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Fetching primary parquet sources and recomputing the published exhibits.
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Fetching primary parquet sources and recomputing the published exhibits.
Autor, Dorn & Hanson’s China Syndrome identifies local labour market effects of rising Chinese import competition in the US, using commuting-zone-level ΔIPW and an instrument built from Chinese exports to other rich economies. We cannot reproduce that research design without commuting-zone employment data. What we canreproduce is the companion cross-country fact that motivates it: how much of each OECD country’s manufactures-import base is now Chinese.
Autor, Dorn & Hanson (2013, AER) build a commuting-zone (CZ) exposure measure ΔIPWj,t = Σk (Lj,k/Lj) · (ΔMk,t/Lk), where the outer weight is industry k’s pre-shock employment share in CZ j, and ΔMk,t/Lk is the change in imports from China to the US in industry k divided by initial US workers in that industry. They instrument US ΔIPW with an other-high-income-country version (Chinese exports to Germany, Japan, etc., per US-industry worker), to purge domestic demand shocks. In their stacked first-difference 2SLS estimates over 1990-2007 (Table 3, Panel I), a $1,000 per-worker rise in Chinese import exposure lowers the manufacturing-employment share of the working-age population by roughly 0.6 percentage pointsper decade in the average CZ (point estimates range from −0.51 to −0.75 across specifications; the column-2 preferred estimate is −0.61). Follow-up work (Acemoglu, Autor, Dorn, Hanson & Price 2016, JoLE) scales this to 2.0-2.4 million manufacturing jobs lost nationally over 1999-2011.
We are emphatic that this page is not ΔIPW. ΔIPW has workers in the denominator; our measure has import dollars in the denominator. ΔIPW uses CZ-level employment weights; our measure uses country-level import aggregates. ΔIPW leverages an IV from other-rich-country Chinese trade to identify causal effects on US labour markets; we estimate no causal effect and run no regression: we simply compute, for each OECD economy, the share of total manufactures imports that come from China in 2000 and 2016. That is a descriptive exposure metric, analogous in spirit to the raw ADH exposure before instrumentation and before CZ weighting, but nothing more. Anyone using this page to attach an employment-displacement number to any OECD country would be misreading it.
What we canshow, BACI-only, is the cross-country pattern the ADH paper points at before building its CZ measure: China’s share of each OECD economy’s manufactures imports roughly tripled in sixteen years, on average.
ADH’s Figure 1 plots the US-only annual import-penetration ratio across 1991-2007 and anchors the WTO-accession date (2001-12-11) as the breakpoint. The cross-OECD analog (mean China-share across the same 38 economies, computed every year from the start of the BACI partitions) is what turns the static 2000 → 2016 endpoint comparison in Figure 1 into a dynamic event study around China’s WTO accession. If the WTO date is the structural break that ADH’s identification leans on, the cross-OECD mean should bend visibly upward in 2002 and continue accelerating until the 2008 financial crisis.
ADH identify exogenous Chinese-import exposure by instrumenting US-industry imports with Chinese exports to other high-income economies (Table 3, Panel II). The first-stage coefficient on the other-country instrument is large and precisely estimated, ranging from 0.63 to 0.79 across their six specifications (first-stage R² of 0.54 to 0.58). That first stage is defined on US-industry-worker variation, which BACI cannot reproduce: there is no worker denominator and no commuting-zone structure in this panel, so we do not attempt a country-level surrogate for it here. The descriptive cross-OECD shares below stand on their own; they are not an instrumented first stage.
ADH’s identification relies on a China-specific supply shock, dated to the 2001 WTO accession and its post-MFA apparel quota phase-out. A natural falsification is to rerun the same 2000→2016 ΔOECD-penetration calculation with other plausible low-cost exporters in place of China. If the secular ΔOECD-penetration is driven by a generic emerging-markets force (commodity supercycle, global wage convergence, containerisation maturing), India, Vietnam, Brazil and Indonesia should each deliver a mean Δ of comparable magnitude to China’s. If instead the ADH “China shock” framing is correct, China’s Δ should tower over every placebo.
ADH find the manufacturing-employment response concentrated in the CZs most exposed to Chinese competition. As a cross-country descriptive analog, we split the OECD-38 at the median 2000 China-penetration ratio into a “low-exposure” bottom half (countries that started the WTO era with very little Chinese content in their manufactures import base) and a “high-exposure” top half. The ADH mechanism is a supply-sideChina shock, so both groups should see level gains in Chinese share; heterogeneity in Δ tells us whether the shock hit lagging or leading economies harder.
ADH motivate their design with the observation that Chinese import growth was concentrated in specific manufacturing industries (apparel, electronics, furniture, footwear) rather than spread evenly. BACI’s bilateral flows have no product dimension in this workbench’s partitioning, but country_year_productexposes China’s world export mix by HS6. Aggregated to HS Section, the ΔChina-share of world exports 2000 → 2016 tells us which industries drove China’s export surge: Section XVI (machinery & electronics) and Section XI (textiles) lead, Sections XIV (gems & metals) and XVII (vehicles) lag. A shock spread uniformly across industries could not generate the within-US labour-market reallocation that ADH document.
| quantity | published (ADH 2013) | our re-estimate |
|---|---|---|
| US ΔChina manuf-import share 2000→2016 | ~0.6% → ~4.6% (1991→2007, Figure 1; denominator is total US goods expenditure, not manufactures imports) | 9.4% → 25.0% |
| OECD avg 2000 | not reported | 4.1% |
| OECD avg 2016 | not reported | 13.4% |
| avg Δ across OECD-38 | n/a | +9.3 pp |
| First-stage coef. (China-to-OTH instrument) | 0.63 to 0.79 (Table 3, Panel II; R² 0.54-0.58) | not computable from BACI |
| CZ employment effect ($1k/worker ΔIPW) | −0.61 pp mfg emp share (Table 3, col. 2) | not computable from BACI |
Four reasons the numbers on this page are notcomparable to ADH’s point estimates. First, denominator: ADH uses US manufacturing employment by industry; we use each country’s total manufactures imports. The ratio differs by orders of magnitude and has a different economic meaning: exposure per worker versus share of trade. Second, geography: ADH’s unit of analysis is the US commuting zone, because the labour-market friction of interest operates locally; we aggregate to whole countries, which averages over within-country heterogeneity that is central to the paper’s story. Third, identification: ADH instruments US Chinese exposure with other-country Chinese exposure, to strip domestic-demand endogeneity; we report raw bilateral shares, which conflate China’s supply shock with each destination’s own cyclical demand. Fourth, sample period: ADH’s headline specification is a stacked first difference over 1990-2007 (subperiods 1990-2000 and 2000-2007); we use 2000-2016, which ends after WTO accession (a clean ADH start) and catches the shock at its mature phase.
What the BACI cross-country pattern doesreproduce is the direction and broad magnitude of exposure growth: China’s share of OECD manufactures imports rises sharply and monotonically in essentially every OECD economy across the 2000-2016 window. That is the aggregate fact that ADH’s CZ-level regressions translate into a causal employment estimate the BACI data alone cannot deliver.
@article{autor_dorn_hanson_2013,
author = {Autor, David H. and Dorn, David and Hanson, Gordon H.},
title = {The China Syndrome: Local Labor Market Effects of Import Competition in the United States},
journal = {American Economic Review},
volume = {103},
number = {6},
pages = {2121--2168},
year = {2013},
doi = {10.1257/aer.103.6.2121}
}For the production-side story of the same shock, see /china-shock. For the 2018-tariff reallocation narrative, see the US-China trade war page. Return to the replication gallery.