TRADE STATUS, TRADE POLICY AND PRODUCTVITY: THE BRAZILIAN CASE
X. Cirera1, D. Lederman2, J.A. Mañez3, M.E. Rochina3 and J. A. Sanchis3
1
IDS, University of Sussex
2 Economics
3
Research World Bank Group
University of Valencia and ERICES
4-september-2012
Abstract.
The literature on firm productivity recognizes the important role played by firm trade
status and trade policy on the evolution of firm productivity. There are many recent studies that
have highlighted the importance of considering trading status (either exporting or importing) as
well as the effects of trade policy in the analysis of total factor productivity. The aim of this paper
is to integrate both firms’ trade status and trade policy in the analysis of productivity in Brazil. We
use a two-steps strategy: first, we estimate a TFP following De Loecker (2010) approach and
Wooldridge (2009) estimation procedure; and, second, we use this estimated TFP as dependent
variable in a model with trade policy and firm trade status as covariates, in order to disentangle
the effects of those variables on TFP. From our results we can conclude that trade liberalisation
(lower input and/or output tariffs) increase productivity. We also get that decreasing input tariffs
has a larger effect increasing productivity for high import intensity firms and that decreasing
output tariffs increases productivity for exporters more than for non-exporters. Finally, even after
controlling for the effects of tariffs, there is still evidence of both learning-by-exporting and
learning-by-importing effects on productivity.
Key words: trade status, trade policy, Total Factor Productivity, GMM
1
1. Introduction.
The literature on firm productivity recognizes the important role played by firm trade status and
trade policy on the evolution of firm productivity. We find recent studies such as De Loecker
(2007, 2010), Van Biesebroeck (2005) or Kasahara and Rodrigue (2008) that have highlighted
the importance of considering trading status in the analysis of total factor productivity (TFP,
hereafter). However, whereas De Loecker (2007, 2010), De Loecker and Warzyniski (2011) or
Van Biesebroeck (2005) only consider the role of exporting, the study by Kasahara and Rodrigue
(2008) only analyses the role of importing. In relation to the effects of trade policy on firm TFP, in
recent years, Fernandes (2007) or Amiti and Konings (2007) have carried sound studies on the
impact of trade policy (proxied by tariffs) on productivity for two developing countries such as
Colombia and Indonesia, respectively. Both studies find that the impact of tariffs reductions on
productivity is large. The extent to which trade policy is expected to affect firm’s level productivity
critically depends on the size of trade policy changes.
The aim of this paper is to integrate both firms’ trade status and trade policy in the
analysis of productivity in Brazil. Further, we aim to consider jointly the role of exporting and
importing to check if there is learning-by-exporting and learning-by-importing.
Our empirical strategy consists of two steps. In the first step, we estimate a TFP following
Wooldridge (2009) estimation procedure. In the second step, we use this estimated TFP as
dependent variable of a model with trade policy and trade status as covariates in order to
disentangle the effects of those variables on TFP.
The TFP estimation procedure used in this study presents various novelties with respect
to the typical control-function based estimation methods (Olley and Pakes, 1996; Levinshon and
Petrin, 2003) used to analyse the effects of trade policy. First, we allow for different demands of
intermediate materials according to firm trade status (non-traders, only exporters, only importers
2
and two-way traders). Second, we move from an exogenous law of motion of productivity to an
endogenous law of motion in which we allow past trading experience to affect productivity
(following De Loecker, 2007, 2010). Third, we do not include firm trade status as a state variable
in the production function but in the demand for intermediate inputs, as it allows the effect of
trading status to vary for firms with different characteristics (see De Loecker, 2007).
In the second step of our estimation strategy, similarly to Amiti and Konings (2007), we
regress our TFP estimate against trade policy measures (input and output tariffs) that we interact
with the trade status variables. Our aim is to analyse the impact of input and output tariffs on firm
productivity and whether they depend on the firm’s trading status.
In order to analyse the relationship between firm productivity and both trade status and
trade policy, we use a Brazilian dataset that links firm’s characteristics, production and export
data for Brazilian firms for the period 2000 to 2008. For production and firm’s characteristics, we
use the PIA (Pesquisa Industrial Anual). PIA is a survey for manufacturing and mining sectors
conducted annually by IBGE (Instituto Brasileiro de Geografia e Estatistica). For exports we use a
dataset created by SECEX (Secretaria de Comercio Exterior). It is importat to note that while
Brazil had undergone an intense period of trade liberalization during the 1980s and 1990s, this
process has slowed down during the 2000s, where trade policy has been quite stable. In general,
tariffs were reduced very slowly until 2007, and increased in 2008.
To anticipate our results, we find that both higher output tariffs (tariffs on imports of final
goods) and higher input tariffs (tariffs on imports of intermediate inputs) decrease productivity.
Higher output tariffs decrease productivity by lowering import competition, as firms are less forced
to improve efficiency. Higher input tariffs decrease productivity by decreasing, for instance,
access to a wider range of foreign inputs, to higher quality inputs, or to foreign technology
incorporated in imported inputs. We also find that trade liberalization, by decreasing input tariffs,
3
has a larger effect increasing productivity for high import intensity firms; and, that trade
liberalization, by decreasing output tariffs, increases productivity for exporters more than for nonexporters. Finally, even after controlling for the effects of tariffs, there is still evidence of both
learning-by-exporting and learning-by-importing effects on productivity.
The rest of the paper is organized as follows. Section 2 summarises the related literature.
Section 3 is devoted to explain the main features of the two-step method pursued and the
production function estimation method. Section 4 describes the data. In section 5 we discuss the
results and some robustness checks carried out. Finally, Section 6 concludes.
2. Related literature.
Most of the relevant literature that analyses the relationship between productivity and trade status
and trade policy focus separately either on the impact of trade status on productivity or the effects
of trade policy on productivity. We start reviewing first the most relevant papers that either
analyse the effects of trade status on productivity or the effects of trade policy on productivity, and
then we will continue with those studies that jointly research the effects of both trade policy and
trade status on productivity.
2.1. Trade status and productivity.
Whereas there is a large amount of papers that have analysed whether exporting improves firm
productivity (learning-by-exporting hypothesis, LBE hereafter), the evidence on the analysis of the
impact of importing on productivity (learning-by-importing hypothesis, LBI) is much more scarce.
According to the LBE mechanism firms improve their productivity after entering a foreign
market (Clerides et al., 1998). These potential productivity gains for firms from participating into
export markets arise from (among others): growth in sales that allows firms to profit from
4
economies of scale, knowledge flows from international customers that provide information about
innovations reducing costs and improving quality, or from increased competition in export markets
that force firms to behave more efficiently. In spite of its large volume, evidence on LBE is far
from conclusive, whereas there are papers that do not find any evidence on LBE, those that find it
differ both on the intensity and the duration of the LBE effect. 1 However, as De Loecker (2010)
has recently shown most previous tests on the existence of the LBE mechanism could be flawed.
The usual empirical strategy is to look at whether a productivity estimate, typically obtained as the
residual of a production function estimation, increases after firms enter in the export market. But
for such an estimate to make sense, past export experience should be allowed to impact future
productivity. Yet some previous studies (implicitly) assume that the productivity term in the
production function specification is just an idiosyncratic shock (Wagner, 2002; Hansson and
Lundin, 2004; Greenaway and Kneller, 2004, 2007b, 2008; Girma et al., 2004; Máñez et al.,
2010), while others assume that this term is governed by an exogenous Markov process (Arnold
and Hussinger, 2005; Serti and Tomassi, 2008). It is this sort of assumptions, often critical to
obtain consistent estimates (Ackerberg et al., 2006), what make these tests of the existence of
LBE to lack internal consistency. To the best of our knowledge, only recent papers by De Loecker
(2007, 2010), De Loecker and Warzyniski (2011) and Manjón et al. (2013) allow past export
experience to impact future productivity.
Similarly, the papers testing for LBI hypothesize that the diffusion and adoption of new
technologies by importing intermediates can be an important source of productivity improvement,
especially in developing countries.2 Among them Kasahara and Rodrigue (2008) test for the LBI
allowing past import experience to affect productivity for Chilean manufacturing plants.
Silva et al. (2010) provide a detailed survey of the learning by exporting literature. Further, Martins and Yang (2009)
provide a meta-analysis of 33 empirical studies. Singh (2010) concludes that studies supporting self-selection
overwhelm studies supporting learning-by-exporting.
2 Previous empirical studies using aggregate country or industry-level data found that importing intermediate goods
1
5
2.2. Trade policy and productivity.
Among the papers that analyse the effects of trade policy and trade liberalization (usually
measured by the evolution tariffs) on firms’ productivity, very likely those more related to our
study are Fernandes (2007) and Schor (2004). For Fernandes (2007), the general argument
linking the reduction of tariffs to productivity is that trade liberalization results in wider exposure to
foreign competition what forces domestic firms to behave more efficiently. Fernandes (2007)
widens the production function to include trade policy as and additional input (which therefore
implies allowing trade policy to shift the mean of the production function). Further, the demand for
intermediate inputs in her production function also depends on trade policy, and so it appears in
the inversion rule for productivity. However, she maintains the assumption of an exogenous
Markov process for the law of motion of productivity. The results from her study strongly support
the presence of competitive pressure-induces TFP gains due to trade liberalization for Colombian
plants.
Schor (2004) estimates the effects of nominal output and import tariffs on productivity
using Brazilian firm data for the period 1986-1998 (which corresponds to the period previous to
the one we use in this study), and she finds that both types of tariffs have a negative effect on
productivity. She uses a two-step methodology: in the first step, he obtains an estimated TFP
using a standard LP methodology that does not include trade policy measures neither as
additional inputs in the production function, nor in the demand for intermediate materials, nor in
the Markov process; and, in a second step, the estimated TFP is regressed on tariffs. Schor
(2004) finds that the estimated coefficient for tariffs in the productivity equation turns out to be
negative. Further, when a measure of tariffs on inputs is added in the productivity equation, the
that embody R&D from an industrial country can boost a country’s productivity, see for example Coe and Helpman,
1995 and Coe et al., 1997.
6
coefficient associated with this measure is also negative, and the inclusion of this new variable
reduces the size of the estimated coefficient of nominal (output) tariffs. Thus, her results seem to
indicate that, along with the increased competition, the new access to inputs that embody better
foreign technology also contributes to productivity gains after trade liberalization.
2.3. Trade policy, trade status and productivity.
Finally, among the papers that analyse jointly the effects of trade policy and trade status on
productivity is worth to mention: Muendler (2004) and Amiti and Konings (2007). Muendler (2004)
uses also data from Brazil but for the period 1986-1998 (as Schor, 2004). Its empirical strategy
consists of two steps: in the first step, he introduces as additional inputs in the production function
the foreign shares of capital and intermediate inputs to measure the impact of differences in
quality between national and foreign inputs; in the second step, the growth of TFP estimated in
the first step is regressed on import penetration3 (as a proxy to control for non-tariff barriers),
output tariffs and the share of capital and intermediate inputs. His empirical results suggest that
the use of foreign inputs plays a negligible role to explain productivity changes, whereas foreign
competition (measured by larger import penetration and lower output tariffs) pressures firms to
raise productivity.
Also Amiti and Konings (2007) use a two-step procedure to analyse the effect of trade
policy on Indonesian firms productivity for the period 1991-2001. In the first step, following the
approach proposed by Kasahara and Rodrigue (2005), Van Biesebroeck (2005) or De Loecker
(2007), they modify the OP two-stage estimation setup to treat the import and export decisions as
additional state variables, as they believe that treating these decisions as exogenous is
inappropriate. This implies that the investment demand function becomes a function of four state
3
Import penetration seems to be very important in Brazil during the analysed period.
7
variables, the standard capital and productivity variables plus the export and import decisions.
However, they do not incorporate firm past trading experience into the law of motion of
productivity (they do not modify the assumption of an exogenous Markov process for the law of
motion of productivity), and therefore they do not allow past trading experience to affect current
productivity. In the second step, they regress the estimated TFP on trade policy variables (output
and input tariffs) and make some robustness checks about the possibility of input tariffs affecting
more to input importers. However, they do not check whether output tariffs affect more intensely
to exporting than non-exporting firms. The results by Amiti and Konings (2007), show that the
effect of reducing input tariffs significantly increases productivity, and that this effect is much
higher than reducing output tariffs. Thus, a 10 percentage point fall in input tariffs leads to a
productivity gain of 12% for firms that import inputs, and this gain is at least twice as high as any
gains from reducing output tariffs.
3. Methodology.
3.1. Some features of our two-step method.
As in Amiti and Konings (2005) and Muendler (2004), our empirical strategy consists of two steps.
In the first step, we estimate TFP using a procedure that introduces some novelties with respect
to the papers reviewed above. In the second step, we use this estimated TFP as dependent
variable in a regression model with trade policy and trade status as covariates.
In this two-step analysis it is crucial to decide whether to include the trade status and
trade policy variables in the TFP estimation or as covariates in the equation explaining the
estimated TFP. Different authors opt for different solutions. Therefore, we devote the following
paragraphs to carry out a detailed reasoning of our choices.
First, following De Loecker (2007) to capture differences in market structure among firms
with different trading status (related to mode of competition, demand conditions, and exit
8
barriers), we allow for different demands of materials according to firm trading status (nontraders, only importers, only exporters and two-way traders). Thus, we do not include firm trade
status as a state variable in the production function but only in the demand for intermediate
inputs.4 According to De Loecker (2007), there are at least three reasons that advice to do so: i)
including a vector of trading status dummies as state inputs (only exporters, only importer, twoway traders dummies) implies to assume, for example, that the impact of importing in productivity
is deterministic, i.e. the productivity of all importers will increase by the estimate of the import
dummy (the same applies to only exporters or two-way traders dummies); ii) including trading
status variables in the demand for intermediate inputs allows trading status to have a different
impact for firms with different characteristics (capital, intermediate materials, etc.); and, iii) the
Cobb-Douglas production function implies that if we include trading status variables as additional
inputs, a firm can substitute any input with being an exporter with a unit elasticity of substitution.5
Second, we move from an exogenous law of motion of productivity to an endogenous law
of motion in which we allow past trading experience to affect productivity (following De Loecker,
2007, 2010 for export status or Kasahara and Rodrigue, 2008 for import status).6
According to Van Biesebroeck (2005) and Kasahara and Rodrigue (2005) the export and import dummies should be
treated also as state variables, in particular, as non-deterministic state variables. However De Loecker (2007, 2010)
and De Loecker and Warzyinski (2011), argue that these variables affect the characteristics of the markets where
firms operate. Notwithstanding, from an empirical point of view both alternatives conduct to the same end: these type
of variables should be included in the investment/inputs demand function. In fact, De Loecker and Warzyinski (2011)
explain that regardless of considering export status as a state variable of the underlying dynamic problem of the firm
or not, it has to be controlled for in the input demand function if exporters face different demand conditions. However,
they recognize that it is reasonable to think that export status is a state variable, given the empirical evidence about
export entry decisions facing significant sunk costs. For us, the same types of arguments also work for the import
status.
5 However, Van Biesebroeck (2005) introduces the export status as an additional input into the production function.
This treatment can be methodologically justified by modelling log productivity, in a previously linearized production
function, as with two components: one observable depending on the export status with its corresponding coefficient
and another unobserved component of productivity. Under this methodological approach if the estimated coefficient
on the variable export status in the production function is greater than zero it can be interpreted like a shifter out of
the production frontier.
6 Kasahara and Rodrigue (2008), analogously to Van Biesebroeck (2005) for exports, introduce a dummy variable for
importers in the production. They introduce this variable not because they consider it as an additional input, but
because they consider that productivity depends both on a productivity shock (unobserved) and on the range of
intermediate inputs available to firms. They expect that having access to a larger range of intermediate inputs will
probably increase productivity. Thus they make the assumption that importing firms have access to more variety of
4
9
Finally, in the same vein than Amiti and Konings (2007), we do not include trade policy
variables (tariffs) either as additional inputs in the production function or in the demand of
intermediate materials, or in the law of motion of productivity. The reasons explaining this
decision are that: i) including them as additional inputs would imply the same problems explained
above for trade status variables; ii) the demand of intermediate inputs should include only firm’s
state variables (as capital) and other factors related to demand conditions, market conditions or
affecting firms’ input choices (as far as they have firm variation); and, iii) trade policy variables are
not firm level decisions that endogenously determine the evolution of productivity and, therefore,
they should not be included in the law of motion of productivity.
With respect to the technique used to estimate the TFP, we follow Wooldridge (2009) that
argues that both Olley and Pakes (1996) and Levinshon and Petrin (2003) two step estimation
procedures can be reconsidered as consisting of two equations that can be jointly estimated by
GMM in a one step procedure. This joint estimation strategy has the advantages of increasing
efficiency with respect to two-step procedures and of making unnecessary bootstrapping for the
calculus of the standard error. Therefore, our estimation technique represents a step further with
respect to both De Loecker (2010) and Amiti and Konings (2007).
In the second step of our estimation strategy, similarly to Amiti and Konings (2007), we
regress our TFP estimate against trade policy measures (input and output tariffs) and interactions
of these variables with the vector of trade status variables. Our aim is to analyse the impact of
inputs and output tariffs on firm productivity and to analyse whether they depend on the firm’s
trading status.
intermediate inputs than domestic firms (assuming that domestic firms have only access to domestic intermediate
inputs). But they also include the import dummy in the intermediate inputs demand function and in the law of motion
for productivity, the latter because it is also considered a firm level decision. They interpret the coefficient associated
to the import dummy in the production function as a static/immediate effect on productivity and, differently, the
coefficient linked to the import dummy in the law of motion for productivity as measuring something dynamic (such as
learning-by-importing).
10
3.2. Productivity estimation.
We assume that firms produce a homogenous good using a Cobb-Douglas technology:
y it = b0 + bl l it + bk kit + bm mit + mt + w it + hit
(1)
where yit is the natural log of production of firm i at time t, lit is the natural log of labour, kit is the
natural log of capital, mit is the natural log of intermediate materials, and t are time effects. As for
the unobservables, ωit is the productivity (not observed by the econometrician but observable or
predictable by firms) and ηit is a standard i.i.d. error term that is neither observed nor predictable
by the firm.
It is also assumed that capital evolves following a certain law of motion that is not directly
related to current productivity shocks (i.e. it is a state variable), whereas labour and intermediate
materials are inputs that can be adjusted whenever the firm faces a productivity shock (i.e. they
are variable factors).7
Under these assumptions, Olley and Pakes (1996) show how to obtain consistent
estimates of the production function coefficients using a semiparametric procedure; see also
Levinshon and Petrin, 200 for a closely related estimation strategy. However, here we follow
Wooldridge (2009), who argues that both OP and LP estimation methods can be reconsidered as
consisting of two equations which can be jointly estimated by GMM: the first equation tackles the
The law of motion for capital follows a deterministic dynamic process according to which kit = (1- d )kit-1 + Iit-1 .
Thus, it is assumed that the capital the firm uses in period t was actually decided in period t-1 (it takes a full
production period for the capital to be ordered, received and installed by the firm before it becomes operative).
Labour and materials (unlike capital) are chosen in period t, the period they actually get used (and, therefore, they
can be a function of it ). These timing assumptions make them non-dynamic inputs, in the sense that (and again
unlike capital) current choices for them have no impact on future choices.
7
11
problem of endogeneity of the non-dynamic inputs (that is, the variable factors); and, the second
equation deals with the issue of the law of motion of productivity. Next we consider each in detail.
Let us start considering first the problem of endogeneity of the non-dynamic inputs.
Correlation between labour and intermediate inputs with productivity complicates the estimation
of equation (1), because it makes the OLS estimator biased and the fixed-effects and
instrumental variables methods generally unreliable (Ackerberg et al., 2006). Both OP and LP
methods use a control function approach to solve this problem, by using investment in capital and
materials, respectively, to proxy for “unobserved” firm productivity.
In particular, the OP method assumes that the demand for investment in capital,
(
)
i it = i kit ,w it , is a function of firms’ capital and productivity. To circumvent the problem of firms
(
)
with zero investment in capital, the LP method uses the demand for materials, mit = m kit ,w it ,
instead, as a proxy variable to recover “unobserved” firm productivity. Since we follow this last
approach, we concentrate on the demand of materials hereafter.8
Therefore, when estimating productivity using these general versions of OP and LP in a
sample where some firms do not participate in foreign markets and others participate either
exporting, importing or both, it is assumed that the demand of intermediate materials for the
different types of firms according to trade status is identical. However, as explained in the
previous section, heterogeneity in trade status may influence the demand function of intermediate
inputs. Therefore, analogously to De Loecker (2007, 2010) when analysing the effects of
exporting on firms’ productivity, we consider different demands of intermediate materials for nontraders, only exporters, only input importers and both exporters and input importers (two way
Both the investment of capital demand function and the demand for intermediate materials are assumed to be
strictly increasing in it (in the case of the investment of capital this is assumed in the region in which iit>0). That is,
conditional on kit, a firm with higher it optimally invests more (or demands more materials).
8
12
traders). In this sense, we extend De Loecker (2010) by introducing firms’ choices related to
imports of intermediate inputs. Thus, we write the demand of materials as:
(
mit = mTS kit ,w it
)
(2)
where we include the subscript TS (trade status) to denote different demands of intermediate
inputs for only exporters, only input importers or both, and non-traders. Also, since the demand of
intermediate materials is assumed to be monotonic in productivity, it can be inverted to generate
the following inverse demand function for materials:
w it = hTS ( kit ,mit )
(3)
where hTS is an unknown function of kit and mit. Then, substituting the above expression (3) into
the production function (1) we get:
(
)
y it = b0 + bl l it + b k kit + bm mit + mt + hTS kit ,mit + hit
(4)
Finally, by considering four different demand functions for intermediate materials (for non-traders,
only exporters, only input importers, two-way traders), our first estimation equation results in:
( k ,m )
( k ,m ) +1(I )H ( k ,m ) +1(EI
y it = b l l it + mt +1(NTit-1 )HNT
+1(Eit-1 )HE
it-1
it
it
it-1
it
it-1
it
Iit-1
it
it
)HEI
it-1
it-1
( k ,m ) + h
it
it
(5)
it
13
where 1(NTit-1), 1(Eit-1), 1(Iit-1) and 1(EIit-1) are indicator functions that take value one for nontraders (NT) , only exporters (E), only input importers (I) and two way traders (EI), respectively.
As for the timing of the firm’s choices on trade status, following Van Biesebroeck (2005), we
assume that the firm decides whether to export or not and whether to import inputs or not in
period t knowing its productivity in t-1 (but not in period t). Therefore, the firm trade status in a
given period only affects its productivity level in the next period. In particular, in equation (5) the
trade exposure indicators refer to period t-1. Doing so solves the potential simultaneity that could
arise if firms were taking their export and import of inputs decisions in t, after observing their
productivity (it).
Further, the unknown functions H in (5) are proxied by second degree polynomials in
their respective arguments. Notice, however, that we cannot identify k and m from (5). This is
achieved by the inclusion of a second estimation equation in the GMM-system that deals with the
law of motion of productivity.
The standard OP/LP approaches consider that productivity evolves according to an
exogenous Markov process:
( )
w it = E éëw it w it-1 ùû + xit = f w it-1 + xit
(6)
where f is an unknown function that relates productivity in t with productivity in t-1 and it is an
innovation term uncorrelated by definition with kit. However, this assumption neglects the
possibility of previous trading experience to affect productivity. Consequently, here we consider a
more general (endogenous Markov) process in which previous trading experience can influence
the dynamics of productivity:
14
(
)
w it = E éëw it w it-1,Eit-1,Iit-1,EIit-1 ùû + xit = f w it-1,Eit-1,Iit-1,EIit-1 + xit
(7)
where Eit-1, Iit-1 and EIit-1 indicate whether the firm, in period t-1, chose to only export, to only
import inputs, or both to export and import inputs, respectively. Obviously, the reference category
is to be a non-trader.
Let us now rewrite the production function (1) using (7) as:
(
)
y it = b0 + bl l it + bk kit + bm mit + mt + f w it-1,Eit-1,Iit-1,EIit-1 + xit + hit
(
)
(
(8)
)
Further, since w it = hTS kit ,mit , we can rewrite f w it-1,Eit-1,Iit-1,EIit-1 as:
(
)
(
)
(
(
)
)
f w it-1,Eit-1 ,Iit-1,EIit-1 = f éë hTS kit-1,mit-1 ,Eit-1,Iit-1,EIit-1 ùû = FTS kit-1,mit-1
= 1(NTit-1 )FNT kit-1,mit-1 +1(Eit-1 )FE kit-1,mit-1 +1(Iit-1 )FI kit-1,mit-1
it-1
+1(EIit-1 )FEI
it-1
(
(k
it-1
,mit-1
)
it-1
)
(
)
it-1
(9)
with F being unknown functions to be proxied by second degree polynomials in their respective
arguments.
Lastly, substituting (9) into (8), our second estimation equation is given by:
y it = b 0 + b l l it + b k k it + b m mit + mt +
1(NTit-1 )FNT
1(EIit-1 )FEI
it-1
it-1
(k
(k
it-1
it-1
)
,mit-1 +1(Eit-1 )FE
)
it-1
(k
it-1
)
,mit-1 +1(Iit-1 )FI
it-1
(k
it-1
)
,mit-1 +
(10)
,mit-1 + uit
where uit=it+it is a composed error term.
15
Wooldridge (2009) proposes to estimate jointly equations (5) and (10) by GMM using the
appropriate instruments and moment conditions for each equation. This joint estimation strategy
has several advantages: i) it increases efficiency relatively to the two step traditional procedures;
ii) it makes unnecessary to do bootstrapping for the calculus of standard errors; and, iii) it solves
the problem, pointed out by Ackerberg et al. (2006), of identification of the labour coefficient in the
separate estimation of equation (5). This procedure allows us to obtain, per each one of the 22
industries considered, both coefficient estimates of the production function and firms’ productivity
estimates. In particular, to estimate firms’ productivity assuming an endogenous Markov process,
we use the corresponding polynomial approximation of expression (10).9
4. Data and descriptive analysis.
In order to analyse firm productivity and trade exposure we use a dataset that links firm
characteristics, production and export data for Brazilian firms for the period 2000 to 2008. For
production and firm characteristics, we use the PIA empresa (Pesquisa Industrial Anual). PIA is a
survey for manufacturing and mining sectors conducted annually by IBGE (Instituto Brasileiro de
Geografia e Estatistica), which focus on firms characteristics. Firms with 30 or more employees
are included in the sample, while smaller firms of up to 29 workers are included randomly in the
sample. In total PIA covers more than 40,000 firms.
For exports we use a dataset created by SECEX (Secretaria Comercio Exterior). SECEX
provides the universe of registered trade flows at the firm level, by HS-8 product and market
destination for the period 2000-2008. The dataset used aggregates export FOB values per year,
product and destination. We complement the dataset with tariffs included in the TRAINS
database.
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The differentiated estimates for the production function coefficients by industry are available upon request.
16
Table A.1 in the Appendix shows the main variables used in the analysis. We proxy
capital with assets and include electricity and energy as intermediate inputs. We use sector
deflators provided by IBGE to deflate the variables in the production function, with the exception
of labour. In order to calculate tariffs for inputs we first calculate the average tariff for each of the
Brazilian Input-Output sectors, and then, for each sector we use the input-output coefficients to
weight the sector tariff for those sectors that provide inputs. These input tariffs are then mapped
from Input-Output sectors to CNAE 4 digits sectors using correspondence tables supplied by
IBGE. For the period 2000 to 2004 we use the 2000 I-O table and for the 2005 to 2008 period we
use the 2005 I-O table. Both tables are available from IBGE national accounts. Changes in yearly
tariffs and the change in I-O table create variation on tariffs in inputs. 10
Regarding tariffs on outputs, each firm is associated to a 4 digits CNAE sector based on
its main sector of production. We first convert HS-8 trade codes with tariffs to the Prodlist code
equivalent (product extension of CNAE classification) using IBGE conversion table. Then we
average the tariff for prodlist products for each CNAE 4 digits sector.11 Finally, since we do not
have information regarding value added, we calculate the effective rate of protection (ERP) as the
difference between tariffs on outputs and inputs. 12
Table 1 reports the main features of our data set. As can be observed, two-way traders
(both exporters and importers) are larger in terms of output, labour, capital and materials and pay
higher wages as compared to one-way traders (either exporters or importers) and to non- traders.
One-way traders are, in general, similar in all variables. If we compare these firms with no traders
we find that are larger in terms of output, labour, capital and materials and pay higher wages.
Due to some missing values for some years, we extrapolate the series using the nearest observation for each firm.
We also calculate an alternative tariff for outputs based on the products produced by each firm. The problem with
this alternative variable is the fact that this information is only available using PIA produto, and prior to 2005 only
large firms entered the sample. Therefore, there are many missing values for smaller firms, which are mainly firms
not engaging in trade and competing with imports.
12 For the estimations we use log transformations of tariff variables, i.e. by taking the log of (1 + tariff) in order to keep
zero values for both tariffs and ERPs.
10
11
17
As regards trade variables, we find that export intensity is larger for only exporters (as
compared to two-way traders) and that import intensity is larger for only importers (also as
compared to two-way traders).
Finally, we do not find significant differences in trade policy variables among the different
trade groups.
5. Results: the effects of trade policy and firm trade status on firm productivity.
In this section we first present the main results from our analysis and in a second section we will
discuss some robustness checks we have carried out.
5.1. Main results.
In the first step of our analysis, using the explained methodology explained above, we estimate
the production function (1) separately for each of the 22 industries, in order to obtain estimates of
the log of TFP.13 Using these estimates, we calculate the (log) of TFP for firm i at time t and
industry s, denoted tfpits , as
tfpits = y it - b0 - bl l it - b k kit - bm mit - mt
(11)
where s denotes industry, and i and t refer to firm and time, respectively. It is important to note
that including a vector of time dummies ( mt ) in the TFP estimation makes the estimated TFP
time effects free. Controlling for time effects in this setup is crucial as we are interested in
disentangling the effects of trade policy from other possible changes in macroeconomic policy or
13
The coefficients estimated at industry level are reported in Table 2.
18
macroeconomic instability, or even from any other uncontrolled events, that occurred in Brazil
during our sample period.
In the second step, we use our TFP estimates as the dependent variable of a series of
reduced form equations that include as covariates either trade policy variables or both trade
policy variables and trade status variables. There are two reasons that advise to include trade
status in the second step estimation: on the one hand, we expect the dynamic evolution of
productivity to be affected by learning-by-importing and learning-by-exporting; and, on the other
hand, we believe that the effects of input and output tariffs on the evolution of firms productivity
may depend on whether the firm imports inputs and/or exports, respectively.
In this second step regression analysis we pool TFP estimates for all industries and use
panel data fixed effects estimation to simultaneously control for individual firm and industry fixed
effects.14,15 Using firm level fixed effects allows us to control for the existence of a self-selection
mechanism, that would arise if only the (a priori) more efficient firms were the ones getting
involved in international markets either as buyers, sellers or both buyers and sellers. This selfselection process is based on the existence of higher sunk entry costs in international markets
that can only be overcome by the more productive firms (see for example Bernard and Jensen,
1999, and Melitz, 2003). The results for these firm fixed effects estimations are reported in Table
3.16
We start our analysis of the effects of trade policy and trade status by using the simplest
possible specification (see equation 12 below), where the only covariate that we include to
We report robust standard errors by clustering at the firm level. Clustering at the industry level gives similar results.
Controlling for industry fixed effects, among other things, allows to account for time-invariant characteristics
coming from trade policy that could make the country policy related to tariffs endogenous with respect to productivity
(due to possible policy pressure from particular industries).
16 We have estimated the same set of reduced form equations linking TFP to trade policy and trade status, using a
random effects approach (these results are reported in Table A.2 in the Appendix). The fact that the random effects
estimates for the export and import status variables are higher than the fixed effects ones suggests that the random
effects estimates suffer from an endogeneity bias problem associated with learning-by-exporting/importing and selfselection by the more productive firms. Further, this bias problem is larger for the import dummy than for the export
dummy.
14
15
19
explain productivity is output tariffs (TO). This specification (specification 1) has been widely used
in the literature on trade liberalization and productivity.
tfpit = a + a i + g 1TO + uit
(12)
In this specification we expect g 1 to be negative. Trade liberalization policies, implying a
reduction of output tariffs on imports (of final goods), may increase competitive pressure from
competing imported products and so force firms to use inputs more efficiently, and, consequently,
this would increase productivity. As the dependent variable is the log of TFP, the effect of a unit
increase in the output tariffs on TFP is computed from the estimated coefficient g 1 as
( ( ) )
100 exp g 1 -1 . This measure shows the percentage change on the TFP when the tariff on
output increases by one unit. The estimate of 1 (see Table 3) shows that, as expected, a
decrease in output tariffs increases productivity. More specifically, a unit decrease in output tariffs
increases TFP by 0.84%.
In our second specification (specification 2), we add as additional covariates both a
dummy that takes value one if the firm exports and zero otherwise (DE), and an interaction that
results from multiplying DE by the output tariff ( TO ×DE ). The aim of the first of these variables is to
capture whether there is a direct effect of exporting on productivity. The role of the second one is
to test whether the effect of output tariffs on productivity is different for exporters and nonexporters.
tfpit = a + a i + g 1TO + g 2TO ×DE + g 3DE + uit
(13)
20
Our results for specification 2 (see second column of Table 3) suggest that a unit decrease in
output tariffs increases productivity by 0.72% for non-exporters and by 0.94% for exporters (we
get that both 1 and 2 are negative). These results mean that trade liberalization (in the form of
reducing tariffs on imports of equivalent competing products) will have a larger effect in the
productivity of exporters than that of non-exporters. This can be the result of two effects that work
in opposite direction: on the one hand, the positive effect of a reduction in output tariffs on
productivity operates tightening competition and forcing both exporting and non-exporting firms to
behave more efficiently; on the other hand, if trade liberalization reduces market shares of
national firms, its impact could be larger in the market shares of the less productive non-exporting
firms (Cirera et al., 2012 show that the self-selection mechanism fully works for Brazilian
manufacturing firms) and this could lessen their incentives to increase productivity. Additionally,
( ( ) )
the transformed estimate 100 exp g 3 -1 shows that the direct effect of exporting (average
difference in TFP between exporters and non-exporters) is of 9.33%. Once we control by firm
level fixed effects, this can be interpreted as evidence of LBE.
Traditional studies on the effects of exports have only considered the effect of firm’s
export status on productivity. The natural evolution of this literature has been to incorporate also
the role of imported inputs. Including an import variable in our analysis could allow disentangling
the effect of exporting on productivity from the effect of importing. Importing inputs may produce
efficiency improvements through the availability both of a wider range of inputs and of inputs of
superior quality for importing firms. Further, closely related to the decision of importing we aim to
analyse the role of import tariffs on productivity. We expect an increase in input tariffs to have a
negative impact on productivity as the increase in the price of the inputs could reduce the range
and quality available for domestic producers.
21
Thus, to start the analysis of imported inputs, in specification 3 we widen our baseline
specification 1 to include as covariates both output (TO) and input tariffs (TI):
tfpit = a + a i + g 1TO + g 2TI + uit
(14)
The negative sign of estimate of 2 in specification 3 confirms that, as expected, a
decrease in inputs tariffs has a positive effect on productivity. More specifically, a unit reduction in
input tariff increases TFP by about 0.50%. As for the effects of output tariffs on productivity, its
correspondent estimate maintains its negative sign. However, once we introduce in the analysis
input tariffs the effect of a unit reduction in output tariffs is lower: whereas in the specification
without input tariffs a unit reduction in the output tariffs increases TFP by 0.84%; when we
consider simultaneously both input and output tariffs this increase is 0.73%.
Finally, in specification 4 we widen specification 2 to take into account both the direct
effect of importing inputs on productivity and whether or not the effect of input tariffs differs
depending on whether the firms import inputs. Thus, we expect a lower impact of changes of
input tariffs for firms that do not import inputs. Therefore, in addition to the covariates already
included in specification 2, we include both a dummy that takes value one if the firm imports and
zero otherwise (DI) and an interaction that results from multiplying DI by the input tariffs variable.
Therefore, this specification allows us to analyse whether the effects of trading policy (proxied by
inputs and output tariffs) depend on the trade status of the firm,
tfpit = a + a i + g 1TO + g 2TO ×DE + g 3DE + g 4TI + g 5TI ×DI + g 6DI + uit
(15)
22
As for the new covariates included in specification 4 (with respect to specification 2), our
estimates for TI and TIDI suggest that a unit decrease in input tariffs increases productivity by
0.45% for non-importers and by 0.66% for importers. Thus our results confirm: i) that a reduction
in input tariffs increases productivity; and, ii) that inputs tariffs have a higher impact on the
productivity of importers as compared to non-importers. Additionally, the direct effect of importing
measured by the average difference in productivity between importers and non-importers (given
( ( ) )
by 100 exp g 6 -1 ) is 8.97%; i.e. importing inputs increases firm productivity by 8.97%, and so
provides evidence in favour of LBI. As for the exporting and output tariff related covariates (DE
and TO ×DE ), the estimates of a unit decrease in output tariffs are lower in specification 4 than in
specification 2, both for exporters and non-exporters. When we account for input tariffs and
whether the firm imports inputs (specification 4), a unit decrease in output tariffs increases TFP
by 0.60% for non-exporters and by 0.82% for exporters. However, these figures are higher when
we do not account for them (specification 2), as they are 0.72% and 0.94%. The direct effect of
exporting (that can be interpreted as a measure of LBE) also gets reduced in specification 4 in
comparison with specification 2 (8.97% and 9.33%, respectively).
The larger size of the estimates obtained for output tariffs and export status in
specification 2 could be due to an omitted variable bias, produced by omitting other relevant
factors affecting TFP such as inputs tariffs and import status. The fact that when we introduce
these variables in specification 4 their coefficients are sizeable and significant confirms the
suspects of an omitted variable bias in specification 2.
5.1. Some further robustness specifications.
We devote this section to present some robustness tests of the former specifications in which we
have analysed the relationship between trade policy, trade status and productivity. The first of
23
these robustness specifications (specification 5) uses as basis specification 4 and simply
substitutes the export and import dummies by export and import intensity variables, respectively.
tfpit = a + a i + g 1TO + g 2TO ×EI + g 3EI + g 4TI + g 5TI ×II + g 6 ×II + uit
(16)
where EE and II stand for export intensity and import intensity, respectively. The most relevant
differences between specifications 4 and 5 are as follows: first, the impact of output tariffs on
productivity is independent of the export intensity of the firm17 (whereas in specification 4, where
we only distinguished between exporters and non-exporters, the effect was higher for exporting
firms); and, second, the negative and significant estimate of the variable TI ×II suggests that the
higher the import intensity of a firm the higher the impact of changes in input tariffs. We also find
that non-importers are also negatively affected by an increase in input tariffs. This result can be
due to indirect negative effects spreading from importing to non-importing firms in the economy.
In our second robustness specification (specification 6), we proxy trade policy by the
effective rate of protection (ERP, hereafter) instead of proxying it using input and output tariffs.
The aim of this second robustness specification is to test which are the drivers of the relationship
between trade policy and productivity in those papers that only include the ERP as measure of
trade policy and do not distinguish between input and output tariffs.
The traditional literature linking trade liberalization and productivity has used the ERP as
the unique measure of trade policy. In this literature, a decrease in input tariffs increases the ERP
and would result in a decrease in productivity via a reduction in the intensity of competition
among national firms. However, the most recent literature on trade liberalization and productivity
suggests using both input and output tariffs to measure trade policy. Within this approach the
17
The estimate of TOEI is not significant at any reasonable significance level.
24
opposite argument arises relating input tariffs and productivity. According to this argument, a
decrease in input tariffs eases productivity increases by domestic firms as it allows them to profit
from: the learning derived from the use of the incorporated technology in imported inputs, and
from the wider range and quality of the inputs available to domestic firms.
In specification 6, we include as covariates not only the ERP but also the exporter and
importer dummies, and the crossed products between these and the ERP:
tfpit = a + a i + g 1ERP + g 2ERP ×DE + g 3DE + g 4ERP ×DI + g 5DI + uit
(17)
The results obtained in the estimation of specification 6 (equation 17) can be summarized as
follows. In comparison with specification 4 (that includes both input and output tariffs instead of
ERP) the direct effect of exporting is lower (8.97% vs. 7.09%) and the direct effect of importing is
higher (10.12% vs. 8.87%). Second, a unit increase in the ERP decreases productivity by 33.10%
for firms that neither export nor import, by 47.53% for exporters and by 56.00% for importers.
Therefore, an increase in the ERP, which relaxes competition in the domestic market, results in a
reduction of firms’ productivity, independently of its trading status. However, from the estimates
associated to the ERP we cannot disentangle whether the effects come from an increase in
output tariffs, from a decrease in input tariffs or from changes in the share of intermediate inputs
in the value of the final good.
Our third robustness exercise (specification 7) checks the effects of the large currency
appreciations that Brazil experienced during the period of analysis, as these can affect
productivity without implying changes in efficiency. To interpret the results in this specification we
have to take into account that an increase in the exchange rate (ER, hereafter) means a
depreciation of the national currency. In specification 7 (see expression 18), we extend
25
specification 4 to include as covariates the cross products of the ER with the export and import
dummies. The aim of including these cross products is to check whether the ER evolution has
different effects on the productivity of importers and exporters:
tfpit = a + a i + g 1TO + g 2TO ×DE + g 3DE + g 4TI + g 5TI ×DI + g 6DI +
g 7ER ×DE + g 8ER ×DI + uit
(18)
Changes in the estimates corresponding to the output and/or input tariffs would suggest
that some of the productivity improvement attributed to a reduction in output and/or input tariffs in
specification 4 could be due to exchange rate changes. Further, the coefficients of the importer
and exporter status dummies could also be affected.
As expected (see column 7 of Table 3) the inclusion of the ER and its interactions with
the export and input dummies reduces the size (in absolute terms) of the estimates
corresponding to the output and input tariffs. Thus, whereas in the specification without ER
(specification 4) a unit reduction of output tariffs increases the productivity of non-exporters and
exporters by 0.60% and 0.82%, respectively, in the specification with the ER variables
(specification 7), the increase in productivity gets reduced to 0.45% both for exporters and nonexporters. Analogously, the effect of a unit decrease in input tariffs on the productivity of
importers is lower in specification 7 than in specification 4 (0.46% vs. 0.66%). However, there is
almost no difference between the effects on productivity of a unit reduction of input tariffs in the
case of non-importers (0.46% in specification 7 vs. 0.45 in specification 4)
Notice that the extra increase in productivity enjoyed by exporters (in comparison with
non-exporters) in specification 4 when the output tariffs increase vanishes with the inclusion, in
specification 7, of the variable interacting ER with the export dummy. Identically, the extra
26
increase in productivity enjoyed by importers (in comparison with non-importers) in specification 4
also disappears in specification 7. This finding suggests that the evolution of the ER has special
incidence in the evolution of the productivity of exporters/importers. Therefore, omitting this
variable can lead to overestimating the effect of input/output tariffs variations in the productivity of
importers/exporters.
However, both the direct effects of exporting and importing in productivity are higher in
the specification including the ER variables than in the specification that does not include them,
confirming the existence of both LBE and LBI processes. Thus, the export premium is 10.20% in
specification 7 in comparison to 8.97% in specification 4. In the same vein, the import premium is
12.52% and 8.87% in specifications 7 and 4, respectively. Finally, the two interaction variables of
the ER with the importer and exporter dummies are negative and significant. This could be
signalling that a real depreciation decreases firm productivity. Very likely, the mechanism
explaining this result is that a real depreciation raises imported input prices what lessens
competition in the inputs markets and so has a negative effect on productivity.
6. Conclusions.
The results from all specifications led us to conclude the following. First, higher output tariffs
(tariffs on imports of final goods) decrease productivity by lowering import competition as firms
are less forced to improve efficiency.
Second, higher input tariffs (tariffs on imports of intermediate inputs) decrease
productivity by reducing access to a wider range of foreign inputs, to higher quality inputs, or to
foreign technology incorporated in imported inputs. Therefore, we do not find for input tariffs the
link with productivity predicted by the literature linking a trade policy measure such as the ERP
27
with productivity, but just the opposite. According to this literature a decrease in input tariffs
increases the ERP and decreases productivity, through the reduction in industry competition.
Third, we do not generally find that trade liberalization (in the form of reducing input
tariffs) has a larger effect increasing productivity for importing firms, except in that specification in
which we interact tariffs with import intensity.
Fourth, for the effects of output tariffs on productivity, for exporters and non-exporter we
find only statistically significant different results coming from the export status, but not from the
export intensity.
Fifth, our results indicate that the effects of tariffs in the economy do spread among all
firms in the economy, and do not only affect exporting or importing firms.
Sixth, we still find evidence of both learning-by-exporting and learning-by-importing
effects on productivity. This evidence comes by the fact that we get significant effects from the
firm importing and exporting status even after controlling for the effects of tariffs.
Seven, according to a trade policy measure such as the ERP we also confirm that an
increase of it, interpreted as a decrease in competition, produces a reduction on productivity.
However, we prefer specifications including separately output and input tariffs to be able to isolate
the effect of competition on productivity from the effect of better access to inputs on productivity.
Finally, from the more complete specification, specification 7, where results over
productivity for other variables are cleaned from the effect of the evolution of exchange rates over
the analysed period, we obtain that the effects of increasing output tariffs on decreasing
productivity are quite similar to the ones coming from increasing input tariffs, but that learning-byimporting (as captured by the import status dummy) is larger than learning-by-exporting (as
captured by the export status dummy). Further, we also obtain that depreciations of the currency
produce a decrease in productivity, being importers more affected than exporters.
28
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31
TABLES.
Table 1.
Exporters &
importers
Production function variables
Output
Labour
Capital
Materials
Wages
Trade variables
Export intensity (%)
Import intensity (%)
Trade policy variables
Effective rate of protection
Inputs tariffs
Output tariffs
Real effective exchange rate
Productivity measures
Firm productivity
Sample size
Notes: Standard errors in parenthesis.
Only
exporters
Only
importers
No
traders
1.35E+08
(1.46E+09)
535.56
(1672.26)
1.64E+08
(2.86E+09)
9.78E+07
(8.94E+08)
9.88E+06
(8.04E+07)
2.20E+07
(1.21E+08)
223.38
(689.63)
2.79E+07
(2.55E+08)
1.65E+07
(8.28E+07)
1.89E+06
(6.63E+06)
2.31E+07
(7.86E+07)
166.85
(383.04)
3.98E+07
(1.69E+09)
1.71E+07
(6.26E+07)
2.10E+06
(6.53E+06)
3.53E+06
(1.52E+07)
73.61
(123.31)
4.05E+06
(3.05E+07)
2.58E+06
(1.25E+07)
4.82E+05
(1.47E+06)
17.08
(24.65)
23.83
(25.49)
19.85
(29.96)
-
28.36
(28.07)
-
0.06
(0.04)
8.23
(2.61)
14.35
(5.01)
0.79
(0.29)
0.06
(0.04)
8.47
(2.85)
14.37
(5.70)
0.72
(0.29)
0.06
(0.04)
7.91
(2.73)
13.88
(5.76)
0.83
(0.32)
0.07
(0.04)
8.82
(3.48)
15.93
(5.94)
0.73
(0.29)
-4.94
-5.27
-5.02
-5.56
35560
41209
11670
179963
32
10
15
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
-
Table 2. Coefficients of the production function.
Industry Classification (CNAE 2 digits)
Labour
Coal Mining
0.178
Food and Beverage Manufacturing
0.126
Textile Product Manufacturing
0.157
Apparel Manufacturing
0.374
Leather processing, Leather products, Luggage and Footwear Manufacturing
0.301
Wood Products Manufacturing
0.182
Pulp, Paper and Paper Products Manufacturing
0.106
Publishing, Printing and Reproduction of Recordings
0.190
Coal Products Manufacturing, Petroleum Refining,
0.059
Nuclear Combustibles Processing and Alcohol Production
Chemical Products Manufacturing
0.145
Rubber and Plastics Product Manufacturing
0.162
Non-metallic Mineral Product Manufacturing
0.159
Metals Production and Basic Processing
0.148
Metal Product Manufacturing (excluding machinery and equipment)
0.256
Machinery and Equipment Manufacturing
0.271
Office Machinery and Data Processing Equipment Manufacturing
0.134
Electrical Machinery, Equipment and Supplies Manufacturing
0.216
Electronic Component and Communication Apparatus and Equipment Manufacturing
0.215
Medical and Therapeutic Equipment, Optical and Precision Instruments,
0.247
Equipment for Industrial Automation and Watch and Clock Manufacturing
Motor Vehicle Assembly and Motor Vehicle, Engine, Trailer and Body Manufacturing
0.212
Other Transportation Equipment Manufacturing
0.290
Furniture and Miscellaneous Manufacturing
0.214
Not distinguishing among industries
0.192
Capital
0.100
0.046
0.047
0.036
0.065
0.067
0.046
0.101
0.048
Materials
0.496
0.551
0.100
0.374
0.321
0.523
0.466
0.332
0.785
0.056
0.046
0.051
0.050
0.059
0.072
0.006
0.048
0.058
0.072
0.528
0.522
0.583
0.459
0.503
0.161
0.804
0.588
0.407
0.478
0.039
0.109
0.062
0.068
0.496
0.187
0.251
0.334
33
Table 3. TFP fixed effects regressions on trade policy and trade exposure variables.
Specification Specification Specification Specification Specification Specification Specification
1
2
3
4
5
6
7
TO
-0.00842*** -0.00722*** -0.00733*** -0.00600*** -0.00748***
-0.00451***
(0.000826) (0.000949) (0.000864) (0.000986)
(0.000895)
(0.00101)
TO*DE
-0.00221**
-0.00226**
-0.000319
(0.00106)
(0.00108)
(0.00111)
DE
0.0892***
0.0859***
0.0685***
0.0971***
(0.0173)
(0.0176)
(0.00979)
(0.0191)
TO*EI
3.82e-05
(2.65e-05)
EI
0.00118***
(0.000389)
TI
-0.00497*** -0.00454*** -0.00437***
-0.00466***
(0.000942)
(0.00101)
(0.000965)
(0.00101)
TI *DI
-0.00209
-0.000104
(0.00197)
(0.00199)
DI
0.0850***
0.0964***
0.118***
(0.0178)
(0.0110)
(0.0207)
TI *II
-0.000165***
(5.77e-05)
II
0.00185***
(0.000493)
ERP
-0.402***
(0.0885)
ERP*DE
-0.243**
(0.123)
ERP*DI
-0.419***
(0.144)
ER*DI
-0.0669***
(0.0161)
ER*DE
-0.0564***
(0.0141)
Constant
-3.449***
-3.488***
-3.426***
-3.483***
-3.443***
-3.586***
-3.503***
(0.0125)
(0.0147)
(0.0133)
(0.0158)
(0.0140)
(0.00633)
(0.0160)
Observations
164,375
164,375
163,123
163,123
163,123
163,350
162,411
Number of firms
31,640
31,640
31,431
31,431
31,431
31,467
31,393
Note: Standard errors are in parentheses; ***, ** and * mean significance at the 1, 5 and 10% level, respectively.
34
APPENDIX
Table A.1. Variables description
Production function variables
Output
Labour
Gross output deflated
Capital
Materials
Wages
Trade variables
Export intensity
Import intensity
Trade policy variables
Effective rate of protection
Inputs tariffs
Output tariffs
Share of sales outside domestic market
-Share of inputs that are imported
-
Real effective exchange rate
Number of employees
Value of assets with extrapolation based on nearest observations deflated
3.53E+06
2.79E+07
3.98E+07
Intermediate inputs including electricity and energy deflated
4.05E+06
Total wages deflated
2.20E+07
2.31E+07
Difference between tariffs on outputs and inputs
Average tariff at CNAE 4 digits sector using Input-Output tables
Average tariff at CNAE 4 digits sector
Average real effective exchange rate at CNAE 4 digits sector
35
Table A.2. TFP non-fixed effects regressions on trade policy and trade exposure variables.
RE:
RE:
RE:
RE:
RE:
RE:
RE:
Specification Specification Specification Specification Specification Specification Specification
1
2
3
4
5
6
7
TO
-0.0204***
-0.0208***
-0.0185***
-0.0193***
-0.0204***
-0.0192***
(0.000783) (0.000903) (0.000829) (0.000954)
(0.000864)
(0.000969)
TO*DE
-1.29e-05
0.000143
0.000447
(0.00106)
(0.00108)
(0.00112)
DE
0.137***
0.129***
0.135***
0.122***
(0.0171)
(0.0176)
(0.00977)
(0.0192)
TO*EI
0.000155***
(2.63e-05)
EI
0.000404
(0.000385)
TI
-0.0112***
-0.0117***
-0.0112***
-0.0119***
(0.000942)
(0.00102)
(0.000974)
(0.00102)
TI *DI
-0.00245
-0.00190
(0.00200)
(0.00202)
DI
0.194***
0.207***
0.212***
(0.0181)
(0.0110)
(0.0210)
TI *II
-0.000159***
(5.83e-05)
II
0.00447***
(0.000496)
ERP
-0.943***
(0.0877)
ERP*DE
-0.112
(0.124)
ERP*DI
-0.657***
(0.145)
ER*DI
-0.0287*
(0.0165)
ER*DE
0.00339
(0.0144)
Constant
-3.412***
-3.448***
-3.345***
-3.402***
-3.346***
-3.729***
-3.401***
(0.0166)
(0.0179)
(0.0172)
(0.0186)
(0.0174)
(0.0127)
(0.0187)
Observations
164,375
164,375
163,123
163,123
163,123
163,350
162,411
Number of firms
31,640
31,640
31,431
31,431
31,431
31,467
31,393
Note: Standard errors are in parentheses; ***, ** and * mean significance at the 1, 5 and 10% level, respectively.
36
37