Abstract
Recent evidence suggests that legal marijuana markets in several U.S. states have decreased violence in Mexican-U.S. border regions. As legal markets for production and distribution displace drug cartel distribution, the violence associated with cartel trafficking and distribution decreases. Prior analysis has not considered an important emerging innovation for drug distribution: online anonymous marketplaces. The increasing volume of drug trade that has occurred on this “Dark Web” could result in reduced drug cartel violence as production and distribution use this substitute network and turn away from the cartel distribution networks. This paper investigates the relationship between border violence and the volume of drug trade that occurs on the Dark Web using a difference in differences model. We examine differences in crime rates at the U.S.-Mexico border and away from the border during the emergence of the Dark Web. Data on Dark Web transactions, users, and markets allows us to measure changes in Dark Web activity and the subsequent impact on crime. We find evidence that the rise in Dark Web marketplaces results in crime reductions at the border of the U.S., relative to non-border counties.
Impact of Dark Web markets on total crime per 100,000 (full sample 1990–2017).
(2) | (2) | (3) | (3) | (4) | (4) | |
---|---|---|---|---|---|---|
Ln (users)*Mexican border*Dark Web Era | −359.8c | −269.2c | ||||
(60.86) | (63.32) | |||||
Ln (users) | −171.5 | 119.3b | ||||
(302.4) | (50.72) | |||||
Ln (users)*Ln (Mexican border distance overall) | −46.29c | −35.58c | ||||
(6.538) | (6.873) | |||||
Ln (users)*Ln (Mexican border distance local) | 3098.1c | 2724.9c | ||||
(627.5) | (638.6) | |||||
Ln ($$ trade volume)*Mexican border*Dark Web Era | −383.5c | −313.9c | ||||
(72.21) | (75.20) | |||||
Ln ($$ trade volume) | 201.5c | 76.35b | ||||
(31.39) | (32.13) | |||||
Ln ($$ trade volume)* Ln (Mexican border distance overall) | −30.18c | −22.75c | ||||
(4.271) | (4.423) | |||||
Ln ($$ trade volume)* Ln (Mexican border distance local) | 178.9c | 153.9c | ||||
(36.68) | (37.68) | |||||
# of markets*Mexican border*Dark Web Era | −879.7c | −734.6c | ||||
(167.4) | (173.2) | |||||
# of markets | 487.9c | 363.0c | ||||
(65.06) | (68.62) | |||||
# of markets* Ln (Mexican border distance overall) | −71.75c | −55.73c | ||||
(8.774) | (9.342) | |||||
# of markets* Ln (Mexican border distance local) | 397.8c | 341.2c | ||||
(82.57) | (84.36) | |||||
Mexican Border | 1403.0c | 826.3c | 1404.0c | 826.3c | 1398.5c | 826.3c |
(218.6) | (143.1) | (218.7) | (143.1) | (218.4) | (143.1) | |
Dark Web Era*Mexican border | −10062.6c | −9502.6c | −584.6a | −850.6c | −384.5c | −546.0c |
(2052.9) | (2084.8) | (335.2) | (319.1) | (87.68) | (103.4) | |
Dark Web Era | 5403.8a | 1352.3c | 341.0* | 1292.7c | 54.48c | −213.2c |
(3225.8) | (80.35) | (186.3) | (84.06) | (20.93) | (28.68) | |
Med MJ legal in state | −2301.5c | −2520.3c | −2378.3c | |||
(880.7) | (892.3) | (864.3) | ||||
Rec MJ legal in state | −7175.3c | −7045.8c | −7180.2c | |||
(2512.4) | (2509.6) | (2506.6) | ||||
Rec MJ legal*Ln (Mexican border distance overall) | 898.4c | 881.8b | 899.9c | |||
(345.6) | (345.3) | (344.8) | ||||
Med MJ legal*Ln (Mexican border distance overall) | 235.5b | 265.1b | 247.0b | |||
(116.3) | (117.7) | (114.1) | ||||
N | 83,868 | 83,868 | 83,868 | 83,868 | 83,868 | 83,868 |
R^2 (within) | 0.0494 | 0.0269 | 0.0495 | 0.0265 | 0.0495 | 0.027 |
-
Standard errors clustered by county in parenthesis. Controls for Median Income Per Captia, Poverty Rate, % Hispanic, % Black, and % Male have all been omitted from this table for brevity. a p < 0.10, b p < 0.05, c p < 0.01.

Dynamic DiD (Border crime trends) coverage indicator 99 %+.
Impact of Dark Web markets on total crime per 100,000 persons (coverage indicator 99 %+).
(2) | (2) | (3) | (3) | (4) | (4) | |
---|---|---|---|---|---|---|
Ln (users)*Mexican border*Dark Web Era | −201.9c | −171.8c | ||||
(51.07) | (53.09) | |||||
Ln (users) | −419.4 | 1.366 | ||||
(298.4) | (40.15) | |||||
Ln (users)*ln (Mexican border distance overall) | −22.60c | −19.40c | ||||
(5.379) | (5.306) | |||||
Ln (users)*ln (Mexican border distance local) | 1869.8c | 1776.2c | ||||
(517.2) | (528.2) | |||||
Ln ($$ trade volume)*Mexican border*Dark Web Era | −213.6c | −202.3c | ||||
(62.55) | (64.66) | |||||
Ln ($$ trade volume) | 81.78c | 0.347 | ||||
(25.64) | (25.33) | |||||
Ln ($$ trade volume)* Ln (Mexican border distance overall) | −14.10c | −12.09c | ||||
(3.518) | (3.406) | |||||
Ln ($$ trade volume)* Ln (Mexican order distance local) | 99.73c | 95.84c | ||||
(31.56) | (32.35) | |||||
# of Markets*Mexican border*Dark Web Era | −541.8c | −493.6c | ||||
(148.2) | (153.6) | |||||
# of markets | 260.1c | 209.1c | ||||
(53.29) | (52.88) | |||||
# of markets* Ln (Mexican border distance overall) | −41.24c | −34.61c | ||||
(7.184) | (7.164) | |||||
# of markets* Ln (Mexican border distance local) | 249.5*** | 231.5*** | ||||
(73.51) | (75.81) | |||||
Mexican border | 406.3b | 499.9c | 412.1b | 500.0c | 412.0b | 499.9c |
(190.2) | (133.8) | (190.3) | (133.8) | (190.4) | (133.8) | |
Dark Web Era*Mexican border | −6123.3c | −6023.8c | −220.2 | −204.0 | −208.4c | −211.3c |
(1702.8) | (1725.8) | (244.5) | (237.9) | (71.81) | (75.77) | |
Dark Web Era | 6165.9a | 1385.0c | 340.7a | 1289.0c | 35.83 | −178.0c |
(3213.1) | (81.69) | (183.5) | (79.94) | (24.14) | (23.74) | |
Med MJ legal in state | 421.3 | 234.6 | 135.2 | |||
(799.8) | (819.4) | (786.9) | ||||
Rec MJ legal in state | −2774.2 | −2647.3 | −3236.3 | |||
(2044.9) | (2043.4) | (2035.6) | ||||
Rec MJ legal*Ln (Mexican border distance) | 327.9 | 311.1 | 391.8 | |||
(283.4) | (283.2) | (282.2) | ||||
Med MJ legal*Ln (Mexican border distance overall) | −80.13 | −55.50 | −39.13 | |||
(105.0) | (107.5) | (103.2) | ||||
N | 48,827 | 48,827 | 48,827 | 48,827 | 48,827 | 48,827 |
R^2 (within) | 0.0789 | 0.0609 | 0.0789 | 0.0596 | 0.0796 | 0.0611 |
-
Standard errors clustered by county in parenthesis. Controls for Median Income Per Captia, Poverty Rate, % Hispanic, % Black, and % Male have all been omitted from this table for brevity. a p < 0.10, b p < 0.05, c p < 0.01.
References
Abu-Hamdeh, S. (2011). The Merida initiative: an effective way of reducing violence in Mexico? Pepperdine Policy Rev. 4: 1–19.Search in Google Scholar
Aldridge, J. and Décary-Hétu, D. (2014). Not an ‘ebay for drugs’: the cryptomarket ‘silk road’ as a paradigm shifting criminal innovation. Available at SSRN 2436643.10.2139/ssrn.2436643Search in Google Scholar
Aldridge, J. and Décary-Hétu, D. (2016). Hidden wholesale: the drug diffusing capacity of online drug cryptomarkets. Int. J. Drug Pol. 35: 7–15. https://doi.org/10.1016/j.drugpo.2016.04.020.Search in Google Scholar
Autor, D.H. (2003). Outsourcing at will: the contribution of unjust dismissal doctrine to the growth of employment outsourcing. J. Labor Econ. 21: 1–42. https://doi.org/10.1086/344122.Search in Google Scholar
Barratt, M.J., Ferris, J.A., and Winstock, A.R. (2016). Safer scoring? Cryptomarkets, social supply and drug market violence. Int. J. Drug Pol. 35: 24–31, https://doi.org/10.1016/j.drugpo.2016.04.019.Search in Google Scholar
Bearman, J. and Hanuka, T. (2015). The rise and fall of silk road, (Part 1): Ross Ulbricht’s journey from libertarian ideaist to savage kingpin. Wired 23: 90–97.Search in Google Scholar
Benson, B.L. (1989). The spontaneous evolution of commercial law. South. Econ. J. 55: 644–661, https://doi.org/10.2307/1059579.Search in Google Scholar
Benson, B.L. and Rasmussen, D.W. (1991). Relationship between illicit drug enforcement policy and property crimes. Contemp. Econ. Pol. 9: 106–115, https://doi.org/10.1111/j.1465-7287.1991.tb00354.x.Search in Google Scholar
Benson, B.L., Leburn, I.S., and Rasmussen, D.W. (2001). The impact of drug enforcement on crime: an investigation of the opportunity cost of police resources. J. Drug Issues 31: 989–1006, https://doi.org/10.1177/002204260103100410.Search in Google Scholar
Bertola, F. (2020). Drug trafficking on darkmarkets: how cryptomarkets are changing drug global trade and the role of organized crime. American J. Qual. Res. 4: 27–34, https://doi.org/10.29333/ajqr/8243.Search in Google Scholar
Bhaskar, V., Linacre, R., and Machin, S. (2019). The economic functioning of online drugs markets. J. Econ. Behav. Organ 159: 426–441, https://doi.org/10.1016/j.jebo.2017.07.022.Search in Google Scholar
Castillo, J.C., Mejía, D., and Restrepo, P. (2020). Scarcity without leviathan: the violent effects of cocaine supply shortages in the Mexican drug war. Rev. Econ. Stat. 102: 269–286, https://doi.org/10.1162/rest_a_00801.Search in Google Scholar
Che, Y. and Benson, B.L. (2014). Drug trafficking wars: (2014) Enforcement versus smugglers and smugglers versus smugglers. J. Drug Issues 44: 150–179, https://doi.org/10.1177/0022042613494839.Search in Google Scholar
Chertoff, M. (2017). A public policy perspective of the Dark Web. J. Cyber Pol. 2: 26–38, https://doi.org/10.1080/23738871.2017.1298643.Search in Google Scholar
Congressional Research Service (2020). Mexico: organized crime and drug trafficking organizations, Available at: <https://fas.org/sgp/crs/row/R41576.pdf>.Search in Google Scholar
Criminal Justice Information Services (CJIS) Division Uniform Crime Reporting (UCR) Program (2013). Summary reporting system (SRS) user manual V.1. Federal Bureau of Investigation, Washington, DC.Search in Google Scholar
El Bahrawy, A., Alessandretti, L., Rusnac, L., Goldsmith, D., Teytelboym, A., and Baronchelli, A. (2020). Collective dynamics of dark web marketplaces. Sci. Rep. 10: 18827, https://doi.org/10.1038/s41598-020-74416-y.Search in Google Scholar
Gavrilova, E., Kamada, T., and Zoutman, F. (2019). Is legal pot crippling Mexican drug trafficking organisations? The effect of medical marijuana laws on US crime. Econ. J. 129: 375–407, https://doi.org/10.1111/ecoj.12521.Search in Google Scholar
Huntington-Klein, N. (2021). The effect: an introduction to research design and causality, 1st ed. Taylor & Francis, New York.10.1201/9781003226055Search in Google Scholar
Kaplan, J. (2019). Jacob Kaplan’s concatenated files: uniform crime reporting program data: offenses known and clearances by arrest, 1960–2017. Inter-University Consortium for Political and Social Research [distributor], Ann Arbor, MI. (Accessed 21 December 2019).Search in Google Scholar
Kaplan, J. (2020). Jacob Kaplan’s concatenated files: uniform crime reporting (UCR) program data: county-level detailed arrest and offense data. Inter-University Consortium for Political and Social Research [distributor], Ann Arbor, MI. (Accessed 15 October 2020).Search in Google Scholar
Maltz, M.D. and Targonski, J. (2002). A note on the use of county-level UCR data. J. Quant. Criminol. 18: 297–318. https://doi.org/10.1023/a:1016060020848.10.1023/A:1016060020848Search in Google Scholar
Martin, J., Cunliffe, J., Decary-Hetu, D., and Aldridge, J. (2018). Effect of restricting the legal supply of prescription opioids on buying through online illicit marketplaces: interrupted time series analysis. bmj 361: k2270, https://doi.org/10.1136/bmj.k2270.Search in Google Scholar
Mendes, S.M. (2000). Property crime and drug enforcement in Portugal. Crim. Justice Pol. Rev. 11: 195–216, https://doi.org/10.1177/0887403400011003001.Search in Google Scholar
Miron, J.A. (1999). Violence and the US prohibitions of drugs and alcohol. Am. Law Econ. Rev. 1: 78–114, https://doi.org/10.1093/aler/1.1.78.Search in Google Scholar
Miron, J.A. (2003). The effect of drug prohibition on drug prices: evidence from the markets for cocaine and heroin. Rev. Econ. Stat. 85: 522–530, https://doi.org/10.1162/003465303322369696.Search in Google Scholar
National conference of state legislatures (2023). State medical marijuana laws, Available at: <https://www.ncsl.org/research/health/state-medical-marijuana-laws.aspx#:∼:text=A%20total%20of%2036%20states,available%20medical%20marijuana%2Fcannabis%20programs>.Search in Google Scholar
Reuter, P., Crawford, G., Cave, J., Murphy, P., Henry, D., Lisowski, W., and Wainstein, E.S. (1988). Sealing the borders: the effects of increased military participation in drug interdiction. RAND Corporation, Santa Monica, CA.10.21236/ADA213737Search in Google Scholar
Soska, K. and Christin, N. (2015). Measuring the longitudinal evolution of the online anonymous marketplace ecosystem. In: 24th {USENIX} security symposium ({USENIX} security 15, pp. 33–48.Search in Google Scholar
Thornton, M. (1998). The potency of illegal drugs. J. Drug Issues 28: 725–740, https://doi.org/10.1177/002204269802800309.Search in Google Scholar
Zambiasi, D. (2020). Drugs on the web, crime in the streets. The impact of dark web marketplaces on street crime. Working Papers 202009. Geary Institute, University, College Dublin.Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Articles
- Expressive Law and Escalating Penalties: Accounting for the Educational Function of Punishment
- Do US State Breach Notification Laws Decrease Firm Data Breaches?
- Dark Web Drug Markets and Cartel Crime
- Intermittent Collusive Agreements: Antitrust Policy and Business Cycles
- Anonymity and Online Search: Measuring the Privacy Impact Of Google’s 2012 Privacy Policy Change
- Law and Economics of the Withdrawal Right in EU Consumer Law
Articles in the same Issue
- Frontmatter
- Articles
- Expressive Law and Escalating Penalties: Accounting for the Educational Function of Punishment
- Do US State Breach Notification Laws Decrease Firm Data Breaches?
- Dark Web Drug Markets and Cartel Crime
- Intermittent Collusive Agreements: Antitrust Policy and Business Cycles
- Anonymity and Online Search: Measuring the Privacy Impact Of Google’s 2012 Privacy Policy Change
- Law and Economics of the Withdrawal Right in EU Consumer Law