Response to Robert Feenstra, Hong Ma, and Yuan Xu’s Comment
on Autor, Dorn, and Hanson (AER 2013)
David Autor∗

David Dorn†

Gordon Hanson‡

April 2, 2017

A 2017 comment by Feenstra, Ma, and Xu (FMX) claims that the estimation results on the impactof import competition on labor-market outcomes in Autor, Dorn and Hanson’s 2013 AER article(ADH) are biased by the exclusion of controls for contemporaneous changes in housing prices. In
investigating these concerns, we find that (a) the trend component of local housing-price changes
on which FMX primarily rely is highly likely to be endogenous to trade shocks, and (b) when one
incorporates measures of plausibly exogenous changes in housing prices into the main ADH specifications, there is little change in ADH’s main empirical results. The key conclusions in FMX’s
comment stem from either using endogenous house price changes—which are themselves affected by
the China trade shock, as documented by extant literature—or by employing an instrumentation
strategy that inadvertently uses the China trade shock as an instrument for house price changes andthen incorrectly infers that house price changes (not the China shock) are the cause of adverse employment outcomes. We conclude that the main critique provided by the comment is not supported
empirically.

∗

MIT Department of Economics and NBER. E-mail: dautor@mit.edu
University of Zurich and CEPR. E-mail: david.dorn@econ.uzh.ch‡
UC San Diego and NBER. E-mail: gohanson@ucsd.edu†

In a recent comment, Robert Feenstra, Hong Ma, and Yuan Xu (2017, FMX hereafter) report thatthe estimation results in Autor, Dorn and Hanson (AER 2013, ADH hereafter) on the impact ofimport competition on labor-market outcomes are sensitive to the inclusion of controls for changes
in housing prices. Based on their analysis, FMX conclude that ADH partially confound trade shocks
with housing-market shocks. This short note contains our response to their comment.
To recap, ADH analyze the impact of import competition from China on local labor markets in
the U.S. They use the 722 commuting zones (CZs) in the continental U.S. to represent local labormarkets and examine the impact of the China trade shock over two time periods, 1990 to 2000
and 2000 to 2007. They measure the trade shock using the growth in China imports per worker(see their equation 3) and instrument for this value using growth in imports from China in otherhigh-income countries (see their equation 4). ADH estimate the impact of the China trade shock onchanges in CZ manufacturing employment; the CZ working-age population; CZ non-manufacturing
employment, unemployment, population not in the labor force, and population receiving Social
Security Disability Insurance; CZ average log weekly wages; CZ government transfer receipts per
capita; and CZ household income by source. They find that CZs more exposed to import competition
from China experience significantly larger reductions in manufacturing employment, increases inunemployment and non-participation in the labor force, decreases in wages in the non-manufacturingsector, increases in government transfer receipts, and reductions in household income. They do notfind significant impacts of trade shocks on changes in CZ non-manufacturing employment, workingage populations, or manufacturing wages.
FMX’s main critique: The comment by FMX claims that the estimation results in
ADH on the impact of import competition on labor-market outcomes are sensitive tothe inclusion of controls for changes in housing prices. Based on their analysis, FMX
conclude that ADH partially confound trade shocks with housing-market shocks.
To begin, it is useful to review the primary regression specification in ADH, which is∆y it = γt + β1 ∆IP Wuit + Xit0 δ + it ,(1)

where ∆yit is the decadal change in labor-market outcome y in commuting zone i, the change inimport exposure ∆IP Wuit is the key explanatory variable of interest, and Xit is a vector of control
variables for initial economic conditions and demographic characteristics for CZ i. When estimating
this model for the interval between 1990 and 2007, ADH stack the 10-year equivalent first differences
for two periods, 1990 to 2000 and 2000 to 2007, and include separate time dummies for each decade(in the vector γt ). The change in import exposure ∆IP Wuit is instrumented by the variable ∆IP Woit
as described in ADH equation (4). This stacked first-difference model is a three-period fixed effects
model but with less restrictive assumptions on the error term (see ADH footnote 26). The vector Xit
contains dummies for Census geographic divisions and controls for CZs’ start-of-decade share of theworking-age population that is college-educated, share that is foreign born, share of women in total
1employment, share of employment in routine occupations, and average offshorability index (see notes
to Table 3 in ADH). These controls allow changes in outcomes to be a function of initial conditions,time trends to vary by geographic region, and the aggregate time trend to vary by decade. Standard
errors are clustered at the state level.
FMX claim that the estimation results in ADH on the impact of import competition on labormarket outcomes are biased by the exclusion of controls for contemporaneous changes in housing
prices. Specifically, the claim is that trade shocks were most pronounced in CZs where house prices
fell by more, thus confounding the impact of the China shock with the adverse impact of housing
shocks. FMX draw inspiration from Charles, Hurst, and Notowidigdo (2016a and 2016b, CHN
hereafter), who observe that the housing boom in the 2000s temporarily ‘masked’ the adverse effectsof employment shocks to manufacturing. Perhaps ironically, FMX’s claims are opposite in spirit
to CHN; FMX’s reasoning implies that adverse housing shocks amplified rather than masked themeasured impact of trade shocks.
FMX propose adding to equation (1) the variable 4Pit , which is the contemporaneous percentagechange in housing prices for commuting zone i, a measure which is available for 522 of the 722
commuting zones used in the ADH analysis. FMX find that some ADH coefficient estimates (on theimpact of trade shocks on CZ changes in total employment, unemployment, and non-participationin the labor force) fall in magnitude and (or) lose statistical significance when one includes CZ-level
housing-price changes in the regression. By failing to control for the “masking effect” of the US
housing-price boom, FMX suggest that ADH overstate the labor-market impacts of the China trade
shock.

Observation 1: Changes in local housing prices are fundamentally endogenous to changes
in local labor demand
There is abundant empirical and theoretical literature which establishes that shocks to local labordemand affect local housing values (e.g., Gyourko and Glaeser, 2005; Notowidigdo, 2011; Diamond,2016). Specifically in the context of import competition, Feler and Senses (2016) find that commuting zones more exposed to the ADH China trade shock (where they employ the ADH instrumentation
strategy) experience larger reductions in housing prices, business activity, tax revenues and expenditure on social welfare programs and pubic housing. In conjunction with the results in ADH, theseresults indicate that adverse shocks to manufacturing employment, be they due to trade or otherforces, are strongly likely to weaken local demand for housing and thereby produce a contraction in
housing values. The comment, however, appears to give short shrift to the sensitivity of local housing
prices to local-labor-market conditions. FMX do not mention the endogeneity issue until rather latein the paper (with no mention in the abstract or introduction) and instrument for housing prices in
only one of the three sections of estimation results. When an instrumentation strategy for housing
price changes is introduced (in section 3), it relies on assumptions that are difficult to support.

2

Observation 2: The instrumentation approach in FMX makes implausible identifying
assumptions
The comment’s proposed solution to the endogeneity of housing prices is to use an empirical modelof structural breaks in housing values to construct an instrument. Specifically, FMX regress log
housing prices for MSAs at a quarterly frequency on an MSA dummy, an MSA-specific time trend,
and two MSA-specific breaks in the time trend (one for the 1990s and one for the 2000s). The two
decadal breaks in the time trend, along with the ADH trade-shock instrument, are then jointly used
to instrument for the changes in local import exposure and changes in local housing prices.
One evident problem with this approach is that it requires the identifying assumption that trend
breaks in local housing prices are uncorrelated with unobserved local-labor-demand shocks, which
are embodied in the disturbance it in equation (1). The literature cited above strongly suggeststhat this assumption is invalid. Indeed, available empirical literature implies that changes in local
labor demand are a driving force behind observed changes in housing values. Just as real-estate
prices have fallen in Youngstown, Ohio, with the shuttering of local manufacturing plants, they
have soared in the San Francisco Bay Area with the proliferation of high-tech startups. The FMX
instrumental variables strategy requires that deviations from a linear change in house prices are
exogenous—which is entirely a functional form restriction on the smoothness of underlying priceshocks. In reality, it is hard to think of any economic shock that necessarily causes a linear but
not a nonlinear change in prices. FMX’s IV approach may be sufficient for identifying discreet
trend breaks in house price trends—which is the purpose for which Charles, Hurst and Notowidigdo
developed it—but it is silent on the underlying causes of these shocks. It is almost certain that the
China trade shock is one of those causes.
There is a further conceptual error in the FMX instrumentation strategy that renders the results
largely uninformative—and in fact, which causes the authors to misinterpret their own results.
Because both sets of instruments—the ADH instrument based on import growth in other highincome countries and the estimated structural breaks in local housing prices—are used jointly to
instrument for two endogenous variables (the ADH trade shock and the local housing-rice changes),part of the predicted change in local housing prices is due to the ADH trade-shock instrument. Thisis directly shown in column 2 of the lower panel of Table 3 of the FMX comment, where the ADH
instrument is a significant, negative predictor of local housing prices changes even conditional on
FMX’s housing instruments. Thus, under the FMX approach, exogenous trade shocks are allowed(statistically) to affect local-labor-market outcomes partly through the variable ∆IP Wuit and partly
through the variable 4Pit . In this statistical model, a change in the coefficient value on ∆IP Wuitin equation (1) when including the variable 4Pit in the regression would not mean that the impactof the trade on the outcome variable ∆y it is lessened; rather, it would mean that trade shocks affectlabor-market outcomes through multiple channels. Specifically, the FMX instrumental variables
strategy measures the causal effect of the China shock on labor market outcomes that accrues boththrough shocks to manufacturing and through shocks to house prices. Though FMX don’t do the3covariance calculations that would be needed to scale this joint effect, it’s plausible that the total
effect of the China shock that works through both the manufacturing and housing channels is aslarge or larger than the marginal effect operating purely through manufacturing shocks. (Simply
including the trade shock as a reduced-form, non-instrumented covariate would account for bothchannels at once without having to specify multiple endogenous variables).

Observation 3: When one uses an alternative measure of housing-price changes that is
plausibly exogenous, the FMX critique is largely neutralized
As an alternative to the FMX approach, we employ a strategy to capture local housing-price changes
that cannot be readily explained by national trade shocks. Using the FMX sample of 522 commuting
zones and their data on house prices, we regress the relative change in CZ house prices on ∆IP Wuit(or ∆IP Wuit plus full ADH controls) in all out-of-state CZs. We then use the estimates to computeresidual house price changes for in-state CZs.1 The resulting residualized housing-price changes arehighly correlated with the the gross values that FMX use (with a correlation coefficient between
0.72 and 0.98, depending on whether no controls or full controls are used). Table 1 reports our
replication of the FMX results on their restricted sample of 522 commuting zones, where we alternatively include the ADH trade shock alone (panel I), the ADH trade shock plus the FMX (plausibly
endogenous) housing-price measure (panel II), or the ADH trade shock plus (plausibly exogenous)
residualized housing-price changes (panel III).2 We report each specification for three outcome measures—manufacturing employment, non-manufacturing employment, and total employment—either
using no additional control variables other than a period dummy (panel A) or full ADH controls(panel B). Table 2 then repeats the analysis separately for the two time periods, 1990-2000 and
2000-2007.
Beginning with Table 1 for the stacked time period, we see that ADH results are largely unaffected
when we go from the ADH specification (panel I) to the specification that includes controls forchanges in housing prices that are plausibly exogenous to the import shock (panel III). In all specifications, the impact of the ADH trade shock on manufacturing employment is strongly negative
1
The exogenous component of the CZ-level house price shock is calculated as follows: (1) regressing the change inthe house price index in each CZ on the instrumented China trade shock using a leave-one-out specification where datafrom the state each encompassing each CZ is omitted (a total of 48 regressions); (2) predicting the China-shock-induced(endogenous) component of CZ-level house price changes by applying the coefficients from the out-of-state regression
to CZ-specific values, including the instrumented CZ-specific trade shock; (3) calculating the exogenous CZ-level house
price shock as the difference between the observed house price change and the predicted (endogenous) change basedon the out-of-state regression coefficient. The first stage regression of house price changes on the instrumented China
trade shock from the leave-one-out specification has a mean coefficient of −0.046 and a mean t-ratio of −5.36. Whenthe 14 ADH standard covariates are added to the model, the mean coefficient increases in absolute magnitude to−0.104 with a mean t-ratio of −3.74. The coefficients and standard errors from these 48 regressions are remarkably
stable across each regression; the standard deviations of the coefficients and standard-errors are 0.0037 and 0.0003,
respectively, for the base specification and 0.005 and 0.009 for the augmented specification. This stability indicatesthat no one state exerts substantial influence on the overall pattern.
2
The results in panel II of Table 1 differ slightly from those in panel III of Table 2 in FMX because we computedecadal changes for both ∆IP Wuit and 4Pit , whereas FMX combine a decadal change in ∆IP Wuit with a seven-yearchange in 4Pit in the second period.

4and precisely estimated, with little change in magnitude across panels. If we compare coefficientestimates on the ADH trade shock for total employment (column 3), which FMX claim is sensitive tochanges in housing prices, we see little change in magnitude (and continued high precision), whether
or not we include residualized housing-price changes (panel I versus panel III). As in ADH, theimpact of trade shocks on non-manufacturing employment is generally imprecisely estimated (withthe addition of housing-price controls not affecting this finding). Only in Panel II, which mixes
endogenous and exogenous variation in house price changes as per FMX, do we see a substantial
reduction in estimated effect of the China Shock on total employment. As explained above, thisapproach is not conceptually sound.
Table 2 analyzes the 1990s and 2000s separately with models that include exogenous housing prices
and either comprise no covariates or full covariates. As in ADH, we see that in the 1990s, when
the China shock was relatively weak and less diversified across industries, we obtain less precisely
estimated coefficients than in the 2000s. This holds whether we use the ADH trade shock, measured
as imports per worker (panel I), or the alternative trade-shock measure of the change in importpenetration (as in Acemoglu, Autor, Dorn, Hanson, and Price, 2016). The impacts of trade shocks on
manufacturing employment and total employment are robustly negative and very precisely estimated(for all but the case of import penetration with no controls in column 3, panel A.II, where the trade
shock is marginally significant). As in ADH, the impacts of trade shocks on non-manufacturing
employment are imprecisely estimated when full controls are included in the specification.
To facilitate comparison, columns (4) and (8) of the table report the ratio of the estimated impactof the China trade shock on non-manufacturing vs. manufacturing employment, both as a share ofworking-age population. The ratio of these coefficients is typically around −0.5 to −0.6, implyingthat employment gains in non-manufacturing offset about 50 to 60 percent of the losses in manufacturing (i.e., less than full offset). The estimates for the 2000s are more sensitive to the inclusion ofcontrols than are those for the 1990s. But when the exogenous housing shock component is included
with the 14 covariates used by ADH (lower row of each panel), the pattern of incomplete employment
offset in non-manufacturing is evident.

Observation 4: FMX inadvertently mischaracterize a core finding of ADH
In contrast to what FMX claim in their conclusion, ADH do not compute job loss outside of manufacturing (precisely because the results for non-manufacturing employment are mixed). Nevertheless,throughout the ADH analysis there is no evidence that CZ employment gains outside of manufacturing fully offset CZ employment losses within manufacturing. The question whether workers areable to smoothly transition from the declining manufacturing sector to other equally lucrative employment outside of manufacturing is of course best addresses by tracing out the individual career
paths of workers in longitudinal data. In a separate research paper, Autor, Dorn, Hanson, and Song(2014) analyze individual-level longitudinal data from the Social Security Administration and findthat over a near two-decade period, trade-exposed workers do not fully replace earnings losses in5

trade-exposed industries with earnings gains in other industries. The implication of FMX’s analysis
that displaced manufacturing workers simply transition to other equally paid jobs after the China
shock is not supported by this evidence.

Summary
The comment by FMX claims that the estimation results in ADH on the impact of import competition on labor-market outcomes are biased by the exclusion of controls for contemporaneous changes
in housing prices. However, further investigation reveals that (a) the trend component of local
housing-price changes is highly likely to be endogenous to trade shocks, and (b) when one incorporates measures of plausibly exogenous changes in housing prices into the main ADH specifications,
there is little change in ADH’s main empirical results. The key findings in FMX’s comment stemfrom either using endogenous house price changes—which are themselves affected by the China trade
shock, as documented by extant literature—or by employing an instrumentation strategy that inadvertently uses the China trade shock as an instrument for house price changes and then incorrectly
infers that house price changes (not the China shock) are the cause of adverse employment outcomes.
We conclude that the main critique provided by the comment is not well-founded methodologicallyor empirically.

6

References
Acemoglu, Daron, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price. 2016. “Import
Competition and the Great US Employment Sag of the 2000s,” Journal of Labor Economics, 34 (S1):S141–S198.
Autor, David H., David Dorn, and Gordon H. Hanson. 2013. “The China Syndrome: Local Labor
Market Effects of Import Competition in the United States.” American Economic Review, 103(6):2121—68.
Autor, David H., David Dorn, Gordon H. Hanson, and Jae Song. 2014. “Trade Adjustment: Worker
Level Evidence.” Quarterly Journal of Economics, 129(4):1799–860.
Charles, Kerwin Kofi, Erik Hurst and Matthew J. Notowidigdo. 2016a. “The Masking of the Decline
in Manufacturing Employment by the Housing Bubble.” Journal of Economic Perspectives, 30(2):179-200.
Charles, Kerwin Kofi, Erik Hurst and Matthew J. Notowidigdo. 2016b. “Housing Booms, Manufacturing Decline, and Labor Market Outcomes.” Northwestern University Working Paper.
Diamond, Rebecca. 2016. “The Determinants and Welfare Implications of US Workers’ Diverging
Location Choices by Skill: 1980-2000.” American Economic Review, 106(3): 479-524.
Feenstra, Robert, Hong Ma, and Xu. 2017. “The China Syndrome: Local Labor Market Effects ofImport Competition in the United States: Comment.” NBER Working Paper No. .
Feler, Leo, and Mine Z. Senses. 2016. “Trade Shocks and the Provision of Local Public Goods.”
Mimeo, Johns Hopkins University.
Glaeser, Edward L. and Joseph Gyourko. 2005. “Urban Decline and Durable Housing.” Journal of
Political Economy, 113(2): 345-375.
Notowidigdo, Matthew J. 2011. “The Incidence of Local Labor Demand Shocks.” NBER Working
Paper No. 17167.

7

Table 1. Imports from China and Employment Status of Working Age Population within Commuting Zones, 19902007: 2SLS Estimates.
A. Models w/o Additional Control
Variables
Non-MfgEmp(2)

Mfg Emp(1)

B. Models with Full ADH Control
Variables

Total
Emp
(3)

Non-MfgEmp(5)

Mfg Emp
(4)

Total
Emp
(6)

I. No Control for House Price Change(Δ Imports from China to
US)/Worker-0.791(0.060)**0.103(0.101)-0.688(0.109)**-0.661(0.100)**-0.177(0.161)-0.837(0.198)**

II. Control for Gross (Exogenous + Endogenous) House Price Change(Δ Imports from China to
US)/Worker-0.707(0.063)**0.381(0.097)**-0.326(0.110)**-0.522(0.079)**0.197(0.207)

Relative Change in Gross
House Price Index

0.922(0.290)**3.056(0.444)**3.978(0.701)**1.333(0.292)**3.590(0.543)-0.325(0.219)**4.923(0.777)**-0.822(0.182)**4.731(0.559)**III. Control for Exogenous House Price Change(Δ Imports from China to
US)/Worker-0.746(0.061)**0.225(0.094)*-0.521(0.103)**-0.655(0.072)**-0.166(0.177)

Relative Change in HPI,
Exogeous to Import Shock

1.037(0.234)**2.794(0.414)**3.831(0.606)**1.583(0.215)**3.148(0.424)**

Notes: N=1044 (522 commuting zones x 2 time periods). Regressions in Panel A control for a period dummy and for the change in thehouse price index (HPI) in subpanels A.II and A.III. Regressions in Panel B control for an additional 14 covariates as indicated in column 6
of Table 3 in Autor, Dorn and Hanson (2013). The exogenous change in HPI in panel A.III for a given CZ is computed as the residual of aregression of the change in out-of-state HPI on imports-per-worker and a period dummy (i.e., change in HPI is first regressed on IPW for all
local labor markets outside the CZ's own state, and the predicted value of that regression for the CZ is then subtracted from the grosschange in HPI for that CZ). The exogenous change in HPI in panel B.III is computed accordingly while controlling for the additional
covariates. Robust standard errors in parentheses are clustered on state. Models are weighted by start of period commuting zone share ofnational population. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.
The exogenous component of the CZ-level house price shock is calculated by: (1) regressing the change in the house price index in each CZ
on the instrumented China trade shock using a leave-one-out specification where data from the state each encompassing each CZ is omitted(a total of 48 regressions); (2) predicting the China-shock-induced (endogenous) component of CZ-level house price changes by applying thecoefficients from the out-of-state regression to CZ-specific values, including the instrumented CZ-specific trade shock; (3) calculating theexogenous CZ-level house price shock as the difference between the observed house price change and the predicted (endogenous) change
based on the out-of-state regression coefficient. The first stage regression of house price changes on the instrumented China trade shockfrom the leave-one-out specification has a mean coefficient of -0.046 and a mean t-ratio of -5.36. When the 14 ADH standard covariates areadded to the model, the mean coefficient increases in absolute magnitude to -0.104 with a mean t-ratio of -3.74. The coefficients andstandard errors from these 48 regressions are remarkably stable across each regression; the standard deviations of the coefficients andstandard-errors are 0.0037 and 0.0003, respectively, for the base specification and 0.005 and 0.009 for the augmented specification. Thisstability indicates that no one state exerts substantial influence on the overall pattern.

8

Table 2. Imports from China and Employment Status of Working Age Population within Commuting Zones, Subperiods 19902000 and 2000-2007: 2SLS Estimates. Dep Vars: 10-Year Equivalent Changes in Population Shares by Employment Status
A. Subperiod 1990-2000
Mfg
Emp
(1)

Non-Mfg
Emp(2)

B. Subperiod 2000-2007
β(2)/
β(1)
(4)

Total
Emp
(3)

Mfg
Emp
(5)

Non-Mfg
Emp
(6)

Total
Emp
(7)

β(2)/
β(1)
(8)

I. Coefficient Estimates for CZ Imports per Worker
Model w/o control
variables-0.990 ** 0.495 ~(0.197)(0.284)-0.495 **(0.109)-0.5-0.755 ** 0.032(0.059)(0.134)-0.723 **(0.148)0.0Model w/ full controls+ exogenous HPI-0.320(0.234)-0.140(0.252)-0.6-0.671 ** 0.252(0.148)(0.160)-0.419 *(0.179)

-0.4

0.180
(0.269)II. Coefficient Estimates for CZ Import Penetration
Model w/o control
variables-2.138 ** 1.228 ** -0.910 ~(0.363)(0.465)(0.505)-0.6-2.077 ** 0.142(0.412)(0.251)-1.936 **(0.379)-0.1

Model w/ full controls+ exogenous HPI-1.552 *(0.668)-0.7-1.060 *(0.431)-0.569 ~(0.325)-0.5

1.091 ~
(0.633)-0.462(0.900)0.491(0.313)

Notes: N=1044 (522 commuting zones x 2 time periods). Each coefficient comes from a separate regression of the outcome on a measure of Chinese
import competition (and additional controls in rows 2 and 4). Panel I measures import competition from China using the import-per-worker measure ofAutor, Dorn and Hanson (2013). Panel II measures import competition using the import penetration measure of Autor, Dorn and Hanson (2017).
Models with full controls include the full vector of control variables from panel B.III of Table 1. Robust standard errors in parentheses are clustered onstate. Models are weighted by start of period commuting zone share of national population. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

9