(Luận văn thạc sĩ) financial constraints and export decision evidence from vietnamese manufacturing listed firms in ho chi minh stock exchange

Le Mai Thy et. al. | 587

Financial constraints and export decision:
Evidence from Vietnamese manufacturing
listed firms in Ho Chi Minh Stock Exchange
LE MAI THY
International University – Vietnam National University HCMC –

PHAM DINH LONG
HCMC Open University –

NGUYEN KIM THU
International University – Vietnam National University HCMC –

Abstract
This research investigates the influences of financial constraints on export decision of 75
Vietnamese manufacturing listed firms in HOSE during 2007–2016. The financial constraints
are approximated by financial variables including long-term debt to total capital, leverage and
liquidity ratios. The researcher then estimates the impact of these financial fundamentals on the
export decision in population average probit models combing with Bootstrap method. The
empirical results confirm the negative influence of financial constraints on the decision of
participating in the export markets which are consistent with previous theoretical works.
Financial constrained manufacturing firms are less likely to become exporters than others. The
robustness check of the findings by adding the interactions between each independent variables
with three control variables also confirm the main findings of this research.
Keywords: export decision; financial constraints; Vietnamese listed firms.

1. Introduction
On 11th January 2007, Vietnam officially joined World Trade Organization (WTO)
(The Ministry of Finance, 2011). This enlarges the export market which creates more
opportunities for Vietnamese firms to develop and join the global market, especially for

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manufacturing firms. It is obvious that the growth of a country and its businesses go in
tandem with the possibility of exporting and penetrating into foreign market. Indeed,
enterprises’ expansion beyond national borders is often promoted by the government’s
policies, such as those on tax. However, such an expansion will face several challenges.
Roberts and Tybout (1997), Bernard and Jensen (2004), Nguyen and Ohta (2007) have
provided evidences of significant fixed (sunk) costs of entry into exporting that affect to
firms’ export decision. Compared with selling to domestic market, exporting requires
higher fixed costs of market entry (Melitz, 2003) and investment expansion. Dario
Fauceglia (2014) finds that better financial system increases the export probability
through the reduction of credit constraints. However, for many developing countries
where financial system is not advanced, the ability of accessing to financing is another
hindrance to firm’s growth because these firms are from small to medium size and need
external financing to cover production costs.
The influences of financial constraints at firm-level have been studied by many
researchers (Chor and Manova, 2012; Freund and Clapper, 2009; Correa et al., 2007;
Muul, 2012; Chaney, 2005; Berman and Hericourt, 2010). However, most of these
researches concentrate on the relationship between financial constraints and the growth
and/or the investment decisions of companies. In addition, empirical work on the
interaction between financial constraints and firm’s export decisions is still few at firm
level in developing countries such as Vietnam. Therefore, this research aims to fill the
research gap for Vietnam, using firm-level data to examine whether financial
constraints have influence on the export decision of Vietnamese manufacturing listed
firms for the period from 2007 - 2016. Besides the contribution to micro-evidence
literature of the impact of financial constraints on international trade, understanding
the relation between financial constraints and export decision is helpful for Vietnamese
Government to setup policies to develop the financial markets from which encourage

firms to export more.
This study employs the panel data including historical data (e.g., sales, employment,
exports, assets, debts proportions and other financial ratios etc.) from the Annual
reports, Financial Statements of 75 manufacturing listed firms for period from 2007 to
2016, collected from the Ho Chi Minh Stock Exchange (HOSE). In particular, there is
about 76% of exporters and 24% of non-exporters in the sample of 750 observations so
the propensity to export is clearly skewed towards one.
Financial constraints in this paper are measured by three financial variables
comprising of leverage and liquidity ratios following Nagaraj (2014) and long – term


Le Mai Thy et. al. | 589

debt to total capital ratio following Egger and Kesina (2010). In order to estimate the
influences of financial constraints on firms’ export decision, the study adapts the
population average probit models combining with Bootstrap method. The marginal
effects of probit models are used to report the quantitative impact on the export
decision in this study. Liquidity is positively related while leverage and long – term to
total capital are negatively related to export decision. All these results support the
hypothesis that the presence of financial constraints influence export decision of
Vietnamese manufacturing listed firms.
The research is then organized as follows. Section 2 provides a literature review on
financial constraints and firms’ export decision, regarding both theoretical and previous
empirical work. Section 3 summarizes the empirical analysis of the research and the last
section is the conclusion.
2. Literature
Theoretical Work
The theoretical literature highlighting the influences of financial constraints on firms’
export decision has been laid out in several studies. Melitz (2003) has theoretically
shown that firms’ heterogeneity in their productivity levels and sunk cost of market

entry are the causality of why firms do not engage in exporting. In specific, Melitz
adapts Hopenhayn’s (1992a) model to a monopolistically competitive industry in a
general equilibrium setting. His model integrates firm heterogeneity in a way such that
a single statistic – an average firm productivity level summarizes the relevance of the
distribution of productivity levels for aggregate outcomes completely. Melitz also
introduces the sunk market entry costs that firms face not only for their domestic
market but also for any potential export market. Firms are required to pay fixed perperiod costs and one-off costs to access the domestic and export markets. The research’s
results show that the more productive firms tend to enter the export market to gain
market share and profit while less productive firms cannot, and the least productive
firms are forced to get out of export market. The causality of more efficient firms find it
profitable to export because they can undertake the investment associated with new
market entry after gaining knowledge of their productivity.
Chaney (2005) extends Melitz’s (2003) framework by including liquidity frictions
and internal finance and assumes firms operating in an imperfectly competitive product
market. He proposes a theory of international trade with liquidity constraints as a key


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determinant of firm’s export behavior. Chaney consequently finds that sunk costs
associated with activities to enter the export market are sensitive to financial variables.
The most productive firms are more likely to become exporters because they are able to
obtain enough liquidity from selling in domestic markets to overcome the liquidity
constraints when starting to export and the less productive firms are unable to export
because they can hardly find access to financial markets and cover foreign market entry
cost. This finding is consistent with the facts that exporters are not liquidity constrained.
Moreover, the scarcity of available liquidity and inequality of the liquidity distribution
among firms lower the total exports.
Subsequently, Manova (2008) took a step forward to provide evidence on the link
between credit constraints and exports of 91 countries in the period of 1980-1997 under

the extension of Melitz’s (2003) model. The paper highlights inter-sectoral differences
in terms of liquidity across countries rather than firm-level financial constraints. She
focuses on the sector (sectors’ differences in tangibility and external finance need) and
country’s comparable advantages in terms of financial development rather than on
credit constraints at firm level. “Vulnerable sectors” in her paper requires more external
finance or use fewer collateralized assets. She concludes that credit constraint has a
negative impact on exports which is higher in countries with lower levels of financial
development and in more financially vulnerable sectors.1She indicates that firms which
have better financial availability export more because they have lower need of external
finance and therefore are able to enter more market destinations, and sell more of each
product. Weak financial firms are less likely to become exporters.
Examining the interaction between firm-level constraints and exports, Muuls’ (2008)
model extends Chaney’s (2005) model by incorporating external financing from Manova
(2008). In particular, she proposes that enterprises have 3 (three) liquidity sources in
order to finance the exporting entry cost consisting of internal financial health, liquidity
shocks and external finances. She also formulates three predictions: (1) there are firms
which could export profitably, but are prevented from doing so because of lacking
sufficient liquidity, (2) if the first prediction holds, those firms which are more
productive and less financial constrained will be able to export to more destinations but
smaller markets. She finds that higher productivity levels and lower credit constraints
motivates firms be more likely to become exporters. Credit constraints are important

1

Manova (2008) defines vulnerable sectors require more external finance and employ fewer collateralized assests.


Le Mai Thy et. al. | 591

determinants of the extensive margin of trade in terms of destinations but not the

intensive margin of trade in that dimension.
Li and Yu (2009) aims to examine the influences of a firm’s credit constraints and its
productivity on its export decisions. They formulate two main propositions: (1) firms for
which it is easier to borrow from financial intermediaries export more, (2) foreign
invested enterprises export more and are less sensitive to the availability of external
finance from financial intermediaries. Research’s findings support both of their model’s
predictions above. They also confirm that firms for which it is easier to borrow from
financial intermediaries export more. Consistent with Li and Yu (2009), Manova et al.
(2015) indicates that multinational firms’ affiliates can tap additional funding from their
parent company or access foreign capital markets. Thus, the credit constraints’ exposure
to these affiliate firms is lesser than independent firms (Antras, Desai, Foley, 2009).
Hence, they are more likely to become exporters than other firms as the fact that
exporting activities requires additional financing. In addition to Li and Yu’s conclusion,
Javorcik and Spatareanu (2009) also find that not only multinational firms but also
suppliers of these multinationals are less influenced by credit constraints.
Empirical Evidence
Egger and Kesina (2010) examine the influence of financial constraints (or credit
constraints) to the extensive and the intensive margin of exports collecting data from
Chinese enterprises of the National Bureau of Statistic of China for the period of 2001 2005. They proxy credit constraints by four financial variables including long-run debt
to capital ratio, financial costs to liquid funds ratio, liquid asset to capital ratio, ratio of
surplus of profits over long-run debts to total assets. They test the relation between
firms’ propensity to export by means of a logit model and the intensive margin of
exports by fractional response model following Papke and Wooldredge (1996). Then
they confirm the negative effect of credit (financial) constraints on exports and
consistent with previous theoretical work. That is those firms are financial constrained
are less likely to become exporters.
Cole, Elliot, Virakul (2010) examined the relationship between firms’ characteristics
and their export decision and mainly emphasize the importance of financial variables as
a proxy for sunk entry costs. The authors used the annual survey of manufacturing
firms in Thailand from 2001 to 2004 issued by Office of Industrial Economics, Ministry

of Industry in Thailand. The survey includes three main types of enterprise (i.e. small,
medium and large firms) covering 79 types of manufacturing activities in 23 industries


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with the final sample including 15,115 observations. By using pooled probit estimation
and GMM, they found that liquidity or leverage influence the export decision because
these ratios explain the capacity to invest in sunk entry cots of firms when participating
in export market. The investment decision depends on firms’ internal financial health.
Moreover, some characteristics of firms including firm size, training, structure of
ownership and R&D are also related to the probability of exporting.
Kiendrebeogo and Minea (2012) also focus on the effects of financial factors on
manufacturing firms’ export participation by presenting the intuition according to
which financial constraints reduce the probability of exporting using Probit model. They
acquire unbalanced panel of 1,655 Egyptian manufacturing firms from World Bank’s
Enterprise Surveys database from 2003 to 2008 and use composite indicators of
financial health based on two financial variables, namely ratio of net income to total
assets and the share of new investment financed by equity. Their main results show that
financial constraints are the cause reducing the export participation of Egyptian firms.
Following Manova (2008), Manova (2013) tests her predictions by investigating data
about 107 countries including 27 sectors from 1985 to 1995 and comes up with a
conclusion that regression findings support her propositions that countries which are
financially developed are more likely to export.
Nagaraj (2014) is also interested in investigating the relationship between financial
constraints and exports decision of Indian manufacturing firms by using multiple
estimators (e.g, fixed effects estimates, Probit estimates, GMM system estimator) and an
unbalanced panel of 7,000 firms from 1989-2008. The rich panel allows Nagaraj (2014)
to analyze the firms’ exporting decision over a long period which is long enough to
address the persistent of exporting behavior. She finds an evidence support on the

negative relations between the two variables. Productivity, a large size and ownership
by foreign firms have a positive influence on firms’ propensity to export. However, even
a foreign firm with adverse financial health does not export. Moreover, financial health
is the cause not the effect of exports. Firms face less financial constraints can increase its
extensive margin of export (increase in export due to new exporters).
3. Empirical Analysis
Data and descriptive statistic
This study utilizes the data from the Annual reports, Financial Statements of
manufacturing listed firms for period from 2007 to 2016 in HOSE. The data was


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collected from the official website of HOSE and other prestigious private websites such
as vietstock.vn, cophieu68.vn and cafef.vn. Those firms with any missing observations
for any variables (e.g., sales, employment, exports, firms’ financial information etc.) in
the model during the research period are dropped. Hence, the final sample contains 75
listed manufacturing firms with adequate requested information during 10 years.
In order to proxy for financial constraints, this study follows the financial variables
approach using two liquidity ratio and leverage ratios from Nagaraj (2014) and long-run
debt to capital ratio from Egger and Kesina (2010). Firstly, leverage is measured as the
ratio of short-term debt to current assets (Nagaraj, 2014). The lower the leverage, the
better the ability of firms to raise funds and obtain the external finance for entry cost in
export markets. Thus, the researcher expects the negative effect of leverage on export
decision. Secondly, liquidity is used to measure firms’ capacity to invest or pay sunk
entry costs in order start exporting (Nagaraj, 2014). Liquidity is measured as the ratio
of the difference between current assets and current liabilities to total assets. The higher
the liquidity ratio, the better would be the financial health of the firm. Finally, long –
term debt to total capital ratio equal to long term debt divided by total capital of firm
level (Egger and Kesina, 2010). This ratio computes the proportion of a company's long

– term debt compared to its available capital for a long time. The higher this ratio, the
stronger the financial constraints the firms are subject to. As a result, a firm is ceteris
paribus more financially constrained the higher is the higher leverage and long – term
debt to total capital ratios and lower is the liquidity ratio.
Bernard and Jensen (2004) find that exporters are more productive, bigger, and
more capital intensive. Hence, the three factors including firm size, labor productivity
and human capital intensity are concerned as control variables and are measured in
logaric. Firm size is expressed by the number of permanent workers of the firm
according to Greenway et al. (2007), Egger and Kesina (2010), Bellone et al. (2010),
Wagner (2015). Labor productivity is measured in this study in term of sales to
employment ratio following Wagner (2015), Egger and Kesina (2010). Human capital
intensity is measured by the fixed assets to employment ratio following Raoul Minetti,
Susan Chun Zhu (2011), Egger and Kesina (2010).
Table 1 below provides the descriptive statistics in terms of mean and standard
deviation of variables which including two main parts. The first part is financial
constraints which summarizes three measurements capturing aspect of financial
constraints. The second part is control variables which summarizes three control
variables in logaric forms.


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Table 1
Descriptive Statistics
Observations

Non exporters

Exporters

181

569

Mean

Standard deviation

Mean

Standard deviation

LTDC

.1019145

.1471238

.099618

.1488349

LVR

.3081978

.2561828

.3504263

.2827597

LQD

.2546156

.1743458

.2423332

.1976783

lnSIZE

6.009017

.8780968

6.851027

.2423332

lnLAP

21.46669

1.169481

20.89699

1.107779

lnHCI

19.59212

1.151961

19.45675

1.270875

Financial Constraints

Control variables

On average, exporters have higher leverage but lower long-term debt to total capital
and lower liquidity ratio than non-exporters. Even thought exporters have bigger size,
their labor productivity and human capital intensity is lower in comparison with nonexporters which are the reason to increase costs and demand for external finance to
these companies. The descriptive statistic is quite different with the data-set of others
papers (Nagaraj, 2014; Manova, 2013; Kiendrebeogo and Minea, 2012, Egger and
Kesina, 2010) due to the context of Vietnam where the study is conducted. In which
majority of Vietnamese exporters are only able to assemble and outsource products due
to the lacking of domestic supplied materials and the competitiveness of Vietnamese
enterprises and their products are not strong enough in comparison with others
countries’.
Moreover, the imperfect capital market and the financial systems are not advanced in
Vietnam; it is still very difficult for firms to obtain external sources of funds regardless
of their high demand for such external finance. Exporters need external funds for their
entry investments in exporting markets because their internal finance is not strong

enough. However, the stock market in Vietnam is not matured, so equity financing is
not very effective for exporters to raise additional capital regularly. Hence, borrowings
seem to be better solution in which long – term debt requires much more collaterals (i.e.
lands, buildings, factories) and higher interest rate (i.e. 9 - 11% per year); exporters
then rely more on short – term debts (i.e. 6 – 9 % per year) to fulfill their need of
investments due to their limited fixed assets.


Le Mai Thy et. al. | 595

Estimation
In order to estimate the influences of financial constraints on export decision, the
study adapts the population average probit models as in Nagaraj (2014) and Egger and
Kesina (2010). The decision whether a company shall participate in the export market is
indicated by a binary outcome model which explains the probability of a firm of starting
to export. A binary variable Expdit for exporting (unity) versus non-exporting (zero) for
firm i in year t is as below:
0, if Expdit * = 0
Expdit =

1, if Expdit * > 0,

where Expdit * is the export revenue that firm i can generate in year t. The export
decision is driven by a latent variable of export revenues Expdit *.
As mentioned above, the three control variables including firm size, labor
productivity, and human capital intensity are measured in logaric. Financial constraints
are represented by each of three measurements above including long-term debt to total
capital, leverage and liquidity ratios. For each of financial constraints’ measurement,
this study will run a separate regression. The three probit models estimates the
influence of financial constraints on export decisions of Vietnamese manufacturing

firms in HOSE from 2007 to 2016 are then as below:
Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LTDCit + zitγ)

(1)

Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LVRit + zitγ)

(2)

Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LQDit + zitγ)

(3)

Results
Running probit models with bootstrap option could give the correct standard
deviation and address the heteroskedasticity, thus, its results are more favorable in this
study. Therefore, the following discussions will be based on the results from probit
model with bootstrap. The empirical results from population average probit models
with bootstrap are given in Table 2 below.
As same as with the researcher expectation, three main independent variables of
interest are all significant. In particular, liquidity is positively related to exporting
probability while long-term debts to capital and leverage show negative relationship
with probability of exporting. Firms with higher liquidity ratio are more likely to start
exporting. If firms do not face with liquidity constraints, these firms are able to have


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sufficient fund so that they can afford to pay the sunk entry costs when entering export
markets.

On the other hand, firms with higher long-term debt to capital and leverage ratios
are less likely to start exporting. This is because the higher these two ratios the lower
the ability of firms to raise funds and obtain the external finance for entry cost in export
markets which hinder them from entering exporting. All these results are consistent
with a negative relationship between financial constraints and export probability. In
other words, the main hypothesis of this study is supported. The results are consistent
with previous papers’ findings, namely Nagaraj (2014), Manova (2013), Kiendrebeogo
and Minea (2012), Egger and Kesina (2010), Cole et al. (2010), Li and Yu (2009).
Table 2
Results
Dependent variable: Export dummy variable
Regression
model

Population average probit models
(bootstrap with 50 replications)

Marginal effects population average probit
models (bootstrap with 50 replications)

(1)

(2)

(3)

(1)

(2)

(3)

0.077**

0.086**

0.086*

0.023**

0.026**

0.026**

(0.036)

(0.036)

(0.050)

(0.010)

(0.011)

(0.013)

lnLAP

0.03**

0.032**

0.032**

0.009**

0.010**

0.010**

(0.015)

(0.016)

(0.014)

(0.004)

(0.005)

(0.005)

lnHCI

0.012**

0.009**

0.019*

0.004**

0.003***

0.006*

(0.006)

(0.004)

(0.010)

(0.002)

(0.001)

(0.003)

lnSIZE

LTDC
LVR
LQD

-0.174

-0.052***

(0.068)**

(0.020)
-0.087**

-0.026**

(0.037)

(0.011)
0.139*

0.042**

(0.076)

(0.021)

Notes: Independent variable is a binary outcome whether a firm is an exporter or not. Three control
variables including lnSIZE, lnLAP, lnHCI are measured in logaric. Financial constraints are measured as
follows: LTDC = long-term debt/(long-term debt + shareholders’ equity); LVR = short-term debt/current
assets; LQD = (current assets – current liabilities)/total assets. Standard errors and bootstrap standard
errors are reported in parentheses. *, **, *** indicate significance at 10%, 5% and 1% respectively. 1.
Marginal effects evaluated at mean.


Le Mai Thy et. al. | 597

For control variables, as can be seen from Table 2, all of three controls variables
including firm size, labor productivity and human capital intensity are positive related
to exporting probability. It can be interpreted that firms with bigger size, higher labor
productivity and higher human capital intensity are more likely to become exporters.

This results are also consistent with Bernard and Jensen (1999 and 2004), Greenaway
and Kneller (2004), Cole, Elliot, Virakul (2010), Egger and Kesina (2010), Wagner
(2012a, 2012b, 2015).
Also in Table 2, the marginal effects of all variables after probit models are all
statistical significant. In particular, liquidity is positively related to exporting probability
while long-term debts to capital and leverage variables have negatively relationship. For
three main measurements of financial constraints, an increase in the long – term debt to
capital causes a reduction in the probability of exporting by 0.052. Similarly, with a
negative sign of the marginal effects, it shows that an increase in the leverage causes a
reduction in the probability of exporting by 0.026. In contrast with the negative
relationship among long-term debts to capital and leverage to export decision, the
liquidity’s marginal effects sign indicates a positive relationship, it can be interpreted
that a decrease in the liquidity causes an increase in the probability of exporting by
0.042.
These findings imply that financial constraints do have negative effects to the entry
decision into export market of Vietnamese manufacturing firms. In other words, for
those firms who have higher long-term debts to capital, higher leverage and lower
liquidity are less likely to become exporters. These findings obviously support the main
hypothesis of this study. These results one more time are consistent with previous
researches including Nagaraj (2014), Manova (2013), Kiendrebeogo and Minea (2012),
Egger and Kesina (2010), Cole et al. (2010), and Li and Yu (2009).
For more information, the empirical results for three control variables in this study
are all significant and consistent with Bernard and Jensen (1999 and 2004), Greenaway
and Kneller (2004), Cole, Elliot, Virakul (2010), Egger and Kesina (2010), Wagner
(2012a, 2012b, 2015). The finding for each of control variable will be discussed in detail
as follows. Firstly, firm size has positive and significant effects across three models. This
means larger firms are more likely to become exporters than smaller ones. The results
shows an increase in firm size by one employee increase the probability of exporting by
0.023, 0.026 and 0.026, respectively. This is supported by the same findings of Bernard
and Jensen (1999 and 2004), Greenaway and Kneller (2004), Cole, Elliot, Virakul

(2010), Egger and Kesina (2010), Wagner (2015). Another control variable that


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determines a firm’s export decision is labor productivity. In all of three columns in Table
7 above, the results show the positive relationship and all significant which means with
one unit increase in labor productivity, the probability of exporting will increase by an
average value of 0.010. This implies that firms that have higher productivity level of
labors are more likely to enter the export market than the others. This finding is
supported by Bernard and Jensen (1999 and 2004), Girma et al. (2004), Greenaway and
Kneller (2004), Egger and Kesina (2010), Wagner (2012a). The last control variable that
the researcher wants to mention here is the human capital intensity. This variable is
positive related to export decision and statistically significant. It means firms that
produce high-quality innovative products are more likely to become exporters. Based on
the result in Table 7, it can be interpreted that an increase in human capital intensity
causes an increase in exporting probability by 0.4, 0.3 and 0.6 percent points. The result
is in line with Bernard and Jensen (1999 and 2004), Wagner (2012b, 2015).
Robustness Check
This study follows Egger and Kesina (2010), Nagaraj (2014) to explore the
robustness of findings by adding the interactions between each of three main
explanatory variables of interest, including long – term to total capital, leverage and
liquidity, respectively, with three control variables firm size, labor productivity, and
human capital intensity into the three probit models. The new three probit models in
this research are then as follows:
Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LTDCit + β5LTDCitxSIZEit
+ β6LTDCitxLAPit + β7LTDCitxHCIit + zitγ)
(1)
Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LVRit + β5LVRitxSIZEit +
β6LVRitxLAPit + β7LVRitxHCIit + zitγ)

(2)
Pr(Expdit = 1|xit) = Ф(β0 + β1SIZEit + β2LAPit + β3HCIit + β4LQDit + β5LQDitxSIZEit +
β6LQDitxLAPit + β7LQDitxHCIit + zitγ)
(3)
The Table 3 below shows the results after running probit models with interactions
between variables. Once again, the results confirm this study’s main hypothesis that
manufacturing listed firms on HOSE that are subject to financial constraints are less
likely to become exporters for the period of 2007 - 2016. In specific, the results show
that the long-term debts to capital and leverage are negatively related to probability of
exporting whilst the liquidity shows the positive relationship. These results are
consistent with the findings of this study’s main models mentioned above as well as


Le Mai Thy et. al. | 599

previous researches including Nagaraj (2014), Manova (2013), Kiendrebeogo and Minea
(2012), Egger and Kesina (2010), Cole et al. (2010), Li and Yu (2009).
The researcher also reports the marginal effects of three new population average
probit models with interaction between variables in Table 3. Adding the interactions
into the population average probit models, the results show that an increase in the long
term debt to capital causes a reduction in the probability of exporting by 0.175; and an
increase in the leverage causes a reduction in the probability of exporting by 0.449. In
contrast, a decrease in the liquidity causes an increase in the probability of exporting by
0.427.
Table 3
Robustness check’s results
Dependent variable: Export dummy variable
Regression
model
lnSIZE

lnLAP
lnHCI
LTDC
LTDCxlnSIZE
LTDCxlnLAP
LTDCxlnHCI
LVR
LVRxlnSIZE
LVRxlnLAP

Population average probit models with
interactions

Marginal effects population average probit
models with interactions

(1)

(2)

(3)

(1)

(2)

(3)

0.029

0.055***

0.108***

0.001

0.017***

0.033***

(0.033)

(0.021)

(0.019)

(0.001)

(0.006)

(0.006)

-0.006

0.011

0.039**

-0.000

0.003

0.012**

(0.027)

(0.018)

(0.016)

(0.001)

(0.005)

(0.005)

0.024

0.014

0.014

0.001

0.004

0.004

(0.17)

(0.011)

(0.014)

(0.001)

(0.003)

(0.004)

-4.213**

-0.175*

(2.061)

(0.092)

0.837***

0.035***

(0.177)

(0.011)

0.177*

0.007

(0.105)

(0.005)

-0.238**

-0.010*

(0.104)

(0.005)
-1.505***

-0.449***

(0.558)

(0.169)

0.1***

0.030***

(0.024)

(0.007)

0.053*

0.016*

(0.029)

(0.009)


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LVRxlnHCI
LQD
LQDxlnSIZE
LQDxlnLAP
LQDxlnHCI

-0.017

-0.005

(0.023)

(0.007)
1.418**

0.427**

(0.713)

(0.217)

-0.107***

-0.032***

(0.028)

(0.009)

-0.041

-0.012

(0.041)

(0.013)

0.012

0.004

(0.030)

(0.009)

Notes: Independent variable is a binary outcome whether a firm is an exporter or not. Three control
variables including lnSIZE, lnLAP, lnHCI are measured in logaric. Financial constraints are measured as
follows: LTDC = long-term debt/(long-term debt + shareholders’ equity); LVR = short-term debt/current
assets; LQD = (current assets – current liabilities)/total assets. Standard errors and bootstrap standard
errors are showed in parentheses. *, **, *** specify significance at 10%, 5% and 1% respectively.
Marginal effects evaluated at mean.

4. Conclusion
This study investigates the influences of financial constraints, through three
measurements including long-term debts to capital, leverage and liquidity ratios, on
export decision of Vietnamese manufacturing listed firms on HOSE for the period of
2007 up to 2016. Applying the population averaged probit models with bootstrap
method, the main hypothesis is supported that Vietnamese manufacturing listed firms
that are entitled to financial constraints are less likely to become exporters. In
particular, only manufacturing firms that have lower long-term debts to capital, lower
leverage and higher liquidity are more likely to become exporters. The results are
consistent with previous researches of Nagaraj (2014), Manova (2013), Kiendrebeogo
and Minea (2012), Egger and Kesina (2010), Cole et al. (2010), Li and Yu (2009).
The results also show that the long – term debt to total capital ratio influence the
export decision at most by 0.052. Next is the liquidity ratio with 0.042 and leverage is
last ranking with 0.026 in the effect on export decision of Vietnamese manufacturing
firms. Moreover, the other control variables used in this study consisting of firm size,
labor productivity and human capital intensity are also positively related to the entry
decision into export market of Vietnamese manufacturing firms. In other words, firms
with bigger size, higher productivity and higher human capital intensity are more likely
to become exporters.


Le Mai Thy et. al. | 601

The study contributes to micro-evidence literature of the impact of financial
constraints on Vietnamese manufacturing firms’ propensity to export by firstly adopting
estimation strategies which estimate population average probit models for panel data in
Ho Chi Minh City context. In addition, by re-confirming the influences of previous
variables consisting of firm size, labor productivity and human capital intensity, this
study also supports the measurements of financial constraint by using financial ratios.
This research is useful for Vietnamese Government to understand the reason why some

manufacturing are less likely to participate in the export market. This calls for more
attention from the Government to setup proper strategies in order to create favorable
conditions and encourage firms to export. In specific, the Government should develop
the financial markets and financial sectors such as banking systems, financial
intermediaries and institution from which financial constrained firms can be easier to
get access into external funding and enter the export markets. In addition, the
Government should also issue some promotion policies (i.e. investments incentives, tax
incentives) for new entry into export so that firms can afford the initial sunk entry cost.
Moreover, international trade economists and researchers can refer to this study’s
finding for their own interest of further researching.

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