COMPETITIVE PRESSURE AND INNOVATION AT THE FIRM LEVEL*

Transcription

1 COMPETITIVE PRESSURE AND INNOVATION AT THE FIRM LEVEL* PILAR BENEITO PAZ COSCOLLÁ-GIRONA MARÍA ENGRACIA ROCHINA-BARRACHINA AMPARO SANCHIS This paper provides empirical evidence on the relationship between market competitive pressure and firms innovation using panel data of Spanish manufacturing firms for We depart from standard measures of competition, and construct variables capturing the fundamentals of competitive pressure (product substitutability, market size and entry costs) to test the theoretical predictions of Vives [28, The Journal of Industrial Economics] for free entry. Our results line up favourably with these predictions. We obtain that greater product substitutability and higher costs of entry lead to more process innovation but less product innovation, whereas market enlargement spurs both product and process innovation. I. INTRODUCTION THE ANALYSIS OF THE EFFECTS OF MARKET COMPETITION on innovation has received a good deal of attention in the economic literature, yet the issue remains far from closed. 1 Theoretical studies on competition and innovation go back to the work of Schumpeter, whose most prevalent hypothesis was that monopoly rents and larger firms size boost firms efforts to innovate. In contrast, a number of authors have stressed that enhanced competition may raise the incremental profits from innovating and, thus, encourage * We would like to thank Jordi Jaumandreu for useful comments and discussion as well as participants at the 9th Annual International Industrial Organization Conference (Boston, 211), the 3rd European Conference on Corporate R&D (Seville, 211), the Zvi Griliches Seminar (Barcelona, 211), the XXVI Jornadas de Economía Industrial (Valencia, 211), and seminars organized by the IESE Business School (Barcelona, 211), and IVIE-Fundación BBVA (Valencia, 212). We are also grateful to the Editor and three anonymous referees for helpful comments. Financial support from the Spanish Ministry of Science and Innovation (project numbers ECO and ECO C3-2), from the Generalitat Valenciana (PROMETEO/29-213/68) and from the Instituto Valenciano de Investigaciones Económicas (IVIE 47/21), is gratefully acknowledged. We also thank Fundación SEPI for providing the data. Authors affiliations: Departamento de Análisis Económico, Universidad de Valencia and ERICES, Facultad de Economía, Avda. de los Naranjos s/n, 4622 Valencia, Spain. pilar.beneito@uv.es Departamento de Análisis Económico, Universidad de Valencia, Facultad de Economía, Avda. de los Naranjos s/n, 4622 Valencia, Spain. paz.coscolla@uv.es Departamento de Estructura Económica, Universidad de Valencia and ERICES, Facultad de Economía, Avda. de los Naranjos s/n, 4622 Valencia, Spain. erochina@uv.es Departamento de Estructura Económica, Universidad de Valencia and ERICES, Facultad de Economía, Avda. de los Naranjos s/n, 4622 Valencia, Spain. sanchisa@uv.es 1 According to Gilbert [26], the amount of literature devoted to the analysis of the relationship between competition and innovation in empirical industrial organization is only surpassed by the analysis of the relationship between competition and profitability.

2 firms R&D investments (the so-called escape competition effect). This line of thought was postulated by Arrow [1962] in the context of perfect protection of innovators property rights. In addition, existing empirical studies on the relationship between competition and innovation provide diverse and often conflicting results, ranging from a positive to a negative, and even an inverted U-shaped relationship. 2 The contradictory results on the empirical relationship between competition and innovation may be partly due to the difficulty, if not the impossibility, of finding an accurate measure of market competition, given the diversity of factors impacting on how market competition may change. The use of synthetic measures of market competition has been widely accepted in empirical work in spite of some potential drawbacks, as stressed early on by authors such as Tirole [1988], amongst others. First, measures such as price-cost margins, concentration measures or other indicators of market structure, are endogenously determined by firms innovation behaviour. Hence, these measures do not always respond in the expected way to changes in the level of competition in the industry, i.e., they do not move always in the same direction as competitive pressure. For instance, high rivalry in cost reduction through process innovation may discourage entry and result in a higher level of concentration, so that in this case higher concentration would not indicate lower competitive pressure in the market. Another example is provided by Boone [2], who pointed out that when competitive pressure intensifies, the price-cost margin may rise as a consequence of some of the most inefficient firms exiting the market, making the price-cost measure an invalid indicator of competitive pressure. A second drawback of using summary measures of competition has to do with their difficult structural modelling, preventing theoretical models from obtaining welldefined predictions on their ultimate expected effects on innovation. Instead, theoretical models typically incorporate a number of parameters that can be identified as fundamental determinants of competitive pressure, and whose impact on competition can be unambiguously established. The degree of product substitutability or the ease of entry are examples of these fundamentals: competition intensifies when products become close substitutes, and lower entry costs create greater competition by increasing the number of firms and/or products in the market. In this paper, we depart from the standard practice of using summary measures of competition and, instead, aim at constructing indicators for what the theoretical literature has addressed as the fundamental determinants of competitive pressure in a market. We consider that constructing empirical indicators of these fundamentals is a more direct and, probably, more suitable way to address the impact of different competitive pressure drivers on firm s incentives to innovate. To this end, we guide our empirical analysis by the theoretical models on competition and innovation as summarised in the work of Vives [28]. We consider that this work is particularly relevant to the aim of our paper for two main reasons. First, from a theoretical point of view, this work provides a general framework with robust results on the effects of several fundamentals of competitive pressure on innovation incentives, reconciling theory with empirical results. Following this author, in a free entry context (endogenous market structure), enhanced competitive pressure may be captured by an increase in the degree of product substitutability, in the size of the market or in the ease of entry (a decrease in entry costs). Regarding innovation, he also distinguishes between incentives to invest in process innovation (reducing variable costs of production) on the one hand, and product innovation (product introduction) on the 2 For a comprehensive survey on the empirical literature of the effects of competition on innovation see, e.g., Gilbert [26]. 1

3 other. Secondly, Vives derives specific implications for the empirical work. In the author s own words: Empirical analysis should consider carefully whether innovation is process or product, whether entry is restricted or not, and include as much as possible of exogenous determinants or instruments like market size, entry costs, or product substitutability variables as well as controlling for technological opportunity' (Vives [28, p. 445]). We use a representative panel sample of Spanish manufacturing firms (Encuesta sobre Estrategias Empresariales, Survey of Entrepreneurial Strategies, ESEE hereafter) for the period to construct measures of product substitutability, market size and entry costs, as well as indicators of firms product and process innovation. We complement the ESEE data with external data drawn from the UNCTAD-TRAINS database and specify a bivariate model to estimate how our empirical measures of competitive pressure affect a firm s innovation incentives, distinguishing between the likelihood of product innovations and of process innovations, while allowing for correlation between these two types of innovation decisions. Our empirical analysis stresses that proper care has to be taken to distinguish the source of change in competition as well as the type of innovation under consideration. Other empirical contributions in the innovation literature have addressed the analysis of the links between innovation and competition. However, these only partially explore dimensions of the analysis we carry out in this paper. Beyond the extensive body of works using summary indicators of competition, and/or a single indicator for firms innovation efforts, such as R&D or patents, few empirical studies attempt to tackle the impact of particular competitive pressure fundamentals on product and/or process innovation. For instance, Kraft [1989] constructs a measure of entry barriers based on capital intensity to explain product innovation by firms; Tang [26] uses an indicator based on firms responses regarding the ease of product substitution by customers to explain product innovation; finally, both Acemoglu and Linn [24] and Cerda [27] investigate the effect of market size on entry of new products in the pharmaceutical industry. However, to our knowledge, our paper is the first to embed into the same empirical framework a comprehensive set of measures for all the basic fundamentals of competitive pressure in order to analyse their likely differential impact on product and process innovation. Furthermore, what is probably even more salient is that this framework is explicitly guided by a priori theoretical predictions for each and every one of these fundamentals. The data we use is remarkably rich in providing the information needed to carry out such an empirical analysis. Our results line up favourably with Vives theoretical predictions. In particular, we obtain that greater product substitutability and higher costs of entry induce greater process innovation and lower product innovation by firms. In addition, market enlargement spurs both firms product and process innovation. These results highlight the importance of taking into account different competitive pressure fundamentals for uncovering their differential and even opposite effects on firms incentives for product and process innovation. Our findings also suggest that the use of alternative indicators of firms innovation, such as total R&D, may not capture the differential effects of competitive pressure fundamentals on product and process innovation. The rest of the paper is organized as follows. Section II presents an overview of the general framework that supports theoretically our empirical analysis. Section III explains the data and variables used. Section IV presents the econometric model and the estimation results. Finally, Section V summarizes the main results and concludes. 2

4 II. THEORETICAL FRAMEWORK In order to guide our empirical study, we follow the theoretical framework of Vives [28] because it provides an extensive and general set of theoretical predictions regarding the differential effect of the fundamentals of competitive pressure on innovation incentives, distinguishing between the incentives for process and product innovation. Vives [28] may be conveniently understood as a survey of the theoretical literature regarding previous results on this topic, explicitly considering the robustness of results to the leading demand systems used in the theoretical literature, and establishing the more salient empirical implications on the impact of fundamentals of competitive pressure on innovation incentives. 3 In what follows we briefly describe Vives general framework to analyse the relationship between competitive pressure and a firm s innovation incentives. 4 We focus on the benchmark model of Bertrand competition with product differentiation and free entry. In his words All cases are empirically relevant although perhaps Bertrand competition with differentiated products and free entry is more salient (Vives [28, p. 421]). His model is a reduced-form non-tournament model, where firms can innovate simultaneously. The reduced form of the model implies, in empirical terms, that endogenous variables, such as the price-cost margin, number of competitors or other endogenous aspects of market structure, are replaced by their basic determinants. These are, in a context of free entry, market size, the ease of entry and the degree of product substitutability. This reduced-form, conditional approach, has then the attractiveness of avoiding both the use of endogenous indicators of competition and their difficult structural modelling. We focus on free entry since the data we analyse correspond to manufacturing firms, where entry is not regulated and, therefore, the number of firms cannot be considered exogenous. The setting is a two stage model where in the first stage firms decide whether to enter the market producing a new variety (product innovation) and paying a fixed cost of entry and, in a second stage, they choose simultaneously the level of investment in cost reduction (process innovation) and price. Vives [28] considers that the profit function for firm i in a market under Bertrand competition with product differentiation takes the form: ( ) (1) i = " p i c z $ # i % &S & D p; i ( ) z i F where p is the price for firm i differentiated variety; c( ) is the constant marginal cost i of production which depends inversely on zi, where z i is firm i investment in cost reduction (process innovation); S is the number of consumers (size of the market) and D p ; is the demand function per consumer for firm i variety, which depends on its i ( ) 3 In relation to previous theoretical work, Vives generalizes some results already obtained in previous papers under specific demand systems, competition modes, nature of market structure and competitive pressure parameters. Vives work considers both restricted and free entry, as well as Bertrand and Cournot competition. For instance, the results he obtains in a market with restricted entry generalize those obtained by, among others, Dasgupta and Stiglitz [198], Spence [1984], Tandon [1984], and Martin [1993] in the context of a Cournot model with constant elasticity or linear specifications of the demand system, and by Raith [23] with price competition and product differentiation à la Salop [1979]. 4 Our description of Vives [28] approach must only be taken as a convenient way to understand the theoretical foundations of our empirical analysis. See Vives [28] for a comprehensive analysis and for further details of his approach. z i 3

5 ( ) own price and the prices of competitors, p = p i, p i, and on a parameter affecting ( ) demand; output (sales) per firm is then x ; and, finally, F represents the i = S " Di p ; cost of entry (cost of introducing a variety, which may be considered as expenditure in product innovation). The parameter affecting individual demand per variety is either the number of firms/varieties (n) or a measure of product substitutability,, usually the elasticity of substitution between any two varieties. In the case of free entry, n is endogenously determined, and the three fundamentals of competitive pressure affecting profit per firm and, thus, firms incentives to invest in process and product innovation are parameterized by S, F and. Note that entry costs, F, have a direct negative impact on profits, whereas S and affect profits through their effect on per-firm output. These three parameters, in turn, have also an induced effect on per-firm profits by affecting the number of firms at equilibrium, n. The profit function in (1) may be used to show how the firm s incentives to invest in process innovation and in product innovation change when there is a change in one of the fundamental determinants of competitive pressure ceteris paribus the others. Incentives for cost-reduction (process innovation) depend on per-firm output, x, because the value of a cost reduction will increase with the output i = S " Di ( p ; ) produced by the firm. Thus, changes in the fundamentals of competitive pressure will affect the incentives for process innovation by changing per-firm output. Incentives for product innovation depend on the profits to be captured as compared to the fixed cost of introducing a new product. Therefore, a change in a fundamental of competitive pressure affecting the difference between the profits (rents) to be captured by a new product and the fixed cost F of its introduction will affect the incentives for product innovation (firms/varieties in the market). This is the so-called Schumpeterian effect. With free entry, the number of firms, n, is endogenously determined by the zero-profit condition and, thus, any change in per-firm profit will have an impact on the number of firms/varieties in the market. Assume now, following Vives [28], that a symmetric regular interior free-entry equilibrium exists for price, the firm s process innovation effort, and number of firms/varieties. 5 Let ˆx i be the equilibrium output per firm in Bertrand equilibrium for a given z i. The effect of a change in on per-firm output, for a given S, can be formally illustrated by the following derivative (see Vives [28, p. 428]): (2) ( ) ˆx # i = S " ˆD i p; % $ % + ˆD i ( p; ) p " p & ( ' ( where ˆD i is the average consumer demand per variety, and where the sign of the derivative depends on two effects: the first effect is the demand size effect, that is, how directly affects firm s demand ( ) ˆD i p;, and the second is the price-pressure effect, ( ) ˆD i p; that is, how affects prices and, thus, demand " p. The latter effect p influences the symmetric Bertrand equilibrium in prices, which is inversely related to 5 See Vives [28] for a formal presentation of all the conditions needed for this equilibrium to exist. 4

6 the elasticity of demand. For this reason the price-pressure effect is also called an elasticity effect. In the case where the demand size effect and the elasticity effect work in opposite directions, it is expected that the direct demand size effect will dominate the indirect elasticity effect. Notice that, in (2), the demand size effect can be identified as a shift in the firm s residual demand (larger demand for constant prices), whereas the elasticity effect could be identified, for a given demand size, as a rotation in the firm s residual demand curve. Hence, any fundamental of competitive pressure in Vives theoretical framework may, in principle, induce shifts and rotations in the firm s residual demand curve. Let us consider first a change in market size S. A standard result in theoretical models of imperfect competition is that market expansion may increase the number of n firms in the market,, 6 but proportionally less than the increase in market size S > because of the fall in margins as firms enter the market (see, e.g., Salop [1979], and Dixit and Stiglitz [1977]). In this case, the demand size effect in (2) is negative due to the increase in the number of varieties, but the price pressure effect is positive, because a higher n reduces prices and so increases average consumer demand per variety. However, the demand size effect dominates, so that the average consumer demand per variety falls. Nevertheless, output per firm increases because S (the number of consumers) grows proportionally more than n and, therefore, it compensates for the decrease in average consumer demand per variety. n Conversely, for some specific demand systems it may be the case that ". 7 S When the derivative is equal to zero, that is, when the number of varieties does not change with S, output per firm increases with S. Furthermore, a derivative smaller than zero is in agreement with some relevant empirical examples (see, e.g., Sutton [1991], where an increase in market size leads to a more concentrated market in some industries). In this case, and independently of the price pressure effect, the demand size effect is positive due to the reduction in the number of varieties and, thus, per-firm output increases both because S increases and because the average consumer demand per variety rises. Therefore, the prediction is that market expansion promotes process innovation since per-firm output increases, independently of the effect of S on n. However, the effect on product innovation incentives is unclear, since n might increase or decrease with market expansion. Vives [28] provides an intuitive explanation for this ambiguous effect. On the one hand, market expansion typically raises profits per firm and, thus, the incentives for product introduction increase. On the other hand, market expansion may reduce perfirm profits if it gives rise to so much rivalry in cost reduction that the incentives for product introduction decrease. However, the author considers that the most likely outcome will be that the direct profitability enhancing effect of a larger market dominates and, thus, an increase in S will increase product innovation incentives. As regards the comparative statics with respect to entry costs, F, a general result in the models of free entry is that increasing F decreases the number of firms/varieties 6 This holds, for instance, for demand systems with constant elasticity with F>, constant expenditureconstant elasticity with F>, and logit demand systems. 7 This derivative is equal to zero for constant elasticity demand systems with F= and constant expenditure-constant elasticity demand systems with F=, and it is strictly lower than zero for a type of constant expenditure-constant elasticity demand systems where F is smaller than a certain parameter in the cost function (see Vives [28]). 5

7 introduced (product innovation). Nonetheless, investments in cost reduction (process innovation) are encouraged because per-firm output increases. Finally, regarding the degree of product substitutability,, for the extensive set of demand systems reviewed by Vives, it holds that the own-price elasticity of demand for an individual firm increases with. This generates price pressure, which decreases the equilibrium price and, therefore, increases average consumer demand per variety. Thus, the price pressure effect of a higher is positive. With regard to the demand size effect, its sign will depend upon the demand system considered. In leading demand systems, such as the linear Shapley-Shubik or constant-expenditure cases, does not affect the ( ) ˆD i p; average consumer demand per variety, that is, =, and, thus, an increase in raises process innovation incentives because of the price pressure effect. However, it reduces product innovation incentives due to a zero demand size effect in combination with a positive price pressure effect that decreases prices, then profits and, thus, the number of entrants according to the zero profits entry condition. 8 In summary, the most relevant theoretical predictions, according to Vives [28], regarding the impact of fundamentals of competitive pressure, such as product substitutability, market size and entry costs, on firms incentives to introduce product and process innovations under the setting of endogenous market structure are those presented in Table I. Although we are aware of the possibility of different results under certain conditions, we regard Table I as the most extensive and robust, and, thus, the most likely set of predictions linking competitive pressure and innovation provided by the theoretical literature so far. The rest of the paper aims at addressing empirically this relationship. [Table I around here] III. DATA AND VARIABLES The main data source used in this paper is the Encuesta sobre Estrategias Empresariales (Survey of Entrepreneurial Strategies) for the period The ESEE is a firm-level annual survey, sponsored by the Spanish Ministry of Industry and representative of Spanish manufacturing firms by industry and size. In the initial year, 199, firms with 1 to 2 employees were randomly sampled, holding around 5% of the population of firms in that year. All firms with more than 2 employees were requested to participate, obtaining a participation rate of about 7% in 199. Since then important efforts are made annually to minimise attrition and to incorporate new firms with the same sampling criteria as in the base year, so that the sample of firms remains representative of the Spanish manufacturing sector over time. 8 ˆD For constant elasticity demand systems, where i p; >, the prediction for process innovation is the same. However, under some particular conditions, the demand size effect may raise profits and encourages entry through the zero profits condition. Finally, there is only one case in Vives revision ( ) where ˆD i p; <, the linear Bowley specification, for which not only product innovation is discouraged (because of lower profits), but also process innovation (because the direct size effect counteracts the price pressure effect on demand, and the direct effect prevails). ( ) 6

8 The richness of information in the ESEE is quite unique. Unlike other surveys designed mainly to gather information on firms innovation activities, the ESEE has the advantage of providing information on a wide range of issues regarding the economic performance of firms and the markets in which they operate. 9 Apart from providing indicators of firms innovation efforts, it allows us to construct an extensive set of variables to proxy for competitive pressure, as well as other side variables that we use to perform robustness analysis. We complement the information of the ESEE with external data from the UNCTAD- TRAINS database to construct an indicator of foreign market enlargement and to instrument product substitutability in our econometric model, as we explain below. 1 In this section, we describe the main variables of the analysis, and leave the description of other variables used as instruments or to perform robustness checks for the section on econometric results below. III (i). Innovation Incentives Indicators: Product and Process Innovation The theoretical predictions described in Section II refer to the impact of the fundamentals of competitive pressure on firms incentives to innovate. The empirical investigation into these incentives is indeed challenging given that they are rarely observable and measurable. R&D expenditures, for instance, may be considered an acceptable measure of innovation incentives since they indicate the aims at innovating instead of actual innovative outcomes. The ESEE provides annual information on firms total R&D expenditures. However, this information does not distinguish between R&D spending on product innovations from that directed towards process innovation. Since our analysis tries to shed light on the differentiated impact of competitive pressure changes on both product and process innovation, as measures of innovation incentives, we use instead firms responses to whether they have introduced product and/or process innovation in a given period. We should interpret these innovation activities as revealed incentives, instead of ex-ante incentives to innovate. We leave the use of R&D expenditures as an alternative measure of innovation to a robustness section below. Regarding the introduction of product innovation, firms provide information on the following question: Indicate if during year t the firm obtained product innovations, either completely new products or products with such important modifications that they are considerably different to those produced in the past. As regards the introduction of process innovation, firms respond to the following: Indicate if during year t the firm introduced any important modifications in the productive process. From the responses to these two questions in the ESEE we construct two binary variables that equal one whenever the firm reports to have introduced in period t either a product innovation or a process innovation, respectively, and zero otherwise. These two binary variables will be used as indicators of firms incentives for product and process innovation, respectively, and constitute the two dependent variables of our baseline bivariate probability model, to be presented in the next section. Table II reports information regarding both product and process innovation frequencies in our data. After selecting those observations with available information on all the variables involved in our baseline estimation, we end up with an estimation 9 See for further details. 1 UNCTAD-TRAINS (TRade Analysis and INformation System) is a comprehensive computerized information system covering tariff, para-tariff and non-tariff measures for more than 14 countries. See 7

9 sample of 1444 observations. Of these, 74.43% correspond to firms with fewer than 2 employees (small firms) and 25.57% to firms with more than 2 employees (large firms). Our estimation sample is an unbalanced panel of 2358 firms, with 1772 small firms and 733 large firms. According to Table II, 32.2% of our sample observations for small firms show positive innovation, whereas this percentage amounts to 57.3% in the case of larger firms. Process innovation seems to be more frequent than product innovation in our sample data. In particular, positive product innovation appears in 17.8% of observations for small firms and in 38.2% of the cases for large firms, whereas process innovation accounts for 23.6% of the sample observations for small firms and for 48.5% of the sample observations for the larger ones. Table II also displays detailed descriptive statistics to show whether firms introduce both product and process innovation or, on the contrary, they focus on a single type of innovation. The percentage of firms with only process innovation is higher among the sample of small firms, whereas large firms seem more prone to combine both product and process innovation. [Table II around here] III (ii). Competitive Pressure Variables In this section we describe how we measure a number of variables that may be considered as providing exogenous variation for product substitutability, the size of the market, and entry costs. 11 The construction of these variables is itself a challenge in the empirical analysis of the competition-innovation relationship, and it constitutes an important intermediate step towards the identification of the causal effects of competitive pressure fundamentals on innovation incentives. Our set of competitive pressure indicators combines firm-level variables, directly drawn from the individual answers to the ESEE questionnaire, and market-level variables that we construct by aggregation of the individual information in the ESEE. In order to construct the market-level variables, we first need to define the relevant market in which firms operate and to use this relevant market as the unit of aggregation. The ESEE classifies firms according to 2 two-digit ISIC industry breakdowns (see Table AI in Appendix A), but these can hardly provide a good approximation to the real market in which firms actually compete. For this reason, in order to proxy for each firm s relevant market, we follow the practice of previous works with the ESEE (see, for example, Huergo [1998] or Beneito [23]) and define marketniches crossing the ESEE information on the 2 industry breakdowns, 3 levels of the geographic scope of the firm s main market (either local-regional, national or foreign market) and 4 possible ranges for the number of significant competitors in its main market (less than 1 competitors, 11 to 25 competitors, more than 25 competitors, and atomistic market, this latter being defined as the case in which there are no firms with significant market share). This procedure defines a total number of 24 market-niches, out of which we discard 31 niches that yield missing values for some of the marketlevel variables needed to perform our baseline estimation. Thus, we end up using Although it could be somewhat arguable whether competitive pressure fundamentals are fully exogenous to the actions of the firms themselves in the very long run, we consider the basic relationships derived in Vives [28] as a reasonable framework for a causal analysis both among industries and in the short and medium term. 8

10 niches to calculate the market-level variables of the analysis. Although we use that number of niches to construct the niche-level variables, in the estimation of our bivariate model for product and process innovation we include instead a set of 8 market dummies since the bivariate estimation fails to converge when introducing the whole set of 29 market-niches dummies. These 8 dummies correspond to the market niches that result from crossing the 2 two-digit ISIC industries with the 4 intervals on the number of competitors in the market. 12 Product Substitutability In theoretical models, the parameter of product substitutability usually refers to the elasticity of substitution between any two products. In empirical work, product substitutability is not a variable typically covered by surveys or official statistics since it is not directly observable. The ESEE does not provide any information that could be taken as a direct measure of product substitutability. However, the theoretical Dorfman- Steiner condition (Dorfman and Steiner [1954]) establishes a formal relationship between the demand own-price elasticity and (an available measure of) advertising intensity, which we use to construct a measure of product substitutability in a market. Let s write the Dorfman-Steiner relationship in log form as follows: (3) ln a $ " # R % & = ln ' ln a p where a denotes advertising expenditures, R stands for the firms sales revenues, a denotes the elasticity of demand with respect to advertising expenditures (i.e, the role of advertising in creating additional demand), and p stands for the own-price demand elasticity. The own-price demand elasticity and the elasticity of substitution between any two products are closely related concepts. In Vives [28] theoretical framework, product substitutability,, changes firms incentives to innovate through its effect on the ownprice demand elasticity, since an increase in product substitutability increases own-price " demand elasticity, p, implying higher price-pressure effects. In turn, the ownprice elasticity of demand for a good may be written as p = s I " I + (1-s I )"", that is, as # > a weighted sum of the income-elasticity of demand for the good, I, and the elasticity of substitution between that good and other goods, ", with weights depending on the income share of the good, s I (Hicks and Allen [1934]). For given values for the income elasticity and income share, the own-price demand elasticity holds a direct linear relationship with the elasticity of substitution, which we denote by g ( ). Thus, we may write: 12 Due to the different sampling procedure of the ESEE for small firms (those with 2 employees or less) and for large firms (those with more than 2 employees), the aggregated values from firm-level variables are obtained using sampling weights. These weights correspond to the inverse of the probability that the observation is included in one of these two size groups. Unless otherwise stated, market-level aggregated variables described and used in estimation are obtained applying this sampling weighting. 9

11 (4) ln a " # R $ % & = ln' a ( ln g () ) Our strategy to obtain an indicator of product substitutability consists, then, in regressing the firm-level information available in the ESEE for a and R on a vector q of proxy variables for a, and retaining what remains unexplained in the regression as the negative of our indicator variable of product substitutability. In reduced regression form, we may write the following expression: (5) ln a $ " # R % & it = ' q it + ( it We estimate by OLS the above expression and set our proxy for product substitutability equal to the negative value of the residual. To run the above regression we construct the dependent variable, log of the ratio a/r, from available data in the ESEE regarding firm-year advertising expenditures and sales. We construct proxies for the advertising elasticity of demand following Strickland and Weis [1976] and Martin [1979]. First, the advertising elasticity of demand is highly dependent on the type of consumer, being higher for individual consumers or households, more prone to respond to advertising than, for example, industrial firms or the public administration. Thus, the higher the share of firms sales going to final consumer demand, the higher the advertising elasticity of demand. Hence, we construct the variable sales_house_retail, defined as the share of sales accounted for by individual consumers, households and retailers. Secondly, the concentration of sales in a low number of customers makes sales to respond less to advertising. The advertising elasticity of demand is likely to depend upon the buyer concentration ratio, which we measure by the variable sales_concentration, defined as the share of sales corresponding to the three major customers of the firm. We include in the regression of (the log of) advertising intensity the log values of these two variables, from which we expect a positive and negative sign, respectively. Further, we include in this regression a set of dummies corresponding to the market-niches explained above. These could also proxy for different advertising elasticities of demand, since such elasticities are determined to a large extent by the type of market in which the product is sold. 13 Finally, we include year dummies, and a dummy for firms with less than 2 employees to control for the different sampling procedure in the ESEE for firms below and above that size threshold. Table III reports the estimated results for the Dorfman-Steiner relationship (descriptive figures on the main explanatory variables are reported in Table IV), in which we observe that the coefficients of sales_house_retail and sales_concentration exhibit the expected signs and are statistically significant. We retain the residual from this regression, and set our variable of substitutability equal to -. In the econometric setting below, we instrument this variable in order to avoid any estimation bias due to, for example, measurement error. We leave for Section IV the description of the instrumental variable procedure. [Table III around here] 13 The inclusion of the market-niche dummies makes our measure of product substitutability to reflect individual differences with respect to the market-niche mean. ˆ it ˆ it 1

12 [Table IV around here] Market Size We use three variables to proxy for market size. The first is a binary indicator taking the value of one whenever firms assess that the market where they operate is expanding (exm). Market expansiveness may come from either macroeconomic changes that affect all firms equally, or, alternatively, from changes that affect in a differential manner to different industries or firms markets over time. 14 The advantage of this variable is that it is directly reported by firms and, hence, it is an indicator related directly to the real market in which the firm operates, and not imputed by the researcher according to industry codes. Further, this variable may also reflect the firm s perceptions regarding market prospects. It indicates not only that a firm s main market is getting larger but also that the firm is aware of that, or even, that the firm is anticipating future market dynamism. Therefore, this variable may capture aspects of market expansiveness other than those captured by the rest of variables of market size, which we describe next. The second variable we use to capture market size is export tariffs (that we denote by ), which is related to the impact of globalization and trade liberalization on innovation. The UNCTAD-TRAINS database provides most favoured nation (m.f.n. henceforth) export tariffs faced by Spanish firms per industry, year and country of destination. We select export tariffs at the two-digit level under the ISIC-Rev. 3 classification, which is equivalent to the 2 industry breakdown information provided by the ESEE. This allows us to match each firm-year in the ESEE with a value of the m.f.n. export tariff from the UNCTAD-TRAINS database. Furthermore, the ESEE provides information on firms export shares by destination, distinguishing among four groups of countries: the European Union, the rest of the OECD countries, Latin America and the rest of the world. Hence, we select from the UNCTAD-TRAINS database those export tariffs corresponding to these four groups of countries, and use the information on the firms export destination shares to construct a weighted export tariff with firm-level weights. As a result our export tariff measure varies across firms depending on their export destination shares, across two-digit industry codes and over time. 15 For non-exporters we impute weights equal to the average export shares by destination of those exporter firms in a same market-niche. This weighting procedure follows Campa [24], who weights exchange rates by firms export destination using the ESEE database. The author imputes the average shares of export destination for exporters in the same industry to non-exporters Doraszelski and Jaumandreu [213] use this information from the ESEE to construct a variable for market dynamism, and show how during the 199s, a period of rapid output growth in Spain, firms in the ESEE tend to report that their markets were expanding. 15 Up to the year 1999, firms export shares by country-group of destination were reported in the ESEE for three country groups: EU, rest of the OECD countries and rest of the world. From 2 onwards, the former rest of the world group was divided into Latin American countries and the rest of countries. We have extracted the export tariffs from the UNCTAD-TRAINS dataset for the three former groups up to the year 1999, and for the four final country groups from 2 onwards. Further, the group of countries whose tariffs are included in the EU group (and, thus, the group of countries that remain in the rest of the world group) has been conveniently updated following the incorporation of new countries into the EU over the sample years. 16 In particular, we impute to non-exporters the average shares of export destination of exporters in the same 8-market-niche. We disregard in this case the 29-market-niches classification that rely also on the geographic scope of the firm s main market, since it yields some niches composed fully of non-exporters. 11

13 The two market size variables described above, that is, the binary indicator of market expansiveness and export tariffs, do not provide, however, a real measure of how large a firm s market really is when compared to other firms market. Hence, we construct a third variable that provides a measure of the firm s market size in terms of the volume of total real sales in its main market. We exploit information provided by the ESEE on firms market shares and volume of sales to construct a measure of the volume of total real sales in their market. To this end, we multiply the inverse of the firm s market share by its own volume of sales (deflated using two-digit industrial price indexes). 17 We include these three different measures of market size in estimation since they capture different dimensions regarding the size of the market. Entry Costs We construct three measures of entry costs that try to capture two ways of understanding the entry of new firms/varieties into the market: the first variable is closely related to the costs of entering and establishing a new firm within an industry (which is also one of the ways by which new products may enter the market). The other two variables approximate the average fixed and marginal R&D costs of introducing a new product into the market. The first entry costs variable is a measure of set-up costs following Sutton [1991]. This variable is defined as the output share of an industry s median-size firm multiplied by the capital-output ratio for the industry as a whole. The former part of this product is considered by Sutton [1991] as a measure for the firm s minimum efficient scale. Therefore, the measure for set-up costs is a proxy for the amount of capital, relative to the industry s market size, required to build such a firm. 18 Firms output is measured as sales plus variation in inventories, whereas the stock of net physical capital is obtained using the perpetual inventory method (details are provided in Appendix A). This variable is defined at the market-level, that is, for each of the 29 market-niches described above. We construct two additional entry cost variables that attempt to proxy for the average fixed and marginal R&D cost of introducing a new product (variety) into the market. We estimate a linear R&D-cost function that consists in regressing firms R&D expenditures on the indicators of process and product innovation, including information on the number of product innovations introduced by firms. To account for the fact that process and product innovation are obtained from lagged values of firms R&D expenditures and, probably, with different frequency over time, we estimate a smoothed moving-average relationship among R&D expenditures and innovations in which we allow the estimated coefficients to vary across the 29 market-niches and over threeyear periods into which we divide our observation window. The estimation equation may be written as follows: 17 We face the problem of a large number of missing values in the market share variable reported by firms. Furthermore some firms may indicate they have a zero market share when their market share is not significant. In order to address these problems, we assume that all firms in the same market-niche have the same value of market size. Under this assumption, all we need is valid information of market size for at least one firm in each market niche. We calculate the sample average of the market size variable from the non-missing information reported by firms in a same market-niche and impute this value to all firms in the same niche. Among the 32 market-niches corresponding to atomistic markets, there are 15 for which all firm-year observations report a null market share. Thus, it is not possible to obtain a valid measure of the size of the market for the observations in these market-niches. This implies discarding 783 observations corresponding mostly to very small firms (around 8% of them with less than 5 workers). 18 This proxy for set-up costs has being used in other studies (see, e.g., Syverson [24]). 12

14 (6) RD it3 = a j q " d j q + b j q " d j q " N _ Product + c i t+2 j q " d j q " Process i t+2 if Product > i t+2 where RD it3 denotes the firm s real R&D expenditures averaged over years t-1, t-2 and t-3, N _ Product stands for the average number of product innovations obtained by i t+2 firm i over years t, t+1 and t+2, and Process indicates the average frequency of i t+2 process innovation over the same years. 19 The underlying assumption is that the average number of product innovations introduced by firms during a three-year period is the outcome of the average R&D investments over the previous three-year period. 2 The introduction of the average frequency of process innovation aims at controlling for the fact that part of R&D expenditures is allocated to process innovation (the ESEE does not provide information on the number of process innovation but only on the yes/no indicator of this type of innovation). We estimate the equation for positive values of the moving average value of product innovation ( Product ). In the expression above, i t+2 > d jq denotes a vector of market-period dummies that result from the interaction of the 29 market-niches dummies with the three-year period dummies. Thus, we estimate marketperiod specific intercepts (to be interpreted as the fixed component of R&D costs) and slopes (R&D marginal costs) of the R&D cost function. Finally, we retain as our indicators of average fixed cost (FRDcost) and marginal R&D costs (MRDcost) of introducing varieties the following measures: (7) FRDcost j q = â jq N _ Prod jq (8) MRDcost j q = ˆb jq where â jq and ˆb jq stand for the estimated coefficients for the market-period constants and slopes, respectively, of the R&D costs equation, and where N _ Prod denotes the jq average number of product innovations introduced in firm i s market-niche during the corresponding three-year period. Descriptive Statistics on the Competitive Pressure Variables Table IV shows descriptive statistics for the competitive pressure variables involved in our benchmark estimation and Table V reports the correlation coefficients among the variables we use to measure the fundamentals of competitive pressure. The figures in Table V indicate, first, that the variable of market expansion has a significant and negative correlation with the average level of export tariffs. This suggests that one part 19 Nominal R&D expenditures are deflated using annual industrial price deflators for the Spanish manufacturing sector. 2 High within-firm year-to-year correlation among R&D expenditures downplays the analysis of the particular R&D lag structure that explains innovation outcomes. Our choice of the previous three-year period is, of course, arbitrary, but sensible among the possible lag structures to relate R&D and innovation outcomes. 13

15 of the exogenous expansion of the firm s market is probably related to easier accessibility to foreign markets as export tariffs decrease. Notice that this qualitative result is independent of being a large or a small firm, but it is quantitatively stronger for the latter, probably because export tariffs represent a higher barrier to foreign markets for small firms. Secondly, the size of the market in which the firm operates is significantly and positively correlated with the market expansion indicator for large firms, whereas this positive correlation is non-significant in the case of small firms. Finally, the variable capturing set-up costs à la Sutton is positively correlated with the average fixed R&D cost and with the marginal R&D cost per new product introduced in the market. In turn, marginal R&D costs are negatively correlated with the average fixed R&D costs. These qualitative results affecting entry costs hold for both small and large firms, but they are quantitatively stronger for the latter. [Table V around here] Other variables in the econometric estimation are control variables (firms age, size, year and market dummies) and variables constructed for robustness purposes. Their construction details are provided in Table AII of Appendix A. IV. ECONOMETRIC MODELLING AND ESTIMATION RESULTS In this section we test the theoretical predictions presented in Section II. First, we present our baseline specification consisting in a two-equation model to estimate the impact of our measures of competitive pressure fundamentals on the likelihood of product and process innovation. Second, we move to several extensions of our baseline specification in order to check the robustness of these results to the use of alternative variables and model specifications. IV (i). Bivariate Model for Product and Process Innovation We use a simultaneous two-equation system to estimate jointly the likelihood of a firm introducing product and process innovation in a given period. The simultaneous estimation allows for the possibility that the error terms in the equations explaining product and process probabilities are not independent of each other. Interrelation between the two error terms may arise for different reasons. A first one is because product and process innovation are examples of two decision variables taken by the same actor (firm) to achieve a given innovation strategy and, thus, they may be actually related. A second source of interdependence in the errors of the two-equations is directly related to possible model misspecification. If, for instance, the true model for the probability of product and process innovation contains omitted variables the two error terms are correlated by construction. Our baseline two-equation model for product and process innovation is specified as a bivariate probit model as follows: 14