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on Project, Program and Portfolio Management |
| By: | Koehler, Mila; Peters, Bettina |
| Abstract: | Little is known about whether and to what extent the outcome of subsidized and non-subsidized R&D projects differ. In this paper we exploit a novel dataset of patent applications filed in Germany between 1995-2005, which allows us to identify if a patent application stems from a subsidized project or not. We use a variety of patent indicators to elucidate to what extent successful subsidized and non-subsidized R&D projects within the same firm differ. Results show that patent applications from subsidized R&D projects have a higher private value, are more often co-applied, more general, but less original, and have larger inventor teams when compared to all other patent applications filed by the same firms. These differences seem to reflect that thematic R&D programs aim to support collaborative R&D projects that have an immediate economic utilization of results. |
| Keywords: | R&D,subsidies,patents,evaluation,project-level analysis,within firm comparison |
| JEL: | H25 H50 O31 O34 O38 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:17042&r=ppm |
| By: | Martin Backfisch (Baden-Wuerttemberg Cooperative State University Center for Advanced Studies and Philipps-Universität Marburg) |
| Abstract: | In the context of the ongoing debate about an innovation crisis in the pharmaceutical industry, we study the success rates of pharmaceutical R&D projects as a measure of innovative productivity. The empirical literature suggests success rates have been decreasing during recent decades. We critically review this literature and only find few studies with a focus on the development of success rates over time. Further, the empirical analysis of success rates imposes difficulties with respect to methodological aspects like data censoring, the definition of success, and the range of firms included in the samples. These difficulties are generally not discussed by the literature. We therefore discuss these issues when critically reviewing the empirical studies and complement this discussion with own empirical results. While most other studies use samples containing a small number of firms and cover just a short time period, we use a broad sample containing firms of different sizes over an observation period of more than 20 years (1989-2010). Descriptive results suggest a declining success rate of pharmaceutical projects during recent years. Correcting for censored observations shows there has been a stabilization of success rates, but at a lower level than before. The main underlying reason for a lower success rate is the start of many more projects in more recent time periods. Results from hazard rate models even suggest there has only been a temporary drop in the success rate for projects between 1995 and 2002. |
| Keywords: | pharmaceutical R&D; drug development; innovation; success rates |
| JEL: | O32 L65 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:201746&r=ppm |
| By: | Ivelin Elenchev; Aleksandar Vasilev (Centre for Economic Theories and Policies, Sofia University St. Kliment Ohridski) |
| Abstract: | The present paper develops three models that help predict the success rate and attainable investment levels of online crowdfunding ventures. This is done by applying standard economic theory and machine learning techniques from computer science to the novel sector of online crowd-based micro-financing. In contrast with previous research in the area, this paper analyzes transaction-level data in addition to information about completed crowdfunding projects. This provides an unique perspective in the ways crowdfinance ventures develop. The models reach an average of 83% accuracy in predicting the outcome of a crowdfunding campaign at any point throughout its duration. These fundings prove that a number of product and project specific parameters are indicative of the success of the venture. Subsequently, the paper provides guidance to capital seekers and investors on the basis of these criteria, and allows participants in the crowdfunding marketplace to make more rational decisions. |
| Keywords: | microfinance, entrepreneur finance, crowdfunding |
| JEL: | M20 G24 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:sko:wpaper:bep-2017-09&r=ppm |
| By: | Elenchev, Ivelin; Vasilev, Aleksandar |
| Abstract: | The present paper develops three models that help predict the success rate and attainable investment levels of online crowdfunding ventures. This is done by applying standard economic theory and machine learning techniques from computer science to the novel sector of on-line crowd-based micro- financing. In contrast with previous research in the area, this paper analyzes transaction-level data in addition to information about completed crowdfunding projects. This provides an unique perspective in the ways crowd finance ventures develop. The models reach an average of 83% accuracy in predicting the outcome of a crowdfunding campaign at any point throughout its duration. These ndings prove that a number of product and project specifi c parameters are indicative of the success of the venture. Subsequently, the paper provides guidance to capital seekers and investors on the basis of these criteria, and allows participants in the crowdfunding marketplace to make more rational decisions. |
| Keywords: | microfinance,entrepreneurial finance,crowdfunding |
| JEL: | C0 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:170681&r=ppm |