Edited by Vanessa Mak, Eric Tjong Tjin Tai and Anna Berlee
Chapter 12: Data science and public administration research: connecting agency rules and red tape
Cutting red tape, or unnecessarily burdensome rules, has become an important objective for policymakers around the world. While there is theoretical support for the notion that rules promulgated by government agencies, or agency rules, are a salient driver of red tape, there is very little empirical research that has tested this relationship. In this chapter, I put forward a research agenda that highlights the potential of data science techniques for studying the relationship between agency rules and red tape empirically. Techniques such as topic modelling and dictionary methods can be used to measure and test different elements of the agency rules – red tape relationship, including the sheer number of agency rules (or rule stock size), the magnitude of information requirements, as well as the red tape content of agency rules in terms of their overall costs and benefits.
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