Intention-based reciprocity and signaling of intentions

Many experiments find that trust intentions are a key determinant of prosociality. If intentions matter, then prosociality should depend on whether trust intentions can be credibly conveyed. This conjecture is formalized and tested in a noisy trust game where I vary the extent to which trust can be credibly signaled. I find that the introduction of noise threatens the onset of trust relations and induces players to form more pessimistic beliefs. Therefore policies that increase transparency of the decision-making environment may foster prosociality. However, the potential impact of such policies could be limited by a large heterogeneity in how individuals respond to changes in their information environment

Choice deferral, indecisiveness and preference for flexibility

In a standard model of menu choice, we examine the behavior of an agent who applies the following Cautious Deferral rule: “Whenever in doubt, don't commit; just leave options open.” Our primitive is a complete preference relation ≽ that represents the agent's choice behavior. The agent's indecisiveness is captured by means of a possibly incomplete (but otherwise rational) preference relation . We ask when ≽ can be viewed as a Cautious Deferral completion of some incomplete . Under the independence and continuity assumptions commonly used in the menu choice literature, we find that even the smallest amount of indecisiveness is enough to force ≽, through the above deferral rule, to exhibit preference for flexibility on its entire domain. Thus we highlight a fundamental tension between non-monotonic preferences, such as preferences for self-control, and tendency to defer choice due to indecisiveness

Essays in applied microeconomics: behaviour and well-being

This thesis explores challenges related to measuring well-being and behaviour, presents novel ways to handle such data and offers some evidence from analysing these types of data which has implications for welfare. Across the three chapters I have two main objectives. Firstly, I present and justify methods to handle data on unobservable feelings, such as satisfaction, motivation or pride. Secondly, the research questions are connected through their preoccupations with understanding human welfare. In Chapter 1, I ask what importance (weightings) do individuals attach to different areas (domains) of life in their overall life satisfaction? In my answer I tackle a fundamental question for economists regarding reported satisfaction data: if we do not wish to assume such data are inter-personally comparable, how can we extract information from them (such as domain weightings)? Existing work uses regression: pooling data across individuals in order to identify average weightings, hence making the inter-personal comparability assumption. I instead use a Maximum Entropy criterion function, which can find a solution for each individual separately: I can identify J domain weightings from a single report of J domain satisfactions and overall satisfaction, and hence find individual-specific weightings. In Chapter 2, jointly with Séverine Toussaert, we investigate the effectiveness of incentives that emerge from a perceived threat to one's image, triggered by another's assessment: an ego threat. We study this in the context of a road-running race. We ask runners to tell us about their time goal for a race and then tell them we doubt they can achieve their goal, or we offer them a reward (a voucher) if they achieve it, or both, or neither. We find that the ego threat message boosts goal achievement by 12 percentage points, but no effect of the voucher. We look for evidence for the mechanisms through which the message may work, and try to evaluate the welfare effect of the treatment. In Chapter 3, I present a method to quantify the ordinal differences between two welfare measurement techniques: traditional utility-based Revealed Preference orderings versus Subjective Well-being orderings (from non-incentivized self-reports of well-being). As a proof of concept, I apply the method to an international dataset, and also outline ideal datasets to which this method could be applied for future research on quantifying differences between well-being measures.</p

Data files

Stata do-file and dta file used to perform the analyses

Peer mentoring and automated text messages for smoking cessation: a randomized pilot trial

Introduction Text-messaging programs for smoking cessation, while efficacious, have high dropout rates. To address this problem, we developed and tested the feasibility and early efficacy of a peer-mentoring intervention for smoking cessation provided by former smokers. Methods Adult US smokers were recruited nationally into a randomized pilot trial (N = 200), comparing 6–8 weeks of automated text-messaging support (SmokefreeTXT) and automated text support plus personalized texts from a peer mentor who formerly smoked. The primary outcome was biochemically verified 7-day point-prevalence abstinence at 3 months post-quit date, assessed on an intention-to-treat basis (missing = smoking). Self-reported abstinence, program acceptability, user engagement, and user perceptions were also assessed. Results Biochemically verified abstinence at 3 months was 7.9% (8/101) in the intervention group and 3.0% (3/99) in the control group (adjusted difference 6.5, 95% CI = 0.7% to 12.3%; p = .03). Self-reported abstinence at 3 months was 23.8% (24/101) in the intervention group versus 13.1% (13/99) in the control group (adjusted difference 12.7, 95% CI = 1.2% to 24.1%; p = .03). The intervention had a positive but insignificant effect on overall satisfaction (78.3% vs. 72.9% control group, p = .55). Having a mentor did not significantly alter duration of interaction with the program nor the proportion unsubscribing, although the intervention group reset their quit date with greater frequency (p Conclusions Peer mentoring combined with automated text messages was feasible and acceptable and increased smoking abstinence compared with automated messages alone. The results highlight the promise of this intervention approach and the need for a full-scale evaluation. Implications Providing quitting assistance by automated text messaging has been shown to increase smoking abstinence. Yet, dropout rates in text-messaging programs are high. No studies have tested the effectiveness of peer mentors who are former smokers as part of a text-messaging intervention, although they represent a promising way to retain, engage, and support smokers. This randomized pilot trial suggests that peer mentors can complement automated text-messaging programs to promote smoking abstinence.</p

Peer mentoring and automated text messages for smoking cessation: a randomized pilot trial

Introduction Text-messaging programs for smoking cessation, while efficacious, have high dropout rates. To address this problem, we developed and tested the feasibility and early efficacy of a peer-mentoring intervention for smoking cessation provided by former smokers. Methods Adult US smokers were recruited nationally into a randomized pilot trial (N = 200), comparing 6–8 weeks of automated text-messaging support (SmokefreeTXT) and automated text support plus personalized texts from a peer mentor who formerly smoked. The primary outcome was biochemically verified 7-day point-prevalence abstinence at 3 months post-quit date, assessed on an intention-to-treat basis (missing = smoking). Self-reported abstinence, program acceptability, user engagement, and user perceptions were also assessed. Results Biochemically verified abstinence at 3 months was 7.9% (8/101) in the intervention group and 3.0% (3/99) in the control group (adjusted difference 6.5, 95% CI = 0.7% to 12.3%; p = .03). Self-reported abstinence at 3 months was 23.8% (24/101) in the intervention group versus 13.1% (13/99) in the control group (adjusted difference 12.7, 95% CI = 1.2% to 24.1%; p = .03). The intervention had a positive but insignificant effect on overall satisfaction (78.3% vs. 72.9% control group, p = .55). Having a mentor did not significantly alter duration of interaction with the program nor the proportion unsubscribing, although the intervention group reset their quit date with greater frequency (p Conclusions Peer mentoring combined with automated text messages was feasible and acceptable and increased smoking abstinence compared with automated messages alone. The results highlight the promise of this intervention approach and the need for a full-scale evaluation. Implications Providing quitting assistance by automated text messaging has been shown to increase smoking abstinence. Yet, dropout rates in text-messaging programs are high. No studies have tested the effectiveness of peer mentors who are former smokers as part of a text-messaging intervention, although they represent a promising way to retain, engage, and support smokers. This randomized pilot trial suggests that peer mentors can complement automated text-messaging programs to promote smoking abstinence.</p

Peer Mentoring and Automated Text Messages for Smoking Cessation: A Randomized Pilot Trial

This randomized trial evaluates the effectiveness of a text messaging intervention for smoking cessation, comparing purely automated text messages to messages provided by a peer mentor. This page contains all survey materials, the database of text messages and the materials used to train mentors

Acceptability of app-based contact tracing for COVID-19: cross-country survey evidence

Background: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. Objective: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. Conclusions: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.</p

Developing a Game (Inner Dragon) Within a Leading Smartphone App for Smoking Cessation: Design and Feasibility Evaluation Study

The project presents the results of a beta test to develop a game embedded in a smoking cessation app. This OSF page contains all survey materials and interview guides used in order to gather feedback during the development of the game (initial focus groups and survey + interview of a small group of testers)