16

Chapter 2

Typology of Spillovers and Literature Review

17

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.

18

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

19

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

20

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

21

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

22

(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

23

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).

24

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.

25

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.

27

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

28

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.