WHY PERCEIVED SIZE MATTERS FOR AGENCY TERMINATION

Agency size is generally assumed to be positively correlated with survival: the bigger an agency, the less likely it is to be terminated. Yet, recent research finds a very small effect of size on survival. The reason, we argue, is that it only addresses size in terms of overall operating budget – what this article calls objective size. By contrast, we use an interpretive historicist approach to show how perceived size – the meanings and beliefs of key actors concerning an agency's size – affects termination. Drawing on a case study of Australia's independent aid agency, AusAID, which endured a tumultuous history of cuts, reorganizations and rebirths, culminating in termination, we show how perceptions of the agency's size mattered. These findings both support and extend recent research showing that adaptation and reputation are critical to the survival of government agencies.     

The News China Can Use

Xinhua, China’s biggest news wire service, began broadcasting English- language programs on its China IXinhua News Network Corp. (CNC) on July 1. CNC will soon go on to broadcast     

SCHOOL DISORDER: THE INFLUENCE OF INDIVIDUAL,INSTITUTIONAL, AND COMMUNITY FACTORS*

Drawing upon control theory, school climate theory, and social disorganization theory, this study examined the relative influence of individual, institutional, and community factors on misconduct in Philadelphia middle schools. Using U.S. census data, school district data, police department data, and school climate survey data obtained from the administration of the Effective School Battery to 7, 583 students in 11 middle schools, we examined the following predictors of student misconduct: community poverty and residential stability; community crime; school size; student perceptions of school climate (school attachment); and individual student characteristics (e.g., age, race, sex, school involvement and effort, belief in rules, positive peer associations). “Community” was conceptualized in two ways: “local” (the census tract around the school), and “imported” (aggregated measures from the census tracts where students actually lived). We used hierarchical linear modeling techniques (HLM) to examine between- and within-school factors. Individual-level factors accounted for 16% of the explained variance; school and community-level factors (both local and imported) added only small increments (an additional 4.1–4.5%). We conclude that simplistic assumptions that “bad” communities typically produce “bad” children or “bad” schools are unwarranted.