Patterns and determinants of trademark use in Portugal

Transcription

1 Patterns and determinants of trademark use in Portugal Ricardo Mamede (ISCTE-University Institute of Lisbon, Dinâmia-CET and Observatório do QREN) Teresa Fernandes (UECE) Paper presented at the DIME Final Conference, 6-8 April 2011, Maastricht Draft version, please do not quote In this paper we provide the first results drawn from an integrated database that crosses information on all trademark applications in Portugal from 1995 to 2006 (collected by the National Institute of Industrial Property, INPI) with microdata of all the firms with employees which were active in the country in the same period. We describe the main patterns of trademark use in Portugal, and attempt to identify the characteristics of firms that are typically associated with trademark use. Keywords: Trademarks; Innovation.

2 1. Introduction A trademark is a sign used within economic activities by a producer or vendor to identify a particular product or service. It enables offerings of goods or services to be somehow differentiated, allowing consumers to distinguish between different goods and recognise their provenance (Ramello, 2006). Even though a trademark is not directly informative about the quality of a product, it often provides this type of information by reference to costumers own, or others, past experience (Economides, 1998). Their economic value, therefore, derives from trademarks being a solution (even if an incomplete one) to problems of asymmetric information. Being a source of visibility and reputation, trademarks become a strategic asset to firms competing on the basis of product differentiation and customer loyalty. When successful, trademarks become associated with perceived value to users and, consequently, are a source of higher margins for the firms that fill them. To the extent that trademarks help firms to appropriate the returns of investments in product quality, they constitute an incentive to the introduction of new or improved goods and services in the economy (Landes and Posner, 1987; Economides, 1998). In other words, by contributing to the distinctiveness of firms offers, trademarks give them an additional incentive to innovate. In fact, trademarks seem to be strongly related with firms innovation activities. Mendonça et al. (2006) found that innovative firms are more intensive users of trademarks, and they use trademarks more often than patents. Millot (2009) points out that trademarks, taken as indicators, can be particularly suited to capture the presence of non-technological innovations and innovation activities service industries cases in which the availability of innovation indicators is typically poor. In this sense, trademarks can be used to deepen our knowledge about innovation dynamics, especially in the context of less knowledge-intensive economic structures. This does not mean, however, that every new trademark is necessarily connected to a new or improved product, nor do they provide any measure of the degree of innovativeness of a new product. Still, firms that fill trademarks are willing to pay a cost in order to obtain and maintain an exclusive right to use a sign which would hardly be the case if they did not believe that new trademarks will allow customers to attach a distinct value to the associated products. Consequently, the use of trademarks as indicators of innovation or, at least, of some sort of entrepreneurial drive, has been growing in recent years. For example, Schmoch (2003) and Schmoch and Gauch (2009) have used trademarks in order to study innovation in services. Krasnikov et al. (2009), Greenhalgh and Rogers (2007), and 2

3 Griffiths et al. (2005) have taken trademarks as a measure of innovativeness and assessed the impact of trademarking on firm performance. And Mangàni (2007) has used trademarks to measure the variety and quality of products. The recent rise in the use of trademark statistics in innovation and marketing studies is partly explained by the increasing availability and reliance of official databases, both at the national and international levels. However, those databases typically include very little information about the trademark holders (e.g., legal status, size, age, industry, etc.), making it necessary for researchers to cross the information drawn from trademark databases with other sources of microdata in order to analyse the use of trademarks at the firm level. And due to different kinds of constraints (unavailability of extensive micro-databases, difficulties in matching different datasets, etc.), micro-level analyses of trademarking are usually restricted to a relatively small sample of firms. In this paper we provide the first results drawn from an integrated database that crosses information on all trademark applications in Portugal from 1995 to 2006 (collected by the National Institute of Industrial Property, INPI) with microdata of all the firms with employees which were active in the country in the same period. We describe the main patterns of trademark use in Portugal, and attempt to identify the characteristics of firms that are typically associated with trademark use. The paper is organised as follows. In the next section we describe the data and the process which led to the integrated database we use in our study. Section 3 presents the basic descriptive statistics, and Section 4 puts the relevant variables in a multivariate framework (a logistic regression), in order to analyse the determinants of trademark use at the firm level. Section 6 concludes. 2. Data The integrated database used in this study was constructed by crossing two main sources of data. One is the Quadros de Pessoal (QP henceforth) database, which contains information on employers and employees collected by the Portuguese Ministry of Employment and Social Solidarity (MTSS) on a yearly basis since 1982 (at present, the data available for research covers the period ), and constitutes mandatory annual reporting for all firms employing paid labour in Portugal. The questionnaire that supports this database includes questions related to the characteristics of both firms (e.g. total employment, industry classification, location, legal status, ownership, etc.) and their employees (gender, date of birth, educational background, professional category, type of contract, etc.). Both firms and workers are identified by their social security or fiscal numbers, making it possible to follow them over time. On the basis of that information one can also cross the QP database with 3

4 information on trademark applications in Portugal. Here we use trademark data collected by the National Institute of Industrial Property (INPI). INPI s data includes the identification of the main applicant of every trademark application that was registered in Portugal since It does not, however, include any detailed information about the applicant, limiting the scope of economic analysis that can be carried out with those data. By crossing the QP and the INPI s databases we were able to unequivocally identify the main applicants of about 52% of the trademarks applications between 1985 and Since the QP includes all firms employing paid labour in Portugal, we believe that the remainder 48% of the trademarks (i.e., the one whose owner we could not identify) were filled mainly by sole proprietorship firms, foreign firms without employees operating in Portugal, or state-owned non-corporate entities. We will not consider these types of entities in this paper. Moreover, we restrict the scope of the analysis to market-oriented industries. These are: Manufacturing (DA to DN, according to NACE Rev. 1.1), Electricity, gas and water supply (EE), Construction (FF), Wholesale and retail trade (GG), Hotels and restaurants (HH), Transport and communication (II), Financial intermediation (JJ), and Real estate, renting and business activities (KK). We also exclude firms with only one employee Descriptive analysis In this section we present some descriptive statistics on the evolution of trademark applications in Portugal, their distribution among industries, regions, firm size and age classes, and the recurrence of trademarks applications by firms. Our data includes firms in In Figure 2 we see that the percentage of the firms applying for trademarks in each year since 1995 remained low, increasing slightly from 0,7% ( firms) in the beginning of the period to 1% in 2006 ( firms). But the percentage of firms that ever applied for trademarks up to each year shows a different pattern with higher growth especially since 2002, which is explained by a higher incidence of trademark use among newer firms. 1 In many firms, the only employee is the firms owner. However, there are several instances of soleproperty firms in which the owner does not declare himself as an employee. The reasons that lead a firm owner to register herself, or not, as an employee are rather arbitrary. However, only those firms declaring at least an employee are registered in the QP database. Thus we decide to exclude firms with only one employee, in order to avoid a bias sample. 4

5 Figure 1. Evolution of the total number of firms and the percentage of firms applying for trademarks ,6% 6,0% ,9% 4,5% 3,5% ,0% ,7% 0,8% 1,0% 1,5% Total number of firms (left) % of existing firms with trademark applications up to this year (right) % of existing firms applying for trademarks in this year (right) 0,0% One can expect trademark use to vary across industries, reflecting differences in innovation regimes. Figure 2 shows that amongst the most intensive users of trademarks are industries classified as knowledge-intensive services. However, it also shows that higher than average use of trademarks can also be found in low and medium-technology manufacturing industries (namely, chemicals, publishing and printing, basic metals, and food products). 5

6 Figure 2. Firms applying for trademarks by industry, NACE rev.1.1 (2006) % 8% 6% 4% 2% 0% 66 Insurance and pension funding 24 Chemicals and chemical products 73 Research and development 64 Post and telecommunications 72 Computer and related activities 65 Financial intermediation 22 Publishing and printing 27 Manufacture of basic metals 40 Electricity and gas 32 Communication equipment 15 Food products and beverages 61 Water transport 37 Recycling 62 Air transport 63 Auxiliary transport activities 51 Wholesale trade 74 Other business activities 25 Rubber and plastic products 31 Electrical machinery and apparatus n.e.c. 19 Leather and footwear 34 Motor vehicles, trailers and semi-trailers 21 Pulp, paper and paper products 41 Collection and distribution of water 17 Manufacture of textiles 33 Medical and optical instruments 70 Real estate activities 71 Renting of machinery and equipment 36 Furniture; manufacturing n.e.c. 26 Other non-metallic mineral products 29 Machinery and equipment n.e.c. 35 Other transport equipment 18 Wearing apparel 52 Retail trade 67 Auxiliary to financial intermediation 55 Hotels and restaurants 20 Wood and of products of wood and cork 28 Fabricated metal products 50 Sale and repair of motor vehicles 60 Land transport Total number of firms (left) % of firms applying for trademarks (right) The distribution of firms applying for trademarks is also asymmetrically distributed across regions. As one could expect, the two biggest Portuguese cities, Lisboa and Porto, stand out both in relative and in absolute terms (Figure 3). 6

7 Figure 3. Firms applying for trademarks by region NUTS III (2006) ,5% Total number of firms (left) % of firms applying for trademarks (right) 2,0% ,5% ,0% ,5% 0 0,0% Grande Lisboa Serra da Estrela Entre Douro e Vouga Grande Porto Baixo Vouga Douro Baixo Mondego Cávado Lezíria do Tejo Cova da Beira Beira Interior Sul Ave Dão Lafões Península de Setubal Pinhal Litoral Oeste Alentejo Central Pinhal Interior Norte Tâmega Alto Alentejo Alentejo Litoral Médio Tejo Baixo Alentejo Algarve Madeira Pinhal Interior Sul Açores Minho Lima Beira Interior Norte Alto Trás-os-Montes The analysis by class of firm size, in Figure 4, shows that the larger the firm, the more likely it is to register trademarks. Figure 4. Firms applying for trademarks by firm size (number of employees, 2006) ,8% 15% 12% ,1% 9% 6% ,6% 1,9% 3% 0 2 to 9 10 to to % Total number of firms (left) % of firms applying for trademarks (right) 7

8 Figure 5 displays the incidence of trademark use by firm age. We can see that the percentage of firms applying for trademarks is clearly higher than average for firms in the first two years of existence, suggesting that trademarks are sometimes an ingredient of firms entry strategies. Figure 5. Firms applying for trademarks by firm age (2006) ,0% ,6% ,2% ,8% ,4% ,0% Total number of firms (left) % of firms applying for trademarks (right) Finally, we are interested in analysing to what extent applying for trademarks constitutes a recurrent practice by firms. Figure 6 shows the distribution of the firms that applied for trademarks in 2006, by the number of years in which they applied from trademarks since It shows that nearly 2/3 of the firms applying for trademarks in 2006 did it for the first time, whereas only 10% of those firms applied for trademarks in more than three years since

9 Figure 6. Recurrence in trademark applications 64% 65% For firms that applied for trademarks in 2006, what is the number of years in which they applied for trademarks since 2000? % 18% 8% 8% 4% 4% 3% 3% 2% 2% In sum, these exploratory results suggest that trademark use in Portugal is: (i) uncommon for most firms; (ii) asymmetrically distributed across industries, with both higher-knowledge intensive and some less knowledge-intensive industries displaying higher than average use of this industrial property right; (iii) strongly concentrated in the metropolitan regions (with some smaller regions also showing a relatively high intensive use of trademarks); (iv) less frequent among firms with less than 10 employees than in higher firm size groups (being disproportionally frequent among big firms); (v) more common among new firms than in older ones; (vi) a recurrent practice for only a small subset of trademark users. In what follows we put these and other variables in a multivariate regression framework, in order to identify the most relevant determinants of trademark use in Portugal. 4. Regression analysis 4.1. The Binary Logistic Regression Model In order to identify the structural determinants of trademarking, we use simple Logit Regression Models. This regression method describes the relationship between a binary dependent variable and one or more explanatory variables, focusing on the 9

10 relative probability (odds) of obtaining a given binomial result category. Hosmer and Lemeshow (2000) use the following specific form for the logistic regression model: e β 0+β 1 x π = 1 + e β 0+β& 1 x which is bounded between zero and 1, and whose errors follow a binomial distribution (Bernoulli), instead of a normal distribution as in Linear Regression Model. Basically, what the logit does is to transform the previous equation into π(x) g(x) = ln 1 π(x) = β 0 + β 1 x. So that it has many of the desirable properties of a linear regression model, like being linear in its parameters and, depending on the range of x, it may be continuous and range from to +. Usually, the coefficients in the logistic model describe the variation in the log-odds when the regressor x increases by one unit, ceteris paribus. The interpretation of those parameters is given by the anti-log of each coefficient, e β, which is the change in the adjusted odds ratio. One can also compute 1 e β to obtain the percentage increase (or decrease) in the adjusted odds-ratio for every one unit increase in the regressor. So, in the present case, this adjusted odds ratio is the variation in odds of a firm applying for trademarks when the value of the regressor increases by one. Although it is easy to move from odds to probabilities, we will considerate the first, in which the interpretation of the coefficients is more simple. We decided to use the Forced Entry Method instead of the Stepwise Procedures, where a random variation in the data can heavily influence the entry and removal of variables in the model (Pallant, 2005) Exploring the database with Logit The original databases plus the crossing transformations we made provide a great deal of information about Portuguese firms that we could use in order to predict firms behaviour of trademarks applications. It contains information into three principal 10

11 categories: demographic characteristics, sectorial characteristics, legal characteristics, human resources management characteristics, and trademarks application characteristics. After a closer look at the available data and the relations among variables and several tests done, we believe that there are three basic factors determining whether a firm applies for trademarks: the concentration of firms applying for trademarks in the same region and/or industry (geographical and industry proximity effects), demographic characteristics of the firm, and idiosyncratic factors which may include characteristics of the labour force composition level and many other factors we cannot account for. Table 1 below displays different specifications of our model. In the first regression, we constructed the continuous variable Industry effect, which is constructed as the proportion of firms in the same industry which applied for trademarks at least once in the last three years (in this case, from 2004 to 2006). This variable attempts to capture the industry propensity for trademark use and, by it self, explains up to 5,3% of the variance in the model. Table 1. Regression results for 2006 Variables Coefficients for each regression* Industry effect 0,12 0,10 0,10 0,09 0,07 0,07 0,06 0,06 Regional effect 0,29 0,22 0,21 0,18 0,16 0,14 0,13 Regional and industry effect 0,04 0,04 0,03 0,03 0,03 0,03 0,03 Metropolitan center (dummy) 0,28 0,26 0,24 0,22 0,19 0,16 New firm (dummy) 0,99 1,08 1,06 1,08 1,09 Size 10 to 49 (dummy) 1,15 1,01 1,01 0,89 0,91 Size 50 or more (dummy) 2,06 1,68 1,72 1,36 1,40 Recurrent TM applicant 2,34 2,31 2,23 2,21 Employer with university degree (dummy) 0,57 0,45 0,47 Employer with degree in business studies (dummy) 0,71 0,68 Goodness of fit Omnibus Tests of Model Coefficients Hosmer and Lemeshow Test Model Summary -2 Log likelihood Cox & Snell R Square 0,6 0,7 0,8 1,3 1,8 1,8 1,9 1,9 Nagelkerke R Square 5,3 7,2 7,3 12,3 17,2 17,5 18,1 18,3 Classification Table overall percentage correct 99,00 99,00 99,00 99,00 99,00 99,00 99,00 99,00 true positives 0,10 0,50 0,50 0,90 3,40 2,90 3,00 3,40 true negatives 100,00 100,00 100,00 100,00 100,00 100,00 100,00 100,00 * all coefficients are significant at 5% level 11

12 In regression 2 we added two more structural variables, one that captures the propensity of local firms to trademark 2 and the other that considers only the use of trademarks by local firms in the same industry 3. All the three variables are statistically significant at 5% level, with the Regional effect showing to be stronger than the Industry effect. We suspected that this result could be determined by the tendency of more sophisticated firms to be located in the two most import cities in the country. As we have seen above (Figure 4), Lisboa and Porto have a disproportionate weight in the number of firms that applied for trademarks in In order to control for this 4 possibility, we added a dummy for the two Metropolitan centers in regression 3. While the results confirm the presence of a capital cities effect, the Regional effect remained statistically significant and relatively high, suggesting that geographical proximity is a relevant factor behind trademark use by firm, even beyond the most important economic centers. 5 Next we look at firm size and firm age, along with the previous four variables. The results of regression 4 confirm that trademark use is more probable among newer firms, and among firms in higher size classes. To test for the weight of idiosyncratic factors in the explanation of our dependent variable, we included in the model an auto-regressive component, the dummy for the 6 past trademark firms behaviour (regression 5). This had a considerable impact in the explanatory power of our model, suggesting that an important part of the explanation for trademark use resides on firm characteristics that are not captured by the structural variables considered before. Our database allows us to go further in the analysis of firm specific features that account for trademark use. Namely, we tested for the relevance of human capital (using different proxies) for explaining trademark use by firms. Two variables have shown to be statistically significant. The first is the presence of at least one individual 2 for each firm i, ECR is the number of firms (excluding the firm i itself) in the same region as the firm i (municipality level) that had applied for trademarks in the last three years, divided by the existing firms in that region. 3 for each firm i, is the number of firms (excluding the firm i itself) in the same region as the firm i (municipality and NACE Rev. 1.1 at two digit level) that had applied for trademarks in the last three years, divided by the existing firms in that sector 4 Dy.Lisboa.Porto is a dummy variable that assumes the value 1 if the firm i is from Lisboa or from Porto (NUT III), and zero otherwise. 5 Dy.Size.10to49, Dy.Size.over49 and Dy.Age.1or2 are dummy variables for firms with 10 to 49 employees, firms with more than 49 employees and firms in its first or second year of existence, respectively, assuming the value 1 if belonging to that characteristic of interest, and zero otherwise. 6 Dy.TM.0405 is a dummy variable which assumes the value 1 if the firm i applied for trademarks in 2004 or 2006, and zero otherwise. 12

13 holding a university degree among the firm owners (regression 7). Even more relevant is the fact that the owner holds a degree in Business Sciences. 7 Even though the variables in the regressions always provide a good fit, the overall performance of our models is rather modest (e.g., none of the models passed the Hosmer and Lemeshow test). This is mainly a result of an inherent feature of the data i.e., our dependent variable having only 1% of yes cases. Still, the coefficients are all statistically significant, with p-values near or equal to zero, allowing us to conclude that the explanatory variables used are all relevant for understanding the still understudied phenomena of trademarking. 5. Conclusion In this paper we provide the first results drawn from an integrated database that crosses information on all trademark applications in Portugal from 1995 to 2006 (collected by the National Institute of Industrial Property, INPI) with microdata of all the firms with employees which were active in the country in the same period. We describe the main patterns of trademark use in Portugal, and attempt to identify the characteristics of firms that are typically associated with trademark use. The descriptive analysis lead to the conclusion that trademark use in Portugal is: (i) uncommon for most firms; (ii) asymmetrically distributed across industries, with both higher-knowledge intensive and some less knowledge-intensive industries displaying higher than average use of this industrial property right; (iii) strongly concentrated in the metropolitan regions (with some smaller regions also showing a relatively high intensive use of trademarks); (iv) less frequent among firms with less than 10 employees than in higher firm size groups (being disproportionally frequent among big firms); (v) more common among new firms than in older ones; (vi) a recurrent practice for only a small subset of trademark users. Our regression results largely confirmed the notion that firm size and age are relevant determinants of trademark use. In particular, new firms reveal a higher propensity for trademarking, while trademark use is more probable among bigger firms than in smaller ones. The regression results also show that geographical proximity plays an important role in explaining the trademark use by firms (even after controlling for the higher propensity of firms located in metropolitan centres to use trade marks). In fact, the regional effect showed to be more important than the industry effect. 7 We also tested for variables related to the qualification of the employees, and with degrees in Science and Technology. These did not show to be statistically significant. 13

14 Notwithstanding, according to our results idiosyncratic features of firms constitute the most important determinants of trademark use. We were able to capture only part of such features, by considering the educational background of the employers. Our results show that having a university degree holder, especially if she graduated in Business Studies, increases the probability of a firm adhering to this type of industrial property. Future work should try to build on these results, following two main directions: first, finding ways to deal with the methodological problem of having to few yes cases in the data; second, further data should be compiled in order to improve our understanding of the idiosyncratic features of firms that increase the propensity for trademark use. 14

15 References Agresti, A. (2007). Building and Applying Logistic Regression Models. An Introduction to Categorical Data Analysis. Hoboken, New Jersey: Wiley.p.138. Blind, K., Edler, J., Frietsch, R., Schmoch, U. (2006). Motives to patent: Empirical evidence from Germany. Research Policy 35 (2006), Hosmer, D., and Lemeshow, S. (2000). Applied Logistic Regression. New York: Wiley Markley, S. (2007). Spatially-Oriented Discrete Choice Predictions: A Case Study of French Supermarket Preferences. France: GREMAQ, Université de Toulouse. Mendonça, S., Pereira, T., Godinho, M. (2004). Trademarks as an Indicator of Industrial change. Research Policy 33 (2004), Millot, V. (2009). Trademarks as an Indicator of Product and Marketing Innovations. Statistical Analysis of Science, Technology and Industry DSTI/DOC(2009)6 Pallant, J. (2005). SPSS Survival Manual. Sydney, Australia: Allen & Unwin. p.160. Sharma, S. (1996). Logistic Regression. Applied Multivariate Techniques. USA: Wiley p.317. Wambalaba, F., Concas, S. and Chavarria, M. (2004). Quantitative Analysis. Public Transportation Research Study. South Florida: Center for Human Transportation.p.12 15