The Role of
The Role of
The Role of
Innovation in Promoting
Productivity in Chile ∗
Jose Miguel Benavente +
Third Draft
November 4, 2002
Abstract
This paper continues the empirical research line started by Crepon
et al. (1998) about the impact of research and development on
innovation and innovation on productivity of firms. In this paper we
estimate a structural model using Asymptotic Least Squares (ALS)
which corrects for selectivity and simultaneity biases taking in
consideration the particular characteristics of the available data. We
find that most of the Schumpeterian hypotheses are confirmed:
research and innovative activities are related with firm size and
market power. However, in the case of Chile, firms productivity is
not affected by innovative results nor by research expenditures in
the short run.
∗
This paper is based on Chapter 6 of my DPhil thesis at Oxford. I wish
to thank Bronwyn Hall, Paul David, Nick von Tunzelmann, Jacques
Mairesse, Emmanuel Duguet, Valpy FitzGerald, participants at the
Gorman workshop at Oxford, and two anonymous referees for their
comments and suggestions.
+
Department of Economics, University of Chile.
1 Introduction
This paper continues the empirical research line started by Crepon et al
(1998) about the impact of research and development on innovation and
innovation on productivity of firms. It is based on a model which takes into
account the whole process of innovation that includes decisions of firms to
engage in R&D activities, the results of these efforts and their impact on
productivity. In this paper we study a less developed country case: Chile, for
which we have data about innovation, research and development and
production activities and its results during the period 1995-1998.
This is the first attempt to analyse research and innovation activities
in a LDC and its further impacts on productivity. As in previous studies
this study presents two main characteristics. On the one hand, it focuses
both on innovation input (e.g. R&D) as well as on innovative output like
innovative sales. That is, firms invest in research and development aiming
either to launch new products and/or reduce their production costs. These,
if successfully accepted by the market, could have an impact on the firms
production performance and economic viability. The model takes into
account the allocation of resources to research and development activities,
the results of the innovation process (where R&D expenditure is one of its
determinants), and productivity, where together with capital and labour,
innovation performance is also included as one of its determinants.
On the other hand, and given the nature of the data: censored, by
interval, truncated together with some already known problems of
selectivity and simultaneity, we have followed the steps of Crepon et al
(1998) by adopting a new technique on the analysis of the data. We rely on
generalised tobit estimation to deal with problems of selectivity and on a
ordered probit in the case of interval data all this implemented in an two
stage estimation procedure to account for simultaneity biases in the system.
The first stage consist in a method of moments estimation while the second
stage uses the asymptotic least squares to obtain consistent estimates of the
structural parameters.
The assumptions made for the estimation are rather reasonable, and
we have tested some of them using the latest available techniques. However,
the main drawbacks of this study are first the cross-sectional nature of the
1
data and second that one of the dependent variables, R&D expenditures, is
measured as a flow variable.
The paper is organised as follows. We start by presenting the basic
statistics of the Chilean case together with the main hypotheses that will be
tested. The presentation of the empirical framework, the equations and
variables is the subject of section 3. In section 4 we present the results of the
estimation and its discussion. Concluding remarks and suggestions for
further research is the subject of section 5. An appendix gives details of the
construction of the data, main sources and definition of the sample following
by the comparison of our results with those obtained by using traditional
econometric techniques.
1
See Benavente & Crespi (1999) for a detailed analysis of this survey.
2
[Table 1 about here]
When firms were asked about the introduction of a new product and/or a
new process into the market, 30% of them claim to have done this in the
last three years. But once extrapolated to the whole universe of firms in
Chile this figure is reduced to 18%.2
In terms of the share of innovative sales in all the sales3, Chilean
results show that they are heavily skewed. Almost half of the firms declare
that innovative sales represents less than 10% of the total sales. Also, only
20% of the plants answered that innovative sales represent more than 30%
of their sales during 1998. On the other hand, when managers where asked
about the importance of demand-pull and technology push factors in the
innovation dynamics of the firms, it appears that both factors are of
considerable importance in the innovation dynamics of Chilean firms.
Almost two third of the managers surveyed consider that these factors
affect, from moderately to strongly, their propensity to innovate, where
technology push indicators are much stronger than demand pull elements.
This result is to be expected in a LDC where important channels of
diffusion of technology are through FDI, purchasing of new machinery and
the introduction of new inputs usually developed overseas and where
demand elements, like customer demands, are less relevant.
Given the available data we aim to test most of the traditional
hypotheses related with industrial innovation.4 These studies stressed the
favourable position that for innovation could have for large firms with a
good degree of market share by exploting not only their size but also their
market power.
On the one hand, market concentration allows the existence of
monopolistic rents that enable firms within the industry to finance R&D
2
A similar study of the French industry (Crepon et al. 1998) shows that on average,
they have roughly the same market share but are less diversified than their Chilean
counterparts. In terms of size the Chileans are slightly bigger and, surprisingly, there exist
more firms in Chile that report R&D activities (around 15%) than in France (10%).
3
As is explained in Appendix I, the average market share and the diversification
index were obtained from the firms sales desegregated by product lines as given by the
Chilean Industry′ Survey (ENIA).
4
For a survey of them see Cohen & Levin (1989), Cohen (1995), and Cohen and Klepper
(1996).
3
projects. In a perfectly competitive world private investment in R&D would
not be feasible. Additionally, when imperfections in capital markets are
recognised, large firms tend to have greater capabilities of securing the
necessary resources to finance R&D projects baring uninsurable risk.
Moreover, the potential total impact of the results of an R&D project can
be significantly greater in large firms given larger sale volumes. Hence, a
product or process innovation that allows for an increase in the price-cost
margin, will have greater absolute effects on profits on a firm with greater
sales. Finally, larger plants have greater absolute incentives to improve
internal process technologies, which in conjunction with economies of scale
should lead firms to make greater relative efforts on process innovations
than their smaller counterparts.
However, there exist several external forces which stimulate firms to
innovate despite their size and market power. The first of such forces is
based on demand factors, such as market growth. Schmookler (1966) first
formulated such rationale known as the “demand-pull hypothesis”. On the
other side, the role of scientific advancements in stimulating industrial
innovative efforts may influence the path and rate of technology advance.
The rationale was that advances in science enabled “technology-push” based
innovations5 through the development of new concepts or when incorporated
in new machinery and/or inputs. The relative opportunities to innovate
within a given industry based on scientific progress form the basis for the
notion of technology opportunities.
The empirical estimation procedure aims to capture the effect of
some of these variables over the research and innovation effort of firms. It
consists in four main equations: two for research and development, one for
innovation and one for productivity. Given the design of the Chilean
Innovation Survey some restrictions are imposed on the data which requires
a different econometric treatment. We will estimate a reduced version of the
model which includes only firm characteristics such as size and market
share, and an expanded version including demand pull and technological
push variables.
5
Firstly mentioned by Rosemberg (1974).
4
3 Empirical Model and Strategy for Estimation
In this section we empirically model the whole innovation process. We
assume that there exists a set of firm characteristics, market structure and
technological variables which conditions and shapes investments in R&D as
well as the successful introduction of new products and processes into
markets.
As in Crepon et al. (1998) we model the process of innovation with
four main equations, two for research and development, one for innovation
and one for productivity. It is important to note that each equation requires
a different econometric treatment depending on the characteristics of the
data. To make this work comparable to the French case, we will estimate a
reduced version of the model which includes only firm characteristics such
as size and market share, and an expanded version including demand pull
and technological push variables. The definition of variables and the
econometric specification of each equation are the subject of the next
sections.
g i* = x i 0 b 0 + u i 0
(1)
5
We then assume that a latent or true intensity of research k i* for firm i is
determined by a second equation :
k i* = x i1b1 + u i1
(2)
u i 0 iid 0 σ 0
2
ρσ 0σ 1
→ N ,
u i1
0 ρσ 0σ 1 σ 12
(3)
xi 0 = x i1 = ( l i , s iw , d i , δ i1 , δ i2 , δ i3 , τ i1 , τ i2 , τ i3 , S i1 , S i9 )
where l i is employment, s iw the one year lagged average market share, and
d i the equivalent number of industry segments, all three variables expressed
6
in logarithms. As in previous works, δ k and τ k with k = 1, 2, 3 are two sets
of demand-pull and technological push dummies. Finally, the S i j are nine
industry dummies equal to one if firm i belongs to industry j and zero
otherwise. These later dummies replace the constant term so that each
industry has a different intercept. This is consistent with the design of the
survey, which contains strata for different productive sector up to 2 digits
ISIC (i.e., nine sectors).
As suggested in the last section we expect a positive impact of size on
investments in R&D once controlled for industry characteristics. On the
other hand, a positive relationship between market power and the
probability of reporting R&D expenditure is expected. We also expect an
impact from technological push and demand pull variables on research
investments once controlled for other characteristics of the industries. In this
later case, we are particularly interested in the significance of the impact
rather than the sign since given the design of the variables negative impact
is unfeasible.
7
t i* = α k k i* + x i 2 b 2 + u i 2
(4)
xi 2 = ( l i , δ i1 , δ i2 , δ i3 , τ i1 , τ i2 , τ i3 , S i1 , S i9 )
with the same notation as above. Note that it is assumed that market share
and diversification do not enter directly in the innovation equation but only
indirectly through research in order to identify the system. By contrast, we
assume that demand-pull and technological push factors could affect
innovation output directly and indirectly. Also, including size in the
innovation equation allows us to test whether the effect of firm size on
innovation passes completely through the size of research activities.
As in the research equations, we include sectorial dummies which
captures technological differences between markets not captured by
technology-push or demand-pull variables.
q i = α I t i* + x i 3 b 3 + u i 3
(5)
8
where q i stands for labour productivity measured as a logarithm. The
vector of the factors, other than innovation output, is :
xi3 = ( l i , c i , E i , Ai , S i1 , S i9 )
with c i being the physical capital per employee as a logarithm, and E i and
Ai being the share of engineers and administrators respectively in the total
number of employees. The coefficient α I is the elasticity of total factor
productivity with respect to innovation output, and b3 is a vector of
coefficients related to the elasticity of scale, that of physical capital and the
skill composition parameters, reflecting percentage differences in efficiency of
skilled labour relative to unskilled labour respectively.6
g i* = x i 0 b 0 + u i 0
(1)
k i
*
= x i1 b1 + u i1
(2)
6
Following Crepon et al. (1998) the interpretation of the parameters of skill composition
is the following. Assume that labour, corrected for quality, enters the production function
as: L* = f U LU + f E L E + f A L A instead of the uncorrected total employment
L = LU + L E + L A where U stands for unskilled, E for engineers and A for administrators.
Then the first expression can be re-written as: L* = f U ( L − LE − L A ) + f E L E + f A L A
or as L = f U L{1 + ( f E / f U − 1) L E / L + ( f A / f U − 1) L A / L} . Using logarithm on
*
9
while the two last equations, for innovation output and productivity, are the
following :
t i* = α I k i* + x i 2 b 2 + u i 2
(4)
qi = α k t *
i + x i3b3 + u i3
(5)
4 Results
4.1 Results of the Basic model
Table 2 presents the results using ALS for the basic model. Results obtained
with more traditional econometric techniques like OLS, 2SLS, ML and ALS
for the equations estimated separately, are presented and discussed in the
Appendix II.
The first two columns of Table 2 show the estimates of the research
equations and the last two the estimates for the innovative sales equation
and the productivity equation with innovative sales. The bottom line shows
10
the results of the Jarque Bera and Lee (1981) for normality in the probit
model and the Pagan and Vella (1989) test for normality in the tobit model.
Starting with the decision to allocate resources in R&D activities
(Probit column), firm size has a positive and significant impact on this
probability. This increase with size is a well-documented fact in the
empirical literature. After controlling for size and sector, it appears that the
probability of doing R&D also increases with the degree of market share. As
suggested by Crepon et al (1998), this is a new link that they also found in
their analysis of French firms.
The analysis with R&D intensity equation shows that firm size has
no significant impact on this variable, suggesting that the elasticity of R&D
expenditure to size is one. This is consistent with previous work in this line
of research.7 However, contrary to the French case, in our study with
Chilean firms neither market share nor firm diversification has a significant
impact on R&D intensity. It seems that these two variables only affect the
decision to enroll in R&D activities but not on the size of it. This last fact
is also consistent with previous work summarised in Cohen and Levin (1989)
and Cohen (1995).
Regarding the innovation equation, current R&D expenditures has
no significant impact on current sales weighted innovations, once controlled
by size and sector. This result can be explained by the dynamic nature of
the innovation process where is difficult to expect instant success related to
R&D activities. However, in the case of Chile, it appears that bigger firms
tend to have more success in their innovative sales compared to their
smaller counterparts.
Finally, estimates for the productivity equation shows that there exist
constant returns to scale and a physical capital elasticity of about 0.7.
However, current sales weighted innovations which proxies innovation
efforts has no statistical significance in the explanation of productivity of
7
Known as Stylised Facts 2 and 3 under Cohen and Kepler’s (1996) terminology.
11
firms.8 This result may be due to the flow characteristic of the Chilean
innovation data.
12
administrative workers, and in a second place engineers, make a massive
difference in their impact on the plant’s productivity compared with other
type of workers. This is obviously reflected in their relative salaries.10
5 Conclusions
One of the main characteristics of empirical studies on R&D and innovation
is the particular structure of the empirical models adopted. Most of the
research in this area has been conducted by using a simple equation relating
a measure of research effort to firm characteristics and/or market structure.
There exists several biases that arise by using single-equation models.
Probably the most important is the selectivity one which is the result of
using samples including firms with no reported R&D expenditure. Recent
work has tackled this problem by estimating a system of equations where
one equation deals with the selectivity issue and the other with the research
intensity. A Generalised Tobit procedure is the adequate tool to estimate
these systems in a consistent and efficient manner. However, we are only
dealing with part of the whole innovative process that takes place at the
level of the firm. One of the main objectives of this paper is to explore the
links between R&D and innovation, and the impact of them on firms’
productivity for a LDC case.
Results show that when all the steps in the innovative process are
incorporated - R&D, introduction of new products and processes, and firm
productivity - most of the traditional hypotheses of firm characteristics and
market structure are validated under this broader set-up.
Indeed, in the case of Chile, firm size is related to the probability
that firms are engaged in research activities. However, size is not related to
the amount of resources allocated for these activities once controlled for
sectorial differences, suggesting a constant return to scale in research
investments. Results also show that technological opportunities do play a
10
See INE (1998).
13
major role in research activities specially when innovative ideas are
embedded in new machinery and output.
There is a constant return to scale in the productivity equation and
after including labour skills in the estimation of the productivity, both
engineers and administrative shares have a positive and significant effect.
The econometric methods used take into to account not only the
particular characteristics of the data such as truncation, interval and
censored but also the selectivity and simultaneity problems that are
embodied in this kind of empirical exercise.
However, some of the findings of our study of the Chilean case were
unexpected. Among them the fact that neither research expenditure nor
innovation has a significant impact on innovation sales and productivity
respectively. These results can be explained partly by the implicit
assumption in the model that there are no lags between the implementation
of innovations and impact on productivity, a subject widely discussed in the
literature. However, a more plausible explanation is related with the fact
that productivity is measured as value added per worker. If innovation is
mainly related with embodied technical change, then this effect is not
captured in this model. Results in related work11 suggest that machinery is
related with the probability to report R&D expenditures but we do not
have the data to test this hypothesis for productivity.
We intent to tackle this problem in the near future when more
information for the Chilean firms will be available.
6 References
Benavente, J.M. (2002): “Determinants of Industrial Research and
Innovation : The case of Chile ”, unpublished DPhil Thesis, University of
Oxford.
11
Benavente (2002)
14
assumption in limited dependent variable models”, International Economic
Review, 25, 563-578.
Cohen, W. (1995): “Empirical Studies of Innovative Activity” in P.
Stoneman (ed.) Handbook of the Economics of Innovation and
Technological Change (Blackwell Handbooks in Economics).
Cohen, W. and R. Levin (1989): “Empirical Studies of Innovation and
Market Structure” in R. Schmalensee and R. Willig (eds.) Handbook of
Industrial Organization (North Holland).
Cohen, W. and S. Klepper (1996): “A Reprise of Size and R+D”. The
Economic Journal, 106, 925-951.
Crepon, B., Duguet, E. and J. Mairesse (1998): “Research, Innovation, and
Productivity : An Econometric Analysis at the Firm Level”. NBER
Working Paper 6696.
15
Appendix I
16
the problem of aggregating answers that are qualitative in nature. In fact,
from an answer of 4 (which represents that the phenomena was very intense
or very important in the firm) cannot be automatically inferred that the
phenomena have taken the same characteristics in all the firms who gave 4
as an answer for the same question. An answer of 4 in two firms to the same
question have to be interpreted that the phenomena has been perceived as
important or intensive in each firm despite differences between each case.
Finally, the evaluation of variations rather than levels of innovative
activity make very difficult the international comparisons.
The information on the firm current accounts and balance sheets, and on
the number of employees comes primarily form the ENIA surveys (Encuesta
Nacional Industrial Anual). From them we have constructed the firm value
added, its fixed assets gross bookvalue and its total number of employees
17
(average over the year), and we have computed labour, productivity and
physical capital intensity as l i = number of employees, q i = value added per
employee and c i = physical capital per employee. These variables are
expressed in logarithm in our estimations.
The information about the distribution of employees comes from the
same source. The data allows to differentiate between administrative and
engineers at a managerial level from blue-collar workers at the floor level
and the rest of the employees.
Average firm market share and diversification indexes are computed
from the same ENIA survey. This survey gives detailed information about
the decomposition of firm’s sales in all its different lines of business up to a
Chilean equivalent of 8 digit ISIC. In our estimations we have defined S i , k as
the sales of firm i for its product k in the industry segment or market k .
S i = ∑ S i , k and S k = ∑ S i , k
k i
are respectively the overall sales of firm i (overall its products) and overall
sales on market k (overall firms) without including its exports. The market
share s i , k of firm i on market k and the share of product k on firm i total
sales are thus equal to:
S i,k S i ,k
s i ,k = and bi , k =
Sk Si
Then for each diversified firm i we can define the weighted average market
share s iw and the diversification index d i as :
1
s iw = ∑ bi , k × s i , k and = hi = ∑ bi2,k
k di k
18
classification (2 digits ISIC) than the average market share and
diversification variables, on the basis of the firm main industrial activity.
19
Appendix II
An assessment of the biases likely to arise in
innovation and productivity studies
One of the main objectives of this study is to correct for selectivity and
simultaneity biases commonly found in applied innovation studies. We have
also taken into account the main characteristics of the available data like
censoring and truncation. We also intend to assess the magnitude of these
biases while using more standard econometric methods. In this section we
present results of the estimation of the same group of equations using
ordinary least squares (OLS), two stage least squares (2SLS), maximum
likelihood (ML) and first step and second step asymptotic least squares
(ALS) estimators. We only perform this exercise for the basic specification
of our model to the sample of innovative plants and when appropriate for
the sub sample of R+D performing firms.
Research Equations
Table 4 compares the OLS estimates for the research intensity equation for
the subsample restricted to the R&D performing firms to the corresponding
ML estimates for the generalised Tobit specification. As can be seen they
differ substantially. Moreover, if OLS estimation was used instead we would
have concluded that market share is an important explanation in the R&D
intensity of firms and that the elasticity of R&D to firm size is bigger than
one, which both are not the case.
20
while comparing results from ML estimation with ALS. For example, note
that in the case of firm size, this coefficient is not significantly different from
zero under ML but is so under ALS. It seems that ML is not taking into
account the effect that firm size does through the probability to perform
R+D activities into innovation sales.
Productivity Equation
The first interesting observation of the results presented in Tables 6 and 7 is
that capital is always significant and with an elasticity about 0.7 in all the
equations. The exception is again the 2SLS, which, as in the French case,
estimates using this technique are quite imprecise. Table 6 also shows that
there exist selectivity problems when only the sub-sample of R+D
performers firms is considered. Estimators differ substantially between both
samples.
In none of the estimations, R+D expenditures and/or innovation
sales has a significant effect on firm s productivity. This confirms the
results obtained with ALS, which corrects for potential problems due to
selectivity and simultaneity, where knowledge-related variables are not
important in explaining productivity changes, at least contemporarily, in the
Chilean manufacturing industry.
21
Table 1. Descriptive statistics of the Innovation Survey and its
expansion to the whole of Chilean Industry. Selected Indicators.
Sample
Sample Data
Sample adjusted by
(488 plants)
weights
Statistics Mean % Mean %
∗
Thousand of 1998 Chilean Pesos CHP
22
Table 2. Results for Basic Model
23
Table 3. Results for the Extended Model
-0.097 -0.153 - -
Equivalent number of (0.132) (0.262)
activities
di
- - - 0.670
(0.021)
Physical capital per employee
ci - - - 0.652
(0.177)
24
Engineers/Personnel - - - 5.952
Ei (1.459)
Administrative/Personnel
Ai
Strong ( δ i3 )
Strong ( τ i3 )
25
Table 4. Research and Development Equation
Dependent variable :
Logarithm of research and development expenditures per employee ( k i ).
(standard errors between parentheses)
Variable li si di
Generalised Tobit
(maximum likelihood)
26
Table 5. Innovation Intensity Equation
Dependent variable :
Logarithm of innovation percentage in sales ( t i ).
(standard errors between parentheses)
Variable ki li gi
All Observations (438 obs.)
*
Ordinary least squares -0.030 0.177 0.360
(0.046) (0.050) (0.223)
+
Maximum likelihood -0.071 0.073 0.594
(0.051) (0.056) (0.247)
*
Ordinary least squares 0.036 0.190 *
(0.056) (0.086)
+
Maximum likelihood -0.093 -0.007 *
(0.057) (0.089)
27
Table 6. Productivity Regression with R&D expenditures
Dependent variable :
Logarithm of value added per employee ( q i ).
(standard errors between parentheses)
Variable ki ci li gi
All Observations (438 obs.)
28
Table 7. Productivity Regression with Innovation Intensity
Dependent variable :
Logarithm of value added per employee ( q i ).
(standard errors between parentheses)
Variable ti ci li
All Observations (438 obs.)
*
Two stage least squares 0.833 0.619 0.009
(0.821) (0.083) (0.126)
29