Similarity and Geographical Issues in evaluating the Impact of R&D Spillovers at firm level. Evidence from Italy. Francesco Aiello & Paola Cardamone Department of Economics and Statistics University of Calabria I Rende (CS) - Italy Adres Conference 2006 Saint Etienne, France September

Research aim: To provide an assessment of the impact of R&D spillovers on the production of Italian manufacturing firms. We introduce some improvements regarding: Determination of R&D spillovers Choice of the production function Estimation method

Related Literature Cincera (2005), Jaffe (1988), Los and Verspagen (2000), Wakelin (2001), Harhoff (2000), Adams and Jaffe (1996) Medda and Piga (2004), Aiello and Pupo (2004), Aiello, Cardamone and Pupo (2005), Aiello and Cardamone (2005) Common denominators:  The use of the Cobb-Douglas production function  The use of R&D capital (or R&D investments) of other firms to determine R&D spillovers

Determination of R&D spillovers Following Griliches (1979), spillovers can be measured by the indirect stock of technological capital, which is determined by the current and past investments in R&D made by other firms Firms are not able to absorb all the technology produced by others, hence absorption capacity differs from one firm to another. In other words, this means that the R&D spillovers of a given firm must be the weighted sum of the R&D stock of the other firms denotes the share of innovation produced by firm j and used by firm i where

Weighting Systems used in literature Input Output Matrices (Medda and Piga, 2004; Aiello and Pupo, 2004; Aiello, Cardamone and Pupo, 2005; Aiello and Cardamone, 2005) Similarity measure using either patents (Cincera, 2005; Jaffe, 1988; Los and Verspagen, 2000) or R&D investiments (Harhoff, 2000; Adams and Jaffe, 1996)

Similarity measure Uncentered correlation metric: Underlying hypothesis: the more similar two firms are, the greater the flow of innovation between them (Jaffe, 1986 and 1988; Cincera, 2005) where X i is a set of variables defining the technological dimension of a firm Variables: value added, skilled (at least high school) and unskilled (primary school) employees, investments in ICT, internal and external R&D investments.

Asymmetric Similarity measure Uncentered correlation gives a symmetric matrix  technology spills over from i to j at the same degree from that occurring from j to i  it is likely that direction matters in determining technological transfers from one firm to another  We consider: where the variable V is the value added

Proximity measure A huge number of papers deals with the theoretical issues of the nexus between spatial agglomeration and knowledge spillovers (Marshall, 1920; Jacobs, 1969; Romer, 1986; Arrow, 1962; Koo, 2005; Audretsch and Feldman, 2003) A weight of geographical proximity is given by: is the spatial distance between a pair of firms and is computed considering the great circle distance where

Asymmetric technological and geographical weighting system It is likely that the closer and more similar firms are the more they benefit from each other’s technology  we average the indices:  with i=1,2,…,N

Production function specification We consider the translog (Christensen et al., 1973)  it does not constrain the elasticity of substitution among inputs to any value Constant returns to scale imply:

Translog production function input cost shares with CRS where S L, S K, S CT denote the cost shares of labour, physical capital and technological capital, respectively.  We obtain a system of equations given by the translog specification and the following cost share equations:

Sample selection : The log-linearization of the translog excludes the firms that do not invest in R&D and thus it does not allow us to control for potential correlation between the “selection process” (to invest or not in R&D) and the substantial model we intend to estimate  Following Wooldridge (2002), we address this issue using the two-steps IV method: in the first step we consider a probit model to explain the decision to invest in R&D, and in the second step we estimate the translog production function using as instruments the fitted probabilities derived from the first step. Estimation Method-1

 This procedure ensures that the usual standard errors and test statistics are asymptotically valid (Wooldridge, 2002) We estimate the system of equations of a balanced panel data by 3SLS (instruments: one-year lagged value of each endogenous regressor). Spillovers are treated as strictly exogenous variables. Estimation Method-2

Data used in this study come from the 8 th and 9 th “Indagine sulle imprese manifatturiere” surveys made by Capitalia (formerly Mediocredito Centrale). The balanced panel data consists of 557 R&D performing firms (the entire sample consists of 1203 firms) and covers the period Data source

Variables Y: value added K: the book value of total assets CT: technological capital determined by perpetual inventory method using R&D investments and a depreciation rate of 15% S L : Labour Cost Share: Labour Cost/Value Added Cost shares of physical and technological capital (S K and S CT ): With Z=K, CT P I =Investment Price Deflator δ=rate of depreciation assumed to be 15% for CT and 5% for K r= interest rate, assumed to be 5%

Results - 1

Asymmetric Technologial & Geografical Spillovers in Italy by Region ( ) Input NORTH-WEST NORTH-EAST CENTRE-SOUTH L *** *** *** K *** *** *** CT *** *** *** SPILL *** *** *** Number of obs F-test Prob > F 000 R-squared

Morishima Elasticity of Substitution It is defined as the percentage change in the ratio of input i and input j due to the percentage change of the price of input j, all other prices being constant: It is a relative measure. If MES ij >0 factors i and j are substitutes, whereas if MES ij <0 they are complementary

Estimated Morishima elasticities of substitution in Italy ( ). Results refer to the use of the asymmetric technological and geographical spillovers

Conclusions/1 Output elasticity with respect to R&D spillovers is always positive and significant (from 0.29 to 0.70). This result stands in sharp contrast to those obtained by other authors, which place the elasticity of spillovers at very low levels. Asymmetry on how technology flows from one firm to another matters in determining the impact of R&D spillovers. All regressions based on the asymmetric similarity index yields an higher value of the output elasticity relative to those which use the “pure” uncentered correlation metric.

Conclusions/2 Geographical dimension is relevant The output elasticity of R&D spillovers is higher in the Centre/South than in the North of Italy

Results - 2

Great circle distance d ij = 69.1 * (180/π) ⋅ ARCOS(SIN(LAT1)*SIN(LAT2)+ +COS(LAT1)*COS(LAT2)* *COS(LONG2+LONG1))

Sample selection : In many cases, firms do not invest in R&D (zero- investment-values)  our sample can be split in the sub-sample of R&D performing firms (with positive values of R&D capital) and in the sub-sample of non- R&D performing firms (with zero values of R&D capital). The log-linearization of translog restricts the sample to the R&D performing entities  it forces to work with a sample which is no longer random, because it ignores the underlying process that leads every firm to invest or not in R&D. Consequently, there might be a selection problem due to likely correlation between the decision process to invest in R&D and the production function we intend to estimate

Sample selection: first step The dependent variable of the probit model is unity if the i-th firm invests in R&D and is zero if R&D investments are zero. The regressors of the probit model are the regressors of the production function and the key determinants of the decision to invest in R&D, that is human capital, cash flow, investments in ICT, a dummy equal to unity if firm i exports and a set of dummies measuring the geographical location and the economic sector of each firm

Italian manufacturing firms by area and industry

The full sample is split in the sub-groups of R&D performing firms - which is composed of the 557 firms (557*6=3342 observations) that invest in R&D for, at least, one year over the period – and of 646 (3876 observations) non-R&D performing firms.

This presentation: Research aim How to measure the R&D Spillovers Production function Data source Results Conclusions