M A N A G E R I A L AND DECISION ECONOMICS, VOL. 10, 101-105 (1989)

R&D, Concentration and Advertising: A Simultaneous Equations Model

JOHN LUNN Department of Economics, Louisiana State University, Baton Rouge, LA, USA

This study employs a simultaneous equations model to test the relationships among industrial R&D spending, market structure and advertising. The R&D data employed are the Line of Business data collected by the FTC and published at the three- and fourdigit SIC level. The results are, in general, complementary to several other recent studies that used data sets involving different degrees of aggregation as well as different measures of research activity.

This study employs a simuftaneous equations model to test the relationships between market structure, industrial R&D spending and advertising. A positive relationship between market power and R&D spend- ing is expected because firms with market power are able to appropriate the returns from innovative out- put better than those without market power. The R&D data employed in this study are the Line of Business (LB) data collected by the FTC and pub- lished at the three- and four-digit SIC level.

THE MODEL

The question addressed by much of the empirical literature concerning industrial research activity is whether interindustry differences in research activity can be explained by interindustry variations in in- dustrial concentration. A system of three equations is estimated to examine the relationships among in- dustrial research, concentration and advertising. The model is written:

R/S =ao + al (CONC) + a2 A/S + a j CASH

+ a4 K A P + a , MKT+a,SIZE + a,GRO W

+ a , P R O C E S S + a , TECH +c1 (1)

C O N C = Po + P' ( R / S ) + P,(MES) + B,(CDR)

+P,(PROCESS)+P,(GRO I+')+&, (2)

A * S = 6, + 6, (R/S) + 6, (CONC) + d3 (CASH)

+G4(CONS)+6,(PROCESS)+~, (3)

Table 1 provides a list of the variables, how they are measured and the sources of the data.

Equation (1) is the central focus of the paper and Eqns (2) and (3) are included because of possible simultaneity bias. Firms that innovate successfully tend to prosper and grow, resulting in increased mar- ket shares and measured concentration. The type of innovation is also likely to be important. Process innovation tends to alter the costs of production,

which are an important determinant of concentration. Advertising is jointly determined with R&D spending (Farber, 1981). Advertising and R&D spending are likely to be complementary activities with respect to product differentiation.'

To save space, the reader is referred to Lunn (1 986) for justification for the specification of Eqns (1) - (3). In general, the explanatory variables in Eqn (1) are in- cluded because they affect either the value of an innov- ation to the innovating firm or the ability of the firm to appropriate the returns from the innovation. PRO- CESS, for example, is expected to have a negative effect on research intensity because property rights provided by patents on new products are likely to be stronger than those for new processes (Lunn, 1986). In summarizing the empirical literature concerning in- dustrial research activity Scherer (1980) notes that the positive relationship between concentration and re- search is weakened when interindustry differences in technological opportunity (TECH) are included. He argues that technological opportunity reflects the in- fluence of technology push&exogenous changes in scientific and engineering knowledge that reduce the costs of new technology. Lunn (1985) argues that TECH may reflect differences across industries in the ability of firms to appropriate the returns from an innovation, and that in industries where patent pro- tection is not strong market power may be a substitute for legal property rights. If so, market power would not be expected to encourage R&D spending in the technically progressive industries but would encour- age greater R&D spending in the less progressive industries with attenuated property rights on new technology. Hence, Eqn (1) is estimated on the subsets of the technically progressive industries and the less progressive industries separately.

DATA

The data employed to test Eqns (1)-(3) are from the Bureau of the Census and the FTC Line of Business

0 1436570/89/020101-05$05.OO 0 1989 by John Wiley & Sons, Ltd.

102 JOHN LUNN

~ ~ ~ - ~~ ~-

Table 1. Variable Definitions (I) Endogenous variables:

RIS" Ratio of R&D spending on the product line to the total sale and transfers on the product line for the line of business sample

CONCb Four-firm concentration ratio Ratio of advertising expenditures on the product line to total sales and transfers on the product line AIS"

( 1 1 ) Exogenous variables:

GROW CASH"

SIZE"

KAP" MKTb M€Sb CDRb

TECH" CONSd

PROCESS"

Percentage change in real total sales and transfers by line of business between 1974 and 1975 Ratio of operating income plus depreciation on the product line to total sales and transfers on the product line Average size of firm measured by total assets of the line of business divided by number of LBs that reported to the FTC in the industry Capital intensity measured by ratio of total assets to total sales and transfers by LB for an industry Size of the market measured by industry value of shipments Output per firm of the top 50% of the firms in an industry divided by total industry output Ratio of average value added per worker in plants supplying the bottom 50% of industry value added to the average in plants supplying the top 50% of industry value added Dummy variable that equals 1 for the technically progressive industries and 0 otherwise Dummy variable that takes the value 1 for consumer goods industries and 0 otherwise Percentage of patents in an industry that were process innovations

a FTC Annual Line of Business Report. 1974 and 1975.

' Scherer (1 983)-industries in the chemical, electrical, and electronic product group. * Ornstein (1977). ' Supplied by William Long at the FTC

Bureau of the Census, Census of Manufacturers, 1972 and 1977.

Reports. The Bureau of Economics at the Federal Trade Commission has collected data from the largest manufacturing firms that are broken down by line of business. These are published at the industry level for 1973-6. A cross-section of four-digit SIC industries are used to estimate Eqns (1)-(3).* Some industries had to be dropped because the FTC's disclosure restrictions prevented them from providing required data in these industries and some are at the three-digit level when required for data compatability.' This left 179 in- dustries for the sample.

The theoretical section does not suggest a specific functional form for estimating Eqns (1)-(3). Since concentration is treated as an endogenous variable and equations that estimate concentration are often log-linear (see Ornstein et al., 1973), a log-linear speci- fication is used.4 All equations are over-identified by the criteria found in Fisher (1976) and are estimated by two-staged least squares (2SLS). Ordinary least squares estimates are provided for purposes of com- parison. Equation (1 ) is also estimated for the pro- gressive industries and for the less progressive in- dustries separately to see if the effects of the expla- natory variables on research intensity differ for tec- hnologically progressive industries from less progres- sive ones.

EMPIRICAL RESULTS

Table 2 presents OLS and 2SLS estimates of Eqns (1)-(3). The differences between OLS and 2SLS are not great, although advertising intensity is statistically

significant in the research intensity equations only on the 2SLS estimates. Further, research activity is sig- nificant in the OLS estimate of the concentration equation but not in the 2SLS estimate. For the entire data set, concentration has a positive effect on re- search activity. In addition, there is evidence that the type of research activity affects market concentration, for PROCESS has a positive and significant effect on concentration. Thus the evidence suggests that market concentration does encourage research activity, and market concentration increases in industries in which firms direct their research activity towards developing process innovations.

Contrary to expectations, advertising intensity has a negative coefficient. This suggests that advertising and research activity should be viewed as substitute activities rather than as complementary ones. It was argued above that advertising and research activity on new products would be complementary activities in terms of product differentiation. However, it may be that the effect of product differentiation on research intensity is being captured by the variable PROCESS rather than by A / S . The negative coefficient on PRO- C E S S indicates that industries with greater propen- sities for product innovations tend to engage in more research activity. If PROCESS is capturing the effects of product differentiation, then A/S would not necess- arily be complementary to R/S. Instead, they may be substitutes in terms of competing for dollars allocated by the firm's management.5

The cash-flow variable ( C A S H ) has a positive coef- ficient that is statistically insignificant at normal confi- dence levels, although it is significant at a 20% confi- dence level. The same is true for the case of the market size variable.

Tab

le 2

. ZS

LS E

stim

ates

of t

he S

yste

m (

?Sta

tistic

s)

Var

iabl

e

Con

stan

t 2.

40'

CO

NC

A/ S

RIS

CA

SH

KA P

MK

T

SIZ

E

GR

OW

PR

OC

ES

S

TEC

H

ME

S

CD

R

CO

NS

N

F GO

F

Tota

l dat

a se

t O

LS

- 5

.63'

(4.7

6)

0.68

0'

(13.

57)

(1.0

5)

-0.0

56

0.46

(0

.37)

0.

908'

(3

.98)

0.

1 52

(1.4

6)

-0.1

86

(1.4

9)

0.07

1 (0

.666

)

(3.9

5)

0.97

4'

(5.8

8)

-0.1

72'

179

11.4

2'

0.36

5

ZSLS

- 3

.93b

(2.2

6)

0.81

8b

(2.4

2)

(3.2

5)

-0.5

77'

0.14

2 (1

.30)

0.

81 0"

(2

.723

0.

203

(1.3

6)

(1.8

3)

(0.3

84)

-0.2

23'

(4.0

9)

1.01

" (4

.91 )

-0.3

30'

- 0.

049

179

8.62

' 0.

222

RIS

Unp

rogr

essi

ves

Prog

ress

ives

C

ON

C

AIS

O

LS

- 5.

01

(3.6

7)

0.65

7'

(2.9

1)

(0.7

3)

-0.0

47

0.07

5 (0

.491

) 0.

741"

(2

.89)

0.

086

(0.6

61 )

(0.9

32)

0.09

5 (0

.838

)

(3.4

7)

-0.1

43

-0.1

72"

141

4.53

0.

21 0

2SLS

O

LS

-3.6

8

(1.9

9)

(2.6

3)

0.97

3'

0.71

7

(2.6

1)

(1.7

6)

-0.5

70'

-0.1

33

(2.7

0)

(1.3

5)

0.1 6

0 -0

.046

(1

.1 2

) (0

.231

) O

.69l

b 2.

03'

(2.1

6)

(3.4

6)

0.21

2

0.37

gb

(1.2

3)

(2.2

9)

(2.0

0)

(2.1

9)

(0.1

67)

(0.8

80)

(3.5

9)

(2.5

2)

-0.4

1 2b

-0

.446

b

-0.0

22

-0.6

65

-0.2

1 2"

-0

.270

b

141

38

4.1 4

" 2.

41

0.1 0

8 0.

355

ZSLS

- 8.0

gb

(0.0

24)

(0.5

92)

(1.7

4)

-0.6

57

- 0.

848'

0.06

5 (0

.352

) 1.

16

(1.0

1 )

0.01

6

(0.0

43)

0.1

88

(036

6)

-0.8

47

(0.7

91 )

(2.0

2)

-0.4

1 7*

38

1.08

0.

090

OLS

-0.1

70

(63.

9)

0.04

7b

(1.9

9)

-0.0

32

(0.8

3)

0.07

8'

(5.1

0)

0.32

C

(1 5.

4)

-0.1

07

(1.2

9)

190

60.6

7'

0.61

5

2SLS

3.24

'

(59.

37)

0.04

4 (0

.924

)

-0.0

29

(0.7

26)

0.08

2'

(4.7

6)

0.32

5 (3

2.84

) -0

.118

(1

.37)

179

53.8

0'

0.61

7

OLS

3.26

'

(3.0

1 )

(7.2

5)

-0.1

15

0.07

8 (1

.OO

) 0.

697"

(5

.55)

-0.1

53'

(3.0

7)

1.83

' (9

.40)

1 9

0 32

.9'

0.47

1

2515

- 1

.95'

(4.4

0)

-0.1

98

(1.2

8)

0.009

(0.0

73)

0.1

45b

(1.9

8)

-0.1

34"

(3

.22)

0.90

9'

(5.8

1)

179

1 1.3

3' 0.

241

E p d Z 4 U

-

~-

Sig

nific

ance

leve

ls a

re a

10%

. 5%

. " 1

%.

The

good

ness

of

fit (

GO

F) i

s th

e sq

uare

of

the

corr

elat

ion

coef

ficie

nt b

etw

een

actu

al a

nd f

itted

val

ues.

See

Hae

ssel

(197

8).

104 JOHN LUNN

As expected, the dummy variable reflecting the tech- nological progressivity of an industry has a positive and significant coefficient. Given the method em- ployed to identify the technologically progressive in- dustries, this is not surprising. Additional information concerning the interrelationships of research intensity, technological opportunity and the other explanatory variables is provided in columns (3)-(6) of Table 1. The data set was split on the basis of the technological opportunity variable and the equation for research intensity was re-estimated on each subsample. For the technologically unprogressive industries the coef- ficients of the variables are similar to those of the full sample. However, for the technologically progressive subsample the pattern of coefficients is very different. There are sign reversals in the case of CONC, M K T , and SIZE, although none of these are statistically different from zero. Only A / S and PROCESS retain statistical significance and the same signs as the full sample.

The PROCESS variable is also an important explanatory variable in the advertising intensity and the concentration equations. Advertising intensity in- creases as the propensity for product innovation in- creases, which is consistent with the view that product innovation is related to product differentiation. Simi- larly, firms in industries characterized as producing consumer goods also advertise more than producer- goods industries. In addition, the results indicate that cash flow has a positive effect on advertising intensity.

In summary, the empirical results suggest that mar- ket concentration does encourage innovative activity, especially for industries labelled as technologically unprogressive. We also found that the type of research carried on has a greater effect on concentration and advertising intensity than the amount of research dol-

complementary to those reported by Lunn and Martin (1986) using the individual LB data set, Connolly and Hirschey (1984) and Lunn (1987) using firm data and Lunn (1986) using patent data at the industry level.

CONCLUSIONS

This paper has related the incentive to engage in research activity to the appropriability of innovative output. Factors that enhance the appropriability of such output encourage more research activity. Two factors were determined as important with regard to the appropriability of innovative output-the prop- erty rights on innovative output and market power. The empirical results are consistent with the hypoth- esis that property rights on innovative output encour- age research activity, but that when these rights are not well defined, market power can act as a substitute for them.

Thus both the theoretical and empirical results suggest that the Schumpeterian paradigm is valid and that the encouragement of research activity may re- quire the acceptance of some market power. This market power may be due to the granting of monopol- ies by the establishment of legally enforceable prop- erty rights on innovative output or it may be a sub- stitute for legal property rights. If society wishes to promote research activity and growth in productivity it may have to accept the existence of some market power.

Acknowledgements lars spent -by firms. Finally, the results are consistent with the view that market power and property rights

innovative output. The results here are, in general,

The author wishes to thank James A. Dunlevy, Stephen Farber, Albert N. Link, F. M. Scherer and an anonymous referee for helpful

research assistance. The usual caveats apply. are in firms to their comments on earlier drafts, and Kamfong M. Cheung for her

NOTES

1. Some studies have included profits as an endogenous vari- able in a system of equations (for example, Connolly and Hirschey, 1984). while others have not (for example, Levin and Reiss, 1984; Farber, 1981; Lunn, 1986). Smyth er a/. (1972) argue that cash flow is a better measure of internal financing, so cash is used in this study instead of profits. Obviously, this implies that a bias may exist if profits should, in fact, be included as an endogenous variable.

2. The endogenous variables are derived from 1975 data while predetermined variables from the LB data set are for 1974 and those from the Census of Manufacturing are from 1972.

3. The LB data set is not a comprehensive data set of all US manufacturing industries. This raises the question of whether

the results here apply to the universe of manufacturing firms. The likelihood of bias is not large because the majority of R&D spending is performed by the largest firms. Scherer estimated that the LB sample companies did 73% of all NSF- reported company-financed R&D.

4. Some regressions using a linear model were run and the results are not sensitive to the specific functional form. GROW is not in logs due to the existence of some negative growth rates.

5. Connolly and Hirschey (1984) also reported a negative coefficient on advertising in the R&D equation, and suggest that advertising and research compete for funds (see also Connolly et a/., 1986).

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R&D, CONCENTRATION AND ADVERTISING 105

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