Expensed and Capitalized Intangibles and Firm Productivity: A Panel Data Assessment

1. by Maria Elena Bontempi, Jacques Mairesse Expensed and Capitalized Intangibles and Firm Productivity:A Panel Data Assessment A COINVEST CONFERENCE Intangible Investments at Macro and Micro Levels and their Role in Innovation, Competitiveness and Growth INSTITUTO SUPERIOR TECNICO, 18-19 March 2010, LISBON

2. Motivation (1) • The question of whether it is better to capitalise or to expense intangibles is one of the most controversial issues to emerge recently in the literature: - Debated by the International Accounting Standards Committee (IASC) when developing the International Financial Reporting Standards (IFRS) - Debated a well from the macroeconomic point of view, in defining a new System of National Accounts (SNA): for example, one of the major changes in the 2008 SNA regards the recognition of R&D as fixed assets. • The link between intangibles and productivity is poorly understood: how to measure intangible capital from information available in companies' accounts? Which types of intangibles (R&D, patents, trademarks, advertising, etc.) should be used? What is the functional link between productivity and intangible input? • Empirical estimates of the relationship between intangibles and productivity may differ substantially, and “sometimes” appear insignificant or fragile even as concerns R&D (Mairesse and Sassenou , 1991; Hall, Mairesse and Mohnen, 2010). COINVEST LISBON, March 2010

3. Motivation (2) • Assessment of the impact of R&D and other intangibles on productivity in the case of Italy, on the basis of detailed data firm balance sheet and current account data. • Extension of the definition of intangibles. This offers the opportunity to disentangle the contribution to productivity of several components of intangibles: “intellectual capital” (R&D and “patents”) versus “customer capital” (trademarks and advertising). • Consideration of the Italian GAAP, which, differently from the US GAAP and the IAS 38/IFRS 3, allow for both capitalising and expensing most of intangible costs. This offers the possibility to enter in the debate about expensing or capitalising (some) intangible investments -- debate pervading most accounting literature (B. Lev, among others). COINVEST LISBON, March 2010

4. Sample Manufacturing firm-level data, drawn from Centrale dei Bilanci Cleaned sample: drop of 46% of the starting observations; unbalanced panel (94,968 observations for 14,254 firms), 1982-1999 period. Many procedures to create a connection between reporting rules, available accounting information, and empirical variables (changes in accounting norms since 1991, when the fourth Directive approved by the European Commission was implemented) (Bontempi, 2005). Classification of observations by size and industry COINVEST LISBON, March 2010

5. Table 1- PANEL A Definition of intangible capital (K = IKBS+IKCA = IK+CK) based on company current and capital accounts COINVEST LISBON, March 2010

6. Table 1- PANEL B Definition of tangible capital (C) based on company current and capital accounts COINVEST LISBON, March 2010

7. Table 2- Magnitude of different forms of intangible capital compared to total tangible capital (C): simple average and [median] (in %) Notes:IK ∩ IKBS = IKBSrd + IKBSpat ; CK ∩ IKBS = IKBSmark ;IK ∩ IKCA = IKCArd + IKCApat ;CK ∩ IKCA = IKCAadv . In columns (1) intangibles capitalised from expenses (IKCA) are at replacement values; intangibles assets (IKBS) and tangible assets (C) are at book values.In columns (2), both intangibles capitalised from expenses and intangible assets (IKCA and IKBS respectively) are estimated at replacement values; while tangibles (C) are at book values.In columns (3), intangibles capitalised from expenses (IKCA), intangible assets (IKBS) and tangible assets (C) are estimated at replacement values. COINVEST LISBON, March 2010

8. Table 3- Magnitude of different forms of intangible capital compared to total intangible capital (K): simple average and [median] (in %) Notes:IK ∩ IKBS = IKBSrd + IKBSpat ; CK ∩ IKBS = IKBSmark ;IK ∩ IKCA = IKCArd + IKCApat ;CK ∩ IKCA = IKCAadv . COINVEST LISBON, March 2010

9. Table 4- Occurrence and relative magnitude of intangible capital for different samples COINVEST LISBON, March 2010

10. Table 5- Descriptive statistics for main variables COINVEST LISBON, March 2010

11. CES specification of total capital: (3)Qit=Ai Bt Lit [C-it +  K-it] - / ecit , Q = value added; L = labour input; C and K = tangible and intangible capital, respectively;  = elasticity of output with respect to labour input;  =  + = returns to scale to the capital inputs, where  and  = elasticity of the output with respect to tangible and intangible capital; Ai and Bt = not measurable firm-specific and time-specific effects, respectively;  = disturbance term capturing omitted variables, measurement errors, and any other error committed in specifying the production function (e.g. not-appropriateness of the functional form, or non validity of the assumption of parameters’ homogeneity);  = constant distribution parameter for capital inputs (or input intensity parameter); -1  = substitution parameter that determines the value of the (constant) elasticity of substitution: where is the marginal rate of technical substitution, i.e. the marginal product of intangibles over that of tangibles. Framework (1) COINVEST LISBON, March 2010

12. Nested in CES are: For  0,  1 Cobb-Douglas production with multiplicative specification of total capital: (1)Qit=Ai Bt Cit Lit Kit emit constant  and  = elasticity of the output with respect to tangible and intangible capital. For  -1,  Cobb-Douglas production with additive specification of total capital: (2)Qit=Ai Bt (Cit+ Kit) Lit eait,  = constant distribution parameter for capital inputs (or input intensity parameter) = MRTS = ratio of marginal products of intangibles and tangibles. Framework (1) COINVEST LISBON, March 2010

13. Table 6 - Production Function parameters COINVEST LISBON, March 2010

14. Take logarithms and model the intercept with year and firm (or industry) effects, that is in case of the multiplicative specification of total capital: Without constant return to scale imposed: (1’) (q-l)it=ai + bt + (cit-lit) + (kit-lit) + ( -1)lit +mit With constant return to scale: (1’’)(q-l)it=ai + bt + (cit-lit) + (kit-lit) + mit And with given labour elasticity,i.e. total factor productivity (TFP): (1’’’) tfpcmit= qit – 0lit - (1- 0)cit tfpcmit = ai + bt + (k-c)it +mit where 0 is set equal to the sample median of the share of labour cost in value added. Framework (2) COINVEST LISBON, March 2010

15. Econometric issues ( 1) A- Specification of the individual and temporal heterogeneity (ai and bt), and of the error term (it) • macro influences (business cycle, macroeconomic shocks, disembodied technical changes); • not measurable firm-specific advantages (like manager ability); • homogeneity of parameters, while firms may have different production functions and diverse rates of utilisation of the various input categories. COINVEST LISBON, March 2010

16. Econometric issues ( 2) B- Endogeneity • Sources of simultaneity: • inputs and output chosen simultaneously; • companies know their efficiency levels: firm-effects correlated with explanatory variables. Sources of measurement errors: • omission of labour and capital intensity-of-utilisation variables, such as hours of work per employees and hours of operation per machine; • problems in intangible stocks computation (accounting normative changes, choice of depreciation rates); • labour input does not distinguish between blue and white collar; • use of price deflators common across companies (lack of individual prices). COINVEST LISBON, March 2010

17. Econometric issues ( 3) C- Non-linearity of (2) and (3) in the ,  and  unknown parameters D- Estimation of the  =MRTS in equation (1), of the elasticity of output with respect to intangibles (and tangibles) in equation (2), of the  =MRTS and the elasticities in equation (3). COINVEST LISBON, March 2010

18. Econometric issues ( 4) Pooled OLS estimators:no individual effects, but per-industry and temporal heterogeneity. Within estimators:two-ways, both individual and temporal, fixed effects. First-differences OLS estimators:yearly growth rates with temporal dummies. Long-differences estimators:5-years growth rates with temporal dummies. GMM-dif, GMM-lev and GMM-sys estimates with imposition of theoretical restrictions on parameters: constant returns to scale; and TFP (conventionally measured, i.e. perfect competition in both the labour and output markets). Grid-searches on the ,  and  unknown parameters to obtain (initial) values minimising the residual sum of squares. Iterative procedures on first-order Taylor-series approximations of equations (2) and (3) around initial values of ,  and  parameters.  and  (MRTS) are computed for the 1st, 2nd and 3rd quartiles of distribution of the ratio of intangibles and tangibles to estimated total capital (tangibles to intangibles ratio). All the models are estimated with the Eicker-Huber-White estimator, robust to the presence of general heteroskedasticity. COINVEST LISBON, March 2010

19. Production Function Estimates with Multiplicative Capital [1] slmed=0.633 is the sample median of labour cost’s share of value added. COINVEST LISBON, March 2010

20. Production Function Estimates with Additive Capital [1] slmed=0.633 is the sample median of labour cost’s share of value added. Estimates of the production function with additive capital are obtained by an iterative procedure on a first-order Taylor-series approximation around an initial value (0). The starting value (0) is selected by a grid search on the  parameter. COINVEST LISBON, March 2010

21. Production Function Estimates with CES Capital [1] slmed=0.633 is the sample median of labour cost’s share of value added. Estimates of the production function with CES capital are obtained by using a grid search on the  and  parameters. Standard errors of  and  parameters are obtained by using the Gauss-Newton regression derived by a first-order Taylor-series approximation around the minimum residual sum of squares estimates of the  and  parameters. Estimates and standard errors of the , -1 and  parameters correspond to the minimum residual sum of squares estimates of the  and parameters. COINVEST LISBON, March 2010

22. Production Function GMM Estimates with multiplicative capital COINVEST LISBON, March 2010

23. Table 7 a- Pooled estimates of the  and  parameters COINVEST LISBON, March 2010

24. Table 7 b- First-differences estimates of  and  parameters COINVEST LISBON, March 2010

25. Table 7 c- Within estimates of  and  parameters COINVEST LISBON, March 2010

26. Table 7 d- Five-year-differences estimates of  and  parameters COINVEST LISBON, March 2010

27. Table 10- Estimates of the  parameters, multiplicative capital and total factor productivity[1] Table 10- Estimates of the  parameters, multiplicative capital and TFP

28. Table 8 a-  and  estimates for intellectual capital (IK) and for customer capital (CK). CES capital, total factor productivity. COINVEST LISBON, March 2010

29. Table 8 b-  and  estimates for intangible assets (IKBS) and for intangible capital estimated from expensed costs (IKCA). CES capital, total factor productivity. COINVEST LISBON, March 2010

30. Figure 1 – Different estimates of relative productivity of intangibles, Non constant returns to scale specification COINVEST LISBON, March 2010

31. Figure 2 – Different estimates of relative productivity of intangibles, Total factor productivity specification Note: By definition, the additive estimates have no range. COINVEST LISBON, March 2010

32. Figure 3 – Different estimates of relative productivity of intellectual and customer capital, CES capital, total factor productivity specification COINVEST LISBON, March 2010

33. Figure 4 – Different estimates of relative productivity of intangible capital capitalised and non-capitalised by the firm, CES capital, total factor productivity specification COINVEST LISBON, March 2010

34. Main findings and conclusions • Measurement errors and endogeneity seem to particularly affect returns to scale and labour elasticity: TFP appears as the most reliable specification for our purpose. • Predominance of between-firms variability over temporal variability: preference for estimates in long-term growth rates. • The order of magnitude of the elasticity of intangibles (and the range of its marginal productivity relative to tangibles) seems overall rather robust. • The highest marginal productivity is that of intellectual capital, followed by customer capital and intangible assets. Intangible capital computed by capitalising expenditures display the lowest level of productivity. • Ignoring the informative content of the Italian GAAP (and measuring intangibles from expenses reported in companies’ current accounts, as the Anglo-Saxon literature does), could lead to downwards biased results. • Companies’ accounting figures for intangible assets are of a genuinely informative nature. • The development of reporting and accounting requirements that allow for the capitalisation of intangibles in companies' accounts (as well as in national accounts) is supported. • Treating intangibles as a form of investment should reduce the information gap between tangible and intangible resources. COINVEST LISBON, March 2010

35. Further developments • Further investigation of the relevance of all the available components of intangible and tangible capital • Investigate using firm- and time-specific depreciation rates, instead of constant ones. • Extension of the analysis at an industry level • Better comprehension of firms’ heterogeneity • Comparison of Italian results with other countries, like France, not dissimilar in the accounting normative on intangibles • … COINVEST LISBON, March 2010