T02P21 - Data for Policy Analysis

Topic : Comparative Public Policy

Panel Chair : Reuben Ng - spprng@nus.edu.sg

Panel Second Chair : MIchael Howlett - howlett@sfu.ca

Panel Third Chair : M Ramesh - mramesh@nus.edu.sg

Objectives and Scientific Relevance of the panel

Call for papers

Session 1 Big Data: Applications in the Policy Sciences

Discussants

Sarah Giest - s.n.giest@fgga.leidenuniv.nl - Leiden University, Institute of Public Administration - Netherlands

An analytics approach to study societal sentiments for policy evaluation: A case study on ageing narratives

Reuben Ng - spprng@nus.edu.sg - Lee Kuan Yew School of Public Policy - Singapore

We present a novel way of measuring societal sentiments over time to provide insights into policy design and evaluation.  This presentation will dive deeper a US case study of ageing narratives.  Scholars argue about whether age stereotypes (beliefs about old people) are becoming more negative or positive over time. No previous study has systematically tested the trend of age stereotypes over more than 20 years, due to lack of suitable data. Our aim was to fill this gap by investigating whether age stereotypes have changed over the last two centuries and, if so, what may be associated with this change.  We found that age stereotypes have become more negative in a linear way over 200 years. In 1880, age stereotypes switched from being positive to being negative. In addition, support was found for two potential explanations. Medicalisation of aging and the growing proportion of the population over the age of 65 were both significantly associated with the increase in negative age stereotypes. The upward trajectory of age-stereotype negativity makes a case for remedial action on a societal level.  Implications for ageing policies are discussed. 

Institutions and temporal dynamic of policy change: empirical evidence from the Structural Topic Model (STM) analysis of development policies in Asia.

maria stella righettini - mariastella.righettini@unipd.it - University of Padova - Italy

Stefano Sbalchiero - stefano.sbalchiero@unipd.it - University of Padova - Italy

In the vast literature on how to identify temporal patterns of policy development, the identification of turning points in policy processes over time and their explanations are at the core of most analytic models and empirical investigations (Howlett, Reynard, 2006). A number of studies have emphasized both the manner in which actors and institutions can promote change as well as stability. Following the punctuated equilibrium model, we consider change in policy trajectories as “outgrowths of earlier trajectories” (Baumgartner and Jones, 2002) driven by actor preferences (Buthe, 2002). Both stability and change emerge from stable or changing preferences. The real challenge for empirical investigation is to identify where, at what level, how and with which documentation ascertain such stability or change, especially when actors perform at different levels (supra national and regional). Our research for this paper focuses on interactions between different institutional actors with different resources in the policy processes in order to detect dynamics at different levels of governance and in different element of development policies in Asia. The Asian Development Bank (ADB) conceived in the early 1960s as a financial institution that would be Asian in character and foster economic growth and cooperation in one of the poorest regions in the world. ADB assists its members, and partners, mainly in Asian countries, by providing loans, technical assistance, grants, and equity investments to promote social and economic development. Drawing upon a database of 1983 titles and descriptions of projects financed from 1990s until 2016, we identify the policy fields covered by the ADB over time in a vast geographic area. We use Latent Direchlet Allocation (LDA) and the Structural Topic Model (STM) to identify the key topics/policies of then ADB financing agenda over the time, identifying topics with  significant increasing and decreasing trend of attention. We use “Hot and cold policy topics” (Griffiths and Steyvers, 2004), a procedure which describes how another metavariable (time) can be used to explore the corpus. We test whether STM facilitates sequencing analysis in the policy processes over time and space and whether it is possible to match different levels of change: macro (that of systemic and strategic development goals); meso (that of policy sectors objectives (topics) in which changes occur over time and space) and micro (that of operational setting, policy tools and instruments adopted in the policy programs examined) (Howlett, 2009). In the frame of the topic analysis of large database, this study explores the opportunity of assessing the evolution of development in terms both of geographical and temporal mapping. Our findings assess the usefulness of LDA and of STM in the analysis of co-occurrence of change at different times and levels of policy processes.

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