D21 | Macro Paper Warehouse

Customer accumulation, returns to scale, and secular trends

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

This paper asks how rising returns to scale in production contributed to three concurrent U.S. secular trends since 1980: declining business dynamism, rising markups, and growing firm expenditures on customer acquisition. The author constructs a firm dynamics model in the Hopenhayn (1992) tradition with endogenous entry and exit, heterogeneous markups, and customer accumulation grounded in directed search in the product market. Firms compete for customers through both prices and selling activities; larger firms gain a competitive edge when returns to scale rise because their marginal costs fall more than those of smaller firms—even though the technological shift is uniform across firms. This demand-based channel triggers winners-and-losers dynamics and the rise of superstar firms.

The empirical foundation rests on Compustat data for U.S. publicly traded firms (1977–2014) and Business Dynamics Statistics (BDS) for aggregate and sector-level dynamism measures. Production-function estimation using Ackerberg, Caves, and Frazer (2015) augmented with sales-share controls documents that aggregate returns to scale rose from approximately 1.0 in 1980 to approximately 1.05 by 2014—a within-sector increase, not a reallocation effect. Over the same period, the cost-weighted markup rose by 42%, the firm entry rate fell by 33%, the excess reallocation rate fell by 29%, and selling costs relative to production costs rose by 60%–90% depending on the measure used.

The model is calibrated to 1980 steady-state moments (firm life-cycle patterns, markups, entry and reallocation rates). A 5% increase in returns to scale—matching the empirical estimate—accounts for: a +15 percentage point rise in the average cost-weighted markup (vs. +42% in the data); a 33% decline in the entry rate (exactly matching the data); a 21% decline in the reallocation rate (vs. 29% in the data); and a 23% increase in selling costs relative to production costs (vs. 60%–90% in the data). The model also generates a 53% rise in the share of firms aged 11 years or older (vs. 50% in the data) and a 58% decline in the employment share of firms aged 5 years or younger (vs. 56% in the data), closely tracking the aging of the U.S. firm population. Firm-level responsiveness to productivity shocks declines by 0.08 in the model, versus about 0.01 in Compustat and 0.09 in Decker et al. (2020).

Sector-level panel regressions with sector fixed effects confirm the model’s directional predictions: within-sector increases in returns to scale are associated with lower entry rates (coefficient −2.89, significant at 1%), lower reallocation rates (−1.16, significant at 1%), higher markups (+3.15, significant at 1%), and higher selling costs relative to production costs (+1.85 for the advertising-based measure; +8.52 for adjusted SG&A).

A key scope condition is that the model yields a constrained-efficient allocation: directed search and full internalization of returns to scale imply decentralized equilibrium efficiency, making the paper a laboratory for assessing how far efficient firm responses to technological change can explain the secular trends without invoking market failures. The model fits the post-2000 transition dynamics better than the 1980s–1990s period, and explains a substantial but incomplete share of the trends, suggesting complementary—possibly inefficient—forces also contributed.

Q: What is the core mechanism through which rising returns to scale generate winners-and-losers dynamics?

A: The marginal cost of production under increasing returns to scale (alpha > 1) is MC(z,n) = l(n,z)^(1−alpha) × (1/alpha) × (W/e^z), which depends on firm size l(n,z). A uniform rise in alpha rotates the marginal cost schedule clockwise by firm size: larger firms see a proportionally larger cost reduction than smaller firms, even though the technological change is identical across all firms. Because firms compete for the same pool of customers, this asymmetric cost advantage allows large firms to offer lower prices while sustaining higher margins, attracting customers away from small firms. The result is a demand-based channel that generates winners-and-losers dynamics and increases market concentration.

Q: How does the model capture customer accumulation, and why is it central to the paper’s argument?

A: The model introduces directed search in the product market, where firms post advertisements and customers—including those already matched with a firm—choose which submarket to enter by trading off offered utility against matching probability. A constant-returns-to-scale matching function governs match creation; in submarket with tightness theta, customers match with probability m(theta) = theta(1+theta)^(−1) and firms attract customers with probability q(theta) = (1+theta)^(−1). The customer accumulation motive creates an investment-harvest trade-off: firms can either post high promised utility (low prices) to grow their customer base or extract surplus through high prices. Rising returns to scale amplify large firms’ ability to resolve this trade-off favorably, linking the technological change directly to markup dynamics, entry incentives, and selling expenditures.

Q: What is the directed search framework’s role in ensuring equilibrium uniqueness and efficiency?

A: The author introduces firm-side commitment contracts—specifying price, separation probability, and continuation utility contingent on productivity realizations—combined with directed search. Because search is directed on both sides and firms fully internalize returns to scale, the decentralized equilibrium is constrained-efficient. This delivers uniquely determined heterogeneous prices in equilibrium (solving the indeterminacy problem common in customer-market models) and establishes the paper’s efficient-mechanism benchmark: it tests how far profit-maximizing firm responses to technological change—without any market failure—can account for the secular trends.

Q: How are prices structured in the model, and what life-cycle pattern do they generate?

A: Each firm charges two distinct prices in each period: one to incumbent customers (the same for all incumbents, since they are identical conditional on being attached to the same firm) and one to newly acquired customers (which varies based on the promised utility in the submarket searched). Firms that are expanding their customer base offer greater promised utility and therefore charge lower prices to attract customers; firms harvesting their existing base charge higher prices. Because firms enter small and grow, this dynamic generates a price life cycle: young firms invest via low prices and mature firms harvest through higher prices, which the model reproduces as a rising markup pattern over the firm life cycle—an untargeted moment the model fits well.

Q: What does the calibration target and what untargeted moments does the model reproduce?

A: The model is calibrated to 1980 using: the number of employees of entrant firms (pinning entry customer base n_e), employees of age-5 firms (pinning convex cost chi_1), share of firms aged 11+ years (pinning chi_2), average firm size (operating cost f), entry rate (entry cost kappa), excess reallocation rate (exit shock delta), and average cost-weighted markup (linear cost c). Untargeted moments reproduced include: a sales-weighted markup of 0.28 (vs. 0.25 in De Loecker et al. 2020), endogenous customer turnover of approximately 9% (vs. 15% in Gourio and Rudanko 2014), and an elasticity of customer base shrinkage to price of 0.08 (within the 0.01–0.16 range from Paciello et al. 2019). The model also matches markup and selling-cost life-cycle patterns that are typically overlooked.

Q: How large is the quantitative contribution of the 5% rise in returns to scale to each secular trend?

A: Comparing the 1980 steady state (alpha = 1) to the 2014 steady state (alpha = 1.05): the average cost-weighted markup rises by 15% in the model versus 42% in the data; the entry rate declines by 33% in the model, exactly matching the data; the reallocation rate declines by 21% in the model versus 29% in the data; and selling costs relative to production costs rise by 23% in the model versus 60%–90% in the data. The model thus explains a substantial share of each trend while leaving a residual requiring additional mechanisms.

Q: How does the model explain the aging of U.S. firms, and how well does it match the data?

A: The winners-and-losers mechanism shifts activity toward larger, older firms, which mechanically ages the firm population. The model generates a 53% increase in the share of firms aged 11 years or older (vs. 50% in the data) and a 58% decline in the employment share of firms aged 5 years or younger (vs. 56% in the data). This aging arises because rising returns to scale increase the cost of customer acquisition, acting as a barrier to entry that disproportionately hurts new, small firms while allowing large incumbents to remain viable at lower productivity thresholds.

Q: What is the channel through which rising returns to scale reduce business dynamism specifically?

A: The unequal reduction in marginal costs intensifies competition for customers and raises customer acquisition costs. This operates through two simultaneous effects on the exit threshold: (i) lower marginal costs allow large firms to remain viable at lower productivity levels despite higher customer acquisition costs; and (ii) heightened competition forces smaller firms to require higher productivity to survive in a market that has become increasingly costly to operate in. Higher customer acquisition costs therefore function as an endogenous barrier to entry, reducing the entry rate and the reallocation of resources across firms.

Q: Does the model attribute the secular trends entirely to efficient firm behavior, and what does it conclude about residual explanations?

A: No. The model is explicitly designed as a constrained-efficient benchmark, and the paper finds that while rising returns to scale account for a substantial share of the trends—particularly in magnitude—the transition dynamics show a less accurate fit before the 2000s. The author concludes that complementary mechanisms, likely involving inefficiencies (such as market power from horizontal product differentiation or barriers to entry beyond those captured by the model), played a significant role in the earlier evolution of these trends and in the portion of the trends not explained by the efficient channel.

Q: What evidence supports the rising returns to scale finding, and what are its limitations?

A: Production-function estimation using the Ackerberg-Caves-Frazer method with sales-share controls on Compustat data shows returns to scale rising from approximately 1.0 in 1980 to approximately 1.05 by 2014, driven primarily by within-sector increases rather than reallocation toward high-returns sectors. A translog production function finds limited evidence of heterogeneous increases across firm sizes within Compustat. However, Compustat predominantly covers large publicly traded firms; smaller firms outside the sample may have experienced minimal or no increase in returns to scale. If technology adoption involves fixed costs, the aggregate impact could be larger than estimated, meaning the quantitative exercises likely represent a conservative lower bound.

Q: How does the paper relate to and extend the directed search literature in product markets?

A: The paper builds on Gourio and Rudanko (2014) and Roldan-Blanco and Gilbukh (2020), where customers are locked in once matched, by introducing labor-search tools from Schaal (2017) to allow: (i) incumbent customer switching between firms at rates of 10%–25% annually (Gourio and Rudanko 2014), and (ii) a non-zero price sensitivity of incumbent customers (Paciello et al. 2019). It also allows firms to invest in demand through selling expenditures, which prior directed search models in product markets typically abstracted from, making it possible to study how technological changes affect customer reallocation and firms’ cost structures jointly.

Customer capital: The stock of customers a firm has accumulated through prior selling and pricing decisions; treated as a state variable that firms invest in (by offering low prices and spending on advertisements) or harvest from (by charging high markups), with a customer turnover rate estimated at 10%–25% annually in the literature.

Directed search in the product market: A market structure in which both firms and customers choose which submarket (indexed by the promised utility level) to enter, trading off match probability against terms; delivers constrained-efficient equilibrium and uniquely determined heterogeneous prices.

Investment-harvest trade-off: The firm’s dynamic choice between offering high promised utility (low prices, low current markups) to grow the customer base versus extracting surplus through high prices from an existing customer base; shaped by the firm’s current size, productivity, and the cost structure implied by returns to scale.

Returns to scale (alpha): The curvature of the production function y = e^z × l^alpha; equals 1.0 under constant returns and approximately 1.05 by 2014 in the empirical estimates; the paper’s central technological change parameter, whose rise disproportionately reduces marginal costs for larger firms.

Winners-and-losers dynamics: The reallocation of customers and market share from small to large firms triggered by the asymmetric cost advantage large firms obtain when returns to scale rise; the demand-based channel through which superstar firms emerge.

Cost-weighted markup: The average markup aggregated using each firm’s costs as weights, as opposed to sales-weighted markup; the primary measure of market power used in the paper, rising by 42% in the data between 1980 and 2014.

Constrained-efficient allocation: An equilibrium outcome in which, given the frictions present (search-and-matching in the product market), no social planner operating under the same constraints could improve welfare; the paper uses this as a benchmark to assess how far efficient firm responses explain secular trends without invoking market failures.

Selling costs relative to production costs: The ratio of customer acquisition expenditures (advertising or adjusted SG&A) to cost of goods sold; rose by 60%–90% in the data between 1980 and 2014 and by 23% in the model’s steady-state comparison.

Optimal Resilience in Multitier Supply Chains

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

Layer 1 — Overview

Research Question

Grossman, Helpman, and Sabal ask what market failures arise in vertical supply chains with multiple production tiers, limited (non-anonymous) supply networks, arms-length transactions, and recurrent risks of disruption at every node. They then ask what government policies would be required to implement the socially efficient (first-best) allocation as a decentralized equilibrium, and — in a second-best environment where subsidies to firm-to-firm transactions are politically infeasible — how optimal policies to promote resilience and network formation differ.

Model and Methodology

The paper develops a general-equilibrium model of a closed economy with an arbitrary number S+1 of vertical production tiers (tier 0 through tier S). A finite measure of “lead” firms in tier S produce differentiated consumer goods under monopolistic competition using labor and a CES bundle of intermediate inputs from tier S-1 suppliers. Firms in each intermediate tier combine labor and inputs from the tier above using a Cobb-Douglas production function. Tier 0 firms produce from labor alone.

Every firm faces an independent, non-zero probability of a catastrophic disruption (complete inability to produce). Firms may invest labor up front to moderate this risk — endogenous “resilience” — or may invest to forge relationships with a larger fraction of potential suppliers in the next upstream tier — endogenous “network thickness.” Each formed relationship costs k units of labor.

After disruption shocks are realized, surviving firms negotiate quantities and payments bilaterally. Bargaining is sequential (beginning with lead firms negotiating with tier S-1, then tier S-1 with tier S-2, and so on to tier 0), and within each round is governed by Nash-in-Nash equilibrium (Horn and Wolinsky, 1988): each firm takes as given the outcomes of its negotiations with all other partners. The Nash surplus is split with exogenous bargaining weight β_s for the downstream buyer in the s-to-s−1 negotiation.

The paper solves the planner’s direct-control problem and then characterizes the three sets of policy instruments needed to decentralize the first best: subsidies to input transactions between adjacent tiers, subsidies to investments in resilience (agility), and subsidies to network formation (redundancy). It then solves the second-best problem in which transaction subsidies are constrained to zero.

Main Findings

Transaction subsidies. In the competitive bargaining equilibrium, each pair of firms undervalues input transactions because the upstream firm anticipates paying a marked-up price when it bargains with its own suppliers. This cascading distortion means the private marginal cost of producing a tier-s good exceeds the social marginal cost. The optimal first-best transaction subsidy on sales by tier s firms (τ*s) equals [γ_s + (1−γ_s)μ{s−1}]^{−1}, where γ_s is the labor share in tier s production and μ_{s−1} is the endogenous markup factor from bargaining at the s-to-s−1 interface. This subsidy depends only on production function parameters and bargaining weights at the immediately adjacent tier. No subsidy is needed at tier 0 (the most upstream tier), and no subsidy is applied to final-good sales. Under Assumption 1 — inputs become weakly less substitutable as goods proceed downstream — the optimal purchase subsidies rise monotonically as one moves downstream along the supply chain.

Resilience subsidies (first best). Two offsetting forces govern the optimal subsidy to resilience investments θ*s at intermediate tiers: (i) firms capture only the fraction (1−β{s+1}) of the joint surplus that their resilience creates for downstream customers, creating underinvestment; (ii) optimal transaction subsidies inflate private profitability, creating a countervailing overinvestment incentive. The net optimal first-best subsidy for intermediate-tier firms is θ*s = (1−β{s+1}) / τ*_s. This formula depends only on technological and bargaining parameters of tier s and the tier immediately adjacent; it does not depend on conditions elsewhere in the chain. When production parameters and bargaining weights are uniform across tiers, the first-best resilience subsidy is the same at every interior tier. If goods become strictly less substitutable downstream, the first-best subsidy for resilience declines monotonically as one moves downstream, and may turn into an optimal tax for middle tiers where the transaction subsidy is large enough to over-incentivize resilience investment. The first-best resilience subsidy always applies at both extreme ends of the chain.

Network formation subsidies (first best). Despite firms’ private incentive to manipulate their number of upstream suppliers to improve bargaining position, the net strategic effect of network formation in general equilibrium exactly cancels the off-equilibrium spillovers to non-partners. As a result, the optimal first-best policy toward network formation at every tier is identical to the optimal policy toward resilience investment.

Second-best policies. When transaction subsidies are unavailable, uncorrected markups downstream from tier s depress demand for tier-s output, reducing profitability and incentives to invest in resilience below the first-best level. Second-best optimal subsidies for resilience and network formation therefore reflect production function parameters and bargaining weights throughout the entire downstream supply chain, not just at the immediately adjacent tier. Specifically, when buyer bargaining weights are non-increasing along the chain (β_{s+1} ≤ β_s for all s), the second-best subsidy to resilience falls monotonically as one moves downstream. This is the opposite pattern from what might be inferred from the first-best analysis when transaction subsidies are available: with non-increasing bargaining weights, second-best subsidies are larger for upstream producers than for downstream producers.

Scope Conditions

Results are derived for a closed economy. Welfare is measured by the CES utility of the representative consumer over differentiated final goods. The sequential bargaining structure assumes contracts are written after disruption shocks are realized. Assumption 1 (σ_1 ≥ σ_2 ≥ … ≥ σ_S > ε, where σ_s is the elasticity of substitution between inputs at tier s and ε is the demand elasticity for final goods) is maintained for sharper monotonicity results on the structure of optimal subsidies across tiers.

In depth

Q1. What is the precise structure of the supply chain in the model, and why does the bargaining take place sequentially rather than simultaneously across all tiers?

A: The economy has S+1 tiers. Tier 0 firms use only labor; tier s firms (s = 1,…,S−1) use labor and a CES bundle of tier s−1 inputs with elasticity of substitution σ_s > 1; tier S firms produce final differentiated goods using labor and tier S−1 inputs under Cobb-Douglas technology. Sequential bargaining is imposed because the vast number of simultaneous negotiations across all tiers makes a grand coalition impractical. The timing is that lead firms (tier S) first negotiate input quantities and payments with their tier S−1 suppliers; those suppliers, now contractually obligated to their downstream customers, then negotiate with tier S−2, and so on up the chain until tier 1 firms contract with tier 0 suppliers.

Q2. How is the markup factor defined, and what parameters determine it?

A: The markup factor μ_s is the ratio of the payment per unit made by tier s+1 firms to the production cost of tier s firms. It equals μ_s = (1−β_{s+1}) · [σ_{s+1}/(σ_{s+1}−1)] + β_{s+1}, where β_{s+1} is the exogenous bargaining weight of the downstream (tier s+1) buyer. When the downstream firm has all bargaining power (β_{s+1} = 1), the markup equals unity (competitive outcome). When the upstream firm has all bargaining power (β_{s+1} = 0), the markup equals the standard monopoly markup σ_{s+1}/(σ_{s+1}−1). For intermediate bargaining weights, the markup is a weighted average. The markup enters the optimal transaction subsidy formula by inflating the private marginal cost of producing tier-s inputs above the social marginal cost.

Q3. Why are no subsidies needed for the most upstream (tier 0) transactions or for final-good sales?

A: For tier 0 transactions: when tier 0 and tier 1 firms bargain, the negotiations occur last sequentially and so do not affect any prior agreements. There are no downstream cascading markup effects — tier 0 firms produce from labor alone, so their private marginal cost equals their social marginal cost. The joint surplus maximization by the pair thus aligns with the planner’s objective, yielding τ*_0 = 1 (no intervention needed). For final-good sales: final producers do mark up above marginal cost under monopolistic competition, but all varieties are symmetric, so the markup affects all goods equally and does not distort relative consumption choices. Hence τ*_S = 1.

Q4. What are the two offsetting forces that determine the optimal first-best subsidy to resilience investments at an intermediate tier?

A: First, a firm in tier s captures only the fraction (1−β_{s+1}) of the joint surplus that its survival creates for its downstream customers (the rest is appropriated through bargaining by those customers), leading to underinvestment relative to the social optimum. Second, the optimal transaction subsidy τ*_s < 1 raises the private profitability of firms in tier s above its social value, because public finances bear part of the cost of their input purchases. This inflated private profitability encourages resilience investment beyond what the planner desires. The net optimal policy is θ*s = (1−β{s+1}) / τ*_s, which may be a subsidy (θ*_s < 1) or a tax (θ*_s > 1) depending on which force dominates.

Q5. Why does the first-best subsidy for resilience at an intermediate tier depend only on local parameters (at tier s and its immediate neighbors), even though resilience investments generate spillovers to firms throughout the network?

A: When optimal transaction subsidies are in place at all tiers, a firm’s value becomes independent of the joint surplus in sales that occur between firms in tiers other than its own. That is, the positive spillovers to all firms farther upstream and downstream in a firm’s own network are exactly offset by the negative spillovers to firms in rival networks (including rival firms in the same tier). What remains after this general-equilibrium cancellation is only the benefit to the firm’s immediate downstream customers and the wedge created by the transaction subsidy. This result implies that the formula θ*s = (1−β{s+1}) / τ*_s does not involve conditions at tiers other than s and s−1.

Q6. Why does the optimal policy for network formation (supplier link investment) equal the optimal policy for resilience investment, despite the fact that network formation also strategically improves a firm’s bargaining position?

A: Firms in intermediate tiers do have a private incentive to form additional supplier links specifically to improve their bargaining position vis-à-vis their upstream suppliers (by improving their outside options) and vis-à-vis their downstream customers (by the same mechanism). However, the authors show by comparing the firm’s first-order condition for link formation with the planner’s first-order condition that this strategic motivation exactly balances the offsetting general-equilibrium effects from rival firms doing the same. After this cancellation, the residual wedge between private and social incentives for network formation is identical to that for resilience investment. Hence #_s = θ_s for all tiers.

Q7. How do second-best policies differ from first-best policies in terms of both the magnitude of subsidies and the information required to set them?

A: In the first best, the subsidy for resilience at tier s depends only on the bargaining weight β_{s+1} and the markup factor μ_{s−1} — parameters relevant to tier s and its immediate neighbors. In the second best, when transaction subsidies are unavailable, the optimal resilience subsidy at tier s is θ†s = J^{−1} · [1 − (cumulative distortion of all downstream tiers)] · (1−β{s+1}), where J captures aggregate labor-market effects of all markups throughout the chain. This formula requires knowledge of production function parameters (labor shares γ_j, markups μ_j, elasticities σ_j) for every tier j downstream from s. The second-best subsidy may be larger or smaller than the first-best subsidy; it is more likely to exceed the first-best subsidy for upstream tiers, where the cumulative downstream distortions (uncorrected markups contracting demand) produce a larger shortfall in private profitability and hence a larger underinvestment in resilience.

Q8. Under what condition do second-best subsidies fall monotonically as one moves downstream, and how does this compare to the first-best pattern?

A: The ratio of second-best subsidies at adjacent tiers (θ†_{s−1} / θ†s) equals [(1−β_s) / (1−β{s+1})] · [τ*s]^{−1}, where τ*s is the first-best transaction subsidy. If buyer bargaining weights are non-increasing along the chain — β{s+1} ≤ β_s for all s — then (1−β_s) ≤ (1−β{s+1}) and, combined with τ*s ≤ 1, the second-best subsidy is larger upstream than downstream (θ†{s−1} ≥ θ†_s). This contrasts with the first-best policy: when parameters are uniform across tiers, first-best resilience subsidies are the same at every interior tier, while second-best subsidies are strictly larger upstream than downstream.

Q9. What role does Assumption 1 (elasticities of substitution non-increasing as goods move downstream) play in the results?

A: Assumption 1 (σ_1 ≥ σ_2 ≥ … ≥ σ_S > ε) ensures that the operating profit function ~v_s(η) is concave in a firm’s network size η, which in turn ensures interior solutions to the network formation problem. It also delivers sharper monotonicity results: under this assumption, if other production parameters and bargaining weights are similar across tiers, the optimal purchase subsidies rise monotonically downstream, and the optimal first-best resilience subsidies decline monotonically downstream (potentially turning into taxes at some interior tiers). The assumption reflects the realistic view that inputs become more differentiated and specialized as they approach the final consumer good.

Q10. What are the limitations the authors identify regarding their model, and what extensions do they suggest?

A: Three main limitations are identified. First, the model assumes bargaining occurs after disruption shocks are realized, ruling out contingent contracts. Pre-disruption bargaining with contingent payments could mitigate double-marginalization inefficiencies and help internalize resilience externalities, though complex network-wide contingent contracts would likely be needed for full efficiency even in the second-best environment. Second, the model assumes symmetric firms within each tier, so downstream firms cannot sort on upstream firms’ observable resilience levels; if observable differences existed, downstream firms could seek out more reliable partners, partially internalizing the resilience externality. Third, the model covers only a closed economy with idiosyncratic (uncorrelated) shocks. Extensions to global supply chains, correlated (geographic) shocks, cross-country differences in wages and technologies, and optimal cooperative versus unilateral policy are identified as important directions for future research.

Key Concepts

Resilience (agility): In the paper’s usage, a firm’s endogenous investment in reducing the probability of a catastrophic disruption to its own operations. A firm in tier s hires r_s units of labor up front, which raises its survival probability φ_s(r_s), with φ’_s > 0 and φ’’_s < 0. Resilience is a relationship-specific investment in the sense that its payoff is realized only conditional on the firm surviving and then trading with its downstream customers.

Network thickness (redundancy): The fraction η_s of firms in the next upstream tier with whom a firm in tier s forms a supply relationship prior to the disruption shock. Forming k units of labor per link creates a thicker network that hedges against supplier disruption, increases input variety (and thus CES productivity), and improves bargaining positions vis-à-vis both upstream suppliers and downstream customers. Distinct from resilience: resilience reduces the firm’s own probability of disruption; network thickness provides substitutability across suppliers should some fail.

Markup factor (μ_s): The ratio of the per-unit payment made by tier s+1 firms to the production cost of tier s firms, as determined by Nash bargaining. Specifically, μ_s = (1−β_{s+1}) · [σ_{s+1}/(σ_{s+1}−1)] + β_{s+1}. The markup distorts private marginal costs above social marginal costs, causing underinvestment in transactions between firms and, transitively, in resilience and network formation.

Nash-in-Nash equilibrium: The bargaining solution concept used in the paper (following Horn and Wolinsky, 1988). Each pair of firms negotiates as if all other bilateral negotiations involving either party proceed at their equilibrium outcomes, both on and off the equilibrium path. This is the appropriate equilibrium concept when grand coalitions across all firms and all tiers are impractical.

Sequential bargaining: The specific timing structure in which negotiations proceed from the most downstream tier (lead firms bargaining with tier S−1 suppliers) sequentially upstream until tier 1 firms bargain with tier 0 suppliers. Each tier of firms, at the time they bargain with their own suppliers, are already contractually obligated to deliver specified quantities to their downstream customers. This obligation anchors the downstream firm’s outside option in any given bilateral negotiation.

First-best transaction subsidy (τ_s):* The fraction of the cost of a tier-s input that, under the optimal policy, the downstream (tier s+1) buyer must pay. Equals [γ_s + (1−γ_s) · μ_{s−1}]^{−1} < 1 for all intermediate tiers, i.e., it is always a subsidy. Designed to align private marginal cost in the bilateral negotiation with the social marginal cost by offsetting the distortion introduced by anticipated markups on the upstream firm’s own inputs.

Second-best subsidy: The optimal policy toward resilience and network formation when subsidizing firm-to-firm transactions is infeasible (constrained to τ_s = 1 for all s). Unlike first-best subsidies — which depend only on local tier parameters — second-best subsidies depend on production function parameters and bargaining weights throughout the entire downstream supply chain due to the uncorrected cumulative markup distortions.