Participants tasted two cups of coffee, decided which they preferred, and then rated each coffee. They were told (in lure) that one of the cups contained “eco-friendly” coffee while the other did not, although the two cups contained identical coffee. In Experiments 1 and 3, but not in Experiment 2, the participants were also told which cup contained which type of coffee before they tasted. The participants preferred the taste of, and were willing to pay more for, the “eco-friendly” coffee, at least those who scored high on a questionnaire on attitudes toward sustainable consumer behavior (Experiment 1). High sustainability consumers were also willing to pay more for “eco-friendly” coffee, even when they were told, after their decision, that they preferred the non-labeled alternative (Experiment 2). Moreover, the eco-label effect does not appear to be a consequence of social desirability, as participants were just as biased when reporting the taste estimates and willingness to pay anonymously (Experiment 3). Eco labels not only promote a willingness to pay more for the product but also lead to a more favorable perceptual experience of it.

We develop theory and a tightly-linked field experiment to explore the supply side implications of corporate social responsibility (CSR). Our natural field experiment, in which we created our own firm and hired actual workers, generates a rich data set on worker behavior and responses to both pecuniary and CSR incentives. Making use of a novel identification framework, we use these data to estimate a structural principal-agent model. This approach permits us to compare and contrast treatment and selection effects of both CSR and financial incentives. Using data from more than 1100 job seekers, we find strong evidence that when a firm advertises work as socially-oriented, it attracts employees who are more productive, produce higher quality work, and have more highly valued leisure time. In terms of enhancing the labor pool, for example, CSR increases the number of applicants by 25 percent, an impact comparable to the effect of a 36 percent increase in wages. We also find an economically important complementarity between CSR and wage offers, highlighting the import of using both to hire and motivate workers. Beyond lending insights into the supply side of CSR, our research design serves as a framework for causal inference on other forms of non-pecuniary incentives and amenities in the workplace, or any other domain more generally.

Several past studies have found health risk to be negatively correlated with the probability of voluntary health insurance. This is contrary to what one would expect from standard textbook models of adverse selection and moral hazard. The two most common explanations to the counter-intuitive result are either (1) that risk-aversion is correlated with health -i.e. that healthier individuals are also more risk-averse -or (2) that insurers are able to discriminate among customers based on observable health-risk characteristics. We revisited these arguments, using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Self-assessed health served as an indicator of risk: better health, lower risk. We did, indeed, observe a negative correlation between risk and insurance but found no evidence of heterogeneous risk-preferences as an explanation to our finding.

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications. * This paper has been designed during the 2015 Santa Fe Institute Complex Systems Summer School. Data preprocessing and post-processing had been performed by Stefano Gurciullo and Michael Smallegan. Dynamic Topic Modeling has been done by Sebastian Poledna, while Alice Patania dealt with Topological Data Analysis. Daniel Hedblom interpreted DTM results, Federico Battiston processed them to obtain insights for individual political agents. Model selection was done by Mara Pereda Garcia and Bahattin Tolga Oztan.