Law and Norms: Empirical Evidence
Tom Lane (),
Daniele Nosenzo and
Silvia Sonderegger ()
Additional contact information
Silvia Sonderegger: School of Economics, University of Nottingham
Economics Working Papers from Department of Economics and Business Economics, Aarhus University
Abstract:
A large theoretical literature argues laws exert a causal effect on norms, but empirical evidence remains scant. Using a novel identification strategy, this paper provides a clean empirical test of this proposition. We use incentivized vignette experiments to directly measure social norms relating to actions subject to legal thresholds. Our large-scale experiments featured around 5,800 subjects drawn from six samples recruited in the UK and China. Results show laws often, but not always, influence norms. Our findings are robust to different methods of measuring norms, and remain qualitatively similar across samples and between two countries with very different legislative environments.
Keywords: Social Norms; Law; Expressive Function of Law (search for similar items in EconPapers)
JEL-codes: C91 C92 D9 K1 K42 (search for similar items in EconPapers)
Pages: 193
Date: 2021-07-01
New Economics Papers: this item is included in nep-evo, nep-exp and nep-soc
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://repec.econ.au.dk/repec/afn/wp/21/wp21_08.pdf (application/pdf)
Related works:
Journal Article: Law and Norms: Empirical Evidence (2023) 
Working Paper: Law and Norms: Empirical Evidence (2020) 
Working Paper: Law and Norms: Empirical Evidence (2019) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:aah:aarhec:2021-08
Access Statistics for this paper
More papers in Economics Working Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().