New publication: "Interventions to Reduce Bureaucratic Discrimination: a Systematic Review of Empirical Behavioural Research"
In a new publication in the Public Management Review, Eva Thomann, Oliver James and Thibaud Deruelle address the question the effectiveness of measures that are implemented to reduce bureaucratic discrimination, based on the findings of previous empirical behavioural research.
Discrimination against individuals based on certain identifiable characteristics is an unwanted by-product of discretion in a bureaucracy. To date, however, there have been few systematic assessments of the evidence on what can be done to counteract this problem of bureaucratic discrimination. Therefore, Thomann, James and Deruelle conduct a systematic literature review to examine the effectiveness of interventions to reduce bureaucratic discrimination. They draw on peer-reviewed publications from the behavioural and social sciences published between 1973 and 2022.
The results of the analysis show that three types of interventions are reliably effective. These include outreach to and engagement with clients, anti-bias training, and passive representation. Passive representation describes measures that ensure that the demographic characteristics of a bureaucracy reflect the demographic characteristics of the population as a whole. Moreover, inclusive practices can also reduce discrimination, however, their impact is context-dependent and the causal mechanisms between intervention and impact remain a “black box”.
This research contributes to the literature on bureaucratic discrimination and intervention strategies. It contributes to a deeper understanding of the challenges of dealing with discrimination in bureaucratic systems and to the development of more effective strategies to promote equity and fairness in a bureaucracy.
The authors point out that further systematic empirical research into how bureaucratic discrimination can be prevented or at least reduced is necessary. They identify three future requirements. First, more empirical research is needed in different countries and policy contexts. Second, researchers need to recognise the contextual nature of bureaucratic discrimination. Many of the studies that found that measures did not reduce discrimination showed that the measures worked in some places but not in others. Third, the promises and pitfalls of digitised public service delivery, automated decision making and the use of artificial intelligence in public sector administration should also be brought into focus and the outcomes of these in terms of impartiality and fairness must be examined.
Link to the article: https://doi.org/10.1080/14719037.2024.2322163