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Regulation on coexisting legal and illegal markets with quality differentiation

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Abstract

This paper considers coexisting legal and illegal markets for goods with undesirable consumption externalities, such as marijuana markets. This paper introduces a quality differentiation between legal and illegal markets, e.g., product differentiation and its consumption externality, and investigates the effects of regulations such as taxation and quality control on legal markets and enforcement against illegal producers and consumers. This paper shows that these regulations of legal and illegal markets are likely to cause divergence between legal and illegal prices and may fail to reduce social costs.

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Notes

  1. Please see the implicit price indexes provided by Statistics Canada (https://www150.statcan.gc.ca/n1/pub/13-610-x/cannabis-eng.htm). The price of marijuana for nonmedical purposes (licensed) is higher than that for nonmedical purposes (unlicensed). 4 mention that in a study of U.S. cities where cannabis is legal, the price of illegally sourced cannabis was at least 10 percent lower than the legal rate.

  2. There are some empirical studies on the effects of medical marijuana legalization. Some papers find no relationship between medical marijuana legalization and crime (Braakman & Jones, 2014, Freisthler et al., 2016, Shepard & Blackley, 2016). In contrast, Gavrilova et al. (2019) find less violent crime associated with drug-related criminal organizations.

  3. For a licensed market, the number of retail outlets can be limited. Initially, state governments restricted the numbers of licensed producers and retailers to make regulation easier. For simplicity, we assume that there exists one licensed producer, which helps us to understand the interaction with an unlicensed producer. This monopolistic market power of a licensed producer is discussed in Hollenbeck and Uetake (2021). For the illegal market, there exists one illegal producer, which can be interpreted as the organized crime group that coordinates and monopolizes the illegal market. This assumption follows the economic analysis of a monopolized illegal market, as shown by Schelling (1967), Buchanan (1973), Garoupa (2000) and Yahagi (2018), who study monopolistic criminal organizations that can limit entry to the controlled illegal market.

  4. As in Amlung et al. (2019), cannabis users treat legal cannabis as a superior commodity compared with illegal cannabis and exhibit asymmetric substitutability favoring legal products.

  5. Consumption benefits of illegal products can be lower in the case of, for example, a major diversification of the cannabis products for sale (Hall & Lynskey, 2020). In addition to cannabis flowers, cannabis retail outlets also sell high-potency cannabis extracts (wax, shatter), edible cannabis (e.g., gummy bears, candy and chocolates), and cannabis-infused beverages. These products presumably meet the needs of a broader range of consumers than products in the illegal market.

  6. Amlung et al. (2019) mention that cannabis users may tolerate somewhat higher prices for legal (and high quality) cannabis.

  7. This can happen if frequent users may be able to obtain products through established relationships with unauthorized dealers, which indicates that licensed products are not so superior for them.

  8. Subscripts denote partial derivatives

  9. We do not consider how total profits are distributed among those involved in cultivation, manufacture, and retail. Furthermore, as mentioned previously, we assume that there is one licensed producer. However, even if there are n producers and each of them provides q/n under the profit maximizing and zero profit condition for them, e.g., \(\pi ^{l}_j =0\) for all \(j\in \{1,2,...,n\}\), our main results are the same.

  10. Furthermore, as for other general health effects, Bahji and Stephenson (2019) mention that the effects of short-term use include impaired short-term memory, impaired motor coordination, altered judgment, paranoia and psychosis. The effects of long-term or heavy cannabinoid use include addiction, altered brain development, poor educational outcomes, cognitive impairment, diminished life satisfaction and achievement, symptoms of chronic bronchitis, and increased risk of chronic psychotic disorders. There are also potentially fatal harms such as risks of injuries (both unintentional and intentional, including exposure to violence), motor vehicle collisions, and suicide.

  11. For example, as summarized by Carnevale et al. (2017), marijuana testing requires that qualified labs test for mold, pesticides, THC and other potent cannabinoids, and other variables. Arguably, as with food safety, states that permit legal use have an obligation to ensure that the final product reaching consumers is safe for consumption. THC and other cannabinoid potency data inform consumers about dosage and may be used to set tax rates. In addition, regulations might place restrictions on potency itself.

  12. This can be confirmed by\(|\pi ^{l}_{\sigma \sigma }|>|\pi ^{l}_{\sigma m}|\) and \(|\pi ^{ul}_{m m}|>|\pi ^{ul}_{m \sigma }|\).

  13. Although examining when \(\sigma ^{*}_{v_1}\) becomes large is complicated, the condition (29) indicates that if \(q_{v_1}\) (thus \(\pi ^{l}_{\sigma v_1}\)) is large, \(\sigma ^{*}_{v_1}\) can be large. Because we have \(q_{v_1}=\partial a^{l}/\partial v_1 -\partial a^{*}/\partial v_1=1/k+1/(1-k)\), a small k may lead to a large \(\sigma ^{*}_{v_1}\).

  14. If consumers can correctly evaluate the health risks of consuming legal products with higher potency, \(v_1\) becomes small. In that case, the outcomes can be different.

  15. Dragone et al. (2019) exploit the staggered legalization of recreational marijuana enacted by the adjacent states of Washington (end of 2012) and Oregon (end of 2014). They find, across different specifications, that the legalization of recreational marijuana has not increased crime. On the contrary, it has reduced rapes and thefts.

  16. Anecdotal evidence indicates that Washington State’s lab testing system is broken (Adlin, 2019).

  17. In this respect, as another policy diversification, Gourdet et al. (2017) summarize the differences in regulating recreational marijuana edibles. For example, Colorado and Washington limit the maximum amount of active THC that can be contained within an edible product to 100 mg, while Oregon limits the total amount to 50 mg. Adults aged 21 and over can purchase up to 7 grams of marijuana concentrates in Washington, 5 grams in Oregon, and 8 grams in Colorado. Although these possession limits may be related to \(v_1\), this effect can be mixed and complicated, as in Proposition 2. Then, compared to these regulation differences, imposing regulation costs on licensed producers such as taxation policies is likely to have a greater effect than policies affecting \(v_1\).

  18. Hao and Cowan (2020) examine the spillover effects of recreational marijuana legalization in Colorado and Washington on neighboring states and find that it causes a sharp increase in marijuana possession arrests in border counties near both Colorado and Washington relative to the number in nonborder counties.

  19. Similar to our paper, Collins and Judge (2010) introduce a simple model to analyze how clients of the paid sex market will be likely to respond to enforcement level differences across jurisdictions.

  20. We assume that there is no quality difference between illegal products in state A and B.

  21. Aflatoxin is a mycotoxin of particular significance to human health due to its carcinogenicity (Strosnider et al., 2006) and emerging evidence of a negative impact on child growth, where aflatoxin is a fungal toxin common in maize, groundnuts, and other crops around the world. Authorities provide regulations to solve these problems.

  22. The formal private-for-profit drug delivery sector in Uganda is regulated and licensed by authorities.

  23. Under the regulation regime, only licensed producers can provide legal services. The authorities permit production only when certain regulatory requirements, such as medical checks, are satisfied by those providing the service.

  24. To analyze the prostitution market, we need to extend our basic framework to consider the endogenous choice of sex workers to work in licensed or unlicensed markets.

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Correspondence to Ken Yahagi.

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Appendix

Appendix

1.1 Appendix A: The effect of regulation costs for the licensed producer

Proof

We provide the explanation for \(\sigma ^{*}_t-m^{*}_t>0\), where

$$\begin{aligned} \sigma ^{*}_t-m^{*}_t=\frac{-\pi ^{l}_{\sigma t}\left( \pi ^{ul}_{m m}+\pi ^{ul}_{m \sigma }\right) }{T}. \end{aligned}$$
(58)

Since \(i_{\sigma } =1/(1-k)>0\) and \(i_{m} =-1/(1-k)<0\), we have

$$\begin{aligned} \pi ^{ul}_{m\sigma }= & {} i_{\sigma } -c^{ul}_{ii}i_\sigma i_m >0 \end{aligned}$$
(59)
$$\begin{aligned} \pi ^{ul}_{m m}= & {} 2i_{m}-c^{ul}_{ii}(i_m)^2<0. \end{aligned}$$
(60)

Since \(|i_\sigma |=|i_m|\) according to (15) and (19), we have \(\pi ^{ul}_{m m}+\pi ^{ul}_{m \sigma }<0\) and \(\sigma ^{*}_t-m^{*}_t>0\). \(\square\)

1.2 Appendix B: The effect of the substitution of licensed products

Proof

The sign of \(\sigma ^{*}_{v_1}\) is determined by

$$\begin{aligned} \underbrace{-\underbrace{\pi ^{l}_{\sigma v_1}}_{+} \underbrace{\pi ^{ul}_{m m}}_{-}}_{+}+\underbrace{\underbrace{\pi ^{ul}_{m v_1}}_{-}\underbrace{\pi ^{l}_{\sigma m}}_{+}}_{-}. \end{aligned}$$
(61)

Furthermore, since

$$\begin{aligned} \pi ^{l}_{\sigma v_1}= & {} q_{v_1}-c^{l}_{qq}q_{\sigma }q_{v_1}>0 \end{aligned}$$
(62)
$$\begin{aligned} \pi ^{l}_{\sigma m}= & {} q_m -c^{l}_{qq}q_\sigma q_m >0 \end{aligned}$$
(63)
$$\begin{aligned} \pi ^{ul}_{m v_1}= & {} i_{v_1}-c^{ul}_{ii}i_m i_{v_1}<0 \end{aligned}$$
(64)
$$\begin{aligned} \pi ^{ul}_{m m}= & {} 2i_{m}-c^{ul}_{ii}(i_m)^2<0, \end{aligned}$$
(65)

and \(|q_{v_1}|>|q_m|\) and \(|i_m|=|i_{v_1}|\) according to (33), (15), and (13). Then, it is likely that \(-\pi ^{l}_{\sigma v_1}\pi ^{ul}_{m m}>0\) is more important than \(\pi ^{ul}_{m v_1}\pi ^{l}_{\sigma m}<0\). Therefore, we are likely to have \(\sigma ^{*}_{v_1}>0\).

The sign of \(m^{*}_{v_1}\) is determined by

$$\begin{aligned} \underbrace{-\underbrace{\pi ^{l}_{\sigma \sigma }}_{-} \underbrace{\pi ^{ul}_{m v_1}}_{-}}_{-}+\underbrace{\underbrace{\pi ^{ul}_{m \sigma }}_{+}\underbrace{\pi ^{l}_{\sigma v_1}}_{+}}_{+}. \end{aligned}$$
(66)

Furthermore, since

$$\begin{aligned} \pi ^{l}_{\sigma v_1}= & {} q_{v_1}-c^{l}_{qq}q_{\sigma }q_{v_1}>0 \end{aligned}$$
(67)
$$\begin{aligned} \pi ^{l}_{\sigma \sigma }= & {} 2q_{\sigma }-c^{l}_{qq}(q_\sigma )^2<0 \end{aligned}$$
(68)
$$\begin{aligned} \pi ^{ul}_{m v_1}= & {} i_{v_1}-c^{ul}_{ii}i_m i_{v_1}<0 \end{aligned}$$
(69)
$$\begin{aligned} \pi ^{ul}_{m\sigma }= & {} i_{\sigma } -c^{ul}_{ii}i_\sigma i_m >0 \end{aligned}$$
(70)

and\(|q_{v_1}|=|q_{\sigma }|\) and \(|i_{\sigma }|=|i_{v_1}|\) according to (33), (19), and (9). Then, it is likely that \(-\pi ^{l}_{\sigma \sigma }\pi ^{ul}_{m v_1}<0\) is more important than \(\pi ^{ul}_{m \sigma }\pi ^{l}_{\sigma v_1}>0\). Therefore, we are likely to have \(m^{*}_{v_1}<0\). As a result, we are likely to have \(\sigma ^{*}_{v_1}-m^{*}_{v_1}>0\). \(\square\)

1.3 Appendix C: The effect of sanctions against the unlicensed producer

Proof

First, we have

$$\begin{aligned} \sigma ^{*}_{e_p}-m^{*}_{e_p}=\frac{\pi ^{ul}_{m e_p}( \pi ^{l}_{\sigma \sigma }+\pi ^{l}_{\sigma m})}{T}. \end{aligned}$$
(71)

Then, in addition to \(\pi ^{ul}_{m e_p}>0\),

$$\begin{aligned} \pi ^{l}_{\sigma m}= & {} q_{m} -c^{l}_{qq}q_\sigma q_m >0 \end{aligned}$$
(72)
$$\begin{aligned} \pi ^{l}_{\sigma \sigma }= & {} 2q_{\sigma }-c^{l}_{qq}(q_\sigma )^2<0. \end{aligned}$$
(73)

Additionally, although we have \(|q_\sigma |>|q_m|\), according to (9) and (13), which indicates \(\pi ^{l}_{\sigma m}+\pi ^{l}_{\sigma \sigma }<0\) and \(\sigma ^{*}_{e_p}-m^{*}_{e_p}<0\). \(\square\)

1.4 Appendix D: The effect of sanctions against illicit consumers

Proof

The sign of \(\sigma ^{*}_{e_d}\) is determined by

$$\begin{aligned} \underbrace{-\underbrace{\pi ^{l}_{\sigma e_d}}_{+} \underbrace{\pi ^{ul}_{m m}}_{-}}_{+}+\underbrace{\underbrace{\pi ^{ul}_{m e_d}}_{-}\underbrace{\pi ^{l}_{\sigma m}}_{+}}_{-}. \end{aligned}$$
(74)

Furthermore, since

$$\begin{aligned} \pi ^{l}_{\sigma e_d}= & {} q_{e_d}-c^{l}_{qq}q_{\sigma }q_{e_d}>0 \end{aligned}$$
(75)
$$\begin{aligned} \pi ^{l}_{\sigma m}= & {} q_m -c^{l}_{qq}q_\sigma q_m >0 \end{aligned}$$
(76)
$$\begin{aligned} \pi ^{ul}_{m e_d}= & {} i_{e_d}-c^{ul}_{ii}i_m i_{e_d}<0 \end{aligned}$$
(77)
$$\begin{aligned} \pi ^{ul}_{m m}= & {} 2i_{m}-c^{ul}_{ii}(i_m)^2<0, \end{aligned}$$
(78)

and \(|q_{e_d}|=|q_m|\) and \(|i_m|=|i_{e_d}|\) according to (50), (15), and (13). Then, it is likely that \(-\pi ^{l}_{\sigma e_d}\pi ^{ul}_{m m}>0\) is more important than \(\pi ^{ul}_{m e_d}\pi ^{l}_{\sigma m}<0\). Therefore, we are likely to have \(\sigma ^{*}_{e_d}>0\).

The sign of \(m^{*}_{e_d}\) is determined by

$$\begin{aligned} \underbrace{-\underbrace{\pi ^{l}_{\sigma \sigma }}_{-} \underbrace{\pi ^{ul}_{m e_d}}_{-}}_{-}+\underbrace{\underbrace{\pi ^{ul}_{m \sigma }}_{+}\underbrace{\pi ^{l}_{\sigma e_d}}_{+}}_{+}. \end{aligned}$$
(79)

Furthermore, since

$$\begin{aligned} \pi ^{l}_{\sigma e_d}= & {} q_{e_d}-c^{l}_{qq}q_{\sigma }q_{e_d}>0 \end{aligned}$$
(80)
$$\begin{aligned} \pi ^{l}_{\sigma \sigma }= & {} 2q_{\sigma }-c^{l}_{qq}(q_\sigma )^2<0 \end{aligned}$$
(81)
$$\begin{aligned} \pi ^{ul}_{m e_d}= & {} i_{e_d}-c^{ul}_{ii}i_m i_{e_d}<0 \end{aligned}$$
(82)
$$\begin{aligned} \pi ^{ul}_{m\sigma }= & {} i_{\sigma } -c^{ul}_{ii}i_\sigma i_m >0 \end{aligned}$$
(83)

and \(|q_{e_d}|<|q_{\sigma }|\) and \(|i_{\sigma }|=|i_{e_d}|\) according to (50), (19), and (9). Then, it is likely that \(-\pi ^{l}_{\sigma \sigma }\pi ^{ul}_{m e_d}<0\) is more important than \(\pi ^{ul}_{m \sigma }\pi ^{l}_{\sigma e_d}>0\). Therefore, we are likely to have \(m^{*}_{e_d}<0\). As a result, we are likely to have \(\sigma ^{*}_{e_d}-m^{*}_{e_d}>0\). \(\square\)

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Yahagi, K. Regulation on coexisting legal and illegal markets with quality differentiation. Eur J Law Econ 53, 235–259 (2022). https://doi.org/10.1007/s10657-022-09724-x

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