L22 | Macro Paper Warehouse

Consumer Credit and the Incidence of Tariffs: Evidence from the Auto Industry

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

Overview

Research Question. Do import tariffs affect consumer credit terms, and does focusing solely on goods prices understate tariff pass-through to consumers? The paper also asks whether vertical integration – specifically, the ownership of a captive finance subsidiary – expands the channels through which manufacturers can pass on cost shocks, and whether tariff incidence falls disproportionately on consumers with less elastic credit demand or in areas with lower credit market competition.

Setting. The Trump administration’s 2018 metal tariffs – a 25 percent tariff on steel and a 10 percent tariff on aluminum – created a large and largely unanticipated cost shock for US auto manufacturers who are heavy consumers of both metals across their supply chains. Crucially, auto manufacturers own captive finance subsidiaries (e.g., Ford Credit, GM Financial, Honda Finance) that originate consumer auto loans alongside independent noncaptive lenders (banks, credit unions, independent finance companies). Because noncaptive lenders had no direct exposure to the metal tariffs, they serve as a natural control group in a difference-in-differences design.

Data. The primary data source is Regulation AB II, which requires issuers of public auto loan asset-backed securities to report loan-level information monthly to the SEC. The final sample covers 1,973,639 auto loans originated between January 2017 and December 2018 across 14 lenders (8 captive, 6 noncaptive). Vehicle invoice price data come from Regulation AB II; consumer sales price data come from the Texas Department of Motor Vehicles (covering approximately 3.9 million vehicle transactions in 2017-2018). Population credit bureau data from Equifax are used for representativeness checks and HHI construction.

Empirical Strategy. The baseline difference-in-differences compares captive auto loans to otherwise-identical noncaptive auto loans originated in the same state, the same quarter, for the same vehicle make-model-condition, and to borrowers in similar income and credit score bins. Parallel pre-trends tests confirm no economically meaningful differential pre-trends across captive and noncaptive lenders for any outcome variable.

Main Findings.

Interest Rate Pass-Through. Relative to noncaptive lenders, captive lenders increased average interest rates by 26 basis points following the tariff announcement, representing a 10 percent increase relative to the pretreatment captive mean of 252 basis points. This corresponds to an average present value increase in total loan payments of $179 per loan (discounted at 5 percent for an average $26,914 principal with 66-month maturity). By the fourth quarter of 2018, the dynamic estimate reaches 48 basis points – nearly double the pooled average – as metal prices continued to rise. The increase is concentrated among more-exposed captive lenders (those whose manufacturers operate two or more domestic production plants), not less-exposed captive lenders (primarily BMW, Mercedes-Benz, Volkswagen), ruling out captive-specific omitted variables.
Non-Price Loan Terms. There is no economically significant change in captive loan amounts, maturities, or loan-to-value ratios following the tariffs. Captive lenders responded to the tariff shock exclusively by raising interest rates, consistent with prior evidence that auto loan demand is less sensitive to interest rates than to non-price terms.
Vehicle Prices. Invoice prices for makes with greater domestic production rose by approximately 1.0 percent (relative to makes with less domestic production), and consumer sales prices rose by approximately 0.7 percent ($225 average increase relative to a pretreatment mean of $32,206) for these same makes.
Relative Magnitude of Pass-Through Channels. After accounting for estimated spillover effects on noncaptive lenders of 7 basis points, the spillover-adjusted estimate implies captive interest rates rose by 33 basis points on average, corresponding to $227 per loan in present value terms. Interest rate pass-through is estimated to be almost two-thirds as large as vehicle price pass-through, meaning that focusing solely on vehicle prices would underestimate tariff incidence on consumers by approximately 37 percent. The population-weighted average cost increase per vehicle is $146 – roughly equally split between higher vehicle prices ($74) and higher financing costs ($72).
Intensive vs. Extensive Margin. The composition of captive borrowers did not deteriorate following the tariffs: average household incomes of captive borrowers increased slightly (economically small), credit scores were unchanged, and future default rates showed no significant change. This confirms that the interest rate increase reflects tariff pass-through to inframarginal borrowers along the intensive margin, not a shift in borrower composition.
Heterogeneity by Credit Demand Elasticity. Pass-through via interest rates was higher for borrowers with lower incomes (33 basis points vs. 20 basis points for higher-income consumers), lower credit scores (36 basis points vs. 15 basis points), and smaller loan amounts (36 basis points vs. 12 basis points). These groups are proxies for less elastic credit demand, consistent with theoretical predictions that cost pass-through is larger where demand is less price sensitive.
Heterogeneity by Market Competition. Tariff pass-through via interest rates was higher in states with lower credit market competition (as measured by state-level Herfindahl-Hirschman Index). Consumers in the lowest competition decile experienced an average captive interest rate increase of 41 basis points, compared to 24 basis points for consumers in the highest competition decile. This 17 basis point differential implies that interest rate pass-through was approximately 88 percent as large as vehicle price pass-through in less competitive markets, versus 57 percent in more competitive markets.

In depth

Q1. What is a captive finance subsidiary, and why does it create a novel channel for tariff pass-through?

A captive finance subsidiary is a wholly owned lending unit of an auto manufacturer (e.g., Ford Credit, GM Financial, American Honda Finance) whose primary purpose is to finance the sale of the manufacturer’s vehicles. Because the captive lender and the manufacturing unit share a parent company, a cost shock to the manufacturing side – such as higher steel and aluminum prices from the tariffs – can be passed on to consumers not only through higher vehicle prices but also through worse financing terms offered by the captive. Prior studies documented tariff pass-through to goods prices but found limited evidence of pass-through to consumer prices; this paper shows that the bundling of a product with captive financing creates a second, previously unmeasured channel. The institutional structure also facilitates “price shrouding”: because consumers are less attentive to financing costs than vehicle sticker prices, captive lenders can exploit this inattention to pass on cost shocks along the financing margin.

Q2. Why is the auto loan market a particularly suitable setting for studying this question?

The auto loan market provides three key advantages. First, both captive lenders (directly exposed to metal tariffs via manufacturing) and noncaptive lenders (with no direct tariff exposure) compete for the same borrowers on the same vehicle purchases, creating a clean within-vehicle, within-period control group. Second, the Regulation AB II data contain vehicle make-model-condition information, allowing the authors to hold vehicle choice fixed and isolate tariff pass-through to loan terms separately from any vehicle switching by consumers. Third, the indirect dealer-intermediated financing process means that consumers typically do not observe the full set of lender bids, weakening their ability to actively arbitrage between captive and noncaptive loan offers.

Q3. What is the Regulation AB II data, and how representative is it?

Under Regulation AB II (effective November 2016), issuers of publicly offered auto loan asset-backed securities must report monthly loan-level data to the SEC, including interest rates, loan amounts, maturities, vehicle characteristics, borrower credit scores and incomes, and loan performance. The final sample covers approximately 8 percent of all open auto loans in the United States and around 30 percent of the total auto loan portfolios of the 14 sampled lenders. Average loan characteristics in the Regulation AB II data closely match population credit bureau data from Equifax, indicating that securitization selection is not a major concern. Average credit scores and incomes are slightly higher in Regulation AB II than in the population, primarily because small banks and credit unions that serve riskier borrowers do not access public securitization markets.

Q4. What is the baseline empirical specification and what identifying variation does it use?

The baseline is a difference-in-differences regression comparing captive loans (treated) to noncaptive loans (control) before and after January 2018 (the date of the Department of Commerce’s initial tariff recommendation, chosen conservatively). The regression includes lender fixed effects, vehicle make-model-condition x origination quarter fixed effects, state x origination quarter fixed effects, $25,000 income bin x origination quarter fixed effects, and 10-point credit score bin x origination quarter fixed effects. The coefficient of interest is estimated using within-lender variation after netting out common vehicle-level shocks, state-level shocks, and shocks common across income and credit score cells. This granular fixed effect structure ensures that the estimate compares captive and noncaptive loans for exactly the same vehicle, in the same state, in the same quarter, to borrowers with similar incomes and credit scores.

Q5. What are the main coefficient estimates on interest rates, and how do they evolve dynamically?

In the full sample, the pooled difference-in-differences estimate is 26 basis points (t = 2.75), representing a 10 percent increase relative to the pretreatment captive mean of 252 basis points. Excluding subvented (subsidized) loans, the estimate is 29 basis points (t = 2.85). Dynamically, captive interest rates started rising within one quarter of the treatment date and continued increasing alongside metal prices, reaching a terminal coefficient of 48 basis points in the fourth quarter of 2018 – nearly double the pooled average. Consistent with the parallel trends assumption, there is no economically significant evidence of differential pre-trends across captive and noncaptive loans in the pretreatment period.

Q6. How do the authors validate that noncaptive lenders constitute a valid counterfactual?

Four alternative specifications are presented. First, when splitting captive lenders by tariff exposure (more exposed: Ford, GM-AmeriCredit, Honda, Toyota; less exposed: BMW, Mercedes-Benz, Volkswagen), only more-exposed captive lenders show a significant increase in interest rates (30 basis points; t = 3.37), while less-exposed captive lenders show no significant increase (-18 basis points; t = -1.33). This rules out captive-specific correlated omitted variables. Second, the authors add interactions of the treatment indicator with changes in the Fed Funds rate and 1-, 5-, and 10-year Treasury yields; results are unchanged in magnitude, ruling out differential sensitivity to the rising interest rate environment of 2018. Third, using CarMax (a noncaptive that also sells and finances vehicles but does not participate in DealerTrack) as the sole control group yields similar results. Fourth, lender-specific borrowing cost controls do not attenuate the estimates.

Q7. Did captive lenders adjust any non-price loan terms in response to the tariffs?

No. Columns 2-4 of Table 3 document that loan amounts, maturities, and loan-to-value ratios showed no economically significant changes for captive lenders relative to noncaptive lenders following the tariffs. Some coefficient estimates in the full sample are statistically significant but economically small, and they lose significance or flip signs once subvented loans are excluded. The event study plots confirm no meaningful pre-trends and no meaningful post-treatment changes in non-price terms. The authors note that this is consistent with prior evidence that auto loan demand is less sensitive to interest rates than to maturity, making interest rates the optimal margin along which to pass through costs.

Q8. How do the authors rule out that the increase in captive interest rates reflects a change in borrower composition rather than intensive-margin pass-through?

The authors estimate a separate regression (equation 4) with log household income, log credit score, and future default rate as outcomes. Relative to noncaptive borrowers, captive borrowers experienced a small but positive increase in average household income (Gamma = 0.012, t = 3.25), no significant change in credit scores (Gamma = 0.001, t = 1.13), and no significant change in 12-month or 24-month default rates. The income increase is of the wrong sign and too small in magnitude to explain the observed interest rate increase from a risk-based pricing perspective. Additionally, captive loan origination volumes declined 6.7 percent after the tariffs, inconsistent with a demand surge driving the interest rate increase.

Q9. How do the authors rule out alternative explanations including demand surges, borrowing cost increases, securitization changes, and dealer markup changes?

For demand surges: vehicle sales volumes showed no noticeable increase following the tariff announcement, and captive loan originations actually declined. For differential borrowing costs: controlling for lender-specific CDS spreads and other borrowing cost measures does not attenuate the main estimate. For securitization changes: combining Regulation AB II and credit bureau data, the authors find no significant change in captive lenders’ securitization rates, the ratio of securitized to total loan amounts, maturities, or monthly payments. For dealer markup changes: noncaptive loans are also subject to dealer markups, so common changes are absorbed in the DiD; additionally, subvented loans (which dealers cannot mark up) also show higher captive interest rates post-tariff, ruling out differential markup changes. For interest rate sensitivity differentials: controlling for changes in risk-free rates does not alter results. For prepayment responses: 12-month and 24-month prepayment rates show no significant change for captive loans.

Q10. How do the authors measure vehicle price pass-through, and what data do they use?

To measure invoice price pass-through, the authors use Regulation AB II data (which contains the invoice price for new vehicles) and estimate a regression comparing the change in log invoice prices for makes with a higher proportion of US-assembled vehicles versus those with lower domestic production, controlling for vehicle make-model fixed effects and price bin x quarter fixed effects. Invoice prices rose approximately 1.0 percent for more-exposed makes. For consumer sales price pass-through, the authors use Texas DMV data (1,819,498 new and 2,105,938 used vehicle transactions in 2017-2018) with the same identification strategy. Sales prices rose approximately 0.7 percent ($225 average increase) for more-exposed makes. Both effects are robust to defining exposure at either the make level or the make-model level.

Q11. How is the overall pass-through rate decomposed between the interest rate and vehicle price channels?

The authors define total tariff pass-through as the sum of interest rate pass-through (change in aggregate captive financing costs divided by aggregate production cost increase) and vehicle price pass-through (change in aggregate new vehicle sales revenue divided by aggregate production cost increase). Taking the ratio of these two components allows them to estimate the relative importance of each channel without needing to directly measure production costs. With a captive loan penetration rate (M) of 0.59, a per-loan present value financing cost increase of $179 (unadjusted) or $227 (adjusted for 7 basis point spillover effect on noncaptives), and a $225 average vehicle price increase, the spillover-adjusted estimate implies interest rate pass-through is almost two-thirds as large as vehicle price pass-through. Focusing solely on vehicle prices would underestimate tariff incidence on consumers by approximately 37 percent. The population-weighted average total cost increase is $146 per vehicle, roughly equally split between vehicle prices ($74) and financing costs ($72).

Q12. How large is the estimated aggregate impact of the tariffs on consumer financing costs?

Using population data of approximately 50 million vehicles sold annually in the United States and a population-weighted average financing cost increase of $72 per vehicle, the authors estimate that the tariffs resulted in approximately $3.6 billion (= 50,000,000 x $72) in additional present value financing costs each year. For reference, Flaaen, Hortacsu, and Tintelnot (2020) estimated that the 2018 tariffs on washing machines led to $1.5 billion in additional annual consumer costs.

The triple-differences results show monotonically higher pass-through for borrowers with less elastic credit demand. Lower-income borrowers (below median) experienced an average captive interest rate increase of 33 basis points versus 20 basis points for higher-income borrowers. Lower-credit-score borrowers experienced an increase of 36 basis points versus 15 basis points for higher-credit-score borrowers. Borrowers with smaller loan amounts (below median) experienced an increase of 36 basis points versus 12 basis points for larger loan amounts. Within income quartiles, consumers in the lowest income quartile experienced a 37 basis point increase compared to 17 basis points in the highest quartile. These patterns are not driven by changes in borrower composition, as default rates show no significant change across any of these subgroups.

Q14. How does credit market competition affect tariff pass-through via interest rates?

States with lower credit market competition (higher Herfindahl-Hirschman Index, constructed from pretreatment lender market shares) experienced higher interest rate pass-through. Comparing above- versus below-median HHI states, the difference is 5 basis points (28 vs. 23 basis points), statistically significant at the 10 percent level. When restricting to the tails of the competition distribution, the difference is substantially larger: consumers in the lowest competition decile experienced an average increase of 41 basis points versus 24 basis points for consumers in the highest competition decile – a 17 basis point differential. This implies interest rate pass-through was 88 percent as large as vehicle price pass-through in less competitive markets versus 57 percent in more competitive markets, consistent with theoretical predictions that firm-specific cost shocks generate higher pass-through when competition is weaker.

Q15. Why do captive lenders spread interest rate increases broadly across vehicle types rather than targeting directly tariff-exposed new vehicle models?

The authors find that captive interest rates increased for both new and used vehicles, and that within more-exposed captive lenders, interest rate increases were not concentrated in domestically produced vehicle models. This is consistent with the hypothesis that firms spread cost shocks across multiple goods and business segments (as documented in the industrial organization literature for multiproduct firms). The authors argue this occurs because vehicles of different makes and models are substitutes for each other (making vehicle-specific price increases costlier in terms of demand loss), whereas auto loans are complementary to vehicle purchases and are offered as an add-on to the sales transaction. This bundled structure, combined with consumer inattention to financing terms, makes it optimal to spread the cost shock across the loan book rather than concentrating it in specific vehicle models.

Key Concepts

Captive Finance Subsidiary: A wholly owned lending unit of a manufacturer (e.g., Ford Credit, GM Financial) whose primary purpose is to originate loans and leases to finance the sale of the manufacturer’s own products. Unlike independent noncaptive lenders, captive lenders are vertically integrated with the manufacturing unit and can, in principle, use financing terms as an additional margin to pass through manufacturing-side cost shocks to consumers.

Tariff Pass-Through (Interest Rate Channel): The extent to which an input cost increase caused by an import tariff is transmitted to consumers via higher interest rates charged by captive lenders, rather than (or in addition to) higher goods prices. The paper defines interest rate pass-through as the ratio of the aggregate present value increase in captive financing costs to the aggregate increase in manufacturing production costs.

Intensive vs. Extensive Lending Margin: The distinction between raising loan prices charged to existing (inframarginal) borrowers (intensive margin) versus changing the pool of borrowers served or lending standards (extensive margin). The paper argues that the observed increase in captive interest rates reflects intensive-margin pass-through because borrower incomes, credit scores, and future default rates did not change significantly after the tariffs.

Price Shrouding: The practice of making price increases less salient to consumers by embedding them in a less-scrutinized component of a bundled transaction. In the auto market, because consumers are documented to be less sensitive to increases in financing costs than to vehicle sticker prices, captive lenders can pass on cost shocks through interest rates with less demand response than if they raised vehicle prices by an equivalent amount. The paper treats this as a key mechanism enabling the financing pass-through channel.

Subvented (Subsidized) Loan: A promotional auto loan offered at a below-market interest rate, often tied to specific vehicle models or sales events (e.g., “1.99 percent APR for well-qualified borrowers”). Subvented loans are typically fixed by the manufacturer and cannot be marked up by dealers. The paper uses the subsample of non-subvented loans as a robustness check and to isolate tariff pass-through from seasonal variation in promotional financing.

Captive Loan Penetration Rate (M): The ratio of captive auto loans originated to new vehicles produced and sold, used in the paper’s decomposition of total tariff pass-through into the interest rate and vehicle price channels. Estimated at approximately 0.59 from population data, this parameter determines how the aggregate present value financing cost increase scales relative to the aggregate vehicle sales price increase when computing the relative importance of the two pass-through channels.

Herfindahl-Hirschman Index (HHI) as Market Competition Measure: The paper constructs state-level HHIs based on pretreatment lender market shares in each state using population credit bureau data, as an inverse measure of credit market competition. Local (direct) auto lending markets exhibit meaningful geographic variation in HHI, in contrast to the largely national scope of indirect (dealer-arranged) lending. The paper uses this variation to test whether pass-through is higher in less competitive credit markets, consistent with theoretical predictions for firm-specific cost shocks.

Peer Effects in Consideration and Preferences

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

This paper develops a general nonparametric model of discrete choice in which peers influence agents through two distinct channels: (1) the set of alternatives an agent considers (consideration set effects) and (2) the agent’s preferences over those alternatives (preference effects). The framework embeds these peer mechanisms in a continuous-time Markov process where agents revise choices at Poisson alarm-clock rates. A peer is classified as a consideration peer, a preference peer, or both, and the network is encoded as two directed edge sets rather than one.

The central identification challenge is recovering network structure, consideration probabilities, and preferences simultaneously, without relying on exogenous variation in covariates or the menu of available options. The paper shows this is achievable using time-series variation in the choices made by connected agents. The key insight is that consideration peers who adopt alternative v change the probability that the focal agent considers v — entering only the “consideration” term of the conditional choice probability (CCP) — while preference peers who adopt alternatives other than v change only the “conditional-on-consideration” selection probability. These cross-alternative patterns in the CCPs allow the researcher to distinguish the two channels. Once consideration-only peers are isolated, their choices serve as exclusion restrictions that mimic artificial menu variation, enabling nonparametric recovery of preferences.

Identification proceeds in stages: (i) recover the full reference group of each agent from changes in CCPs; (ii) separate consideration-only peers from preference-affecting peers using cross-order effects across alternatives; (iii) distinguish preference-only peers from consideration-and-preference peers under an exclusion restriction (Assumption 4) requiring that an agent with a dual-channel peer also has at least one single-channel peer; (iv) recover consideration ratios Q(v|n+1)/Q(v|n) and then the full choice rule. The results allow arbitrary heterogeneity across agents and do not require exogenous menu variation or covariate shifters.

For continuous-time data (Dataset 1), the CCPs and Poisson rates are exactly identified from the observed revision history. For discrete-time panel data (Dataset 2), identification is generic under a mild eigenvalue condition on the transition rate matrix.

The empirical application studies store-opening decisions by China’s two dominant high-end tea chains — Heytea and Nayuki — across prefecture-level cities from their founding through end-2020. By that date, Nayuki had 485 stores in 57 cities and Heytea had 729 stores in 46 cities, in an industry whose total revenue grew from 42.2 to 83.1 billion yuan between 2017 and 2020. Each firm-market pair is modeled as an agent deciding whether to open a new store. The key exclusion restriction is that the cumulative store count of either firm in geographically neighboring markets shifts consideration probabilities but does not enter marginal profitability directly.

Estimation via maximum likelihood yields four substantive findings: (1) Firms exhibit limited consideration — consideration probabilities for markets with no prior presence by either firm are substantially below one. (2) Stores in neighboring markets significantly raise consideration probabilities for a given market, for both own-firm and rival stores; this peer effect in consideration is described as economically large. (3) Own-market store density raises marginal profitability (density economies) while rival presence lowers it (competitive effects). (4) A full-consideration model that omits the attention stage overestimates the negative competitive effect and underestimates positive density effects.

Counterfactual simulations show that removing attention constraints (full consideration) accelerates market penetration substantially: firms enter new markets earlier and achieve broader geographic coverage. Removing peer effects in consideration only — while retaining attention constraints — slows the diffusion of store openings across neighboring markets, because peer effects in consideration function as an informational cascade. Limited consideration also reduces competition by delaying rival entry into high-profitability markets, explaining a significant share of the geographic concentration in first- and second-tier cities during the early expansion phase. The paper’s scope is limited to settings with repeated, non-durable choices; it does not model forward-looking behavior or multiple equilibria, which the authors note as directions for future research.

Q: What are the two peer-effect channels in the model, and how do they differ structurally? A: A consideration peer influences whether an alternative enters the agent’s consideration set — specifically, the probability Q_a(v | n) that alternative v is considered is a function of the number n of consideration peers currently adopting v. A preference peer influences the choice rule R_a(v | y, C) — the probability that v is selected conditional on it being in the consideration set. Importantly, the paper models the two channels as affecting logically separate stages of the decision process, so the observed CCP factors into a consideration term and a conditional-selection term that respond to distinct sets of peers.

Q: Why does the standard identification approach of varying menus fail here, and how does the paper substitute for it? A: Menu variation requires the researcher to observe the same agent facing different sets of available alternatives, which is unavailable in many empirical settings. The paper replaces exogenous menu variation with endogenous variation generated by consideration-only peers: when a consideration-only peer adopts alternative v, the focal agent’s probability of considering v rises, effectively mimicking the removal of other alternatives from her consideration set. This peer-induced variation in consideration is then used to trace out the choice rule R_a over counterfactual menus without any actual menu changes.

Q: How does the paper separate consideration peers from preference peers in the data? A: The decomposition exploits an asymmetry in how the two peer types appear in the log-CCP. When a consideration peer switches to alternative v, the term ln Q_a(v | .) changes but the conditional-selection term ln D_a(v | .) remains unchanged, because the agent already considers v. Conversely, when a preference peer adopts an alternative other than v, only the conditional-selection term shifts. The paper formalizes this via cross-order effects of peers across alternatives in the CCPs (Propositions 3.1–3.3) and invokes Assumption 4 — requiring at least one single-channel peer when a dual-channel peer exists — to complete the separation.

Q: What is Assumption 4 and why is it necessary? A: Assumption 4 states that if agent a has a peer in N_CR_a (a peer affecting both consideration and preferences), then a also has at least one additional peer affecting only consideration or only preferences. Without this exclusion restriction, the consideration and preference effects of a dual-channel peer are not separately identified from each other; the single-channel peer provides the variation needed to pin down each component separately.

Q: What does Proposition 2.1 establish and what does it require? A: Proposition 2.1 establishes existence and uniqueness of an invariant equilibrium distribution mu over choice configurations, with full support. It requires Assumptions 1 (independent consideration), 2(i) (strictly positive consideration probability for every alternative), and 3(i) (strictly positive probability of selecting any non-default alternative from some reachable consideration set). The continuous-time Poisson structure ensures zero probability of simultaneous revisions, which rules out multiple equilibria in the data-generating process.

Q: How does the paper handle discrete-time panel data, where only periodic snapshots of choices are observed? A: The paper invokes results from Blevins (2017, 2026) to show that the transition rate matrix W of the continuous-time process is generically identified from the discrete-time transition matrix observed at interval Delta, provided the eigenvalues of W do not differ by integer multiples of 2pii/Delta. Once W is identified, the CCPs P and Poisson rates lambda_a are recovered. This result is described as generic, meaning it holds except on a measure-zero set of parameter values.

Q: What data does the empirical application use, and what are the key sample statistics? A: The application uses city-level store registration data sourced from the National Enterprise Credit Information Publicity System (via CnOpenData, 2021), supplemented by regional statistics from the China City Statistical Yearbook (2016–2021). The sample ends in 2020 to avoid COVID-19 demand shifts. By end-2020, Nayuki had 485 stores across 57 cities and Heytea had 729 stores across 46 cities. The high-end tea industry’s total revenue grew from 42.2 to 83.1 billion yuan between 2017 and 2020.

Q: What is the key exclusion restriction in the empirical specification, and why is it plausible? A: Stores in geographically neighboring markets (parameterized by distance bins d(m,m’)) enter the attention index pi_tilde but are excluded from the marginal profit index pi_bar. The rationale is that nearby store counts are informative signals that draw managerial attention to a market (an informational spillover) but do not directly alter the profitability of operating in that market — profitability depends on local demand, competition within the market, and own firm density, not on activity in adjacent markets. This restriction identifies the consideration-only peer channel.

Q: What does the paper find about biases from ignoring limited consideration? A: When the two-stage model (consideration + choice) is replaced by a single-stage full-consideration model, the estimated payoff parameters differ substantially. Specifically, the full-consideration model overestimates the negative effect of competition (rival presence in the same market) and underestimates the positive effect of own-store density. The intuition is that correlated entry patterns driven by shared consideration spillovers are misattributed to payoff interactions when the consideration stage is omitted.

Q: What do the counterfactual simulations show about the role of limited consideration in market dynamics? A: Three counterfactuals are compared against the baseline. Under full consideration (no attention constraints), market penetration is substantially faster — firms enter new markets earlier and achieve broader geographic coverage. Removing peer effects in consideration while retaining attention constraints slows geographic diffusion because the informational cascade that propagates entry to neighboring markets is eliminated. Limited consideration also reduces competition by delaying rival entry into high-profitability markets; markets with high potential demand remain underserved for longer. Collectively, limited consideration explains a significant portion of the geographic concentration of tea chain stores in first- and second-tier cities during the early expansion period.

Q: What forms of heterogeneity does the identification allow, and what does it not require? A: The nonparametric identification results accommodate arbitrary heterogeneity across agents in consideration mechanisms Q_a, choice rules R_a, Poisson revision rates lambda_a, and network positions. The identification requires neither exogenous covariates that shift preferences or consideration, nor variation in the set of available alternatives across observations. It relies solely on time-series variation in the choices made by connected agents, which are endogenous to the model and are themselves identified in the first stage.

Q: How does the paper model history dependence, and does it change the main identification results? A: Section 4.1 extends the model to allow consideration probabilities and choice rules to depend on the agent’s own choice history h_t in addition to the current configuration y. Proposition 4.1 states that under Assumptions 1–4 applied conditional on both y_{at} and h_t, all identification propositions from Section 3.1 remain valid. The extension also allows consideration probabilities to equal one, enabling nontrivial dynamics in consideration sets driven by past choices.

Q: How is the unobservable default handled in the empirical application? A: When the default alternative (e.g., “do not open a store”) is unobserved, the Poisson revision rate lambda_a cannot be separately identified from the CCPs without normalization. The paper normalizes lambda_a = 1 for each agent in the empirical application, treating the revision opportunity rate as fixed and recovering all remaining primitives under this normalization.

Consideration set: The subset C of the full menu Y that agent a actually attends to at the moment of revision; formed before the choice rule is applied. Alternative v enters C independently with probability Q_a(v | n), where n is the number of consideration peers currently adopting v. The default alternative is always in the consideration set.

Conditional choice probability (CCP): P_a(v | y), the ex-ante probability that agent a selects alternative v given choice configuration y; equal to the product of the consideration probability Q_a(v | .) and the conditional-selection probability D_a(v | .), integrated over all possible consideration sets.

Choice configuration: The vector y = (y_a)_{a in A} recording the current alternative selected by every agent in the network simultaneously; the state variable of the continuous-time Markov process.

Consideration-only peer: A peer a’ in N_C_a \ N_R_a whose choices enter the consideration probability Q_a but not the choice rule R_a. Variation in the choices of consideration-only peers serves as an exclusion restriction that mimics artificial menu variation for identifying preferences.

Preference-only peer: A peer a’ in N_R_a \ N_C_a whose choices enter the choice rule R_a but not the consideration probability Q_a.

Cross-order peer effect: The pattern in the CCP by which a consideration peer’s adoption of alternative v changes ln P_a(v | .) but not the conditional-selection component, while a preference peer’s adoption of a different alternative v’ changes the conditional-selection component but not the consideration component; this asymmetry is the key to separating the two channels.

Limited consideration: The situation in which Q_a(v | n) is strictly less than one for at least some alternatives v and peer counts n, so that the agent does not evaluate all available options before choosing; distinct from full rationality in which all alternatives are always considered.

Mean attention index (pi_tilde): The latent index governing the consideration probability in the empirical specification; it depends on own and rival store counts in the same and neighboring markets and on firm fixed effects, but is excluded from the marginal profit index — constituting the empirical exclusion restriction that separates the consideration and payoff channels.