Import Liberalization as Export Destruction? Evidence from the United States
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. How does import liberalization affect a country’s export performance and welfare? Economic theory (Graham 1923, Ethier 1982, Krugman 1984) shows the answer hinges on whether production exhibits increasing returns to scale at the sector level. Krugman (1984) argued that with scale economies, import protection can be export-promoting because a protected industry expands, exploits scale economies, becomes more productive, and exports more — so conversely import liberalization is “export destroying.” The paper turns this logic into an empirical test: the sign of the import-liberalization-to-export relationship discriminates between constant-returns and increasing-returns trade models. Researchers otherwise lack tools to choose between these model classes, yet the choice matters greatly for multi-sector trade policy analysis.
Model and data. The authors build a multi-sector general-equilibrium gravity model generalizing Krugman (1980) to many countries/sectors with input-output linkages (as in Caliendo-Parro 2015). The model nests constant returns (Armington, σ→∞) and increasing returns. The “scale elasticity” is 1/(σ−1); the “output elasticity” of exports equals the trade elasticity (ε−1) times the scale elasticity, and is positive iff there are increasing returns. The empirical application exploits US Permanent Normal Trade Relations with China (PNTR), passed Oct 2000, which removed tariff-revocation uncertainty. Exposure is measured by Pierce-Schott’s NTR gap (log gap between non-NTR and NTR tariffs; mean 0.23, SD 0.13, range 0–0.59). Trade data are from CEPII BACI; the baseline sample covers exports from 23 OECD countries (including the US) to 141 importers across 444 NAICS goods industries, in long differences (1995–2000 pre-period vs 2000–07 post-period).
Main findings. Reduced-form: US export growth fell in higher-NTR-gap industries after PNTR. The raw Figure 1 slope is −0.51 (SE 0.057); a 10-log-point NTR-gap increase is associated with 5.0 log points lower annual export growth, and the NTR gap explains 18% of cross-industry variation. This is inconsistent with constant returns and implies increasing returns in US goods production. An offsetting input cost effect (lower imported-input costs) raises exports: PNTR reduced 2007 exports by 13% more for a 75th- vs 25th-percentile NTR-gap industry, but raised them 20% more for a 75th- vs 25th-percentile input-cost-shock industry; net effects range from −18% (Cigarettes) to +56% (Automobiles). A structural IV (NTR gap instrumenting output growth) yields an output elasticity of 0.74 (SE 0.41, preferred column).
Quantitative GE results. Calibrating the output elasticity to 0.821 (matching the −0.10 conditional NTR-gap effect; trade elasticity set to 5), PNTR raised aggregate US exports/GDP by 3.2%, decomposed into −1.8% real market potential (export destruction), +2.4% input cost, and +2.7% foreign demand. Aggregate export growth is 28% larger with scale economies than without, because scale economies make the input-cost effect almost five times stronger (2.4% vs 0.5%). Exports nevertheless declined in the most exposed sectors (Textiles & Leather, Other Manufacturing), shifting US comparative advantage away from high-NTR-gap sectors. Welfare: PNTR raised US real income 0.068% (real expenditure 0.087%); gains are ~30% smaller than under constant returns because a negative specialization effect (−0.15%) offsets a larger ACR openness gain (0.22%). Chinese gains exceed US gains tenfold.
Layer 2: Deep Dive
What is the core theoretical test and why does the sign of the import-liberalization-to-export relationship identify returns to scale?
From the bilateral trade equation, the elasticity of exports to output equals the output elasticity (ε−1)/(σ−1), which is strictly positive iff there are increasing sector-level returns. Under constant returns (Proposition 1), conditional on foreign demand and domestic input costs, import liberalization does not affect exports (α1=0). Under increasing returns (Proposition 2), import liberalization shrinks domestic real market potential, lowers output, and — because productivity falls with output under scale economies — reduces exports to ALL destinations (α1<0), with the effect’s magnitude strictly increasing in the output elasticity. So estimating whether export growth falls in more-liberalized industries distinguishes the two model classes.
What is the identification strategy and its main threats?
A triple-difference: changes in US bilateral export growth by sector after PNTR relative to changes in other OECD exporters’ growth, identified from the NTR gap interacted with Post and a US-exporter dummy. The estimating equation (12) uses importer-exporter-industry, importer-exporter-period, and importer-industry-period fixed effects to absorb importer demand, common-across-exporter technology shocks, and industry trends in supply capacity and trade costs. The NTR gap is plausibly exogenous because variation stems mostly from Smoot-Hawley (1930) non-NTR tariffs, unlikely related to economic conditions 70 years later; any endogeneity from NTR tariffs being higher in weak-growth industries would bias against finding a negative effect. Threat 1: unobserved US-specific technology shocks negatively correlated with the NTR gap not captured by input/skill/capital intensity controls. Addressed by re-estimating at HS 6-digit level with NAICS-industry-exporter-period fixed effects (Table 3), still finding negative effects. Threat 2: US-China competition in third markets — if PNTR shifted China’s export basket toward US-type products in high-NTR-gap industries. Tested by interacting with China’s market share (Table 4); the quadruple interaction is positive and insignificant, ruling this out.
What are the three mechanisms and how are they distinguished empirically and quantitatively?
(1) Real market potential / export destruction: import liberalization lowers the US price index, makes the domestic market more competitive, shrinks real market potential and output, and (under scale economies) cuts productivity and exports — identified by the negative α1 on the NTR gap. (2) Input cost effect: lower imported-input costs cut production costs and raise exports — identified by α2 on the input-output-weighted upstream NTR gap (CostShock), found negative and significant (lower input costs → higher exports). (3) Foreign demand effect: GE expansion of global demand and the trade-balance link between imports and exports — absorbed by fixed effects in the regression but recovered in the calibrated model’s decomposition (equation 16). In GE: −1.8% (market potential), +2.4% (input cost), +2.7% (foreign demand), netting +3.2%.
What heterogeneity is documented?
Sector-level: the real market potential effect is negative in all goods sectors and stronger where the NTR gap is higher; the input cost effect is positively correlated with the NTR gap (due to heavy diagonal weight in the I-O table); the foreign demand effect is positive everywhere but uncorrelated with the NTR gap. Net exports/GDP rise in 12 of 15 goods sectors but fall in the highest-NTR-gap sectors — Textiles & Leather falls 22% (−32% market potential, +8.5% input cost, +4.6% foreign demand) and exports decline in 3 of the 4 highest-NTR-gap sectors. Under constant returns, by contrast, export growth is positive in all sectors and weakly POSITIVELY correlated with the NTR gap — qualitatively opposite. The correlation between sector-level export growth with vs without scale economies is insignificant (excluding Textiles & Leather) or significantly negative (including it).
What robustness checks are run?
Appendix C checks robustness to: starting the post-period in 2001 instead of 2000; alternative NTR-gap definitions; aggregating exports across destinations; varying the exporter/importer/industry samples; allowing PNTR to affect domestic expenditure; and controlling for China import growth driven by non-PNTR shocks. An event study (equation 13, Figure 2) shows no NTR-gap/export relationship before 2000 and a negative one from 2001 until the 2007–08 financial crisis, ruling out pre-trends. The first-stage (Table 5) confirms higher-NTR-gap industries had lower OUTPUT growth (paralleling Pierce-Schott’s employment result). Alternative calibrations (Appendix D.5): without I-O linkages the market potential effect weakens but total export growth is roughly unchanged; allowing services scale economies raises US gains; combining Textiles & Leather with Other Manufacturing preserves results; using Bartelme et al. (2019) sector-varying elasticities still yields a negative specialization effect.
How is the output elasticity calibrated and how does it compare to the structural estimate?
The output elasticity for goods is calibrated to 0.821 by matching the simulated NTR-gap effect to the −0.10 conditional reduced-form estimate (Table 2, column i), with services output elasticity set to zero and trade elasticity (ε−1) set to 5 (Head-Mayer 2014). This is below the value of 1 implied by Krugman (1980) or the Pareto-Melitz model but close to the Bartelme et al. (2019) mean of 0.83. It is reassuringly close to the independent structural IV estimate of 0.74 (SE 0.41). The simulated effect is decreasing in the output elasticity (consistent with Proposition 2 part ii) and rises sharply as the elasticity approaches one; the model has a unique solution for output elasticities below 0.95.
How does the welfare decomposition work and why are gains smaller with scale economies?
Following Costinot-Rodríguez-Clare (2014), real-income gains decompose into an ACR term (changes in domestic expenditure share / trade openness) and a specialization term that exists only with scale economies (welfare from sectoral reallocation of employment, weighted by adjusted Leontief forward-linkage coefficients). With scale economies the ACR effect is +0.22% (vs +0.10% without), but it is more than offset by a −0.15% specialization effect, netting +0.068% real income — about 30% below the constant-returns gain. The specialization effect is negative because PNTR shifted resources toward services (weaker scale economies; goods output −0.55%, services +0.11%) and, more importantly per Appendix D.5, toward sectors with weaker FORWARD input-output linkages; cross-sectoral heterogeneity in scale economies alone contributes negligibly.
How does this relate to and differ from closely related prior work?
It extends Krugman (1984)’s partial-equilibrium oligopoly mechanism to a class of quantitative GE trade models (love-of-variety, external economies, Melitz-Pareto, or endogenous innovation — shown equivalent in Appendix A.3). Unlike prior scale-economy estimates (Antweiler-Trefler 2002, Lashkaripour-Lugovskyy 2018, Bartelme et al. 2019) and home-market-effect tests (Davis-Weinstein 2003, Costinot et al. 2019), it uses TRADE POLICY variation (not factor content, market size, or exchange rates) for identification and performs an ex-post policy analysis (echoing Goldberg-Pavcnik 2016). Relative to the PNTR/China-shock literature (Pierce-Schott 2016, Handley-Limão 2017, Autor-Dorn-Hanson 2013), it adds a new outcome — US EXPORTS and comparative advantage — and argues the ‘surprisingly swift’ manufacturing decline would have been smaller absent scale economies. It complements Juhász (2018)’s infant-industry evidence (Napoleonic France) by quantifying the export-destruction cost while showing PNTR’s net effect on exports and welfare is positive. Dick (1994) tested the same hypothesis cross-sectionally for 1970 US data but found little support.
What are the policy implications and their scope conditions?
The findings support the existence of the scale-economies channel traditionally invoked to justify protection: pre-PNTR import protection shifted US comparative advantage toward the most-protected industries, and in the calibrated model targeted import protection CAN promote sector-level exports — but not under constant returns. However, the export-destruction effect is dominated, for most sectors and in aggregate, by export-promoting channels (input cost, foreign demand); total export growth is even greater WITH scale economies; and the negative specialization effect is more than offset by traditional gains from trade, so US gains from PNTR remain positive (+0.068% real income). Scope conditions: results rest on the calibrated output elasticity (0.821) and trade elasticity (5); the model assumes constant markups and full employment, so welfare excludes pro-competitive effects (Jaravel-Sager 2020, Amiti et al. 2020) and employment effects (Autor-Dorn-Hanson 2013); it studies a single liberalization episode; and the analysis cannot distinguish among alternative SOURCES of increasing returns. The authors stress accounting for scale economies (or their absence) is a prerequisite for correctly evaluating sector-level trade flows and welfare.
What other notable findings or caveats appear?
PNTR is calibrated as a reduced-form openness shock (α5=0.43; equation 15), equivalent to a 13% average trade-cost reduction on US imports from China (SD 6.6% across industries) given trade elasticity 5 — matching Handley-Limão’s 13-percentage-point estimate. The calibrated economy has 12 economies and 24 sectors (15 goods). Chinese gains exceed US gains more than tenfold (because the US was much larger in 2000, so PNTR was a bigger shock to China), and China’s nominal wage rose 6.0% relative to the US, contributing to factor-price convergence. For comparison, Caliendo-Parro (2015) find NAFTA raised US welfare 0.08% and Fajgelbaum et al. (2020) find the Trump trade war cut US real income 0.04%. The model in changes is solved via exact hat algebra, holding each country’s trade deficit as a constant share of global value-added (which induces the positive import-export link in the foreign-demand term).