nep-ppm New Economics Papers
on Project, Program and Portfolio Management
Issue of 2020‒07‒20
six papers chosen by
Arvi Kuura
Tartu Ülikool

  1. Dataset of projects co-funded by the ERDF during the multi-annual financial framework 2014-2020 By Julia Bachtroegler; Mathieu Doussineau; Peter Reschenhofer
  2. Roadmap and action plan for the first cross-border solar project By Natalia Caldes-Gomez; Ana R. Diaz Vazquez
  3. Higher Education for Smart Specialisation in Lubelskie, Poland By Marcin Kardas; Krzysztof Mieszkowski; John Huw Edwards
  4. Higher Education for Smart Specialisation: The Case of Lithuania By Zilvinas Martinaitis; Eskarne Arregui-Pabollet; Lina Stanionyte
  5. Connective Financing - Chinese Infrastructure Projects and the Diffusion of Economic Activity in Developing Countries By Richard Bluhm; Axel Dreher; Andreas Fuchs; Bradley C. Parks; Austin M. Strange; Michael J. Tierney
  6. Teamwise Mean Field Competitions By Xiang Yu; Yuchong Zhang; Zhou Zhou

  1. By: Julia Bachtroegler (Österreichisches Institut für Wirtschaftsforschung (WIFO),Vienna, Austria); Mathieu Doussineau (European Commission - JRC); Peter Reschenhofer (Österreichisches Institut für Wirtschaftsforschung (WIFO),Vienna, Austria)
    Abstract: Over the 2014-2020 financial period, 451 €bn of ESIF are invested in Cohesion policies, of which more than 40 €bn are dedicated directly to the thematic objective related to research and innovation. Among ESIF funds, ERDF is the main source of funding of innovation through the implementation of Smart specialisation strategies with a management shared between the Commission and the Regional or national authorities. The monitoring and the evaluation of ESIF are largely implemented at regional or national level. At European Commission level, the monitoring of Cohesion policy is carried out mainly at operational programme (OP) level, meaning that only very limited information is available at project and beneficiaries' level, also with limited accuracy in terms of geographical information (an OP can be national or in some cases multiregional). Reporting at project and beneficiary level is carried out through national or regional databases gathering information on beneficiaries (type of organisation, localisation etc.) and also details on funded projects (e.g. titles and abstracts and possibly other additional info depending the territory). Conversely to Horizon 2020 and due to the shared management, an integrated database of ESIF projects does not exist. While information on ESIF beneficiaries and projects is recorded, this is held by individual regional or national managing authorities. Bringing together this information on projects and beneficiaries in a single structured database would greatly benefit policy monitoring and evaluation, and the identification and creation of synergies with Horizon 2020 funding. This database would also feed ex-ante impact assessment analysis for the future multi-annual financial period (2021-2027), for the future cohesion policies and Horizon-Europe programme. To fill this gap, an initiative was launched under the Stairway to Excellence project, to design a structured and comprehensive database of projects funded by ERDF for the programming period 2014-2020 (as of June 2019), building on the systematic collection of all information available at national and regional levels. The whole content of the database is translated into English (including project titles and abstracts) using the European Commission Machine translation tool. The database offers also additional information such as keywords associated with each project in order to be able to bridge easily information contained in the ERDF projects database with, among others, Eye@RIS3 and the Horizon 2020 database. This report explains the origins of the information, the processing of collected information and the proxies used to obtain the most complete and comprehensive picture possible of ERDF funding support since 2014.
    Keywords: ERDF, ESIF, Cohesion policies, database
    JEL: O30 O38 O32
    Date: 2020–05
    URL: http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc120637&r=all
  2. By: Natalia Caldes-Gomez; Ana R. Diaz Vazquez (European Commission - JRC)
    Abstract: This report proposes the use of a legitimization function of the Technological Innovation System (TIS), as an analytical framework to develop a roadmap and action plan for deploying cross-border renewable projects in Europe. This approach assesses the role, competences and critical issues of a subset of the key stakeholders. Based on this information, a set of actions are proposed as to achieve the social acceptance towards cross-border renewable projects in Europe. To conclude, a solar project in Extremadura is studied in order to validate this approach.
    Keywords: Concentrated Solar Power, cross-border renewable European project
    Date: 2019–03
    URL: http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc115467&r=all
  3. By: Marcin Kardas; Krzysztof Mieszkowski; John Huw Edwards (European Commission - JRC)
    Abstract: This technical report contains the findings of action research that was carried out in the Polish region of Lubelskie on the role of Higher Education Institutions (HEIs) in the design and implementation of its Smart Specialisation Strategy (S3). It is one of ten case studies undertaken in the project on Higher Education for Smart Specialisation (HESS), an initiative of the Joint Research Centre and DG Education, Youth, Sport and Culture. The research shows that HEIs in Lubelskie have great potential to contribute to innovation and regional development, with strong and complementary universities as well as research institutions and higher vocational institutions. The overall issue of S3 governance is tackled and shows a good partnership between the Marshal’s Office and the regional HEIs, built on a history of cooperation in developing innovation strategies, which is longer than that of other Polish regions. However, the role of HEIs in implementing S3 is hampered by a lack of targeted instruments in the EU co-financed regional operational programme. In particular, the current situation of subcontracting HEIs by companies for research and innovation projects has not led to their successful mobilisation. As for education, there are several interesting instruments in the relevant national education programme but they are not linked to S3 implementation. In addition to the governance of S3, the action research focused on the role of HEIs in two priority domains, namely the bioeconomy and photonics. It reveals two contrasting approaches to the Entrepreneurial Discovery Process, a key element of the Smart Specialisation concept: One relies on technology push by building on the research strengths of the regional HEIs, with the other being led more by the market. Both priorities have a lot to offer the region but appropriate lessons are needed on how to increase their impact and critical mass. Based on the results of this action research specific recommendations are put forward to support the process of revising the Lubelskie S3.
    Keywords: Higher Education Institutions, Lubelskie, Poland, S3
    Date: 2020–05
    URL: http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc120453&r=all
  4. By: Zilvinas Martinaitis (Visionary Analytics); Eskarne Arregui-Pabollet (European Commission - JRC); Lina Stanionyte (European Commission - JRC)
    Abstract: This technical report presents the findings of the case study carried out in Lithuania on the role of Lithuanian Higher Education Institutions (HEIs) in the design and implementation of the Smart Specialisation Strategy (S3). It is one of the case studies undertaken in the project Higher Education for Smart Specialisation (HESS), an initiative of the European Commission's Joint Research Centre (JRC) and the Directorate General for Education, Youth, Sport and Culture. The research shows that the Smart Specialisation Strategy in Lithuania has constituted an important framework to coordinate research and innovation policies and investments with a significant improvement from past experiences, creating a space for a participatory process of innovation stakeholders. The higher education institutions are actively participating in the S3 process, with a good correlation of the S3 selected priority areas and the higher education research capacities, but with no significant changes in the internal decision-making. The higher education system presents an unbalanced funding model, with most incomes devoted to education activities rather than research and innovation. The research and innovation system of Lithuania is highly dependent on European Structural and Investment Funds, as national funding is comparatively very small, creating specific challenges in the implementation of the Smart Specialisation concept. Too narrowly defined priority areas can create a lock down effect in terms of broad support to innovation with limited funding sources to counterbalance. There is a growing demand of the productive sector of skilled students in engineering/STEM fields. This has increased demands of discussion spaces between public authorities, business and higher education to re-balance the attraction of students from social sciences to STEM studies, as well as a stronger policy to attract international talent. A long-term agreement between the Government and HEIs regarding the future HE educational offer, research priorities and resources could strengthen the contribution of higher education to S3, building on the experience of this case study and bringing forward its recommendations.
    Keywords: Smart specialisation strategies, higher education institutions, universities, territorial development, human capital, skills, innovation and growth, entrepreneurship
    Date: 2020–06
    URL: http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc120527&r=all
  5. By: Richard Bluhm; Axel Dreher; Andreas Fuchs; Bradley C. Parks; Austin M. Strange; Michael J. Tierney
    Abstract: This paper studies the causal effect of transport infrastructure on the spatial concentration of economic activity. Leveraging a new global dataset of geo-located Chinese government-financed projects over the period from 2000 to 2014 together with measures of spatial inequality based on remotely-sensed data, we analyse the effects of transport projects on the spatial distribution of economic activity within and between regions in a large number of developing countries. We find that Chinese-financed transportation projects reduce spatial concentration within but not between regions. In line with land use theory, we document a range of results which are consistent with a relocation of activity from city centers to their immediate periphery. Transport projects decentralize activity particularly strongly in regions that are more urbanized, located closer to the coast, and less developed. .
    Keywords: transport costs, infrastructure, development finance, foreign aid, spatial concentration China
    JEL: F15 F35 R11 R12 P33 O18 O19
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_8344&r=all
  6. By: Xiang Yu; Yuchong Zhang; Zhou Zhou
    Abstract: This paper studies competitions with rank-based reward among a large number of teams. Within each sizable team, we consider a mean-field contribution game in which each team member contributes to the jump intensity of a common Poisson project process; across all teams, a mean field competition game is formulated on the rank of the completion time, namely the jump time of Poisson project process, and the reward to each team is paid based on its ranking. On the layer of teamwise competition game, three optimization problems are introduced when the team size is determined by: (i) the team manager; (ii) the central planner; (iii) the team members' voting as partnership. We propose a relative performance criteria for each team member to share the team's reward and formulate some mean field games of mean field games, which are new to the literature. In all problems with homogeneous parameters, the equilibrium control of each worker and the equilibrium or optimal team size can be computed in an explicit manner, allowing us to analytically examine the impacts of some model parameters and discuss their economic implications. Two numerical examples are also presented to illustrate the parameter dependence and comparison between different team size decision making.
    Date: 2020–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2006.14472&r=all

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