Do Campaign Finance Policies Really Improve Voters' Welfare?

In an electoral race, interest groups will be willing to finance political candidates’ campaigns in return for favors that are costly to voters. Starting from the empirical observation of split contributions, we develop a theoretical model of directly informative campaign advertising with rational voters. In this setting, interest groups that demand more favors are less likely to finance candidates to enhance their electoral prospects. We find that the only feasible Pareto improving policy involves providing specific limits and subsidies to each candidate. Unfortunately, this policy is very demanding in terms of information for the policy maker and always involves candidates providing favors to interest groups. We argue that bans on contributions without public subsidies may not be welfare improving, since they negatively affect the informational value of advertisements.Campaign Finance, Interest Groups, Elections, Welfare

ELECTORAL CONTRIBUTIONS AND THE COST OF UNPOPULARITY

When considering electoral campaigns, candidates receiving contributions from relatively unpopular industries should be regarded less favorably by voters that have information on the sources of funding. To offset this unpopularity effect, politicians may either demand more money for campaign advertising from these industries in order to persuade less informed voters, or shy away from unpopular contributors to avoid losing the support of the informed electorate. Our model predicts that the first effect dominates, and electoral contributions are increasing in industry unpopularity. By using U.S. House elections data and different identification strategies, we provide robust evidence in favor of our predictions

Experts, Conflicts of Interest, and the Controversial Role of Reputation

This paper studies the impact of reputation on the reporting strategy of experts that face conflicts of interest. The framework we propose applies to different settings involv- ing decision makers that rely on experts for making informed decisions, such as financial analysts and goverment agencies. We show that reputation has a non-monotonic effect on the degree of information revelation. In general, truthful revelation is more likely to occur when there is more uncertainty on an expert's ability. Furthermore, above a certain threshold, an increase in reputation always makes truthful revelation more difficult to achieve. Our results shed light on the relationship between the institutional features of the reporting environment and informational efficiency.

Influential Listeners: An Experiment on Persuasion Bias in Social Networks

This paper presents an experimental investigation of persuasion bias, a form of bounded rationality whereby agents communicating through a social network are unable to account for possible repetitions in the information they receive. The results indicate that network structure plays a significant role in determining social influence. However, the most influential agents are not those with more outgoing links, as predicted by the persuasion bias hypothesis, but those with more incoming links. We show that a boundedly rational updating rule that takes into account not only agents' outdegree, but also their indegree, provides a better explanation of the experimental data. In this framework, consensus beliefs tend to be swayed towards the opinions of influential listeners. We then present an effort-weighted updating model as a more general characterization of information aggregation in social networks.

Trust in the Helth System and Covid-19 Treatment

COVID-19 continues to spread across the globe at an exponential speed, infecting millions and overwhelming even the most prepared healthcare systems. Concerns are looming that the healthcare systems in low- and middle-income countries (LMICs) are mostly unprepared to combat the virus because of limited resources. The problems in LMICs are exacerbated by the fact that citizens in these countries generally exhibit low trust in the healthcare system because of its low quality, which could trigger a number of uncooperative behaviors. In this paper, we focus on one such behavior and investigate the relationship between trust in the healthcare system and the probability of potential treatment-seeking behavior upon the appearance of the first symptoms of COVID-19. First, we provide motivating evidence from a unique national online survey administered in Armenia–a post-Soviet LMIC country. We then present results from a large-scale survey experiment in Armenia that provides causal evidence supporting the investigated relationship. Our main finding is that a more trustworthy healthcare system enhances the probability of potential treatment-seeking behavior when observing the initial symptoms

Political Narratives and the US Partisan Gender Gap

Social scientists have devoted considerable research effort to investigate the determinants of the Partisan Gender Gap (PGG), whereby US women (men) tend to exhibit more liberal (conservative) political preferences over time. Results of a survey experiment run during the COVID-19 emergency and involving 3,086 US residents show that exposing subjects to alternative narratives on the causes of the pandemic increases the PGG: relative to a baseline treatment in which no narrative manipulation is implemented, exposing subjects to either the Lab narrative (claiming that COVID-19 was caused by a lab accident in Wuhan) or the Nature narrative (according to which COVID-19 originated in the wildlife) makes women more liberal. The polarization effect documented in our experiment is magnified by the political orientation of participants' state of residence: the largest PGG effect is between men residing in Republican-leaning states and women living in Democratic-leaning states.JEL Classification: J16, D83, C83, C99, P16, D72

Political Narratives and the US Partisan Gender Gap

Social scientists have devoted considerable research effort to investigate the determinants of the Partisan Gender Gap (PGG), whereby US women (men) tend to exhibit more liberal (conservative) political preferences over time. Results of a survey experiment run during the COVID-19 emergency and involving 3,086 US residents show that exposing subjects to alternative narratives on the causes of the pandemic increases the PGG: relative to a baseline treatment in which no narrative manipulation is implemented, exposing subjects to either the Lab narrative (claiming that COVID-19 was caused by a lab accident in Wuhan) or the Nature narrative (according to which COVID-19 originated in the wildlife) makes women more liberal. The polarization effect documented in our experiment is magnified by the political orientation of participants' state of residence: the largest PGG effect is between men residing in Republican-leaning states and women living in Democratic-leaning states

Method to Support Circular Economy of aggregates (Sand / Gravel)

The objectives of the Green Deal require new circular economy approaches also starting from the management of construction and demolition waste. Circularity processes are affected both by regulatory actions and by physical, urban and industrial geographical aspects. The processes of industrial circularity are inserted in the territory where linear economies already exist by hybridizing them. In fact, localization processes of circular economies present cluster configurations. Furthermore, these circular economies are configured differently in relation to physical geography, to urban centers and transport infrastructures. The paper therefore aims to evaluate two case studies to extrapolate the risks and opportunities of the circular economy from construction and demolition waste (CDW)

Artificial Intelligence – impact on total factor productivity, e-commerce & fintech

Scholars, and not only, believe that Artificial Intelligence will be among the main sources of innovation in the years to come. A relevant issue involves understanding the impact that these technologies have on productivity. Indeed, the stylized facts related to past introductions of disruptive innovations such as the industrial revolution and the mechanization of agriculture, suggest that the automation of existing tasks produced extraordinary increases in productivity (Acemoglu & Restrepo, 2019). In order to assess the growth potential of this new wave of innovation, we study the impact of AI technologies on total factor productivity, TFP, We analyse improvements in productivity that originate from firms that have patented AI technologies, In particular, we exploit a novel data set on AI patents at the firm level to analyze whether the extent with which firms develop AI technologies positively affects productivity and wages. Also, we draw attention to e-commerce and fintech firms which are more extensively adopting AI.JRC.I.1-Monitoring, Indicators & Impact Evaluatio