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Chapter 2
Typology of Spillovers and Literature Review
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2.1 Introduction The concept of technology spillover lies in the heart of the new growth theory. It is a complex
concept which is frequently misunderstood or confused with other related issues, such as
technology adoption or technology transfers. In addition, it is often used as interchangeably with
R&D spillovers. The R&D process is essentially a knowledge generation process in which one
utilizes resources (scientist, engineers, technicians and research equipment, etc) to create new
knowledge. The innovative activities of firms not only leads to new product (whose benefits the
firms can appropriate) but also it contributes to the general stock of new knowledge upon which
subsequent innovations can be built. So the direct and indirect benefit of innovation accrues not
only to the innovators, but its spillover benefits other firms in raising the level of knowledge upon
which new innovations can be used. This is referred to as knowledge spillover. The growth in total
factor productivity in a country depends not only on the domestic R&D capital stock but also on
the foreign R&D capital stocks.
FDI can be seen as enabling an international exchange of information and dissemination of
knowledge because a country’s productivity depends on its own R&D as well as on the R&D
effects of its transaction partners. FDI constitutes an important source of the technology as well as
knowledge spillover and it can improve the domestic firms’ productivity through products
available with the use of foreign knowledge and information. A large share of benefits may well
come from the FDI as in the form of spillovers of knowledge to the domestic firms, for example,
local firms improve their productivity by imitating the technology used by MNCs affiliates
operating in the local market (demonstration effect). Another kind of spillover effect occurs if the
entry of foreign-owned firms leads to greater competition in the host economy, so the local firms
are forced to use existing technology and use of the resources in a most efficient way (competitive
effect). The third type of spillover effect is the availability of workers trained by MNCs to firms in
the same industry, firms in other industry or the economy as a whole. Another type of spillovers
occurs if competition forces the local firms to search for new knowledge and efficient
technologies. Finally, improving managerial practices employed by MNCs such as JIT (Just in
time), QA (quality assurance), QC (quality circles) etc., which are prerequisites for effective and
efficient use of new technology can be viewed as a form of spillovers to the rest of the industry in
the host country.
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Section 2.2 explains the different channels of technology spillovers from FDI in broader
ramifications of the inter-industry and intra-industry technology spillovers from FDI. Section 2.3
discusses the typology of spillovers studies, which is divided into four parts. The four parts
correspond to technology flow approach, cost function approach, production function approach
and paper trial approach and its related research. Section 2.4 discusses an empirical literature
review of technical efficiency, productivity, and R&D spillovers from FDI; MNCs, technology
transfers, and technology spillovers from FDI and technology spillovers in Indian manufacturing
industries.
2.2 Linkages and Different Channels of Technology Transfers and Spillovers from FDI
The most important aspect of technology spillover is in the form of externalities. Technology
spillover occurs when one firm obtains an economic benefit from another firm’s R&D activity
without sharing any cost. So the most important and significant difference between technology
spillover and technology transfer consists in whether the innovator can appropriate the welfare
surplus from the transfer of knowledge. The empirical issues on spillover effects of FDI are based
on the notion that MNCs possess superior organizational and production techniques compared to
the domestic firms (Hymer, 1976). MNCs can transfer technology through various means like
licensing, trade, FDI, subcontracting, franchising, and strategic alliances. The different channels of
technology transfers and spillovers from MNCs are illustrated in Figure 2.1. Commonly, the
preferred mode of technology transfer is through FDI since it can internalize the transfer of
superior technological assets at low or no extra cost (Caves, 1996). In addition, FDI is considered
as the best means to retain control over the technological knowledge. Since technology has the
characteristics of a public good, a part of the technology spills over from the MNCs subsidiaries to
the domestic firms. The technology spillovers can be in the form of improvement in productivity
of the domestic firms. This is the neo-classical view on spillover effects. However, the spillover
effects from FDI can be broadly classified into two groups. Griliches (1979, 1992) distinguished
two different types of technology spillover concepts. But in practice it is very difficult to separate
them operationally. These are as follows:
(i) The first concept is often called as a vertical, welfare, pecuniary, or rent spillover. It is basically
a matter of price measurement. R&D performed in one firm (seller) can benefit another firm
(buyer) because the quality improvement embodied in inputs is often not appropriated fully by the
sellers because of competition.
Fig 2.1: Different Channels of Technology Transfers and Spillovers from FDI
Source: Sasidharan (2006)
Thus, this type of spillover focuses on the transaction
through buyer-supplier chains. In welfare terms, a cost reducing innovation of a seller firm lowers
the cost of a buyer firm and thereby increases the level of the buyer firm’s producer’s surplus. In
this case, the welfare benefits are passed among the purchasers of the new
of welfare effect is rarely shown in the transaction data because the price indexes do not correctly
reflect the quality improvements in a timely manner (Moen
industry is the best choice for vertical spillovers. The quality of computer related products has
been improved dramatically, even
type of inter-industry vertical spillover as a
Schmookler (1966). Terleckyj (1974), Sveikauskas (1981), Scherer (1982
estimate such effects. This kind of spillover drives the endogenous growth in
Licensing Trade
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Channels of Technology Transfers and Spillovers from FDI
Thus, this type of spillover focuses on the transaction-based linkages and usually occurs
lier chains. In welfare terms, a cost reducing innovation of a seller firm lowers
a buyer firm and thereby increases the level of the buyer firm’s producer’s surplus. In
the welfare benefits are passed among the purchasers of the new innovations. This type
rarely shown in the transaction data because the price indexes do not correctly
reflect the quality improvements in a timely manner (Moen, 2000). As for example
industry is the best choice for vertical spillovers. The quality of computer related products has
been improved dramatically, even while their prices are stable or fallen. The importance of this
industry vertical spillover as a source of technology flow was originally suggested by
Schmookler (1966). Terleckyj (1974), Sveikauskas (1981), Scherer (1982, 1984
estimate such effects. This kind of spillover drives the endogenous growth in
MNCs
Subcontracting FDI
Host country
subsidiary
Vertical welfare,
pecuniary, or
rent spillover
(Inter-industry)
Horizontal,
knowledge, non
pecuniary, or
technological spillover
(Intra-industry)
Franchising
Channels of Technology Transfers and Spillovers from FDI
based linkages and usually occurs
lier chains. In welfare terms, a cost reducing innovation of a seller firm lowers
a buyer firm and thereby increases the level of the buyer firm’s producer’s surplus. In
innovations. This type
rarely shown in the transaction data because the price indexes do not correctly
). As for example, the computer
industry is the best choice for vertical spillovers. The quality of computer related products has
their prices are stable or fallen. The importance of this
source of technology flow was originally suggested by
, 1984) made efforts to
estimate such effects. This kind of spillover drives the endogenous growth in Grossman and
Horizontal,
knowledge, non-
pecuniary, or
technological spillover
industry)
Franchising Strategic
alliances
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Helpman model (1991a, 1991b). Thus, this type of spillover helps the firms to move along the
existing production frontier to the optimal level of production (Branstetter, 2000).
(ii) The second concept is often known as a horizontal, knowledge, or non-pecuniary spillover.
These things are concerned with the knowledge transmission. R&D performed in one firm can
stimulate the creation of new knowledge or result in the fruition of previous ideas in another firm.
In this case, new knowledge is disembodied from new goods and becomes part of a general pool
of knowledge (i.e., public goods). Subsequent innovations are built by this disembodied pool of
knowledge. It is the kind of spillover that begets further innovations and changes the production
capacity of an economy. Thus, this type of spillover focuses on the technology-based linkages and
can occur without direct input-output linkages among the industries. One example of the
technology-based linkages is the technological closeness concept which is developed by Jaffe
(1986). It supposes that one industry may benefit from new discoveries made by another industry
if two industries are using similar process (not necessarily connected by value chains).
Arrow (1962) argues that the first type of spillover is an appropriability problem. If an
innovator has a perfectly discriminating monopoly power, she may get the entire welfare benefit
from the technological advancement. On the other hand, if the market is perfectly competitive,
consumers reap all the welfare benefits. In most of the cases, however, a firm faces a situation
between these two extremes.
Thus, innovative firms cannot fully appropriate welfare benefit from the newly cost
reducing innovations. So, it is a result of a competitive market. The second kind of spillover on the
other hand is a question of knowledge (or technology) as considered to be a social non-rival good.
Since knowledge is partially a non-rival and non-excludable commodity, borrowing an idea from
someone else’s research does not reduce the available stock of knowledge for the original
innovator. In other words, it is the result of unique characteristics of knowledge as a commodity.
Intra-Industry and Inter-Industry Spillover Effect
FDI can generate benefits to domestic companies in host countries via increasing productivity.
The R&D process associated with FDI is an innovative effort, which acts as a major engine of
technological progress and productivity growth. It is essentially a knowledge generating process,
which utilizes resources to create new knowledge. Innovation originates from knowledge which is
the outcome of cumulative R&D experience and is a contribution to the stock of knowledge. The
innovative activities of the firm or industry may not lead to a new product but it can spread to
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other firms by raising their productivity and innovative knowledge which can form a threshold on
which their new innovation can be based. This is referred to as the ‘technology spillover’.
The intra-industry spillovers or horizontal spillovers are based on knowledge transmission.
The research performed in one firm can stimulate the creation of new knowledge in another firm.
In this case, the new knowledge is disembodied from new goods and becomes part of a general
pool of knowledge that is a public good. These types of spillovers recreate further innovations and
try to change the production capacity of an economy. Thus, it is based on the technology-based
linkages. However, the inter-industry spillover or vertical spillover is based on the factor of price
measurement. Research performed in one firm (seller) can benefit another firm (buyer) because
the quality improvement embodied in input is often not appropriated fully by the seller because of
competition. So, it is based on the transaction-based linkage which usually happens through the
buyers and supplier chain. The main difference between horizontal (intra-industry) and vertical
(inter-industry) spillover is that the former is involuntary and (generally) undesirable from the
point of view of innovating firms, whereas the latter is desirable (and more often voluntary).
Another difference is that while horizontal R&D cooperation may mitigate competition between
firms and it is often closely monitored by competition authorities while vertical cooperation is less
likely to hinder competition. Inter-industry cooperation is generally sufficient for firms to
internalize horizontal spillovers. However, the internalization of vertical spillover requires inter-
industry coordination.
Tomohara and Yokota (2007) explained the relationship between FDI and productivity and
consensus regarding the FDI impact on productivity of domestic companies in the host country.
Taking Thailand as an example, they try to show the vertical linkages across industries by
introducing the endogenous input decision making of Thai manufacturing firms. They found that
on an average, FDI improves domestic companies’ productivity through horizontal and backward
channels but does not affect increase in productivity of the domestic companies by forward
linkages. In addition, the mechanisms of backward spillovers are varied and it is dependent on the
industry’s structures. Further, they examine the horizontal and vertical linkages between
companies’ productivity and foreign direct investment by using the following time-variant, sector-
specific variables. ( ) jtHorizontal , measures intra-industry spillovers of an industry. They
calculate an average foreign presence in sector j at time t by using the weight of firm i’s output
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(denoted by Y) to total output in the sector which firm ibelongs. The weight captures the
magnitude of firm i’s effects on other companies in the same sector.
( )( ) ( )
( )∑
∑=
∈ jiit
iitit
jt Y
YForeign
Horizontal
*
From the above, the right hand side variable involves the summation over the foreign firms within
an industry with respect to the total industry output.
( ) jtBackward measures spillover effect on domestic companies which supply intermediate goods
to the same industry j :
( ) ( )ktk
jkjt HorizontalBackward ∑= α ;
where jkα is the share of sector sj ' output supplied to sector k . This measure excludes goods
supply for final consumption, imports of intermediate goods and input supply within the sector.
( ) jtForward measures spillover effect on domestic companies that purchase intermediate goods
from the same industry j :
( ) ( ) ( ) ( )( )∑∑∑= Y itY ititForeignm
jmjtForward *σ .
From the above equation, jmσ stands for the share of input that industry j buys from industry m
among sector j’s total input purchases. As inputs purchased within the sector are not included.
( ) jtVertical measures the inter-industry spillover effect. It is the sum of horizontal spillovers over
jth industry at time t using weights of ith firm output to total output in the sector to which firm i
belongs,
( ) ( ) jtHorizontal
jiijjtVertical ∑
≠= α
Peters (1995) studied a model of vertical spillovers. He found that more concentrated
industries tend to spend more on R&D (however, this result may be reversed for high values of
inter-industry spillovers and some specific values of horizontal spillovers), horizontal spillovers
may increase or decrease R&D and vertical spillovers increase R&D investments, profits and
welfare. The model suffers from the following restrictive assumptions: spillovers are in one
direction only, from suppliers to customers; upstream firms do not benefit from their own R&D
investment; all benefits accrue to downstream firms and upstream firms cannot adjust their output
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to their R&D investments. Finally, cooperation is not addressed across industries or between
industries.
Harhoff (1991) presents a model of product R&D spillovers between the vertically related
industries. He finds that upstream and downstream R&D are generally substitutes: with an
exogenous market structure and perfect vertical spillovers in one direction only, and only one of
the two industries depends on imperfectly appropriable R&D. However, his model suffers from
criticisms because of the restrictive assumptions. The presence of the Stackelberg upstream
monopolist makes the result applicable only to asymmetric markets. Moreover, these market
structures make it possible to study the upstream horizontal spillovers along with the downstream
horizontal spillovers. Another important restrictive assumption behind the model was that when
downstream horizontal spillovers were allowed then the upstream prices were fixed exogenously.
Atallah (2000) found that the vertical spillovers effect of R&D investments directly and
indirectly influence upon the horizontal spillovers and R&D cooperation.2 The horizontal
spillovers may increase or decrease innovation and welfare depending on the prevailing types of
cooperation and vertical spillovers always increase innovation and welfare. Cooperative settings
are compared in terms of R&D. And it has been shown that none of cooperation uniformly
dominates the others. The types of cooperation that yields more R&D is dependent on horizontal
spillovers, vertical spillovers and the market structure. Different kinds of cooperation induce the
firms to internalize a larger positive sum of the competitive externalities (vertical, horizontal and
diagonal) which yield more R&D. Finally, on the basis of the strategic investment literature,
cooperation between the competitors increases or decreases R&D when the horizontal spillovers
are high or low and the model shows that these results do not necessarily hold when vertical
spillovers and vertical cooperation are not taken into account.
2.3 Typology of Spillover Studies
Nadiri (1991) developed a typology of spillover studies. He categorized the spillover studies into
two groups: technology flow approach and cost function approach. The technology flow approach
uses input-output linkages (or a technology flow matrix developed from the patent data) to
position a firm or an industry in the technology space and to examine technology spillover
2 The result of Atallah (2000) is quite contrary to the results of Steurs (1994, 1995), Peters and Becker (1997/98).
Under the Atallah model, it is found that there is a strong complementarity between inter-industry research efforts. Vertical R&D cooperation has been briefly addressed in the agricultural economics literature. For more detail see Freebairn et al. (1982) and Alston and Scobie (1983).
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patterns from an R&D performing firm or an industry to the remaining firms or industries. The
cost function approach however is based on the econometric techniques which estimate the cost
reducing effect of the technology spillovers. Further, there are also other approaches often used in
the literature, but are not covered under the Nadiri typology of spillover studies, for example, the
production function approach is a dual to the cost function approach and aims to measure the
effects of spillovers on total factor productivity (TFP) or output using econometric models. Unlike
the three previously mentioned approaches, the paper trail approach utilizes the patent and patent
citation data to directly measure the spillovers. The literature concerning the four different aspects
of spillover studies are given below.
2.3.1 Technology Flow Approach and Cost Function approach
Technology Flow Approach
The technology flow approach is based on the vertical spillover (i.e., research performed in one
industry can improve technology in another industry). In most cases, the increase in inter-industry
spillover decreases the labor and material demand. A number of studies use input-output linkages
or technology flow matrices (i.e., based on the input-output relationships) to measure the spillover
among industries. The most important limitations of this approach are that it only considers the
knowledge flows among industries linked through buyer-supplier chains (i.e., vertical spillovers).
Many more interactions which are located outside such linkages are not taken into consideration.
Jaffe (1986) explained in his ‘technology closeness’ concept, technology spillovers arise not only
among industries that are closely related through input-output linkages but also among those that
are engaged in technologically similar procedures and activities. So, the technology flow approach
does not take into account the horizontal technology spillovers. In addition, the input-output
accounts examine the inter-industry relations at a given point in time; whereas the inter-temporal
aspect of R&D gets ignored. The overall conclusion from this line of studies is that there are
substantial spillovers among different industries.
Terleckyj (1974, 1980) is among the first researchers to make use of the technology flow
matrix to estimate the size of spillover among industries. He introduced the borrowed R&D
concept to take into account the potential knowledge flows between an R&D performing industry
and its recipient industries. The reported rate of return in the manufacturing industry is 45% for
borrowed R&D and 28% for its own R&D.
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Scherer (1982, 1984) developed a technology flow matrix based on the industry R&D
spending patterns and patent data to distinguish between industries of origin and users of
innovations.3 Used R&D, defined as the sum of its own process R&D and embodied R&D
borrowed from other industries through purchase linkages, show a 70-100% rate of return while
the rate of return for the own product R&D is relatively small. Griliches and Lichtenberg (1984)
reconfirmed the role of interdisciplinary technology flows in promoting productivity growth with
using R&D having larger coefficients than own product R&D components.
Wolff and Nadiri (1993) find that there are significant spillovers from the R&D embodied
in capital stock (i.e., borrowed R&D). An industry’s own total factor productivity (TFP) growth
has been significantly related to the sector’s supplying industries; the degree of industries forward
linkages has been positively associated with the sector’s R&D intensity; and private R&D
embodied in inputs has a bigger impact than government financing R&D. In addition, Verspagen
(1997) criticized the previous studies for overly focusing on rent spillovers. According to his
opinion the existing methods for measuring inter-industry technology spillover mostly aims to
capture the input-output linkage based rent spillovers but not knowledge spillover which is
another aspect of the spillover process.
As he compared the transaction-based technology flow matrices such as the Yale
Technology Concordance and the matrix which was earlier developed by Scherer (1982) with the
technological linkages based on the U.S. patent matrix, he came to conclusion that they indeed
measure the different aspects of the technology spillovers. Along similar lines, Keller (1997) also
used the Yale matrix to identify technology flow among sectors. Alternatively, drawing attention
towards the Jaffe’s technology similarity matrix (Jaffe, 1986), Park (1995) uses the distribution of
researchers in 30 major academic fields to describe the technology flows approach.
Cost Function Approach
The cost function approach focuses on cost reduction which is most common and important
beneficial aspect of technology spillover. This line of research utilizes the cost function
formulation which is based on output and relative factor prices for variables and quasi-fixed
inputs. The empirical evidence has been given to show the strong cost-reducing effect of
3 It often takes two to three years to finish an R&D project and another several years to develop a new
technology or a product (Mansfield, Rapoport, Schnee, Wagner, & Hamburger, 1971). Ravenscraft and Scherer (1982) identified a roughly bell-shaped lag structure with a mean lag of four to six years
26
technology spillovers. In addition, it is also expected that inter-firm technology spillover can serve
as a substitute for the R&D input and therefore create an incentive for free-riding. However,
Rosenberg (1974), Nelson and Winter (1982), and Cohen and Levinthal (1989) argued that firms
have to invest in R&D to able to explore externally available knowledge which is often obtained
by spillovers. So, the spillover can be an incentive and a disincentive factor for a firm to perform
its own R&D. Hence, from the above, the overall results from this line of research suggests that
(a) there exists a significant intra and inter-industry spillover and (b) spillover affects not only
productivity growth but also the demand pattern of production factors like labor, capital, energy
and materials.
Bernstein (1988) measures both intra-industry and inter-industry spillovers and find that
both spillovers effects are significantly associated with cost variables. Bernstein and Nadiri (1988)
find that spillovers decrease variables cost, decrease the demand for labor and materials (i.e.,
substitute), increase the demand for physical capital (i.e., complement) and produce a larger social
rate of return rather than private rate of returns. The paper by Bernstein and Nadiri (1988) was the
first study which paid attention to the potential differences in spillover patterns among industries.
A common approach of all previous studies was based on the measurement of spillover as a single
variable. The previous models ignored the importance of industrial heterogeneity in spillover
patterns. In fact, Bernstein and Nadiri investigate the effect of R&D spillovers in five high-tech
industries, where each industry is treated as a separate spillover source.
Bernstein and Nadiri (1991) in another paper introduce a vector of spillover which
represents the R&D stocks of different industries. An earlier study had suggested the vectorization
of borrowed R&D models in each spillover source to generate distinct effects of industry. The
1991 study illustrates that spillovers decrease variable cost and increase output in all industries
and thereby lead to a fall in price. Output growth outweighs the initial cost reductions and
therefore the total variable cost increases when output expands. Spillover increases labor and
material demand. Some of the industries are more important than others from the spillover angle
and finally, the social rate of return to R&D capital is significantly greater than the private rate of
returns.
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2.3.2 Production Function Approach and Paper Trail Approach
Production function Approach
The production function approach is based on the influence of technology spillover on
productivity and innovation. This line of research utilizes the econometric models to estimate the
effects of spillover on TFP (or innovation in a knowledge production function case). In common
terminology, output is regressed on standard inputs such as labor, capital, material and R&D
within the Cobb-Douglas or translog function framework. Empirical studies show that spillovers
have a significant impact upon firm’s productivity or propensity to innovate. It becomes very
important at this point to distinguish and categorize the technology flow approaches and
production function approaches. However, the production function is used as a standard model for
both approaches; the former is designed to capture the first type of spillover (i.e., rent spillovers)
because it relies mostly upon input-output linkages. On the other hand, the production function
approach focuses on capturing the second type of spillover (i.e., knowledge spillovers).
Jaffe (1986, 1989) who is one of the earliest researchers employed the knowledge
production function framework which is suggested by Griliches (1979). In his 1986 study, he finds
some evidence of spillovers from various technological success indicators. Firms in which the
research area overlaps to other firms that are invested heavily in R&D have on an average more
patents per dollar R&D and a higher return to R&D both in accounting profits and in market
value. The most important innovation of Jaffe’s (1989) study is the incorporation of a spatial
aspect. He uses a modified knowledge production function by a spatial component that measures
the importance of geographic proximity for university and industry research. Initially, he
introduces the geographic considerations into the study of spillover analysis. The study provides
some evidence for the importance of geographically-mediated spillover from the university
research, especially in drugs, chemicals and in electronics. However, there are some recent
developments, wherein spatial aspects of spillover analysis and their implications for regional
development are studied. The incorporation of spatial aspect is the most important distinction in
comparison to the first two approaches (technology flow and cost function) and the other two
approaches (production function and paper trial). The geographic aspect of the technology
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spillover becomes a major issue in more recent studies which uses a production function
framework or patent citations.4
From the knowledge of production function approach, Feldman (1994) demonstrates that
region with more knowledge inputs tend to generate more innovation. This suggests that
knowledge spillovers are geographically bounded where new knowledge is being created. In
addition, Anselin et al. (1997) finds strong evidence of localized spillovers at the same level. They
illustrated that there exist local externalities between university research and high-tech innovative
activity at the metropolitan statistical areas (MSAs). Finally, Adams and Jaffe (1996) measure
intra-industry spillovers based on the transformed Cobb-Douglas production function to take into
account the effect of geographic and technological proximity of R&D activity. They show that the
productivity-enhancing effect of R&D diminishes when the geographic and technological distance
increases. And finally, it explains the R&D intensity rather than the total amount of R&D which
substantially affect the level of productivity and spillovers.
Paper Trail Approach
The paper trial approach uses the direct measure of spillovers while the other three approaches
(technology flow, cost function and production function) use both indirect and suggestive
measures of spillovers. In addition, from the direct measure of spillover, the paper trail approach
gives importance to explicit analysis of the localized technology spillovers. Krugman (1991)
argues that knowledge flow are invisible and do not leave any paper trails by which they can be
measured or traced. Jaffe, Trajtenberg, and Henderson (1993), however pointed out that
knowledge flows do indeed leave paper trials often in the form of patented inventions. This related
research it may be stated uses the patent data to measure inter-firm technology spillovers. Since
the use of patent and patent citation data in spillover research is a relatively new concept being
discussed by many more economists which are as follows.
A patent is a property right granted to the inventor of a device or a process based on its
novelty and potential utility. When a patent is granted, a public document is created that includes
extensive information of the inventor, employer and technological predecessors (i.e., citations).
4 In a standard production function case, output or productivity is used as a dependent variable. In a knowledge production function case, which was originally introduced by Griliches (1979), an innovation or patent count is used instead of output as a dependent variable.
29
Patent citations define the scope of the property right, which is granted to a new invention.
Granting a patent is a legal confirmation to the invention’s novelty and original contribution over
previously existing knowledge and research. Therefore, when patent Y cites patent X, in principle,
it usually means that patent Y is built upon preexisting knowledge embodied in patent X (Jaffe
and Trajtenberg, 1992). It is worthwhile to note that the patent citations are added by patent
examiners.
There have been some researchers who are against the use of patent data in spillover
research. Griliches (1990) summarized the issues from two major perspectives: one, classification
and two, intrinsic variability. First, the patent classification system does not match existing
product or industry categories adequately. In addition, the propensity to patent inventions varies
according to the different fields of research. Therefore, use of patent data inherently raise issues
such as whether to assign a patent to the industry where it was made or where it would be used or
whether patent data across industries are comparable to measure technological change. Second,
there exist significant variations in terms of technological and economic significance among the
patents. However, no information has been available for the relative importance and accordingly
every patent is treated equally when it is counted.
In spite of above criticism, the use of patent data in spillover research can be substantially
helpful in analyzing the process of invention and innovation. Jaffe et al. (1998) examined 26
national aeronautics and space administration (NASA) citing patents that received more than 10
citations and found about 58 percent of them were involved in reliable technology spillovers
analysis. The study provided evidence in favor of using patent citation as a proxy for technological
impact and knowledge spillovers. Jaffe et al. (2000) again surveyed 380 citing and cited inventors
and examined what patent citations really measure. The result suggested that patent citations
could be a valuable and reliable indicator for newly-developed technological and knowledge
spillover.
Jaffe and Trajtenberg’s (1992) study demonstrate that patents cite other patents more
frequently when they originate in the same city, which implies an existence of the localization of
technology spillover. Jaffe and Trajtenberg (1996) find that the frequency and duration of citations
30
vary according to the scientific field and type of institutions where the patents are originated.5 For
instance, industries facing rapid technological change like electronics, optics and nuclear
technology have a higher rate of immediate citations, but the rate fades rapidly over time. Further,
government patents tend have a fewer citations as compared to the university and corporate
patents. Almeida and Kogut (1997) apply Jaffe’s methodology to the US semiconductor industry
and find that patent citations are highly localized, thus recommending Jaffe’s earlier studies for
indicating the existence of geographical boundaries in technology spillovers.
2.4 Literature Review
This section reviews the literature on FDI and technology spillover. It includes empirical and
some aspects of the theoretical review of FDI and technology spillovers, efficiency dynamics and
productivity, and R&D spillovers. The sub-sections of the review are as follows.
2.4.1 Technical Efficiency and R&D Spillovers from FDI
Mukherjee and Ray (2005) analyzed state-level data for the aggregate manufacturing sectors in
India for the period 1986-87 to 1999-00 to study the efficiency dynamics of individual states.
They used the non-parametric method of Data-envelopment Analysis (DEA) and by using the
super-efficiency models they ranked the states in terms of their performance. The method of DEA
developed by Charnes, Cooper and Rhodes (CCR) (1978) and further generalized by Banker,
Charnes, and Cooper (BCC) (1984) provides a nonparametric alternative to parametric frontier
production function analysis. In DEA, only a few weak assumptions have to be made for the
underlying production technology. In particular, no functional specification is necessary. Based on
these assumptions a production frontier is empirically constructed by using mathematical
programming methods from the observed input-output data of the sample firms. Then the
efficiency of the firms is measured in terms of how far they are from the production frontier.
The study further analyzes various aspects of the dynamics efficiency of ranking through
concordance analysis, convergence analysis and Markov chains analysis. Economic reforms,
liberalization of government control and greater reliance on market forces have so far failed to
create an environment conducive for efficient utilization of resources in most of the states in India.
They found no major changes in the efficiency ranking of the states after the economic reforms. In
5In this study, Jaffe used real citations and placebo citations (patent that are similar but not actually cited) to examine the relationship between a patent and its citations. He found that in spillovers scores of real citations and placebos are statistically significantly different, which implies that meaningful links (i.e., spillovers) between cited and citing patents exist. And the results also suggested that the more important patents are perceived to generate more citations.
31
fact, a detailed sector-wise analysis could have provided a more relative and informative idea of
the effect of reforms on the relative efficiency of state’s manufacturing sectors.
Wang and Blomstrom (1992) developed a model in which international technology
transfer emerges from the parent company decisions on the expected strategies for interacting with
their foreign subsidiaries and the technological characteristics of host country firms. By solving
dynamic optimization problem they found that technology transfer from a parent company to a
subsidiary company is positively related to the level and cost efficiency of the domestic firms
learning investment. They further found that the higher operation risk like the political instability
and low potential economic growth then more reluctant of the foreign firms to transfer the
technology.
Bernstein (1988) and Jaffe (1986) find that inter-industry spillover has more effects on cost
reduction than intra-industry spillovers. Bernstein finds that unit costs decrease more in response
to an increase in intra-industry (inter-industry) spillovers in industries with large (small) R&D cost
shares.
Bernstein and Nadiri (1989) estimate a model of production and investment based on the
theory of dynamic duality. The dynamics arise from the adjustments costs associated with the
accumulation of both physical and R&D capital stocks. R&D capital stock is distinguished by the
fact that the returns to R&D investment are not perfectly appropriable because spillovers have
been generated between firms from the process of R&D capital accumulation. Drawing on the
literature of previous studies, the Bernstein and Nadiri study analyzes the effect of R&D spillovers
and its effect in calculating the social and private rate of returns. There are three associated effects
with intra-industry R&D spillover. These are as follows: (i) Cost decline as knowledge expands
for the externality-receiving firms (ii) Production structures are affected as factor demand changes
in response to the spillover and (iii) the rates of capital accumulation are affected by the R&D
spillover. These cost reducing factor bias and capital adjustments of the spillovers are estimated
for four industries.
The R&D spillovers are embodied in technology of firms which can be represented by the
following equation:
( ))(),(),(),(),(),()( tI rtI ptX rtK rtLtK pFty θ=
32
where )(ty is the output flow, F is the production function, )(tK p is the physical capital service
flow, )(tL is the variable factor service flow, and )(tK r is the R&D capital service flow. The
R&D spillover is given by the variable )(tX r , which is the R&D capital service flow of other
firms in the industry. Indeed, ),()( tff
rKtX r ∑= where the summations are taken for all firms in
an industry. The parameterθ captures the extent to which R&D capital is appropriate. If 0=θ then
R&D capital is completely in-appropriable and there are no spillovers; if 1=θ then R&D capital is
appropriable and all knowledge is common; and if 10 << θ , then there is complete
appropriability. The presence of investment, which is given by )(),( physicalpitI i = and
)&( DRri = in the specification of technology implies that there is internal adjustment cost
associated with changes in the level of the capital inputs. These adjustment costs are measured in
terms of foregone output (Treadway, 1971; Mortensen, 1973; and Epstein, 1981).
There are three effects associated with the R&D spillovers. First, from the production
functions given the inputs and investments, changes in the spillover generate change in the
quantity of output. This is presented as the productivity effect. Second, given input levels and the
investment rates, changes in the R&D spillovers can cause factor substitution. However, the
variable factor like R&D capital may be complements or substitutes to spillovers. It is important to
note that not only R&D capital responds to the spillover, but in principle each factor of production
can be affected by the knowledge obtained from other firms in the economy. Third, the technology
incorporates adjustment costs. Given output and factor quantities in the production functions,
change in R&D spillover cause quasi-fixed factor adjustments when the rates of investment
change. The existence of R&D spillovers implies that the social and private rate of returns to R&D
capital differs. The analysis shows that the social return exceeds the private return in each
industry. Moreover, there is significant variation across industries with respect to the difference
between the social and private rates of return.
2.4.2 MNCs, Technology Transfer and Technology Spillovers from FDI
The spillover effect of FDI in the empirical study by Kokko, Tansini and Zehan (1996) focuses on
technology spillover conditioned by a country’s trade policy regime and is based on Uruguayan
firm-level inter-industry analysis. In this study, the Uruguay deployment of trade liberalization in
1973 is used as a benchmark to separate export promoting (EP) FDI from import substituting (IS)
33
FDI. Foreign firms set up before 1973 are classified as IS firms and those after 1973 are classified
as EP firms. There are two limitations of this study. First, the classification of EP and IS FDI
using 1973 as the base year was problematic because the trade liberalization implemented in that
year was partial and some industries continued to remain under heavy protection (Favaro &
Spiller, 1991). Moreover, the analysis suffers from a failure to address the possible simultaneity
involved in the relationship between productivity of local firms and the foreign presence. The
positive relationship between the foreign presence and productivity of local firms is not covered
by the single equation model and might simply reflect the fact that foreign investment gravitates
towards more productive industries rather than less productive industries and as representing any
technology spillover from FDI (Aitken & Harrison, 1999).
The literature of optimal market penetration strategy by MNCs emphasizes the
minimization of probability of imitation, especially under imperfect intellectual property rights in
the host country. Organizational choices can be used to delay the evaluation by domestic
producers through absorptive capacity. In an incomplete contracts environment, resource and
information transfer within the MNCs minimizes the transactions cost (Ethier, 1986).
The economies of scope stemming from product-specific R&D can explain the vertically
integrated nature of the MNCs (Helpman, 1984). Trade secrecy and efficiency wages are also used
to mitigate technology leakages from FDI. Over time the dissipation of technological knowledge
rents if intra-industry spillovers materialized are mitigated, and MNCs organize production to
maximize the imitation lag. The location of the MNCs subsidiary minimizes rent erosion due to
copying by local firms. Proximity to potential competitors with absorptive capacity reverse
engineer proprietary technology would be detrimental to the MNCs and subsidiaries being set up
where potential rivals cannot erode its market share (Markusen & Venables, 1998). Since MNCs
can get benefit from knowledge diffusion when it reaches downstream clients and upstream
suppliers, it encourages vertical flow of generic knowledge leading to inter-industry spillovers.
Linkages can be a propagation mechanism for technological externalities above and beyond the
pecuniary externalities highlighted by Hirschman (1997).
The literature on backward linkages emphasizes the static effect of increased demand by
the MNCs for local intermediate inputs (Rivera-Batiz and Romer, 1991). Recent models
emphasize the dynamic effect in host-country productivity ensuing expansion of both demand and
supply of intermediate inputs and services. Not only do incumbent upstream sector producers
34
benefit but also MNCs may start by providing goods and services that were previously unavailable
in the host country. Thus, MNCs operations can induce local availability of new intermediate
services and inputs and thereby a nexus between FDI penetration and growth in productivity of the
downstream manufacturers (Romer, 1994; Rodriguez-Clare, 1996).
A group of research papers has addressed the issues of FDI from the ‘bottom up’ focusing
primarily on the development of subsidiaries as a unique and differentiated organizational entity.
Variation in innovative capabilities occur among subsidiaries and over time the subsidiary
depends on own decision rather than the centralized decisions of the parent company, i.e. the
innovation capabilities of subsidiaries get determined by: (i) the decisions and strategies of
subsidiaries themselves; and (ii) aspects of the local environment which create constraints and
opportunities for subsidiaries (Birkinshaw and Hood, 1998). One notable implication is that the
subsidiaries may themselves affect the potential for generating spillovers into the domestic
economy. MNCs subsidiaries R&D expenditure can therefore be a better measure of FDI activities
than the more commonly used measure of total FDI financial flows. However, Birkinshaw and
Hood found no evidence of spillovers by using that indicator. Todo and Miyamoto (2002) used
two indicators of technological activities in MNC subsidiaries to estimate spillovers in Indonesia;
on the commonly used R&D based indicator (R&D expenditures) and what they call the human
resources development indicator (measured by subsidiaries expenditure on training). They find
that only subsidiaries engaged in R&D and training have a positive impact on the productivity of
domestic firms.
The usual perspective in technology spillovers from FDI holds the MNCs subsidiary as a
passive actor. It presumes that the technological superiority that spreads from subsidiaries to other
firms in the host economy is initially created outside by the MNCs parent companies and the
MNCs deliver through subsidiaries via international technology transfer. The role of subsidiaries
is seen as being a little more than a ‘leaky container’ lying between the technology transfer
pipeline and the absorption of spillovers by domestic firms (Marin and Bell, 2005). The Marin and
Bell study empirically explores the effects of own knowledge-creating and accumulating activities
of the local subsidiaries on technology spillover from FDI by using data of industrial firms in
Argentina over the period 1992-96. The analysis suggests that significant results can be obtained
by incorporating subsidiaries own technological activities as an explanatory variables in the
35
spillover process. The analysis follows a model for spillovers which is within the familiar
production framework. The basic model used by them is given below:
εηδλ ijjdij
dijj
dij
dij IGZFDIpartInputy ++++∆+∆=∆ lnln ;
where d denotes domestic firms, subscript i and j stand for the plant and industry, respectively and
∆ represents the changes in the variables between 1992 and 1996 (t-4); and ηδλ and,, are
parameters to be estimated in the model. FDIpart is a measure of the scale of the FDI presence in
each industry. Z is a set of plant and industry level control variables. G and I are dummies for
corporate groups and industries, respectively. Change in FDI participation in industries is treated
as an additional input which explains the productivity growth of domestic firms and its coefficient
is taken as the evidence of spillover effect from FDI. The study gives importance to the absorptive
capabilities of the domestic firms which constitutes an important part of the spillovers studies. The
study analyzes the hypothesis that investment by domestic firms in capital embodied technology
and to a lesser extent in skill training is associated with spillovers effects. Another issue examined
is based on the conventional pipeline model of the spillovers that is driven by the knowledge
assets of multinational corporations. It is seen that the mere existence of MNCs subsidiaries linked
to the superior knowledge resources of the parent company does not by itself generate spillovers.
Instead, subsidiaries own knowledge creation and accumulation seems to be significant in
spillover potential. This suggests that the knowledge asset model with its smoothly operating
technology transfer pipeline to subsidiaries is not an appropriate framework for analyzing the
significance of technology spillovers from FDI to domestic firms (Marin and Bell, 2005).
Xu (2000) investigates the US multinational enterprises (MNEs) as a channel of
international technology diffusion in 40 countries in the period 1966 to 1994. The data has been
used on technology transfer to distinguish between the technology diffusion effect and other
productivity enhancing effects of MNEs. The study uses a panel data regression equation which is
as follows:
ε itititittiit MNEaHaGAPaaaGTFP +++++= 32100 ;
where itGTFP is the growth rate of total factor productivity (TFP) of country iat time t , 0ia is a
country-specific constant, 0ta is time-specific constant, GAP is the technology gap measured by
TFP of the country relative to the TFP of the US, H is the human capital level of the country and
36
MNE is a measure of the activities of MNE affiliates that affect host country productivity growth.
The idea here is incorporated from the technology diffusion model of Barro and Sala-i-Martin
(1997). The model has technology leading countries that innovates new technologies and a
follower country that imitates the technologies. The study finds that the technology transfer
provided by US MNEs contributes to the productivity growth in Developing Countries (DCs) but
not in the least developing countries (LDCs). The analysis shows that a country has to reach a
minimum human capital threshold level in order to benefit from the technology transfer of US
MNEs. However, most of the LDCs do not come under the minimum threshold level, so they
cannot absorb any benefit from US MNEs.
Kugler’s (2006) paper contributes an estimation framework to measure both technological
and linkage externalities from FDI. Empirical research mainly dealt with intra-industry spillovers
from FDI with restrictive treatment of inter-industry effects until recently. However, optimal
organization of the multinational corporation (MNC) involves minimization of loss of profits due
to leakage of technical information to competitors, with the consequence that host country firms
within the MNCs sector experience limited productivity gains from ensuing FDI. MNCs transfer
knowledge to local downstream clients, or outsource to local upstream suppliers. Hence, FDI
substitute’s domestic investment within the sectors but it complements across the sectors. The net
impact on aggregate capital formation by host country producers hinges on the interaction
between linkages and spillovers. Estimations based on the Colombian Manufacturing Census yield
the sectoral pattern of FDI spillovers displaying knowledge propagation between but not within
industries. Empirical cross-country estimation reveals that there exist contemporaneous
correlations between FDI inflows and domestic productivity consistent with the diffusion of
externalities from MNCs operations. The study find evidence in diffusion of generic knowledge,
namely spillovers of exporting know how from MNCs to neighboring Mexican manufactures
(Aitken et al., 1997). This suggests that the absence of intra-industry FDI spillovers does not rule
out the prevalence of inter-industry spillovers.
Keller (2000) suggests a model whether the pattern of a country’s intermediate goods
imports affects its level of productivity. A country which imports such goods primarily from
technological leaders receives more technology than a country which imports primarily from the
follower countries. The importance of trade patterns in determining technology flows is quantified
by using industry-level data for machinery goods imports and productivity from eight member
37
countries of the Organization for Economic Cooperation and Development (OECD) between 1970
and 1991. In fact, the analysis develops an empirical model in which domestic productivity is
related to the varieties of imported differentiated inputs that are employed domestically. The
number of varieties of intermediate inputs from the partner countries is related to imports from
these countries, and it estimates the relation between domestic as well as import-weighted foreign
R&D and domestic productivity. Three conclusions can be drawn from this analysis. First, the
eight country studies indicate benefits as being greater from domestic R&D rather than from R&D
of the average foreign country. Second, conditional on technological diffusion from domestic
R&D, a country’s import composition matters only if it is strongly biased toward or away from
technological leaders. Third, differences in technology inflows are related to the pattern of imports
explaining about 20 percent of the total variation in country productivity growth rates.
Amiti and Konings (2007) estimate the productivity gains caused by reduction in tariffs on
intermediate inputs. Lower output tariffs can increase productivity and productivity spillovers by
inducing tougher import competition, whereas cheaper imported inputs can raise the productivity
via learning, variety and quality effects. The study uses the Indonesian manufacturing census data
from 1991 to 2001, which include plant-level information on imported inputs. The effects of trade
liberalization on productivity can be analyzed for a plant by means of the simple Cobb-Douglas
production framework, which is as follows:
( ) mit
kit
litAitY it MKL
βββτ= ;
where output of firm iat time t ,Y it is a function of labor, Lit , capital, K it , and intermediate inputs,
M it . The study analyzes whether the productivity of plant i is a function of trade policy, denoted
byτ . In the first step it estimates the plant level TFP, and in the second step it specifies how
productivity can be affected by trade policy. From the above functional form the following
regression equation can be made by taking the logarithmic expression in both sides of the
equation.
eitmitmk itkl itly it ++++= ββββ 0 .
The dependent variable is total revenue at the plant level, deflated by the five-digit
industry-level producer price indices. Domestic and imported inputs are adjusted by separate
deflators and domestically purchased material inputs are deflated by five-digit price deflators. The
study uses the Olley and Pakes (1996) methodology to estimate the above equation in order to
38
avoid the simultaneity problem between input choices and productivity shocks. The study shows
that the effect of reducing input tariffs significantly increases productivity and this input tariff
effect is much higher than reducing output tariffs. A 10 percent point fall in input tariffs leads to a
productivity gain of 12 percent for firms and at least twice time high as any gains from reducing
output tariffs. The productivity estimates from reducing output tariffs range between 1 to 6
percent. Excluding input tariffs could result in an omitted variables bias problem. Overestimating
the competition effects arises by lowering output tariffs. After including the input tariffs, the
coefficient of output tariffs reduces significantly in some specifications and size of the coefficient
of output tariffs reduces by more than half.
The paper by Ekholm and Hakkala (2007) analyzes the location choice by firms operating
in the high-tech sector on the assumption that there are two sources of agglomeration economies:
knowledge spillovers from R&D activities and home-market effects which are based on the
combination of scale economies and trade costs. Both activities use the inputs of skilled labor. The
tendency for production to concentrate in the larger economy puts upward pressure in the return to
skilled labor and creates factor cost reduction for locating R&D in the smaller economy. Because
of R&D spillovers, the smaller economy may end up hosting an agglomeration of R&D activities.
In the next analysis, allowing for agglomeration forces in both production and R&D which
generates an outcome where production agglomerates in a larger economy and R&D in a smaller
economy is one of the possibilities. In fact, it might lead to multiple equilibria for the intermediate
level of trade costs that is the R&D activities are completely concentrated in the smaller economy
and in the other case they are spread out. With stronger R&D spillovers, R&D concentrates in
either low to medium trade costs country, while it becomes concentrated in the larger economy for
high trade costs.
Pavitt (1984) finds that out of 2000 innovations in the UK, only 40% emanated from the
sector using the innovation. Goto and Suzuki (1989) find that in the electronics industry,
technological diffusion through spillovers is more important than technological diffusion through
inputs. Ward and Dranove (1995) find important vertical spillovers within the American
pharmaceutical industry. Inter-industry knowledge spillovers are more likely to occur when one
innovation naturally brings forth the development of complementary products or innovations in an
upstream input supply sector can reach to its full potential.
39
Suzuki (1993) and Branstetter et al. (1999) find significant vertical spillovers in Japanese
keiretsu. As regards the vertical spillover when firms have a higher level of vertical integration, a
good part of vertical spillovers are internalized. And, as much as the outsourcing is concerned,
spillovers which are intra-firms become inter-firm/inter-industry spillovers. Suzuki finds that the
spillovers from the core firms to its subcontractors are significant; a percentage increase in
technology transfer reduces the unit variable cost of the subcontractors by 0.09%. In his study he
takes the sample of 208 Japanese manufacturing firms and finds that production keiretsu promote
innovative activity which is measured by firm-level spending on research and development. In
addition, he finds that affiliation with production keiretsu groups promotes the exchange of
technological knowledge across firms and within groups.
2.4.3 Total Factor Productivity and Technology Spillovers in Indian Manufacturing
Industries
In the longer run, the increases in TFP in industries take place through the application of
advanced technological knowledge. This makes investments in technology acquisition through
R&D expenditure and technology purchase, and technology flowing from foreign collaborators
important contributors to productivity growth. Also, local firms may gain from technology
spillovers from foreign industrial firms. All these issues have received a good deal of attention in
the studies undertaken on these aspects in the context of Indian manufacturing industries.
An earlier study on the impact of technology related investments on productivity in Indian
industries was undertaken by Basant and Fikkert (1996). They used firm-level data for the period
1974-75 to 1981-82 and studied the impact of R&D, technology purchases and domestic and
international spillovers. The results of the study indicated significant productivity gains from
technology purchase (technology imports). No strong impact of R&D investment done by a firm
on its productivity was found, but there were clear indications of spillovers effects from domestic
R&D investment.
Another study on the impacts of technological investments was done by Hasan (2002).
This study covered the period 1977 to 1987. Hasan found that imported technologies, especially
those of disembodied nature and obtained through contractual arrangements with foreign firms,
impact productivity positively and significantly. Firms own R&D efforts, on the other hand, were
not found to be very productive, corroborating the findings of Basant and Fikkert (1996). His
results showed that domestically produced capital goods impact productivity positively and
40
significantly, but this impact according to Hasan stems primarily from the technological know-
how imported by domestic producers of capital goods.
Among the relatively more recent studies on the impact of technological investments on
productivity which cover the post-reform period, Ray (2009) finds that Indian industrial firms
have made significant productivity gains from technology transfers. The technology transfer have
occurred either because of trade (imports and exports) or through more direct channels of
technology transfers such as imports of technology against royalty payments. She notes that a
positive effect of exports activity on productivity (through the inducement for technological
advance it creates) does not occur in all industries. This positive effect exports on productivity is
found only for high tech industries such as electronics, pharmaceuticals and chemicals.
Banga and Goldar (2007) use panel data for 41 industry groups for the years 1980-81 to
1999-00 to estimate an econometric model for explaining variations in TFP, and find a significant
positive effect of technology acquisition (R&D, technology imports, and advanced technology
embodied in imported capital goods) on productivity. A positive effect of technology imports on
efficiency is found also by Parameswaran (2002) in his study covering a few select industries. On
the other hand, Mazumdar et al. (2009) find that neither R&D and export expenditure nor the uses
of imported technology improve technical efficiency of pharmaceuticals firms. This is at variance
with the findings of Pradhan (2002) who found that R&D and technology imports exert a
significant positive influence on productivity of pharmaceuticals firms.
Kathuria (2001) paper uses techniques of stochastic production frontier and panel data
literature to examine the spillover hypothesis that the presence of foreign-owned firms and
disembodied technology import in a sector leads to higher productivity growth of domestic firms.
The study uses panel data covering 368 medium and large sized Indian manufacturing firms for
the period 1975- 1976 to 1988-1989. The empirical results indicate that there exist positive
spillovers from the presence of foreign-owned firms but the nature and type of spillovers vary
depending upon the industries to which the firms’ belongs. Further, there exist significant positive
spillovers for the domestic firms belonging to the scientific subgroup provided the firms
themselves possess significant R&D capabilities. However, for the non-scientific subgroup
presence of foreign firms itself forces the local firms to be more productive by inducing greater
competition. Further, the results change marginally when the technological gap is considered in
Indian manufacturing.
41
Sasidharan (2006) study empirically examines the spillover effects from the entry of
foreign firms by using the firm level data of Indian manufacturing industries over the period 1994-
2002. He considers both the horizontal and vertical spillover effects of FDI. Consistent with the
findings of the previous studies, the study finds no evidence of significant horizontal spillover
effects. In contrast, the study finds negative vertical spillovers effects; however it is not
statistically significant.
Kathuria (2002) and Sasidharan and Ramanathan (2007) do not find evidence of any
significant horizontal spillovers. By contrast, Bhattacharya et al. (2008) find that foreign presence
has positive spillovers on productivity. This study finds that other channels like R&D activity or
export initiatives have no impact on productivity. Siddharthan and Lal (2004), similarly, find a
significant spillover effect. According to them, the spillover effects were modest in the initial
years of liberalization, but increases sharply later on. They also point out that if the productivity
gap between the local firms and the foreign firms is high, beneficial spillover effects may not
occur. In a recent study, Pant and Mondal (2010) find evidence of significant technology spillover
effects. According to them, technology spillovers and transfer from to domestic firms is more
likely to be achieved by the presence of foreign firms than by simple purchase of foreign
technology. A common point emerging from the studies on spillover effects undertaken for Indian
industries is that technology transfer and spillover depend crucially on the absorptive capacity of
the local firms, particularly the R&D efforts of the local firms. As a result, some local firms may
gain from the presence of foreign firms.
Marin and Sasidharan (2010) study find that the local innovative activity of subsidiaries
plays a critical role in accounting for both the possibility of positive or negative effects. Moreover,
they distinguish between three types of subsidiaries: ‘competence creating’, ‘competence
exploiting’ and passive; and explore conceptually and empirically the spillover effects of each
type. They find that in less advanced contexts such as India, only creative subsidiaries have a
positive effect on host country firms; that competence exploiting subsidiaries generate negative
effects when domestic firms are more advanced; and passive subsidiaries have no effects.
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