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.”