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Using a panel data quantile regression approach, this study analyses the linkage between export profit growth when using OLS.. However, when using a quantile approach, export participatio

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This article was downloaded by: [University of Waikato]

On: 14 January 2014, At: 18:22

Publisher: Routledge

Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Applied Economics Letters

Publication details, including instructions for authors and subscription information:

http://www.tandfonline.com/loi/rael20

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

To link to this article: http://dx.doi.org/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

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

I Introduction

Since the ground-breaking study of Bernard and Jensen

perfor-mance’, many empirical studies have shown that the

rea-son for exporters having higher productivity than

nonexporters stems from a self-selection mechanism

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 offirms 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 betweenfirm exporting and profitability

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

current literature The quantile approach has further advantages The results are robust to the existence of

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

Vol 21, No 6, 442–445, http://dx.doi.org/10.1080/13504851.2013.866197

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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 Thefinal section summarizes

II The Data and Methodology

The data source

con-ducted in 2005, 2007 and 2009 as collaboration between

the Institute of Labour Science and Social Affairs,

University of Copenhagen The original dataset included

2821 enterprises which were interviewed in 2005 The

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 asfirm 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 onfirm profit growth (e.g Fryges and

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:

where yitis the profit growth of firm i through time and uit

represents for unobservable factors such as products

quality and management quality A vector of independent

variable, export participation, is captured by a dummy

variable to minimize measurement errors We also con-sider other types of exporting activities Continuous exporters arefirms that export through the study period, whereas starting exporters are enterprises that do not

addition, the behaviour of SMEs may be different among various sectors, kinds of ownership and locations

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:

β

i¼1

yit xitβq

i :y it x i β qjyit xitβqj

i:y it <x it β ð1  qÞjyit xitβqj

 (2)

A series of studies have discussed the problem of

these approaches According to Canay (2011), the estima-tion procedure comprises two stages In thefirst stage, the conditional mean of uitis 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

average regression approaches There is a statistically

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

2

Variables 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

4

We also usefirm size squared and firm age squared to capture the nonlinear nature of the linkages

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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 ofTable 1and 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

findings here suggest that the productivity advantages of

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 mixedfindings 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 ofTable 1reports an insignificant

growth However, the negative and significant links

quantiles are still being observed in the case of both continuous and starting exporters This is further demon-strated using the confidence intervals shown inFig 1

are generally consistent with other international empirical studies For example, wefind that a higher profit growth is associated withfirms of a larger size In addition, while innovators gain a higher profit growth than

growth in profitability compared to firms with less busi-ness experience

IV Summary

previous studies of the role of exporting onfirms’ 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

Table 1 Exports and profit growth

Variables

OLS

Fixed effect quantile regression

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 forfirm 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

a

This model also controls for other independent variables as Model I

5

The results are available on request

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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

support from the Vietnamese government

References

Bernard, A B and Jensen, J B (1995) Exporters, jobs, and

wages in US manufacturing: 1976–1987, Brookings Papers on Economic Activity, Microeconomics, 1995,

67–119

Buchinsky, M (1994) Changes in the US wage structure 1963–

1987: application of quantile regression, Econometrica:

Journal of the Econometric Society, 62, 405–58

Cameron, A C and Trivedi, P K (2009) Microeconometrics

Using Stata, Vol 5, Stata Press, College Station, TX

Canay, I A (2011) A simple approach to quantile regression for panel data, The Econometrics Journal, 14, 368–86 Fryges, H and Wagner, J (2010) Exports and profitability: first evidence for German manufacturing firms, The World Economy, 33, 399–423

Grazzi, M (2011) Export andfirm performance: evidence on productivity and profitability of Italian companies, Journal

of Industry, Competition and Trade, 12, 413–44

Kizhakethalackal, E T., Mukherjee, D and Alvi, E (2013) Quantile regression analysis of health-aid and infant mor-tality: a note, Applied Economics Letters, 20, 1197–201 Koenker, R (2004) Quantile regression for longitudinal data, Journal of Multivariate Analysis, 91, 74–89

Lu, J W and Beamish, P W (2006) SME internationalization and performance: growth vs profitability, Journal of International Entrepreneurship, 4, 27–48

Rand, J and Torm, N (2011) The benefits of formalization: evidence from Vietnamese manufacturing SMEs, World Development, 40, 983–98

Vu, H., Lim, S., Holmes, M et al (2013) Firm exporting and employee benefits: first evidence from Vietnam Manufacturing SMEs, Economics Bulletin, 33, 519–35 Wagner, J (2007) Exports and productivity: a survey of the evidence from firm‐level data, The World Economy, 30, 60–82

Wagner, J (2011) Exports, imports and profitability: first evi-dence for manufacturing enterprises, Open Economies Review, 23, 747–65

Wagner, J (2012) International trade and firm performance: a survey of empirical studies since 2006, Review of World Economics, 148, 235–67

Quantile Quantile impact 90% upper CI

Export participation and profit growth

Continuous exporters and profit growth

Starting exporters and profit growth

Exporting stoppers and profit growth

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

Quantile Quantile impact 90% upper CI

90% lower CI OLS impact

Fig 1 Slope and 90% coefficient intervals for quantile treatment regression

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