Cannabis Grow

The cannabis industry has historically resisted widespread farm consolidation, perhaps due to its status as an unregulated, and illicit or semi-licit, activity. While the amount of cannabis produced in California is substantial , evidence from 2016 suggests that most outdoor cannabis was then produced on farms smaller than one acre . When Proposition 64 legalized non-medicinal cannabis in 2016, its size provisions explicitly acknowledged the state’s desire to see cannabis farms remain small . Initial regulations limited each permit to an area no greater than one acre and limited each entity to only one permit. Federal laws against cannabis have also encouraged small farms: Farmers with more than 99 plants potentially face federal minimum sentences of five years in prison . Local permitting may also favor smaller producers. Each jurisdiction in California can create its own permitting system, and possessing a local permit is a condition for obtaining a state permit. Most local jurisdictions place limitations on field sizes, and these limitations can encourage small-scale farming. While local permits may provide an avenue for local governments to protect small farmers , they also add another layer of regulation, potentially increasing entry costs. Beginning with California’s first attempt to implement a comprehensive regulatory system for the cultivation and distribution of legal cannabis, through the 2015 passage of the Medical Marijuana Regulation and Safety Act, stakeholders have expressed concerns that the permitting process privileges large farms over small. MacEwan et al. calculate that, due to the nature of regulatory costs, the type of small cannabis farmer prevalent in Northern California is the “least likely to participate in the regulated market.” Yet to date, empirical evidence on cannabis producers’ engagement with the formal market under the new regulatory framework has been lacking. In particular,hydroponic drain table there is a large evidence gap about the types of farms that participate in the regulated market and those that do not.

The gap exists partly because of a lack of public data about growers who have not applied for permits. We remedy that gap by combining information about farmers who have started the permit application process with a unique dataset of cannabis farms in Humboldt County in 2012 and 2016. Humboldt County is one of the largest cannabis producing regions in California and perhaps the world. Cannabis farming began there in the early 1960s, with rapid expansion following in the 1970s, and cannabis has been among the most valuable crops in the county at least since a proposition legalizing medical cannabis was approved by voters in 1996 . Recent studies suggest that at least 5,000 cannabis farms operate in Humboldt County . In the lead-up to the enactment of regulated cultivation of cannabis — which began for the medicinal market in 2016 and for the adult-use market in 2018 — the region experienced a cannabis boom, with the number of plants under cultivation increasing by 150% between 2012 and 2016 . This time of massive cannabis expansion is often referred to locally as the “green rush.” To track both permitted and unpermitted cannabis growers, we used data created by Butsic et al. . In their study, Butsic et al. hand-digitized cannabis farms using very high resolution satellite imagery. Cannabis production was measured in both 2012 and in 2016. Outdoor plants were counted and the number of plants inside greenhouses was estimated based on greenhouse size. Of the 1,724 farms in the dataset, 942 started producing cannabis between 2012 and 2016 and 782 produced at least some positive amount in both 2012 and 2016 . For permit data, we used publicly available data from the Humboldt County Planning Department, compiled from applications for commercial cannabis cultivation permits . We were able to combine the farm location data with the permit data based on the unique parcel identification that existed in both datasets. In total, applications were received for cultivation on 1,945 unique parcels. Of these, 533 were located within our study area .

We also include data describing farm/parcel characteristics. Locational variables such as distance to public roads and cities are used to proxy for transportation cost, while distances to endangered and threatened fish species habitat proxy for the environmental sensitivity of a site. Biophysical characteristics such as slope and presence of prime agricultural soils are used to describe the growing conditions of a site, while zoning designations are used to identify areas where growing cannabis is allowed . We also determined if a timber harvest plan had been associated with a parcel at any point since 1997. We include the quadratic term on farm size to increase the goodness of fit in our model and allow a more flexible relationship between farm size and permit application. The other covariates included in our regression are useful predictors of permit application, as they explain site-specific characteristics as well as proxy for potential land-use opportunities. They have been found to be significant predictors of farm location or farm abandonment . Importantly, these other covariates are primarily time-invariant or predetermined at the time growers decide whether to apply for permits. Specifically, we include variables of environmental sensitivity as proxies for potential challenges in obtaining approval from the Regional Water Quality Control Board. We include zoning information to help describe the other potential uses of the parcel if it were not being used for cannabis. Finally, we include a variable indicating if the area had ever had a timber harvest plan since 1997. We include this variable to see if past land use influences the likelihood of permit application. The average farm size in 2016 was 432 plants, with a median of 263 plants, a minimum of 14 and a maximum of 12,901 . Over 90% of farms produced fewer than 1,000 plants and fewer than 2% produced more than 2,000. Examining permit application rates by farm size reveals a distinct size gradient , as application rates increase substantially over farm-size categories. This pattern holds for both existing and new farms, but the rise is much sharper for the latter. Approximately 10% of small new farms apply for a permit, but rates jump to 61% and 50%, respectively, for the largest farm size groupings. We found a significant difference in size between farms that applied for a cannabis permit in 2016 relative to those that did not apply . The trend according to which larger farms applied for permits at higher rates held true regardless of production type .

The size differences are proportionally similar for both greenhouse and outdoor plants, so we do not find evidence that the relationship between farm size and permit application is solely driven by production method. Our regression models confirm that this result is robust to controlling for other covariates. In all our regression specifications, the coefficient on the total number of plants in 2016 is positive and statistically significant at the 1% level. The effect size of the number of plants indicates that, controlling for parcel characteristics, an increase of 100 plants increases the probability of applying for a permit by 2.4% , with the slope of the relationship declining for extremely large farms . The overall marginal effect is similar for existing and new farms, , though the declining marginal effect for very large farms is driven by new farms , and is robust to the inclusion of watershed fixed effects . The pattern also holds for size in 2012. Restricting the sample to existing farms, an increase of 100 plants in 2012 increases the probability of application by 3.1%.We first categorize growth of existing farms according to the proportionate change in plants produced between 2012 and 2016. The “declining production” group consists of farms that shrank by more than 5% ; “minimal change” farms experienced between −5% and 5% growth ; “moderate growth” farms grew between 5% and 50% and “high growth” farms grew by more than 50% . Within the sample of existing farms, there is a clear gradient of application rates with respect to growth between 2012 and 2016 . The farms least likely to apply are those that declined in size, followed by those with minimal growth. Application rates for existing farms that grew moderately jump to over 40%,rolling benches hydroponics with high growth farms the most likely to apply. Note that across all expansion rates for existing farms, application rates are significantly higher than the average rate for new farms. Statistical tests confirm this trend. Existing farms that applied for permits displayed a mean expansion of 212 plants between 2012 and 2016, while the mean expansion for farms that did not apply was 130 plants . This difference of 82 plants is significant at the 1% level. Our regression results also find expansion associated with permit application . In column 6, an increase of 100 plants among existing growers is associated with a 1.5% higher probability of applying for a permit, with the result positive and statistically significant at the 1% level. farms than existing farms . However, new farms are far less likely to apply for permits than existing farms. The univariate comparison shows that, on average, a new farm was 22% less likely to apply for a permit than a farm that already existed in 2012. Our regression results indicate that this relationship is robust to controlling for associated covariates, including farm size. The coefficient on new farms is statistically significant and negative in all regression specifications.

Controlling for other factors, new farms are approximately 7.3% less likely than existing farms to apply for a permit, with the magnitude of the effect slightly reduced when relying only on within-watershed variation . Small new farms are very unlikely to apply for a permit, even in comparison with existing farms of similar size . Regression results indicate that farms which have not applied for permits tend to be located further north, closer to both cities and the coast and further away from roads . They are also more likely to be located on prime agricultural soils, which is a listed requirement for obtaining a permit. However, there seems to be no effect associated with flat terrain or agricultural zones, which are also requirements for permits. These results suggest that siting criteria in the permit ordinance do not appear to be positive independent drivers of application decisions. In contrast, farms that did apply for permits tend to be located closer to streams and chinook salmon habitat, even as permit eligibility requires the use of non-diversionary water sources . Applying farms are also more likely to be located in forest recreation or timber production zones and to have been transacted at least once since 2015. They also tend be located on larger parcels. However, from comparing the results in columns and , it is clear that a number of regression outcomes between permit applications and parcel characteristics are not robust to the inclusion of watershed fixed effects. This suggests the existence of underlying geographic drivers which might influence these relationships.Cannabis has been profitably produced in California, primarily on small farms, for decades . As cannabis becomes increasingly legal, production practices have become more standardized, and many small farms fear that the increased regulatory costs associated with formalization will force them to either shut down or remain on the black market . Here, we use empirical data on farm location and permit status to investigate differences between cannabis farms that applied for permits to produce in the legal market and those that did not. We find strong evidence that farms with more plants are more likely to apply for permits than farms that grow fewer plants. This is consistent with the argument that increased formalization disfavors small-scale farms . A potential implication of this trend is that continued cannabis expansion in California may disproportionately favor the establishment of large farms, despite measures seemingly designed to prevent this outcome. Small cannabis farms may face challenges similar to those faced by small farms producing other crops — and if small farms are valued, additional policy solutions are required. While our results point toward a robust positive relationship between size and permit application , we cannot definitively attribute the cause to either the fixed cost of initial application or ongoing costs associated with regulatory compliance.

Similar to findings by Lopez-Larson discussed above, the concept of deleterious effects related to early initiation of cannabis has been explored in the neuroimaging literature as well. According to Wilson and colleagues , individuals reporting marijuana use prior to age 17 demonstrated decreased whole brain and cortical gray matter in addition to increased percent white matter volume. Findings also included higher cerebral blood flow in males reporting early initiation of marijuana use. While findings do not necessarily support a clear and consistent pattern of changes in cortical/sub-cortical volume and thickness measurements, as emphasized by Lopez-Larson and colleagues, we can conclude that marijuana may influence the trajectories of appreciable gray matter changes in several ways. The compound may illicit premature tissue development, impose a marijuana-related effect on regressive changes , and alter ongoing myelination of fiber tracts that are impacting gray matter estimates. Functional changes likely affect the mechanics that underlie structural brain changes, and interactions between these processes cannot be ruled out.White matter tissue integrity is believed to be important for efficient cortical connectivity in the developing brain. The literature has shown linear increases in white matter over early development. As the brain becomes increasingly myelinated and fiber bundles mature from infancy to late adolescence, restriction of diffusing water molecules along the principal axis of an axon is commonly observed due to increasingly compact fibers and with more limited intracellular space. Diffusion tensor imaging commonly utilizes two indices of white matter tract coherence to reflect water diffusion in white matter, fractional anisotropy and mean diffusivity , which are thought to help to identify alterations in the health of white matter fibers. Increases in FA and decreases in MD are typically seen in healthy white matter development from young children to early adulthood. In 2006, DeLisi and colleagues published one of the earlier studies to explore the potential for deleterious effects of cannabis on developing white matter.

The authors found higher FA and lower in MD in several tracts in MJ users compared to matched controls; they conclude no evidence of pathological white matter changes despite finding differences between groups. Since this study,greenhouse growing racks findings do suggest some evidence of alterations in white matter integrity in adolescent cannabis users. While DeLisi and colleagues suggest no evidence of pathology per se, subsequent studies have since shown changes in unanticipated directions. While this may not represent a typical pathological process, group differences in either direction may still be reflective of a neural alterations. For instance, increased MD in the prefrontal fiber bundles of the corpus callosum in heavy cannabis using adults who initiated use during adolescence suggest changes in white matter development associated with cannabis use. Ashtari and colleagues found that adolescents with heavy cannabis use enrolled in residential drug treatment had reduced FA and increased MD in cortical association areas such as the temporal-parietal fiber tracts. Recently, in a small sample of adolescents approximately 18 years of age, WM alterations were found in cannabis users compared to controls. Decreased FA in cortical and sub-cortical areas was found in cannabis users compared to controls with no history of substance abuse. In our laboratory, we have found white matter alterations in our abstinent teen marijuana users compared to controls. In two studies published in 2008 and 2009, we found poorer white matter integrity in several association and projection fiber tracts in adolescent cannabis users with concomitant alcohol use. Areas showing between group differences included tracts linked to fronto-parietal circuitry. White matter integrity in several of these regions was linked to neurocognitive performance on measures of attention, working memory, and processing speed; we have also seen white matter linked to emotional functioning and prospective risk taking in our substance users.

To better understand micro-structural differences in tissue integrity among adolescent marijuana users as compared to binge drinkers, we looked at white matter differences between adolescent binge drinkers compared to binge drinkers with histories of heavy marijuana use . While between group differences persisted between marijuana users and controls, surprisingly, teens engaging in binge drinking only looked significantly worse on indices of white matter integrity in several areas as compared to marijuana users, highlighting the need for further research to disentangle the effects of marijuana and alcohol on the developing brain. In general, research points to poorer white matter integrity in adolescent marijuana users compared to non-substance using controls. While white matter findings are subtle in nature, we have observed poorer white matter integrity correlated with poorer neurocognitive functioning in our studies, which underscores the impact that slight alterations in white matter health during this time could have on optimal cognitive functioning. Interestingly, some preliminary evidence supports that marijuana-related toxicity on white matter integrity may be more modest compared to the impact adolescent alcohol use has on the developing brain, although more research in needed in this area.Changes in cognitive performance after acute and longer-term cannabis use are fairly well documented, even if residual effects are suspected to largely resolve. However, less is known on how brain functioning, or neural activation/signaling, may be changed by marijuana use and thereby reflected in declines in neuropsychological performance. Comparisons between blood oxygen dependent signal in adolescent marijuana users and controls in response to cognitive tasks have revealed subtle differences in brain activation patters in marijuana users. Jacobsen and colleagues were the first to pilot an auditory working memory fMRI study comparing marijuana users compared to a tobacco using group and control group. The authors found cannabis users performed the task less accurately and failed to deactivate the right hippocampus across conditions. In another study by the same authors, nicotine withdrawal elicited increased activation across brain regions in the marijuana group, including parietal cortex, superior temporal gyrus, posterior cingulate gyrus, and the right hippocampus. The same effect was not found in the tobacco-only control group suggesting marijuana use may lead to developmental changes masked by nicotine use.We have conducted several BOLD fMRI studies evaluating differences in activation patters between our sample of abstinent marijuana users and matched controls.

In 2007, we found marijuana users to have substantially more activation than non-using peers in response to an inhibitory processing task, particularly in parietal and dorsolateral prefrontal cortices, suggesting additional neural resources required to maintain adequate executive control during response inhibition. In evaluating response patterns to a spatial working memory task, adolescent marijuana users exhibited increased activation in the right parietal lobe along with diminished activation in the right dorsolateral prefrontal cortex to achieve good task performance, which was not observed in controls. In a follow-up investigation using the same spatial working memory task, we evaluated teens with more recent abstinence compared to prolonged abstinence from marijuana, as well as matched controls. Recent users showed greater brain activation in prefrontal cortices, regions needed for working memory processes, and bilateral insula. In response to a third task assessing verbal encoding, marijuana users demonstrated increased encoding-related activation in anterior brain regions as compared to decreased activation in posterior regions, despite no differences in task performance; findings may suggest increased recruitment of neural resources in brain areas sub-serving task-related processing in marijuana using teens. Several recent studies outside of our laboratory have shown similar findings. For example, Jager and colleagues evaluated boys with frequent cannabis use compared to matched controls and found that cannabis users showed excessive activity in prefrontal regions in response to a working memory task, studies from this same research group with young adults have yielded similar, although modest, aberrant findings of the working memory system. In 2010, an investigation comprising chronic marijuana users and matched controls , suggest increased activity in the prefrontal cortex in response to a task requiring executive aspects of attention. Cousijn and colleauges recently found increased activation in heavy cannabis users in response to the Iowa Gambling task during win evaluations in brain areas such as the insula, caudate, and temporal gyrus, which was also positively related to weekly cannabis use; win-related increase in brain activity also predicted increased cannabis use six months later.Lopez-Larson and colleagues found differences in cortico-cerebellar activity in older adolescents with heavy marijuana use. The authors describe decreased activation in response to a bilateral finger-tapping task,vertical hydroponic garden and motor function activation was negatively correlated with total lifetime marijuana use.

Age of onset also continues to play an important role, as early-onset cannabis users demonstrated increased activation in the left superior parietal lobe in response to a verbal working memory challenge , and earlier initiation of use was associated with increased BOLD activity. The majority of findings suggest increased recruitment of neural resources in brain areas sub-serving task-related processing in marijuana using teens. There has been limited research on brain functioning using EEG among adolescent cannabis users. The strength in using EEG is the degree of temporal resolution that is not possible with BOLD imaging. Information on the degree of attentional bias to marijuana cues may provide some indication of brain-based differences in cue-reactivity resulting in heavier use of marijuana among certain teenagers. For instance, one lab based paradigm of cue reactivity found increased skin conductivity among teens diagnosed with cannabis use disorde. Nickerson and colleagues found that among adolescents ages 14–17, P300 response was larger among cannabis users, and response increased in the user group after handling marijuana paraphernalia; findings suggest attentional bias, increased arousal, and possible neural differences that may elucidate discrepancies among teen substance use engagement. The neurovascular effect of marijuana use in adolescence has not been studied extensively. Understanding vascular changes in cerebral blood flow can help us better understand neural signaling and vascular alterations that may be related to changes in neurocognitive functioning and/or changes in neural signaling related to the BOLD signal. Adult studies typically report increased CBF after acute exposure and lower or stabilized CBF after a period of abstinence in heavy users, although this has varied to some degree. To our knowledge, there has only been one study in adolescent blood perfusion in heavy cannabis users. In a recent study in our laboratory utilizing arterial spin labeling , we found that heavy marijuana users assessed pre-and post 28 days of monitored abstinence showed reduced CBF in 4 cortical regions, including the left superior and middle temporal gyri, left insula, left and right medial frontal gyrus, and left supramarginal gyrus at baseline; users showed increased CBF in the right precuneus at baseline, as compared to controls. We did not observe group differences in neurovascular functioning after four weeks of abstinence, suggesting marijuana may influence cerebral blood flow acutely with a possible return to baseline with prolonged abstinence. A study evaluating young adults found that acute THC administration increased blood perfusion in areas important for emotional and cognitive processing, such as the anterior cingulate, frontal cortex, and insula, and reduced perfusion in posterior brain regions. Resting state activity was also altered, as THC increased baseline activity. Very few studies have looked at neurochemical brain changes related to marijuana use in adolescence. Prescott and colleagues found decreases in metabolite concentrations in the anterior cingulate, suggesting poorer underlying neuronal health in adolescent marijuana users, While the exact mechanisms by which cannabis would affect neuronal health is unclear, it is possible that modulation of neurotransmitters such as glutamate and GABAhave adverse consequences on cellular development and neuron integrity; changes in neuronal health is one suggested mechanism which may underlie neuroimaging and neurocognitive findings discussed above.A detailed analysis of the preclinical studies is beyond the scope of this review, however briefly discussing the existing literature is important for translation to human models. Studies also focus on various cannabinoids beside Δ9 -tetrahydrocannabinol , the principal psychoactive component of marijuana; for example increasing attention is being given to cannabidiol, a nonpsychoactive cannabinoid that may have promising therapeutic effects independent of THC. However, this brief summary will focus on models of exposure to the natural compound or cannabinoid agonists, which mimic the structure Δ9 -THC. A great benefit of animal studies is lack of heterogeneity that corresponds with human consumption and substance use reporting. In animals, postnatal days 28–49 correspond with human adolescent development. Studies during this postnatal time period in rats have evaluated both emotional behavior as well as cognitive/behavioral functioning.

There are many rare social facts, but some become pathological based due to their infrequency, and others do not. Why is that so? To tailor the Durkheimian theory and make it more suitable for my empirical analysis, I incorporate Mary Douglas’s idea of pollution and power. Rather than speaking about pathological forms, Douglas focuses on marginal conditions. Her basic premise is that objects, practices, behaviors, and ideas that do not fit the existing social classifications are considered polluting, impure, and even dangerous and thus should be separated . Managing spatiality is a technique of power that allows the legitimate authority to reject “inappropriate” elements and protect what it deems normal, natural, and right. In this paradigm, space is not value-free but constructed through politics and power relations. Take, for example, racial segregation, building the wall to isolate immigrants, hot spot policing, skid rows, mental hospitals, jails and prisons—in all these cases, devaluation of individuals involves their spatial separation. Socio-spatial stigmatization is a mutually constitutive process, in which places inherit the stigma of persons, but persons also can be stigmatized through their interaction with places . For instance, concentrating homeless shelters into specific areas of a town tends to reinforce the stigmatized understanding of such areas. In similar ways, the stigma attached to a homeless shelter extends to individuals using it.Those who live in areas with a high concentration of “disordered” facilities, practices, and individuals tend to oppose them physically, ideologically, and discursively . For example, in his research of addiction treatment clinics in Toronto, Christopher Smith shows that residents perceive these facilities as a threat to the productive places and try to enforce certain socio-spatial borders .

Previous research showed that medical cannabis drying trays dispensaries were more likely to be located in less desirable parts of a neighborhood, characterized by high poverty level, unemployment, and homelessness . However, we know very little about the recreational cannabis facilities: Are they perceived as a “matter out of place”? Do they blur, contradict, and otherwise confuse the moral and social order of the communities? This study investigates the extent to which cannabis is normalized in California. Normalization is a barometer of changes in social behavior and cultural perspectives . Drawing from the Thomas theorem—stating that if men define situations as real, they are real in their consequences—I suggest that if cannabis is conceived as legitimate, it will not be pushed to the geographical and/or social margins. By the same token, if it is viewed as dangerous and illegitimate, then cannabis dispensaries will be regarded as sites of contagion, which are to be marginalized and isolated. I conduct a regression analysis to identify factors that explain variations in cannabis practices at the city level. In particular, I examine the relationship between the support of cannabis legalization in California cities and the number of cannabis licenses issued by local governments. Following the normalization theory, I expect that cities whose residents supported cannabis legalization are more likely to permit legal cannabis dispensaries within their borders. If residents view cannabis as legitimate and socially acceptable, local governments will favor cannabis-related activities on their territories. I also expect homogeneity in the characteristics of cities whose citizens supported cannabis legalization and those that permitted cannabis businesses. For example, if cities whose residents voted for cannabis legalization have a higher percentage of the middle and upper class, then cities that de facto legalized cannabis would also have a higher percentage of the middle and upper class. Since cannabis businesses create jobs and bring tax revenues to city budgets, local governments have strong incentives to permit cannabis-related activities, especially when most citizens favor legalization. But imagine situations in which citizens voted for cannabis legalization, but governments forbade any cannabis businesses, or, on the contrary, citizens did not support the legalization, but governments adopted pro-cannabis policies. These examples demonstrate the dissociation between the public’s wishes and the government’s deeds and cast doubts on the legitimacy of cannabis in a given jurisdiction. As I discussed earlier, cannabis users and distributors bear a stigma that can potentially extend to other people and places.

Prosperous communities may decide to distance themselves from the possible harm of cannabis stigma and forbid any cannabis related activities . In contrast, for economically disadvantaged communities, financial benefits may outweigh the harm of stigma and reinforce the marginalization of places with already limited resources. I look at the adoption of pro-cannabis regulation as an example of morality policies, through which local governments draw a boundary between “pure” and “polluted”, “ordered” and “disordered”, “safe” and “dangerous.” To get a more nuanced picture of the legalization and normalization processes, it is important to understand the moral-economic rationale behind decision making at the city level. The question is not only whether cannabis is legal, but where, how, and to what degree it is legal. The goal of the quantitative analysis is to reveal variations in local regulation practices and define factors impeding or fostering the adoption of permissive cannabis policies at the city level. By exploring which cities are more likely to allow cannabis businesses, this research contributes to understanding the relationship between legitimacy and legality and helps determine the current status of cannabis in California. Moreover, the focus on city-level data provides an insight into how boundaries of normality vary across local contexts. Acting as moral entrepreneurs, local governments rely on principles of the politics of pollution and create a cognitive map of acceptable and non-acceptable places . Previous studies have highlighted the importance of religiosity, economic development, political competition, community composition, organizational perviousness, and historical legacies in explaining moral policy outcomes . This research takes a different path and sets out to clarify the relationship between changes in public attitudes and the adoption of morality policies . I posit that greater social and cultural accommodation of cannabis can explain permissive cannabis policies only to a certain degree. Socio-economic characteristics of communities and their racial composition also matter. Licensing agencies collect information at the individual level.For the current project, I aggregate the number of issued licenses at the city level, which excludes any personal identification from the dataset . The reasoning behind aggregating data at the city level is that according to the Medical and Adult Use Cannabis Regulation and Safety Act , cities have the full power and authority to enforce cannabis regulation and complete responsibility for any regulatory function relating to the licensees within the city limits.Local jurisdictions decide whether cannabis businesses will be legal on their territories or not, define which types of cannabis activity to allow , and establish regulatory schemes for activities involving growing or selling cannabis.Before applying for a cannabis license, an applicant has to obtain a permit from the city administration that would enable him/her to conduct commercial cannabis activity. The permit does not guarantee each applicant a cannabis license, but it gives him/her the green light to advance to the final stage and submit the application to a licensing agency.

The dependent variable has three different measures: the total number of cannabis licenses, the number of cultivation cannabis licenses , and the number of sale and distribution cannabis licenses . I suggest that factors explaining the permissiveness of local governments towards cannabis cultivation and cannabis distribution are not exactly the same. Cultivation primarily occurs in private spaces and thus is hidden from the public eye. On the contrary, retail is associated with public display: shop-windows, street signs, and advertising boards make cannabis dispensaries visible and accessible. I expect that the public display of cannabis will be more stigmatized than its private cultivation. The normalization of cannabis is a gradual process, and we cannot expect it to progress at the same pace in different localities. But we can assume that cities whose residents supported cannabis legalization will be more likely to pass pro-cannabis laws. As seen in Tab. 2, 72% of California cities supported the legalization of cannabis in 2016, but only 45% of them legalized cannabis-related economic activities within their borders. Moreover,of those cities whose residents did not support Proposition 64, 22% eventually permitted cannabis companies,what is need to grow marijuana despite the lack of public support . There is an obvious gap between people’s preferences and governments’ actions, which should be explained. Before turning to the description of other independent variables, I should address the issue of moral hypocrisy. Greater cultural acceptance of cannabis does not necessarily translate into moral acceptance of its sale and use. In particular, we do not know whether people who supported legalization are amenable to cannabis dispensaries in their neighborhoods—i.e., we cannot exclude the NIMBY phenomenon.The willingness to legalize cannabis may follow a pragmatic logic: decriminalizing cannabis generates tax revenues, creates jobs, and diminishes law enforcement costs. People may also support legalization because it gives an opportunity to begin repairing the damages caused by the criminal justice system in the past. Moreover, it may be perceived as a progressive move that fits general liberalizing trends, including same-sex marriage, abortion, pre-marital sex, drinking, gambling, and so on. And yet, people may be moral hypocrites: they may support the idea of cannabis legalization and act in discord with it by opposing the location of cannabis dispensaries in their backyards. The statistical analysis cannot account for these nuances and, thus, simplifications are inevitable.For the purposes of this analysis, “legitimacy” means tolerance of cannabis use rather than its total acceptance; it is what people are ready to declare publicly rather than act upon. Legitimacy is a necessary but not sufficient condition for legality.

What other factors can explain the responsiveness of local governments to morally controversial issues? Since only 38% of California cities allowed cannabis companies, the dependent variable has many zeros. To account for excessive zeros and overdispersion, I use a negative-binomial regression, which allows the variance to exceed the mean and is appropriate for analyses with a count outcome variable.87 Table 4 reports the estimated coefficients of the negative binomial regression models. Model 1 contains all cannabis licenses issued in California cities in 2018 and 2019. Model 2 tests hypotheses only for licenses issued by the California Department of Food and Agriculture. Model 3 tests hypotheses only for licenses issued by the Bureau of Cannabis Control . The analysis demonstrates that cities whose residents supported cannabis legalization are more likely to permit cannabis-related activities within their borders . It is not surprising since, as I mentioned above, 45% of cities supporting cannabis legalization allow legal cannabis companies. The main question is: What other city properties are associated with the adoption of pro-cannabis legislation? Opinion polls show that the middle- and upper-class representatives, young adults, and non-Hispanic citizens support cannabis legalization at higher rates than other social groups. I ran a separate model regressing the percent of support for Proposition 64 on the index of economic prosperity, percent of people aged 20 to 29, and percent of the Hispanic population . The results confirm that the support of cannabis legalization is associated with a higher index of economic prosperity, a larger percentage of young adults, and a lower percentage of the Hispanic population. This association is significant at the 0.01 level. However, as we see in Table 4, cities that eventually allowed legal cannabis companies, on the contrary, are more likely have a lower index of economic prosperity , a lower percentage of young population , and a higher percentage of the Hispanic population . The disparity between the demand and supply offers an intriguing puzzle. Economically prosperous cities, on average, express higher support of cannabis legalization, but it does not mean that they are more likely to permit legal cannabis companies within their borders. Moreover, there are significant differences between licenses issued for sale and cultivation. Cultivation licenses are more likely to be issued in cities with a lower percentage of the young population, which can be explained by the fact that these are mostly rural remote areas, and young adults live in more urbanized places. Sale licenses are associated with three other factors: a higher percentage of the Hispanic population, a lower city’s fiscal score, and higher violent crime rates. There is substantial evidence in the findings that socio-spatial stigmatization of cannabis persists despite its legalization.