Multiple Public Service Performance Indicators: Toward an Integrated Statistical Approach

Public service organizations usually produce multiple outputs, measured on different scales, giving rise to a suite of performance indicators. The traditional approach to statistical analysis of organizational performance has been to develop a separate regression model for each performance indicator. This piecemeal approach, the article argues, may discard valuable information, as it ignores potentially important relationships between individual performance measures. We therefore propose modeling an organization’s performance measures simultaneously, using the methods of seemingly unrelated regressions. The approach implicitly introduces a latent organizational variable into the regressions and may therefore economize on the need to assemble explicit measures of organizational characteristics. The method is illustrated using an example from English public hospitals. In most industrialized nations, measuring the performance of public services has assumed central political importance, as governments come under increased pressure to reduce taxation and ensure that tax revenues are spent cost-effectively. To that end, governments have put in place extensive systems for measuring the performance of public service organizations, with a view to improving accountability thorough improved political and managerial scrutiny of those organizations (Bird 2004). Yet only a few years ago such performance data were sparse, selective, and slow to emerge. Now the revolution in information technology is rapidly leading to a situation in which public services are overwhelmed with indicators of activity and performance. In this new world of data overload, an emerging challenge is to turn the data into meaningful messages that are useful for informing both managerial decisions and democratic debate. Without technologies to address this difficulty, there is a risk that the superabundance of data will be used ineffectively. In interpreting performance data, one of the most pressing concerns is often that public service organizations operate in different circumstances, and therefore direct We would like to thank the participants at the Cardiff Seminar, the referees, and our colleagues Katharina Hauck and Andrew Street. Smith is funded by ESRC grant R000271253. Address correspondence to Peter C. Smith at