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macro · FX elasticity
How do sector exports respond to the real effective exchange rate? Mexico , Machines
Panel REER elasticity estimates for Mexico by HS section, a cross-country heterogeneity view for Machines, and an FX-shock scenario table anchored on the published long-run export price elasticity from Hooper, Johnson and Marquez (2000). Methodological caveats are spelled out on each figure: REER is jointly determined with trade flows, so cross-sectional fixed-effects slopes are descriptive only and the simulator uses the literature benchmark for point estimates.
countryMEX · Mexico
section16 · Machines
REER window1995-2025
exports 2024$237.2B
HJM 2000 long-run-0.90G-7 mean, no country series
Machines within-beta0.25
Sector-level pass-through: HS-section elasticities under two-way fixed effects
For each HS section we estimate ln(Xict) = αic+ λct + βc ln(REERit) + uict across the 93-country IMF IFS panel with annual coverage 1995-2025. Double-demeaning sweeps out country-within-section and year-within-section fixed effects, so βc is identified off within-country deviations from global year-specific trends. Standard errors are classical OLS-on-demeaned residuals and should be read as lower bounds: clustering by country or block bootstrap would typically widen them (Bertrand, Duflo and Mullainathan 2004, QJE). We report these as transparency on the data; the identified elasticities from HJM 2000 are preferred for the simulator below.
Figure 1
Within-panel REER elasticity by HS section, 1995-2025
Estimated β by HS section from the two-way FE panel. A negative coefficient is the Marshall-Lerner direction: real appreciation → lower exports. Most sections cluster near zero or slightly positive, which is the signature of the identification problem (Imbs and Mejean 2015, AEJ:Macro): within a country-year, REER and exports co-move with productivity and income shocks that also raise demand for exports, attenuating the coefficient toward zero or flipping its sign. For Machines the within-β is 0.25; HJM 2000 aggregate long-run estimate for MEX is -0.90. Read the spread across sections as a within-sample heterogeneity diagnostic, not as causal export elasticities.
Sources: CEPII BACI 202501 (retrieved 2026-04-28) (export values, in thousands USD, x1000 for display); IMF IFS annual REER (CPI-based, 2010=100). Specification: double-demeaned OLS, country + year FE within HS section; OLS-on-demeaned standard errors (Bertrand, Duflo and Mullainathan 2004 QJE document clustered-SE are typically wider). Sample: 93 countries x 30 years x 21 HS sections. Benchmarks: Hooper, Johnson and Marquez (2000) long-run export elasticity.
Country heterogeneity in the selected sector
A large literature documents that FX pass-through into prices and quantities varies systematically with country characteristics. Campa and Goldberg (2005, REStat) 'Exchange Rate Pass-Through into Import Prices' document heterogeneity in short- and long-run pass-through across 23 OECD countries, correlated with trade openness and the macro-policy environment. Gopinath and Itskhoki (2010, AER) 'Frequency of Price Adjustment and Pass-Through' show that the frequency of price adjustment by exporters is a structural determinant: low-frequency adjusters pass through less.
Figure 2
Per-country time-series slope of ln(Machines exports) on ln(REER) vs trade openness
FX shock simulator
The scenario table applies the Hooper-Johnson-Marquez (2000) aggregate long-run export elasticity for MEX (G-7 mean as fallback)to a set of real exchange rate shocks spanning a one-decile band. The HJM elasticity is a log-linearised long-run response: %ΔX ≈ β · ln(1 + shock). We apply it uniformly to the selected sector; heterogeneity across sectors is real and non-trivial (Imbs and Mejean 2015 AEJ:Macro estimate Armington sectoral elasticities of substitution in the 2-6 range, generally higher for tradable goods and lower for services and commodities), but mapping their σ to an export supply elasticity requires a demand-side closure we do not impose here.
Figure 3
FX shock simulator: Machines exports from Mexico, 2024 baseline $237.2B
REER shock
HJM benchmark ΔX (%)
HJM ΔX (USD)
Within-FE ΔX (%)
Within-FE ΔX (USD)
-20% REER (depreciation)
+20.1%
$47.6B
-5.6%
-$13.4B
-10% REER (depreciation)
+9.5%
$22.5B
-2.7%
-$6.3B
+10% REER (appreciation)
-8.6%
-$20.4B
+2.4%
$5.7B
+20% REER (appreciation)
-16.4%
-$38.9B
+4.6%
$10.9B
Each row is a counterfactual REER shock applied to the 2024baseline. The 'HJM benchmark' column uses β = -0.90 from Hooper, Johnson and Marquez (2000); the 'within estimate' column uses the Figure 1 two-way FE slope β = . The gap between the two columns quantifies the OLS attenuation bias documented in Imbs and Mejean (2015). For treasury planning, use the HJM column as central and the within-FE column as a lower bound; Bahmani-Oskooee and Ratha (2004, ) survey the J-curve evidence for shorter-horizon asymmetries.
Recent history: does the overlay match?
The acid test is whether the model tracks reality. We overlay the actual REER path for Mexico against actual Machines exports over the mexico peso real appreciation 2020-2024. Both series are indexed to 100 at the first year with complete coverage. If the HJM-class elasticity is right in magnitude, a large REER move should show a visible opposite-signed export move over three to five years (Bahmani-Oskooee and Ratha 2004 document a typical J- curve lag of this length).
Figure 4
Overlay: Mexico REER and Machines exports, 2019-2024 (both = 100 at start)
Over 2019-2024, Mexico REER moved +20.0%; Machines exports moved +29.7%. The ratio of the two cumulative moves is a point-to-point ex-post 'implied elasticity' of 1.49. This is noisier than any regression coefficient because it bundles world growth, sector composition change, and policy shocks; the HJM 2000 benchmark of -0.90 sits in a different universe from raw point-to-point ratios in short windows and that is expected. Use the overlay to gut-check direction and timing, not to back out a coefficient.
Sources: IMF IFS annual REER (CPI-based, 2010=100) and CEPII BACI 202501 (retrieved 2026-04-28) (export values, x1000 for display). Window: 2019-2024.
Distribution of country-level slopes: what does the panel look like?
How does the country-specific pass-through for Machinesdistribute across the 93-country IMF REER panel? The histogram below bins the Figure 2 per-country slopes into 0.5-wide buckets and overlays two benchmarks: the G-7 mean from Hooper-Johnson-Marquez (2000) at β = -0.90, and zero (the null of no FX pass-through). Following Imbs and Mejean (2015), we expect the panel mass to sit above the HJM benchmark (i.e. less negative), reflecting attenuation toward zero from the joint endogeneity of REER and exports within a country-year. A cleanly identified elasticity requires either instrumental variation (Fitzgerald 2012, AER) or structural assumptions; the raw descriptive histogram still informs the size of the attenuation gap that the simulator must correct for.
Figure 5
Distribution of per-country ln(Machines exports)-on-ln(REER) slopes, 1995-2025
Lag structure: 1-year vs 3-year REER pass-through by income tier
The J-curve says the export response to a real depreciation builds over time: small or wrong-signed in year 1, larger and correctly signed three to five years out (Bahmani-Oskooee and Ratha 2004). Does this lag structure vary with country income? The dominant-currency-pricing framework (Gopinath 2015, NBER 21646) predicts that low- and middle-income exporters that price in USD face a flatter and more delayed response, because their invoice currency decouples prices from the bilateral nominal rate. We stratify the 93-country IMF REER panel by 2018 GNI per capita (PPP; World Bank income tiers) and run a two-way fixed-effects regression of ln(total exports) on lagged ln(REER), separately for horizons h = 1 and h = 3.
Figure 6
Within-country REER elasticity of total exports, by income tier and lag
Threshold: is pass-through nonlinear above 15% depreciation?
A widely-documented nonlinearity in REER pass-through says the coefficient is steeper for large moves, above roughly 15% annual depreciation the response is closer to textbook long-run elasticities, while small moves within the threshold band are noisy or near-zero. Bussière (2013, Oxford Bull. Econ. Stat.75(5): 731-758) and Bussière, López & Tille (2015, JIE97(1): 21-33) identify this asymmetry in G-7 import and export price pass-through; it survives invoicing-currency controls. The regression below partitions country-year first-differences into three regimes defined by log-change cutoffs: big depreciation (ΔlnREER ≤ ln(0.85), i.e. ≥15% real depreciation), big appreciation (ΔlnREER ≥ ln(1.15)), and small moves in between; then estimates Δln(total exports) on ΔlnREER separately within each regime.
Figure 7
Threshold regression: REER pass-through by move-size regime
Across the 93-country annual panel, the pooled OLS slope of Δln(total exports) on ΔlnREER is βsmall = 0.22 (n = 2553) inside the threshold band and βbig-dep = -0.23 (n = 49) for ≥15% depreciations, βbig-app = -0.73 (n = 67) for ≥15% appreciations. A more negative βbig-dep than βsmallis the Bussière (2013) 'large-move' regime: big real depreciations coincide with (small) export-value contractions in the same year, consistent with short-run J-curve dynamics where the price term dominates the quantity term. Large appreciations, by contrast, often accompany commodity and income booms that also raise exports, so the raw bivariate slope can flip sign. This is a pooled first-difference regression without country FE or clustering; treat it as a shape diagnostic, not a causal estimate.
Specification: Δln(total exports)_it = α + β_regime · ΔlnREER_it + u_it, pooled OLS on first differences, regime defined by ΔlnREER cutoffs ln(0.85) ≈ -0.163 and ln(1.15) ≈ +0.140 (corresponding to ±15% annual REER moves). Sample: 93-country IMF IFS REER panel, CEPII BACI total exports ×1000 for USD. SEs are classical OLS and are not country-clustered. Literature: Bussière (2013) Oxford Bull. Econ. Stat. 75(5): 731-758; Bussière, López & Tille (2015) JIE 97(1): 21-33; Goldberg & Knetter (1997) JEL 35(3): 1243-1272 (asymmetric pass-through survey).
REER volatility and estimated pass-through: where is identification cleanest?
A standard signal-extraction argument (Devereux & Engel 2003, RES70(4): 765-783) says that the within-country slope of log exports on log REER is most precisely identified in countries whose REER moves a lot relative to other shocks: when the regressor has high sample variance, attenuation toward zero from co-movement with productivity and demand shocks is mechanically smaller. The figure below scatters the per-country slope from Figure 2 against the country's REER volatility (sample standard deviation of annual Δln REER over 1995-2025). Countries on the right of the plot are where the cross-section reading of the export elasticity is most credible; countries clustered on the left are where the slope is dominated by simultaneity and the HJM-class benchmark in Figure 3 is the better prior.
Figure 8
Per-country REER volatility (sigma of dlnREER) vs Figure-2 slope, 1995-2025
Policy read, 2025 Article IV context
The IMF Integrated Policy Framework (IMF 2020) treats the REER-export elasticity as a primitive input: a country with a high-magnitude elasticity needs less FX intervention to absorb terms-of-trade shocks because expenditure-switching does the work, while a low-elasticity economy with dominant-currency-priced trade (Gopinath 2015, NBER 21646) may legitimately lean on reserves or capital-flow measures. Three readings follow for Mexico. First, Figure 1's within-β for Machines at 0.25 must be read as a lower bound; the HJM-anchored simulator in Figure 3 is the central estimate. Second, Figure 2's openness-elasticity pattern is the Campa-Goldberg (2005) empirical regularity: small-open economies pass through more, and MEX's position flags whether FX-driven export adjustment is a first-order channel in Article IV scenario analysis. Third, the Figure 5 distribution says the panel mass sits closer to zero than the HJM benchmark, so naive cross-country averages understate the true elasticity; treasuries should use HJM or Imbs- Mejean (2015) sectoral Armington estimates as priors and calibrate the within-sample slope only as a conservative floor.
How treasury and macro advisory use this
FX scenario planning. Figure 3 translates a board-level REER assumption into a headline export-value impact for Machines, with both a literature-anchored benchmark and a within-sample lower bound so that range-based planning is explicit.
Country-specific exposure reviews. Figure 2 flags whether the client country sits in a high-elasticity or low-elasticity part of the cross section for this sector; an exporter with a more elastic profile is a higher-beta trade partner for the counterparty's own FX positioning.
Ex-post validation. Figure 4 is the history check that every scenario product should ship with: if the model's implied elasticity cannot make sense of the last decade of actual moves for the selected country, use it only with explicit caveats.
References
Bahmani-Oskooee, M., & Ratha, A. (2004). 'The J-curve: a literature review.' Applied Economics 36(13): 1377-1398.
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). 'How much should we trust difference-in-differences estimates?' Quarterly Journal of Economics 119(1): 249-275.
Campa, J. M., & Goldberg, L. S. (2005). 'Exchange rate pass-through into import prices.' Review of Economics and Statistics 87(4): 679-690.
Gopinath, G., & Itskhoki, O. (2010). 'Frequency of price adjustment and pass-through.' American Economic Review 100(1): 304-336.
Hooper, P., Johnson, K., & Marquez, J. (2000). 'Trade elasticities for the G-7 countries.' Princeton Studies in International Economics No. 87. Princeton University Press.
Imbs, J., & Mejean, I. (2015). 'Elasticity optimism.' American Economic Journal: Macroeconomics 7(3): 43-83.
Meese, R., & Rogoff, K. (1983). 'Empirical exchange rate models of the seventies.' Journal of International Economics 14(1-2): 3-24.
Each point is one country in the IMF REER panel: y is the country-by-country time-series slope of log Machines exports on log REER over 1995-2025, x is the 2010-2022 average trade openness (exports + imports over GDP, WDI NE.TRD.GNFS.ZS). Negative slopes are the Marshall-Lerner direction, positive slopes reflect strong correlated demand shocks or expenditure-switching that moves REER with exports. Median across 93 countries: 0.13; mean: 0.15. The Campa-Goldberg 2005 prediction is that small, open economies face higher pass-through and therefore a more elastic export response; the raw cross-section here is consistent with that expectation in direction, but the slopes are time-series bivariate regressions with no FE controls and the well-known REER endogeneity (see Figure 1). MEX is highlighted.
Sources: CEPII BACI 202501 (retrieved 2026-04-28), IMF IFS annual REER, WDI NE.TRD.GNFS.ZS. Specification: per-country bivariate OLS of ln(exports) on ln(REER), 1995-2025, n>=15 paired years. References: Campa and Goldberg (2005) REStat 87(4): 679-690; Gopinath and Itskhoki (2010) AER 100(1): 304-336.
0.25
Applied Economics
Sources: HJM 2000 Table 1 long-run export price elasticities (USA -1.5, CAN -0.9, JPN -1.0, GBR -1.6, FRA -0.2, DEU -0.3, ITA -0.9; G-7 mean for countries outside this set). CEPII BACI 202501 (retrieved 2026-04-28) for baseline export value. %DeltaX = beta * ln(1 + shock).
Across 93 countries with at least 15 paired observations on Machines exports and REER, the median within-country slope is 0.13, the interquartile range spans -1.84 to 3.52, and 43 of 93 countries (46%) have the Marshall-Lerner-signed (negative) slope. Only 32 sit below the HJM G-7 benchmark of -0.90, consistent with the Imbs-Mejean (2015) elasticity attenuation story: within-country co-movement of REER with income and productivity shocks pulls the raw slope toward zero.
Sources: CEPII BACI 202501 (retrieved 2026-04-28) exports (in thousands USD, x1000 for display), IMF IFS REER (CPI-based, 2010=100). Histogram of per-country OLS slopes from Figure 2, binned into width-0.5 buckets. Benchmarks: HJM 2000 G-7 mean (-0.90) and zero.
Across the four World Bank income tiers, the short-run (h = 1) and long-run (h = 3) elasticities are: Low income: β1= 0.13 (SE 0.10, n=16), β3= -0.03 (SE 0.10, n=16); Lower-middle: β1= 0.46 (SE 0.12, n=17), β3= 0.29 (SE 0.13, n=17); Upper-middle: β1= 0.17 (SE 0.09, n=26), β3= 0.19 (SE 0.10, n=26); High income: β1= 0.77 (SE 0.06, n=33), β3= 0.66 (SE 0.06, n=33). Where the long-run slope is larger in absolute magnitude than the short-run slope, the J-curve is visible in the raw panel; where they are similar, pass-through is contemporaneous or the three-year horizon is too short to capture build-up. Gopinath (2015)'s dominant-currency-pricing prediction is that lower-income tiers show flatter responses because USD-invoiced exports decouple the bilateral REER from local-currency pricing.
Specification: ln(X_it) = α_i + λ_t + β_h · ln(REER_{i,t−h}) + u_it, two-way FE via double-demeaning, run separately within each income tier at lag h ∈ {1, 3}. Income tiers are sample quartiles of 2018 GNI per capita PPP (WDI NY.GNP.PCAP.PP.CD). Labels mirror the four-tier World Bank income taxonomy but cut-offs are in-sample NTILE(4), not the WB Atlas thresholds (Atlas GNI is not in this parquet build). Sources: CEPII BACI 202501 (retrieved 2026-04-28) (total exports, ×1000 for USD); IMF IFS annual REER; WDI NY.GNP.PCAP.PP.CD. Literature: Bahmani-Oskooee & Ratha (2004) Applied Econ.; Gopinath (2015) NBER 21646; Campa & Goldberg (2005) REStat.
Across the 93 countries with matched volatility and slope, sample REER volatility ranges from near zero (currency-board and pegged regimes) up to double-digit annual log-change standard deviations (commodity-exporters and EM crisis countries). Median cross-country volatility is 6.2% per year. MEX sits at 12.6%, above the cross-country median, so the Figure 2 slope is comparatively well-identified for this country. The Devereux-Engel (2003) signal-extraction logic predicts that the per-country slope's attenuation bias shrinks as REER variance rises, so high-volatility countries on the right of the chart should display, on average, more negative (Marshall-Lerner-signed) estimates than the near-pegged left tail.
Method: REER volatility = sample stddev of annual dln(REER) per country, 1995-2025, dropped countries with fewer than 15 paired observations. Slope = Figure 2 per-country bivariate OLS. References: Devereux & Engel (2003) RES 70(4): 765-783; Imbs & Mejean (2015) AEJ:Macro 7(3): 43-83 (attenuation in within-country FX-trade slopes). Sources: IMF IFS REER (CPI-based, 2010=100); CEPII BACI 202501 (retrieved 2026-04-28).