direct and indirect effects of industrial research and ... · pdf file361 direct and indirect...
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This PDF is a selection from an out-of-print volume from the National Bureau of Economic Research
Volume Title: New Developments in Productivity Measurement
Volume Author/Editor: John W. Kendrick and Beatrice N. Vaccara, eds.
Volume Publisher: University of Chicago Press
Volume ISBN: 0-226-43080-4
Volume URL: http://www.nber.org/books/kend80-1
Publication Date: 1980
Chapter Title: Direct and Indirect Effects of Industrial Research and Development on the Productivity Growth of Industries
Chapter Author: Nestor Terleckyj
Chapter URL: http://www.nber.org/chapters/c3917
Chapter pages in book: (p. 357 - 386)
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III. The Effects of Research andDevelopment, Energy, andEnvironment on ProductivityGrowth
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6 Direct and Indirect Effectsof Industrial Research andDevelopment on theProductivity Growthof IndustriesNestor E. Terleckyj
6.1 Introduction
In an earlier study by the author, rather high estimates were obtainedof the effeëts of industrial R and D on the rate of productivity growthof industries (Terleckyj 1974). Two kinds of such effects were identifiedusing the total factor productivity data compiled by John W. Kendrick(1973): direct effects in the industries in which the R and D is con-ducted, and indirect effects in the industries purchasing intermediate andcapital inputs from the industries conducting the R and D. Indications ofpresence of these effects were obtained for the privately financed R andD, but not for government financed R and D, and for the manufacturingindustries but not for the nonmanufacturing industries. Because humancapital was not included in earlier research, the resulting estimates ofproductivity returns to R and D might be inflated by the possible effectsof increases in the employment of human capital if such increases werecorrelated with increased use of R and D inputs.
In this paper these results are tested for independence from possibleeffects of increased use of human capital and other input characteristics,first by introduction of an explicit variable measuring increases in theuse of human capital by industry and then by repeating estimation ofthe R and D effects using the measures of total factor productivity
Nestor E. Terleckyj is with the National Planning Association.The author greatly appreciates the helpful suggestions and comments received
from Milton Moss, Neil McMullen, and B. I. Stone, and the help in obtainingdata given by Dale W. Jorgenson, John W. Kendrick, and Elizabeth Wehle. Thepresent revised version of this paper incorporates changes made in response tothe original comments by Steven Globerman, who discussed this paper at the con-ference. The research for this paper was supported by grant GSOC74—17574 fromthe National Science Foundation.
359
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360 Nestor E. Terleckyj
growth prepared by Gollop and Jorgenson (1979) which are alreadyadjusted for the use of human capital, as well as for other characteristicsof input.
6.2 The Analytical Model
The theoretical model underlying the present analysis treats the re-search capital as a third input in addition to the labor and capital inputs.This model has been formulated by Griliches (1973). Its adaptation tothe present analysis has been discussed in earlier work by the author(Terleckyj 1974, pp. 3—8, 19). Hence, only a very brief discussion ofthe model will be provided here.
In this model the production function for time, t, is represented by:(1)where Q, L, K, and R are the output, and the inputs of labor, tangiblecapital, and R and D capital, respectively; p, and a are therespective elasticity parameters for the three inputs; A is a constantspecifying the level of output in the base year; and the parameter Arepresents the disembodied growth of productivity.
The productivity ratio at time t can then be expressed as a productof a component cumulative effects of disembodied techni-cal change and the stock of R and D capital raised to an exponentrepresenting the elasticity of output with respect to research capital:
(2) = = AeXt
Differentiating this equation with respect to time, one can show that therate of growth in productivity is the sum of the autonomous disembodiedcomponent and a component representing the product of the relativegrowth of research capital and the elasticity of output with respect tothat capital:
(3) p4=A+c4.Because the rate of growth of research capital usually cannot be
directly observed, an alternative formulation of equation (3) may beused, provided one assumes that the gross investment in R and D,i.e., the expenditure for R and D in the base year, also representsthe net R and D investment (i.e., that there is no depreciation of Rand D or that it can be ignored). One can then express the second termon the right-hand side of equation (3) by a product of the marginalproduct of capital, v, and the ratio of R and D investment to output, 1:(4) p=A+vi,(because v = dQ/dR, I = R/Q, and a = dQ/dR R/Q, aR/R = vi).
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361 Direct and Indirect Effects of Industrial Research and Development
The present research results are based on the statistical estimates ofan expanded version of equation (4). The equation is expanded byintroducing successively different components offirst, by separating R and D conducted in industry according to itssource of financing—into the cost of research conducted with privatefunds of the industry and research conducted with the federal govern-ment funds—and then by estimating the R and D embodied in purchasesfrom other industries separately for the privately financed and the gov-ernment-financed R and D. The equation is also expanded by the intro-duction of variables other than R and D which have been found to becorrelated with productivity growth and the omission of which mightdistort estimates of the net effect of R and D on productivity. The gen-eral form of the equations estimated in this study can be stated asfollows:(5) p = a0 + aIX,. + b,l,.
Here a0 is the constant of regression representing (when normalized)the remaining unexplained residual growth. The as's are the regressioncoefficients of the variables which represent factors other than R andD, and the b,'s are the coefficients of the research intensity ratios, I,'s.
The coefficients b, are the estimates of the marginal products of therespective types of R and D capital. They also represent the produc-tivity rates of return on the different types of R and D expenditures.Because the costs of labor and of capital used in R and D are alreadyincluded in the input index used to estimate productivity, the regressioncoefficients b, measure the "excess" or additional rates of return toR and D, i.e., net of its cost. Thus, the statistical estimates of the pro-ductivity rates of return approximate the concept of "internal rates ofreturn." The accuracy of this statistical approximation actually achieveddepends among other things on the extent to which the cost and theeffects of same R and D projects are included in the data for the periodused. The period 1948—66 used in the present analysis covers 18 years.It should be sufficient to include a predominance of complete lifetimesof R and D projects and their productivity effects. The time series dataon costs and on private and social returns to innovations developed byMansfield and his associates in their case studies of specific innovationssupport this assumption (Mansfield Ct al. 1975).
6.3 Previous Estimates
In the following discussion, only the main results and the basic dataof previous research are summarized. The methods of estimation ofdata, various qualifications, and details regarding specific assumptionswere discussed in the publication in which these results were first given(Terleckyj 1974).
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362 Nestor E. Terleckyj
The basic data are shown in tables 6.1 and 6.2. Kendrick's (1973,pp. 78, 79) data for the rates of change in total factor productivity forthe period 1948—66 which were used as the dependent variable, areshown in table 6.1 together with the author's estimates of the four Rand D investment intensity ratios for the base year 1958. Estimates ofR and D embodied in purchased goods were derived by summing theR and D of the conducting industries, redistributed in proportion totheir sales as given in the 1958 Department of Commerce input-outputmatrices for intermediate and capital flows among industries. Table 6.2contains the data for the three non—R and D variables introduced tohold productivity effects of R and D constant. One of these variablesis the percent of sales to the private sector, which tends to have positiveeffects on productivity. Another is the unionization rate of the workforce of the industry. It was found by Kendrick (1973) to have signifi-cant negative correlation with productivity growth. It is also includedhere for a different year (1953 rather than 1958, because nonmanufac-turing data could not be obtained for 1958). The third variable is anindex of cyclical instability of output of the industry which in anotherstudy by the present author was found to have a negative effect on pro-ductivity (Terleckyj 1960).
The highlights of the results obtained are shown in table 6.3, whichcontains a series of estimates of equation (5) for twenty manufacturingindustries. The table follows the sequence of analysis of the R and Dvariables. First, the total ratio for all R and D conducted in the industrywas introduced. Its coefficient was not statistically significant at the 5%level. After dividing the total R and D into privately financed and gov-ernment-financed R and D, the estimated rate of productivity return toprivate R and D was highly significant and amounted to 37%, whilethe return estimated for the government-financed R and D was notsignificant (and numerically near zero). Based on this result, the ratioof government-financed R and D was omitted from further analyses,and the total R and D embodied in purchases from other industries wasintroduced. The coefficient for industry's own R and D continued to besignificant, but its magnitude dropped from 37 to 28%. The estimate 2of return to total imputed R and D was 45% and significant at the 5%level. External R and D was then divided into government-financedand privately financed components. This division of the embodied R andD resulted in an overall improvement in the fit of the equation andincreases in the significance of almost all coefficients but one (salesnot to government). The coefficient obtained for privately financedR and D embodied in purchased inputs is equivalent to a 78% rate ofproductivity return, while the coefficient estimated for the government-financed purchased R and D is near zero, negative, and not significant.
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pT
able
6.1
Rat
esof
Gro
wth
in T
otal
Fac
tor P
rodu
ctiv
ity, 1
948-
66, a
nd th
e 19
58R
and
D V
alue
Add
ed R
atio
s for
Priv
atel
y Fi
-na
nced
R a
nd D
and
for G
over
nmen
t Fin
ance
d R
and
D C
ondu
cted
in th
e In
dusf
ry a
nd fo
r Priv
atel
y Fi
nanc
ed a
ndG
over
nmen
t Fin
ance
d R
and
D E
mbo
died
in P
urch
ased
Goo
ds, T
hirty
-thre
e In
dust
ries i
n th
e Pr
ivat
e D
omes
tic E
con-
omy
(R a
nd D
am
ount
s as p
erce
nt o
f val
ue a
dded
)
R a
nd D
Inte
nsity
Rat
ios,
1958
R a
ndD
Con
duct
ed in
R a
nd D
Em
bodi
ed in
Prod
uctiv
ityG
row
th R
ate,
Ann
ual
Indu
stry
Purc
hase
d G
oods
Priv
atel
yG
over
nmen
tPr
ivat
ely
Gov
ernm
ent
Ave
rage
Fina
nced
Fina
nced
Fina
nced
Fina
nced
Indu
stry
1948
—66
R a
nd D
R a
nd D
R a
nd D
R a
nd D
(arr
ange
d in
ord
er o
f des
cend
ing
prod
uctiv
ity g
row
th)
(per
cent
)(p
erce
nt)
(per
cent
)(p
erce
nt)
(per
cent
).
4Air
trans
porta
tion
*Coa
l min
ing
*Raj
I roa
dsC
hem
ical
s*E
lect
ric a
nd g
as u
tiliti
esTe
xtile
sR
ubbe
r pro
duct
s*C
omm
unic
atio
n ut
ilitie
sEl
ectri
cal m
achi
nery
Lum
ber p
rodu
cts
*Far
min
g*O
il an
d ga
s ext
ract
ion
Tran
spor
tatio
n eq
uipm
ent a
nd o
rdna
nce
Food
sPe
trole
um re
finin
gFu
rnitu
reIn
stru
men
ts
8.0
5.2
5.2
4.9
4.9
4.0
3.9
3.8
3.7
3.5
3.3
3.2
3.2
3.0
3.0
2.9
2.9
0 0 06.
8 .1 .4 1.1 .3 5.5 .2 .5 .2 6.9 .5
4.3 .2 4.2
0 0 01.
8 0 0 .4 .112
.0 0 0 018
.6 0 .2 03.
6
1.6 .3 .2 1.8 .3 2.1
2.3 .6 1.9 .3 .6 .1 3.8 .6 .9 .4 2.1
1.5 .1 .1 .8 .3 .1 .3 1.1
3.3 .1 .1 .1 3.8 .1 .3 .1 2.2
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R a
nd D
Inte
nsity
Rat
ios,
1958
R a
nd D
Con
duct
ed in
R a
nd D
Em
bodi
ed in
Prod
uctiv
ityG
row
th R
ate,
Ann
ual
Indu
stry
Purc
hase
d G
oods
Priv
atel
yG
over
nmen
tPr
ivat
ely
Gov
ernm
ent
Ave
rage
Fina
nced
Fina
nced
Fina
nced
Fina
nced
Indu
stry
1948
—66
R a
nd D
R a
nd D
R a
nd D
R a
nd D
(arr
ange
d in
ord
er o
f des
cend
ing
prod
uctiv
itygr
owth
)(p
erce
nt)
(per
cent
)(p
erce
nt)
(per
cent
)(p
erce
nt)
Prin
ting
and
publ
ishi
ng2.
70
0.2
.2M
achi
nery
, exc
ludi
ng e
lect
rical
2.6
3.7
2.5
1.1
.9*N
onm
etal
min
ing
2.6
00
.4.1
Pape
r2.
5.9
0.5
.1tra
de2.
50
0.1
.1*M
etal
min
ing
2.4
00
.4.2
*Ret
ail t
rade
2.4
00
.1.1
Ston
e, c
lay,
and
gla
ss p
rodu
cts
2.4
.90
.5.1
Bev
erag
es2.
20
0.6
.1
App
arel
1.9
.10
.30
Fabr
icat
ed m
etal
pro
duct
s1.
9.7
.5.7
.3Le
athe
r Pro
duct
s1.
7.2
0.2
.1
Prim
ary
met
al p
rodu
cts
1.6
.9.1
.6.2
*Con
tract
con
stru
ctio
n1.
50
0.6
.3To
bacc
o pr
oduc
ts1.
10
0.3
0*W
ater
tran
spor
tatio
n0.
50
0.4
0
*Non
man
ufac
turin
g in
dust
ries.
SOU
RC
ES: P
rodu
ctiv
ity G
row
th R
ate
data
repr
oduc
ed b
y pe
rmis
sion
from
the
auth
or: K
endr
ick
(197
3) ta
ble
5—1,
pp.
78—
79; R
and
Din
tens
ity ra
tios f
rom
Ter
leck
yj (1
974)
, pp.
13,
15.
Not
e re
visi
on o
f dat
a fo
r bev
erag
es in
dust
ry R
& D
con
duct
ed in
indu
stry
ratio
s.
jT
able
6.2
-V
aria
btes
Oth
er th
an R
and
I) T
este
d fo
r Pos
sibl
e Ef
fect
s on
Prod
uctiv
ity
r-T
able
6.1
(con
t.)
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——
— /
fiji.
&%
4*.
.SJ.
tin
tens
ityra
tios f
rom
Ter
leck
yj (1
974)
, pp.
13,
15. N
ote
revi
sion
of d
ata
for b
ever
ages
indu
stry
R &
D c
ondu
cted
in in
dust
ry ra
tios.
Tabl
e 6.
2V
aria
bles
Oth
er th
an R
and
D T
este
d fo
r Pos
sibl
e Ef
fect
s on
Prod
uctiv
ity G
row
th,
Thirt
y-th
ree
Indu
strie
s in
the
Priv
age
Dom
estic
Eco
nom
y
Uni
on M
embe
rsas
Per
cent
of A
llSa
les O
ther
than
Wor
kers
inIn
dex
of C
yclic
alto
Gov
ernm
ent
Prod
ucin
gIn
stab
ility
of
as P
erce
nt o
fEs
tabl
ishm
ents
Indu
stry
Out
put
Tota
l Sal
es 1
958
1953
1948
—66
Indu
stry
PVTS
UN
CY
C(a
rran
ged
in o
rder
of d
esce
ndin
g pr
oduc
tivity
gro
wth
)(p
erce
nt)
(per
cent
)(p
erce
nt)
trans
porta
tion
9551
0*C
oal m
inin
g98
8416
.1*R
ailro
ads
9595
10.4
Che
mic
als
955.
7*E
lect
ric a
nd g
as u
tiliti
es96
410
Text
iles
100
307.
4R
ubbe
r pro
duct
s97
5495
*Com
mun
icat
ion
utili
ties
9752
0El
ectri
cal m
achi
nery
9056
11.0
Lum
ber p
rodu
cts
100
218.
7
tFar
min
g98
13.
2SO
il an
d ga
s ext
ract
ion
100
145.
4Tr
ansp
orta
tion
equi
pmen
t and
ord
nanc
e77
6511
.0Fo
ods
100
450
Petro
leum
refin
ing
9467
2.5
Furn
iture
9529
8.7
Inst
rum
ents
8550
9.2
Prin
ting
and
publ
ishi
ng98
383.
2M
achi
nery
, exc
ludi
ng e
lect
rical
9545
13.1
*Non
met
al m
inin
g99
324.
6
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P
Tabl
e 6.
2 (c
ont.)
Uni
on M
embe
rsas
Per
cent
of A
llSa
les O
ther
than
Wor
kers
inIn
dex
of C
yclic
alto
Gov
ernm
ent
Prod
ucin
gIn
stab
ility
of
as P
erce
nt o
fEs
tabl
ishm
ents
Indu
stry
Out
put
Tota
l Sal
es 1
958
1953
1948
—66
Indu
stry
PVTS
UN
CY
C(a
rran
ged
in o
rder
s of d
esce
ndin
g pr
oduc
tivity
gro
wth
)(p
erce
nt)
(per
cent
)(p
erce
nt)
Pape
r99
456.
7*W
hole
sale
trad
e99
44.
1*M
etal
min
ing
9270
10.6
*Ret
ajj t
rade
9914
3.6
Ston
e, c
lay
and
glas
s pro
duct
s10
045
7.4
Bev
erag
es10
044
3.2
App
arel
9953
4.1
Fabr
icat
ed m
etal
pro
duct
s99
.45
8.4
Leat
her p
rodu
cts
9939
5.0
Prim
ary
met
al p
rodu
cts
9855
lS.2
*Con
tract
con
stru
ctio
n71
724.
1To
bacc
o pr
oduc
ts10
058
2.0
*Wat
er tr
ansp
orta
tion
9576
7.0
indu
strie
s.SO
UR
CES
: Dat
a on
indu
stry
sale
s to
gove
rnm
ent i
s fro
m U
.S. D
epar
tmen
t of C
omm
erce
, Off
ice
of B
usin
ess E
cono
mic
s, "T
he T
rans
actio
nsTa
ble
of th
e 19
58 In
put-O
utpu
t Stu
dy a
nd R
evis
ed D
irect
and
Tot
al R
equi
rem
ents
Dat
a,"
Surv
ey o
f Cur
rent
Bus
ines
s, 9
(196
5): 3
4—39
;pe
rcen
t uni
oniz
atio
n is
from
H.G
. Lew
is, U
nion
izat
ion
and
Wag
es in
the
Uni
ted
Stat
es (C
hica
go: U
nive
rsity
of C
hica
go P
ress
, 196
3), p
p.28
9—29
0; c
yclic
al in
stab
ility
inde
x of
indu
stry
out
put i
s bas
ed o
n an
nual
out
put d
ata
from
Ken
dric
k (1
973)
cal
cula
ted
in a
man
ner d
escr
ibed
in T
erle
ckyj
(196
0). S
ee n
ote
6, p
. 16,
in T
erle
ckyj
(197
4).
4——
—
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ItioL
eUIII
Iy II
IUC
A U
t UlU
US
ttjf
15 U
4SC
U U
fl dt
IlIU
iiua
ia It
OH
!ca
iiuia
teu
ina
man
ner
Uca
s.lo
UeU
inTe
rleck
yj (1
960)
. See
not
e 6,
p. 1
6, in
Ter
leck
yj (1
974)
.
Tabl
e 6.
3R
egre
ssio
n C
oeff
icie
nts O
btai
ned
in th
e A
naly
sis o
f the
Ann
ual R
ates
of C
hang
e in
Tot
al F
acto
r Pro
duct
ivity
for t
hePe
riod
1948
—66
, Tw
enty
Man
ufac
turin
g In
dust
ries (
t—ra
tios i
n pa
rent
hese
s)
Cos
t of R
and
D/V
alue
-add
ed R
atio
s for
Diff
eren
t R a
nd D
Com
pone
nts
Oth
er V
aria
bles
Ran
d D
Con
duct
edR
and
D E
mbo
died
in P
ur-
in In
dust
rych
ases
from
Oth
er In
dust
ries
Con
stan
t of
Reg
ress
ion
Tota
l,19
58
Priv
atel
yG
over
nmen
tPr
ivat
ely
Gov
ernm
ent
Fina
nced
,Fi
nanc
ed,
Tota
l,Fi
nanc
ed,
Fina
nced
,19
5819
5819
5819
5819
58
Perc
ent o
f Sal
esN
ot to
Gov
ernm
ent,
1958
Uni
on M
embe
rs a
sPe
rcen
t of W
orke
rsin
Pro
duci
ng E
stab
-lis
hmen
ts, 1
953
Ann
ual R
ate
ofC
yclic
al C
hang
ein
Out
put,
1948
—66
R2
Cor
-re
cted
a0IR
DI
IRD
NIR
DG
PRD
IPR
DN
PRD
GPV
TSU
NC
YC
2.29
(0.3
1)1.
62(0
.25)
0.12
(1.7
6)0.
370.
01(3
.31)
(0.1
2)
0.02
(0.3
3)0.
03(0
.46)
—0.
04(2
.38)
—0.
05(2
.94)
—0.
03(0
.56)
—0.
03(0
.56)
0.39
0.44
—7.
83(1
.36)
0.28
0.45
(2.8
4)(2
.49)
0.12
(2.2
0)—
0.05
(3.5
4)—
0.05
(1.2
5)0.
62
—3.
72(0
.71)
0.29
0.78
—0.
09(3
.43)
(3.7
9)(0
.34)
0.08
(1.5
4)—
0.05
(4.0
3)—
0.04
(1.3
0)0.
72
Sous
ca: T
erle
ckyj
(197
4), p
p. 2
0, 2
2, 2
6, 2
9.
—-i
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368 Nestor E. Terleckyj
The effects estimated for the three non—R and D variables remainedrather stable, in the course of these substitutions of the R and D vari-ables.
These results for the manufacturing industries constituted the mainfindings of the earlier study. The results for nonmanufacturing (notreproduced here) were rather erratic. No indication of positive returnsto R and D conducted in the industry was obtained. But, for this groupof industries, it is not surprising. It may simply reflect the fact that mostnonmanufacturing industries conduct little or no R and D. (Also, theR and D data for the nonmanufacturing industries were derived fromthe NSF data by a series of additional assumptions.) On the other hand,a statistically significant coefficient was obtained for indirect returns,suggesting a very high rate of productivity return of 187%. Dividingthe indirect R and D by sources of financing gave large and positivecoefficients for both components which, however, were not statisticallysignificant.
The overall fit of the equation for all industries combined was con-sistently lower than for either manufacturing or nonmanufacturing in-dustries alone. Among the coefficients for the R and D intensity ratios,only the one for the total R and D cost embodied in purchases wasstatistically significant.
6.4 Productivity Returns to R and D with Considerationof Human Capital
Human capital is not included in the measures of input used in esti-mating productivity growth. The underlying indexes of labor input usedin constructing the indexes of total factor productivity from whichthe growth data are derived are based on the number of man-hours.Consequently, a part of growth in productivity may represent produc-tivity returns to a growing stock of human capital. Moreover, if in-creases in the use of human capital are correlated with increased useof R and D capital, the regression coefficients intended to measure theproductivity return to R and D may be biased upward to the extent thatthey (also) reflect returns to human capital. Use of human capital maybe correlated either with own R and D, if use of human capital inproduction is complementary with the conduct of R and D, or withpurchased R and D, if human capital is complementary with the useof R and D—intensive, "high-technology" inputs. Thus, the regressioncoefficients for both direct and indirect return to R and D are subjectto potential bias from this source.
In testing the hypothesis that the previously obtained statistical esti- tmates of returns to R and D do not include the returns to human capitalas well, the estimating equations are revised to include human capital
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369 Direct and Indirect Effects of Industrial Research and Development
investment intensity as an additional variable. This variable is formu-lated in the same manner as the R and D investment intensity variable.After the introduction of human capital, equations (2), (3), and (4)discussed earlier become equations (6), (7), and (8):(6) Pt AeAtRatHYt,
J;T
(7)
(8)
Here y is the elasticity parameter, analogous to a, and r in equation(8) is the productivity rate of return to human capital, analogous to vfor research capital. Both parameters are estimated as regression coeffi-cients of the respective net investment intensity ratios in the estimatingequation for productivity growth. However, while the basic cost ofRand D activities conducted in an industry is included in the labor andcapital input indexes (except for the effect of differences in labor costper man-hour), the cost of human capital is not included in the inputdata.
No direct measures of human capital stock or investment exist thatcould be readily applied in estimating its productivity effects. Humancapital has to be measured indirectly, by schooling or by an indicatorof its market value. In this paper, the indicator of human capital is in-vestment intensity. It is based on the increases in real wages per man-hour worked.
This indicator of growth of human capital is based on its evidentmarket value. The advantage of basing the measure of human capitalon wages rather than on schooling is that such a measure includes bothschooling and experience components of earning power (Mincer 1974),which presumably reflects productivity and at the same time excludesconsumption components of schooling and the variability of the learn-ing content of years of schooling over time. However, there are alsodisadvantages in using the relative real wage growth as a measure ofhuman capital investment. One is that other factors unrelated to workerproductivity affect this growth to an unknown extent. Such factors aswage bargaining or legislation have autonomous effects on wages.
Also, because, statistically, the growth in labor compensation is amajor component of growth in output and output per man-hour is bydefinition a large part of the total factor productivity growth, the humancapital investment rate estimated from growth in real wages is subjectto some possibility of the simultaneous equation bias. However, thisbias would not arise if the competitive working of the labor marketequalized the earnings within occupations rapidly relative to the length
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370 Nestor E. Terleckyj
of time over which the productivity growth is measured. Then theobserved long-term increases in wages in the individual industries wouldbe independent of the increases in productivity in the same industriesbecause the period of observation would be sufficiently long for thecorrelation to reflect only the effects of increases in human capital onproductivity.
The best approach to resolve the uncertainties resulting from thenature of the indicator used for human capital is to test the resultsobtained by alternative indicators. Use of alternative indicators was notpossible within the scope of this paper because of the lack of the avail-able data in comparable industry detail or for the period studied. Itshould be possible in future research to develop or adapt indicators ofaverage education and of the skill mix of labor input in individualindustries. Here, a more general further test of independence of theestimated effects of R and D from previously unmeasured inputs isundertaken by estimating the effects of industrial R and D on the totalfactor productivity growth measured after an adjustment for humancapital and other inputs.
The real wage growth was calculated from unpublished NBER dataused in Kendrick's study. The original data were in the form of periodaverages of actual hourly earnings for the period 1948—5 3 and againfor the period 1960—66. These averages were converted to 1958 dollarsby the Consumer Price Index. The difference between the two periodaverages was divided by 12.5, the number of years between the mid-points of the two periods. The result is shown in the first column intable 6.4. Equation (8) requires the net rate of investment in humancapital in the aggregate in the industry rather than per hour. Therefore,the amount per hour in the first column is multiplied by the man-hoursworked in the industry in the base year, 1958, used in this study. Theresult, giving the investment in human capital in millions of 1958 dol-lars, is shown in the second column. This result is then divided by the1958 value added by industry in order to obtain the desired variable,H/Q, i.e., the human capital investment intensity ratio which is shownin percentages in the third column.
It may be noted that, while, theoretically, human capital investmentintensity ratios are commensurate with the R and D investment intensityratios used in the regression analysis, statistically their estimates aremuch stronger. The human capital data are actually based on changefor the entire period rather than on the experience in the base year.Also, they are based on net investment in human capital rather than onthe gross investment as was the R and D ratio.
The results of introducing human capital investment into the estimat-ing equation for productivity growth for the twenty manufacturing in-dustries are given in table 6.5. The estimated effect of the human capital
I
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Tabl
e 6.
4Es
timat
es o
f the
Net
Inve
stm
ent R
ate
in H
uman
Cap
ital,
This
ty-th
ree
Man
ufac
turin
g an
d N
onm
anuf
actu
ring
Indu
strie
s,19
58 (d
ofla
r am
ount
s in
1958
doH
ars)
Ave
rage
Ann
ual
Indu
stry
Inve
stm
ent
Incr
ease
in R
eal
Estim
ated
Indu
stry
as P
erce
nt o
fW
age
per H
our
Inve
stm
enta
1958
Val
ue A
dded
,In
dust
ry(li
sted
in o
rder
of d
esce
ndin
g ra
te o
f pro
duct
ivity
gro
wth
)W
orke
d, 1
948—
66(1
)(m
illio
ns)
(2)
1958
(3)
*Air
trans
porta
tion
$.10
15$
36.5
2.4%
*Coa
l min
ing
.071
127
.61.
6*R
ajlro
ads
.111
221
6.4
2.6
Che
mic
als
.108
017
2.7
1.9
*Ele
ctric
and
gas
util
ities
.095
212
7.4
1.2
Text
iles
.034
060
.91.
5R
ubbe
r pro
duct
s.0
678
46.2
1.6
*Com
mun
jcat
ions
.081
714
1.2
1.6
Elec
trica
l mac
hine
ry.0
751
182.
22.
0Lu
mbe
r pro
duct
s.0
503
67.9
2.1
*Far
mjn
g.0
166
208.
81.
0SO
il an
d ga
s ext
ract
ion
.059
144
.5.5
Tran
spor
tatio
n eq
uipm
ent a
nd o
rdna
nce
.fl71
414.
42.
6Fo
ods
.074
323
5.2
1.9
Petro
leum
refin
ing
.143
063
.52.
0
Furn
iture
.047
736
.21.
9In
stru
men
ts.0
9 17
59.4
2.3
Prin
ting
and
publ
ishi
ng.0
433
77.1
1.3
Mac
hine
ry, e
xclu
ding
ele
ctric
al.0
847
232.
62.
15N
onm
etal
min
ing
.069
519
.31.
9
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Tabl
e 6.
4 (c
ont.)
Ave
rage
Ann
ual
Indu
stry
Inve
stm
ent
Incr
ease
in R
eal
Estim
ated
Indu
stry
as P
erce
nt o
fW
age
per H
our
Inve
smen
ta19
58V
alue
Add
ed,
Indu
stry
Wor
ked,
194
8—66
(mill
ions
)19
58(a
rran
ged
in o
rder
of d
esce
ndin
g pr
oduc
tivity
grow
th)
(1)
(2)
(3)
Pape
r.0
769
89.5
1.9
*Who
lesa
le tr
ade
.070
646
8.7
1.6
*Met
al m
inin
g.0
800
15.3
1.4
trade
.041
589
2.4
2.0
Ston
e, c
lay,
and
gla
ss p
rodu
cts
.08
1293
.42.
0
Bev
erag
es.0
569
23.8
1.0
App
arel
.028
460
.01.
3Fa
bric
ated
met
al p
rodu
cts
.073
615
9.6
2.0
Leat
her p
rodu
cts
.036
924
.11.
6
Prim
ary
met
al p
rodu
cts
.114
225
0.7
2.3
cons
truct
ion
.085
860
7.6
3.0
Toba
cco
prod
ucts
.090
716
.2.6
*Wat
er tr
ansp
orta
tion
.045
543
.02.
9
*Non
man
ufac
turin
g in
dust
ries.
aAve
rage
ann
ual i
ncre
ase
in re
al w
ages
per
hou
r wor
ked
from
col
umn
(I) m
ultip
led
by th
e nu
mbe
r of m
an-h
ours
wor
ked
in 1
958,
as
SOU
RC
ES: J
ohn
W. K
endr
ick
and
the
Nat
iona
l Bur
eau
of E
cono
mic
giv
en in
Ken
dric
k (1
973)
, pp.
196
—97
.R
esea
rch,
unp
ublis
hed
data
on
real
wag
es.
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Tabl
e 6.
5R
egre
ssio
nC
oeff
icie
nts O
btai
ned
in th
e A
naly
sis o
f the
Ann
ual R
ates
of C
hang
e in
Tot
al F
acto
r Pro
duct
ivity
for t
hePe
riod
1948
—66
with
Add
ition
al V
aria
bles
, Tw
enty
Man
ufac
turin
g In
dust
ries (
t-rat
ios i
n pa
rent
hese
s)
Cos
t of R
and
D/ V
alue
-add
ed R
atio
s for
Diff
eren
t R a
nd D
Com
pone
nts
R a
nd D
Em
bodi
ed in
R a
nd D
Con
duct
edPu
rcha
ses f
rom
in In
dust
ryO
ther
Indu
strie
sO
ther
Var
iabl
es•
Hum
anC
apita
lIn
vest
men
t Int
ensi
tyPe
rcen
t of S
ales
Not
to.
Uni
onM
embe
rs a
sPe
rcen
t of W
orke
rsA
nnua
l Rat
e of
Cyc
lical
Cha
nge
Priv
atel
yG
over
nmen
tPr
ivat
ely
Gov
ernm
ent
Con
stan
t of
Reg
ress
ion
Fina
nced
,Fi
nanc
ed,
Fina
nced
,Fi
nanc
ed,
1958
1958
1958
1958
Gov
ernm
ent,
1958
in P
rodu
cing
Est
ab-
lishm
ents
,195
3in
Out
put,
1948
—66
Rat
io B
ased
on
Gro
wth
ofR
ealW
ages
l958
R2
Cor
-re
cted
a0IR
DN
IRD
OPR
DN
PRD
GPV
TSU
NC
YC
HC
IW—
6.58
.25
.82
.10
—.0
4—
.07
.39
.73
(1.5
3)(3
.00)
(3.9
9)(2
.62)
(3.3
8)(1
.73)
(1.0
4)—
6.17
.25
—.0
5.8
5.1
7.1
0—
.04
—.0
7.3
8.6
9(1
.02)
(2.5
0)(.5
9)(3
.78)
(.37)
(1.7
0)(3
.41)
(1.5
6)(.9
3)
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374 Nestor E. Terleckyj
variable is not statistically significant, while, on the whole, the previousresults remain unaffected by its introduction.
6.5 Results with Productivity Data Based on an InclusiveConcept of Input
In their paper, Gollop and Jorgenson have developed a set of esti-mates of output, input, and total factor productivity for individualindustries (as well as economic sectors and the economy) which arebased on different measurement concepts than the estimates by Ken-drick, which are used to derive the estimates of the effects of R and Don productivity. Thus, while Kendrick measures the net output and thenet input, Gollop and Jorgenson use gross measures of output and input,i.e., including intermediate inputs.
Also, the Gollop-Jorgenson method attempts to account for qualitycharacteristics of inputs, and their input index accordingly includes"quality" index components for labor and for capital.
To the extent that research and development activities result in im-proved intermediate and capital inputs, one would expect the estimatesof productivity rates of returns to R and D to be lower when produc-tivity is derived from the input data already adjusted for the qualitychanges than when the calculation of productivity growth is based onthe unadjusted inputs.
The two sets of productivity data also differ in the underlying theo-retical formula. Kendrick implicitly used a reduced form of the Cobb-Douglas production function. The Gollop-Jorgenson data are derivedfrom a translog production function. The two sets of productivity esti-mates also differ in the method of estimating the man-hours input andin the estimate of depreciation of capital. But in contrast to theinput quality adjustments, there is no apparent reason to expect thesedifferences in measurement to have a systematic effect on the observedcorrelations between productivity growth and the R and D variables.
The results of substituting Gollop-Jorgenson estimates for Kendrickestimates of productivity change' for the twenty manufacturing indus-tries and with the same independent variables are shown in table 6.6.The Gollop-Jorgenson estimates are not available for the nonmanufac-turing industries.
1. With two minor changes in the Gollop-Jorgenson data to make consistentthe industry definitions: (1) Productivity growth for "food and kindred products"is used for both "food products" and "beverages," and (2) the arithmetic averageof growth rates for "motor vehicles" and for "transportation equipment, otherthan motor vehicles, and ordnance" was used for "Transportation equipment andordnance."
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0.I
ici
ici
i.—
.i
Tab
le6.
6R
egre
ssio
n C
oeff
icie
fits O
btai
ned
with
Alte
rnat
ive
Estim
ates
of t
he A
nuua
l Rat
es o
f Cha
nge
in T
otal
Fac
tor P
rodu
ctiv
ity,
Twen
ty M
anuf
actu
ring
Indu
strie
s, 19
48—
66 (i
-rat
ios i
n pa
rent
hese
s)
Con
stan
t of
Reg
ress
ion
Cos
t of R
and
D/V
alue
-add
edR
atio
sfo
rD
iffer
ent R
and
D C
ompo
nent
s
Oth
er V
aria
bles
R a
nd D
Em
bodi
edR
and
D C
ondu
cted
in P
urch
ases
from
in In
dust
ryO
ther
Indu
strie
sPe
rcen
t of S
ales
Not
toG
over
nmen
t,19
58
Uni
on M
embe
rs a
sPe
rcen
t of W
orke
rsin
Pro
duci
ng E
stab
-lis
hmen
ts, 1
953
Ann
ua' R
ate
ofC
yclic
al C
hang
ein
Out
put,
1948
—66
R2
Cor
-re
cted
Priv
atel
yG
over
nmen
tPr
ivat
ely
Gov
ernm
ent
Fina
nced
,Fi
nanc
ed,
Fina
nced
,Fi
nanc
ed,
1958
1958
1958
1958
a0JR
DN
IRD
GPR
DN
PRD
GPV
TSU
NC
YC
Ken
dric
kda
ta—
4.01 (.73)
.27
—.0
5.8
1.1
2(2
.92)
(.53)
(3.6
6)(.
26)
.08
(1.5
2)—
.04
(3.6
9)—
.05
(1.3
7).6
9G
ollo
p-Jo
rgen
son
dala
—32
.26
(1.7
9).2
0—
.18
1.83
1.67
( .64
)(.6
3)(2
.53)
(1.1
0).3
3(1
.87)
03 (.88)
m02 (.1
4).3
0
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376 Nestor E. Terleckyj
Compared to the estimates based on Kendrick's data, the estimatesderived with the Gollop-Jorgenson data in table 6.6 have a much poorerstatistical fit. But the two sets of estimates are qualitatively consistent.The signs of all coefficients are the same in both equations, and theirgeneral magnitudes are comparable. Among the four R and D variables,only the coefficient for privately financed purchased R and D is statis-tically significant at the 5% level. It is also considerably larger (1.83)than the coefficient derived with the Kendrick data (0.81), despite themuch lower estimates of productivity growth by Gollop-Jorgenson(averaging 0.9% a year for the twenty manufacturing industries duringthe period 1948—66) than by Kendrick (averaging 2.8%).
Somewhat surprisingly, perhaps, the equation based on Gollop-Jor-genson data suggests the possibility of positive "spillover" effects ofgovernment R and D to the industries purchasing inputs from the in-dustries conducting government-financed R and D. In this equation therespective coefficient for the government-financed R and D is larger andstronger than in all the earlier analyses, but it is still not statisticallysignificant.
Among the non—R and D variables, the estimated coefficients aregenerally similar, but their statistical significance is eliminated, exceptperhaps for the share of sales in private markets.
6.6 Conclusions
Significant effects of the privately financed industrial R and D onproductivity growth of manufacturing industries were found in earlierresearch by the author. These effects were two-fold: (1) direct increasesin productivity of industries conducting the privately financed R and D,and (2) indirect increases in productivity of industries purchasing capi-tal and intermediate inputs from the industries conducting the privatelyfinanced R and D. The estimated indirect effects were considerablylarger, per dollar of R and D expenditure, than the direct effects. Nocomparable effects were found for government-financed industrial Rand D.
These findings were tested in this paper for independence of the Rand D effects from the possible effects of the previously unmeasuredinputs and input characteristics, in general, for human capital, interme-diate goods, and composition of physical capital, and for human capitalin particular.
The results continue to uphold strongly significant and large estimatesof indirect effects of privately financed R and D. There was a weakeningof the significance of the estimated direct effects of privately financedR and D, a continued absence of any indication of direct productivityeffects of government-financed R and D, and some indication of pos-
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sible indirect effects of the government-financed industrial R and D.Qualitatively, the results of the tests were consistent with the earlierfindings. More detailed research is needed in the future to permit anevaluation of the possible effects of the previously unmeasured inputs,one at a time.
References
Gollop, Frank, and Jorgenson, Dale W. 1979. U.S. total factor produc-tivity by industry, 1947—73, this volume.
Griliches, Zvi. 1973. Research expenditures and growth accounting. InScience and technology in economic growth, ed. B. R. Williams. NewYork: John Wiley & Sons, Halsted Press.
Kendrick, John W. 1973. Postwar productivity trends in the UnitedStates, 1948—1969. New York: National Bureau of Economic Re-search.
Mansfield, Edwin; Rapoport, John; Romeo, Anthony; Wagner, Samuel;and Beardsley, George. 1975. Returns from industrial innovation:Detailed description of 17 case studies. University of Pennsylvania,preliminary and unpublished.
Mincer, Jacob. 1974. Schooling, experience, and earnings. New York:National Bureau of Economic Research.
Terleckyj, Nestor E. 1960. Sources of productivity advance. A pilotstudy of manufacturing industries, 1899—1953. Ph.D. diss., ColumbiaUniversity.
1974. Effects of R&D on the productivity growth of industries:an exploratory study. Washington: National Planning Association.
Comment Steven Globerman
IntroductionTerleckyj's paper is an extension of a rich and interesting study of a
slightly earlier vintage (Terleckyj 1974). The earlier study sought toidentify separately returns to the R and D conducted within industriesand the R and D "purchased" from other industries in the form of em-bodied technology in capital and intermediate goods. A related concernwas to estimate the separate returns to privately financed R and D (both
V
Steven Globerman is at York University.
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•
.378 Nestor E. Terleckyj
"own" and "purchased") and government-financed R and D. The mainfindings of that study are as follows:
1. For manufacturing industries, direct returns to private R and Dwere on the order of 30%, in terms of productivity growth; indirectreturns were on the order of 80%. The productivity returns, both directand indirect, to government-financedR and D were estimated at zero.
2. For nonmanufacturing industries, no indication of positive returnsto R and D conducted in the industry was obtained. Indirect returnswere on the order of 187%.
This conference paper extends earlier findings by attempting to holdconstant (either explicitly or implicitly) the effects of increased use ofhuman capital, relative increases in the use of intermediate inputs, andchanges in the composition of labor and capital which may have beencorrelated with increased direct and indirect investment in R and D bythe industry.
Before summarizing Terleckyj's empirical results, the basic modelunderlying the estimation procedure should be briefly reviewed. Theunderlying model for most estimations is a Cobb-Douglas productionfunction with labor, physical capital, research capital, and a "disem-bodied" rate of growth of productivity as arguments of the function.By making the usual assumption about equality of factor prices to mar-ginal value products and taking time derivatives of the variables, areduced-form equation is derived in which the rate of change in anindustry's total factor productivity is a linear function of the rate ofgrowth of the intangible stock of R and D capital. Since the growth rateof the R and D capital stock cannot be directly measured, the ratio ofgross investment in R and D to output is substituted into the equation.For purposes of estimation, it is assumed that depreciation in R and Dcapital can be ignored and that the gross investment in R and D capitalover the sample period can be measured by the industry's R and D in-tensity in 1958.
In the estimating equations, the aggregate R and D intensity variableis decomposed by source of financing (private versus public) and bylocation of conduct (within or without the industry) Additional non—R and D standardizing variables included in all reported estimatingequations are percent of sales not made to government, union membersas a percent of workers in producing establishments, and annual rateof cyclical change in output. The initial dependent variable is Kendrick'sindex of total factor productivity compiled for thirty-three manufactur-ing and nonmanufacturing industries, covering the period 1948—66.
The initial estimation results reported for the sample of twenty man-ufacturing industries are essentially those of Terleckyj's earlier study,and exclude the effect of changes in input quality and in the use of
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379 Direct and Indirect Effects of Industrial Research and Development
intermediate inputs. Results for the nonmanufacturing sample are notreproduced, but are reported as above.
In the first extension of his preceding findings, Terleckyj introducesa measure of human capital intensity, based upon the increase in realwages per man-hour worked over the period 1948—66, into earlier esti-mating equations. He finds that introduction of the human capital mea-sure leaves previous results essentially unaffected, and the coefficientfor the human capital variable itself is statistically insignificant.
In a second extension, Gollop-Jorgenson total factor productivityestimates for the twenty manufacturing industries are substituted forKendrick's estimates in the initial set of estimating equations. The over-all statistical results are much poorer than those obtained employingthe Kendrick estimates: the adjusted R2 coefficients decrease substan-tially, and the "own" privately financed R and D coefficient becomesstatistically insignificant. Interestingly, the "purchased" privately fi-nanced R and D coefficient remains statistically significant using theGollop-Jorgenson measure of total factor productivity, and, furthermore,has a greater impact upon productivity than in the earlier equations.Thus, the various specifications of the productivity/R and D relationshipprovide a range of estimates of the direct and indirect effects of R andD expenditures.
The following discussion will focus primarily upon issues relating tothe model specifications, measurement of variables, and potential prob-lems associated with the single-equation estimation procedure. I haveno great difficulty in accepting the general nature of Terleckyj's findings.Specifically, to the extent that the performance (or purchase) of R andD is associated with improved quality of conventional factor inputs, onewould expect the estimated returns to R and D to be lower when in-creases in the "quality" of labor and capital are otherwise accountedfor. Furthermore, since the numerator of Kendrick's total factor pro-ductivity measure is gross output while the denominator excludes inter-mediate inputs, one would expect estimates of returns to "purchased"R and D to be biased upward if embodied R and D expenditures arepositively correlated with the relative use of intermediate inputs. Thisis bound to be the case for Terleckyj's sample since the imputations ofembodied R and D were done by redistributing the R and D expendi-tures of each industry in proportion to the distribution of sales of thatindustry to other industries and them summing the amounts attributedto each of the sample industries. The fact that the "purchased" privatelyfinanced R and D coefficient increases substantially in the equationemploying the Gollop-Jorgenson data suggests the possibility that other,unspecified sources of bias may be present in the estimations.
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380 Nestor E. Terleckyj
The observation that the productivity effect of "own" privately fi-nanced R and D essentially disappears when "quality" adjustments forlabor and capital and the relative use of intermediate inputs are incor-porated into the dependent variable (i.e., the Gollop-Jorgenson index)is somewhat difficult to accept on intuitive grounds. If labor and capitalresources employed in R and D activities have less risky employmentalternatives, one would expect R and D resources to earn their users"excess" returns or be shifted into other activities over time. Part of theexplanation for the different results reported in table 6.6 may, indeed,rest in the different methodologies employed to construct the dependentvariables. In any case. since the attribution of the productivity effectsof R and D will ultimately depend upon how the factor productivityresidual is defined, we are somewhat less concerned about explanationsof differences in estimated rates of return to R and D across differentproductivity measures than we are with the identification of returns toR and D employing any given productivity measure. It is the latterconcern we will primarily address in our discussion.
Model SpecificationThe inclusion of input "quality" measures reflects Terleckyj's concern
about possible estimation biases arising from the omission from theestimating equation of one (or more) variables whose values differedacross sample industries. To the extent that differences in the values ofother omitted variables are unsystematically related to the includedvariables over the sample period, the slope parameters would remainunaffected. However, the brace of standardizing variables employed byTerleckyj excludes certain variables that may be systematically relatedto the rate of growth in R and D investment.
One such variable is the rate of growth in output in the sample indus-tries over the estimation period. There is ample evidence that industriesenjoying above-average productivity growth rates (associated in part,with investments in R and D) also enjoy above-average rates of growthin output. Relative increases in output growth rates could, in turn,affect industry differences in productivity through differential scale andlearning economies. Including an output growth rate variable in theestimating equation would raise an identification problem; however,one suspects that its exclusion leads to an upward bias in the estimatedR and D parameters. it
Another potential source of bias arises from changes in the relativedegree of product specialization within industries over time. The deriva-tion of productivity and R and D data along establishment (and prod- duct) lines reduces but does not obviate this possibility, particularlygiven the level of aggregation of the sample industries. One might hy-pothesize that over the sample period, R and D—intensive industries
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381 Direct and Indirect Effects of Industrial Research and Development
were becoming relatively more specialized (on an establishment basis),both because a rapidly expanding market for their output facilitatedincreased specialization and because specialization facilitated entry intohigh-technology industries.' The production and, perhaps more impor-tantly, the learning economies associated with increased product spe-cialization would support the productivity effects of both disembodiedand embodied R and D, leading to upward biases in the estimatedparameters.
The effect of differential rates of growth in "x-efficiency," in turnrelated to changes in industrial market structure, might also be em-bodied in the estimated R and D parameters. An upward bias could beimparted to the R and D coefficients if technological change bringsabout a decline in average plant size relative to market size. Such adecline could facilitate easier entry into the industry, thereby fosteringgreater competition and a more efficient allocation of resources, includ-ing faster interfirm rates of technological diffusion. Evidence relatingchanges in concentration ratios to technological change is far from con-clusive but, on balance, points to the existence of a negative relationshipbetween the two variables.2
The variable used by Terleckyj to standardize for changes in laborquality is the increase in real wages per man-hour worked. The originaldata for this variable were in the form of period averages of actualhourly earnings for the periods 1948—53 and 1960—66, deflated to 1958dollars. The annualized percentage change between the two periods wasmultiplied by the man-hours worked in the industry in the base year,1958, to obtain the rate of growth in aggregate human capital. Theresulting term was then divided by the 1958 value added by the industryto obtain the desired variable.' The author acknowledges the possibilityof a simultaneous-equation bias arising from the feedback of increasedproductivity to higher wages, but argues that this bias would not ariseif the competitive working of the labor market equalized the earningswithin occupations rapidly relative to the length of the period over whichthe productivity growth is measured. Even if adjustments in the relativesupply of labor for different occupations proceeded rapidly (a phenom-enon which is certainly at variance with recent evidence on the signifi-
1. The process of entry through specialization in the innovation process is illus-trated in the case of the semiconductor industry. See John Tilton, InternationalDiffusion of Technology: The Case of Semi-Conductors (Washington: The Brook-ings Institute), 1971.
2. Examining concentration ratios at the establishment level for different indus-tries over time might provide some feel for the magnitude of this potential bias.
3. Since the numerator of the productivity measure is gross output, it is notevident why the human capital variable, as well as the other standardizing vari-ables, are deflated by value-added in the base year rather than by gross output.
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382 Nestor E. Terleckyj
cance of information costs in factor markets), a simultaneous-equationbias might still be obtained if wages (to any significant extent) incor-porate expected productivity gains. While it is likely that the Gollop-Jorgenson labor quality measure, based on the shift of workers amongdifferent categories, is subject to a smaller simultaneity bias, some biaswill still be present if higher wages, in part, are realized in the form ofjob upgrading.
In light of the acknowledged shortcomings of the labor-quality vari-able employed, other measures, including the average education levelof employees, might have been tested. Median years of schooling of thelabor force by industry are available for censal years. While the avail-able censal years 1959 and 1969 do not provide a perfect overlap withthe estimation sample period, such exact concordance might not benecessary if relative differences in education levels across industries arereasonably stable over time. The assumption that differences in educa-tion levels at a point in time reflect differences in growth rates over thepreceding period seems no more heroic than a similar assumption in-voked by the author in specifying the R and D variables.
In fact, we reestimated the basic productivity equation across thetwenty manufacturing industry sample using Kendrick's index of averageeducation per employee for 1959 (Kendrick 1973) and obtained resultsquite similar to those obtained by Terleckyj for the growth-in-real-wagesvariable. The failure to identify a statistically significant positive returnto human capital investment for those estimations employing Kendrick'sproductivity index as the dependent variable is a surprising result. Apossible explanation of this result is the existence of multicollinearitybetween the human capital variables and other included variables. In-deed, the zero-order correlation coefficient between Terleckyj's humancapital measure and "own" privately financed R and D equals .58,while the correlation coefficient between the human capital measure andthe cyclical change in output is .69. Thus, the influence of human capi-tal investment on productivity growth may be largely assigned to col-linear variables in the estimation process.
Measurement of VariablesI will not comment extensively on the problems associated with mea-
surement of variables since Terleckyj considers most of the obviousproblems in some detail in his earlier study. Many of the problems are,in any case, intractable given the state of the available data.
The failure of cotiventional productivity measurements to adequatelyreflect product quality changes is well-recognized and presumably leadsto an underestimation of real rates of productivity change. Insofar as thebias differs among products, the industry comparisons would be affectedand, on balance, this probably imparts a downward bias to the estimated
nI'
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I383 Direct and Indirect Effects of Industrial Research and Development
R and D parameters. To the extent that federally financed R and D is.primarily directed towards improvements in product quality as opposedto cost reduction, the methodology used in deriving industry produc-tivity estimates could contribute to the finding that government-financedR and D is not significantly related to productivity change. Furthermore,while the period 1948—66 might be long enough compared to the timelag one would expect between privately financed R and D intensityratios in 1958 and their productivity effects, it might be too short tofully incorporate the effects of federally financed R and D which ispresumably aimed at effecting greater changes in underlying productionconditions. While Terleckyj's finding of no productivity return to gov-ernment-financed R and D accords with similar results obtained byLeonard (1971) in a study of interindustry output growth differences,the latter study is subject to the same sorts of criticisms.
The imputations of purchased R and D to the sample industries weredone by redistributing the R and D expenditures of each industry inproportion to the distribution of sales of that industry to other indus-tries and then summing the amounts attributed to each of the industries.While imputing embodied R and D as a strict proportion of sales mightbe no more arbitrary than any other procedure, I would expect thatindustries performing R and D intensively are more likely to purchasecomplex and technically advanced equipment than are those industrieswhich perform little R and D. Another imputation procedure mighttherefore attribute the R and D transferred from a supplier industry toany given purchasing industry as a proportion of the percentage of totalsales made to the purchasing industry weighted by the relative "own"R and D intensity of the purchasing industry. In any case, it would beenlightening to evaluate the sensitivity of the statistical results to theimputation procedure used.
A more serious concern is the use of a single year's set of observa-tions to calculate interindustry flows of purchased inputs.4 Albeit theyare dictated by available data, it is certainly possible that observedfactor ratios in the given year were not long-run equilibrium values.Indeed, a similar concern might be expressed about most of the inde-pendent variables. For example, the "own" R and D intensity variablesare measured for a given base year, 1958. In his earlier study, Terleckyjargues that differences in base year R and D intensity levels are likelyto reflect differences in preceding R and D growth rates. While thisassumption might be quite tenable when comparing high and low R andD—intensive industries, it is somewhat more objectionable for the seven
4. Data for 1958 were employed in developing the "purchased" privately if.nanced R and D intensity variable and the relative growth rate in intermediateinputs variable.
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I384 Nestor E. Terleckyj
manufacturing industries with "own" privately financed R and D inten-sity ratios ranging between .2 and .5.
Estimation ProcedureGiven the substantial differences in the parameters estimated across
the separate samples of manufacturing and nonmanufacturing industries,it is inappropriate to fit common slope parameters for the R and Dvariables across the full sample of thirty-three industries.
Terleckyj notes that the results for nonmanufacturing industries wereerratic, and they can be shown to be highly sensitive, in particular, tothe inclusion or exclusion of the air transportation industry. Part of thereason for the sampling instability of the parameters for the nonmanu-facturing industry sample might be the substantial collinearity that existsalong the basic set of independent variables. Substantial collinearity alsoexists among specific independent variables for the manufacturing in-dustry sample. Specifically, the zero-order correlation coefficients forpairs of the four R and D intensity variables provided in Terleckyj'stable 6.1 (for the sample of twenty manufacturing industries) rangebetween .73 and .95. The simple correlation coefficients between salesother than to government as a percent of total sales and the variousR and D intensity variables range between —.79 and — .91. The factthat coefficients for the privately financed R and D variables tend to beunaffected by inclusion or exclusion of the government-financed R andD variables provides a reason for optimism about the reliability of theestimated returns to privately financed R and D. However, this stabilitymight simply reflect the fact that rate-of-return estimates for govern-ment-financed R and D are confounded by collinearity with other van-ables, and particularly with the nongovernment sales variable, so thatthe private R and D variables are assigned the joint effect of the en-tire set.
One might also conjecture that the relationship between productivitychange and returns to federally funded R and D depends upon thenature and the level of the R and D performed. Specifically, contractR and D performed in the electrical machinery and transportationequipment industries is largely defense-related. Thus, the effects ofR and D performance on productivity may be atypical for those twoindustries. Our own estimation provides some indication that returnsto government-financed R and D (both embodied and disembodied) arehigher in the above-mentioned industries than in other manufacturingindustries. The difference might reflect the existence of increasing re-turns to federally financed R and D or the possibility that governmentR and D support to other manufacturing industries is, in effect, a sub-sidy to offset low rates of productivity growth. These possibilities maybe worthy of further investigation.
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385 Direct and Indirect Effects of Industrial Research and Development
Concluding CommentsTerleckyj's general findings (for the specifications employing Ken-
drick's productivity index) are directionally in accord with existingevidence in the literature; however, differences among the various studiesin the nature of the samples, the measurement of variables, and the tech-niques of estimation make specific comparisons rather tenuous. Forexample, Griliches (1973) obtained an estimated productivity returnto R and D of around 40% for a sample of eighty-five two, three, andfour-digit manufacturing industries. This estimate includes both direct(i.e., intraindustry) and indirect (i.e., interindustry) productivity effects.Terleckyj's estimates of total R and D returns (employing Kendrick'sproductivity index) range between 100 and 110% and lie substantiallyabove Griliches's estimated total returns. In another study using a par-tial productivity measure, Griliches (1975) estimated the rate of returnto R and D for 883 large R and D—performing U.S. manufacturingcompanies. The average gross excess rate of return to R and D was27% in 1963. It should be noted that this estimate includes the produc-tivity effects of intercompany as well as interindustry technology trans-fers. Mansfield (1965) found that marginal rates of return to R and Daveraged about 40 to 60% for a sample of petroleum firms; for asample of chemical firms, returns averaged about 30% if technicalchange was assumed to be capital-embodied but only about 7% if itwas disembodied.5
Thus, various alternative estimates of the productivity returns to Rand D tend to lie somewhat below estimates obtained by Terleckyj,although all of the estimated returns are substantial. Terleckyj's studyperforms the valuable service of demonstrating that these substantialreturns (at least for one productivity measure) cannot be significantlyreduced by statistically standardizing for human capital investments inan industry. It also provides us with dramatic evidence that estimatedreturns to R and D are extremely sensitive to the way in which produc-tivity is measured. However, Terleckyj's study shares a weakness withother studies in failing to hold constant the effects of such factors aschanges in average plant size, changes in plant-level specialization, andchanges in organizational structure (including intrafirm and interfirmrates of technology diffusion) which may be related to the performanceof R and D. An attempt to incorporate the influence of the above-mentioned factors into the basic productivity equation might make asignificant contribution to our understanding of the influence of R andD on industrial performance.
5. Mansfield's estimated returns also include the indirect effects of the R and Dconducted outside the sample firms.
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386 Nestor E. Terleckyj
Finally, Terleckyj should be complimented for the thoughtful inclu-sion and careful description of the data series included in his estimationwork. The inclusion facilitates extension of his interesting work byother researchers.
References
Griliches, Zvi. 1973. Research expenditures and growth accounting. InScience and technology in economic growth, ed. B. R. Williams. NewYork: John Wiley and Sons.
1975. Returns to research and development expenditures inthe private sector. Paper presented at Conference on Research in In-come and Wealth.
Kendrick, John. 1973. Post-war productivity trends in the UnitedStates, 1948—1959. New York: National Bureau of Economic Re-search.
Leonard, W. N. 1971. Research and development in industrial growth.Journal of Political Economy 79:232—56.
Mansfield, Edwin. 1965. Rates of return from industrial research anddevelopment. American Economic Review 55:310—22.
Terleckyj, Nestor E. 1974. Effects of R & D on the productivity growthof industries: an exploratory study. Washington: National PlanningAssociation.