Multinational Enterprises and Technology Frontier:

Productivity and Competitiveness in Central Europe

Peter Zámborský∗

November 2004

Abstract

This paper analyzes the impact of foreign presence and knowledge intensity on performance of

major manufacturing sectors in the Czech Republic, Hungary and Poland in 1994-2000. Inspired

by Kosova (2004), I model the interaction between a foreign investor and local firms by

combining a dominant firm/competitive fringe framework with a model of firm and industry

dynamics by Jovanovic (1982) and Sun (2002). The model distinguishes between static and

dynamic “crowding out“ of local firms by the multinational entrant and “technology spillovers“

from foreign direct investment (FDI). My empirical analysis, based on industry-level data,

confirms firm-level findings of Kosova (2004) that crowding out (negative impact on

productivity) is a short-term phenomenon and technology spillovers (positive impact on

productivity) a longer-term one for technologically advanced sectors. However, unlike her study

analyzing FDI’s impact on local firms in the Czech Republic, I do not find much support for

static crowding out and dynamic technology spillovers in the “low-tech“ (including foreign

investors) in Central Europe. This finding of mine is consistent with the results Keller and Yeaple

(2002) for the US. I also devise a conceptual framework to analyze the impact of FDI on

productivity and competitiveness of sectors in emerging and developed economies. Empirically, I

find a weak competitive effect of FDI in Central Europe, particularly in the “high-tech”.

JEL classification numbers: O32, F23 ∗ Brandeis University, International Business School. Work in progress, comments are welcome. I would like to thank Jeremy Dalletezze for insightful remarks on the first draft of this paper.

1. Introduction

Central Europe has become an attractive location for some multinational enteprises.

When General Motors, a car producer, announced this year that it would cut 12,000 jobs at its

European units, almost a fifth of its workforce in the region, Germany was labelled as the likely

loser and Poland as the one benefiting from the move1. For other types of production, Central

Europe is not globally competitive. Flextronics, the world’s largest contract maker of electronics

equipment, shut down Microsoft’s Xbox game console line in Hungary after about one year of

operations in 2002 and moved it to China, where wages are much lower than those in Hungary2.

These two cases show that productivity is a narrow measure of performance. FDI could arguably

have had a positive impact on productivity of Polish car industry and Hungarian game console

production, but technology and labor costs considerations made only the former globally

competitive. My study explains why multinationals have had a different impact on productivity

and competitiveness of „high-tech“ and „low-tech“ industries in Central Europe.

I primarily contribute to two streams of the international business literature: (1) political

economy of FDI (host country effects of FDI); and (2) competitive strategy in emerging

economies. This literature asks questions of vital importance to policy makers and corporate

executives, most notably: (1) which (if any) multinationals should governments subsidize because

of positive spillovers to the host economy firms; and (2) which types of production (and for how

long) should multinationals locate in emerging economies to stay globally competitive.

Technology spillovers from foreign to local companies and their impact on host country‘s and

multinational enterprise’s strategy are key concerns for scholars in both of these two lines of

research (Blomstrom, Kokko, Zejan, 2000). Although technology transfer has been subject to

numerous economic analyses, the implications for corporate strategy of inter-firm learning and

technological convergence of emerging and developed economies is still relatively under-

researched (Peng, 2000).

1 Carl-Peter Forster, the president of General Motors Europe, said that GM’s German and Swedish plants need to

improve performance against other General Motors plants as well as against rivals. „They have to be absolutely

competitive to get allocations“ of new models‘ production, Forster said. Labor costs at General Motors‘ plant in Poland

are 15 percent of those in German factories (Bloomberg, October 12, 2004). 2 Thanks to other contracts - including, in a twist, one to assemble TV sets for a Chinese company - employment at the

Flextronics Hungarian factory today is higher than when Microsoft was a customer (International Herald Tribune,

March 3, 2004).

2

My paper is partly inspired by the emerging literature on the „world technology frontier“.

The concept has been eloquently introduced by Caselli and Coleman (2000). On this frontier,

increases in the efficiency of unskilled labor are obtained at the cost of declines in the efficiency

of skilled labor and capital. Caselli and Coleman (2000) find that poor countries tend

disproportionately to be inside the world technology frontier. Recent theoretical arguments

(Aghion et al., 2002 and 2003, Acemoglu et al., 2002 and 2003) and empirical research

(Sabirianova Peter, Svejnar and Terrell, 2004a and 2004b) have extended this concept to industry

and firm-level analysis, suggesting that local firms in sectors closer to the technology frontier are

more likely to benefit from FDI. Unlike these economic studies, my approach attempts to

integrate productivity and competitiveness considerations in one framework, providing lessons

both for economic policy and corporate strategy. This ambitious tactic is justified because

interests of governments, local firms and multinational corporations today have to be seen as

inter-dependent, intertwined within an „alliance compact“ (Brewer and Young, 2003).

My analysis shows that multinationals had a negative immediate impact on productivity,

measured as value added per worker, in technologically more advanced sectors. With a one-year

lag, though, this impact was overwhelmingly positive in Central Europe. The technologically less

advanced sectors had not seen any significant technology spillovers, suggesting that if

governments choose to subsidize any foreign investors, it should be the more technologically

sophisticated ones. I also tackle the static and dynamic effects of FDI on competitiveness,

measured as value added per labor costs, finding virtually no effect. I hypothetise that this may be

due to the fact that Central Europe has been crossing the frontier between an emerging and

developed economy in the period under study (all three countries became members of the

Organization for Economic Cooperation and Developement, a club of „developed economies“, by

2000). The economic transition may require patience of corporate executives who may see a

declining competitiveness of their „low-tech“ investments while the „high-tech“ sector is not yet

ready to compete globally. As an emerging economy becomes developed, the „low-tech“ can be

expected to experience marginalization while the „high-tech“ will be in the ascendancy.

The rest of the paper is organized as follows. In section 2, I review the relevant literature

and present my hypotheses. In section 3, I embed them in a simple theoretical model of static and

dynamic crowding out and technology spillovers. In section 4, I discuss the data sources and

definitions and then I describe the data. Section 5 focuses on my empirical methodology,

including estimation framework and alternative specifications. Section 6 presents the analysis of

the results and section 7 concludes. The appendix presents my conceptual and analytical

frameworks, regression results and selected data summaries.

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2. Literature review

The literature on host country effects of FDI has initially seen the impact of

multinationals through rose-colored glasses, with a support for positive „technology spillovers“

hypothesis found by Caves (1974). However, later firm-level panel data research, such as Haddad

and Harrison (1993), Chung et al (1998), Aitken and Harrison (1999), has put to doubt the

hypothesis of domestic firms mostly benefiting from FDI through technological and know-how

externalities. Thus until recently, the conventional wisdom on FDI spillovers was that they do not

exist or are at best of minor economic importance. Keller and Yeaple (2002) review panel studies

based on micro data (to which this literature has gravitated recently), and find only two studies

that find statistically significant positive effects of FDI on domestic firm productivity. Moreover,

these effects are small in an economic sense.

Studies concentrating on the FDI spillovers in transition economies have generally

yielded similar conclusions. Zemplinerova and Jarolim (2001) do find productivity spillovers in

the Czech Republic but most other studies of East European economies did not. Konings (1999)

found negative spillovers in Bulgaria, Romania and Poland and Djankov and Hoekman (2000) in

the Czech Republic. Using evidence on the effects of FDI in the Czech Republic between 1995

and 1998, Kinoshita (2001) also fails to find positive spillovers to local industry from inward

FDI; however, there is a robust effect if the FDI variable is interacted with the local firms’ R&D

spending, which may be understood as a precondition for technology spillovers from FDI.

New methodologies have challenged the conventional wisdom of negative FDI

spillovers. One novelty was the use of the Olley-Pakes production function estimation method

(Olley and Pakes, 1996) that controls for the possible correlation of inputs with the productivity

shocks. Keller and Yeaple (2002) use this method to find that FDI spillovers accounted for

economically significant 13% of productivity growth in U.S. firms between 1987 and 1996. The

other major recent new method was introduced by Kosova (2004), who does not rely on

production function or productivity estimation. Instead, she estimates the domestic firm growth

equation and firm survival/exit equations to find that domestic firms were not “crowded out” by

foreign firms in the Czech Republic in 1994-2001. Initial foreign entry leads to increased exit of

domestic firms, but over time growth of the foreign industry segment actually relates positively to

the growth rate and survival of domestic firms.

Both studies deal with the differences between the productivity impact of FDI on

technologically more and less advanced sectors over time. They acknowledge Kinoshita’s (2001)

insight about the importance of R&D characteristics of local firms in their capacity to absorb

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knowledge and productivity spillovers. Kosova (2004) claims that while her findings on crowding

out effects are robust across different subsamples, the results also suggest that the primary

beneficiaries of the technology spillovers were firms in technologically more advanced industries

in 1994-2001. Keller and Yeaple (2002) find a statistically significant positive impact of FDI on

productivity growth in the U.S. high-tech industries and statistically insignificant impact in low-

tech sectors. Time also matters. Kosova (2004) finds that while there is short-term „crowding

out“ in most sectors, over time they benefit from FDI spillovers. Keller and Yeaple (2002) found

technology spillovers in the „high-tech“ with no or one-year lag but not with a two-year lag. They

also found negative same-year and two-year lagged spillovers while the one-year lag produced a

positive estimate of spillovers in the low-tech sector (but all were statistically insignificant).

Keller and Yeaple (2002) results also suggest that FDI spillovers in low-tech industries

may mostly take the form of inter-industry spillovers from high-tech industries. Smarzynska

Javorcik (2002) focuses on the understudied issue of FDI spillovers through backward linkages.

Based on a firm-level panel data set from Lithuania, she finds that a 10 percent increase in the

foreign presence in downstream sectors is associated with 0.38 percent rise in output of each

domestic firm in the supplying industry. Smarzynska Javorcik and Spatareanu (2003) analyzed

intrasectoral spillovers from foreign direct investment in Romania. Based on a 1998-2000 firm-

level panel data, she provides evidence consistent with positive intrasectoral spillovers resulting

from fully-owned foreign affiliates but not from joint venture projects.

Several theories vindicate the hypothesis of the different impact of FDI and R&D on

more and less technologically intensive sectors. On the macroeconomic level, this is in line with

theories of absorptive capacity such as that of Bosworth & Collins (1999). Their endogenous

growth model highlights the roles of not only the introduction of more advanced technology, but

also the requirement of absorptive capability in the host country as determinants of economic

growth. In a similar vein, Borensztein, De Gregorio and Lee (1998) have used endogenous

growth theory to explain why FDI increases economic growth only when the level of education in

the host country is high. Other way to define the absorptive capacity was suggested by Kinoshita

(2001), who stressed R&D at local firms as a precondition for positive productivity spillovers.

Blomstrom et al (2000) argue that positive FDI spillovers are less likely in the industries

where the gap between the technologies of domestic and foreign firms is large, which allows

foreign affiliates to “crowd out” local firms from the domestic market because domestic firms are

less efficient. In a similar vein, Aghion et al. (2002 and 2003) and Acemoglu et al. (2002 and

2003) suggest that an increase in competition encourages innovative behavior of firms that are

near the technological frontier but stifles those that lag significantly behind.

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Theories also acknowledge that FDI and R&D impact industrial sectors differently over

time. Aitken and Harrison (1999) argue that even though technology spillovers may exist, foreign

producers can draw demand from less efficient domestic producers, thereby forcing them to cut

production. They refer to this competitive effect as market stealing. If such effect appears

between time t and t+1, the foreign presence should be negatively correlated with domestic firm

growth rates, because part of the domestic production is crowded out by the foreign competition.

Thus market stealing can be a one-time phenomenon, realized at the time of foreign entry

into the domestic industry, or it can arise gradually over time as foreign firms increase their

production in the domestic markets. The latter phenomenon can be denoted as “dynamic

crowding out” (Kosova 2004), while the former represents a static crowding out effect. If

crowding out is a dynamic phenomenon, then holding domestic market constant, foreign sales

expansion/growth should reduce the sales of domestic firms over time and thus lower domestic

growth rates. In that case foreign growth should have a negative impact on growth rates and mean

survival time of domestic firms, and hence a positive effect on a probability of exit at a point of

time.

Several hypotheses emerge from the reviewed theoretical and empirical literature:

Hypotheses 1: FDI has (does not have) an economically and statistically significant positive

(negative) impact on productivity (competitiveness) of technologically more advanced sectors in

emerging ( developed) economies in the short (long) run.

Hypotheses 2: FDI has (does not have) an economically and statistically significant positive

(negative) impact on productivity (competitiveness) of technologically less advanced sectors in

emerging ( developed) economies in the (short) long run.

Hypotheses 3: FDI in technologically more advanced sectors has (does not have) an

economically and statistically significant positive (negative) impact on productivity

(competitiveness) of technologically less advanced sectors in emerging (developed) economies in

the short (long) run.

Hypotheses 4: FDI in technologically less advanced sectors has (doesn’t have) an economically

and statistically significant positive (negative) impact on productivity (competitiveness) of

technologically more advanced sectors in emerging ( developed) countries in the short (long) run.

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3. Theoretical framework

These hypotheses may be embedded in the conceptual and analytical frameworks that I

present in the appendix (Figures 2 and 3). A more rigorous explanation for FDI’s impact on

industrial performance is a theoretical model deriving the predictions for domestic firm growth

and exit, inspired by Kosova (2004). Firm growth in this model stands for productivity growth

while firm exit is a decline of competitiveness. The framework combines a standard model of

dominant firm and competitive fringe and a stochastic model of firm dynamics with cumulative

technology shock by Sun (2002), which is inspired by Jovanovic’s (1982) seminal work on

industry dynamics. In Jovanovic’s framework, the heterogeneous firms operate in a competitive

industry with incomplete information. Firm heterogeneity arises from differences in the firm true

cost of efficiency, which is unknown by the firms themselves, and about which firms learn over

time by operating in the industry. As a result, efficient firms grow and survive, while inefficient

firms decline and fail. In the end, firms differ in size because some of them discover that they are

more efficient than others. Jovanovic’s (1982) model explains the stylized empirical facts that

smaller and younger firms grow faster and are less likely to survive than old and large firms.

Following Kosova (2004), I assume that the domestic market with foreign presence

resembles a dominant firm competitive fringe industry structure. For simplicity, let us assume

that foreign firms as a group are a single dominant firm (DF) while domestic firms form the

“competitive fringe” in the industry. The main assumption of the classical dominant

firm/competitive fringe model is that a dominant firm has higher market share than individual

firms in the fringe and thus it has impact on market price, while the firms in competitive fringe

take price as given. However, collectively CF firms may have a substantial market share. The

dominant firm behaves as a monopoly with respect to its residual demand (market demand minus

total supply of the competitive fringe), so the existence of the competitive fringe limits the market

power of the dominant firm. In this model a single firm becomes dominant in a market when it

benefits from at least some of these competitive advantages:

(1) Lower costs than the firms in the competitive fringe due to better management or technology,

possibly protected by patents; early entry into other market, thus learning by doing; economies of

scale; favorable public policy (subsidies, lower taxes, other privileges).

(2) A superior product in a differentiated product market. This superiority may be due to the

firm’s reputation for quality, or technical superiority protected by patents.

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Whether a dominant firm can exercise market power depends on the number of firms that

can enter and exit the competitive fringe, how fast they can enter and exit, and the differences in

production costs between the dominant and competitive fringe firms. The use of the dominant

firm/competitive fringe model fits well with the asset ownership advantages of multinational

enterprises stressed in the OLI paradigm of the multinational enterprise (Dunning, 1988). The

model also fits well the particular institutional environment of Central Europe, where one can

expect the assumptions of significant differences between domestic and foreign firms to hold

because of the legacy of the planned economy. Hence one can expect that foreign firms with

significant experience in competitive markets and with advanced technologies and products can

enter the markets such as the Czech Republic, Poland and Hungary with significant advantages

and easily gain market leadership.

3.1. Static “crowding out” effect

Figure 1 presents the standard dominant firm/competitive fringe model. It demonstrates

the static crowding out effect related to foreign entry into a domestic industry. Figure 1a shows

the total market demand for a homogeneous product D(p), and total competitive fringe supply

S(p). Price p is the shut-down price of the fringe (domestic firms). Figure 1b shows the situation

8

from the perspective of the dominant firm, whose residual demand curve is given by the

horizontal difference between market demand D(p) and total competitive fringe supply S(p). The

dominant firm maximizes profits by choosing the output QDF, where MR = MCDF. This in turn

determines the market price and hence the total quantity the CF will sell on the market, QCF. After

the foreign entry, the domestic firms in the CF must, as a whole, produce less. This appears

through reduction in every firm’s output level, or firm exit, or both. The amount of crowding out

depends on the difference between the marginal costs of the foreign and domestic firms. If the

marginal costs of the dominant firm are very low compared to MC of domestic firms (say MC2DF

in Figure 1b) then all the fridge firms would be crowded out and would exit the market, because

MC2DF intersects the MR of the DF at the point such as the new price is below p . However, if the

costs of the DF are higher, say MC1DF, then the equilibrium price is p* and the domestic firms

with shutdown price below p* survive, but produce less.

3.2. Dynamic “crowding out” effect

How foreign output expansion affects domestic output and survival over time depends

also on exogenous shifts in market demand D(p), technology spillover effects. On Figure 1, these

lead to downward shifts in total supply of CF, but the process repeats the same logic. To analyze

the impact of foreign presence on the domestic firm growth rate and exit over time I integrate the

DF/CF model into Jovanovic’s (1982) framework with cumulative technology shocks by Sun

(2002). The model is solved backwards. First, given the price sequence, the competitive fringe

firms choose output and decide whether to exit in every period. Second, given the total supply of

CF as a function of prices, dominant firm chooses an equilibrium price sequence and makes it

public at the beginning of the game. Since the driving force behind firm dynamics is domestic

firms’ learning process about their efficiency in the competitive environment, I assume that the

game starts after the foreign (dominant) firm enters. Only after that the domestic firms begin to

learn about their efficiency, so that entry and exit occurs. This is consistent with the situation in

Central Europe, where before the transition started there was no market competition and virtually

no foreign presence, so domestic firms could not learn about their true relative efficiency.

Let us start with the domestic firms and assume that there are many of them in the

competitive fringe, each of them too small to affect price. Every period a firm chooses output qt to

maximize its expected profit E(πt):

E(πt) = [ p qt

max t qt – C(qt) Tt E(xt) ] (1)

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where the price sequence p ≡ { pt } is known by all firms in time zero. C(q∞0 t) Tt xt* represents

firm total costs and C(qt) is a function that satisfies C(0)=0, C′(0)=0, C′(q)>0, C′′(q)>0.

Moreover, C′(q) → ∞ and ( C′ / qC″ ) = k > 0. x∞→q

lim t is a random variable independent across

firms that represents the inverse of firm production efficiency, where xt = f (δt) and δt = θ + εt.

Then E(xt) is expectation of xt conditional on information received prior to time t. The function f

is a positive, strictly increasing and continuous with A−∞→ tδ

lim 1 > 0 (for δt → - ∞) and A∞→ tδ

lim 2 ≤ ∞

for (for δt → ∞). The parameter θ represents the firm’s true cost efficiency (or firm type) which is

normally distributed among all potential firms with mean θ and variance σθ2.

A firm does not know its θ but learns about it while operating in the industry by Bayesian

updating according to signals that arrive every period. The signals are generated by random

productivity shocks, εt ∼ N (O, σε2), independent across firms and time. A firm learns about them

at the end of the period through realized profits, respectively inferred realizations of δt and adjusts

its expectations for the next period, E(xt+1).

Following Sun (2002), I also assume that a firm experiences each period an additional

i.i.d. technological shock, ut ∼ N (u , σu2) where u represents the trend in technological progress.

ut > 0 represents plausible (marginal costs decreasing) technological shock, which is bounded

from above by 1 to prevent negative costs. ut cumulates over time, so then firm technology level,

Tt = (1- u∏−=

t

ntjj) for j = 1 to t – n, is the cumulated value of all technology shocks a firm has

experienced in the past up to and including period t, where n is firm age. I assume that at an entry

the firm does not have any technology improvements yet, so T0 = 1.

This technology shock can represent any shocks to firm production process that have

persistent effects on firm efficiency, including firm’s innovation or changes in management.

However, it can also be interpreted as an impact of a macro-level shock, where the i.i.d. property

is preserved if ut is seen as a firm specific adjustment to the common macroeconomic shock.

Following Kosova (2004), I take ut to represent technology spillovers, because FDI flows

constitute an equivalent of a macroeconomic shock to domestic firms, where the technology

spillovers are them domestic firm specific adjustments to FDI inflows. In this way Sun’s (2002)

framework provides a convenient way to incorporate the effect of the FDI technology spillovers

into the profit maximization problem of domestic firms.

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A firm chooses qt at the beginning of period t before it observes xt, but after it observes ut.

Then the optimal output choice, qt* = q [ pt , Tt , E(xt) ] that maximizes E(πt) satisfies the first-

order condition: pt = C′ (qt*) Tt E(xt) and firm discrete output growth rate is:

( qt+1* - qt* ) / qt* = k . { [ (pt+1 - pt) / pt ] - [ E(xt+1) - E(xt) ] / E(xt) + ut+1 } (2)

where k is a growth multiplier. Hence the main result from Jovanovic-Sun framework is that the

firm growth rate increases with larger prices and the positive technology shock, but decreases

with firm’s expected inefficiency E(xt+1) > E(xt). Firm updating process implies that firm age and

size have a negative impact on firm growth.

3.3. Domestic firm exit and growth

Besides choosing an output every period, a fringe firm also decides whether to stay or

exit the industry. This decision process determines the critical value of firm efficiency, x t , and

consequently the critical output size, q t (pt , Tt , x t) at which a firm exits. If a firm decides to exit

at the beginning of period t + 1, then qt+1* must be smaller than q t+1 . This exit size can be then

expressed in terms of an exit growth rate, = (g~ q t+1 - qt*)/ qt*. If the firm’s optimal growth rate

would be less than , firm exits, so qg~ t+1* = 0, and its observed growth rate is -1. Thus the same

variables that affect firm growth should also affect firm exit. So the model gives also predictions

for firm exit rates. Specifically, firm exit rates should decrease with: higher prices, positive

technology shock ut+1 and more plausible expectations of firm efficiency, which imply that larger

and older firms should have lower exit rates.

Allowing for firm exit the firm expected growth rate, E (qt+1* - qt*)/ qt* = g can be expressed as:

g = gs Ps + gexit (1 - Ps ) = gs Ps – ( 1 - Ps ) (3)

where: gs is the mean growth rate of surviving firms, Ps is the probability that a randomly drawn

firm will survive and gexit is the mean growth rate of exiting firms, equal to -1.

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The model can further be extended to derive how the price changes are affected by the presence

of the dominant firm and how the growth rate of a local firm, (q * - q *t ) / q *

t , depends on the

growth rate in the output of the dominant firm, (Q - Q ) / Q , firm age, size, technology

shock and the cross products of industries and time trend:

1+t

dt 1+

dt

dt

(q * - q *t ) / q = −km1+t

*t t [(Q - Q ) / Q ] – k(aged

t 1+dt

dt t , size t) + ku t+1 + ind × trend (4)

Thus the model can give us predictions about how expansion of foreign output over time affects

the output of individual domestic firms, their growth and exit rates, i.e. their productivity and

competitiveness. To test whether these effects are static or dynamic, one can introduces into the

equations for domestic firm growth and exit rates dummy for the year of foreign entry in to a

particular industry.

3.4. Technology shock and spillovers

While the crowding out effect occurs via changes in prices associated with the foreign

output changes, the positive “technology spillovers” enter via the technology shock u t+1. Since

ut+1 is an exogenous shock, the model does not provide direct relationship between u t+1 and other

parameters. However, the literature on technology transfer and technology spillovers suggest that

spillovers might be correlated with different firm and industry characteristics. This literature may

be used to measure u t+1 empirically. In the theoretical model u t+1 is assumed to be firm specific

and observable at the beginning of t+1. Thus we should allow for unobserved firm effects and

measure all the technology variables at time t.

Wang and Blomstrom (1992) argue that the technology spillovers should be proportional

to the foreign presence in the domestic market. These intra-industry spillovers can be measured

by the foreign employment share, ESjt. A domestic firm can also benefit from intra-firm

spillovers if it has some foreign shareholders, FDIijt (Aitken & Harrison, 1999, Kinoshita, 2000).

The positive effect of firm innovation on its growth and survival has also been demonstrated

(Mowery, Oxley, Silverman 1996). Variables like the firm intangible asset ratio, INTANGijt, can

be used to control for firm absorptive capacity and innovation. The magnitude of technology

spillovers also depends on the technology gap, GAP ijt, i.e. the differences in the technological

capabilities between domestic and foreign firms (Sjoholm 1999, Caves 1999). The technology

transfer literature argues that the large gap between technology donor and recipient increases the

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costs of the technology transfer and reduces thus the likelihood of technology transfer. On the

other hand, some FDI spillover studies (Haddad & Harrison 1993, Haskel et al 2001) conclude

that the larger gap represents more opportunities for technology exchange and thus more

technology spillovers can be generated.

uijt+1 = α1 ESjt + α2FDIijt + α3 INTANGijt + α4 GAP ijt + ESjt * GAP ijt + µi (5)

4. Data sources and features

4.1. General data description

I use data on six groups of major manufacturing sectors in the Czech Republic, Hungary

and Poland for the years 1995-2000, 1999-2000 and 1994-1999 respectively, with one Czech

industry having observation only in 1997-01 and one having no observations. This makes for a

total of 76 annual observations, with 28 for the Czech Republic, 12 for Hungary and 36 for

Poland. The data are sourced from the OECD databases and the choice of countries, industries

and time periods is largely dictated by data availability. For example, Slovakia was excluded

from the analysis because of insufficient data on labor productivity. The industries were

assembled into six specific groups because this was the breakdown used in OECD’s FDI by

industry statistics. The industries covered are food, beverages and tobacco (ISIC 31); textiles,

apparel and leather (ISIC 32) and wood products and furniture (ISIC 33); chemicals (ISIC 351),

rubber (ISIC 355) and plastics (ISIC 356); basic metals (ISIC 37) and metal products (ISIC 381);

non-electrical machinery and instruments (ISIC 382+385) and electrical machinery (ISIC 383);

and transportation equipment (ISIC 384). The choice of time period was due to the joint

availability of FDI and productivity data, hence only two annual observations for Hungary.

4.1.1. Sources of productivity data

Productivity data are sourced from the OECD STAN database. Ideally, one would wish

to obtain data both on labor and capital inputs to calculate total factor productivity, but for the

Central European economies only labor data is available on the level of industrial sectors.

Specifically, it is the value added, number of employees and total wages to employees. Value

added is calculated as the difference between production and intermediate inputs and comprises

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labor costs, consumption of fixed capital, indirect taxes less subsidies and net operating surplus

and mixed income. Number of employees stands for headcounts, so that those with more than one

job (full- or part-time) are counted only once. Hours worked or full-time equivalent jobs where

adjustments are made for part-time employment were not available for Central Europe. Total

wages to employees comprise of wages and salaries of employees paid by producers but not

supplements such as contributions to social security, private pensions, health insurance, life

insurance and similar schemes, for which there were no historical data in the OECD database.

Out of these three variables, one can compute productivity in terms of value added per worker

and value added divided by total wages.

4.1.2. Sources of FDI and R&D data

FDI data are taken from OECD’s International Direct Investment Statistics Yearbook. I

converted the figures in national currencies to US dollars using average annual market exchange

rates provided by the OECD in the same publication. FDI inflows are provided only for groups of

industrial sectors described above. R&D data, on the other hand, are unfortunately only available

as national aggregates for Central European economies. The R&D gross expenditures are in

million constant 1995 US dollars and purchasing power parities. Of the R&D breakdown

available, R&D financed by local enterprises (not by government subsidies or funds from abroad)

is most relevant for my analysis. I want to use the R&D measure as a proxy for the social capacity

of the local industry to virtuously interact with FDI. For this purpose, the local business R&D is

more appropriate than R&D financed or done by the government or the universities because the

data in Central Europe for public and academic R&D are not very trustworthy (they are like

‘paper profits’). The business R&D financed by local enterprises accounted for 84% of total

business R&D in the Czech Republic in 2001, 76% of the total in Hungary in 2000, and 68% of

the total in Poland in 2001. Total business R&D made for 60% of total R&D in the Czech

Republic in 2001, 44% of the total in Hungary in 2000, and 36% of the total in Poland in 2001

(see Figures 4-6 in the Appendix).

4.2.1. General features of productivity data

This paper aims to explain productivity variation between six major industrial sectors in

the Czech Republic, Hungary and Poland. Before I proceed to the analysis of the impact of FDI

and R&D on industrial productivity, it would be useful to describe the features of the productivity

14

data in their own right. Hungary leads Central Europe when it comes to value added per worker in

more productive sectors such as vehicles, chemicals, machinery and metals. Poland has the lowest

productivity across all industries with an exception of chemicals and textiles. The Czech Republic

leads the pack in the food & beverages sector, while it ranks at the bottom in the chemicals

industry. However, when competitiveness, or value added per wage costs is taken as a measure of

economic performance, these results change somewhat. Hungary is no longer a clear leader in

machinery, chemicals, and metals. Poland scores on par with Hungary in machinery as well as in

food. The Czech Republic is a leader not only in food, but also in textiles, metals and chemicals,

in which it scored as the last in value added per worker indicator. These variations reflect

Hungary’s relatively high wages and the Czech Republic relatively low wages and show how

much of the value added created goes to workers. The two measures of productivity also often

show different trends. Examples of this divergence include the productivity trend in Poland,

where value added per worker goes up and value added per wage costs goes down in all sectors,

reflecting significant wage rises. A similar divergence in productivity trends can also be noticed

in the Czech food sector and Hungarian metals sector.

4.2.2. General features of R&D data

How does one explain these productivity trends? On the aggregate level, R&D data may

provide some illumination. The general upward trend in value added per worker may reflect

growing total R&D expenditures in the analyzed period in all countries. However, although the

total expenditures rose by 40% between 1995 and 2000 in the Czech Republic and by 13%

between 1994 and 1999 in Poland, they fell by 1% between 1999 and 2000 in Hungary.

Moreover, as I have already mentioned, these aggregate data that include government financed

R&D can not be considered very reliable. The trend in business financed R&D needs to be taken

into consideration. Here we get slightly different results, but an unambiguous upward trend,

namely a 13% rise in local business R&D between 1995 and 2000 in the Czech Republic, a 20%

increase between 1999 and 2000 in Hungary, and a 44% increase in local business R&D between

1994 and 1999 in Poland.

4.2.3. General features of FDI data

On the level of specific industrial sectors, I will analyze FDI as a possible factor affecting

productivity growth. The capital investments and know-how of the foreign parent could be

15

expected to increase value added per worker. It is more uncertain whether FDI should increase

value added per total wage compensation, depending on the negotiating position of the investor in

a particular industry with respect to workers and other firms regarding wage compensation. FDI

inflows to industrial sectors of Central Europe vary significantly from year to year (coefficient of

variation of about 100%), much more than productivity (20% coefficient of variation for value

added per worker and 35% for value added per wages). This is due to the nature of FDI in Central

Europe, particularly in small countries, which often depend on a small number of major deals

rather than on a steady flow of smaller investments. Thus the Czech Republic saw major

investments in its food, textiles and chemicals sectors in 1999, while in the recession year of 1997

it saw little FDI. Hungary saw in several industries a strike contrast between a rapid rise and rapid

fall in FDI inflows in 1999 and 2000, reflecting the second stage of globalization in Central

Europe, when some foreign firms started to relocate to East Asia because of rapidly rising wage

costs in Hungary. Poland, a country more than four times as populous as Hungary or the Czech

Republic, saw lower variability in its FDI inflows, but still the variance of this measure was much

higher than variance of productivity figures. The FDI data show the Czech Republic’s superior

attractiveness per worker compared to Poland in all sectors. Hungarian FDI data are only

available for 1999-2000 when the country started to lose its luster following its stellar FDI

performance before that. Industry which attracted most FDI per worker was the foodstuffs sector,

while the textiles industry fared worst in terms of inflows per worker (see Tables 1 and 2). The

magnitude of FDI inflows can be expected to play a potentially significant role in affecting the

size of FDI’s impact on productivity growth.

5. Methodology 5.1. Empirical approaches

Most existing studies of FDI productivity spillovers have relied on macroeconomic

framework, estimating production functions or total factor productivity (TFP). Kosova (2004) is

an exception who calls for alternative methodologies. She suggests that the pitfalls of the TFP-

based analysis can be rectified by addressing the FDI spillovers question by the models of firm

and industry dynamics from industrial organization economics. These models not only provide a

general framework to analyze various competitive effects, but incorporate firm learning,

innovation and technology imitation, all emphasized in the international business literature. One

trouble with production functions approach is that we neither know the firm production function

16

nor the variety of all inputs different firms use. Even if we agreed to approximate the production

function, e.g. by standard Cobb Douglas form, as many of the previous studies have done, there

remains the problem of proper input measurement. This problem is especially acute in analyses

based on industry level data, where it is impossible to control for the efficiency of input usage at

the firm level.

Firm-level studies, controlling for firm fixed effects, can provide at least a partial solution

to the problem. However if inputs are correlated with firm time-varying idiosyncratic shocks

there is still an endogeneity problem in input measurement. Olley and Pakes (1996) argue that

inputs are endogenous because they are made according to a firm’s expectation about future

productivity shocks. The literature on quality improvement due to technical change and “vintage

capital models” provide other reasons why inputs might be correlated with the productivity

shocks. These models argue that newer inputs are more productive than the old ones. Because of

that firm total capital stock does not have to reflect firm productive capital. Olley and Pakes

(1996) develop a semi-parametric procedure that should control for input endogeneity problem,

but most studies on FDI spillovers do not use this technique. Exceptions are the recent studies by

Smarzynska Javorcik (2003), Blalock (2002) and Keller (2002). Smarzynska Javorcik and

Spatareanu (2003) use an alternative approach by Levinsohn and Petrin (2000).

5.2. Econometric considerations

My study builds on the theories and firm-level empirical studies of technology spillovers.

Instead of firm-level data, I use industry level data on productivity and FDI. A significant

econometric challenge that I encounter in this analysis is the small data set. I have fewer than 15

observations for each of the industrial groups that I study. Moreover, I only have 12 data points

for Hungary, which makes it difficult to conduct the analysis on an industry by industry or

country by country basis. The best way to overcome this complication appears to be to group the

data in some sensible way both on the level of industries and countries. All three countries from

Central Europe share the same characteristics of a rapidly changing transition economy that

succeeded in becoming an OECD member in the second half of the 1990s, thus it is not a major

distortion to include them in one panel. As for the industrial groups, I propose to group them

according to their R&D intensity and productivity. There are differences between countries, but

overall, we can classify food, textiles and metals sectors as relatively low value added or “low-

tech’ (giving us total of 40 observations) and chemicals, machinery and vehicles as relatively

high value added or “high-tech” industries (36 observations).

17

One can have valid doubts about this seemingly arbitrary clustering technique. My

aggregation into “low-tech” and “high-tech” follows Keller and Yeaple (2002) who justify it by

the fact that about 90% of R&D in the G-7 industrialized countries in 2000 was done in the

chemicals, machinery and vehicles sectors (Keller, 2001). The grouping also reflects insights

from Kinoshita (2001) who suggests that FDI has an impact on domestic productivity only when

virtuously interacting with domestic R&D, which is significantly different across high- and low-

tech. I do not have an access to industry level R&D data in Central Europe, therefore ‘knowledge

intensity’ of industry derived from the G-7 figures, possibly in interaction with the national R&D

level, is my best proxy for this capacity of the local industry to absorb FDI and translate it into

productivity gains. Kosova (2004) has classified industries as “technology leaders” if the mean of

differences between intangible asset ratio of domestic firms and foreign intangible asset ratio is

greater or equal to zero. She also controlled for firm-level clusters in her regressions. I might

attempt to follow her approach in the future with a better data set.

5.3. Estimation framework

My empirical analysis relates firm’s productivity and competitiveness to changes in the

stock of foreign investment at the industry level and to research and development expenditures at

the national level. My data do not allow me to measure productivity in terms of total factor

productivity as Kinoshita (2001) and Keller and Yeaple (2002) did. I have to rely on value added

per worker instead. In specification 1, I enter this measure as a natural logarithm of value added

per worker, ln VApW, in specifications 2 and 3 as a first difference of logarithm, ∆ ln VApW, to

account for annual percentage change. I measure competitiveness as value added per total wage

costs, as this is best what my data allow me. This variable also enters my analysis as a natural

logarithm of value added per total wages in Specification 1, ln VApTW, and as first difference of

logarithm, ∆ ln VApTW, in specifications 2-3.

The degree of foreign activity had been commonly measured as a share of foreign

employment in total employment (Kinoshita, 2001, Keller and Yeaple, 2002) and as a dummy

variable taking a value of 1 for firms exceeding a certain threshold of foreign ownership

(Kinoshita, 2001, Kosova, 2004). My data do not allow me to use these definitions; I rely on

annual flows to a particular industrial sector divided by the number of workers there, denoted as

FDI. A large positive flow can conceivably have a different impact than a small positive flow or

an outflow. I also include in my third baseline specification a change in the logarithm of R&D

expenditures at the national level, ∆ ln R&D, as a factor which may impact productivity of high-

18

tech and low-tech sectors in a given year. Inspired by Kinoshita (2001), who interacted his FDI

variables (share on employment and dummy for foreign ownership) with R&D (R&D divided by

value added), I also include an interaction of my FDI term with the logarithm of R&D, FDIxRD,

in all three specifications. A limitation of this approach in my case is that I do not have an access

to R&D data on a disaggregated level. However, my specification allows me to account for the

spillovers from the industry-wide business R&D effort across high-tech and low-tech sectors. It

can be seen as an indicator of an impact of the overall research climate on productivity and thus

provide more lessons for policy than an approach relying purely on firm-level data.

It is also important to recognize that the impact of FDI on productivity and

competitiveness need not be simultaneous, e.g. there may be some lagged and/or cumulative

impact of FDI. Kinoshita (2001) did not consider lagged effects but other researchers such as

Keller and Yeaple (2002) and Kosova (2004) did. Most of the productivity improvements may be

expected in the year when the investor’s capital arrives and in the following year. Some effect of

FDI inflow may be longer term than one year, particularly spillovers in high tech and to other

sectors. Given a short data set, I have to trade off lags of over one year for data observations that I

would lose. Thus I will capture the lagged effect only by relating productivity in year t to FDI per

worker in year t, FDI, and in t-1, FDILAG, and by including an interaction of my FDI variable

with ln of R&D, FDIxRDLAG, in all specifications. Finally, my estimations also include a mean-

zero error term ε:

Baseline Specification 1

ln VApW = α + β1FDI + β2FDILAG + β3FDIxRD + β4FDIxRDLAG + ε

ln VApTW = α + β1FDI + β2FDILAG + β3FDIxRD + β4FDIxRDLAG + ε

Baseline Specification 2

∆ ln VApW = α + β1FDI + β2FDILAG + β3FDIxRD + β4FDIxRDLAG + ε

∆ ln VApTW = α + β1FDI + β2FDILAG + β3FDIxRD + β4FDIxRDLAG + ε

Baseline Specification 3

∆ ln VApW = α + β1FDI + β2FDILAG + β3 ∆ln R&D + β4 FDIxRD + β5FDIxRDLAG + ε

∆ ln VApTW = α + β1FDI + β2FDILAG + β3 ∆ln R&D + β4 FDIxRD + β5FDIxRDLAG + ε

19

5.4. Alternative specifications

One should also try control for other influences to isolate the particular effect of FDI on

productivity and to better judge its economic and statistical significance. Instrumental variable

estimation is a way to address this issue; however, here we do not have good instruments, because

the variables that are highly correlated with FDI are also likely to be correlated with productivity.

To test for endogeneity, I ran reverse regressions to check the hypothesis that it is not FDI that

increases productivity but high (or low) productivity is what attracts FDI. I did not find any

statistically significant relationship of this sort, but this perhaps reflects the limitations of my

data. More robustness checks such as fixed industry and time effects and random effects will be

necessary when I expand my data set. There is also a substantial amount of work showing that the

link between R&D spending in one industry and productivity in another can be used to estimate

knowledge spillovers (Griliches 1995, Smarzynska Javorcik 2003, 2004). Therefore I tested the

hypothesis that FDI flows to high-tech sectors have an impact on productivity of low-tech

industries and vice versa. Here I found intriguing results suggesting that FDI in low-tech industry

increases both productivity and competitiveness in the high-tech. I also found a statistically

significant negative contemporaneous effect of FDI in the high-tech sector on the competitiveness

of the low-tech sector. These results were not robust to alternative specifications. Nevertheless,

they suggest that inter-sectoral spillovers and linkages between high-tech and low-tech may

remain a promising area of research.

6. Analysis

My analytical framework has distinguished between the static and dynamic impact of

multinational enterprises on productivity and competitiveness of low-tech and high-tech

manufacturing sectors in less developed in developed countries. It predicts that in a less

developed economy, crowding out of local firms by multinationals is a short term effect in all

sectors while in the long run, the high-tech sector experiences positive „technology spillovers“

from FDI. In a developed economy, only the low-tech sector is crowded out, in the long run,

while the high-tech sector enjoys both static and dynamic productivity spillovers. Central Europe

in 1994-2001 was a region transitioning from a status of „less developed“ to „developed“.

Therefore for the low-tech, I expect no crowding out effect or at worst a static one. For the high-

tech, I definitely expect some evidence of spillovers in the longer run and possibly a short-run

crowding out effect.

20

Regarding the impact on competitiveness, my conceptual framework predicts that in the

less developed economies, only the low-tech sector can expect „ascendancy“ towards global

competitiveness due to FDI, and this effect will only be temporary. A more divergent trend can be

expected in the developed countries, where globalization can be expected to bring not only

ascendant high-tech sectors, but in a longer run also a marginalized low-tech. For Central Europe,

a region on the frontier between the less developed and developed during the period analyzed in

this study, we can therefore hypothetize that the increased presence of multinational enterprises

will have a limited impact on competitiveness, but if it will occur, it will be generally positive

both in the low-tech and the high-tech.

My empirical results generally confirm these theoretical predictions. In all three baseline

specifications, I find a strong statistical support for a static crowding out in the high-tech sector.

The positive long run technology spillovers, another prediction of my framework, do exist within

the first two of the specifications and receive a reasonably strong statistical corroboration in the

third framework. The absence of a significant competitive effect of FDI on local industries also

receives a substantial support in my analysis. The only notable discrepancy between my

hypotheses and the results are static technology spillovers in the low-tech sector under the first

baseline specification. This effect is not supported by the other two specifications, which have a

change in productivity growth, not just a logarithm of productivity, as the dependent variable,

suggesting that the results of the first specification should be interpreted separately from the other

two.

The regression results in Tables 3 and 4 show the results for the first baseline

specification. FDI shows a statistically significant impact on value added per worker in more

R&D intensive industries: a negative contemporaneous one and a positive one with a lag of one

year. Under a fully specified model, we get a decent adjusted R squared of 0.37 and coefficients

on FDI, FDI lagged and both interactions of FDI and R&D significant at 5% p-level. The F-

statistic is low, thus the results are not due to multicollinearity. Curiously, when the FDI variable

is interacted with R&D expenditures, we get a statistically significant positive impact in the short

run and a negative one in the longer run, although the long-run impact is relatively small in

economic terms. This may be interpreted as a support for Kinoshita’s (2001) finding that the

absorptive capacity of local economy, closely correlated with its R&D activity, conditions FDI’s

positive productivity impact. I also get a statistically significant coefficient on FDI in a simple

regression of productivity on FDI for industries of low R&D intensity but the adjusted R squared

for that regression is lower than 0.1 and the results are not supported under richer specifications.

21

When it comes to the impact of FDI and R&D on value added per total wages, it is only

for the less technologically sophisticated industries that I get some statistically significant results.

Both FDI and FDIxRD are significant at 5% level in explaining value added per total wage

compensation in a partially specified estimation equation, with R squared of 0.18 suggesting

these results are not too strong but acceptable. The F-test for this regression does not show a high

level of correlation between the independent variables. FDI also gets a statistically significant, but

negative, coefficient, in a fully specified baseline regression of the first type for industries of low

R&D intensity, with R squared of 0.13 and the F-statistic exceeding the critical value only at

10.3% level of significance. The mixed results may be due to the fact that my model predicts

opposing results for less developed and developing economies, while Central Europe was both

and neither of them at once in 1994-2001.

The second and the third baseline estimation frameworks offer fewer statistically

significant conclusions (see tables 5-8). They both agree about the negative short-run impact of

FDI on productivity of the high-tech sector. A fully specified baseline estimation 2 gives us an

agreeable adjusted R squared of 0.20 and a negative coefficient on FDI at 1% significance level.

This finding is corroborated by a partially specified second framework and even more so by a full

third baseline specification, which shows a pleasing adjusted R squared of 0.50. The statistically

significant positive impact of FDI on productivity with a one year lag, found in the specification

one, gets a reasonable support here, in particular in the second framework. The third baseline

specification also shows a positive coefficient very close to the 10% significance level. In these

two specifications, particularly in the third one, we get again the reversed and statistically

significant signs for interacted terms of FDI and R&D. The findings appear to reinforce the

notion that FDI can have an immediate positive effect given substantial R&D effort in the

recipient country.

We get no significant results for the impact of FDI on productivity or competitiveness in

the second and third estimation frameworks, leaving us with little clue about the significance of

the conflicting findings in the first specification which were at odds with our hypotheses and even

with each other. FDI appears to have absolutely no impact on competitiveness in these two

specifications. The specification 3 differs from the first two in the fact that it includes a change in

national R&D expenditures as an independent term, not interacted with the FDI variables. In fact,

business R&D is very strongly positively related to productivity in the high-tech sector (1%

significance level and high R squared) but not in the low-tech sector. This is clearly another

support for the Kinoshita argument about the virtuous interaction of FDI and R&D, with a

possible extension of his argument that it only holds for the high-tech sectors. On the other hand,

22

a curious finding is that R&D had a statistically significant positive impact on competitiveness,

measured as value added per total wages, in the low-tech sectors, and no impact on the low-tech

sectors. This appears to be in line with my theory that it is only the low-tech sectors that can

enjoy an „ascendancy“ after increased multinational investment, but it points to the potential

crucial role of R&D as distinct from foreign investment.

How can these results be interpreted, in particular with respect to performance in

particular industries and countries? The statistically significant impact of lagged FDI on value

added per worker in the chemicals, machinery and vehicles sectors appear to have one caveat. All

but one outlier observation lie within or very close to the 95% confidence band around the

regression line. The outlier is the performance of the Hungarian vehicles industry in 2000, which

was high in spite of the almost zero FDI inflow in 1999. On this example we can see the

limitations of the model with a one-year lag, as Hungarian productivity was clearly dependent on

huge FDI inflows in the years preceding 1999. A poor FDI inflow in the current or the previous

year thus cannot be taken as a predictor of poor productivity, particularly in countries where FDI

flows fluctuate significantly. If more data were available, a moving average or persistence model

would be definitely better for explaining the relationship between FDI flows and industrial

productivity.

Moreover, the Hungarian productivity in the vehicles sector actually declined between

1999 and 2000 in spite of a small positive inflow of FDI in 1999 (and due to the negative outflow

in 2000). This fact is not entirely captured in the logarithmic specification due to only one annual

observation for Hungary. The impact of FDI on value added per total wages is also not clear- cut.

The most notable group of outliers here are the observations of value added per total wage costs

smaller than 2.5 and those over 3.5. The low-productivity observations are mainly those for the

Hungarian and Polish food sectors and the Polish metals sector. The high-productivity ones are

those for the Czech textiles and metals sectors. In analyzing these outliers, which actually

constitute majority of observations, we might consider two separate trends for lower and higher

productivity countries/sectors within this group. This consideration suggests that aggregating the

industries into subgroups according to their R&D intensity may be tricky because of substantial

heterogeneity across the industries. Again, the small data set is a problem; the analysis would not

be statistically robust on the level of individual sectors though.

23

7. Conclusions

My analysis shows that FDI had a statistically significant negative contemporaneous

impact on value added per worker in the chemicals, machinery and vehicles sectors, the most

R&D intensive industries I studied in Central Europe. On the contrary, the impact was significant

and positive when I considered a one-year lag. This suggests that the entry of a multinational

company may initially be a negative “technology shock” for local firms but after some time, the

whole “high-tech” industry benefits from new capital investments and translates them into

productivity gains for an emerging economy. The significance of the time lag and the weak

impact of FDI in these industries on competitiveness, measured in terms of value added per wage

costs, point to the potential necessity to train and compensate workers substantially in order to

make them productive in using new technologies.

To illustrate my point, let’s consider ABB, a Swiss-Swedish engineering conglomerate. It

had to spend more on management training in Central and Eastern Europe than it did on its

acquisition transactions in the region (Radosevic, 2002). The corporation had to shut down its

Polish R&D facilities in Elblag because research staff there was not considered effective (CASE,

2004). Miroslaw Gryszka, the ABB Group’s representative in Poland, said that the personnel of

the R&D center created by ABB in Cracow was largely recruited from among Poles living

outside Poland. One of the reasons for this, according to Mr Gryszka, was that Polish engineers

and scientists living in Poland have cultural barriers that are an impediment to innovation (CASE,

2004). A key implication of my results is therefore the idea that costs associated with increasing

productivity of employees and business allies of „high-tech“ foreign ventures in emerging

economies may be higher than expected and pose a challenge to their global competitiveness.

My results for the technologically less advances sectors (food & beverages, textiles and

metals) are less conclusive. I find little support for the contention of Kosova (2004) that local

firms are crowded out by multinationals in the short run and experience technology spillovers

from FDI in the long run. Indeed, the only statistically significant result that I get for these sectors

are technology spillovers (positive impact on productivity) in the short run (although this finding

is not robust across all specifications). My findings are more consistent with Keller and Yeaple

(2002). They found that in the United States in 1987-96, the current year and one year lagged

impact of FDI on the productivity of “low-tech” were not statistically significant, although the

immediate impact was negative and the lagged one positive. To be fair, while Kosova (2004)

found some positive spillovers in the “low-tech”, she admitted that firms in technologically more

advanced industries were the primary beneficiaries of technology spillovers in her sample.

24

My results for the impact of FDI on competitiveness (measured as value added per total

labor costs), are mixed for the low-tech industries. However, this finding stands in contrast to no

impact at all found in the high-tech sector. Although the impact of R&D expenditures on

productivity and competitiveness is not a central concern of this study, I do find some interesting

results in this respect that may offer promising areas for future research. My study suggests that

nation-wide local business R&D expenditures changed in tandem with productivity of the “high-

tech” sector and competitiveness of the “low-tech” sector in Central Europe (while there was no

statistically significant relationship between R&D expenditures and productivity of “low-tech”

and competitiveness of “high-tech”). This finding points to the complementarity between FDI and

local firms’ R&D in their productivity impact on the host economy, as suggested by Kinoshita

(2001). It also reinforces my main point that globalization of industry has a different impact on

productivity and competitiveness of sectors in emerging economies depending on their distance

from the technology frontier.

8. References Acemoglu D., Aghion P., Zilibotti F. (2003), “Distance to frontier, selection and economic growth”, Harvard University (mimeo) Acemoglu D., Aghion P., Zilibotti F. (2002), “Vertical integration and distance to frontier”, Harvard University (mimeo) Aghion P., Bloom N., Blundell R., Griffith R., Howitt P. (2002), “Competition and Innovation: An inverted U-relationship”, Harvard University (mimeo) Aghion P., Burges R., Redding S., Zilibotti F. (2003), “The unequal effects of liberalization: Theory and evidence from India”, Harvard University (mimeo) Aitken B. and Harrison A. (1999), “Do domestic firms benefit from direct foreign investment? Evidence from Venezuela”, American Economic Review, Vol. 89 (3) Blalock G. (2002), “Technology from FDI: Strategic Transfer Through Supply Chains”, University of Michigan PhD thesis (mimeo) Blomstrom M, Kokko A. and Zejan M. (2000), „Foreign Direct Investment: Firm and Host Country Strategies“, Macmillan Press Ltd, London, UK Borensztein, De Gregorio and Lee (1998), “How does foreign direct investment affect economic growth?’, Journal of International Economics 45 Bosworth & Collins (1999), “Capital flows to developing countries: implications for saving and investment”, Brookings Papers in Economic Activity

25

Brewer T., Young S. and Guisinger S. (2003), “The New Economic Analysis of Multinationals”, Edward Elgar Publishing Caselli F. and Wilbur J. (2000), “The World Technology Frontier”, NBER Working Paper 7904 Caves R. (1974), “Multinational firms, Competition and productivity in host country markets”, Economica, 41 Caves R. (1999), “Spillovers from multinationals to developing countries: The mechanisms at work”, WDI conference paper, June 1999 Center for Social and Economic Research (CASE, 2004), “Critical Synthesis, review of the main findings, methodologies and current thought on the role of foreign and domestic firms in changes in competitiveness, Warsaw, Poland Chung W., Mitchell W. and Yeoung B. (1998), “Foreign direct investment and host country productivity: The American automotive component industry in the 1980s”, New York University (mimeo) Djankov S. and Hoekman B. (1998), “Avenue of technology transfer: foreign investment and productivity change in the Czech Republic”, CEPR Discussion Paper no. 1883 Dunning J. (1988), “The eclectic paradigm of international production: A restatement and some possible extensions”, Journal of International Business Studies Griliches Z. (1995), “R&D and Productivity: Econometric Results and Measurement Isssues” in P. Stoneman, Handbook of Innovation and Technological Change, Blackwell, Oxford Haddad D. and Harrison A. (1993), “Are there positive spillovers from foreign direct investment? Evidence from panel data for Morocco”, Journal of Development Economics, 42 Haskel J., Pereira S., and Slaughter M. (2001), “Does Inward FDI Boost Productivity of Domestic Firms?”, NBER WP 8724 Jovanovic B. (1982), “Selection and the Evolution of Industry”, Econometrica, Vol. 50(3) Keller W. (2001), “The geography and channels of diffusion at the world’s technology frontier”, NBER Working Paper 8150 Keller W. and Yeaple S. (2002), “Multinational Enteprises, International Trade, and Productivity Growth: Firm-Level Evidence from the United States”, NBER Working Paper no. 9504 Kinoshita (2000), “R&D and technology spillovers via FDI: Innovation and absorptive capacity”, CERGE Working Paper (http://www.cerge-ei.cz/pdf/wp/Wp163.pdf) Konings J. (1999), “The effects of direct foreign investment on domestic firms: Evidence from firm-level panel data in emerging economies”, WDI Working Paper no. 344 Kosova (2004), “Do Foreign Firms Crowd Out Domestic Firms? The Evidence From the Czech Republic”, University of Michigan PhD thesis (mimeo)

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Levinsohn J. and Petrin A. (2000), Estimating production functions using inputs to control for unobservables (mimeo) Mowery D., Oxley J. and Silverman B. (1996), “Strategic alliances and interfirm knowledge transfer”, Strategic Management Journal Vol. 17 Olley S. and Pakes A. (1996), “The dynamics of productivity in the telecommunications equipment industry”, Econometrica 64

Peng M. (2000), “Business Strategies in Transition Economies”, Thousand Oaks: Sage Publications

Porter M. (1998), "Clusters and the New Economics of Competition," Harvard Business Review, November-December

Radosevic S. (2002), European Integration and Complementarities Driven Network Alignment: The case of ABB in Central and Eastern Europe” (mimeo)

Sabirianova Peter K., Svejnar J., Terrell K. (2004a), ”Foreign Investment, Corporate Ownership and Development: Are firms in emerging markets catching up to the world standard?, University of Michigan (mimeo)

Sabirianova Peter K., Svejnar J., Terrell K. (2004b), ”Distance to the frontier and FDI spillovers”, University of Michigan (mimeo)

Sjoholm F. (1999), Technology Gap, Competition and Spillovers from FDI: Evidence from Establishment data”, Journal of Development Studies, Vol. 36

Smarzynska Javorcik B. (2002), “ Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers through Backward Linkages.” Policy Research Working Paper Serier 2923, The World Bank

Smarzynska Javorcik B. and Spatareanu M. (2003), “To share or not to share: Does local participation matter for spillovers from FDI" Policy Research Working Paper no. 3118, The World Bank

Sun S. (2002), “Is firm growth proportional or disproportional? A reconciliation with an application using franchising data”, University of Michigan PhD thesis (mimeo)

Zemplinerova A. and Jarolim M. (2001), “Modes of FDI entry and firm performance: The Czech case”, Transnational Corporations, vol. 10, no. 3 (December)

27

9. Appendix

Figures 2 and 3 (Conceptual and analytical frameworks)

Impact of multinational enterprises on performance of manufacturing sectors

Productivity

Competitiveness

Positive impact

Technology spillovers

Ascendancy

Negative impact

Crowding out

Marginalization

Impact of multinational enterprises on sectors and countries over time

Emerging economy

Developed economy

Productivity

Competitiveness

Productivity

Competitiveness

Static

Crowd’ out

Ascendancy

?

?

Low

Tech

Dynamic

?

?

Crowd’ out

Marginalization

Static

Crowd’ out

?

Spillovers

Ascendancy

High

Tech

Dynamic

Spillovers

?

Spillovers

Ascendancy

28

Table 1 Summary statistics for “low-tech” sectors in Central Europe, 1994-2000

Sector

Country

Statistics

VApW

VApTW

FDIpW

mean 13,551 3.28 1,055

CZE stdev 827 0.13 716

mean 10,812 2.16 961

HUN stdev 600 0.05 438

mean 9,467 2.43 931

Food & Beverages

POL stdev 1,436 0.26 417

mean 6,277 3.62 644

CZE stdev 706 0.32 440

mean 5,969 2.79 -170

HUN stdev 366 0.10 326

mean 6,179 3.36 306

Textiles & Leather

POL stdev 531 0.16 166

mean 10,149 3.56 249

CZE stdev 233 0.12 120

mean 14,344 3.49 52

HUN stdev 226 0.23 218

mean 9,641 2.35 102

Metals & metal goods

POL stdev 1,442 0.58 65

Notes. CZE stands for the Czech Republic, HUN for Hungary and POL for Poland. VApW stands for value added per worker and FDIpW for FDI per worker in thousands of US dollars. VApTW is a ratio of value added to total wage costs. Mean is the arithmetic mean, stdev is the standard deviation. Total number of observations for the “low-tech” industries is 40.

29

Table 2 Summary statistics for “high-tech” sectors in Central Europe, 1994-2000

Sector

Country

Statistics

VApW

VApTW

FDIpW

Mean 14,006 5.71 3,636

CZE Stdev 1,214 0.41 2,210

Mean 19,231 4.92 2,767

HUN Stdev 82 0.93 2,207

Mean 14,236 5.18 1,185

Chemicals & plastics

POL Stdev 1,889 0.60 584

Mean 9,206 4.13 1,339

CZE Stdev 495 0.10 1,268

Mean 13,669 3.89 837

HUN Stdev 301 0.60 622

Mean 9,484 4.14 93

Machinery& Electronics

POL Stdev 1,976 0.21 24

Mean na Na na

CZE Stdev na Na na

Mean 28,913 4.68 -3,768

HUN Stdev 791 0.26 5,685

Mean 8,198 1.82 1,131

Transport Vehicles

POL Stdev 1,295 0.24 748

Notes. CZE stands for the Czech Republic, HUN for Hungary and POL for Poland. VApW stands for value added per worker and FDIpW for FDI per worker in thousands of US dollars. VApTW is a ratio of value added to total wage costs. Mean is the arithmetic mean, stdev is the standard deviation. Total number of observations for the “high-tech” industries is 36.

30

Figures 4-6

R&D Expenditures in the Czech Republic

0

500

1000

1500

2000

1995 1996 1997 1998 1999 2000 2001

Year

Mill

ion

1995

US$

PPP

Academic

Government

Other Business

Local Business

R&D Expenditures in Hungary

0

200

400

600

800

1000

1993 1994 1995 1996 1997 1998 1999 2000

Year

Mill

ion

1995

US$

PPP

Academic

Government

Other BusinessLocal Business

R&D Expenditures in Poland

0

500

1000

1500

2000

2500

3000

1994 1995 1996 1997 1998 1999 2000 2001

Year

Mill

ion

1995

US$

PPP

Academic

Government

Other BusinessLocal Business

Source: Organisation for Economic Cooperation and Development (OECD) Note. Aggregate R&D expenditures in million 1995 US$ at purchasing power parity.

31

Regression results (Specification 1) Table 3 VALUE ADDED PER WORKER

FDI FDIXRD ADJ R^2 F STAT

VA PER TOTAL WAGES

FDI FDIXRD ADJ R^2 F Low Tech

.188** (.092)

- 0.07

- .013 (.064)

- -0.03 -

.226 (1.263)

-.006 (.194)

0.05 <0.15 7.06** (2.20)

1.089** (.337)

0.18

0.01

High Tech

-.015 (.028)

- -0.02 - .0251 (.03)

- -0.01 -

-.279 (.33)

.042 (.053)

-0.03 <0.63 -.284 (.350)

.050 (.056)

-0.02 0.48

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDI stands for FDI flows per worker. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of R&D. In this specification, value added and value added per total wages are entered as logarithm. Table 4 VALUE ADDED PER WORKER

FDI FDILAG FDIXRD FDIXRDLAG ADJ R^2 F STAT

Low Tech

.111 (.122)

.077 (.117)

-

-

0.03

< 0.26

.116 (.301)

.073 (.134)

0.689 (1.09)

-.069 (0.899)

-0.05

< 0.63

High Tech

-.054** (.025)

.081** (.037)

-

-

0.15

< 0.05

-.194*** (.054)

.287*** (.090)

0.290*** (.096)

-.035** (.013)

0.37

< 0.01

VALUE ADDED PER TOTAL WAGES FDI FDILAG FDIXRD FDIXRDLAG ADJ R^2 F STAT

Low Tech

.080 (.091)

-.023 (.087)

-

-

-0.04

< 0.66

-.415** (.198)

-.055 (.088)

0.680 (0.718)

.017 (.592)

0.13

< 0.10

High Tech

.003 (.037)

.057 (.054)

-

-

-0.02

< 0.49

-.081 (.094)

.080 (.156)

.016 (.017)

-.006 (.023)

-0.06

< 0.67

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDILAG is FDI flow per worker one year before that of FDI. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of RD. FDIXRDLAG is FDI lagged interacted with ln R&D lagged by one year. In these specifications, value added and value added per total wages are entered as logarithm.

32

Specification 2 Table 5 VALUE ADDED PER WORKER

FDI FDIXRD ADJ R^2 F STAT

VA PER TOTAL WAGES

FDI FDIXRD ADJ R^2 F Low Tech

-.024 (.061)

- -0.03 - .017 (.064)

- -0.03 -

-.171 (.176)

.200 (.225)

-0.04 <0.62 -.151 (.183)

.229 (.233)

-0.04 0.60

High Tech

-.047 (.030)

- 0.05 <0.14 -.007 (.017)

- -0.03 0.66

-.651 (.389)

.097 (.062)

0.10 <0.10 -.240 (.218)

.037 (.035)

-0.03 0.52

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDI are flows per worker. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of R&D. In this specification, value added per worker and total wages enter as first logarithmic difference. Table 6 VALUE ADDED PER WORKER

FDI FDILAG FDIXRD FDIXRDLAG ADJ R^2 F STAT

Low Tech

-.017 (.076)

-.012 (.073)

- - -0.07 < 0.91

-.176 (.183)

-.047 (.083)

.666 (.683)

-.397 (.560)

-0.10 < 0.82

High Tech

-.057* (.033)

.037 (.048)

- - 0.03 < 0.24

-1.08*** (.435)

.269*** (.123)

.170*** (.071)

-.386 (.178)

0.20 < 0.06

VALUE ADDED PER TOTAL WAGES FDI FDILAG FDIXRD FDIXRDLAG ADJ R^2 F STAT

Low Tech

.037 (.078)

-.034 (.075)

- - -0.07 < 0.87

-.131 (.189)

-.024 (.085)

-.139 (.706)

.339 (.578)

-0.09 < 0.79

High Tech

-.006 (18.4)

-.006 (.026)

- - -0.07 < 0.88

-.329 (.267)

.018 (.075)

52.7 (43.5)

-.050 (.109)

-0.09 < 0.78

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDILAG is FDI flow per worker one year before that of FDI. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of R&D. FDIXRDLAG is FDI lagged interacted with ln R&D lagged by one year. In this specification, value added per worker and total wages enter as first logarithmic difference.

33

Specification 3 Table 7 VALUE ADDED PER WORKER

FDI R&D ADJ R^2 F STAT

VA PER TOTAL WAGES

FDI R&D ADJ R^2 F Low Tech

-.024 (.061)

- 0.03

- .017 (.064)

- -0.03 -

-.025 (.062)

-.044 (.088)

-0.06 <0.82 .021 (.059)

.191** (.084)

0.10

0.09

High Tech

-.047 (.030)

0.05 - -.007 (.017)

-0.03 -

-.019 (.026)

1.96*** (.557)

0.35 <0.01 -.005 (.018)

.126 (.374)

-0.07 0.86

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDI are flows per worker. R&D is a first ln difference of R&D. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of R&D. In this specification, value added per worker and total wages enter as first logarithmic difference. Table 8 VALUE ADDED PER WORKER

FDI FDILAG R&D FDIXRD FDIXRDLAG ADJ R^2

Low Tech

-.023 (.078)

-.003 (.076)

-.043 (.093)

- - -0.10

-.217 (.193)

-.037 (.085)

-.070 (.097)

.725 (.694)

-.410 (.565)

-0.12

High Tech

-.022 (.030)

.126 .407

1.930*** .577

- - 0.32

-1.12*** (.342)

.158 (.101)

1.96*** (.516)

.001*** (.000)

-.026* (.014)

0.50

VALUE ADDED PER TOTAL WAGES FDI FDILAG R&D FDIXRD FDIXRDLAG ADJ R^2

Low Tech

.066 (.073)

-.076 (.071)

.217** (.086)

- - 0.11

-.012 (.183)

-.055 (.080)

.203** (.091)

-.031 (.659)

.376 (.536)

0.06

High Tech

-.001 (.001)

-.001 (.026)

.147 (.387)

- -

-0.11

-.332 (.271)

.001 (.080)

.205 (.408)

53.7 (44.3)

-.003 (.114)

-0.13

Notes. * is 10% significance level, ** is 5% and *** is 1%, standard error in brackets. Low Tech stands for the group of technologically less sophisticated industries (foodstuffs, textiles, metals). High Tech industries are chemicals, machinery and vehicles. FDILAG is FDI flow per worker one year before that of FDI. R&D is a first difference of ln R&D. FDIXRD captures the joint impact of FDI and R&D by interacting FDI flows and ln of R&D. FDIXRDLAG is FDI lagged interacted with ln R&D lagged by one year. In this specification, value added per worker and total wages enter as first logarithmic difference.

34