Designing Systems for the Co-Production of Public Knowledge: Considerations for National Statistical Systems

I am pleased to say that “Designing Systems for the Co-Production of Public Knowledge: Considerations for National Statistical Systems,” written with Derrick Anderson of Arizona State, is now forthcoming at Policy Design and Practice. PDP is a new journal from Taylor & Francis. Here’s the abstract:

The functions of government are increasingly complex and information-driven. However, for many developing countries, the quality of information is poor and the consequences of that information poverty are substantial. If the goal is to establish or advance effective systems of government – in terms of formulating or implementing public policies by laws or rules – we have to consider how the design process can help attain that goal through improved information, data and evidence. National statistics are problems of governance, knowledge and design. While governments are primary users of national statistical systems, national statistical capacity is jointly determined because without contributions from non-state actors there is little hope of observing accurate data that expresses important social, economic and natural phenomena in any state – but especially so in failed, transitioning or struggling states. This paper discusses several findings from research studies for those who design and implement systems that collect, disseminate and interpret government statistics. These findings are derived from the literature on the co-production of public knowledge. The growth of complex, high dimensional data, accompanied by calls for investment in “big data” technologies and methods, will change how we collect and interpret data in many countries. Yet, our most important data enterprises are built on a human infrastructure with prospects that are both limited and supported by social factors. Organizations themselves must expend resources to navigate a world in which data is growing at exponential rates. But organizations are constrained and enabled by broader aspects of society that go well beyond government’s role in collecting, processing, and disseminating statistical data. As we discuss, one notable example is the relative presence of general purpose information technologies.

Please contact me or Derrick if you’d like a prepublication copy.

John Gaus Award nominations sought

Along with Kelly Leroux of the University of Illinois, Chicago (Committee Chair) and Jill Nicholson-Crotty of Indiana University, I am serving on the APSA John Gaus Award Committee. The John Gaus Award is one of APSA’s ten career awards. We welcome nominations!

The John Gaus Award and Lectureship honors the recipient’s lifetime of exemplary scholarship in the joint tradition of political science and public administration and, more generally, recognizes and encourages scholarship in public administration. The award carries a $2,000 prize and the recipient delivers a lecture at the APSA Annual Meeting.

The deadline for nominations from individuals is Monday, February 12, 2018. Nominations are made online through an electronic form. Please submit at this link:

“Developing knowledge states: Technology and the enhancement of national statistical capacity”

New with Derrick Anderson of Arizona State University, this paper is now published at the Review of Policy Research. Here’s the abstract:

National statistical systems are enterprises tasked with collecting, validating and reporting societal attributes. These data serve many purposes–they allow governments to improve services, economic actors to traverse markets, and academics to assess social theories. National statistical systems vary in quality, especially in developing countries. This study examines determinants of national statistical capacity in developing countries, focusing on the impact of technological attainment. Just as technological progress helps to explain differences in economic growth, we argue that states with greater technological attainment have greater capacity for gathering and processing quality data. Analysis using panel methods shows a strong, statistically significant positive linear relationship between technological attainment and national statistical capacity.