R&D, Spatial Spillovers and Productivity Growth: Evidence from

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R&D, Spatial Spillovers and Productivity Growth: Evidence

from Dynamic Panel

Wen-Cheng Lu, Jong-Rong Chen,* Chia-Ling Wang

Abstract

This paper investigates the relationship between R&D stock and productivitygrowth, while taking into account the effect of spatial spillovers. We propose ahomogeneous dynamic panel data model and three heterogeneous dynamic panel datamodels to consider the individual effect as well as endogenous. We also distinguishbetween the estimated long-run and short-run results. Our results indicate that boththe R&D stock and R&D spatial spillovers positively affect productivity growth in theshort-run as well as in the long-run.

Key words: Industrial cluster, dynamic panel data model, R&D spatial spillovers,productivity growth.

JEL classification code: L63, O30, R10, D24

* Corresponding author: Graduate Institute of Industrial Economics, National Central University,Taiwan 320. Tel: 886-3-4227791, Fax: 886-3-4226134, E-mail: jrchen@cc.ncu.edu.tw.

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1. Introduction

The purpose of this paper is to recognize the importance of technological

accumulation and to evaluate its contribution to productivity growth in the context of

spatial spillover effects. R&D behavior may be enhanced by the location of R&D

facilities such as Silicon Valley or Taiwan’s Hsinchu Science-based Industrial Park.

Total factor productivity (TFP), which measures productivity improvements generated

from technical progress and changes in efficiency, has been a commonly-used

indicator of the role of the state of technology on input productivity. Many economists

have found that R&D serves as the main engine of technological progress and

productivity growth. In particular, a firm’s investment plays an important role in TFP

growth. The R&D expenditures of individual firms contribute to the sustained

long-run growth of an economy (Grossman and Helpman, 1990a, 1990b; Romer,

1990). Based on this view, individual firms invest in R&D in order to acquire private

knowledge that increases their productivity and profit. During the process, the private

technology of individual firm spills over to other firms and becomes social knowledge

that gives rise to an external effect in promoting the productivity of all firms. R&D

spatial spillover effect is mainly viewed as externality. In the previous studies, the

spatial spillover effects can be divided into two categories: Marshall-Arrow-Romer

externality and Jocobs externality. No matter what kind of externality, Geographic

location for R&D is very important.

The size of the external effect depends on the technological characteristics and the

respective locations of firms. Because the spillover effects can’t be observable, we

measure them by their properties of accessibility and R&D intensity. Accessibility

reflects the interaction between firms in the neighborhood while R&D intensity

described by R&D stock. In particular, if the distance between firms is smaller, the

external effect will be larger. In other words, the size of spillover effect is captured by

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industrial clustering. In this paper, we use the industrial cluster multiplied by the R&D

stock of other firms to measure the spatial spillovers.

In this paper, we study the relationship between productivity growth and R&D

stock while considering the spatial spillovers. Our reasons for studying this issue are

as follows. First, productivity growth may reflect different states of technology. As

technology accumulates over time, using the R&D stock to represent different states

of technology is more suitable. Firms may need to engage in persistent and long-run

R&D investment. Secondly, it is important to emphasize that the concept of

geographical space may also be defined in an economic sense with distance in terms

of economic connections (e.g., TFP growth, bilateral trade). Many papers have used

economic theory to predict very specific forms of spatial correlation. In this study, we

use the latitude and longitude data to calculate the distance from the center of the

latitude and longitude. The distance stands for the industrial cluster effects from

which we can then estimate the R&D spillover effects.

Previous studies usually focused on the domestic/foreign R&D stock in relation to

TFP at the country level and only rarely at the firm level.1 This paper expects that the

results at the firm level may be different from those at the country level. The research

at the country level cannot explain the industrial situation and the country-wide data

are aggregate data that may result in the loss of important information regarding

microeconomic units. The firm’s R&D stock in terms of its own R&D and other

firms’ R&D stocks plays an important role in TFP growth because of the R&D

1 According to the country evidence, domestic productivity depends not only on domestic but also onforeign R&D (Hayami and Ruttan, 1985; Johnson and Evenson, 1999). Domestic international R&Dspillovers play an important role in the productivity issue. If we ignore international spillovers, wewill overestimate productivity growth and the rates of return to research (Alston and Pardey, 2001).Gutierrez and Gutierrez (2003) find that total factor productivity is strongly influenced by domesticand foreign R&D spending in the agricultural sector. They also show that geographical factors matterand that the countries located in temperate zones benefit more from technological spillovers thancountries located in tropical zones. Based on the country evidence, we can conclude that thedomestic R&D stock and foreign R&D stock are both very closely related to TFP growth, and thatthe spillover effect is important for estimating TFP in relation to this.

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accumulation characteristics and R&D uncertainty. The R&D spatial spillover effects

are unobserved and influence each firm in a given industry through intra-industry (or

inter-industry) sales and geographical location. Whatever the benefit of intra-industry

sales or geographical location, R&D spillover effects are involuntary and viewed as

externalities. The main reason for these relationships in recent papers has to do with

the external economies that a firm faces. (Hoogstra, et al., 2004; Campi, et al., 2004;

Honjo, 2004)

This paper addresses the empirical relationship between a firm’s R&D stock, the

R&D stocks of other firms with their spatial spillover effects and TFP growth for the

electronic firms over the period 1991-2002. More specifically, this paper contributes

the following:

(1) Many of the previous studies viewed the firm structure as being homogeneous. In

other words, thefirm’s structure in a given industry is seen as being the same and

these parameters are estimated in the panel. Such an approach may lead to a

biased conclusion because previous studies did not consider the heterogeneity

between firms. In the paper, we propose the adoption of three heterogeneous

dynamic panel data models that consider both the individual effects and

endogenous. We also distinguish between long-run and short-run estimated

results.

(2) Spatial spillover effects are composed of industry clusters and the R&D stock.

Recently, spillover effects resulting from R&D stocks and industry clusters have

been viewed as an R&D external effect. We have applied the relative distance

among firms as a measure of industrial clustering. Industrial clustering may affect

a firm’s R&D behavior.We investigate the relationship between a firm’s R&D

stock and the R&D stocks of other firms through spatial spillover effects.

The remainder of the paper is organized as follows. In Section 2, we present a brief

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review of the empirical literature. In Section 3, we define the variables and describe

empirical models. The empirical results are discussed in Section 4. Finally, we

conclude with a summary of our results.

2. Previous Empirical Studies

In this paper, we begin by examining the relationship between the R&D stock and

productivity, with a view to investigating the relationship between the firm’s R&D

stock, the other firms’ R&D stocks with their spatial spillover effects and productivity

growth at the firm level. Since R&D expenditure may reduce a firm’s average cost

and promote the firm’s productivity, productivity may be seen as being connected

with the firm’s R&D behavior. Recently, many empirical studies have dealt with the

relationship between R&D expenditure/R&D capital and productivity growth.

Audretsch and Feldman (1996) find that industries with high levels of innovative

activity have a greater tendency to cluster. Raut (1995) estimates the effects of

individual R&D expenditures and industry-wide R&D spillovers on the individual

firm’s productivity growth. Raut (1995) also uses the R&D capital of a firm as a

factor of production. He finds that the R&D spillover is a highly significant

determinant of productivity growth that has an insignificant effect on own R&D

capital due to the non-reporting problem.

Bernstein and Mohnen (1998) empirically investigate bilateral spillovers between

the U.S. and Japan and show that international spillovers exist from the U.S. to Japan.

U.S. R&D capital accumulation leads productivity growth and reduces average cost in

Japan. Coe and Helpman (1995), among other researchers, state that

commercially-oriented innovation efforts that respond to economic incentives are the

major engine of technological progress and productivity growth. Coe and Helpman

(1995) argue that a country’s productivity depends on its own R&D efforts as well as

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the R&D efforts of its trading partners. Using data from 21 OECD countries plus

Israel during 1971-1990, they find that both domestic and foreign R&D capital stocks

have important effects on TFP. Kao, et al. (1999) studied the issue of international

R&D spillovers and the effect of domestic and foreign R&D capital stocks. Madden

and Savage (2000) applied these methods to a sample of OECD and Asian economies

from 1980 to 1995 to determine the extent to which total factor productivity was

related to domestic and foreign R&D activity, trade, and information technology and

telecommunications (ITT). They found that the benefits of R&D could spill over to

other countries through trade. Branstetter (2001) has estimated the size of

international spillovers using micro-level data. He shows that technological

externalities can generate persistent growth differentials. Bottazzi and Peri (2003) use

European regional data to test for the existence of spatial spillovers of R&D. They

find that R&D spillovers exist and localized only within a distance of 300 km.

Doubling R&D spending in a region would increase the output of new ideas in other

regions within 300 km only by 2-3%.

In sum, spatial spillover effect plays an important role in the issue of productivity

growth and R&D as many previous papers shown.

3. Theoretical and empirical framework

3.1 The measure of TFP and the spatial spillover effect

A firm will choose a location that maximizes profit. When a location is determined,

the way in which that firm is clustered with other firms and its spatial R&D spillover

effect will affect its productivity. The firm’s spatial spillover effect may be calculated

in terms of its relative distance from other firms. The regional stock of human capital

is a suitable means of explaining persistent regional differences and determines the

firm’s ability to absorb and use new technology. Geographical clustering and

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knowledge diffusion may also contribute to regional growth, with geographical

clustering playing an important part in the R&D spillover effect. At the same time, the

R&D spillover effect may have an effect on TFP.

TFP is measured as follows:

LKYTFP log)1(logloglog , (1)

where it is assumed that the production function is of a Cobb-Douglas form in which

logarithms are taken on both sides of the equation. Term K denotes capital

accumulation, L is the firm’s hired amount of employees, and Y is final output.

However, firms within a given industry often face different internal and external

environments and are able to obtain or absorb distinct spillover effects. Bernstein

(1989) constructed a model for seven Canadian industries based on Equation (2). He

argued that it is implausible for every firm to be able to gain equally from the

aggregate stock of knowledge. To account for the different abilities of firms to

internalize other firms’ knowledge, Bernstein (1989) provides the following indicator:

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ijjiji KwS , (2)

where jK is R&D stock, iS is the spillover effect of firm i, and ijw represents

the absorptive ability of firm i. Equation (2) is characterized by the weights, ijw ,

which represent firm i’s ability to internalize pieces of firm j’s stock of knowledge.

The larger these weights are, the more that firm i can gain from firm j’s stock of

knowledge. There are many suggestions for the calculation of spillover effects that

can be found in the literature: (1) Distance in technology space. Jaffe (1986),

Adam (1990), Inkmann and Pohlmeier (1995) belong to this classification. (2)

Geographical distance. Beise and Stahl (1999) use the inverse of the geographical

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distance between firms i and j to calculate the weights ijw . (3) Direct measures

based on innovation survey data.

In this paper, we use the latitude and longitude of a firm to define the firm’s

position. Our method involves finding the center of gravity and computing the

distance of every firm from the core. Head and Mayer (2002) use the weighted

latitude and longitude of each firm to estimate the center of gravity in a given industry.

The purpose in using weighted latitude and longitude is to exhibit the effect of firm

size on industrial clustering. We thus exploit the latitude and longitude data of the

firms and their distance from the industry center to estimate the industry cluster and

the R&D spillover effects. Within this framework, we assume that the more that an

industry is clustered; the greater will be the R&D spillover effects. Similarly, a firm

near the center of gravity will have larger R&D spillover effects.

The latitude and longitude data are obtained from the Industry, Commerce and

Service (ICS) Census conducted by the Directorate-General of Budget, Accounting

and Statistics (DGBAS) in Taiwan. The purpose of the database development is to

meet the increasing demand for national censuses and academic research. The ICS

census is designed to collect basic data on economic activities such as the operational

status of an industry, the commerce and services sectors, the distribution of resources,

major equipment, capital utilization, economic structure, changes in sales and

production, and other relevant matters. To estimate the center of gravity, we assemble

the information on the latitude and longitude for each village and define the

geographical position of the various firms. By exploiting the differences in terms of

the latitude and longitude, we can estimate the real degree of industrial clustering. The

latitude and longitude are calculated as follows

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ix

xxi longpconlong (3)

xix

xi latpconlat

, (4)

where iconlong and iconlat represent the center of longitude and latitude in a

given industry, respectively, and xlong and xlat stand for the longitude and latitude

of a firm, respectively. Term xp is the share of employees in the industry as a whole.

The distance from the center of longitude and latitude to firm x is shown as follows

360(min()2958.57/()2958.57/((6370 COSlatCOSconlatCOSARCOSdist xixi

)2958.57/()2958.57/))(, ixixi conlatSINlongconlonglongconlong

)2958.57/( xlatSIN . (5)

Finally, the industrial cluster effects are expressed as

xii dist

m1

. (6)

We use im to measure the industrial cluster effects. When the extent of the clustering

is higher, xidist is smaller, and the firm in the neighborhood of the industry core

absorbs more spillover effects and has a significant effect on productivity. In addition,

im multiplied by other firms’ R&D stocks ( ftiS , ) is used to measure R&D spatial

spillovers ( ftii Sm , ). A diagram of the relations between industrial gravity and

industrial members is shown in Figure 1.

3.2 Estimation of parameters using the dynamic panel data method

For simplicity, we design a dynamic panel data model to describe the relationship

between a firm’s own R&D capital stock and the R&D capital stock of other firms

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taking into consideration the spatial spillover effects and TFP growth as follows

titiftidtiti TFPSmSTFP ,1,21, log)log(loglog

Ni ,,2,1 Tt ,,2,1 , (7)

where tiTFP , and 1, tiTFP are the TFP growth rates at times t and 1t ,

respectively, dtS is the firm’s own R&D stock, f

tii Sm , is the R&D spatial spillover

effect and ftS represents the R&D stocks of other firms. 1 and 2 denote the

elasticities of the firm’s own R&D and the R&D spatial spillovers.

In equation (8), i stands for the individual effects and is different across firms.

, 1 and 2 are homogeneous, implying that the lagged dependent variables and

exogenous variables have the same effects on the dependent variables. Due to the

endogenous (i.e. where the expectations regarding the explanatory variables and

residuals are not equal to zero), the OLS estimation procedure may lead to biased

estimators. It is clear that such an equation (7) is very restrictive, since it particularly

implies that causality does not exist for any individual. We adopt the econometric

procedure of Ahn and Schmidt (1995) and Baum, et al. (2002) to eliminate individual

specific effects to estimate parameters. The presence of such heterogeneity can result

in serious mis-specification biases in the subsequent estimation that imposes

homogeneous parameter values. In particular, if the dynamics are heterogeneous

across firms and they are assumed to be equal, Pesaran and Smith (1995) show that

estimates will be biased and inconsistent.

We apply the following simple heterogeneous dynamic model proposed by Pesaran

and Smith (1995):

titiitiiti xTFPTFP ,1,1,, Ni ,,2,1 Tt ,,2,1 (8)

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with its coefficients i and i varying across groups according to the following

random coefficients model:

iiH 10 : , ii 2

where fti

dtiti SmiSx 1,1,1, , , and ),( 21 ii . i1 and i2 are assumed to

have zero means and constant covariances. The model introduces parameter

heterogeneity through the short-run coefficients i and i and long-run parameters

)1( i

i

. There is a sizeable literature on the small sample bias of the least

squares estimators of the short-run slope coefficients i and i . Many earlier

econometric studies provide an estimation method to obtain a consistent parameter

estimate for short-run parameters on the small sample: (1) the Mean group estimator

of Pesaran and Smith (1995); (2) the Pooled mean group estimator of Pesaran, et al.

(1999); (3) the Bayesian estimator of Hsiao and Tahmiscioglu (1997); and (4) the

bias-corrected method of Kiviet and Phillips (1993) to estimate the short-run slope

coefficients. The long-run parameters can be obtained by indirectly deriving from the

short-run parameters or directly estimated through the long-run approach. Therefore,

we apply the following methods to estimate the long-run parameters: (1) The OLS

method is used to estimate the short-run slope coefficients and to derive the long-run

parameters. (2) The bias-corrected method of Kiviet and Phillips (1993) is used to

estimate the short-run slope coefficients and to construct the long-run parameters. (3)

The bootstrap bias-corrected method of Pesaran and Zhao (1999), who proposed a

bootstrap bias-corrected method to directly estimate i, is used to show that the next

source of potential bias is due to the non-linearity of i in terms of i and i.

3.4 Data description

The firms’ operational dataare derived from the Taiwan Economic Journal (TEJ)

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database. There are 90 firms covering 10 years (1992-2002) in our sample. To define

the spatial relationship between firms, we must have longitude and latitude data. The

longitude and latitude data are obtained from the Industry, Commerce, and Service

(ICS) Census provided by the Directorate-General of Budget, Accounting and

Statistics (DGBAS). The basic statistics are listed in Table 1.

4. Empirical Results

In this paper, we use the dynamic panel data method for a sample of Taiwanese

electronics companies that are listed on the Taiwan stock exchange. The success and

excellent performance of Taiwan’s electronics industry has attracted worldwide

attention. There are several science parks in Taiwan, in which firms are integrated

both horizontally and vertically. While this is conducive to a firm’s R&D, it does not

mean that there is no technology diffusion or R&D spillovers. Distance from the

center of gravity is adopted to measure the industrial cluster. The data used are

suitable for investigating the issue of TFP growth and R&D stock.

The dynamic panel data model has already been mentioned as equation (8). The

estimation results are presented in Table 2. Model 1 is expressed by equation (8) and

we test the robustness of our model in order to estimate Model 2 and Model 3 as

follows:

tiitdititi STFPTFP ,1,11,, Ni ,,2,1 Tt ,,2,1 (Model 2)

tiitfiititi SmTFPTFP ,1,21,, Ni ,,2,1 Tt ,,2,1 (Model 3)

In Model 1, we find that the lag-one period TFP growth rate has a negative effect on

the current TFP growth rate. dS and fi Sm have a positive effect on the current

TFP growth rate. This means that the TFP growth rate for firm i converges over

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time. dS and fi Sm play important roles in TFP growth. To maintain the TFP

growth, we have to accumulate R&D effort. Model 2 and Model 3 also exhibit the

same results. 1 and 2 measure the effect of the short-run R&D stock on TFP

growth. In Table 2, we can find that the short-run effects are both positive and

significant. In previous empirical studies such as Pesaran and Smith (1995), we can

study the heterogeneous panel data and estimate different slopes for the explanatory

variables across firms. The heterogeneous model has a short-run slope that varies

across firms and that is used to derive the long-run effects. As mentioned in Section 3,

there are two long-run effects associated with TFP growth—the long-run effect from

diS to TFP growth and also that from fi Sm to TFP growth. In this paper, there are

three estimation methods used to obtain the long-run effects. In a recent paper, Judson

and Owen (1999) recommended the corrected fixed estimator of Kiviet (1995) as the

best choice for balanced macro-panels, with GMM being the second best choice, and

for long panels, the computationally simpler Anderson and Hsiao (1982) estimator. In

this paper, we use the OLS procedure, the bias-corrected estimator of Kiviet and

Phillips (1993), and the bootstrap bias-corrected method of Pesaran and Zhao (1999)

to estimate the long-run effects. The long-run effects are shown in Table 4. It can be

shown that the long-run effects are positive and significant and the conclusion is

similar to that for the short-run effects. The results are similar to those of Raut (1995).

Raut (1995) uses panel data for a sample of Indian private manufacturing firms, in

which he considers correlation in the error terms and simultaneity in the determination

of output and input levels, but not the endogeneity of the dynamic panel data method.

Raut (1995) points out that the spillover R&D is a highly significant determinant of

productivity growth but that own R&D capital has an insignificant effect on the light

and petrochemical industries.

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5. Conclusion

In this paper, we also investigate the relationship between the R&D stock and the

R&D spatial spillover effects and TFP growth. R&D spillover effects have been

viewed as external economies and have been measured their contribution to TFP

(Madden and Savage, 2000; Raut, 1995). Previous studies measured the spillover

effects based on bilateral trade or sales. From the viewpoints of geographic, urban,

and regional economics, spatial correlations play an important role in analyzing

cross-firm TFP growth. From the viewpoint of a firm’s choice of location, different

locations may offer different external resources such as learning opportunities and

specifically-skilled workers. In this paper, we use the inverse of the distance between

a given firm and the center of gravity to measure the extent of the industrial clustering.

When the industrial squeeze is higher or the other firms’ R&D stocks are larger, the

spillover effects are larger. Furthermore, we propose three heterogeneous dynamic

panel data models to consider the individual effects and endogenous. We also

distinguish between the long-run and short-run estimated results. Our results indicate

that both the R&D stock and R&D spatial spillovers positively affect productivity

growth in the short run as well as in the long run.

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Table 1. Basic statistics

Variable Definition MeanStandarddeviation

TFP )ln()ln( 1,, titi TFPTFP 0.001 0.485

dSThe logarithm of own R&Dstock

11.513 2.442

fi Sm The logarithm of ( fii

Sd

1

) -0.783 0.697

Table 2. The results of the dynamic panel data model (dependent variable: productivity growth rate)

VariablesModel 1

(full model)Model 2 Model 3

Constant-0.040**

(-2.193)

-0.025**

(-2.116)

-0.032***

(-7.246)

1 tTFP-0.463***

(-5.527)

-0.539***

(-14.545)

-0.545***

(-22.435)

dtiS 1,

0.002**

(2.061)--

0.001**

(3.348)

ftii Sm 1,

1.265**

(2.055)

0.842**

(2.060)

--

1.“***”and“**” stand for significance at the 1 percent level and 5 percent level, respectively.

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Table 3. Long-run effects of the dynamic panel data model

Denote OLSKiviet-Phillips

(1993)estimator

Pesaran andZhao (1999)

Bootstrapbias-corrected

estimator

Long-run effect

from dtiS 1, to

TFP1

11 1

0.002**

(2.011)0.007***(2.567)

0.003(1.154)

Long-run effect

from ftii Sm 1,

to TFP2

22 1

0.003

(1.002)0.052*(1.731)

0.008***(2.667)

1.“***”, “**” and “*” stand for significance at the 1 percent level, 5 percent level and 10 percent

level, respectively.

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Figure 1. A diagram of relation of industrial gravity and industrial members

Geographicscope

Geographicscope

Industrialgravity

Industrialgravity

Note: As Figure 1 shows, the dotted line periphery is the geographic boundary. The left diagram has

more clusters than the right diagram. The industrial gravity is mentioned by the main text and the

distances between it and every industrial member measures the spatial spillover effects. When the

distances are smaller, it means highly-squeezed and more spatial spillover effects like the left diagram.

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References

Abuaf, N. and Jorion, P. (1990) Purchasing power parity in the long run, The Journal

of Finance, March, 45: 157--174.

Adams, J.D. (1990) Fundamental stocks of knowledge and productivity growth,

Journal of Political Economy, 98, 673--702.

Ahn, S. and Schmidt, P. (1995) Efficient estimation of models for dynamic panel data,

Journal of Econometrics, 68, 5--27.

Alston, J.M. and Pardey, P.G. (2001) Attribution and other problems in assessing the

returns to agricultural R&D, Agricultural Economics, 25, 141--152.

Anderson, T.W. and Hsiao, C. (1982) Estimation of dynamic models with error

components, Journal of the American Statistical Association, 76, 589--606.

Audretsch, D.B. and Feldman, M.P. (1996) Knowledge spillovers and the geography

of innovation and production, American Economic Review, 86, 630--640.

Baum, C.F., Schaffer, M.E. and Stillman, S. (2002) Instrumental variables and GMM:

estimation and testing, Boston College Economics Working Paper 545.

Beise, M. and Stahl, H. (1999) Public research and industrial innovations in Germany,

Research Policy, 28, 397--422.

Bernstein, J.I. and Mohnen, P. (1998) International R&D spillovers between U.S. and

Japanese R&D intensive sectors, Journal of International Economics, 44, 315--338.

Bernstein, J.I. (1989) The structure of Canadian inter industry R&D spillovers, and

the rates of return to R&D, Journal of Industrial Economics, 37, 315--28.

Bottazzi, L. and Peri, G. (2003) Innovation and spillovers in regions: Evidence from

European patent data, European Economic Review, 47, 687-710.

Branstetter, L.G. (2001) Are knowledge spillovers international or intranational in

scope? Microeconometric evidence from the U.S. and Japan, Journal of

19

International Economics, 53, 53-79

Campi, M.T.C., Blasco, A.S., and Marsal, E.V. (2004) The location of new firms and

the life cycle of industries, Small Business Economics, 22, 265--281.

Chen, J.R. and Lu, W.C. (2003) Panel unit root tests of firm size and its growth,

Applied Economics Letters, 10, 343--345.

Christopoulos, D.K. and Tsionas, E.G. (2004) Financial development and economic

growth: evidence from panel unit root and cointegration tests, Journal of

Development Economics, 73, 55--74.

Coe, D. and Helpman, E. (1995) International R&D spillovers, European Economic

Review, 39, 859--887.

Grossman, G.M. and Helpman, E. (1991a) Innovation and growth in the global

economy, Cambridge, Mass.: MIT press.

Grossman, G.M. and Helpman, E. (1991b) Quality ladders and product cycles,

Quarterly Journal of Economics, 106, 557--86.

Gutierrez, L. and Gutierrez, M.M. (2003) International R&D spillovers and

productivity growth in the agricultural sector: A panel cointegration approach,

European Review of Agricultural Economics, 30:3, 281--303.

Hayami, Y. and Ruttan, V.W. (1985) Agricultural development, an international

perspective, Baltimore, MD: Johns Hopkins University Press.

Head, K. and Mayer, T. (2002) Illusory border effects: Distance mismeasurement

inflates estimates of home bias in trade, Working Paper 2002-01, CEPII.

Holtz-Eakin, D., Newey, W. and Rosen, H. (1988) Estimating vector autoregressions

with panel data, Econometrica, 56, 1371--1395.

Honjo, Y. (2004) Growth of new start-up firms: Evidence from the Japanese

20

manufacturing industry, Applied Economics, 36, 343--55.

Hoogstra, G.J. and Dijk, J.V. (2004) Explaining firm employment growth: Does

location matter? Small Business Economics, 22, 179--192.

Hsiao, C. and Tahmiscioglu, A.K. (1997) A panel analysis of liquidity constraints and

firm investment, Journal of the American Statistical Association, 92, 455--465.

Hurlin, C. and Venet, B. (2001) Granger causality tests in panel data models with

fixed coefficients, Mimeo, University of Paris IX.

Im, K.S., Pesaran, M.H. and Shin, Y. (1997) Testing for unit roots in heterogeneouspanels, Mimeo, Department of Applied Economics, University of Cambridge.

Inkmann, J. and Pohlmeier, W. (1995) R&D spillovers, technological distance and

innovation success, Presented at the IFS conference on “R&D, innovation and

productivity,” London.

Jaffe, A.B. (1986) Technological opportunity and spillovers of R&D: Evidence from

firms’ patents, profits, and market value, American Economic Review, 76 no.5:

984--1001.

Johnson, D. and Evenson, R.E. (1999) R&D spillovers to agriculture: Measurement

and application, Contemporary Economic Policy, 17:4, 432--456.

Judson, R.A. and Owen, A.L. (1999) Estimating dynamic panel data models: A guide

for macroeconomists, Economic Letters, 65, 9--15.

Kao, C., Chiang, M.H. and Chen, B. (1999) International R&D spillovers: An

application of estimation and inference in panel cointegration, Oxford Bulletin of

Economics and Statistics, 61, 691--709.

Keller, W. (1998) Are international spillovers trade-related? Analyzing spillovers

among randomly matched trade partners, European Economic Review, 42,

1469--1481.

Kiviet, J.F. and Phillips, G.D.A. (1993) Alternative bias approximations in regressions

21

with a lagged-dependent variable, Econometric Theory, 9, 62--80.

Kiviet, J.F. (1995) On bias, inconsistency, and efficiency of various estimators in

dynamic panel data models, Journal of Econometrics, 68, 53--78.

Levin, A. and Lin, C.F. (1992) Unit root tests in panel data: Asymptotic and

finite-sample properties, Mimeo, University of California, San Diego.

Maddala, G.S. and Wu, S. (1999) Comparative study of unit root tests with panel data

and a new simple test, Oxford Bulletin of Economics and Statistics, 61, 631--652.

Madden, G. and Savage, S.J. (2000) R&D spillovers, information technology and

telecommunications, and productivity in ASIA and the OECD, Information

Economics and Policy, 12, 367--392.

Moon, H.R. and Perron, B. (2002a) Power comparison of panel unit root tests. In:

Working Paper. University of Southern California.

Moon, H.R. and Perron, B. (2002b) Testing for a unit root in panels with dynamic

factors. In: Working Paper. University of Southern California.

O’Connell, P. (1998), The overvaluation of purchasing power parity, Journal of

International Economics, 44, 1--19.

Pedroni, P. (1995) Panel cointegration: asymptotic and finite sample properties of

pooled time series tests with an application to the PPP hypothesis. Indiana

University Working Papers in Economics, June.

Pedroni, P. (1999) Critical values for cointegration tests in heterogeneous panels with

multiple regressors, Oxford Bulletin of Economics and Statistics, November,

Special Issue, 653--669.

Pesaran, M.H. and Smith, R. (1995) Estimating long-run relationships from dynamic

heterogeneous panel, Journal of Econometrics, 68, 79--113.

22

Pesaran, M.H., Shin, Y. and Smith, R. (1999) Pooled mean group estimation of

dynamic heterogeneous panels, Journal of the American Statistical Association, 94,

621--634.

Pesaran, M.H. and Zhao, Z. (1999) Bias reduction in estimating long-run relationships

from dynamic heterogeneous panels, Chapter 12 in C. Hsiao, K. Lahiri, L.F. Lee

and M.H. Pesaran, eds., Analysis of panels and limited dependent variable models,

Cambridge University Press, Cambridge, pp.297--322.

Phillips, P.C.B. and Sul, D. (2002) Optimal testing for unit roots in panel data, mimeo,

Yale University.

Raut, L.K. (1995) R&D spillover and productivity growth: Evidence from Indian

private firms, Journal of Development Economics, 48, 1--23.

Romer, P.M. (1990) Endogenous technological change, Journal of Political Economy,

XCVIII: S71--S103.

Taylor, M.P. and Sarno, L. (1998) The behavior of real exchange rates during thepost-Bretton Woods period, Journal of International Economics, 46, 281-312.