Do Importing and Exporting Increase the Demandfor Skilled Workers? Plant-level Evidence fromIndonesia
Joel Rodrigue
Department of Economics
Vanderbilt University
joel.b.rodrigue@vanderbilt.edu

Hiroyuki Kasahara
Department of Economics
University of British Columbia
hkasahar@interchange.ubc.ca

December 3, 2009

Preliminary and Incomplete

Abstract
This paper estimates the impact of plant-level import and export decisions on the demand
for skilled labor. We allow for import and export eﬀects to vary across plants and forplants to self-select into importing and exporting with some knowledge of their gains fromsuch actions. Using an Indonesian manufacturing data set that contains the detailed plantlevel information on workers’ education levels, we apply the treatment eﬀect framework
developed by Heckman and Vytlacil (1999, 2005, 2007a,b) to identify the average eﬀect
of importing and exporting on skill demand across diﬀerent sub-groups of plants, such asimporters, non-importers, exporters, and non-exporters. We ﬁnd that the eﬀect of importingon skill demand among importers is substantially larger than among non-importers, providing
evidence that the eﬀect of importing on skill demand is heterogeneous and that plants selfselection into importing based on the gains from importing. The results also indicate alarge positive eﬀect of importing on skill demand both within production workers and within
non-production workers. On the other hand, exporting leads to skill upgrading within nonproduction workers but it does not necessarily increase the skill demand for production
workers.

1

Introduction

Understanding the eﬀect of trade on income inequality has been emphasized as a key issue
in developing countries (Goldberg and Pavcnik, 2006). Numerous studies provide empiricalevidence that trade liberalization may have contributed to an increase in the skill premium1across several developing countries (Hanson and Harrison (1999), Gindling and Robbins (2001),
Attanasio et al. (2004)). While empirical evidence suggests that the skill premium increasein developing countries was driven by an increase in the demand for skilled workers (SanchezParamo and Schady, 2003; Goldberg and Pavcnik, 2007), the underling cause of the increased
demand for skilled workers is still an unresolved, but equally important, issue.
The rise in skill premium after trade liberalization in developing countries is inconsistent withthe predictions of Hecksher-Ohlin (H-O) theory. Speciﬁcally, lower trade barriers are predicted
to lead to a decrease skill premiums by decreasing the demand for skilled labor through a shiftof resources from skill-intensive industries to skill-scarce industries in skill-scarce developing
countries. In contrast, empirical evidence indicates that the reallocation of non-production andproduction workers across industries explains little of the changes in the demand for skilledworkers; most of increase in the demand for skilled labor happens within ﬁrms rather thanacross ﬁrms/industries (Berman, Bound, and Griliches, 1994; Bernard and Jensen, 1997; andBiscourp and Kramarz, 2006). Thus, to understand the eﬀect of trade on skill premium, it isimportant to understand how the demand for skilled labor at the ﬁrm-level is aﬀected by trade.
This paper empirically examines how the demand for skilled workers is related to plant-level
import and export decisions using the exceptionally detailed plant-level panel data covering
all the plants employing more than 20 workers in the Indonesian manufacturing sector. Using
foreign intermediate goods in production often requires the adoption of more sophisticated technology, particularly when it is imported from industrialized nations for which there is substantial
evidence of skill-biased technological change.1 Similarly, exporting may provide an opportunity
to get better access to skill-biased foreign technology (Clerides et al., 1998). Skill-biased technological change may also have a signiﬁcant eﬀect on the skill premium in developing countries(e.g., Kijima, 2006), and importing and exporting at plant-level may induce the adoption ofskill-biased technology.
The impact of trade on the demand for skill within ﬁrms may vary substantially acrossheterogeneous ﬁrms. For instance, importing foreign intermediate goods may provide ﬁrms
with an incentive to hire more skilled workers, but the degree of skill-upgrading may depend
crucially on the ﬁrm’s existing (potentially unobserved) heterogeneous ability to implement
foreign technology. When the eﬀect of importing on skill demand varies across ﬁrms, there isno single “eﬀect” of importing on skill demand. Further, we expect ﬁrms with greater ability
to adopt technology will self-select into importing, leading to a diﬃcult selection problem. In
such a case, the instrumental variable (IV) estimator does not identify the average impact ofimporting. Imbens and Angrist (1994) show that, under some conditions, instrumental variables
1

Doms, Dunne, and Troske (1997) provide evidence that the adoption of new factory automation technologies
lead to skill upgrading. Further, recent literature on trade and heterogeneous ﬁrms suggests that importing foreignintermediate goods increases productivity. Muendler (2004), Halpern, Koren, and Szeidl (2006), Kasahara andRodrigue (2008), Amiti and Konings (2008), Kugler and Verhoogen (2009). Adoption of skilled-biased technology
may be the reason why importing intermediates increases productivity.

2

identify the average eﬀect of importing among ﬁrms induced to change their import status by
the instrument.
In this study, allowing for a possibility that the impact of trade on skill demand is heterogeneous across ﬁrms, we employ the treatment eﬀect framework developed by Heckman and
Vytlacil (1999, 2005, 2007a,b) to estimate the average eﬀect among all ﬁrms, the average eﬀect
among importers (or exporters), and the average eﬀect among non-importers (or non-exporters);
they are called the Average Treatment Eﬀect (ATE), the Treatment Eﬀect on the Treated (TT),and the Treatment Eﬀect on the Untreated (TUT), respectively.
The data set contains plant-level information on the education level of workers (e.g., juniorhigh-school, high-school, bachlor, Ph.D, etc.) across production and non-production occupational categories for the years of 1996 and 1997. We examine the eﬀect of importing andexporting on the relative demand for skills measured by the logarithm of the ratio of the number of workers with at least a high school degree and that with less than a high school degree.
To control for the endogeneity of importing and exporting decisions, we utilize the variationsin input and output tariﬀs at 5-digit industry level and their interaction terms with plant laborproductivity.
Using the skill measure based on worker’s education level is the major advantage over the
existing empirical literature studying the eﬀect of international trade on the demand for skills
which uses occupation categories, such as non-production or white-collar, to construct a proxyfor skilled labor (Bernard and Jensen, 1999; Harrison and Hanson, 1999; Pavcnik, 2003; Biscourp
and Kramarz, 2006). The exception is Bustos (2007). Bustos examines the impact of exportingon skill updating using a panel of Argentinean manufacturing ﬁrms with the detailed information
on worker’s education level and ﬁnds that exporting increases the demand for skills, while ourresults suggest that importing is more important for skill upgrading than exporting.
Our analysis provides evidence for the heterogeneous eﬀect of importing on skill demandacross ﬁrms. We ﬁnd that the average eﬀect of importing on skill demand among ﬁrms who
self-select into importing (TT) is substantially larger than the average eﬀect among those whochoose not to import (TUT), indicating that the impact of importing on skill demand is positive
even among ﬁrms who does not import. On the other hand, the eﬀect of exporting on skill
demand is less clear.
We also examine the impact of importing and exporting on skill-upgrading within occupational classes of workers. We ﬁnd a large positive eﬀect of importing on skill demand, especiallyfor importers, both within production workers and within non-production workers. Thus, missing the skill upgrading within each of occupation categories, the conventional skill measure
based on the ratio of non-production workers and production workers in the previous studies
may underestimate the impact of importing on skill demand. Our estimates also indicate thatthe impact of exporting on skill demand within non-production workers is signiﬁcantly positive,
but we ﬁnd no evidence for the impact of exporting on skill demand within production workers.
3

The next section describes our data set. Our empirical framework is discussed in Section 3.
Section 4 provides the results, and the last section concludes.

2

Data and Descriptive Statistics

Our data set is based on the Indonesian manufacturing census between 1991 and 1997 which
covers all plants with at least 20 employees. For this study, we mainly use the data for the yearsof 1996 and 1997 since these two years contain detailed information on workers’ education levels.
This data provides numerous advantages in this context.
First, the data set contains plant-level information on the education level of workers foreach of production and non-production (or white-collar and blue-collar) occupational categories
for the years of 1996 and 1997, which allows us to construct the relative skill measures basedon workers’ education levels. This is a major advantage over the existing empirical literature
studying the eﬀect of international trade on the demand for skilled labor which often uses
occupation categories, such as non-production or white-collar, to construct a proxy for skilledlabor (Bernard and Jensen, 1999; Harrison and Hanson, 1999; Pavcnik, 2003; Biscourp andKramarz, 2006).
Second, the data set includes information on export sales and the cost of imported intermediates to identify the eﬀect of exporting and importing on the demand for skilled labor. Additional
plant-level information contained in the data includes the percentage of foreign ownership, total
expenses on research and development (R&D), and total expenses on training. If R&D activities are necessary for the adoption of foreign skill-based technology, investigating how R&D
expenditures are aﬀected by export and import decisions provides important information about
the eﬀect of international trade on the demand for skills. Total expenses on training can beinterpreted as the measure of human capital investment within a ﬁrm. On the one hand, a ﬁrm
may try to meet the increased demand for skills by training the existing workforce in addition tohiring better educated workers; on the other hand, training workers may improve ﬁrm’s ability
to adopt foreign technology.
Finally, as mentioned in Amiti and Konings (2008) and Amiti and Davis (2008), Indonesia
experienced trade liberalization during the period covered by this data set and the data on
input and output tariﬀs are available at 5 digit ISIC industry-level.2 Both types of tariﬀs vary
substantially across years and industries. We utilize the variations in input and output tariﬀs tocontrol for the endogeneity of importing and exporting decisions when we estimate the eﬀect ofimporting and exporting on the demand for skilled workers. Then, we use the estimated results
to quantify how much changes in input/output tariﬀs aﬀect the demand for skilled workers
through their eﬀects on the adoption of skill biased technology.
2

Given the detailed information on individual inputs at plant level, input tariﬀs can be constructed at 5 digit
ISIC industry-level. See Amiti and Konings (2008).

4

Table 1 presents the descriptive statistics on the relationship between various skill measuresand four diﬀerent export/import status for 1996-1997 for domestic and foreign plants. As
reported in the last columns, the sample size is large at 38393 even we restrict our sample to1996 and 1997. We classify a plant as foreign plant when at least 10 percent of its equity is held byforeign investors. Since there are few examples of data describing how workers’ education levels
diﬀer across export/import status in the existing studies, we provide the detailed descriptive
statistics for the education-based skill measures, including a fraction of workers with six diﬀerent
education categories classiﬁed by the highest degree completed: no primary education, primaryeducation, junior high school, high school, college, and graduate school.
In Table 1, importers and exporters tend to employ more educated workers than domesticallyoriented plants. For instance, on average, a fraction of college graduates in total workers is
6.3 percent for plants that both export and import while the corresponding ﬁgure for plantsthat neither export nor import are only 1.8 percent. Similarly, a fraction of workers beyond
high-school degree for plants that both export and import is 56.5 percent but, for domestic nonexporters and non-importers, it is only 22.6 percent. The workers of foreign plants are generally
more educated than those of domestically owned plants.
Conditional on import status, the worker’s education level of non-exporting plants appears
to be higher than that of exporting plants. This pattern is especially apparent for foreign plants.
This is possibly because foreign ﬁrms are conducting vertical foreign direct investment by setting
up a production facility that is specialized in unskilled-labor intensive production processes inIndonesia. As opposed to foreign plants, the average education level of exporting domestic plants
is higher than that of non-exporting plants although importing domestic plants employ moreeducated workers than exporting domestic plants. On the other hand, conditional on export
status, importers hire more educated workers than non-importers; for instance, the workers ofplants that import but do not export, denoted by “No-Ex/Im,” are more educated than thoseof plants that both exports and imports.
The last three columns report alternative skill related variables. “Training/Worker” and“R&D/Worker” report the average per worker expenditures on training and R&D, respectively,in the unit of 1983 hundred thousands Indonesia rupiah while “Non-Prod./All Workers” reports
a fraction of non-production workers in all workers. Importers and exporters engage in moretraining and R& D activities, and employ more non-production workers than plants that neither
export nor import. The per worker expenditures on training and R&D activities of importers
are higher than those of exporters. Foreign plants, especially those who import intermediate
goods from abroad, tend to spend more resources on training and R&D activities and hire more
non-production workers than domestically owned plants.
Table 2 reports the descriptive statics for each of production and non-production occupation
categories. Non-production workers are much more educated than production workers acrossexport/import status and domestic/foreign ownership. For instance, on average across all plants,
5

a fraction of college graduates for non-production workers is 13.1 percent while a fraction ofcollege graduates for production workers is substantially smaller at 1.1 percent.
The education levels of workers are diﬀerent across import and export status even within
production and non-production occupation categories. On average, a fraction of college graduates for production workers is 3.0 percent at plants that both import and export while lessthan 1 percent of production workers have college degrees at domestically oriented plants. Similarly, 25.0 percent of non-production workers are college graduates at importing and exporting
plants while a fraction of college graduates among non-production workers is only 9.8 percent
at non-importing and non-exporting plants. Given the observed large diﬀerences in skill demands across import and export status within occupation categories, this paper examine theeﬀect of importing and exporting on the demand for skills separately for production workers
and non-production workers.
The last column of Table 2 reports a fraction of plants that do not hire any non-production
worker. Overall, 20.6 percent of plants do not hire any non-production worker. Plants thatimport and export are more likely to hire at least one non-production worker while domesticallyoriented plants that neither export nor import is most likely to hire no one in non-production
occupation category. Foreign plants are more likely to hire non-production workers than domestic plants. Thus, whether plants hire at least one non-production workers or not is related toimport and export status as well as foreign ownership.

3

Empirical Framework

This section develops our empirical framework to analyze the impact of importing and exportingon skill demand. For brevity, our discussion mainly focuses on importing but it is also possible
to incorporate exporting into the model as in Kasahara and Lapham (2008).

3.1

Model with Heterogeneous Adoption of Skill-Biased Technology

Consider a constant elasticity of substitution (CES) production function as follows:{}σ/(σ−1)f (Ls , Lu , A, φ) = φ [ALs ](σ−1)/σ + L(σ−1)/σ
,
u

(1)

where Ls is the skilled labor input, Lu is the unskilled labor input, σ > 1 is elasticity of
substitution between skilled and unskilled labor, φ is Hicks neutral technology term, and A isskilled labor augmenting technology term.
Denote the log of demand for skilled labor input relative to unskilled labor input by S ≡ln(Ls /Lu ). Given the market wages, the relative demand for skilled labor input is determinedby equating the ratio of the marginal product of skilled and unskilled labor to the ratio of their6

wages as
S = (σ − 1) ln A − σ ln (ws /wu ) ,(2)

where ws and wu are the wages for skilled and unskilled labor inputs, respectively.
To test whether importing increases the relative demand for skilled labor input, we consider apossibility that importing foreign inputs aﬀects the level of skilled labor augmenting technologyas(σ − 1) ln A(X, D) = Dβ + Xγ ′ + U,

(3)

where β is a parameter that captures the eﬀect of importing on skill-biased technology level; X
is a vector of observables; U is skill-biased technology shock. Then, the skill demand is given by
S = Dβ + Xγ ′ + U,

(4)

where, with some abuse of notation, the second term on the right hand side of (2), −σ ln (ws /wu ),is incorporated into one of the variables in X, for instance, as industry/time dummies.
It is plausible to think that import decision D and skill-biased technology shock U are
correlated. A ﬁrm with more advanced skill-biased technology (i.e., high value of U ) may selfselect into importing (D = 1) because the gain from importing is increasing in the value of U .
In such a case, the ordinary least squares estimator (OLS) of β is subject to this “standard”
endogeneity bias. If an instrument that is correlated with D but uncorrelated with U is available,
the method of instrumental variable (IV) identiﬁes β when β is a constant parameter.
The standard model assumes that the eﬀect of D on S is the same across ﬁrms. This is astrong assumption. What if the eﬀect of importing on skilled demand is heterogeneous acrossplants so that β is diﬀerent across plants? In such a case, there is no single parameter β toestimate and, further, plants may sort into importing on the basis of their knowledge of β.
For instance, importing may provide better opportunities for a ﬁrm to adopt foreign skillbiased technology. If we interpret the value of β as the ability for a ﬁrm to adopt the skill-biased
technology upon importing, the impact of importing on the demand for skilled workers would beheterogeneous whenever the ﬁrms’ ability to adopt foreign technology is heterogeneous.3 Whena ﬁrm knows its ability to adopt skill-biased technology, a ﬁrm with greater ability to adopt
skilled-biased technology will self-select into importing because it can successfully adopt foreigntechnology upon importing. Because of this positive sorting on the gain from importing, thevalue of β tends to be higher for a plant that chooses to import than for a plant that chooses
not to import.
3

The adoption of skill-biased technology is speciﬁed in reduced-form through equation (3). In principle, we may
extend the model further by explicitly considering a decision to adopt foreign technology, and this is important
direction for future research.

7

We extend the model in (4) by assuming that β is a variable coeﬃcient withβ = β̄ + ϵ,

(5)where β̄ is the mean of β while ϵ is unobservable to a researcher but is, at least partially, known
to the ﬁrm’s manager. Then (4) is written as
S = [β̄ + ϵ]D + Xγ ′ + U.

(6)

In this “random-coeﬃcient” setup, the eﬀect of importing on skill-demand is heterogeneous.
Imbens and Angrist (1994) show that, under some conditions, an instrumental variable estimator
identiﬁes the average value of β for firms induced change their import choice by the instrument.
Since not all ﬁrms will be induced to switch their import status by an instrument, the averagevalue of β across all ﬁrms, β̄, is not identiﬁed by the instrumental variable (IV) estimator.
As Card (2001), Heckman, Urzua, and Vytlacil (2006), and Heckman and Vytlacil (1999,
2005, 2007a,b) further argue that, in this random coeﬃcient case, diﬀerent instrumental variables(IV) lead to diﬀerent IV estimates of the impact of importing on skill demand. To see this, let
Z is a vector that contains X and an instrument that is correlated with the import decision D
but is uncorrelated with the error term U in (4) so that Cov(Z, D) > 0 and Cov(Z, U ) = 0.
Then, conditional on X, as the sample size goes to inﬁnity, the instrumental variable estimatorapproaches the probability limit ofCov(Z, S)
Cov(Z, ϵD)= β̄ +.
Cov(Z, D)
Cov(Z, D)In general, Cov(Z, ϵD) ̸= 0 since D and ϵ are correlated when ﬁrms self-select into importing
based on their knowledge of ϵ. Then, the instrumental variable estimator does not identify theaverage impact β̄. Furthermore, diﬀerent instruments lead to diﬀerent “bias terms” because ingeneral Cov(Z, ϵD)/Cov(Z, D) ̸= Cov(Z ′ , ϵD)/Cov(Z ′ , D) for two diﬀerent instruments Z and
Z ′ . Two distinct empirical studies on the impact of importing and exporting on skill demand
could produce diﬀerent conclusions when they use diﬀerent instrumental variables.
Since the treatment eﬀect of importing on skill demand is heterogeneous, we will construct
and report diﬀerent “summary statistics” for the treatment eﬀect.4 In particular, we consider
the average eﬀect of importing on skill-demand among importers (the treatment eﬀect on thetreated, TT), the average eﬀect among non-importers (the treatment eﬀect on the untreated,
TUT), and the average eﬀect among all ﬁrms (the average treatment eﬀect, ATE). If a ﬁrm withhigh value of ϵ self-selects into importing, the average value of β among importers (TT) would
be higher than the average value of β among non-importers (TUT).
4

While the main object of interest is the (conditional) distribution of β, identifying the distribution of β is adiﬃcult task. In this paper, we attempt to construct some summary measure of the population distribution of β.

8

3.2

The Import Decision

To clarify the nature of self-selection into importing, we provide a simple static model of ﬁrm’s
import decision in which a ﬁrm’s ability to adopt skill-biased technology is heterogeneous.
The marginal cost of producing one unit of output is given by c(A, φ) = min{Ls ,Lu } ws Ls +wu Lu subject to f (Ls , Lu , A, φ) ≥ 1. As speciﬁed in (3) and (5), importing may increase theskill-biased technology level A and, further, such an eﬀect of importing is heterogeneous acrossﬁrms. For simplicity, we assume that importing does not aﬀect the Hicks neutral technology
level φ.5
Let Z denote a vector of observed variables containing X and other observables that serve as
instruments. The ﬁrm faces demand function q = B(Z)p−η , where q is quantity demanded, p is
output price, and B(Z) is demand shifter. Denote gross proﬁt by r(A, φ, Z) = maxq pq−c(A, φ)q.
When a ﬁrm chooses to import, it incurs an importing cost fm (Z). Then ﬁrm’s net proﬁt functionis π(A, φ, Z, D) = r(A, φ, Z) − Dfm (Z).
A ﬁrm imports if π(A(X, 1), φ, Z, 1) − π(A(X, 0), φ, Z, 0) ≥ 0. Deﬁne a latent variableD∗ = π(A(X, 1), φ, Z, 1) − π(A(X, 0), φ, Z, 0) = µD (Z) − V,
where µD (Z) = E[π(A(X, 1), φ, Z, 1) − π(A(X, 0), φ, Z, 0)|Z] is a deterministic function of observable variables Z while V = [π(A(X, 1), φ, Z, 1) − π(A(X, 0), φ, Z, 0)] − µD (Z) is a mean-zero
unobserved stochastic component. This leads to a latent variable model asD∗ = µD (Z) − V,
D=1

(7)

if D∗ ≥ 0,

D=0

otherwise.

A ﬁrm imports, i.e., D = 1, if D∗ ≥ 0; it does not import otherwise.
The random variable V captures both idiosyncratic productivity shock φ and the random components in the skill-biased technology level, ϵ and U . Since π(A(X, 1), φ, Z, 1) −π(A(X, 0), φ, Z, 0) is strictly increasing in the value of ϵ, the random variables V and ϵ are
negatively correlated when ϵ is independent of φ and U . This implies that a ﬁrm with highvalue of ϵ—high ability of adopting foreign technology—is more likely to self-select into importing. That is, a ﬁrm with high ability of adopting the skill-biased technology is more likely
to import because its gain from adopting more advanced skill-biased technology induced byimporting is larger.
5This is an extreme assumption but the similar argument goes through when the impact of importing on theskill-biased technology A is suﬃciently large relative to its impact on the Hicks neutral technology level φ.

9

3.3

The Marginal Treatment Eﬀect

To evaluate the heterogeneous impact of importing on skill demand, we use the framework
developed by Heckman and Vytlacil (1999, 2005, 2007a,b). Let S1 be the log of potential
demand for skilled labor relative to unskilled labor input as an importer, and S0 be the log ofpotential relative skill demand as a non-importer. We can write the relative demand for skilledlabor input, depending on import status, as
S1 = µ1 (X) + U1 and S0 = µ0 (X) + U0 ,(8)where, allowing for the average value of β to depend on X in (6), µ1 (X) ≡ E[S1 |X] = β̄(X)+Xγ ′and µ0 (X) ≡ E[S1 |X] = Xγ ′ while U1 = ϵ + U and U0 = U . The impact of importing on theskill demand depends on the ability of adopting skill-biased technology as
S1 − S0 = β̄(X) + U1 − U0 .
For a ﬁrm’s decision to import, we consider a latent variable model (7). In (7), we assume
that Z is the vector of observed variables that are assumed to be independent of (U0 , U1 , V ).
The set of variables in X can be a subset of Z but, for identiﬁcation, we assume that Z contains
at least one “instrument” that is not in X.
The distribution of V , denoted by FV , is assumed to be continuous and strictly increasing.
Further, we allow V to be dependent on U1 and U0 ; as discussed in the previous section, we
expect that a ﬁrm with a larger value of U1 − U0 = ϵ has a larger gain from importing due
to higher ability of adopting technology, and hence the value of V is low. This means thatE[U1 − U0 |V ] is decreasing in V .
Denote the probability of importing conditional on Z by P (Z) so that P (Z) = Prob(µD (Z) >V ) = FV (µD (Z)). Define a uniform random variable UD ≡ FV (V ) and then the importing
decision (7) is alternatively written as D = 1 if P (Z) ≥ UD and D = 0 otherwise. Note that
if E[U1 − U0 |V ] is strictly decreasing in V , so is E[U1 − U0 |UD ] in UD . Deﬁne the marginal
treatment eﬀect (MTE) as∆M T E (x, p) = E[S1 − S0 |X = x, UD = p] = E[µ1 (X) − µ0 (X) + U1 − U0 |X = x, UD = p]. (9)This is the mean treatment eﬀect for ﬁrms with X = x and P (Z) = p when UD = p. That is, itis the mean impact from importing on skill demand for ﬁrms with X = x and P (Z) = p when
the realization of unobserved random variable UD is such that ﬁrms are indiﬀerent between
importing and not importing. The MTE helps us to identify the heterogeneity in the impact ofimporting on skill demand across ﬁrms. Furthermore, as Heckman and Vytlacil (2005, 2007a,
2007b) establish, we can express all the conventional treatment parameters (ATE, TT, TUT,

10

IV, and OLS) as weighted averages of MTE with diﬀerent weighting function. In particular,
diﬀerent instrumental variables weight MTE diﬀerently. The appendix discusses how to estimate
various treatment parameters in detail.

3.4

Skill measure

We classify between skilled labor and unskilled labor based on the worker’s education level. In
the large empirical literature in labor economics on skill premium and skill biased technologicalchange (Berman, Bound, and Griliches, 1994; Autor, Katz, and Krueger, 1998; and Card and
DiNardo, 2002), the number of college graduates and the number of high school graduates areoften used as the measures of skilled labor and unskilled labor, respectively. Since the average
education level in Indonesia is low as indicated in Table 1, we deﬁne workers with at least
high-school degree as skilled labor while those with less than high-school degree as unskilledlabor.
Speciﬁcally, we construct skilled labor Ls by converting workers with some college education,bachelor, master, and doctor degrees to high-school graduate equivalent using an 8.7 percent
rate of return to education as Ls = high-school+ some-college× exp(2r) + bachelor× exp(4r) +master× exp(6r) + doctor× exp(9r) with r = 0.087.6 Similarly, the measure of unskilled labor
Lu is constructed by converting workers with primary education and junior-high-school degree toworkers without any primary education (no-primary) as Lu = no-primary + primary× exp(6r)+ junior-high-school× exp(9r). The 8.7 percent rate of return to education is from Duﬂo (2001)
who estimates the wage regression in Indonesia using regional variation in the number schools
across cohorts induced by the timing of the school construction programs.

3.5

Instruments

We construct a set of instruments based on input and output tariﬀs at 5 digit ISIC industry-levelobtained from Amiti and Konings (2008). As Amiti and Konings (2008) show, both types oftariﬀs vary substantially across industries and years between 1991-1997.
To capture the non-linear eﬀect of tariﬀ rates, we ﬁrst construct dummy variables based on
10, 50, and 90 percentile of the distribution of tariﬀ rate in 1996 and 1997.7 Speciﬁcally, for
input tariﬀ, we deﬁne Dτm ,1 = 1(0.085 ≤ τm < 0.156), Dτm ,2 = 1(0.037 ≤ τm < 0.085), andDτm ,3 = 1(τm < 0.037), where τm is input tariﬀ rate of 5-digit ISIC industry each plant belongs
to. The baseline tariﬀ rate dummy excluded from a regression is a dummy of the highest range
6

When we examine skill demand equation within production workers, we use Ls = junior-high-school + highschool× exp(3r)+ some-college× exp(5r) + bachelor× exp(7r) + master× exp(9r) + doctor× exp(12r).
7
Estimating the decision of Canadian plants to start exporting to the United States by the probit, Lileeva andTreﬂer (2009) ﬁnd that continuous variable of tariﬀ cut in the U.S. is not statistically signiﬁcant while a binary
variable constructed from the threshold tariﬀ-cut value is signiﬁcant with expected sign. They argue that this isbecause of the nonlinear eﬀect of tariﬀ cut.

11

of tariﬀ rates, Dτm ,0 = 1(0.156 ≤ τm ), so that the coeﬃcients on Dτm ,1 , Dτm ,2 , and Dτm ,3 inimport decision regression captures the eﬀect of reducing tariﬀ rates on importing.
Further, as suggested in the model of Melitz (2003) and Kasahara and Lapham (2008), theeﬀect of tariﬀ rates on importing and exporting may crucially depend on plant productivity level;a plant with very low productivity may not respond even to a large reduction of tariﬀ rate whilea plant with high productivity may start importing with a small reduction of tariﬀ rate. To
capture such an eﬀect, we also consider the interaction terms between plant labor productivity
and tariﬀ dummies as additional instruments. In sum, we use Dτm ,1 , Dτm ,2 , Dτm ,3 , and theirinteraction terms with plant labor productivity are included in import decision equation but
excluded from skill demand equation (4) and will serve as our instrument for importing.
A set of instruments for exporting is similarly constructed as Dτx ,1 = 1(0.150 ≤ τx < 0.238),
Dτx ,2 = 1(0.048 ≤ τx < 0.150), Dτx ,3 = 1(τx < 0.048), and their interaction terms with plantlabor productivity, where τx denotes output tariﬀ rate of 5-digit ISIC industry each plant belongs
to.

4

Results

4.1

Decision to Import and Export

To examine the validity of our instruments for importing and exporting, Table 3 reports theresults from the logit of the probability of importing or exporting with diﬀerent set of regressors.
The set of plant-level controls are as follows: “Labor Prod” is the logarithm of labor productivity;“Foreign” indicates a dummy for foreign ownership; “R&D” takes a value of one when R&D
expenditure is strictly positive, and zero otherwise; “Training” is also a dummy for trainingthat takes a value of one when training expenditure is positive; and “Capital/Worker” is thelogarithm of capital per worker. All regressions include 3 digit industry eﬀects and year eﬀects.
Column (1) of Table 3 includes both input tariﬀ rate and its interaction term with laborproductivity with labor productivity and foreign ownership as additional controls. As expected,
the sign on the interaction term is negative and signiﬁcant, suggesting that a reduction of input
tariﬀ rates encourages high productivity plants to start importing more than low productivity
plants. Column (2) uses three input tariﬀ dummies, Dτm ,1 , Dτm ,2 , and Dτm ,3 , in place of input
tariﬀ rate τm . The interaction terms of Dτm ,1 , Dτm ,2 , and Dτm ,3 with labor productivity are
signiﬁcantly positive; since the highest range of tariﬀ rates is set to be a baseline, this also
indicates that the eﬀect of a reduction of input tariﬀ rates on importing is positively related toplant productivity. Adding more plant-level controls in column (3) does not change the main
result; the interaction terms between labor productivity and tariﬀ dummies are signiﬁcantly
positive with additional controls of R&D, Training, and capital per worker. Overall, theseresults conﬁrm that our instruments for import decision are not weak.

12Columns (4)-(6) of Table 3 provide the results from the logit of exporting decision. As in thecase of import decision, the interaction terms between labor productivity and output tariﬀ rateor output tariﬀ dummies are signiﬁcant and have the expected sign, indicating that a reductionof output tariﬀ rates has a positive impact on exporting, especially among highly productive
ﬁrms.

4.2

Estimation of the Impact of Importing and Exporting on Skill Demand

Table 4 reports the results from estimating skill demand equation (4) using three diﬀerent
methods: OLS, IV with the estimated probability of importing or exporting as instrument,
and IV with tariﬀ dummies and their interactions with labor productivity as instrument. The
dependent variable is the logarithm of the ratio of the number of workers with more than highschool degree to the number of workers with less than high-school degree, where education-levelis adjusted within each category of skilled and unskilled labors.
As shown in Columns (1)-(6) of Table 4, the impact of importing on skill demand is signiﬁcantly positive across three diﬀerent methods and diﬀerent sets of controls. The IV estimates inColumns (3)-(4) or (5)-(6) are substantially larger than their OLS counterparts. The presence ofunobserved heterogeneous response may be responsible for this result; as argued in Imbens andAngrist (1994) and Card (2001), when β in skill demand equation (4) is heterogeneous acrossﬁrms, the IV estimator will give the weighted average of the impact of importing on skill demandacross plants that switch their import status due to a change in the value of instruments.
On the other hand, in Column (9)-(12) of Table 4, the IV estimates of the eﬀect of exportingon skill demand is not signiﬁcant although the OLS estimates in Column (7)-(8) are signiﬁcantly
positive. One possible explanation is that, to take advantage of low wage for unskilled labor inIndonesia, exporting may encourage plants to adopt skill-scarce technology or to switch theirproducts toward skill-scarce products, canceling the positive eﬀect of exporting on skill demand
from the adoption of skill-biased foreign technology.
Table 5 reports the IV estimates of skill demand equation (4) separately for ﬁve diﬀerent
set of subsamples classiﬁed by the estimated probability of importing or exporting conditionalon observable characteristics, where P̂ (Dm = 1) and P̂ (Dx = 1) are the estimated probabilities
of importing and exporting obtained from the speciﬁcations in Columns (3) and (6) of Table
3, respectively. The IV regressions applied to the subsamples use tariﬀ dummies and theirinteractions with labor productivity as instruments and include a full set of controls as reported
in Columns (6) and (12) of Table 4.
As reported in the second column of Table 5, the IV estimates of the impact of importingare larger for plants with less 10 percent of importing probability than for those with more than
10 percent of importing probability. This result suggests the presence of unobserved heterogeneous response. Namely, plants with observable characteristics that lead to low probability of13

importing would be induced to start importing by the instruments only if they expect a large
gain from importing. Thus, for plants with low value of P̂ (Dm = 1), the marginal plants who
would be induced to start importing by the instruments must have high ability to adopt foreign
skill-biased technology and their coeﬃcients of Dm would be high. Similarly, in the sixth columnof Table 6, the IV estimates of the impact of exporting on skill demand are larger for plants
with low probability of exporting than those with high probability of exporting, suggesting thepresence of unobserved heterogeneous response of exporting on skill demand.
To further examine the possibility of heterogeneous impact of importing on skill demand, we
follow the methodology suggested by Heckman and Vytlacil and estimate the marginal treatmenteﬀect (MTE) to compute the TT, the ATE, and the TUT.
Figures 1 and 2 show the MTE of importing and exporting, respectively, on skill skill demand, where the dotted lines indicate the 90 percent bootstrap conﬁdence interval. In Figure
1, the MTE of importing on skill demand is decreasing in the value of UD that makes plants to
be indiﬀerent between importing and not importing. That is, for a plant with low probabilityof importing conditional on observable variables (i.e., low UD ), the high value of unobserved
heterogeneous response β is necessary for the ﬁrm to self-select into importing. The observed
downward sloping MTE is evidence for unobserved response heterogeneity; if there is no unobserved heterogeneity in the impact of importing, the slope of the MTE would be ﬂat. On theother hand, in Figure 2, the MTE of exporting is not downward sloping, providing little evidence
for unobserved heterogeneity in the impact of exporting.
Table 6 shows the TT, the ATE, and the TUT of importing and exporting computed fromthe estimated MTE with diﬀerent weighting functions. In the ﬁrst row of Table 6, the TT, theATE, and the TUT of importing are signiﬁcantly positive with point estimates of 10.54, 0.86,
and 0.67, respectively. The magnitude of the TT is much larger than the ATE and the TUT,
indicating that importing plants self-select into importing because their gain from importingis larger than the gain for non-importing plants. The small value of the TUT suggests that afurther reduction of importing tariﬀ rates in Indonesia would have a relatively small impact onskill demand because those plants that are not currently importing are the ones with low valuesof β.
On the other hand, as the second row of Table 6 reports, the TT, the ATE, and TUT ofexporting on skill demand are not signiﬁcant, and the point estimates of the ATE and the
TUT are even negative. Thus, our analysis on the impact of exporting on skill demand using
the framework of MTE provides evidence neither for the positive impact of exporting on skill
demand nor for the presence of heterogeneous impact of exporting.
We also examine how importing and exporting induces an increase in the demand for skill
within production workers or within non-production workers. When we examine the skill demand
equation within production workers, since education level of production workers is low, we deﬁneworkers with at least junior high-school degree as skilled labor and those without junior high14

school degree as unskilled labor.
Table 7 shows the TT, the ATE, and the TUT of importing and exporting within production
workers and within non-production workers. As shown in the ﬁrst row of Table 7, the TT ofimporting is substantially larger than the ATE and the TUT for both within production workers
and non-production workers although both the ATE and the TUT are also signiﬁcantly positiveat 5 percent. Thus, the eﬀect of importing on skill demand is strong, especially for plants thatchoose to import, both within production workers and within non-production workers.
On the other hand, as reported in the second row of Table 7, there is an interesting diﬀerence in the eﬀect of exporting on skill demand between within production workers and within
non-production workers; the TT, the ATE, and the TUT of exporting on skill demand within
production workers are not signiﬁcant with negative point estimates while the treatment eﬀects
of exporting within non-production workers are all signiﬁcantly positive. This result suggeststhat exporting leads to skill upgrading within non-production workers but it does not necessarily
increase the skill demand for production workers.

5

Concluding Remark

This paper empirically examines the eﬀect of importing and exporting on skill demand using
the Indonesian manufacturing data set that contains the detailed plant-level information on
worker’s education level. With input and output tariﬀs at 5-digit industry level as instruments
for endogenous import and export decisions, we estimate the skill demand equation to quantify
the causal eﬀect of importing and exporting on skill demand. Using the framework developed
by Heckman and Vytlacil, we allow for a possibility that the impact of importing and exportingon skill demand is heterogeneous across plants and estimate the treatment eﬀect on the treated(TT), the average treatment eﬀect (ATE), and the treatment eﬀect on the untreated (TT)separately for within production workers and within non-production workers.
The results suggest that the eﬀect of importing on skill demand is large while the evidence
for the positive eﬀect of exporting on skill demand is mixed. Importing increases skill demandfor both within production workers and non-production workers. Furthermore, we ﬁnd thatthe eﬀect of importing on skill demand among importers (TT) is substantially larger than thatof importing among non-importers (TUT), suggesting that importers self-select into importing
because their gains from importing is large. Our results also indicate that exporting induces skillupgrading within non-production workers, especially for plants that choose to export. We do
not ﬁnd, however, any supportive evidence for the positive eﬀect of exporting on skill-upgrading
within production workers. Thus, a plant that starts exporting may employ more educatednon-production workers but it does not necessarily employ more production workers who are
educated.

15

6

Appendix: Estimation of MTE, ATE, TT, and TUT

Heckman and Vytlacil (2005) shows that the MTE deﬁned in (9) can be identiﬁed by thederivative of E[S|X = x, P (Z) = p] with respect to p as follows. First write the skill demand
equation as S = DS1 + (1 − D)S0 = µ0 (X) + D[µ1 (X) − µ0 (X)] + U0 + D(U1 − U0 ). We assume
that (U1 , U0 ) is independent of X. Taking the conditional expectation of S given X and P (Z),and noting that E[D|X, P (Z)] = P (Z), we have
E[S|X, P (Z)] = µ0 (X) + P (Z)[µ1 (X) − µ0 (X)] + E[U1 − U0 |UD ≤ P (Z)]P (Z).
Then, the MTE can be identiﬁed within the support of P (Z) as∆M T E (x, p) =∂E[S|X, P (Z)]= µ1 (x) − µ0 (x) + E[U1 − U0 |UD = p].
∂P (Z)
X=x,P (Z)=p

The term µ1 (x) − µ0 (x) represents the component of the MTE depending on X while E[U1 −U0 |UD = p] represents the component of the MTE that depends on UD .
To estimate the MTE, we impose the linear function form of µ0 (X) = α0 +Xβ0′ and µ1 (X) =α1 + Xβ1′ , where α0 and α1 represent the intercept terms. Then,E[S|X, P (Z)] = α0 + P (Z)∆α + Xβ0′ + P (Z)X∆β ′ + E[U1 − U0 |UD ≤ P (Z)]P (Z),(10)

where ∆α = α1 − α0 and ∆β = β1 − β0 . This suggests that β0 and ∆β can be estimated by apartially linear regression of S on X and P (Z). The easiest way to do this is to approximate
E[U1 − U0 |UD ≤ P (Z)]P (Z), say, by a fourth order polynomials in P (Z) as E[U1 − U0 |UD ≤P (Z)]P (Z) ≈ ρ0 + ρ1 P (Z) + ρ2 (P (Z))2 + ρ3 (P (Z))3 + ρ4 (P (Z))4 .8 While we are not able
to separately identify α0 and ∆α from the intercept and the linear term of E[U1 − U0 |UD ≤P (Z)]P (Z), we are able to identify the MTE as follows. We ﬁrst estimate P (Z) by a logit
regression of D on polynomials in Z. In the second step, we run OLS of S on constant, X,
P̂ (Z)X, and P̂ (Z), (P̂ (Z))2 , (P̂ (Z))3 , and (P̂ (Z))4 :S = (α0 + ρ0 ) + Xβ0′ + P̂ (Z)X∆β ′ + (ρ1 + ∆α)P̂ (Z) + ρ2 (P̂ (Z))2 + ρ3 (P̂ (Z))3 + ρ4 (P̂ (Z))4 + v.
Once E[S|X, P (Z)] is estimated, then we may take a derivative with respect to P to estimate
the MTE asˆ ′ + (ρ1 +ˆ ∆α) + 2ρ̂2 p + 3ρ̂3 p2 + 4ρ̂3 p3 .
ˆ M T E (x, p) = x∆β∆ˆ MT E.
We use bootstrap to construct conﬁdence interval bands for ∆8
An alternative to polynomial approximation is local quadratic estimator as suggested in footnote 17, page 17,of Carneiro, Heckman, and Vytlacil (2006).

16

The Average Treatment Eﬀect is computed as∫ 1
AT E(x) =ˆ ′ + (ρ1 +ˆ ∆α) + ρ̂2 + ρ̂3 + ρ̂4 .
∆M T E (x, p)dp = x∆β0The treatment eﬀect for a ﬁrm with (X, Z) = (x, z) who decides to export or import isT T (x, P (z)) = E[S1 − S0 |X = x, D = 1] = E[S1 − S0 |X = x, UD ≤ P (z)].
Since the probability density function of UD conditional on UD ≤ P (z) is 1/P (z), we may write∫ P (z)T T (x, P (z)) =E[S1 − S0 |X = x, UD = p]0

∫ P (z)∆M T E (x, p)

=
0

1
dp
P (z)

1
dp
P (z)

The average impact of treatment on ﬁrms who actually export or import (TT) is computed byintegrating out P (z) from the T T (x, P (z)) using the distribution of P (Z) conditional on X = x
and D = 1, denoted by fP (Z)|X,D=1 , as:∫ 1 [∫ P (z)T T (x) ==]1∆M T E (x, p)
dp fP (Z)|X,D (P (z)|X = x, D = 1)dP (z)
P (z)
0
0
]
∫ 1 [∫ P (z)1
MT E∆(x, p)dp fP (Z)|X (P (z)|X = x)dP (z)Pr(D = 1|X = x) 00

where the second equality follows from applying the Bayes’ rule as fP (Z)|X,D (P (z)|X = x, D =(Z)=P (z))
P (z)1) = Pr(D=1|X=x,P
fP (Z)|X (P (z)|X = x) = Pr(D=1|X=x)
fP (Z)|X (P (z)|X = x). HeckPr(D=1|X=x)
man and Vytlacil (p.5100, 2007) shows that the right hand side of the above equation can be
rewritten as∫ 1∆M T E (x, p)ωT T (x, p)dp

T T (x) =0withωT T (x, p) = ∫ 1

1 − FP (Z)|X (p|x)

0 [1 − FP (Z)|X (t|x)]dt=

Pr(P (Z) > p|X = x),
E[P (Z)|X = x]where FP (Z)|X is the distribution function of P (Z) conditional on X. When the dimensionof Z conditioning X = x is small and the sample size is suﬃciently large at X = x, it isstraightforward to estimate Pr(P (Z) > p|X = x) and E[P (Z)|X = x]. For instance, if there is
only one instrument (i.e., dim(Z)=dim(X)+1), then we can just use a sample frequency or its
smoothed version.99In practice, P (Z) often have a limited support so that we would like to integrate over the region, say, [0.05, 0.95](see p. 22 of Carneiro et al.). Further, as Appendix E of Carneiro et al. discusses, we need to integrate out x

17

The average impact of treatment on ﬁrms that choose not to export or import (TUT) is∫ 1∆M T E (x, p)ωT U T (x, p)dp

T U T (x) =0(P (Z)≤p|X=x)
with ωT U T (x, p) = Pr
E[1−P (Z)|X=x] .

using the conditional distribution of x given D = 1. In the current version, we partition the space of P (Z) intoﬁnite bins over the region [0.05, 0.95] and use a sample frequency to integrate out p across those bins.

18

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21

Table 1: Descriptive Statistics: Trade and Skill Intensity 1996-1997, All Workers

Trade
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/ImAll
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/ImAll
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/Im
All

Owner
-ship
All
All
All
All
All
For.
For.
For.
For.
For.
Dom.
Dom.
Dom.
Dom.
Dom.

No
Primary
0.108
0.068
0.042
0.021
0.091
0.040
0.047
0.011
0.011
0.020
0.109
0.070
0.048
0.028
0.095

Highest Degree Completed/Fraction
Jr.
PrimaryHigh
High
College
0.430
0.235
0.208
0.018
0.310
0.264
0.329
0.029
0.264
0.239
0.395
0.058
0.165
0.249
0.502
0.062
0.382
0.239
0.261
0.027
0.198
0.221
0.475
0.066
0.211
0.243
0.452
0.046
0.106
0.181
0.591
0.109
0.105
0.198
0.605
0.080
0.134
0.202
0.559
0.082
0.433
0.235
0.204
0.018
0.318
0.266
0.318
0.027
0.292
0.249
0.361
0.049
0.204
0.283
0.434
0.049
0.397
0.242
0.242
0.023

Grad.
School0.00030.00050.001
0.001
0.00050.00060.0004
0.002
0.002
0.002
0.00030.00050.001
0.001
0.0004Training
Worker
0.024
0.077
0.180
0.212
0.060
0.241
0.049
0.576
0.283
0.337
0.022
0.079
0.111
0.166
0.043

R& D
Worker
0.024
0.116
0.254
0.315
0.079
0.161
0.257
0.829
0.594
0.555
0.022
0.104
0.153
0.131
0.049

Non-Prod.
All Workers
0.132
0.152
0.202
0.175
0.145
0.231
0.172
0.258
0.182
0.212
0.131
0.150
0.192
0.170
0.141

No. of
Obs.
29153
4184
5091
2356
40784
369
332
755
935
2391
28784
3852
4336
1421
38393

Notes: Standard deviations are in parentheses. The ﬁrst column indicates import and export status, where “No-Ex/No-Im”
denotes plants that neither export nor import; “Ex/No-Im” denotes plants that exports but do not import; “No-Ex/Im”
denotes plants that do not export but import; and “Ex/Im” denotes plants that both import and export. The second
column indicates whether plants are owned by foreign investors, where “For.” denotes plants for which at least 10% of
equity is held by foreign investors while “Dom.” denotes plants for which at least 90 % of equity is held by domestic
investors.

Table 2: Descriptive Statistics: Trade and Skill Intensity 1996-1997, Production vs. NonProduction Workers

Trade
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/ImAll
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/ImAll
No-Ex/No-Im
Ex/No-Im
No-Ex/Im
Ex/Im
All

Owner
-ship
All
All
All
All
All
For.
For.
For.
For.
For.
Dom.
Dom.
Dom.
Dom.
Dom.

Production Workers
Less thanJr.
CollegePrimary
High
High
Grad.
0.569
0.246
0.178
0.007
0.408
0.283
0.297
0.012
0.333
0.260
0.380
0.027
0.201
0.269
0.501
0.030
0.501
0.253
0.234
0.011
0.254
0.236
0.483
0.028
0.279
0.259
0.438
0.024
0.127
0.198
0.627
0.047
0.123
0.212
0.627
0.039
0.166
0.218
0.578
0.038
0.573
0.246
0.174
0.007
0.419
0.285
0.285
0.011
0.369
0.271
0.337
0.023
0.251
0.306
0.418
0.024
0.523
0.255
0.213
0.010

Non-Production Workers
Less thanJr.
CollegePrimary
High
High
Grad.
0.187
0.192 0.524
0.098
0.112
0.140 0.587
0.159
0.101
0.143 0.553
0.203
0.076
0.128 0.547
0.250
0.158
0.174 0.537
0.131
0.110
0.152 0.493
0.245
0.107
0.125 0.539
0.228
0.076
0.107 0.492
0.325
0.064
0.108 0.526
0.301
0.081
0.117 0.512
0.290
0.188
0.192 0.524
0.096
0.113
0.142 0.592
0.153
0.106
0.149 0.564
0.180
0.084
0.141 0.561
0.215
0.163
0.179 0.539
0.119

Frac. of Plants
Without Any
Non-prod. workers
0.254
0.102
0.102
0.030
0.206
0.073
0.042
0.025
0.027
0.036
0.256
0.107
0.115
0.032
0.217

Notes: Standard deviations are in parentheses. The ﬁrst column indicates import and export status, where “No-Ex/No-Im”
denotes plants that neither export nor import; “Ex/No-Im” denotes plants that exports but do not import; “No-Ex/Im”
denotes plants that do not export but import; and “Ex/Im” denotes plants that both import and export. The second
column indicates whether plants are owned by foreign investors, where “For.” denotes plants for which at least 10% of
equity is held by foreign investors while “Dom.” denotes plants for which at least 90 % of equity is held by domestic
investors.

22

Table 3: Logit of the Probability of Importing and Exporting using tariﬀ level as instruments
Dependent Variable:
τ
Labor Prod×τ
Dτ,1
Dτ,2
Dτ,3
Labor Prod×Dτ,1
Labor Prod×Dτ,2
Labor Prod×Dτ,3
Labor Prod
Foreign
R&D
Training
Capital/Worker
No. of Obs.

Import Status
(1)
(2)
(3)
11.969(1.657)-1.778(0.349)-2.531-2.336(0.188)(0.190)-2.017-1.833(0.194)(0.196)-3.243-2.943(0.373)(0.375)0.520
0.457(0.042)(0.041)0.397
0.330(0.042)(0.041)0.473
0.394(0.079)(0.079)
0.664
0.076
0.010
(0.037)(0.036)(0.035)
1.798
1.817
1.576
(0.055)(0.056)(0.058)0.340(0.053)0.708(0.044)0.194(0.017)
39030
39030
39030

Export Status
(4)
(5)
(6)
2.988
(0.467)-0.311(0.104)-1.572-1.324(0.182) (0.180)-1.361-1.173(0.186) (0.183)-1.953-1.705(0.255) (0.252)
0.344
0.286
(0.039) (0.038)
0.222
0.174
(0.038) (0.037)
0.283
0.219
(0.052) (0.051)
0.464
0.182
0.121
(0.021) (0.031) (0.030)
1.566
1.562
1.336
(0.053) (0.053) (0.055)
0.282(0.056)0.695(0.045)0.171(0.019)
39030
39030
39030

Notes: Standard errors are in parentheses. 3-Digit Industry Eﬀects and Year eﬀects are included. When import decision
is estimated, we use τ = τm , Dτ,1 = Dτm ,1 = 1(0.085 ≤ τm < 0.156), Dτ,2 = Dτm ,2 = 1(0.037 ≤ τm < 0.085), andDτ,3 = Dτm ,3 = 1(τm < 0.037); when export decision is estimated, we use τ = τx , Dτ,1 = Dτx ,1 = 1(0.150 ≤ τx < 0.238),
Dτ,2 = Dτx ,2 = 1(0.048 ≤ τx < 0.150), and Dτ,3 = Dτx ,3 = 1(τx < 0.048).

23

Table 4: Skill Demand Equation, All Workers

Import Status
Export Status
Labor Prod
Foreign
R&D
Training
Capital/Worker
No. of Obs.

Dependent Variable: log((High school Grad)/(Less than High school))
IV with
IV with
IV withOLSP̂ (Dm = 1)
τm ’s
OLS
P̂ (Dx = 1)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.404
0.293
2.053
1.239
2.371
0.983
(0.021)(0.021)(0.186) (0.151)(0.329) (0.142)
0.298
0.213
-0.292-0.401(0.021) (0.020)(0.152) (0.138)
0.339
0.241
0.233
0.196
0.212
0.208
0.351
0.248
0.379
0.270
(0.007)(0.008)(0.014) (0.011)(0.022) (0.010)(0.007) (0.008)(0.011) (0.009)
0.767
0.563
0.171
0.260
0.056
0.342
0.824
0.600
1.000
0.763
(0.035)(0.035)(0.076) (0.060)(0.126) (0.057)(0.035) (0.035)(0.057) (0.051)
0.151
0.098
0.113
0.161
0.181
(0.027)(0.029)(0.028)(0.027)(0.027)
0.423
0.312
0.342
0.435
0.500
(0.023)(0.029)(0.028)(0.023)(0.027)
0.234
0.211
0.217
0.238
0.247
(0.008)(0.009)(0.009)(0.009)(0.009)
30479
30479
30479
30479
30479
30479
30479
30479
30479
30479

IV with
τx ’s
(11)
(12)

0.214
(0.271)
0.355(0.015)0.849(0.087)30479

Notes: Standard errors are in parentheses. 3-Digit Industry Eﬀects and Year eﬀects are included. For the sixth and theseventh columns, import status is instrumented with Dτm ,1 , Dτm ,2 , Dτm ,3 , and their interaction terms with Labor Prod.
For the last two columns, export status is instrumented with Dτx ,1 , Dτx ,2 , Dτx ,3 , and their interaction terms with Labor
Prod.

Table 5: Some Evidence for Heterogeneous Impact of Importing on Skill Demand

SubsampleP̂ (Dm = 1) ≤ 0.05
0.05 < P̂ (Dm = 1) ≤ 0.10
0.10 < P̂ (Dm = 1) ≤ 0.25
0.25 < P̂ (Dm = 1) ≤ 0.45
0.45 < P̂ (Dm = 1) ≤ 0.65
0.65 < P̂ (Dm = 1)

Dependent Variable: log((High school Grad)/(Less than High school))
Coeﬃcient on Dm
No. ofCoeﬃcient on Dx
IV with τm ’s
OLS
Obs.
Subsample
IV with τx ’s
OLS
4.763
0.389
7095
P̂ (Dx = 1) ≤ 0.05
9.817
0.201
(1.634)(0.068)(3.359)(0.096)
2.681
0.352
4946
0.05 < P̂ (Dx = 1) ≤ 0.10
5.554
0.220
(0.885)(0.063)(1.668)(0.061)
1.505
0.244
9931
0.10 < P̂ (Dx = 1) ≤ 0.25
1.132
0.310
(0.564)(0.037)(0.415)(0.034)
0.874
0.248
4737
0.25 < P̂ (Dx = 1) ≤ 0.45
0.905
0.161
(0.473)(0.040)(0.284)(0.036)
1.887
0.386
1853
0.45 < P̂ (Dx = 1) ≤ 0.65
-0.1930.068(0.555)(0.063)(0.886)(0.070)-4.5240.126
1917
0.65 < P̂ (Dx = 1)
0.543
0.068
(1.965)(0.082)(1.352)(0.118)Notes: Standard errors are in parentheses. The estimated coeﬃcients on import status Dm or export status Dx are reported
for diﬀerent set of subsamples classiﬁed by the estimated probability of importing or importing. Other variables includedin the regression are: Labor Prod, Foreign, R&D, Training, Capital/Worker, 3-Digit Industry Eﬀects, and Year eﬀects.

24

No. of
Obs.
6242
6625
10025
5059
1853
776

0.257
(0.2500.246(0.0120.588(0.0750.159(0.0280.430(0.0350.237(0.00930479

Table 6: TT, ATE, and TUT of Importing and Exporting on Skill Demand, All Workers
ImportingExportingTT
10.536(1.552)1.398(2.206)ATE
0.862(0.211)-0.204(0.160)TUT
0.671(0.268)-0.244(0.153)

Table 7: TT, ATE, and TUT of Importing and Exporting on Skill Demand, Within Production
Workers and Within Non-Production Workers

ImportingExportingWithin Production Workers
TT
ATE
TUT
8.259
0.872
0.676
(1.637)(0.299)(0.370)-0.057-0.571-0.709(2.380)(0.308)(0.320)25

Within Non-Production Workers
TT
ATE
TUT
9.046
1.085
0.859
(1.680) (0.426)(0.327)
9.432
1.250
1.194
(2.309) (0.323)(0.337)

Import, S2
8

6

Marginal Treatment Effect

4

2

0

−2

−4

−6

0

0.1

0.2

0.3

0.4

0.5
UD

0.6

0.7

0.8

0.9

1

Figure 1: MTE of Importing on Skill Demand

Export, S2
7

65

Marginal Treatment Effect

4

3

2

1

0

−1

−2

−3

0

0.1

0.2

0.3

0.4

0.5
UD

0.6

0.7

0.8

0.9

Figure 2: MTE of Exporting on Skill Demand261