Productivity and Exporting Status of Manufacturing Firms:
Evidence from Quantile Regressions
Mahmut Yasar*
Emory University
Carl H. Nelson
University of Illinois at Urbana-Champaign
Roderick Rejesus
Texas Tech University
ABSTRACT
Past literature has established the relationship between productivity and exporting. This paper
provides further understanding about this issue by examining the productivity effects of export
status at different points of the conditional output distribution and by investigating the
productivity effects of firms with different exporting status (i.e. new exporters versus continuous
exporters). Plant-level data of Turkish manufacturing firms are analyzed using quantile
regression techniques. The empirical results indicate that the productivity effect of exporting is
present at all points along the conditional output distribution, and this effect increases as one
moves from the lower tail to the upper tail of the distribution. Exporting firms that continuously
exported throughout the time-period have more pronounced productivity effects compared to
firms in other categories (i.e. new exporting firms, exporting firms that exit, and exporting firms
that switch exporting practices). These results have implications for firm behavior and for
targeting policy prescriptions to augment manufacturing competitiveness.
Keywords: Exporters; Manufacturing Firms; Productivity; Quantile Regressions
JEL Classification: L11, L60
*Corresponding author: M. Yasar, Department of Economics, Emory University, 306C Rich
Bldg., Atlanta, GA 30322. Phone No. (404)712-8253. Fax No. (404)727-4639. Email:
myasar@emory.edu.
*Acknowledgements: The authors would like to thank Omer Gebizlioglu, Emine Kocberber,
and Ilhami Mintemur at State Institute of Statistics in Turkey for allowing us access to the data
for this study.
Productivity and Exporting Status of Manufacturing Firms:
Evidence from Quantile Regressions
Introduction
Recent studies based on firm- or plant-level data have found that exporting firms have
higher productivity than non-exporting firms. Bernard and Jensen (1995, 1998a, 1998b, 1999a,
1999b) have shown that this behavior holds for industrial firms in the United States (U.S.).
Several authors have indicated the presence of this behavior in other countries (Bernard and
Wagner (1997) and Wagner (2002) for the case of Germany; Aw et al. (1998) for the case of
Taiwan and Korea; Clerides et al. (1998) for the case of Colombia, Mexico and Morocco; Girma
et al. (2003, 2004) for the case of the U.K). These studies typically conclude that exporting
plants have better technical productivity relative to non-exporting plants within the same
industry, even after controlling for plant or firm observed characteristics.
These studies, however, typically show the productivity effects of export status at the
average firm level only. That is, most of these studies use least squares regression analysis which
expresses the expected value of the dependent variable (production output) as a function of the
independent variables of interest (export status). If there is significant firm heterogeneity, the
average output effect of exports may not describe how the output of each type of firm is affected
by exporting. This is especially important in a lower middle income country like Turkey, where
significant inter-plant variation in capital intensity, productivity, and size is likely. For example,
it is important to understand if there is an export effect for small plants as well as large plants
because many small plants can make important contributions to economic growth. Further, least
squares coefficient estimates are known to be strongly influenced by extreme observations. Thus,
an estimated effect of exports on output could be caused by the largest firms in the sample if the
data is significantly positively skewed.
1
A more robust estimator that can take heterogeneity of the dependent variable into
account is quantile regression (Koenker and Basset, 1978). This involves the estimation of
conditional quantiles, rather than estimation of coefficients at a single measure of central
tendency. The approach enables the evaluation of the relative effects of export status and other
important regressors, such as production inputs (i.e. labor and capital), at different points of the
conditional output distribution. The main objective of this paper, therefore, is to determine the
effects of export status and other regressors at different quantiles of the conditional production
output distribution, and to learn more about these effects if they are found to vary across the
output distribution. We are especially interested to test whether smaller firms who export exhibit
a positive relationship between exporting and output. The results of this study provide further
understanding about the productivity effects of exporting status because the procedures used here
give a more complete picture about the effects of exporting status on productivity and about the
elasticity of outputs with respect to various production inputs. Furthermore, our unique data set
allows us to examine the productivity effects of different export status categories, not just the
effects of exporters versus non-exporters.
The remainder of the paper is organized as follows. The next section describes the
theoretical basis of the relationship between exporting status and productivity. Section three
discusses the plant-level data used in the study. The econometric framework is described in the
fourth section, the results of the analysis are presented in the fifth section, and the last section
provides some concluding comments.
The Effect of Exporting Status on Productivity: Theoretical Justification
The theory behind the effect of exporting status on firm/plant productivity has been
described in the literature as ‘learning-by-exporting’ (Lucas, 1988; Clerides et al. 1998).
2
Krugman (1979) and Jovanovic and Lach (1991) have modeled the productivity gains from
exporting as being caused by: (1) firms learning and adopting international best practice
production and distribution methods, (2) firms receiving feedback from international customer,
suppliers, and competitors, and (3) other knowledge spillovers. In addition, various studies in the
endogenous growth literature argue that exports enhance productivity through innovation
(Grossman and Helpman, 1991 and Rivera-Batiz and Romer, 1991), technology transfer and
adoption from leading nations (Barro and Sala-I-Martin, 1995; Parente and Prescott, 1994), and
learning-by-doing gains (Lucas, 1988; Clerides et al., 1998). The innovation argument is where
firms are forced to continually improve technology and product standards to compete in the
international market. The technological and learning-by-doing gains arise because of the
exposure of exporting firms to cutting-edge technology and managerial skills from their
international counterparts. Economies of scale from operating in several international markets
are also often cited as one other explanation for the learning-by-exporting hypothesis.
The mechanism behind the type of learning-by-exporting effect described above can be
further conceptualized using Rosenberg’s (1982) “learning-by-using” hypothesis. Rosenberg
(1982) defines learning-by-using as knowledge that can only be acquired after a product/process
has been used. In the exporting context, this refers to learning acquired after being in the export
market continuously over time. This differs from Arrow’s (1962) classical “learning-by-doing”
interpretation where the largest learning (and, for that reason, productivity increases) only takes a
place as firms enter the export market. Arrow (1962) hypothesized that learning and productivity
turn down afterwards since firms have learned the proverbial ropes of exporting. Hence, the
general conceptual mechanism behind the learning-by-exporting studies above follow
Rosenberg’s (1982) intuitively appealing learning-by-using effect, where exporting firms
3
continually learn over time through their interaction with the international competitors and
consumers (Muroyama, 2004). In this framework, knowledge is freely borrowed or exchanged
and the knowledge spillover to exporting firms increases with more interaction with the
international firms and consumers.
The intuitive appeal of the learning-by-exporting hypothesis has resulted in several
authors empirically testing this theory in several different countries. For example, studies by
Kraay (1999), Castellani (2001), Bigsten et al. (2002), Girma et al (2003), Van Biesebroeck
(2003) and Yasar and Rejesus (2005) found strong empirical support for the presence of
learning-by-exporting. However, as we mentioned earlier, none of these studies have examined
the relationship between exporting status and productivity at different percentiles of the
conditional output distribution. This article contributes to the literature in this regard.
Data
This study uses unbalanced panel-data on manufacturing plants with more than 25
employees for the apparel 1 , food, and textile industries from 1990-1996. The data were collected
by Turkey’s State Institute of Statistics from the Annual Surveys of Manufacturing Industries,
and the data are classified based on the International Standard Industrial Classification (ISIC).
The data consists of plant-level information about: output, material inputs, energy inputs, labor
inputs, capital inputs, investment levels, depreciation rates, exports, and several other plant
characteristics (i.e. size). Summary statistics for these variables are reported in Table 1. The
reported minimum and maximum values show that there is significant variation in all of the
variables.
Output (Y) is the value of aggregate output deflated by the corresponding price index.
Changes in the stock of output are considered in the calculation of Y. As for the calculation of
1
This industry includes all wearing apparel, except for fur and leather.
4
material input (M), the expenditure on these inputs is used, considering the changes in stocks.
The energy input (E) variable includes the value of electricity and fuel used by the plant. The
quantity of labor input (L) is based on data about the total number of hours worked in production
(i.e. the product of average hours worked times the number of employees in manufacturing).2
We divide the nominal values of all the inputs above by the corresponding price deflators to find
the constant dollar value quantities of inputs at 1987 prices.
The calculation of the capital input (K) variable is based on information on gross
investment levels 3 and depreciation rates. The process of calculating K is as follows. First, we
compute an initial (t-1) capital benchmark by taking a three-year average of investment and
dividing it by the depreciation rate (see Harper et al., 1989). Then, the aggregate K at time t is
estimated
by
applying
the
perpetual
inventory
method
(PIM)
on
the
fixed
assets: K t = K t −1 (1 − δ ) + I t −1 , where K t is the capital stock in period t; δ is the depreciation rate
of capital 4 ; and I t is the level of the investment during the period. We assume the following
service lives for the fixed assets: 40 years for building and construction; 15 years for
transportation equipment; and 15 years for machinery and equipment.
Using the export variable data, we were able to group the Turkish manufacturing plants
into five export status categories: 1) Non-exporters (ones that did not export at any point in the
time period); 2) Exporters (those that continuously exported the entire time period); 3) Exiters
(those that started the time period as exporters, then stopped exporting for the remainder of the
sample period); 4) Switchers (those that altered exporting practices more than once during the
2
The labor input consists of administrative personnel, technical personnel, and unskilled workers.
The gross investment data used here includes domestic plus imported capital purchases (less sales), with
maintenance investment added. Further, the gross investment data are deflated based on the capital price index.
4
We used Diewert and Lawrence’s (1999) method of computing the depreciation rates. The depreciation rates
corresponding to the assumed service lives of the fixed assets are as follows: 12.5 percent for transportation
equipment; 12.5 percent for machinery and equipment; and 4.9 percent for building and construction.
3
5
time period); and 5) Entrants (non-exporters that began and continued to export during the time
period). Dummy variables corresponding to the exporting categories were created, with nonexporters set as the base.
Econometric Framework
Traditional production function analysis is used to estimate the productivity effects of a
firm’s exporting status across different points of the conditional output distribution. Production
function analysis allows for controlling the effects of observed plant-specific characteristics and
enables inference about the productivity differences between exporters and non-exporters.
Productivity differences can be inferred from the estimated production functions because the
coefficient of an export status dummy variable gives the percentage difference between the
productivity of exporters and non-exporters.
We assume that the production function of Turkish manufacturing plants can be
approximated by a Cobb-Douglas specification: 5
ln yit = α 0 + ∑ β j ln x jit + ∑ δ im ( EX im ) + ∑ψ k zkit + μit
j
m
(1)
k
where i and t are plant and time subscripts, y is output, x j is the jth input in the production
process (where j = K, L, M, E), EX m is a dummy variable for export status ( EX m = 1 if the plant
falls in the specific export status category in the current year; EX m = 0 otherwise), z k are a
vector of dummy variables representing plant size (small, medium, large) 6 , year (1990-1996),
5
We also tried a different specification of the production function where we interacted the export status dummies
with a time trend to look at the differences in total factor productivity growth (instead of productivity level) across
the five firm categories based on export status, and estimated our equation in a labor productivity form. The results
we found are similar to the ones reported above (i.e. continuous exporters and entrants still have the highest
productivity growth compared to the non exporters).
6
Plant size is defined as follows: (1) Small plants -- less than 50 employees, (2) medium plants -- between 50 and
100 employees, and (3) large plants -- 100 employees or more.
6
region (i.e. 6 region variables) 7 , and industry (apparel, food, textile) categories, and μit is the
residual. 8 The coefficients β j , δ m and ψ k represent the parameters to be estimated. In particular,
β j is the elasticity of output with respect to the respective j inputs. The coefficients δ m , on the
other hand, denote the productivity differences between plants in a particular exporting status
relative to firms not in that status (i.e. exporter vs. non-exporter). For example, this coefficient
can show whether continuous exporters (those who exported for the whole time-period) have
higher unexplained contributions to output, than non-exporters (those who did not export for the
whole time-period).
Plant size is included in the specification to capture differences in the production
technology across plants of different sizes. Year dummies are included in the model to capture
macroeconomic shocks and changes in the institutional environment over time. Regional
dummies are also included to correct for the exogenous disparities in the productivity differences
across the regions. Industry dummies are included to account for production differences across
the three industries in the pooled data. 9
If traditional least squares regression is used to estimate (1) and there is unobserved
heterogeneity, then the estimated coefficients are not representative of the entire conditional
output distribution (Mata and Machado (1996); Dimelis and Louri (2002)). To account for some
of the heterogeneity in the sample, observed plant-level characteristics (i.e. regions, size, etc.) are
explicitly included in the regression equation. However, plants may also have sources of
7
There are seven main geographic regions in Turkey. The regions are East Anatolia, South-East Anatolia, Central
Anatolia, Black sea, Agean, Marmara, and Mediterranean. Since there were not enough observations for the East
Anatolian region we combined it with the Southeast Anatolian region and used only six region dummies.
8
μit can be interpreted as the Total Factor Productivity (TFP) based on Solow’s model. This is the unexplained
contribution to output.
9
One could also estimate the model by running separate regressions for each industry. However, due to the small
sample sizes in the food and textile industries pooling the data and using industry dummy variables is more
appropriate in this case.
7
heterogeneity that cannot easily be observed and accounted for. For example, in our case,
unobserved characteristics like entrepreneurial ability and initial endowments are not taken into
account in the data and these factors may cause unobserved heterogeneity. Furthermore, recent
studies have shown that idiosyncratic shocks and uncertainty in technology affect plants
differently even within the same industry (see Jovanovic (1982); Hopenhayn (1992); Ericsson
and Pakes (1995); and Olley and Pakes (1996)). This unobserved heterogeneity may render the
dependent variable in (1), and the error term ( μit ), to be independently, but not identically
distributed across plants. When observations are not identically distributed least squares
estimates will be inefficient, and if there are long tails, extreme observations will have significant
influence on estimated coefficients. Quantile regression estimates are considered robust relative
to least squares estimates. In contrast to the least squares estimator, the quantile regression
estimates place less weight on outliers and are found to be robust to departures from normality.
Quantile regression can be illustrated as follows (see Koenker and Basset (1978) and
Buchinsky (1998)): 10
ln y it = x 'it βθ + uit with Qθ (ln y it / xit ) = x 'it β θ
(2)
where ln y is the vector of log output, x is a vector of all the regressors in (1), β is the vector of
parameters to be estimated, and u is a vector of residuals.
10
Qθ (ln y it / xit ) denotes the θ th
For discussion and implementation of quantile regressions with longitudinal data please see Koenker (2004). In
this paper, Koenker (2004) suggests that unobserved fixed effects can be controlled by including firm dummies in
the regression. This can be interpreted as a firm specific location-shift effect. Since we have a large sample size
with about 1332 firms, the approach of using firm-specific dummies is not practically and computationally
implementable (i.e. convergence problems occur). However, we have also estimated our models using a fixed effect
OLS model as a means to check the "robustness" of our results. We find similar mean results when we use the fixed
effects OLS approach. In addition, we undertook another "robustness" check by using a semiparametric model that
controls for simultaneity and selection bias. Again, we obtained mean results similar to the ones reported here.
Since our goal in this paper is to examine the productivity effect of export status at different points of conditional
output distribution, we do not explicitly report the mean results from the alternate estimation procedures. However,
they are available upon request.
8
conditional quantile of ln y it given xit . The θ th regression quantile, 0 < θ < 1 , solves the
following problem:
n
⎧⎪
⎫⎪
Min 1n ⎨ ∑ θ ln y it − x 'it β + ∑ (1 − θ ) ln y it − x 'it β ⎬ = Min 1n ∑ ρθ (u θit )
β
β
i ,t :ln y it < x 'it β
i =1
⎪⎩i ,t:ln yit ≥ x 'it β
⎪⎭
(3)
where ρθ (.) is called the “check function” which can be defined as:
if u θit ≥ 0 ⎫
⎧θ u θit
ρθ (u θit ) = ⎨
⎬
⎩(θ − 1)u θit if u θit < 0 ⎭
(4)
By changing θ continuously from zero to one, any quantile of the distribution of ln y it
conditional on xit can be obtained. Changing θ from zero to one relaxes the assumption made in
least squares regression where the parameter estimates are assumed to be the same at all points
on the conditional distribution because of the i.i.d assumption. Linear programming methods can
be used to minimize the sum of weighted absolute deviations and perform the estimation. It has
also been shown that the function can be fit into a GMM form, where the consistency and
asymptotic normality of βθ can be confirmed and the asymptotic covariance matrix can be
found.
In contrast to the least squares estimator, which provides information only about the
effect of regressors at the conditional mean of the dependent variable, the results of quantile
regression give parameter estimates at different quantiles. Thus, this technique provides
information regarding the variation in the effect of regressors on the dependent variable at
different quantiles. The coefficients can be interpreted as the partial derivative of the conditional
quantile of y with respect to particular regressors, ∂Qθ (ln y it / xit ) / ∂x . The derivative is
interpreted as the marginal change in y at the θ th conditional quantile due to marginal change in
a particular regressor. For example, in the case of equation (1), if a plant lies in the θ th quantile
9
of output distribution, then the estimated output elasticity with respect to inputs (conditional on
the set of covariates) equals to βθj , where j = K, L, M, E.
To test for equality of the coefficient estimates at the various quantiles, estimation of the
variance-covariance matrix is required. The test statistic is computed by using the variancecovariance matrix of the coefficients of the system of quantile regressions. The variancecovariance matrix is estimated using bootstrapping techniques. 11 The null hypothesis is that the
jth coefficient at the θ z
th
quantile is statistically the same as the one in the θ y
th
quantile
( H 0 : βθz j = βθ y j ); and the alternative hypothesis is where the coefficients are not equal across
quartiles ( H a : βθz j ≠ βθ y j ). This test allows us to examine whether the productivity effects of
exporting status and the estimated output elasticities vary significantly across the conditional
output distribution.
Results
Before running our regressions we tested the normality of the output variable (y). We
used the skewness and kurtosis tests of D’Agostino et al. (1990) to statistically show (at the 1%
level of significance) that the dependent variable is positively skewed and leptokurtic (skewness
= 7.10 and kurtosis = 92.85). Thus, there are a large number of firms with relatively small output
and the firms with above average output are significantly above average. Skewness and kurtosis
tests for the natural logarithm of y also show statistically significant departures from normality;
the p-values of the skewness and kurtosis tests are zero to three significant digits. These results
suggest that the distribution of the dependent variable significantly departs from normality and
justifies the use of quantile regression.
11
See Bassett and Koenker (1982) and Hendricks and Koenker (1991) for further information on the computation
of the test statistic. For an excellent review of quantile regressions see Koenker and Hallock (2001) and Buchinsky
(1998).
10
We used two alternative specifications of equation (1) to explore the productivity effects
of export status ( δ m ) and the output elasticities with respect to the relevant inputs ( β j ). The first
specification uses only one exporting status dummy variable (Export). The Export variable in
this specification indicates whether a firm exported at some point in the time-period of the data
set. Hence, in the nomenclature elucidated in the second section of this paper, all plants that are
in categories (2) – (4) are considered exporters and the Export dummy variable will take a value
of one if the plant falls into categories (2) – (4); zero, otherwise. The empirical results for this
specification are seen in Table 2.
The second specification of equation (1) uses dummy variables for the following four
categories (as described in the data section of this paper): (1) Exporters, (2) Exiters, (3)
Switchers, and (4) Entrants. For example, the dummy variable Entrants will equal one if a plant
is an entrant; zero, otherwise. The omitted dummy variable in the regression is the non-exporter
dummy. This specification allows for more detailed insights about the productivity effects of the
different kinds of exporters. The empirical results for this specification are seen in Table 3.
In Tables 2 and 3, the first column shows the parameter estimates for the ordinary least
squares (OLS) regression.
Before running the quantile regressions, we estimated the two
specifications of equation (1) using OLS regression and applied the Jarque-Bera test to examine
the normality of the conditional distribution of the residuals (Jarque and Bera, 1980). The
hypothesis of normality is rejected at the 0.01 significance level in both specifications. This
finding further supports the use quantile regression as a robust alternative to least squares.
The OLS estimates in Table 2 suggest that all the parameters have positive signs and are
statistically significant at 0.01 significance level. The parameter associated with the Export
dummy provides an estimate of the conditional difference between the productivity of exporters
11
and non-exporters at the sample mean, since the omitted category is the non-exporters. The
Export dummy coefficient is significant and indicates that manufacturing plants that exported at
some point in 1990-1996 are around 19% more productive than plants that did not export at all
during the period, conditional on the region, size and year dummies. 12
In Tables 2 and 3, the third, fourth, fifth, sixth, and seventh columns presents the results
of the quantile regression at the following quantiles: 0.10, 0.25, 0.50, 0.75 and 0.90. In Table 2,
the quantile regression estimates indicate that there are significant differences in the parameter
estimates across the five quantiles. The coefficient associated with the Export dummy ( δ ) varies
significantly from 9% to 21% as we move from the lowest quantile to the highest quantile. This
provides important evidence that the positive export productivity effect is present across the
entire conditional output distribution. Smaller exporting plants at the lower tail of the distribution
exhibit a positive export productivity effect. The positive shift of all the quantiles means that the
exporter output distribution first order stochastic dominates the non-exporter output distribution.
And the larger positive shifts at the higher quantiles means that the exporter output distribution is
more positively skewed than the non-exporter output distribution.
This means that the
productivity enhancement of exporters is stronger and more significant for the larger firms.
The estimated output elasticities with respect to capital and energy inputs also increase as
one moves from the lowest quantile to the highest quantile (Table 2). A one percent increase in
capital input would result in a proportionately higher output effect at the upper tails of the
conditional output distribution than in the lower tails of the distribution. This indicates that the
plants with higher production output level are more responsive to these variables. The output
elasticity for labor, on the other hand, is relatively stable across quantiles, indicating a constant
12
Note that, in the interest of space, the coefficients for the region, year, and industry dummies are not reported in
Tables 2 and 3. Results are available from the authors upon request.
12
elasticity of output with respect to labor at all points of the conditional output distribution.
Lastly, the output elasticity with respect to material input decreases as one moves from the
lowest quantile to the highest quantile. Thus, material inputs have a lower output effect at the
upper tail of the conditional output distribution as compared to the lower tail of the distribution,
indicating that the material inputs contribute less to the production output at the upper tail of the
conditional output size distribution.
We include size dummies in the production function to capture some differences in the
production technology by the scale of plants. In Table 2, the coefficient associated with the plant
size dummies increases as one moves from the lowest quantile to the highest quantile of the
conditional output distribution. This means that firms with large and/or medium plant size have
higher productivity effects (relative to firms with small plant size) at the upper tails of the
conditional output distribution. The productivity effects of larger firms become more pronounced
at the upper tails of the conditional output distribution.
Estimating the second specification of the production function allows us to see the
magnitudes of the productivity effects for more specific export status categories. The estimation
results for the second specification are presented in Table 3. The OLS estimates reveal that
among the four export status categories, the continuous exporters (Exporters) have the largest
productivity difference over non-exporters; followed by Entrants, Exiters, and Switchers. Based
on the OLS estimates firms that are continuous exporters are around 30% more productive than
non-exporters, while firms that alter exporting practices are only 17% more productive than nonexporters. Note, however, that the magnitude of the productivity difference of Exiters and
Switchers are very similar at 17%. The productivity of Entrants, on the other hand, is about 23%.
13
In most cases, the quantile regression results also follow the ranking of the productivity
differences of the export status dummy variables estimated with OLS (See Table 3). The
continuous exporters (Exporters) always have the largest productivity difference as compared to
the other exporting categories. The continuous exporters’ productivity difference ranges from
16% to 30% as one moves from the lowest quantile to the highest quantile. This pattern of
having higher productivity differences as one moves to the upper tail of the conditional output
distributions is also evident in the other export categories. This suggests that the productivity
impact of exporting is lower at the lower end of the conditional output distribution and becomes
larger as you move up to the upper tail of the conditional output distribution. The largest
potential productivity returns for the practice of exporting may be felt at higher output levels.
But, unlike least squares evidence, these results show that even firms at the lower tail of the
conditional output distribution exhibit a positive export-productivity enhancement.
The behavioral patterns of the estimated output elasticities and the coefficients associated
with plant size dummies in the second model are very similar to the ones estimated in the first
model. The output elasticities associated with capital and energy tend to be higher at the higher
quantiles, while the output elasticity associated with the material inputs tend to be lower at the
higher quantiles. The output elasticity with respect to labor is relatively stable across quantiles.
Larger and medium plant sizes still have higher productivity differences at higher quantiles.
In the interest of space, detailed results of the hypothesis tests that evaluate the statistical
significance of the difference of parameter estimates at all quantiles and between pairs of
quantiles are presented in Appendix Tables 1 and 2. Based on these tables, the null hypothesis
that the coefficients are equal across and between pairs of quantiles is rejected. This indicates
that there are statistically significant differences among the estimated quantile regression
14
parameters. This holds for all of the regressors used in both model specifications. The
coefficients suggest considerable variation in all of the right hand-side variables at the different
points in the conditional output distribution. Results of the hypothesis tests confirm the presence
of unobserved heterogeneity and validate the use of quantile regression techniques.
To investigate the sensitivity of the behavioral patterns observed in Tables 2 and 3,
additional quantile regression runs were undertaken at the following alternative quantiles: 0.10,
0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80 and 0.90. The estimated coefficients at these quantiles are
graphically presented in Figures 1 and 2. These plots indicate that the behavioral patterns
observed in Tables 2 and 3 are robust to changes in the quantiles. For example, Figure 1 still
shows that the productivity effect of the Export dummy variable increases as one moves up to
higher quantiles.
Conclusions
There have been recent studies that establish the existence of productivity effects of
exporting practices. However, none of these studies examined the productivity effects at different
points of the conditional output distribution and none have carefully investigated the productivity
effects of different exporting status categories (i.e. new exporters versus exiting exporters). This
paper provides further insights about the productivity and export status relationship by more
carefully exploring the productivity effects at different points of the conditional output
distribution and for different export status categories. Turkish manufacturing firm data was
analyzed using quantile regression techniques. This approach allows for unobserved
heterogeneity and enables determination of the productivity effects at different points of the
conditional output distribution.
15
The empirical results support the notion that the productivity effect of exporting increases
as one move from the lower tail to the upper tail of the conditional output distribution. And it
provides clear evidence that the productivity effect of exporting is present for small plants who
export. We also found that exporting firms that continuously exported have more pronounced
productivity effects, as compared to other exporting firms in other categories (i.e. new exporting
firms, exporting firms that exit, and exporting firms that switch exporting practices). The
quantile regression results also showed that, in general, there is significant variation in the output
elasticities at different points in the conditional output distribution. These results further support
the idea that, when data sets have significant unobserved heterogeneity, substantial informational
gains can be achieved by using techniques that allow a thorough analysis at different points of
the conditional distribution. This paper not only established the relationship between productivity
and export status, but also provided insights about its relative variation along the conditional
output distribution.
The results of the study also have implications for manufacturing firm behavior. Given
that the productivity effects of exporting tend to be more pronounced at the upper tail, Turkish
exporting firms with lower production volume may want to take advantage of this “scale” effect
by expanding output. On the policy side, the differential productivity effects of exporting firms
with higher production volume and continuously exporting firms should be taken into
consideration in any policy prescription that aims to improve the competitiveness and
productivity of export manufacturing firms. Government policies to improve manufacturing
productivity should probably target lower volume firms that have entered the export market.
Supporting these newly established manufacturing firms may allow them to stabilize export
practices, survive international competition, and continually be involved in the export market.
16
References:
Arrow, K. (1962) The economic implications of learning by doing. Review of Economic Studies
29:155-173.
Aw, B., S. Chung, and M. Roberts (1998). Productivity and the Decision to Export Market:
Micro Evidence from Taiwan and South Korea. Cambridge MA, NBER.
Barro, R. J., Sala-I-Martin, X. (1995). Economic Growth. McGraw-Hill, New York.
Bassett, G. and Koenker, R. (1982). “An Empirical Quantile Function for Linear Models with iid
Errors.” Journal of the American Statistical Association 77: 407-415.
Bernard, A. B. and J. B. Jensen (1995). “Exporters, Jobs and Wages in U.S. Manufacturing,
1976–1987. The Brooking papers on economic activity.” Microeconomics 67–112.
Bernard, A. B. and J. B. Jensen (1998a). Understanding the U.S. Export Boom. Cambridge, MA,
NBER.
Bernard, A. B. and J. B. Jensen (1998b). Why Some Firms Export: Experience, Entry Costs,
Spillovers, and Subsidies. Yale Mimeo.
Bernard, A. B. and J. B. Jensen (1999a). “Exceptional Exporter Performance: Cause, Effect, or
Both?” Journal of International Economics 47(1): 1-26.
Bernard, A. B. and J. B. Jensen (1999b). Exporting and Productivity. Cambridge, MA, NBER.
Bernard, A. B. and J. Wagner (1997). “Exports and Success in German Manufacturing.”
Weltwirtschaftliches Archive 133: 134-157.
Bigsten, A., Collier, P., Dercon, S., Fafchamps, M., Gauthier, B., Gunning, J.W., Habarurema, J.,
Oduro, A., Oostendorp, R., Pattillo, C., Soderbom, M., Teal, F. & Zeufack, A. (2002) Do
African manufacturing firms learn from exporting?, Oxford University, Centre for the
Study of African Economies Working Paper Series, WPS/2002-09.
17
Buchinsky, M. (1998). “Recent Advances in Quantile Regression Models: A Practical Guide for
Empirical Research.” Journal of Human Resources 33(1): 88-126.
Castellani, D. (2001) Export behavior and productivity growth: Evidence from Italian
manufacturing firms, mimeo, ISE-Università di Urbino.
Caves, R.E. (1989). “International Differences in Industrial Organization,” In R. Schmalensee
and R. Willig (eds.), Handbook of Industrial Organization, v.II. Amsterdam:NorthHolland.
Clerides, S., S. Lach, and J. Tybout (1998). “Is learning-by-exporting important? Micro Dynamic
Evidence from Colombia, Mexico, and Morocco.” Quarterly Journal of Economics
CXIII, 903–947.
D’Agostino, R.B., A. Balanger, and R.B. D’Agostino Jr. (1990). “A suggestion for using
powerful and informative tests of normality.” The Amer. Statistician 44(4): 316-321.
Diewert, E. and D. Lawrence (1999). Progress in Measuring the Price and Quantity of Capital,
University of British Columbia Dept. of Economics Discussion Paper 99/17.
Dimelis, S. and H. Louri (2002). “Foreign ownership and production efficiency: a quantile
regression analysis.” Oxford Economic Papers 54: 449-469.
Ericson, R. E. and A. Pakes (1995). “Markov-perfect Industry Dynamics: A Framework
for Empirical Work.” Review of Economic Studies 62: 53-82.
Girma, S., D. Greenaway and R. Kneller (2003). “Export Market Exit and Performance
Dynamics: A Causality Analysis of Matched Firms.” Economics Letters 80:181-187.
Girma, S., D. Greenaway and R. Kneller (2004). “Entry to Export Markets and Productivity:
Microeconometric Analysis of Matched Firms.” Review of International Economics,
forthcoming.
18
Grossman, G., Helpman, E. (1991). Innovation and Growth in the Global Economy. MIT Press,
Cambridge.
Harper, M., E. Berndt, and D. Wood (1989). “Rates of Return and Capital Aggregation Using
Alternative Rental Prices,” In D.W. Jorgenson and R. Landau, (eds.), Technology and
Capital Formation, Cambridge, MA: The MIT Press, pp. 331-372.
Hendricks, W. and R. Koenker (1991). “Hierarchial Spline Models for Conditional Quantiles and
the Demand for Electricity.” Journal of American Statistical Association 87: 58-68.
Hopenhayn, H. (1992). “Entry, Exit, and Firm Dynamics in Long-Run Equilibrium.”
Econometrica 60: 1127-1150.
Jarque, C.M. and A.K. Bera (1980). “Efficient Tests for Normality, Heteroskedasticity, and
Serial Independence of Regression Residuals.” Economics Letters 6: 255-259.
Jovanovic, B. (1982). “Selection and the Evaluation of Industry.” Econometrica 50: 649-670.
Jovanovic, B. and S. Lach (1991). “The Diffusion of Technology and Inequality among
Nations,” NBER Working Paper, Cambridge, MA.
Koenker, R. and G. Basett (1978). “Regression Quantiles.” Econometrics 46: 33-50.
Koenker, R. and K. F. Hallock (2001). “Quantile Regression.” Journal of Economic Perspectives
15(4), 143-156.
Koenker, R. (2004). “Quantile Regression for Longitidinal Data.” Working Paper, University of
Illinois at Urbana Champaign.
Kraay, A. (1999) Exports and economic performance: Evidence from a panel of Chinese
enterprises, mimeo, World Bank.
Krugman, P. (1979). “A Model of Innovation, Technology Transfer, and the World Distribution
of Income,” Journal of Political Economy 87: 253-266.
19
Lucas, R.E. (1988). “On the Mechanics of Economic Development Planning,” Journal of
Monetary Economics 22: 3-42.
Mata, J. and J.A.F. Machado (1996). “Firm Start-Up Size: A conditional Quantile Approach”
European Economic Review 40: 1305-1323.
Mukoyama, T. (2004). “Rosenberg’s `Learning by Using’ and Technology Diffusion,” Working
Paper, Concordia University, Montreal, Canada.
Olley, G.S. and A. Pakes (1996). “The Dynamics of Productivity in the Telecommunications
Equipment Industry.” Econometrica. 64(6): 1263-97.
Parente, S., Prescott, E. (1994). “Barriers to technology adaptation and development” Journal of
Political Economy 102: 298-321.
Rivera-Batiz, L. A., Romer, P. (1991) “Economic integration and endogenous growth” Quarterly
Journal of Economics 106: 531-555.
Rosenberg, N. (1982). Inside the Black Box: Technology and Economics (Cambridge U.P,
Cambridge, UK)
Solow, R. (1957). “Technical Change and the Aggregate Production Function.” Review of
Economics and Statistics 39(3): 312-320.
Van Biesebrock, J. (2003) Exporting raises productivity in Sub-Saharan African manufacturing
plants, NBER Working Paper.
Wagner, J. (2002). “The Causal Effects of Exports on Firm Size and Labor Productivity: First
Evidence from a Matching Approach.” Economics Letters 77(2): 287-292.
Yasar, M. and R.M. Rejesus. (2005) “Exporting Status and Firm Performance: Evidence from a
Matched Sample.” Economics Letters 88(3), 397-402.
20
Table 1. Descriptive statistics for relevant variables (Constant Value Quantities at 1987 Prices, in
‘000 Turkish Liras)
A. Continuous variables
Mean
Standard deviation
Minimum
Maximum
Output (Y)
Capital (K)
Energy (E)
Labor (L)
Material (M)
B. Dummy variables
2657.26
1283.29
72.67
195.24
2293.10
5169.11
9919.96
350.25
331.55
4064.69
3.65
0.16
0.06
1.72
0.03
115994.70
384023.70
13469.05
7960.80
56239.88
Percentage of plants (%)
49.74
50.26
9.57
12.28
15.70
12.19
55.96
20.46
23.57
Export
Non-exporters
Exporters
Entrants
Exiters
Switchers
Small
Medium
Large
Note: (1) Total number of observations: 6055. There are 8806 observations in the data set, but
after cleaning the data we end up with 6055 observations.
(2) The summary statistics for industry, year, and region dummy variables are not
reported here in the interest of space. It is available from the authors upon request.
21
Table 2: Estimation Results of the Production Function using the First Specification
Independent
OLS
Quantile regression estimates
variables
estimates
0.10
0.25
0.50
0.75
0.90
Material (M)
0.531
(0.005)*
0.815
(0.011)*
0.760
(0.008)*
0.675
(0.009)*
0.562
(0.012)*
0.442
(0.017)*
Labor (L)
0.209
(0.013)*
0.174
(0.016)*
0.154
(0.013)*
0.161
(0.016)*
0.155
(0.023)*
0.150
(0.039)*
Capital (K)
0.050
(0.005)*
0.019
(0.005)*
0.030
(0.004)*
0.036
(0.004)*
0.049
(0.005)*
0.067
(0.009)*
Energy (E)
0.094
(0.008)*
0.028
(0.008)*
0.040
(0.006)*
0.069
(0.006)*
0.091
(0.009)*
0.105
(0.018)*
Export
0.193
(0.016)*
0.085
(0.016)*
0.108
(0.012)*
0.150
(0.013)*
0.197
(0.017)*
0.220
(0.030)*
Medium
0.160
(0.021)*
0.014
(0.023)
0.059
(0.019)**
0.1003
(0.019)*
0.148
(0.025)*
0.215
(0.040)*
Large
0.325
(0.031)*
0.046
(0.033)
0.108
(0.024)*
0.181
(0.032)*
0.327
(0.041)*
0.446
(0.069)*
Intercept
1.906
(0.073)*
0.325
(0.080)*
0.841
(0.053)*
1.433
(0.072)*
2.276
(0.109)*
3.231
(0.159)*
Number of observations = 6055
Notes: (1) *Significant at the 1% level. **Significant at the 5 percent level.
(2) The P-value for Jarque-Bera normality test is <0.01, which indicates that errors are
not normally distributed.
(3) The null hypothesis that the coefficients above are equal across and between pairs of
quantiles is rejected (p-value ≤ 0.01; See Appendix Table 1)
(4) The standard errors in parenthesis for quantile regressions are bootstrapped with 500
repetitions.
(5) The regression runs in this table includes dummy variables that control for region,
year, and industry characteristics. However, they are not reported here in the interest of
space. They are available from the authors upon request.
22
Table 3: Estimation Results of the Production Function using the Second Specification
Independent
OLS
Quantile regression estimates
variables
estimates
.10
.25
.50
.75
.90
Material (M)
0.528
(0.005)*
0.809
(0.011)*
0.754
(0.009)*
0.674
(0.009)*
0.559
(0.012)*
0.439
(0.016)*
Labor (L)
0.206
(0.013)*
0.165
(0.018)*
0.164
(0.013)*
0.150
(0.016)*
0.154
(0.023)*
0.132
(0.053)*
Capital (K)
0.050
(0.005)*
0.017
(0.004)*
0.029
(0.004)*
0.036
(0.004)*
0.049
(0.005)*
0.065
(0.008)*
Energy (E)
0.093
(0.007)*
0.033
(0.008)*
0.039
(0.006)*
0.067
(0.007)*
0.090
(0.009)*
0.103
(0.016)*
Exporters
0.299
(0.025)*
0.159
(0.023)*
0.194
(0.018)*
0.239
(0.022)*
0.262
(0.024)*
0.302
(0.040)*
Entrants
0.226
(0.021)*
0.111
(0.020)*
0.111
(0.015)*
0.160
(0.020)*
0.202
(0.023)*
0.209
(0.041)*
Exiters
0.174
(0.021)*
0.074
(0.022)*
0.111
(0.015)*
0.135
(0.017)*
0.174
(0.025)*
0.208
(0.036)*
Switchers
0.171
(0.022)*
0.082
(0.023)*
0.066
(0.018)**
0.126
(0.019)*
0.169
(0.027)*
0.230
(0.048)*
Medium
0.155
(0.021)*
0.017
(0.024)
0.050
(0.017)*
0.099
(0.018)*
0.140
(0.030)*
0.215
(0.038)*
Large
0.325
(0.030)*
0.057
(0.037)
0.100
(0.023)*
0.201
(0.030)*
0.313
(0.042)*
0.477
(0.071)*
Intercept
2.036
(0.073)*
0.403
(0.077)*
0.831
(0.051)*
1.476
(0.071)*
2.296
(0.109)*
3.374
(0.165)*
Number of observations = 6055
Notes: (1) *Significant at the 1% level. **Significant at the 5 percent level.
(2) The P-value for Jarque-Bera normality test is <0.01, which indicates that errors are
not normally distributed.
(3) The null hypothesis that the coefficients above are equal across and between pairs of
quantiles is rejected (p-value < 0.01; See Appendix Table 2)
(3) The standard errors in parenthesis for quantile regressions are bootstrapped with 500
repetitions.
23
(4) The regression runs in this table includes dummy variables that control for region,
year, and industry characteristics. However, they are not reported here in the interest of
space. They are available from the authors upon request.
24
Figure 1: Coefficients on Inputs and Export Dummy
0.90
0.80
0.70
Coefficient
0.60
Material
0.50
Labor
Capital
0.40
Energy
0.30
Export Dummy
0.20
0.10
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Quantile
Figure 2: Coefficients on Export History Dummies
0.35
0.30
Continuous Exporters
Entrants
0.25
Exiters
Coefficient
Switchers
0.20
0.15
0.10
0.05
0.00
0.1
0.2
0.3
0.4
0.5
Quantile
25
0.6
0.7
0.8
0.9
APPENDIX
26
Appendix Table 1: Test for Coefficient Equality between Pair-wise Quntiles and across all
Quantiles for the First Model Specification
Quantiles being
P-values of the independent variables
tested
M
L
K
E
Export Medium Large
A. Pair-wise:
0.10 vs. 0.25
0.10 vs. 0.50
0.10 vs. 0.75
0.10 vs. 0.90
0.25 vs. 0.50
0.25 vs. 0.75
0.25 vs. 0.90
0.50 vs. 0.75
0.50 vs. 0.90
0.75 vs. 0.90
B. Joint test for all
quantiles
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Notes: (1) The null hypothesis is that the coefficients are equal between pairwise quantiles (A.)
and/or the coefficients are equal across all quantiles (B.). The test statistic is computed by
using the variance-covariance matrix of the coefficients of system of quantile regressions.
The p-values of F-tests estimated from the system of quantile regressions are reported in
the table. If the p-value is less than the level of significance, we reject the null hypothesis
of equality.
(2) The regression runs in this table includes dummy variables that control for region,
year, and industry characteristics. However, they are not reported here in the interest of
space. They are available from the authors upon request.
27
Appendix Table 2: Test for Coefficient Equality between Pair-wise Quntiles and across all
Quantiles for the Second Model Specification
P-values of the independent variables
Quantiles
being tested
M
L
K
E
Exporters Entrants Exiters Switchers Medium Large
A. Pair-wise:
0.10 vs. 0.25
0.10 vs. 0.50
0.10 vs. 0.75
0.10 vs. 0.90
0.25 vs. 0.50
0.25 vs. 0.75
0.25 vs. 0.90
0.50 vs. 0.75
0.50 vs. 0.90
0.75 vs. 0.90
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
B. Joint test <0.01 <0.01 <0.01 <0.01
for all
quantiles
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Notes: (1) The null hypothesis is that the coefficients are equal between pairwise quantiles (A.)
and/or the coefficients are equal across all quantiles (B.). The test statistic is computed by using
the variance-covariance matrix of the coefficients of system of quantile regressions. The p-values
of F-tests estimated from the system of quantile regressions are reported in the table. If the pvalue is less than the level of significance, we reject the null hypothesis of equality.
(2) The regression runs in this table includes dummy variables that control for region,
year, and industry characteristics. However, they are not reported here in the interest of space.
They are available from the authors upon request.
28