Responsibility utility and the difference between preference and desirance: implications for welfare evaluation

Actions can provide “responsibility utility” when they signal the actors’ identities or values to others or to themselves. This paper considers a novel implication of this responsibility utility for welfare analysis: fully informed incentive-compatible choice data can give a biased measure of the utility delivered by exogenously determined outcomes. A person’s choice of a policy outcome may be informed by responsibility utility that would be strictly absent if that same person were a passive recipient of that same policy outcome. We introduce the term “desirance” to describe a rank ordering over exogenously determined outcomes and present evidence that desirance captures the welfare consequences of exogenously determined outcomes more accurately than preference. We review literatures showing that preference is sensitive to contextual variations that influence responsibility utility and show experimentally that responsibility utility can explain discrepancies between welfare estimates derived from choice data and subjective well-being data. We close by discussing subjective well-being as a potential measure of desirance.

Compliance with Covid-19 measures: evidence from New Zealand

Governments around the world are seeking to slow the spread of Covid-19 by implementing measures that encourage, or mandate, changes in people's behaviour. These changes include the wearing of face masks, social distancing, and testing and self-isolating when unwell. The success of these measures depends on the commitment of individuals to change their behaviour accordingly. Understanding and predicting the motivation of individuals to change their behaviour is therefore critical in assessing the likely effectiveness of these measures in slowing the spread of the virus. In this paper we draw on a novel framework, the I3 Compliance Response Framework, to understand and predict the motivation of residents in Auckland, New Zealand, to comply with measures to prevent the spread of Covid-19. The Framework is based on two concepts. The first uses the involvement construct to predict the motivation of individuals to comply. The second separates the influence of the policy measure from the influence of the policy outcome on the motivation of individuals to comply. The Framework differentiates between the strength of individuals' motivation and their beliefs about the advantages and disadvantages of policy outcomes and policy measures. We show this differentiation is useful in predicting an individual's possible behavioural responses to a measure and how it assists government agencies to develop strategies to enhance compliance.

Input from Whom? Public Reactions to Consultation Measures

Most legislation neither affects nor interests citizens equally. But should this variation in interest and affectedness impact who gets to influence policy reforms? This article examines US public opinion on this issue using a national survey experiment varying both the policy outcome (a bill’s passage/failure) and the type of constituency input granted by elected representatives (none/constituency surveys/targeting interested constituents/targeting affected constituents). It then compares reactions across treatment groups, examining the impact of outcome favourability as well as external and internal political efficacy. Results suggest that granting constituents explicit policy influence consistently affected perceived responsiveness in the expected manner, but that the different consultation procedures had more varied effects on decision acceptance. Furthermore, where the procedures impacted decision acceptance, they pushed the reactions of both the pleased and the displeased towards more muted responses. Finally, similar ‘cushion effects’ were present when external and internal political efficacy were incorporated into the analysis.

The contribution of policy design to realist evaluation

The literature on policy design provides insightful resources to improve policy evaluation. We compare how this literature addresses causation, evaluation, instrumentation, and intervention, based on different methodologies. Then, we explain how the realist approach can contribute to theory-based evaluation with multi-methods. We go through the process leading from the adoption of policy aims to the production of a policy outcome by a causal mechanism linking agenda setting to policy formulation, cross-sectorial coordination, political interplays, and this policy’s outcome. This hypothesis is then tested against a typology of policy instruments classified by the State resources. These instruments are hereby treated as expected empirical observations that provide an empirical basis to assess the theoretical process following a Bayesian logic. We eventually present a five-step protocol aimed at making realist policy evaluation easier, which includes process theorization and operationalization, empirical tests design, case selection and justification, congruence analysis, and deep within-case study.