An analysis of competition, innovation and productivity in Australia

Transcription

1 An analsis of competition, innovation and productivit in Australia A Conference Paper for the 202 International Schumpeterian Societ Conference 2-5 Jul 202, Brisbane. Leo Soames and Dr Donald Brunker Leo Soames (leo.soames@pc.gov.au) is a research economist at the Productivit Commission ( Donald Brunker is a former assistant commissioner of the Productivit Commission. The authors appreciate the advice and comments provided b Professor Robert Breunig of the ANU College of Business and Economics on earlier versions of material in this paper, and the helpful comments provided b Jenn Gordon, Alan Johnston, Lisa Gropp, Shiji Zhao and Cind Li of the Productivit Commission. The views expressed in this paper are those of the authors and do not necessaril reflect the views of the Productivit Commission. An errors or omissions in the paper are the responsibilit of the authors alone.

2 Abstract Productivit growth is a fundamental determinant of growth in average income, living standards and ultimatel national well-being. Competition and innovation are important in influencing productivit growth, but competition, innovation and productivit can interact in non-trivial was. This adds complexit to efforts to identif and quantif the ke factors that drive productivit growth. In this paper, a new source of data, the Australian Bureau of Statistics Business Longitudinal Database (BLD), is used to analse the relationships between competition, innovation and productivit. The BLD comprises firm level information on a variet of competition, innovation and productivit characteristics, linked with compan tax and other administrative data. A cross-sectional approach is emploed to analse two potential relationships between product-market competition and innovation: a Schumpeterian relationship, where innovation is negativel associated with greater levels of competition; an anti-schumpeterian relationship, where innovation is positivel associated with greater levels of competition. A multivariate probit model is used to explain the effect of competitive pressures on the propensit of firms to complete particular tpes of innovations. Most of the competition indicators analsed (namel the number of competitors faced, a (lower) price-cost margin, a declaration of the firm being hampered b competition, and being an exporter) indicate a positive association between stronger competition and a higher likelihood of firms completing an innovation of an tpe. A positive association between stronger competition and the probabilit of a firm completing a wider range of innovation tpes is also found. The remaining competition variable tested, the market share of the firm, is found to be positivel associated with the probabilit of innovating, suggesting that competition, to the extent it is associated with market share, is negativel linked to innovation. On the whole, these results indicate that, for Australian firms, the relationship between competition and innovation is largel anti- Schumpeterian in nature. Turning to the link between innovation and productivit, a firm was found to be more likel to report an increase in productivit if it completed some sort of innovation in the previous ear, particularl either a 'goods and services' innovation or an 'operational process innovation. Put another wa, firms that do not innovate are predicted to be more likel to report a decline in productivit than businesses that report completing innovations. Kewords: Product-market competition, innovation, productivit, micro-data, multivariate probit, ordered probit, business longitudinal database. 2 ISS 202

3 Introduction and theor Productivit growth is a fundamental determinant of growth in average income, living standards and ultimatel national well-being. Competition and innovation are important in influencing productivit growth, but competition, innovation and productivit can interact in non-trivial was. This adds complexit to efforts to identif and quantif the ke factors that drive productivit growth. The goal of this paper is to empiricall determine which of a variet of economic theories regarding competition and innovation is most accurate in Australia. In order to achieve this, firm-level data from the Australian Bureau of Statistics (ABS) Business Longitudinal Database (BLD) is used to econometricall quantif the relationship between product market competition and innovation outcomes, as well as to identif the link between a variet of innovation tpes and productivit outcomes as reported b the firms. Theor The influence of product market competition on the incentive for firms to innovate has been the subject of debate since the first half of the 20 th centur. The theoretical role of competition in influencing the incentive to innovate occurs through two primar mechanisms: static allocative efficienc and dnamic efficienc. An allocation of scarce economic resources (capital and labour) among competing economic activities is said to be efficient, at a point in time, if no other allocation can result in at least one person being better off while at the same time making noone worse off. The extent and intensit of product market competition can affect the allocation of resources, and hence allocative efficienc and, potentiall, the aggregate well-being of the communit. Competition, as discussed here, improves allocative efficienc b reducing the capacit of firms to appropriate economic rent from restricting suppl to the market. An example of a violation of product market competition is a profit maximising monopol firm. Standard microeconomic theor demonstrates that a profit maximising monopolist will produce less output and emplo less capital and labour than is sociall desirable, resulting in lower aggregate economic welfare (this is an example of the loss of economic welfare from static allocative inefficienc). 3

4 The second reason competition is important is that it is generall considered to provide an incentive for firms to innovate. The discover and application of less costl production processes and the introduction of new and improved products and services provides a firm with an edge over its competitors. The extent and effectiveness of the incentive to innovate is often referred to as dnamic efficienc. However, as discussed below, the relationship between competition and dnamic efficienc is not straightforward. Competition can also influence productivit growth directl through the process of creative destruction, a term coined b Joseph Schumpeter (942). In competitive markets, firms with relativel poorer productivit than their counterparts are more likel either to be forced to improve their productivit to retain their market share or lose market share and possibl be driven out of business. Theor and analsis of the effect of competition on the incentive for firms to innovate goes back to the mid-20 th centur. Schumpeter argued that greater competition is associated with a reduced incentive to innovate (Schumpeter 942). The reasoning behind this Schumpeterian theor of competition and innovation is that firms facing less competition have a greater abilit to appropriate the economic returns from innovation than do firms facing strong competition, where the returns are competed awa and accrue to the consumer. A rejoinder to this argument can be found in the work of Kenneth Arrow with his suggestion of a replacement effect (Arrow 962). In markets where there is a lowlevel of competition, there can be little incentive to undertake costl innovation and replace existing products, especiall in the case where such innovation does not lead to substantial changes in demand. To use the monopolist example of earlier, there is little incentive for the monopolist firm to replace its existing supernormal profit with the same profit less the costs of innovation b the introduction of a new good. In contrast, those firms facing intense competition ma be able to secure additional market share and potential supernormal profits (temporaril) b innovating. 2 2 A more recent theor developed b Aghion et al. (2005) suggests that both Schumpeterian and anti-schumpeterian responses to competition can occur depending on the technological diversit of the particular product market. Where the product market is characterised b firms of similar technological sophistication, an increase in competition will lead to a greater innovation effort the so-called escape competition effect. However, where firms are widel dispersed in terms of technological sophistication, an increase in competition (among technological leaders) will lead to a decrease in innovation effort. The balance of these technological market characteristics across the various product markets in an econom determines whether an increase in competition will generall be expected to increase or decrease the aggregate flow of innovation. If competition is at a relativel low level, an increase in competition is more likel to lead to an increase in innovation, but if the level of competition is alread high, an increase in competition is more likel to decrease aggregate innovation. In this wa the relationship between aggregate 4 ISS 202

5 Determining which of these theories, a Schumpeterian relationship or an anti- Schumpeterian relationship, best applies to Australian firms is the empirical question addressed in this paper. There is a considerable literature on the subject of competition and its effect on innovation and productivit. Man good treatises on the subject, at various levels of detail, are readil available to the interested reader and include Tirole (988) and the more abridged, expositor papers b Gilbert (2006a and 2006b), and Ahn (2002). This paper provides onl a brief summar as background to the empirical analsis. Previous Australian work While empirical work on these matters at the firm level has been relativel sparse in Australia, there are three papers of particular interest and relevance: Rogers (2004) uses 4500 firm-level observations from the ABS Growth and Performance Surve (GAPS) in a cross-sectional, discrete choice context to identif those factors associated with a firm being more likel to innovate. The stud finds that firm size and whether the firm innovated previousl are positivel associated with the probabilit of a firm innovating, but that market share (measured in two different was) is not significant in explaining innovation activit. Wong et al. (2007) use the 2003 ABS innovation surve linked with the Economic Activit Surve (EAS) datasets for multiple ears as well as incorporating unpublished business income tax and business activit statement data from the ATO. These datasets contain information on firm characteristics and innovation activit, and are used with tax return information in a Crepon, Duguet and Mairesse (CDM) - tpe model to examine those factors that affect innovation inputs, innovation outputs and productivit. While the authors emphasise that their results are indicative and explorator onl (due to the short period of data examined), the include a finding that firms with higher market share and larger firms are more likel to innovate. Griffiths and Webster (2009) analse a panel of 4802 Australian firms over a sixteen ear period to investigate how external factors (such as competition) and internal (firm-specific) factors affect research and development (R&D) expenditure. The find that most variation in R&D expenditure at the firm level is related to internal, unspecified, time-invariant characteristics that are specific to the firm. The authors conclude that even over a ver long time period, drivers innovation and the level of competition follows an inverted U shape. Data limitations prevent the testing of such a relationship in this paper. 5

6 of innovation ma not easil be separatel identified from firm-specific indicators. In this paper, a new source of data is used to analse the relationships between competition, innovation and productivit. The ABS BLD comprises of firm level information from the ABS Business Characteristics Surve (BCS) and is linked with compan tax and other administrative data (ABS 2009). As time progresses, the BLD will consist of several panels of firm-level data, which will be useful for panel-data analsis. 6 ISS 202

7 2 Competition and innovation Product market competition arises as firms endeavour to establish an advantageous position over other businesses ving to maintain or increase their share of the product market in question. It is a complex process with the extent and nature of competition changing over time as firms enter and leave the market, as new products and processes are introduced, and as firms emplo different competitive strategies. The introduction of new or improved goods and services, or new or improved processes is one of the ke means of establishing a competitive advantage. In addition to the capacit to provide an advantage to the initiating firm, the introduction of new or improved products and processes should in turn give rise to improvements in productivit. Because the foundations of product market competition are microeconomic in nature, firm level data are fundamental to an investigation of the role of competition in stimulating innovation and productivit. This section provides the results of a firm level econometric analsis of the relationship between product market competition and innovation, and innovation and productivit. 3 The data used in the analsis were sourced from the main-unit record file of the ABS Business Longitudinal Database (BLD). The database contains information on firm level responses to the ABS Business Characteristics Surve (BCS) augmented with administrative customs trade data and tax data. Much of the data sourced from responses to the BCS is subjective in nature for example, a question in the surve which asks whether the productivit of the business decreased, staed the same or increased compared with the previous ear. The analsis in this section is essentiall cross-sectional in nature. It uses information from a snapshot of firms at a point in time to investigate the nature and strength of associations between competition, innovation and productivit. 4 The econometric work is presented in two parts. The first examines the relationship between competition and innovation, and the second presents the relationship between innovation and productivit. 3 The modelling and results are sourced or adapted from Competition, Innovation and Productivit in Australian Businesses (Soames, Brunker and Talgaswatta 20) - a research paper released jointl b the Australian Bureau of Statistics and the Productivit Commission. Full details of the data, variables and modelling techniques are available in that publication, with the relevant models reproduced in the appendix to this paper. 4 The BLD has not been established long enough to provide a longitudinal component that was sufficientl well populated for a panel analsis approach. See ABS (20) for further detail on the BLD. 7

8 Measurement and data Product market competition is a difficult thing to measure. Past empirical work has emploed a variet of approaches, including measures of market share, market concentration, price/cost margins, and more recentl profit elasticities. 5 The presence of competitive pressures is likel to be manifest in different behavioural traits at the firm level. Competition is eas to talk about in the abstract, but an empirical analsis requires the identification and use of appropriate indicators that can be measured. In practice this proves challenging. There are man different dimensions to competition that are difficult to combine into a single measure. Frequentl, a measure of profitabilit is used to reflect the extent to which a firm (or an industr) is subject to product market competition higher profitabilit is taken to indicate less competition. A variet of competition-related measures derived from the BLD are considered in the analsis in order to reflect the likel diversit of firm behaviour in response to the evolution of product market competition. These include measures of the firm s market share, the number of competitors it faces, and whether or not it considered its performance to have been hampered b the need to keep profit margins low in order to remain competitive. Information is also used to construct other competition related measures including a firm s export status, export intensit, and a price-cost margin (PCM). Other variables that are expected to be associated with innovation are also included in the modelling. These include the age and size of the business, as well as the degree of foreign ownership, and industr of operation. 6 A description of each of these variables and the competition measures is provided in table. 5 For example, see A new wa to measure competition, Boone (2008) 6 The firm s industr of operation and its business size are also stratification variables of the BCS. As unweighted regression techniques are emploed, the inclusion of these variables is necessar to avoid bias in the estimates. 8 ISS 202

9 Table Competition and other variables a Variable Tpe Values Market Share Discrete {<0%, 0-50%, 50%+} Number of competitors Discrete {no competition, -2 competitors, 3 or more competitors} Hampered b Binar {Yes, No} Business Size Discrete {sole trader, -4 emploees, 5-9 emploees, emploees, emploees, 500+ emploees} Export Status Binar {Exporter, Non-exporter} Export Intensit Continuous on unit interval Ratio of export sales to total sales Foreign Ownership Discrete {no foreign ownership, <0% foreign ownership, 0-50% foreign ownership, 50%+ foreign ownership} Price-Cost Margin (PCM) Continuous Sales less intermediate inputs and wages as a ratio of sales Industr of operation Binar Dumm variable for each ANZSIC industr division Age of operation Continuous The number of ears the business has been operating a The descriptive statistics for these variables are available in the data section of the appendix to the source identified below. b A declaration that business operations were hampered b the need to keep profit margins low due to strong competition. Source: ABS (20), Soames, Brunker and Talgaswatta (20) Man of the variables used in the analsis are discrete or categorical in nature. For example, each firm in the dataset reports a market share in one of the categories: less than 0 per cent, between 0 and 50 per cent, or greater than 50 per cent. The measures of innovation and of productivit change, which form the dependent variables in this analsis (and are discussed further below), are also categorical. The categorical nature of these data dictates the use of discrete response modelling techniques in this analsis. Most studies investigating the role of competition in the innovation decision use either some form of R&D expenditure or a measure of patents granted or patent citations as an indicator of innovation or innovation effort. However, the innovation component of the BCS (and the Australian Innovation Surve prior to that), consistent with the Oslo Manual (OECD 2005), takes a much broader view of what constitutes innovation. It is appropriate to examine innovation, rather than the 9

10 narrower subset of R&D, as previous studies have indicated that less than half the expenditure on innovation is R&D expenditure in Australia. 7 The BCS does enquire about the introduction of new or significantl improved goods, services or processes b surveed businesses. In the analsis presented in this section, a business is defined to have been an innovator if it reports having introduced new or significantl improved products or processes. A variet of innovation measures are available from the BCS, which are defined in table 2: Table 2 Innovation Tpe Innovation measures used in the modelling When a firm implements Goods and services Operational process Organisational process Marketing A product where its intended uses differ significantl from those previousl produced/offered b [the] firm A significant change for [the] business in its methods of producing or delivering goods or services. A significant change in this business strategies, structures or routines which aim to improve the performance of [the] business A significant change in designs, promotion or sales methods aimed to increase the appeal of this business goods or services or to enter new markets. Source: ABS (20), Soames, Brunker and Talgaswatta (20) A multivariate probit model of competition and innovation A simple model, which examines whether a firm innovates, conditional on its competitive pressures, is documented in Soames, Brunker and Talgaswatta (20). However, the association between competition and innovation can be nuanced further b recognising that the act of a firm completing a particular sort of innovation ma not be completel independent of completing other tpes of innovation. To this end, a multivariate probit model, which can account for such outcomes, is used. 8 A single ear of BLD data from provides the data for the subsequent analsis. 9 Table 3 reports the results of the multivariate probit model, where each of the innovation tpes completed b a firm (table 2) are regressed against the competition and other explanator variables (table ) as a sstem of binar probit equations. Of particular interest are the following associations found from the model results: 7 In , R&D comprised onl 3% of innovation expenditure (ABS-DITR 2006). 8 The full specifications and results of all models in this paper are detailed in the appendix. 9 The ear of the BLD is useful for innovation-related analsis as it contains information on additional innovation measures that are not asked as part of the BCS in ever ear. 0 ISS 202

11 A greater market share is found to be significantl and positivel associated with the likelihood of a firm being an innovator for most different innovation tpes. The sole exception to this finding is the completion of a marketing innovation, where the degree of market share is statisticall insignificant in explaining the occurrence of that particular sort of innovation. Conversel, the effect is strongest in goods and services innovation, with those firms possessing a greater market share more likel to complete an innovation of that tpe. The number of competitors faced b a business is also found to be significantl associated with a greater likelihood of being an innovator for each of the different innovation tpes. The declaration of being hampered b competition is found to be significantl associated with a greater likelihood of being an innovator (relative to unhampered firms) for each of the different innovation tpes. A lower price-cost margin is associated with a higher likelihood of innovating in each innovation tpe. Exporting firms are found to have a higher likelihood of being an innovator in each of the innovation tpes, relative to non-innovators, but the export intensit term is onl found to be statisticall significant in the case of operational process innovation, where it is negativel associated with the likelihood of innovating. 0 While not strictl a competition variable, the modelling indicates that those firms with a greater number of emploees are more likel to innovate, relative to smaller firms. 0 Recall that export behaviour is captured b two variables: a simple indicator that defines whether a firm exports at all and an export intensit measure that defines the proportion of its sales comprised of exports. In the case of operational process innovation, being an exporter is positivel associated with a higher likelihood of innovating, while greater export intensit is negativel associated with the probabilit of innovating. It is the case that the effect of the indicator is larger than that of the intensit measure, so exporting firms are more likel to innovate in operational process innovations.

12 Table 3 Multivariate Probit Model a Goods and Services Innovation Organisational Process Innovation Coeff. b M.E. c Coeff. M.E. 0-50% market share 0.93*** 5 (3%) 0.60*** 4 (25%) 50%+ market share 0.357*** (6%) 0.255*** 7 (42%) or 2 competitors 0.459*** (00%) 0.303*** 7 (58%) 3+ competitors 0.420*** 0 (90%) 0.338*** 8 (66%) PCM -0.67*** -4 (-2%) *** -6 (-30%) -4 emploees 0.442*** 9 (0%) 0.393** 6 (03%) 5-9 emploees 0.627*** 4 (74%) 0.765*** 6 (253%) emploees 0.730*** 7 (23%).09*** 27 (425%) emploees 0.92*** 23 (294%).298*** 34 (550%) 500+ emploees.88*** 33 (49%).77*** 30 (476%) Exporting Business 0.44*** 3 (70%) 0.60** 5 (24%) Export intensit (-34%) (-8%) 'Hampered b competition' 0.2*** 6 (33%) 0.50*** 4 (23%) Constant -.80*** na -.99*** na Operational Process Innovation Marketing Innovation Coeff. M.E. Coeff. M.E. 0-50% market share 0.85*** 5 (28%) (%) 50%+ market share 0.242*** 7 (38%) (9%) or 2 competitors 0.208** 6 (35%) 0.342*** 6 (8%) 3+ competitors 0.256*** 7 (44%) 0.434*** 8 (08%) PCM -0.29** -4 (-7%) *** -4 (-38%) -4 emploees 0.568*** 0 (73%) 0.634*** 9 (238%) 5-9 emploees 0.84*** 7 (304%) 0.837*** 3 (365%) emploees.53*** 28 (488%) 0.856*** 4 (378%) emploees.245*** 3 (549%).32*** 22 (597%) 500+ emploees.620*** 46 (808%).26*** 22 (59%) Exporting Business 0.382*** 2 (62%) 0.242*** 6 (44%) Export intensit ** -8 (-39%) 0.06 (0%) 'Hampered b competition' 0.238*** 7 (36%) 0.222*** 5 (40%) Constant -.765*** na -2.40*** na a Full specification and results of the model are shown in the appendix to the source below. Code from Capellari and Jenkins (2003, 2006) is used to calculate the multivariate probit results. b Shows the estimated coefficient as predicted b the model, where ***, ** and * denote the variable is statisticall significantl different from zero at the %, 5% and 0% level respectivel. c Marginal effects. Calculated while holding all other continuous variables at their average values. Categorical variables are incremented from a value of zero to unit in the specific categor in question. In the case of continuous variables the marginal effect is the value of the partial derivative of the probabilit of being an innovator with respect to that variable, evaluated at the mean. Source: Authors estimates and Soames, Brunker and Talgaswatta (20). The results of the multivariate probit indicate that an increase in market share, which is usuall interpreted to indicate a drop in competition, is associated with a higher likelihood of innovation for most of the innovation tpes. At the same time, 2 ISS 202

13 the other statisticall significant competition-related variables indicate that changes to a more competitive outcome are associated with a higher likelihood of innovation. It is worthwhile to tr and understand wh this potential inconsistenc ma arise. One possible explanation goes to the interpretation of modelling. The sign of the estimated coefficients in the model indicate the direction of change in predicted probabilities resulting from a given change in the selected explanator variable, with all other conditioning variables held fixed. Does it make sense to impose an increase in market share with all other competition variables held fixed? Businesses with the same number of competitors and the same number of emploees could have ver different market shares if the operate in different product markets of different size. In that case the business operating in the smaller product market might be expected to have higher market share. However, with the same PCM, such a result could reflect a more concentrated (although not necessaril less competitive) market, and the result found here could reflect an association between innovation and concentration, rather than innovation and competition. The ke issue is thus how well concentration reflects competition. The results are consistent with a positive relationship between firm concentration in an industr and the extent to which an industr is more likel to pursue innovation. Another possible explanation lies in the categorical nature of some of the explanator variables. For example, the all other things equal constraint in the interpretation of coefficients is ver weak for some variables. (For example, holding the business size categor fixed within the 20 to 99 emploees categor allows for a great deal of variation in business size.) The impact of market share, holding categorical business size fixed, ma still be influenced b substantial variation in business size within that categor. Similar arguments appl to the number of competitors categor three or more. Although onl categorical data are available for number of competitors, continuous data on number of emploees are available (categorical variables for emploment are used in the main models as it was categorical classes that were used in the sample surve design). 2 It is worth noting that the two other studies that used ABS micro data (Rogers 2004, Wong et al 2007) also found a greater market share to be associated with innovation propensit. Given the different dimensions of competition examined in this paper, it is not unreasonable to expect that each ma have a different effect on the propensit to innovate. Thus finding that a greater market share is associated with a Schumpeterian result and the other competition variables are associated with an anti-schumpeterian result is not necessaril inconsistent. 2 However, even in the presence of a continuous emploment variable in the regressions (as an alternative specification), the coefficient on the market share variable remained positive and statisticall significant. 3

14 Further, it ma be the case that the model is picking up reverse causalit. That is, it could be that innovation leads to the firm increasing its market share the act of innovating is a competitive act designed to seize market share from competitors. Unfortunatel, the model as it stands cannot provide guidance as to the direction of causalit. 3 Another result of the modelling is that a significant degree of dependence between the innovation tpes is found. Put simpl, the model indicates that the innovation tpes completed b a firm are not independent of one another. This dependence is particularl strong between the process innovation tpes, and to a lesser degree, between goods and services and operational process innovations. 4 Another wa to think about such a relationship is that particular combinations of innovations are more likel to occur, given a particular set of competitive pressures. The multivariate probit allows the calculation of the (estimated) probabilit of particular combinations of innovation outcomes arising. A numerical example A numerical example that demonstrates the significance of dependence between innovation outcomes can illustrate the occurrence of combinations of innovations more clearl. Consider a firm with the following characteristics: A manufacturing firm with 0 50 per cent market share, 2 competitors, a PCM of 0.2, and 5 9 emploees. It is not an exporter, it is not hampered b the need to keep profit margins low in order to remain competitive, and has no foreign ownership. 5 The likelihood of such a firm completing a goods and services innovation is estimated to be 25.5 per cent, and the likelihood of the same firm completing an organisational process innovation is estimated to be 22.2 per cent. If the likelihood of completing these innovation tpes was independent of other innovation tpes completed, then the probabilit of the firm completing both tpes of innovations would simpl be the product of the two probabilities: 5.7 per cent. However, it is not the case that innovations in different tpes are completed independentl of one another. Given a significant variance-covariance term between 3 To address issues of causalit, time series models must be emploed rather than the crosssectional methods emploed in this paper. Such analsis of causal effects will become more viable as the time series dimension of the BLD populates more full in the future. 4 The formal definition of the multivariate probit specification is detailed in the appendix, with the estimated off-diagonal elements of the variance-covariance matrix that define the dependence between the innovation tpes. 5 These characteristics represent the mean firm from the sample. 4 ISS 202

15 the goods and services innovation and organisational process innovation tpes, the true probabilit of the firm completing both tpes of innovation is 9.4 per cent - significantl higher. The same issue of dependence between innovation tpes is present when considering marginal effects. The marginal effects given in table 3 provide information as to the magnitude of the effect of incrementing each of the explanator variables, but the marginal effect with respect to a particular combination of innovation tpes being completed cannot be calculated. Using the same firm characteristics above, but this time with the difference that the firm is hampered b competition, the likelihoods of that firm innovating in goods and services and organisational processes are 3.6 per cent and 27.4 per cent, respectivel. Jointl, the likelihood of the firm innovating in both rises (from 9.4 per cent) to 2.6 per cent. The full innovation outcome set for a firm with those characteristics and the change in hampered state is show in figure. 5

16 Figure The full innovation outcome set estimated b the multivariate probit model a a The base case in the scenarios is a manufacturing firm with 0 50% market share, 2 competitors, a PCM of 0.2, and 5 9 emploees. It is not an exporter, it is not hampered b the need to keep profit margins low in order to remain competitive, and has no foreign ownership. The changed scenario in this case is that where the firm reports itself to be hampered, while keeping all other characteristics of the base case unchanged. Data source: Soames, Talgaswatta and Brunker (20). The last piece of information that can be gleaned from the multivariate probit is the estimation of the number of different innovation tpes a firm completes, based on its competitive (and other) characteristics. The probabilities estimated b the model are shown in table 4. 6 ISS 202

17 Table 4 Probabilities of a firm innovating in a particular number of innovation tpes Non- Innovator -tpe 2-tpes 3-tpes 4-tpes Base probabilit of innovating in the number of innovation tpes above a pp (%) b pp (%) pp (%) pp (%) pp (%) Probabilit of innovating in the above number of tpes, given a change from the base case to: 50%+ Market Share 47.2 (-7.8) 6.6 (3.8) 23.2 (0.5) 2.4 (9.7) 0.7 (40) 3+ competitors 49.9 (-2.5) 5.7 (-.9) 2.9 (4.3).9 (5.3) 0.6 (20) PCM of (2.9) 6. (0.6) 20 (-4.8) 0.8 (-4.4) 0.3 (-40) Hampered 4.9 (-8.2) 5.6 (-2.5) 26.6 (26.7) 4.8 (3).2 (40) Exporter with 5% export intensit 33.8 (-34) 9 (8.8) 29.5 (40.5) 6.2 (43.4).5 (200) a The base case in all the scenarios below is a manufacturing firm with 0 50% market share, 2 competitors, a PCM of 0.2, and 5 9 emploees. It is not an exporter, it is not hampered b the need to keep profit margins low in order to remain competitive, and has no foreign ownership. b Probabilit given in percentage points, with the proportional change in parentheses. Source: Soames, Talgaswatta and Brunker (20). For example, a firm with the base characteristics listed above has a 6 per cent probabilit of completing one innovation tpe. If that firm was instead a small exporter (with 5% export intensit and its other competition characteristics unchanged), then the model predicts it would have a 9 per cent likelihood of completing one tpe of innovation (a proportional change of 8.8 per cent). Broadl speaking, those factors found to be statisticall significant in explaining whether a firm innovates in each innovation tpe are also found to determine the firm s likelihood in completing a particular number of innovation tpes. Specificall: a greater market share is associated with completing more innovation tpes; facing more competitors is associated with completing more tpes of innovation; a higher PCM is associated with completing fewer tpes of innovation (recall that a higher PCM indicates that the firm is more profitable, which is an indicator of less competition); a firm that is hampered is more likel to complete a greater number of innovation tpes, relative to the firm that is not hampered; and 7

18 an exporter is more likel to complete a greater number of innovation tpes, relative to non-exporters. Competition and the novelt of innovations There is a further element of the competition-innovation relationship that can be examined using BLD data the effect of competition on the degree of innovation novelt completed. The BLD provides data on the greatest degree of novelt of innovation that a firm completes, whether it is new to the firm, new to the industr, new to Australia or new to the world. A multivariate probit model is inappropriate in this context as the outcomes are mutuall exclusive. Instead, an ordered probit model is estimated with the highest degree of innovation novelt completed b the firm as the dependent variable. The explanator variables used in the model include all those from the multivariate probit model (table ), as well as some additional indicators regarding the intellectual propert (IP) protection methods used b the firm. 6 The ordered probit model is restricted to the subset of innovating firms. The reason for this restriction is that in an ordered probit model there should be some sense of continuit when moving from one categor in the dependent variable to the next. This makes sense when the dependent variable is the greatest degree of novelt completed as there is a clear ordering. To include non-innovators in such a model violates this ordering it is not clear where a non-innovator would sit compared to the greatest degree of innovation novelt. Far fewer of the competition variables tested are found to be statisticall significant in explaining the likelihood of a firm completing an innovation of greater novelt. A greater market share and the export status are the onl competition measures found to be statisticall significant in explaining a higher likelihood of a firm s innovation being of higher novelt. The marginal effects for these variables, keeping the other conditioning variables at the mean of the innovators-onl sample, are shown in table 5. 6 Such IP measures are included in the analsis in order to check whether the availabilit of IP protection is associated with a greater likelihood of firms undertaking innovation in spite of greater competition (an Arrow -like argument). At the same time, it could be expected that results from these variables ma be strongl subject to reverse causalit: the more novel the innovation, the greater the likelihood a firm ma seek to protect it through the use of IP protection. Discussion of the nature of the IP results is beond the scope of this paper, but the full results are included in the appendix for the sake of completeness. 8 ISS 202

19 Table 5 Marginal effects of significant competition explanator variables in highest degree of novelt completed a New to the firm New to industr New to Australia New to world pp (%) pp (%) pp (%) pp (%) 0-50% Market Share -2 (-3) 0.7 (9) 0.7 (2) 0.8 (8) 50%+ Market Share -9 (-) 3 (36) 3 (53) 4 (89) Positive export status -0 (-2) 3 (34) 3 (50) 4 (79) a Where the number reported is the percentage point change in novelt outcome, with proportional change in parentheses. Rows ma not sum to zero due to rounding. The marginal effects are calculated while holding all other variables fixed at their average values. Categorical variables are incremented from a value of zero to unit in the specific categor in question. Results are reported as the percentage point change followed b the proportionate change in parentheses. In the case of continuous variables, the marginal effect is the value of the partial derivative of the probabilit of the outcome in question with respect to that variable, evaluated at the mean. Source: Authors estimates derived from Soames, Talgaswatta and Brunker (20). For example, the highest degree of innovation novelt completed b an innovating business, based on that business having average characteristics, is 8 per cent likel to be new to the firm, 8.4 per cent likel to be new to the industr, 5.7 per cent likel to be new to Australia and 4.7 per cent likel to be new to the world. If that firm were an exporter, the likelihood of completing an innovation tpe of novelt greater than new to the firm increases, indicating that exporting is associated with completing more novel innovations. Both market share and export status were found to be positivel associated with the likelihood of a firm innovating; these results tend to suggest that these innovations are likel to be of a more novel tpe as well. Summar of competition-innovation modelling at the firm level The multivariate probit modelling uses a variet of competition-related measures to explain the propensit of firms to complete particular tpes of innovations and combinations of those innovation tpes. With the exception of the market share variable, the other competition-related measures namel the number of competitors faced, a (lower) price-cost margin, a declaration of the firm being hampered b competition, and being an exporter are all found to be positivel associated with a firm completing an innovation of an tpe and a greater number of innovations of different tpes. Such a result is anti-schumpeterian in nature. An additional model that examines the greatest novelt of innovation completed b innovating firms, based on the competitive pressures that it faces, provides less clear-cut results. A greater market share and being an exporter are both associated with firms completing more novel innovations (other than new to the firm). The market share result supports the Schumpeterian case, whereas the exporting result 9

20 could be interpreted as an anti-schumpeterian outcome. The possibilit of reverse causalit should be considered when interpreting these results more novel innovations ma well lead to firms increasing their market share. It also raises the question of whether industries with higher levels of concentration are more likel to see firms compete through innovation something that could be examined longitudinall as the BLD dataset develops. 20 ISS 202

21 3 Innovation and productivit This section provides the results of an analsis of the relationship between innovation status and productivit outcomes at the firm level. Three ears of BLD data ( to ) are pooled to provide the information base for the analsis. The productivit measure used here is drawn from responses to a question on the BCS, which asks how the firm s productivit has changed relative to the previous ear, with the options of declined, staed the same or improved. It should be noted that in addition to being categorical, this variable is explicitl subjective and ma reflect a number of interpretations of the term productivit on the part of the different respondents to the surve. The results of the modelling should therefore be interpreted in that context. An ordered probit model is used to examine the relationship between innovations completed in the previous ear and firms perceptions of their productivit change. Variables that detail firm size and industr of operation are also included in the model. The results from the ordered probit model, along with the more instructive marginal effects (for the same representative firm examined earlier), are detailed in table 6. Table 6 Ordered probit model of innovation and productivit with marginal effects on productivit outcome a Coefficient in ordered probit model Marginal effect on the probabilit of productivit declining Marginal effect on the probabilit of productivit improving pp (%) b pp (%) Lag of goods and services innovation *** -5 (-3) 9 (26) Lag of organisational process innovation ** - (-9) 2 (7) Lag of operational process innovation 0.508*** -3 (-2) 6 (7) Lag of marketing methods innovation 0.084*** -2 (-2) 3 (9) Constant *** Constant *** a Full model presented in table 9 of the appendix. The marginal effects examined here relate to the representative firm detailed on p4. b Change in probabilit in percentage points (proportional change in parentheses). Source: Author s estimates and Soames, Brunker and Talgaswatta (20). 2

22 Completing an innovation of an tpe is associated with a firm reporting a better productivit outcome that is reporting an improvement or not reporting a reduction in the subsequent ear. Appling these results, a small firm that does not innovate is five percentage points more likel to report a decline in productivit, relative to the firm that completes a goods and services innovation. 7 Each of the innovation tpes is associated with a reduced probabilit of a firm reporting a productivit decline. Innovating in each of the innovation tpes is also associated with a firm reporting a productivit improvement. For example, a small firm that is an operational process innovator has about a 40 per cent probabilit of reporting an increase in productivit, whereas a similar firm that does not innovate has a 34 per cent probabilit of the same outcome. 8 Innovation in the previous period is not onl associated with a lower chance of reporting a decline in productivit, but also of a higher probabilit of reporting that productivit improved. 9 While all of the innovation tpes are associated with an improvement in the productivit outcome of firms, the different tpes of innovations have a different magnitude of effect. Goods and services innovations have the strongest positive effect on productivit outcomes, followed b organisational process innovations. The effects of marketing and organisational process innovations are statisticall indistinguishable from one another. In summar, the model estimates indicate that a firm is more likel to report an increase in productivit if it completed some sort of innovation in the previous ear, particularl either a goods and services innovation or an operational process innovation. Put another wa, firms that do not innovate are predicted to be more likel to report a decline in productivit than businesses that complete innovations. 7 Specificall, the non-innovating firm has a 7 per cent chance of reporting a decline in productivit, whereas the goods and services-innovating firm has a 2 per cent chance of reporting a productivit decline. The change is statisticall significant at the five per cent level of significance. 8 Such a difference is found to be statisticall significant at the five per cent level of significance. 9 Given the different responses available to firms, it could have been the case that innovation was associated with avoiding a productivit decline, rather than being associated with productivit improvement. The results of the modelling indicate that innovation is associated with higher likelihoods of both avoiding a productivit decline and achieving a productivit improvement. 22 ISS 202

23 4 Conclusions Two relationships between product-market competition and innovation have been analsed: the first was a Schumpeterian relationship, where innovation is negativel associated with greater levels of competition; the second was an anti-schumpeterian relationship, where innovation is positivel associated with greater levels of competition. At the firm level, most of the competition variables examined appear to be associated with a greater likelihood of a firm completing an innovation and a greater likelihood of a firm completing multiple innovation tpes. More specificall, those firms with a greater number of competitors, lower profit margins, which are exporters, or who otherwise describe themselves as hampered b competition, tend to have higher levels of innovation. Such findings would tend to point to an anti-schumpeterian association between competition and innovation. There is an important caveat from the firm-level analsis, however, in that the modelling indicates those firms that are larger and have greater market share are more likel (relative to smaller firms with less market share) to complete innovations and to complete a greater number of innovation tpes. On the whole, however, it can be concluded from the firm-level analsis that innovation and competition are broadl anti-schumpeterian in nature. The links between innovation and productivit have also been examined. Firms that innovate are unambiguousl found to be more likel to report an improvement in productivit relative to those firms that are non-innovators. Innovations in goods and services and operational processes are found to have the greatest effect on increasing the likelihood of firms reporting a productivit improvement. It follows from the previous results that an increase in competition (via its effect on increasing the likelihood of innovating as detailed above) will, more often than not, lead to an improvement in productivit amongst firms. To conclude, a variet of simplified relationships, based on theor relating to how product-market competition, innovation and productivit interact were tested at the firm-level. The analsis suggests, with some exceptions, that greater competition is associated with a higher likelihood of Australian firms completing an innovation a mostl anti-schumpeterian outcome. These innovations, in turn, are associated with a firm being more likel to report a productivit improvement. 23

24 5 References ABS 20, Technical Manual: Business Longitudinal Database, CURF, Australia to , Cat. no , ABS, Canberra. ABS-DITR (Department of Industr, Tourism and Resources) 2006, Patterns of Innovation in Australian Businesses, Joint ABS-DITR Research Paper, Cat. no , ABS, Canberra. Aghion. P.; Bloom, N.; Blundell, R.; Griffith, R. and Howitt, P. 2005, Competition and Innovation: An Inverted-U Relationship, Quarterl Journal of Economics, vol. 20, no. 2, pp Ahn, S. 2002, Competition, Innovation and Productivit Growth: A Review of Theor and Evidence, OECD Economics Department Working Papers, no. 37, OECD Publishing. Arrow, K. 962, The Implications of Learning-b-Doing, Review of Economic Studies, vol. 29, no., pp Boone, J. 2008, A New Wa to Measure Competition, The Economic Journal, vol. 8, pp Capellari, L. and Jenkins, S. 2003, Multivariate Probit Regression Using Simulated Maximum Likelihood, The Stata Journal, vol. 3, no. 3, pp , Calculation of Multivariate Normal Probabilities b Simulation, with Applications to Maximum Simulated Likelihood Estimation, Institute for the Stud of Labor, Discussion Paper 22. Gilbert, R. 2006a, Competition and Innovation, Journal of Industrial Organization Education, Berkele Electronic Press, vol., no., p b, Competition and Innovation in Collins, W. (ed), Issues in Competition Law and Polic, American Bar Association Antitrust Section, chapter 26. Griffiths W. & Webster, E. 2009, What Governs Firm-Level R&D: Internal or External Factors? Universit of Melbourne, Melbourne Institute Working Paper Series #3/09. Long, J. and Freese, J. (2006) Regression Models for Categorical Dependent Variables using Stata, (Second edition), Stata Press Publication, College Station, Texas. OECD 2005, Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3 rd Edition, OECD Publishing. Rogers, M. 2004, Networks, Firm Size and Innovation, Small Business Economics, vol 22, pp ISS 202