Public Administration as a Science of the Artificial: A Methodology for Prescription

Public administration, in the words of Herbert Simon (1969), is a science of the artificial. Unlike natural sciences, it is concerned not so much with how things are as how things might be. Simon's position is not so different from Max Weber's (1946) emphasis on the ideal type, in that both are seeking to illustrate or obtain some optimum level of performance. Consistent with these values, many believe that the study of public administration should focus on finding ways to improve administrative performance rather than seeking knowledge for its own sake. This view of research is part of the chasm that separates academics from practitioners in the field. Academics, while occasionally finding things with practical applications, focus on how things are, on the empirical description of the administrative world. Scholarly journals reinforce that emphasis. We argue that at partial fault for the separation between academics and practitioners is the dominant methodology used by both scholars and practitioners - regression-based techniques. Regression methods, by their very nature, tend to downplay the unusual and focus on the norm or the average. Recent developments in regression diagnostics (Belsley, Kuh, and Welsch, 1980; Rousseeuw and Leroy, 1987), although rarely used in public administration thus far, downplay the unusual cases even more. As a result, while these techniques add useful information, they increase the academic-practitioner gap. Our research introduces a new approach that we feel has the potential to transform the basic quantitative method of public administration (regression) from a tool that explains what is to a tool that can be used to search for what might be. We call this new approach substantively weighted least squares (SWLS). We illustrate this method with an analysis of child-support enforcement at the state level. The key question is why do some organizations learn to perform their functions at a faster rate than others do? Our argument will introduce the substantive case, child-support enforcement, and briefly present the regression findings of a traditional study. We will introduce some recent techniques of regression diagnostics and argue that these methods take us in a completely wrong direction. Finally, we will illustrate substantively weighted least squares, a technique that puts more weight on the highest performing agencies. The method shows that some variables are far more important for effective performance than normal regression techniques demonstrate. Child-Support Enforcement In 1975, the federal government required that state governments set up procedures to compel absent parents to support their dependent children. Although some states had long operated such programs, many did not. In the past 20 years, all states have gained substantial experience operating these programs, and academic research has designated some effective collection techniques (Klawitter and Garfinkel, 1991; Michalopoulas and Garfinkel, 1989). The relative effectiveness of these programs, however, varies a great deal. In 1991, Michigan collected about $74 per capita in child support while Arizona was able to collect only nine dollars per capita. Although this state to state variation is an interesting topic in itself (Keiser, forthcoming), our concern is with how well these agencies have improved their performance over time, something that we call "organizational learning. " To measure learning, we took child-support collections (per 1,000 population) in constant 1991 dollars for all years from 1982 to 1991 (see appendix A for sources of all data). We plotted these data and ran a regression. The slope of that regression is essentially the annual average improvement in real dollars collected per 1,000 population. (1) The Original Study Seven variables have been hypothesized to affect organization learning by the child-support bureaucracy. (2) These can be divided into three types - support by advocates, subject characteristics, and bureaucratic capacity. …