The Journal of Industrial Statistics (2016), 5 (1), 1 - 21
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Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
Abhishikta Acharyya (Roychowdhury),1 Confederation of Indian Industries,
Kolkata, India
Bibek Ray Chaudhuri, Indian Institute of Foreign Trade, Kolkata, India
Abstract
Internationalization experience of Indian manufacturing firms gained momentum during
the last decade or so. In this paper we look at the determinants of entry mode choices
made by these firms. Exploiting the firm-level Outward Foreign Direct Investment data
published by RBI this study tries to explain the determinants of entry mode choices made
by Indian manufacturing firms during the years 2008-09 to 2012-13. This data has been
supplemented by matching firm-level characteristics obtained from CMIE PROWESS
database to that of RBI data to test the various hypotheses. Results show that size,
productivity lag and past international experience are significant determinants of entry
mode choice made by Indian manufacturing firms.
1.
Introduction
1.1
Indian firms are getting increasingly internationalized. The major drivers behind
this trend are increase in liquidity in the economy given favorable macroeconomic policies,
low-cost labor, efforts towards moving up the value chain, leveraging insights gained from
servicing customers in the domestic economy, and strong economic growth during the
years 2001-2005 which improved balance sheets of the Indian firms making it possible for
them to raise capital (Financial Times (2006)). Indian exports grew at 14.14% CAGR between
the years 2004-05 to 2013-14 from US$ 83535.93 million to US $ 313542.89 million (See Figure
1). Top five export destinations being: United Arab Emirates, USA, Singapore, China and
Hong Kong comprising around 37% of India’s exports. The main export items are petroleum
(crude & products), gems & jewelry, transport -equipment, machinery and instruments,
drugs, pharmaceuticals & fine chemicals.
1.2
On the other hand, India accounts for 0.6% of outward foreign direct investment
of the world. Top five FDI destinations are: Singapore, Mauritius, Netherlands, USA and
UAE. Indian outward FDI grew at 23.89% between 2000-01 and 2011-12 from US$ 677.67
million to US $ 8861.46 million (See Figure 2). Major sectors engaging in overseas
investments are: Manufacturing (including oil sector), Financial Insurance, Real Estate
Business & Business Services, Wholesale & Retail Trade, Agriculture & Allied activities,
Transport, Communication & Storage Services, Restaurants & Hotels, Construction,
Community, Social & Personal Services and Electricity and Gas & Water.
1.3
The increase in export and foreign direct investment implies rising integration
with world economy. In traditional theories of international trade and multinationals these
act as substitutes to serve overseas markets. Brainard (1997) empirically established that,
firms export when there are cost advantages to concentration (economies of scale) and
e-mail : [email protected]
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establish foreign production facility when proximity to local market is more important. But,
that could not explain why firms within each industry facing same industry costs make
different entry choices. Helpman, Melitz and Yeaple (2002) explained that with firm
heterogeneity, only the most productive firms find it profitable to meet the higher costs
associated with FDI; the next set of firms find it profitable to serve foreign markets through
exporting; while the least productive firms find it profitable to serve only the domestic
market. Findings of Clerides, Lach and Tybout (1998); Bernard and Jensen (1999, 2004);
Head and Ries (2003); Tomiura (2007); and Todo (2009) among many others are consistent
with theoretical predictions of the heterogeneous-firm trade models.
1.4
The consistency between theory and empirics has given a direction to understand
firms’ internationalization strategy. However, in some cases, it is found that a number of
firms that are as productive as those engaged in export or FDI do not take part in either of
the international activities. Hence four types of firms have been conceived in the literature
according to degree of internationalization, namely those serving only the domestic market
(“domestic firms”), those engaging in export but not in FDI (“pure exporters”), those engaging
in FDI but not in export (“pure FDI firms”), and those engaging in both (“export and FDI
firms”). On an average, firms serving only the domestic market are less productive than
exporters and FDI firms, but the distribution of the four types of firms overlaps with each
other to a great extent. In other words, many firms do not serve foreign markets although
they are as productive as many exporters and FDI firms. This evidence suggests that there
should be other key determinants of firm-level internationalization besides productivity
(Mayer and Ottaviano, 2007; Todo 2009).
1.5
Thus, this study re-examines determinants of the export and FDI decision,
incorporating firms characteristics like R & D intensity, firm size, age of firms, credit constraint,
international experience etc; paying special attention to the magnitude of the impact of
each determinant in addition to its statistical significance. This study should be particularly
relevant as work on Indian firms in this sphere has been few. Again, emerging country
strategies for internationalization may be different from firms from developed countries.
Moreover, in case of Indian firms as predicted by Melitz (2003) that only larger and more
productive firms export, may not be true. In this country around 40% of the exports are by
small and medium enterprises (EXIM Bank 2012). Hence the theory that only bigger firm
engages in internationalization is not true in the Indian context. Armenter and Koren (2009)
shows that latent heterogeneity across firms can explain export by relatively small firms.
Even low productivity firms may export if e.g. costs of transportation of their product are
less. Thus it would be interesting to test certain hypotheses related to this phenomenon in
the Indian context. This paper contributes in understanding the decision regarding FDI vs.
exports by considering firm-level characteristics for the manufacturing sector as a whole.
Matching RBI OFDI data with other firm-level variables obtained from CMIE PROWESS
this is one of the first attempts to look at internationalizing decisions by Indian
Manufacturing firms.
2.
Research hypotheses
2.1
Productivity
Recent empirical studies on international trade at the firm level have found that firms
engaged in export or foreign direct investment (FDI) are generally more productive and
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
3
larger in size than firms serving only the domestic market (Clerides, Lach and Tybout, 1998;
Bernard and Jensen, 1999, 2004; Head and Ries, 2003; and Tomiura, 2007, among many
others). This finding is backed by theoretical predictions of heterogeneous-firm trade models,
most notably those of Melitz (2003) and Helpman, Melitz, and Yeaple, (known as HMY
(2004)), in which only productive firms can pay higher costs associated with export and FDI
and hence can serve foreign markets. The consistency between theory and empirics has
deepened our understanding on firms’ internationalization. The productivity of all
multinational firms is stated to be greater than that of all exporting firms. These explanations
lead to the following hypothesis:
Hypothesis 1(H1): Productivity level positively affects the probability of engaging in export
and FDI.
2.2
Size of Firms and R & D
2.2.1
Arnold and Hussinger (2006) has shown that among non-exporter firms, exporter
firms and FDI firms, on average, exporting firms are larger than non-exporters, both in terms
of employment and sales or value added. Head and Ries (2003) has considered sales, value
added, and employment of firms as the size variable and has shown that they are correlated
with entry decisions. Firms with foreign investment tend to be the largest of the three
subsets. Interestingly, this ordering also matches with the propensity to engage in R&D
activities (the variable “Innovator”), and to the amount of investment into such activities.
However, it is interesting to investigate whether R & D which leads to the so called ownership
advantage of a firm (Dunning (1979)), really plays an important role in firms’
internationalization decision in developing countries like India.
2.2.2
Much of the early literature on foreign market entry was concerned with the
choice between exporting and FDI (Root, 1987; Young, et al.1989; Buckley and Ghauri,
1993). The cost-based view related to this decision suggested that the firm must possess a
“compensating advantage” in order to overcome the “costs of foreignness” (Hymer, 1976;
Kindleberger, 1969). This led to the identification of technological and marketing skills as
the key elements in successful foreign entry (Hirsh, 1976; Horst, 1972). The literature on
core competences arising from the Penrosian tradition (Penrose, 1959; Prahalad and Hamel,
1990) connects well with this tradition of firm-specific advantages (Caves, 1971; Rugman,
1981). In this case, firm specific knowledge advantage mainly stems from R& D. Firms with
high asset specificity will try to avoid transaction cost of handling agents who may misuse
the proprietary contents of product.
2.2.3
However, transaction cost theory states that ownership advantages are not
required for FDI. Whereas the eclectic paradigm argues that ownership advantages are
internally generated, transaction cost school argues that such advantages may be generated
externally and internalised by the firm. Thus knowledge acquired abroad by the subsidiary
get transferred to the parent firm ( Furu 2000, Lindqvist, Solvell et al 2000, Peng and Wang,
2000, Lundan and Hagedoorn 2001, Madhok and Phene 2001,Randoy and Dibrell 2002).
Hence, ownership advantage stemming from knowledge can be generated within the firm
as well as through internalization from exogenous sources in a strategic manner. Dunning
(1998) accepted the increasing role of strategy in the market entry decision stating that
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possibly the most significant change in motives for FDI over the last two decades has been
the rapid growth of asset seeking FDI, which invest less for exploiting its own ownership
advantage and more for exploiting the advantage of a foreign resource or asset.
2.2.4
Porter’s model of inputs includes innovation as an important factor in market
entry. The innovative input emerges from a unique mix of factors in a particular location and
they are immobile. Porter’s diamond model of national advantage states that critical masses
of firms tend to cluster together in a particular location given this unique blend of factors
available.
2.2.5
Literature provides mixed findings regarding complementarities and substitutability
of exports and OFDI (Thomas and Narayanan (2013)). Among various reasons, trade barriers,
transport costs and low scale economies at the firm-level have been cited as reasons for
choosing OFDI over exports. Sasidharan and Kathuria (2011) and Narayanan and Bhat
(2011) found strong relationship between R&D and OFDI for manufacturing and IT firms
respectively in India. Helpman et al. (2004) found robust impact of within industry firm
heterogeneity measured in terms of firm-size on decision of the firms to substitute FDI for
exports. Goldar (2013) also found size to be an important determinant of OFDI (but does not
affect the direction) in case of Indian Manufacturing firms.
2.2.6
Thus we can form the following two hypotheses:
Hypothesis 2(H2): R & D intensity (Knowledge variable) positively affects the probability
of engaging in export and FDI and probability of FDI rather than export.
Hypothesis 3(H3): Firm size positively impacts the probability of engaging in export and
FDI and probability of FDI rather than export.
2.3
Financial Constraint
2.3.1
We test a theoretical result that extends models of exporting (Chaney 2005, Manova
2010) to include, both, exporting and FDI. This allows us to explore how productivity and
financial constraints affect firms’ choices between FDI and exports when firms have limited
internal funds. It is assumed that the upfront investment costs in case of FDI exceed those
of exporting, whereas the marginal cost of exporting are higher due to iceberg transportation
cost. In addition to the well-known effect that firms need to be more productive to engage
in FDI, earlier research has also suggested that financial frictions should matter relatively
more for the decision to engage in FDI as compared to exporting (Buch et al, 2010, Todo,
2009). Evidence shows that a higher debt-equity ratio has a negative impact on being an
exporter for the large firms in the sample. Simple interaction terms would indicate a significant
negative impact on both types of activities.
2.3.2
Furthermore, studying the interaction of productivity and financial constraints,
we expect a threshold effect: financial constraints should matter only above a critical
threshold of productivity (or: size) because firms with lower productivity do not consider
investing abroad or to export in the first place.
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
2.3.3
5
Thus we form the hypothesis:
Hypothesis 4(H4): High debt-equity ratio negatively affects the probability of engaging in
export and FDI.
2.4
International experience
2.4.1
Previous studies have suggested that location familiarity significantly affects the
choice of market entry mode. Firms with greater location familiarity are more likely to adopt
a direct investment mode (Hill, Hwang and Kim, 1990; Kim and Hwang, 1992). Past research
has shown that one way to acquire location familiarity is by having direct experience with
a market, as this reduces the perceived distance between the home and host markets. It has
also been asserted that when firms first enter a country which is culturally different from the
home country, they are generally reluctant to adopt a FDI mode. However once the barriers
of language and culture are overcome, the probability of foreign direct investment increases
(Hirsch, 1976; Luostarinen, 1980).
2.4.2
Melitz (2003) and Helpman, Melitz, and Yeaple (2004) suggested, costs of exporting
and FDI include initial fixed costs like, researching foreign markets and developing sales
networks. Therefore, costs of exporting (or FDI) are lower for firms that are already engaged
in these activities.
2.4.3
Todo (2009) has also shown that, the impact of firms’ status in the previous year
is quite large. The predicted probability that the average domestic firm remains domestic in
the next year is 99 percent, and the probability does not change much even when the firm’s
characteristics such as the level of productivity and employment improve so much that the
characteristics are better than the average of exporters and FDI firms. Although the positive
effect of firms’ previous status has been found in existing studies, this study highlights the
extremely large degree of stickiness of the export and FDI behavior by performing a number
of numerical exercises.
2.4.4
Based on the above considerations, the following hypothesis is formed:
Hypothesis 5(H5): Firms that have prior experience with the host market are more likely to
adopt a FDI market entry mode, while those who do not have such experience tend to start
as an exporter.
3.
Data Sources
3.1
Monthly outward FDI data at firm-level has been obtained from Reserve Bank of
India database for the years 2008 to 2012. Data period selected for the study is purely based
on availability of robust and consistent data provided by RBI. This data was then aggregated
to obtain the year-wise data. Company level balance sheet data like PBDITA (Profit before
interest, depreciation, tax and amortization), Salaries, wages, bonus, ex-gratia, PF & gratuities
paid, amortization, sales, year of incorporation, borrowing, equity, export sales have been
taken from CMIE Prowess database. FDI data from RBI was then matched with that of
Prowess data. The data were suitably deflated to account for price changes. Table 1 lists
the variables, definitions and corresponding deflators used in this study.
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3.2
The Journal of Industrial Statistics, Vol. 5, No. 1
The variables were deflated to get their real value as per the following equation
Real Valuei 
Nominal Valuei
Price Indexj where ‘i’ is the variable and ‘j’ is the corresponding price index.
100
3.3
Gross and Net capital formation data were collected from Reserve Bank of India
database and price indices were collected from Department of Economic Affairs, Government
of India.
3.4
Number of firms finally considered reduced substantially from what was available
from PROWESS database. Only firms which have GVA>0 could be considered due to the
requirement of only positive values for inputs and outputs to calculate productivity using
the Data Envelopment Analysis (DEA) technique. Thus, for example in 2012 we could
consider only 1426 firms out of data available for 10,216 firms. For the period spanning
years 2008-12 we could use a total 7130 observations on firms for this study. The companies
under our consideration represent various industries like machinery (15%), textile (14%),
metal (13%), agriculture and food products (13%), automobile (10%), drugs and
pharmaceuticals (8%), plastic and plastic items (6%), cement, ceramic and refractory (4%)
electronics (3%), jewellery (1%), footwear (1%) etc. (see figure 3).
3.5
Most of the firms in our sample are only exporting firms (Table 2) followed by
domestic and FDI & Export firms. In terms of sectors (Figure 3) Machinery, Textile and
Clothing, Metal and Agriculture and Food products dominate the sample. There is not
much of a difference in the average age of the firms across the four categories considered
(Table 3). It is slightly higher (37.88 years) for firms which are domestic in the year 2012. In
terms of size measured by real value of sales firms choosing to do both export and FDI have
a significantly higher size than the others though variability in size is also significantly
high. A reverse trend is observed in terms of Debt-equity ratio with domestic firms having
the highest value followed by exporting firms, FDI firms and those indulging in both exports
and FDI. Together with size this implies that firms facing financing constraints resort to
domestic sales, whereas those facing lower financing constraints engage in international
business. On an average domestic firms resort to higher proportion of R&D to sales than
those engaging internationally. This may imply that external engagement by Indian firms is
not due to ownership specific advantages. With respect to productivity firms engaging in
only FDI have higher mean value followed by exporting firms and those involved in both
exports and FDI. Distribution of productivity is depicted in Figure 4. It can be seen that
there are overlaps towards the tails of the distributions. Both at lower and higher values,
productivity of domestic firms are more than that of other firms. So a clear cut distinction
between the different types of firms in terms of distribution of productivity is not observed
in our sample. None of the distributions can be said to be strictly dominating the others.
This is the reason why we did not resort to stochastic dominance to test our hypotheses.
The other reason being we are not using only productivity to explain internationalization of
Indian firms.
3.6
Movement from one status to the other is also found to be limited in case of Indian
firms for the time period considered in the study. It can be seen from Table 4 that only in
case of movement from status ‘domestic’ to status ‘FDI’ the proportion of firms is high. But
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
7
in this case there are very few firms in the sample who engage in only FDI. For all other
choices, the proportion is quite low. On the other hand, the proportion of firms continuing
with the initial choice is quite high. Table 5 shows that it is highest for exporting firms (more
than 90% in most cases) followed by domestic (more than 84% in most cases). This may be
due to the comfort level enjoyed by domestic firms within the borders or reluctance to try
new things. For exporting firms it may be due to initial fixed costs of knowing the international
markets. As their engagement with the world increases they get the benefit of scale
economies. This is apart from other benefits like higher profitability, increasing returns due
to other initial costs like research and development, advertising, expenditure on setting up
production facilities etc.
4.
Methodology
4.1
For testing the various hypotheses we have used a multinomial logit model. As
we have four choices (domestic, export, FDI and Export and FDI) by the firms it is appropriate
to include random intercepts to account for unobserved heterogeneity or spurious
dependence between them.
Suppose that firm i has T categorical observations and let Yit denote the tth observation for
firm i, t = 1,…,T. If there are J possible response states then Pr(Yit  j | X it ) , j = 1,…,J,
is the probability that firm i has response j at time t given Xit,a column vector of explanatory
variables for that observation.
4.2
The multinomial logit model is expressed as
 itj
 Pr(Yit  j | X it ) 
e
J
X it  j
e
X it  k
.
k 1
4.3
The logit model pairs each response category with an arbitrary baseline category.
In our analysis the response has four states (j =1 to 4): domestic (j = 1), export (j = 2), FDI (j
= 3) and export &FDI (j = 4). In our case Xit is the vector of regressors real value of sales
(size), debt-equity ratio, research and development expenditure to sales ratio, productivity,
lag of productivity, past experience of exports and past experience of FDI and exports. For
identifiability export is set as the reference category so that 2= 0. The multinomial logit
model then has the form
log(
 itj
 it 3
)  X it'  j
(1)
where j=1,3,4. This has a latent variable interpretation where we define the utility of choosing
a particular response, for example entry mode, by the random variables Uitj (j = 1,…,J), with
the function
U itj  X it  j  eitj
(2)
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consisting of an observable component and random elements eitj that arise from an
independent extreme value distribution. The firm chooses the response state j if and only if
the utility is greatest for this state, that is, U itj  max{U itk }, j  1,..., J .
1 k  J
4.4
If we also introduce firm-specific random effects ij and let Zij denote a vector of
coefficients for the random effects, then the logit model has the form
log(
 itj
 it 3
)  log(v itj )  X it'  j  Z ij'  ij j = 1,2.
(3)
4.5
The random effects i={ i1,…, iJ ) capture non-observable individual effects
that are specified to arise from a multivariate normal distribution with mean zero and variancecovariance matrix .
The relative probability of y=1 to the base outcome is
Pr( y  1)
 e X1
Pr( y  2)
(4)
4.6
This may be termed as relative risk ratio, and if we further assume that X and 1k are
vectors equal to (x1, x2, …,xk) and (11, 12,….., 1k)’ respectively. The ratio of relative risk for
one unit change in xi is then given by,
e 11 x1 ... i ( xi 1) ... 1 k xk
 e 1i
11 x1 ...   i xi ... 1 k xk
e
(5)
4.7
Thus the exponential of the coefficient is the relative risk ratio for a one unit
change in the corresponding variable. In this model there are no single conditional mean of
the dependent variable. If one wants to calculate the change in probabilities due to change
in regressors, for this model it can be shown that
p ij
xi
 pij (  j   i )
(6)
where,  i is the probability weighted average of the coefficient. Marginal effects vary with
the point of evaluation xi because pij varies with xi. The signs of the regression coefficients
don’t give the signs of the marginal effects. For a variable x, the marginal effect is positive
if j> i .
4.8
The productivity for the firms has been calculated using Malmquist Productivity
Index (MPI). MPI is a tool to measure change of performance of a firm with respect to time.
It was introduced by Caves, Christensen and Diewert (1982) and it is measured by the ratio
of output distance functions and it did not require aggregation of inputs in case of multiple
inputs. There after Fare, Grosskopf, Lindgren and Ross (1992) used mathematical
programming to calculate the distance functions for Malmquist Productivity Index. This
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
9
advance method decomposes the MPI into two parts, one showing movement towards or
away from the production frontier and the other shows shift of the frontier. This method
assumes constant returns to scale (CRS) production process. Subsequent papers by Fare,
Grosskopf, Norris and Zhang (FGNZ 1994) and Fare, Grosskopf and Lovell (1994) incorporate
scale effect in MPI, assuming VRS.
4.9
Output based MPI is a geometric mean of two output based Mamlquist TFP
indices. One uses technology of current year and other index uses preceding year’s
technology. The change of productivity of a firm either could be due to change in technical
efficiency or change in technology in industry or for both. Again technical efficiency
change can be decomposed into pure technical efficiency change and scale efficiency
change. Pure technical efficiency refers to the firm’s ability to avoid waste by producing as
much output from the given input level, or by reducing input to produce the current level of
output. Scale efficiency measures firms’s ability to work at its optimal scale.
TFP =
SE 0 (x1, y1) SE 1 (x1, y1)
*
SE 0 (x0 , y0) SE 1 (x0 , y0)
1/2
D 0v (x1, y1) D 0v (x0 , y0)
*
D 1v (x0 , y0) D 1v (x1, y1)
1/2
D 1v (x1, y1)
D 0v (x0, y0)
= [scale efficiency change] * [technological change] * [pure technical efficiency change] ... (7)
The first term in the above equation (7) within bracket measures scale efficiency change.
Similarly the second term measures technological change and the last term measures pure
technical efficiency change.
4.10
Technical efficiency of a firm shows its ability to produce maximum output with a
given level of input. If the firm is operating with CRS technology then there will be no scale
effect. But if the firm is operating with VRS technology then the difference between technical
efficiency score under CRS and VRS gives scale efficiency (SE) score for the firm. And
technical efficiency change (TEC) is a product of scale efficiency change (SE) and pure
technical efficiency change (TE).
4.11
Scale efficiency index is the geometric mean of scale index of previous period and
the current period. It measures the deviation of scale form MPSS. Similarly, pure technical
efficiency change measures the change of production due to change in usage of technology
and input in the current period compared to that of previous period in terms of VRS and it
is caused by change in management practices. But to measure the effect of change in
technology we calculate technological change. It takes care of change in production due to
change in technology in current period compared to previous period. For our study we
have used the output oriented DEA. Gross Value Added is considered as output and inputs
are wages and salaries, capital employed, raw material expenses and power, fuel and water
expenses. As mentioned earlier these were suitably deflated before calculating MPI.
5.
Results
5.1
The Table 6 presents the results of the multinomial logit regression. The log
likelihood tests for the overall significance of the model. Lower the value the better, as it is
being minimized to obtain the estimates. It can help in model selection. It can be seen that
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The Journal of Industrial Statistics, Vol. 5, No. 1
the model with productivity lag gives a better model fit with lower log likelihood than the
model without it. The Likelihood Ratio (LR) Chi-Square tests that for all the equations
whether at least one of the predictors’ regression coefficients are not equal to zero. The
number in the parentheses indicates the degrees of freedom of the Chi-Square distribution
used to test the LR Chi-Square statistic and is defined by the number of models estimated
(2) times the number of predictors in the model (3). The LR Chi-Square statistic can be
calculated by -2*(L(null model) - L(fitted model)), where L(null model) is from the log
likelihood with just the response variable in the model (Iteration 0) and L(fitted model) is the
log likelihood from the final iteration (assuming the model converged) with all the parameters.
5.2
Prob>chisquare is the probability of getting a LR test statistic as extreme as
possible than the observed under the null hypothesis that all of the regression coefficients
across both models are simultaneously equal to zero. In other words, this is the probability
of obtaining the chi-square statistic if there is in fact no effect of the predictor variables.
This p-value is compared to a specified alpha level (our willingness to accept a type I error)
which is typically set at 0.05 or 0.01. The small p-value from the LR test, <0.00001, would
lead us to conclude that at least one of the regression coefficients in the model is not equal
to zero. It can be seen that for both the models at least one of the regressors significantly
contribute in explaining the outcome variable.
5.3
McFadden’s pseudo R-squared is similar to R-square statistic. Since this statistic
is not measured like R-square in OLS regressions (the proportion of variance for the response
variable explained by the predictors), inferences cannot be similar. Hence we refrain from
explaining its significance here.
5.4
From Table5 (Appendix) the results show that it is more likely that smaller firms
choose domestic over exports as a choice of mode of operation. As the size of the firms
increase it is more likely that they choose exports over domestic as entry mode choice (H3
is satisfied). Firms who had selected exports as the entry mode in the previous period are
99% more likely to choose exports over domestic in the current period. This result is
consistent across the two models considered. In the first model productivity, debt-toequity ratio and R&D to sales ratio are found to be insignificant in determining the outcomes.
This is true for the second model also except for lagged values of productivity.
5.5
In case of choice of FDI as an entry mode over exports it is found that firms
engaging in FDI in the previous year are more likely to choose FDI over exports. For our
sample firms choosing export in the previous period are more likely to choose export over
FDI. It is almost 99% more likely (H5 is satisfied). Even though impact of productivity is not
found to be significant lagged productivity has significant impact on choice of FDI over
exports. Log of odds ratio of firms with higher productivity in the previous period of
choosing FDI over exports is 2.4 times of log of odds of firms with lower productivity, other
factors remaining the same. Thus it seems that more productive among the Indian
manufacturing firms choose FDI over exports in the next period (H1 is true in lagged
productivity terms).
5.6
Firms’ choice of export and FDI over exports is influenced by size among other
factors. Larger firms are more likely to go for both exports and FDI than only exports.
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
11
Moreover, this result is also true for firms with higher productivity in the previous period
and those which opted for FDI in the previous period. Results show that the log odds of
firms with higher productivity in the previous period are 3.36 times of log odds of firms with
lower productivity in the previous period in determining the choice of exports and FDI over
only exports other things remaining the same. Results do not support hypotheses 2 and 4.
Neither R&D nor debt/equity ratio have any significant impact of entry mode choice by
Indian manufacturing firms.
5.7
A more useful way to look at choice of entry mode may be to find out the change
in probability of choosing a particular mode as the regressors change. Table 6 (Appendix)
shows the results for the various entry modes. In terms of size we find that as the sales
value increases by one unit (Rs. 1 crore) the probability of choosing domestic rather than
other modes decreases by 0.0001. Though the magnitude of impact is less it is statistically
significant. For all the other entry modes size is an important determinant of probability of
entry mode choice even though magnitude of impact is low. Productivity lag is found to be
significantly positively impacting choice of export and FDI over other modes. A one unit
change in productivity in the previous period influences the probability of choosing export
and FDI over other modes in the current period by 0.05. FDI choice in previous period
impacts the probability of choice of domestic and export modes negatively but influences
probability of choice of export and FDI over other modes positively. If a firm had chosen
FDI in the previous period rather than over other modes it increases the probability of
choosing export and FDI in the current period over other modes by 0.43. For firms who have
chosen export in the previous period the probability of choosing domestic mode in the
current period is lower and that of choosing exports and export and FDI in the current
period is higher. Probability of choosing exports over other modes in the current period
increases by 0.76 for firms who have chosen export mode in the previous period and by 0.05
for firms choosing export and FDI in the current period. For probability of choice of domestic
rather than other modes the decrease is 0.81 if the firms hand chosen exports in the previous
period.
6.
Conclusions
6.1
This paper examines determinants of choice of export and/or FDI by Indian
manufacturing firms vis-a-vis remaining domestic. An attempt has been made to test the
theories in a developing country context. A multinomial logit model has been used on panel
data to incorporate time and firm-specific impact of variables like productivity, financial
constraint, size, international experience & R &D on entry mode choice decision by such
firms. Earlier work mainly dealt with industry-level data or specific sectors. Our study is one
of the first attempts at exploiting the firm-level data on OFDI made available by RBI.
6.2
There is not much of a difference in the average age of the firms across the four
categories considered. However, in terms of size measured by real value of Sales firms
choosing to do both export and FDI have a significantly higher size than the others. Also,
as the size increases, probability of internationalization increases, though the impact low in
magnitude, it is statistically significant.
12
The Journal of Industrial Statistics, Vol. 5, No. 1
6.3
The higher the financial constraint, more the firms are inclined to serve only
domestic market rather than FDI and/or export, although the relation is not statistically
significant. The reason for the result not being significant might be due to omission of firms
whose net worth is negative reducing the variability in the data. If one looks at huge
outward FDI deals like TATA-CORUS and others most of the outward FDI deals of Indian
firms at least in US have been debt-financed with cash being the popular mode of payment2.
Hence firms resorting to both domestic and foreign mode choice have high debt/equity
ratio in India which may have made its influence statistically insignificant.
6.4
The results also show that, productivity of firms positively influences their entry
mode choice. Firms with higher productivity are more likely to choose FDI over exports and
exports and FDI over exports. Again contrary to theoretical predictions choice of domestic
over exports is also positively impacted by productivity. But all these relationships are not
statistically significant. This is in contrast with Goldar (2013) results. But may be ignored
due to statistical insignificance. In case of Indian manufacturing firms we find that
productivity impacts entry mode choice with a lag. Firms with higher productivity in the
previous year are more likely to choose FDI over exports and exports and FDI over exports.
These relationships are statistically significant. This shows that Indian firms are cautious
in their internationalization decision. Only when they achieve higher productivity do they
plan for internationalization and this decision on an average takes one year to implement.
6.5
Firms’ status regarding internationalization in the previous year is a significant
determinant of the entry mode choice in the current year. This lends credence to the theories
that familiarity with the foreign market is a significant determinant of entry choice by the
firms. Initial fixed costs incurred in entering the foreign market leads to increasing returns
with more familiarity. This reduces cost of entry for existing international firms in the current
year.
6.6
The findings related to R & D are rather interesting. Although more R & D is
leading to choice of export over domestic, it is not true in case of decision regarding FDI vs
export. In this case more R&D expenditure to sales is increasing the probability of choosing
export over FDI. One reason for this apparently unexpected result can be due to unreliable
R & D data of Indian firms. Often smaller firms tend to exaggerate their R & D data to avail
tax breaks. Also though the major activity of firms included in this study is manufacturing,
out of 166 FDI firms many are engaged in producing steel and other metal products which
require procurement of minerals (Table 7 in Appendix). Hence, motive for outward FDI by
Indian firms is not predominantly market seeking for exploiting their ownership advantage,
but they are mainly strategic asset and resource seeking. Thus Indian firms are investing in
strategic resource rich countries like UK, USA, Korea, Singapore, Australia, Russia, and
Canada. Kumar (2008) shows that the source of Indian firms’ ownership or competitive
advantage lies in their accumulation of skills for managing large multi-location operations
across diverse cultures in India and in their ability to deliver value for money with their
frugal engineering skills honed up while catering to the large consumer base within the
country. Hence R & D data may not be the ideal indicator of their ownership advantage.
Thus the firms engaged in FDI may not have large R & D investment but may have managerial
http://www.thehindubusinessline.com/economy/indian-investments-in-the-us-showing-strong-traction/
article6821406.ece
2
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
13
knowledge based on experience gathered in the home country which they leverage while
internationalizing.
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Appendix
Figure 1: India’s Export (in Million US $)
Figure 2: India’s Outward Foreign Direct Investment (in Million US $)
Source: Reserve Bank of India and EXIM Bank research
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
Figure 3
Distribution of Industries (in 2012)
Figure 4: Distribution of Productivity among Indian Manufacturing Firms
17
18
The Journal of Industrial Statistics, Vol. 5, No. 1
Table 1: Variables, Definitions and Deflators used
Variables
Wages
GVA
Capital employed
Raw material
Sales
Power, fuel,
electricity
Definition of Variables
Deflators1
Sum of salaries and wages, bonus and ex -gratia,
contribution to provident fund and gratuities paid
CPI (IW)
to employees.
Operating Profit (before tax) + Employee Costs
WPI (Manufacturing)
+ Depreciation + Amortisation
Capital employed is the sum of all shareholders
funds and total borrowings. This is the total
funds deployed into the business from owners of
equity and preference capital and from lenders.
Obtained by adding the raw material purchases
to opening stock of raw materials and deducting
cenvat credit and closing stock of raw materials.
Sale of goods and income from various
associated activities. This includes the sale of
scrap, of raw materials and stores, income from
job-work done, from repairs and maintenance,
construction and utilities. It also includes fiscal
benefits received by the company.
Power and fuel expenses are the cost of
consumption of energy for carrying out the
business of a company. This would include the
cost of consumption of electricity, petroleum
products such as diesel, naphtha, etc, coal and
other sources of energy.
Implicit
deflator=Gross / Net
Domestic Capital
Formation
WPI ( Intermediate)
WPI Manufacturing
WPI ( Fuel & Power)
Table 2: Forms of Internationalization
Forms of Internationalization
Domestic
Only export
Only FDI
Export and FDI
Number of firms ( in 2012)
281
980
13
152
Table 3: Data Description
Entry Mode
Domestic
Export
FDI
Export and FDI
3
Age
37.8855
(124.0012)
35.1303
(20.4118)
30.4615
(22.1608)
37.2171
(22.1676)
Real Sales
26.9068
(80.89000)
126.5177
(807.1504)
217.1865
(398.8777)
667.1601
(3332.8392)
DebtEquity
1.8634
(3.9955)
1.3033
(3.0488)
1.2758
(1.2457)
0.9905
(1.00310
R&D –
Productivity
Sales
0.0065
0.2154
(0.0956)
(0.1152)
0.0043
0.2445
(0.0439)
(0.1169)
0.0000
0.2455
(0.0000)
(0.1026)
0.0022
0.2312
(0.0234)
(0.1007)
WPI at disaggregated level obtained from www.eaindustry.nic.in/choose_item.asp
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
19
Table 4: Firms with Domestic Status in Previous Year moving to
Other Statuses in the Next Year
Previous
Domestic
Year
Mode in the
Current Year
2009
2010
2011
2012
Export
0.036122 0.039347 0.029923 0.021898
FDI
100 0.666667
0.5 0.461538
Export & FDI
0
0
0 0.013072
Table 5: Current Year Mode Similar to Previous Year Mode
Mode
Domestic
Export
FDI
Export & FDI
2009
0.811111
0.879278
0
0.636364
2010
0.886719
0.941459
0
0.465909
2011
0.873518
0.933398
0.25
0.569892
2012
0.847328
0.941606
0.461538
0.407895
Table 6: Results of Multinomial Logit Regressions
Dependent Variable
Observations
LR chi square
Prob>chi square
Log likelihood
Pseudo R2
Domestic over Exports
Sales
Debt-Equity Ratio
R&D/Sales
Productivity
Model 1
Entry decision
5543
4054.35
0.00
-2124.6033
0.4883
Coefficients
eβ
-0.0021208**
(0.0005922)
0.0052126
(0.0096771)
-0.0191372
(1.066436)
0.0880325
(0.4491953)
0.9978815
1.005226
0.9810447
1.092024
Productivity lag
FDI in previous period
Export in previous
period
Constant
FDI over Exports
Sales
Debt-Equity Ratio
R&D/Sales
-0.183135
(0.4409185)
-5.273319**
(0.1296487)
2.005472**
(0.1421272)
0.0000876
(0.0001577)
-0.0240861
(0.0906888)
-500.752
(744.9934)
0.8326557
0.0051266
1.000088
0.9762016
3.36E-218
Model 2
Entry decision
5543
4067.68
0.00
-2117.9366
0.4899
Coefficients
eβ
-0.0020996**
(0.0005932)
0.0051959
(0.009686)
-0.0196656
(1.067586)
0.0912376
(0.4499497)
-0.0409576
(0.4687005)
-0.1790853
(0.4389565)
-5.273731**
(0.1296565)
2.012961**
(0.1719283)
0.0000823
(0.0001685)
-0.0229907
(0.0898154)
-458.5626
(712.1791)
0.997903
1.005209
0.980527
1.095529
0.95987
0.836035
0.005125
1.000082
0.977272
7.06E-200
20
The Journal of Industrial Statistics, Vol. 5, No. 1
Table 6: Results of Multinomial Logit Regressions (Contd)
Dependent Variable
Productivity
Model 1
Entry decision
0.2885164
(1.579929)
1.334446
Productivity lag
FDI in previous period
Export in previous
period
Constant
FDI & Exports over
Exports
Sales
Debt-Equity Ratio
R&D/Sales
Productivity
4.005717**
(0.5909737)
-5.77424**
(0.6506129)
-2.189447**
(0.4928016)
0.0000904**
(0.0000314)
-0.0201439
(0.0202506)
-2.687411
(3.564733)
0.295852
(0.4536119)
54.9112
0.0031066
1.00009
0.9800576
0.0680569
1.344271
Productivity lag
FDI in previous period
Export in previous
period
Constant
3.119387**
(0.1304382)
0.9405374
(0.5929654)
-3.970522**
(0.6004295)
22.63251
2.561358
Model 2
Entry decision
0.210167
(1.658316)
2.42446*
(1.116635)
3.995827**
(0.5938161)
-5.770291**
(0.6507021)
-2.8066**
(0.6003783)
0.0000874**
(0.0000315)
-0.0217798
(0.020651)
-2.103833
(3.329449)
0.2175562
(0.4595444)
1.213786**
(0.387162)
3.129679**
(0.1310912)
0.9374497
(0.5933242)
-4.24837**
(0.6084391)
1.233884
11.29613
54.37077
0.003119
1.000087
0.978456
0.121988
1.243035
3.366206
22.86664
2.553461
Note: Figures in brackets are standard errors. (*) stands for significant at 10% level and (**)
indicate significance at 5% level.
Table 6: Marginal effects
Entry Mode
Predicted Probability
Variables
Sales
Debt-Equity Ratio
R&D/Sales
Productivity
Productivity lag
FDI in previous period
Export in previous period
Domestic
FDI
Export &
Firm
Export Firm Firm
FDI Firm
0.06869529
0.88578736
0.000161
0.045356
dy/dx
dy/dx
dy/dx
dy/dx
-0.0001346**
(0.00003)
0.0004005
(0.00062)
0.0103808
(0.06941)
0.0051569
(0.02876)
-0.006429
(0.02996)
-0.0376108**
(0.01458)
-0.8152777**
(0.02034)
0.0001242**
3.59E-08
(0.00003) (0.00001)
0.0005621 -3.61E-06
(0.00103) (0.00002)
0.1512738 -0.073979
(0.182) (0.11734)
-0.0143223 0.0000313
(0.03372) (0.00029)
-0.0466191 0.0003828
(0.03367)
(0.0012)
-0.398021** 0.0036187
(0.03673) (0.01112)
0.7656244** 0.0028982
(0.01881) (0.00891)
0.0000103**
(0.00001)
-0.0009591
(0.0009)
-0.0876755
(0.14411)
0.0091341
(0.0199)
0.0526654**
(0.01739)
0.432013**
(0.03614)
0.0525514**
(0.00428)
Note: Figures in brackets are standard errors. (*) stands for significant at 10% level and (**)
indicate significance at 5% level.
Outward FDI vs. Exports: The Case of Indian Manufacturing Firms
Table 7: Activities of firms engaged in FDI
Drugs & pharmaceuticals
Chemical
machinery and machine tools
Steel & steel products
Textile
Other automobile ancillaries
Plastic & plastic products
Food processing
Other Metal products
Cement
Diversified
Other agricultural products
Gems & jewellery
Lubricants, etc.
Commercial vehicles
Ferro alloys
Mining & construction equipment
Castings & forgings
Generators, transformers & switchgears
Other construction materials
Other transport equipment
Paper & newsprint
Refinery
Tyres & tubes
Wires & cables
Abrasives
Air-conditioners & refrigerators
Aluminium & aluminium products
Ceramic products
Computers, peripherals & storage devices
Copper & copper products
Footwear
Glass & glassware
Misc. electrical machinery
Other electronics
Other non-ferrous metals
Sponge iron
Storage batteries
23
21
10
10
10
8
8
6
6
5
5
7
4
4
3
3
3
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
21