A Brief Discussion on KPI Development in Public Administration

TL;DR

Problem: KPI development in Public Administration is complex due to diverse services and political dynamics. The paper proposes a framework that uses Random Forest variable importance and expert input to derive micro-KPIs from macro-KPIs, with a data pipeline that includes data collection, processing, and continuous monitoring. It contributes a structured process for goal alignment, data-driven KPI identification, and iterative evaluation to ensure targeted, transparent performance improvements. The approach enables agile, citizen-centered public service optimization through real-time KPI updates and potential cross-office generalization.

Abstract

Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.

Figures (2)

• Figure 1: KPI details
• Figure 2: Framework for KPI identification