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Eurasian Business Review
Published in: Eurasian Business Review 1/2017

10-02-2016 | Original Paper

Multilevel heterogeneity of R&D cooperation and innovation determinants

Author: Sara Amoroso

Published in: Eurasian Business Review | Issue 1/2017

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Abstract

Assessing the impact of public support to innovation on R&D collaboration may require a more complex multilevel design, that describes the likely correlation present among firms characteristics within a particular sector. Using data from the 2006 edition of the Community Innovation Survey (CIS) for the Netherlands, we propose a methodology to study the effect of firm-level characteristics on the propensity to undertake a research collaborative agreement. In particular, we show that controlling for a richer variance structure yields a different picture with respect to simpler regression frameworks adopted in the literature of R&D cooperation determinants. Moreover, such a hierarchical framework can be generalized allowing for clustering at higher levels, such as sectors or geographical areas. Besides the link between public funding and R&D collaboration, our results confirm the findings of the literature: technological spillovers, risk and cost sharing rationales, firm’s size, and type of innovative activity are related to the decision of engaging in different sorts of research alliances.
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Appendix
Available only for authorised users
Footnotes
1
Recent research employing spectral analysis has confirmed the presence of sinusoidal-like cycles (called Kondratiev) in the world GDP dynamics at an acceptable level of statistical significance. Korotayev and Tsirel (2010) detected shorter (on average 17 years) business cycles, approximately one third of the Kondratiev cycles.
 
2
Depending on the model assumptions, and compatibly with the data at hand, one could allow for a richer specification of the clusters, such as the geographical district, or the relevant markets. We limit ourselves to a frugal, yet general representation of a multilevel design in the context of research cooperation determinants.
 
3
The labels radical and incremental belong mostly to the managerial literature (see Dewar and Dutton 1986; Henderson 1993).
 
4
The class of mixed logit models is a highly flexible as it can approximate any random utility model (Train 2009). The results we present can be generalized and extended to panel data.
 
5
The Community Innovation Surveys are designed to provide an extensive description of the general structure of innovative activities at the country and industry levels. Within the guidelines of the OSLO Manual on performing innovation surveys (OECD 1997), information about innovation activities is collected.
 
6
Following the guidelines of the OECD Directorate for Science, Technology and Industry, the manufacturing industry can be classified into four categories according to technology intensity using the ISIC Rev. 3 breakdown of activity: high technology sectors (aircraft and spacecraft; pharmaceuticals; office, accounting and computing machinery; radio, TV and communications equipment; medical, precision and optical instruments), medium/high-technology industries (electrical machinery and apparatus; motor vehicles, trailers and semi-trailers; chemicals excluding pharmaceuticals; railroad equipment and transport equipment; machinery and equipment), medium/low-technology industries (building and repairing of ships and boats; rubber and plastics products; coke, refined petroleum products and nuclear fuel; non-metallic mineral products; basic metals and fabricated metal products), and low-technology industries (recycling; wood, pulp, paper, paper products, printing and publishing; food products, beverages and tobacco; textiles, textile products, leather and footwear).
 
7
We formally test differences in the estimated coefficients using a Welch two-sample t test.
 
8
Since the MLE estimator and the mean of the posterior are asymptotically equivalent and their difference depends on the inverse of the square root of the sample size, the larger the sample size the narrower this difference. As our sample is pretty large (1929 observations), this difference is likely to be negligible.
 
9
If we set the error component \(z^{\prime }_{ij}\alpha ^c_j=d^{\prime }_{ij}\alpha ^c_j\), where \(d_{ij}\) is a dummy variable that takes the value 1 if firm i is nested in sector j and zero otherwise, \(\alpha ^c_j\) is reduced to a category-specific random intercept. In such a case \(W=w\) and \(\mathbf {A_2}=diag(w_1,\dots ,w_J)\) would be a simple diagonal matrix of dimension \(J\times J\).
 
10
Coull and Agresti (2000) derive a multivariate Binomial logit-normal distribution, where the c responses \(Y_i=(Y_{i1},\dots ,Y_{ic})\) with index vector \(m_i=(m_{i1},\dots ,m_{ic})\) are assumed to be independent binomial distributions, with success parameter vector \(\pi _i\). Then the multivariate Binomial logit-normal model is expressed by incorporating a random effect, such that \(logit(\pi _i)=X_i\beta +z_i\). where \(X_i\) is a \(c\times p\) covariate matrix and \(z_i\) is a \(c\times 1\) vector of random effects and is distributed as a multivariate normal distribution with mean vector 0 and covariance matrix \(\Sigma \). Then the probability density function of y is written as
$$\begin{aligned} p(y;\pi , m,\Sigma )=\int _{[0,1]^c}f_B(y|\pi , m)f_N(z;\Sigma )dz \end{aligned}$$
(4)
where \(f_B(y|\pi , m)\) denotes the binomial probability mass function with m trials and success probability \(\pi \) and \(f_N(z;\Sigma )\) denotes the multivariate normal density function of z.
 
11
One of the many advantages of the package MCMCglmm resides in the great flexibility in the specification of various residual and random-effect variance structures. MCMCglmm allows variance structures of the form \(\mathbf G=V \otimes \mathbf A \): unstructured and completely parameterized covariance matrices. However, binary responses pose a special problem because the residual variance cannot be estimated because the variance is uniquely determined by the mean. Therefore, following Hadfield and Kruuk (2010), we apply restrictions on the prior distribution of the residual covariance matrix. In particular, we fix the parameters of the prior distribution at some value (1 for variances and 0 for covariances).
 
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Metadata
Title
Multilevel heterogeneity of R&D cooperation and innovation determinants
Author
Sara Amoroso
Publication date
10-02-2016
Publisher
Springer International Publishing
Published in
Eurasian Business Review / Issue 1/2017
Print ISSN: 1309-4297
Electronic ISSN: 2147-4281
DOI
https://doi.org/10.1007/s40821-015-0041-1

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