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on Project, Program and Portfolio Management |
| By: | L. Serafini; E. Marrocu; R. Paci |
| Abstract: | This paper focuses on the impact of the Smart Specialisation Strategy (S3) and Industry 4.0 (I4) initiatives during the 2014-2020 programming period on firms' performance in Italy. By analysing European Regional Development Fund (ERDF)-funded projects under these frameworks, we use OpenCoesione data and a Difference-in-Differences approach to assess the effectiveness of S3 and I4 initiatives. Our results reveal that projects integrating I4 technologies within the S3 framework (S3I4 projects) significantly enhance firms' performance. This is particularly evident when compared to projects funded under other ERDF initiatives. The study highlights the importance of aligning S3 and I4 strategies with regional economic profiles and innovation capacities to maximise their impact. Our analysis underscores the role of these initiatives in driving innovation and economic growth. The results offer key insights for policymakers, suggesting that focused and strategic investment in S3 and I4 can lead to more effective regional innovation and development. |
| Keywords: | Industry 4.0;Innovation and firm Performance;Cohesion Policy;Counterfactual Impact Analysis;Smart Specialisation Strategy |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:202403&r=ppm |
| By: | Barbara Biasi; Julien Lafortune; David Schönholzer |
| Abstract: | This paper identifies which investments in school facilities help students and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices for 29 U.S. states and a research design that exploits close elections with staggered timing, we show that increased school capital spending raises test scores and house prices on average. However, impacts differ vastly across types of funded projects. Spending on basic infrastructure (such as HVAC) or on the removal of pollutants raises test scores but not house prices; conversely, spending on athletic facilities raises house prices but not test scores. Socio-economically disadvantaged districts benefit more from capital outlays, even conditioning on project type and the existing capital stock. Our estimates suggest that closing the spending gap between high- and low-SES districts and targeting spending towards high-impact projects may close as much as 25% of the observed achievement gap between these districts. |
| Keywords: | school expenditures, school capital, test scores, real estate |
| JEL: | H41 H75 I22 I24 R30 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10884&r=ppm |
| By: | Biasi, Barbara (Yale School of Management); Lafortune, Julien (Public Policy Institute of California); Schönholzer, David (Stockholm University) |
| Abstract: | This paper identifies which investments in school facilities help students and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices for 29 U.S. states and a research design that exploits close elections with staggered timing, we show that increased school capital spending raises test scores and house prices on average. However, impacts differ vastly across types of funded projects. Spending on basic infrastructure (such as HVAC) or on the removal of pollutants raises test scores but not house prices; conversely, spending on athletic facilities raises house prices but not test scores. Socio-economically disadvantaged districts benefit more from capital outlays, even conditioning on project type and the existing capital stock. Our estimates suggest that closing the spending gap between high- and low-SES districts and targeting spending towards high-impact projects may close as much as 25% of the observed achievement gap between these districts. |
| Keywords: | test scores, school capital, school expenditures, real estate |
| JEL: | H41 H75 I22 I24 R30 R53 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16713&r=ppm |
| By: | Heyard, Rachel; Held, Leonhard |
| Abstract: | Recent large-scale replication projects (RPs) have estimated alarmingly low replicability rates have been estimated. Within these RPs, the original-replication study pairs can vary substantially with respect to aspects of study design, outcome measures, and descriptive features of both the original and replication study population and study team. When broader claims about the replicability of an entire field are based on such heterogeneous data, it becomes imperative to conduct a rigorous analysis of the heterogeneity among study pairs included in the RP. Methodology from the meta-analysis literature provides an approach for quantifying the heterogeneity present in RPs, as additive or multiplicative parameter. Meta-analysis methodology further allows for an investigation of the sources of the heterogeneity through meta-regressions. Notably, we propose the use of location-scale meta-regressions as a means to directly relate the identified characteristics with shrinkage (represented by the location) and the heterogeneity variance (represented by the scale). The proposed methodology is illustrated using data from the Reproducibility Project Psychology and the Reproducibility Project Experimental Economics. All analysis scripts and data are available online. |
| Date: | 2024–01–29 |
| URL: | http://d.repec.org/n?u=RePEc:osf:metaar:e9nw2&r=ppm |
| By: | Haapamäki, Taina; Riukula, Krista; Väänänen, Touko |
| Abstract: | Abstract In economics, the regional densification of economic activity is referred to as agglomeration. The effects of agglomeration are often referred to when discussing the wider economic benefits of transportation infrastructure projects. The magnitude of these effects has not been extensively studied in Finland. In this brief, we present results from a study that examines the effect of agglomeration on productivity in the Helsinki region. Agglomeration, defined as job-to-job accessibility, was found to have a positive effect on employees’ wages. However, the results at the establishment-level are less precise and statistically insignificant. According to the results, increased accessibility increases other operating expenses such as rents, potentially explaining the lack of statistically significant effects on establishment-level productivity. The results indicate that agglomeration benefits are predominantly intraregional, with interregional accessibility having little impact on these benefits. Consequently, the ratio between agglomeration benefits and direct benefits of transportation infrastructure projects varies depending on the project. Taxes and similar payments on increased wages due to accessibility increases could be included as a separate item in cost-benefit analysis. |
| Keywords: | Agglomeration, Productivity, Transport project, Cost-benefit analysis, Accessibility, Wider economic impacts |
| JEL: | R12 R41 R42 |
| Date: | 2024–01–23 |
| URL: | http://d.repec.org/n?u=RePEc:rif:briefs:132&r=ppm |
| By: | Brunelle Marche (ERPI - Equipe de Recherche sur les Processus Innovatifs - UL - Université de Lorraine); Brice Corrigeux (ERPI - Equipe de Recherche sur les Processus Innovatifs - UL - Université de Lorraine); Mauricio Camargo (ERPI - Equipe de Recherche sur les Processus Innovatifs - UL - Université de Lorraine); Christophe Bachmann |
| Date: | 2023–06–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04381955&r=ppm |
| By: | Assen Slim (CREE EA 4513 - Centre de recherches Europes-Eurasie - Inalco - Institut National des Langues et Civilisations Orientales, CESSMA UMRD 245 - Centre d'études en sciences sociales sur les mondes africains, américains et asiatiques - IRD - Institut de Recherche pour le Développement - Inalco - Institut National des Langues et Civilisations Orientales - UPCité - Université Paris Cité) |
| Abstract: | In einem internationalen Kontext, in dem die Blockchain-Technologie zunehmend an Attraktivität gewinnt, wurden zahlreiche Projekte für digitale Zentralbankwährungen (MDBC) ins Leben gerufen. Das von der ukrainischen Zentralbank (NBU) initiierte e-hryvnia-CBDM-Projekt ist eines der am weitesten fortgeschrittenen in Europa. Nach einer Definition des Begriffs MDBC gibt dieser Artikel einen Überblick über die Erwartungen der NBU, die Ergebnisse des 2018 gestarteten Pilotprojekts MDBC e-hryvnia und die noch zu beseitigenden Hindernisse für die endgültige Einführung dieser Zentralbankwährung der neuen Generation. |
| Abstract: | In an international context marked by a growing attraction for blockchain technology, many central bank digital currency (CBD) projects have emerged. The e-Hryvnia CBDC project initiated by the National Bank of Ukraine (NBU) is one of the most advanced in Europe. After defining the concept of CBDC, this article reviews the NBU's expectations, the findings of the CBDC e-hryvnia pilot project launched in 2018, and the hurdles to be cleared to launch this new generation of central bank currency. |
| Abstract: | La MDBC e-hryvnia : une monnaie banque centrale en projet Dans un contexte international marqué par un attrait croissant pour la technologie blockchain, de nombreux projets de monnaies digitales de banques centrales (MDBC) ont vu le jour. Le projet de MDBC e-hryvnia engagé par la banque centrale d'Ukraine (NBU) est l'un des plus avancés d'Europe. Après avoir défini la notion de MDBC, cet article fait le point sur les attentes de la NBU, les conclusions du projet pilote de MDBC e-hryvnia lancé en 2018 et les obstacles qui restent à lever pour lancer définitivement cette monnaie banque centrale de nouvelle génération. |
| Keywords: | Central bank digital currency, CDBC, e-hryvnya, Ukraine, National Bank of Ukraine, Monnaie digitale de banque centrale, MDBC, e-hryvnia, Banque nationale d'Ukraine |
| Date: | 2022–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03937410&r=ppm |
| By: | OECD |
| Abstract: | This scoping review examines the effectiveness of online and blended learning in fostering higher-order thinking skills in higher education, focussing on creativity and critical thinking. The paper finds that whilst there is a growing body of research in this area, its scope and generalisability remain limited. Current evidence suggests that, for most students and contexts, in-person learning yields better or equivalent outcomes for higher-order thinking skills than fully online learning. However, blended and flipped learning show promise. In some cases, they may be more effective than in-person learning to develop higher-order skills. The review aims to be of use to higher education practitioners by synthesising, for the first time at such a scale, the diverse literature on what supports students to develop these skills online. This has been linked to active and interactive online learning, well-structured project-based learning, disciplined questioning, students labelling relevant dimensions of their thinking, and regular, quality instructor and peer feedback. The review calls for improved research design to understand the effectiveness of different modes of learning and address gaps in the literature, which include fostering creativity online and ensuring equitable online skills development across disciplines and teaching contexts. Policy implications include the need to integrate attention to higher-order thinking skills into professional learning, innovation funds, national networks and quality assurance to support effective online teaching of these skills across higher education systems. |
| Date: | 2024–02–05 |
| URL: | http://d.repec.org/n?u=RePEc:oec:eduaab:306-en&r=ppm |
| By: | Lezhi Li; Ting-Yu Chang; Hai Wang |
| Abstract: | This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06164&r=ppm |