Policy Evaluation

PSJ 54/3 2026-08-03 Original It Does Matter. A New Framework on the Influence of Policy Evaluation Pirmin Bundi, Valérie Pattyn

PNAS 123/31 pp. e2602426123-e2602426123 2026-07-27 Original Can stimulating ownership increase fertility: Evidence from housing interventions in China Zhang Xin, Dongxue Wu, William A. V. Clark Abstract Declining fertility and the emergence of very low TFR's in the Asian economies has increased the focus on how to change the direction of the fertility trend. Several countries in Europe and Asia have explored a variety of stimulus packages to increase the overall TFR. In this research we review those policies and examine one of those interventions which has the potential to stimulate fertility-the role of access to housing. The core of housing approaches to stimulating fertility is to make housing more accessible and to use various forms of credit assistance with the aim of making ownership easier and more attractive to young families. The research asks how effective are these approaches to reversing the decline in fertility? And, are they a solution to very low fertility? The results provide some evidence that the focus on housing stimulus packages may increase fertility although most successfully for socioeconomically advantaged households.

JPART — 2026-06-15 Original Mapping the temporal evolution of causal effects in public administration and policy research Lefteris Jason Anastasopoulos, Inkyu Kang Abstract Abstract Recent growth in the use of randomized and quasi-experiments in public administration and policy research has advanced the ability to establish cause-and-effect relationships. However, many studies adopt static conceptions of causality, focusing on snapshots or time-averaged effects while overlooking how the effects change over time. This oversight is problematic, as interventions of scholarly interest, such as leadership training or the adoption of new technologies, are likely to produce impacts that unfold in various ways. In this paper, we propose a conceptual framework for understanding the temporal dynamics of causal effects and their implications for research hypotheses and design. We then introduce Bayesian changepoint models (BCMs) as a methodological tool for detecting shifts in the average, variance, or trend of causal effect series, providing a more rigorous yet accessible alternative to visually inspecting graphs. Next, we demonstrate the application of BCMs with an illustrative example derived from simulation data as well as a real-world case examining the effect of body-worn cameras on police officers’ use of force. Finally, we discuss how examining temporal changes in effects can advance theoretical understanding of why and how they occur, as well as inform the design and implementation of policies and strategies in practice.