Working Paper: CEPR ID: DP14377
Authors: Jean-Edouard Colliard; Copierre Georg
Abstract: Despite a heated debate on the perceived increasing complexity of fi nancial regulation, there is no available measure of regulatory complexity other than the mere length of regulatory documents. To fill this gap, we propose to apply simple measures from the computer science literature by treating regulation like an algorithm: a fixed set of rules that determine how an input (e.g., a bank balance sheet) leads to an output (a regulatory decision). We apply our measures to the regulation of a bank in a theoretical model, to an algorithm computing capital requirements based on Basel I, and to actual regulatory texts. Our measures capture dimensions of complexity beyond the mere length of a regulation. In particular, shorter regulations are not necessarily less complex, as they can also use more "high-level" language and concepts. Finally, we propose an experimental protocol to validate measures of regulatory complexity.
Keywords: financial regulation; capital regulation; regulatory complexity; Basel accords
JEL Codes: G18; G28; G41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| regulatory complexity (K20) | measured by algorithmic complexity (C69) |
| algorithmic complexity measures (C63) | capture complexity of regulations (G18) |
| complexity of regulations (L51) | influences effectiveness of regulatory frameworks (G38) |
| dimensions of regulatory complexity (K20) | influence effectiveness of regulatory frameworks (G38) |
| understanding complexity of regulations (K20) | help regulators make informed decisions (G18) |