Derrick M. Anderson

Unfulfilled Promise: Laboratory experiments in public management research

Abstract We make the case for increased laboratory experimentation in public management research. Laboratory experiments can generate useful knowledge, particularly in testing causal relationships among constructs of interest. The challenge in this regard is one of identifying the appropriate role for experiments in a greater knowledge production enterprise. Although laboratory experiments are underutilized, they have proffered important knowledge contributions to the field, especially in areas of decision-making and, increasingly, motivation. Because practical problems may pose a greater obstacle to laboratory experimentation in public management than epistemological issues, we address external validity and the cost of conducting laboratory experiments before concluding with suggestions for future research.

The public value of nanotechnology

Science and innovation policy (SIP) is typically justified in terms of public values while SIP program assessments are typically limited to economic terms that imperfectly take into account these values. The study of public values through public value mapping (PVM) lacks widely-accepted methods for systematically identifying value structures within SIP and its public policy processes, especially when there are multiple stakeholder groups. This paper advances the study of public values in SIP using nanoscale science and engineering (NSE) policy by demonstrating that quantitative analysis of value statements can provide a credible and robust basis for policy analysis. We use content analysis of over 1,000 documents with over 100,000 pages from major contributors to the NSE policy discourse to identify and analyze a wide range of public value statements. Data analysis and reduction methods reveal a multifactor structure of public values that has been consistently cited by a range of actors in an NSE research policy network.

Public Policy and the Origins of Bureaucratic Red Tape Implications of the Stanford Yacht Scandal

The so-called “Stanford yacht scandal” is used as a case to examine the public policy origins of bureaucratic red tape. The 1988 erroneous charge by Stanford University accountants of depreciation of the university Yacht to federal research accounts resulted in disastrous audits and then changes to U.S. research policies that adversely affect research universities. The seeds of red tape and organizational disaster were planted in ill-designed public policies and then exacerbated by Stanford officials’ missteps. Overcompliance, misplaced precision, and overcontrol are causes of red tape. A “convergent indicators” approach is offered to help judge when rules have become red tape.

Designing systems for the co-production of public knowledge: considerations for national statistical systems

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 poor information 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.

Developing Knowledge States: Technology and the Enhancement of National Statistical Capacity

National statistical systems are the 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 general purpose technologies (GPTs). Just as technological progress helps to explain differences in economic growth, states with markets with greater technological attainment (specifically, general purpose technologies) arguably have greater capacity for gathering and processing quality data. Analysis using panel methods shows a strong, statistically significant positive linear relationship between GPTs and national statistical capacity. There is no evidence to support a non-linear function in this relationship. Which is to say, there does not appear to be a marginal depreciating National Statistical Capacity benefit associated with increases in GPTs.