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CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "Biases and Nudges in the Circular Economy: A Review" 
CEIS Working Paper No. 583 LUCA CONGIU, Tor Vergata University of Rome
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ENRICO BOTTA, Bocconi University - IEFE Centre for Research on Energy and Environmental Economics and Policy
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MARIANGELA ZOLI, University of Rome Tor Vergata
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The circular economy transition requires consumers to make further efforts in their waste disposal behaviors, by increasing waste sorting, repairing and reusing products, and reducing the amount of waste produced. The literature has identified several barriers to consumers' adoption of these practices. In this paper, we posit that such barriers can be ultimately linked to well-known decisional biases and proceed to offer a review. In doing so, we categorize biases into "cognitive", referring to deviations from normatively correct behavior, and "motivational", encompassing behavior driven by desirability concerns. We also survey the existing behavioral policies addressing the identified biases, focusing on "nudges", that is, interventions leveraging biases to improve welfare. In our review, we call attention to the importance of recognizing the relevant bias behind specific behaviors to identify the best interventions to implement. By offering a conceptual link between biases and circular economy practices, we also lay the groundwork for future experimental investigation. "With a Little Help From the Crowd: Estimating Election Fraud with Forensic Methods"
CEIS Research Paper No 584 CHRISTOPH KOENIG, University of Rome Tor Vergata
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Election forensics are a widespread tool for diagnosing electoral manipulation out of statistical anomalies in publicly available election micro-data. Yet, in spite of their popularity, they are only rarely used to measure and compare variation in election fraud at the sub-national level. The typical challenges faced by researchers are the wide range of forensic indicators to choose from, the potential variation in manipulation methods across time and space and the difficulty in creating a measure of fraud intensity that is comparable across geographic units and elections. This paper outlines a procedure to overcome these issues by making use of directly observed instances of fraud and machine learning methods. I demonstrate the performance of this procedure for the case of post-2000 Russia and discuss advantages and pitfalls. The resulting estimates of fraud intensity are closely in line with quantitative and qualitative secondary data at the cross-sectional and time-series level. "The Long-Run Effects of R&D Subsidies on High-Tech Start-Ups: Insights From Italy"
CEIS Research Paper No 585 CHRISTOPH KOENIG, University of Rome Tor Vergata
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LETIZIA BORGOMEO, Intesa Sanpaolo SpA
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MARTINA MIOTTO, Università di Padova
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We study the impact of a government subsidy program in Italy targeted at R&D-intensive projects presented by high-tech startups in 2009. Using the score assigned by the scientific commission to each project, we employ a Regression Discontinuity Design to study how the subsidy affected successful firms' innovation activity and performance over more than 10 years. We show that the subsidy led to substantial increases in intangible assets and had a lasting positive effect on various dimensions of firm performance. Innovation as measured by patents did not respond to the subsidy. | | ^top
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