Working Papers (available upon request)
Court Resolution and Predictive Justice, with S. Massoni
Under revision in the European Journal of Law and Economics
Algorithm Control and Responsibility: Shifting Blame to the Programmer?, with M. Chevrier
Under revision in the Journal of Economic Psychology (minor revision)
Fairness preferences in gain and losses: evidence from an experiment, with E-W. Tchalanga
Under review in the Journal of Economic Science Association
Ambiguity Attitudes and the Value of Partial Information, with S. Massoni
Submitted
Choosing Between Algorithmic Forecasters: What drives Delegation?, with M. Chevrier and F. Fidanoski
Measuring Perceived Inequality: an Empirical Comparison of Methods, with Y. Kaouane, E. Kemel and E.W. Tchalanga
Replication of Banovetz and Oprea (2023), American Economic Journal: Microeconomics, conducted within the I4Replication Lab, with A. Boufarsi, P. Crosetto, A-G. Maltese, and D. Mayaux.
Work in progress
General population survey in Morocco on the causes and consequences of perceived inequality, with E. Kemel
Is the Gender Pay Gap Fair? Perceptions and Policy Support in Morocco, with S. Abouri and E. Kemel
Eliciting Risk Preferences: Overcoming Probability Distortions, with M. Abdellaoui, S. Massoni and L. Page
Abstract of the papers
Ambiguity Attitudes and the Value of Partial Information, with S. Massoni
Decision-makers often receive predictive information that refines uncertainty without eliminating it. This paper examines why such partial refinements may receive limited value under ambiguity. We decompose ambiguity attitudes into ambiguity aversion and ambiguity-generated insensitivity, and relate these components to willingness to pay for predictive information in two incentivized experiments involving artificial Ellsberg-type uncertainty and structured natural uncertainty from a predictive-justice environment. Ambiguity aversion is strongly source-dependent, with higher levels under artificial uncertainty than under judicial uncertainty. Ambiguity-generated insensitivity remains substantial under complete ambiguity in both sources and declines when likelihood information is introduced. In the information-valuation task, willingness to pay responds more clearly to full resolution of likelihood ambiguity in the artificial source, whereas valuation is nearly flat across information types in the structured natural source. Ambiguity-generated insensitivity predicts weaker demand for predictive information, mainly at the extensive margin, while ambiguity aversion is more closely related to valuation conditional on positive demand.
Court Resolution and Predictive Justice, with S. Massoni
This article examines the impact of predictive justice on litigants’ decisions in French divorce cases. Court backlog is a persistent issue, and predictive justice tools aim to facilitate out-of-court settlements by informing litigants about the likely judicial outcome. We conducted a laboratory experiment in which pairs of subjects—one representing the claimant and the other the defendant—negotiated over alimony payments across three rounds. If no agreement was reached, the case was decided by a judge and legal costs were imposed. Depending on the treatment, one or both parties received information from a predictive justice algorithm. Contrary to expectations, providing information to the claimant or to both parties reduced the likelihood of reaching an out-of-court settlement, a pattern consistent with both parties initially underestimating the likely judicial award. However, when settlements did occur, the alimony amounts were closer to those that the judge would have awarded, suggesting that predictive justice can improve the legal accuracy of agreements even as it decreases their frequency. Our experiment thus reveals a trade-off between increasing settlement rates and enhancing the legal accuracy of negotiated outcomes.
Algorithm Control and Responsibility: Shifting Blame to the Programmer?, with M. Chevrier
In a laboratory experiment, we investigate whether individuals delegate allocation decisions to an intermediary in order to shift blame. Depending on the treatment, the intermediary is either a human, a rule-based algorithm (RA), or an artificial intelligence algorithm (AI). Behind these algorithms, a programmer fully controls the decisions of the RA and partially controls the decisions of the AI. We find that delegation rates do not differ across intermediary types (human, RA, or AI). Human intermediaries and RA programmers are judged more responsible for inegalitarian allocations than the respective delegators. By contrast, when the intermediary is an AI, programmers are perceived as less responsible for the AI's inegalitarian allocations, while delegators bear most of the blame. Nevertheless, recipients are less likely to punish when the intermediary is an AI, implying that AI users ultimately receive a level of expected punishment similar to that of RA users. Finally, because the AI condition reduced programmer control and the subsequent threat of punishment, programmers were more likely to select unequal allocations.
Fairness preferences in gain and losses: evidence from an experiment, with E-W. Tchalanga
Do fairness judgments depend on whether people allocate gains or losses? We investigate this question using a preregistered online experiment (N=602) in which impartial spectators redistribute a gain or a loss between two workers after observing (im)perfect information about the source of the inequality (performance or luck). Our data do not allow us to reject the hypothesis that fairness views are invariant to the domain of the outcome. A notable exception arises among meritocrats when it is certain that the initial inequality is based on performance: they redistribute less in the Loss treatment than in the Gain treatment, suggesting a stronger tendency to preserve performance-based allocations in losses than in gains. Finally, we find that spectators allocate more to the high-earning worker as the probability that the initial inequality reflects performance increases, and that meritocrats become increasingly egalitarian as uncertainty about the source of inequality rises.
Measuring Perceived Inequality: an Empirical Comparison of Methods, with Y. Kaouane, E. Kemel and E-W. Tchalanga
Beyond the reality of income inequality, its perception also matters. Several methods have been developed for the measurement of perceived inequality, and mixed results are observed regarding (i) the comparison between perceived and real inequality, and (ii) its power to predict redistribution preferences. This study compares five quantitative methods for measuring perceived income distributions, including a novel method adapted from the measurement of beliefs in behavioral economics. We assess their consistency, accuracy, and ability to predict preferences for redistribution . The methods are implemented in an incentivized and choice-based within-subjects experiment. Notably, our subjects are from Morocco, and they considered inequality and redistribution in France, thereby taking a spectator perspective. Following a pre-registered plan, we use econometric methods to estimate perceived Gini indices. This allows us to test the calibration and consistency of perceived inequality between methods as well as its power to predict redistributive choices. The different methods give consistent results regarding the perception of average income, and more heterogeneous results regarding the perceived Gini. The method based on the elicitation of histograms provides the best results according to our comparisons.