Import Competition and Innovation at the Plant Level:
Evidence from Mexico
Kensuke Teshima †Job Market Paper
First Version: Nov. 2008. This Version: Feb. 2009.

Abstract
A key idea in the literature on trade and growth is that trade liberalization may affect plants’innovative activities through increased competition. Theoretical predictions remain ambiguous,
however, and it has been difficult to investigate this relationship empirically because R&D expenditure data is rarely available at the plant level. This paper takes the advantage of a newly
constructed combination of Mexican plant-level datasets to examine the extent to which tariffchanges lead to changes in R&D through increased competition. The combined dataset has two
unique features: it contains (1) the amount of R&D on product innovation and on process innovation, and (2) the trade-classification categories of plants’ outputs and inputs, which allows me
to construct plant-level tariff changes and to control for industry time effects. The degree of tariff
reduction is not correlated with initial plant characteristics, suggesting that the tariff reduction
is exogenous for plants. The key finding is that the reduction of tariffs of the goods produced
by Mexican plants induced those plants to increase total R&D. This suggests that trade liberalization stimulates plants’ innovative activities through increased competition. I also find thatthe pattern would not be discernable using the measures of plant behavior and trade exposure
available in typical plant-level datasets – measured total factor productivity and industry-levelaverage tariffs. Additional results using process R&D and product R&D expenditure informationsuggest that trade liberalization affects plants’ capability through the effects of competition onplants’ incentives to increase cost efficiency rather than through the effects on incentives to create
new products or to upgrade quality.

——————————†

Columbia University, email: kt2123@columbia.edu. I am truly indebted to Eric Verhoogen and David Weinsteinfor continuous guidance and support at every stage of this project. I would like to thank Gerald Leyva, Abigail
Duran, Adriana Ramirez, and Gabriel Romero of INEGI for assistance with establishment surveys. I also thankAlberto Zarco of Secretarı́a de Economı́a for his help on the SIEM data. I am grateful to Pierre-Andre Chiappori,
Rajeev Cherukupalli, Donald Davis, Jan De Loecker, Ronald Findlay, Jessie Handbury, James Harrigan, Kate Ho,
Takakazu Honryo, Amit Khandelwal, Ayako Kondo, Dilip Mookherjee, Christian Pop-Eleches, Michael Riordan,
Prasana Sethupathy, Hitoshi Shigeoka, Yoichi Sugita, Mamiko Terasaki, Daniel Trefler and seminar participants at
Columbia and the BREAD summer school for helpful conversations. I gratefully acknowledge the funding from theInstitute of Latin American Studies and the Center for International Business Education Research at Columbia
University. I am solely responsible for any errors.

1

Introduction

Economists have shown growing interest in whether international trade can generate dynamic
gains through innovative activities. In contrast to traditional trade theories that predict static
gains from trade from reallocation of resources across sectors, this recent literature emphasizes
the idea that trade may increase growth. Beginning with the seminal work by Rivera-Batiz
and Romer (1991) and Grossman and Helpman (1991a), a key idea in this literature has beenthat trade liberalization creates tougher market competition and that this increase in market
competition may affect firms’ incentive to innovate.
Identifying such effects empirically has been challenging, however, because of a scarcity of direct information on the innovative activities of plants. Many researchers have tried to identify theeffects of trade liberalization on plants’ productivity using total factor productivity (TFP).1 However, measured TFP is known to suffer from many biases and may reflect a number of differences
across plants apart from technical efficiency (Katayama, Lu and Tybout (2006)). In particular,
measured TFP reflects mark-ups, which are also likely to be affected by trade liberalization.2 As
a consequence, changes in measured TFP due to trade liberalization may not reflect innovative
activities.
This paper takes the advantage of a newly constructed combination of Mexican plant-level
datasets to examine whether trade liberalization leads to changes in a direct measure of innovative
activities – total R&D expenditures – at the plant level. The combined dataset has two uniquefeatures: it contains (1) the amount of total R&D as well as R&D on product innovation andprocess innovation, and (2) the trade-classification categories of plants’ outputs and inputs, whichallows me to construct plant-level tariff changes and therefore to control for industry-year effects.
I use tariff changes of Mexico between 2000-2003 as a source of exogenous variation in market
1

Plant-level studies on the effects of output tariff reduction on productivity include Harrison (1994), Tybout
and Westbrook (1995), Krishna and Mitra (1998), Head and Ries (1999), Pavcnik (2002), Schor (2004), Topalova(2004), and Trefler (2004). See Tybout (2000) for a survey of literature on trade and productivity in developing
countries and Tybout (2003) for a survey of literature on plant-level evidence on international trade theories.
2
See Tybout (2000) for a further discussion of the issues arising when interpreting measured TFP and measured
mark-ups. Klette and Griliches (1996) discuss the issues arising when estimating a production function or a costfunction using revenue-based output measures.

1

competition. The tariff changes are an attractive source of variation because they are driven
mainly by the reduction of tariffs due to free trade agreements (North American Free Trade
Agreement (NAFTA) and the free trade agreement with the European Union (EU)), which are
likely to be exogenous to Mexican plants.3 This is the first paper to combine direct evidence on
R&D expenditures at the plant level with credibly exogenous variation in exposure to international
competition due to trade liberalization.
I find robust evidence that the reduction on the tariffs of goods produced by Mexican plants
induced those plants to increase total R&D. This suggests that trade liberalization stimulates
innovative activities through competition. I do not find any evidence that the same output tariff
reduction affected TFP, which suggests that relying solely on TFP, as much of the literature has
done, is likely to be misleading.
As an extension, this study separates total R&D expenditures into process R&D (i.e. R&D
for improvement of the efficiency of production system) and product R&D (i.e. R&D for creationof new products or upgrading of the quality of existing products). Separating these types ofinnovative activities allows me to explore further the sources of gains from international trade. I
find evidence that reductions in the output tariffs increased process R&D. I do not find evidence
that the same output tariff reductions affected product R&D. These findings suggest that theeffects of trade liberalization come primarily through cost-cutting and improvements in technicalefficiency rather than product innovation.
All the results mentioned above are based on within-industry variation, from regressions that
include industry-year effects. The same patterns are not discernable in regressions that do not control for such effects. Because typical plant-level datasets lack detailed product-level information,4tariffs can be constructed only at the industry level and the within-industry dimension cannot
be explored.5 The results of this paper thus underline the value of product-level information in
3

Kowalczyk and Davis (1996) provide evidence that the Mexican tariff reductions through NAFTA were notdriven by Mexican interests. I will further discuss endogeneity issues in Section 5.1.
4
Exceptions include the Indonesian data used by Amiti and Konings (2007), the Colombian data used by Kugler
and Verhoogen (2008) and the Canadian data used by Lileeva and Trefler (2007).
5
See Levinsohn (1993) that noted the difficulty of capturing the effects of tariff changes on plant-level wages andemployment.

2

analyzing the effects of tariff changes on plant-level innovative activities.
This paper is related to a number of strands of literature. Building on the early work of Grossman and Helpman (1991b, 1991c) and Rivera-Batiz and Romer (1991), recent studies incorporate
innovation into the heterogeneous-firm model of Melitz (2003) to analyze the relation between
international trade and innovation at the plant level (Baldwin and Robert-Nicoud (2008), Constantini and Melitz (2007) and Atkeson and Burstein (2007)). The effects of trade liberalization
on innovation through increased competition depend on whether market competition increases
innovation. Theoretical predictions on the effects of increased competition on innovation are ambiguous. On one hand, an increase in competition may hurt innovation through negative effects
on the market share of firms (Grossman and Helpman (1991a) and Aghion and Howitt (1992)).6
On the other hand, an influential set of recent papers suggest that competition may increase
innovation: tougher competition increases the threat to rents of incumbent firms at or near thetechnology frontier; such firms maybe induced to innovate to preserve their rents (Aghion, Harrisand Vickers (1997) and Aghion, Harris, Howitt and Vickers (2001)).7 Competition may also reduce managerial slack or X-inefficiency among manufacturers, which may stimulate innovation.8
Thus, it is an empirical question whether increased import competition encourages or discourages
innovation.
In the empirical literature, this study is most closely related to papers by Sherer and Huh(1992), Bloom, Draca and Van Reenen (2008), and Gorodnichenko, Svejnar and Terrell (2008)
which examine the relationship between import competition and plant-level R&D or information
technology use.9 Sherer and Huh (1992) find negative effects of high technology imports on U.S.
6
See Schumpeter (1942) for the original work pushing the idea that monopoly rents stimulate innovation; in thisview, competition is bad for innovation.
7
This line of research is summarized in Aghion and Howitt (2005). See also Acemoglu, Aghion and Zilibotti(2002) for the implications of distance from technology frontier on the effects of openness on growth. See also
Sutton (2007) for another analysis of the effects of global competition on innovation in developing countries.
8
See Hicks (1932) and Corden (1974) for classical treatment. Schmidt (1997) builds a model in which competition
induces more managerial efforts by increasing the risk of bankruptcy. Bertrand and Mullainathan (2003) provide
evidence on managerial slack.
9
There exist also papers analyzing the effects of competition without focusing on import competition. Early
plant-level studies on domestic market competition and innovation have typically shown a positive correlation
between the two (Nickell (1996), Blundell, Griffith and Van Reenen (1999)). See also Aghion, Bloom, Blundell,
Griffith and Howitt (2005) that show empirically that the relation between competition and innovation is U-shaped
as suggested by their models.

3

firms’ R&D, Bloom, Draca and Van Reenen (2008) find positive effects of Chinese imports on
European firms’ technology use, and Gorodnichenko, Svejnar and Terrell (2008) find a negative
correlation between firms’ perception on the degree of competition and innovative activities inemerging countries. All the three studies are subject to concerns about omitted variables bias,
however.10 Since the degree of import competition could be affected by innovative activities orother factors that are related to them, exogenous variation in import competition is needed. In
this study, tariff changes provide a more credibly exogenous source of variation with which toestimate the causal effects of import competition on R&D at the plant level.
This paper is also related to a number of papers highlighting alternative channels throughwhich trade may affect innovation. Early empirical papers tried to estimate the effects of openness on growth using country-level data (Sachs and Werner (1995) and Frankel and Romer (1999)).
Typical cross-country studies do not allow one to separate the mechanisms through which international trade could affect innovation and are subject to endogeneity concerns. Recently, several
empirical papers have shown that indicators of plants’ quality and technology choices respond positively to positive shocks to export market access (Verhoogen (2008), Bustos (2007), and Lileeva
and Trefler (2007)). Another set of empirical papers investigate the effects of increased availabilityof imported intermediate inputs: Schor (2004) and Amiti and Konings (2007) analyze the effects
of input tariff reduction on productivity and Broda, Greenfield and Weinstein (2007), and Goldberg, Khandelwal, Pavcnik and Topalova (2008) analyze the effects of availability of imported
intermediate goods on variety expansion. Recent papers by Acharya and Keller (2007, 2008) also
analyze the effects of imports on productivity in industries in developed countries stressing therole of imports in generating R&D spillovers. None of the papers above investigate empirically
10

For example, growing industries may have more competition and more innovation at the same time, which
would result in positive biases in the estimated effects of competition on innovation. This concern may apply to all
cross-sectional studies. On the other hand, the plants that fail to do innovative activities might be more likely tolose from competition, which would result in negative biases in the estimated effects of competition on innovation.
Gorodnichenko, Svejnar and Terrell (2008) are susceptible to this concern. Finally, in case of Chinese competition,
China may have comparative advantage in industries with initially more scope for technology upgrading, andimports from China may be growing in such industries, which would result in positive biases in the estimatedeffects of Chinese competition on innovation. Identification strategies using current or initial imports from China
as sources of variation, as in Bloom, Draca and Van Reenen (2008), are susceptible to this concern. (Bloom, Draca
and Van Reenen (2008) mention other estimation strategies but do not report the results of those strategies.)4the effects of increased import competition on innovative activities at the plant level. I show thatmy findings are not driven by these other mechanisms.
The paper is organized as follows. The next section describes the new combination of datasets,
and presents descriptive statistics of plant-level variables and tariff changes. Section 3 describes
my econometric strategy. Section 4 presents the baseline results. Section 4.1 shows that the output
tariff reduction decreased total sales, domestic sales, employment of the plants affected, but notexport sales. Section 4.2 shows that the output tariff reduction did not affect TFP. Section 4.3
presents the key results on the effects of competition on R&D. In section 5, I present additional
analysis to provide evidence that alternative hypothesis cannot fully explain the results of section
4. Section 5.1 deals with endogeneity concerns. Section 5.2 deals with an alternative hypothesis:
export market access. Section 5.3 shows that the results are robust to the inclusion of regionyear effects. Section 6 presents the additional results separating product and process innovation.
Section 7 provides evidence that the results would be inconclusive without the availability ofwithin-industry variation exposure to international competition. Section 8 concludes.

2

Data

This section describes the data. The first subsection describes the plant-level data and presents
descriptive statistics of plant-level variables. The second subsection describes the imports andtariff data and presents descriptive statistics of tariff changes.

2.1

Plant-level Data

I combine three types of plant-level data for the analysis.11 The first is a specialized survey on
innovative activities. The second is a standard plant-level survey from which I draw basic variables
such as sales and employment. The third is a registry of plants that includes information on the
trade-classification category of plants’ outputs and inputs from which I will construct measuresof plant-level tariff changes.
11
To the possible extent that the ownership information could be checked, a firm owns at most one plant in mostof the cases. The results of the paper do not change if I aggregate the unit of analysis to the firm level.

52.1.1ESIDET

The source of R&D information is the Encuesta Sobre Investigación y Desarrollo de Tecnologı́a(ESIDET) [Survey on Research and Development of Technology]. This is a confidential survey carried out by the Instituto Nacional de Estadı́sticas, Geografı́a (INEGI) [National Institute of Statistics and Geography] of Mexico for the Consejo Nacional de Ciencia y Tecnologı́a (CONACYT)[National Council of Science and Technology]. It has surveys for three sectors: manufacturing,education, and government. I will use the data for manufacturing. The survey contains information on several aspects of innovative activities: expenditures, human resources and collaborating
firms and institutions. It includes information on expenditures for each type of R&D: productR&D and process R&D. I use the 2002 and 2004 surveys.12 Each survey elicits information forthe previous two years. This makes unbalanced panel data from 2000 to 2003.
The survey asks how much plants spend on (1) introduction of new products, (2) substantial
quality upgrading of existing products, (3) routine quality upgrading existing products, (4) creation of new production process, (5) substantial improvement of existing production process and(6) routine improvement of existing production process. I classify (1) - (3) as product R&D and(4) - (6) as process R&D.13

2.1.2

EIA

In order to obtain various basic information such as domestic sales, export sales, capital stock, investment, and employment, I use the Encuesta Industrial Anual (EIA) [Annual Industrial Survey].
The EIA is a longitudinal plant level dataset in 205 of the 305 6-digit industries in manufacturing.
The EIA is also compiled by INEGI. For further details, see Appendix II of Verhoogen (2008).
12

Surveys were done in 1996, 1998, 2002, 2004 and 2006. The 1998 survey suffers from a number of data quality
issues, and few plants can be linked between the 1996 and 2002 waves. In addition, the product-level information
is available beginning 2001. The 2006 survey has become available too recently to be included in this version of thepaper.
13
I do not distinguish between creation of new products and quality upgrading because there is not a clear conceptual distinction between a new product and a substantially upgraded version of an existing product. Theoretically,
the two types of innovation are often isomorphic (Grossman and Helpman (1991a)).

6

2.1.3SIEM

For information on the output and input categories of the firms to calculate output and input
tariffs at the plant level, I use the Sistema de Información Empresarial Mexicano (SIEM) [Mexican Company Information System] compiled by Mexico’s Secretarı́a de Economı́a [Ministry of
Economy]. It is a directory of firms in Mexico to facilitate business contacts between firms in
Mexico and foreign firms. SIEM lists firms’ inputs and outputs at the 6-digit or 8-digit tradeclassification level regardless of whether the firms export or import. It does not have informationon the volumes of each output or input, or on whether the plants export or import. The SIEM
starts in 1997, but detailed information about firms’ inputs and outputs are available only from2001. Firms are legally obliged to report; therefore in principle the SIEM can be regarded as acensus of firms in the formal economy. The SIEM has been linked to the EIA and ESIDET using
information on firm name, state, municipality, street address, and industry.

2.1.4

Descriptive Statistics of Plant-level Variables

Table 1a presents summary statistics for the EIA-SIEM panel. I report the mean and the standard
deviation of each variable by export status. Consistent with patterns found for many countries,
exporting plants have larger volumes of sales and employment.14 Exporters are also more likely
to be importers and to have larger volumes of imports than non-exporters.
Table 1b presents summary statistics for the ESIDET-SIEM panel. Exporters are more likely
to be doing R&D and have higher expenditure on R&D. However, only 23% of these exporters
report positive amount of total R&D expenditure. This ratio is 17% for all the plants and 9% for
non-exporters. The intensity of R&D (R&D expenditure/ Total sales) is about 1% unconditionally, and about 4% conditional on spending a positive amount on R&D. On average, conditionalon spending a positive amount of R&D expenditure, plants spend 24% of total R&D expenditure on creation of new products, 29% on quality upgrading of existing products, and 34% on
improvement of production system.15 The first two refer to product innovation and the last to
14
15

For the U.S., see Bernard and Jensen (1999)
These does not add up to 100% because there is uncategorized R&D expenditure in the data.

7

process innovation. The distribution of R&D is very different between exporter plants and nonexporter plants. Exporters are more likely to spend R&D for product innovation rather thanprocess innovation.

2.2

Tariff Data

I construct tariff data using (1) Mexican import statistics published in trade statistics yearbooksand (2) tariff information from the tariff law of Mexico and from the documents of the free trade
agreements between Mexico and other countries. The first subsection describes the method to
calculate plant-level tariffs. The second subsection describes the summary statistics for the tariff
data.

2.2.1

Construction of Plant-level Tariff Measures

Because of free trade agreements, tariffs for one product differ depending on the country of origin.
I first aggregate the country-good specific tariffs to good-level tariffs by taking the weighted
average with the initial volume of imports used as weights. Importsgjct is imports of good g inindustry j from country c at time t. Tariffgjct is tariff of good g in industry j from country c attime t.

Tariffgjt =X

αc Tariffgjct(1)cImports

gjc2000
where αc = P Imports.
gjc2000c

Next, using this good-level tariff data Tariffgjt , I take the simple average of the tariffs of eachplant’s outputs to construct the output tariffs at the plant level.16
P
Output Tariffigt =g∈Gi Tariffgjt

(2)

Ni

16

I have to use the simple average because SIEM data does not allow one to obtain the information on the volumesof each product by plant.

8where Gi is the set of products that plant i produces, and Ni is the number of products of planti produces, respectively.
Similarly, I take the simple average of the tariffs of each plant’s inputs in the initial period
to construct the input tariffs at the plant level. Note that I always use the outputs and inputs
information from year 2001 to compute the output and input tariffs for each year. Thus all thevariation of the tariff of a good is coming from the changes in the tariff of the good but not fromthe changes in the volume of the imports of the good. This is to avoid bias due to the changes
in output mix or in input mix in response to the tariff reduction. In Section 4, I report theresults from two unbalanced panels: EIA-SIEM panel and ESIDET-panel. I used EIA-SIEM forthe analysis of sales, employment and TFP, and I used ESIDET-SIEM for the analysis of R&D
variables.17

2.2.2

Descriptive Statistics on Tariffs

Table 2a presents summary statistics for tariffs. I report the weighted average tariffs for imports
from all the countries as well as from four groups of sets of countries: NAFTA, EU, countries towhich most favored nations (MFN) tariffs are applied, and other countries that are not in NAFTA
or in EU and that have a free trade agreement with Mexico. The purpose of the table is to show
the sources of tariff changes during the period of the study. It shows that the tariff changes arelargely coming from tariff changes scheduled in free trade agreements.
Table 2b presents summary statistics of plant-level tariffs for the ESIDET-SIEM panel. On
average, both output and input tariffs at the plant-level are higher than the tariffs shown in
Table 2b. Plant-level average output tariffs decreased from 7.9% in 2000 to 4.3% in 2003, whileplant-level average input tariffs decreased from 6.3% to 3.4%.
17

I also constructed another panel EIA-ESIDET-SIEM, and the results are qualitatively similar.

93Specification

The baseline econometric model is the following:

Yijt = β1 Output Tariffit + λi + µjt + ²ijt

(3)where i, j, and t index plants, industries, and years, respectively; Yijt denotes the dependent
variable: total sales, domestic sales, employment, and exports, TFP measures or Total R&D,
process R&D, or product R&D; Output Tariffit is output tariffs at the plant level constructed in
the manner described in the previous section; λi is a plant fixed effect; µjt is an industry-year
fixed effect; ²ijt is an error term.
The coefficient of interest in these regressions is β1 , which corresponds to the changes inthe dependent variables in response to a one percent point change in the output tariff. The
plant fixed effects capture all observed or unobserved time-invariant heterogeneity across plants.
The industry-year fixed effects capture all observed or unobserved shocks at the industry level.
Thus, the coefficient of interest is identified on the basis of within-plant changes in output tariffs
and within-plant changes in the dependent variables controlling for industry-level idiosyncratic
shocks. The identification assumption of this econometric model is that no unobservable factors
are correlated with the output tariffs after controlling for time-invariant plant-level heterogeneityand industry-level idiosyncratic shocks.
Note that a positive value of the coefficient means that output tariff reduction affects the dependent variable negatively. If output tariff reduction affects plants’ behavior through increase incompetition, the coefficients of β1 for the regressions with sales and employment as the dependent
variables are expected to be positive. For TFP measures and R&D variables, there is no clear
theoretical prediction on whether the coefficient should be positive or negative.
I also run regressions of the following form:

Yijt = β1 Output Tariffit + β2 Input Tariffit + λi + µjt + ²ijt

10

(4)

This specification controls for the changes in input tariffs. It is important to control these
input tariffs because the changes in output tariffs and those in input tariffs might be correlated.
It is also important to note that most of the previous studies focusing on the effects of output
tariff changes are not able to control for input tariff changes.18 In Section 5.2, I further control
for U.S. tariffs in order to control for the changes in export market access, and In section 5.3, I
also control for state-year fixed effects to control for any shocks at the region level.

4

Results

This section reports the results from the regressions. There are three types of results. The first
subsection reports the results for sales and employment as the dependent variables. The subsection
4.2 reports the results for various kinds of TFP measures as the dependent variables. The last
subsection reports the key results of the paper: the regression results for R&D expenditure as thedependent variables.

4.1

Results on Plant-level Basic Variables

Before presenting the results for innovative activities, I report the results from the regressions using
total sales, domestic sales, employment and exports as the dependent variables. The purpose is
to show that the output tariff reduction did have an impact on basic plant behavior. Since output
tariff reduction should affect plants’ behavior through an increase in competition, it is important
to confirm that the tariff reduction affected negatively the basic performance of the plants affected.
Table 3a shows the results. The regression results from EIA-SIEM panel confirm that output
tariff reduction negatively affected the total sales, domestic sales, and total employment. I do
not find significant effects of output tariff reduction on exports. There is no reason to believe theoutput tariff reductions affect the export behavior of the plants unless the output tariff reductions
are correlated with tariff reductions of other countries. The fact that I do not find evidence of theeffects of output tariff reduction on exports suggests that output tariff reductions affect plants’
18

Schor (2004) and Amiti and Konings (2007) are exceptions.

11behavior only through an increase in competition and are not correlated with the tariff reductionsof other countries.19
Table 3b shows the results controlling for input tariff changes. The results are qualitatively
similar in that the output tariff reduction decreased sales and employment, but not exports. Thissuggests that the effects of the output tariff changes found in Table 3a are not driven by changes
in input tariffs that happens to be correlated with the changes in output tariffs.

4.2

Results on TFP

Next, I report the results from the regressions using TFP measures as the dependent variables.
I estimated TFP using the estimation methods of production function by OLS, by the method
developed by Olley and Pakes (1996) and by the method of Levinsohn and Petrin (2003).20 The
first three columns of Table 4 show the results from the basic specification. The coefficients arenegative, which suggests that output tariff reductions positively affect productivity. But the effects
are not significant for any of the TFP measures. One possibility is that increased competition
due to the tariff reductions decreased mark-ups, which would reduce measured TFP.
The last three columns show the results from the regressions that control for input tariffchanges. The results are qualitatively similar in that the coefficients are negative but not significant. This suggests that the effects of the output tariff changes found above are not driven by
the changes in input tariffs correlated with the changes in output tariffs. From these results, one
would be tempted to conclude that the trade liberalization had little effect on productivity, or,by extension, plants’ innovative activities.

4.3

Results on R&D

Finally, I report the results from the regressions using R&D as the dependent variables. The first
three columns show the results from the basic specification; equation (3). I find significant effectsof output tariff reduction on R&D. I use three types of total R&D measures: Log Total R&D,
19

In Section 5.2, I further control for U.S. tariffs in order to control for the changes in export market access.
The methods by Olley and Pakes (1996) and Levinsohn and Petrin (2003) are standard methods to correct for
endogeneity problems arising from the correlation between inputs and productivity.
20

12

R&D intensity, and R&D Dummy. They are defined in the following way. Log T otal R&Dit =otalR&Dit
log(T otal R&Dit + 1), R&D Intensityit = TT otalSales, where 0 indicates the year I first observei0

the plants,21 and R&D Dummyit is a dummy variable taking value 1 if total R&D is positive,
and 0 otherwise. The intensity variable is likely to capture the intensive margin and the dummy
will capture the extensive margin.
For all variables, the effect of output tariff on R&D is statistically significant. It seems that theeffects are generated both through the intensive margin and the extensive margin. One percent
reduction of the average tariffs of the goods that a plant produces increases total R&D by 8%,
R&D intensity by 0.03% points, and the probability of undertaking R&D by 1% points. Given
that the average R&D intensity is 1% in 2000, the increase in R&D intensity by 0.03% points is
relatively small. Most of positive effect of increased competition on R&D seems to come from theextensive margin.
Columns (4)-(6) show the results controlling for input tariff changes, which is the specificationin equation (4). The results are qualitatively similar in that estimated coefficients of the output
tariffs are similar with and without controlling for the input tariffs. This suggests again that theeffects of the output tariff changes found in the first three columns are not driven by the changes
in input tariffs correlated with the changes in output tariffs.
In sum, I find significant negative effects of output tariff reduction on total and domestic
sales and employment. I find the significant positive effects of output tariff reduction on R&D.
However, I do not find evidence that TFP has responded. It suggests that the effects that I havebeen finding are thanks to the data that allows me to observe direct information on the innovative
activities that are not contaminated by mark-ups.
21

I already showed that the output tariff reduction affected total sales negatively. The reduction of total sales
would automatically increase the R&D intensity. I fixed the period of denominator at the initial period in order toavoid this.

13

5

Robustness Checks

I perform two kinds of analysis to provide evidence that alternative hypotheses cannot fully explain
the results of Section 4. Section 5.1 deals with endogeneity concerns. Section 5.2 deals with analternative hypothesis: export market access. Section 5.3 show the additional results from theregressions that control for region-year fixed effects.

5.1

Endogeneity Check

Tariff reductions could be endogenous because the government could have set the tariffs to favor
some firms over others. Since I have been relying on within-industry variation of tariff changes,
correlation between tariffs and omitted industry characteristics would not invalidate my results.
However, the government could have set trade policies to favor some particular plants even within
industries, which would invalidate my assumption that the tariff changes are exogenous at least
within industry. One way to investigate this story is to check whether the degree of tariff reduction
was correlated with plants’ initial characteristics within industry.
To do so, I run regressions of the following form:

Yij2000 = β1 4Output Tariffij + µj + ²ij(5)where Yij2000 is either total sales, domestic sales, exporter dummies, exports, total employment
or TFP22 of plant i in 2000; 4Output Tariffij is a change in tariff of plant i from 2000 to 2003;µj is a industry fixed effect. A significant coefficient β1 tells that smaller or larger plants weremore likely to face tougher import competition induced by tariff reduction, even within industry,
which would imply that the governments might have set the tariffs to favor plants with particularcharacteristics over other plants.
Table 6a shows the results. It shows that there is no correlation between plant characteristics
and subsequent output tariff reduction within an industry. None of the dependent variables in the22

I report the results for TFP estimated using Olley-Pakes method, but the results are qualitatively similar if I
use other TFP measures.

14

initial year, initial total sales -domestic sales, export status, exports or employment- are correlatedwith the degree of the output tariff reduction between 2000 and 2003.
I also run regressions of the following form to see whether this conclusion is robust to theinclusion of the degree of input tariff reduction:

Yij2000 = β1 4Output Tariffij + β2 4Input Tariffij + µj + ²ij(6)Table 6b shows the results. The conclusion above is robust to the inclusion of the changes
in input tariffs as controls: none of the initial total sales, domestic sales, export status, exports,
employment are correlated with the degree of the output tariff reduction between 2000 and 2003.
However, there is a significant positive correlation between the size of the plants and the degreeof the subsequent input tariff reduction. One possibility is that larger and more productive plants
use high-technology inputs,23 which had a higher degree of tariff reduction.24
The fact that the degree of input tariff reduction is correlated with the initial plant size
may be consistent with the findings of the previous sections that there seem to be no significant
effects of the input tariff reductions on sales, employment or R&D. The period between 2000 and2003 is a period in which the performance of Mexican exports was relatively sluggish. Exporting
plants, which are likely to be bigger in size, might have been relatively more likely to reduce
sales, employment and R&D. If this is the case, it would produce positive biases in the estimatedcoefficients (negative biases in the effects of input tariff reductions) and it might have prevented
me from finding the positive effects of the input tariff reductions.

5.2

Other Hypothesis: Export Market Access

All the results reported so far are from the specifications with plant fixed effects and industry-year
fixed effects. Therefore, the results are robust to any changes in domestic or external demand
23This story is consistent with Kugler and Verhoogen (2008) who show that more productive plants are more
likely to produce higher quality products and to therefore need higher quality inputs.
24
In 2002, Mexico ratified Information Technology Agreement (ITA)-plus, which reduced the tariffs of manyhigh-technology inputs.

15

at the industry level. A potential confounding factor is the tariff changes of other countries.25
This is especially relevant in the context of my study, because the Mexican tariff changes that
I am focusing on are due to free trade agreements. The other countries that have a free trade
agreement with Mexico must have also decreased their tariffs. If the degree of Mexican tariff
reduction and the tariff reduction of the other countries are correlated, then the results might not
be due to the effects of increased competition but due to the effects of export market access. As
Table 4a and 4b show, however, output tariffs did not cause any changes in exports; therefore it
is not likely that the results are driven by the changes in export market access.
In order to further examine this alternative hypothesis, I run regressions of the following form
for the main R&D variables:

Yijt = β1 Output Tariffit + β2 Input Tariffit + β3 U S Tariffit + λi + µjt + ²ijt

(7)This is the basic specification with U.S. tariffs as a further control variable. The trade classifications of U.S. and those of Mexico are harmonized up to 6 digits, therefore I aggregated U.S.
tariffs to 6 digits, and calculated plant-level US tariffs in the manner described in Section 2.2.1.
The US tariffs are the tariffs that Mexican producer would face. Therefore higher US tariffs meanless export market access. Since approximately 90% of Mexican exports over the study period
are to the U.S., I treat U.S. market access as equivalent to export market access.
Table 7 shows the results. The first three columns are the results with U.S. tariffs as anonly control, and the next three columns are the results with both U.S tariffs and input tariffs.
The magnitude of the coefficients for the output tariffs are similar to the results in Table 5: one
percent reduction of the average tariffs of the goods that a plant produces increases total R&D
by 8%, R&D intensity by 0.03% points, and the probability of undertaking R&D by 1% point.
The results confirm that the effects of output reduction are not driven by the effects of export
25
Another potential confounding factor may be tax incentives for R&D investment. To the extent I know, however,there are no such incentives that are correlated with output tariffs.

16

market access.
In some specifications, the U.S tariff reductions significantly increased Log of R&D and R&D
dummy. The results are consistent with other papers that investigate the effects of export market
access on innovative activities at the plant level (Verhoogen (2008), Bustos (2007) and Lileeva
and Trefler (2007)).

5.3

Regional Factors

I also run regressions of the form (3) and (4) with state-year effects to control any regional growth
factors and changes in policies at the state level. Table 8 shows the results. The qualitative
conclusions are very similar to the previous subsection. Moreover, the extensive margin effects(the effect of output reduction on the R&D dummy) are now stronger and have stronger statisticalsignificance.

6

Extension: Product vs Process R&D

One of the unique features of the dataset is that it has separate information on product R&D andprocess R&D. I run regressions of the form (3) and (4) to examine whether competition affects
innovative activity mainly through product innovation or through process innovation, or both.
Tables 9a and 9b show the results. For all specification of the R&D variables, product R&D is
not significantly affected by the output tariff reduction. However, process R&D responded significantly to the output tariff reduction. The findings suggest that trade liberalization affects plants’capability primary through increases in cost efficiency rather than through the creation of new
products or quality upgrading. This is somewhat surprising because most of the papers on tradeand growth model innovation as occurring either through variety expansion or through quality
upgrading. Recent models of industrial organization that try to predict a positive correlation
between the market competition and innovation also focus on product innovation. The results
are consistent, however, with Lawrence (2000) and Lawrence and Weinstein (2001), which stressthe positive effects of competition on plants’ incentive to be more cost efficient. Building a formal

17

model to explain the findings is left to future research.26

7

The Results without Industry-year Effects

All the regressions mentioned up to the previous section controlled for industry-year fixed effects.
To show the importance of controlling for industry-year fixed effects, I run all the regressions
without them. Table 10a shows the results for the basic plant-level variables such as sales andemployment, and table 10b shows the results for R&D variables. None of the coefficients are
significant. These results for both the basic plant-level variables and R&D variables share acommon feature that the effects of competition are smaller. One explanation may be that theindustries with a higher degree of tariff reduction faced some offsetting forces. Note that the table
2a shows that the tariff reduction for the period of analysis is due to the NAFTA and the free
trade agreement with the EU. However, during this period occurred a surge of Chinese imports,
which do not experience tariff reduction.27 Since Chinese imports are likely to have a negative
correlation with plants’ performances as well as with the degree of the tariff reduction, it may
be difficult to capture the effects of the tariff reductions with typical plant-level datasets. Thisis because typical plant-level data have information only on the industry classification of plants’
main products but not on detailed product-level information. The results of this paper suggest
caution when relying on typical plant-level data in analyzing the effects of tariff changes when
external factors are changing greatly.

8

Conclusion

This paper has found evidence that the reduction of tariffs on the goods produced by Mexican plants induced those plants to increase total R&D. This suggests that trade liberalization
stimulates innovative activities through competition. I do not find any evidence that the same
26
Models with both product and process innovation have been proposed by Cohen and Klepper (1996), Lin and
Saggi (2002) and Rosenkranz (2003).
27
See López Córdova (2004) and Shigeoka, Verhoogen and Wai-Poi (2006) for analysis on the effects of Chinesecompetition on manufacturing performance of Mexico.

18

output tariff reduction affects measured TFP, which suggests that relying on measured TFP —which includes mark-ups — might lead to wrong conclusions on the effects of trade liberalization
on innovative activities. Additional results using process R&D and product R&D expenditure
information suggest that trade liberalization affects plants’ capability through increases in cost
efficiency rather than through the creation of new products quality upgrading.
These results suggest that competitive pressure stimulates plants to be more efficient. The
results are evidence against the infant-industry argument that assumes that competition fromabroad will hurt the capacity and the incentive of plants’ to innovate. The proponents of infantindustry protection might argue that the government could have selectively reduced tariffs onplants that were in the position to innovate. However, this does not appear to be the case. Initialplant characteristics are not correlated with the degree of the output tariff changes.
The overall analysis demonstrates the value of using direct measures of innovative activities.
The effects of competition on innovation would not be discernable using standard TFP measures.
The effects overall would not be discernable without the data that allows me to control forindustry-specific shocks. This suggests caution in using typical plant-level datasets to analyze theeffects of trade liberalization on plant-level outcomes, especially in situations where the degree oftariff reduction is modest and the global business environment is rapidly changing.

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Table 1a: Summary statistics of basic plant variables in 2000 (EIA-SIEM panel)
Total Sales(millions of pesos)
Exports(millions of pesos)
Exports Share
Importer Dummy
Use of Imported Materials(millions of pesos)
Employment
N

Non-Exporter
168.6***(13.8)
0.00**(0.00)
0.00***(0.00)
0.45***(0.02)
19.1***(3.2)
236.18***(16.26)606

Exporter
607.9***(116.7)241.0**(9.5)
0.21***(0.01)
0.78***(0.02)
161.7***(51.1)
489.36***(34.95)
641

Total
394.4
(60.7)
130.1
(3.7)
0.11
(0.01)
0.62(0.01)
93.23(26.5)
366.33(19.94)
1247

Notes: The table reports summary statistics of basic plant variables. The first column is the statistics for nonexporter plants, while the second for exporter plants, and the third for all plants pooled together. Standard deviationof the means in parentheses. Sales and exports are in nominal pesos (A dollar was 9.5 pesos in the beginning of
2000). Significance of the test of the equality of the mean of the two groups: * 10 percent, ** 5 percent, *** 1
percent.

Table 1b: Summary statistics of R&D variables in 2000 (ESIDET-SIEM panel)
Non-Exporter
614.51**(294.81)
R&D Dummy (1 if R&D > 0)0.09***(0.02)
R&D/Total Sales
0.00(0.00)
R&D/Total Sales (if R&D > 0)0.01
(0.005)
Share of R&D for creation of new products
0.16(if R&D > 0)(0.06)
Share of R&D for quality improvement of products
0.16*(if R&D > 0)(0.05)
Share of R&D for improvement of production process
0.66***(if R&D > 0)(0.07)N
257
R&D Expenditure (thousands of pesos)

Exporter
4267.34**(1550.14)
0.23***(0.02)
0.01(0.01)
0.05(0.03)
0.26(0.04)
0.32*(0.04)
0.34***(0.05)
337

Total
2686.91(892.68)
0.17(0.02)
0.01(0.005)0.04(0.02)
0.24(0.03)
0.29(0.04)
0.41(0.04)
592

Notes: The table reports summary statistics of R&D variables. The first column is the statistics for non-exporter
plants, while the second for exporter plants, and the third for all plants pooled together. Standard deviation of
the means in parentheses. R&D expenditure is in nominal pesos (A dollar was 9.5 pesos in the beginning of 2000).
Significance of the test of the equality of the mean of the two groups: * 10 percent, ** 5 percent, *** 1 percent.

Table 2a: Weighted average tariffs by type of country. Goods level data.
Tariff in 2000
Tariff in 2001
Tariff in 2002
Tariff in 2003

All
5.1
3.9
3.3
2.7

NAFTA
2.7
1.9
1.2
0.5

EU
13.5
5.8
4.7
4.0

MFN
12.5
12.4
12.6
12.5

Others
7.2
5.4
3.1
3.2

Notes: The table reports the weighted average of tariffs by type of country. The second column shows the tariffs
for each year for the imports from the NAFTA countries (US and Canada). The third column shows the tariffs
for each year for the imports from the EU countries. The fourth column shows MFN tariffs. The fifth column
shows the tariffs for the imports from other countries with free trade agreements or with Mexico during the periodof this study. They are Colombia (1995), Venezuela (1995), Costa Rica (1995), Bolivia (1995), Nicaragua (1995),
Chile (1999), Israel (2000), Guatemala, El Salvador (2001), Honduras (2001), Iceland (2001), Liechtenstein (2001),
Norway (2001), and Switzerland (2001).

Table 2b: Plant-level output and input tariffs (ESIDET-SIEM Panel).
Variable
Output tariff 2000
Output tariff 2001
Output tariff 2002
Output tariff 2003
Input tariff 2000
Input tariff 2001
Input tariff 2002
Input tariff 2003

Mean
7.87
5.60
5.05
4.26
6.28
4.69
4.12
3.41

Std. Dev.
7.63
5.82
5.87
5.74
8.46
7.03
7.20
6.31

N
527
527
602
602
559
559
629
629

Notes: The table reports summary statistics of output tariffs and input tariffs at the plant level. The method of
construction of plant-level output tariffs and input tariffs are in section 2.2. Briefly, the plant-level output tariff fora plant is the simple averages of the product-level tariffs of the products that the plants produce. Similarly, theplant-level input tariff for a plant is the simple averages of the product-level tariffs of the products that the plantuses as intermediate products.

Table 3a: Regressions of sales and employment on output tariffs, EIA-SIEM panel
2000-2003.

Output Tariff
Plant fixed effects
Industry-year effects
Observation
R2

(1)
Log
Total
Sales
0.0088**(0.0047)
Yes
Yes
4105
0.0522(2)
Log
Domestic
Sales
0.0115**(0.0056)
Yes
Yes
4105
0.0410(3)
Exporter
Dummy

(4)
Log
Exports(5)
Log
Employment-0.0012(0.00203)
Yes
Yes
4105
0.07310.0025(0.0033)
Yes
Yes
2753
0.08130.0067*(0.0038)
Yes
Yes
4105
0.0868Notes: The table reports coefficients on the output tariffs from plant-level regressions of sales, employment orexport-related variables on the output tariffs, plant fixed effects and industry-year fixed effects. All dependent
variables are in logs except the exporter dummy. Plant-level output tariff for a plant is the simple averages of the
product-level tariffs of the products that the plants produce. Similarly, the plant-level input tariff for a plant is thesimple averages of the product-level tariffs of the products that the plant uses as intermediate products. Robust
standard errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 3b: Regression of sales and employment on output tariffs, EIA-SIEM panel
2000-2003 (with input tariffs as control variables).

Output Tariff
Input Tariff
Plant fixed effects
Industry-year effects
Observation
R2

(1)
Log
Total
Sales
0.0132*(0.0065)-0.0083(0.0089)
Yes
Yes
4105
0.0538(2)Log
Domestic
Sales
0.0155**(0.0081)-0.0055(0.0093)
Yes
Yes
4105
0.0422(3)
Exporter
Dummy

(4)
Log
Exports(5)
Log
Employment-0.0013(0.0013)-0.0025(0.0018)
Yes
Yes
4105
0.07450.0032(0.0043)0.0022(0.0042)
Yes
Yes
2753
0.08650.0048*(0.0029)0.0001(0.0019)
Yes
Yes
4105
0.0941

Notes: The table reports coefficients on the output tariffs from plant-level regressions of sales, employment orexport-related variables on the output tariffs, plant fixed effects and industry-year fixed effects. All dependent
variables are in logs except the exporter dummy. Plant-level output tariff for a plant is the simple averages of the
product-level tariffs of the products that the plants produce. Similarly, the plant-level input tariff for a plant is thesimple averages of the product-level tariffs of the products that the plant uses as intermediate products. Robust
standard errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 4: Regressions of TFP measures on output tariffs, EIA-SIEM panel 20002003.
(1)
Log
TFP
OLS
Output Tariff-0.0103(0.0119)

(2)
Log
TFP
LevinsonPetrin-0.0066(0.0113)

(3)
Log
TFP
OlleyPakes-0.0128(0.0122)

Yes
Yes
2612
0.121

Yes
Yes
2612
0.116

Yes
Yes
2612
0.121

Input Tariff
Plant fixed effects
Industry-year effects
Observations
R2

(4)
Log
TFP
OLS
-0.0083(0.0121)0.0058(0.0070)
Yes
Yes
2612
0.135

(5)
Log
TFP
LevinsohnPetrin-0.0069(0.0122)0.0014(0.0066)
Yes
Yes
2612
0.121

(6)
Log
TFP
OlleyPakes-0.0059(0.0118)0.0056(0.0067)
Yes
Yes
2612
0.125

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the TFP on the output
tariffs, plant fixed effects and industry-year fixed effects. All dependent variables are in logs. TFP is estimatedusing OLS, the method by Olley and Pakes (1996), and the method by Levinsohn and Petrin (2003). Plant-level
output tariff for a plant is the simple averages of the product-level tariffs of the products that the plants produce.
Similarly, the plant-level input tariff for a plant is the simple averages of the product-level tariffs of the productsthat the plant uses as intermediate products. Robust standard errors in parentheses. Robust standard errors inparentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 5: Regressions of total R&D on output tariffs, ESIDET-SIEM panel 20002003.

Output Tariff

(1)
Log
R&D
-0.0833**(0.0421)(2)
R&D
Intensity-0.0345*(0.0182)

(3)
R&D
dummy
-0.0102*(0.0057)

(4)
Log
R&D
-0.0814**(0.041)-0.0090(0.0269)(5)
R&D
Intensity-0.0364*(0.0196)-0.0043(0.0122)

(6)
R&D
dummy
-0.0116*(0.0068)0.0006(0.0034)

Yes
Yes
2178
0.13

Yes
Yes
2178
0.70

Yes
Yes
2178
0.22

Yes
Yes
2178
0.12

Yes
Yes
2178
0.71

Yes
Yes
2178
0.21

Input Tariff
Plant fixed effects
Industry-year effects
Observations
R2

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the total R&D on the output tariffs,
plant fixed effects and industry-year fixed effects. Robust standard errors in parentheses. Log R&Dit = log(R&Dit + 1),
R&Dit
R&D Intensityit = T otal
Sales

i0

∗ 100, where 0 is the time first I observe the plants, and R&D Dummyit is a dummy

variable taking value 1 if R&D is positive, and 0 otherwise. Plant-level output tariff for a plant is the simple averages of the
product-level tariffs of the products that the plants produce. Similarly, the plant-level input tariff for a plant is the simple
averages of the product-level tariffs of the products that the plant uses as intermediate products. Robust standard errors inparentheses. Robust standard errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 6a: Regressions of initial sales and employment on the changes in output tariffs,
EIA-SIEM panel 2000-2003.
(1)
Log
Total
Sales
Output Tariff Changes 0.0113(0.0157)
Industry dummies
Yes
Observations
649
2
R
0.316

(2)
Log
Domestic
Sales
0.0096(0.0153)
Yes
649
0.318

(3)
Exporter
Dummy

(4)
Log
Exports(5)
Log
Employment

(6)
Log
TFP

-0.00541(0.00569)
Yes
649
0.231

0.0278(0.0322)
Yes
433
0.253

0.0133(0.0118)
Yes
649
0.305

-0.0062(0.0112)
Yes
649
0.133

Notes: The table reports coefficients on the changes in output tariffs from plant-level regressions of the initial plantcharacteristics on the changes in output tariffs and industry fixed effects. Robust standard errors in parentheses.
All dependent variables are in logs except the exporter dummy. TFP is estimated using Olley-Pakes method.
Plant-level output tariff for a plant is the simple averages of the product-level tariffs of the products that the plants
produce. Robust standard errors in parentheses. Robust standard errors in parentheses. Significance: * 10 percent,** 5 percent, *** 1 percent.

Table 6b: Regressions of initial sales and employment on the changes in output
tariffs, EIA-SIEM panel 2000-2003 (with input tariffs).
(1)
Log
Total
Sales
Output Tariff Changes 0.0205(0.0164)
Input Tariff Changes -0.0268**(0.0128)
Industry dummies
Yes
Observations
649
2
R
0.335

(2)
Log
Domestic
Sales
0.0189(0.0158)-0.0254**(0.0125)
Yes
649
0.336

(3)
Exporter
Dummy

(4)
Log
Exports(5)
Log
Employment

(6)
Log
TFP

0.0028(0.0054)-0.0183***(0.0040)
Yes
649
0.242

0.0296(0.0319)-0.0461(0.0353)
Yes
427
0.259

0.0186(0.0133)-0.0117(0.00857)
Yes
649
0.318

-0.0022(0.0122)-0.0108(0.0079)
Yes
649
0.143

Notes: The table reports coefficients on the changes in output tariffs from plant-level regressions of the initial plantcharacteristics on the changes in output tariffs and industry effects. Robust standard errors in parentheses. All
dependent variables are in logs except the exporter dummy. TFP is estimated using Olley-Pakes method. Plantlevel output tariff for a plant is the simple averages of the product-level tariffs of the products that the plants
produce. Similarly, the plant-level input tariff for a plant is the simple averages of the product-level tariffs of the
products that the plant uses as intermediate products. Robust standard errors in parentheses. Robust standard
errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 7: Regressions of total R&D on output tariffs, ESIDET-SIEM panel 2000-2003(with US tariffs and input tariffs as control variables).

Output Tariff
US Tariff

(1)
Log
R&D
-0.0862**(0.0423)-0.0920**(0.0442)(2)
R&D
Intensity-0.0391*(0.0213)0.0160(0.231)

(3)
R&D
dummy
-0.0123*(0.0068)-0.0134*(0.0076)

Yes
Yes
2137
0.71

Yes
Yes
2137
0.23

Input Tariff
Plant fixed effects
Yes
Industry-year effects Yes
Observations
2137
2
R
0.23

(4)
Log
R&D
-0.0854**(0.043)-0.0870*(0.0474)-0.0070(0.0272)
Yes
Yes
2137
0.22

(5)
R&D
Intensity-0.0406*(0.0221)0.0014(0.239)-0.0036(0.0123)
Yes
Yes
2137
0.70

(6)
R&D
dummy
-0.0121*(0.0069)-0.0122(0.0078)-0.0007(0.0035)
Yes
Yes
2137
0.22

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the total R&D on the output tariffs,
plant fixed effects and industry-year fixed effects with U.S. tariffs and input tariffs as control variables. Robust standard errors
R&Dit
in parentheses. Log R&Dit = log(R&Dit + 1) ∗ 100, R&D Intensityit = T otal
, where 0 is the time first I observe the
Sales
i0

plants, and R&D Dummyit is a dummy variable taking value 1 if R&D is positive, and 0 otherwise. The plant-level output
tariff for a plant is the simple averages of the product-level tariffs of the products that the plants produce. The plant-level
U.S. tariff for a plant is the simple averages of the U.S. tariffs for U.S imports from Mexico of the products that the plants
produce. Similarly, the plant-level input tariff for a plant is the simple averages of the product-level tariffs of the productsthat the plant uses as intermediate products. Robust standard errors in parentheses. Robust standard errors in parentheses.
Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 8: Regressions of total R&D on output tariffs, ESIDET-SIEM panel 2000-2003(with US tariffs, input tariffs and region-year effects as control variables).

Output Tariff
US Tariff

(1)
Log
R&D
-0.1087**(0.0512)-0.102**(0.0447)(2)
R&D
Intensity-0.0409*(0.0222)0.0404(0.379)

(3)
R&D
dummy
-0.0133**(0.0064)-0.0150*(0.0083)

Yes
Yes
Yes
2137
0.29

Yes
Yes
Yes
2137
0.73

Yes
Yes
Yes
2137
0.29

Input Tariff
Plant fixed effects
Industry-year effects
Region-year effects
Observation
R2

(4)
Log
R&D
-0.1123**(0.0532)-0.101*(0.0496)-0.0102(0.0272)
Yes
Yes
Yes
2137
0.28

(5)
R&D
Intensity-0.0406*(0.0223)
0.0361(0.339)-0.0046(0.0123)
Yes
Yes
Yes
2137
0.70

(6)
R&D
dummy
-0.0143**(0.0069)-0.0145(0.0089)-0.0008(0.0035)
Yes
Yes
Yes
2137
0.30

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the total R&D on the output tariffs,
plant fixed effects, industry-year fixed effects and region-year effects with US tariffs and input tariffs as control variables.
R&Dit, where 0 is the
Robust standard errors in parentheses. Log R&Dit = log(R&Dit + 1) ∗ 100, R&D Intensityit = T otal
Sales
i0

time first I observe the plants, and R&D Dummyit is a dummy variable taking value 1 if R&D is positive, and 0 otherwise.
The plant-level output tariff for a plant is the simple averages of the product-level tariffs of the products that the plants
produce. The plant-level U.S. tariff for a plant is the simple averages of the U.S. tariffs for U.S imports from Mexico of the
products that the plants produce. Similarly, the plant-level input tariff for a plant is the simple averages of the product-leveltariffs of the products that the plant uses as intermediate products. Robust standard errors in parentheses. Robust standard
errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 9a: Regressions of product and process R&D on output tariffs, ESIDET-SIEM
panel 2000-2003.
(1)
Log
Product
R&D
Output Tariff-0.0288(0.0532)
Plant fixed effects
Yes
Industry-year effects
Yes
Observation
2178
R2
0.21

(2)
Product
R&D
Intensity-0.0133(0.0148)
Yes
Yes
2178
0.13

(3)
Product
R&D
dummy-0.0045(0.0061)
Yes
Yes
2178
0.17

(4)
Log
Process
R&D-0.0735*(0.041)
Yes
Yes
2178
0.19

(5)
Process
R&D
Intensity-0.0250*(0.0138)
Yes
Yes
2178
0.15

(6)
Process
R&D
dummy-0.106*(0.0064)
Yes
Yes
2178
0.19

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the product R&D or process R&D
on the output tariffs, plant fixed effects and industry-year fixed effects. Robust standard errors in parentheses. Log R&Dit =R&Ditlog(R&Dit + 1), R&D Intensityit = T otal
Sales

i0

∗ 100, where 0 is the time first I observe the plants, and R&D Dummyit

is a dummy variable taking value 1 if R&D is positive, and 0 otherwise. The plant-level output tariff for a plant is thesimple averages of the product-level tariffs of the products that the plants produce. Robust standard errors in parentheses.
Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 9b: Regressions of product and process R&D on output tariffs, ESIDET-SIEM
panel 2000-2003. With input tariffs.

Output Tariff
Input Tariff
Plant fixed effects
Industry-year effects
Observation
R2

(1)
Log
Product
R&D-0.0314(0.056)-0.0245(0.018)
Yes
Yes
2178
0.22

(2)
Product
R&D
Intensity-0.0135(0.0158)-0.0064(0.0078)
Yes
Yes
2178
0.14

(3)
Product
R&D
dummy-0.0054(0.0068)-0.0025(0.0023)
Yes
Yes
2178
0.18

(4)
Log
Process
R&D-0.0715*(0.036)-0.009(0.026)
Yes
Yes
2178
0.21

(5)
Process
R&D
Intensity-0.0224*(0.0133)
0.021(0.0082)
Yes
Yes
2178
0.16

(6)
Process
R&D
dummy-0.0107*(0.0066)
0.0007(0.0034)
Yes
Yes
2178
0.20

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the product R&D or process R&D
on the output tariffs, plant fixed effects and industry-year fixed effects. Robust standard errors in parentheses. Log R&Dit =R&Ditlog(R&Dit + 1), R&D Intensityit = T otal
Sales

i0

∗ 100, where 0 is the time first I observe the plants, and R&D Dummyit isa dummy variable taking value 1 if R&D is positive, and 0 otherwise. Robust standard errors in parentheses. The plant-level
output tariff for a plant is the simple averages of the product-level tariffs of the products that the plants produce. Similarly,
the plant-level input tariff for a plant is the simple averages of the product-level tariffs of the products that the plant uses asintermediate products. Robust standard errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.

Table 10a: Regressions of sales and employment on output tariffs, EIA-SIEM panel
2000-2003 (without controlling for industry-year effects).

Output Tariff
Input Tariff
Plant fixed effects
Industry-year effects
Observation
R2

(1)
Log
Total
Sales
0.0062(0.0084)-0.0032(0.0076)
Yes
No
4105
0.0040(2)
Log
Domestic
Sales
0.0071(0.0088)-0.0041(0.0077)
Yes
No
4105
0.0025(3)
Exporter
Dummy

(4)
Log
Exports(5)
Log
Employment-0.0004(0.0019)-0.0016(0.0021)
Yes
No
4105
0.01150.0024(0.0023)-0.0017(0.0027)
Yes
No
2753
0.02650.0008(0.0034)0.0007(0.0019)
Yes
No
4105
0.0499Notes: The table reports coefficients on the output tariffs and the input tariffs from plant-level regressions of sales,
employment or export-related variables on the output tariffs, plant fixed effects without controlling for industry-year
fixed effects. All dependent variables are in logs except the exporter dummy. The plant-level output tariff for aplant is the simple averages of the product-level tariffs of the products that the plants produce. Similarly, theplant-level input tariff for a plant is the simple averages of the product-level tariffs of the products that the plantuses as intermediate products. Robust standard errors in parentheses. Significance: * 10 percent, ** 5 percent, ***1 percent.

Table 10b: Regressions of total R&D on output tariffs, ESIDET-SIEM panel 20002003 (without controlling for industry-year effects).

Output Tariff

(1)
Log
R&D
0.0316(0.352)(2)
R&D
Intensity
0.0102(0.0183)

(3)
R&D
dummy
0.0056(0.0053)

Yes
No
2178
0.11

Yes
No
2178
0.69

Yes
No
2178
0.19

Input Tariff
Plant fixed effects
Industry-year effects
Observation
R2

(4)
Log
R&D
0.0218(0.0371)
0.0158(0.0162)
Yes
No
2178
0.12

(5)
R&D
Intensity
0.0040(0.0192)
0.0103(0.0116)
Yes
No
2178
0.71

(6)
R&D
dummy
0.030
(0.0046)
0.0028(0.0020)
Yes
No
2178
0.21

Notes: The table reports coefficients on the output tariffs from plant-level regressions of the total R&D on the output tariffs,
plant fixed effects without controlling for industry-year fixed effects. Robust standard errors in parentheses. Log R&Dit =R&Ditlog(R&Dit + 1), R&D Intensityit = T otal
Sales

i0

∗ 100, where 0 is the time first I observe the plants, and R&D Dummyit isa dummy variable taking value 1 if R&D is positive, and 0 otherwise. The plant-level output tariff for a plant is the simple
averages of the product-level tariffs of the products that the plants produce. Similarly, the plant-level input tariff for a plant is
the simple averages of the product-level tariffs of the products that the plant uses as intermediate products. Robust standard
errors in parentheses. Significance: * 10 percent, ** 5 percent, *** 1 percent.