Q54 | Macro Paper Warehouse

Can Trade Policy Mitigate Climate Change?

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

Overview

Farrokhi and Lashkaripour (2025) study the interaction between trade policy and climate change. The central research question is whether and how countries can use trade policy — specifically import tariffs — to address carbon leakage arising from domestic carbon pricing. When a country prices carbon domestically, production and emissions can shift to countries without carbon pricing, partially offsetting domestic emissions reductions. The paper asks how optimal import tariffs should be designed to internalize this leakage, how they relate to standard terms-of-trade tariffs, and what additional gains multilateral coordination can deliver.

Methodology and Data. The paper develops a multi-country, multi-sector trade model in which carbon emissions are proportional to output with sector-specific emission intensities, and countries choose trade taxes and subsidies strategically in Nash equilibrium alongside domestic carbon prices. The model is calibrated to 43 countries and 56 sectors using the 2014 baseline from the World Input-Output Database (WIOD 2016) for trade flows and input-output linkages, IEA data for sector-level carbon emissions, and GTAP for trade elasticities.

Main Findings. The paper’s first key result is that the optimal unilateral import tariff decomposes additively into a standard terms-of-trade component and a carbon leakage correction component. The carbon leakage correction is proportional to the emission intensity of imports from the exporting country in that sector and to the gap between the social cost of carbon and the actual domestic carbon price in the exporting country, divided by the import price. This decomposition implies that countries have incentives to impose import tariffs beyond those justified by standard terms-of-trade arguments, specifically to correct for the carbon embodied in imports from countries with insufficient carbon pricing.

The paper derives a sufficient statistic for the optimal carbon tariff that depends only on observable trade elasticities and emission intensities, enabling calibration without full structural estimation beyond the model’s standard parameters.

Quantitative Magnitudes. In the calibrated model, optimal unilateral carbon tariffs are on average 30% above standard optimal tariffs globally (28% above for the EU; 33% above for the US). The excess is largest in carbon-intensive sectors: petroleum products (41% above standard optimal), cement and non-metallic minerals (45% above standard optimal), basic metals (38% above standard optimal), and chemicals (32% above standard optimal). Imposing the optimal unilateral carbon tariff yields a welfare gain of +0.8% consumption equivalent for the imposing country, with trading partners losing on average 0.3%, and a net global gain of +0.4%.

Multilateral coordination — a symmetric global carbon pricing agreement — eliminates the strategic motive for carbon trade wars, delivers an additional global welfare gain of +0.6% above the unilateral optimum, and eliminates 85% of the carbon leakage remaining under unilateral policy.

CBAM Analysis. The paper evaluates the EU Carbon Border Adjustment Mechanism (CBAM) against the theoretically optimal carbon tariff. The EU CBAM as currently implemented — covering only direct emissions — captures 60% of the theoretically optimal carbon tariff. Extending coverage to indirect (supply-chain) emissions would capture 85% of optimal. The welfare gain to the EU from CBAM relative to no border adjustment is +0.4%.

Scope Conditions and Robustness. Results are qualitatively robust to trade elasticity assumptions but quantitatively sensitive to them. Optimal carbon tariffs are regressive with respect to developing countries; multilateral coordination mitigates this distributional effect via income transfers. General equilibrium labor market effects reduce welfare gains by approximately 20% but do not change the qualitative ranking of policies.

Q&A

Q1: What is the formal structure of the optimal unilateral import tariff in the presence of carbon externalities? The optimal import tariff from country j in sector s is tau*_js = tau^ToT_js + tau^carbon_js, where tau^ToT is the standard terms-of-trade optimal tariff (inverse of the export supply elasticity) and tau^carbon is a carbon leakage correction equal to e_js × (lambda_j − lambda*) / P_js. Here e_js is the emission intensity of country j in sector s, lambda_j is the social cost of carbon in the importing country, lambda* is the actual domestic carbon price in the exporting country, and P_js is the import price. Countries therefore have two distinct and additive incentives to impose import tariffs: the classical terms-of-trade motive and a novel carbon leakage correction motive.

Q2: What is the sufficient statistic result and why does it matter for implementation? The paper shows that the optimal carbon tariff can be expressed as a function of observable trade elasticities and emission intensities alone, without requiring estimation of structural parameters beyond those standard to the trade model. This sufficient statistic result matters because it means regulators can in principle calculate and implement the theoretically optimal carbon border adjustment using data that are already collected — sectoral emission intensities and trade elasticities — rather than relying on unobservable structural primitives.

Q3: By how much do optimal carbon tariffs exceed standard optimal tariffs in the aggregate and in the most carbon-intensive sectors? Globally, optimal unilateral carbon tariffs are on average 30% above standard optimal tariffs (28% above for the EU, 33% above for the US). The excess is largest in highly carbon-intensive sectors: cement and non-metallic minerals (45% above), petroleum products (41% above), basic metals (38% above), and chemicals (32% above). These are precisely the sectors where emission intensities are highest, consistent with the carbon leakage correction being proportional to emission intensity.

Q4: What are the welfare effects of unilateral optimal carbon tariff policy? For the country imposing the optimal unilateral carbon tariff, the welfare gain is +0.8% in consumption-equivalent terms relative to no carbon tariff. Trading partners lose on average 0.3%. The net global welfare gain is +0.4%. These numbers reflect the fact that unilateral carbon tariffs are partly beggar-thy-neighbor in structure — they improve the imposing country’s terms of trade in addition to correcting leakage — which is why multilateral coordination is needed to eliminate the strategic distortion.

Q5: What additional gains does multilateral coordination deliver over unilateral policy? Multilateral coordination — modeled as a symmetric global carbon pricing agreement — generates an additional global welfare gain of +0.6% above the unilateral optimum. It also eliminates 85% of the carbon leakage that persists under unilateral policy. The mechanism is that coordination removes the strategic motive for trade wars over carbon policy: under unilateral policy, each country has an incentive to impose carbon tariffs partly for terms-of-trade reasons, but under a coordinated agreement these beggar-thy-neighbor components are internalized.

Q6: How well does the EU’s CBAM as actually implemented capture the theoretically optimal carbon border adjustment? The EU CBAM as implemented — covering only direct emissions from covered sectors — captures 60% of the theoretically optimal carbon tariff. Extending the CBAM to include indirect emissions embedded in supply chains would raise this to 85% of optimal. The remaining gap (15% under the extended CBAM) reflects the difficulty of accounting for all upstream emission intensities across complex global supply chains.

Q7: What is the welfare gain to the EU from CBAM relative to no border adjustment? The welfare gain to the EU from implementing CBAM (relative to having no carbon border adjustment at all) is +0.4% in consumption-equivalent terms. This figure corresponds to the direct CBAM as implemented, covering only direct emissions.

Q8: How sensitive are the results to trade elasticity assumptions, and what are the distributional implications for developing countries? The results are qualitatively robust to trade elasticity assumptions but quantitatively sensitive — the magnitude of optimal carbon tariffs and welfare effects depends on the specific elasticities used. On distributional grounds, optimal carbon tariffs are regressive with respect to developing countries, meaning developing economies bear disproportionate costs from carbon border adjustments. Multilateral coordination partially mitigates this distributional concern through income transfers implied by the symmetric global agreement.

Q9: How do general equilibrium labor market effects alter the conclusions? General equilibrium labor market effects reduce the welfare gains by approximately 20% relative to the baseline estimates, but do not change the qualitative ranking of policies (unilateral carbon tariff better than no border adjustment; multilateral coordination better than unilateral). This suggests that the core policy conclusions are robust to incorporating labor market general equilibrium effects, even if the precise magnitudes are somewhat smaller.

Key Concepts

Carbon Leakage. In this paper, carbon leakage refers specifically to the shift in production and emissions to countries without domestic carbon pricing that occurs when one country implements a carbon price. It is the mechanism by which domestic carbon pricing is partially offset, motivating the use of trade policy as a complementary instrument.

Carbon Leakage Correction (tau^carbon). The component of the optimal import tariff that is distinct from the standard terms-of-trade tariff. It equals emission intensity × (social cost of carbon − domestic carbon price in exporter) / import price. It corrects for the fact that imports from countries with insufficient carbon pricing embody unpriced carbon externalities.

Terms-of-Trade Tariff (tau^ToT). The standard optimal import tariff arising from a large country’s ability to manipulate its terms of trade. Equal to the inverse of the export supply elasticity of the trading partner. The paper establishes that carbon tariffs add to — rather than replace — this classical component.

Sufficient Statistic for Optimal Carbon Tariff. A formula expressing the optimal carbon tariff as a function of observable trade elasticities and emission intensities, without requiring estimation of unobservable structural parameters beyond those standard to the trade model. The term is used in the paper’s specific sense of an empirically implementable formula that is exact within the model.

Emission Intensity. Sector-specific carbon emissions per unit of output in a given country, denoted e_js for country j and sector s. Used as the key observable that scales the carbon leakage correction component of the optimal tariff.

Multilateral Coordination. Modeled as a symmetric global carbon pricing agreement in which all countries simultaneously adopt optimal carbon pricing. In the paper’s framework, this eliminates the strategic motive for unilateral carbon trade wars and achieves additional welfare gains and leakage reductions beyond what any single country can achieve unilaterally.

Carbon Border Adjustment Mechanism (CBAM). The EU policy instrument that imposes a carbon price on imports from sectors covered by the EU Emissions Trading System, evaluated in the paper against the theoretically optimal carbon tariff. The paper distinguishes between the direct-emissions-only CBAM as implemented (capturing 60% of optimal) and a hypothetical full CBAM including indirect supply-chain emissions (capturing 85% of optimal).

Cap‐and‐Trade and Carbon Tax Meet Arrow–Debreu

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

Layer 1 — Overview

Research Question

Anderson and Duanmu (2025) ask how general equilibrium (GE) interactions — factor reallocation across sectors, capital misallocation under climate uncertainty, and the distributional incidence of damages — alter the social cost of carbon (SCC) relative to the partial equilibrium (PE) estimates embedded in standard integrated assessment models (IAMs). The paper also characterizes conditions for Pareto improvements through climate policy and derives the optimal carbon tax in second-best environments with pre-existing distortions.

Framework

The authors build a dynamic Arrow-Debreu economy with L goods, K capital stocks (including climate stocks), and T periods. The climate module specifies that the carbon stock evolves as S_{t+1} = S_t + sum_j e_j(q_j) − alpha·S_t, and climate damage functions D_j(S_t) = 1 − d_j·(S_t − S_0) reduce sector-specific production possibilities sets. Firms and households take the climate trajectory as given and do not internalize their own emissions’ impact, generating the externality. Under standard regularity conditions, the authors prove existence of a competitive equilibrium and establish that it is inefficient: output is too high and climate-intensive sectors are too large relative to the social optimum.

General Formula for the SCC

The paper derives a general SCC formula — SCC_t = Sum_{tau >= t} beta^(tau−t) · [dW/dS_tau / (dW/dY_t)] — that decomposes into four components: (1) the standard direct productivity-loss term, (2) a GE factor-reallocation term capturing inefficient reallocation as damages shift relative prices, (3) a capital-misallocation term reflecting distortions in investment from climate uncertainty, and (4) a distribution term reflecting the welfare losses from the regressive incidence of climate damages. All three correction terms are positive under standard conditions, so the GE SCC exceeds the PE SCC. The paper shows that this formula nests existing IAM frameworks as special cases.

Quantitative Findings

Calibrating to three leading IAMs, the authors find that general equilibrium interactions raise the SCC by 15–40% above standard PE estimates:

DICE-calibrated: GE correction of 18% above the PE estimate.
FUND-calibrated: GE correction of 15% above the PE estimate.
PAGE-calibrated: GE correction of 40% above the PE estimate, the largest correction owing to greater sector heterogeneity in that model.
Median calibration: a PE SCC of $51/tCO₂ rises to a GE SCC of $62/tCO₂.

Decomposing the aggregate GE correction: factor reallocation across sectors accounts for 55%, capital misallocation due to climate uncertainty for 30%, and the distributional regressivity of damages for 15%.

Second-Best Policy and Uncertainty

In environments with pre-existing distortions, the optimal carbon tax deviates from the SCC: revenue recycling through labor tax cuts generates additional welfare gains of 10–15% of carbon tax revenue; undertaxed capital implies the optimal carbon tax should be set above the SCC (double dividend); and in monopolistically competitive sectors the optimal carbon tax is below the SCC because the carbon tax amplifies monopoly distortions. Under climate uncertainty, the SCC carries a risk premium proportional to the variance of damage estimates times the coefficient of relative risk aversion, estimated at +$8–15/tCO₂ (15–25% of the base SCC).

Scope Conditions

The quantitative corrections are calibrated to DICE, FUND, and PAGE and therefore inherit those models’ parameterizations of damage functions and discount rates. The GE factor-reallocation and capital-misallocation channels are larger when sectors are more heterogeneous in damage exposure — as is explicit in the PAGE result. Second-best corrections depend on the sign and magnitude of pre-existing distortions (labor taxes, capital taxes, market structure).

Layer 2 — Q&A

Q1. What is the core inefficiency result, and what does it imply about the competitive equilibrium?

The paper’s efficiency theorem establishes that the competitive equilibrium is Pareto inefficient because firms and households take the climate trajectory as given and do not internalize the impact of their own emissions on the carbon stock. As a consequence, output is too high and climate-intensive sectors are too large relative to the social optimum. This externality is the fundamental justification for climate policy in the model.

Q2. How does the paper’s general SCC formula extend existing approaches, and what are the novel terms?

The general formula SCC_t = Sum_{tau >= t} beta^(tau−t) · [dW/dS_tau / (dW/dY_t)] nests standard IAM SCC formulas as special cases. The novel terms relative to partial equilibrium are: (i) a GE reallocation term capturing losses from inefficient factor reallocation as climate damages change relative prices across sectors; (ii) a capital-misallocation term capturing distortions in investment arising from climate uncertainty; and (iii) a distribution term capturing welfare losses from the regressive incidence of damages. All three terms are positive under standard conditions, implying GE SCC > PE SCC in all calibrations.

Q3. How are the quantitative GE corrections decomposed, and which channel dominates?

Of the total GE correction above the PE baseline, factor reallocation across sectors contributes 55%, capital misallocation due to climate uncertainty contributes 30%, and the distributional regressivity of damages contributes 15%. Factor reallocation is the dominant channel because, as climate damages alter relative prices, production shifts toward less-damaged sectors in ways that are distorted by the original carbon externality — generating second-order losses absent from PE damage functions.

Q4. Why does the PAGE calibration produce a larger GE correction (40%) than DICE (18%) or FUND (15%)?

The paper attributes PAGE’s larger GE correction to greater sector heterogeneity in that model’s parameterization. When damage exposure is more heterogeneous across sectors, the relative-price effects of marginal carbon are larger, amplifying the factor-reallocation channel. DICE and FUND, with more uniform sector-level damage structures, exhibit smaller reallocation corrections.

Q5. What is the median-calibration implication for the SCC in dollar terms?

In the median calibration, a PE SCC of $51/tCO₂ rises to a GE SCC of $62/tCO₂, an increase of roughly $11/tCO₂ or approximately 22%. This figure is directly computable from observable trade elasticities and sector-level damage estimates.

Q6. How should the carbon tax be adjusted when pre-existing labor market distortions are present, and what is the magnitude of the welfare gain from revenue recycling?

When labor taxes create a pre-existing wedge, using carbon tax revenue to reduce labor taxes generates additional welfare gains of 10–15% of total carbon tax revenue — the double dividend in the labor market dimension. The optimal carbon tax in this case includes the SCC plus a correction term for the labor-market distortion.

Q7. How do capital market distortions alter the optimal carbon tax relative to the SCC?

If capital is undertaxed (a pre-existing distortion in capital markets), the optimal carbon tax is set above the SCC. The intuition is that a higher carbon tax partially offsets the under-taxation of capital by raising the effective cost of carbon-intensive investment, capturing a double-dividend in the capital market.

Q8. How does monopolistic competition modify the optimal carbon tax?

For monopolistically competitive sectors, the optimal carbon tax is below the SCC. The reasoning is that applying a carbon tax to these sectors amplifies existing monopoly markups and associated distortions, so the social cost of the carbon tax exceeds the raw SCC in those sectors. The optimal policy trades off carbon correction against monopoly amplification.

Q9. What is the risk premium in the SCC under climate uncertainty, and how is it estimated?

The paper adds a term to the SCC proportional to the variance of damage estimates times the coefficient of relative risk aversion. Using empirical estimates of damage uncertainty, this risk premium is estimated at +$8–15/tCO₂, representing 15–25% of the base SCC. This term is absent from deterministic SCC calculations and constitutes a further reason standard PE estimates understate the true social cost.

Q10. What is the paper’s claim regarding computability of the GE correction?

The paper states that the novel GE terms are computable from observable trade elasticities and sector-level damage estimates, implying the GE correction is not merely a theoretical construct but can be implemented in quantitative policy analysis using data sources already available to researchers and policymakers.

Key Concepts

Social Cost of Carbon (General Equilibrium Formula) Defined in the paper as SCC_t = Sum_{tau >= t} beta^(tau−t) · [dW/dS_tau / (dW/dY_t)], the present discounted value of the marginal welfare loss from an additional unit of carbon, expressed relative to the marginal utility of current output. The paper’s version adds GE reallocation, capital-misallocation, and distributional terms absent from standard PE formulations.

GE Adjustment Factor The ratio of the general equilibrium SCC to the partial equilibrium SCC, expressed as GE/PE = 1 + phi_realloc + phi_capital + phi_distribution. Under standard conditions all three phi terms are positive, so the GE SCC strictly exceeds the PE SCC.

Climate Damage Function (Sector-Specific) Specified as D_j(S_t) = 1 − d_j·(S_t − S_0), a sector-specific multiplicative reduction in the production possibilities set as the carbon stock rises above the pre-industrial level S_0. Heterogeneity in d_j across sectors is the driver of the factor-reallocation GE correction.

Carbon Stock Evolution S_{t+1} = S_t + sum_j e_j(q_j) − alpha·S_t, where alpha is the natural decay rate of atmospheric carbon and e_j(q_j) is sectoral emissions as a function of output. Firms and households treat S_t as exogenous, generating the externality.

Double Dividend In second-best environments, a carbon tax can generate two welfare gains simultaneously: correcting the carbon externality and reducing the deadweight loss from a pre-existing distortion (labor or capital tax). The paper finds revenue recycling via labor tax cuts yields 10–15% of carbon tax revenue as additional welfare gain; undertaxed capital implies the optimal carbon tax is set above the SCC.

Risk Premium in the SCC An additive term in the SCC under climate uncertainty, proportional to the variance of damage estimates times the coefficient of relative risk aversion. Empirically estimated at +$8–15/tCO₂, representing 15–25% of the base SCC.

Second-Best Optimal Carbon Tax Written as tau*_carbon = SCC + CORRECTION, where the correction depends on the sign and magnitude of pre-existing distortions. The correction is positive under undertaxed capital (raise above SCC), negative under monopolistic competition (lower below SCC), and augmented by revenue-recycling gains when labor taxes are present.

Catastrophes, Delays, and Learning

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

This paper develops a general model of experimentation under catastrophe risk in which the catastrophe is triggered when a stock variable exceeds an unknown threshold, but occurs only after a stochastic delay. The central contribution is the concept of the “legacy of the past”: at any planning date, past experiments may have already triggered a catastrophe that has not yet materialized, and the planner cannot observe whether triggering has occurred. The legacy is formally defined as the probability, conditional on survival, that a catastrophe was triggered in the past.

The model unifies two canonical but previously incompatible approaches in the literature. In the hazard-rate approach, the catastrophe is bound to happen and the planner manages its timing and severity. In the unknown-threshold approach, learning is instantaneous and the catastrophe is certainly avoided if the stock has not yet exceeded the threshold. Neither approach captures the intermediate case where the planner remains uncertain about whether the catastrophe is already underway. By introducing a delay governed by an exponential distribution with parameter α, the authors show that both approaches are limiting special cases: as α → ∞ (no delay), the legacy vanishes and the unknown-threshold approach is recovered; when the legacy is set permanently to one (catastrophe triggered with certainty), the hazard-rate approach is recovered.

Three benchmark stock levels anchor the analysis. QN is the long-run target absent any catastrophe risk. QD (“Damages”) is the optimal stabilization target when the planner knows a catastrophe was triggered in the past — it lies weakly below QN because the planner trades off current gains against the discounted marginal damage from raising the stock at the moment of eventual catastrophe occurrence. QE (“Experimentation”) is the stock level below which stabilization is suboptimal when the planner is certain no triggering has occurred — it also lies weakly below QN.

The paper’s two main theorems are distinguished by the ranking of QD and QE, which reflects whether mitigation strategies are effective.

Theorem 1 (QE < QD): When damage is not highly sensitive to the stock level at catastrophe time — so mitigation is relatively ineffective — optimal paths are monotonically increasing and converge to a long-run stock level Q∞ ∈ [QE, QD]. The stopping condition equates the marginal benefit of experimentation to a weighted average of the expected cost under the unknown-threshold approach (weight 1 − π) and the marginal damage under the hazard-rate approach (weight π), where π is the legacy at stopping time. A higher legacy at the stopping time is associated with a higher long-run stock level. A higher initial legacy induces fatalism: since the catastrophe is more likely already triggered, the planner shifts priority toward current consumption rather than caution, leading to more total experimentation.

Theorem 2 (QD < QE): When damage is highly sensitive to the stock level — so mitigation is valuable — the long-run target is uniquely QE regardless of the initial legacy. However, the short-run path is non-monotonic: for a sufficiently high initial legacy, the planner first reduces the stock sharply (lockdown, emissions cut) to mitigate pending catastrophe damages, then, as the legacy declines because no catastrophe occurs, gradually allows the stock to rise back toward QE. The direction of caution reverses relative to Theorem 1: a higher legacy now induces more caution, not less.

Applications include pandemic management (stock = infected population, catastrophe = health system collapse) and climate change (stock = cumulative CO2 emissions or atmospheric pollution stock). In the disease control application, whether a planner prioritizes economic production or mortality reduction determines which theorem governs, with the key ratio being production losses relative to mortality increases. For pandemic policy, Theorem 2 produces a formal learning-based rationale for non-monotonic “hammer-and-dance” policies (strict early lockdown followed by relaxation) that differs from prior explanations in the literature. In the carbon budget application, Proposition 5 formally proves that higher initial legacy raises the optimal carbon budget under Theorem 1 conditions, and can imply unbounded consumption (certainty of catastrophe) above a critical legacy threshold π*. Under Theorem 2 conditions (Proposition 6), the optimal policy can involve first reducing then expanding the stock before stabilizing, with both transition dates increasing in the initial legacy.

Q: What is the “legacy of the past” and how is it computed? A: The legacy πt is defined as the probability, conditional on survival to date t, that a catastrophe was already triggered by past experiments. Formally, πt = 1 − [1 − F(Qt)] / pt, where Qt is the highest stock level ever reached, F is the prior distribution over the threshold, and pt is the survival probability. A past experiment at time t’ contributes to the current legacy with weight exp[−α(t − t’)], so recent experiments matter more than distant ones. As time passes without catastrophe, the legacy of any fixed past experiment declines geometrically at rate α.

Q: How do the three benchmark stock levels QN, QD, and QE relate to each other? A: QN is the optimal long-run stock without any catastrophe. QD is defined by the condition where the marginal net benefit of increasing the stock — ν(Q) − [α/(α+δ)]D’(Q) — equals zero, and satisfies QD ≤ QN. QE is defined by ν(Q) − [α/(α+δ)]ρ(Q)D(Q) = zero, and also satisfies QE ≤ QN. The ranking between QD and QE depends on whether damage is more sensitive to the marginal increase in stock at catastrophe time (which pushes QD below QE) or to the level of the stock at triggering (which pulls QD above QE).

Q: What is the key optimality condition in Theorem 1 and how does it unify prior approaches? A: The stopping condition (equation 15) states: ν(QT) = [α/(α+δ)] × [(1 − πT)ρ(QT)D(QT) + πT D’(QT)]. When πT = 0 (no legacy, unknown-threshold limit), this reduces to the experimentation stopping condition of Tsur and Zemel, governed by the hazard rate ρ(QT) times expected loss D(QT). When πT = 1 (full legacy, hazard-rate limit), it reduces to the damage-mitigation condition governed by marginal damage D’(QT). The legacy at stopping time thus serves as the mixing weight between the two canonical approaches, embedding both as special cases.

Q: How does the initial legacy affect total experimentation under Theorem 1 versus Theorem 2? A: Under Theorem 1 (QE < QD), a higher initial legacy π0 leads to more total experimentation (higher Q∞), because the planner becomes fatalistic — since the catastrophe is more likely already triggered and mitigation is relatively ineffective, current consumption is prioritized. Proposition 5 formally proves this for the carbon budget application: the optimal stopping date T and optimal budget QT are nondecreasing in π0. Under Theorem 2 (QD < QE), a higher legacy triggers more caution in the short run (larger reduction in the stock during the mitigation phase), but the long-run target QE remains the same regardless of π0.

Q: What generates non-monotonic policies in Theorem 2, and what does this look like in the pandemic application? A: Non-monotonicity arises because the optimal response to a high legacy is first to reduce the stock sharply to limit catastrophe damages (since damage is sensitive to the stock level), and then, as time passes without catastrophe and the legacy declines, to allow the stock to recover. In the disease control application with high mortality weight, a complete lockdown is optimal in the first phase whenever the legacy is strictly positive. As the legacy declines, the lockdown is gradually relaxed, and eventually the infection level returns to its pre-lockdown level. Figures 3 and 4 show that a higher initial legacy (π0 = 0.1, 0.5, or 0.9) leads to a longer lockdown and slower recovery, though all paths converge to the same long-run infection level.

Q: How does the model’s disease control application determine which theorem governs? A: Lemma 2 states that if 1 / [1 + (Y(r+d) − Y*) / (wµdI^D)] < ρ(I^D), then I^E < I^D and Theorem 1 applies; otherwise I^E > I^D and Theorem 2 applies. The key ratio is (Y(r+d) − Y) / (wµ*d), the production loss relative to mortality increase. A planner who weights economic activity heavily (large production loss ratio) falls under Theorem 1 and tolerates rising infections; a planner who weights mortality heavily falls under Theorem 2 and imposes an initial lockdown.

Q: What is the carbon budget result under Theorem 1 (Proposition 5)? A: Under the condition u1 > [α/(α+δ)]v0 (marginal consumption value exceeds discounted marginal damage), Theorem 1 applies and there exists a critical legacy threshold π* such that: below π*, the planner consumes maximally (qt = q-bar) until a finite date T and then stops, with QE < QT < QD; above π*, the planner consumes maximally forever, triggering the catastrophe with certainty. The stopping date T and the optimal budget QT are nondecreasing functions of initial legacy π0, formally proving that higher past emissions (captured through legacy) justify higher future carbon budgets in this model.

Q: What is the carbon budget result under Theorem 2 (Proposition 6)? A: Under condition u1 < [α/(α+δ)]v0, QD < QE and Theorem 2 applies. Starting from Q0 above QE, if π0 is small enough (specifically u1 > π0[α/(α+δ)]v0), the optimal policy is to stabilize the stock forever at Q0. Otherwise, there exist two finite dates t1 < t2, both increasing in π0, such that the planner first reduces the stock at maximum rate (qt = q-bar-negative) for t < t1, then expands at maximum rate for t1 < t < t2, then stabilizes at Q0 forever. The optimal carbon budget is Q0 in all cases, showing that the long-run target is independent of legacy under Theorem 2.

Q: How does the model relate to the hazard-rate literature formally? A: Papers such as Nordhaus and others that use an exogenous hazard rate h(Qt) for catastrophe — yielding survival probability pt = p0 exp(−∫h(Qτ)dτ) — are shown to be equivalent to the special case where the catastrophe was triggered in the past (legacy = 1 permanently). Their formulation corresponds to assuming α is constant and the legacy is identically one, which reduces the law of motion for pt to pt = p0 exp(−αt). The key difference is that in the hazard-rate approach the planner can reduce the arrival rate by lowering the stock (h is increasing in Q), whereas in the authors’ model the delay parameter α is constant and policy affects only damages.

Q: What is the role of the exponential delay distribution assumption? A: The assumption that the delay τ follows an exponential distribution with parameter α is made for tractability. Under this assumption, the entire past trajectory of the stock (Qt)t≤0 can be summarized by just two state variables — the highest stock on record Q0-bar and the initial legacy π0 — because the exponential “memoryless” property means that the additional expected waiting time until catastrophe occurrence does not depend on how long the triggering has already been in effect. Without this assumption, the full chronicle of past experiments would be required as a state variable, making the problem intractable.

Q: What happens when the delay parameter α approaches zero or infinity? A: When α → ∞ (instantaneous catastrophe upon triggering), pt = 1 − F(Qt) and the legacy is identically zero, recovering the Tsur-Zemel unknown-threshold approach (Proposition 3). The optimal path converges to QE0 from below or stabilizes if already above QE0. When α → 0 (infinite delay, effectively no catastrophe), QE = QD = QN and the problem reduces to the simple stock-flow problem (Proposition 1), with the optimal path converging monotonically to QN.

Q: Does the model allow for damage mitigation after triggering but before occurrence? A: Yes, this is a key feature. The continuation payoff after catastrophe occurrence is V(QT) where QT is the stock level at the time of occurrence T, not at triggering time T(S). This means the planner can reduce the stock after triggering to lower damages — analogous to a skater turning back toward shore after the ice first cracks. The assumption that V depends on the stock at occurrence rather than at triggering or at the maximum historical level is what allows this mitigation channel and is explicitly noted as a modeling choice.

Legacy of the past (πt): The probability, conditional on survival to date t, that past experiments have already triggered a catastrophe. Formally πt = 1 − [1 − F(Qt)] / pt. Recent experiments contribute more to the legacy than distant ones, with contribution decaying at rate α. The legacy is zero when α → ∞ and is the central state variable bridging the paper’s two canonical extremes.

QE (“Experimentation” threshold): The stock level at which the net marginal gain from further experimentation, defined as ν(Q) − [α/(α+δ)]ρ(Q)D(Q), equals zero, under the assumption that no catastrophe has been triggered. Below QE, stabilization is suboptimal; above QE, the planner does not experiment further when the legacy is zero.

QD (“Damages” threshold): The stock level at which the net marginal benefit from holding the stock, defined as ν(Q) − [α/(α+δ)]D’(Q), equals zero, under the assumption that the catastrophe is known to have been triggered. QD ≤ QN and represents the optimal long-run target when the hazard-rate approach applies.

Marginal payoff ν(Q): Defined as uq(0, Q) + (1/δ)uQ(0, Q), it measures the net gain from marginally increasing the flow when the stock is stabilized at Q. It is strictly decreasing in Q under Assumption 1 and equals zero at QN.

Damage function D(Q): Defined as (1/δ)u(0, Q) − V(Q), it measures the welfare loss from catastrophe occurrence when the stock is Q at occurrence time, relative to permanent stabilization at Q. Assumed weakly positive and weakly increasing in Q.

Survival probability (pt): The probability, computed from prior beliefs F at the beginning of times, that the catastrophe has not yet occurred by date t. Its law of motion is ṗt = α[1 − F(Qt) − pt], driven solely by the catastrophe parameter α and the current maximum stock Qt.

Fatalism (under Theorem 1): The policy implication that a higher legacy — meaning a higher probability the catastrophe is already triggered — leads the planner to increase the stock further and accept more experimentation, because mitigation is relatively ineffective (QE < QD) and current consumption must be enjoyed before the catastrophe arrives.

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.

Insuring Peace: Index-Based Livestock Insurance, Droughts, and Conflict

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

This paper provides quasi-experimental evidence that Index-Based Livestock Insurance (IBLI) — a remote-sensing-triggered, automated payout scheme for pastoralists — substantially reduces drought-induced conflict in Kenya over the 2001–2020 period.

The research question is whether a market-based financial instrument can mitigate the causal chain running from drought shocks to violent conflict between nomadic pastoralists and sedentary farmers and other land users. The authors motivate the study by documenting that droughts force pastoralists out of their traditional grazing grounds and into mixed-land-use areas (farms, ranches, urban settlements, nature reserves), where miscoordination with other land users escalates into violence. A case study of the Samburu-Laikipia-Isiolo-Meru region in central Kenya — drawing on georeferenced survey data from Lengoiboni et al. (2010) and ACLED conflict events — validates this spatial mechanism: during droughts, roughly 60–90% of non-pastoral land users report encounters with pastoralists, and conflicts accumulate precisely where drought migration routes cross into non-pastoral land.

The empirical design combines two sources of variation: (1) plausibly exogenous changes in rainfall deficits at the 0.1 × 0.1-degree grid-cell level (roughly 10 × 10 km), derived from NASA GPM satellite data; and (2) the staggered, five-wave rollout of IBLI across 146 insurance districts in Kenya from 2010 onward, which the authors argue was driven primarily by technical challenges rather than pre-existing conflict or drought patterns. The unit of observation is 94,300 cell-periods. Because conflicts due to pastoralist drought migration occur in the neighborhood of affected areas rather than within them, both drought and IBLI coverage are measured as inverse-distance-weighted averages over surrounding cells. The estimating equation is a linear probability model with cell and period fixed effects, interacting neighborhood rainfall deficit with neighborhood IBLI coverage; the coefficient on this interaction term (delta3) is the parameter of interest.

The main finding is that a one-standard-deviation increase in neighborhood IBLI coverage reduces the semi-elasticity of neighborhood rainfall deficit on conflict probability by approximately 23%. In absolute terms, a one-percentage-point increase in the rainfall deficit raises the probability of conflict by 6.92 percentage points at average IBLI coverage; with one additional standard deviation of neighborhood IBLI, that same deficit raises conflict probability by only 5.34 percentage points — a reduction of 1.58 percentage points against a baseline conflict probability of roughly 2.5%.

Scope conditions: the effect is estimated for Kenya specifically, over a pastoralist-heavy population of approximately 8.8 million out of 53 million Kenyans, during 2001–2020. The conflict-mitigating effect is approximately four times larger in mixed-land-use areas (nine times when rollout-cluster-times-period fixed effects are included), consistent with the theoretical expectation that IBLI matters most where pastoralists are most likely to encounter other land users during drought migration.

Two mechanisms are identified. First, IBLI reduces migratory pressure: when pastoral homelands have IBLI coverage, the distance between the ethnic homeland centroid and conflict events involving that group decreases, indicating reduced drought migration. Second, IBLI smooths incomes — corroborated with Afrobarometer geo-coded data — raising the opportunity cost of fighting. An instrumental-variable specification finds that actual IBLI payouts in the neighborhood reduce conflict probability by approximately 150% relative to the baseline risk.

A cost-effectiveness analysis finds that even using conservative World Health Organization or World Bank estimates of the value of statistical life, IBLI delivers fatality savings of between 10 and 22 cents per dollar spent on government subsidies for the program, making it a cost-effective complement to political and institutional conflict-mitigation approaches.

Q: What is the core causal mechanism linking droughts to conflict that IBLI interrupts?

A: Droughts deplete forage in pastoralists’ traditional grazing grounds, forcing them to migrate into mixed-land-use areas — farms, ranches, urban settlements, and nature reserves — where encounters with other land users are more likely to escalate into violence. Without insurance, pastoralists hold excess livestock as precautionary savings, amplifying the extent of necessary migration during dry periods. IBLI payouts allow pastoralists to purchase forage locally, reducing migration distance and intensity, and also smooth income, raising the opportunity cost of engaging in violence.

Q: How does IBLI work technically, and why does it overcome problems of traditional livestock insurance?

A: IBLI uses satellite remote sensing to calculate whether a district-specific drought threshold has been crossed; if so, automated payments are triggered immediately without requiring direct loss assessment or field inspections. This design eliminates moral hazard and adverse selection problems inherent in traditional indemnity insurance, reduces monitoring costs, and enables fast delivery via mobile payment platforms such as MPESA even to remote households. The Kenyan government rebranded the program as the Kenyan Livestock Insurance Program (KLIP) in 2015 and fully subsidizes coverage for up to five tropical livestock units per household.

Q: What is the magnitude of the main conflict-mitigation result?

A: A one-standard-deviation increase in neighborhood IBLI coverage reduces the semi-elasticity of the neighborhood rainfall deficit on conflict probability by approximately 23% (delta3/delta1 = -0.0158/0.0692). In absolute terms, this translates to a reduction from a 6.92 percentage-point increase in conflict probability per one-percentage-point rainfall deficit to a 5.34 percentage-point increase — a decline of 1.58 percentage points against a mean conflict probability of roughly 2.5%.

Q: Why do the authors use a neighborhood rather than cell-level treatment measure?

A: Drought-induced pastoralist conflicts occur primarily not in the pastoral home areas themselves but in neighboring regions where drought migration routes cross into non-pastoral land. The case study documents this pattern directly: ACLED conflict events accumulate where migration routes from Namelok, Lodungokwe, and Ngaremara communities intersect urban or agricultural areas, not within the pastoral zones. The neighborhood approach, using inverse-distance-weighted averages, captures both the probability of migration from surrounding cells and the declining probability of migration with distance.

Q: What is the main identification concern and how do the authors address it?

A: The main concern is that the timing of the IBLI rollout is endogenously determined — areas with a higher latent drought-conflict elasticity might receive coverage earlier or later, biasing the interaction coefficient. The authors show that the pre-treatment drought-conflict elasticity has no systematic correlation with either IBLI eligibility or the timing of coverage receipt. Placebo tests interacting the neighborhood rainfall deficit with pre-treatment eligibility or eventual coverage indicators yield positive, statistically insignificant coefficients, suggesting any bias would run in the direction of underestimating the mitigation effect. A permutation test randomly reassigning IBLI coverage across the six rollout clusters finds the actual point estimate is in the bottom 2.2% of the simulated distribution, indicating it is unlikely to arise from cluster-level confounders.

Q: How do the authors rule out that other programs — cash transfers or development aid — explain the result?

A: The authors control for cell-level and neighborhood-level coverage of Kenya’s Hunger Safety Net Programme (HSNP), which provides unconditional cash transfers to vulnerable households and covers most IBLI-eligible areas, as well as for World Bank agricultural aid projects. Across these specifications, the estimated conflict mitigation ranges from -19.16% to -42.24%, with the baseline estimate of -22.79% remaining robust, indicating neither HSNP nor development aid is a plausible alternative explanation.

Q: What is the alternative identification strategy using within-rollout-cluster variation?

A: The authors exploit pre-determined (1984 government land-use map) variation in mixed-land-use status across cells within the same IBLI rollout cluster-period, including rollout-cluster-times-period fixed effects that absorb any omitted variable related to the potentially endogenous rollout steps. The conflict-mitigating effect of IBLI is approximately four times larger in mixed-land-use cells, and approximately nine times larger in the most restrictive specification with rollout-cluster-times-period fixed effects, consistent with the prediction that IBLI matters most where pastoralists encounter other land users.

Q: How do the authors establish the migratory pressure mechanism?

A: Following Eberle et al. (2023), the authors match conflict actors to ethnic homelands using Murdock (1967) boundaries and test whether IBLI coverage in a homeland reduces the distance between the homeland centroid and conflict events involving that group. They find that it does, indicating that IBLI coverage reduces the spatial range of pastoralist drought migration and thus the probability of conflict-generating encounters with other land users.

Q: How do the authors establish the income-smoothing mechanism?

A: Using geo-coded Afrobarometer survey data, the authors show that IBLI coverage is associated with higher reported incomes among pastoralist households, consistent with Jensen et al. (2017). Higher incomes raise the opportunity cost of fighting (following Grossman, 1991), contributing to the overall conflict-mitigating effect alongside reduced migratory pressure.

Q: What does the instrumental variable specification find?

A: The authors instrument inverse-distance-weighted IBLI payouts in the neighborhood with the interaction of neighborhood rainfall deficit and neighborhood IBLI coverage. The first stage confirms that rainfall deficits trigger payouts conditional on coverage. The second stage finds that the occurrence of payouts in the neighborhood reduces the probability of conflict by approximately 150% relative to the baseline risk, corroborating the reduced-form results.

Q: How do the authors assess cost-effectiveness?

A: The authors predict plausible drought-induced conflict fatalities in Kenya over the pre-treatment period and calculate yearly lives saved from the main estimates, then compare the monetary value of saved lives to government subsidy expenditures on IBLI. Using conservative VSL estimates from the WHO and World Bank, IBLI delivers between 10 and 22 cents of pure fatality savings per dollar of public subsidy expenditure.

Q: How robust are the results to alternative drought and conflict measures?

A: Results are qualitatively similar using an Aridity Index or Dry Matter Productivity (DMP) as drought proxies instead of rainfall deficit. The estimated interaction effect maintains a t-statistic above two for spatial decay functions ranging from distance^-0.5 to distance^-1.5 and for Conley standard error cutoffs from 200 km up to 400 km. Results also hold when restricting to conflict events not involving the government, or to battles, riots, and violence against civilians only, and when excluding the pre-IBLI period (2000–2009) entirely.

Q: What are the policy implications regarding scalability?

A: Pastoralism covers 43% of the African landmass across 36 countries, supporting approximately 268 million people (FAO, 2018). The World Bank and private equity were planning to invest close to 900 million dollars in East African pastoralist programs over 2023–2027. The authors argue that IBLI’s cost structure — high fixed costs of technology and setup but low marginal costs of expansion — gives it a scalability advantage over cash transfer programs or public works schemes that require sustained state capacity. Market-based IBLI complements rather than substitutes for political and institutional reforms.

Index-Based Livestock Insurance (IBLI): A financial instrument that uses satellite remote sensing to automatically trigger preemptive cash payouts to pastoralists when a pre-determined district-specific drought threshold is crossed, bypassing direct loss assessment and thereby eliminating moral hazard and adverse selection problems inherent in traditional indemnity insurance.

Drought-conflict semi-elasticity: The percentage-point change in the probability of conflict associated with a one-percentage-point increase in the rainfall deficit; the paper’s main outcome quantity, estimated at 6.92 percentage points at mean IBLI coverage, reduced by 23% for a one-standard-deviation increase in neighborhood IBLI coverage.

Neighborhood approach: An empirical strategy that measures both drought severity and IBLI coverage as inverse-distance-weighted averages over all surrounding grid cells, reflecting the authors’ finding that pastoralist drought-migration generates conflicts not in the pastoral home area but in neighboring mixed-land-use zones where migration routes intersect other land users.

Migratory pressure: The mechanism by which drought forces pastoralists — who hold excess livestock as precautionary savings in the absence of insurance — to migrate farther from traditional grazing grounds into mixed-land-use areas, increasing the probability of encounters and violent miscoordination with farmers, urban dwellers, and protected-area managers.

Mixed land use: Areas, designated using a 1984 Kenyan government land-use map, where pastoral grazing zones are proximate to farms, ranches, urban settlements, or nature reserves; the paper identifies these as the locations with the highest expected treatment intensity, where IBLI coverage reduces drought-induced conflict approximately four to nine times more than elsewhere.

Tropical Livestock Unit (TLU): The standard unit of account for IBLI contracts in Kenya; one TLU corresponds to one head of cattle or ten goats or sheep; the Kenyan government fully subsidizes IBLI for up to five TLUs per household.

Rollout-cluster-times-period fixed effects: A restrictive set of fixed effects included in the alternative identification strategy that absorbs all omitted variables varying at the level of the six IBLI spatial rollout clusters over time, allowing the authors to identify the conflict-mitigating effect purely from within-cluster variation in mixed-land-use exposure.

Pigovian Transport Pricing in Practice

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

This paper reports on the MOBIS experiment, a large-scale randomized controlled trial (RCT) implementing a multi-modal Pigovian transport pricing scheme in urban areas of German- and French-speaking Switzerland. The central research question is whether a first-best transport pricing scheme — one that charges users the full marginal external costs of their travel choices, varying across time, space, and mode — generates meaningful behavioral responses, and how those responses compare to a pure information intervention.

The study recruited participants from urban areas, requiring them to be between 18 and 65 years old and to use a car at least two days per week. After contacting over 90,000 individuals and an initial online screening of 21,800 respondents, 3,656 participants completed the RCT. Each participant agreed to have their daily travel tracked via a smartphone app (“Catch-My-Day”) for eight weeks: four weeks of observation followed by four weeks of treatment. Assignment to treatment and control groups was fully randomized without stratification.

The pricing treatment gave participants a budget equal to their observed external costs during the observation period plus a 20% buffer, from which the external costs of their actual travel were deducted in real time; any remaining balance was theirs to keep. External costs were computed across all modes using official Swiss Federal Roads Office monetization factors, including congestion (via a MATSim-based average marginal cost approach), CO2 climate costs (CHF 136.08/ton), health costs from air pollution (PM10 and NOx), and accident and physical activity effects for active and public modes. Public transport also carried a peak-hour surcharge of CHF 0.10/km for congested zone-pairs. A second “information-only” treatment provided identical information about external costs but imposed no financial charge. A control group received only weekly summaries of kilometers traveled by mode.

The regression framework is a difference-in-differences specification with person, calendar-day, and day-of-study fixed effects, estimated in levels for external-cost outcomes (due to negative values from walking’s net external benefit) and via Poisson Pseudo-Maximum Likelihood for non-negative outcomes.

The pricing treatment reduced total external costs by CHF 0.215 per day (p < 0.01), a 5.1% reduction relative to the control group. The average private cost of transport for the control group during the treatment period was CHF 25.72 per day; the external cost was CHF 4.22 per day, implying that Pigovian pricing raised total transport costs by 16.4% on average. The implied price elasticity of external costs with respect to this price increase is -0.31. The reduction is attributable to mode substitution toward public transport and active modes and to departure time shifting away from peak hours, but not to a reduction in total distance traveled.

The information-only treatment produced a coefficient of -0.087, which is not statistically significant at conventional levels for the full sample. The differential effect of adding pricing to information is -0.127 (marginally significant, p < 0.1), with the pricing increment particularly important for reducing congestion costs. Sensitivity analysis shows that removing the control group and time fixed effects inflates the before-vs.-after elasticity to between -0.57 and -0.71, substantially larger than the preferred estimate of -0.31, underscoring the importance of the experimental design.

Heterogeneity analysis reveals that men respond more strongly than women, German speakers more than French speakers, participants under 30 more than older participants, and those with above-median altruistic values respond significantly even to information alone. Correct knowledge of the definition of external costs (present in 45% of the sample) is a key driver of the pricing treatment effect. These scope conditions — mode availability, urban Swiss context, short 4-week treatment window, mandatory car use eligibility, and the specific external cost monetization framework — bound the generalizability of the elasticity estimate.

Q: What is the main treatment effect of the Pigovian pricing scheme on external transport costs? A: The pricing treatment reduced total external costs by CHF 0.215 per day, which is a 5.1% reduction relative to the control group (p < 0.01). About half of the reduction came from health costs, with congestion and climate costs following in magnitude. The implied elasticity of external costs with respect to the Pigovian price increase is -0.31, meaning a 10% increase in total transport costs from Pigovian pricing would reduce external costs by approximately 3.1% in the short run.

Q: How was the Pigovian price increase calculated, and what was its magnitude relative to private costs? A: The average private cost of transport for the control group during the treatment period was CHF 25.72 per day, and the average external cost was CHF 4.22 per day. The external cost thus represents 16.4% of total (private plus external) transport costs, and dividing the 5.1% reduction in external costs by this 16.4% price increase yields the elasticity of -0.31.

Q: What mechanisms drove the reduction in external costs? A: The reduction resulted from a combination of mode substitution — a shift away from car use toward public transport and active modes — and departure time shifting away from peak hours. Critically, total distance traveled did not decline; the behavioral adjustment operated entirely through changes in how and when people traveled, not in how much.

Q: What was the effect of the information-only treatment? A: The information-only treatment produced a coefficient of -0.087 CHF per day, which was not statistically significant at conventional levels for the full sample. It was statistically significant only for subgroups, notably participants with above-median altruistic values. The differential effect of adding pricing to information (alpha_P minus alpha_I = -0.127) was marginally significant (p < 0.1) and was particularly concentrated in congestion cost reductions, suggesting that the monetary incentive is especially important for internalizing the congestion externality.

Q: Why is the control group critical, and how does removing it affect the estimated elasticity? A: The tracking data show a seasonal negative trend in external costs over the study period; without a control group, this trend would be incorrectly attributed to the treatment, inflating the estimated effect. When both day-of-study and calendar-day fixed effects are removed (approximating a before-vs.-after design without a control group), the estimated elasticity rises to between -0.57 and -0.71, roughly double the preferred estimate of -0.31. This highlights that most prior studies in the literature, which lack control groups, are likely to overestimate treatment effects.

Q: What heterogeneity is observed in the treatment response? A: Men respond more strongly than women to both treatments, with the gender gap particularly pronounced for congestion costs. German speakers respond more strongly than French speakers. Participants under age 30 show stronger responses than older participants. Those scoring above the median on an altruistic values index respond significantly not only to pricing but also to information alone. Participants who correctly defined external costs (45% of the sample) drive the pricing treatment effect; a causal forest analysis confirms knowledge of external costs, age below 30, and language region as key heterogeneity drivers.

Q: How were external costs computed across modes, and what are the key monetization parameters? A: For private road transport, GPS tracks were map-matched using Graphhopper and processed via MATSim modules; emission factors came from the HBEFA 3.3 database, and congestion was assessed via an average marginal cost approach incorporating spillback effects. Externalities were monetized at CHF 136.08/ton for CO2, CHF 515,497–1,358,461/ton for PM10 (rural vs. urban), CHF 7,109/ton for NOx (regional), and a value of travel time savings of CHF 25.77/hour. For other modes, per-km values from the Swiss Federal Roads Office were applied. Walking carries net external benefits (negative external costs), while cycling carries small net external costs because accident costs exceed physical activity benefits.

Q: How was public transport priced in the experiment, and why was it simplified? A: A second-best zonal peak-hour surcharge of CHF 0.10/km was applied to public transport stages between zone-pairs experiencing peak demand, with peak windows set at 7–9 am and 5–7 pm. Full first-best pricing of public transport crowding was deemed infeasible because crowding effects are highly heterogeneous spatially and temporally, often concentrated in very short windows on specific lines, making aggregate distribution unreasonable.

Q: Was there evidence of gaming the mode detection system? A: Because participants could manually correct the app’s algorithmic mode assignments — and the pricing group had an incentive to overclaim low-cost modes — the potential for strategic misreporting was examined. While the analysis could not rule out some gaming, the main results were shown to be robust to excluding potential gamers, suggesting that gaming did not materially distort the treatment effect estimates.

Q: What does the study imply for transport pricing policy? A: The elasticity of -0.31 provides a benchmark for policymakers: a full Pigovian pricing scheme that raises total transport costs by about 16% can be expected to reduce external costs by about 5% in the short run in an urban context. The finding that congestion costs respond more to pricing than to information alone suggests the monetary component is essential for this externality. Heterogeneous responses — particularly the weaker responses by women and French speakers — have distributional implications. The experiment is a proof of concept that first-best transport pricing can generate meaningful behavioral responses, but scaling it would require addressing privacy concerns from GPS tracking, technical infrastructure, and political economy challenges.

Pigovian transport pricing: A pricing scheme that charges each user the marginal external costs of their transport choices — including health, climate, congestion, and noise costs — as they vary across time, space, and mode, intended to internalize the gap between private and social costs of travel.

External costs of transport: Costs borne by society rather than the individual traveler, including congestion (delay imposed on others), climate damages (CO2 emissions), health costs (local air pollution, accidents), and noise; in this paper, computed in real time from tracked trips using official Swiss monetization values.

Average treatment effect (ATE): The difference-in-differences estimate of the causal effect of the pricing or information treatment on outcomes, identified from the randomized assignment and controlling for person, calendar-day, and day-of-study fixed effects.

Mode substitution: The behavioral response in which travelers shift from higher-external-cost modes (primarily car) to lower-external-cost modes (public transport, walking, cycling) in response to pricing, as distinct from reducing total travel distance.

Departure time shifting: The behavioral response in which travelers adjust when they depart to avoid peak-hour congestion surcharges, contributing to reduced congestion externalities without reducing total distance traveled.

Information-only treatment: An experimental arm receiving identical information about external costs as the pricing group but facing no financial charge, used to isolate the informational component of the pricing treatment from the monetary incentive component.

Source text origin: pdf

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

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

The paper shows that the macroeconomic impact of climate change is an order of magnitude larger than what standard country-level panel estimates suggest. The key identification innovation is to measure the effect of global mean temperature shocks using time-series local projections, rather than using cross-country variation in local temperatures as in the conventional panel literature. A shock to global mean temperature tracks extreme weather events (droughts, heat waves, wind, precipitation anomalies) that affect all countries simultaneously; a local temperature anomaly in one country does not.

Empirical approach: The authors estimate local projections of world GDP growth on exogenous global mean temperature shocks. The shock is the innovation to global mean temperature after removing a 2-year autoregressive component and a low-frequency trend, following Hamilton (2018). Two estimation samples: BU (Barro-Ursúa macro history, 43 countries, 1860–2019) and PWT (Penn World Tables, 173 countries, 1960–2019).

Key empirical results (Section 3): A 1°C shock to global mean temperature causes world GDP to fall by 14% after 6 years in the PWT sample (95% CI: 6%–22%); significant at the 5% level in years 2–8; does not mean-revert within the 10-year sample horizon. In the BU sample, the peak GDP decline is 18% after 5 years (95% CI: 6%–30%). Converting the cumulative IRF ratio to a permanent temperature change yields a 22–34% long-run GDP decline per 1°C of permanent global warming (PWT and BU respectively). By contrast, local temperature shocks — estimated from a standard cross-country panel with country and year fixed effects — generate effects of 1–3% per °C, not statistically significant at the 5% level.

Why global > local (Section 4): Four categories of extreme climatic events (heat waves, droughts, wind, precipitation anomalies) jointly account for roughly half of the estimated global temperature effect on GDP. None of these are strongly correlated with local temperature anomalies because extreme weather reflects ocean-atmosphere dynamics (El Niño/ENSO) that elevate global mean temperature rather than any single country’s local temperature. In addition, capital and investment both decline persistently after global temperature shocks (capital response significant at 5% level), and warm/low-income countries are disproportionately affected.

Structural model (Section 5): A parsimonious neoclassical growth model embeds climate change as aggregate TFP changes. Households maximize ∫e^{−ρt}U(C_t)dt; firms use Cobb-Douglas technology Z_t K_t^α L_t^{1−α}. The damage function governing TFP is:

Z_t = Z_0 exp( ∫0^t ζ_s T̂{t−s} ds )

where T̂_t is excess global mean temperature above baseline and ζ_s = A(e^{−Bs} − e^{−Cs}) is the structural damage function. When ζ_s → 0, shocks have level but not growth effects; no statistically significant evidence of growth effects is found in Figure 3 of the paper. The model is calibrated with: risk aversion γ = 1 (log utility), capital share α = 0.33, annual capital depreciation δ = 0.08, and pure time preference ρ = 0.02. Proposition 1 (model inversion) shows that, to first order, ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model. This delivers identification: observed output impulse responses recover the structural TFP damage function ζ_s without imposing functional form on the capital channel.

Estimation results (Section 5.3, Figure 12): The estimated damage function implies a 4% peak short-run productivity decline 2 years after a 1°C transitory global temperature shock; the effect decays slowly and remains significant for up to 10 years. The capital response (non-targeted moment) closely matches its empirical counterpart, providing an overidentification check. The local temperature damage function, estimated by targeting the local-panel output IRF, peaks at only 0.5% and is more than 8× smaller in cumulative productivity effect; it is not statistically different from zero at the 5% level.

Business-as-usual counterfactual (Section 6.1–6.2): Temperature rises from 2024, reaching 3°C above preindustrial by 2100 (asymptoting to 3.3°C), equivalent to 2°C of additional warming since 2024. Under the global temperature damage function:

World output by 2050: −28% vs. no-warming baseline
World output by 2100: −53% (accumulated TFP losses reach −40%)
Capital by 2100: −51% (investment initially rises as households anticipate lower permanent income, then decumulates rapidly)
Consumption by 2100: −53%
2024 welfare loss (consumption equivalent): 35%; welfare continues declining as temperatures rise, eventually reaching 56%
95% CI for 2100 output loss: 29%–77%
All effects statistically significant at the 5% level

Under the local temperature damage function with the same warming scenario: long-run output declines only 9%, welfare loss is 5%, and neither is statistically significant at the 5% or 10% level — consistent with conventional estimates (Nordhaus 1992, Dell et al. 2012, Burke et al. 2015).

Social Cost of Carbon (Section 6.2, Panel F): The SCC is defined as the consumption-equivalent amount households would pay at time 0 to avoid one additional ton of CO2, using the temperature-response function from Dietz et al. (2021a). Baseline result: $1,207 per ton (2024 international dollars), more than 6× larger than the $185/ton estimate in Rennert et al. (2022). 95% CI: $399–$2,015 per ton. Climate sensitivity range (half/double median): $600–$2,400 per ton. BU sample (larger damage functions): >$1,500 per ton. Using the local temperature damage function yields an SCC of only $149/ton, consistent with conventional estimates.

Sensitivity (Section 6.4): Higher time preference ρ > 0.04 lowers welfare losses below 20% and the SCC below 3× conventional high-end estimates — the only scenario where results converge toward prior estimates. Near-Stern discount rates (ρ → 0): welfare loss >40% and SCC >$2,500/ton. A 6°C-by-2100 scenario yields welfare losses >60%.

Historical growth accounting (Section 6.3): Starting the model in 1960 and imposing the realized 1960–2019 warming path, then holding temperature constant at its 2019 level, reveals:

World GDP per capita would be 25% higher today without warming since 1960
By 2040, output is 32% below potential from past warming — one-quarter of losses from historical warming are yet to materialize (due to delayed damage function and transitional capital dynamics)
Climate change reduced the annual world growth rate by as much as a third of baseline by the 21st century

Policy implication: Most decarbonization interventions cost ~$80/ton on average (Bistline et al. 2023). Under conventional SCC estimates based on local temperature ($149/ton), the US Domestic Climate Cost (DCC) falls below policy cost, making unilateral emissions reduction prohibitively expensive. Under the paper’s global temperature SCC of $1,207/ton, the DCC of the United States exceeds $80/ton even accounting for the fraction of global climate benefits that accrue domestically — unilateral decarbonization becomes cost-effective for large economies such as the US.

Scope conditions: The neoclassical model abstracts from adaptation, mitigation, trade, urbanization, and endogenous emissions. The identification assumption requires that global mean temperature innovations are uncorrelated with other global economic confounders at business-cycle and trend frequencies; the paper checks robustness against alternative detrending, exclusion of WWII and COVID-19 years, El Niño/ENSO controls, and instrumental variables for temperature based on solar/volcanic forcing. The conversion from medium-run to long-run effects relies on the constrained ζ_s = A(e^{−Bs} − e^{−Cs}) functional form ruling out growth effects — consistent with the data but not formally testable beyond the 10-year horizon. Counterfactuals involve 2–3°C temperature changes substantially beyond the sample’s moderate perturbations; the model’s extrapolation may understate damages if nonlinearities exist at extreme temperatures (the authors note their conservative constrained-form approach).

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 do global temperature shocks produce GDP effects an order of magnitude larger than local temperature panel estimates?

Global mean temperature shocks are strongly correlated with extreme weather events — heat waves, droughts, wind storms, and precipitation anomalies — that simultaneously affect all countries; these four event categories jointly account for roughly half of the global temperature effect on GDP. Local temperature anomalies in a given country (as measured in standard cross-country panels with year fixed effects absorbed) are not correlated with these same events, because El Niño/ENSO and related ocean-atmosphere dynamics elevate global mean temperature without proportionally elevating any one country’s local temperature. Local panel studies also implicitly allow economic activity to shift toward cooler regions within a given year — an option unavailable when global warming affects all locations simultaneously. The resulting bias in local-panel estimates is not “aggregation bias” in the sense of Jensen’s inequality, but rather an identification problem: local panels identify a different object (the effect of temperature relative to other countries in the same year) rather than the aggregate climate impact the paper measures.

Q2. What is the identification strategy and what are the main threats?

The global temperature shock is identified as the innovation to global mean temperature after removing a 2-year AR component and a Hamilton (2018) low-frequency trend, yielding a shock orthogonal to its own recent history and to long-run trends. The main threats are: (i) global business-cycle confounders (worldwide recessions that simultaneously lower activity and emissions), addressed by controlling for quadratic time trends and global aggregate demand proxies; (ii) reverse causality (economic expansion warming the atmosphere), addressed by IV estimates using solar/volcanic forcing as instruments; (iii) low-frequency correlation between climate trends and productivity growth, addressed by flexible detrending and robustness to sample period. All major specification checks generate quantitatively similar results, and the paper passes placebo tests for large global confounders (WWII, COVID-19).

Q3. How does the structural model translate medium-run shock responses into long-run warming effects?

Proposition 1 (model inversion) shows that the output impulse response decomposes into a direct TFP effect ẑ_t and a capital channel ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model (Auclert et al. 2021); this allows recovery of the structural TFP damage function {ζ_s} from the observed 10-year output IRF by non-linear least squares, without having to observe TFP directly. The counterfactual for a gradually rising temperature path (BAU scenario with 2°C additional warming since 2024) is then solved via the full nonlinear model — not via the log-linearization used in estimation — because the 2–3°C excursion far exceeds the sample’s modest temperature perturbations. The capital response (non-targeted moment) closely tracks its empirical counterpart, providing a strong overidentification check that the model’s capital dynamics are correctly specified.

Q4. Why does capital initially rise in the BAU counterfactual before declining?

Following standard permanent-income logic, when households learn at date 0 that global temperatures will rise and future TFP will fall, they temporarily increase saving and investment to accumulate buffer capital before the productivity decline materializes; this front-loads some capital accumulation in the early transition years (2024–2030s), briefly pushing capital above baseline, before the accumulated TFP losses overwhelm the saving motive and capital begins an extended decline. The net effect is still a 51% capital shortfall by 2100 because persistently lower TFP reduces the marginal product of capital over decades, depressing investment and allowing the capital stock to drift far below its no-warming balanced growth path.

The SCC is defined as the dollar amount C such that households are indifferent between (a) a world where one additional ton of CO2 is emitted at time 0 and (b) a world in steady-state where the household has paid C at time 0 (equation 7: V^{ss}(K^{ss} − C) = V^{SCC}_0(K^{ss})). The temperature response to a 1-ton CO2 pulse is taken from Dietz et al. (2021a) — temperature peaks at 0.002°C after a 1-gigaton pulse and stabilizes. The model generates the productivity path {Z^{SCC}_t} via the structural damage function, solves for equilibrium capital and consumption paths, and computes the value function V^{SCC}_0. The resulting $1,207/ton exceeds prior estimates by 6× because the global-temperature damage function implies 4% peak TFP losses per 1°C transitory shock, compared to the ~0.5% peak implied by local temperature — and the SCC is essentially the capitalized sum of these future productivity losses, so the ratio scales proportionally.

Q6. Why are historical climate losses so large if year-to-year warming is small?

The key is cumulation: annual warming increments are individually small (tenths of a degree), but the damage function {ζ_s} is persistent (effects last 10+ years), so each year’s increment adds a flow of persistent TFP losses that stack on top of prior increments. The paper’s growth accounting shows that climate change reduced the world growth rate by up to one-third of baseline in the 21st century — a number that appears modest in any single year but, compounded over decades, translates into a 25% GDP per capita shortfall by 2019. Additionally, because the estimated damage function has a 2-year lag before peak TFP impact, a substantial share of past warming’s losses are yet to be realized — the paper estimates GDP will be 32% below its potential by 2040 even with no further warming.

Q7. What does the sensitivity analysis reveal about the robustness of the results?

The key sensitivity is the rate of time preference ρ: at ρ = 0.02 (baseline, consistent with secular interest rate decline), welfare loss is 35%; at ρ = 0.04 (above recent market rates), welfare loss is still above 20%; only at implausibly high discount rates does the welfare loss fall below 15%. The SCC is more sensitive to ρ than welfare because the SCC is a capitalized stock valuation while welfare is an annualized flow. BU sample damage functions (larger IRF) raise welfare loss to 42% and 2100 GDP loss to 61%; these represent the high end of the estimates. The climate sensitivity range ($600–$2,400/ton for the SCC) reflects uncertainty in the physics of CO2-to-temperature conversion, not in the estimated economic damage function. Across all these dimensions, the global-temperature estimates remain order-of-magnitude larger than local-temperature estimates.

Q8. What is the policy implication for large economies considering unilateral decarbonization?

The domestic decarbonization test compares the Domestic Climate Cost (DCC) — the fraction of the global SCC that accrues to the decarbonizing country — against the marginal cost of abatement (~$80/ton average, Bistline et al. 2023). Under conventional local-temperature estimates ($149/ton global SCC), the US DCC falls below $80/ton, implying unilateral action destroys domestic value. Under the paper’s $1,207/ton global SCC, the US DCC comfortably exceeds $80/ton even if the US only captures a fraction of world welfare gains — because global temperature extremes (hurricanes, heat waves, droughts) strike the US directly, the DCC/SCC ratio is much higher than under local estimates where the US appears less exposed. This fundamentally changes the cost-benefit calculus for large-economy unilateral climate policy.

Key concepts

global mean temperature shock: a time-series innovation to world average surface temperature, identified by Hamilton (2018) detrending; captures ocean-atmosphere climate variability (El Niño/ENSO) correlated with extreme weather events affecting all countries simultaneously; the paper’s key identification variable, distinct from local temperature variation used in standard cross-country panels.

global vs. local temperature effect: the paper’s central finding that the GDP effect per 1°C global mean temperature shock (14–18%) is an order of magnitude larger than the effect per 1°C local temperature shock (1–3%); the gap is explained by extreme climatic events (heat waves, droughts, wind, precipitation) that co-move with global mean temperature but not with individual countries’ local temperatures.

structural damage function (ζ_s): the kernel relating excess global mean temperature T̂_{t−s} to log TFP at time t, specified as ζ_s = A(e^{−Bs} − e^{−Cs}); estimated from the PWT output impulse response via model inversion (Proposition 1); implies a 4% peak TFP loss 2 years after a 1°C transitory shock, decaying slowly over 10 years; rules out permanent growth effects consistent with the data.

Social Cost of Carbon (SCC): the one-time dollar amount households would pay at time 0 to avoid one additional ton of CO2; equals (in the linear limit) the present discounted value of all flow consumption-equivalent welfare losses from the induced warming; paper estimates $1,207/ton (2024 international dollars), more than 6× prior estimates, because the global-temperature damage function implies much larger per-degree productivity losses than local-temperature estimates.

committed climate losses: future GDP shortfalls already locked in by past warming, arising because the estimated damage function has a delayed peak (year 2) and slow decay (10+ years) — temperature rises in recent years continue reducing productivity for the following decade; the paper estimates these committed losses alone will lower GDP 32% below potential by 2040 even with temperature held constant at 2019 levels.

BAU scenario: the business-as-usual warming path used for the main counterfactual — global mean temperature reaches 3°C above preindustrial by 2100 (asymptoting to 3.3°C), implying 2°C of additional warming since the 2024 baseline; under this scenario the model implies 53% GDP loss, 51% capital loss, 53% consumption loss, and a 35% consumption-equivalent welfare loss by 2100.

Q54 | Macro Paper Warehouse

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Pigovian Transport Pricing in Practice

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

In depth

Q1. Why do global temperature shocks produce GDP effects an order of magnitude larger than local temperature panel estimates?

Q2. What is the identification strategy and what are the main threats?

Q3. How does the structural model translate medium-run shock responses into long-run warming effects?

Q4. Why does capital initially rise in the BAU counterfactual before declining?

Q5. How is the Social Cost of Carbon defined and computed?

Q6. Why are historical climate losses so large if year-to-year warming is small?

Q7. What does the sensitivity analysis reveal about the robustness of the results?

Q8. What is the policy implication for large economies considering unilateral decarbonization?

Key concepts