TRADE STATUS, TRADE POLICY AND PRODUCTVITY: THE BRAZILIAN CASE
X. Cirera 1 , D. Lederman 2 , J.A. Mañez 3 , M.E. Rochina 3 and J. A. Sanchis 3
1 IDS, University of Sussex
2 Economics Research World Bank Group
3 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
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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,
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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
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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.
1 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
2 overwhelm studies supporting learning-by-exporting.
Previous empirical studies using aggregate country or industry-level data found that importing intermediate goods
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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.
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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 penetration 3 (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.
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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
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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.
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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
4 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
5 status.
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
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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).
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3.2. Productivity estimation.
We assume that firms produce a homogenous good using a Cobb-Douglas technology: y it
= b
0
+ b l l it
+ b k k it
+ b m m it
+ m t
+ w it
+ h it
(1) where y it
is the natural log of production of firm i at time t, l it
is the natural log of labour, k it
is the natural log of capital, m it
is the natural log of intermediate materials, and  t
are time effects. As for the unobservables, ω it
is the productivity (not observed by the econometrician but observable or predictable by firms) and η it
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 facto rs).
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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
7 The law of motion for capital follows a deterministic dynamic process according to which
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 k it
= (1 d ) k it 1
+ I it 1
.
). 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.
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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
=
( it
, 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, m it
=
( it
, 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
8 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 i it
>0). That is, conditional on k it
, a firm with higher  it
optimally invests more (or demands more materials).
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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: m it
= m
TS
( k it
, 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
= h
TS
( k it
, m it
)
(3) where h
TS
is an unknown function of k it
and m it
. Then, substituting the above expression (3) into the production function (1) we get: y it
= b
0
+ b l l it
+ b k k it
+ b m m it
+ m t
+ h
TS
( k it
, m it
)
+ h it
(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: y it
= b l l it
+
+ 1( E it 1 m t
+ 1( NT it 1
) H
E it 1
( k it
) H
NT it 1
, m it
)
+ 1(
(
I k it it 1
, m it
) H
I it 1
)
( k it
, m it
)
+ 1( EI it 1
) H
EI it 1
( )
+ h it
(5)
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where 1(NT it-1
), 1(E it-1
), 1(I it-1
) and 1(EI it-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 éé w it w it 1
éé+ x it
= f
( )
+ x it
(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 k it
. 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:
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w it
= E éé w it w it 1
, E it 1
, I it 1
, EI it 1
éé+ x it
= f
( w it 1
, E it 1
, I it 1
, EI it 1
)
+ x it
(7) where E it-1
, I it-1
and EI it-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
= b
0
+ b l l it
+ b k k it
+ b m m it
+ m t
+ f
( w it 1
, E it 1
, I it 1
, EI it 1
)
+ x it
+ h it
Further, since w it
= h
TS
( k it
, m it
)
, we can rewrite f
( w it 1
, E it 1
, I it 1
, EI it 1
)
as: f
( w it 1
, E it 1
, I it 1
= 1( NT it 1
) F
NT it 1
+ 1( EI it 1
) F
EI it 1
, EI it 1
)
=
( k it 1
, m it 1 k it 1
, m it 1
) f h
TS
( )
+ 1( E
( k it 1
, m it 1 it 1
) F
E it 1
(
,
)
E it 1
, I it 1
, EI it 1 k it 1
, m it 1
)
+ 1( I
éé= it 1
F
TS
) F
I it 1
(
( k it 1
, m it 1 k it 1
, m it 1
)
)
(8)
(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
1( NT it 1
0
+
1( EI it 1
) F
EI it 1 b
) F
NT it 1 l
( l it
(
+ b k k it
+ k it 1
, m it 1 k it 1
, m it 1
)
) b
+
+ m m it u
1( it
E
+ it 1 m t
+
) F
E it 1
( k it 1
, m it 1
)
+ 1( I it 1
) F
I it 1
( k it 1
, m it 1
)
+ where u it
=  it
+ 
it is a composed error term.
(10)
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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).
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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.
9 The differentiated estimates for the production function coefficients by industry are available upon request.
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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.
10 Due to some missing values for some years, we extrapolate the series using the nearest observation for each firm.
11 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.
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 tfp it s , as tfp s it
= y it
b
0
b l l it
b k k it
b m m it
m t
(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 ( m t
) 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
14 We report robust standard errors by clustering at the firm level. Clustering at the industry level gives similar results.
15 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.
19

explain productivity is output tariffs (T
O
). This specification (specification 1) has been widely used in the literature on trade liberalization and productivity. tfp it
= a + a i
+ g
1
T
O
+ u it
(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
( ( )
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 (D
E
), and an interaction that results from multiplying D
E
by the output tariff ( T
O
× D
E
). 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. tfp it
= a + a i
+ g
1
T
O
+ g
2
T
O
× D
E
+ g
3
D
E
+ u it
(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
( ( )
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 (T
O
) and input tariffs (T
I
): tfp it
= a + a i
+ g
1
T
O
+ g
2
T
I
+ u it
(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 (D
I
) and an interaction that results from multiplying D
I
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, tfp it
= a + a i
+ g
1
T
O
+ g
2
T
O
× D
E
+ g
3
D
E
+ g
4
T
I
+ g
5
T
I
× D
I
+ g
6
D
I
+ u it
(15)
22

As for the new covariates included in specification 4 (with respect to specification 2), our estimates for T
I
and T
I
D
I
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
( ( )
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 (D
E and T
O
× D
E
), 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. tfp it
= a + a i
+ g
1
T
O
+ g
2
T
O
× EI + g
3
EI + g
4
T
I
+ g
5
T
I
× II + g
6
× II + u it
(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 firm 17 (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 T
I
× 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 T
O
EI 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: tfp it
= a + a i
+ g
1
ERP + g
2
ERP × D
E
+ g
3
D
E
+ g
4
ERP × D
I
+ g
5
D
I
+ u it
(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: tfp it
= a g
+ a i
+
7
ER × D
E g
1
T
O
+ g
+ g
2
T
O
8
ER × D
I
× D
E
+ u it
+ g
3
D
E
+ g
4
T
I
+ g
5
T
I
× D
I
+ g
6
D
I
+
(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.
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.
Table 1.
Exporters & importers
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)
17.08
(24.65)
23.83
(25.49)
0.06
(0.04)
8.23
(2.61)
14.35
(5.01)
0.79
(0.29)
-4.94
35560
Only exporters
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)
19.85
(29.96)
-
-
0.06
(0.04)
8.47
(2.85)
14.37
(5.70)
0.72
(0.29)
-5.27
41209
Only importers
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)
-
-
28.36
(28.07)
0.06
(0.04)
7.91
(2.73)
13.88
(5.76)
0.83
(0.32)
-5.02
11670
No traders
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)
0.07
(0.04)
8.82
(3.48)
15.93
(5.94)
0.73
(0.29)
-
-
-
-
-5.56
179963
32

Industry Classification (CNAE 2 digits)
10 Coal Mining
15 Food and Beverage Manufacturing
17 Textile Product Manufacturing
Table 2. Coefficients of the production function.
Labour
0.178
0.126
0.157
18 Apparel Manufacturing
19 Leather processing, Leather products, Luggage and Footwear Manufacturing
20 Wood Products Manufacturing
21 Pulp, Paper and Paper Products Manufacturing
22 Publishing, Printing and Reproduction of Recordings
0.374
0.301
0.182
0.106
0.190
0.059 23 Coal Products Manufacturing, Petroleum Refining,
Nuclear Combustibles Processing and Alcohol Production
24 Chemical Products Manufacturing
25 Rubber and Plastics Product Manufacturing
26 Non-metallic Mineral Product Manufacturing
27 Metals Production and Basic Processing
28 Metal Product Manufacturing (excluding machinery and equipment)
29 Machinery and Equipment Manufacturing
0.145
0.162
0.159
0.148
0.256
0.271
0.134
0.216
30 Office Machinery and Data Processing Equipment Manufacturing
31 Electrical Machinery, Equipment and Supplies Manufacturing
32 Electronic Component and Communication Apparatus and Equipment Manufacturing
33 Medical and Therapeutic Equipment, Optical and Precision Instruments,
Equipment for Industrial Automation and Watch and Clock Manufacturing
34 Motor Vehicle Assembly and Motor Vehicle, Engine, Trailer and Body Manufacturing
35 Other Transportation Equipment Manufacturing
36 Furniture and Miscellaneous Manufacturing
- Not distinguishing among industries
0.215
0.247
0.212
0.290
0.214
0.192
Capital
0.100
0.046
0.047
0.036
0.065
0.067
0.046
0.101
0.048
0.056
0.046
0.051
0.050
0.059
0.072
0.006
0.048
0.058
0.072
0.039
0.109
0.062
0.068
Materials
0.496
0.551
0.100
0.374
0.321
0.523
0.466
0.332
0.785
0.528
0.522
0.583
0.459
0.503
0.161
0.804
0.588
0.407
0.478
0.496
0.187
0.251
0.334
33

II
T
I
*II
ERP
ERP*D
E
ERP*D
I
ER*D
I
ER*D
E
Constant
EI
T
I
T
I
*D
I
D
I
T
O
T
O
*D
E
D
E
T
O
*EI
Table 3. TFP fixed effects regressions on trade policy and trade exposure variables.
Specification
1
-0.00842*** -0.00722*** -0.00733*** -0.00600*** -0.00748***
(0.000826) (0.000949) (0.000864) (0.000986) (0.000895)
Specification
2
-0.00221**
(0.00106)
0.0892***
(0.0173)
-3.449*** -3.488***
(0.0125) (0.0147)
Specification
3
3.82e-05
(2.65e-05)
0.00118***
(0.000389)
-0.00497*** -0.00454*** -0.00437***
(0.000942) (0.00101) (0.000965)
-0.00209
(0.00197)
0.0850***
(0.0178)
-3.426***
(0.0133)
Specification
4
-0.00226**
(0.00108)
0.0859***
(0.0176)
-3.483***
(0.0158)
Specification
5
-0.000165***
(5.77e-05)
0.00185***
(0.000493)
-3.443***
(0.0140)
Specification
6
Specification
7
0.0964***
(0.0110)
-0.402***
(0.0885)
-0.243**
(0.123)
-0.00451***
(0.00101)
-0.000319
(0.00111)
0.0685*** 0.0971***
(0.00979) (0.0191)
-0.00466***
(0.00101)
-0.000104
(0.00199)
-0.419***
(0.144)
-3.586*** -3.503***
(0.00633) (0.0160)
0.118***
(0.0207)
-0.0669***
(0.0161)
-0.0564***
(0.0141)
Observations
Number of firms
164,375
31,640
164,375
31,640
163,123
31,431
163,123
31,431
163,123
31,431
163,350
31,467
162,411
31,393
Note: Standard errors are in parentheses; ***, ** and * mean significance at the 1, 5 and 10% level, respectively.
34

APPENDIX
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
Table A.1. Variables description
Gross output deflated
Number of employees
2.79E+07
3.98E+07
Share of sales outside domestic market
-
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
2.20E+07
2.31E+07
35

Table A.2. TFP non-fixed effects regressions on trade policy and trade exposure variables.
RE:
Specification
1
RE:
Specification
2
RE:
Specification
3
RE:
Specification
4
RE:
Specification
5
RE:
Specification
6
RE:
Specification
7
II
T
I
*II
ERP
ERP*D
E
ERP*D
I
ER*D
I
ER*D
E
Constant
EI
T
I
T
I
*D
I
D
I
T
O
T
O
*D
E
D
E
T
O
*EI
-3.412*** -3.448***
(0.0166) (0.0179)
-0.0204*** -0.0208*** -0.0185*** -0.0193*** -0.0204***
(0.000783) (0.000903) (0.000829) (0.000954) (0.000864)
-1.29e-05
(0.00106)
0.137***
(0.0171)
0.000143
(0.00108)
0.129***
(0.0176)
0.000155***
(2.63e-05)
0.000404
(0.000385)
-0.0112*** -0.0117*** -0.0112***
(0.000942) (0.00102) (0.000974)
-0.00245
(0.00200)
0.194***
(0.0181)
-0.000159***
(5.83e-05)
0.00447***
(0.000496)
-3.345*** -3.402*** -3.346***
(0.0172) (0.0186) (0.0174)
0.207***
(0.0110)
-0.943***
(0.0877)
-0.112
(0.124)
-0.0192***
(0.000969)
0.000447
(0.00112)
0.135*** 0.122***
(0.00977) (0.0192)
-0.0119***
(0.00102)
-0.00190
(0.00202)
-0.657***
(0.145)
0.212***
(0.0210)
-0.0287*
(0.0165)
0.00339
(0.0144)
-3.729*** -3.401***
(0.0127) (0.0187)
Observations 164,375
Number of firms 31,640
164,375
31,640
163,123
31,431
163,123
31,431
163,123
31,431
163,350
31,467
162,411
31,393
Note: Standard errors are in parentheses; ***, ** and * mean significance at the 1, 5 and 10% level, respectively.
36

37