Der Staat fragt Produkte und Dienstleistungen mit ganz unterschiedlichem technologischen Niveau nach – von Büroklammern bis zu Forschungssatelliten. Dieser Beitrag zeigt zunächst in einem theoretischen Modell, dass der Staat durch die technologische Intensität seiner Nachfrage den Markt für technologieintensive Produkte und Dienstleistungen erweitern kann. Denn eine stärkere staatliche Nachfrage nach innovativen Produkten und Dienstleistungen erlaubt es privaten Unternehmen, die überwiegend fixen Kosten für Forschung und Entwicklung auf größere Absatzmengen umzulegen, lässt die privaten Erträge aus Forschung und Entwicklung ansteigen und generiert somit zusätzliche Anreize, in die Entwicklung neuer Technologien zu investieren. Anhand von Daten aus den USA wird auch empirisch belegt, dass eine – budgetneutrale – Erhöhung der technologischen Intensität der staatlichen Nachfrage die privaten FuE-Ausgaben erhöht. Damit rückt die staatliche Nachfrage erneut in die Diskussion über mögliche Instrumente einer effektiven Wirtschafts- und Innovationspolitik.

Nahezu alle entwickelten Volkswirtschaften haben Programme zur Förderung von Forschung und Entwicklung (FuE) eingeführt. Allerdings ist sehr wenig darüber bekannt, welche Programme eigentlich genau zur Anwendung kommen, welche finanziellen Mittel dafür aufgebracht werden und ob die Programme in der Art und Weise wirken, wie sie ursprünglich intendiert waren. Evaluationsstudien, die auf kausalen Untersuchungsdesigns basieren, können einen wertvollen Beitrag zur Beantwortung der Frage leisten, ob ein Programm tatsächlich Wirkungen entfaltet und welcher der verschiedenen Ansätze am erfolgversprechendsten ist.

Übergeordnetes Ziel der hier vorgestellten Routinen ist die Abbildung von Kooperationsbeziehungen von Unternehmen, Universitäten, außeruniversitären Forschungseinrichtung sowie sonstiger Institutionen auf drei Ebenen von Innovationstätigkeit (Verbundprojekte; Publikationen; Patente). Dazu werden a) die drei innovationsbezogenen Datenbanken (Förderkatalog; Web of Knowledge; DPMA Patente) vereinheitlicht und zusammengeführt sowie b) diese kombinierten Datenbestände mittels Record-Linkage-Techniken mit den Daten aus den Institutionendatensätzen Amadeus und Research Explorer verknüpft. Die Zusammenführung erstreckt sich für dieses Projekt auf die sechs Fallregionen, die in RegDemo vorgesehen sind. Die Abgrenzung erfolgt anhand von Raumordnungsregionen: 501 - Aachen; 513 - Siegen; 602 - Nordhessen (= „Kassel“); 1302 – Mittleres Mecklenburg/Rostock (= „Rostock“); 1401 - Oberes Elbtal/Osterzgebirge (= „Dresden“); 1504 - Magdeburg.

Technological complementarity is argued to be a crucial element for effective R&D collaboration. The real structure is, however, still largely unknown. Based on the argument that organizations’ knowledge resources must fit for enabling collective learning and innovation, we use the co-occurrence of firms in collaborative R&D projects in Germany to assess inter-sectoral technological complementarity between 129 sectors. The results are mapped as complementarity space for the Germany economy. The space and its dynamics from 1990 to 2011 are analyzed by means of social network analysis. The results illustrate sectors being complements both from a dyadic and portfolio/network perspective. This latter is important, as complementarities may only become fully effective when integrated in a complete set of different knowledge resources from multiple sectors. The dynamic perspective moreover reveals the shifting demand for knowledge resources among sectors at different time periods.

Early access to technological knowledge embodied in the industry’s innovation network can provide an important competitive advantage to firms. While the literature provides much evidence on the positive effects of innovation networks on firms’ performance, not much is known about the determinants of firms’ initial entry into such networks. We analyze firms’ timing and propensity to enter the industry’s innovation network. More precisely, we seek to shed some light on the factors affecting the duration between firm founding and its first cooperation event. In doing so, we apply a unique longitudinal event history dataset based on the full population of German laser source manufacturers. Innovation network data stem from official databases providing detailed information on the organizations involved, subject of joint research and development (R&D) efforts as well as start and end times for all publically funded R&D projects between 1990 and 2010. Estimation results from a non-parametric event history model indicate that micro firms enter the network later than small-sized or large firms. An in-depth analysis of the size effects for medium-sized firms provides some unexpected findings. The choice of cooperation type makes no significant difference for the firms’ timing to enter the network. Finally, the analysis of geographical determinants shows that cluster membership can, but do not necessarily, affect a firm’s timing to cooperate.

The collapse of the communist system has initiated the transformation of Central and Eastern European (CEE) countries from a social-planned towards a market economy. The institutional transition, structural change and privatization process evolved at a different extent across CEE countries. The former satellite states implemented a rapid transformation of the economic system and joined the European Union (EU), whereas the countries of the Commonwealth of Independent States (CIS) faced severe difficulties in adapting their system to the new environment. Due to the lack of capital and knowledge, foreign direct investment (FDI) has played a crucial role in the process of technological renewal and economic development. This thesis consists of two research objectives; the location decision of multinational enterprises (MNE) in CEE regions and the impact of FDI in host economies. This thesis is based on firm level information and takes three theoretical frameworks on FDI into account: International Economics, Regional Economics and International Business. Taking the different transition paths of CEE countries into account, the findings suggest that the regional distribution of FDI differs across sectors and is affected by agglomeration economies and by the access to locally bounded inputs. Therefore, FDI amplifies the concentration of economic activities. The investigation of FDI spillovers provides evidence that FDI contributes to the competitiveness of domestic firms in CEE economies. Notwithstanding, the results show that local firms only benefit from FDI if foreign affiliates are sufficiently embedded in the host economy and global production networks.

Die gesamtwirtschaftliche Produktion ist in Deutschland im Jahr 2014 wieder spürbar ausgeweitet worden, insbesondere dank einer Belebung der Investitionstätigkeit und des privaten Konsums. Im Winterhalbjahr 2014/2015 dürfte der Produktionsanstieg besonders kräftig ausfallen. Auch wenn das Expansionstempo im weiteren Jahresverlauf wieder etwas abflacht, sprechen günstige Rahmenbedingungen wie gesunkene Energiekosten und sehr niedrige Finanzierungskosten für eine gute Konjunktur in den Jahren 2015 und 2016. Das Bruttoinlandsprodukt dürfte im Jahr 2015 um 2,0% zulegen (66%-Prognoseintervall: 1,6% bis 2,5%).

Drawing from confidential firm-level balance sheets for 17 European countries (13 Euro-Area), the paper documents the newly expanded database of cross-country comparable competitiveness-related indicators built by the Competitiveness Research Network (CompNet). The new database provides information on the distribution of labour productivity, TFP, ULC or size of firms in detailed 2-digit industries but also within broad macrosectors or considering the full economy. Most importantly, the expanded database includes detailed information on critical determinants of competitiveness such as the financial position of the firm, its exporting intensity, employment creation or price-cost margins. Both the distribution of all those variables, within each industry, but also their joint analysis with the productivity of the firm provides critical insights to both policy-makers and researchers regarding aggregate trends dynamics. The current database comprises 17 EU countries, with information for 56 industries, including both manufacturing and services, over the period 1995-2012. The paper aims at analysing the structure and characteristics of this novel database, pointing out a number of results that are relevant to study productivity developments and its drivers. For instance, by using covariances between productivity and employment the paper shows that the drop in employment which occurred during the recent crisis appears to have had “cleansing effects” on EU economies, as it seems to have accelerated resource reallocation towards the most productive firms, particularly in economies under stress. Lastly, this paper will be complemented by four forthcoming papers, each providing an in-depth description and methodological overview of each of the main groups of CompNet indicators (financial, trade-related, product and labour market).

Previous research indicates that firm innovativeness can either be determined by a firm’s position within the network dimension or by its position within the geographical dimension. Integrative studies addressing both distinct and combined proximity effects remains rare (cf. Whittington et al. 2009). Thus, we address in this Chapter the following research question: Are firm-level innovation outcomes positively or negatively related to network positioning effects, geographical co-location effects or combined proximity effects; and if the latter case is true, are the combined effects substitutional or complementary in nature? Panel data count models with fixed and random effects were used to analyze a firm’s innovative performance as measured by patent application counts. This last empirical analysis is organized as follows: We start with a short introduction in Sect. 12.1. Next, we provide a brief discussion of theoretical background in Sect. 12.2. In Sect. 12.3 we introduce our conceptual framework and derive our hypotheses. In Sect. 12.4 we introduce the data and methods used. Next, we outline the estimation strategy and report our empirical results in Sect. 12.5. Finally, we discuss our findings and conclude with a number of critical remarks in Sect. 12.6.

Technological complementarity is argued to be a crucial element for effective Research and Development (R&D) collaboration. The real structure is, however, still largely unknown. Based on the argument that organizations’ knowledge resources must fit for enabling collective learning and innovation, we use the co-occurrence of firms in collaborative R&D projects in Germany to assess inter-sectoral technological complementarity between 129 sectors. The results are mapped as complementarity space for the Germany economy. The space and its dynamics from 1990 to 2011 are analyzed by means of social network analysis.

The results illustrate sectors being complements both from a dyadic and portfolio/ network perspective. This latter is important, as complementarities may only become fully effective when integrated in a complete set of different knowledge resources from multiple sectors. The dynamic perspective moreover reveals the shifting demand for knowledge resources among sectors at different time periods.