Policy analysis and policy analytics

Working from a description of what policy analysis entails, we review the emergence of the recent field of analytics and how it may impact public policy making. In particular, we seek to expose current applications of, and future possibilities for, new analytic methods that can be used to support public policy problem-solving and decision processes, which we term policy analytics. We then review key contributions to this special volume, which seek to support policy making or delivery in the areas of energy planning, urban transportation planning, medical emergency planning, healthcare, social services, national security, defence, government finance allocation, understanding public opinion, and fire and police services. An identified challenge, which is specific to policy analytics, is to recognize that public sector applications must balance the need for robust and convincing analysis with the need for satisfying legitimate public expectations about transparency and opportunities for participation. This opens up a range of forms of analysis relevant to public policy distinct from those most common in business, including those that can support democratization and mediation of value conflicts within policy processes. We conclude by identifying some potential research and development issues for the emerging field of policy analytics

Using a Model-driven Approach in Building a Provenance Framework for Tracking Policy-making Processes in Smart Cities

The significance of provenance in various settings has emphasised its potential in the policy-making process for analytics in Smart Cities. At present, there exists no framework that can capture the provenance in a policy-making setting. This research therefore aims at defining a novel framework, namely, the Policy Cycle Provenance (PCP) Framework, to capture the provenance of the policy-making process. However, it is not straightforward to design the provenance framework due to a number of associated policy design challenges. The design challenges revealed the need for an adaptive system for tracking policies therefore a model-driven approach has been considered in designing the PCP framework. Also, suitability of a networking approach is proposed for designing workflows for tracking the policy-making process.Comment: 15 pages, 5 figures, 2 tables, Proc of the 21st International Database Engineering & Applications Symposium (IDEAS 2017

A Methodology for Economic Crisis Policy Analytics

The development and success of the ‘business analytics’ in the private sector, in combination with the growing availability of large quantities of useful data in government agencies, gives rise to the emergence of the ‘policy analytics’ in the public sector. However, though some knowledge has already been developed in this area, extensive research is required in order to increase our knowledge base concerning the exploitation of these exponentially increasing quantities of data available in government, in combination with data from private sector firms as well, using advanced analytical techniques (from various areas, such as machine learning, statistics, simulation, etc.), in order to provide substantial support for all stages of public policies in various important policy domains. This paper makes a contribution in this direction, by describing a methodology for policy analytics in the economic policy domain, concerning a highly important problem: the economic crises, which repeatedly occur in market-based economies being an inevitable trait of them. Our methodology aims at the identification of firm’s characteristics that affect positively or negatively their sensitivity to the economic crisis, which enables a deeper understanding of the kinds of firms that exhibit higher sensitivity to economic crisis (i.e. have more negative consequences) and provides a basis for the design of public policies for supporting such firms. It exploits existing data from various public sources (e.g. Ministries of Finance, Statistical Authorities), in combination with data from private sources (e.g. business information firms, consulting firms), from which firm-level crisis sensitivity models are estimated. Furthermore, an application of the proposed methodology is presented, using data from Greek firms for the crisis period 2009 – 2014, which provides interesting insights

C-KE/I: A pragmatic framework for policy innovation

Improving policy making is key to address numerous contemporary challenges such as the environmental crisis, climate change, global inequality, financial crises, or pandemics. Policy making is a sequence of stages structuring policy problems and choices made to address them. Among these stages, policy design is a crucial phase since it impacts the quality of the policy alternatives being considered. Policy design is, however, largely neglected in the scientific literature, and in practice it is mainly conducted informally. Design theory, and more specifically Concept-Knowledge (C-K) theory, originally aimed at assisting the process of creating marketable objects, offers promises to formalize and rationalize policy design. We critically analyze this theory, showing that, despite its strengths, as it stands it is ill-adapted to support the innovative design of policy alternatives. For that purpose, we propose a framework, C-KE/I. This framework, which is inspired by and compatible with C-K, appraises innovation based on the explicit or implicit modal statements held by a certain individual or group (“E/I” stands for Explicit vs. Implicit). Through an ex-post analysis of a case study—the search for innovative policy solutions to water management problems in the Apulia Region, Italy—we illustrate the practical applicability and usefulness of our framework

Big Data and AI – A transformational shift for government: So, what next for research?

Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed

Research themes in big data analytics for policymaking:Insights from a mixed-methods systematic literature review

The use of big data and data analytics are slowly emerging in public policy-making, and there are calls for systematic reviews and research agendas focusing on the impacts that big data and analytics have on policy processes. This paper examines the nascent field of big data and data analytics in public policy by reviewing the literature with bibliometric and qualitative analyses. The study encompassed scientific publications gathered from SCOPUS (N = 538). Nine bibliographically coupled clusters were identified, with the three largest clusters being big data's impact on the policy cycle, data-based decision-making, and productivity. Through the qualitative coding of the literature, our study highlights the core of the discussions and proposes a research agenda for further studies.publishedVersionPeer reviewe

Behavioral challenges in policy analysis with conflicting objectives

Public policy problems are rife with conflicting objectives: efficiency versus fairness, technical criteria versus political goals, costs versus multiple benefits. Multi-Criteria Decision Analysis provides robust methodologies to support policy makers in making tough choices and in designing better policy options when considering these conflicting objectives. However, important behavioral challenges exist in developing these models: the use of expert judgments, whenever evidence is not available; the elicitation of preferences and priorities from policy makers and communities; and the effective management of group decision processes. The extensive developments in behavioral decision research, social psychology, facilitated decision modeling, and incomplete preference models shed light on how decision analysts should address these issues, so we can provide better decision support and develop high quality decision models. In this tutorial I discuss the main findings of these extensive, but rather fragmented, literatures providing a coherent and practical framework for managing behavioral issues, minimizing behavioral biases, and optimizing the quality of human judgments in policy analysis models with conflicting objectives. I illustrate these guidelines with policy analysis interventions that we have conducted over the last decade for several organizations, such as the World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), the UK Department of Environment Food and Rural Affairs (DEFRA), the Malaria Consortium/USAID, the UK National Audit Office, among others

Силабус навчальної дисципліни «Аналіз та прогнозування в міжнародних економічних відносинах» для здобувачів другого (магістерського) рівня спеціальності 292 «Міжнародні економічні відносини», які навчаються за освітньо-професійною програмою "Міжнародні економічні відносини"

Даний курс орієнтований на засвоєння студентами теоретичних положень та вироблення практичних навичок щодо застосування конкретних механізмів аналізу і прогнозування в міжнародних економічних відносинах. Метою навчальної дисципліни є досягнення студентами сучасного фундаментального, конструктивного мислення та системи спеціальних знань з аналізу і прогнозування в міжнародних економічних відносинах. Використовуються такі методи викладання та технології: проблемна лекція, аналіз конкретних ситуацій (case study), обговорення, презентації, ситуаційні дослідження, навчальна дискусія та інші

A Policy Analysis of the Endangered Species Act

The goal of this thesis was to identify and analyze common characteristics that are shared by recovering species listed in the Endangered Species Act (ESA). NatureServe population data was used to determine which listed species were recovering, and a logistic regression analysis was performed to identify which aspects of the ESA most contribute to recovery. Of the 747 species tested, only 24% had a population that was stable or improving. Time listed and classification group were found to significantly influence recovery, and recovery plan presence and critical habitat designation also increase the odds of recovery. The analysis found no relationship between species recovery and average annual funding,and species listed as “threatened” were just as likely to be recovering as those listed as “endangered”