discussion papers
FS IV 88 - 17
Entry, Innovation and Productivity GrowthPaul A. Geroski London Business School
October 1988
ISSN Nr. 0722 - 6748
Forschungs Schwerpunkt Marktprozeß und Unternehmensentwicklung (IIMV)Research UnitMarket Processes and Corporate Development (UM)
Wissenschaftszentrum Berlin für Sozialforschung Reichpietschufer 50 D-1000 Berlin 30
Tel.: (030) 25491-0
ABSTRACTEntry, Innovation and Productivity GrowthThis paper computes measures of total factor productivity in 79 three digit UK industries for the period 1976-1979, and associates them with current and post domestic entry, foreign entry and innovative activity. All three are seen to have significant effects (positive, negative and positive respectively) that persist (in varying degrees over time), with innovative activity accounting for perhaps 50% or more of total factor productivity growth on average, and domestic entry for about 30%.
ZUSAMMENFASSONGEintritt, Innovation und ProduktivitätswachstumIn diesem Beitrag werden Maße für die Gesamtfaktorproduktivität für 79 dreistellige Industriezweige Großbritanniens für die Periode 1976 bis 1979 berechnet, und es wird der Zusammenhang zu den Variablen aktueller und vergangener inländischer Markteintritt, ausländischer Markteintritt und Innovationsaktivität überprüft. Alle drei Größen haben einen signifikanten Einfluß (positiv, negativ und wiederum positiv), der beständig ist (in unterschiedlichem Grade im Zeitablauf), mit einem Einfluß der Innovationsaktivität von 50% und mehr und einem Einfluß des inländischen Markteintritts von etwa 30% auf das durchschnittliche Wachstum der Gesamtfaktor- produktivität.
1
I. INTRODUCTION
Almost no one doubts the proposition that monopoly breeds inefficiency and
slack. Competition, by contrast, is thought likely to stimulate a more economical use
o f scarce resources through the displacement of the less by the more efficien t, and,
indirectly, through self-im provem ent undertaken in response to the threat of
displacement. These gains come about because a vigorous competitive process throws up
alternatives in the form o f new firms and new ideas, and selection amongst them
induces movements to and movements o f the production frontier. As is often the case
with convention 1 wisdom, the extent o f agreement on the existence o f an effect o f
competition on efficiency all but dwarfs whatever consensus there is on it’s size.
The evidence that is available is surprisingly patchy and our goal in this paper is to
remedy this lacuna.
The vehicle that we shall use to explore the subject is an inter-industry
analysis o f the association between the extent o f competition and total factor
productivity growth. The level o f market concentration is a widely used proxy
measure o f competition, but it is rather static and does not really capture the idea
o f a competitive process which throws up new firms and new ideas to challenge a
possibly second best set o f current practices. An attractive alternative procedure is
to interpret "the extent o f competition'* in a broad sense by taking the
conceptualization "new firm s and new ideas* literally. The drawback o f this decision
is obvious: entry rates and innovation rates are not entirely unambiguous measures o f
the intensity of "competition", and they are certainly not exhaustive. They are,
however, observable, and an effect on productivity associated with them almost
certainly captures at least part o f the effect one would want to attribute to
competition. Similarly, the decision to examine total factor productivity growth has
advantages and disadvantages. The main advantage o f looking at productivity growth is
2
that one does not need to impose strong assumptions on the data in order to separate
out movements to from movements o f the production frontier. However, this means that
one may confuse the potentially quite different effects that competition can have in
reducing X -inefficiency with those it may or may not have in stimulating innovation.
Further, the effect that one can attribute to competition through this procedure is
partial; that is, it is conditional on increases in conventional inputs conventionally
measured. Still, while an exploration o f the effects o f new firms and new ideas in
total factor productivity growth may not provide all the answers, it will at least
provide some that are o f interest.
The plan o f operations is as follows. To assess the determinants of
productivity growth, one must first measure it. This is not an unfamiliar problem,
and we shall outline our not unfamiliar solution to it in Section II. The work that
we shall report uses data on UK three digit industries in the 1970s. However, since
productivity growth is (apparently) subject to periodic slowdowns and speedups, one
must choose one’s dates with care. Indeed, we regard the precise definition o f our
sample as an important prior specification decision on which the data can be
informative. The choices that we have made are outlined in Section m , together with
some purely descriptive information on productivity growth at the three digit level.
Our results on entry, innovation and productivity growth are presented in Section IV,
and Section V summarizes the several conclusions that can be drawn from them.
3
II EXPERIMENTAL DESIGN: THE MODEL
Total factor productivity growth is, by definition, the rate o f growth of
output less an appropriately weighted sum of the rates o f growth in the various inputs
used. The basic procedure for isolating that percentage o f the increase in production
not associated with increases in inputs is well known. Using any simple production
function relating, say, labour and capital inputs, L and K , to output Y, one cano
express the rates o f growth o f output, y, in terms o f the rates of growth o f labour
and capital, ! and k, as
( !) y = oc 1 + ß k + 9,
where <x and ß are the output elasticities o f labour and capital, and & describes all other
changes. Our goal is to associate variations in 6 with entry and innovation, and the
major problem to be solved is that o f observing oc and ß. In principle, the solution is
straight forward. If the industry in question were in a long run perfectly competitive
equilibrium, then flows o f labour and capital services would be proportional to the
stocks, L and K . Further, in these circumstances, factors will be hired and goods priced
in such a way as to equate factor shares with the two output elasticities, ac and ß , and
consequently, the unobserved a and ß can be measured by observing labour and capitals
share in total revenue.
This happy state o f affairs is, however, unlikely to correspond exactly to those
from which the data has been generated. Two particular problems are worth noting.
First, i f price deviates from marginal cost, then factor shares in total revenue will differ
from shares in total imputed revenue (marginal cost times output). It follows that, at
equilibrium, a will not equal labours share in total revenue, but, rather, that quantity
times the ratio o f price to marginal cost, and similarly with ß (in Hall, 1986, this
4
observation is used as the basis for a test o f the equality between price and marginal
cost; the point is also made in Nadiri and Shankerman, 1981). Second, inputs that are
fixed in the short run will not necessarily be fully utilized all o f the time, and, when
they are not fully utilized, their value to firms is misstated by market prices. It follows
that, at a short run or temporary equilibrium, the contribution of factors will be valued
at shadow prices that define factors shares which, in general, will differ from those
defined in terms of observed market revenue (see Berndt and Fuss, 1986 and the
exposition by Hulten, 1986). Both problems together suggest that identifying a and ß
with observed factor shares o f revenue may induce measurement error.
These observations have a major impact on the structure of the work that
follows. Broadly speaking, two methods have been used to identify 6. The first uses
observed factor shares to measure a and ß, and these are then combined with observede o o
values o f y, 1 and k to construct measures o f 9. The second method identifies 9 fromo o o
regressions o f y on 1 and k. If, as the previous discussion suggests, oc and ß cannot
reliably be associated with observed factor shares, then the regression route seems to be
the more sensible one to follow, and this is the one that we have chosen to follow. Y,
K and L are treated as observable across industries i and over time t; <x, ß and 9 are
treated as unknown. However, since there is every reason to think that output
elasticities and total factor productivity are industry and time specific, there are no real
grounds for assuming that« , ß and 9 are constant across a sample of industry years. To
allow these parameters to vary across i and t, we shall model them in terms o f
observables, and it is the time and industry invariant parameters associated with the
observable determinants o f « , ß and 9 which w ill claim our attention. Estimates o f <x, ß
and 9 follow straight forwardly from estimates o f these primal parameters.
Eliminating ß through the assumption o f constant returns, the regression
programme can be written as
5
(2) Yit ■ ^it “ a it (’it “ ^it) + it>
(3) <xjt = « X it + €jt, and
and
(4) 0jt - *Zit + pjt,
where Xjj and Zjt are the observable determinants o f « jt and and ej are the
unobservable determinants, and oc and 8 are the objects of estimation. The formulation (2)
- (4) effectively allows each industry to have its own production function, and allows
each industry specific production function to vary at an industry spe. ific rate over time.
To complete the specification o f (2) - (4), it is necessary to be more explicit about the
observables and Z jt , and we shall consider each in turn.
Factor shares are widely thought to be roughly constant over at least the
medium run, and there is quite a bit o f evidence to suggest that this is also broadly true
o f the relationship between price and marginal cost. ( ’ ) These considerations suggest
that variations in the ocjt’s can be parsimoniously described using industry specific fixed
effects if , as will be the case below, one works with relatively short time series.
However, temporal variations in the ocjt may nevertheless occur in the short run when
the shadow prices on factors diverge strongly from market prices. Measuring labour and
capital utilization rates at industry level is extremely difficult, but, i f one assumes that
variations over time in factor utilization is highly correlated across industries, then a
parsimonious description o f variations in utilization rates can be had by introducing
annual dummies for each period in the sample. Since the oqt are o f no interest per se in
this exercise, parsimony is well worth having (although not, o f course, at any price), and
we write (3) as:
(5) « jt = « ; + « t + Mit,
where the oq and « t are parameters to be estimated.
6
The 0jt are, o f course, the objects o f interest here. Our hypothesis is that
increases in the intensity o f competition will induce either movements to or movements
of the production function (or, more likely, both), leading to increases in output for
given levels of factor input. Examining the association between total factor productivity
and competition isolates only that percentage o f the effect o f competition which is
disembodied, and fails to account for effects embodied in new capital which replaces
older vintages. Thus, our procedure almost certainly understates the total e ffect o f
competition on productivity growth. The interesting question is, of course, that of
measuring the intensity o f competitive pressure. Much the most common solution found
in the literature involves using concentrat? n indices as proxy measures o f market power,
and thus as inverse measures of competition. The strengths and weaknesses o f this
approach are well known and, for better or worse, we wish to push beyond concentration
indices and explore other proxies/^) Most attractive in this context are proxies that
reflect notions of competition as a dynamic process, as a flow of new firms and new
ideas which result in new competitors and in a rejuvenation of incumbents. The specific
proxies that we shall use to capture this conceptualization are gross entry penetration by
new domestic producers, Ejt, net entry penetration by foreign producers, Mjt, and a
count o f major innovations first introduced into each industry i at time t, lit since
it is more than possible that the effects o f any one o f these factors will extend beyond
the current period, we shall model as a function o f the history o f all three,
(6) *it - Eit + ^ (L ) Mit + <?3(L)Iit + €jt,
where we also have allowed for industry specific fixed effects, 9j, time varying effects
common to all industries, and where the ^ (L )ji » 1, 2, 3, are polynomials in the lag
operator.
7
Substituting (5) and (6) into (2) yields a model that enables us to estimate the
determinants o f 9\,
(7) Yit - ^it ■ (« i + « t) A t ’ ^it) + *i + *t + ^ (L ) Eit + ^ ( L) Mit + *3(L) lit + "it’
where i/jt is a residual error which conflates and Mit- Eor a sample o f N industries
observed over T years and using p lags in the three competitiveness variables, the model
contains 2N + 2 ( T - l) + 3p +1 parameters to estimate from NT observations, requiring
that at least three cross section panels be pooled. The output, therefore, takes the form
of NT estimates o f ocjt and constructed from the estimated parameters, plus estimates
o f the standard error o f each.
Like most experimental vehicles, (7) is not perfectly designed for our purposes,
and it is worth closing with a few remarks on the likely consequences o f potential
problems. The aggregation and simultaneity problems implicit in (7) are well known, and
we shall have nothing and relatively little (respectively) to say about them here.
Omitted variables are a further set of problems, and these come in two forms: omitted
factors o f production and omitted determinants o f 0jt. Allowing for raw materials and
intermediate inputs, (4) a range o f factor utilization measures and, possibly, using an
explicit disequilibrium model (5) are all possible generalizations o f (1) worth considering,
although we have pursued only the first and last o f these. Practically speaking, errors
in specifying (1) are likely to be highly cyclical, and may generate a (presumably upward)
bias to estimates o f the 0^, 9 and 9$ to the extent that entry and innovation is pro
cyclical. There are, o f course, numerous possible determinants o f the 0jt , in addition to
those that we have allowed for explicitly (see, for example, Caves and Davies, 1987, and
references therein). The effects o f many o f these factors will be picked up by the
industry specific fixed effects and time dummies, and this is particularly the case with
variables like concentration which do not vary very much over time. Still one must
acknowledge that the risk o f bias remains.
8
III. EXPERIMENTAL DESIGN: THE SAMPLE
The experience o f productivity growth in the UK of the 1970s (and most of
the rest o f the world for that matter) was unusual by post-War standards. At the
aggregate level, the growth o f labour productivity, steady but not spectacular prior to
the 1970s, slumped in 1973, gradually recovered from 1975 onwards, and then slumped
again at the end of 1979. Total factor productivity growth was extremely low
throughout, both absolutely and relative to the 1960s/6) in aggregate, output, labour
demand and, for that matter, labour utilization all slumped in both 1973 and 1979, and
this seems to have been the experience o f the vast majority o f individual industries.
Only 26 o f a sample o f 115 three digit industries observed by Wenban-Smith had higher
labour productivity in the period 1973-79 than in 1968-73, and only 12 had both higher
productivity and higher output growth. This said, the experiences o f particular industries
differed widely, and the range o f productivity growth rates a three digit level was large
by any standards. Productivity slowdowns at three digit level were clearly related to
output growth rates and, less clearly, to energy and capital intensity (see Wenban-Smith,
1981, and Kilpatrick and Naisbitt, 1984).
Our data covers 79 three digit industries for which we have information on
employment, real output and real capital stock for the period 1970-1979. The most
striking feature o f the data is the relatively high "within group" variation in the growth
rate o f output (85% o f the total variation is within industries over tim e), employment
growth (62%), capital stock growth (88%), the growth o f output per head (88%) and the
growth o f output per unit o f capital (89%). Further, the total variation in input growth
rates is much smaller than for output growth (about 19% for labour and 43% for capital),
leading to productivity growth rates nearly (89%) as variable over industry and time as
output growth rates. The impression o f high diversity in individual industry experience
is also clearly revealed in the data. Variations in average output, input and productivity
9
growth rates across all industries between years were dominated by variation across
industries within years. Only 16% o f the total variation in output growth was
attributable to shifts in industry averages across years; the figures for employment
growth, capital growth, and the growth in output per head and per unit o f capital were
9%, 11%, 14% and 14% respectively. The correlation in labour growth rates across
industries between 1979 and 1978 was 0.1522, between 1979 and 1977 it was 0.2642,
between 1979 and 1976 it was 0.0300, and, in general, it never exceeded 0.30 between any
pair o f years from 1970 to 1979. Rank correlations were also extremely low. All of this
is not to deny, however, that the purely cross section variation in the data is substantial
by any standards. The mean output growth rate over all years and industries was 0.0063
with a standard deviation of 0.0846; the overall mean of labour productivity growth was
0.0269 and had a standard deviation o f 0.0797 (the distribution of output, input and
productivity growth rates was in all cases approximately normal)/?)
The first observation to be made about the raw data, then, is that the growth
rates in output, inputs and productivity were highly variable across industries, being
roughly normally distributed with standard deviations three to ten times their means.
More interesting was the unusually high variation over time within industries in these
growth rates. While the distribution year by year was normal, the location o f any
particular industry on that distribution varied enormously over time. Todays high
performer often turned out to be tomorrows loser, and the indications are that, in
general, high productivity performance simply did not persist over time. The important
point about this observation is the large class o f possible determinants o f variations in
productivity growth that are incapable o f accounting for this pattern. Levels o f industry
concentration, factor usage intensity, levels o f import penetration and so on are all
variables with a very low ratio o f "within" to "between" variation, and, while they may
satisfactorily explain average industry growth rates over a five or ten year period, they
will not contribute much to explaining annual variations by industry.
10
The second observation to make is that there do appear to be "structural"
shifts over time in the data, and, even if they are not statistically significant, they are
troubling. Table 1 displays some data on annual average output and labour productivity
growth rates for the 79 industries in the sample. The mean output growth rate varied
enormously from 1971 to 1975, settling down from 1976 onwards at a fairly even level.
Other aspects of the distribution show weaker traces o f change, most notably the range
(which widened considerably in 1978 and 1979). The mean growth rate o f output per
head (and, for that matter, per unit o f capital) also stabilized (relatively) post-1976 with,
perhaps, some slight tightening in the overall distribution of growth rates across
industries. Although not decisive, these numbers do suggest the need for caution. We
therefore ran a number o f variants of the basic model (7) on samples constructed from
all three, four and five consecutive year pools o f the individual year cross sections from
1974 (the earliest year for which entry data was available) until 1979, and performed
tests of structural stability on the parameters (following Pesaran et al, 1985). Taking
the five year pool 1975-1979 as a base, the test statistics indicated that, of the four
marginal years 1979, 1978, 1976 and 1975, only 1975 appeared to be structurally distinct.
That is, there is evidence o f a structural break between 1975 and the years 1976-1979,
but no evidence o f any breaks within the period 1976-1979. It is not at all obvious that
this break in 1975 has anything to do with entry or innovation, and, this being the case,
the natural course o f action is to pool the data for the period 1976-1979.
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Table 1: Annual Average Output and Productivity Growth Rates
1971
1972
1973
1974
1975
1976
1977
1978
1979
1971
1972
1973
1974
1975
1976
1977
1978
1979
MEAN STD DEV MINIMUM M AXIMUM
-0.00431 0.07381 -0.26581 0.19215
0.01971 0.07922 -0.22768 0.14246
0.08203 0.06997 -0.08956 0.28501
-0.01582 0.08891 -0.30337 0.14556
-0.05046 0.09082 -0.26620 0.16842
0.01380 0.07457 -0.18272 0.21107
0.01398 0.06226 -0.18417 0.17145
0.01182 0.07382 -0.22113 0.27916
-0.01382 0.08394 -0.31516 0.39397
labour productivity
MEAN STD DEV MINIMUM M AXIM UM
0.01844 0.07854 -0.28669 0.19240
0.05705 0.08456 -0.18248 0.22668
0.07859 0.07970 -0.15422 0.28996
-0.02829 0.07188 -0.23303 0.16238
-0.00623 0.08752 -0.22653 0.19709
0.03667 0.06158 -0.15326 0.20133
0.02414 0.05947 -0.12212 0.17621
0.03587 0.07444 -0.20363 0.38349
0.02552 0.06561 -0.12558 0.30556
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IV. SOME RESULTS
Our goal is to detect the effect o f entry and innovation on the rate o f growth
o f output per unit o f capital conditional on the growth of factor inputs and on a range
on industry specific and time dummies. One obvious concern is whether (7) is an
equilibrium relationship, and, if not, whether it ought to be embedded in a dynamic
model. A variety o f experiments were undertaken in this direction, and almost none o f
the included variables (e.g. lag dependent variable, lagged first differences in the
dependent or independent variables) added significantly to the overall explanations or
materially affected estimates of 0*. f t a 'd tfi. A further, but, as it turns out, lesser
concern, involves relaxing the assumption o f constant returns.^) More substantively,
previous work on total factor productivity growth has also made it plain that the
measurement of inputs is crucial (e.g Jorgenson and Griliches, 1967, and Gollop and
Jorgenson, 1980). The most that we could do in this respect was to include the growth
o f materials usage as a determinant o f output growth, and, to conserve degrees of
freedom, we constructed an observed factor share weighted sum of labour and materials
per unit o f capital/^) Denoting this composite input variable as fjt, then (roughly
o o ospeaking) the difference between implementing (7) using (Ijt - kjt) and using fjt is that,
in principle, the former specification omits information on the rate o f growth o f material
inputs per unit o f capital. As it turns out, virtually the only important difference in
results that emerged from our data when using rather than (ijt - kjt) is that the
estimated effect o f entry and innovation on total factor productivity was somewhat larger
in the latter case. This is probably the result o f cyclical effects not properly controlled
for, and we shall, in the main, concentrate on discussing the results obtained when ?it
was used.
13
Having experimented with the broad features o f (7) and found a fairly
satisfactory version o f it to work with, the next step is to explore the correlations
associated with innovation and entry. Equation (i) on Table II shows estimates of 9 ,
and (P using fjt in (7). The effect o f domestic entry and innovations is positive and
that o f foreign based entry is negative, with short and long run effects taking the same
sign in all three cases. The means of Ejt, Mjt and Ijf were 0.023022, 0.04592 and 1.21835
respectively, and thus it follows readily that the total effect o f innovation on
productivity growth is by far the largest effect (in absolute value) of the three (in the
short and long run), despite the fact that domestic entry has the largest marginal effect.
Further, the tot:.' and marginal effects of domestic entry are (in absolute value) far more
substantial than those associated with foreign based entry.
There seems little doubt that the effects associated with both types o f entry
and innovation are individually and collectively significant, but it is not clear exactly
how much they contribute to the explanation o f productivity growth. The obvious
benchmark to use in ascertaining this is estimates of (7) when tf^(L) = 9^(L) =• 0^(L) » 0.
Imposing this restriction inflated the sum of squared residuals by about 18.6% and the
calculated x^(9) statistic on these restrictions was more than three times its critical
level, indicating that entry and innovations made a significant contribution to the overall
explanation o f output per unit capital. Entry and innovations do not, however, explain
all the systematic variation in total factor productivity growth that is apparent in the
data. The intercept time dummies (i.e the 0t) indicate that, all other factors constant,
total factor productivity growth was highest in 1978, lowest in 1976 and higher in 1977
than in 1979. Including the time dummies for both slope (the a t) and intercept lowered
the sum o f squared residuals by about 10%; the calculated x^(3) statistic on their
exclusion was four times its critical level. The industry specific slope dummies (the ocj)
played a major role in accounting for movements in (yjt - k^). Eliminating them raised
the sum o f squared residuals by nearly 50%, and the calculated x^(79) statistic testing
14
their exclusion exceeded its critical level by more than a factor of four. Inclusion of
the industry specific intercept dummies (the 9$ lowered the sum of squared residuals by
nearly 27%, and the calculated x^(79) statistic on their exclusion was about twice its
critical level. There seems to be no doubt that the « j contribute far more than the 0j,
indicating that factor shares, while differing across industries, are far more stable over
time within industries than total factor productivity growth. Thus, as a group, entry
and innovation go some, but by no means all the way towards accounting for variations
in total factor productivity growth.
The estimated effects of entry are fairly robust to a variety of© o o
respecifications. As discussed above, they emerge when (kjt - ljt) is used instead of fit-
and their features are evident regardless of whether they are measured conditional on
the various intercept dummies which account for persistent inter-industry differences in
productivity, and conditional on the assumption that production functions differ industry
by industry and over time. Further, these effects are also observable when current
values o f entry and innovation are allowed to be endogenous. Equation (ii) on Table II
uses instruments for Ejt, Mjt and Ijt, and produces results which differ from (i) mainly
by suggesting that (i) understates 01, and Simultaneity bias, i f it exists,
appears to operate in a way which minimizes the impact o f entry and innovation, largely,
one imagines, because current growth shocks affect current entry flows. The strongest
impression that one gets from these various experiments is that the effects o f entry and
innovation are possibly also pro-cyclical, or, at least, are difficult to disentangle from
pro-cyclical effects. This is, perhaps, as it should be. The precise mechanisms by which
Verdoons Law is supposed to operate have never been made clear, but it is not hard to
believe that, at base, it is flows o f new firms and new ideas which do much o f the heavy
work.
Entangling the precise contribution o f innovation and each of the two types of
15
Table II: Estimates o f 01, Ä2 an(j #3 Using (7)*
Eit
(i)
0.0851
(H)
0.4947
(iii)
0.8316
(iv)
-0.00691
(v)
Eit-1(0.5427)0.4202
(0.4641)0.4529
(0.7098)0.4910
(0.0078)0.4116
Eit-2(2.281)-0.2682
(1.669)-0.2382
(1.501)-0.2990
(1.678)-0.2107
Mit(2.616)-0.0411
(2.352)-0.1141
(2.93) (2.0329)-0.1209 -0.1178
Mit-1(3.705)-0.0312
(4.33)-0.0183
(4.562)-0.0077
(4.400)-0.0162
Mit-2(1.094)0.0176
(1.014)-0.0031
(0.4411)0.0008
(0.9194)-0.0015
lit(1.277)0.0030
(0.3579)0.0114 0.0169
(0.0929) (0.1785)0.0119
Iit-1(1.536)0.0024
(2.195)0.0071
(2.671)0.0096
(2.323)0.0649
Iit-2(0.9184)
0.0053(1.75)0.0062
(2.242)0.0059
(1.694)0.0053
(1.824) (2.208) (2.105) (1.88)
R2 0.8846 0.8917 0.8787 0.8875 0.8859SSR 0.221172 0.207535 0.232328 0.215584 0.218546LL’h 699.415 709.471 691.641 703.459 701.303
* All estimated equations include 79 industry and 3 time intercept dummies, and allow for 79 + 3 slope co-efficients on f jt. Equation (ii) uses instruments for Ejt, Mjt and Ijt, (iii) for Ejt and Ijt, (iv) for Ijt and Mjt and (v) for Mjt and I^. The construction o f these instruments is discussed in footnote 10. Absolute values o f t-statistics are given in brackets below the estimate parameters; all estimates are heteroscelastic consistent.
16
entry is rather difficult, and, indeed, one is tempted to focus only on their joint effect.
None of the three variables that we are using to capture the effects o f entry and
innovation can be excluded from (i) or (ii) without significantly reducing the explanatory
power o f the regression. Further, all three provide similar information, the result being
that exclusion o f any one has some effect on the estimated impact o f the others.
Equations (iii) - (v) drop each in turn and show, broadly speaking, that the effects
attributed to any o f the three variables in (ii) are robust to inclusion of the other two.
This again seems sensible. The processes by which new firms and new ideas are
generated are bound to be deeply intertwined, and all three series ought to exhibit the
effects of many common exogenous determinants.
Much more interesting than the separate effects of each of the three variables
is the set of effects that each apparently generates over time. In all three cases, lagged
values of the entry and innovation variables appear to play an important role. The
effect of innovations is more or less constant over the three years, and, indeed, further
experiments using five lags in innovations revealed that the effect is roughly constant as
long as that. Using (ii) on Table II, the ultimate impact o f one more innovation on total
factor productivity is (at least) more that twice its initial impact. By contrast, the
effects associated with the two entry variables in (ii) diminish over time: the total effect
of domestic entry is, perhaps, 50% larger than its initial impact, while that o f foreign
entry is, perhaps, 15% larger in absolute magnitude. The effects associated with foreign
entry diminish regularly over time, even for as long as five years lagged. The effects of
the two entry variables seems to be more clear in total than in their distribution over
time, and this is particularly the case with domestic entry, the size o f whose initial
impact is very hard to establish with any precision. What is puzzling (but quite robust)
is the negative effect o f Ejt_2 on tota' factor productivity growth, and it must be
interpreted either as reflecting a rather complex dynamic adjustment or as a consequence
o f truncation b ia s .^ ^
17
The basic message that emerges is that domestic entry and innovation have the
expected positive effect on productivity growth, but that foreign based entry has a
negative effect. There are, perhaps, two explanations for the latter. First, given total
demand, an increase in input penetration displaces domestic production without, however,O 4
reducing domestic capital stock, thus lowering (yjt - kjt). Second, it is often the case in
the U K (e.g. in kitchen manufacturing, as discussed by Steedman and Wagner, 1987) that
high quality foreign imports displace high quality domestic goods, forcing domestic
producers down market. Whenever this causes domestic firms to specialize in lower value
added, lower productivity goods, then one will observe a diminution in productivity
growth during the displacement process. Either way, the negative ef; cts observed must
be considered to be net o f whatever incentive effects exist associated with new
competition. If those incentive effects are similar for both types of entrant, then a
comparison between 8 and 8 suggests that the restructuring or lower utilization of
capital stock effects consequent on foreign entry are fairly large indeed, and o f an order
o f magnitude just slightly larger than the incentive effects themselves.
Table III presents descriptive statistics on the estimated values o f derived
from (i) and (ii) respectively. As can be seen, the estimates o f are fairly robust to
alternative assumptions about the endogeneity o f E;t, and Ijt . Their movement over
time and, indeed, their average values in each year are broadly consistent with aggregate
estimates o f total factor productivity growth for this period, painting a familiar (and
gloomy) picture o f productivity in the UK. As can be seen in the last three columns,
the diversity o f individual industry experience is enormous. However, reference to Table
I and the discussion in Section III suggests that raw productivity measures, like the
growth in output per head or per unit o f capital, are a good deal more variable than
total factor productivity growth. There are three interrelated senses in which this is
true. First, raw productivity measures have a much higher standard deviation and range;
second, about 50% o f the variation in is between industry variation, much higher than
18
Table III: Estimates Values o f 0jt
from (i) on Table II
MEAN STD DEV MINIMUM M AXIM UM
1976 0.0031 0.02798 -0.1027 0.0722
1977 -0.0131 0.02272 -0.0537 0.0776
1978 0.0205 0.02817 -0.0324 0.1603
1979 0.0157 0.02943 -0.0379 0.1917
from (ii) on Table II
MEAN STD DEV MINIMUM M AXIM UM
1976 0.0023 0.0248 -0.0776 0.0759
1977 -0.0128 0.0210 -0.0564 0.0533
1978 0.0197 0.0217 -0.0423 0.1191
1979 0.0075 0.0349 -0.1748 0.0997
19
for the raw measures; and, third, the variation in productivity across industries over time
is more stable than it was for the raw productivity measures (the correlation between 0jt
across industries was in the 0.5 to 0.73 range for adjacent years, falling to 0.1754
between 1976 and 1979). Like raw productivity measures, total factor productivity
appears to be roughly normally distributed across industries, although it’s inter-industry
spread is much much lower. It seems plain from this that much of the enormous
variability in output or output per head growth rates is accounted for by variability in
inputs, leaving a much more modest variability in total factor productivity to be
accounted for. This said, total factor productivity is still extremely variable, exhibiting a
much higher rai.o o f within to between variation than is common in panel data sets.
Entry and innovation rates tend to have a similar type o f variability, and play an
important role in accounting for variations in total factor productivity. For the period
as a whole, the estimates from column (i) suggest that the contribution of domestic entry
to productivity growth is 0.002 percentage points; the estimates from (ii) suggest that it
is 0.0121 percentage points. For foreign entry, the figures are 0.0001 and -0.006, and,
for innovation, they are 0.0121 and 0.0291. Clearly innovation plays the major role in
stimulating productivity growth and, in its absence, no growth would have been observed
on average. More modestly but still of importance, domestic entry accounts for about
30% o f observed productivity growth on average (adopting the more conservative
estimates); foreign based entry plays almost no quantitively important role.
Although it is not o f major interest, the other output produced in (i) and (ii)
is estimates o f ocjt, and these turn out to be very similar between the two estimated
equations. On average across industries and years, oc^ « 1.11, and almost all o f its
variation is cross-sectional in nature. In several further experiments, we included
variables like industry concentration, entry, import penetration, innovations and industry
growth as possible determinants of but only the latter was ever significant it had a
positive effect). It proved to be the case that including, say, entry as a determinant o f
20
both ocjt and 0jt made it difficult to sort out their separate effects, but, in general, most
of the observables could be restricted out o f the explanation o f given that they were
included as determinants o f The time dummies indicated that the « jt rose marginally
in every year from 1976 to 1979 on average across industries. If we assume that all
factors are fully utilized, then these estimates o f the oqt suggest a mark up o f prices
over marginal costs o f about 11%, slightly less that the 17% estimate one gets from
standard Census sources (eg see Geroski and Toker, 1988). Unsurprisingly, ocjt and «it
were mildly positively correlated across industries, and the oqt were roughly normally
distributed across industries, (although the range was fairly severely truncated).
Our final task is to relate these results to the literature (see footnotes 2
and 3 above). The effects o f innovation are not dissimilar to those that have been
observed using R & D or patents data, and the general tenor of the results on entry are
consistent with those observed in the literature with respect to barriers to entry. The
trade effects observed here differ from previous results associated with trade intensity.
As it happens, using trade intensity rather than foreign entry in (7) tended to produce
positive co-efficients that were generally significant but more modest in size than those
associated with domestic entry flows. One concludes that the effects o f foreign
competitors on domestic productivity are negative during the period when their market
penetration is increasing. At a steady state, however, high import penetration levels
enhance productivity. Much the same effects are observable with concentration, which
attracted a positive and often significant co-efficient (without, however, much affecting
estimates o f 01, and 0^)/12) seems plain that the process o f deconcentration
induced by entry stimulates productivity growth, but in the steady state, highly
concentrated markets still seem to be slightly more productive than less concentrated
ones. It is clear that there remains a slight puzzle with these results, particularly with
respect to industry concentration, but this puzzle may have more to do with what one
means by "competition" than with the effects that one thinks it might have on
21
productivity growth. Although one can observe significant effects on productivity
associated with industry concentration and import intensity, the contribution they make
is largely captured by 0j in their absence, and these variables do not help to account for
what is, perhaps, the most striking feature o f the data - the large year to year variation
productivity growth observed for each industry taken alone. High import intensity and,
even more strikingly, industry concentration are (relatively) permanent features o f
industries; high productivity growth is not.
V. CONCLUSIONS
Our goal in this paper has been to explore the effects of competition - "new
firms and new ideas" - on productivity growth rates. Although entry and innovation
rates do not fully capture the ebb and flow o f competitive challenges that incumbents
must deal with, they do at least convey some o f the flavour o f change and development
that does occur in markets. Industries which experience rapid changes in technology or
host numerous new faces year in and year out are likely to be those in which incumbents
are under some pressure to perform well. This rather uncontroversial proposition finds
strong support in our data. Competition plays a significant role in stimulating
productivity, with both new firms and new ideas provoking movements to and outward
movements o f the production frontier which, the data suggests, simply would not have
occurred in their absence. O f the two, innovations play the more substantive role, but
observed domestic entry rates account for at least 30% of total factor productivity
growth observed. Foreign based entry, on the other hand, causes a m ild fall in
productivity growth.
Another way to summarize both our results and the puzzles that remain is in
terms o f simple data description. The strongest and most overwhelming feature o f the
data is the sheer variation in productivity growth that one observes. In any year,
22
industries differ enormously in how much they grow, and productivity growth fluctuates
enormously in any given industry over time. Almost no industries display persistently
high or persistently low productivity growth over time, and rankings by performance vary
year by year. One can account for a fair bit of variation in output or output per input
growth rates by using input growth rates, particularly if the input-output relations are
allowed to be industry specific (as they clearly are). Variables like entry and innovation
also contributes significantly to the overall explanation o f productivity growth. Very
little permanence or persistence in productivity growth is perceptible in the data, but
what little there is seems (puzzlingly) to be positively associated with industry
concentration and import intensity.
23
DATA APPENDIX
The real output data was that used by Wenban-Smith, J 98 J; employment data came from
the Census of Production; capital stock data was taken from calculations made by Dick
Allard for the Office o f Fair Trading; materials inputs were computed by subtracting net
from gross input and dividing by the ratio o f gross to real output. Domestic entry was
measured as the market share o f all new firms appearing in the annual Census, the
calculations being made by the Business Statistics Office; foreign entry is the change in
import share, derived from figures in the Business Monitor; and the innovations data
were counts of major innovation derived from the series on iajor innovations
constructed by SPRU at Sussex (discussed in Pavitt et al, 1987) and provided on tape by
the ESRC Data Archive at Essex University.
24
NOTES
(1) This is the broad drift of a range o f studies at firm and industry level of the persistence o f profits; Geroski and Toker, 1988, observe an extremely high stability of inter-industry differences in price-cost margins for the period 1970-1979 in the UK for the industries in our sample (plus several more).
(2) Work exploring the effects of concentration on efficiency is surveyed by Scherer, 1980, and Siegfried and Wheeler, 1981. Calculations by Primeaux, 1977, and White, 1976, suggest that concentration reduces efficiency, but Carlsson, 1972, reports positive effects. On concentration and productivity growth, see Greer and Rhoades, 1976, and references therein.
(3) Net foreign entry, the change in import share, was used largely because gross foreign entry data is not available. We did experiment using exit rates in addition to entry rates, the two variables are positively correlated to each other and to productivity growth, suggesting that the effects attributable to entry arise from displacement. The innovation variables are, to my knowledge, new to this kind of work, but much work has been d i e on the effects of R&D and patents on productivity; see the last five papers in Griliches, 1984, and references therein.
(4) Bruno, 1984, has argued that raw materials price rises will appear as autonomous technical regress, an effect that may even plague estimates of (7) using net output measures. To the extent that entry and innovation are important elements o f the response to raw materials price shocks in heavy raw material using industries, then one expects that failing to account for such price shocks will bias estimates of
the effects o f such shocks.
(5) See Bernanke, 1983, Chaterji and Wickens, 1982, Mohr, 1980 and others; interesting attempts to measure labour and capital services include Bailey, 1981, and Muellbauer, 1984. Note that leaving the acjt to be determined (in principle) allows factors to be valued at shadow prices determined by the degree of utilization o f stocks (see Berndt and Fuss, 1986).
(6) See, for example, Bruno and Sachs, 1982, Mendis and Muellbauer, 1984.
(7) In general, the tails o f the distributions were too truncated to make them exactly normal. For the rate o f growth of labour productivity (pooled over time and industries), the mean was 0.027, the median was 0.030, kurtosis « 1.410, skew » 0.092 and the range was [ -0.287, 0.383].
(8) We relaxed the assumption of constant returns to scale in a weak way by introducing the term of kjt into (7), forcing the co-efficient on it to take a common value across industries and over time. The variable attracted a negative and significant co-efficient (indicating decreasing returns to scale) and. more to the point, its inclusion had almost no effects on the estimates of 01, and Properly relaxing constant returns effectively adds N + T - 1 parameters to be estimated.
(9) This procedure assumes that the output elasticities o f labour, capital and materials all d iffer from their observed factors shares by the same proportional amount. This will be true i f the shadow prices o f the three all bear the same relation to their market prices (i.e. if all three are utilized to the same degree), since the divergence of price from marginal cost affects all three factor shares to the same degree. Making this assumption effectively means that (ljt ~ ^it) can replaced by an
25
observed factor share weighted sum o f labour and materials per unit o f capital, the co-efficient on which will reflect divergences o f price from marginal costs and shadow from market prices o f inputs common to all factors of production.
(10) The instruments have been generated from reduced form regressions o f Ejj, Mjt and Iit on industry specific and time effects, domestic entry, domestic exit, industry concentration, the level o f import penetration, and innovation all lagged up to six times. The broad features o f the estimates in (ii) on Table II are insensitive to alterations in this set o f exogenous variables.
(11) If it has any effect on Table II, truncation bias is likely to affect the estimates of the effect o f domestic entry, since the effects associated with innovation and foreign entry appear robust to the inclusion o f extra lagged terms. Data limitations mean that only two lagged domestic entry variables are available and, since entry appears to follow a second or third under autoregression, problems can in principle be expected (eg see Griliches and Pakes, 1984). Whether this accounts for the negative effect o f Ejt_2 is not clear, but it seems possible.
(12) The Schumpeterian hypothesis suggests that concentration will be linked to innovation and this may offset any static efficiency reducing effects that it has, leading a net positive effect on productivity. This offsetting seems to correspond to the general tenor o f results reported in the literature. However, the positive effect of concentration on innovation is probably spurious, reflecting the fact that highly concentrated industries are often rich in technological opportunity (Geroski, 1988). Scherer, 1983, observed that the effect o f concentration in labour productivity growth disappeared when R & D expenditures were included, which is consistent with this view.
26
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ZITIERWEISE/CITATION:Paul A. Georski:
Entry, Innovation andProductivity Growth,Discussion Paper FS IV 88 - 17, Wissenschaftszentrum Berlin für Sozialforschung 1988.
