SIMPATIC working paper no. 11November 2013

Innovation and employment inSpanish manufacturing firms

Felipe Rojas Pizarro

The SIMPATIC project is coordinated by Bruegel (Belgium) and involves the following partner organisations: KU Leuven (Bel-gium), UNU-Merit (Netherlands), SEURECO (France), E3MLab (Greece), Univesidad Complutense de Madrid (Spain), FederalPlanning Bureau (Belgium), Imperial College (United Kingdom), Institut za ekonomska raziskovanja (Slovenia). Project website:http://simpatic.eu/

LEGAL NOTICE: The research leading to these results has received funding from the Socio-economic Sciences andHumanities Programme of the European Union's Seventh Framework Programme (FP7/2007-2013) under grantagreement no. 290597. The views expressed in this publication are the sole responsibility of the authors and donot necessarily reflect the views of the European Commission.

WORKING PAPER

INNOVATION AND EMPLOYMENT IN SPANISH MANUFACTURING FIRMS

Felipe Rojas Pizarro

GRIPICO- Universidad Complutense de Madrid

2

Innovation and Employment in Spanish Manufacturing Firms

Felipe Rojas Pizarro*

GRIPICO-Universidad Complutense de Madrid

May 2013

Abstract: In this article, the relationship between innovation and employment for Spanish

manufacturing firms is reviewed, using data from the Panel of Technological Innovation

covering the period 2004 to 2010. Following the model developed by Harrison, Jaumandreu,

Mairesse and Peters (2008), the results provide evidence that there are different patterns in

the impacts of process and product innovation on employment for the period prior to and

during the economic crisis.

Key words: innovation, employment, Spain, panel data.

Códigos J.E.L.: L6, O31, O33.

_____________________ * Address for correspondence: GRIPICO (Group for Research in Productivity, Innovation and Competition). Dpto. Fundamentos del Análisis Económico I. Facultad de CC. Económicas y Empresariales. Universidad Complutense de Madrid. Campus de Somosaguas. 28223 Madrid. España. E-mail: ferojas@ucm.es

This research has been partially financed by the European Union’s Seventh Framework Programme (SIMPATIC project. GA No. 290597) .

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I Introduction

The classical question “Does technology create or destroy jobs?” has been posed

since the beginning of classical economics (Bogliacino and Vivarelli, 2010), and in well-

defined contexts (Pianta, 2005). The analysis of the relationship between innovation and

employment is complex, and in recent years, it has expanded to include new aspects like the

level of employment skill, and new contexts like developing countries and the service sector.

For many economists, it is essential to measure the impact of innovation on employment

because of the fear of unemployment which arises in radical periods of technological change

(Vivarelli, 2007, Bogliacino and Vivarelli, 2010, Vivarelli, 2012). As such, an evaluation of the

effects of innovation on employment is important in order to understand how the product

market and the institutionality of the job market can be affected by innovations in firms, and

consequently, the impact of innovation policies on employment.

To address this problem, we must distinguish between different innovations

promoted by firms and their influence on employment via multiple channels. In general, the

literature identifies four kinds of innovations, process, product, marketing and

organisational, focusing the discussion on the first two, which are considered technological

innovations.

The goal of the study is to discover the impact of technological innovations on

employment in Spanish firms in the context of economic growth and recession, following the

methodology developed by Harrison, Jaumandreu, Mairesse and Peters (2008). This model,

which from here on will be called the HJMP model (2008), is used in most of the emprical

research papers produced with firms’ micro-data. Information from the Panel of

Technological Innovation is used for firms in the Spanish manufacturing sector from 2004 to

2010.

Given the chance to use data which cover a large range of years, including those

when the Spanish economy grew considerably and also when that growth declined, we

identify different effects of process and product innovation in Spanish firms. Not only are

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there changes in the magnitude of the impact of product innovation on employment, but

also the production of old products loses efficiency during the period studied.

This paper is organised as follows. In Section II, the main theoretical relationships

between innovation and employment are analysed from a microeconomic perspective. In

Section III, the HJMP model (2008) is reviewed and the main empirical results found in

previous articles that use this model are summarised, for both developed and developing

countries. In Section IV, the estimation strategy and the implications of using panel data are

set out in detail. Section V describes the behaviour of the model’s main variables. Section VI

presents the main results, closing with an assessment of the contribution that innovation

makes to employment growth. Lastly, Section VII summarises the main conclusions.

II. Compensation effect versus displacement effect

Authors like Peters (2004), Vivarelli (2007) and HJMP (2008) review and synthesise

the literature that analyses the effects of innovation on employment, pointing out the

different channels through which innovations destroy jobs (displacement effect), and

mechanisms for creating new jobs (compensation effect). The total effect will vary according

to the firm’s sector of activity, national factors and the time frame of analysis, which

depends on: the existing production technology and the nature of the technological progress

(product or process innovation); the purpose of the innovation (to save labour or capital,

neutral, or biased towards skills); the dimension of the innovation (radical or incremental

innovation); consumer preferences; the existing competition in intermediate inputs and the

labour market; and, lastly, the structure of workforce skills.

With regard to the compensation effect, economic theory identifies the existence of

elements that would generate new jobs as a consequence of technological progress. In a

historical perspective of economics, the discussion is focused on the existence of

mechanisms which compensate for technological advances. Vivarelli (2007, 2012), for

example, borrows a compensation theory from Marx that tries to identify different market

5

mechanisms that operate because of technological change, and which could neutralise the

initial impact on the lower demand for labour.

In process innovation, compensation mechanisms include the price and income

effects: Bogliacino and Vivarelli (2010) note that process innovations lead to a decline in unit

production costs and, assuming a competitive market exists, this effect translates to a

reduction in prices. Then, the fall in prices stimulates the demand for products, at the same

time increasing production and employment. As for the income effect, when market

adjustments are not instantaneous, it is found that during the time between the fall in costs

because of the innovation process and the resulting drop in prices, innovative entrepreneurs

and their employees can accumulate profits and/or additional wages by reinvesting the extra

profits from innovation or creating new jobs. Furthermore, additional wages can translate to

greater consumption, which would lead to an increase in employment, compensating for the

initial job losses.

As Pianta (2005) notes, in product innovation, the compensation effect comes from

the possibility of opening new markets and, when elasticity is high, it is associated with

greater production and employment and cost reductions, with an impact similar to process

innovations. In general, increases in demand will depend on the nature of the market

competition, in addition to the delay in the reaction of competitors due to the release of

new products. New product sales can to some extent supplant a firm’s current sales from

old products, reducing the compensation effect, with limited economic effects.

Moreover, Edquist et al. (2001) add that new products enter the economy as

consumer goods, intermediate goods or investment goods, as a result of demand from

consumers, firms or investors. Product innovation in investment goods has an effect on

industries that produce capital goods and at the same time can become process innovations

in industries that purchase them. As a consequence, it is likely that employment increases in

machine production sectors and diminishes in industries where said capital goods are

incorporated. As such, as HJMP (2008) highlights, it is necessary to distinguish when possible

between a gross effect and a net effect, the latter taking into account the resulting reduction

6

in current sales. A summary of compensation mechanisms can be found in Spiezia and

Vivarelli (2002) and Vivarelli (2007 and 2012):

1. Due to a greater demand for labour in the capital goods sector.

2. Through the falling of prices, due to lower production costs, which stimulate the

demand for products and, as a result, the demand for employment.

3. Through new investments; assuming that the convergence between diminshing costs

and the drop in prices is not instantaneous, extra profits that are accumulated can be

appropriated by innovative firms for new investments.

4. Through a reduction of wages, and in a neoclassical approach, increasing the demand

for labour.

5. By increasing income, supposing that some of the improvements in productivity

transfer to wages, which at the same time result in greater consumption, an increase

in (aggregate) demand and an increase in employment.

6. Through new products resulting from innovation; these entail the creation of new

jobs, or even new branches of economic activity.

Critics of these mechanisms, summarised by Vivarelli (2012), emphasise that said

compensation effects can be partial or non-existent. For the first mechanism described

above, the compensation will be only partial, given that the reason for acquiring capital

goods is to create a more efficient workforce, replacing machinery or workers.

The next mechanism will depend on the level of competition that exists in the

relevant market, given that imperfections in the product market can reduce the price

reduction effect. In addition, criticism of the compensation effect of new investments

stresses the role of agents’ expectations when deciding to increase investments.

Furthermore, new investments can be capital-intensive, mitigating the compensation effect

even more.

For the fourth mechanism, the reduction of wages could affect expectations of

aggregate consumption, causing only a partial effect on the increase in the demand for

employment. However, the increase in income, especially among workers, is gradually

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weakened, because the gains in productivity are distributed to workers less than they were

in previous decades.

Lastly, the incorporation of new products, in the words of Vivarelli (2012), is “the

most powerful way to counteract the saving of work that results from process innovations,”

although the impact will depend on the different technological paradigms.

To summarise, in accordance with Bogliacino and Vivarelli (2010), the price or income

effects and compensation mechanisms of process innovation and the increase in the

demand derived from product innovation can be more or less efficient in compensating for

job losses according to: 1) the degree of competition in the market, 2) the elasticity of the

demand, 3) agents’ expectations and the institutional contexts that promote competition in

the labour market through greater flexibility in regulation.1 Vivarelli (2012) concludes that

the compensation effect can only be partial and depends on historical and institutional

circumstances. As such, economic theory has no clear answer regarding the effect of

innovation on employment.

Is the displacement effect greater than the compensation effect? The answer to this

question is crucial in order to evaluate the effect of innovation on employment. Innovations

improve the reach or the quality of products offered by a firm, increasing the production of

the firm. Non-neutral technical change (in Hicks’s sense) is also associated with changes in

the combination of factors. In this regard, the strength of the contrasting effects allows us to

know the impact of innovation on employment. The result of this mechanism provides only

the maximum value of the impact on employment.

The behaviour of firm agents, who seek to gain revenue from innovation, can

indirectly intensify the displacement effect and weaken the compensation effects. Two

evident ways, argued by García et al. (2002), are negotiation via wage increases, which

1 Pianta (2005) notes that the conditions of the labour market and institutions are important. The results of employment caused by technological changes will depend on the way the creation and destruction of jobs is carried out, along with the setting of wages, the training period, flexibility and the administration of social protection. Additionally, labour market institutions influence the supply of employment, which must coincide with the skill and knowledge requirements of the new technologies.

8

counteract the savings in costs obtained through innovation, and exploitation of the increase

of market power through price rigidity, deficiently translating the change in costs.

Furthermore, if the firm operates in an oligopolic market, total compensation might

be weakened, given that the saving of costs does not necessarily mean a reduction of costs.

In addition, if the firm participates in the labour market as a monopsony, it can also weaken

the increase in consumption owing to smaller wage increases compared to a competitive

labour market.

Finally, the compensation effect by new products is compared to the substitution

effect of industries with mature products, considering that new branches can have different

consequences according to the degree of labour intensity they show.

III. The HJMP Model (2008): previous empirical evidence

To try to distinguish between the displacement effect and the compensation effect

with firm data, most recent empirical research follows the methodology developed in HJMP

(2008). This model includes the estimation of some structural parameters and proposes

some alternative ways to solve the problem of endogeneity.

The model assumes that the firm can produce two types of products, old and new.

Chronologically, in a first period (t1), there are only old products, and in a second period (t2),

if some product innovation has been successfully carried out between periods, new products

are generated. The production of old and new products is designated ��� and ���, respectively. By definition, in the first period, the firm only produces old produts (���), and

therefore, ��� �0; if there are no product innovations in the firm, the production of new

products is zero in the second period. Firms use a production technology with constant

returns to scale, in capital (K), labour (L) and intermediate inputs (M). The production

functions for both products are equal, identical and separable, with technological

parameters different from Hicks’s neutral type θ. Specifically:

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��� � ������, �� , ����������� � � 1, � � 1,2; � � 2, � � 2

where �� is an unobserved fixed effect of the firm, and ���represents a non-technological,

specific productivity shock, with zero mean. The previous specification allows for the

presence of economies of scope.

With regard to technological activities, the goal of investing in R&D is to increase the

number of innovative products and create more efficient processes in the production of old

and new goods or services. This efficiency will be measured in relative terms according to

the ratio between the technological parameters of old products in both periods (��� ���⁄ ),

and likewise for new products (��� ���⁄ ).

Decisions about employment and other factors are made assuming cost-minimising

behaviour by the firm given the technology. In this context, HJMP (2008) show that the

variation of employment between two periods can be expressed in the following mannner:

Δ ≅ !"��� !"���� # !"��� !"���� #

������

������ ��� ����$�

Equation (1) explains the growth of employment in terms of four components:

• The change in the efficiency of the production process of old products: !"��� !"���� • The variation in the demand of old products: !"��� !"����. • The increase in production derived from new products:

%&&%''

(''(&&.

• The impacts of non-technological productivity disturbances: ��� ����.

The first component is expected to be greater in firms that undertake process

innovations (positive for a negative value inside the parentheses). The third component

incorporates the impact of the product innovations into the growth of employment derived

from the relative efficiency of the production of old and new products (��� ���⁄ ). If the new

products are produced more efficiently than the old products, this ratio will be less than the

unit.

10

Rewriting Equation (1) in terms of variation rates, we obtain:

! � )* # )�+� # ,� # -,� # ./�

where l corresponds to the variation rate of the employment between consecutive periods,

d is a dummy variable that identifies the process innovation (in absence of product

innovation), ,�is the variation rate of old products, and ,� is the variation of new products.

Finally, . � ��� ���� # 0 is a random disturbance, where ξ represents different

uncorrelated errors. Subtracting the variable ,� from both sides of the equation, we obtain:

! ,� � )* # )�+ # -,� # ./′�

In this equation, which is the equation to estimate, the parameter )* represents the

average growth of efficiency in producing old goods or services ( !"��� !"����)(with an

opposite sign), )� picks up the effect of only the process innovation, while -captures the

relative efficiency of producing old and new products. With time, an increase in the

efficiency of producing old products is expected, and therefore, that the parameter )* will

be negative, while )� will also be negative given that process innovation increases the

efficiency of producing old products, in absence of product innovation. At the same time, the

parameter -captures the relative efficiency of producing old and new products. If new

products are produced more efficiently than old products, this ratio will be less than the unit

and, therefore, employment will not increase as much as sales due to new products.

Because of its simplicity, this equation limits the distinction of other effects on

employment, given that they cannot be separated without the existence of more data. We

know, for example, that ,� is affected by three different forces: (1) the “autonomous”

variation of the demand for old products because of exogenous market conditions, (2) the

compensation effect brought about by changes in the prices of old products after process

innovations, and (3) the substitution effect caused by the introduction of new products. The

last two effects are expected to be positive and negative, respectively, for growth in the

sales due to old products. Without more data on demand, it is impossible to separate these

three effects, in accordance with Hall et al. (2007).

11

What follows is a summary of the results found by different authors using the HJMP

model (2008) for different countries in Europe and Latin America. The main characteristics of

the data bases and the coefficients obtained in these papers are presented in Table 1.2

Insert Table 1

In their seminal paper, HJMP (2008) study the relationship between innovation and

employment for Germany, Spain, France and the United Kingdom. Among their conclusions

for the manufacturing sector, they highlight that process innovation tends to displace

employment, although the compensation effect predominates, while product innovation is

associated with employment growth. There is no evidence of a displacement effect as a

result of product innovation, but there is a compensation effect, even when the

cannibalisation of old products is taken into account. In general, the results are similar in all

the countries. Specifically for Spain, there is no evidence of the displacement effect of

process innovation, perhaps because of a greater transfer of productivity in low prices.

Product innovation stands out in Germany and Spain, although the increase in employment

resulting from a greater production of old products is superior in Spain.

For the case of Italy, Hall et al. (2007) use data on manufacturing firms from the years

1995-2003, and adjust the variation of new products with the industrial price index. Again,

there is no evidence of the impact of process innovation on employment, while employment

growth originates from the growth of sales due to old products and sales due to new

products resulting from product innovation. In addition, the authors conclude that the

contribution of product innovation to employment growth is somewhat smaller than in the

four European countries studied in HJMP (2008).

In a first approach developed by Benavente and Lauterbach (2008) for Chilean firms

from the period 1998-2001, it is found that product innovations increase employment more

than previous studies had reported. With regard to process innovation, there is no evidence

to conclude that process innovation affects employment.

2 For a summary at the macroeconomic and industrial sector level, see Pianta (2005).

12

For the case of Brazil, Guerra and Leiva (2008) use data on the manufacturing sector

for the years 2001-2003, with some adjustments like the measurement of the different

impact on skilled and unskilled personnel in intensive firms.3 They find evidence that the

impact of technological innovation is biased because of the qualification of the personnel,

and they confirm that process innovation has no major effect on employment, while product

innovation tends to increase it significantly.

In 2011, the Inter-American Development Bank (IADB) undertook a project called

“Employment Generation, Firm Size and Innovation in Latin America”. The study includes

manufacturing firms from Argentina, Chile4, Costa Rica and Uruguay. In all cases, the impact

of process and product innovation on employment is estimated using the HJMP model. At

the same time, the estimations try to describe different possible impacts according to firm

size, the composition of employment and the innovation strategies used by the firms. The

preliminary results were presented at the Fifth Conference on Micro Evidence on Innovation

in Developing Economies (MEIDE) in the papers by Aboal et al. (2011a y b), de Elejalde et al.

(2011), Monge-González et al. (2011) and Álvarez et al. (2011).

Specifically, using data on Uruguayan manufacturing firms5 from the years 1998-

2009, Aboal et al. (2011a) conclude that process innovation marginally displaces labour,

while product innovation is complementary to employment. Unlike other studies, a decline

in sales due to old products is detected, and as such, employment shrinks. In estimations

that differ according to the magnitude, technological level and strategy of the innovation,

they obtain similar results.

In the case of Argentina, de Elejalde et al. (2011), estimate the HJMP model for a

sample of manufacturing firms, obtained from a single survey from the year 2003 which

collects retrospective information about the state of the firms for every year in the period

3 This paper is not included in Table 1 because it is based on a previous version of the HJMP model and it does not deconstruct employment growth to identify the compensation and displacements effects. 4 For the case of Chile, the survey uses the establishment as a unit of analysis. 5 The same authors study the service sector in Uruguay, for the years 2004-2009. They conclude that the impact of product innovation on employment is positive, while process innovation does not seem to have any effect.

13

1998-20016. They find that, for those years, there is no significant evidence of the impact of

process innovation on employment. For product innovation, there are no important

differences between the efficiency of producing new products and the efficiency of

producing old ones. Therefore, on deconstructing employment growth, they conclude that

there is no evidence of the displacement effect as a consequence of introducing product

innovations. Employment is created only because of the effect of a greater demand for old

products. Furthermore, they do not find that process innovation affects employment,

because process innovation does not generate significant profits in productivity.

For Costa Rica, Monge-González et al. (2011) apply the HJMP model to a sample of

211 firms7, estimating the annual variations between the years 2006 and 2007. They

conclude that process innovation does not have a significant effect, and that there are no

appreciable differences in the efficiency between old and new products.

Lastly, Álvarez et al. (2011), using data on Chilean manufacturing firms from the

period 1999 to 2007, conclude that process innovation has no relevant impact on the

determination of employment growth, while product innovation is positively associated with

increases in employment, although there is greater efficiency in the production of old

products compared to new products.8 Unlike previous results, the net contribution of

product innovation to employment is negative, while there is evidence of a decrease in the

production of old products.

In another recent paper for fourteen European countries, Leitner et al. (2011)

estimate the HJMP model for manufacturing as well as services firms, incorporating

organisational innovation as an additional explanatory variable.9 They use three groups of

countries, according to geographic location and level of development, and they find that

6 That is, it has information about those firms that survived the crisis of 2001. 7 This study uses the smallest sample size of all the studies reviewed. This could be one of the reasons why there is a high value in the estimation of the parameter related to process innovation. 8 This paper uses surveys from the following years: 1995, 1998, 2001 and 2007. Moreover, the Innovation for Chile survey does not directly provide the percentage of sales associated with new products, but instead this information is obtained as categorised in large groups of percentages. In accordance with Álvarez et al. (2011), this makes the analysis and comparison of results more difficult. 9 For this reason, the results are not strictly comparable to those of previous studies and they are not included in the summary in Table 1.

14

there is a different impact of innovation on employment according to the country group in

question. Specifically, in the cases of manufacturing firms in countries in southern, central

and eastern Europe, they detect a positive effect of product innovation, while process

innovation operates contrary to the traditional model, creating an increase in employment.

For the central European countries (Denmark, France, Luxembourg and Sweden), they find

evidence of greater efficiency in producing new products due to product innovation

compared to the other countries studied.

Therefore, in most of the papers reviewed, there is little evidence of the impact of

process innovation on employment, although HJMP (2008) and Aboal et al. (2011a) suggest

the existence of some displacement effect, which is exceeded by the compensation effect.

As for the impact of product innovation on the relative efficiency of new products compared

to old products, a positive effect is found in most of the studies. Generally speaking, the net

contribution of product innovation is positive. However, in the context of a recession like

those observed in the papers that refer to Argentina and Uruguay, the net effect of product

innovation is negative, showing that despite the increase in sales due to new products, the

decrease in sales due to old products is much greater.

It is worth noting that other authors have studied the relationship between

innovation and employment with firm data that refer to longer periods of time and by using

approaches different from the HJMP model (2008). For Spain, Giulidori and Stucchi (2010)

use the Encuesta de Estrategias Empresariales (Survey of Business Strategies) to quantify, on

the basis of an equation for employment demand, the impact of process and product

innovation on permanent as well as temporary workers. Given the change in Spanish labour

laws, this condition is analysed to estimate the impact according to different periods of time.

In general, they suggest that their results are consistent with those obtained by HJMP

(2008), there is a greater effect on temporary workers than on permanent ones and this

effect is more immediate in the former than in the latter.

For a large sample of countries and European manufacturing and services firms,

Vivarelli et al. (2011) estimate an equation for employment demand, using panel data

15

techniques for the years 1990-2008. In particular, in this equation, similar to the one used by

Van Reenen (1997), the impact of innovation expenses on employment is analysed. The

conclusion reached is that R&D benefits not only European productivity and

competitiveness, but also the creation of employment.

Another important recent example is the paper by Lachenmaier and Rottmann

(2011), who study the effect of innovation on employment for German firms, covering the

period from 1982 to 2002. Using a dynamic panel, they find that the effects of innovation on

employment are robust and, surprisingly, that process innovation has a greater effect than

product innovation.

IV. Estimation strategy

As mentioned above, this paper will follow the methodology set out by HJMP (2008).

This methodology has frequently been applied to cross-sectional data. As will be seen below,

this study uses panel data, which is why separate estimations will be made not only for each

cross-section but for the entire period as well. This requires making some clarifications about

the estimation strategy, and they are included at the end of this section.

In Equation (2), the variables for growth in sales due to old products,,�, and growth

in sales due to new products, ,�, are in real terms, but often only the nominal growth of

sales is observed in data bases. That is, in the case of old products, we observe:

2� � 34����� 4�����4����� 5,

where 2� is the nominal growth rate of sales due to old products, which can be

approximated as the sum of the variation of real production and the variation of prices of

old products; that is, 2� � ,� # 6�. For new products, given that they are not produced in

Period 1, only nominal sales from Period 2 are observed and it is possible to estimate the

variation in relation to the level of sales of all the products from Period 1. Then, the nominal

growth rate of new products is 2� � 78''(''8&&(&&9, or 2� � ,�1 # 6�� � ,� # ,�6�, where 6� �

16

78'':8&&8&& 9 is the proportional difference in the prices of new products relative to the prices of

old products.

Substituting the relationships in Equation (2’), the resulting equation to estimate

would be

! 2� � )* # )�+ # -2� # ;,<�

where the new disturbance is ; � 6� -6�,� # ., assuming a not-null average 6�. The

model will now include – >6�� in the constant and 6�–>6��� in the disturbance.

Notice that, given that 2� � ,� # 6�,�, 2� will be correlated to the error term,

which will generate biases in estimation by Ordinary Least Squares (OLS).

In accordance with HJMP (2008), an alternative would be to incorporate the price

index corresponding to each industry (6) as a proxy for 6�. This means that:

! 2� � )* # )�+ # -2� 6� -6�,� # .3´�

! 2� 6� � )* # )�+ # -2� # 6� 6� -6�,�� ��� ���� # 0,3´´�

so that the dependent variable becomes: ! 2� 6�.

To estimate the relevant parameters, the independent variables and the error term

need to be uncorrelated or, failing that, the existence of instrumental variables (VI)

correlated to independent variables but not to the error term. The lack of correlation

between the variables and the error term depends on this model’s supposed specific theory

about the characteristics of the disturbance and the firm’s timing of investments in

technological innovations. That is:

• If it is expected that the decisions to invest in technological innovations are made before

the impact of the disturbance, then the innovations will not be correlated with the error

term nor with its components.

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• If it is supposed that firms make investments between periods 1 and 2, where

disturbances ω are also present, then a correlation would exist. One possible solution

would be to use the lagged values of the variables assuming they are not correlated with

the error. These values could be valid instruments. On the other hand, if autocorrelation

is observed in ω, then the present value of the error term depends on past values, and as

such, the innovations would be correlated with the past values of the error term. In this

case, lagged variables would not be a valid instrument, and the identification problem

must be solved with instrumental variables uncorrelated with the error term.

Give these caveats, in general, the circumstances in which investments are made

(and when they are planned) do not consider unforeseeable (or inaccurately predicted)

productivity shocks. As a result, an estimation by OLS makes no sense. In addition,

investments in R&D are usually made long enough in advance of the innovations that they

possibly generate with a certain persistence, so they might be positively related to the

disturbances of productivity ω, which is why the estimated parameters would be downward

biased.

Note that in the HJMP model (2008), the key variables are defined as variation rates

between two periods which, given the type of information available in the innovation

surveys, usually correspond to the years t and t-3. The consistent estimation of this model

requires non-correlation between the independent variable and the error term; that is,

>@A�� A��:B�.�� .��:B�C � 0. This condition is weaker than strict exogeneity, but the

important point is that the previous supposition is erroneous if .�� is correlated with A��:B ,

A�� or A���B, or in general terms A�E is correlated with .�� for all t and s. This point is

problematic when .�� is correlated with future values of A��.

The solution endorsed by HJMP (2008) for this problem is the use of instrumental

variables. In their case, these variables are related to the effects of product innovation, as

are the increase in the range of products, the improvement in the quality of the products

and the larger market share. Furthermore, clients are evaluated as instruments like sources

of information and variables related to internal R&D activities. Other studies that use the

HJMP model (2008) use similar instruments, and they will also be used in this paper.

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To summarise, given the character of the model, first equation (3’) will be estimated

separately for each triannual period using a basic version of the OLS-type, to correct

afterwards through the use of instrumental variables, verifying the existence of endogeneity

and rejecting those weak instruments.

Secondly, the model will be estimated for the entire period, that is, using the

variations of the four triannual periods, first as a pooled model, then pooled with

instrumental variables. Finally, using the panel structure of the data, fixed effects models as

well as random effects models are estimated with instrumental variables procedures.

Note that, in this second case, to estimate the standard error of the model

parameters, it must be considered that ∆.�� could present serial correlation. In the presence

of autocorrelation, the standard errors and the usual statistical contrasts are not valid. To

evaluate the supposition of non-correlation of the errors in the differences model, the

simple test proposed by Wooldridge (2002) will be used.

V. Data base

The data used come from the Panel of Technological Innovation (PITEC) for the years

2004-2010. The PITEC is a statistical instrument for following technological innovation

activities of Spanish firms, and is constructed with the format of a data panel by the National

Institute of Statistics (initialled INE in Spanish) on the basis of the Survey about Innovation in

Firms and Statistics about R&D Activities. The panel covers all the business sectors gathered

in the National Classification of Economic Activities (NACE)10. In general, the sample sizes

differ among years because of firm demographics, with an exceptional increase in 2005.

The PITEC contains detailed information about firm characteristics, sales,

employment, innovative activity, the effects of this activity, and sources of information for

10 The NACE Rev. 1.1 classification corresponds to the years 2004-2008, while the NACE Rev. 2 corresponds to the years 2008-2010. Given the panel structure of PITEC, for years 2004-2007 it was possible to reclassify most of the firms with the most updated code of the NACE Rev.2, assuming the economic activity did not change during the period.

19

innovation. In general, the questions gather information using two reference periods. The

reference period for questions about sales and number of workers is the year immediately

before the year the information was collected. The variables related to the existence and

characteristics of technological innovations implemented by the firm refer to the last three

years in order to facilitate international comparability (INE, 2009). Owing to these two

different reference periods, there are difficulties in making interannual estimations with

regard to the impact of process or product innovations on employment. The present study

will use only manufacturing firm data that refer to the following four overlapping triennia:

2004-2007, 2005-2008, 2006-2009, 2007-2010.11

The data described below refer to the average of the triannual variations of the main

variables used in the model. The description of the variables will be given for all

manufacturing firms, by type of innovating firm and by triannual period. To define the sales

due to old products in real terms, the industrial price index (base year 2010) produced by the

INE will be used as a deflator.

Considering all the triannual variations, a decrease in employment is observed, lower

among firms that undertake both types of innovations and higher among firms that do not

innovate at all (see Table 2). For the first and second triennia, employment increases, then

falls during years of economic recession in Spain. Among non-innovating firms, all periods

show a decrease in employment.

Insert Table 2

Considering all three-year periods, the sales of products grow by an average of 2.4%.

The first triennium observed shows an increase of 25.7%, while in the last period, the

decrease is by 17.6%. By type of good, sales due to old products fall an average of 30%,

while sales due to new products increase 24% in real terms. As such, sales of new or

improved products are an important component in the increase in total sales. Sales due to

new products supplant part of the production of old products among firms that undertake

product innovations.

11 A detailed description of the variables used and the reference periods can be found in Appendices 1 and 2, respectively.

20

To summarize, employment on average diminishes less in firms that innovate,

especially firms with simultaneous product and process innovations. For firms with product

innovations, the demand for old products always diminishes, but the increase in the number

of sales due to new products compensates and exceeds this diminishing. All of this suggests

that compensation effects due to the introduction of new products are more frequent in the

first periods and less frequent during periods of recession.

Table 3 summarises the main statistics of the different instruments used according to

the HJMP model (2008). For Spain, and considering the period 2007-2010, the main source

of information for the innovation is still the firm itself, followed by clients and suppliers.

Noteworthy among the effects of product innovation is the increase in the range of

products, and increased markets.

Insert Table 3

With regard to internal R&D activities, three possible instruments are defined: first,

quantifying the number of years that the firm spends on internal R&D during the last three

years; second, identifying those firms for which this expense is constant throughout the

triennium; and finally, the proportion of spending on internal R&D with regard to firm sales.

Seventy-three percent of firms that obtain both innovations (product and process) maintain

their spending on internal R&D throughout the triennium. It should be noted that, on

average, firms commit more spending when they undertake at least one product innovation,

tripling the spending of firms that do not obtain process and product innovations.

VI. Econometric results

The results of estimating equation (3’) are estimated below.12 All the estimations are

made including dummy variables from the industrial sector.13 Initially, the estimations for

the triannual variations 2004-2007, 2005-2008, 2006-2009, and 2007-2010 are presented.

12 The adjusted and successful models are presented according to different endogeneity tests, strength of instruments and validity tests of overidentifying restrictions. 13 The coefficients of the dummy variables are restricted to add up to zero with the purpose of conserving the interpretation of the constant (Suits, 1984).

21

Then, these periods are estimated together via panel data techniques. Finally, the

contribution of innovation to employment growth is decomposed using the average values

of the variables.

As can be observed in the first five columns of Table 4, on estimating the model by

OLS, one obtains a positive coefficient for the constant, which represents the average

growth of efficiency in producing old products with an opposite sign. Different from the

findings of other studies, there is a loss of average efficiency in the production of old

products, especially for the period 2007-2010. Among firms that undertake only process

innovation, the signs and values are the ones expected, although decreasing in time. Finally,

the relative efficiency of producing new products compared to old products increases with

the passage of time. Note that, if the parameter is less than 1, it suggests that new products

are produced more efficiently than old products. However, given the endogeneity of

unobserved prices or measurement errors, it is likely that the bias is low for this coefficient,

presenting an apparently greater efficiency of old products compared to new ones.

Insert Table 4

The last five columns of Table 4 present the results of applying instrumental variables

to correct the endogeneity problem that exists among unobserved non-technological shocks

and product innovation. These instrumental variables define the importance that the firm

gives to innovation for increasing the range of products and improving the quality of the

products, along with the number of years firms spend internally on R&D during the

triennium14. It is confirmed that overidentification restrictions are valid for each period and

for all the years as a whole.

In general, on estimating with instrumental variables, a drop in the average efficiency

of producing old products is observed. The constant in the first two triennia has the

expected sign according to the empirical evidence of other studies. However, for the

variations of the years 2006-2009 and 2007-2010, the positive estimations reflect a loss of

14The definition and the construction of the variables are outlined in Appendix 1.

22

efficiency of old products, showing a bigger decrease in sales due to old products than in

employment in these firms.

On evaluating the impact of process innovation on the efficiency of old products, the

result is statistically negative only for the 2005-2008 triennium. For the other triennia, there

is no significant evidence. This conclusion is in line with most empirical studies based on the

HJMP model (2008).

As for the impact of product innovation, all the values are less than 1. From the

triennium 2004-2007 to the triennium 2006 to 2009, a loss of relative efficiency is observed.

However, for the 2007-2010 triennium, there is a strong downward correction This

adjustment is probably due to the greater efficiency in the production of new products

among the firms that maintain them during this period.

As a test of robustness, Table 5 shows the estimations of the model, distinguishing

between products that are new to the market and products that are new to the firm. Using

an OLS estimation, the process innovation estimations are lower compared to the estimation

which does not distinguish between types of product innovation. For relative efficiency of

old and new products, in general, the values are slightly higher than the estimation that does

not distinguish between types of product innovation. Also, when comparing the parameters

of new-to-the-market and new-to-the-firm product innovations, it is observed that there is

no significant difference for the trienia analysed except the 2006-2009 triennium, in which

there is a difference shading to a greater relative efficiency of new-to-the-market products

relative to old products. This estimation is preliminary, given that it has not been corrected

because of the endogeneity in the variables related to sales growth. Furthermore, a possible

interaction effect among both types of product innovation has not been evaluated.15

Insert Table 5

15 Preliminarily, estimations for both endogenous variables were made, and no significant values were found for the estimations of sales variations of new goods for the firm and for the market. However, for the years 2004-2007, there is an estimation that is coherent with the theory. These results are available upon request.

23

Also, separate estimations were made for firms with fewer and more than 200

workers because of the PITEC’s distinct degree of representativity in each of these

subsamples (see Table 6). The results in general confirm the results obtained for the whole

of the sample, although with some different features. With regard to the relative efficiency

of new products compared to old ones, in the first three triennia, small and medium-sized

enterprises (SMEs) present better numbers than large firms (a value further from 1), but in

the last triennium, it is observed that this regularity is inverted.

Insert Table 6

In conclusion, the estimations for each triennium show two regularities. During the

two triennia prior to the crisis (2004-2007 and 2005-2008), the results are similar to other

studies: the efficiency variation of old products has a negative sign, associated with an

improvement in the efficiency of said products. Furthermore, process innovation is

significant only for the 2005-2008 triennium. At the same time, the coefficient associated

with product innovation is less than one, and as such, it is a clear sign that new products are

produced efficiently. However, for the following triennia, in the context of economic

recession, the results change significantly. For one, the production of old products loses

efficiency (values with a positive sign in the constant), and process innovation, as in previous

research, ceases to be important. Finally, efficiency in the production of new products

increases dramatically for the last triennium. This could be a consequence of a greater

adjustment in the firms’ labour force even though the sale of new products represents a

smaller proportion of sales than in previous periods.

It is possible to obtain better estimations using the panel data available in the PITEC.

In general, panel data allows one to exploit the time variation to separate the permanent

cross-variation so that consistent estimations of regression parameters may be obtained,

given certain conditions.16 The HJMP model (2008) by definition eliminates the unobserved

fixed factors through the first difference among the years that make up the triennium. This

allows us to have an unbalanced panel for four periods.

16 For more details, see Arellano and Bover (1990) and Baltagi (2008).

24

The results obtained considering the structure of panel data are presented in Table 7.

To establish a point of comparison, in the first two columns of the table the results of

estimating by OLS and VI are repeated, taking all the information as a pool. Given the

possible existence of first-order serial correlation, the Wooldridge test (2002) is estimated,

and it is found that the errors are serially uncorrelated.

With both procedures, the values and signs obtained for the parameters of the

structural model confirm what was expected. However, the parameters corresponding to

the time dummies are positive, explaining a continuous loss of efficiency in the production of

old products as the crisis deepens. Said loss is clear on comparing the values of the 2005-

2008 and 2006-2009 triennia. This would confirm that there are two clear periods in the

sample: the first two triennia, which correspond to the years prior to or at the start of the

Spanish economic crisis; and the last two triennia, which include the years of the crisis. There

is no statistically significant difference between the parameters of these last two triennia.

Insert Table 7

The last three columns of Table 7 present the results of estimating the model by

using VI and considering alternatively different suppositions about unobservable individual

effects, fixed or random, although in this case, given the construction in differences of the

structural model, specifying random effects seems to be preferable, which is confirmed on

performing the Hausman test.

The results again confirm the signs expected by the HJMP model (2008), with process

innovation that increases the efficiency of producing old products, a constant that indicates

the average increase in the efficiency of producing old products in the entire sample, and a

greater relative efficiency in the production of new products compared to old ones.

Therefore, there is clear evidence of significant differences in the relative efficiency

of producing old products, among periods of growth and periods of recession. Process

innovation does displace employment, while product innovation presents a greater relative

efficiency compared to old products.

25

To finish this section, now we proceed to calculate the contribution of innovation to

employment growth. The HJMP model (2008) makes it possible to decompose the average

employment growth in the following four components:

! � ∑ )H* #I )H*I��"+I # )H�+ # J1 12� K 0�L2� 6�� # 12� K 0�M2� 6� # -N2�O # .H ,

where )H*I are the estimated coefficients; �"+I denotes the industry dummies;

∑ )H* #I )H*I��"+I measures the change in employment attributable to the trend of

productivity in the production of old products; )H�+ reflects the change in employment

associated with the effect of the gross productivity of process innovation in the production

of old products; J1 12� K 0�L2� 6�� is the change in employment related to the

growth of producing old products from firms that do not create new products; 12� K0�M2� 6� # -N2�O corresponds to the net contribution of product innovation (that is,

contribution after taking into account the substitution of new products for old products);

and .H is the error term with zero mean.

Table 8 shows the decomposition performed on the basis of the results obtained

through instrumental variables for each triannual period17. In accordance with the empirical

evidence collected, the contributions obtained for the periods 2004-2007 and 2005-2008

have the expected signs. The increase in the efficiency of old products contributes to a

reduction in employment. Furthermore, process innovation displaces employment, although

marginally with regard to the total change in employment for these periods. As a

compensation effect, the increase in the production of old products among those firms that

do not have product innovations counteracts part of the displacement effect resulting from

the greater efficiency in the production of old products. Among firms that undertake product

innovations, new products contribute to the increase in employment by 25.6% (2004-2007)

and 26.4% (2005-2008), an increase that counteracts the loss of employment as a result of a

lower production of old products among these firms. As such, the net contribution of

product innovators generates an important compensation effect, explaining the increase in

17 The descriptive statististics from Table A.1 in Appendix 3 and the estimations from instrumental variables in Table 4 are used.

26

employment in these periods. These results are in line with the estimations obtained by

Harrison et al (2008).

Insert Table 8

How do different contributions behave in periods of recession? We can see a

decrease in employment in the 2006-2009 and 2007-2010 triennia. In these periods, process

innovation maintains values similar to other periods and other studies. However, the

contribution of sales due to old products among firms that do not undertake product

innovation has a negative impact on employment as a consequence of the big drop in sales.

Meanwhile, in firms that have product innovation, the effect of the fall of sales due

to old products reduces employment more than what is oberved in previous triennia, while

production of new products increases employment, but less than those observed previously.

Therefore, it does not manage to counteract the fall in the sales due to old products. This

occurs through less sales due to new products, going from almost 50% of sales in the first

two triennia to only 33% of sales in the last two. Moreover, sales due to old products fall in

the first two triennia between 19% and 28% and in the last two between 44% and 48%.

However, given the loss of efficiency in the production of old products in relation to

the years 2004-2006 and 2005-2007, respectively, a compensation effect counteracts the

decrease in employment. This loss of efficiency allows employment to increase between

12.2% and 12.9%. This unexpected effect contributes to counteracting the decrease in

employment during these periods, which totaled 7.7% (2006-2009) and 11.8% (2007-2010).

Table 9 shows the same decomposition, distinguishing between sub-samples of firms

with fewer and more than 200 workers.18 The results confirm the differences between the

periods prior to and during the economic crisis. For the triannual periods 2004-2007 and

2005-2008, large firms increase employment more than SMEs. Among large firms, the main

factor that contributes to the growth of employment is the net contribution of product

innovation. This same factor is quite high among SMEs for the period 2004-2007, but in the

18 The descriptive statistics from Table A.2 in Appendix 3 and the estimations by instrumental variables from Table 6 are used.

27

following triannual period, it greatly decreases. Another factor that contributes to the

average growth of employment is the greater production of old products, a stable factor

among large firms, but a decreasing factor among SMEs in the last period. These factors

confirm that the impacts of the crisis are first noted in small firms.

Insert Table 9

In the 2006-2009 and 2007-2010 triennia, employment falls in both types of firms.

For the first triennium indicated, the decrease in employment among large firms is

substantially smaller, although with a negative contribution from product innovation and

also from the sales due to old products among firms that do not undertake product

innovation. Only the loss of efficiency in the production of old products can contain the

decreases in employment. For the 2007-2010 triennium, these factors increase their fall,

especially product innovation, whose net contribution in both types of firms is -15%. Again,

the loss of efficiency in the production of old products is configured as an important

compensation effect.

To summarise, employment drops more sharply in SMEs than in large firms. Among

SMEs, product innovation does not compensate for the fall of sales due to old products, but

instead transforms into a displacement effect. Only the loss of efficiency in the production of

old products reduces the falls in employment. Among large firms, these effects are smaller in

the first triennia, although in 2007-2010, they behave similarly to SMEs.

VII. Conclusions

It is important to know the impact of innovation on employment and how this impact

is transmitted to the economy. Those who make policy decisions see a great opportunity to

promote innovation as a motor of growth stemming from improved efficiency in firms, in

their production process as well as in the insertion of new or improved products into the

market.

With the aim of contributing to this debate, this study estimates the contribution of

product and process innovations of Spanish manufacturing firms, using the structural model

28

proposed by HJMP (2008). This makes an excellent case study, as it has data for a period of

the Spanish economy that includes years of growth as well as years of recession.

The results of estimating said model with different econometric methods

demonstrate the existence of two periods with different effects of product innovation on

employment. On one hand, in periods prior to the crisis, employment increases as a

consequence of the increase of sales due to old products among firms that do not undertake

product innovation, but employment also increases among firms that do pursue product

innovation, although they displace part of the production of old products for new ones.

However, for the periods covered by the crisis, employment falls as a consequence of

lower sales. Process innovation maintains its displacement effect to the same degree as in

the previous periods, and the contribution of sales due to old products among firms that do

not pursue product innovation is around -9%, while the net contribution among firms that

do undertake product innovation is between -11% and -15%.

Why do innovating firms present more negative impacts? For one, the proportion of

firms that undertake product innovation increase in the period of the crisis, and the

participation of the sales due to new products over total sales loses importance, such that

the compensation effect of product innovation does not exceed the displacement effect of

lower sales due to old products among these firms. The only factor that compensates for

these displacement effects comes from the lower efficiency in the production of old

products, which increases the employment required for a given level of production. This

might be a result of a smaller adjustment in the employment of old products, which is linked

to a greater proportion of permanent workers, with less chance of being fired according to

adjustments in the firm’s level of sales.

Finally, on distinguishing according to firm size, better efficiency in the production of

new products is observed in those firms that have fewer than 200 workers. With regard to

process innovation, regardless of size, it is not significant in the periods studied. Again it is

confirmed that there is a continuous loss in the efficiency of producing old products,

especially in large firms.

29

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31

Appendix 1: Construction of variables

Variables Description

Employment growth Growth rate of employment between t and t-3.

Percentage of sales due to old products

Percentage of the sales due to old products with respect to total sales from the same year.

Percentage of sales due to new-to-the-market products

Percentage of the sales due to products that are new to the market with respect to total sales from the same year.

Percentage of sales due to new-to-the-firm products

Percentage of the sales due to products that are new to the firm with respect to total sales from the same year.

Sales growth due to old products Growth rate of the sales due to old products between t and t-3.

Sales growth due to new products Growth rate of the sales due to new products between t and t-3.

Prices growth Growth rate of prices between t and t-3. The series of “industry price indices” from the Spanish Statistical Institute are used (base year: 2010).

Process innovation Dummy variable that takes the value 1 if the firm reports that it has implemented new or significantly improved production processes during the period.

Process innovation only Dummy variable that takes the value 1 if the firm reports that it has implemented new or significantly improved production processes during the period but has not implemented any product innovation.

Product innovation Dummy variable that takes the value 1 if the firm reports that it has introduced new or significantly improved products during the period.

Industry variables PITEC’s own classifier is used, based on the NACE 2009.

Sources

These variables refer to multiple channels that the firm uses as sources of information for innovations. The answers are classified as 1 if the firm reports the source was irrelevant, 2 if it had a small impact, 3 if it had a moderate impact and 4 if it had a large impact.

Effects

These variables refer to multiple effects or aims of the innovation undertaken by the firm. The answers are classified as 1 if the firm reports that the source was irrelevant, 2 if it had a small impact, 3 if it had a moderate impact and 4 if it had a large impact.

Years of R&D engagement Variable that adds the number of years that the firm undertakes intramural R&D expenditures from t-2 to t.

Continuous R&D engagement Dummy variable that takes the value 1 if the firms reports continuous engagement in intramural R&D activities during the period.

R&D effort Average R&D expenditure with respect to total sales during the period.

32

Appendix 2: Time structure of the questions about innovation, sales and employment

In general, the PITEC panel has information for the years 2004-2010. In each survey

year, information is gathered on employees in the firm, investments in technology, turnover,

and developments in process and product innovation. This last bit of information uses the

last three years (including the year of the survey) as a reference period. As such, the

definition of old products in accordance with the HJMP model (2008) requires period 1 not

to cover the possible innovations declared in order to measure the effect of innovation on

the sales due to new products.

33

Appendix 3: Complementary Statistics

Table A.1: Descriptive statistics by type of innovator (2004-2010)

2004-

2007

2005-

2008

2006-

2009

2007-

2010 Total

No. of firms 4518 5259 5132 4873 19784

Non-innovators (%) 23.7 22.5 21.4 20.6 22.0

Process innovators only (%) 16.8 16.7 16.1 15.5 16.3

Product innovators (%) 59.5 60.8 62.5 63.9 61.7

Product innovators only 16.8 16.4 15.0 14.5 15.7

Product and process innovators 42.7 44.4 47.5 49.4 46.1

Employment growth (%)

All firms 4.0 1.7 -7.7 -11.8 -3.6

Non-innovators -2.7 -4.4 -12.8 -17.4 -9.0

Process innovators only 5.5 2.5 -8.5 -11.5 -3.1

Product innovators 6.1 3.7 -5.7 -10.1 -1.9

Product innovators only 2.5 1.1 -10.1 -14.7 -5.1

Product and process innovators 7.6 4.6 -4.3 -8.7 -0.8

Sales growth (%)

All firms 25.7 17.3 -12.6 -17.6 2.4

Non-innovators 16.6 7.8 -18.7 -23.9 -4.3

Process innovators only 27.2 21.3 -11.9 -18.2 4.1

Product innovators 28.9 19.7 -10.7 -15.4 4.3

Sales growth due to old products -18.9 -27.5 -44.1 -47.6 -35.4

Sales growth due to new products 47.9 47.2 33.4 32.3 39.7

Prices growth (%)

All firms 10.9 10.2 5.8 5.0 7.9

Non-innovators 11.1 10.5 6.5 5.3 8.4

Process innovators only 11.5 10.9 6.3 5.3 8.5

Product innovators 10.6 9.9 5.5 4.9 7.6

Product innovators only 9.9 9.6 5.4 4.4 7.4

Product and process innovators 10.9 10.1 5.5 5.1 7.7

Source: own calculations, from the PITEC data base 2004-2010.

Table A.2: Descriptive statistics by type of innovator and size of firm (2004-2010)

Fewer than 200 employees Equal to or more than 200 employees

2004-2007 2005-2008 2006-2009 2007-2010 Total 2004-

2007

2005-

2008

2006-

2009

2007-

2010

Total

No. of firms 1052 1025 894 857 3822 3466 4234 4238 4016 15963

Non-innovators (%) 18.3 15.1 13.9 12.5 15.1 25.3 24.2 23.0 22.4 23.7

Process innovators only (%) 17.2 15.8 13.5 12.6 14.9 16.7 16.9 16.6 16.1 16.6

Product innovators (%) 64.5 69.1 72.6 74.9 70.0 57.9 58.8 60.4 61.6 59.7

Product innovators only 10.3 10.1 8.8 7.9 9.4 18.7 17.9 16.3 15.9 17.2

Product and process innovators 54.3 58.9 63.8 67.0 60.6 39.2 40.9 44.1 45.7 42.6

Employment growth (%)

All firms 7.8 5.8 -1.2 -4.6 2.2 2.8 0.7 -9.1 -13.4 -5.1

Non-innovators 6.6 7.4 3.4 0.8 5.3 -4.7 -6.2 -14.9 -19.6 -11.2

Process innovators only 4.9 2.0 -4.9 -5.2 -0.2 5.7 2.6 -9.1 -12.6 -3.7

Product innovators 8.9 6.3 -1.4 -5.4 2.1 5.2 2.9 -6.8 -11.3 -3.0

Product innovators only 7.0 3.6 0.5 -4.1 2.3 1.7 0.8 -11.4 -15.8 -6.0

Product and process innovators 9.2 6.8 -1.6 -5.5 2.0 6.9 3.8 -5.2 -9.8 -1.8

Sales growth (%) All firms 27.0 21.9 -4.5 -9.0 9.2 25.3 16.1 -14.3 -19.4 0.7 Non-innovators 22.3 18.4 -4.3 -8.6 9.0 15.4 6.2 -20.5 -25.7 -6.4 Process innovators only 21.1 24.2 -1.9 -8.8 9.6 29.1 20.6 -13.7 -19.7 3.0 Product innovators 29.9 22.1 -5.1 -9.1 9.1 28.6 19.0 -12.1 -17.0 2.9 Sales growth due to old products -12.6 -16.7 -34.5 -39.2 -26.2 -21.1 -30.6 -46.5 -49.8 -38.0

Sales growth due to new products 42.5 38.8 29.5 30.1 35.2 49.7 49.5 34.4 32.8 41.0

Prices growth (%)

All firms 11.3 9.7 4.9 4.9 7.9 10.7 10.4 6.0 5.1 8.0

Non-innovators 11.3 10.0 6.3 5.5 8.8 11.1 10.6 6.5 5.2 8.4

Process innovators only 12.2 10.9 4.6 5.7 9.0 11.2 10.9 6.6 5.2 8.4

Product innovators 11.0 9.3 4.7 4.6 7.5 10.5 10.1 5.7 5.0 7.7

Product innovators only 10.1 8.9 4.0 4.3 7.3 9.8 9.7 5.6 4.4 7.5

Product and process innovators 11.2 9.4 4.8 4.7 7.5 10.7 10.3 5.7 5.2 7.7

36

Source: own calculations, from the PITEC data base 2004-2010.

Table 1: Summary of previous estimations of the HJMP model (2008)

Authors Country Period Survey

Estimated Coefficients

Sargan Test

(p-value)

No. of

firms

Constant

J PQR$/ PQR$$�L

Process

innovation only

PQS$/ PQS$$�

Sales growth

due to new

products

HJMP (2008)

France 1998-2002 CIS3 -3.44*** (0.77) -1.37 (1.55) 0.97*** (0.06) 2.08 (0.36) 4631

Germany 1998-2001 CIS3 -6.32*** (1.30) -6.80*** (2.90) 0.97*** (0.06) 2.74 (0.25) 1319

Spain 1998-2000 CIS3 5.99*** (0.88) 2.35 (1.76) 1.01*** (0.04) 0.54 (0.76) 4548

U. K. 1998-2003 CIS3 6.30*** (0.85) 3.51** (1.85) 0.99*** (0.05) 1.93 (0.38) 2533

Germany 1998-2001 CIS3 -7.30*** (1.36) -6.11*** (2.92) 1.02*** (0.07) 0.03 (0.86) 1319

Hall et al. (2007)

Italy 1995-1997 MCC -2.98** (1.23) -1.84 (1.29) 0.96*** (0.10) 1.45 (0.69) 4290

Italy 1998-2000 MCC -5.84*** (0.71) 0.18 (0.87) 0.94*** (0.04) 0.91 (0.82) 4618

Italy 2001-2003 MCC 1.91* (0.91) -1.15 (1.31) 1.07*** (0.07) 13.53 (0.00) 4040

Italy 2001-2004 MCC -2.80*** (1.14) -1.22** (0.66) 0.95*** (0.04) 1.74 (0.63) 12948

Benavente y Lauterbach (2008) Chile 1998-2001 ChNIS -0.42*** (0.12) 0.13 (0.24) 0.55*** (0.18) 0.05 (0.83) 514

De Elejalde et al. (2011) Argentina 1998-2001 ENIT01 -0.70 (3.03) 0.77 (1.59) 1.16*** (0.12) 3.21 (0.07) 1415

Aboal et al. (2011a) Uruguay 1998-2009 MIS 1.54*** (0.65) -2.61*** (1.10) 0.96*** (0.04) 2.78 (0.73) 2532

Monge-González et al. (2011) Costa Rica 2006-2007 CRIS -8.80** (4.59) 18.86** (10.13) 1.02*** (0.05) 2.18 (0.14) 208

Álvarez et al. (2011) Chile 1995, 1998, 2001, 2007

ChNIS -1.99 (2.79) 0.30 (2.50) 1.74*** (0.63) 2.32 (0.31) 2049

Source: Own elaboration. See references. Note: Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01.

37

Table 2: Descriptive statistics by type of innovator. Manufacturing firms (2004-2010)(a)

Non-

innovator

Process

innovator only

Product

innovator only

Process and product

innovator Total

Number of firms 4357 3218 3099 9113 19787

Employment growth(b) -7.49 -3.31 -4.42 -0.38 -3.04

Percentage of sales due to old products 100 100 60.64 61.68 76.19

Percentage of sales due to new-to-the-market products 0 0 15.56 15.44 9.55

Percentage of sales due to new-to-the-firm products 0 0 23.81 22.88 14.26

Sales growth due to old products (nominal) -4.27 4.21 -37.97 -34.60 -22.14

Sales growth due to old products (real) -12.69 -4.31 -45.43 -42.27 -30.08

Sales growth due to new-to-the-market products (nominal) 0 0 15.82 16.22 9.95

Sales growth due to new-to-the-firm products (nominal) 0 0 23.29 23.72 14.57

Sales growth due to new products (nominal) 0 0 39.11 39.94 24.52

Sales growth due to new products (real) 0 0 31.65 32.26 19.82

Sales growth (real) -12.69 -4.31 -6.32 -2.34 -5.56

Productivity growth (base 2005) 1.17 2.78 1.02 1.47 1.55

Prices growth (%) 8.43 8.52 7.46 7.68 7.95

Source: own calculations, from the PITEC data base 2004-2010. Notes: (a) Growth rates correspond to the following triannual periods: 2004-2007, 2005-2008, 2006-2009, 2007-2010. (b) The final sample is composed of firms that have triannual variations with an average growth lower than the 99th percentile, in all the growth rates of number of employees, sales, for the total as well as for new and old products.

38

Table 3: Sample means of evaluated instruments by type of innovator (2004-2010)

Non-

innovator

Process innovator

only

Product innovator

only

Process and product

innovator Total

Sources (a)(b)

Within firm or group 1.50 3.00 3.20 3.50 2.90

Supply team 1.30 2.40 2.30 2.70 2.30

Clients 1.30 2.10 2.60 2.90 2.30

Competitors 1.30 1.80 2.20 2.40 2.00

Consultants, laboratories and private institutes 1.20 1.80 1.80 2.10 1.80

Universities 1.20 1.50 1.60 1.80 1.60

Public research institutes 1.10 1.40 1.50 1.70 1.50

Technological centers 1.20 1.60 1.70 2.00 1.70

Conferences, fairs, expositions 1.20 1.80 2.10 2.40 2.00

Scientific journals, technical publications 1.20 1.70 2.00 2.20 1.90

Professional or Industrial Association 1.20 1.60 1.70 1.90 1.70

Effects (a)(b)

Expansion of range of products 1.40 2.10 3.10 3.30 2.70

Market Increase 1.50 2.20 2.90 3.20 2.70

Improvement in quality of products 1.40 2.50 3.00 3.30 2.70

Improvement in production flexibility 1.30 2.80 2.10 3.00 2.40

Increase in productive capacity 1.30 2.70 2.10 3.00 2.40

Reduction of labor costs 1.20 2.40 2.00 2.80 2.20

Reduction of materials and energy 1.30 2.20 1.90 2.60 2.20

Reduction of environmental impact 1.30 2.20 2.20 2.70 2.20

Compliance with regulations 1.10 1.30 1.40 1.50 1.30

Years of R&D engagement (c) 0.73 2.06 2.42 2.73 2.12

Continuous R&D engagement (%) (d) 16.57 36.36 64.62 73.01 53.01

R&D expenditure/sales (%) 1.48 2.93 4.42 4.77 3.67

Source: own calculations, from the PITEC data base 2004-2010.

39

Notes: (a) The value presented in each category corresponds to the average considering the following categories of answers: Does not apply/Not important (1), low importance (2), moderate importance (3), high importance (4). (b) Although firms do not undertake process or product innovations, they might pursue some other innovation (organizational or marketing) and therefore respond to these questions. (c) Years of R&D engagement: Qualitative variable that adds the number of years that the firm undertakes innovation from t-2 to t. (d) Dummy variable which takes the value 1 if the firms reports continuous engagement in intramural R&D activities during the period.

40

Table 4: Estimations by OLS and IV

OLS IV

2004-2007 2005-2008 2006-2009 2007-2010 Total 2004-2007 2005-2008 2006-2009 2007-2010 Total

Constant 3.91** 0.36 12.72*** 13.43*** -1.98* -0.66 -4.28* 9.21*** 11.61*** -5.44***

(1.39) (1.28) (0.86) (0.89) (0.91) (1.97) (1.93) (1.54) (1.42) (1.01)

Process innovation only -8.63*** -11.30*** -5.98*** -2.44* -7.05*** -3.57 -6.57** -2.61 -0.64 -3.73***

(1.95) (1.78) (1.26) (1.20) (0.89) (2.34) (2.22) (1.75) (1.64) (0.99) Sales growth due to new products 0.75*** 0.77*** 0.82*** 0.82*** 0.79*** 0.90*** 0.92*** 0.95*** 0.90*** 0.90***

(0.02) (0.02) (0.02) (0.01) (0.01) (0.05) (0.04) (0.05) (0.05) (0.02)

No. of firms 4518 5259 5132 4873 19784 4518 5259 5132 4873 19785

R2 0.45 0.43 0.31 0.29 0.36 0.39 0.41 0.44 0.47 0.44

R2 first stage 0.38 0.41 0.44 0.47 0.44

Sargan test 1.98 0.11 0.40 1.26 2.99

p-value 0.37 0.94 0.81 0.53 0.22

Endogeneity test 11.60 11.30 7.90 2.90 26.50

p-value 0.00 0.00 0.00 0.09 0.00

Source: own calculations, from the PITEC data base 2004-2010. Notes: Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01. Instruments: Effect of innovation on the expansion of the range of products, effect of innovation on the quality of products, and R&D effort.

41

Table 5 : Estimations by OLS distinguishing between new-to-the-firm and new-to-the-market products

2004-2007 2005-2008 2006-2009 2007-2010 Total

Constant

2.43 -1.20 12.15*** 13.11*** -2.90**

(1.35) (1.27) (0.85) (0.90) (0.94)

Process innovation only -7.09*** -9.68*** -5.37*** -2.11 -6.15***

(1.93) (1.77) (1.26) (1.21) (0.75)

Sales growth due to new-to-the-firm products 0.81*** 0.83*** 0.87*** 0.84*** 0.84***

(0.03) (0.02) (0.02) (0.02) (0.01)

Sales growth due to new-to-the-market products 0.82*** 0.85*** 0.83*** 0.84*** 0.82***

(0.02) (0.02) (0.02) (0.03) (0.01)

Number of firms 4481 5218 5088 4829 19628

R2 0.44 0.43 0.31 0.29 0.36

Source: own calculations, from the PITEC data base 2004-2010. Notes: Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01. The triannual totals are lower with respect to previous estimations. Also, the estimation is restricted, excluding cases from the last percentile.

42

Table 6: Estimations by size of firm

OLS Fewer than 200 employees Equal to or more than 200 employees

2004-2007 2005-2008 2006-2009 2007-2010 Total 2004-2007 2005-2008 2006-2009 2007-2010 Total

Constant 1.25 1.08 13.01*** 15.13*** -2.78* 3.63 -3.6 9.85*** 14.47*** -0.54 (2.61) (2.17) (1.64) (1.59) (1.38) (2.13) (1.93) (1.75) (1.86) (1.52)

Process innovation only -10.13*** -11.23*** -4.83*** -1.83 -6.96*** -3.47 -11.30* -12.51*** -5.64 -7.32*** (2.29) (1.92) (1.35) (1.30) (1.00) (3.60) (4.59) (3.51) (3.13) (1.94)

Sales growth due to new products 0.73*** 0.75*** 0.82*** 0.83*** 0.78*** 0.79*** 0.88*** 0.83*** 0.78*** 0.82*** (0.03) (0.02) (0.02) (0.01) (0.01) (0.03) (0.03) (0.04) (0.05) (0.02)

No. of firms 3466 4234 4238 4016 15963 1052 1025 894 857 3821 R2 0.45 0.43 0.31 0.29 0.36 0.4291 0.4114 0.3092 0.2983 0.344

Instrumental Variables Fewer than 200 employees Equal to or more than 200 employees

2004-2007 2005-2008 2006-2009 2007-2010 Total 2004-2007 2005-2008 2006-2009 2007-2010 Total

Constant -5.79** -2.80 10.28*** 9.72*** -7.71*** -1.77 -8.56 2.69 15.85*** -2.48 (2.05) (1.93) (1.55) (1.41) (1.07) (4.31) (4.94) (4.92) (4.51) (2.56)

Process innovation only -6.10* -6.63** -1.66 0.40 -3.54*** 2.36 -6.54 -6.08 -6.92 -5.38* (2.68) (2.41) (1.79) (1.70) (1.05) (5.35) (5.61) (5.20) (4.99) (2.54)

Sales growth due to new products 0.85*** 0.89*** 0.95*** 0.92*** 0.90*** 0.98*** 1.03*** 1.09*** 0.73*** 0.89*** (0.05) (0.05) (0.05) (0.05) (0.02) (0.12) (0.14) (0.17) (0.15) (0.07)

No. of firms 3466 4234 4238 4016 15963 1052 1025 894 857 3822 R2 0.38 0.41 0.44 0.48 0.44 0.42 0.45 0.44 0.46 0.47 R2 first stage 0.19 0.19 0.17 0.17 0.17 0.16 0.17 0.14 0.16 0.14 Sargan test 2.59 0.12 0.12 2.2 3.3 0.15 0.43 1.76 0.47 0.82 p-value 0.27 0.93 0.94 0.33 0.18 0.92 0.80 0.41 0.82 0.18 Endogeneity test 7.10 9.90 7.40 4.30 26.8 3.81 1.71 1.32 0.60 1.32 p-value 0.01 0.00 0.01 0.04 0.00 0.05 0.19 0.25 0.44 0.25

Source: own calculations, from the PITEC data base 2004-2010. Note: Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01.

43

Table 7: Estimations for all triennia

OLS Pooled IV FE2SLS RE2SLS

Constant -1.98* -5.44*** -4.47**

(0.91) (1.01) (1.38)

Process innovation only -7.05*** -3.73*** -6.11* -4.29***

(0.89) (0.99) (2.87) (1.17)

Sales growth due to new products 0.79*** 0.90*** 0.81*** 0.88***

(0.01) (0.02) (0.10) (0.03)

Years 2005-2008 5.27*** 5.07*** 5.23*** 4.98***

(0.67) (0.74) (0.66) (0.65)

Years 2006-2009 17.29*** 17.78*** 18.08*** 17.94***

(0.73) (0.76) (1.00) (0.68

Years 2007-2010 17.34*** 17.82*** 18.18*** 18.00***

(0.78) (0.77) (1.06) (0.69)

No. of firms 19784 19785 19390 19785

R2 0.36 0.44 0.31 0.44

Sargan test 2.99 3.04 3.12

p-value 0.22 0.22 0.21

Hausman test (FE2SLS vs. RE2SLS ) 39.01 (0.049)

Source: own calculations, from the PITEC data base 2004-2010. Note: Robust standard errors in parentheses; * p<0.1, ** p<0.05, *** p<0.01.

44

Table 8

Contribution of innovation to employment growth

2004-2007 2005-2008 2006-2009 2007-2010 Total

Employment growth 4.0 1.7 -7.7 -11.8 -3.6

Contributions:

Productivity trend in production of old products -7.5 -2.0 12.2 12.9 5.0

Gross effect of process innovation in the production of old products -0.6 -1.1 -0.4 -0.1 -0.6

Output growth of old products contribution 3.9 1.1 -8.3 -9.6 -3.5

Net contribution of product innovators 8.1 3.6 -11.2 -15.0 -4.5

Contribution of old products -17.6 -22.8 -31.0 -33.6 -26.6

Contribution of new products 25.6 26.4 19.8 18.6 22.1

Contribution of new-to-the-firm products 15.1 15.7 11.8 11.3 13.1

Contribution of new-to-the-market products 10.6 10.7 8.1 7.2 8.9

Source: own calculations, from the PITEC data base 2004-2010

45

Table 9: Contribution of innovation to employment growth, by size of firm.

Firms with more than 200 employees 2004-2007 2005-2008 2006-2009 2007-2010 Total

Employment growth 7.8 5.8 -1.2 -4.6 2.2

Contributions:

Productivity trend in production of old products -7.8 -6.2 7.1 16.3 4.5

Gross effect of process innovation in the production of old products 0.4 -1.0 -0.8 -0.9 -0.8

Output growth of old products contribution 3.5 3.4 -2.3 -3.6 0.1

Net contribution of product innovators 11.7 9.6 -5.1 -16.4 -1.6

Contribution of old products -15.2 -18.0 -28.5 -32.8 -23.5

Contribution of new products 26.9 27.6 23.3 16.4 22.0

Contribution of new-to-the-firm products 16.1 16.9 14.1 9.6 13.1

Contribution of new-to-the-market products 10.8 10.7 9.2 6.9 8.9

Firms with fewer than 200 employees 2004-2007 2005-2008 2006-2009 2007-2010 Total

Employment growth 2.8 0.7 -9.1 -13.4 -5.1

Contributions:

Productivity trend in production of old products -6.5 -0.8 12.6 12.6 5.2

Gross effect of process innovation in the production of old products -1.0 -1.1 -0.3 0.1 -0.6

Output growth of old products contribution 4.1 0.6 -9.6 -10.9 -4.4

Net contribution of product innovators 6.2 2.0 -11.8 -15.1 -5.3

Contribution of old products -18.3 -23.9 -31.5 -33.7 -27.3

Contribution of new products 24.5 25.9 19.7 18.6 22.0

Contribution of new-to-the-firm products 14.3 15.4 11.7 11.5 13.1

Contribution of new-to-the-market products 10.1 10.6 8.1 7.1 8.9

Source: own calculations, from the PITEC data base 2004-2010.