We argue that anti-corruption laws may provide an efficiency rationale for why political parties should meddle in the distribution of political nominations and government contracts. Anti-corruption laws forbid trade in spoils that politicians distribute. However, citizens may pay for gaining...

Typically, laboratory experiments suffer from homogeneous subject pools and selfselection biases. The usefulness of survey data is limited by measurement error and by the questionability of their behavioral relevance. Here we present a method integrating interactive experiments and...

Typically, laboratory experiments suffer from homogeneous subject pools and self-selection biases. The usefulness of survey data is limited by measurement error and by the questionability of their behavioral relevance. Here we present a method integrating interactive experiments and...

In recent years a large number of experimental studies have documented the existence of strong reciprocity among humans. Strong reciprocity means that people willingly repay gifts and punish the violation of cooperation and fairness norms even in anonymous one-shot encounters with genetically...

This paper discusses recent neuroeconomic evidence related to other-regarding behaviors and the decision to trust in other people's other-regarding behavior. This evidence supports the view that people derive nonpecuniary utility (i) from mutual cooperation in social dilemma (SD) games and (ii)...

This paper investigates the driving forces behind informal sanctions in cooperation games and the extent to which theories of fairness and reciprocity capture these forces. We find that cooperators' punishment is almost exclusively targeted towards the defectors but the latter also impose a...

Typically, laboratory experiments suffer from homogeneous subject pools and self-selection biases. The usefulness of survey data is limited by measurement error and by the questionability of their behavioral relevance. Here we present a method integrating interactive experiments and...