Data Matters…Sometimes: Revisiting the Connection between Problem Indicators and Policy Maker Attention

DeLeoRob De Leo

An extended version of this blog post was originally published on Discover Society.

From the number of drug overdoses to annual average temperatures, public transportation ridership rates to Gross Domestic Product (GDP), government is inundated with data documenting social problems. In theory, these statistics should lead to more informed decision making. In practice, they are heavily politicized. Organized interests compete to ensure that their preferred statistic is adopted as the preferred measure of a given policy problem, a testament to these so-called “problem indicators” are important determinants of policy maker attention.  

Virtually every major theory of policy making suggests indicators and other forms of information play an important role in stimulating issue attention and provoking policy maker action. My recent paper, “Indicators, agendas, and stream: Analysing the Politics of Preparedness,” applies the Multiple Streams Framework (MSF), which argues policy change is facilitated by the coupling of three distinct streams: (1) the problem stream, which consists of the various social issues competing for policy maker attention; (2) the policy stream, which encompasses the various policies and programs designed to address items in the problem stream; and (3) the politics stream, which broadly describes the current political environment, including trends in public opinion as well as the composition of government. Coupling is aided by a policy entrepreneur or an individual or organization willing to invest considerable amounts of time and energy to secure policy change. Once the three streams are coupled, a policy window is opened providing organized interests with an opportunity to push their pet issues onto the policy agenda and, ideally, secure policy change.  Continue reading

Crowdsourcing Data to Improve Macro-Comparative Research

Nate Breznau
Nate Breznau

by Nate Breznau, University of Bremen

Early in my studies a supervisor recommended that I replicate a key publication in my research area on the relationship of public opinion and social welfare policy. Throughout my entire dissertation studies I couldn’t do it. This is how I arrived at the following conclusion:

Different researchers (or teams) who work with the same data and employ the same statistical models will not arrive at the same results.

 My study was actually a reanalysis, not a replication because I took the same data and methods as the original researchers. Of course in true replication studies researchers do not expect to arrive at identical or even similar results. The subjective perceptions of the scientists and the unique observational contexts lead to variations in results. But with secondary data and reproduction of statistical models how are different outcomes possible?! These secondary observer effects, as I label them,

Continue reading