Publications

Evaluations may perform a key role in political systems as they provide a basis for parliaments to hold their executives accountable. This is equally the case in the European Union. Yet, several factors may work against the usage of European Union evaluations for accountability purposes. Members of the European Parliament work under great time pressure and executives may have little incentives to produce high-quality evaluations. This article therefore addresses the question of to what extent and when Members of the European Parliament use ex post legislative evaluations. We present an analysis of 220 evaluations, studying how many were referred to in parliamentary questions. Our main finding is that 16% of the evaluations are followed up through questions. However, the parliamentary questions hardly serve accountability aims. Members of the European Parliament mostly use evaluations for agenda-setting purposes. The main variable explaining differences in the usage of evaluations is the level of conflict between the European Parliament and Commission during the legislative process.

This article provides a comprehensive overview of how policy makers, practitioners, and scholars can fruitfully use behavioral science to tackle public administration, management, and policy issues. The article systematically reviews 109 articles in the public administration discipline that are inspired by the behavioral sciences to identify emerging research trajectories, significant gaps, and promising applied research directions. In an attempt to systematize and take stock of the nascent behavioral public administration scholarship, the authors trace it back to the seminal works of three Nobel Laureates—Herbert Simon, Daniel Kahneman, and Richard Thaler—and their work on bounded rationality, cognitive biases, and nudging, respectively. The cognitive biases investigated by the studies reviewed fall into the categories of accessibility, loss aversion, and overconfidence/optimism. Nudging and choice architecture are discussed as viable strategies for leveraging these cognitive traps in an attempt to alter behavior for the better, among both citizens and public servants.

Il presente articolo illustra le sinergie che potrebbero essere ottenute dall’utilizzo di alcuni indicatori del Benessere equo e sostenibile (Bes) all’interno dei processi di Analisi d’impatto della regolazione (Air) e di Valutazione d’impatto della regolazione (Vir). Uno dei limiti spesso attribuiti alle Air e alle poche Vir realizzate nel nostro paese, infatti, è quello di valutare (nella migliore delle ipotesi) le opzioni prendendo in considerazione soltanto stime qualitative dei loro impatti, tralasciando di effettuare stime quantitative. I motivi di tale limite sono diversi, non ultimo la scarsa dimestichezza della nostra classe dirigente con strumenti di analisi valutazione delle politiche pubbliche. La crisi economica e lo stato dei conti pubblici, che, soprattutto in tempi recenti, hanno ridotto le possibilità di scelta dei decisori pubblici, chiamandoli ad una maggiore responsabilità nella scelta di dove destinare le sempre più scarse risorse pubbliche, impongono tuttavia di far ricorso a strumenti di better regulation che consentono di aumentare la qualità delle politiche pubbliche. Il ricorso ad alcuni indicatori del Bes, nonché la possibilità di elaborarne alcuni nuovi e ad hoc, potrebbero permettere, almeno nel breve periodo, di effettuare Air e Vir basate sulla stima quantitativa degli effetti delle regolazioni e, perciò, più efficaci e precise.

Innovations in networked digital communications technologies, including the rise of ‘Big Data’, ubiquitous computing and cloud storage systems, may be giving rise to a new system of social ordering known as algorithmic regulation. Algorithmic regulation refers to decision-making systems that regulate a domain of activity in order to manage risk or alter behaviour through continual computational generation of knowledge by systematically collecting data (in real time on a continuous basis) emitted directly from numerous dynamic components pertaining to the regulated environment in order to identify and, if necessary, automatically refine (or prompt refinement of) the system’s operations to attain a pre-specified goal. It provides a descriptive analysis of algorithmic regulation, classifying these decision-making systems as either reactive or pre-emptive, and offers a taxonomy that identifies 8 different forms of algorithmic regulation based on their configuration at each of the three stages of the cybernetic process: notably, at the level of standard setting (adaptive vs. fixed behavioural standards); information-gathering and monitoring (historic data vs. predictions based on inferred data) and at the level of sanction and behavioural change (automatic execution vs. recommender systems). It maps the contours of several emerging debates surrounding algorithmic regulation, drawing upon insights from regulatory governance studies, legal critiques, surveillance studies and critical data studies to highlight various concerns about the legitimacy of algorithmic regulation.