MISSIONS FOR EU INNOVATION POLICY WHY THE RIGHT SET-UP MATTERS. Bertelsmann Stiftung Policy Paper N0. 224 29 May 2018

The proposed introduction of research & innovation (R&I) missions in Horizon Europe, the next EU research programme, seems to be the most significant and ambitious change on previous programmes, especially given its implications for the governance of research projects. R&I missions are an innovation policy instrument where the government sets the objective of solving a certain technological or societal problem within a pre-defined time-frame that cannot yet be reached technologically. Governments may employ various policy instruments ranging from financial support for R&I activities to regulation to achieve this objective

Knowledge production and patterns of proximity: French sme¿s of biotechnology

Nowadays, innovation is the main factor of firms and regions competitiveness. But, the conditions necessary to improve its development remain still unknown, particularly there are few works about spatial aspects of its appearance. Few monographs exist on the efficiency of 'local' networks, but no theoretical patterns. We want to check if innovative networks are really more efficient if they are local, and bring to the fore the conditions of this efficiency. This conditions seem to lie in the notion of proximity. Instead of considering geographic and organizational proximity as substitutes, we define them as complementary. So we propose a schedule of innovative networks linking geographic and organizational proximity. We shall test it in the biotechnology field, to build a typology of innovative networks. Key words: innovative networks, geographic and organizational proximity, biotechnology.

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

A NEW PERSPECTIVE ON UNDERINVESTMENT IN AGRICULTURAL R&D

During the past 40 years, the returns to agricultural R&D have been on average in the range of 40-60% (Alston, et al 2000, Evenson 2001). Many agricultural economists see this high average as convincing evidence that there is significant underinvestment in public agricultural R&D (Ruttan 1980, Pinstrup-Andersen 2001). This paper sheds new light on the underinvestment hypothesis by introducing a simple model of the selection of R&D projects and confronting it with the rate-of-return evidence accumulated over the years worldwide. The model assumes that the distribution of all possible R&D projects on an expected rate-of-return (ERR) scale declines asymptotically. Under the neoclassical conditions of full information and profit maximization, R&D project selection starts with the project with the highest ERR and continues until the budget is finished or the last project hits the social cutoff rate, whichever comes first. Hence the underinvestment gap can be defined as the difference between the ERR of the marginal R&D project (the actual cutoff rate) and the social cutoff rate. Only three variables need to be known to estimate the underinvestment gap: the social cutoff rate, the actual cutoff rate, and the slope coefficient. Taking less than full information and economic rationality into account, the paper discusses how the latter two can be derived from a sufficiently large and representative sample of ex-post rates of return on agricultural R&D. Important findings of the model are: · Not the mean but the mode of the ex-post rate-of-return distribution is the relevant variable for assessing underinvestment in agricultural R&D. · Under the assumption of full information and profit maximization, developed countries could have invested about 40% more in public agricultural R&D and developing countries about 137% more. In terms of agricultural R&D intensity (i.e., R&D expenditures as a percentage of AgGDP), developed countries could have invested 2.8% rather than 2.0%, and developing countries 1.0% rather than 0.4% in 1981-85. · Low investment in public agricultural R&D in developing countries is caused foremost by a relatively smaller portfolio of profitable R&D projects to choose from. Underinvestment certainly plays a role (the gap is bigger for developing countries), but it explains only a small part of the difference in agricultural R&D intensity between developed and developing countries. · While efforts to reduce the underinvestment gap should continue (e.g., better priority setting and mobilization of political support), more emphasis should be placed on designing policies that help to shift (the portfolio of) R&D projects higher up on the ERR scale, even at the risk of increasing the underinvestment gap. Key words: agricultural R&D, underinvestment, rate of return, research intensitiesagricultural R&D, underinvestment, rate of return, research intensities, Research and Development/Tech Change/Emerging Technologies,

Defining and Measuring The Creation of Quality Jobs

Our research is intended to support our peers in the Community Development Financial Institution (CDFI) industry who, through their financing, have served low-income and other disadvantaged communities for two decades.  While the CDFI industry has been instrumental in supporting job creation across the U.S., we believe that now is the time to focus greater attention on the quality of the jobs created in order to combat rising income and wealth inequality.Through a better understanding of what defines a quality job and a set of practical methods for measuring the quality of jobs created, we believe CDFIs and others in the impact investing community will be better positioned to make more effective investments that support good jobs for workers, businesses, and communities

Towards the Framing of Venture Capital Policies: a Systems-Evolutionary Perspective with Particular Reference to the UK/Scotland and Israeli Experiences

We compare some of the policies that have been attempted in Europe (UK/Scotland) and Israel over the past fifteen years to elaborate a new Systems Evolutionary (SE) framework for rethinking VC policy and related ITP. We argue that this perspective is useful for both real world (‘positive’) analysis and policy (‘normative’) analys is. Our SE framework is shaped by (i) a multidimensional view of VC; (ii) strong between VC, VC policy and the development of EHTCs; and (iii) a strategic approach to policy. In contrast, many VC policies in Europe up to and including the 1990s took a ‘static’ financial view of VC that focused on ‘bridging existng early phase finance gaps of innovative companies’ rather than creating of a new mechanism to assure the timely growth of EHTCs. We aim to present the new framework rather than to provide specific recommendations. The main conclusion is that the success of VC policies depend on factors such as the phase of evolution of (i) VC or related innovation finance organizations; (ii) the underlying segment of start up companies and of high tech industries; (iii) the specific country/region institutional setting. While in some contexts it may be worth considering the targeting of a new VC industry/market (and associated EHTC) in others the focus of policy should center in improving pre-emergence conditions. More specifically it may be, given that VC searches for ‘investment ready opportunities’, that ITP should, in many contexts, precede VC policies. Another key conclusion is that implementing this perspective necessitates the creation of a strategic level of policy, with a view of specifying a set of strategic priorities for Scie nce, Technology, and Innovation, priorities that should precede rather than follow policy design and implementation. A major challenge is to extend the present framework that was initially based on VCs oriented towards ICT to LS.

Catalog of Approaches to Impact Measurement: Assessing Social Impact in Private Ventures

To inform action impact investors could take to measure impact in a coordinated manner, The Rockefeller Foundation commissioned the study of impact assessment approaches presented here.It is natural to hope to find a single, turnkey solution that can address all measurement needs. In this study we conducted a survey of impact investors and complemented it with seven years of experience in the field of impact investing to discover what these investors want from impact measurement, and conducted in-depth interviews with over twenty entities that have developed and implemented approaches to measuring impact. Our survey of existing approaches was thorough but surely is not comprehensive; however the approaches are a good representation of the current state of play. What we found is that there is not one single measurement answer. Instead the answer depends on what solution is most appropriate for a particular investor's "impact profile" defined as the investor's level of risk tolerance and desired financial return, the particular sector in which the investor operates, geography, and credibility level of information about impact that the investor requires