F15 | Macro Paper Warehouse

Input Sourcing under Climate Risk: Evidence from U.S. Manufacturing Firms

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

Blaum, Esposito, and Heise study how supply chain risk — specifically, the risk of unexpected shipping delays caused by ocean weather conditions — affects U.S. manufacturing firms’ import sourcing decisions. The paper asks three related questions: Do weather-induced shipping delays harm firm performance? Do firms adapt their sourcing strategies ex ante in response to shipping time risk? And what are the aggregate welfare costs of heightened supply chain risk from climate change, geopolitical tensions, and port congestion?

The empirical foundation is the U.S. Census Bureau’s Longitudinal Firm Trade Transactions Database (LFTTD), covering the universe of U.S. import transactions from 1992 to 2016, merged with the Longitudinal Business Database and Annual Survey of Manufacturers for firm-level outcomes. For ocean shipments, the authors reconstruct vessel routes using vessel names, foreign port stops, and U.S. ports of entry, then map those routes to hourly wave height and direction data from NOAA’s WaveWatch III model at 0.5-degree resolution across more than 40,000 distinct maritime routes (period: 2011–2016 for weather data).

The identification strategy proceeds in two steps. First, observed shipping times are regressed on a rich set of fixed effects — supplier, product, route-month, vessel, buyer, relationship status — plus controls for shipping charges and weight, to strip out anticipated determinants of delivery time. Second, the residuals are projected onto realized wave height and direction along the vessel’s route to isolate the weather-induced, unexpected component of shipping time variation. The identifying assumption is that realized wave conditions along the entire multi-week ocean crossing are not predictable by importers at the time orders are placed, beyond seasonal patterns absorbed by route-month fixed effects. This assumption is supported by the literature on weather forecasting, which finds accuracy degrades sharply beyond seven days.

The paper’s first empirical result concerns the consequences of weather-induced delays. Defining an extreme delay as a weather-induced shipping time above the 95th percentile for a given product-route, the authors estimate that a one standard deviation increase in the share of input costs that are weather-delayed (2.66 percentage points) reduces firm sales by 6.5%, profits by 3.5%, and employment by 1.0% within the same year. These effects are estimated from panel regressions for 2011–2016, with importer, product, and year fixed effects. The magnitudes indicate that firms are typically unable to fully hedge supply chain disruptions through insurance or financial instruments.

The paper’s second empirical result concerns ex ante adaptation. Risk exposure is measured as the standard deviation of weather-induced shipping times over three-year rolling windows for each supplier-route-product combination, then aggregated to the importer-product-year level using pre-determined import shares as weights (Bartik shift-share). Moving from the 25th to the 75th percentile of this shipping risk distribution increases the number of routes used by 7.7% and the number of foreign suppliers by 4.9%, while reducing total import value by 5.1%, route concentration (HHI) by 4.6%, and supplier concentration (HHI) by 3.2%. The risk effect on imports is estimated conditional on average shipping time, indicating that uncertainty exerts an additional, independent negative effect on import demand beyond the level of delays.

To rationalize these findings, the authors build a quantitative general equilibrium model of importing with firm heterogeneity. Firms source domestic and foreign inputs; foreign input quality is reduced when delivery is late, and firms face uncertainty about shipping times when placing orders. Risk-neutral firms nonetheless face a concavity in expected revenues from monopolistic competition, so higher variance in input quality reduces expected profits. Firms can diversify by adding foreign suppliers (at a per-supplier fixed cost), and a key theoretical result is that a mean-preserving spread in supplier quality variance increases the optimal number of suppliers but, because the extensive-margin elasticity is less than one, total import value necessarily falls.

The calibrated model is used to evaluate three counterfactual scenarios. Ocean wave height volatility increased by 0.34% per year on average between 2011 and 2023; projecting this trend forward 50 years generates a climate change scenario. The Houthi attacks in the Red Sea caused rerouting that raised both the mean and variance of navigation time. Post-Covid port congestion (2021–2022) increased the variance of port waiting times. Across all three scenarios, U.S. real income falls by 0.4% to 1.33%, driven by firms substituting toward more expensive domestic inputs as they reduce exposure to risky foreign sourcing.

The sample scope is U.S. manufacturing importers using ocean shipping during 2011–2016 for the main empirical results (weather data period), with an extended robustness sample of 1992–2016 using residualized shipping time volatility. The study covers 43,080 origin-destination port pairs, 401,700 unique vessels, and approximately 35.8 million seaborne transactions.

Q: What is the paper’s core research question? A: The paper asks how supply chain risk — specifically, the risk of unexpected delays in ocean shipping caused by weather conditions — affects U.S. manufacturing firms’ import sourcing decisions and aggregate welfare. It examines both the disruption effects of realized delays and the ex ante adaptation of sourcing strategies to risk exposure, then quantifies aggregate costs through a calibrated general equilibrium model.

Q: What data sources underpin the empirical analysis? A: The primary dataset is the LFTTD, which covers the universe of U.S. import transactions from 1992 to 2016, recording importer and exporter identities, HS-10 product codes, values, quantities, shipping dates, vessel names, and port pairs. This is merged with the Longitudinal Business Database for employment and industry, and with Census of Manufactures and Annual Survey of Manufacturers for sales, material costs, and payroll. Weather data come from NOAA’s WaveWatch III model at hourly, 0.5-degree resolution for 2011–2016. Ocean routes are constructed using Eurostat’s SeaRoute program, covering over 40,000 distinct routes across approximately 10,500 route segments.

Q: How do the authors isolate the unexpected component of shipping time variation? A: They use a two-step residualization. In step one, observed log shipping times are regressed on supplier, product, route-month, vessel, buyer, and relationship-status fixed effects, plus controls for log shipping charges and log weight; the residuals capture variation not explained by anticipated factors. In step two, these residuals are projected onto realized average wave height and relative wave direction along the vessel’s route to extract the weather-induced component. The identifying assumption is that importers cannot forecast realized wave conditions beyond seasonal patterns when placing orders that initiate multi-week ocean crossings, consistent with evidence that weather forecasts lose accuracy beyond seven days and that ocean wave height is particularly hard to predict.

Q: What are the estimated effects of weather-induced shipping delays on firm performance? A: A one standard deviation increase in the share of input costs that are weather-delayed (2.66 percentage points) reduces firm sales by 6.5%, profits by 3.5%, and employment by 1.0% within the same year. Using a broader measure of residualized shipping time delays (not restricted to the weather-induced component) produces similar results: a one standard deviation increase reduces sales by 6%, profits by 3.2%, and employment by 0.9%. These effects are estimated from panel regressions for 2011–2016 with importer, product, and year fixed effects.

Q: How do firms adjust their sourcing strategies in response to higher shipping time risk? A: Moving from the 25th to the 75th percentile of the shipping risk distribution (a 61 log-point increase) raises the number of routes used by 7.7% and the number of foreign suppliers by 4.9%, while reducing route HHI by 4.6%, supplier HHI by 3.2%, and total import value by 5.1%. The margin of route diversification is larger than supplier diversification, consistent with shipping risk being determined primarily at the route level. Higher risk also increases the likelihood of switching to air freight by 1.0% over the same interquartile range.

Q: Does the risk effect on imports operate independently of the level of shipping times? A: Yes. The regressions of total import demand on risk exposure control for average shipping time, and the coefficient on risk remains negative and significant after this control. This indicates that the variance of shipping times has an independent negative effect on import demand beyond the first-moment effect of longer average delays.

Q: What is the theoretical mechanism through which shipping time risk reduces import demand? A: In the model, firms are risk-neutral but face monopolistically competitive output markets, which introduces curvature in the revenue function. Higher variance in input quality (stemming from unpredictable shipping times) reduces expected revenues even for risk-neutral firms. Firms can diversify by adding foreign suppliers at a per-supplier fixed cost, which reduces variance in average input quality. However, the elasticity of the optimal number of suppliers with respect to quality variance is less than one, so total import expenditure necessarily falls as variance rises — diversification is incomplete and firms substitute toward domestic inputs.

Q: What does Proposition 1 state about the extensive margin response to risk? A: Proposition 1 establishes that, under the condition that shipping time risk is small relative to expected revenues, a mean-preserving spread in the variance of supplier quality increases the optimal number of foreign suppliers. However, the elasticity of the optimal number of suppliers with respect to quality variance is strictly less than one, which implies that total import value necessarily falls whenever quality variance increases, regardless of the extensive margin diversification response.

Q: How is the calibration structured and what moments does it target? A: The model features firm heterogeneity in both productivity and shipping time risk (variance of delivery times). The calibration targets three sets of moments: the estimated effect of shipping time risk on the extensive margin of importing (number of suppliers), the negative association between firm sales and average shipping times (which disciplines the timeliness elasticity parameter tau), and the joint distribution of firm size and risk observed in the data — specifically, the empirical finding that larger importers are matched with safer (lower-risk) foreign suppliers, with a correlation of -0.12. The calibrated model replicates the key moments of shipping time risk and import demand.

Q: What are the three counterfactual scenarios and their aggregate welfare costs? A: (1) Climate change: ocean wave height volatility increased by 0.34% per year on average between 2011 and 2023; projecting this trend forward 50 years and passing the resulting increase in shipping time variance through the model. (2) Red Sea/Houthi attacks: re-routing around the Suez Canal raises both the mean and variance of navigation time. (3) Post-Covid port congestion: greater variability in port waiting times during 2021–2022. Across all three scenarios, U.S. real income falls by 0.4% to 1.33%, driven by firms substituting from cheaper foreign inputs toward more expensive domestic production to reduce risk exposure.

Q: What is the role of the shift-share (Bartik) instrument in the risk exposure measure? A: The exposure measure aggregates supplier-route-product level risk (standard deviation of weather-induced shipping times over three-year rolling windows) to the importer-product-year level using pre-determined import shares from the prior three years as weights. Using lagged shares rather than contemporaneous shares ensures that the weights are not endogenous to current sourcing decisions. This construction is standard in the Bartik shift-share literature and helps isolate variation in risk that is plausibly exogenous to the firm’s current sourcing choices.

Q: How do the authors handle the endogeneity concern that firms may select into riskier routes? A: The weather-induced component of shipping time variation is by construction driven by realized ocean conditions that are unpredictable at the time orders are placed. The residualization removes all fixed-effect variation associated with route, season, vessel, supplier, and buyer characteristics. Additionally, the shift-share construction uses pre-determined weights, so risk exposure does not mechanically reflect current sourcing decisions. The authors also show robustness using the longer 1992–2016 sample with residualized (rather than weather-specific) shipping time volatility, obtaining qualitatively and quantitatively similar results.

Q: What does the paper contribute relative to the literature on shipping times and trade? A: Prior work by Evans and Harrigan (2005) and Hummels and Schaur (2010, 2013) focused on the level of shipping times (the first moment) as a trade cost. This paper is the first to systematically study the variance of shipping times (the second moment) as an independent determinant of import demand and sourcing structure, both empirically and theoretically. The authors show that uncertainty around delivery times has negative effects on trade that are separate from the effects of longer average delays.

Q: What are the robustness checks reported for the main empirical results? A: For the effects of risk on sourcing behavior, the authors show that using residualized shipping time volatility over the longer 1992–2016 sample (rather than the weather-induced measure over 2011–2016) produces similar results: moving from the 25th to the 75th percentile increases routes by 6.6%, suppliers by 3.7%, decreases route HHI by 3.9%, and supplier HHI by 2.5%, while reducing total imports by 10.5%. For the effects of delays on firm performance, applying the same specification with residualized (not weather-induced) delay shares yields coefficients on sales, profits, and employment that are very close to the baseline estimates.

Q: What are the welfare implications for firms that cannot hedge through financial markets? A: The large negative effects of weather-induced delays on sales, profits, and employment — and the finding that firms respond by ex ante restructuring their supply chains rather than relying on insurance — indicate that financial hedging instruments are largely unavailable or insufficient for managing input delivery risk. This motivates the model’s assumption that firms must manage risk through sourcing diversification, which is costly because of per-supplier fixed costs and because it ultimately requires substituting toward more expensive domestic inputs.

Weather-induced unexpected shipping time: The component of shipping time variation explained by realized ocean wave height and direction along the vessel’s route, after removing all variation attributable to anticipated factors (route, season, vessel, supplier, buyer characteristics, shipping charges, weight). Interpreted as unexpected because multi-week ocean crossings begin before accurate weather forecasts are available.

Shipping time risk: Measured as the standard deviation of weather-induced residualized shipping times over three-year rolling windows for each foreign supplier-route-product combination. This captures the second moment (variance) of delivery time uncertainty, distinct from the first moment (average shipping time level).

Shift-share risk exposure: An importer-product-year level risk measure constructed as a weighted average of supplier-route-product level risk, using pre-determined import shares from the prior three years as weights. This Bartik-style construction ensures exposure weights are not endogenous to current sourcing decisions.

Timeliness elasticity (tau): A structural parameter in the model governing how rapidly input quality degrades when delivery is later than expected. Specifically, when a shipment arrives di days late, quality is reduced by the factor exp(-tau*(di - E[di])). Calibrated to match the observed negative association between firm sales and average shipping times in the data.

Extensive margin diversification: The response of firms to higher shipping time risk by increasing the number of foreign suppliers and shipping routes used for a given product, rather than increasing the volume sourced from existing suppliers. In the model and data, this margin is the primary channel through which firms hedge delivery risk.

Mean-preserving spread condition: The theoretical condition (Proposition 1) under which higher variance in supplier quality increases the optimal number of foreign suppliers. The condition requires that shipping time risk be small relative to expected revenues, so that the diversification benefit of adding suppliers (reducing variance in average quality) dominates the revenue-reducing effect of higher variance.

Per-supplier fixed cost: A fixed cost in the model that must be paid for each foreign supplier relationship maintained. This cost limits the extent of diversification, ensuring that firms cannot fully eliminate shipping time risk by adding arbitrarily many suppliers, and that higher risk raises (rather than eliminates) per-unit sourcing costs.

The Micro and Macro Dynamics of Capital Flows

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

Using the 2001 Hungarian capital account liberalization as a quasi-natural experiment and census-level firm data covering the entire economy (1992–2008), the paper identifies two channels through which capital inflows affect resource allocation: an input-cost channel (lower cost of capital benefits capital-intensive sectors) and a consumption channel (higher household incomes benefit high-expenditure-elasticity sectors, chiefly services). The paper finds the consumption channel dominates: one standard deviation increase in expenditure elasticity is associated with 8.4% greater real value-added growth, versus 4.2% for one standard deviation in capital elasticity. Along the extensive margin, high-expenditure-elasticity sectors experience 15% higher net entry and 19% higher gross entry. A calibrated multi-sector heterogeneous-firm model with non-homothetic preferences (à la Comin–Lashkari–Mestieri 2021) replicates 12 non-targeted moments and reproduces 70% of the reallocation toward services observed in Hungary. Counterfactual exercises show that a neoclassical homothetic model underpredicts reallocation by a factor of ten and generates counterfactual real exchange rate depreciation. Despite reallocation toward less productive service firms (a negative composition effect), aggregate TFP increased 11.4% in Hungary — driven by a love-of-variety effect from entry (mass-of-firms effect of +3.5% versus composition effect of −1.9%). Non-homothetic preferences amplify this mechanism: capital-scarce economies experience 21.9% larger TFP gains than homothetic models predict.

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 is Hungary’s 2001 capital account liberalization a clean quasi-natural experiment?

Hungary deregulated only cross-border financial flows, without simultaneous trade or FDI liberalization, and the reform was predetermined by the Copenhagen Criteria of 1993 as a condition for EU accession. The content and timing of the reform were not driven by Hungarian firm-level fundamentals: by March 2001, financial liberalization was the sole remaining EU accession requirement, and neither trade nor FDI changed around the reform (Figures C.4–C.5). Exports to the EU already accounted for 80% of total exports before 2001. The nine other EU accession candidates at the time did not experience comparable patterns of capital inflows, consumption booms, or sectoral reallocation (Tables C.2–C.3), ruling out EU accession itself as the driver.

Q2. How does the paper identify the input-cost and consumption channels separately?

The identification strategy exploits three sources of variation: pre- versus post-reform timing, heterogeneous capital elasticities across four-digit industries (input-cost channel), and heterogeneous expenditure elasticities across two-digit industries (consumption channel), derived from model-implied structural relationships. Using equation (4), the DiD regression estimates γ₁ (capital elasticity × reform dummy) and γ₂ (expenditure elasticity × reform dummy). These two structural parameters are nearly orthogonal (correlation 2.1% between USDA capital and expenditure elasticities), allowing separate identification. The capital elasticities are estimated using the Petrin–Levinsohn–Wooldridge method on pre-reform data; expenditure elasticities come from USDA Seale–Regmi–Bernstein (2003) estimated for Hungary in 1996. Parallel trends hold: firms across elasticity levels shared similar pre-reform growth trajectories (Table C.9).

Q3. What do the baseline regression results show about which channel dominates?

In the preferred specification with both channels and all controls (column 4, Panel A of Table 1), capital elasticity raises value added by 4.2% per standard deviation (0.045 SD), while expenditure elasticity raises it by 8.4% per standard deviation (0.223 SD USDA); standardized beta coefficients confirm the consumption channel is larger. For capital accumulation (Panel B), only the capital elasticity coefficient is significant: a one standard deviation increase in capital elasticity is associated with 4.4% more firm-level capital, while expenditure elasticity has no significant effect — firms in high-expenditure-elasticity sectors do not accumulate more capital, they hire more workers. Employment (Panel C) shows 9.3% higher employment per standard deviation in expenditure elasticity (5.9% using Bils–Klenow–Malin elasticities). These patterns survive controls for non-tradability, financial frictions (Rajan–Zingales, Raddatz inventories-to-sales, cash conversion cycle), and firm-level debt obligations.

Q4. How does the model fit the non-targeted moments for Hungary?

Calibrated to 13 internally targeted moments (including the 3.5 percentage point decline in the domestic real interest rate and sectoral firm-size distributions), the model matches 12 non-targeted moments spanning consumption, capital accumulation, cross-sector reallocation, and within-sector selection (Table 6). Key matches: household consumption +5.8% (data), +7.2% (model); within-firm capital accumulation +22.5% vs +24.9%; value-added share of services +3.9pp vs +2.7pp (70% match); relative operational cutoff of services vs manufacturing −2.3% vs −1.7% (74% match); relative export cutoff +4.6% vs +4.5% (98% match). The model accounts for roughly 60% of the 2.9% relative price appreciation (real exchange rate). The model also reproduces the differential increase in entry rates: services +10.8pp (data) vs +18.4pp (model), manufacturing +5.7pp vs +8.6pp.

Q5. What do counterfactual exercises reveal about the role of non-homothetic preferences?

A neoclassical representative-firm model with homothetic preferences generates only 0.4 percentage points of reallocation toward services — ten times less than the 3.9pp observed in Hungary — and produces a counterfactual real exchange rate depreciation. In Table 7, four counterfactuals are compared: (1) baseline model (εS ≠ εM, αS ≠ αM): consumption ratio CS/CM +6.9pp, service value-added share +2.7pp, relative price appreciation +1.7%; (2) consumption channel only (εS ≠ εM, αS = αM): similar service reallocation but no RER appreciation; (3) input-cost channel only (εS = εM, αS ≠ αM): modest reallocation (~1.1pp) but correct RER appreciation; (4) homothetic heterogeneous-firm model (εS = εM, αS = αM): ~0.7pp reallocation, wrong RER; (5) neoclassical model: ~0.4pp, wrong RER. Non-homothetic preferences account for about two-thirds of the service reallocation; differential capital elasticities are necessary to replicate exchange rate dynamics.

Q6. How can aggregate TFP increase when resources move toward less productive services?

Financial liberalization induces firm entry — especially in high-expenditure-elasticity services — generating a love-of-variety effect that increases aggregate output more than proportionally with the number of varieties (since σ > 1), overwhelming the negative composition effect from reallocation to lower-productivity service firms. The TFP decomposition (Table 9) shows: composition effect −1.9%, mass-of-firms effect +3.5%, interaction +0.7%, sum +2.3% model (data: +11.4%). The composition effect is consistently negative across all capital-scarcity levels because service firms are less productive. But the mass-of-firms effect is consistently larger and positive. Non-homothetic preferences amplify entry in services (the high-expenditure-elasticity sector), strengthening the love-of-variety channel.

Q7. How do non-homothetic preferences affect TFP gains in capital-scarce economies, and what are the policy implications?

Capital-scarce economies experience larger consumption booms upon financial liberalization (given lower initial capital levels and higher intertemporal borrowing gains), inducing stronger entry in high-expenditure-elasticity services and larger mass-of-firms TFP effects; non-homothetic preferences amplify this gradient by 21.9% relative to homothetic preferences (Table 10). Specifically, an economy liberalizing at 25% of its open-economy steady-state capital stock gains 5.5× more TFP than one liberalizing at 70%; under homothetic preferences the ratio is 4.5×, yielding a 21.9% amplification from non-homotheticity. This helps explain the empirical puzzle documented by Bekaert–Harvey–Lundblad (2011) and Bonfiglioli (2008) that financial liberalization episodes associate with productivity gains in capital-scarce economies, which neoclassical models predict incorrectly as productivity declines. The policy implication is that the gains from financial openness are largest — and most driven by consumption-driven entry — when economies are capital-scarce, but these gains also carry macro-financial risks (as in Gyongyosi–Rariga–Verner 2023 on the 2008 Hungarian forint depreciation).

Key concepts

input-cost channel : the mechanism through which capital inflows reduce firms’ cost of capital (borrowing rate), benefiting sectors with higher capital elasticity; identified in Hungary through the differential expansion of firms in high-capital-elasticity industries after the 2001 deregulation.

consumption channel : the mechanism through which capital inflows increase household consumption, benefiting sectors with higher expenditure elasticity; found to dominate the input-cost channel in Hungary, explaining the reallocation toward services.

non-homothetic preferences : demand preferences (modeled following Comin–Lashkari–Mestieri 2021) in which sectoral expenditure shares change with income levels — goods with expenditure elasticity above one gain share as income rises; these preferences are quantitatively necessary to explain the 3.9pp reallocation toward services in Hungary (versus 0.4pp under homothetic preferences).

mass-of-firms effect : the aggregate productivity gain from an increase in the number of active firm varieties under CES demand (σ > 1), whereby output grows more than proportionally with the number of varieties; this love-of-variety mechanism explains why aggregate TFP increases in Hungary despite resource reallocation toward less productive service firms.

expenditure elasticity : the sector-level responsiveness of consumption to a proportional increase in aggregate income; used in the paper’s DiD identification to separate the consumption channel from the input-cost channel, measured using USDA (Seale–Regmi–Bernstein 2003) estimates for Hungary, with services having higher elasticity (1.18 in model calibration) than manufacturing (0.75).