Department of Economics, Delhi School of Economics, University of Delhi Does Productivity Differ in Domestic and Foreign Firms? Evidence from the Indian Machinery Industry Author(s): CHANDAN SHARMA Source: Indian Economic Review, New Series, Vol. 45, No. 1 (January-June 2010), pp. 87-110 Published by: Department of Economics, Delhi School of Economics, University of Delhi Stable URL: http://www.jstor.org/stable/29793955 . Accessed: 28/06/2014 08:52 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. . Department of Economics, Delhi School of Economics, University of Delhi is collaborating with JSTOR to digitize, preserve and extend access to Indian Economic Review. http://www.jstor.org This content downloaded from 185.31.195.106 on Sat, 28 Jun 2014 08:52:04 AM All use subject to JSTOR Terms and Conditions
Transcript
Department of Economics, Delhi School of Economics, University of Delhi
Does Productivity Differ in Domestic and Foreign Firms? Evidence from the IndianMachinery IndustryAuthor(s): CHANDAN SHARMASource: Indian Economic Review, New Series, Vol. 45, No. 1 (January-June 2010), pp. 87-110Published by: Department of Economics, Delhi School of Economics, University of DelhiStable URL: http://www.jstor.org/stable/29793955 .
Accessed: 28/06/2014 08:52
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp
.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].
.
Department of Economics, Delhi School of Economics, University of Delhi is collaborating with JSTOR todigitize, preserve and extend access to Indian Economic Review.
http://www.jstor.org
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Indian Economic Review, Vol. XXXXV, No. I, 2010, pp. 87-110
Does Productivity Differ in Domestic and Foreign Firms? Evidence from the Indian Machinery Industry
CHANDAN SHARMA Economics and Business Policy, FORE School of Management,
New Delhi-110016, India.
Abstract
This study aims to evaluate the productivity performance of foreign and domestic firms of the machinery industry in India. Using information of more than 200 firms of three sub-industries namely electrical, electronics and non-electrical, we compare both types of
firms' total factor productivity (TFP) for the period of 1994-2006. At the first stage, our
empirical analysis utilizes a non-parametric approach based on the principle of first order stochastic dominance. Comparing the distributions of the measures of firms' performance across the groups, we find that the distributions for foreign firms dominate that of
domestically owned Indian firms in two industries. In the next stage, the study estimates
determinants of productivity growth of firms. The results of our analysis suggest that in
the electrical industry foreign ownership matters, however, in other two industries there
is no significant difference between both types of firms. The results also reveal that those firms which import and have in-house R&D facilities are more productive. Finally, the
role of public infrastructure is found to be vital in the firms' productivity growth for the
sample of industries considered.
Key Words: Total Factor Productivity; Machinery Industry; Foreign firms
JEL Classification: L64, 03, D24, F23
1. INTRODUCTION
Ever since the beginning of the economic reforms (1991) in India, the successive
governments have been liberalizing the foreign direct investment (FDI) and industrial
policies to encourage foreign firms to invest and operate in the country. It is commonly believed that the Multinational Corporations (MNCs) add directly to employment, capital, exports, and new technology in the host country.1 In addition, local firms benefit from
indirect effects of improved productivity through demonstration effects and labor mobility. This externality which is also known as spillovers occurs because foreign investors
1 Acknowledgments: I thank an anonymous referee for his/her useful comments and helpful suggestions on the previous version of this paper. I would also like to thank Prof. B.N. Goldar, Prof. N. S. Siddharthan and other conference
participants on 'Corporate Sector, Industrialization and Economic Development in India' (March 27-28, 2009) at ISID, New Delhi, for their constructive criticisms and detailed suggestions. I am also grateful to Prof. Arup Mitra for his
valuable suggestions on the earlier draft of this paper. Any errors or omission are solely my own.
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cannot appropriate them fully. Perhaps expectation of attaining the spillovers has motivated
the policy makers in India to pursue policies aimed of attracting MNCs. However, this
is not the case always and domestic firms may also suffer negative externalities, for
instance, the loss of skilled employees to MNC affiliates. In the short run, increased
competition from MNCs may reduce the local firms' market share, even as it induces
some firms to upgrade their resource utilization and improve their competitiveness (Sinani and Meyer, 2004). This makes the issue controversial and debatable in the related literature.
Much of the literature focused on direct spillover from foreign to domestic firms (e.g. Haddad and Harrison, 1993; Aitken and Harrison, 1999; Djankov and Hoekman, 2000;
Konings, 2001, Haskel et al., 2002, Kathuria, 2002, Keller and Yeaple, 2003 and Sinani
and Meyer, 2004). In this context, the present study adopts a different approach and
examines the issue with an innovative way by comparing the both types of firms' total
factor productivity (TFP) and estimating their determinants.
In the existing literature, the theoretical models suggest that performances of foreign owned firms are better than domestically-owned firms (e.g., see Helpman, Melitz and
Yeaple, 2004). This is mainly because superiority of firm-specific assets, particularly in
intangible assets related to production processes, marketing networks, and management
capability, which is a necessary condition for a firm to become a multinational corporation
(MNC). On the other hand, many theorists assert that Internationalization alone is not an
essential condition for a firm to become a MNC and that ownership advantages such as
the possession of firm-specific assets are sufficient but not necessary condition for a firm
to become a MNC (e.g., Buckley and Casson, 1992; Casson, 1987; Rugman, 1980, 1985).
However, theorists agree on a point that MNCs have advantages in possession in firm
specific assets, thereby they spend relatively higher on the R&D activities and
advertisement; and keep relatively more patents (Dunning, 1993).
Empirical findings on this issue are very mixed and often contrary to each other. For
instance, studies of Hill (1988), Blomstr?m (1990), Sj?holm (1998), Ramstetter (1999), Takii and Ramstetter (2000), Takii (2002), Hallward-Driemeier, Iarossi and Sokoloff
(2002), and Bernard and Bradford (2004) found that foreign firms use better technology in production process than domestically owned firms. On the other hand, studies of
Aitken and Harrison (1999), Konings and Murphey (2001), Oguchi (2002), and Barbosa and Louri (2005) results could not find any significant difference in performance of both
types of firms. These evidences include studies involving both individual countries as
well as cross country. In case of India, Pandit and Siddharthan (1998, 2003) have shown
that MNCs having many advantages over domestic firms (therefore better performer) while Chhibber and Majumdar (1997) failed to find any difference between both types of firms. Recently, Goldar, Renganathan and Banga (2003) found that foreign firms are
more efficient than domestic firms in the engineering industry of India. The contrary
findings in different countries/industries can be explained by two reasons. First, if the
technological spillover from MNCs to domestic firms is taking place at the speedy rate
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Does Productivity Differ in Domestic and Foreign Firms? 89
in a country/industry then TFP differentials between both types of firms would not exist
otherwise it may be present.3 Second, in a country/industry, local firms may be as
productive as foreign firms if they have already enough foreign exposure through trade
relationship (e.g., see Bernard and Jensen, 1999, 2004; Girma et al., 2004; Arnold and
Hussinger, 2005).
Against this background, this paper intends to answer two important questions by an
empirical investigation. First, are foreign firms more productive than domestically owned
firms? Second, what factors determine productivity growth of the firms and is the foreign
ownership one of them? The study focuses on the machinery industry of India and our
scale of measuring productivity is TFP.4 Overall, our approach is different from that of
previous research studies in many ways. First, though the literature on productivity and
efficiency of Indian manufacturing industry is considerably large but, to best of our
knowledge, none of these has specifically focused on the Indian machinery industry.5 It
is a known fact that machinery industry is the core of Indian industrial development and
the industry has outpaced overall performance of manufacturing sector in the post-reform era. Textile and many other industries are heavily dependent on the machinery industry for capital equipments; therefore, performance of this industry has direct implications for other industries as well.6 In the second place, our approach is different from previous studies in terms of methodology used for the estimation of TFP. We apply the most recent
Levinsohn and Petrin (2003) methodology for TFP estimation, which is expected to yield better results. Third, the study applies a non-parametric Kolmogorov-Smirnov test for the
comparison purpose between both types of firms. Fourth, this study not only compares both types of firms' performance but also attempts to know the role of other important factors (viz. export, import, R&D and infrastructure) on their performance. Finally, to examine the role of public infrastructure on productivity, we construct a composite infrastructure index and test its role in firms' productivity growth.
3 The spillover of productivity and efficiency gains depend on a range of factors, for instance absorptive capacity (Borzenstein et al., 1998; Alfaro et al, 2003; Edison et al., 2002; Durham, 2004) of the country. These initial conditions that capture the absorptive capacity of host countries include the initial level of development (Blomstr?m et al., 1992), existing human capital development (Borensztein et al., 1998), trade policy (Balaubramanyam et al., 1996), financial
development (Durham, 2003; Alfaro et al., 2003), legal-based variables (Durham, 2004; Edison et al., 2002), and
general government policy (Edison et al., 2002).
4 The concept of total factor productivity (TFP) is important because output growth can not be fuelled by continuous input growth in the long run due to the nature of diminishing returns for input use. For sustained output growth, TFP growth is essential and therefore, TFP growth became synonymous with long-term growth as it reflects the potential for growth (Mahadevan, 2002).
5 Goldar, Renganathan and Banga (2003) study focused on the engineering industry. Their study compared technical
efficiency of firms under different type of ownership. However, TFP growth comparison was not covered in the study. 6 Among the developing countries, India is one of the largest exporters of machinery industry, pertaining to light and
heavy engineering equipments, bulk capital equipments for fertilizer industry, power projects, cement industry, petrochemical manufacturing units, mining equipments, and steel industry.
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Rest of paper is organized as follows: Section 2 discusses about the machinery industry in India. Section 3 describes the methodology that is adopted for TFP estimation and data related issues. Section 4 contains empirical results of production function
estimation and; provides industry and group wise TFP comparisons. Section 5, discusses
determinants of the TFP growth in the industry. The final section concludes the discussion
and gives policy suggestions on the basis of empirical findings.
In the present phase of high growth economic scenario of the Indian economy, the
machinery industry occupies an important place. The industry is currently experiencing a fast growth and it has performed better than overall Indian manufacturing industries in
Figure 1: Performance of Machinary and Manufacturing Sector in India
Source: Central Statistical Organization, India, 2007. (http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/
87411.pdf).
Note: Manufacturing sector index constitutes 17 industries, and Machinery industry is one of them.
A large part of the machinery industry production is exported i.e. pertaining to light and heavy engineering equipments, bulk capital equipments for fertilizer industry, power projects, cement industry, petrochemical manufacturing units, mining equipments, and
steel industry. The Indian machinery industry also produces and exports construction
equipments, diesel engines, equipment for irrigation projects, transport vehicles, tractors,
and sugar mill machinery among others.
The Indian machinery industry has already made known its capabilities in the
production of huge manufacturing units and instrumentations for various industrial sectors
2. THE MACHINERY INDUSTRY IN INDIA
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Does Productivity Differ in Domestic and Foreign Firms? 91
such as cement, power, fertilizer, etc. The machinery industry in India also caters to
Indian textile industry which is the largest industry in India. It produces state of the art
textile equipments which are highly efficient and easy to handle. In recent times, the
Indian machinery industry has also indulged in the production of heavy electrical
equipments and air pollution control equipments. With India experiencing a boom in the
economy, the development pertaining to the country's infrastructure has been high on the
agenda. Construction activities, development of the industries, and improvement in the
transportation facilities offer huge scope for the India's machinery industry to grow, not
only at the national level but also globally. These developments have helped the domestic
industry to be less dependent on the imported equipments, which makes production efficient. Also, maintenance of machinery has become comparatively easy and timely for
firms because it is domestically produced and locally available.
Table 1
EXPORT AND IMPORT PERFORMANCE OF THE INDIAN MACHINERY INDUSTRY
Year % of Total
Export
%Growth in
Export of
Machinery Industry
% of Total
Import
%Growth in
Import of
Machinery Industry
1991- 92
1992- 93
1993- 94
1994- 95
1995- 96
1996- 97
1997- 98
1998- 99
1999- 00
2Q00-01 2001- 02
2002- 03
2003- 04
2004- 05
2005- 06
2006- 07
2007- 08
4.74
4.07
4.24
4.33
4.72
5.50
5.59
4.99
5.06
5.91
6.63
6.19
7.06
6.65
7.03
7.58
7.56
-8.83
-10.95
25.02
20.84
31.70
22.73
6.22
-15.23
12.46
41.17
10.40
12.24
38.13
23.23
30.61
32.08
25.51
11.64
12.09
13.53
15.43
17.55
15.12
15.69
14.24
12.56
13.69
14.67
16.41
17.38
16.70
17.32
17.91
18.85
-31.76
17.06
19.18
40.21
45.56
-8.03
10.00
-7.29
3.40
10.84
9.00
33.63
34.80
37.14
38.68
28.77
35.82
Notes: Machinery export includes Machinery & instruments and Electronic goods. Machinery import includes Machine tools, Machinery except electrical, Electrical machinery and Electronic goods.
Source: Directorate General of Commercial Intelligence and Statistics, India.
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The faster growth and high potential of the industry have attracted a large amount of
foreign direct investment (FDI) in this industry. This sector has received around 17% of
total FDI inflows of the manufacturing industry for the period 1990-2006, which is
highest among all industries (DIPP, 2008). Further, the trade data reveals that the industry has transformed itself into a world class industry. The machinery industry's export and
import have been increasing at the speedy rate in the post- reform era (since 1991), and
it has further been augmenting since 2003 (see Table 1).
3. DATA AND METHODOLOGY
Our yardsticks of relative performance of firms are total factor productivity. Deviating from previous studies on the similar issue in terms of methodology, we use an innovative
technique to estimate productivity of the firms. Details of this methodology are discussed
below.
3.1 Levinsohn and Petrin (2003) Method of TFP Estimation
An important issue in estimation of production function is the presence of strong correlation between unobservable productivity shocks and input levels, which lead to
ordinary least squares (OLS) estimates of production functions biased and further, lead
to biased estimates of productivity. To correct the problem Olley and Pakes (1996)
developed an estimator that uses investment as a proxy for these unobservable shocks.
However, firm-level datasets suggest investment is very lumpy. If this is true, the investment
proxy may not smoothly respond to the productivity shock, violating the consistency condition (Levinsohn and Petrin, 2003). Using intermediate inputs could be a remedy of
this simultaneity problem. Levinsohn and Petrin (2003) have given three advantages for
using intermediate inputs as proxy in the specification. The first advantage is that
intermediate inputs will generally respond to the entire productivity term, while investment
may respond only to the news in the unobserved term. A second advantage is that
intermediate inputs provide a simpler link between the estimation strategy and the economic
theory, primarily because intermediate inputs are not typically state variables. Finally,
using intermediate input proxies avoids the potential truncation of a large number of firms
in industries with pronounced adjustment costs of capital. This is because, in general, firms always report positive use of intermediate inputs like electricity or materials.
Basic model of the estimation procedure is as follow: consider a Cobb-Douglas
production function
LYt =a0+alLNt+a2LKt+a3LMt + Q)t+rit ...(1)
where LY, LN, LK and LM are the firm's output (value added), labor, capital and
intermediate (material) input respectively (all variables are logged). In the model; error
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Does Productivity Differ in Domestic and Foreign Firms? 93
has two parts, first is co, which represents the transmitted productivity component while
r| an error term that is not correlated with inputs, <x> is affected by firm's policy, and
unobserved. Since it can affect input choice, therefore may lead to simultaneity problem in production function estimation. If correlation between inputs and this unobservable
factor is ignored (as it is done in OLS estimation), will provide inconsistent results. In
the model material demand function is assumed to be dependent on capital and co.
LMt =
f((OnLKt) ...(2)
Levinsohn and Petrin (2003) have shown that material demand function is
monotonically increasing in co. Therefore, material demand function can be inverted and
written as a function of observed variables
cot =
f(LKnLMt) ...(3)
Now, it is assumed that productivity of the firm is derived by a first-order Markov
process.
(Ot=E[(D\coiA] + $t ...(4) where ?t is an innovation to productivity that is uncorrelated with the state variable
LKt, however, its relationship with labor term is unclear which leads to simultaneity
problem.
Substituting equation 3 into 1 gives
LYt =alLNt+a2LKt + f(LMnLKt) + rit ...(5)
which can be rewritten as
LYt =axLNt+<l)t{LMnLKt) + r]t ...(6)
where <pt =
f (LKt, LMt) =
a0 + a2LKt + cot (LKt, LMt)
Thereafter third order polynomial approximation is substituted in LKt and LMt for
(LKt and LMt), and model is estimated, this is first stage of Levinsohn and Petrin (2003). This stage of estimation yields estimated value of ocr
In the second stage, oc2 is identified. It is done by computing the estimated value of
?t using
(j) =
LYt-a2LKt ...(7)
For any candidate value a2*, a predication of TFP (w) for all the periods can be done
as
(bt =(j>-a2LKt ..(8)
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This way of production function estimation resolves most of the problems that are
mentioned earlier. Therefore it is likely to improve our results.7
3.2 Data and its Sources
Firm data are obtained from the Prowess database provided by Center for Monitoring Indian Economy (CMIE). Although the database collects annual data on all listed firms
of Indian industry, but our sample only includes firms in three sub-industries of the
machinery industry namely electrical, electronics and non electrical industries. In electrical
industry our sample cover 77 firms in which 13 are foreign firms, in electronics industry we include 49 firms in which 10 are foreign owned firms and in non-electrical industry, we have 80 firms, out of which 17 are foreign firms. Foreign firms are considered those
having at least 20% equity owned by foreigners.8 Our time horizon for the study is 1994
to 2006. The primary reason behind taking 1994 as a starting year is that the Indian
economy underwent through an economic reform process in early 1990's, which has
brought many changes in the manufacturing sector as well. Another reason is price indices and deflators for all variables are available for this duration. We attempt to make
a balance panel of the industries, thus firms which have missing data for more than two
years are excluded from the study.
We use data of gross value added of the firms as a measure of output and it is deflated
by industry specific Wholesale Price indices (WPI).9 This deflator is obtained from Office of the Economic Adviser (OEA), the Ministry of Commerce & Industry of India.
For number of workers, data of the firms is taken from Prowess and Annual Survey of
Industries (ASI). Prowess database does not provide number of workers information, but
it does provide data on salaries and wages. We obtain average wages rate (total emoluments/
total mandays) data of the industry from ASI database and each firm's salaries and wages divided by the average wages rate, which gives number of workers information of firms.
For capital, we follow Krishna and Mitra (1998) and each firm's net fixed asset data is
7 To implement this method, there are three pre-conditions to hold: first, it requires a monotonicity condition in order to
be able to invert the intermediate demand function and express the transmitted productivity shock as a function of the
intermediate input and capital. Second, firms operate in the perfect competition condition. Third, it also requires
separability condition which implies the production function to be weakly separable in the particular input that is used
as a proxy. These pre-conditions are biggest criticisms are leveled against the LP method. However, in this study, we
are unlikely to face a situation in which these conditions do not hold.
8 This definition is used by Djankov and Hoekman (1998). Other researchers, e.g., Sjoholm (1999), take 15% of equity owned by foreigners as the threshold. Haddad and Harrison (1993) consider firms with at least 5% equity owned by
foreigners to be foreign firms. While Sinani and Meyer (2004) considered this value 10-20%.
9 Using gross value added (GVAD) at constant prices as a measure of output is a commonly practice in the Indian
empirical literature (e.g., Goldar, 1986; Balakrishnan and Pushpangadan, 1994; Ahluwalia, 1991; and Unel, 2003). There
are many advantages of using this process over gross output. Firstly, using GVAD allows comparison between the firms
that are using heterogeneous raw materials (Griliches and Ringstad, 1971). Secondly, the use of gross output in place of GVAD added necessitates the use of raw materials, which may obscure the role of labor and capital in the productivity
growth (Hossain and Karunaratne, 2004). Finally, use of gross value added accounts for differences and changes in the
quality of inputs (Salim and Kalirajan, 1999).
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Does Productivity Differ in Domestic and Foreign Firms? 95
deflated by capital deflators.10 Expenses incurred on raw materials and; power, fuel and
energy are used as indicators of materials (M) and energy (E), respectively. Materials data are deflated by the all commodities WPI while energy data are deflated using the Energy Price Index as provided by the OEA. In this study we also use data of export, import, R&D expenditure and sales of the firms, which are also culled from Prowess database.
For infrastructure, we consider only physical (economic) infrastructure of India.
Transportation, information and communication technology (ICT) and Energy sectors are
included in infrastructure.11 Our data sources of these variables are World Development Indicators (WDI) online and CMIE. Instead of using all infrastructure variables separately in the model, we construct a composite infrastructure index for India by using Principal
Components Analysis (PCA). (For details see Appendix)
4. ESTIMATING AND COMPARING TFP OF FIRMS
4.1 Estimating the Production Functions
To estimate TFP of the firms, we construct three separate panels of firms for electrical, electronics and non- electrical industry. Using Levinsohn-Petrin (LP) productivity estimator, we estimate Cobb- Douglas production functions (as discussed in sub-section 3.1).
Following the LP method, raw material and power fuel expenses of firms are considered as proxy variables in the models. The estimated production function results are reported in Table 2. The results suggest that for all three industries, coefficients of workers and
capital are significant at 5% critical level. The results also reveal that electrical and
Table 2
COBB- DOUGLAS PRODUCTION FUNCTION ESTIMATION USING LEVINSOHN-PETRIN PRODUCTIVITY ESTIMATOR (Dependent Variable: LY)
Variables Electrical Electronics Non- Electrical
LK 0.41813* 0.73035* 0.40118*
(9.12) (5.91) (4.17)
LN 0.46775* 0.49859* 0.16086*
(3.89) (5.81) (3.66)
Wald test (P-Value) 0.3087 0.1262 0.0002*
Notes: 1. Z-test statistics in parenthesis, 2. Wald test of constant returns to scale, 3. Proxy variables: Power
and fuel expenses; and Raw material expenses.
10 Data for the capital deflator is obtained from Handbook of Statistics on Indian Economy (http://www.rbi.org.in) 11 We could not consider Water and Sanitation in infrastructure, because unavailability of their time series data.
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electronics industries are operating under the constant return to scale, while in non?
electronic industry, results indicate for presence of decreasing returns to scale. On the
basis of the estimation, TFP of firms are predicted for all three industries (as shown in
equation 8).
42 Estimated TFP of Industries
The estimated TFP of firms are presented in Table 3. An industry-wise comparison of TFP suggests that non-electrical industry is highly productive in respect of electrical
and electronic industries.12 The period of 1998 to 2003 seems to be a slowdown period
Table 3
AVERAGE OF ESTIMATED TFP OF MACHINERY INDUSTRY'S FIRMS, 1994-2006
Does Productivity Differ in Domestic and Foreign Firms? 97
for these industries as TFP has decline during the period across the machinery industry. Results also suggest that TFP of electrical and non-electrical industries have increased
significantly in the last three years of the study (2004-2006). Further, estimated TFP of electronics industry suggest that its productivity has been falling in the study period.
Perhaps, electronics industry could not adopt well in the fast changing world of the
electronic market, especially cheap Chinese products in the industry have taken over a
lead both in domestic as well as in the world market.
The ownership group-wise comparison provides interesting results. On average foreign firms in electrical and non-electrical industries are more productive than the domestic
counterparts, however, the gap is not very substantial. Surprisingly, in electronic industry, domestic firms have better TFP than foreign owned firms. A close look on the trend of
this industry reveals in last three years that is the economic recovery phase; domestically owned firms are losing the productivity, while foreign firms are gaining it. Therefore, reverse convergence seems to be taking place in this industry.
43 The Test of Equality: Kolmogorov-Smirnov Test
In the next stage, we conduct a two-sided non-parametric Kolmogorov-Smirnov test
(KS-test) (see a technical note on this methodology in 2.A. of Appendix) to determine
whether the TFP distributions between the two groups differ significantly. The KS-test
calculates the largest difference between the observed and expected cumulative frequencies, which is called D-statistics. These statistics are compared against the critical D-statistic
for the sample size. The results of the two-sided KS-test for all three industries are shown in Table 4.
In the electronics industry, the largest difference of distribution of TFP between
foreign and domestic firms is 0.0082, which is statistically not significant. Thus, the null
hypothesis that both TFP distributions are equal can not be rejected. The largest difference between the firms of domestic and foreign distribution functions is -0.1165, which is
again not significant at 5% significant level; however, the null can be rejected at 10%
significant level. In short, the results for this industry indicate for existence of no significant difference between firms.
In the case of electrical industry, the largest difference of distribution between the firms of foreign and domestic distribution functions is 0.2246, which is statistically significant. Thus, the null hypothesis that both TFP distributions are equal is convincingly rejected. Further, the largest difference between the firms of domestic and foreign distribution functions is -0.0120, which is statistically not significant at 5% level. Therefore, the results indicate that in electrical industry foreign firms has superiority over the domestic firms.
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Finally, in non-electrical industry foreign firms are found to be more productivity than domestically owned firms as the null is rejected in the first case while in the second
case it can not be rejected (see Table 4). Therefore, at this stage, we conclude that at least
in two industries foreign firms are more productive than local Indian firms.
Table 4
RESULTS OF KOLMOGOROV-SMIRNOV TEST FOR EQUALITY OF DISTRIBUTION FUNCTIONS, TFP
Group Largest Largest Largest Difference (D) Difference (D) Difference (D)
Electronics Electrical Non- Electrical
H0: Foreign-Domestic < 0 0.0082 0.2246* 0.1576*
(0.987) (0.000) (0.000)
H0: Domestic-Foreign< 0 -0.1165 -0.0120 -0.0337
(0.073) (0.960) (0.656)
Combined K-S: 0.1165 0.2246* 0.1576*
(0.146) (0.000) (0.000)
Notes: P-value in parenthesis,* denotes significant at 5% level.
5. DETERMINANTS OF TFP GROWTH
5.7 The Empirical Model
Moving further to see the role of foreign ownership on firms' productivity, we now
turn to investigate the determinants of estimated TFP growth. For this purpose, we choose
a set of variables which could potentially determine the TFP growth of firms in all
industries of our sample. These variables are discussed below in details:
Research and Development (R&D) intensity: It is well established in the related literature that R&D intensity is an important determinant of productivity growth. Pioneer
study of Griliches (1979) has shown in the 'R&D Capital Stock Model' that this factor has a direct effect on the productivity of firms. Empirical evidence of Cuneo and Mairesse
(1984), Lichtenberg and Siegal (1989) and Hall and Mairesse (1995) also supported the Griliches's view.
To capture the Research and Development (R&D) activities of firms, the study considers
the ratio of R&D expenditure to the firm's total sales. This variable is a measure of R&D
intensity of firms; thus, it is expected to have a positive impact on firms' productivity
growth.
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Does Productivity Differ in Domestic and Foreign Firms? 99
Export & Import intensities: Several previous studies have shown that exporting and importing firms are more productive than others firms (e.g., see Bernard and Jensen,
1999; Ben-David, 1993; Sachs and Warner, 1995; Bernard and Wagner, 1997; Wagner, 2002; Aw et al., 1998; Clerides et al., 1998; Girma et al., 2004; Bernard and Bradford,
2004). To capture the export intensity of firms, we use ratio of export to value of sales
of firms. Since exporting firms make themselves more productive to compete in foreign markets, therefore, we expect positive impact of this variable. On the other side, the
import intensity of firms is captured by ratio of total import (imports of both raw material
and finished goods) to value of sales of firms. Generally importing firms receive
technological transfers as well as better inputs, which can potentially help firms to enhance their productivity performance.
Ownership of firm: In this present study, our main objective is to find the effect of foreign ownership of firms. To accommodate this factor in the model, we keep a dummy variable for the foreign firms. Positive sign of coefficient of this variable would indicate for productivity superiority of foreign firms over that of the domestic Indian firms.
Public infrastructure: A range of pervious studies have found that public infrastructure is a significant predictor of productivity of firms. In case of India, findings of Mitra et
al. (2002) and Hulten et al. (2006) revealed that infrastructure is a crucial and significant
predicator of the Indian manufacturing productivity. Hence, to control our model, we
intend to include this variable in the model. We construct an index for infrastructure and it is included in the productivity model. We expect positive sign of this variable on the
productivity growth of firms.
Size of firm: Geroski (1998), and Halkos and Tzeremes (2007) argued that size of the firm exerts an indirect effect on the productivity of firms, as it conditions the impact of other factors on productivity. Bearing this in mind, we accommodate the size of firms in the model by using value of sales of firms. Theoretically, because of economy of scale, a larger size and increasing output should have a positive influence on the productivity of firms. Therefore, we expect positive sign of this variable.
On the basis of above discussion, we construct following empirical model of TFP
growth for estimation:
MTFPijt =
axrdqijt +a2exqijt +a3impqijt + aAFD +
a5LQijt +
a6LGijt+a1LTFPijt_^eijt ...(9)
where rdq is ratio of R&D expenditure to value of sales, exq is ratio of export to
value of sales, impq is ratio of import to value of sales, FD is a dummy for foreign firms,
LQ is sales of firms and LG is composite infrastructure index, which is common for all firms, i, j, and t denote firm, industry and year respectively.
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A preliminary step in our study of determinates has consisted in testing the stationarity of the series which are included in the empirical model. The test is done by using the
cross-sectionally augmented Im-Pesaran-Shin (IPS) panel unit-root test, which is based
on the simple averages of the individual cross-sectionally augmented Dickey-Fuller statistics. The main advantages of this approach are that it incorporates potential cross
sectional dependence and that it does not pool directly the autoregressive parameter in the
unit root regression so that it allows for the possibility of heterogeneous coefficients of
the autoregressive parameters under the alternative hypothesis that the process does not
contain a unit root. The results of this test are reported in Table 5, which leads to rejecting the unit root hypothesis, except in one variable case. This allows us to conduct regression at the level of the variables.
Table 5
TEST FOR PANEL UNIT ROOT APPLYING IPS-W STATISTICS (AT LEVEL)
Variables Electrical Electronics Non-Electrical
rdq -7.00834* -5.32581* -5.29664*
exq -3.48947* -9.00480* -3.64206*
impq -5.85737* -6.52121* -4.41714*
LQ -2.13900* -0.67282 -1.74413*
LG 11.6558
ALTFP -6.84373* -11.5437* -12.8282*
Notes, (i) All statistics are based on AR(p) specifications, (ii)* denotes for significant at 5% level, (iii) Probabilities are computed assuming asymptotic normality (iv) optimal lag(s) decided by AIC criteria.
5.3 Results of the Model Estimation
The equation (1) for all three industries panels are estimated separately with GLS Random effect method, and estimation results are reported in Table 6.13 The second
column of the table provides estimated results of the electrical industry. The results reveal
that R&D, public infrastructure and size of firms have positive impact on the growth of
TFP as hypothesized. The dummy for foreign firms (FD) is found to be significant and
positive, which implies that in the electrical industry foreign ownership matter. This also
validates our previous section findings that foreign ownership has positive spillover
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Does Productivity Differ in Domestic and Foreign Firms? 101
effects on the productivity of firms. Surprisingly, we fail to find any significant impact of export and import intensity on the firms' TFP performance for this industry. This may be because of the reason that trade exposure has still not reached at a level where it could
affect firms' performance in the industry.
The next column of Table 6 reports the results of the electronic industry. The results
of this industry are somewhat different from the electrical industry. Our prime focus
variable the foreign firm dummy is found be negative, however, statistically insignificant. It implies that for this industry our evidence does not support the hypothesis that foreign
ownership has any impact on the productivity performance of firms, which is quite consistent with our previous section findings. Import intensity is found to have positive influence on the firms' performance, which is on the expected line that technological transfers help firms to improve their productive. Especially the considering the nature of
this industry, it seems obvious. Other variable such as infrastructure, R& D intensity and
value of sales of firms are also found to have significant and positive effects. However, consistent with previous industry results, in this industry too, the export intensity coefficient
is not found to be significant.
Finally, we turn to discuss the determinants of TFP in the most productive industry in our sample. The estimation results of the non-electrical industry are presented in the
last column of Table 6. Again, the export intensity is not found to be significant for this
industry. Most importantly, the foreign firm dummy is not found to be significant, this
is indeed surprising. All other variables have positive and significant impact on the
productivity growth of firms. The R&D intensity is found to be significant, only at 10%
level. It seems that higher productivity growth of this sector is mainly explained by the
infrastructure, output-level and import intensity. The result regarding infrastructure makes
sense because in recent years in India, development of electricity infrastructure is a core
of governments' policy, which has a direct linkage with this industry. Perhaps, this has
played a key role in transformation of the industry and which is also reflected in our
results.
It is noteworthy here that import intensity is positive and significant for the two
industries, while export intensity is not found to be significant for any industries. This
may be because exporters generally import their intermediate materials in order to keep their production costs under control. It is possible that the level of productivity is better
explained by participation in the import market than by the intensity of exports.
13 We employ GLS for panel data to avoid the hetroskedasticity and autocorrelation problems, which are likely with our
dataset.
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Does Productivity Differ in Domestic and Foreign Firms? 103
studies in terms of methodology of TFP estimation. We apply the most recent Levinsohn
and Petrin (2003) methodology for TFP estimation. Thirdly, this study not only compares both types of firms' performance but also attempts to know the determinants of their
performance. Finally, to examine the role of public infrastructure on productivity, we
construct a composite infrastructure index and test its role in firms4 productivity growth.
The main findings of this study are somewhat surprising as well as very relevant in
many senses. Our results of TFP estimation suggest that in the machinery sector, non?
electrical industry is the most productive industry, followed by electrical and electronic
industry. These results also reveal that overall machinery industry has shown only marginal
improvement in TFP in the study period. The most striking results we receive for electronic
industry, in which the productivity has decline in the study period. For the same industry, we also find that foreign firms are not better productive than domestically owned firm
and this result is further validated by the test of equality (KS test). The test of equality results suggests that in electricity and non-electricity industries foreign firms have
productivity superiority over local Indian firms.
Our investigation on the determinants of TFP also yields interesting results. The
results suggest that foreign ownership is a significant determinant only in electrical industry. Most disappointing results we find for the role of export intensity, which suggest that it
does not affect firms' productivity of any of our sample industry. However, we find some
evidence for roles of import and R&D intensities in firms' productivity performance. This is consistence with findings of Goldar et al. (2003) for the engineering industry of India. Further, the role of infrastructure is found to be positive and crucial in determining the
productivity, which is also consistent with findings of previous studies in India.
These results have important, however, complex policy implication. The productivity difference indicates that the conventional wisdom should hold and that government should
encourage foreign firms because potential positive spillovers from foreign firms to the
productivity of domestic firms are expected. Further, the technology gap may inhibit the utilization of foreign technologies by domestic firms; in this situation the government should provide support to domestic firms to help them learn from foreigners. The policies such as targeting the increasing local learning capabilities and labor skills may be critical to increasing the absorptive capacity of domestic firms. Further, as Wang and Blomstr?m
(1992) argued that competition is essential for reduction in the technology gap between domestic and foreign firms, which forces foreign firms to transfer more technology to the host country. Therefore, we recommend to policy makers to formulate policies which increase competition in the machinery sector in particular and in manufacturing in general. Before concluding, we also suggest for some types of incentives for R& D activities in the sector and increasing the quality and quantity of public infrastructure to increase the
productivity of firms in India.
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To construct a composite infrastructure index, we use principal component analysis (PCA). In this
exercise, nine normalized variables across the sectors are used. Using PCA technique, our estimate
suggests that two of components are statistically significant and they jointly explaining 96% variations (see Table-1 A).
14 On this basis, in the next stage, we obtain factor loadings of the variables (see Table-1 A). These loadings are used as weights of respective variables to construct the composite Infrastructure index.
? Air Transportation Air transport, freight (million tons
per km)
? Roadspaved Transportation Roads, paved (% of total roads)
? Port Transportation Port (commodities wise Traffic, 000 tomes)
2. A A Technical Note on Kolmogorov-Smirnov (KS)
The basic concept of nonparametric of the two-sided Kolmogorov-Smirnov (KS) tests is follows:
suppose we have two independent random samples of productivity realizations. One sample C0j, con, is drawn from a distribution function Qt and the other sample, u)n+1, con is drawn from a
distribution function Qr The hypothesis of interest is that -Q2(?)) < OVc? e 9^ . If this
hypothesis holds, and the inequality is strict for at least some co e , we say that Ql dominates
?22 stochastically.
Stochastic dominance we test using the two-sided Kolmogorov-Smirnov test, for which the
asymptotic distribution of the test statistic under the assumption of independently drawn samples evaluating two related null hypotheses. We reject the equality of distributions as in the null
hypothesis:
H0 :Q.l(G))-Q2(?)) = 0 V<oe9t
then we can conclude that Q,{ (co) stochastically dominates Q2 (co)
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