Project overview
Is it fair that all kids, independently of their parents’ income, get free school books? Is it fair that in an economic downturn caused by a pandemic situation, all citizens receive helicopter money to boost the economy? Is it fair to spend public money on a playground specially designed for few disabled children? Different people will give different answers as their perceptions of what is fair or unfair varies according to their notion of fairness, as well as whether they are the ones who bear the costs or reap the benefits of certain policies. Among the most controversial policies due to different fairness perceptions are affirmative actions, such as quotas, which are used to create equal representation among genders within legislation and to contribute to promoting gender equality. Passionate arguments exist on whether or not these actions, which create inefficiency, foster equality or inequality. Contrasting to the inefficiency created by quotas, algorithms, nowadays commonly used to select workers, can generate efficiency. However, delegating a decision to a non-human mechanism may also be perceived as procedurally unfair. The impact of quotas, as well as the use of algorithms, is twofold. On one hand, they may directly promote social inclusion (quotas), or promote efficiency (algorithms), on the other hand, they may generate a feeling of intentional unfairness that triggers negative behavioural responses. As shown by a large body of behavioural and experimental evidence, individuals are not solely motivated by self-interest but exhibit social preferences, in particular a preference for reciprocal fairness, i.e. they are prepared to sacrifice some money to reward (punish) good (bad) intentional behaviour of others. In an overview article, [16] report that 40-66 percent of subjects display non-selfish, reciprocal behaviour in a laboratory gi exchange game. In this game they observe employers pay non-minimal wages to workers, who in response choose higher than minimum effort levels. This gi-exchange behaviour is robust to different contexts as shown by [10, 33, 34]. The existent research on reciprocal fairness in general assumes no subjective fairness perceptions of (un)kind actions. An important research question that needs to be answered is: how do people react to “good” intentional policies and mechanisms that are perceived by some as unfair? In reality, will people protest by joining populist movements? By behaving less cooperatively in the society? By undermining their labour market productivity? By engaging in corruption and sabotage? The answer to these questions is crucial for a correct policy evaluation. Only by understanding the dynamics of fairness perceptions and the behavioural consequences of perceived unfairness, we are able to compute the hidden costs and benefits of public and private policy measures, such as policies that aim at fighting discrimination and social exclusion, and the use of efficient mechanisms like algorithms. Acknowledging the importance of this matter and the existence of only a few studies that use exogenous sources of variation to study fairness, this project aims at conducting controlled laboratory experiments that will deepen our understanding of unfairness perceptions and its behavioural consequences.
