Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application

Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application By JACQUES MAIRESSE AND PIERRE MOHNEN* The purpose of this paper is to propose and illustrate an accounting framework for. We characterize the of by a sales-weighted measure of : the share of sales in innovative products, but other output indicators of could be considered as well. Comparing statistics on the share of innovative sales across countries, industries, or firms measures but does not explain the intercountry, interindustry, or interfirm differences in. To have a more meaningful basis of comparison we need a model. If an exact model of in its various dimensions existed and if we knew it, we would be able to understand fully why differs among countries, industries, or firms. Of course, such a model does not exist. Nevertheless it is worth trying to account for differences, if only very roughly. In such an endeavor, what remains to be explained is as important to consider as what can be explained, because it reflects the extent of innovative ability or capacity, or innovativeness. To motivate our approach better and make it more explicit, we find it appealing to draw a comparison with the standard framework for growth accounting and the underlying model of a production function. Production output is viewed as resulting from a process of transformation of inputs into output that can be represented and analyzed in terms of a production function. Based on the production function, an accounting framework can be constructed in which changes in output between periods (years, decades) or differences between spatial * Mairesse: CREST, Timbre J390, 15 Boulevard Gabriel Peri, 92245 Malakoff, Cedex, France, ENSAE, NBER, and EHESS; Mohnen: MERIT, University of Maastricht, Tongersestraat 49, NL-6211 LM Maastricht, Netherlands, UQAM, and CIRANO. We thank EUROSTAT and OCDE for access to the data, and Bronwyn Hall for her stimulating remarks. This study has benefited from the financial support of SSHRC and CNRS. units (firms, industries, countries) are ascribed to changes or differences in the inputs and in a residual that is known as total factor or multifactor productivity (TFP or MFP) or simply productivity. Likewise, output can be viewed as resulting from inputs, such as R&D efforts, and other contextual determinants, such as the pressure of competition. This linkage can be represented in terms of an function and an accounting framework, on the basis of which changes in output between periods or differences between spatial units can be ascribed to changes or differences in the factors of and in a residual that we call innovativeness, or the unexplained ability to turn inputs into output. Innovativeness is thus to what TFP is to production. Innovativeness is conditional on a model of an function and a set of factors of, just as TFP is conditional on an assumed specification of the production function and measured factors of production. Both correspond to omitted factors of performance such as technological, organizational, cultural, or environmental factors (and to other sources of misspecification errors), although TFP is commonly interpreted as being mainly an indicator of technology. 1 The produc- 1 The analogy between innovativeness and TFP is particularly straightforward when both are measured on the basis of an econometrically estimated production function or function. However, it is not as close when production accounting is done using index numbers and TFP is computed as the ratio of an output index to an overall weighted index of inputs, where the weights are taken to be equal to the corresponding input shares (in total revenue or total cost) available from firms current accounts or country national accounts. In practice, it is impossible (although ideally not unconceivable) to measure innovativeness by such methods in the absence of similar information on appropriate weights and, more fundamentally, in the absence of well-functioning markets for outputs and inputs where one could assume that in the long run relative prices and marginal productivities would tend to become equal. 226

VOL. 92 NO. 2 THE ECONOMICS OF TECHNOLOGY AND INNOVATION 227 tion accounting framework is generally applied to comparisons of output in the time dimension (growth accounting), but it can also be extended to comparisons in the spatial dimension (see e.g., Douglas W. Caves et al., 1982). The same holds for the -accounting framework. We shall illustrate it here by proposing a comparison of in seven European countries (Belgium, Denmark, Germany, Ireland, Italy, the Netherlands, and Norway), based on cross-sectional data from the first European Community Innovation Survey. 2,3 I. Accounting for Innovation: An Illustrative Framework 2 It should be possible in the future to make intertemporal comparisons using consecutive surveys. 3 Michael Porter and Scott Stern (1999) do a very similar type of exercise, although they do not cast it explicitly in terms of accounting. They estimate a national -capacity equation by regressing international patenting per head for various countries on a number of variables that are supposed to capture the basic determinants of. The European Community Innovation Survey questionnaire is set up in a way that gives rise to a selection problem. First, firms are asked some general questions on their identity, their total sales and number of employees, their industry affiliation, and whether they belong to a group. Then comes a set of questions which define innovating firms versus non-innovating firms. Only innovating firms have to fill out the rest of the questionnaire and provide information on their outputs and inputs, as well as on various modalities of their innovative activities. We are thus driven to specify the function as a generalized Tobit model with two equations: the first one accounting for the propensity to innovate (binary variable y 1i ), and the second one for the of of an innovating firm (measured by the share of innovative sales variable y 2i or the corresponding logit-share variable z 2i ). As explanatory variables (x 1i ) for the propensity to innovate ( y 1i ), we introduce industry dummies, size, and group membership. Industry dummies capture technological opportunity conditions (i.e., it is easier to innovate in certain fields than in others), industry-targeted policies, industry-specific effects of differential demand growth (e.g., a rapidly growing demand for electronic products), or structural effects like the of competition. Size, measured by the number of employees, reflects access to finance, scale economies, and differences in the organization of work. Firms that are members of a group are expected to benefit, for instance, from intra-group knowledge spillovers and internal access to finance. As explanatory variables (x 2i ) for the share of innovative sales ( y 2i ) or the logit share ( z 2i ), we introduce six additional variables. 4 Four of these variables are related to R&D: a dummy for R&D-performing firms, the R&D/sales ratio for R&D-performing firms, a dummy for R&D done on a continuous basis, and a dummy for collaborative R&D. The other two describe the environment in which the firm operates: a measure of the strength of perceived competition and a measure of proximity to basic research. 5 The data come from the first European Community Innovation Survey pertaining to the year 1992, as assembled and harmonized by the Statistical Office of the European Community (Eurostat, 1997). 6 Although the original data are firm data, Eurostat can make them available only in micro-aggregated form for reasons of statistical confidentiality. 7 We restrict here 4 More precisely, as defined in the Oslo manual (Organization for Economic Cooperation and Development, 1997), the share of innovative sales y 2i corresponds to the share of total sales in the survey year due to incrementally changed, significantly changed, or entirely new products introduced in that year and the two preceding years. The logit share is defined as z 2i ln[ y 2i /(1 y 2i )]. 5 Competition is deemed to be strong when increasing or maintaining market share as an objective of receives a mark greater than or equal to 4, and proximity to basic research is deemed to be important when information from universities/higher education or government laboratories is given a score greater than 1 (both on a five-point Likert scale). These cutoff values correspond roughly to the median responses. 6 A second round of the European Community Innovation Survey has since been performed for the year 1996, and a third round is currently under way. 7 On the basis of French survey data, we have shown that the results of a model like this one are not sensitive to the use of micro-aggregated versus original data (Mairesse and Mohnen, 2001).

228 AEA PAPERS AND PROCEEDINGS MAY 2002 the analysis to firms that belong to the R&Dintensive manufacturing industries, that is, the chemicals, machinery, electrical, and vehicles industries. After some data-cleaning, we end up with 182 observations in Belgium, 223 in Denmark, 1,070 in Germany, 259 in Ireland, 845 in Italy, 666 in the Netherlands, and 150 in Norway. In order to compare performance across countries, we estimate our model on the pooled data of the seven countries with a common structure that applies to all countries. Had we estimated different structures for each country, we would not be able to disentangle differences in the structure of the model from differences in the explanatory variables. Based on our estimates, we compute for each country its expected share of innovative sales (regardless of knowing whether firms are innovative or not). As in the case of multilateral productivity comparison, it is appropriate to have some kind of average sample point as a fixed base of comparison. We thus choose as a hypothetical reference country the average European country giving equal weight to the seven countries (i.e., taking for each variable the average of the seven country average values). For the accounting of the intercountry differences in we then simply take the linear expansion of the average expected share of innovative sales for each country around the average European country, with respect to all the explanatory variables. 8 II. An Application to an Intercountry Comparison of Innovation 8 This decomposition and the accounting framework for are presented more formally in an Appendix, available from the authors upon request. TABLE 1 ACCOUNTING FOR INTERCOUNTRY DIFFERENCES IN INNOVATION INTENSITY IN THE R&D-INTENSIVE INDUSTRIES OF SEVEN EUROPEAN COUNTRIES A. Structural Effects Country Industry effect Size group effects R&D effects Environment effects Total Belgium 1.2 2.6 0.9 0.7 3.0 Denmark 1.3 0.7 0.4 0.4 1.4 Germany 1.3 0.6 0.9 1.7 4.5 Ireland 0.6 2.2 0.1 0.1 2.6 Italy 0.4 1.1 0.9 1.6 1.0 Netherlands 0.8 1.1 0.6 0.1 2.4 Norway 0.5 0.2 0.7 1.5 2.9 Average country 0.0 0.0 0.0 0.0 0.0 B. Innovativeness Country European Expected Innovativeness Observed Belgium 34.7 37.7 0.2 37.9 Denmark 34.7 36.1 0.7 36.8 Germany 34.7 39.2 4.6 43.8 Ireland 34.7 32.1 3.1 35.2 Italy 34.7 33.7 8.1 25.6 Netherlands 34.7 32.3 1.0 33.3 Norway 34.7 31.8 1.6 30.2 Average country 34.7 34.7 0.0 34.7 Notes: Small discrepancies are due to rounding errors. The results of applying the accounting framework to the comparison of the performance of the seven European countries are shown in Table 1. The expected is the share of innovative sales that we would expect in each country given its industry composition, the average size and group membership of its firms, its R&D activities, and characteristics of its environment. These various structural effects and their total are measured in terms of deviation from the average European country of reference. Thus, together with innovativeness, by definition the unexplained residual in observed, they account for the bilateral differences in between each country and the average European country, and hence between any pair of individual countries. If we compare Italy and Germany, which appear to have respectively the lowest (25.6 percent) and highest (43.8 percent) share of innovative sales, our framework accounts for about one-third (5.5 percent) of the total difference (18.2 percent) between them and attributes the remaining two-thirds (12.7 percent) to innovativeness. Of the total difference of 5.5 percent accounted for, 3.3 percent, 1.8 percent, and 0.9 percent are respectively imputed to relatively favorable differences in the environment conditions, R&D activities, and industry compositions, while only 0.5 percent corresponds to unfavorable size and group-membership effects.

VOL. 92 NO. 2 THE ECONOMICS OF TECHNOLOGY AND INNOVATION 229 On the whole, focusing on the comparison with the average European country, the framework performs rather well. The magnitude of the various structural effects seems reasonable. The estimated figures for country innovativeness also look plausible, although they can be rather large. They are larger than the differences in the totals of structural effects in two of the seven countries: Italy and Ireland. They are about equal in Germany and much smaller in the other four countries. These differences in country innovativeness can reveal genuine differences in the national systems of and their effectiveness in producing output. They can also reflect remaining imperfections and discrepancies between countries in the Community Innovation Survey design and implementation, and of course shortcomings and misspecifications in our -accounting framework as it stands. In any case, they are indicative of points and querries that could be of interest for further investigation. III. Conclusion We propose in this paper an accounting framework for and illustrate it by an application based on the data from the first European Community Innovation Survey, for the year 1992 and the R&D-intensive manufacturing industries in seven European countries. In this application, we measure by the share of innovative sales, but our framework can also be applied to other sources of data and other measures of. Trying to make the best use of the qualitative and quantitative information available in the survey, we select a certain number of explanatory variables for the propensity to innovate and the of, and we specify and estimate an function as a generalized Tobit model. Based on this model, we compute the expected share of innovative sales and define innovativeness as the part of the observed share of innovative sales that remains unexplained and corresponds to the notion of total factor productivity, or simply productivity, in the standard growth-accounting framework and production-function analysis. As it stands, with relatively few explanatory variables, our framework already accounts for sizable differences in country. It also shows, however, that differences in country innovativeness can be quite sizable as well. Given some of the limitations of our attempt, in particular that related to using only the data from the first round of the European Community Innovation Survey, these initial results should be merely taken as illustrative. We hope they will suffice to indicate the potential interest and advantages of explicitly implementing an accounting framework for when comparing performances between countries (as here), or between industries or firms, and in terms of absolute levels in a given period (as here) or changes over given periods. These advantages should be similar to those of the standard growth-accounting framework, in spite of the facts that in both cases many conventional decisions have to be made and many variants may be considered in setting up an appropriate framework. To make progress in future work, besides gaining experience in using surveys and improving them, it will be important to be able to match the specific information they provide with the usual current accounts, balance sheets, and stockmarket data, as well as with complementary data from other sources such as on patents and R&D. In view of the fundamental role of research and activities in increasingly knowledge-based economies, it will also be of interest to make attempts to combine and production accounting frameworks in some systematic way, and thus contribute to the development of productivity analysis in relation to R&D and. 9 REFERENCES Caves, Douglas W.; Christensen, Laurits R. and Diewert, Erwin W. Multilateral Comparisons of Output, Input and Productivity Using Superlative Index Numbers. Economic Journal, March 1982, 92(365), pp. 73 86. 9 For a first step in this direction, see the four-equations and productivity model considered in Bruno Crépon et al. (1998).

230 AEA PAPERS AND PROCEEDINGS MAY 2002 Crépon, Bruno; Duguet, Emmanuel and Mairesse, Jacques. Research, Innovation and Productivity: An Econometric Analysis at the Firm Level. Economics of Innovation and New Technology, 1998, 7(2), pp. 115 58. Eurostat. Community survey 1 [CD- ROM]. Luxembourg: Statistical Office of the European Communities, 1997. Mairesse, Jacques and Mohnen, Pierre. To Be or Not To Be Innovative: An Exercise in Measurement. Science, Technology and Industry Review (Organization for Economic Cooperation and Development), 2001, 27, pp. 103 29. Organization for Economic Cooperation and Development. OECD proposed guidelines for collecting and interpreting technological data Oslo manual, 2nd Ed. Paris: Organization for Economic Cooperation and Development, 1997. Porter, Michael and Stern, Scott. The new challenge to America s prosperity: Findings from an index. Washington, DC: Council on Competitiveness, 1999.