Dollar Dominance and the Transmission of Monetary Policy: McLeay & Tenreyro (2026)

What this is. The paper’s core results, the model, and the empirical specifications: enough to know what it found and how, without reading all 62 pages. To replicate or extend, read the full source at the original.

TL;DR

McLeay and Tenreyro challenge the dominant-currency pricing (DCP) view that dollar invoicing undermines exchange-rate-based monetary policy transmission. They build a mixed currency pricing (MCP) framework in which competitive, homogeneous-good exporters price in dollars with flexible prices, while differentiated-good exporters retain sticky monopoly-power pricing. In the MCP model, a monetary loosening that depreciates the currency lowers domestic production costs expressed in dollars, allowing competitive exporters to expand supply substantially. The binding constraint is export supply capacity (upward-sloping marginal cost from decreasing returns to scale), not demand. The model replicates the empirical fact of limited exchange rate pass-through to dollar export prices (as in DCP), yet delivers a strong export quantity response (as in the classic PCP framework of Obstfeld and Rogoff (1995)), and challenges the optimal-DCP-policy conclusions of Egorov and Mukhin (2023) by showing price flexibility relaxes the binding dollar-pricing constraint. Three empirical exercises confirm the mechanism: monetary policy-induced depreciations raise exports significantly in a panel of 37 emerging and developing economies, in Canada and Chile (where dollar-priced commodity exports dominate), and large devaluations in Argentina, Brazil, and Mexico were followed by visible export expansions relative to trend.

Core results

Magnitudes as reported; \*\*\* = 1%. Locators point to the source PDF.

#	Result	Locator	Magnitude
R1	MCP model: export quantity response to monetary easing is 7x the sticky-price DCP response	Table III, p. 639	Year-1 avg: MCP 0.95%, DCP 0.14%, PCP 0.69%; impact (Figure VI, p. 637): MCP ~1.34%, DCP ~0.05%
R2	MCP model: aggregate output response is 2.5x the DCP response	Table III, p. 639	Year-1 avg: MCP 0.81%, DCP 0.32%; common exchange rate depreciation: 0.52%
R3	Dollar invoicing is strongly positively associated with homogeneous-goods export share	Table I, p. 620	OLS coefficient 0.712-0.799*** (1,173 obs, R² 0.29-0.37); 10 pp more homogeneous goods → 7-8 pp more dollar invoicing
R4	Panel LP: monetary tightening causes significant fall in exports in 37 emerging and developing economies	Figure X, p. 652	Dollar exports peak fall ~1.5% at 11 months; year-1 avg ~0.99%; 68% CI excludes zero at peak
R5	Canada: monetary tightening causes large export falls consistent with MCP predictions	Figure XI, p. 656	Energy exports peak -1.5% after 3 months; chemicals -1% after 7 months per 1 pp policy rate increase
R6	Chile: monetary tightening causes large mining export falls consistent with MCP	Figure XII, p. 657	Mining exports fall ~10% on impact; manufacturing exports avg -1.25% first 6 months per 1 pp tightening
R7	MCP dollar export price pass-through is small (-0.06%), matching DCP, but arises from equilibrium not stickiness	Table III, p. 639	Year-1 avg: MCP -0.06%, PCP -0.34%, DCP -0.07% (100 bps easing)
R8	Homogeneous goods make up a larger export share in developing and emerging economies	Figure IV, p. 617; text p. 617	1985-2023 averages: developing economies above 70%, emerging economies around 60%, advanced economies around 35%
R9	Mixed dollar and producer-currency pricing still yields an export expansion at both tested exporter shares	Figure VII, p. 641	Impact export quantity response is approximately 0.9% with an 80% PCP export sector and 1.4% with a 20% PCP sector; both responses unwind over about 10 quarters
R10	Export supply capacity controls how much exports expand after a depreciation	Figure VIII, p. 643; text pp. 643-644	Impact export quantity response is approximately 4.8% at returns to scale 1, 1.3% at 0.85, and below 1% at 0.72
R11	A hybrid VAR can recover impact effects on simulated data, but dynamic estimates for homogeneous exports are imprecise	Figure IX, p. 648; text p. 648	VAR estimated on 1,000 simulated periods; zero lies within the 68% confidence bands for homogeneous-good exports
R12	Exports visibly accelerate relative to U.S. exports after large Latin American devaluations	Figure XIII, p. 659; text pp. 659-660	Six-month exchange-rate increase: Argentina 130%, Brazil 40%, Mexico 50%; normalized exports turn to faster growth after the devaluations, with no coefficient reported
R13	Auto exports are a counterexample: they rise significantly after an appreciation	Text p. 658 n. 30; Online Appendix Figure A.14	Statistically significant positive response; the main text reports no point magnitude
R14	Longer dollar-price rigidity curtails the export response	Text pp. 641-642; Online Appendix Figure A.2	Durations up to two quarters have little qualitative effect; durations of three quarters or longer significantly reduce the response
R15	Panel estimates also show CPI and industrial production fall after monetary tightening	Figure X, p. 652; text p. 651	Both responses are plotted for a normalized 1 percentage point policy-rate increase; no text point estimates are reported
R16	Canadian CPI and GDP fall significantly after monetary tightening	Figure XI, p. 656; text p. 656	Both outcomes decline; no point magnitudes are reported in the main text
R17	Chilean non-mining activity falls gradually after monetary tightening	Figure XII, p. 657; text p. 658	Gradual decline; no point magnitude is reported in the main text
R18	The Chilean CPI response is not statistically significant	Figure XII, p. 657; text p. 658	CPI falls, but the response is statistically insignificant

Overall (paper’s conclusion). The pass-through of monetary policy via the export channel is strong even when goods are priced in dollars, as long as dollar-pricing exporters face competitive markets with flexible prices. The standard interpretation of low exchange rate pass-through to dollar export prices as evidence of nominal rigidities is misleading: in the MCP model, low pass-through is an equilibrium result of high demand elasticity and rising marginal costs, not a friction. Monetary policy and the exchange rate remain effective stabilization tools in a world of dollar dominance. The policy implications of dollar pricing may need to be reassessed.

Theory / model

The model economy consists of households who consume domestic and imported goods and provide labor for firms that produce for home consumption and exports. A monetary authority sets domestic interest rates via a Taylor rule. The key structural innovation is a nested CES demand system that reverses the standard open-economy nesting, placing intra-sector variety competition at the inner level and cross-industry substitution at the outer level.

Household preferences. Each household in country $j$ maximizes lifetime expected utility (equation 1, p. 622):

$$\mathbb{E}_0 \sum_{t=0}^\infty \beta^t \left( \frac{C_{j,t}^{1-\sigma_c}}{1-\sigma_c} - \frac{N_{j,t}(h)^{1+\varphi}}{1+\varphi} \right), \tag{1}$$

where $C_{j,t}$ is total consumption, $N_{j,t}(h)$ is labor supply, $\sigma_c$ is the coefficient of relative risk aversion (equal to the inverse of the intertemporal elasticity of substitution), and $\varphi$ is the reciprocal of the labor supply elasticity.

Demand structure. Total consumption aggregates across goods $g$ (equation 2, p. 622):

$$C_{j,t} \equiv \left( \int_0^1 C_{j,t}(g)^{\frac{\sigma-1}{\sigma}} dg \right)^{\frac{\sigma}{\sigma-1}}, \tag{2}$$

where $\sigma$ is the cross-industry elasticity of substitution. Within each good $g$, consumption aggregates varieties from all countries (equation 3, p. 623):

$$C_{j,t}(g) \equiv \left( \sum_i \left( \frac{\gamma^g_{ij}}{|\Omega^g_i|} \right)^{\frac{1}{\eta^g}} \int_{\omega \in \Omega^g_i} C^g_{ij,t}(\omega)^{\frac{\eta^g-1}{\eta^g}} d\omega \right)^{\frac{\eta^g}{\eta^g-1}}, \tag{3}$$

where $\eta^g$ is the within-good cross-variety elasticity (which may vary across goods) and $\gamma^g_{ij}$ captures preference for varieties from country $i$, arising from home bias and trade costs. Setting $\eta^g \gg \sigma$ for homogeneous goods means the relevant price for export demand is the variety price relative to competing foreign varieties, not the aggregate price index. This makes demand highly elastic at the variety level, enabling large quantity adjustments in response to small price changes.

Firms and production. A firm in country $j$ producing variety $\omega$ of good $g$ uses labor and intermediate inputs (equation 15, p. 626):

$$Y^g_{j,t}(\omega) = A^g_{j,t} (L^g_{j,t}(\omega))^{1-\alpha} (X^g_{j,t}(\omega))^\alpha \left[ (L^g_{j,t})^{1-\alpha} (X^g_{j,t})^\alpha \right]^{v_g - 1}, \tag{15}$$

where $\alpha$ is the intermediate input share, $1-\alpha$ is the labor share, and $v_g \leq 1$ governs returns to scale at the industry level. The term $[(L^g_{j,t})^{1-\alpha}(X^g_{j,t})^\alpha]^{v_g-1}$ generates decreasing returns when $v_g < 1$, capturing fixed good-specific factors such as structures. The resulting industry-level marginal cost (equation 24, p. 629) is:

$$MC^g_{j,t} = \frac{1}{(1-\alpha)^{1-\alpha} \alpha^\alpha} \frac{W^{1-\alpha}_{j,t} P^\alpha_{j,t} \left[ L^{1-\alpha}_{j,t} X^\alpha_{j,t} \right]^{1-v_g}}{A^g_{j,t}}, \tag{24}$$

which rises with industry output when $v_g < 1$, generating an upward-sloping marginal cost curve. This supply-side constraint, not demand, limits the export expansion after a depreciation.

Pricing. Each firm resets its price with good-specific Calvo probability $1 - \delta^g_p$ each period. Dollar-pricing firms solve (equation 21, p. 628):

$$\mathbb{E}_t \left[ \sum_{s=0}^\infty (\beta \delta^g_p)^s \frac{C^{-\sigma_c}_{j,t} P_{j,t}}{C^{-\sigma_c}_{j,t+s} P_{j,t+s}} Y^g_{ji,t+s}(\omega) \left( \bar{P}^{g,\$}_{ji,t}(\omega) - \frac{\eta^g}{\eta^g - 1} \frac{MC_{j,t+s}(\omega)}{\mathcal{E}_{\$j,t+s}} \right) \right] = 0, \tag{21}$$

setting the dollar reset price as a markup $\eta^g / (\eta^g - 1)$ over the weighted average of future dollar marginal costs. When prices are flexible ($\delta^g_p \to 0$), the optimal dollar price depends only on current dollar marginal costs and the invoicing currency is irrelevant: a depreciation that lowers home costs in dollar terms leads to a small equilibrium price cut (smaller when demand is more elastic) and a large quantity increase.

Monetary policy. The central bank sets domestic nominal interest rates via a Taylor rule (equation 25, p. 629):

$$\frac{1 + i_{j,t}}{1 + \bar{i}_j} = \left( \frac{1 + i_{j,t-1}}{1 + \bar{i}_j} \right)^\rho (1 + \pi_{j,t})^{(1-\rho)\phi_\pi} \zeta^M_{j,t}, \tag{25}$$

where $\rho$ is policy smoothing, $\phi_\pi > 1$ is the inflation response coefficient, $\bar{i}_j$ is the steady-state nominal rate, and $\zeta^M_{j,t}$ is an AR(1) monetary policy shock. A negative shock (easing) reduces the policy rate, leading to a nominal exchange rate depreciation.

The central finding. The depreciation lowers domestic dollar costs (wages expressed in dollars fall). For a monopolistic sticky-price DCP exporter, the price cannot adjust so markups rise but quantities stay flat. For a competitive flexible-price exporter with high $\eta^g$, the optimal reset price falls only slightly (elastic demand means profits respond more to volume than to margin). The quantity adjustment is large, continuing until rising marginal cost from expanding production offsets the improved profitability. The supply constraint parameter $v_g$ determines the size of the export response: under constant returns ($v_g = 1$), the quantity response is very large; under decreasing returns ($v_g = 0.85$), it is still substantially larger than in the DCP model. The MCP model nests sticky-price DCP (set $\eta^g = \sigma$, $\delta^g_p = 0.75$) and PCP (set $\delta^g_p = 0$, $\eta^g = \sigma$) as special cases.

Remaining numbered model equations

The numbered system also defines the household demand, asset-market, firm-pricing, aggregation, and calibrated small-open-economy relationships below. Equations (1)-(3), (15), (21), (24), and (25) appear above.

Consumption demand and price indices, equations (4)-(7), pp. 623-624:

$$\text{C}_{j,t}(g)=\left(\frac{\text{P}_{j,t}(g)}{\text{P}_{j,t}}\right)^{-\sigma}\text{C}_{j,t} \tag{4}$$ $$\text{C}^{g}_{ij,t}(\omega)=\frac{\gamma^{g}_{ij}}{|\Omega^{g}_{i}|}\left(\frac{\text{P}^{g}_{ij,t}(\omega)}{\text{P}_{j,t}(g)}\right)^{-\eta_g}\text{C}_{j,t}(g) \tag{5}$$ $$\text{P}_{j,t}(g)=\left(\sum_i\frac{\gamma^{g}_{ij}}{|\Omega^{g}_{i}|}\int_{\omega\in\Omega^{g}_{i}}\text{P}^{g}_{ij,t}(\omega)^{1-\eta_g}d\omega\right)^{\frac{1}{1-\eta_g}} \tag{6}$$ $$\text{P}_{j,t}=\left(\int_0^1\text{P}_{j,t}(g)^{1-\sigma}dg\right)^{\frac{1}{1-\sigma}} \tag{7}$$

Labor aggregation and labor demand, equations (8)-(9), p. 625:

$$\text{L}_{j,t}=\left(\int_0^1\text{N}_{j,t}(h)^{\frac{\vartheta-1}{\vartheta}}dh\right)^{\frac{\vartheta}{\vartheta-1}} \tag{8}$$ $$\text{N}_{j,t}(h)=\left(\frac{\text{W}_{j,t}(h)}{\text{W}_{j,t}}\right)^{-\vartheta}\text{L}_{j,t} \tag{9}$$

The household budget constraint and risk-premium rule are equations (10)-(11), p. 625:

$$\text{P}_{j,t}\text{C}_{j,t}+\mathcal{E}_{\$,j,t}(1+i^{\$}_{j,t})\text{B}^{\$}_{j,t}+\text{B}_{j,t}=\text{W}_{j,t}(h)\text{N}_{j,t}(h)+\Pi_{j,t}+\mathcal{E}_{\$,j,t}\text{B}^{\$}_{j,t+1}+\sum_{s\in S}\text{Q}_{j,t+1}(s)\text{B}_{j,t+1}(s) \tag{10}$$ $$i^{\$}_{j,t}=\bar{i}^{\$}_{j}+\psi\left(\exp\left(\frac{\text{B}^{\$}_{j,t}}{\text{P}^{\$}_{\$,t}}-\bar{\text{B}}^{\$}_{j}\right)-1\right) \tag{11}$$

The domestic Euler equation, UIP condition, and reset-wage condition are (12)-(14), p. 626:

$$\text{C}_{j,t}^{-\sigma_c}=\beta(1+i_{j,t+1})\mathbb{E}_t\left[\text{C}_{j,t+1}^{-\sigma_c}\frac{\text{P}_{j,t}}{\text{P}_{j,t+1}}\right] \tag{12}$$ $$(1+i_{j,t+1})\mathbb{E}_t\left[\text{C}_{j,t+1}^{-\sigma_c}\frac{1}{\text{P}_{j,t+1}}\right]=(1+i^{\$}_{j,t+1})\mathbb{E}_t\left[\text{C}_{j,t+1}^{-\sigma_c}\frac{\mathcal{E}_{\$,j,t+1}}{\text{P}_{j,t+1}\mathcal{E}_{\$,j,t}}\right] \tag{13}$$ $$\mathbb{E}_t\sum_{s=0}^{\infty}(\beta\delta_w)^s\text{N}_{j,t+s}(h)\text{C}_{j,t+s}^{-\sigma_c}\left[\frac{\bar{\text{W}}_{j,t}(h)}{\text{P}_{j,t+s}}-\frac{\vartheta}{\vartheta-1}\text{N}_{j,t+s}(h)^\varphi\text{C}_{j,t+s}^{\sigma_c}\right]=0 \tag{14}$$

Intermediate-input aggregation and export demand are equations (16)-(17), p. 627:

$$\text{X}_{j,t}=\left(\int_0^1\text{X}_{j,t}(g)^{\frac{\sigma-1}{\sigma}}dg\right)^{\frac{\sigma}{\sigma-1}} \tag{16}$$ $$\text{Y}^{g}_{ji,t}(\omega)=\frac{\gamma^{g}_{ji}}{|\Omega^{g}_{j}|}\left(\frac{\text{P}^{g}_{ji,t}(\omega)}{\text{P}_{i,t}(g)}\right)^{-\eta_g}\left(\frac{\text{P}_{i,t}(g)}{\text{P}_{i,t}}\right)^{-\sigma}(\text{C}_{i,t}+\text{X}_{i,t}) \tag{17}$$

Firm profits and producer-currency reset pricing are equations (18)-(20), pp. 627-628:

$$\Pi_{j,t}(\omega)=\sum_i\left(\text{P}^{g,j}_{ji,t}(\omega)\text{Y}^{g}_{ji,t}(\omega)-\text{MC}^{g}_{j,t}(\omega)\text{Y}^{g}_{ji,t}(\omega)\right) \tag{18}$$ $$\Pi^{\$}_{j,t}(\omega)=\sum_i\left(\text{P}^{g,\$}_{ji,t}(\omega)\text{Y}^{g}_{ji,t}(\omega)-\frac{\text{MC}^{g}_{j,t}(\omega)\text{Y}^{g}_{ji,t}(\omega)}{\mathcal{E}_{\$,j,t}}\right) \tag{19}$$ $$\mathbb{E}_t\sum_{s=0}^{\infty}(\beta\delta_p^g)^s\frac{\text{C}_{j,t}^{\sigma_c}\text{P}_{j,t}}{\text{C}_{j,t+s}^{\sigma_c}\text{P}_{j,t+s}}\text{Y}^{g}_{ji,t+s}(\omega)\left[\bar{\text{P}}^{g,j}_{ji,t}(\omega)-\frac{\eta_g}{\eta_g-1}\text{MC}^{g}_{j,t+s}(\omega)\right]=0 \tag{20}$$

The marginal-cost conditions for labor and intermediate inputs are (22)-(23), p. 629:

$$\text{MC}^{g}_{j,t}(\omega)=\frac{\text{W}_{j,t}\text{L}_{j,t}(\omega)}{(1-\alpha)\text{Y}^{g}_{j,t}(\omega)} \tag{22}$$ $$\text{MC}^{g}_{j,t}(\omega)=\frac{\text{P}_{j,t}\text{X}_{j,t}(\omega)}{\alpha\text{Y}^{g}_{j,t}(\omega)} \tag{23}$$

Market clearing and aggregate trade/output definitions are equations (26)-(31), pp. 629-630:

$$\text{Y}^{g}_{j,t}(\omega)=\sum_i\text{Y}^{g}_{ji,t}(\omega) \tag{26}$$ $$\text{L}_{j,t}=\int_0^1\text{L}^{g}_{j,t}dg \tag{27}$$ $$\text{X}_{j,t}=\int_0^1\text{X}^{g}_{j,t}dg \tag{28}$$ $$\text{NTB}_{ji,t}=\int_0^1\left[\int_{\omega\in\Omega_j^g}\text{P}^{g}_{ji,t}(\omega)\text{Y}^{g}_{ji,t}(\omega)d\omega-\int_{\omega\in\Omega_i^g}\text{P}^{g}_{ij,t}(\omega)\text{Y}^{g}_{ij,t}(\omega)d\omega\right]dg \tag{29}$$ $$\text{NTB}_{j,t}\equiv\sum_{i\ne j}\text{NTB}_{ji,t} \tag{30}$$ $$\text{Y}_{j,t}=\frac{\text{P}_{j,t}\text{C}_{j,t}+\text{NTB}_{j,t}}{\text{P}_{j,t}} \tag{31}$$

The export and import price indices are defined in equations (32)-(33), p. 630; the paper then deflates nominal flows by these indices to obtain real trade quantities:

$$\text{P}_{ji,t}\equiv\int_0^1\left(\frac{\gamma^g_{ji}}{|\Omega_j^g|\int_0^1\gamma^g_{ji}dg}\int_{\omega\in\Omega_j^g}\text{P}^{g}_{ji,t}(\omega)d\omega\right)dg \tag{32}$$ $$\text{P}_{ij,t}\equiv\int_0^1\left(\frac{\gamma^g_{ij}}{|\Omega_i^g|\int_0^1\gamma^g_{ij}dg}\int_{\omega\in\Omega_i^g}\text{P}^{g}_{ij,t}(\omega)d\omega\right)dg \tag{33}$$

The calibrated small-open-economy demand relationships are equations (34)-(36), pp. 632-633:

$$\text{C}_{H,t}=\left[\kappa_M\text{C}_{H,t}(g_M)^{\frac{\sigma-1}{\sigma}}+(1-\kappa_M)\text{C}_{N,t}(g_N)^{\frac{\sigma-1}{\sigma}}\right]^{\frac{\sigma}{\sigma-1}} \tag{34}$$ $$\text{Y}^{g_N}_{H,t}(\omega)=\text{Y}^{g_N}_{HH,t}(\omega)=\frac{1}{|\Omega^{g_N}_{H}|}\left(\frac{\text{P}^{g_N}_{HH,t}(\omega)}{\text{P}_{H,t}}\right)^{-\sigma}(\text{C}_{H,t}+\text{X}_{H,t}) \tag{35}$$ $$\text{Y}^{g_H}_{HU,t}(\omega)\approx\frac{1}{|\Omega^{g_H}_{H}|}\left(\frac{\text{P}^{\$,g_H}_{HU,t}(\omega)}{\text{P}^{\$}_{U,t}(g_H)}\right)^{-\eta_{g_H}}\gamma^{g_H}_{HU}(\text{C}_{U,t}+\text{X}_{U,t}) \tag{36}$$

Method

Model calibration and simulation. The model is linearized around a deterministic steady state and simulated using impulse response functions (Figure VI, p. 637). The baseline calibration for households and policy follows Gopinath et al. (2020): cross-product elasticity $\sigma = 2$, labor demand elasticity $\vartheta = 4$, Calvo price rigidity $\delta_p = 0.75$ (four-quarter mean duration), Calvo wage rigidity $\delta_w = 0.75$ (Table II, pp. 634-635). The key departures for the homogeneous export sector: fully flexible prices ($\delta^{g_H}_p = 0$), cross-variety elasticity $\eta^{g_H} = 17$ (from Broda and Weinstein (2006) for crude oil 1972-1988), and decreasing returns $v_{g_H} = 0.85$ (calibrated from the share of structures in Canadian mining value-added). Country-specific calibrations for Canada and Chile are in Table IV (p. 654).

The method builds on local-projections (Jordà 2005) for the empirical tests and panel-regression for the motivating cross-country facts, with the proposed nk-soe-dsge framework as the structural basis.

Monetary policy shock identification. Because the exchange rate is endogenous, the empirical strategy uses monetary policy shocks identified by purging the interest rate of its response to current macroeconomic conditions. Shocks are obtained as residuals $\hat{\epsilon}_{i,t}$ from a forward-looking interest rate rule (equation 38, p. 650):

Before the empirical shock construction, the paper checks identification in a simulated-data exercise using the one-lag hybrid VAR (equation 37, p. 647):

$$\text{X}_t=\text{c}+\text{B}\text{X}_{t-1}+\epsilon_t,\qquad \text{X}_t=[\zeta^M_t,\mathcal{E}_{\$,H,t},\text{Y}^{g_{H2}}_t,\text{Y}^{g_{H1}}_t,\text{Y}_t]' \tag{37}$$

The VAR uses one lag, orders the simulated monetary shock first for recursive identification, and is estimated on 1,000 simulated periods. Homogeneous-export responses are imprecise, with zero inside the 68% intervals (Figure IX, p. 648).

$$\Delta i_{i,t} = \alpha + \phi_\pi E_t \pi^f_{i,t+12} + \phi_y E_t \Delta y^f_{i,t+12} + \sum_{j=1}^2 \phi_\pi \pi_{i,t-j} + \sum_{j=1}^2 \phi_y \Delta y_{i,t-j} + \sum_{j=1}^2 \phi_e \Delta NEER_{i,t-j} + \sum_{j=1}^2 \phi_i i_{i,t-j} + \epsilon_{i,t}, \tag{38}$$

where $E_t \pi^f_{i,t+12}$ and $E_t \Delta y^f_{i,t+12}$ are 12-month-ahead forecasts of inflation and output growth. The residual $\hat{\epsilon}_{i,t}$ is by construction uncorrelated with past macro conditions and current forecasts, providing an exogenous driver of exchange rate changes.

Empirical specifications

Section III: Fact 3 invoicing regression (Table I, p. 620). Cross-country OLS establishes that dollar invoicing is concentrated in homogeneous-good sectors. The regression uses four-digit SITC data from UN Comtrade (Rauch (1999) homogeneous-goods classification) and invoicing data from Boz et al. (2022):

$$\text{Dollar share}_{i,t} = \beta_0 + \beta_1 \text{Homogeneous share}_{i,t} + \mu_t + \epsilon_{i,t},$$

with specifications adding year fixed effects and GDP weighting. Estimated on 1,173 observations across 101 countries (1990-2019) with robust standard errors. Coefficient $\hat{\beta}_1 = 0.712$ to $0.799$ (all significant at 1%).

Section V.B: Panel local projections in 37 EMEs (equation 39, p. 651). Macroeconomic effects of identified monetary shocks on exports and activity are estimated using Jordà (2005)‘s LP method with country fixed effects:

$$z_{i,t+h} = \mu^h_i + \sum_{j=0}^2 \gamma^h_j \hat{\epsilon}_{i,t-j} + \delta^h_0 \Delta NEER_{i,t} \times \hat{\epsilon}_{i,t} + \sum_{j=0}^2 \beta^h_j \times \text{controls}_{i,t-j} + \omega^h_{i,t}, \tag{39}$$

where $h$ is the horizon in months, the interaction term $\Delta NEER_{i,t} \times \hat{\epsilon}_{i,t}$ captures the differential effect through the exchange rate, and $\omega^h_{i,t}$ is the residual. Impulse responses are normalized to a 1 percentage point interest rate increase on impact (so all results correspond to a monetary tightening). The panel database is from Brandao-Marques et al. (2021), covering 37 countries.

Country fixed effects are included at each horizon. Figure X reports 68% confidence intervals; the main-text equation and caption do not specify a clustering or other standard-error formula. The monthly horizon and 11-month export peak are reported in the text (p. 652).

Section V.C: Country VARs for Canada and Chile (equation 40, p. 655). For each economy, a hybrid VAR with six lags (Canada) or four lags (Chile) is estimated:

$$\mathbf{X}_t = \mathbf{c} + \delta t + B(\mathbf{L}) \mathbf{X}_{t-1} + C(\mathbf{L}) \mathbf{W}_{t-1} + \boldsymbol{\epsilon}_t, \tag{40}$$

where $\mathbf{X}_t$ contains the monetary policy shock series (ordered first for recursive identification), exchange rate, CPI, GDP, and sectoral exports; $\mathbf{W}_t$ includes the U.S. dollar price of Canadian commodities (Canada only). Identification is recursive (Cholesky), with the cumulative monetary shock ordered first. For Canada: Champagne and Sekkel (2018) narrative shocks, monthly, 1981-2015. For Chile: Brandao-Marques et al. (2021) shocks, monthly, 2003-2017. Model impulse responses (solid red lines in Figures XI-XII) are scaled to match the average estimated exchange rate response over the first six months.

The figure captions report 68% confidence intervals. The main-text specification does not state a separate standard-error or bootstrap procedure.

Datasets used

Dataset	Role in paper	Wiki page
UN Comtrade (four-digit SITC)	Share of homogeneous goods in total goods exports, 1985-2023 (Figure IV); base data for invoicing regression (Table I)	no page yet
Boz et al. (2022) invoicing database	Share of exports invoiced in dollars, 1990-2019, used in Table I regression	no page yet
Brandao-Marques et al. (2021) panel	Monetary policy shocks and macro data for 37 emerging and developing economies; used in Section V.B LP estimation (equation 39, Figure X)	no page yet
Champagne and Sekkel (2018) shock series	Narrative monetary policy shocks for Canada, 1974-2015; used in Canada VAR (equation 40, Figure XI)	no page yet
Canadian national statistics (Bank of Canada / Statistics Canada)	Monthly interest rate, CPI, GDP, energy and chemicals exports, exchange rate, 1981-2015	no page yet
Chilean national statistics (Banco Central de Chile)	Monthly IMACEC (output), policy rate, CPI, mining and manufacturing exports, exchange rate, 2003-2017	no page yet
Harvard Dataverse replication files	Assembled replication dataset (McLeay and Tenreyro 2025, doi:10.7910/DVN/SASVME)	no page yet

Sample: panel LP covers monthly data for 37 countries (1990-2019; response horizons reported in months); country VARs use monthly data (Canada: January 1981-October 2015; Chile: April 2003-July 2017); invoicing regression covers 1,173 country-year observations across 101 countries.

When to read the full paper

Read the original if you are: building or evaluating open-economy monetary models where the invoicing currency choice matters; empirically studying exchange rate pass-through and its interpretation for monetary policy; assessing whether monetary policy transmission through exports remains effective in highly dollar-invoiced developing and emerging economies; or using local projections to identify monetary policy effects on trade flows in a panel with heterogeneous export structures.

Attribution and rights

Source: peer-reviewed, The Quarterly Journal of Economics 141(1), 2026. This distillation was extracted by an LLM on 2026-06-28 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). McLeay, Michael, and Silvana Tenreyro. “Dollar Dominance and the Transmission of Monetary Policy.” The Quarterly Journal of Economics 141, no. 1 (2026): 605-666. DOI: 10.1093/qje/qjaf043. © 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.