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. Older farms are 25% larger, on average, than new farms. Both predominantly produce cannabis in greenhouses, where multiple crops can be produced each year. This increases potential revenue, though the share of greenhouse production is slightly higher among new 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, rolling grow tables 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. Small farms, for example, may be less able to engage with the legal supply chain or obtain favorable pricing in the legal market, or they may systematically differ from larger farms in risk tolerance. Thus, because we are unable to directly control for these factors in the regression analysis, it is unclear which of these potentially omitted variables might be driving the size-application relationship. That ambiguity suggests a topic for future study. We also find that existing farms that expanded during the “green rush” years were more likely to apply for permits. This finding could arise via multiple pathways. Perhaps farms that expanded during this time were those endowed with, or able to accumulate, sufficient capital to enter the regulated market. Alternatively, some farms may have invested more heavily specifically in anticipation of formalization and legal marketing opportunities. We also found that farms that were established after 2012 were less likely to apply for permits, all else equal. Whether these newer farms will continue to operate illegally or abandon their operations remains unknown.
Nevertheless, it suggests potential divergence in formalization strategies between newer entrants and older producers. Whether that divergence is driven by systematic differences in operators’ human capital and experience levels, in financial capital or in other unobserved factors like risk tolerance or “taste”- based considerations remains a subject for further research. Indeed, while formalization is clearly favored by larger farms, we do find evidence that smaller farms traditionally associated with Northern California cannabis production have not been completely shut out of the legal market. Though permit application rates for the smallest farms are substantially lower than those for large farms, the small farms that do apply tend to be farms with longer production histories. Our work documents permit applications at a dynamic moment in formalization, and we suggest that the trends we have seen to this point may change going forward. Many farms that applied for permits may not complete the application or gain approval, or may fail to receive necessary permits from state offices. Likewise, new cannabis investments continue in the county and some farms that initially resisted formalization may now decide to join the market. New cooperative businesses that specifically focus on supporting small farms are emerging, and these organizations are assisting small farmers in the permitting process. The final chapter of formalization is yet to be written. In the US, tobacco and cannabis use are common, particularly among adolescents and young adults Simultaneous tobacco and cannabis use is common in Europe and increasingly common in the US . As companies promote new smoking devices that may be used for both tobacco and cannabis, such as e-cigarettes and vaporizers, those who previously used either tobacco or cannabis alone may be more prone to using both drugs . Some users believe that combining tobacco with cannabis in a single occasion can enhance or prolong the psychoactive effects of cannabis use , and those who vape tobacco and cannabis believe that vaping is a safe form of ingesting each substance . Simultaneous use is associated with more symptoms of dependence, reduced motivation to quit, and greater social problems that separate use , however, flood drain table prevalence data assessing simultaneous use among other vulnerable population groups has been limited. These findings suggest a need to make explicit distinctions in surveillance data between two types of tobacco and cannabis use: simultaneous use, meaning both products are consumed during the same occasion, and separate use, in which respondents consume both products but do not do so during the same occasion. The current policy landscape provides multiple pathways that could lead to increased simultaneous use. Electronic nicotine delivery systems , including e-cigarettes, may encourage those who vape nicotine to also vape cannabis. Legalization of recreational cannabis can lead to use among some adults who would not have used cannabis when it was illegal . Given that more states have legalized medical marijuana and recreational cannabis, use is likely to increase . Given the likely increases in use, predicting patterns of tobacco and cannabis consumption in these states is critically important. Many ongoing surveys collect information about alcohol, tobacco, and other drug use in the US, including the National Survey on Drug Use and Health , Monitoring the Future, and the National Adult Tobacco Survey . While these surveys provide extensive information on the national level about the prevalence of substance use, data on different possible patterns of tobacco and cannabis couse at the state level are not necessarily available.
California was a policy innovator in the late 1990s, the first in the US to decriminalize the use of medical marijuana and eliminate smoking in bars . This combination of policies changed attitudes about the use of tobacco and cannabis, making tobacco use less socially acceptable and cannabis use more so. Consistent with these changes in attitudes, in 2016 California passed a number of innovative policies again, legalizing the recreational use, cultivation, and sale of cannabis, raising the minimum age of legal access for tobacco and cannabis to 21 years, and regulating e-cigarettes under existing tobacco control laws. Despite a growing number of studies examining the relationship between tobacco and cannabis use, no surveillance studies have characterized the prevalence of simultaneous use in California relative to the separate use of tobacco and cannabis. We report on the results of a 2016 population-based survey of adults in California, two decades after the first major changes to tobacco and medical marijuana laws but prior to the implementation of the 2016 policy changes. We specifically investigated characteristics of respondents who reported simultaneous use of tobacco and cannabis, distinguishing these users from those who reported separate use, as well as the use of each substance individually. This study relied on a cross-sectional analysis of survey data drawn from the California Adult Tobacco Survey , a weighted representative sample of approximately 3,000 California adult residents. Data are collected under a contract with the California Tobacco Control Program, which requires that the data be collected from an existing probability based online sample that has been used in peer reviewed publications. In 2016, the contractor for CATS data collection was the GfK Group , The Growth from Knowledge \ Group , a survey research firm based in Germany. The 2016 wave was completed prior to the implementation of 2016 policy changes for cannabis and tobacco. The online study was conducted in English and in Spanish using computer-assisted self-interview and the panel related on probability-based sampling of addresses. Response rates from GfK surveys are reported by GfK to be approximately 65%, with the possibility of minor variations due to survey length, topic, and other fielding characteristics, The Growth from Knowledge \ Group . A detailed description of the study design and sampling strategy is available from the California Tobacco Control Program . Respondents were queried about tobacco and cannabis use, and modality of use, in the past 30 days. Modalities of tobacco use included cigarettes, smokeless, cigars, little cigars/ cigarillos, pipes, hookah, and e-cigarettes. Modalities of cannabis use, whether medical or recreational, included smoking, eating, drinking, vaping, dabbing, or “another way” volunteered by the respondent. Questions were based on the National Adult Tobacco Survey conducted by the US Centers for Disease Control and Prevention . Simultaneous users were identified by asking participants if they had used cannabis in the past 30 days; the modality by which they had used cannabis, and then, “You said you used marijuana [in the past 30 days]. Did you use marijuana with any form of tobacco in it, such as a blunt or a joint with a combination of tobacco and marijuana?’” Separate users were those who indicated consuming both cannabis and tobacco in the past 30 days, but who did not report simultaneous use. After collection, data were weighted based on California demographics drawn from the 2010 and 2015 Current Population Surveys. We identified weighted 30-day prevalence estimates and cross-tabulations for tobacco and cannabis use using the software package STATA version 15. We then created weighted odds ratios based on multivariate logistic regression to identify any statistically significant associations between 30-day tobacco and cannabis use, considering whether respondents were users of individual substances, were separate users, or were simultaneous users, relative to sociodemographic characteristics and age of first use or initiation.