IV. Summary
In an attempt to reconcile the ambiguous findings in
previous studies of the role of exporting on firms’ profit
growth, we argue that the empirical linkage between
exporting and profit growth has been clouded by the
use of a mean approach. Using an OLS approach, our
results do not show a linkage between export participation
and the growth of profit estimates. However, quantile
treatment effects estimates reveal that export
participation has a positive association for those firms
with high profit growth at the higher quantiles but a
negative link with low profit growth for those firms at
the lower quantiles
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Applied Economics Letters
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Exports and profitability: a note from quantile
regression approach
Huong Vua, Mark Holmesa, Steven Lima & Tuyen Trana
a Department of Economics, Waikato University, Hamilton, New Zealand
Published online: 14 Jan 2014.
To cite this article: Huong Vu, Mark Holmes, Steven Lim & Tuyen Tran (2014) Exports and profitability: a note from quantile
regression approach, Applied Economics Letters, 21:6, 442-445, DOI: 10.1080/13504851.2013.866197
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Exports and profitability: a note from
quantile regression approach
Huong Vu*, Mark Holmes, Steven Lim and Tuyen Tran
Department of Economics, Waikato University, Hamilton, New Zealand
Studies of the linkage between exports and profitability often use mean regres-
sion approaches and focus only on European countries. Using a panel data
quantile regression approach, this study analyses the linkage between export
behaviour and profit growth in Vietnam. Using a panel dataset from 2005 to
2009, our results show an insignificant linkage between export status and firm
profit growth when using OLS. However, when using a quantile approach, export
participation is found to be positively related to profitability for those firms with
high profit growth but negatively related for those firms with low profit growth.
This might suggest that the productivity advantages of exporters with low profit
growth are absorbed by costs relating to trading activities in overseas markets.
Keywords: exports; profitability; quantile approach; SMEs
JEL Classification: F14; O12
I. Introduction
Since the ground-breaking study of Bernard and Jensen
(1995), which described ‘exceptional export perfor-
mance’, many empirical studies have shown that the rea-
son for exporters having higher productivity than
nonexporters stems from a self-selection mechanism
rather than learning by exporting (e.g. Wagner, 2007).
Such work has led to a further interesting question that
has drawn the attention of some recent studies in interna-
tional trade. Do exporters with the advantage of higher
productivity gain higher profitability, or is this advantage
absorbed by extra costs relating to trading activities in
overseas markets? The motivation for us to pursue this
topic stems from several reasons. First, as noted by
Wagner (2011), considering profitability instead of pro-
ductivity is very important because profitability reflects
the success of firms and the first priority of firms is profit
maximization rather than productivity. Second, some stu-
dies have found export participation to have a positive
impact, but other studies indicate a negative or insignif-
icant relationship between firm exporting and profitability
(e.g. Lu and Beamish, 2006; Fryges and Wagner, 2010;
Grazzi, 2011). Third, all the investigations carried out so
far are in the context of European countries (Wagner,
2012). Hence, no generalized inferences can be made.
In terms of methodology, existing regression ana-
lysis of the relationship between profitability and
exports typically relies on OLS or least absolute
deviation methods and so only estimate the marginal
effects of the covariates on the conditional mean
(median) function of profitability. Such estimates
sidestep the potentially heterogeneous patterns of
the influence of the covariates in the conditional
distribution, and this approach provides limited infor-
mation about the relationship. Using a quantile
regression approach for panel data, this research
therefore aims to bring empirical evidence of the
role of exporting on profitability in a transitional
economy, Vietnam, to fill the gap that exists in the
current literature. The quantile approach has further
advantages. The results are robust to the existence of
outliers (Kizhakethalackal et al., 2013), and they also
offer a detailed picture of the relationship. In our
*Corresponding author. E-mail: vhv1@waikato.ac.nz
Present affiliation for Tuyen Tran: VNU University of Economics and Business, Hanoi, Vietnam.
Applied Economics Letters, 2014
Vol. 21, No. 6, 442–445,
442 © 2014 Taylor & Francis
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case, we have evidence of a nonlinear relationship
between exports and profitability. This has the poten-
tial to reconcile the ambiguity in the earlier studies.
The remainder of the article is as follows. Section II
presents data sources and methodology. Section III dis-
cusses the empirical results. The final section summarizes
main findings.
II. The Data and Methodology
The data source
The research uses data from the ‘Small and Medium Scale
Enterprise Survey in Vietnam’. The surveys were con-
ducted in 2005, 2007 and 2009 as collaboration between
the Institute of Labour Science and Social Affairs,
Vietnam, and Department of Economics of the
University of Copenhagen. The original dataset included
2821 enterprises which were interviewed in 2005. The
number is 2635 firms in 2007, while a slightly larger
number of 2655 were interviewed in 2009.1
The dataset has some inherent advantages. This is a
uniquely rich dataset of manufacturing SMEs that covered
10 provinces in 3 regions in Vietnam. It also covers all the
major manufacturing sectors namely food processing,
wood products and other sectors. Furthermore, the main
information on export status of the enterprise and economic
indicators as well as firm characteristics is contained in the
dataset. Hence, the data enable us to conduct a test of the
linkage between export status and profitability.2
Methodology
Previous studies often use OLS estimation to consider the
role of export status on firm profit growth (e.g. Fryges and
Wagner, 2010). However, as Buchinsky (1994) suggests,
mean regression techniques have never been satisfactory
approaches when considering heterogeneous populations.
To consider the potential heterogeneous impacts, we specify
the qth quantile (0 < q < 1) of conditional distribution of the
dependent variable, given a set of variables Xi, as follows:
Qqðyit=xitÞ ¼ aq þ xitβq þ uitαq (1)
where yit is the profit growth of firm i through time and uit
represents for unobservable factors such as products
quality and management quality. Avector of independent
variables (xit) is included. First, the main interest
variable, export participation, is captured by a dummy
variable to minimize measurement errors. We also con-
sider other types of exporting activities. Continuous
exporters are firms that export through the study period,
whereas starting exporters are enterprises that do not
export in year t – 1 but export in year t. Exporting
stoppers are firms that export in year t – 1 but do not
export in year t.3 Second, firms’ profitability may be
determined by standard firm characters such as firm
size, firm age and innovation (e.g. Grazzi, 2011).4 In
addition, the behaviour of SMEs may be different
among various sectors, kinds of ownership and locations
in Vietnam (Rand and Torm, 2011; Vu et al., 2013). We
control for all these by using a dummy for low technol-
ogy sectors, a dummy for household ownership and an
urban dummy.
Cameron and Trivedi (2009) show that estimation of
Equation 1 based on the qth quantile regression involves
minimizing the absolute value of the residual using the
following objective function:
QðβqÞ ¼ min
β
Xn
i¼1
yit xitβq
h i
¼ min
X
i:yitxiβ
qjyit xitβqj
þ
X
i:yit <xitβ
ð1 qÞjyit xitβqj
(2)
A series of studies have discussed the problem of captur-
ing unobserved factors through a fixed effects quantile
model (e.g. Koenker, 2004; Canay, 2011). We also follow
these approaches. According to Canay (2011), the estima-
tion procedure comprises two stages. In the first stage, the
conditional mean of uit is estimated. In the second stage,
this component is subtracted from the original dependent
variable, and then the standard estimation of quantile
regression is used.
III. Empirical Results
Column 1 in Table 1 reports results based on the
average regression approaches. There is a statistically
insignificant difference in profit growth between
exporters and nonexporters, a finding consistent
with previous studies (e.g. Wagner, 2011). Similar
results in the linkage with profit growth have also
been found when using export participation at
1 This data has been kindly shared by Prof. John Rand.
2Variables with current prices are deflated to 1994 prices using GDP deflators to avoid biases that might arise from inflation.
3 The direct information of import status is not available. Export intensity is also unavailable in 2007. The caveats of data prevent us from
considering these covariates in the regression.
4We also use firm size squared and firm age squared to capture the nonlinear nature of the linkages.
Exports and profitability 443
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different stages. Fixed effect estimations do not pro-
vide qualitatively different results.5
A totally different picture emerges when using quantile
regression. As displayed by the top half of Table 1 and the
top left graph in Fig. 1, there is a positive relationship
between export participation and profit growth at the 70th
and 80th percentiles, but a negative linkage is observed
between export participation with enterprises having low
profit growth at the 10th percentile. These results imply
that the average approach has clouded the role of export
activities in firm profit growth at different points. The
findings here suggest that the productivity advantages of
exporters compared with nonexporters are found for firms
having high profit growth at 70th and 80th percentiles. For
firms with low profit growth at 10th percentile, these
advantages are possibly absorbed by costs relating to
trading activities on overseas markets such as entry costs
and advertisement costs. Our results hence move towards
reconciling the mixed findings of previous results in the
literature (See Wagner (2012) for a review). If export
participation is replaced by each exporting activity in
turn, then the lower half of Table 1 reports an insignificant
linkage between exporting stoppers and firm profit
growth. However, the negative and significant links
between firms having low profit growth at the lower
quantiles are still being observed in the case of both
continuous and starting exporters. This is further demon-
strated using the confidence intervals shown in Fig. 1.
Regarding firm characteristics, our findings in Table 1
are generally consistent with other international empirical
studies. For example, we find that a higher profit growth is
associated with firms of a larger size. In addition, while
innovators gain a higher profit growth than noninnova-
tors, firms with more business experience have a lower
growth in profitability compared to firms with less busi-
ness experience.
IV. Summary
In an attempt to reconcile the ambiguous findings in
previous studies of the role of exporting on firms’ profit
growth, we argue that the empirical linkage between
exporting and profit growth has been clouded by the
use of a mean approach. Using an OLS approach, our
results do not show a linkage between export participation
and the growth of profit estimates. However, quantile
treatment effects estimates reveal that export
Table 1. Exports and profit growth
Variables
OLS
Fixed effect quantile regression
q10 q20 q30 q40 q50 q60 q70 q80 q90
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Export participation and profit growthI
Export −0.02 −0.09*** −0.04 −0.02 −0.02 −0.01 0.02 0.05* 0.09* 0.06
(0.035) (0.050) (0.030) (0.015) (0.017) (0.019) (0.021) (0.024) (0.034) (0.037)
Size in log 0.02* −0.01 0.01*** 0.02** 0.02** 0.02** 0.02** 0.03** 0.04** 0.05**
(0.008) (0.009) (0.006) (0.004) (0.003) (0.003) (0.004) (0.004) (0.005) (0.010)
Firm age −0.01** −0.01** −0.01** −0.01** −0.01** −0.01** −0.01** −0.01** −0.01** −0.01**
(0.002) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002)
Innovation dummy 0.03* 0.02 −0.00 0.00 0.00 0.01*** 0.02* 0.02** 0.02** 0.03*
(0.011) (0.012) (0.008) (0.007) (0.005) (0.005) (0.006) (0.006) (0.008) (0.014)
Constant 0.13** −0.19** −0.08** −0.03* 0.01 0.04** 0.09** 0.14** 0.23** 0.43**
(0.032) (0.029) (0.019) (0.015) (0.013) (0.011) (0.013) (0.013) (0.022) (0.036)
Other exporting activities and profit growtha
Continuous exporters −0.06 −0.16 −0.13*** −0.07*** −0.07*** −0.01 0.01 0.02 0.07 −0.01
(0.062) (0.101) (0.076) (0.039) (0.044) (0.051) (0.052) (0.067) (0.079) (0.116)
Starting exporters −0.04 −0.03 −0.04 −0.05*** −0.06*** −0.04*** −0.04 0.01 0.00 −0.06
(0.046) (0.069) (0.034) (0.027) (0.033) (0.026) (0.044) (0.049) (0.051) (0.074)
Exporting stoppers −0.11 −0.17 −0.06 −0.05 −0.02 −0.03 −0.03 0.04 0.02 0.00
(0.071) (0.112) (0.092) (0.042) (0.036) (0.029) (0.051) (0.056) (0.056) (0.075)
Notes: Bootstrap SEs in parentheses with 2000 replications. *** significant at 10%, * at 5% and ** at 1%. OLS SEs are robust. Model I
controls for firm size squared, firm age squared, year 2009, urban dummy, a dummy for household ownership and a dummy for low
technology sectors. The number of observations is 7612.
aThis model also controls for other independent variables as Model I.
5 The results are available on request.
444 H. Vu et al.
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participation has a positive association for those firms
with high profit growth at the higher quantiles but a
negative link with low profit growth for those firms at
the lower quantiles.
Acknowledgement
The author Huong Vu is grateful for the helpful feedback
from Prof. Canay regarding this article.
Funding
The author Huong Vu gratefully acknowledges financial
support from the Vietnamese government.
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Quantile
Quantile impact
90% upper CI
Export participation and profit growth
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OLS impact
Quantile
Quantile impact
90% upper CI
90% lower CI
OLS impact
Quantile
Quantile impact
90% upper CI
90% lower CI
OLS impact
Quantile
Quantile impact
90% upper CI
90% lower CI
OLS impact
Fig. 1. Slope and 90% coefficient intervals for quantile treatment regression
Exports and profitability 445
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