Law and Norms: Lane, Nosenzo & Sonderegger (2023)

What this is. This is an LLM-distilled skeleton of Lane, Nosenzo, and Sonderegger (2023); read the original to replicate or extend.

TL;DR

Lane, Nosenzo, and Sonderegger develop a legal-threshold identification strategy to test whether laws causally influence social norms. They exploit a special class of laws that regulate behavior via legal thresholds (age of consent, legal drinking age, maximum undeclared cash import, BAC drink-driving limit, motorway speed limit), measuring the social norm function S(o, 1) on both sides of each threshold using incentivized vignette experiments with 1,248 UK subjects across three samples. Under the identifying assumption that norms vary continuously near the threshold absent the law, any sharp discontinuity at the legal threshold is causally attributable to the law. Laws produce large, statistically significant drops in perceived social appropriateness for three of five behaviors (age of consent, alcohol to youth, cash at customs) and smaller effects for drink driving and speeding. Robustness experiments with 5,771 additional UK, US, and Chinese subjects show: (i) placebo thresholds produce no comparable discontinuities, ruling out focal-point and information-transmission alternatives; (ii) perceived prosocial traits (trustworthiness, honesty, altruism) shift discontinuously at legal thresholds in the same direction, consistent with a social-image signaling mechanism; and (iii) a “bad law” (CANO nuisance ordinance) reverses the direction of discontinuity in honesty, ruling out the meta-norm (rule-following) explanation.

Core results

#	Result	Locator	Magnitude as reported
R1	Age of consent: strong norm discontinuity at legal threshold	Table 2 col A1/B1/C1/D1, Fig 1, p. 1273	beta2 = -0.778*** (students, SE 0.184); -0.890*** (gen pop 2019, SE 0.127); -0.803*** / -0.813*** (gen pop 2021 first/second order)
R2	Alcohol to youth: largest norm discontinuity across all samples	Table 2 col A2/B2/C2/D2, Fig 1, p. 1273	beta2 = -1.035*** (students, SE 0.138); -0.920*** (gen pop 2019, SE 0.118); up to -1.137*** (gen pop 2021 second order)
R3	Cash at customs: strong norm discontinuity	Table 2 col A3/B3/C3/D3, Fig 1, p. 1273	beta2 = -0.866*** (students, SE 0.132); -0.948*** (gen pop 2019, SE 0.124); -0.821*** / -0.971*** (gen pop 2021)
R4	Drink driving: weaker norm discontinuity	Table 2 col A4/B4, p. 1273-1274	beta2 = -0.326 (students, SE 0.178, p=0.068); -0.522*** (gen pop 2019, SE 0.143); roughly half of R1/R2/R3 magnitudes
R5	Speeding: weakest effect, insignificant for student sample	Table 2 col A5/B5, p. 1273-1274	beta2 = -0.103 (students, SE 0.107, n.s.); -0.461*** (gen pop 2019, SE 0.127)
R6	Placebo thresholds: no systematic discontinuities	Fig 2, Appendix F, p. 1278-1280	Krupka-Weber: placebo discontinuity = 0.22 vs legal threshold = 0.88 (difference p=0.000); no significant placebo for age-of-consent and alcohol-to-youth vignettes under either method
R7	Perceived trustworthiness and honesty drop sharply at legal threshold (Exp 2)	Fig 3, Appendix G, p. 1282-1283	Trustworthiness drops 0.45-0.63 units; honesty drops 0.51-0.81 units; altruism drops 0.26-0.42 units across high-effect vignettes (all p <= 0.009)
R8	”Bad” law (CANO): upward discontinuity in honesty rules out rule-following mechanism (Exp 3)	Fig 4, Appendix I, p. 1286	Honesty +0.36 (p=0.002); trustworthiness +0.11; altruism +0.17 (both n.s.); rule-compliance shows near-zero discontinuity (-0.04)

Overall. Laws exert causal expressive power on social norms, creating sharp discontinuities in perceived social appropriateness at legal thresholds. The effect is large for behaviors with high perceived intentionality and high law-enforcement detectability (age of consent, alcohol to youth, cash at customs), and weaker for behaviors that are harder to detect or likely to be unintentional (drink driving, speeding). The pattern of results across all five robustness experiments is consistent with a social-image signaling mechanism (Benabou and Tirole 2011) in which laws make illegality a credible signal of low prosociality.

Theory / model

The model (Section I, pp. 1260-1264) adapts the social-image framework of Benabou and Tirole (2006, 2011). An individual with privately known type theta (measuring prosociality) faces an opportunity o drawn from a distribution with density g(.) and full support [o_min, o_max] with o_min > 0, where o is the magnitude of the negative externality imposed on others. The individual can seize or leave the opportunity. A law sets a threshold o-bar above which seizing the opportunity is illegal; if caught (with probability p), the individual incurs a material penalty K.

Utility. Utility depends on the material payoff, the psychological cost of imposing externalities, and social esteem from observers’ inferences about type (eq. 1, p. 1260):

u_a(o;\theta) = (t - \theta o - pK I_{o>\bar{o}})a + S(o,a), \tag{1}

where t is the material gain, $I_{o>\bar{o}}$ is an indicator for illegal action, a = 1 if the opportunity is seized (a = 0 otherwise), and S(o, a) is the social esteem accruing to the individual. Social esteem equals observers’ expectation of the individual’s type upon observing opportunity o and action a (pp. 1260-1261):

$S(o,1) \equiv E(\theta \mid o, a=1), \qquad S(o,0) \equiv E(\theta \mid o, a=0).$

Absence of law (Proposition 1, p. 1262). When no law sets a threshold (p = 0), equilibrium esteem is determined by the highest type theta-hat who seizes opportunity o, defined by the indifference condition (eq. 3, p. 1261):

t - \hat{\theta}_o \, o - \Delta(\hat{\theta}_o) = 0, \tag{3}

where $\Delta(\hat{\theta}_o) \equiv \mathcal{M}^+(\hat{\theta}_o) - \mathcal{M}^-(\hat{\theta}_o)$ is the esteem gap, with $\mathcal{M}^-(\theta_o) \equiv E(\theta \mid \theta < \theta_o)$ and $\mathcal{M}^+(\theta_o) \equiv E(\theta \mid \theta > \theta_o)$. Since theta-hat is continuously decreasing in o, S(o, 1) is also continuously decreasing in o: without law, norms do not create sharp distinctions between arbitrarily close behaviors.

Presence of law (Proposition 2, p. 1263). When a law sets a threshold o-bar, legal sanctions create a payoff discontinuity. The threshold type for illegal actions solves (eq. 5, p. 1263):

t - pK - \bar{\theta}_o \, o - \Delta(\bar{\theta}_o) = 0. \tag{5}

Since theta-hat (highest type taking legal action just below o-bar) always lies strictly above theta-bar (highest type taking illegal action just above o-bar), and $\mathcal{M}^-(\cdot)$ is increasing, the esteem function exhibits a downward discontinuity at o-bar (eq. 4, p. 1263):

S(o,1) = \begin{cases} \mathcal{M}^-(\hat{\theta}_o), & \text{if } o \le \bar{o}; \\ \mathcal{M}^-(\bar{\theta}_o), & \text{if } o > \bar{o}, \end{cases} \tag{4}

with jump:

$\lim_{\epsilon\to 0}\bigl[S(\bar{o}-\epsilon,1) - S(\bar{o}+\epsilon,1)\bigr] = \mathcal{M}^-(\hat{\theta}_{\bar{o}}) - \mathcal{M}^-(\bar{\theta}_{\bar{o}}) > 0.$

Proposition 3 (p. 1264) extends the result to distant observers (who see the criminal record but not the action directly): the discontinuity holds even when K = 0, since visibility of illegal behavior to a wider audience is itself a mechanism.

Method

The empirical approach (Section II, pp. 1265-1270) exploits laws with threshold rules to identify the causal effect of law on norms, using a design analogous to regression discontinuity. The identifying assumption is that the social norm function S(.) is continuous in the vicinity of the legal threshold absent the law’s expressive effect. Under this assumption, any discrete jump in measured norms at the legal threshold is causally attributable to the law (p. 1265).

Norm elicitation. Two incentivized methods from Krupka and Weber (2013) and Bursztyn, Gonzalez, and Yanagizawa-Drott (2020) are used:

• Krupka-Weber method: subjects rate vignette behavior on a four-point scale (very/somewhat socially appropriate or inappropriate) and earn a bonus when their response matches the modal response in their sample. This coordination game incentivizes truthful second-order belief reporting.
• Opinion matching method: a first group states personal appropriateness opinions (unincentivized); a second group guesses the first group’s modal response for a financial bonus. This elicits second-order beliefs without legality as a coordination device.

Experimental design. Subjects are randomly assigned between-subjects to one of 4 or 8 versions of each vignette, varying the behavior’s distance from the legal threshold (e.g., age of the younger person in the age-of-consent vignette being 1-4 months above or below the threshold). The random assignment of subjects to versions means the identification design is randomized, not observational; the RDD-analogy is in the analysis, not in whether subjects select their treatment. Prior work such as Tankard and Paluck (2017) and Casoria, Galeotti, and Villeval (2020) exploited natural changes in existing laws to study law’s effect on norms; those designs face the challenge that simultaneous events may confound the measured effect. The present design instead uses legal thresholds, requiring only the local continuity assumption rather than assuming all other norm-relevant factors are unchanged by a legislative change.

Samples. The main experiment (Section II.C, pp. 1269-1270, Table 1, p. 1270) ran between September 2017 and March 2021 with 1,248 UK participants across three samples: 197 students (Krupka-Weber, 2017), 375 representative general-population subjects (Krupka-Weber, 2019), and 676 representative general-population subjects (opinion matching, 2021). Robustness experiments added 5,771 subjects (Table 3, p. 1277): 1,554 UK general-population (Exp 1 placebo), 2,767 UK general-population (Exp 2 prosocial traits), 1,202 US general-population (Exp 3 bad law), and 248 Chinese students (Exp 4 weak rule of law).

Empirical specifications

Main regression (eq. 7, p. 1273). For each vignette and sample, OLS is estimated with heteroskedasticity-robust standard errors:

s(o_i) = \alpha + \beta_1(T - o_i) + \beta_2 \, \text{Illegal}_i + \beta_3(T - o_i) \times \text{Illegal}_i + \epsilon_i, \tag{7}

where $s(o_i)$ is subject i’s social appropriateness evaluation (coded +1 to -1 for four-point scale), $(T - o_i)$ is the signed distance from the legal threshold T (positive for legal, negative for illegal actions), $\text{Illegal}_i$ is a dummy equal to one for the vignette version describing illegal behavior, and $\beta_2$ is the key coefficient of interest: the estimated norm discontinuity at the legal threshold (the causal effect of law on normative appropriateness). Regressions for the two general-population samples include demographic controls (age, gender, income); no controls were collected for the student sample.

Identification check. Chow tests confirm no statistically significant differences in $\beta_2$ across the three high-effect vignettes (age of consent, alcohol to youth, cash at customs) within each sample (all p >= 0.136), but statistically significant differences between the first group (high effect) and the second group (drink driving, speeding), supporting the model’s prediction that intentionality and law-enforcement detectability moderate the effect size (p. 1273-1276, Appendix E).

Placebo regression (Exp 1). An extended version of equation (7) adds dummy variables for a placebo threshold placed 5-6 units from the legal threshold and its interaction with threshold distance. The placebo produces no significant discontinuities in four of five vignettes (Krupka-Weber method); the legal-threshold discontinuity is significantly larger than the placebo in all five cases where a comparison is possible (p. 1278-1280, Appendix F).

Prosocial-traits regression (Exp 2). The same regression specification is applied to subjects’ ratings of the likelihood that the vignette person engages in trustworthy, honest, and altruistic behavior, using the opinion-matching method with 1,984 incentivized and 783 unincentivized UK general-population subjects. Each subject sees one version of one target vignette, randomly assigned. Results (Appendix G, p. 1282-1283): statistically significant discontinuities in trustworthiness and honesty across all high-effect vignettes (all p <= 0.009), confirming the social-image mechanism.

Datasets used

Dataset	Role in paper	Wiki page
Original vignette experiment, UK samples (1,248 subjects, 2017-2021)	Main experiment measuring social norm functions; Krupka-Weber and opinion-matching methods	no page yet
Robustness experiment 1 (1,554 UK subjects, 2021)	Placebo-threshold test via Prolific	no page yet
Robustness experiment 2 (2,767 UK subjects, 2021)	Prosocial-traits elicitation via Prolific	no page yet
Robustness experiment 3 (1,202 US subjects, 2022)	“Bad law” CANO vignette via Prolific	no page yet
Robustness experiment 4 (248 Chinese students, 2017)	Weak-rule-of-law generalizability test	no page yet

All data are author-collected online vignette experiments; replication data are deposited at https://doi.org/10.3886/E182995V1.

When to read the full paper

Read this paper to understand the expressive function of law from a causal empirical standpoint. The theoretical model (Section I) is compact and shows formally how a social-image mechanism (Benabou and Tirole 2011) predicts exactly the pattern observed. The norm-elicitation methods (Section II.B) using Krupka and Weber (2013) and Bursztyn, Gonzalez, and Yanagizawa-Drott (2020) are carefully contrasted. The online appendices (A-K) contain full proofs, vignette wordings, robustness analyses, regression tables, and an alternative conformity-based model. Figure 1 (p. 1272) gives a visual overview of all five norm functions across all three UK samples; Table 2 (pp. 1273-1275) gives the full regression results.

Attribution and rights

Lane, T., Nosenzo, D., and Sonderegger, S. (2023). “Law and Norms: Empirical Evidence.” American Economic Review 113(5): 1255-1293. https://doi.org/10.1257/aer.20210970

This page is an LLM-distilled extract (claude-sonnet-4-6, 2026-06-25). Not human-verified; not reproduced. The source article is paywalled; no CC licence is recorded in Crossref metadata. An open author manuscript is available at https://eprints.ncl.ac.uk/file_store/production/293182/ACD73151-8B74-4985-A1E4-B8D65A151BE8.pdf. Replication data are available at https://doi.org/10.3886/E182995V1.