How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions
Open accessAbstract
Abstract Despite dozens of studies, research on crime has struggled to reach consensus about the impact of right-to-carry (RTC) gun laws. With this in mind, we formalize and apply a class of bounded-variation assumptions that flexibly restrict the degree to which outcomes may vary across time and space. Using these assumptions, we present empirical analysis of the effect of RTC laws on violent and property crimes in Virginia, Maryland, and Illinois. Imposing specific assumptions that we believe worthy of consideration, we find that RTC laws increase some crimes, decrease other crimes, and have effects that vary over time for others.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Manski and Pepper (2018) also consider bounded time and bounded state restrictions that bound how much the mean of Y(0) can differ either across treatment groups or within groups over time.
“Manski and Pepper (2018) also consider”
From A More Credible Approach to Parallel Trends (2023) · cites this paper as Manski and Pepper (2018)
Section C (p. 6) describes bounded-time and bounded-interstate assumptions that limit how much outcomes under a given treatment can differ across years or states. But Section B (p. 5) explicitly says the analysis focuses on individual states rather than groups of states. Describing these as restrictions across or within treatment groups extends the paper's stated setting. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRambachan and Roth (2022b) propose an approach for robust inference and sensitivity analysis when parallel trends may be violated, building on earlier work by Manski and Pepper (2018).
“Rambachan and Roth (2022b) propose an approach for robust inference and sensitivity analysis when parallel trends may be violated, building on earlier work by Manski and Pepper (2018).”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Manski and Pepper (2018)
The approach builds on the foundational partial identification analysis for DiD in Manski and Pepper (2018).
“The approach in this paper builds on the foundational partial identification analysis for DiD in Manski and Pepper (2018).”
From A More Credible Approach to Parallel Trends (2023) · cites this paper as Manski and Pepper (2018)
While Manski and Pepper (2018) provide a framework for identification, this paper provides inference methods to construct uniformly valid confidence sets for the treatment effect of interest.
“Most importantly, while Manski and Pepper (2018) provide a framework for identification, we provide inference methods to construct uniformly valid confidence sets for the treatment effect of interest.”
From A More Credible Approach to Parallel Trends (2023) · cites this paper as Manski and Pepper (2018)
The identification framework generalizes the partial identification framework in Manski and Pepper (2018).
“Our identification framework, which generalizes the partial identification framework in Manski and Pepper (2018), thus helps formalize the intuition motivating pre-trends testing.”
From A More Credible Approach to Parallel Trends (2023) · cites this paper as Manski and Pepper (2018)
Similar breakdown concepts appear in the partial identification settings of Horowitz and Manski (1995), Kline and Santos (2013), Manski and Pepper (2018), and Masten and Poirier (2020).
“similar”
From A More Credible Approach to Parallel Trends (2023) · cites this paper as Manski and Pepper (2018)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 29 · Cited by 158
Research works printed but not linked: 2
- Cochran, William G., Sampling Techniques (New York: Wiley, 1977).
- National Research Council, Firearms and Violence: A Critical Review , edited by C. Wellford, J. Pepper, and C. Petrie (Washington, DC: National Academy Press, 2005).
- Authors
- Manski, C. F., & Pepper, J. V.
- Affiliations
- Northwestern University (US)
- University of Virginia (US)
- ORCID
- Manski, Charles F.
- Venue
- Review of Economics and Statistics
- Publisher
- MIT Press
- Published
- 2018-05-01
- Volume
- 100
- Issue
- 2
- Pages
- 232-244
- ISSN
- 0034-6535 (ISSN-L)
- 1530-9142
- OpenAlex
- W2891256222
- Cited by
- 158
- References
- 29
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 20 · 2023: 27 · 2022: 24 · 2021: 14 · 2020: 22 · 2019: 9 · 2018: 7 · 2017: 3 · 2016: 2
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Review of Economics and Statistics (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Health › Gun Ownership and Violence Research
- Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Law, Economics, and Judicial Systems
- Keywords
- right-to-carry laws, gun laws, crime rates, violent crime, property crime, ambiguity, time-dependent effects, spatial variability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
