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on Investment |
| By: | Nagler, Markus (Friedrich Alexander Universität Erlangen-Nürnberg, Germany); Winkler, Erwin (Friedrich Alexander Universität Erlangen-Nürnberg, Germany) |
| Abstract: | A large literature investigates the employment effects of minimum wages, with comparatively little evidence on other adjustment margins. In this paper, we analyze the impact of a nationwide introduction of minimum wages in Germany on employer-induced work pressure, using detailed worker-level survey data. Applying a difference-in-differences approach, we show that the introduction of minimum wages increased work pressure in occupations more exposed to the minimum wage. The increase in work pressure cannot be explained by compositional changes in terms of demographics, job complexity, or hours worked. |
| Keywords: | minimum wage, work pressure, non-wage amenities, working conditions, compensating differentials |
| JEL: | J28 J31 J32 J33 J81 H80 I31 I38 K31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18398 |
| By: | Boeing, Geoff (Northeastern University); Zhou, Yuquan |
| Abstract: | Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naïve travel times from open data then developing a random forest model to predict travel times as a function of those naïve times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements. |
| Date: | 2026–02–15 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:qepc6_v1 |
| By: | Katharina Agethen (Paderborn University & OWL University of Applied Sciences and Arts) |
| Abstract: | Managers often assume that collectively oriented employees perform well in organizations, yet prior meta-analyses have yielded inconsistent findings regarding the relationship between employee collective orientation and job performance. This study addresses these inconsistencies through an updated and comprehensive meta-analysis of 128 articles, comprising 144 samples and 390 effect sizes. Specifically, I examine how different conceptualizations and measures of collective orientation (global vs. work context-specific and unidimensional vs. multidimensional) as well as different performance types (general, in-role, and extra-role) moderate the relationship between collective orientation and job performance. Employing a three-level meta-analytic approach, the results reveal that collective orientation, overall, is positively related to job performance (r̅ = .17). Contrary to my expectations, global collective orientation (r̅ = .18) shows a stronger relationship with performance than work context-specific collective orientation (r̅ = .13), challenging prevailing assumptions about the need for context-specific measures. Multidimensional measures (r̅ = .21) further outperform unidimensional ones (r̅ = .14). Among the types of performance, collective orientation is most strongly associated with extra-role performance (r̅ = .19) compared to general (r̅ = .09) and in-role performance (r̅ = .08). These findings clarify long-standing inconsistencies in the literature and offer theoretical and practical implications. In particular, they inform human resource management practices related to employee selection and performance evaluation. |
| Keywords: | collective orientation, collectivism, team orientation, job performance, meta-analysis |
| JEL: | M54 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:172 |
| By: | Persson, Rebecka (Center for Education and Leadership Excellence) |
| Abstract: | This working paper evaluates the measurement properties of teacher collaboration items from a survey distributed by LegiLexi to Swedish primary school teachers (N = 2, 124). Nine items capturing the frequency and quality of professional interactions with teacher teams, school leaders, and broader school climate were examined using descriptive statistics, principal component analysis, confirmatory factor analysis, internal consistency estimates, and intraclass correlations. A two-factor confirmatory model distinguishing horizontal collaboration (teacher team) from vertical collaboration (principal/school leaders), with correlated uniquenesses for parallel item content, provided excellent fit to the data (χ²(5) = 7.37, p = .195, CFI = .999, RMSEA = .016). The two factors were moderately correlated (r = .36), confirming empirically distinct dimensions. Preliminary school-level correlations with student literacy outcomes from LegiLexi reading assessments were explored. The results establish a measurement foundation for future research on teacher collaboration and student reading outcomes in Swedish primary schools. |
| Keywords: | teacher collaboration; measurement validation; confirmatory factor analysis; primary school; LegiLexi; Swedish School Inspectorate |
| Date: | 2026–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:hhb:hastel:2025_003 |
| By: | Cecilia Carvalho; Nicolas Goulart; Daniel Monte; Emanuel Ornelas |
| Abstract: | We study the sustainability of international trading rules in a multipolar world. A rules-based equilibrium is shaped by three forces. A static temptation to exploit market power undermines cooperation, while two dynamic forces support it: the efficiency gains from rules and the cost of reestablishing the regime once a country becomes hegemonic. When multipolarity is short-lived and involves few co-leaders, a strong enough prospect of future hegemony ensures rules cooperation. However, in a more fragmented world, the sustainability of rules is more likely if shared leadership is expected to persist, to ensure long-lasting efficiency gains. |
| Keywords: | hegemonic stability theory, World Trade Organization, trade agreements, multipolarity |
| JEL: | F02 F13 F53 H87 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12395 |
| By: | Marcela Mello; Jo\~ao Garcia |
| Abstract: | A growing literature documents how religious institutions shape behavior through social influence, but less is known about what happens when religious movements gain political power and use the tools of government to advance their agenda. We use a regression discontinuity design on close mayoral elections in Brazil to show that mayors from parties institutionally tied to Pentecostal denominations increase teenage fertility 3 per 1, 000 higher (a 40% increase). This effect appears for cohorts exposed to middle school during the administration. Consistent with a school-based mechanism, we find that the likelihood that municipal schools offer sexual education programs falls by 12.5 percentage points, with no changes in state schools outside mayoral control. We also find elevated STD rates, and higher middle school dropout rates, while slightly older cohorts show no effects. Results are not explained by changes in contraceptive availability in public clinics, pointing to sexual education as the primary mechanism. We also find no effects from other right-wing parties, indicating the importance of institutional links to Pentecostal parties. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.19388 |
| By: | - |
| Abstract: | Caribbean small island developing States face a complex convergence of structural vulnerabilities that profoundly threaten their long-term development. These challenges include acute climate vulnerability (manifesting as hurricanes, sea-level rise, salination, biodiversity loss, sargassum influxes and drought), limited land and natural resources, high dependence on imported food, and challenging geographic and economic isolation due to small domestic markets. Compounding these issues are significant fiscal constraints, high debt levels and persistent brain drain, which collectively limit investment in long-term resilience and development strategies. While Caribbean SIDS increasingly recognize the transformative potential of STI to drive resilience, sustainability and economic diversification, this crucial input remains critically underfunded, underrepresented and inadequately integrated into national, regional and global policy settings and planning frameworks. The Caribbean allocates a mere 0.1–0.2% of its GDP to research and development, substantially below the 2% or more invested by leading science economies like the Republic of Korea or the United States of America. This underinvestment, heavily concentrated in a few larger economies within Latin America and the Caribbean, leaves smaller economies with minimal scientific capacity. Furthermore, existing national STI strategies are often outdated or disconnected from core sustainable development policies, such as those addressing climate change, biodiversity, energy production or sustainable tourism. |
| Date: | 2026–01–29 |
| URL: | https://d.repec.org/n?u=RePEc:ecr:col095:85930 |
| By: | Anderie, Lutz; Agel, Harald |
| Abstract: | Dieses Working Paper beleuchtet die Integration Künstlicher Intelligenz (KI) in Krankenhausumfeldern, mit dem Ziel, die Qualität der Patientenversorgung zu steigern, klinische und administrative Prozesse zu optimieren und Kosten im Gesundheitswesen zu senken. Es startet mit einer Einführung in zentrale KIDefinitionen - wie maschinelles Lernen, Deep Learning und natürliche Sprachverarbeitung - und erläutert deren spezifische Relevanz im Krankenhauskontext, wobei zwischen schwacher und starker KI unterschieden wird und der Fokus auf aufgabenbezogenen Anwendungen liegt. Das Paper identifiziert systematisch KI-Anwendungsfälle und priorisiert diese anhand von Kriterien wie klinischem Nutzen, wirtschaftlicher Effizienz, technischer Machbarkeit, ethischer Konformität und Skalierbarkeit. Eine detaillierte Fallstudie zum KI-Einsatz im Entlassmanagement (§ 39 Abs. 1a SGB V) demonstriert das transformative Potenzial, einschließlich prädiktiver Analytik für Wiedereinweisungsrisiken, automatisierter Planung durch Tools wie Recare PREDICT und VOICE sowie agentischer KI zur Prozessorchestrierung, die Verweildauern verkürzen und erhebliche Kosteneinsparungen ermöglichen kann (z. B. 75.000 €/Monat für ein 850-Betten-Krankenhaus mit ca. 35.000 stationären Patienten). Weitere effizienzsteigernde Anwendungsfälle wie KI in der bildgebenden Diagnostik, Ressourcenplanung, Patientenüberwachung und personalisierten Medizin werden skizziert. Eine strategische Roadmap für die KI-Implementierung wird vorgeschlagen, in Kooperation mit Partnern wie HA&P Beratungsgesellschaft mbH, umfassend Bedarfsanalysen, Pilotprojekte (z. B. Chatbot-Prototypen) und modulare Skalierung. Ethische und regulatorische Aspekte werden kritisch bewertet, mit Schwerpunkt auf dem EU AI Act (Verordnung (EU) 2024/1689) und seinem risikobasierten Ansatz sowie DSGVO-Anforderungen an Datenschutz und Bias-Minimierung durch erklärbare KI. Maßnahmen zur Skalierung umfassen Serious Games wie "Keine Angst vor KI" und "Entlassmanagement Training" (inspiriert von Charités GameEducation-Initiativen) zur Personal-Schulung und gewährleisten Konformität bis August 2026 für Hochrisikosysteme. Das Paper schließt mit einem umfassenden, handlungsorientierten Rahmen ab, der Krankenhäusern den Weg zu ethischer, effizienter und nachhaltiger KI-Adoption ebnet und Innovationen fördert, ohne die Patientensicherheit und das Vertrauen zu gefährden. |
| Abstract: | This working paper examines the integration of Artificial Intelligence (AI) into hospital environments, with the objective of enhancing patient care quality, streamlining clinical and administrative processes, and reducing healthcare costs. It begins with an introduction to key AI definitions-such as machine learning, deep learning, and natural language processing-and delineates their specific relevance in hospital contexts, distinguishing between weak and strong AI while emphasizing the predominance of taskspecific applications. The paper systematically identifies and prioritizes AI use cases based on criteria including clinical benefits, economic efficiency, technical feasibility, ethical compliance, and scalability. A detailed case study on AI in discharge management (§ 39 Abs. 1a SGB V) illustrates transformative potential, featuring predictive analytics for readmission risks, automated planning via tools like Recare PREDICT and VOICE, and agentic AI for process orchestration, which can shorten hospital stays and yield significant cost savings (e.g., €75, 000/month for an 850-bed facility). Additional efficiency-optimizing use cases, such as AI in imaging diagnostics, resource planning, patient monitoring, and personalized medicine, are outlined. A strategic roadmap for AI implementation is proposed, in collaboration with partners like HA&P, encompassing needs analysis, pilot projects (e.g., Chatbot prototypes), and modular scaling. Ethical and regulatory aspects are critically evaluated, highlighting the EU AI Act (Regulation (EU) 2024/1689) with its risk-based approach, alongside DSGVO requirements for data protection and bias mitigation through explainable AI. Measures for scaling include serious games like "Keine Angst vor KI" and "Entlassmanagement Training" (inspired by Charité's Game Education initiatives) for staff training, ensuring regulatory conformity by August 2026 for high-risk systems. The paper concludes by providing hospitals with a comprehensive, actionable framework for ethical, efficient, and sustainable AI adoption, fostering innovation while safeguarding patient safety and trust. |
| Keywords: | Krankenhausmanagement, Künstliche Intelligenz, Patienten, Fallmanagement, Schnittstellenmanagement |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:fhfwps:337483 |
| By: | Chepeliev, Maksym |
| Abstract: | Rapidly increasing material extraction is putting major pressure on ecosystems. Future increases in incomes and population could result in over 2.5 times growth in global material demand by 2050, putting even more pressure on the environment. Thus, an absolute decoupling of material use from GDP and income is of major importance to preserve safe operating boundaries. It is vital to understand how current policy efforts, including climate mitigation, could impact material use patterns and what complementary circular economy (CE) policies could be implemented to support dematerialization. At the same time, there is a lack of global datasets and related modeling tools that could support such an analysis. To address this limitation, here we develop a special version of the Global Trade Analysis Project (GTAP) Circular Economy (GTAP-CE) Data Base with detailed representation of primary, secondary, and recycling activities for metals (steel, aluminum, copper, etc.) and plastics, detailed representation of fertilizers, as well as disaggregated cement activity. The GTAP-CE Data Base is based on the v11c of the GTAP-Power Data Base with the 2017 reference year, representing the global economy across 99 activities, 141 individual countries and 19 composite regions. Introduced sectoral splits are designed to facilitate both the assessment of the circular economy policies, as well as Carbon Border Adjustment Mechanism (CBAM) measures. The developed GTAP-CE Data Base is distributed in model-friendly formats and can be readily linked to the GTAP-based CGE models for the assessment of various policy scenarios either in the dynamic or static frameworks. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:gta:resmem:7674 |
| By: | Fichter, Klaus; Neumann, Thomas; Olteanu, Yasmin; Grothey, Tim; Block, Jörn |
| Abstract: | Der Green Startup Report 2026, herausgegeben vom Borderstep Institut für Innovation und Nachhaltigkeit, analysiert die aktuelle Entwicklung der grünen Gründungslandschaft in Deutschland und setzt die seit 2013 kontinuierlich durchgeführte wissenschaftliche Beobachtung der Szene fort. Die Langzeitperspektive ermöglicht eine belastbare Einordnung struktureller Trends, technologischer Entwicklungen und Veränderungen der Gründungsdynamik. Die grüne Start-up-Community ist weiter gewachsen und umfasst inzwischen 4.668 Unternehmen (Gründungszeitraum 2016 - 2025). Grüne Start-ups leisten einen zentralen Beitrag zur technologischen Innovationsfähigkeit und zur Klimaschutzleistung des Wirtschaftsstandorts Deutschland. Sie zeichnen sich durch eine hohe Patentquote, überdurchschnittliche Forschungsintensität und ein erhebliches CO2-Minderungspotenzial aus. Im Durchschnitt reduzieren ihre Lösungen Treibhausgasemissionen um mehr als 70 Prozent gegenüber marktüblichen Technologien. Gleichzeitig zeigt der Report erstmals seit Jahren eine rückläufige Gründungsdynamik. Trotz stabiler und wachsender GreenTech-Märkte bremsen Aufmerksamkeitsverschiebungen in Politik und Öffentlichkeit sowie regulatorische Unsicherheiten insbesondere in kapitalintensiven Sektoren das weitere Wachstum. Der Green Startup Report 2026 liefert damit eine zentrale Datengrundlage für die strategische Weiterentwicklung der deutschen Start-up-, Innovations- und Klimapolitik. |
| Keywords: | grüne Start-ups, Gründungen, Kapital, Einhorn, Klimaschutzpotenzial, Finanzierung, Start-ups mit Investment, Patent, Nachhaltigkeit und Wettbewerbsfähigkeit, Innovationskraft, Scale-up-Strategie, Klimaschutzmaßnahmen, Förderung, Green-Startup-Ökosystem, Marktzugang, Künstliche Intelligenz KI, Geschäftsmodell, nachhaltiges Geschäftsmodell, Volatilität, Branchen, Green-Tech-Geschäftsmodell, Wirkungspotenzial, Frauenanteil, Business Angels, Venture Capital, Corporate Investment, Crowd-Investment, Kooperation, Purpose, Finanzierungsrunde, Technologie, Umweltinnovation, Energiewende, nachhaltige Chemie, Förderinstrumente, Klimaschutz, doppelte Dividende, Naturschutz, CO2-Emissionen, Impact, Materialkreislauf, Produktlebensweg, Lieferkette, Hackathon, Biodiversität, Skalierung, Nachhaltigkeit, GreenTech, Grand Challenges, Transformation, EU, Monitoring, Gründungsförderprogramme, Climate Forward Financing |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esrepo:337499 |
| By: | Cheaheon Lim |
| Abstract: | This paper develops a theory of learning under ambiguity induced by the decision maker's beliefs about the collection of data correlated with the true state of the world. Within our framework, two classical results on Bayesian learning extend to the setting with ambiguity: experiments are equivalent to distributions over posterior beliefs, and Blackwell's more informative and more valuable orders coincide. When applied to the setting of robust Bayesian analysis, our results clarify the source of time inconsistency in the Gamma-minimax problem and provide an argument in favor of the conditional Gamma-minimax criterion. We also apply our results to a persuasion game to illustrate that our model provides a natural benchmark for communication under ambiguity. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07634 |
| By: | Peyton, Alicea |
| Abstract: | Hymenal-related distress—experienced as fear, pain, or avoidance of penetration—arises from both physical factors, such as anatomical variations, and psychological influences, including cultural beliefs, limited anatomical knowledge, and experiences of abuse. Hymenectomy, a straightforward outpatient procedure that removes or modifies hymenal tissue, is a well-supported treatment for structural hymenal problems, with literature primarily supporting its use in the relatively rare cases where the diagnostic presentation is clearly physical. However, little empirical evidence exists regarding how the procedure functions when the primary source of fear or phobia is psychological and the anatomy is normal. Recent conceptual work distinguishes fear-based penetration avoidance without pain—termed vaginal penetration phobia (VPP)—as a separate clinical phenomenon (Rabinowitz et al., 2017), further underscoring the need to examine psychological contributors to penetration distress. Using a biopsychosocial‑spiritual framework, this review underscores that no empirical studies have examined individuals’ awareness of hymenectomy, their anticipated or lived experiences of the procedure, or how it is positioned in relation to psychological distress and perceived normalcy with vaginal penetration. |
| Date: | 2026–01–26 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:8df7w_v1 |
| By: | Ari Hyytinen (Hanken School of Economics and Helsinki GSE); Jan Jääskeläinen (Finnish Consumer and Competition Authority); Antti Sieppi (Finnish Consumer and Competition Authority); Vesa-Heikki Soini (Hanken School of Economics and University of Turku); Janne Tukiainen (Department of Economics, University of Turku) |
| Abstract: | Limited competition in public procurement remains a persistent concern, yet the reasons for low participation are not well understood. We conduct a conjoint survey experiment that targets both potential and actual bidders in Finland. We present real firms with hypothetical tender scenarios, randomly varying key attributes values, asking which tender they would enter. The time required to prepare a bid is the most significant entry barrier. Moreover, tenders evaluated solely on the lowest price, those involving cross-border participation, and higher expected competition reduce entry. Uncertainty over the number of competitors deters entry as much as for certain facing high competition. |
| Keywords: | Entry, Conjoint Experiment, Public Procurement |
| JEL: | C83 D44 H57 L22 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:tkk:dpaper:dp176 |
| By: | Norman Jones III |
| Abstract: | Small Business Credit Survey respondents signaled that higher costs, staffing issues, and increased difficulty growing their sales are their most prevalent issues. |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00100:102778 |
| By: | Becker, Sascha O (University of Warwick and Monash University) |
| Abstract: | This chapter explores the intersection of religion and economics on the 250th anniversary of Adam Smith's The Wealth of Nations, first published in 1776. While Smith is often viewed as a secular figure in economics, his work was deeply influenced by the moral philosophy of his time, which was shaped by Christian thought. I discuss how economists think about the religious themes in Smith's work in the 21st century and review what we know today about the connection between religion and economic outcomes. |
| Keywords: | Adam Smith; religion JEL Classification: B1, B2, N3, N9, P5, Z12 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cge:wacage:789 |
| By: | Neda Bagheri Renani; Daniel Sevcovic |
| Abstract: | We propose a fourth--order compact finite--difference (HOC--FD) scheme for the transformed Bates partial integro--differential equation (PIDE). The method employs an implicit--explicit (IMEX) Crank--Nicolson framework for local terms and Simpson quadrature for the jump integral. Benchmarks against second--order finite differences (FD) and quadratic finite elements (FEM, p=2) confirm near--fourth--order spatial accuracy for HOC--FD, near--second--order for FEM, and second--order temporal convergence for all time integrators. Efficiency tests show that HOC--FD achieves similar accuracy at up to two orders of magnitude lower runtime than FEM, establishing it as a practical baseline for option pricing under stochastic volatility jump--diffusion models. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.19092 |
| By: | Hanna Wang |
| Abstract: | I develop and estimate a life-cycle discrete-choice model of fertility and female labor supply to study the optimal design of a range of child-related policies. First, I examine two German reforms that introduced wage-contingent parental leave payments and expanded access to low-cost public childcare. I find that both reforms raised completed fertility, with the parental leave reform having a particularly strong impact on highly educated women. Second, I solve for a budget-neutral optimal policy portfolio that maximizes either aggregate welfare or fertility, while ensuring that welfare and fertility do not decline for any education group. I consider four prominent child subsidies as well as the degree of tax jointness. My results show that optimal policy has the potential to increase welfare by 0.5% or fertility by 5.7%. While the solutions are qualitatively similar, they prioritize different policy instruments depending on the specific objective being targeted. |
| Keywords: | fertility, parental leave, childcare subsidies, optimal policy |
| JEL: | H21 J13 J24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12416 |