Do Financing Constraints Impact Outward
Foreign Direct Investment?
Evidence from India
Subash Sasidharan and M. Padmaja∗

This study examines the role of financing constraints in explaining outward
foreign direct investment (FDI) using unique firm-level panel data on Indian
manufacturing during the period 2007–2014. We consider the role of both
internal and external finance, and employ instrumental variable probit and Tobit
models to examine financing constraints in outward FDI decisions and intensity.
We find that internal finance impacts the likelihood of outward FDI. 更远,
using count data models, we examine financing constraints in determining
strategies regarding a firm’s number of affiliates abroad. Our findings reveal
that firms with greater cash flows and liquidity are likely to have more foreign
affiliates.

关键词: financing constraints, outward FDI, total factor productivity
JEL codes: F14, F21, F23

我. 介绍

Firm-level internationalization decisions regarding foreign direct investment
(FDI) have recently garnered attention in the literature on international trade. 这
theoretical models, which explain the process of internationalization, focus on
firm heterogeneity in terms of productivity (Melitz 2003; Helpman, Melitz, 和
Yeaple 2004; Yeaple 2009). Productivity is highlighted as the determining factor
in firm decisions to enter foreign markets, either through FDI or exports. 这些
models posit that exporting and FDI involve sunk costs and fixed costs. Firms
above a minimum threshold level of productivity engage in exporting while highly
productive firms undertake FDI. Recent theoretical models extend this argument
by emphasizing the role of financing constraints as a barrier to entering foreign

∗Subash Sasidharan (corresponding author): Associate Professor, Department of Humanities and Social Sciences,
Indian Institute of Technology Madras, 印度. 电子邮件: subash@iitm.ac.in; 中号. Padmaja: Research Assistant Professor,
SRM Institute of Science and Technology, Chennai, 印度. 电子邮件: padmaja251988@gmail.com. An earlier version of
this paper was presented at the Conference on Leveraging FDI for Sustainable Economic Development in South Asia
organized by the Copenhagen Business School in Denmark on 2–3 October 2015. The authors would like to thank
Aradhna Aggarwal, Ari Kokko, 氮. S. Siddharthan, Jijo Lukose, Vinish Kathuria, and all the conference participants
for helpful discussions and comments. We would also like to thank Joe Varghese Yeldho and Arun Sudarsan for
excellent editorial and research assistance; the managing editor and two anonymous referees for helpful comments
and suggestions. The usual disclaimer applies.

Asian Development Review, 卷. 35, 不. 1, PP. 108–132
https://doi.org/10.1162/adev_a_00107

© 2018 Asian Development Bank
and Asian Development Bank Institute

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Do Financing Constraints Impact Outward Foreign Direct Investment? 109

市场 (Chaney 2013, Manova 2013, Muuls 2015). These models incorporate
financing constraints in well-known firm heterogeneity models, following Melitz
(2003). The problem of financing constraints assumes greater significance in setting
up affiliates abroad since firms face bigger barriers in the form of huge upfront
fixed costs (Helpman, Melitz, and Yeaple 2004). During the previous 2 几十年,
the entry of firms from emerging economies like India into foreign markets has
increasingly become a global phenomenon. Previously, firms from these economies
were unable to expand beyond their own borders due to regulatory hurdles and
resource constraints. 自20世纪90年代以来, reform measures adopted by policy makers in
India have enabled firms to escape domestic resource constraints and integrate with
global markets (Gaur, Kumar, 和辛格 2014). The rapid pace at which these firms
have integrated with the global economy requires thorough empirical examination
given that these firms operate in an underdeveloped institutional environment that
inhibits them from accessing resources (Khanna and Palepu 1997).

The much-acclaimed OLI framework (Dunning 1993) and resource-based
view of FDI (巴尼 1991; 彭 2001; Westhead, 赖特, and Ucbasaran 2001)
consider resources as the key determinant of FDI. Resources constitute both
technology and capital. 另一方面, firm heterogeneity theory is based on
an economic approach with a focus on efficiency considerations. Firms in emerging
economies are not technologically superior but their investment decisions can also
be affected by financial constraints. 出奇, the role of financial factors is
overlooked in the above-mentioned approaches since traditionally FDI has emerged
from advanced economies where capital markets are developed and financial
constraints may not pose serious obstacles in making outward FDI decisions.
然而, the recent proliferation of multinationals in emerging economies like
India poses a puzzle since capital markets are not developed in these economies.
因此, the question of how multinationals arise in resource-poor economies like
India assumes greater significance. Unlike the People’s Republic of China, 在哪里
outward FDI is mainly driven by state-owned enterprises (Morck, Yeung, 和
赵 2008), in India outward FDI is predominantly driven by private sector firms.
所以, it is important to understand whether financing constraints play a major
role in the outward FDI decisions of firms in emerging economies like India.

Outward FDI is considered a means to escape from the “institutional voids”
encountered by firms in emerging economies (Khanna and Palepu 2006). 尝试
have been made to study the internationalization process of emerging market
multinationals. 然而, the focus of these studies is mainly on entry-mode choices
and determinants of outward FDI identified by using firm-level and aggregate
economy-level data (Chittoor and Ray 2007; Woodcock, Beamish, and Makino
1994; Kumar 2007; Pradhan 2004). Buch et al. (2014) extended the theoretical
models of internationalization strategy to the case of outward FDI in the presence
of financing constraints. Since outward FDI involves high fixed costs, 哪个是
incurred upfront, firms depend on their own internal financing and/or external

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110 Asian Development Review

sources for financing FDI. 然而, very few empirical studies have explored the
role of financing constraints in determining outward FDI decisions (Buch et al.
2014, Duanmu 2015). Financing constraints are regarded as an important factor
in determining firm-specific decisions such as capital investment, research and
发展 (右&D) 投资, and exports. 然而, financing constraints’ role
in determining outward FDI decisions has not received much empirical attention.

The present study attempts to bridge this gap in the literature by examining
the role of financing constraints in determining outward FDI decisions as well as
the extent of outward FDI undertaken by firms. The standard empirical approach
is the use of cash flow sensitivity analysis in identifying the existence of financing
constraints. A recent strand of literature argues that firms lacking internal funds may
be able to obtain external finance provided they have adequate collateral (Manova
2013).1 This proposition has been verified by studies on firm-specific decisions
on outward FDI (Duanmu 2015).2 Outward FDI from emerging economies like
India is increasingly becoming an important component of the world’s investment
flows. India’s outward FDI stock registered a quantum jump over the past 2 几十年,
rising from a negligible $25 million during the early 1990s to $241 十亿 2013.
The momentum of these investment outflows picked up during the second half
of the 2000s. One can attribute this increasing trend of outward FDI by Indian
firms to market-oriented reforms undertaken by the Government of India during
the early 1990s. Indian policy makers have recognized the importance of these
investments and take measures to ease the stringent regulatory rules on overseas
investments.3 India’s share of total outward FDI from Asia recorded a significant
increase from 0.4% 到 4.3% 之间 2001 和 2011 (Export–Import Bank of
印度 2014). The bulk of outward FDI flows originate from the manufacturing
sector, which accounted for 32% of the total outward FDI from India in 2011–2012
(Export–Import Bank of India 2014). Existing studies on outward FDI in the context
of India have overlooked the role of financing constraints. 所以, the objective
of the present study is to examine financing factors in determining outward FDI
based on the experience of Indian firms. We analyze the role of both internal
and external financing constraints in determining outward FDI decisions and the
amount of outward FDI made by Indian manufacturing firms.4 Further, we extend
our analysis to examine the role of financing constraints in determining the number

1In our empirical analysis, we test for the role of external finance following this line of argument.
2Duanmu (2015) finds a significant role for external financing constraints in determining the outward FDI

decisions of manufacturing firms in the People’s Republic of China.

3The Reserve Bank of India (RBI) relaxed the guidelines for investing overseas by raising the annual overseas
investment ceiling for Indians to establish joint ventures and wholly owned subsidiaries from $75,000 到 $125,000.
4Recent studies on sources of financing in the context of Indian manufacturing firms point to the increasing
role of internal funds as a major source of financing. External sources of funding, such as banks and the corporate
bond market, play a meager role in India compared with other emerging economies, reflecting the underdevelopment
of Indian financial markets (Allen et al. 2012).

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Do Financing Constraints Impact Outward Foreign Direct Investment? 111

of a firm’s foreign affiliates. This additional exercise is undertaken since establishing
more foreign subsidiaries incurs higher fixed costs.

Our study contributes to the existing literature in the following ways. 第一的,
empirical studies on India’s experience with outward FDI concentrate on its
determinants. We add to the nascent but growing body of literature on the effects
of financing constraints on FDI—controlling for firm productivity, 尺寸, 所有权,
and export status—based on the experience of an emerging economy like India.
Unlike previous studies that considered the significance of either internal or external
finance, we focus on both aspects. 第二, our study uses a novel firm-level data set
of outward FDI from India, which allows us to comprehensively analyze the role
of financing factors in determining outward FDI. We combine data for the years
2007–2014 from the Prowess firm-level database with outward FDI data provided
by the Reserve Bank of India (RBI). 更远, our data set contains information
pertaining to the number of affiliates and the entry mode of these firms, 哪个
enables us to understand their complex business strategies. 最后, unlike previous
studies that focus on the likelihood of engaging in outward FDI, our data set permits
us to account for the total amount of foreign investments, which enables us to test
the relationship between financing constraints and the probability of undertaking
foreign investments, as well as the amount of outward FDI.

The remainder of the paper is organized as follows. Section II explains the
data and descriptive statistics. Section III provides the methodology and empirical
模型. The findings are discussed in section IV. The final section concludes.

二. Theoretical Underpinnings and Literature Review

The standard industrial organization approach considers FDI arising out
of product and technology market imperfections (Hymer 1976, Rugman 1981).
Recent theoretical models attribute the decision of a domestic firm to export or
undertake FDI to productivity effects (Melitz 2003; Helpman, Melitz, and Yeaple
2004). According to this set of models, the presence of fixed costs in entering
foreign markets leads more productive firms to export, with the most productive
firms engaging in FDI. Following these models, numerous studies investigated the
findings of Helpman, Melitz, and Yeaple (2004) and their theoretical predictions.
Yeaple (2009) provides strong empirical evidence to support the findings of
Helpman, Melitz, and Yeaple (2004) based on the FDI experience in the United
状态. Similar findings were reported by Kimura and Kiyota (2006); Girma,
Kneller, and Pisu (2005); 瓦格纳 (2006); 和李 (2010) for Japan, 联合
王国, 德国, and the Republic of Korea, 分别.

As mentioned above, productivity is not the only decisive factor driving
the decision to serve foreign markets. Some of the recent models extend the
Melitz (2003) model to incorporate financing factors in explaining the decision
to undertake FDI and exporting (Chaney 2013, Buch et al. 2014). 然而, 这样的

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112 Asian Development Review

empirical studies on firms’ internationalization process and financing constraints
are confined mainly to export decisions. The inclusion in the literature of the
relationship between financing constraints and outward FDI is very recent. Buch
等人. (2014) develop a theoretical model similar to firm heterogeneity models that
show outward FDI being more vulnerable to financing constraints than exports.
Firms undertaking FDI use internal funds for their international investments rather
than using external finance. Firms rely more on internal funds since banks or other
creditors may be unwilling to lend due to the information asymmetry surrounding
the uncertainty and riskiness of investments in foreign markets. Buch et al. (2014)
provide empirical support for their theoretical predictions based on the experience
of German firms.

Studies on financing constraints and firm decisions in the context of India
focus mainly on capital investment, 右&D, and exports (Athey and Laumas 1994;
戈什 2006; Bhaduri 2005; Bhattacharyya 2008; Sasidharan, Lukose, and Komera
2015). Some recent empirical studies have extended this framework to explain the
export decisions of Indian firms. Lancheros and Demirel (2012) examined the role
of credit constraints in the export behavior of Indian service firms and found that
financing factors have no major impact. 反而, nonfinancing variables such as
size and total factor productivity were found to be significant. In a recent study,
Nagaraj (2014) analyzed the role of financing constraints in the export participation
decisions of manufacturing firms in India and found that financing constraints affect
the probability of firm exports. Previous research on outward FDI by Indian firms
has largely been descriptive in nature (Nayyar 2008). Among these studies, 一些
focus on the push factors of outward FDI using firm-level data (Kumar 2007,
Pradhan 2004). Others concentrate on the locational choices of Indian outward FDI
and motivational factors using a gravity model (Hattari and Rajan 2010). Exceptions
include the firm-level studies of Goldar (2013) and Thomas and Narayanan (2013)
that investigated the relationship between outward FDI and productivity. 然而,
as mentioned above, existing studies in the context of emerging economies have
overlooked the role of financing factors in determining outward FDI.

二. Data Sources

To carry out the empirical analysis, we combine two different data sources.
第一的, financing information and firm-specific characteristics such as sales, assets,
export status, and ownership information are obtained from the Prowess database
provided by the Center for Monitoring Indian Economy. The Prowess database is
generated from the annual reports and balance sheets of over 27,000 firms belonging
to the utilities, manufacturing, and service sectors. The database contains both listed
and unlisted firms, and has previously been employed in many firm-level studies
analyzing financing constraints related to fixed investments and R&D (戈什 2006;
Sasidharan, Lukose, and Komera 2015). 第二, outward FDI data were obtained

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Do Financing Constraints Impact Outward Foreign Direct Investment? 113

from the RBI to compile a database containing information about the investments
of around 3,600 Indian firms in the utilities, manufacturing, and service sectors.
更远, this database provides information on FDI destinations and the number
and nature of affiliates (例如, joint venture versus wholly owned subsidiary).

In our empirical analysis, we restrict the sample to firms belonging to the
manufacturing sector since the fixed costs of investing abroad (例如, setting up
foreign affiliates) are more significant and higher for manufacturing firms than
service firms (Helpman, Melitz, and Yeaple 2004). 更远, manufacturing firms
were more likely to venture abroad, with manufacturing firms accounting for about
40% of India’s total outward FDI during the review period (Goldar 2013). 我们
matched the RBI data with the Prowess data on financing characteristics and other
major firm-specific characteristics to yield a subset of 329 firms engaged in outward
FDI.5 The data comprises various industry sectors.6 We use unbalanced panel data
covering the period 2007–2014.7 The sample firms were selected based on the
following criteria.8 First, we include only those firms with positive sales and fixed
assets. 第二, firms reporting a negative cash flow were excluded from the sample
since they were considered to be financially distressed (Sasidharan, Lukose, 和
Komera 2015). Flow variables such as sales are deflated with the corresponding
industry Wholesale Price Index obtained from the Central Statistical Organisation.
To remove the effect of outliers, variables were winsorized at the upper and lower
0.5 percentiles.

三、. 方法

We estimate the following specification using the instrumental variable probit
(ivprobit) regression to analyze the role of financing constraints in determining FDI
决定:9

Pr (OFDI)它

= β0 + β1Zi,t−1 + β2Xi,t−1 + 英石 + εi,t

(1)

5We matched the RBI data on outward FDI at the firm level with firm-level data on financial statements and
other major firm-specific characteristics provided by the Prowess database. We were able to match 628 outward FDI
firms belonging to the manufacturing sector. We applied filters to the matched data to clean the data. After applying
the first filter of positive sales and fixed assets, the number of firms was reduced to 596. 下一个, we excluded those
firms with a negative cash flow, reducing our sample to 568 firms. 最后, we dropped those firms with missing
values for the financing constraint variables, leaving us with 329 firms.

6It is evident from the data that the bulk of FDI stems from the machinery and electrical equipment (39%),

transport equipment (29%), chemicals and chemical products (19%), and pharmaceutical (12%) 行业.

7The RBI provides outward FDI information at the firm level from 2007 向前. The absence of information

prior to 2007 restricts our study to the period 2007–2014.

8We compared the characteristics of the selected sample of firms with those firms engaged in outward FDI
that were excluded from the sample. The comparison shows that the selected sample for the present study is not
biased. The descriptive statistics of the excluded sample are reported in column 7 of Table 1.

9An ivprobit model is used since the endogenous regressors included are continuous variables and the

dependent variable is of a binary nature.

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114 Asian Development Review

where i and t denote firm and year, 分别. To account for endogeneity and
simultaneity among explanatory variables, we use lagged values of the time-varying
explanatory variables. The dependent variable, OFDIit, denotes whether firm i has
undertaken outward direct investment or not. OFDIit is defined as a binary variable
taking a value of 1 if a firm has reported outward FDI and 0 否则. Zit−1 and
Xit−1 represent one period lagged values of vector-of-financing constraint variables
and firm-specific control variables, 分别. St denotes a set of time dummies
to account for macroeconomic factors.

In addition to the role of financing constraints in the likelihood of engaging
in outward FDI, we also examine the effect of financing constraints on the amount
of outward FDI (defined as the ratio of outward FDI to total assets of the firm).10
We employ a random-effects panel Tobit model to examine the effect of financing
constraints in determining the outward FDI share (Bhaumik, Driffield, and Pal
2010).11 Since a large number of firms in our data set report no FDI, left censoring
has to be taken into account. The use of a Tobit model helps to account for the
problem of left censoring. 方程 (2) below shows the model specification for
examining the role of financing constraints on the share of outward FDI:

OFDIit = max[0, 乙0 + β1Zi,t−1 + β2Xi,t−1 + 英石 + εi,t if OFDIit > 0]

(2)

where OFDIit is the share of outward FDI, which is the ratio of outward FDI to total
assets of the firm. The explanatory variables and other control variables are similar
to the basic specification. We also control for firm-specific characteristics such as
尺寸, 年龄, export orientation, and ownership status. 更远, we undertake another
empirical exercise to test the complex strategies of firms having multiple affiliates
by including the number of affiliates as a count variable. This variable is used as
a proxy to determine the outward investment decisions of the sample firms. 在这个
set of analysis, we employ count data models to analyze factors that determine the
number of foreign affiliates.

A.

Explanatory Variables

1. Measures of Financing Constraints

Our main variable of interest is the financing constraints. We have used
both internal and external financing measures to examine the role of financing
constraints in determining a firm’s outward FDI. 然而, the measurement of
financing constraints is a complex issue. Previous studies have employed various

10The RBI data report the value of outward FDI in dollar terms. We converted to rupees and took the ratio of

these converted values to the total assets of a firm.

11We have also estimated the model using the generalized least squares method and the results were found to

be consistent. Results of this estimation are available from the authors upon request.

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Do Financing Constraints Impact Outward Foreign Direct Investment? 115

direct and indirect proxies of financing constraints based on firm characteristics
(Farre-Mensa and Ljungqvist 2016).

2.

Internal Finance Measures

Cash flow. The standard approach in measuring financing constraints in the
literature is using a cash flow indicator. The cash flow sensitivity of an investment
is considered to be evidence of the existence of financing constraints, following the
pioneering work of Fazzari, Hubbard, and Petersen (1988). The sensitivity of a firm’s
investments to cash flow is interpreted as evidence of financing constraints.12 Many
subsequent empirical studies used cash flow as a measure of financing constraints
(Bond and Meghir 1994; Carpenter, Fazzari, and Petersen 1998). Firms with a
higher degree of internal finance find it easier to meet investment costs even if
they do not have access to external finance. We define cash flow as the ratio to
total assets, where cash flow is measured as profit after tax plus depreciation and
amortization.

Liquidity. 此外, we use liquidity as an alternative measure of financing
constraints, which is also widely used in literature. The liquidity ratio is measured
as current assets minus current liabilities scaled by total assets. We expect a positive
effect of liquidity on the probability of firms investing abroad. The availability of
higher liquidity enables firms to meet fixed costs. In addition to the possibility of
using internal funds, firms can obtain financing resources from external sources.
Liquidity is a standard measure of financing constraints used by various empirical
学习 (Greenaway, Guariglia, and Kneller 2007; Stiebale 2011).

3.

External Finance Measures

External finance is another important source of financing for firms. 外部的
finance becomes important because of the existence of upfront costs and the lag
between the expenses incurred and receipts received (Manova 2015). 下列的
Manova (2015) and Duanmu (2015), to account for the role of external finance,
we include two measures: (我) capital expenditure not financed by cash flow, 和 (二)
access to finance (defined as a ratio of long-term bank credit to total assets). 这
first measure (capital expenditure not financed by cash flow) accounts for outside
funding required by firms to undertake long-term investment projects and relates to
fixed costs (Manova 2015). The second measure (access to finance) is an alternative

12Cash flow as a measure of financing constraint has been questioned by various researchers (Kaplan and
Zingales 1997). They point out that it captures the future investment opportunity and is nonmonotonic in nature.
While Fazzari, Hubbard, and Petersen (2000) point out certain limitations in the approach followed by Kaplan and
Zingales (1997), arguing that their theoretical model fails to capture the approach used in the literature and pointing
out that their empirical classification system is flawed in identifying whether firms are constrained and the degree of
financing constraints across firm groups.

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116 Asian Development Review

measure of dependence on external finance and it accounts for a firm’s access
to bank credit. Both variables are expected to have a positive impact on a firm’s
outward FDI decisions.

4.

Other Firm-Specific Characteristics

Firms that are heavily indebted have very little collateral to offer, which acts
as a constraint on their expansion abroad (Buch et al. 2014). 所以, we control
for a firm’s leverage (debt ratio) measured as the ratio of debt to total assets. The size
of the firm is considered one of the major firm-specific factors affecting firm-level
决定. This accounts for scale effects (Krugman 1980), with larger firms
always having the advantage of lower average costs, better information, and easier
access to funds. Exporting is another means of serving the foreign market. Size is
measured as the ratio of a firm’s total assets to the industry median value. 自从
exporting entails ample learning opportunities about international markets, it acts
as a stimulant to FDI. 所以, we include export status as a control with a value
的 1 if it exports and 0 否则. Total factor productivity (TFP) is an important
determinant of outward FDI (Helpman, Melitz, and Yeaple 2004). We estimate
TFP using the Levinsohn and Petrin (2003) procedure; we measure productivity
as the ratio of a firm’s TFP to mean industry TFP. Business group affiliates are a
salient feature of the Indian corporate sector. Since group affiliates have access to
the headquarters, they may face fewer constraints in terms of obtaining finance.
所以, we control for group association by assigning a value of 1 for group
affiliates and 0 否则. Regarding the effect of the age of the firm and the
decision to invest abroad, previous findings in the literature are inconclusive. 一些
studies report that older firms are more likely to undertake FDI (Blomstrom and
Lipsey 1991). 然而, other studies obtain mixed results (Asiedu and Esfahani
2001). We measure the age of the firm as the number of years since incorporation.
Higher fixed costs involved in establishing an affiliate abroad are expected to have
a negative impact on the number of affiliates owned by investing firms. 为了
account for fixed costs, we include asset tangibility measured as the ratio of fixed
assets to the total book value of assets (fixed costs) in the model on determinants
of the number of foreign affiliates. 更远, higher fixed costs are a proxy for the
amount of collateral or tangibility.

乙.

Econometric Issues

We employ limited dependent variable models like an ivprobit model,
a random-effects Tobit model, and a count data model to identify financing
constraints in explaining outward FDI decisions, the amount of outward FDI, 和
number of foreign affiliates, 分别. The endogeneity of financing constraints
is a major concern in empirical models that examine firm-level outward FDI

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Do Financing Constraints Impact Outward Foreign Direct Investment? 117

决定. Endogeneity arises due to the possibility that firm internationalization
can enhance the financing status of firms through access to international financial
markets or through export receipts (Buch et al. 2014). To control for endogeneity,
we use an ivprobit model. Specifically, we control for endogeneity issues using the
financing constraints of competitors of a particular firm as instruments (Buch et al.
2014). It is expected that the financing constraints of competitors are exogenous and
independent of the investment decisions of a specific firm. Mean industry cash flow
and mean industry liquidity, where we exclude the values of these measures specific
to the firm from mean values, are employed as instruments.13

We use another measure, credit rating, as an alternative instrument for
financing constraints.14 Empirical evidence shows that a credit rating can be taken
as a measure of financing constraints for the following reasons: (我) unrated firms are
assumed to have no access to public debt markets and therefore are dependent on
other intermediaries such as banks; 和 (二) a credit rating reduces the information
asymmetries between investors and firms, and thus implies that unrated firms are
more opaque and more likely to be rationed by lenders (Farre-Mensa and Ljungqvist
2016). The reason behind employing a credit rating as an alternative instrument
is that we expect firms with a credit rating to have a better financial status than
unrated firms (亚当 2009, 瓦格纳 2014, Muuls 2015). We define credit rating as
a binary variable taking a value of 1 if a firm is rated by Credit Rating Information
Services of India Limited (CRISIL) 或者 0 否则. 然而, such a measure
may be inadequate since rating status may not reflect whether firms are financially
constrained or not since unrated firms may not be financially constrained in a true
感觉 (Farre-Mensa and Ljungqvist 2016). To overcome this problem, we consider
firms that are rated and have been downgraded from their initial rating as financially
constrained firms. Downgrading has been considered in some studies that use credit
rating as a measure of financing constraints (Kisgen 2009, Tsoukas and Spaliara
2014).

To examine firm strategies for owning affiliates, we rely on the count data
型号. Count variables are characterized by excessive zeros, but have nonnegative
价值观. The count models control for excess zeros in the data. The basic count
model is the Poisson model, which is based on an equidispersion assumption. 自从
the assumption of equidispersion rarely holds, negative binomial and zero-inflated
negative binomial (ZINB) regression models are often used as alternatives because
they allow for overdispersion and unobserved heterogeneity (Hilbe 2014). Since in
our sample there are many zero counts, in addition to the Poisson and negative

13We test for the potential quality of instruments using an ordinary least squares regression. The results show
that all major variables are significant. The major interest variables—sector mean cash flow, sector mean liquidity, 和
credit rating—were found to be positively correlated to a firm’s financing condition, which confirms the endogeneity
问题. The results are not reported here for brevity and are available from the authors upon request.

14We have taken credit ratings assigned by the Credit Rating Information Services of India Limited (CRISIL)

from the Prowess database.

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118 Asian Development Review

binomial models, we employ a ZINB model to examine the role of financing
constraints in determining the number of foreign affiliates. Another econometric
issue with respect to count data models is the initial conditions problem associated
with the data. Initial conditions account for persistence in the nature of firm-level
decisions on these variables and determine the future values (Lemmon, 罗伯茨,
and Zender 2008) in the context of firm decisions like exporting and the number
of foreign affiliates. 所以, we control for the effect of initial conditions by
dropping the initial year count of number of foreign affiliates in the count data
model specification.

C.

Descriptive Statistics

桌子 1 provides the definition of the variables discussed above, 他们的
measurement, and descriptive statistics. Column 6 provides the results of the
equality of mean difference between outward FDI and domestic firms using a two-
tail t-test. The results of the t-test for the difference between outward FDI and
domestic firms indicate that, 一般, outward FDI firms are larger in terms
of size and cash flow, maintain more liquidity, and are less leveraged. Column
7 reports the descriptive statistics of firms engaging in FDI that were excluded
from the sample after the matching process. The average values of the firm-specific
characteristics are similar to those of firms that are included in the sample. 我们
reported this to provide evidence of our sample’s unbiasedness. 人物 1(A), 1(乙),
和 1(C) confirm the hypothesis that the outward FDI firms are larger, have greater
cash flow, and maintain more liquidity compared to their counterparts. 数字 1(d)
shows that in the case of TFP, the corresponding figures are overlapping, 哪个
provides evidence that some firms with higher productivity are not engaging in
outward FDI. 数字 1(e) shows no significant difference between the two groups
in terms of asset tangibility (proxy for fixed costs). Based on this exercise, 这
heterogeneity of outward FDI and non-FDI firms with regard to their financing
status is evident. 然而, there seems to be no clear difference in the case of asset
tangibility and TFP.

IV. Results and Discussion

桌子 2 presents the relationship between internal finance and the probability
of firms investing abroad using an ivprobit model. Columns 1 和 2 report the
estimates using cash flow and liquidity as financing indicators. 是一致的
theoretical predictions, our results confirm that financing constraints (内部的
finance) measured by cash flow and liquidity matter for outward FDI decisions.
We include size, 年龄, 生产率 (TFP), export status, leverage (debt ratio), 和
business group association as additional control variables. The size of the firm is
expected to have a positive impact on the firm’s investment. 另一方面,

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Do Financing Constraints Impact Outward Foreign Direct Investment? 119

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122 Asian Development Review

in the presence of financing constraints, the size of the firm may have a negative
impact on the probability of firms investing abroad.15 We observe that larger
firms have a higher probability of undertaking outward FDI. The TFP of firms
has a positive effect on outward FDI decisions. Our results are consistent with
other studies that report the significant effect of TFP on outward FDI (Duanmu
2015). 相似地, firms with international market experience in exporting have
a significantly higher probability of investing abroad. Firms that are exposed to
international markets through exports are more likely to invest abroad. 然而,
debt ratio fails to have a significant impact on outward FDI decisions. Firm age is
found to have a negative effect, which implies that young firms tend to invest more
in comparison with their counterparts. The coefficient of business group affiliation
is negative and significant. Even though a bit surprising, the slightly unexpected
result may be because firms affiliated with business groups prefer to focus
predominantly on the domestic market. Perhaps this is because family-owned and
business-group-affiliated firms find the institutional context in their home economy
optimal in comparison with the overseas environment. This is mainly due to the
risks involved, an unwillingness to allow dilution of ownership, and a lack of
strategic relationships with foreign investors (Bhaumik, Driffield, and Pal 2010).

Columns 5 和 6 report the results of the model with two external finance
措施: (我) the ratio of capital expenditure not financed by cash flow to total
assets, 和 (二) the ratio of long-term bank credit to total assets as a proxy for a firm’s
access to finance. We expect a positive effect for these two measures, which implies
that firms with access to external funds will have a higher probability of investing
国外. We retain all other explanatory variables, including the internal finance
措施. Contrary to the expectation, evidence of external finance ameliorating
financing constraints is weak. 相当, the present findings confirm the hypothesis
that a firm’s foreign investment decisions rely more on the availability of internal
funds. 正如预期的那样, the sign and significance of other control variables such as
尺寸, TFP, and exports are found to be consistent with the previous specifications.
Columns 7 和 8 report the results using credit rating as an instrument for
internal financing constraints instead of the mean industry values of cash flow and
liquidity.16 The result shows that the use of alternative instruments does not change
our results.

桌子 2 also reports the results of the interaction term between financing
constraints and productivity. The objective of including these variables is to examine
whether higher productivity helps firms compensate for undertaking FDI. 我们
control for the mitigating effect of productivity by including an interaction term

15Buch et al. (2014) argue that this result further depends on the instrumentation strategy.
16We have also carried out an ivprobit estimation using credit rating as an instrument where credit rating is
defined as 1 或者 0 based on credit rating status without considering changes in grading. The results were found to be
consistent with the results reported in columns 7 和 8 of Table 3. We have not reported these results in Table 2 为了
brevity, 然而, they are available from the authors upon request.

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3

Do Financing Constraints Impact Outward Foreign Direct Investment? 123

桌子 3. Financing Constraints and Outward Foreign Direct Investment Share

Variable

Cash flowt−1

Liquidityt−1

Sizet−1

Capext−1

Long-term borrowingst−1

年龄

TFPt−1

Exporter

Business group

Debt ratiot−1

(Cash flow × TFP)t−1

(Liquidity × TFP)t−1

Time dummies
Wald Chi2
Rho
问题. > Chi2
观察结果

(1)

(2)

0.028**
(0.008)

0.016
(0.041)

0.038**
(0.012)
−0.027
(0.057)

(3)

0.018*
(0.009)

0.019
(0.041)

(4)

0.031*
(0.013)
−0.022
(0.057)

(6)

0.037*
(0.015)
0.027
(0.069)

(5)

0.017*
(0.009)

0.081
(0.111)
−0.107
(0.124)

−0.029*
(0.012)
0.010*
(0.004)
0.012
(0.015)
−0.006
(0.013)
−0.023
(0.017)

−0.028
(0.012)
0.009*
(0.004)
0.009
(0.015)
−0.004
(0.013)
−0.023
(0.017)

−0.003
(0.038)
−0.029* −0.029* −0.040** −0.018
(0.234)
(0.012)
−0.007
0.104*
(0.006)
(0.009)
0.011
0.011
(0.018)
(0.015)
−0.043**
−0.007
(0.017)
(0.013)
−0.050
−0.024
(0.017)
(0.032)
0.008*
(0.004)

(0.0148
(0.012)
−0.011
0.012*
(0.006)
(0.013)
0.019
0.009
(0.016)
(0.019)
−0.0007 −0.005
(0.015)
(0.013)
−0.064
−0.027
(0.032)
(0.017)

是的
63.66
0.110
0.0000
5,645

是的
61.15

0.113
0.0000
5,645

是的
69.48

0.109
0.0000
5,645

0.007*
(0.004)
是的
64.08

0.113
0.0000
5,645

是的
51.01

0.143
0.0000
4,297

是的
57.21

0.191
0.0000
4,297

TFP = total factor productivity.
Notes: This table reports the marginal effects of a random-effects Tobit model where the dependent variable is a
share of outward foreign direct investment defined as the ratio of outward foreign direct investment to total assets.
Cash flow, 尺寸, 年龄, and TFP are measured in logs. Exporter is a dummy for export status. Standard errors are
reported in parentheses. ***, **, 和 * denote significance at the 1%, 5%, 和 10% 等级, 分别. The mismatch
of observations is due to missing values for the external finance variable.
来源: 作者的计算.

of the financing indicators with productivity. A significant negative impact of the
variable implies that higher productivity fails to compensate a firm’s financing
constraints and reduces the probability of a firm investing abroad. Columns 3 和
4 report the results of the empirical model controlling for the mitigating effect of
生产率. The negative and significant impact of the interaction term indicates
that productive firms that are financially constrained are less likely to invest abroad.
桌子 3 presents the results of the role of financing constraints in determining
the share of outward FDI. Columns 1 和 2 report the marginal effects of the Tobit
model on the role of financing constraints in determining the share of outward FDI,
while columns 3 和 4 report the estimation results of the Tobit model, 包括

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