1. Trimming and Bucking:
   • Properly trimming and bucking the harvested cannabis flowers involve removing excess leaves and stems. This not only improves the visual appeal of the buds but also enhances the efficiency of subsequent processing steps.
2. Cannabis Extraction:
   • Cannabis extraction methods, such as solvent-based extraction (using ethanol, CO2, or hydrocarbons) or solventless extraction (such as rosin pressing), can be employed to isolate cannabinoids and terpenes from the plant material. This is commonly used to produce concentrates, oils, and tinctures with higher cannabinoid concentrations.
3. Decarboxylation:
   • Decarboxylation is a process that involves heating cannabis to convert non-psychoactive cannabinoids (such as THCA and CBDA) into their active forms (THC and CBD). This step is crucial for making edibles, tinctures, and other products where the cannabinoids need to be in their activated state.
4. Infusions:
   • Cannabis-infused products, such as edibles, beverages, and topicals, involve incorporating cannabis extracts or decarboxylated cannabis material into various mediums. This allows for precise dosing and diverse consumption methods.
5. Terpene Preservation:
   • Some post-harvest processing techniques focus on preserving and enhancing the terpene profile of cannabis. For example, cold extraction methods, like live resin extraction, aim to capture and retain the full spectrum of terpenes found in fresh, uncured cannabis.
6. Microbial and Contaminant Testing:
   • Rigorous testing for microbial contaminants, pesticides, heavy metals, and other impurities is a crucial post-harvest step to ensure the safety and quality of the final cannabis products.
7. Quality Control and Testing:
   • Regular quality control measures involve testing for cannabinoid and terpene profiles, ensuring accurate labeling of products,dry cannabis and monitoring for any changes in quality over time.
8. Packaging and Storage:
   • Proper packaging is essential to maintain the freshness, potency, and quality of cannabis products. Packaging should be airtight, light-resistant, and compliant with local regulations. Proper storage conditions, including temperature and humidity control, also contribute to preserving the quality of the final product.
9. Product Formulation:
   • In the case of infused products, careful formulation can enhance the overall experience for the consumer. Balancing cannabinoids, terpenes, and other ingredients can create products with specific effects, flavors, and aromas.
10. Product Innovation:
    • Continuous research and development lead to innovative post-harvest processing techniques and product formulations. This includes exploring new extraction methods, delivery systems, and product categories.

Each of these post-harvest processing techniques plays a role in creating a diverse range of cannabis products that cater to different consumer preferences and needs. The specific techniques employed depend on the intended end product and the goals of the cultivator or processor.

Notably, although the associations between cord plasma concentrations of chlorpyrifos and diazinon were highly significant in children born before the US EPA started to phase out residential use of these pesticides, they were no longer detected in children born after. However, only cord plasma chlorpyrifos, but not diazinon, levels were significantly decreased in the relevant period. In a different cohort of pregnant women in New York, no association was detected between self-reported pesticide use during pregnancy, urinary levels of TCPy, or pyrethroid metabolites obtained during the third trimester and birth weight, length, head circumference, or gestational age . However, when maternal activity of the phase-II detoxifying enzyme paraoxonase 1 activity was accounted for, maternal urinary chlorpyrifos metabolite levels were associated with a small, but significant, decrease in head circumference. Most of the enzymes involved in the metabolism, activation, and detoxification of OP pesticides and other chemicals discussed here exhibit polymorphisms that greatly influence enzyme activity. This study represents one of the rare examples where at least one of these polymorphisms was accounted for. Notably, urinary levels of pesticide metabolites are highly variable, and measurements obtained at three different time-points show significant within-person variability . Therefore, one or two spot-urine samples are unlikely to provide a reliable measure of pesticide exposure throughout pregnancy. This may partially explain the inconsistent findings regarding birth outcomes in the aforementioned studies. Whether cord plasma or meconium concentrations constitute a more reliable measure remains to be established.Chronic exposure of rats to the pesticide rotenone has been found to constitute an animal model of Parkinson’s disease that reproduces the typical biochemical, molecular, anatomical, and behavioral findings in Parkinson’s disease . These include binding to complex I in the brain, selective nigrostriatal dopaminergic degeneration with relative sparing of the dopaminergic fibers in medial aspects of striatum, cytoplasmic inclusions containing ubiquitin and α-synuclein resembling the Lewy bodies associated with Parkinson’s disease, and hypokinesia and rigidity.

Notably, rotenone is a “natural” plant-derived compound that even organic farmers use on vegetable crops. Several epidemiological studies have suggested an association between agricultural work, which usually includes pesticide exposure, or pesticide exposure per se and idiopathic Parkinson’s disease , although others have found only suggestive evidence for such an association or have found no association . There is increasing evidence that occupational exposure to certain pesticides increases the risk of several cancers, including cancers of the brain and lungs , acute myeloid leukemia , and possibly multiple myeloma . Children may be particularly sensitive to the carcinogenic effects of pesticides, as suggested by numerous reports of associations between residential pesticide exposure and childhood cancers—particularly brain cancer and leukemia but also Wilm’s tumor, Ewing’s sarcoma, and germ cell tumors . Because cholinergic nerves in the vagi provide the major neural control of airway tone and reactivity, it seems plausible that OPs could induce airway hyperreactivity and asthma . Seven days after a single subcutaneous injection of 70 mg/kg of chlorpyrifos, vagally induced bronchoconstriction was found to be potentiated in guinea pigs in the absence of AChE inhibition . This effect was accompanied by decreased M2 muscarinic receptor function, whereas M3 receptor function was not affected. Similar results were obtained 24 h after administration of 1 or 10 mg/kg of parathion and 0.75 or 75 mg/kg of diazinon, although only the higher doses inhibited AChE . Intraperitoneal administration of parathion to guinea pigs increased lung resistance and mucus secretion and induced pulmonary edema . These broncho-obstructive effects were demonstrated to depend on the biotransformation of parathion by P450 enzymes. Even doses that did not increase lung resistance were able to induce airway hyper responsiveness not only to ACh but also to histamine. The latter was prevented by atropine, suggesting the involvement of a cholinergic mechanism. In the Agricultural Health Study, data collected on more than 20,000 farmers indicated that use of the OPs malathion and chlorpyrifos dose-dependently increased the risk of wheeze, and parathion also carried an elevated OR . It remains to be established whether OP pesticides at environmental exposure levels increase the risk of asthma and asthma-like symptoms.

OCs comprise a diverse group of synthetic chemicals that include not only pesticides but polychlorinated biphenyls , polybrominated biphenyls, polychlorinated dibenzofurans , and polychlorinated dibenzodioxins . OC pesticides include 1,1,1-trichloro- 2,2-bisethane ; lindane and other hexachlorocyclohexanes; cyclodienes such as dieldrin, chlordane, and heptachlor; and hexachlorobenzene. Many OCs—particularly the more heavily chlorinated ones—resist biotic and abiotic degradation and are lipophilic; therefore, they not only bio-accumulate in all parts of the environment,curing marijuana but are bio-concentrated from one trophic level to the next. PCDDs and PCDFs are tricyclic aromatic compounds. Because they can be substituted with between one and eight chlorine atoms, there are potentially 75 different PCDD and 135 PCDF congeners . However, the actual number present in biotic samples is much lower, and mainly 2,3,7,8-substituted congeners are detected. The most toxic congener is 2,3,7,8-tetrachlorodibenzo-p-dioxin , often referred to simply as “dioxin,” whereas the PCDDs are called dioxins. There are 209 possible PCB congeners, which differ in the degree of chlorination and the position of the chlorine atom; however, depending on the species and its trophic level, only between 50 and 150 congeners are detectable in biotic samples . Whereas PCDDs and PCDFs have rigid planar structures, the two rings of PCB molecules are joined by a single carbon–carbon bond, thus allowing axial rotation of the benzene rings. This freedom is restricted by the number and positions of the chlorine substituents and decreases from nonortho via mono-ortho to di-, tri-, and tetraortho PCBs. Planar PCBs exhibit the greatest resemblance to the dioxins. Whereas PCBs and polybrominated biphenyls were purposely produced for use as dielectric fluid in transformers and capacitors, hydraulic fluid, plasticizers, and fire retardants, PCDD/Fs arise as byproducts of thermal and industrial processes, particularly via incineration of municipal and hazardous waste. PCBs were produced in the United States from the 1920s until they were banned in 1977, with peak production occurring during the 1960s and 1970s. Historical global production of PCBs is conservatively estimated at 1.3 million tons, which were used almost exclusively in the Northern hemisphere . Emissions of PCBs were estimated to be in the range of 440 and 92,000 tons , and other data strongly have suggested that actual emissions were closer to the upper estimate . The environmental residence times of two of the major PCB congeners, PCBs 153 and 180, were recently estimated to be 110 and 70 yr, respectively ,suggesting that although the production of PCBs was halted approx 30 yr ago, exposure will continue for decades, if not centuries.Because persistent OCs are lipophilic, resist metabolism and bio-degradation, and bio-accumulate to similar extents in various biota, humans are simultaneously exposed to complex mixtures of these compounds. However, the precise nature of the mixture depends on various factors such as solubility, volatility, and rates of degradation as well as dietary and other lifestyle factors and geographic location. For the purposes of risk assessment and regulatory action, the concept of toxic equivalency factors has been developed . It is based on evidence that PCDDs, PCDFs, and certain PCBs exert their toxicity via binding to the aryl hydrocarbon receptor and subsequent induction of gene expression, particularly of various cytochrome P450 isozymes. The TEF concept assumes that the combined effects of these OCs can be predicted by a model of concentration addition. TEF values can then be used to calculate toxic equivalent concentrations by multiplying the concentrations of each PCDD, PCDF, or PCB by its TEF. Commonly, either the World Health Organization TEQs or the international TEQs developed by the NATO are used.

Inhalation of airborne OCs, stemming mostly from municipal and industrial incinerators and open burning of household trash, and dermal exposure make comparatively minor contributions to exposure. More than 90% of current exposure to background levels of PCBs and DDT and its metabolite dichlorophenyl dichloroethylene is believed to come from the dietary intake of contaminated foods—particularly dairy products, meat, and fish . Fish can contribute 75% or more of total PCDD/F and PCB TEQ ingestion in countries with high fish consumption , and in several studies, intake of fish—particularly from highly contaminated waters like the Great Lakes or the Baltic sea— has shown a significant association with serum concentrations of PCBs and their metabolites and PCDD/Fs . Notably, the traditional diet of many Arctic populations includes substantial amounts of marine foods, including sea mammals. Although OCs have been produced and used primarily in the lower and middle latitudes of the Northern hemisphere, long-range transport via the predominantly northward flow of rivers and ocean and atmospheric currents results in high exposure levels in the Arctic . Because of their lipophilicity and resistance to bio-degradation, many OCs bio-accumulate in fatty tissues and are bio-magnified in the aquatic food webs. Their consumption is associated with concentrations of PCBs and other OCs in serum, breast milk, and adipose tissue samples obtained from various Inuit populations that are up to fivefold higher than in other North American or European populations . In the United States, daily dietary intake of dioxin TEQs in the early 1990s was estimated to be 0.3 to 3.0 pg/kg body weight TEQs for an adult who weighed 65 kg . Estimates in eight European countries during the 1990s varied between 65 pg I-TEQ/d in the Netherlands and 210 pg I-TEQ/d in Spain, which is equivalent to 1 to 3 pg I-TEQ/kg body weight/d assuming a body weight of 70 kg . A more recent market basket study conducted in Finland on almost 4000 samples representing 228 food items, combined with results of a 1997 dietary survey, produced a similar estimate of 115 pg WHOTEQ/d, or 1.5 pg WHO-TEQ/kg body weight using an average weight of 76 kg . Up to threefold higher values for mean daily PCB and dioxin intake estimates have been reported for children . In most of the countries,the contributions of dioxins and dioxin-like PCBs to total TEQs were roughly equal, varying between approx 40 and 60%. Together, these data indicate that the daily intake of dioxin TEQs of many Europeans exceeded and probably still exceeds the TDI of 1 to 4 pg/kg/d recommended by the WHO .

Prior to the SUPERB interview, participants were given a list with pictures of current pesticide products in order to facilitate the recall of product names and brands. Most data collected for SUPERB pertained specifically to insecticide usage in the last year. SUPERB also contained a small subset of questions pertaining to indoor and outdoor pesticide use frequency from ages 18–50, for this age period it was possible for participants to report use of any type of pesticide. PEG and SUPERB recorded self-reported information on pesticide storage and personal protection methods used during application. The emphasis of PEG and CGEP was less on recent but more on lifetime residential pesticide usage. Thus, product names, purposes of use, and frequency of use of specific pesticides were collected for four periods of the participant’s lifetime: young adult , adult , middle age , and senior . It is important to note that not all participants had reached the age of 65 at the time of interview, therefore questions regarding the 65 and older period often had a smaller sample size than the three younger age periods. PEG and CGEP participants did not receive additional materials to aid with their recall of product names and brands. We relied only on the product names recalled by participants and the purpose of using the reported pesticide. If a participant could not remember data was recorded as missing. Relying on pesticide product information and years of use reported by PEG/CGEP participants, we utilized the California Department of Pesticide Registry online database to identify the pesticide products’ active ingredients [19]. The CDPR database contains information on pesticide formulations sold in California as far back as 1945. This extensive database allowed us to identify active ingredients for pesticides that participants reported using throughout their lifetime in a time specific manner. When participants did not provide sufficient information to accurately identify the correct product, active ingredient information was treated as missing. When the product was identifiable, among active ingredients, the one with the highest concentration was considered the main active ingredient. A chemical class was then assigned to the corresponding main active ingredient of a particular product. Chemical class information was primarily identified from CDPR and the Pesticide Action Network Pesticide Database . For analysis, we assigned active ingredients to one of the following chemical classes: pyrethroids, organophosphates, nitrogen containing lactones, carbamate, halogenated, metals/inorganic compounds, organochlorine, and botanicals .

In some cases a product’s chemical composition changed over time, thus we assumed subjects were exposed to all possible active ingredients in a product during the reported years of usage. Depending on the composition of the product, some were assigned more than one main ingredient and chemical class. We used data from all three studies to evaluate pesticide use throughout a person’s lifetime. We generated frequencies and percentages to describe the prevalence of various pesticide usage and exposure related behaviors. Most variables were multinomial rather than normally distributed. We also compared pesticide use within age groups by education and race. In many instances breaking our study population into smaller subgroups for comparison purposes created small cell sizes, necessitating the use of Fisher’s exact test. Another goal was to compare pesticide use during younger and older adulthood. In order to pool data from all three studies, it was necessary to reorganize the data, defining younger adulthood and older adulthood in each study in a slightly different manner prior to pooling; i.e., as <45 vs. ≥45 for CGEP and PEG and <50 and ≥50 years of age in SUPERB. Because our data on use of pesticides during younger and older adulthood was dichotomous we employed the Phi coefficient to compare pesticide usage across age groups. Data analysis was conducted using SAS 9.2 . Overall 89% of participants from all three study populations used indoor and/or outdoor pesticides at some point during their lifetimes, 72% of participants from all three studies reported ever using pesticides outdoors and 74% ever using pesticides indoors at any point during their lifetime. Our data on ever use frequency are comparable to previous studies that investigated residential pesticide use. In an older study by Savage et al. for the EPA region IX, which contains California and other western states, 62% of all participants reported ever using pesticides in the yard, 28% reported using pesticides in the garden, and 83% of households reported ever using pesticides inside their home within 12 months of being interviewed. A more recent study conducted by Colt et al. in Los Angeles, Detroit, Iowa, and Seattle reported that 94% of subjects had ever used insecticides in or around their current or former residences during a 30 year period prior to interview. In our study we found that 18% of individuals had used indoor pesticides only during their lifetime, 16% had used outdoor pesticides only,cannabis drying and 56% had used both. We speculate that many factors may contribute to this pattern of use, such as type of dwelling and urban versus suburban or rural address.

Unfortunately our three studies did not collect any or comparable information about such factors. We also examined frequency of pesticide use by race and education in each of the four age periods, young adult , adult , middle age , and senior . We found that there was no statistically significant difference of outdoor pesticide frequency of use by race. For indoor pesticide frequency of use, the only statistically significant finding was that nonwhites, ages 16–24, appeared to use indoor pesticides more frequently than whites ages 16–24. When examining frequency of outdoor pesticide use by education the only statistically significant difference in use during lifetime was seen after age 65 , such that individuals with <12 years of education appeared to use outdoor pesticides more frequently. Individuals with <12 years of education used indoor pesticides significantly more frequently throughout most of their adult lives In PEG and CGEP, lifetime frequency of pesticide use, both indoors and outdoors, increased with increasing age .We speculate that increased use of pesticides during middle age may be a reflection of changes in lifestyle during middle age. For example, individuals in middle age may be more likely to own their homes, whereas young adults may still live with family or rent an apartment. A homeowner may be more likely to apply residential pesticides in his/her home compared to an apartment tenant who does not have or take care of yards and gardens and may rely on a landlord to eliminate pests. Relying on information from all three studies we found that 50% of participants had ever used outdoor pesticides during younger adulthood and 63% had ever used outdoor pesticides during older adulthood . More than half of all participants had ever applied pesticides indoors during both younger and older adulthood . Outdoor and indoor pesticide use during younger adulthood was positively correlated with pesticide use during older adulthood . Our findings suggest that people are likely to use pesticides throughout their lifetimes, albeit at relatively low frequency. While the overall frequency of pesticide use was low, as people age there seems to be an increase in use. Our results also show that use of pesticides at younger ages may be related to use of pesticides at older ages. However, further studies are necessary to examine whether general attitudes toward pesticide use, which may be influenced by occupation or education, influence an individual to adopt and continue residential pesticide use throughout lifetime. Additionally it appears that at certain ages, race and education may also influence frequency of pesticide use. Higher use frequencies among older adults are important since pesticides may act more strongly as neurotoxins due to the nervous system’s inability to properly handle and repair damage from toxins during this stage of life. Future risk assessment for pesticides may need to take into account a possible increased vulnerability for older adulthood exposures as well as considering the accumulation of effects from even low-level exposures over a person’s lifetime. According to reports in our PEG and CGEP studies, organophosphates, halogenated pesticides, botanicals, and organochlorines were used more often outdoors, while pyrethroids, carbamates, and aromatic, nitrogen containing lactones were the most common active ingredients in pesticides used indoors. Metal/inorganic pesticides were used both in- and outdoors in similar proportion . However it is important to acknowledge that chemical classes found in consumer use pesticides have changed over the years due to the introduction of new chemicals and regulations limiting use of certain chemicals. For example pyrethroids have only become more common in the past twenty-five to thirty years, while regulations restricted the use of many organochlorines especially for residential and indoor uses. Therefore many individuals in our sample would likely not have used pyrethroids as young adults but also would have decreased their residential use of organochlorines and organophosphates as older adults.Both indoors and outdoors, pesticides were most commonly applied as sprays when participants were asked to report method ever used or method used in the last year . Ever usage of bait and granule varies greatly between indoor and outdoor pesticides; however use of bait and granule is more similar for indoor and outdoor use if we consider their use only in the last year. Participants also reported other application methods for indoor pests, e.g., foggers, gel, and stakes, and for outdoor application, e.g., powder, candles, foam, strips, and traps . Some of the differences in methods used over lifetime versus used in the past year may be due to the fact that PEG and CGEP collected data on any type of pesticide over lifetime, while in SUPERB use in the last year focused specifically on insecticide use. Relatively few participants reported the use of foggers in all three studies. In the case of PEG and CGEP it is possible that participants may have reported foggers as sprays. In SUPERB foggers were clearly separated from sprays. Foggers are a major source of potential insecticide exposure to residential users as they release far more chemical into the environment than the other methods of application mentioned here.

In order to continue tracking as many of the original 542 retailers as possible, we attempted to follow businesses that moved to new locations or that temporarily closed and then re-opened.We coded retailers by county, city and phone number.When a retailer’s listing disappeared, we searched for other listings under the same name or phone number.When we found the same retailer or a branch of the same retail chain elsewhere in the same county, we kept the retailer in the data set.If a retailer disappeared and then reappeared in a later round of data collection, we kept it in the data set.If a retailer removed its online price list, or moved its only location outside the original seven counties, we removed it from the data set for that data collection round.Between January 2017 and August 2017, we observed significant attrition from the initial group of 542 retailers in the October 2016 seven-county sample.By August 2017, 389 of the original 542 retailers remained in the data set.As shown in tables 2 and 3, average prices for these retailers changed little during this 11-month period.We call this “attrition” because the data collection method was consistent over this time period.In our 2018 rounds of data collection, we impose the additional condition that retailers must be licensed, thus changing the data collection method.Thus, for 2018 data collection rounds, the percentage of retailers dropping out of the data set from the original October 2016 sample of 542 retailers should not be thought of as “attrition.” Some retailers may have removed their online price lists from both Weed maps and Leafly but continued to operate.Attrition from the initial 542 retailers thus should not be interpreted solely as a measure of how many cannabis retailers left the legal cannabis segment.In January 2018, mandatory licensing laws went into effect, thus rendering illegal under state law any cannabis retailer without a temporary license from the Bureau of Cannabis Control.We verified licensing status by cross-referencing all Weed maps and Leafly listings in California with the publicly available lists of temporary licenses granted by the Bureau of Cannabis Control.If both a Weed maps and a Leafly listing were found, we used the Weed maps data and dropped the Leafly data.

In computing averages for our last three data collection rounds , we calculated “legally marketed” minimum and maximum price averages at California cannabis retailers that listed prices on Weed maps and that had obtained temporary licenses to sell cannabis in compliance with state regulations at the time of each data collection round.For comparative purposes, we also collected a sample of about 90 unlicensed retailers in 20 counties from Weed maps or Leafly, distributed similarly to the licensed retailers.We chose these retailers from within a set of 20 representative counties, approximately in proportion to the relative populations of those counties.We selected retailers for this “20-county unlicensed sample” arbitrarily from the first page of search results on Weed maps for retailers in each of the 20 counties, but we did not use mathematical randomization to select the counties or the listings we chose within counties.These data may not be fully representative of legal cannabis price ranges for several reasons.First, as discussed above, not all legal retailers use Weed maps or Leafly, and prices may not be representative of all prices.The price data we collected also may not fully represent the range of products in the market, which may have varied in different rounds of data collection.As is suggested by the changing prevalence of 1-ounce flower packages and 500-milligram oil cartridge packages, product assortments may have changed within each of these categories.This problem plagues price data in many different industries, but changes in product assortments and price listings may have been especially rapid in the emerging cannabis market.The differences in price ranges we report here should not be interpreted as measures of price dispersion, because we are not observing maximum and minimum prices for exactly the same products at different retailers and thus are not comparing “apples to apples,” as is traditionally required to measure price dispersion.However, concrete differences in product attributes — such as potency or grow type for minimum-priced or maximum-priced cannabis — may also vary between retailers, and may correlate with price differences , even if price differences between agricultural products do not necessarily correlate with sensory characteristics.For instance, the minimum price for one-eighth ounce of flower at a particular retailer might represent a price for outdoor-grown cannabis with a THC concentration of 15%, whereas the minimum price for one-eighth ounce of flower at another retailer might represent a price for indoor-grown cannabis with a THC concentration of 20%.

By analogy, if one were to collect minimum and maximum prices for all wine at retailers around California, the minimum-maximum range could not be used to measure price dispersion in a traditional sense; in order to measure dispersion, one would have to compare, for instance,cannabis drying the price of the same Kendall-Jackson Chardonnay at different stores.For our research, comparing prices for identical products across retailers would not have been feasible, given the Weed maps format and our data collection methods.Our approach here, in reporting cannabis price ranges, is to make no assumptions about quality and assume that minimum and maximum prices are simply prices for different types of products.It would be interesting, in future work, to explore dispersion by collecting and comparing data on standard product types across retailers.Beyond requiring product standardization, an analysis of cannabis price dispersion with respect to geographic areas would also likely require a larger data set than ours.Hollenbeck and Uetake comment that regulatory barriers to entry can facilitate the exercise of monopolistic behavior by retailers.Dispersion measures, as proxies for competition, might help illuminate regulatory impacts.As more tax and sales data are released by government agencies, it might soon become possible for researchers to collect data sets of sufficient size and precision for dispersion to be measured.Table 2 shows average minimum and maximum prices over the course of the 21-month data collection period for the three product types that we studied, along with the number of observations in each period.In the last four rounds of data collection , we generally observe only relatively slight differences in both average prices and upward or downward movements among the three retailer groups.Both statewide and within the seven-county sample, average minimum and maximum prices for one-eighth ounce of flower and for 1 ounce of flower differed by 2.5% or less, but averages differed by up to 8.8% for 500-milligram cartridges.In table 3, we report prices over the 21-month period for the non-attrited sample of the original retail store locations whose prices we collected in October 2016.These retailers may not be representative of overall state averages, particularly after the substantial attrition from the original group of retailers that we observed beginning in November 2017.However, this set of observations avoids potentially confounding factors introduced by the changing sample composition over time.Table 3 shows substantial attrition from the original seven-county sample of 542 retailers that listed prices on Weed maps in October 2016.By July 2018, 21 months after the first round of price collection, only 74 non-attrited retailers from the original sample remained active on Weed maps or Leafly.Local police crackdowns and municipal bans in some counties surely contributed to this 86% attrition rate, which should not be interpreted as representative of statewide attrition from Weed maps or evidence of the general rate of business closures.What is more interesting, perhaps, is the basic observation that only 270 licensed cannabis retailers were listed on Weed maps in all of California in July 2018, whereas in November 2017, near the end of the unregulated market, about 2,500 California cannabis businesses operated without the need for a license.

This observation suggests, at least, that many medicinal cannabis retailers that had been operating legally in 2017 had not yet obtained licenses and entered the new legal market as of mid-2018.Figures 1, 2 and 3 show average minimum and maximum prices for one-eighth ounce of flower, 1 ounce of flower and 500-milligram oil cartridges for each round of data collection, both for legally marketed cannabis and for the 20-county unlicensed sample.In the 2016 and 2017 price data, before mandatory licensing, regulation and taxation, we observe relative stability in California cannabis price ranges for all three product types.In 2018, after licensing, regulation and taxation, we observe three patterns.First, we observe falling prices for all products between February and May 2018, which may be related to retailers’ need to liquidate untested inventory that would become illegal as of July 2018.Second, we observe generally rising prices between May and July 2018, which may be related to the introduction of mandatory testing rules.However, because of the limitations and uncertain representativeness of the Weed maps sample, as well as changes to our sampling methods in different rounds, we do not have a basis for inferring a causal relationship between testing rules or other regulatory events and our minimum and maximum price averages.Third, we observe rising maximum prices for 500-milligram oil cartridges over our last four data collection rounds.At all retailers statewide that listed prices on Weed maps or Leafly, we observed a 33% increase in maximum prices from November 2017 to July 2018.Table 2 shows that the latter pattern can be observed, with some variation, in prices both in the original seven counties and in all of California.We do not know to what extent the maximum price increases for cartridges might be attributed to the introduction of new, higher-end products with differentiated sensory or functional attributes as the market has evolved; to differentiated packaging attributes; to price increases generated by increased high-end demand; to supply-side factors; or to other market effects.In general, the price patterns we observe demonstrate little evidence of seasonality, even though wholesale cannabis prices are known to vary seasonally because of the annual outdoor harvest and consequent increase in outdoor cannabis supply in the fall and winter months.We collected eight rounds of price data from the legal California retail cannabis market during a 21-month period of regulatory transition, as cannabis was being decriminalized, legalized and regulated in stages.Given the differences between the data sets we collected and the unknowns about Weed maps that we have discussed above, readers should be especially cautious in interpreting the movements we observe as “trends.”