Process versus Product Innovation: What difference does it
make?∗
Jordi Jaumandreu†and Jacques Mairesse‡
March 16, 2015
Abstract
This note explores the structural specification of the impact of innovation on the
cost and demand functions of the firms and estimates several models with a role for
both indicators. We find that the introduction of process and product innovations
affects differently the demand and production functions of the firm. Both product and
process innovation shift the demand for the products of the firm. Process innovation
reduces production marginal cost, but not always. A possibility, that we cannot neither
prove nor reject with the current specification, is that process innovations associated to
product innovations raise marginal cost. Another relevant finding is that advertising
significantly affects demand but we can exclude, as expected, that affects production
marginal cost. To obtain richer conclusions, an enlargement of the model will be needed.
∗
We thank Xin Qi for research assistance, Sabien Dobbelaere, Ivan Fernadez-Val and Mark Roberts for
comments, and the audiences at the London and Paris CDM workshops.
†
* Boston University and CEPR.Department of Economics, Boston University, 270 Bay State Road, MA
02215, Boston, USA. E-mail: jordij@bu.edu
‡
** CREST-ENSAE (Paris), UNU-MERIT (Maastricht University) and NBER.CREST-ENSAE, 15,
Boulevard Gabriel Peri, 92245 Malakoff cedex, France. E-mail: Mairesse@ensae.fr.
1
1. Introduction
Firms perform R&D to obtain and introduce process and product innovations. The process of discovery and development of innovations embodies uncertainty, and hence the timing
of the resulting innovations and its introduction is also be characterized by some randomness. The distinction between process and product innovations, formalized by OECD in the
Oslo manual in 1992, picks up a fundamental distinction of innovative activities. Sometimes
the firm wants to change cost by altering the process of production, sometimes to enhance
demand by developing the products that sells. It seems clear, however, that at least some
process innovations may also change the nature of the product and affect demand, and
some product innovations need associated process changes. Firm-level data collection in
different surveys have made available counts of innovations since 1992 distinguishing process and product innovations. Researchers have struggled to use both indicators in their
productivity equations, with quite diverse results, and many problems about this use of the
indicators and the interpretation of results remain.
Crepon, Duguet and Mairesse (1998), CDM, was the first paper that formalized the process of R&D in different stages. They first allowed for an equation representing the decision
of the firm to carry out R&D. Then they introduced a production function of knowledge,
that is a function of production of innovations as the result of the R&D investments. Finally,
they specified the introduction of these innovations as impacting productivity over time in
a Cobb-Douglas production function. R&D was the natural instrument to asses the role
of these innovations in the production function. CDM measured innovations alternatively
by patents and the share of innovative sales. Notice that the first variable is an innovation
indicator and the second a product innovation measurement.
Since then many papers have adopted the staggered modeling of the production of innovations and the impact of innovations on productivity. In doing this, they use to take
advantage of the availability of process and product innovation indicators. A good example
is Griffith, Huergo, Mairesse and Peters (2006), who introduce process and product innovation in a Cobb-Douglas production function, after modeling separately the production of
2
both type of innovations. GHMP use firm samples for France, Germany, Spain and UK,
find a significant impact of product innovation everywhere except in Germany, but process
innovation is only significant in France. The authors are surprised by the lack of association between productivity and process innovation and conjecture in page 493 that “...could
correspond to the fact that we are measuring revenue productivity (deflated by industry
deflators, not by individual firm deflators)”.
More recently, Peters, Roberts, Vuong and Fryges (2014) constitute a notable example of
dynamic paper that also carefully models the generation of process and product innovations.
Productivity follows a Markov process that shifts with their introduction. Product innovations turn out to be only significant, however, in the high-tech industries, while process
innovations only show a weaker significance in low-tech industries. The productivity to be
explained in PRVF is explicitly a combined form that mixes the efficiency of the firm with
the demand shifts and that it is called “revenue productivity”.1 In this sense it inherits the
same context as in GHMP.
A somewhat more structural use of the indicators of process and product innovation is
done in Harrison, Jaumandreu, Mairesse and Peters (2014). HJMP derive a labor demand
equation where there is a separated role for the increase of the efficiency of production, as
a direct driver of employment reductions for a given output, and an output effect coming
through the demand enlargements induced by new products. Product innovation effects
are always well estimated and process innovations are often significant despite the lack of
deflators. Another related paper is Jaumandreu and Lin (2014), which emphasizes the
modeling of firms’ marginal cost as depending on both process and product innovation and
starts an exploration of its transmission to prices. Preliminary estimates point out that
both process and product innovations increase productivity, but with non-trivial patterns.
Jaumandreu and Mairesse (2010), henceforth JM, is up to our knowledge the first paper
1
There is a literature started with Klette and Griliches (1996) which tries to solve the problem of esti-
mating without prices by nesting the production function in the (inverted) demad function of the firm. A
recent example is de Loecker (2011). PRVF nest maginal cost in the direct demand and explicitly consider
a persistent demand shifter.
3
that has tried an structural modeling of process and product innovation, separating their
effects in the marginal cost and demand functions of the firm, i.e. on productivity and
demand shifts. There is a version currently in progress, Jaumandreu and Mairesse (2013).
This note reports the work done and the problems encountered in the structural specification
of the effects of innovation.
The rest of this note is organized as follows. In Section 2 we describe the context of
estimation. Section 3 biefly comments the data. Then Section 4 discusses the results of
carrying out estimations of models with exogenous innovation, and Section 5 instrumenting
innovation. Section 6 briefly discusses the exclusion of advertising from the marginal cost
equation. Section 7 ends the note with some concluding remarks.
2. Context
JM specify a system of two equations, the short-run marginal cost function and the
demand function of a firm. Marginal cost is the short-run marginal cost because capital
is given, and hence it depends on capital, the prices of the variables factors -labor and
materials-, and output. Marginal cost depends in addition on the unobservable level of productivity of the firm 1  Demand represents the demand relationship of a monopolistically
competitive firm, which depends on the price set by the firm given the prices of the other
firms (picked up by the time dummies). Demand also depends on an unobserved demand
advantage of the firm represented by 2 . The system can be written as
  = (         1 )
 = (   2 )
(1)
where  stands for capital,  and  for the price of labor and materials,  for output,
and  for output price.
In this note, we are going to assume that  1 and 2 follow random walk with drift
4
processes, that is
 1 =  1−1 + 1  1 +  1 
 2 =  2−1 + 2  2 +  2 
(2)
where 1 and 2 represent variables that shift productivity and demand advantages and
hence marginal cost and demand.2 As a link between the two equations a pricing rule is
assumed to hold: firms set price with a margin on marginal cost according to the elasticity
of demand,  =

−1  
As a first approximation, the elasticity of demand is considered
constant.
Both equations are hence subject to persistent displacements that represent changes in
the firm productivity and improvements of demand, respectively. The value of the shifters
1 and 2 may be endogenously determined by the firm but, as long as this determination
is previous to the current period and the process for 1 and  2 are well specified, this value
is going to be orthogonal to the unforecastable random shocks  1 and  2 
We are going to consider the following shifters: the state of the market, advertising of
the firm and innovation. Variable  stands for the state of the market of the firm (an
index which takes three values corresponding to three states: recessive, stable or expansive).
Variable  is the rate of change in advertising expenditures of the firm. And variables 1
and 2 are generic indicators of the innovations introduced by the firm (see below for their
specification).
Log differentiating equations (1), inserting equations (2) and specifying the shifters, representing rates of growth by lowercase letters, the system can be written as



1
 +  +
 + ( − 1) +  1  + 1  1 +  1 




= − +  2  +    + 2  2 +  2 
 = −

2
(3)
The random walk process is only a simplifying assumption to be relaxed and tested in more complex
specifications. Notice, however, that a number of works that assume an autoregressive process in productivity
get as a result an autoregressive coefficient quite near to one (see, for example, Peters, Roberts, Vuong and
Fryges (2014)).
5
where we write the short run elasticity of scale as (≡  +  ) Variables , , , ,  and
 are the rates of growth of marginal cost,3 price, output, capital, and the prices of the
two variable factors, labor and materials, respectively.
Market dynamism is included in both equations, advertising only in the demand equation,
and innovation effects in both equations. That dynamism of the market is a shifter of
demand seems obvious. But it is less clear why this variable should be included in the
cost equation. We include it because in practice seems to be important but we should
precise two things. First, that its impact in the cost equation is significant but small and
systematically negative. Second, that we can drop this variable from the cost equation
without a significant change in the estimates (except that the error is then correlated with
the cycle). The reasons for these facts are worthy of further investigation that we leave for
the future.
We carry later a test to confirm that advertising only affects demand, as the theory
of production establishes (advertising is not a factor of production, but an investment to
impact consumers demand), i.e. that advertising can be excluded as a shifter of the cost
equation.
Our main interest in this note is to establish how process and product innovation should
structurally enter cost and demand. We are going to consider four alternative specifications,
that we will call unrestricted, non-specialized, specialized and mixed, respectively.
We observe the introduction of two different type of innovations, process and product
innovations. A-priory it is not completely clear if they impact cost, demand, or both
relationships. Hence, we may want to start with a general model that allows for all type of
effects:
1  1 =  11  +  12  +  13  ×  
2  2 =  21  +  22  +  23  ×  
3
(4)
Notice that, with constant short-run elasticity of scale, the growth of marginal and average costs are the
same, so we simply write  for the rate of growth of average variable cost.
6
where  is a dummy of process innovation and  a dummy of product innovation. This
is the unrestricted model.
The effects in (4) nest three models of particular interest. If product, process innovations
and simultaneous process and product innovations have the same impact, the right model
can be obtained by imposing the restrictions  11 =  12 = − 13 and  21 =  22 = − 23  The
model can then simply be written as
1  1 =  1  
2  2 =  2  
(5)
where  = 1( = 1 or  = 1) is a simple dummy of innovation We call this the
non-specialized model or specification, where the only thing that really maters is innovation
whatever is its character.
If, alternatively, process innovation affects cost only and product innovation affects demand only, the restrictions  12 = 0  13 = 0 and  22 = 0  23 = 0 determine the model
1  1 =  1  
2  2 =  2  
(6)
We call this specification the specialized model, since each kind of innovation affects a
particular equation.
Of course there is little economic sense if any in defining a model that reverses the role of
process and product innovation (affecting to demand and cost respectively). But the data
have convinced us of the possible validity of a combination of models (5) and (6),
1  1 =  1  
2  2 =  2  
(7)
that we call mixed model. Process innovation is the only innovation affecting cost but both
process and product innovation affect demand in the same way.
7
3. Data
We present estimates based on ten (unbalanced) industry samples, which in total amount
to more than 2,400 Spanish manufacturing firms and 20,000 observations, corresponding to
the period 1990-2006. All variables come from the survey ESEE (Encuesta Sobre Estrategias Empresariales), a firm-level panel survey of Spanish manufacturing starting in 1990.
The survey provides a random sample of Spanish manufacturing with the largest firms oversampled. A Data Appendix provides the details on the sample composition and variables
definition.
Let us briefly detail the variables that we use. On the one hand, we have the more usual
variables: output (deflated production) and an estimate of capital stock. We compute
variable cost as the sum of the wage bill and intermediate consumption, and estimate the
hourly wage by dividing the wage bill by total hours of work.
But we also have some less usual firm-level variables which play an important role in our
estimations. Firstly, we have the yearly (average) output price change as reported by the
firm that we use to get the output index from nominal production. Secondly, firms also
provide an (average) estimate of the change in the cost of inputs grouped in three sets:
energy, materials and services, which are combined in a price index for materials. Finally,
we can compute a firm specific user cost of capital using the interest rate paid by the longterm debt of the firm plus the rough estimate of a 0.15 depreciation rate and minus the
consumer prices index variation.
We are going to use the following shifters: a dummy representing the introduction of
process innovations, a dummy reporting the introduction of product innovations, the rate
of increase of firm advertising, and an index of the dynamism of the firm’s specific market.
Finally, we have the R&D expenditures of the firm that we are going to use to instrument
the innovations.
Table 1 shows the size of the samples and descriptive statistics. Table 2 provides detail
on the innovation variables.
8
4. Results with exogenous innovation
Marginal cost and demand functions
Let’s start by describing how we estimate the equations corresponding to the specification
of the marginal cost and demand functions leaving aside the shifters. Quantity and price
are the endogenous variables included in the right side of the equations, respectively. Other
variables are capital and prices, and both equations include constant and 15 time dummies.
Our capital variable is in fact capital times the utilization of capacity.
Capital and prices should be both exogenous but we need, however, to instrument them
in the first equation, presumably by a problem of errors in variables. The fact that we
are estimating in differences may be, as it is well known, exacerbating this problem. We
employ the user cost of capital and the variable utilization of capacity as instruments aimed
at picking up the variations of capital. Simultaneously, we use the levels of the wage and
price of materials index to help the estimation of the price coefficients (a solution in the
tradition of panel data GMM estimates). This is enough for identification, because we have
to estimate only three elasticities in the first equation and the elasticity of demand in the
second equation.4
Two important aspects should be precised. The right variable to include in the right hand
side of the demand equation is price, but we are only able to include it in six industries. In
industries 2,4,5 and 10 we have not been able to get meaningful estimates with the (variation
of) price and we have replaced it by the (variation of) cost. If the markup were invariant,
these two forms of the equations should be completely equivalent. As they are not, at
least for some industries, this means that in the future we should do an effort to model
independently the price in a third equation and try to include it properly in all industries.
Also, when we use price as explanatory variable in the demand equation, as a matter of
4
In fact this way of instrumenting gives one overidentification restriction in the first equation and three
in the second. When we take the shifters as exogenous this gives another overidentification restriction in the
first equation, where advertising is not included as regressor. This gives a total of 5 restrictions, which drop
to 4 when we do not use utilization of capacity to instrument the second equation (see below). In industry
10, the restrictions fall because we take capital as exogenous.
9
fact we have to drop utilization of capacity as instrument in this particular equation.
We are going to keep fixed this form of instrumenting in all our subsequent experiments.
We can see the results in tables 3 and 4. Table 1 allows to check that the estimation of
the elasticities is very reasonable, although sometimes a little imprecise. Notice that we
are estimating production elasticities with variation in prices, something fully supported by
duality but not usual. In particular, all long-run returns to scale are sensible and not too far
from unity and all the elasticities of demand significantly above unity as expected. Table
4 shows that the specification test is however not passed at standard levels of signification
in five industries. We attribute this to the specification of the shifters of both equations
and continue with our exercises in the hope that a better specification will improve these
overidentification tests.
Unrestricted innovation effects
Our first estimate, reported in Table 3, includes the unrestricted innovation effects of
equation (4). All shifters, including these unrestricted innovation effects are taken as exogenous. The innovation effects are imprecisely estimated, what is the likely result of a high
degree of multicollinearity of the innovation dummies (correlation between the dummies is
high, see Table 2). But the results also point out to a high heterogeneity of the effects. A
detailed reading of the cost effects shows a somewhat dominant pattern. In six industries
both process and product innovation tend to show a negative sign together with a positive
interaction effect. And a detailed reading of the demand effects reveals a different dominant
pattern, a little more significant. In seven industries both process and product innovation
are positive while the interaction effect is negative. We conclude that innovations seem to
reduce cost but not as clearly when process is associated to product innovation. And also
that both innovations tend to increase demand, both in isolation and when associated.
Restricting the effect of innovation
We try to get a more precise idea by restricting the effects to give the models implied by
equations (5), (6) and (7), the non-specialized, specialized and mixed models respectively.
As each restricted model is nested in the general equations (4), the restrictions can be tested
by means of a difference of the values of the second stage objective functions, conveniently
10
scaled, when they have been obtained by using the weighting matrix of the unrestricted
estimate. This difference is distributed as an 2 with degrees of freedom equal to the
number of restrictions, that are always 4. The results of the test are reported in columns 3
to 8 of Table 4.
The results are somewhat defavorable to restrict the innovation effects to the effects of
the specialized model. In five industries the restriction is statistically rejected at standard
levels of significance. Less defavorable are the results of the non-specialized and mixed
models. The non-specialized model is only rejected in one industry, the mixed model in
three. No one of these two models seems to be clearly better. We conclude that it seems
quite well statistically established that both process and product innovations have an impact
in demand but we have no ground to clearly reject that the relevant cost variable is process
innovation.
A model with exogenous innovation
The previous result suggests that there is a chance that the non-specialized model, a
model that simply includes an (exogenous) dummy of innovation in each equation, works
properly. Table 5 reveals, however, that this is not the case. We tried to include innovation
in the cost function both contemporaneously and lagged. We retain the results with lagged
innovation because they are slightly better. Elasticities and the effects of the rest of the
shifters stay basically the same. But innovation is only fully significant in the demand
equation in six industries and process innovation in none. Moreover, the specification test
is now not passed in six industries.
5. Results instrumenting innovation
What kind of endogeneity?
From the previous exercises we conclude that, in addition of heterogeneous innovation
effects, we have a problem of errors in variables that is likely to create correlation of the
dummies with the residuals and hence some biases. Errors in variables in all the innovation
dummies (process, product and the innovation dummy) are likely to arise by several motives.
11
The first reason is the unweighted character of these count indicators. Reduction of cost or
increase of demand are likely to change in size according to the character of the innovation,
in particular the degree of research and hence the novelty that it embodies. One can argue
that the replacement of the relevant quantitative innovation variable by the dummy is likely
to bias towards zero the estimated coefficient.5 A second reason is that these yearly count
indicators can hardly give account of the relevant timing of the introduction of innovations
and the lags in their impact.
Part of the problem can be addressed by using R&D expenditure as instrument.6 For
example, in this exercise we construct an instrument that is equal to the (log of) the sum of
the R&D expenses accumulated by the firm since the latest innovation, if there is innovation,
and zero if there is no innovation. This instrument can help to address the problem because
its correlation with the size of the innovation effect. But we cannot distinguish between R&D
expenditures in process and product innovation and hence we have only one instrument.
This sets a radical limitation about what we can do in practice, because we cannot hope
to instrument unrestricted innovation effects with only one instrument. A possible solution
would be the discovery of more instruments by setting a structural model of process and
product innovation. But this is a complex task in itself that lies out of the scope of this
note.
Models, instruments and tests
Hence, we are going to estimate and compare the three models that only include one
innovation indicator in each equation using the same set of instruments. Recall that the
non-specialized model includes a dummy of innovation in both equations, and the specialized
includes the dummy of process innovation in the cost equation and the dummy of product
innovation in the demand equation. The mixed model includes process innovation in the
cost equation and the dummy of innovation in the demand equation. Whatever indicator is
5
Assume that the true model should include a term  and the variable size is replaced by the dummy,
that is related to size by the linear predictor  =  0 +  1  +  The included term will be 
and the disturbance of the equation will contain − 1  The dummy is negatively correlated with this
component of the error.
6
This is a arrangement of the type that Wooldridge (2010) calls "multiple indicator solution".
12
used, we lag it one period in the cost equation (they seem to give better results). In what
follows, we describe the common way to instrument innovation.
We firstly include product innovation as instrument in the cost equation and process
innovation as instrument in the demand equation. We hope that their possible errors do
not invalidate them as instruments. Their main paper is to help to predict the endogenous
variable of the equation in which they are used as instruments (hence product innovation
helps to predict output and process innovation cost). Secondly, we include the variable accumulated R&D (since the latest innovation) in both equations, lagged in the cost equation
because the innovation regressor is going to be lagged.
The three models hence diverge in the innovation regressors and use the same set of
instruments. We therefore compare them by means of a Rivers-Vuong type of test for non
nested models (see Rivers and Vuong, 2002). The test consists of computing the difference
of the value of the objective functions of the first stage estimates, divided by an estimate of
the variance of this difference.7 The test is distributed as an standard Normal and then a
positive (negative) significant value implies that the second (first) model is better than the
other. Table 6 reports the value of the objective functions (columns 1 to 3), and the values
of the test with their probability values in columns 4 to 6.
The non-specialized model turns out to be significantly better than the specialized model
in four industries, at different degrees of signification (5% in industry 1, 10% in industry
3 and 15% in industries 7 and 9). The mixed model is also significantly better than the
specialized model in four industries (1 and 3 at 10%, 7 and 9 at 15%). The non-specialized
and mixed models fit the data quite similarly in any industry. We are going however to
retain the mixed model because it seems to marginally give better results. Table 7 reports
the estimates generated by this model.
An structural model of product and process innovation
In this instrumental variables estimation we have added 2 overidentification restrictions,
since we have one additional endogenous variable and two additional instruments per equation. This makes a total of 6 restrictions in most of industries (but see footnote 2). Despite
7
See e.g. Doraszelski and Jaumandreu (2013) for an application.
13
that the specification has improved we still have four industries in which the specification
test is not passed. So they remain some specification problems.
The elasticities in Table 7 are sensible, with little changes with respect to the previous estimates and again sometimes a little imprecisely estimated. The market dynamism variable
diminishes cost and increases demand in all cases, 8 of them significantly in each equation.
The advertising variable shifts demand significantly in all 10 cases. Product innovation
increases demand in all cases, 8 of them significantly. Process innovation at moment  − 1
that is the variable included in the cost function, is significant in 5 cases. In 3 out of the 5
it decreases cost but in the other 2 it increases it. In four more cases the effects are close
to statistical significance, again split in two negative and two positive.
Our interpretation is that we are only able to estimate a reduced form of the effects in
the cost function, and that this reduced form implies both global negative and real positive
cases. Part of the process innovations seem to increase cost very clearly. But we do not know
which is this part and why it dominates in some industries. The most likely explanation
is that process innovations associated to product innovations decrease the efficiency of the
firm. This seems something sensible, as these new processes may well imply some not fully
observed changes in the inputs (as labor skills, quality of materials, managerial abilities
or production organization). We cannot disentangle the effects without, at least, more
instruments, and these instruments are unavailable.
6. Advertising
We have included market dynamism in both equations and discussed te best specification
for the innovation dummies but, is advertising rightly excluded from the cost equation?
To answer this question we estimate the model that we have retained extended to include
advertising in both equations. Using this unrestricted estimate we apply the same type of
test that for restricting the effects of innovation. In this case the restriction to be tested is
simply if we can consider that the estimated coefficient on advertising in the cost equation
can be shut down to zero.
14
The result of the test is quite drastic as can be checked in Table 6 columns 7 and 8.
In seven sectors we can accept the exclusion of advertising in the cost equation with high
levels of confidence. In the remaining 3 sectors we cannot, but we cannot because just the
role that advertising has picked up in the unrestricted model is perverse. In industries 4
and 5 advertising has in the cost function a significant positive effect at the expense of the
coefficient on capital (the coefficient of capital, that is negative, becomes positive or zero
respectively). In industry 6, the impact of advertising is negative, diminishing cost. We
conclude that the exclusion of advertising from the cost equation constitutes the imposition
of a theoretical restriction fully accepted by the data.
5. Concluding remarks
We have explored the structural specification of innovation effects by means of dummy
variables representing the introduction of process innovations, product innovations or simply innovations. We want to pickup the effects of innovation in the productivity-induced
variations of the marginal cost function of the firm and in the shifts of its demand function. The specification of unrestricted exogenous innovation effects detects heterogeneity
that cannot be fully assessed because the signs of colinearity in the estimation of the coefficients and a likely problem of errors in variables. The models instrumenting innovation are
however compelled to the use of only one instrument for the innovation effects, because is
all what we have correlated with the size of the effects. Comparing different specifications
we arrive to two main conclusions that reinforce the suggestions of the previous estimates.
On the one hand, it seems to be not much difference in the impact on demand of product
innovation only, process innovation only and simultaneous process and product innovations.
On the other, it seems clear that process innovations sometimes reduce cost and sometimes
increase cost. The problem is that we can only get a reduced form of the effects in the cost
function. The conjecture, that we cannot prove with our unique instrument, is that some
process innovations associated to product innovations raise costs.
These findings are important because they can explain the lack of robustness of innova-
15
tion effects estimated in different productivity equations. First, if the researcher includes
both process and product innovation in an specification that embodies both productivity
evolution and demand shifts, the results can be very different according to the correlations
which dominate in the data set. Process innovation can be picking up efficiency and demand
shifts but also may turn out to have no effect or a less significant effect if it is negatively
related to efficiency. At the same time, a product innovation dummy may be mainly estimating the demand effect but also show a reduced or even non significant effect because of
its possible negative efficiency effect. Second, if the equation can be argued not to significantly include demand shifts (for example because revenue has been effectively deflated),
a positive significant effect for process innovation seems not to be warranted and obtaining
it for product innovation may be something definitely harder. Third, the substitution of an
innovation dummy for the two effects is likely to mix the effects in any way.
Are there any solutions for the structural estimation of process and product innovation
effects? One first solution, already mentioned, is the enlargement of the set of available
instruments. The reasonable way to do this is specifying and estimating equations for the
production of process and product innovations that can select some variables particularly
correlated with the introduction of one or the other type of innovation. These variables
would be suitable instruments. These equations are not easy to specify because, with panel
data, in addition of the problem of the type of innovation we need to predict the timing
of the introduction. A second possible solution is to construct a sort of latent variable
model, in which the effects of the unique dummy introduced in each equation correspond to
a particular theoretically restricted combination of process and product effects. A model of
this type can possibly infer structural effects from the estimated coefficients and correlations
in the data. Both avenues seem worthy of being pursued in future research.
16
Data Appendix:
All employed variables come from the information furnished by firms to the ESEE survey
(Encuesta Sobre Estrategias Empresariales), a firm-level panel survey of Spanish manufacturing starting in 1990 and sponsored by the Ministry of Industry. The unit surveyed is
the firm, not the plant or establishment, and some closely related firms answer as a group.
At the beginning of this survey, firms with fewer than 200 workers were sampled randomly
by industry and size strata, retaining 5%, while firms with more than 200 workers were all
requested to participate, and the positive answers represented a more or less self-selected
60%. To preserve representation, samples of newly created firms were added to the initial
sample every subsequent year. At the same time, there are exits from the sample, coming
from both death and attrition. The two motives can be distinguished and attrition was
maintained to sensible limits. Table A1 gives the composition of the sample according to
the number of years that the firms stay in it.
Definition of variables
Advertising: Firm’s advertising expenditure.
Average cost: Firm’s variable costs (wage bill plus the cost of materials) divided by output.
Capital : Capital at current replacement values is computed recursively from an initial
estimate and the data on current firms’ investments in equipment goods (but not buildings
or financial assets), updated by means of a price index of capital goods, and using industry
estimates of the rates of depreciation. Real capital is then obtained by deflating the current
replacement values. In the regressions we the utilization of capacity times capital (see below
for utilization of capacity).
Market dynamism: Weighted index of the market dynamism reported by the firm for the
markets in which it operates. The index can take the values 0 (slump), 0.5 (stable markets),
and 1 (expansion).
Output: Production of goods and services. Sales plus the variation of inventories, deflated
by the firm’s output price index.
17
Price of materials: Paasche-type price index computed starting from the percentage
variations in the prices of purchased materials, energy and services reported by the firms.
Price of the output: Paasche type price index computed starting from the percentage
price changes that the firm reports to have made in the markets in which it operates.
Product innovation: Dummy variable that takes the value 1 when the firm reports the
introduction of product innovations.
Process innovation: Dummy variable that takes the value 1 when the firm reports the
introduction of a process innovation in its productive process.
R&D: Total R&D expenditures of the firm. From this expenses and the innovation counts
we construct an instrument that is equal to the (log of) the sum of the R&D expenses
accumulated by the firm since the latest innovation, if there is innovation, and zero if there
is no innovation.
Utilization of capacity: Yearly average rate of capacity utilization as reported by the firm.
User cost of capital : Weighted sum of the cost of the firm values for two types of long-term
debt ( long-term debt with banks and other long-term debt), plus a common depreciation
rate of 0.15 and minus the rate of growth of the consumer price index.
Wage: Firm’s hourly wage rate (total labour cost divided by effective total hours of
work).
18
References
Crepon, B., E. Duguet and J. Mairesse (1998), "Research, Innovation, and Productivity: An Econometric Analysis at the Firm Level," Economics of Innovation and new
Technology, 7, 115-158.
de Loecker (2011), "Product Differentiation, Multiproduct Firms, and Estimating the
Impact of Trade Liberalization on Productivity," Econometrica, 79, 1407-1451.
Doraszelski, U. and J Jaumandreu (2013), "R&D and Productivity: Estimating Endogenous Productivity," Review of Economic Studies, 80. 1338-1383.
Griffith, R., R. Harrison, J. Mairesse and B. Peters (2006), " Innovation and Productivity
across Four European Countries", Oxford Review of Economic Policy, 22, 483-498.
Harrison, R., J. Jaumandreu, J. Mairesse and B. Peters (2014), "Does Innovation Stimulate
Employment? A Firm-level Analysis using Comparable Micro Data on Four European
Countries," International Journal of Industrial Organization, 35, 29-43.
Jaumandreu, J. and J. Mairesse (2010), "Innovation and Welfare: Results from Joint
Estimation of Production and Demand Functions," Working Paper no. 16221, NEBR,
Cambridge.
Jaumandreu, J. and J. Mairesse (2013), "Innovation and Welfare: Results from Joint
Estimation of Production and Demand Functions," mimeo, Boston University.
Jaumandreu, J and S. Lin (2014), "Innovation and Prices," mimeo, Boston University.
Klette J. and Z. Griliches (1996), “The Inconsistency of Common Scale Estimators when
Output Prices are Unobserved and Endogenous,” Journal of Applied Econometrics,
11, 343-361.
Peters, B., M. Roberts, V.A. Vuong and H. Fryges (2013), "Estimating Dynamic R&D
Demand: An Analysis of Cost and Long-Run Benefits," mimeo, Penn State University.
19
Rivers, D. and Q. Vuong (2002), “Model Selection Tests for Nonlinear Dynamic Models,”
Econometrics Journal, 5, 1-39.
Wooldridge, J. (2010), Econometric Analysis of Cross-section and Panel Data, MIT Press.
20
Table 1. Descriptive statistics, 1990-2006
Output
growth
(s.d.)
(3)
Capital
growth
(s.d.)
(4)
Wage
growth
(s.d.)
(5)
Mat’s price
growth
(s.d)
(6)
Advertising
growth
(s.d.)
(7)
Market dynamism
index value
(s.d.)
(8)
Industry
Obs.
(1)
Firms
(2)
1. Metals and metal products
2365
313
0.045
(0.235)
0.055
(0.200)
0.049
(0.163)
0.041
(0.067)
0.04
(0.939)
0.600
(0.344)
2. Non-metallic minerals
1270
163
0.046
(0.228)
0.058
(0.215)
0.043
(0.144)
0.031
(0.034)
0.044
(0.876)
0.581
(0.337)
3. Chemical products
2168
299
0.060
(0.228)
0.063
(0.185)
0.047
(0.138)
0.032
(0.065)
0.026
(0.794)
0.589
(0.330)
4. Agric. and ind. machinery
1411
178
0.031
(0.252)
0.044
(0.198)
0.045
(0.150)
0.030
(0.038)
0.020
(0.786)
0.569
(0.352)
5 Electrical products
1505
209
0.059
(0.269)
0.046
(0.178)
0.051
(0.168)
0.030
(0.044)
0.036
(0.839)
0.557
(0.353)
6. Transport equipment
1206
161
0.060
(0.287)
0.050
(0.174)
0.047
(0.162)
0.028
(0.048)
0.029
(0.857)
0.569
(0.372)
7. Food, drink and tobacco
2455
327
0.023
(0.206)
0.051
(0.184)
0.052
(0.170)
0.033
(0.058)
0.046
(0.840)
0.540
(0.313)
8. Textile, leather and shoes
2368
335
0.004
(0.229)
0.035
(0.197)
0.052
(0.178)
0.031
(0.044)
0.049
(0.851)
0.436
(0.343)
9. Timber and furniture
1445
207
0.025
(0.225)
0.049
(0.174)
0.054
(0.166)
0.035
(0.039)
0.048
(0.940)
0.530
(0.338)
10. Paper and printing products
1414
183
0.031
(0.187)
0.052
(0.226)
0.052
(0.139)
0.035
(0.076)
0.029
(0.848)
0.533
(0.324)
Industry
Table 2: Descriptive statistics on the introduction of innovations, 1991-2006
Proportion of obs.
Correlation
Proc. (s.d.) Prod. (s.d.) Proc. and Prod. (s.d.)
Proc. and Prod.
(1)
(2)
(3)
(4)
1. Metal and metal products
0.373
(0.484)
0.184
(0.387)
0.127
(0.333)
0.310
2. Non-metallic minerals
0.265
(0.442)
0.172
(0.378)
0.093
(0.290)
0.281
3. Chemical products
0.403
(0.490)
0.345
(0.476)
0.221
(0.415)
0.352
4. Agric. and ind. machinery
0.332
(0.471)
0.354
(0.478)
0.190
(0.392)
0.320
5. Electrical goods
0.375
(0.484)
0.365
(0.481)
0.213
(0.409)
0.326
6. Transport equipment
0.464
(0.499)
0.313
(0.464)
0.222
(0.416)
0.333
7. Food, drink and tobacco
0.305
(0.461)
0.223
(0.416)
0.154
(0.361)
0.443
8. Textile, leather and shoes
0.242
(0.482)
0.230
(0.421)
0.116
(0.320)
0.296
9. Timber and furniture
0.285
(0.451)
0.257
(0.437)
0.134
(0.340)
0.304
10. Paper and printing products
0.293
(0.455)
0.141
(0.348)
(0.083)
0.277
0.265
Industry
Table 3
Cost and demand functions with exogenous unrestricted process and product effects1
Cost function
Shifters
Price
Elasticities
Market Process Product
elasticity Market



dyn.
innov.
innov.
Prc.×Prd.

dyn.
(s.e.)
(s.e.)
(s.e.)
(s.e)
(s.e.)
(s.e.)
(s.e.)
(s.e)
(s.e)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Demand function
Shifters
Product Process
Adv.
innov.
innov.
(s.e.)
(s.e)
(s.e.)
(10)
(11)
(12)
Prc.×Prd.
(s.e.)
(13)
1. Metals and metal products
0.117
(0.034)
0.057
(0.043)
0.591
(0.099)
-0.096
(0.032)
-0.010
(0.011)
-0.018
(0.014)
0.002
(0.018)
2.158
(0.657)
0.180
(0.018)
0.008
(0.005)
0.030
(0.011)
0.025
(0.018)
-0.013
(0.022)
2. Non-metallic minerals
0.062
(0.059)
0.236
(0.161)
0.929
(0.290)
-0.036
(0.028)
0.010
(0.012)
-0.014
(0.017)
0.018
(0.024)
2.268
(1.293)
0.009
(0.073)
0.017
(0.014)
0.024
(0.031)
-0.060
(0.035)
0.044
(0.048)
3. Chemical products
0.105
(0.022)
0.084
(0.082)
0.648
(0.081)
-0.048
(0.016)
-0.017
(0.012)
-0.004
(0.012)
0.008
(0.016)
1.789
(0.556)
0.132
(0.017)
0.024
(0.008)
0.039
(0.013)
0.004
(0.014)
-0.020
(0.020)
4. Agric. and ind. machinery
0.091
(0.113)
0.172
(0.146)
0.884
(0.355)
-0.026
(0.063)
0.012
(0.012)
-0.009
(0.012)
0.015
(0.025)
3.305
(0.989)
0.023
(0.055)
0.024
(0.019)
0.031
(0.026)
0.010
(0.025)
-0.012
(0.041)
5. Electrical products
0.052
(0.049)
0.330
(0.095)
0.614
(0.159)
-0.048
(0.026)
-0.010
(0.014)
-0.004
(0.010)
0.020
(0.015)
2.009
(0.866)
0.084
(0.035)
0.045
(0.014)
0.008
(0.027)
-0.044
(0.019)
0.065
(0.037)
6. Transport equipment
0.178
(0.057)
0.285
(0.060)
0.463
(0.104)
-0.074
(0.024)
-0.010
(0.011)
0.010
(0.013)
-0.020
(0.020)
3.021
(1.414)
0.166
(0.023)
0.018
(0.010)
0.007
(0.024)
0.021
(0.022)
0.004
(0.035)
7. Food, drink and tobacco
0.057
(0.024)
0.111
(0.057)
0.767
(0.081)
-0.026
(0.013)
-0.002
(0.009)
-0.016
(0.008)
0.008
(0.012)
2.291
(0.608)
0.112
(0.014)
0.024
(0.007)
0.019
(0.010)
0.040
(0.011)
-0.016
(0.018)
8. Textile, leather and shoes
0.070
(0.033)
0.074
(0.055)
0.648
(0.110)
-0.082
(0.033)
-0.012
(0.011)
-0.012
(0.010)
0.030
(0.018)
4.299
(2.669)
0.240
(0.049)
0.027
(0.008)
0.027
(0.017)
0.018
(0.026)
-0.056
(0.037)
9. Timber and furniture
0.074
(0.023)
0.135
(0.057)
0.606
(0.138)
-0.083
(0.039)
-0.021
(0.018)
-0.016
(0.017)
0.018
(0.025)
3.149
(1.447)
0.171
(0.020)
0.013
(0.007)
0.053
(0.016)
0.042
(0.023)
-0.028
(0.030)
10. Paper and printing products
0.041
(0.020)
0.198
(0.120)
0.663
(0.206)
-0.043
(0.025)
0.008
(0.011)
0.015
(0.017)
-0.026
(0.022)
1.875
(1.106)
0.058
(0.034)
0.019
(0.012)
0.043
(0.025)
0.031
(0.043)
-0.069
(0.067)
1
Standard errors in parentheses, robust to arbitrary autocorrelation over time and heteroskedasticity across firms.
Industry
Table 4
Testing the specification and restrictions
Restricting the effects to model
Specif. test
Non-specialized
Specialized
Mixed
2 ( )  val.
2 (4)  val.
2 (4)  val.
2 (4)  val.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1. Metals and metal products
5.855
(4)
0.210
3.610
0.461
17.000
0.002
6.379
0.173
2. Non-metallic minerals
11.264
(5)
0.046
6.944
0.139
4.557
0.336
5.923
0.205
3. Chemical products
14.445
(4)
0.006
8.622
0.071
16.975
0.002
12.051
0.017
4. Agric. and ind. machinery
10.138
(5)
0.071
3.554
0.470
4.964
0.291
1.565
0.815
5. Electrical products
16.406
(5)
0.006
24.930
0.000
24.141
0.000
24.889
0.000
6. Transport equipment
18.381
(4)
0.001
9.028
0.060
3.144
0.534
2.501
0.644
7. Food, drink and tobacco
13.756
(4)
0.008
6.813
0.146
11.678
0.020
11.303
0.023
8. Textile, leather and shoes
0.348
(4)
0.986
4.525
0.340
5.626
0.229
5.541
0.236
9. Timber and furniture
5.130
(4)
0.274
2.341
0.673
20.140
0.000
7.537
0.110
10. Paper and printing products
4.156
(3)
0.245
1.998
0.736
5.569
0.234
2.792
0.593
Industry
Table 5
Cost and demand functions with exogenous innovation dummies
Cost function
Demand function
Shifters
Price
Shifters
Elasticities
Market
Innovation
elasticity
Market



dynamism at ( − 1)

dynamism Advertising
(s.e.)
(s.e.)
(s.e.)
(s.e.)
(s.e.)
(s.e)
(s.e)
(s.e.)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Innovation
(s.e)
(9)
Specification test
2 ( )
 val.
(10)
(11)
1. Metals and metal products
0.115
(0.035)
0.036
(0.054)
0.634
(0.094)
-0.092
(0.032)
-0.004
(0.007)
2.293
(0.634)
0.183
(0.018)
0.009
(0.005)
0.032
(0.009)
6.970
(4)
0.137
2. Non-metallic minerals
0.064
(0.065)
0.270
(0.189)
0.853
(0.275)
-0.038
(0.030)
-0.004
(0.010)
2.097
(1.144)
0.016
(0.065)
0.020
(0.013)
0.004
(0.018)
13.391
(5)
0.020
3. Chemical products
0.104
(0.022)
0.061
(0.084)
0.679
(0.080)
-0.048
(0.016)
0.003
(0.006)
1.814
(0.567)
0.131
(0.017)
0.024
(0.008)
0.023
(0.010)
15.002
(4)
0.005
4. Agric. and ind. machinery
0.050
(0.129)
0.220
(0.172)
0.991
(0.403)
-0.004
(0.055)
0.001
(0.008)
3.409
(1.026)
0.022
(0.054)
0.021
(0.019)
0.025
(0.019)
12.339
(5)
0.030
5. Electrical products
0.054
(0.048)
0.301
(0.092)
0.611
(0.150)
-0.054
(0.027)
0.011
(0.007)
1.962
(0.859)
0.092
(0.034)
0.046
(0.014)
0.001
(0.015)
15.981
(5)
0.007
6. Transport equipment
0.181
(0.062)
0.264
(0.069)
0.487
(0.104)
-0.073
(0.025)
0.003
(0.011)
3.119
(1.398)
0.164
(0.023)
0.018
(0.010)
0.017
(0.016)
18.958
(4)
0.001
7. Food, drink and tobacco
0.055
(0.025)
0.095
(0.057)
0.801
(0.078)
-0.024
(0.014)
-0.004
(0.005)
2.284
(0.610)
0.114
(0.014)
0.025
(0.007)
0.033
(0.007)
14.286
(4)
0.006
8. Textile, leather and shoes
0.071
(0.033)
0.057
(0.055)
0.664
(0.109)
-0.082
(0.033)
0.004
(0.008)
4.058
(2.574)
0.234
(0.046)
0.026
(0.008)
0.012
(0.012)
0.737
(4)
0.947
9. Timber and furniture
0.074
(0.024)
0.097
(0.059)
0.689
(0.139)
-0.074
(0.037)
0.010
(0.007)
3.685
(1.438)
0.175
(0.021)
0.015
(0.007)
0.057
(0.015)
4.450
(4)
0.349
10. Paper and printing products
0.041
(0.020)
0.217
(0.118)
0.628
(0.201)
-0.044
(0.025)
-0.007
(0.008)
1.821
(1.117)
0.058
(0.035)
0.019
(0.013)
0.031
(0.021)
4.604
(3)
0.203
1
Standard errors in parentheses, robust to arbitrary autocorrelation over time and heteroskedasticity across firms.
Value functions
M1
M2
M3
(1)
(2)
(3)
Table 6
Testing the IV models
Specialized vs Non-spec. Specialized vs Mixed
 (0 1)
 (0 1)
( val.)
( val.)
(4)
(5)
Non-spec. vs Mixed
(0 1)
( val.)
(6)
Advertising
in the cost
2 (1)
(7)
exclusion
function
 val.
(8)
1. Metals and metal products
0.545
0.181
0.195
1.660
(0.952)
1.637
(0.949)
-0.749
(0.227)
1.984
0.159
2. Non-metallic minerals
1.290
1.104
1.075
0.523
(0.669)
0.606
(0.728)
0.832
(0.797)
0.906
0.341
3. Chemical products
0.632
0.357
0.347
1.524
(0.936)
1.561
(0.941)
0.513
(0.696)
0.475
0.491
4. Agric. and ind. machinery
0.169
0.204
0.167
-0.134
(0.447)
0.006
(0.503)
0.689
(0.755)
5.362
0.021
5. Electrical products
0.381
0.378
0.382
0.017
(0.507)
-0.008
(0.497)
0.263
(0.396)
17.360
0.000
6. Transport equipment
0.764
0.726
0.707
0.298
(0.617)
0.458
(0.676)
0.874
(0.809)
4.540
0.033
7. Food, drink and tobacco
0.322
0.192
0.191
1.072
(0.858)
1.070
(0.858)
0.115
(0.546)
3.525
0.060
8. Textile, leather and shoes
0.086
0.131
0.131
-0.496
(0.310)
-0.488
(0.313)
0.003
(0.501)
0.016
0.899
9. Timber and furniture
0.558
0.214
0.225
1.204
(0.886)
1.165
(0.878)
-0.470
(0.319)
0.022
0.882
10. Paper and printing products
0.407
0.236
0.231
0.931
(0.824)
0.9787
(0.836)
0.397
(0.654)
0.245
0.621
Industry
Table 7
Cost and demand functions with endogenous process innovation and innovation dummies
Cost function
Demand function
Shifters
Price
Shifters
Elasticities
Market
Process In.
elasticity
Market



dynamism
at ( − 1)

dynamism Advertising
(s.e.)
(s.e.)
(s.e.)
(s.e.)
(s.e.)
(s.e)
(s.e)
(s.e.)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Innovation
(s.e)
(9)
Specification test
2 ( )
 val.
(10)
(11)
1. Metals and metal products
0.107
(0.036)
0.139
(0.048)
0.583
(0.095)
-0.079
(0.027)
-0.033
(0.014)
1.984
(0.619)
0.178
(0.018)
0.010
(0.005)
0.032
(0.009)
9.873
(6)
0.130
2. Non-metallic minerals
0.094
(0.048)
0.147
(0.127)
0.747
(0.174)
-0.059
(0.029)
0.040
(0.019)
2.158
(1.125)
0.015
(0.069)
0.023
(0.013)
0.033
(0.021)
10.400
(7)
0.167
3. Chemical products
0.108
(0.022)
0.103
(0.075)
0.620
(0.080)
-0.050
(0.016)
-0.028
(0.017)
1.599
(0.554)
0.130
(0.017)
0.023
(0.008)
0.032
(0.012)
15.887
(6)
0.014
4. Agric. and ind. machinery
0.096
(0.087)
0.110
(0.101)
0.837
(0.266)
-0.044
(0.050)
0.042
(0.018)
3.766
(1.057)
0.006
(0.057)
0.032
(0.019)
0.047
(0.024)
12.482
(7)
0.086
5. Electrical products
0.053
(0.044)
0.296
(0.087)
0.606
(0.134)
-0.056
(0.021)
0.020
(0.017)
1.994
(0.885)
0.085
(0.036)
0.045
(0.015)
0.041
(0.020)
16.232
(6)
0.013
6. Transport equipment
0.158
(0.052)
0.276
(0.062)
0.514
(0.114)
-0.061
(0.024)
0.018
(0.017)
3.440
(1.413)
0.159
(0.023)
0.018
(0.010)
0.007
(0.019)
22.827
(6)
0.001
7. Food, drink and tobacco
0.056
(0.025)
0.126
(0.058)
0.811
(0.080)
-0.018
(0.012)
-0.014
(0.010)
2.280
(0.606)
0.116
(0.014)
0.026
(0.007)
0.031
(0.008)
15.612
(6)
0.016
8. Textile, leather and shoes
0.071
(0.032)
0.051
(0.048)
0.644
(0.103)
-0.089
(0.032)
0.006
(0.016)
2.936
(1.481)
0.215
(0.029)
0.025
(0.007)
0.011
(0.012)
1.998
(6)
0.920
9. Timber and furniture
0.073
(0.023)
0.123
(0.057)
0.563
(0.167)
-0.098
(0.047)
-0.024
(0.038)
3.178
(1.346)
0.172
(0.020)
0.013
(0.007)
0.065
(0.015)
6.740
(6)
0.346
10. Paper and printing products
0.027
(0.022)
0.252
(0.121)
0.658
(0.207)
-0.036
(0.024)
-0.026
(0.024)
1.904
(1.129)
0.053
(0.034)
0.017
(0.013)
0.031
(0.022)
7.106
(4)
0.130
1
Standard errors in parentheses, robust to arbitrary autocorrelation over time and heteroskedasticity across firms.
Table A1. Sample detail
N of years
in sample
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Total
1990-2006
N of firms Observations
319
957
244
976
221
1105
217
1302
213
1491
178
1424
123
1107
194
1940
131
1441
91
1092
106
1378
57
798
73
1095
97
1552
162
2754
2426
20412