Cannabis Grow

Table E.1 compares estimates of the effects of growth in the legal market on past month use restricting the sample to only include Montana and its bordering states in Panel A, to the estimates using the entire sample of states in Panel B. If there are substantial supply spillovers from MML states with large markets to other states, then we would expect the effect sizes in Panel A to exceed those in Panel B. From Table E.1, for youths aged 12-25, the effects of legal market size on past-month use are larger in Panel A than in Panel B, but for adults aged 26 and older they are quite similar. This is consistent with growth in the legal medical marijuana market having supply spillovers across states in the black market, where adolescents and young adults have substantially greater access than older adults. Table E.2 replicates the analysis of Table E.1 using prevalence of past-year initiation as the outcome variable. The results are similar. Thus, there appear to be supply spillovers from medical marijuana markets to recreational marijuana markets used by youths in other states. The differences between the estimates from Montana’s case study to those using the entire sample suggests that the effects of medical marijuana market growth on adolescent and young adult use may be twice as large as shown in the primary results if cross-state supply spillovers are accounted for. If the decision to report marijuana use is more closely related to beliefs about legal penalties or social disapproval compared to availability,flood drain tray then the results from Table 2.8 suggest that the effects of legal market growth on adolescent marijuana use are a true measure of consumption changes and not of reporting behavior.

Tables F.1-F.2 provide additional supporting evidence that the primary results of this paper are not driven by reporting bias. Table F.1 reports estimates for the effects of registration rates on the prevalence of past-month marijuana use by adolescents separately for the time period before the Ogden Memo and after the Cole Memo. If changes in reporting behavior are more likely to be driven by law passage than by legal market size, then registration rates should have no effect on reported past-month except due to the federal government’s memos. As evidenced in Table F.1, the coefficient estimates for adolescent past-month use are not significantly different if examined before the federal policy reduced enforcement with the Ogden Memo, or after the federal government increased enforcement with the Cole Memo. However, adolescent reporting behavior may be more sensitive to changes in risks from social or community disapproval than to changes in perceived disapproval from law enforcement. If this were the case, then changes in state marijuana policy or changes in federal enforcement policy may have less effect on adolescent reporting behavior than changes in perceived social stigma associated with cannabis consumption, which is likely highly correlated with the number of legal users and suppliers visible in the community. To address this potential concern, estimates of the effects of legal medical marijuana market size on juvenile arrests for marijuana possession are shown in Table F.2. Since adolescents for the most part do not qualify as medical marijuana patients, it is unlikely that there were significant state enforcement changes regarding juvenile arrests for marijuana-related crimes, and thus effects of legal market size on adolescent marijuana arrests are likely highly correlated with effects of legal market size on adolescent cannabis use.

Annual data on juvenile arrests from 1994-2012 were obtained from the Uniform Crime Reports County-Level Detailed Arrest Files compiled by the Inter-University Consortium for Political and Social Research. County data were aggregated up to the state level. Table F.2 reports coefficient estimates for the effect of registration rates on the juvenile marijuana possession arrest rates. In Columns -, a log-linear ordinary least-squares specification is employed, with the dependent variable constructed as the natural log of the number of juvenile arrests for marijuana possession per 100,000 of the relevant-aged population for Columns -, or the natural log of the number of juvenile marijuana possession arrests in Columns -. Columns – employ a negative binomial specification. For all model specifications, growth in the legal market size has a positive effect on juvenile arrests for marijuana possession of similar effect size to that found for the effects on adolescent past-month use. This suggests that the observed effects on self-reported use are not driven solely by changes in reporting behavior. According to estimates by Miron , state and federal expenditures on enforcement of marijuana prohibition exceed $7 billion annually. Citing these costs, hundreds of economists signed a petition in 2012 encouraging state and federal officials to rethink marijuana policy in the United States. While a growing number of states are liberalizing the use and distribution of marijuana, the federal government still maintains that prohibition is necessary to limit the costs of increased marijuana use that would occur under a legalized regime . Chapter 2 shows that growth in the size of legal medical marijuana markets significantly increases recreational use, but the welfare effects of this increased consumption alone are ambiguous.

If individuals are rational and fully anticipate the potential negative consequences of marijuana consumption on future utility, then any increase in use induced by liberalization increases consumer welfare . If, however, individuals underestimate the potential negative consequences or make mistakes in their consumption choices triggered by environmental cues, this increased marijuana use may decrease social welfare . Under any theory of individual decision-making, if marijuana use generates negative externalities, then the socially optimal level of consumption is below the individually optimal level of consumption achieved under a free market regime. This paper contributes toward understanding the potential welfare consequences of legalization by studying how growth in the legal market for medical marijuana affects traffic fatalities and deaths related to alcohol and opioid poisonings. These outcomes will reflect direct externalities from marijuana use due to impaired driving,flood and drain tray as well as indirect externalities resulting from substitution or complementarity with alcohol and opioids. Since past research has found evidence of heterogeneity by age in the elasticity of substitution between marijuana and other substances , particular attention is given to differences in these outcomes by age. In the aggregate, I find that greater medical marijuana access decreases mortality from traffic accidents and substance-related poisonings. However, the aggregate effect masks an important welfare trade-off generated by age differences in the elasticity of substitution between marijuana and alcohol. For older adults aged 45-64, increased medical marijuana availability has positive health consequences, as growth in registration rates reduces mortality related to alcohol and opioid poisonings by 7-11% and 12-16% respectively. In contrast, for youths, greater marijuana access generates negative externalities in the form of a 6% increase in traffic fatalities caused by drivers aged 15-20, with large and significant effects on alcohol-related accidents. These results are consistent with complementarity between alcohol and marijuana among youth and substitution among older adults. The paper proceeds as follows. Section 3.2 provides a brief description of medical marijuana markets in the United States, and section 3.3 discusses the literature on marijuana’s role as a substitute or complement for other additive substances. The data and empirical framework are described in sections 3.4 and 3.5 respectively. Section 3.6 presents the empirical results of the effects of growth in medical marijuana availability on traffic fatalities and substance-related poisonings. Finally, sections 3.7 and 3.8 conclude with discussion and directions for future work. The past few decades have seen a growing movement, both worldwide but particularly in the United States, away from a strict policy of marijuana prohibition. Over half of the U.S. population now live in states with medical marijuana laws , which provide legal protections for the medical use of marijuana by qualifying patients and allow for the legal supply and distribution of medical marijuana. As of 2013, in all but three of the twenty MML states, an individual who wants medical marijuana must obtain a physician’s certification that the individual has a medical condition which could benefit from marijuana use and register with the state.The share of adults registered as patients reflects the extent of medical marijuana participation in a given state and provides a measure of market penetration.

Variation in how production is regulated has led to heterogeneity in medical marijuana take-up across states, and data on state counts of registered medical marijuana patients highlights this variation. Figure 3.1 plots the percent of the state adult population registered as legal medical marijuana users in December 2014 against months since the effective date of MML enactment. As expected, the size of the market is positively correlated with the length of time the MML has been in place. However, another important determinant of market size is the strictness of regulations facing suppliers. States that allowed large-scale production with little oversight or monitoring show growth in market size above trend, while states without operational dispensaries have seen little growth.While Figure 3.1 presents a static snapshot of the current state of MML market size, the structure and size of state medical marijuana markets have undergone substantial changes over the past decade. Although several MML policies had established legal protections for large-scale producers prior to 2009, the threat of federal prosecution and product seizure served as a sufficient barrier to entry in the state-legal market. However, in October 2009, the federal government released the Ogden Memo which formally de-prioritized prosecution of users and producers in MML states who were compliant with state law . This decrease in perceived federal enforcement risk resulted in a significant shift in the structure of the medical marijuana industry. Prior to 2009, California was the only state which had seen the rise of large scale medical marijuana production collectives . However, as detailed in Table 3.1, in states where caregivers could legally produce for multiple patients, the number of operational large-scale producers increased rapidly after the Ogden Memo. For example, in Colorado, very few medical marijuana commercial operations had opened between initial MML enactment and 2009; by mid-2010, over 900 operational dispensaries were identified by law enforcement . In Montana, which did not have state-licensed dispensaries but allowed caregivers to produce for and sell marijuana to an unlimited number of patients, the number of caregivers serving ten or more patients grew from 84 in October 2009 to more than 480 by September 2010. Trends in the number of registered medical marijuana patients reflect this rapid expansion in the size of the market for medical marijuana following the Ogden Memo. Figure 3.2 depicts trends in the total number of registered patients, aggregated over all states with registration rate data that had MMLs effective prior to the Ogden Memo. Consistent with the increase in medical marijuana production, the number of registered patients spiked following the announcement of decreased federal enforcement. The flattening in trend in 2011 was driven by the federal government’s reversal in stance and re-prioritization of involvement in MML states . As documented in Chapter 2, growth in medical marijuana market size significantly increases marijuana consumption among all age groups. The welfare implications of this increased marijuana use will depend largely on whether marijuana consumption itself generates externalities, and on the extent to which marijuana is a substitute or complement to other addictive substances. While prior work has sought to address the effects of medical marijuana liberalization on traffic fatalities, alcohol consumption, and opioid use, there is little agreement as yet. Alcohol and marijuana are the drugs most frequently detected in fatally injured drivers , and the effect of marijuana legalization on drugged driving is a potential negative externality of primary concern in the current policy debate. While a large body of research has established the role of alcohol in increasing crash risk , the effects of cannabis on driving impairment are less clear. Cognitive studies show that marijuana use impairs a number of tasks associated with driving ability , but experimental research has found the effects of marijuana on driver impairment to be only modest when compared to the effects of alcohol. Still, the consumption of both alcohol and marijuana has an additive or even multiplicative effect on driver impairment , with one study finding these effects to be particularly strong during nighttime driving simulations .

By reducing the perceived risk of federal prosecution for legal producers in compliance with state law, the Ogden Memo should have increased benefits to patients by increasing medical marijuana availability. The Cole Memo should have had the opposite effect. If supply-side factors are an important determinant of the relative value of medical marijuana participation, To measure medical marijuana participation, I collected data on the number of registered medical marijuana patients for all states with mandatory registration programs as of 2014. The full listing of data sources for each state — which include direct contact with state officials, state department reports and websites , academic papers, and local news articles — is provided in Appendix A. This paper uses monthly data from 1999-2014, and Table 1.2 presents count tabulations of data availability by year and state. The measure of interest is the registration rate, calculated as the percent of the resident adult population registered as medical marijuana patients.16 As shown in Table 1.2, data availability on registered patient counts varies across states. Some states provide monthly statistics, while others collect data quarterly or annually. For states with smaller registration programs , administrative records were not made available and had to be collected from older news articles and archived web pages. For states with more developed registration systems, statistics could be found starting from the program’s inception,2×4 flood tray but the frequency of data collection increased substantially following the Ogden Memo in 2009.

For months with missing data, registered patient counts were linearly interpolated using the two closest months of data. This new dataset presents the most comprehensive state panel of medical marijuana participation made available as yet. The solid line in Figure 1.2 plots the total number of individuals registered as medical marijuana patients from 1997-2014 in states that required patient registration. As the data show, registered patient counts were relatively flat during the period of federal intervention from 1997-2008, but the Ogden Memo led to a rapid increase in medical marijuana patient participation. The spike in patient take-up coincided with significant growth in the number of legal medical marijuana producers. According to estimates by Sevigny et al. , from 2008-2010 the number of medical marijuana dispensaries increased from around 1,400 to 3,800, and the number of legal producers grew from less than 20,000 to almost 90,000. As shown in Figure 1.2, medical marijuana participation stalled following the Cole Memo. Patient registration rates resumed growth in mid-2013 when Deputy Attorney General James Cole released a second memorandum re-clarifying that federal enforcement resources should focus on large-scale marijuana operations only if they are suspected of engaging in certain criminal activities such as trafficking across states lines, distributing to minors, and supporting cartels . While the aggregate data suggest that these federal memos significantly affected trends in medical marijuana participation, the magnitude of these changes varied widely across states.

To illustrate this variation, Figure 1.3 graphs trends in adult per capita patient registration rates for states with effective registry dates prior to 2010. Some states saw exponential growth in registration rates following the Ogden Memo and declines in registered patient counts at the time of the first Cole Memo. Other states show an up-tick in patient registration with the Ogden Memo but appear to have been relatively unaffected by the Cole Memos. Finally, a few states have seen relatively flat trends in medical marijuana participation since program enactment. Summary statistics for the variables used in this paper’s regression analysis are presented in Table 1.3. Columns and show the mean and standard deviation in monthly medical marijuana registration rate data and for the other included control variables in the models. Column presents the standard deviation across state averages, such that comparing columns and indicates how much of the data variation comes from differences across versus within states. Based on the conceptual framework outlined in section 1.3, this study considers the effects of federal policy changes, state regulations, and their interactions on medical marijuana participation. Due to data limitations, I take a reduced-form approach and do not separately model eligibility, take-up conditional on eligibility, or entry and exit. then the federal memos may have influenced patient take-up through their effects on medical marijuana access. The magnitude of these effects will depend on the regulatory framework for legal production established by state MML policy. These findings suggest that the effects of the federal memos on medical marijuana suppliers was an important driver of patient registration. For Colorado, there is sufficient data to disaggregate registered patient counts by those patients with and without a designated caregiver.

For Colorado, Figure 1.4 shows that, indeed, the most substantial growth in registered patient counts was seen by patients reporting a primary caregiver as their source of marijuana; similarly, the Cole Memo led a larger reduction in registered patient counts among patients with caregivers compared to patients without caregivers. Figure 1.5 provides further evidence that interest in medical marijuana flows from producers to patients. The graph shows quarterly data for Google search interest in the phrases “how to become a patient” and “how to become a caregiver.” Data was collected from Google Trends, which measures relative search interest over time for these phrases from a sample of total searches. The spike in search interest for becoming a caregiver occurs at the time of the Ogden Memo, and it clearly precedes that of search interest in becoming a patient. This suggests that producers responded more rapidly to the announcement effects of the Ogden Memo than users, and is consistent with evidence that incentives to obtain information about a program are influenced by the expected net benefit of participating . To assess the relative role of supply and demand in driving medical marijuana patient registration, ideally one would have detailed state-level time series data on potency-adjusted marijuana prices. Unfortunately, since marijuana remains illegal at the federal level, accurate price data is highly limited. High Times is an online magazine where users can submit the price they paid for their last marijuana purchase. The magazine reports individual price submissions by city and strain of cannabis. Priceofweed.com is a website that collects user-submitted data in real-time on the price of marijuana purchases and classifies them into “high”, “medium”, or “low” quality. For completeness, I present evidence based on this crowd-sourced data, but they are intended only as suggestive evidence and should be interpreted with caution. Table 1.6 presents estimates for the effects of registration rates on the natural log of price per ounce of high-potency marijuana. For the regressions, data on high quality marijuana prices was aggregated at the state-quarter level and converted to price-per-ounce. Outlying price values were dropped.17 The results from Table 1.6 show that increases in registration rates significantly predict lower prices. This suggests that,flood and drain table even if higher medical marijuana participation rates to some extent reflect increased demand, they reflect even larger effects on supply. A number of studies have exploited state-time variation in the enactment of MMLs to estimate their effects on marijuana use in the general population. Findings have varied substantially, with estimates ranging from significantly negative, to statistically insignificant, to significantly positive for an excellent review. However, the standard difference-in-differences approach employed in these studies implicitly assumes that the “treatment effect” of MML enactment is dichotomous, i.e. the policy change occurs at a specified date, and it is implemented completely and equally across states. Whether this assumption holds will depend on the mechanisms by which MMLs induce changes in behavior. According to deterrence theory, by reducing the perceived severity of legal or informal sanctions associated with marijuana consumption, MML enactment should ceteris paribus increase demand. Since the passage of MMLs provided similar legal protections and represented a shift in either governmental or social acceptance of marijuana, ex-ante these effects should occur simultaneously with law enactment and be similar across states. This prediction relies on three conditions: that the statutory policy change is actually implemented, that no offsetting changes in enforcement occur simultaneously, and that the public is aware of the change in policy . Since MMLs provide protection from state-level but not federal prosecution, citizens may be even less likely to update their expectations about potential prosecution until it is observed or known that the federal government will not intervene.

Since awareness about laws and enforcement policies will be diffused through social networks, personal experience, and the mass media, the federal memos and their coverage by the media and marijuana advocacy groups may have had far greater effects on public perception than MML enactment alone. To provide suggestive evidence that knowledge about MMLs was limited prior to the Ogden Memo, Table 1.7 presents state-representative data on MML awareness from the National Survey of Drug Use and Health , which starting in 2002, asked respondents the following: “In your state, has marijuana been approved for medical use?” Table 1.7 reports cross-sectional variation in the percent of youths and adults who responded “yes” to this question, comparing the 2008-2009 and 2010-2011 for each state with an MML prior to 2009.18 Although these are not causal effects, they provide some useful insights. The first striking feature of Table 1.7 is the wide range of awareness across MML states. Oregon was the only state in 2008-2009 where over half of adult respondents correctly reported that the state had an MML. In contrast, less than 18% of adults in Nevada correctly responded that their state had an MML. On average, youths aged 12-17 are less aware of MML existence, but there is similar variation across states. The share of adolescents correctly reporting their state had an MML in 2008-2009 ranged from 25% in Vermont to 47% in Oregon. This variation in awareness is not explained by differences in how long the MML has been in effect. Also of note is the substantial increase in awareness of MML status following the Ogden Memo for Colorado, Montana, and Michigan. In two years, the share of adults who correctly responded that their state law allowed for the use of medical marijuana nearly doubled. These states also show the largest increase in awareness among youths. From Tables 1.1 and 1.5, these were also states with MMLs allowing caregivers to serve multiple patients and experiencing the greatest growth in medical marijuana patient participation following the Ogden Memo. The evidence from Table 1.7 suggests that the perceptual effect of state medical marijuana liberalization was relatively unrealized until after the Ogden Memo. It is thus unsurprising that studies only covering a time period prior to 2009 find insignificant effects of MML enactment on use for both adolescents and adults.19 Another mechanism by which MMLs may affect marijuana consumption is through increasing availability or decreasing prices. Research by Pacula et al. recognized that, if these are important channels through which MMLs generate spillovers, estimation needs to account for heterogeneity in the specifics of MML provisions. Accordingly, more recent studies have, in addition to using a binary measure of MML enactment, also included indicator variables for allowance for the legal operation of retail dispensaries and allowance for home cultivation by patients and/or caregivers. Still, findings have varied . While this approach is an improvement to treating MMLs as a homogeneous set of policies, it still relies on the assumptions of the DID approach and thus suffers from similar limitations. For example, including a categorical measure of “home cultivation allowance” implicitly assumes that all home cultivation laws are created equal. As Table 1.1 shows, this is clearly not the case. A binary variable for whether a state allowed patients or caregivers to cultivate would take a value of one for both Colorado and Vermont. However, a caregiver in Vermont was limited to growing for only one patient, and thus could only legally cultivate three plants; a caregiver in Colorado could grow for an unlimited number of patients, and could thus theoretically be legally protected for maintaining a large-scale grow operation with hundreds of plants. Additionally, it is unclear whether the dummy variables for any specific policy measure should “turn on” when the law is passed, when it becomes statutorily effective, or when it becomes effective “on the ground” . This is especially problematic for the measurement of dispensary legalization. As shown in Table 1.1, some states did not explicitly permit dispensaries but they did not explicitly prohibit them either.

Tobacco policies at the state, county, and city jurisdiction levels had similar degrees and patterns of co-occurrence among policies. For example, comprehensive clean-air laws for bars and comprehensive clean-air laws for restaurants frequently co-occurred at the state, county, and city levels . Most policy measures were positively correlated, but we also found pockets of negative correlations. For example, country-years with child tax credits tended not to have child tax allowances . The heat maps also revealed groups of co-occurring and independent policies. For example, labor policies requiring licensing for different professions frequently co-occurred, but this set was relatively independent of policies regarding collective bargaining and minimum wages .Most of the variability in policy measures across jurisdictions and times was explained by the other policies in the same database. Figure 4 displays the distributions of R2 values: the higher the R2, the less unique variation there is for an individual policy, to a maximum of 1.0. The impacts of policy co-occurrence on identifiability were generally substantial: of all 502 policies considered, 65% had R2 values greater than 0.90 when regressed on other policies in the same database. Child benefits had the lowest R2 distribution, with a median of 0.19; policies on poverty and social welfare, family leave, fertility/immigration, firearms, cannabis, alcohol, state tobacco control, and county tobacco control had R2 distributions with medians of approximately 0.9 or greater. In some cases,rolling benches correlations between predictor policy variables were so strong that the statistical software forced certain variables from the model .

Policy co-occurrence substantially reduced the precision of possible effect estimates in all cases . Across policy measures, databases, and simulation iterations, policy co-occurrence effectively increased the variance of effect estimates by a median of 57-fold. Across policies, the lowest degree of variance inflation observed was 7% for country child tax rebates. For other policies, particularly family leave, variance inflation was so substantial as to render estimates effectively indeterminate. Again, some predictors were dropped from models due to strong correlations with other predictors .We analyzed 13 social policy databases drawn from contemporary research in top epidemiology, clinical, and social science journals. These exemplar databases represented diverse policy domains, geographies, and times to describe the pervasiveness and impacts of policy co-occurrence on estimation of health effects. We found that high degrees of co-occurrence were the norm rather than the exception. For a majority of policies, greater than 90% of the variation across jurisdictions and times was explained by other related policies in the same database. Unbiased studies attempting to isolate individual policy effects must control for these related policies, so for many applications, there may be little independent variation left with which to study the policy of interest. Consistent with this, we found that adequate control for co-occurring policies is also likely to substantially reduce the precision of estimated effects, often so dramatically that informative effect estimates are unlikely to be derived.Several factors make the pervasiveness and consequences of policy co-occurrence likely to be even greater than we have estimated. First, we only examined policy cooccurrence within domain-specific databases.

Yet social policy changes may happen in multiple domains simultaneously. For health outcomes affected by diverse types of policies , researchers must consider policy co-occurrence across domains, which likely will indicate even more severe co-occurrence. Second, each policy database we considered included only 1 jurisdictional level, but true policy environments involve complex overlays of national, state or province, county, municipal, employer, and/or school policies. Third, we did not incorporate lagged effects or nonlinear relationships between variables. Fourth, policy variables that perfectly or near-perfectly predicted one another were dropped from the regression models. Finally, we did not consider the multitude of social, economic, political, or societal factors that may also co-occur with policies of primary interest, including changes in social norms, implementation, or enforcement that can be conflated with policy changes. Some such confounders can be controlled with jurisdiction or time fixed effects; measured confounders that are jurisdiction-specific and time-varying could be evaluated using the same methods illustrated here. This is a formidable task; data sharing efforts would facilitate its assessment and handling. We found that the overall degree of policy co-occurrence varied across databases, ranging from very high for state level recreational cannabis policies to low for country-level sexual minority rights policies. Several different factors may drive this variation. Our finding that tobacco policies at the state, county, and city levels had similar degrees and patterns of co-occurrence among similar sets of policies suggests that co-occurrence may be a characteristic of the domain. Political polarization may result in greater co-occurrence for certain policy domains versus others . Databases with rarer policies, fewer umbrella policies , or more nested policies also tended to have more co-occurrence. Databases with more unique policies also generally had more cooccurrence; with a fixed number of jurisdictions and times of observation, considering more policies creates more opportunities for alignment.

Importantly, these patterns highlight that the measured degree of co-occurrence depends not only on the policies themselves but also on the investigator’s choices of policy measures. Furthermore, policies that could be considered alternatives rather than complements co-occurred less frequently and may offer the opportunity for more robust studies of causal impacts. Differences in the ways that policies are adopted across different political systems and different jurisdictional levels may also matter. In our examples, country-level policies appeared to co-occur less frequently than state-level policies, implying that estimating causal effects of country-level policies may be more feasible. Similar considerations apply to the temporal scale of analyses as well: The feasibility of estimating health effects may depend on whether analyses are conducted at the level of the year, month, or even election cycle. Our analysis could not determine which of these factors drives variation in policy cooccurrence; this would be a fruitful area for future research.Several other limitations of this study must be noted. Certain policy domains were not covered, either because no social policy studies for that domain were sampled or because no corresponding policy database was identified or accessed. We did not review all potentially relevant journals. Our results may not generalize to policy domains or journals not examined. Our approach also assumes that all the policies in each domain-specific database are relevant to the health outcome of interest; this is plausible for social interventions that likely affect a broad range of health outcomes, but for some outcomes, only a subset of the policies in a database may need to be controlled to isolate the effect of the index policy. In addition, our approach is only relevant when a database of the relevant policies exists or can be constructed. Developing policy databases is often an arduous task requiring systematic review of legal language. We did not consider the quality of the underlying databases. Our selections serve to illustrate the policy co-occurrence problem, but for applied researchers, the optimal policy database may differ from the one used here. The problem of correlated exposures arises in many domains, including environmental health, and although social policies are distinct in important regards, methods in other domains may nonetheless prove helpful. Furthermore, our analysis did not examine the distinctions between policy adoption, implementation, promulgation, or changes in norms that precede or follow from policy changes,grow tray but these considerations are essential in applied policy research. Finally, data sparsity arising from co-occurring policies can lead to bias, not just imprecision. Our simulations did not incorporate this because this type of bias is less relevant to studies of the health effects of social policies and is highly context specific. Simulation results on the magnitude of bias from positivity violations are therefore unlikely to be generalizable. Specifically, bias arising from positivity problems depends on the estimation method. For methods that rely on modeling the outcome , positivity-related bias arises from model-based extrapolation. For methods that involve modeling the exposure mechanism , bias can result from disproportionate reliance on the experiences of a just a few units or on the absence of certain confounder strata . Because our simulations were based on outcome regressions—the most common approach for differences-in differences, panel fixed effects, and related designs—bias would only arise from model-based extrapolation. However, for the vast majority of policies identified in this study, measures were binary, and thus extrapolation cannot occur. For continuous policy measures , model-based extrapolation is possible but application dependent. Thus, simulating the potential degrees of bias resulting from model-based extrapolation requires either tenuous generalizations or substantive assumptions about each policy area. We suspect that extremely nonlinear relationships that would lead to large extrapolation bias are rare for policy effects, but this remains an open question.Researchers should be cautious when seeking to make causal inferences about the health effects of single social policies using methodological approaches premised on arbitrary or quasi-random variation in policies across jurisdictions and time. Not every policy change offers a valid differences-in-differences or panel fixed-effects study design. These methods are most compelling when policy implementation is staggered across jurisdictions and dates independently from other policies and for plausibly like-random or arbitrary reasons. For example, there could be differing timing of elections, legislative sessions, crises that provoke specific policy changes, or lottery-type roll outs. In these cases, such research can be very persuasive, or at least constrain the set of co-occurring policies.

Our finding of pervasive policy co-occurrence across numerous databases suggests that many policies do not fit this criterion. Inadequate control for co-occurring policies or differences in the set of policies controlled may explain surprising or conflicting results in previous studies. Investigators should base interpretations of social policy research on the plausibility that policy adoption is distributed arbitrarily with respect to other uncontrolled policies or social changes, a phenomenon that, in reality, may be rare. This evaluation should be based on deep content knowledge of law, politics,and society—a compelling argument for involving policymakers in the design and interpretation of studies.We illustrate an approach for researchers to assess whether the effects of individual policies can be estimated. Although other simulation-based methods for assessing positivity exist , the approach we propose is tailored to the policy co-occurrence problem and facilitates examining how a full set of policies substantively occur together. For a given application, if the heat map indicates high correlations, and estimated R2 values and variance inflation are high, it may be necessary to alter the research question and corresponding analytic approach. Researchers have applied numerous analytic approaches to address the challenge of highly co-occurring policies, ranging from machine-learning algorithms that identify policy measures most strongly related to an outcome of interest to methods that characterize overall policy environments based on expert panels. The second article in this issue on policy co-occurrence provides a systematic assessment of available methods. We briefly review 3 promising analytic options here, and refer the reader to the other article for more detail. One approach is to focus on outcomes that are affected by the index policy of interest but not the co-occurring policies. For example, changes in state Earned Income Tax Credits co-occur with changes in other social welfare policies . Rehkopf et al. took advantage of seasonality in the disbursement of EITC cash benefits versus benefits without the same seasonal dispersal pattern, to examine the association of EITC with health using a differences-in differences design. By comparing health outcomes that can change monthly for EITC-eligible versus non-eligible individuals in months of income supplementation versus non-supplementation, the authors measured potential short-term impacts of EITC independent of other social welfare policies. Another approach is to move beyond binary measures of policy adoption to more detailed characterizations . These measures may co-occur less frequently with related policies or provide opportunities to examine dose– response effects among jurisdictions adopting the policy. For example, the adoption of certain unemployment benefit policies co-occurs frequently with other social welfare policies across state-years. Researchers have successfully assessed these policies’ health impacts by comparing varying levels of unemployment benefit generosity—measured as the total maximum allowable benefit per bout of unemployment—across states and years . Heatmaps like those presented in this study may help researchers identify specific policy measures that are more independent from related policies. A final option is to conceptualize policy clusters, instead of individual policies, as exposures. This is promising if policies are typically adopted as a group, as is the case with the large omnibus bills that are increasingly common at the state and federal levels.

The repeater last stage exhibits large gain at the transition period that amplifies the stage internal noise sources as well as preceding stages noise. When transition completes, the gain drops and output noise also drops drastically. Thus the average noise is low compared to transition noise. Jitter at the output of the repeater is thus given by , where Vn,rms is the root mean square noise at t0. Clock edges are sampling the noise every clock cycle, hence we only need to integrate noise from 0 to fclk/2. Building a simulation model for the cable link shown in Fig.1.2 is a necessary step to estimate clock accumulation and jitter of the synchronous link, and thus predict its performance. The most accurate way to perform this simulation, given the strong non-linear and time variantnature of the clock path is to use a transient noise analysis, where the different noise sources inside the SPICE model are internally replaced by a transient random sources that satisfy the power density and bandwidth of the original noise source. For a white noise source, this requires transient simulation step to be small enough to sample the highest frequency components of the noise source, and simulation time needs to be long enough to take at least one full cycle of lowest frequency components of noise. Those requirements are known obstructions for circuit designers and hinder practicality of transient noise simulation to cable links. On top of that,vertical grow system long simulation time needed to propagate the clock across the cable section. For instance, for CAT 7 cables, delay is almost 4ns/meter.

For a 100 meter cable, this is a 400ns of simulation time that contains no information. Such simulations would typically take hours to a day. On the other hand, Steady state analysis could also be used for this purpose where noise analysis is solved as a small signal analysis on top of a linear time variant solution of the circuit, as we explained in the previous section. However, a circuit that contains cable model represented by S-Parameters is very tough to solve with state of the art steady state simulators, in particular when those models exhibit excessive delays as in the case with cable model. Instead, we propose using a fast linear time variant model to estimate jitter accumulation and power of the clock forwarded cable link. Fig. 3.6 shows a block diagram of the clock forward link expressed in freq. domain transfer functions. The cable is a linear time variant element so it’s expressed as Hc, where l is the cable section length. As explained in section 3.2.1 the clock repeater transfer function can be expressed in linear time variant model as Hr, and it’s output noise power spectral density expressed by Srn, where t0 is the observation time set as the zero crossing point of the output clock. For most practical considerations we can assume that the clock driver output has a 50% duty cycle and fast edges compared to clock period. The Fourier expansion of such a clock contains only odd harmonics n scaled by 2n 1 +1 . The harmonics are filtered by the cable transfer function before being applied to the next clock repeater. A single cable and repeater section can thus be expressed as shown Fig. 3.6 with a voltage source VS representing the filtered clock signal.For most practical cases, the clock signal at the output of the clock repeater has fast rise and fall times i.e. negligible fraction of the clock cycle. It’s at this window of rise and fall time where the repeater has non zero gain for noise and signal. The rest of the clock cycle, gain is almost zero.

As a result, the linear time variant impulse response can be expressed as an almost ideal train of narrow impulses in the time domain. The frequency domain transfer function which can obtained by equation consists of identical side bands over a wide range of frequency much bigger than the repeater bandwidth. Fig. 3.7 shows the frequency domain transfer function of the clock repeater, Hr, with 5 side bands obtained by a Steady state simulation of the clock repeater. As can be seen from the figure the side bands have the same magnitude and shifted by a frequency that’s double the clock frequency. This is because the train of linear time variant impulses repeat for the rising and falling edges of the clock, i.e. The Fourier fundamental frequency is twice the clock frequency.The aforementioned analysis proposes a fast and accurate model to estimate jitter accumulation in repeater based synchronous links. The cable used in the link design is a CAT7 cable with a channel response shown in Fig 3.9. The cable has a 2.2dB attenuation per meter at the Nyquist frequency of 12Gbps. Only a single repeater needs to be simulated to obtain LTV transfer function and output noise for a given driver amplitude and cable section length. Fig. 3.10 shows the jitter accumulation profile at the end of a 100m cable for a 500mV clock driver swing, for different cable length sections and clock frequencies. As frequency increases, loss of clock amplitude and slope inside the cable increase which results in SNR degradation. A smaller clock amplitude also implies that earlier stages inside the repeater possess more gain, as depicted in Fig. 3.3, which causes more noise contribution from those stages. Consequently, more degradation of SNR and jitter increases with the increase of clock frequency. A similar effect occurs with increasing cable section length. Fig.3.10 shows that 40ps RMS jitter is observed at end of 100m if we used 19m cable section length, and 800MHz Clock frequency. An amount of jitter that’s practically intolerable by a receiver at the other end of the cable. Increasing the clock amplitude increases the SNR and reduces jitter on the expense of total clocking power. Fig. 3.11 shows the total repeating clocking power, excluding clock multiplication, needed to meet a 4ps RMS jitter requirement at the end of the 100m cable.

As expected, power increases when cable section length and clock frequency increase, to compensate for SNR loss and jitter accumulation. Figures 3.10 and 3.11 suggest that a shorter cable section length and lower clock frequency are favorable for lower jitter accumulation along the entire cable. Shorter cable section means more number of sections needed to meet the required distance. Thus, more connectors are needed to connect cables to the repeaters which adds cost to the link and poses more mechanical week points. Detailed analysis of this issue is beyond the scope of this work, but generally less number of cable sections are needed to achieve the required length. On the other hand lowering the clock frequency reduces jitter accumulation because of less SNR degradation inside the cable but this doesn’t come without a price. The lower the clock frequency,indoor weed growing accessories the larger the multiplication ratio needed inside the CMU in fig. 1.2 to multiply the clock up to the data rate. To understand the impact of large CMU multiplication ratio on the performance of the link we need to have a closer look at jitter accumulation inside the CMU. Fig. 3.12 shows the RMS jitter observed as time elapses from some reference edge inside a typical ring oscillator. Jitter accumulates indefinitely inside an open loop oscillator with the square root of observation time. When the VCO is used inside a PLL CMU, the jitter accumulation plateaus at an observation time approximately equals to the CMU time constant. For over damped PLLs which are commonly used in repeater and jitter filtering applications, the PLL time constant is inversely proportional to the loop bandwidth. Uncorrelated jitter is amplified at frequencies inversely proportional to clock-data delay. This suggests that filtering of high frequency jitter is advantageous in clock forwarded systems to mitigate uncorrelated jitter accumulation. Because the jitter filtering element is inserted only in the clock path, jitter filtering bandwidth should be controlled to track correlated jitter and pass it un-filtered, meanwhile it rejects uncorrelated high frequency jitter. There are several candidates for jitter filtering. For instance, a tuned clock buffer where a differential inductor is used at the clock amplifier filters the phase noise around the center frequency. While effective, the main disadvantage is the large silicon area for the inductor needed at propagated clock frequency. A 5nH differential inductor that resonates with 5pF cap at 1GHz can easily consume 300×300µm2 . Additionally, an LC-based filter does not accommodate a wide range of frequencies easily without needed large varactors that can compromise the filter performance. Another widely used jitter filtering circuit is a cleanup PLL with the appropriate bandwidth. A PLL is already needed for the CMU and hence with proper design may serve both purposes. In the example above with a filter bandwidth of 75MHz, a cleanup PLL with similar bandwidth can be difficult to over-damp due to the delay within the loop. Furthermore, with a cascade of PLLs in the clock repeaters results in accumulated peaking of the PLL transfer function. Sufficient damping of the transfer function is very challenging with wide tracking bandwidths and results in jitter amplification near the PLL bandwidth.

This work proposes a third option of using delay elements to implement a finite impulse response phase filter to perform the high-frequency jitter filtering. As shown in Fig. 3.15, a first-order phase FIR needs a delay and summation. The summation can be implemented as a phase interpolator as described in the next section. The filter resembles phase averaging used in implementing a DLL in, where the phases of the delay cells are added to average timing mismatches. Fig. 3.16 compares different filtering approaches for uncorrelated jitter and absolute jitter . The analysis assumes a CAT7 cable link with 13m clock repeating distance, 250mV clock amplitude and observing jitter at the end of the 100m cable. The plot shows the impact of filtering with clock forwarding at clock frequencies ranging from 200MHz to 800MHz. The uncorrelated jitter is a filtered version of the absolute jitter. The reference is without any filtering and the decorrelation between clock and data stems from the 1 clock cycle delay inside the clock multiplication unit and noise from the clock repeaters. Additional filtering can reduce the high frequency noise but at the expense of further decorrelating the clocks and hence the filter is designed for high bandwidth. The LC-tuned amplifier design uses an inductor with quality factor of 4 at 1GHz. The PLL design assumes a bandwidth of 1/10 of the input frequency and 60o phase margin. The FIR filter design is a 1+αD first order filter at each repeater with the delay set at one clock cycle. The FIR zero falls at half the clock frequency. As shown in both figures, a PLL has superior performance at lower frequencies due to a higher order of its filter but suffers at high frequencies due to the peaking in its transfer function. The FIR and LC filtering have very similar performance making the FIR an attractive option for a low-area implementation. Fig. 3.17 compares FIR and PLL filtering with different noise sources. In mixed signal environments, supply noise from on-chip switching activity and external noise coupled to the chip can be a dominant component to the total output noise. This noise generally has a high-pass or band-pass characteristic due to high frequency capacitive and inductive coupling or behavior of the PLL. The FIR filter approach matches the PLL filtering performance at low frequency but outperforms the PLL at high frequency. As shown in Fig. 3.15, a simple first order FIR has a delay of 1 clock cycle. We opportunistically observe that with the proper architecture the CMU for frequency multiplying and generating the sampling clock can produce this delay. By injecting the reference clock edge into the VCO, MDLLs do not accumulate jitter in comparison with VCO-based PLLs for data sampling. The divided output of the MDLL has an intrinsic delay of 1 clock cycle between the input and the feedback clocks and has an all-pass transfer function. To implement the FIR, at the output of the MDLL, we insert a phase interpolator that takes as inputs the incoming reference clock and feedback clock of the CMU.

We audited the locations and point-of-sale marketing activities of RMDs in school neighborhoods and merged auditing data with school survey data on a large sample of adolescents in California. We paid particular attention to child-appealing marketing activities, which were presumably more influential to adolescents than general marketing activities. Instead of aggregating data at zip code or census tract level, we examined individual-level outcomes and simultaneously accounted for between- and within-school variations. Our first hypothesis that a closer proximity of RMDs is associated with a greater likelihood of adolescents’ marijuana use was not supported by the findings. In fact, a closer proximity was found to be associated with lower likelihoods of some outcomes in some model specifications in sensitivity analysis. Although no similar studies on RMDs can be used to compare to our findings, existing evidence on medical marijuana dispensaries did show mixed relationships between dispensaries’ proximity and marijuana use among adolescents. Whether and how the proximity of RMDs in school neighborhoods is associated with adolescents’ marijuana use outcomes deserve further research. Our second hypothesis that the presence of child-appealing marketing activities in RMDs is associated with a greater likelihood of adolescents’ marijuana use was not supported by the findings, either. However, when we examined the third hypothesis , we did find some evidence that child-appealing products, packages,vertical farming systems and paraphernalia in RMDs in very close proximity to schools might be associated with a greater odds of current use or heavy use.

It is likely that these items were resold or freely distributed to adolescents by third party adults, such as older friends, relatives, street dealers, who resided or worked in school neighborhoods. The interaction effects of RMDs’ proximity and marketing activities were not found on child-appealing advertisements. One plausible explanation is that nearly all RMDs we audited complied with age restrictions by ID check. Adolescents therefore had little chance to see advertisements inside of the RMDs, which could not be taken out by third party adults. It should be noted that the findings on interaction effects were sensitive to the selection of proximity cutoffs and model specifications. This is why we considered the strength of the evidence on interaction effects to be only moderate.The findings have policy implications. If the impacts of point-of-sale child-appealing marketing activities depend upon the proximity of RMDs to schools, stronger surveillance may be needed to monitor marijuana-related perceptions and behaviors in schools that have RMDs located near to them. Even though almost all states with legal sales of recreational marijuana prohibit products and advertisements specifically targeting children, our dispensary auditing data demonstrated a wide presence of these prohibited items in school neighborhoods. Actions should be taken to reduce child-appealing marketing activities and prevent adolescents from potential exposure. This study has limitations. First, the cross-sectional examination of associations should not be interpreted as causality. Second, the study sample was restricted to 73% of the CSTS 2017-8 schools that completed the survey on or after February 1st, 2018.

The generalizability of the findings to the entire California may be a concern. Third, we audited RMDs after the CSTS 2017-8 was completed in order to have an accurate and complete list of surveyed schools and conduct auditing in a cost-efficient manner. To what extent our observations on RMDs applied to the time when the schools were actually surveyed was unknown. Fourth, the marketing activity predictors were indicators of presence instead of continuous quantity measures due to feasibility considerations in fieldwork. We were not able to examine the quantity of marketing items . Lastly, our findings may not be applied to RMDs around adolescents’ homes, adolescents in private schools, or jurisdictions outside of California. With the dynamics in marijuana retail environments and government surveillance and law enforcement, the findings in the early stage of recreational marijuana commercialization may also lack generalizability to the most recent regulatory and retail contexts. In the past decade, the massive scale-up of insecticide treated bed nets and indoor residual spraying , together with the use of artemisinin-based combination treatments, have led to major changes in malaria epidemiology and vector biology. Overall malaria prevalence and incidence have been greatly reduced worldwide. But the reductions in malaria have not been achieved uniformly; some sites have experienced continued reductions in both clinical malaria and overall parasite prevalence, while other sites showed stability or resurgence in malaria despite high coverage of ITNs and IRS. Persistence and resurgence of vector populations continues to be an important issue for malaria control and elimination. More importantly, extensive use of ITNs and IRS has created intensive selection pressures for malaria vector insecticide resistance as well as for potential outdoor transmission, which appears to be limiting the success of ITNs and IRS. For example, in Africa, where malaria is most prevalent and pyrethroid-impregnated ITNs have been used for more than a decade, there is ample evidence of the emergence and spread of pyrethroid resistance in Anopheles gambiae s.s., the major African malaria vector, as well as in An. arabiensis and An. funestus s.l.. Both the prevalence of An. gambiae s.s. resistance to pyrethroids and DDT and the frequency of knock-down resistance have reached alarming levels throughout Africa from 2010–2012.

Unfortunately, pyrethroids are the only class of insecticides that the World Health Organization recommends for the treatment of ITNs . Furthermore, a number of recent studies have documented a shift in the biting behavior of An. gambiae s.s. and An. funestus, from biting exclusively indoors at night to biting both indoors and outdoors during early evening and morning hours when people are active but not protected by IRS or ITNs, or to biting indoors but resting outdoors. Apart from these intraspecific changes in biting behavior, shifts in vector species composition, i.e., from the previously predominant indoor-biting An. gambiae s.s. to the concurrently predominant species An. arabiensis, which prefers to bite and rest outdoors in some parts of Africa, can also increase outdoor transmission. Because IRS and ITNs have little impact on outdoor-resting and outdoor and early-biting vectors, outdoor transmission represents one of the most important challenges in malaria control. New interventions are urgently needed to augment current public health measures and reduce outdoor transmission. Larval control has historically been very successful and is widely used for mosquito control in many parts of the developed world, but is not commonly used in Africa. Field evaluation of anopheline mosquitoes in Africa found that larviciding was effective in killing anopheline larvae and reducing adult malaria vector abundance in various sites. Microbial larvicides are effective in controlling malaria vectors,cannabis grow room and they can be used on a large scale in combination with ongoing ITN and IRS programs. However, conventional larvicide formulations are associated with high material and operational costs due to the need for frequent habitat re-treatment, i.e., weekly re-treatment, as well as logistical issues in the field. Recently, an improved slow-release larvicide formulation was field-tested for controlling Anopheles mosquitoes, yielding an effective duration of approximately 4 weeks. Considering the monthly reapplication interval, this still may not be a cost-effective product for large-scale application. The new US EPA-approved long-lasting formulation, FourStar Microbial Briquets , is potentially effective for up to 6 months , and preliminary data suggest that it is effective in malaria mosquito control [GZ, unpublished data]. Field-testing is needed to determine the efficacy and cost-effectiveness of this long-lasting larvicide. The central objective of this study is to determine the effectiveness and cost-effectiveness of long-lasting microbial larviciding on the incidence of clinical malaria and the reduction of transmission intensity. The hypothesis is that adding LLML to ongoing ITN and IRS programs will lead to significant reductions in both indoor and outdoor malaria transmission and malaria incidence as well as cost savings. This paper describes a protocol for evaluating the impact of LLML in reducing malaria vector populations and clinical malaria incidence.For purposes of planning and conducting an evaluation of the intervention, we will subdivide the field area into villages , which is the smallest administrative unit in Kenya. Using villages as clusters has advantages over random sampling. First, the clinical records in health centers or hospitals in Kenya generally include the name of the village and sublocation ; therefore, clinical malaria cases can be traced back to the village level. Second, villages have been conveniently used as intervention/ control clusters in previous trials.

Our field team will conduct the demographic surveys before the start of the intervention. Each team will be provided with a printed overview map and a handheld Google Nexus 7 tablet. A surveillance team, comprising a field technician, a reporter, and a local guide, will visit every compound to explain the study procedures, tally inhabitants, and collect information on house characteristics. If the head of the compound agrees to participate, we will record the geographical coordinates of the main house of the compound and compound codes will be written in permanent marker on the front wall next to the door. We will record the genders and ages of all compound members on questionnaire forms using the on-site Google Nexus 7, which will update the database in real time together with the GPS coordinates of the surveyed compound. We will map the locations of all compounds using ArcGIS 10 . Demographic surveillance will be done in year 1, 6–12 months prior to intervention . We will draw village boundaries based on the demographic surveys and confirm it with the field teams and the database manager. If a village is too small , we will combine the village with a neighboring village to form one cluster. Total and age- and gender-specific populations will be aggregated at the cluster level.Clinical malaria records will be collected from 8 to 12 months prior to intervention, to calculate baseline incidence rate at each cluster for cluster randomization, through to 8 to 12 months after all interventions . We will collect information on clinical malaria cases retrospectively from all government-run hospitals, health care centers, and clinics located either within the study area itself or within catchment areas overlapping the study area. We will obtain clinical data from the treatment centers through the malaria control office of Kakamega and Vihiga counties, Kenya. We will also collect patient- and treatment-related information, including age, gender, date of diagnosis, parasite species, village of patient , and prescriptions given. All personal identifiers will be excluded from this study. A clinical malaria case is defined as an individual with fever and other related symptoms such as chills, severe malaise, headache, or vomiting in the presence of a Plasmodium-positive blood smear. The clinical malaria incidence rate is calculated as the number of clinical malaria episodes divided by the total person time at risk based on demographic surveys. We will also collect the aggregated monthly diarrhea data at each site along with clinical malaria records from local health clinics and hospitals. We will not conduct prospective passive surveillance, active home visits, or cross-sectional blood surveys. We will calculate the clinical malaria incidence rate separately for each cluster, different study period and different age group . We will include all clinical malaria cases in our study, including cases diagnosed during the four study periods : preintervention period: baseline clinical malaria records started at least 8–12 months prior to the application of long-lasting microbial larvicides till intervention, intervention period: all clinical records during the intervention period, the 8-month wash-out period, and post intervention period: clinical malaria records till 8–12 months after the last round of larvicide application.Permission to use microbial larvicides for malaria vector control has been obtained from the Pest Control Products Board of Kenya. Ethical clearance has been approved by the Scientific and Ethical Unit of the Kenya Medical Research Institute . As described, aggregated clinical data will be obtained from the treatment centers through the malaria control offices of Kakamega and Vihiga counties, Kenya. According to US Department of Health and Human Services Code of Federal Regulations 45 CFR 46.101 part 4 , these data are in the category of exempt human subjects research, which involves the study of existing data, documents, or records, with no collection of subject-level information. Informed consent will be obtained from each participant. All investigative team members in the United States, Kenya, and Australia have no financial conflict of interest with the larvicide manufacturer, Central Life Sciences.We will conduct baseline malaria vector surveillance at least 4 months prior to any application of LLMLs .

The process of designing, building, and validating a cell stretching platform and using this device to study the effect of mechanical stimulation on different cell types requires the development of several skills, which formed the basis of the learning outcomes required for the successful completion of the project. Through participating in this project, undergraduate students should have developed knowledge and skills in engineering design, basic cell biology, and experimental design. This required students to gain experience in programming, using computer aided design -related software, such as SOLIDWORKS, and cell culture. Students were also encouraged to apply for either individual or project related funding through the Edwards Life sciences Summer Undergraduate Research Program and Undergraduate Research Opportunities Program at the University of California, Irvine , respectively. Through these programs, students gained experience not only in reading and writing scientific articles,rolling grow benches but also presentation of research findings in formal settings including at the annual Undergraduate Research Symposium at UCI. The learning outcomes associated with this project were similar to those of other experiential learning modules as key knowledge and skills that promote learning and individual development are gained through student involvement.

Over the next four years, junior undergraduate students were recruited and mentored by their senior counterparts. This approach created a continuity of knowledge over multiple years and provided the students with mentoring experiences. The undergraduate students were from biomedical and mechanical engineering programs in their sophomore or junior years. As the project progressed, some of the students wanted to continue and stayed to pursue graduate degrees and participated more directly in recruitment. After working on this project, the students were given surveys to gauge learning and to provide feedback on how the project can be improved for a better learning experience.The uniaxial cell stretching device is composed of two experimental substrates housed in a 10.16 cm  15.24 cm 6061-T6 aluminum channel. The substrates, made by joining silicone sheets and 2.54 cm inner diameter silicone tubing, are held in place by a movable center clamp and fixed outer clamps, with top clamps and wing nuts used to apply pressure and maintain substrate tension during application of cyclic stretch. Cyclic strain is generated by using a programmable servomotor to move the center clamp, which is coupled to a gear and gear rack system. This center clamp slides on two 0.635 cm rails and is aided by bronze bushings to reduce friction and wear. Once the device is assembled, the experimental substrate has a length of 3.81 cm in the direction parallel to the uniaxial stretch. Different amplitudes of strain can be generated by programming the servomotor to rotate a circumferential distance corresponding to a fraction of the experimental substrate length. For example, 5, 10, 15, and 20% strain amplitudes can be generated through rotating the servogear 0.191, 0.381, 0.572, and 0.762 cm , respectively. In addition, an aluminum block is used to either extend both experimental substrates and generate static strain, or extend one experimental substrate and create a temporary static strain until the servomotor is powered resulting in equal cyclic strain in both experimental substrates.

A 1 Hz cyclic stretch was used for all experiments in this study. The CAD files and drawings have been made publicly available. The parts and their costs as well as detailed assembly instructions are provided in the Appendix, which is available under the “Supplemental Materials” tab for this paper on the ASME Digital Collection.The experimental substrates were fabricated through sealing silicone tubing to a 0.05 cm thick silicone sheet using polydimethylsiloxane , which was then cured at 60  C. To validate the strain profiles generated by the cell stretcher, videos were captured of the servo gear rotating and the experimental substrate stretching and deforming. The videos were processed through IMAGEJ software to track either a single point of interest on the servogear or a 5 5 matrix of markers on the surface of the substrate using the MTrack2 plugin. The data obtained were analyzed using a custom python code to validate the waveform output by the servo or the resulting strains parallel and perpendicular to the direction of stretch, respectively.Experimental substrates were sterilized by autoclave, several 70% ethanol and phosphate buffered saline washes, and then coated with a 10 lg/mL fibronectin solution and incubated at 4  C overnight. The substrates were further rinsed with phosphate buffered saline before cells were seeded onto the surface. Bone marrow derived macrophages were obtained by flushing the bone marrow from the femurs of 6–12 week old female C57BL/ 6J mice . This was accomplished using Dulbecco’s modified eagle medium supplemented with 10% heat-inactivated fetal bovine serum , 1% penicillin/ streptomycin, 2 mM L-glutamine , and a 10% conditioned media, produced from CMG 14–12 cells that express recombinant mouse macrophage colony stimulating factor, which differentiates bone marrow cells to macrophages. Red blood cells were removed by treating the collected bone marrow cells with a red cell lysis buffer.

The cells were then centrifuged, resuspended in the culture media, and seeded onto non-tissue culture treated petri dishes for 7 days, before being harvested using an enzyme-free dissociation buffer and seeded onto experimental substrates. The resulting macrophages were seeded at a density of 2 105 cells per substrate. Following 4 h of incubation, the media was replaced with either regular or 0.3 ng/mL interferon-gamma and 0.3 ng/mL lipopolysaccharide containing media then cyclically stretched at a 10% stretch amplitude for a period of 18 h. Following stretch, supernatants were collected and analyzed for the presence of tumor necrosis factor-alpha , interleukin-6 and monocyte chemoattractant protein- 1 cytokine secretion using ELISA kits following the manufacturer’s instructions.The designed, low cost, uniaxial cell stretching device was subjected to a number of tests to ensure adequate and repeatable mechanical function. For example, by tracking and analyzing the rotation of the servogear and positional markers on the experimental substrates, the waveform and the strain profiles output by the servo were computed. The servo was capable of generating sine, triangle, and square waves through fine adjustments in the rotation speed and acceleration of the servo gear. Similarly, changing the degree of rotation of the servo gear generated different strain amplitudes. The parallel and perpendicular cyclic strains generated by the device were similar to theoretical 10% and 20% stretch amplitudes, respectively. This device is, therefore, able to generate uniform strain profiles in the center of the well that is comparable to other, more expensive, cell stretching systems. However, decreases in strain were observed toward the transverse boundaries on the stretchable membrane . Once the mechanical functions were validated, the uniaxial system was used to mechanically stimulate macrophages and cardiomyocytes.When subjected to cyclic uniaxial stretch, bone marrow derived macrophages were observed to alter their cell morphology. Following stretch,drying cannabis the degree of macrophage alignment was quantified by calculating the OOP. The OOP ranges from 0 for a completely isotropic arrangement to 1 for perfectly aligned organization. Significantly higher OOP values were obtained for unstimulated macrophages in response to cyclic stretch when compared to static controls. This alignment of macrophages in response to a 1 Hz uniaxial strain at a 10% amplitude was previously observed. IFN-c/LPS stimulated macrophages are also aligned in the direction of stretch and displayed significant elongation in response to cyclic stretch. However, no significant differences were observed in inflammatory cytokine secretion for IFN-c/LPS stimulated macrophages subjected to cyclic strain at a 10% amplitude , which has also been previously reported.This project was initiated as a collaboration between two labs with different biological interest , but a common goal of promoting undergraduate research projects. The initial group of students focused mainly on device design and manufacturing and worked under the supervision of both principal investigator’s as a team. They were given a wide degree of autonomy to research and design the stretchers. For example, it was through their independent research that the possibility of using low-cost servomotors was discovered. Beyond mentoring the newly recruited team-members, these students also mentored high school students who were part of the center’s CardioStart high school summer program.

As the project progressed, the students were more involved in the biological experiments that aligned with the research programs of each lab. However, they continued to work as a team on optimizing the device design. Thus, the students gained experience in design and manufacturing, biological experimental design, and working on an interdisciplinary team, all of which are valuable for careers in industry or academia. Overall, the students indicated that the project had a positive impact on their educational growth and helped to influence their career decisions . Through working on this project, the students perceived that they gained valuable knowledge and skills in engineering design, experimental design, and basic cell biology. They also indicated that they gained experience in reading and writing scientific articles, programming, using CAD-related software, and cell culture. In addition, working in interdisciplinary teams and presenting their work to a wide range of audiences resulted in improved communication skills. The students were also asked to assess the impact the project had on their careers. They indicated that their direct involvement with this project helped to influence their chosen career paths and the experience also helped to develop the skills necessary for their career decisions. The first two students on the project originally intended to pursue industry careers, but both chose to also acquire master’s degrees . Of the latter group of students, one went to industry and two are pursuing doctoral degrees.This study describes a low cost uniaxial cell stretcher that produces consistent strain profiles, similar to alternative commercially available systems. While the mechanical functions are similar, the fabrication and maintenance costs of this device are only a fraction of those required for other similar systems, thus potentially increasing the widespread availability of this apparatus. The proposed device is composed of readily available materials and utilizes a low cost servomotor to perform the mechanical motions necessary to elicit a uniaxial strain. The device is capable of multiple strain profiles with differing amplitudes and frequencies, and can maintain applied strains for a minimum of three days under continuous use, thus validating the clamping mechanism. In addition, no heating issues resulting from the continuous operation of the servomotor were observed. When compared to other commercially available stretchers, the proposed design is considerably less expensive to maintain, but has comparable functions . However, this uniaxial cell stretcher has several limitations that can be addressed with minor modifications. For example, the servomotor used is unable to generate strains greater than a 20% amplitude. In addition, at maximum amplitude, the motor is capable of generating strains up to a frequency of 3 Hz, whereas higher frequency waves can be generated at lower amplitudes. These limitations can be addressed by substituting the given servomotor for another higher specification Hitech standard servo, which nominally increases the total cost. The standard servos typically have the same frame size, as a result, no additional modifications to the overall design of the cell stretcher should be needed. The designed cell stretcher was used to verify the alignment of macrophages and cardiomyocytes in response to cyclic uniaxial stretch. Macrophages were subjected to a 1 Hz strain at a 10% amplitude for a period of 18 h, whereas cardiomyocytes were subjected to a 1 Hz strain at a 20% amplitude for a period of 6 h. The chosen frequencies and strain amplitudes have previously been described as within normal physiological ranges for stretch experienced by macrophages recruited to blood vessels and cardiomyocytes in the heart. In the experiments performed, cardiomyocytes were seeded at a higher density to form a confluent monolayer that is more representative of cardiac tissues and analysis of alignment was dependent on actin organization, as determined by immunofluorescence. Macrophages were seeded moresparsely to allow ample space for cell spreading in response to cyclic stretch and to distinguish between individual cells using phase contrast images, which was necessary for the analysis of alignment and elongation, since macrophages do not display actin stress fibers. The stretch duration is also unique to each cell type but is typically long enough to allow for cytoskeletal rearrangement. Although the experimental setups were somewhat different, both macrophages and cardiomyocytes displayed alignment in response to stretch.

What was an illegal behavior not so long ago, became a legal behavior for some approximately twenty-two years ago, and is now a legal behavior for all in some states and countries. With that said, although for some individuals marijuana use may be purely medical or purely recreational, for many, medical and recreational use of marijuana overlaps . This study considers motives of marijuana use and associated mental health outcomes in a sample of young adults comprised of individuals who use marijuana exclusively for medical reasons, exclusively for recreational reasons or for both medical and recreational reasons, in a context with a longstanding history of legalized medical marijuana. It does so using an instrument that operationalizes marijuana motives of use to include both recreational as well as medical motives of use, which is a departure from motives of use questionnaires found thus far in the literature. Moreover, a better understanding of the association between motives of use and symptoms of depression and motives of use and symptoms of anxiety might allow one to detangle the association between marijuana use and diagnoses of depression and anxiety, and provides an avenue ripe for intervention. Finally, most of the work around marijuana use has not examined gender differences. But, as the gap in use prevalence between gender is decreasing and gender norms are changing, , it is imperative to better understand how marijuana use affects women differently than men. This work confirms that gender matters when examining the association between marijuana use and mental health outcomes,botanicare rolling benches and begins to lay the groundwork to better understand how motives of use may influence mental health outcomes differently for men and women.

Taken together, the findings presented in this dissertation contribute to the literature on motives of marijuana use and associated outcomes by demonstrating that there is a differential effect of motives of marijuana use on symptoms of mental health in young adults of Los Angeles who use marijuana for medical and/or recreational reasons. Whereas marijuana use driven by a coping motive is significantly associated with increases in symptoms of depression, symptoms of anxiety, and overall psychological distress, marijuana use driven by other motives does not appear to be directly associated with these mental health outcomes. However, when considering frequency of marijuana use, it becomes apparent that motives of pain, conformity and attention also influence mental health outcomes. Finally, associations for some of the motives, namely social anxiety, play out differently based on gender. These findings also have concrete implications for the development of interventions targeting marijuana use and mental health in young adults. Mainly, by targeting maladaptive coping practices. The findings also highlight the need for gender specific interventions as men and women engage in use differently, particularly in social settings. Given the exploratory nature of this work, these findings set forth an avenue of research on motives of marijuana use and mental health outcomes in young adults who use marijuana for medical and/or recreational reasons. First and foremost, although beyond the purposes of this dissertation, these associations should be compared between user groups , and looked at longitudinally. These findings should also be replicated using a larger, randomly selected sample.

To address some of the previously mentioned limitations, work should be pursued considering whether the strain of marijuana and concentration of cannabinoids versus tetrahydrocannabinol used play a role in the association between motives of marijuana use and symptoms of depression, symptoms of anxiety, and psychological distress. Finally, more work should be done to better understand and capture motives of marijuana use at time of use in order to eliminate the recall bias and get a better understanding of the associations between motives of marijuana use and mental health outcomes. As of January 2018, marijuana, in all its forms, is legal in California to over seventy five percent of its population. This comes after twenty-two years of medical marijuana being legal in California. Being only one of nine states to legalize all forms of marijuana, but being the more populous one, California has become the site of a large social experiment. The legalization of marijuana in all its forms, comes with little knowledge of what the social and health implications of what such an endeavor might be. In a context of legalized marijuana, there is an urgency to continue to detangle the associations between marijuana use and mental health in young adults to help ensure a successful transition to adulthood. Following medical marijuana legalization in over half states in the US and a few countries in Europe and America, in 2012, Colorado and Washington in the US first passed laws to legalize marijuana use by adults aged 21 or older. Since then, recreational marijuana legalization has been adopted in eight states and DC where one fifth of US population live . These state-wide laws emboldened other jurisdictions in the world to enable recreational marijuana market, with Uruguay and Canada passing country-level legalization in 2014 and 2017 , respectively. While intense debates are ongoing surrounding recreational marijuana legalization, little empirical evidence has been provided regarding its impacts on public health.

Primarily constrained by data availability, existing research typically conducted pre- and post-legalization evaluations on one or two states in the US controlling for contemporaneous trends in a limited number of comparison states . Study findings were mixed. Some states with recreational marijuana legalization saw an increase in marijuana use but no changes in motor vehicle crash fatality rates . The impacts of recreational marijuana legalization on other drugs remain unclear. Particularly, there have been considerable concerns about whether and how the opioid crisis may be influenced. Prescription opioid related harms are becoming a global problem, especially in the US . In the past 2 decades, the volume of opioid prescriptions quadrupled and opioid overdose deaths more than doubled . It is estimated that opioid misuse and overdose imposed an economic burden of $56 billion to the US each year . In 2017, opioid crisis was declared a “National Public Health Emergency” . There have been two hypotheses regarding the impacts of marijuana laws on opioid use. Marijuana is suggested to be effective in pain management and could be used medically by patients as substitutes for opioids. There were emerging population studies suggesting that medical marijuana patients reported substituting marijuana for opioids . The first hypothesis therefore suggested that liberalization of marijuana laws could reduce opioid use and related consequences if it increased marijuana use for medical purposes. In contrast, the competing hypothesis argued that marijuana, when used for non-medical purposes, could act as a gateway drug to opioids and result in increased opioid misuse and related outcomes. A recent study reported that non-medical marijuana use was associated with increased odds of prescription opioid misuse and opioid use disorder in a longitudinal, nationally representative sample in the US . Liberalization of marijuana laws may thus lead to a deterioration of opioid crisis if it encouraged non-medical use of marijuana. Both hypotheses regarding the impacts of marijuana laws on opioid use may be valid. The net effects of medical or recreational marijuana legalization could be either positive or negative, depending on which of the two hypotheses dominated in reality. Recent studies on medical marijuana legalization reported that substantial reductions in opioid-related deaths, misuse, drug prescriptions, traffic fatalities,commercial plant racks and inpatient stays were observed after medical marijuana was legalized . These findings appeared to support the first hypothesis, albeit indirectly, if marijuana use for medical purposes increased more than marijuana use for non-medical purposes as a result of medical marijuana legalization. Regarding recreational marijuana legalization, there has been continuous concern that the legalization may exacerbate opioid crisis if the legalization primarily impacted non-medical marijuana use. The empirical support is very limited. The only study focusing on recreational marijuana legalization indicated that the increasing trends in opioid-related deaths in Colorado were reversed following recreational marijuana legalization . However, data on a single state without comparison states lack generalizability and causal inferences. This study aimed to provide empirical evidence about the relationship between recreational marijuana legalization and prescription opioids. We focused on Medicaid enrollees in the US. Medicaid is a US health insurance program jointly funded by the federal government and states, primarily covering beneficiaries with low income and disabilities. Medicaid enrollees are a priority population for opioid control with a disproportionate burden of pain as well as a higher risk of opioid overdose and misuse . Using 2010–2017 state Medicaid drug prescription data, we were able to examine all the eight states and DC that have legalized recreational marijuana in the US. We explored the heterogeneity in policy responses by analyzing different drug schedules separately.

The primary outcome, prescription opioids received, were measured in three population adjusted variables: 1) number of opioid prescriptions, 2) total doses of opioid prescriptions ) , and 3) Medicaid spending on opioid prescriptions, per quarter per 100 Medicaid enrollees. Nominal spending was converted to 2017 constant US dollars using consumer price index. The number of Medicaid enrollees by state and year was obtained from annual Medicaid Managed Care Enrollment Reports . Prescription opioids were identified by linking the National Drug Code numbers in Medicaid State Drug Utilization Data to drug information in the Approved Drug Products with Therapeutic Equivalence Evaluations published by the US Food and Drug Administration . Because we were primarily interested in prescription opioids potentially substitutable by marijuana, we followed previous studies to exclude buprenorphine drugs typically used to treat opioid use disorder and included buprenorphine drugs commonly used in pain management . All methadone drugs were included because they were generally prescribed for pain management in outpatient settings that our data source captured. Schedule II and Schedule III opioids were categorized separately to reflect their differences in drug misuse and overdose potential. According to the most recent classifications by the Drug Enforcement Agency , Hydrocodone-combination drugs such as Vicodin and Lortab were classified as Schedule II drugs. The types of prescription opioids included in our analysis were listed in Table 1 by drug schedule. Following previous research , the primary policy variable was the implementation of statewide recreational marijuana legalization identified by law implementation dates. During the study period, eight states and DC implemented recreational marijuana legalization . Because state-level heterogeneity in the duration of legalization may have differential impacts on prescription opioids, three dichotomous policy variables were created to indicate recreational marijuana legalization taking effect at different time points : 4th quarter of 2012 , around 2nd quarter of 2015 , or around 4th quarter of 2016 . We also controlled for state-level time-varying covariates in the regressions, including a dichotomous variable indicating statewide medical marijuana legalization in effect, a dichotomous variable indicating statewide prescription drug monitoring program in effect, a dichotomous variable indicating statewide Medicaid expansion under the Affordable Care Act that provided insurance to all adults with income up to 138% of the US federal poverty level, a continuous variable for median household annual income adjusted to 2017 dollars with consumer price index, a continuous variable for annualized poverty rate, and a continuous variable for annualized unemployment rate .The analysis was conducted at state-quarter level. A difference-in-difference approach was used to assess the associations of legalizing recreational marijuana with the three log transformed continuous outcomes for Schedule II and Schedule III prescription opioids, separately. The coefficients in regression models can be interpreted as the average percentage change in prescription opioid outcomes in association with the implementation of recreational marijuana legalization. The underlying assumption in the difference-in-difference approach is parallel trends in treatment and comparison states in the absence of policy change . In our study, treatment states were eight states and DC that adopted recreational marijuana legalization in the study period. Before they adopted recreational marijuana legalization, they all had adopted medical marijuana legalization. Because medical marijuana legalization had significant impacts on trends in opioid-related outcomes including prescribing in Medicaid population , comparison states should have had medical marijuana legalization in effect to ensure their comparability with these treatment states prior to recreational marijuana legalization. We therefore made comparisons in two difference-in-difference models. Model A compared among eight states and DC themselves. Because they implemented recreational marijuana legalization at different time points, at a given time point, states that had not implemented legalization served as controls. Model B compared eight states and DC to six states that had implemented medical marijuana legalization as of January 1st, 2010 but had not implemented recreational marijuana legalization during the study period.

In particular, I observe the weigh-in time, the berry picker’s unique employee identifier, the field where the berries were picked, and the weight of the picker’s harvest. I divide the harvest’s weight by the time elapsed since the picker’s previous weigh-in to obtain a weight-per-hour measure of worker productivity. For the first weigh-in of the day, I use time elapsed since morning check-in to calculate this measure. As reported in table 1.1, average productivity pooled across both farms is just over nineteen pounds picked per hour. This number, however, masks significant heterogeneity across farm, day, and worker. At the San Diego farm, which grows organic berries, average productivity is slightly under fourteen pounds per hour, while at the Bakersfield farm, which grows conventional berries, average productivity is over twenty-two pounds per hour. Figure 1.3 plots the distribution of workers’ average productivities, while figure 1.4 plots the distribution of each day’s average productivity, in both cases separated by farm. These two figures highlight substantial variation in picker skill, as well as in daily productivity. In southern California and the central valley, where the farms I study are located, temperatures peak in the mid-to-late afternoon. To avoid the hottest part of the day, most pickers begin work as early as 6:00 a.m. and end around 3:00 p.m. This pattern is reflected in figure 1.5: most fruit picking ends by mid-afternoon. The average picker works around eight hours each day, as shown in figure 1.6. Under California law in my sample period , agricultural workers do not earn overtime pay until after working ten hours in a single day. In my data,rolling tables only the San Diego farm ever lets pickers work more than ten hours in any given day. Farms employ pickers on a day-to-day basis, either directly or through a labor contractor.Some pickers only work for a day or two, but others work continuously for several weeks or months as shown in figure 1.7.

Indeed, several employees in my data work for a farm in two or all three of the years I study. Unfortunately, I do not observe each worker’s initial date of hire, so I am unable to confidently measure lifetime worker tenure on either farm.While I know each farm’s daily piece rate wage from the its payroll data, I obtain information on market prices for California blueberries from the Blueberry Marketing Research Information Center of the California Blueberry Commission . As an official agricultural commission, the CBC legally requires all blueberry producers in the state to report daily production and sales figures. The CBC then publishes daily summary statistics of these data through BMRIC. Individual blueberry producers are able to access a daily BMRIC report online that summarizes the high, low, and weighted average prices received by blueberry producers throughout the state on the previous day. Separate statistics are provided for conventional and organic blueberries. In order to capture the information a farmer could have accessed on any particular day, I use each day’s most recent previous BMRIC report as the relevant measure of market prices. Because BMRIC publishes a daily report each weekday except for holidays, the relevant market price data for harvest data collected on a Thursday is from the Wednesday prior. Similarly, the relevant market price data for harvest data collected on a Monday is from the Friday prior. Based on personal conversations, the blueberry farmers I study track these BMRIC reports quite closely throughout the season. From April to June each year, both market prices and piece rate wages fall as the California blueberry season progresses. Figure 1.10 documents this relationship across the three years and two farms in my dataset. Recall that the San Diego farm grows organic blueberries while the Bakersfield farm grows conventional berries. This distinction accounts for why the two farms face differing market prices in the same year.

Market prices and piece rate wages are highly correlated over time, due in large part to seasonality in blueberry production. Figure 1.11 plots each farm’s daily total production over time for each season. At times of high production, blueberry bushes are likely to be full of easily-pickable ripe berries. This abundance of fruit leads farmers to cut the piece rate as described in the previous section. In order to disentangle the various factors that affect farms’ piece rate wages in my empirical exercises, I control both for seasonality in production as well as the field where berries are harvested. In my subsequent econometric analyses, I estimate the causal effects of piece rate wages and temperature on picker productivity. Figure 1.12, in contrast, plots the naïve relationship between average picker productivity and piece rate wages, temperature, and two other observable characteristics: time of observation and worker tenure by season. First, note that productivity and piece rate are negatively correlated, since farmers lower the piece rate when fruit is plentiful in the fields.Second, note that there are no sharp decreases to average productivity at particularly high temperatures, as one may hypothesize. Finally, note that there is a clear increasing and concave relationship between worker tenure within a season and productivity. In other words, there is learning-by-doing in berry picking, and this learning has decreasing marginal returns over time. While most employees out-earn the hourly minimum wage under the piece rate system, some fall below this threshold and are paid according to the minimum wage for the day. As Graff Zivin and Neidell note, if there is not a credible threat that these workers could be fired for their low output, they may shirk and provide less effort than they otherwise would. Figure 1.13 plots the distribution of normalized daily productivity that identifies those picker days where shirking could be a problem. Observations to the left of one are picker-days where the picker’s effective hourly wage is below the minimum wage, and observations to the right of one are picker-days where the picker out-earns minimum wage under the piece rate scheme. A picker with a normalized productivity measure of two is earning twice the minimum wage. Productivity in this figure is normalized because both piece rate wages and the hourly minimum wage vary over the sample period. Shirking, if it occurs, could bias my results. In particular, if high temperatures or low wages lead to more pickers earning the minimum wage, and these pickers subsequently shirk, my econometric estimates will be biased upward. I address this concern in section 1.6 by re-estimating my primary results using only those picker-days where employees out-earn the minimum wage. My findings do not change when I eliminate these observations, suggesting that the threat to a picker of being fired if they consistently slack off is a sufficient incentive to keep them from shirking. The model presented in section 1.2.1 motivates my empirical strategy. In particular, my goal is to estimate the relationship between piece rate wages and labor productivity . The primary challenges to this undertaking are twofold. First,cannabis grow supplies many observable and unobservable factors contribute to worker productivity which – if unaccounted for – could lead to omitted variable bias in my estimates of temperature and wage effects. Second, piece rate wages are endogenous to labor productivity.

To address factors other than the piece rate wage that could drive labor productivity, I exploit the richness of my data and include flexible controls for temperature, and a host of fixed effects. Most importantly, I include time fixed effects to capture seasonality , work patterns , and season-specific shocks . I also include field-level fixed effects to capture variation in the productivity of different varieties and plantings of blueberry bushes. The combination of time- and field-level fixed effects gives me a credible control for the average density of blueberries available for harvest at a given time in a given field. In other words, these fixed effects allow me to control for resource abundance . Further, I include worker-specific fixed effects to capture heterogeneity in picker ability. Lastly, I include a quadratic of worker tenure to allow for learning-by-doing. When estimating the effect of temperature on productivity, my identifying assumption is that individual realizations of temperature are as good as random after including the controls described here and the piece rate wage. To address the endogeneity of piece rate wages to labor productivity, I instrument for these wages using California market prices for blueberries. In order for these prices to be a valid instrument for wages, they must be correlated with farms’ piece rates, but not affect labor productivity through any other channel. Figure 1.10 plots piece rate wages and market prices over time and suggests a strong correlation between the two variables. I provide formal evidence of this relationship in table 1.4, which I describe in detail in the following section. As evidence that the exclusion restriction holds – that market prices do not affect labor productivity except through wages – I rely on the size and heterogeneity of the California blueberry industry. Statewide market prices capture supply shocks from growing regions around the globe, each with different weather, growing conditions, and labor markets. To the extent that environmental conditions agronomically drive blueberry production, they do so differentially across different growing regions of California. Therefore, any one farm’s temperature shocks in a given growing season do not determine aggregate blueberry supply.Additionally, both of the farms I study are quite small in comparison to the statewide market: they are price-takers and cannot independently affect average prices. As a result, market prices capture exogenous variation in aggregate supply shocks and serve as an effective instrument for piece rate wages. Table 1.2 presents the results of estimating my primary specification, equation , with different sets of controls. In column , I include only the instrumented piece rate wage and five-degree temperature bins. As expected, without controlling for seasonality or harvest field, I find a statistically significant negative effect of wages on productivity. I also find large and negative effects of cool temperatures on productivity. In each subsequent column, I add more controls: farm fixed effects, field fixed effects, worker tenure controls, time fixed effects , and worker fixed effects. Including time fixed effects to column makes the largest difference to the sign and significance of my results. This makes sense, since seasonality and time-of-day dynamics are particularly relevant in the California blueberry context. Column of table 1.2 contains the results of my preferred specification using the temperature bins described in equation . By controlling for field and time fixed effects, , the point-estimate for piece rate wages’ effect on worker productivity switches from negative and statistically significant to positive but statistically indistinguishable from zero. The standard error on this effect is qualitatively small, meaning that I can reject even modest effects of wage on productivity. I also find statistically significant negative effects of both cool temperatures and very hot temperatures on picker productivity. The solid line in figure 1.14 plots this temperature-response function with a 95%- confidence interval. The relevant temperature point estimates represent the change in conditional average picker productivity expected by replacing a picking period with a time-weighted average temperature between 80–85◦F with a picking period having a time-weighted average temperature within the corresponding temperature bin. I find that temperatures between 50 and 55 degrees lower productivity by 3.22 pounds per hour – a nearly 17% decrease, while temperatures over 100 degrees lower productivity by 2.33 pounds per hour – just over a 12% decrease. Table 1.3 re-estimates my preferred specification using the piece wise-linear spline described in equation . I find that at temperatures below 88.5 degrees Fahrenheit, an additional degree of heat increases productivity by 0.088 pounds per hour, on average. At temperatures above 88.5 degrees, however, an additional degree of heat lowers productivity by 0.20 pounds per hour. The dashed line in figure 1.14 plots these effects, which are significant at the 0.001 and 0.05 levels, respectively. In table 1.4, I provide evidence that blueberry market prices are an effective instrument for piece rate wages. Column reports the results of estimating equation by ordinary least squares without instrumenting for wages. While the estimated effect of wages on productivity in this specification is statistically insignificant, the point estimate is negative. Column presents the results of regressing market prices, temperature, and other controls on the piece rate wage: my first stage. There is a large, positive, and statistically significant effect of prices on wages, while temperature has no meaningful effects on piece rates below95 degrees Fahrenheit.

We hypothesized that: a) motives that promote positive experiences would not be associated with symptoms of depression, symptoms of anxiety, or overall psychological distress; b) motives for avoidance of negative experiences would be associated with higher levels of symptoms of depression and symptoms of anxiety, or overall psychological distress; c) motives focused on medicinal use would be associated with lower levels of symptoms of depression and symptoms of anxiety, or overall psychological distress; and d) there would be no association between motives of boredom, relative low risk, and availability with depression or anxiety symptoms of depression and symptoms of anxiety, or overall psychological distress. As a first step, multiple linear regression analyses were used to investigate the associations between motives of marijuana use and symptoms of depression and symptoms of anxiety as well as overall psychological distress in our sample. Variables were entered in two blocks using the “enter” function for regressions in SPSS. The first block consisted of the 17 motives of use and the second block entered contained the control variables: age, race/ethnicity, user group, and gender. Given the number of variables entered in the model and the number of comparisons to be made, Bonferroni corrections were used to counteract potential Type I errors. Thus, the Bonferroni corrected alpha value of 0.003 was used to assess significance. Post hoc power analyses, or the probability of finding a statistical difference from zero,vertical grow room design were also performed. Second, mediation analyses using a non-parametric bootstrapping approach were conducted to assess whether past 90 days marijuana use or daily number of marijuana hits influenced the association between motives of marijuana use and mental health in our sample.

The mediation analyses followed PROCESS Model 4 . A cross product test of the coefficients was favored over causal step mediation as it is a superior method to detect indirect effects and assess their significance . The cross product of the coefficients test provides a single test for the relation between the independent variable, the mediator, and the dependent variable by multiplying coefficients for a and b paths, therefore directly assessing the statistical significance of the indirect effect using bootstrapped confidence intervals. Testing the cross product of coefficients using a nonparametric bootstrapping method is advantageous as it does not require for the assumption of normality to be met, and is appropriate for smaller to moderate sample sizes . To assess for significant indirect effects, 95% bias corrected confidence intervals were calculated using 10,000 bootstraps. Indirect effects were considered significant if the 95% bias corrected confidence intervals for ab point estimates did not contain zero . To further correct for Type I errors, a supplemental analysis using 99% bias corrected confidence intervals were also calculated using 10,000 bootstraps. To better quantify and compare the effect size of each indirect effects, completely standardized effects were calculated . Completely standardized effects express the indirect effects as the change in the standard deviation for the dependent variable between two cases of the independent variable that differ by one standard deviation . Analyses were conducted using Version 3 of the PROCESS macro in SPSS Version 24, first without any control variables and subsequently controlling gender, age, user group, and race/ethnicity. Men, non-patient users, and Non-Hispanic Whites were used as reference categories for gender, user group, and race/ethnicity respectively. The purpose of this third aim was to determine whether associations between motives of use and our mental health outcomes of interest varied by gender. First, moderation analyses were performed to examine whether the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress differ by gender in young adults who use marijuana. Second, conditional process analyses were done to test for gender differences for the significant indirect associations between motives of marijuana use and mental health outcomes uncovered in aim 2.

Men was used as the reference category for all moderation and conditional process analyses. Analyses were performed using the PROCESS Version 3 macro in SPSS Version 24. PROCESS Model 1 was used to assess moderation . Per Hayes , a moderation is deemed significant if the coefficient for the interaction term between the independent variable and the moderator is significant. In this scenario, the coefficient will properly estimate the moderation of the independent variable’s effect by the moderator . An interaction term was deemed significant if p ≤ 0.05. Conditional process analyses, also called moderated mediation, were conducted to determine whether gender influences the indirect effects found to be significant in aim 2. In these moderated mediation models, the strength of the relationship between motives of marijuana use on symptoms of depression, symptoms of anxiety, or psychiatric distress is conditional on the value of the moderator; gender. Given that our interest was to test the effect of gender on the three paths of the mediated model X→M, M→Y, X→Y, Hayes’ PROCESS Model 59 was used for the conditional process analyses . By using this model, a test of moderation for each path is available in the form of the regression coefficients for the products along with their tests of significance. PROCESS also generates tests of significance and bootstrapped confidence intervals for the conditional direct and indirect effects. PROCESS also automatically conducts a test of the difference between the indirect effects in the two groups called the index of moderated mediation, with a bootstrapped confidence interval. The index of moderated mediation and its bootstrap confidence interval therefore act as an inferential test for the conditional process analysis of the indirect effect . In summary, by conducting conditional process analyses using PROCESS Model 59, we were able to determine which path, if any, was significantly moderated, and whether the indirect effect was moderated.

Bootstrapped confidence intervals for the conditional indirect effects were calculated using 10,000 bootstraps. Using bootstrapped confidence intervals can help avoid power problems introduced by asymmetric and other non-normal distributions of an indirect effect . Descriptive statistics for the sample are presented in Table 3.4. Two cases were eliminated from the original dataset as their gender identity was defined as “other”. Participants were on average 21 years old and mostly men . Forty-five percent of respondents identified as Hispanic/Latino, 26% as Non-Hispanic White, and 19% as NonHispanic African American/Black, 4% as Asian/Pacific Islander, and 6% as multi-racial. This racial/ethnic distribution is somewhat comparable to that of Los Angeles County . Past year annual income was relatively low with 83% of the sample falling in the $1-$25,000 bracket. Most participants reported part-time employment. With regards to education, about half of the sample reported having completed some college and/or being currently enrolled in either a four year or community college. Marijuana was the most frequently used drug in the past 90 days. On average, participants reported using marijuana 69 out of the past 90 days. This means that, on average, participants used marijuana between on 5 to 6 days per week,grow vertical thus classifying their use as heavy . Use of heroin was only reported by one participant over the past 90 day period. The average daily number of marijuana hits was 23.5. There was no difference between men and women with regards to either past 90 days use or daily number of hits. Overwhelmingly, participants reported smoking buds/flowers as the primary form and way of marijuana use. On about 26 of the past 90 days, marijuana was used with other drugs, primarily alcohol about 43% of the time. Fifty-seven percent of the sample had a valid medical marijuana recommendations and thus identified as medical marijuana patients or medical marijuana users. With regards to motives of use, the motive of enjoyment was the motive with the highest mean score indicating that “most of the time” participants in the sample used marijuana for enjoyment purposes . This is followed by motives of sleep and relative low risk. When examining the mode of motives , “always” is the most frequent answer for motives of sleep, relative low risk, pain, and enjoyment. Motives of altered perceptions, availability and celebration follow with “most of the time”. There was a significant difference in mean scores of reported motives of use between men and women for motives of attention, celebration, enjoyment, natural remedy, nausea, pain, sleep and social anxiety . For all these motives, women scored higher than men. Brief Symptoms Inventory-18 scores averaged between 3 and 4 out of a possible 24 for both symptomatology of depression and symptomatology of anxiety, indicating that participants in our sample endorsed some symptoms of depression or anxiety. For the Global Severity Index, which is used to operationalize psychological distress, the average score for the sample was 9.89 out of a possible 72. Only for the symptomatology of anxiety and psychological distress scales was there a significant difference of scores by gender . Table 4.41 presents the regression estimates of symptoms of depression on motives of marijuana use without and with control variables. Motives of use account for 22% of the variance in symptoms of depression. At p ≤ 0.05, motives of celebration, coping and pain were significantly associated with symptoms of depression in the analyses without control variables.

After controlling for age, gender, race/ethnicity, and user group, only coping remained significantly associated with symptoms of depression. At a Bonferroni corrected alpha of ≤ 0.003., only coping was positively, significantly associated with symptoms of depression in models without and with control variables. None of the control variables included in the model were significantly associated with symptoms of depression. The association between the coping motive of marijuana use with symptoms of depression is positive indicating that the more often marijuana use is motivated by coping, the higher the score for symptoms of depression. The magnitude of changes in symptoms of depression for a one unit increase in motives of use is of almost 2 points. Post hoc power analyses indicate that the statistical power is greater than 0.9. Results from the mediation analysis with past 90 days marijuana use as a mediator are presented in Tables 4.42a-d. From a simple mediation analysis without control variables , marijuana use motives of availability, conformity, pain, and social anxiety indirectly influenced symptoms of depression through their effect on past 90 days marijuana use. For motives of availability and conformity, the indirect association through past 90 days use is positive , whereas it is negative for motives of pain and social anxiety . For each of these indirect effects, a 95% bootstrap confidence interval based on 10,000 bootstraps did not contain zero . For motives of conformity, coping, and social anxiety, there is also evidence of a direct effect with symptoms of depression independent of their effect on past 90 days marijuana use . The effect is positive for motives of coping and social anxiety with symptoms of depression whereas the direct effect between conformity and symptoms of depression is negative. After controlling for age, gender, race/ethnicity, and user group , the indirect effect of motives of availability on symptoms of depression and social anxiety on symptoms of depression through past 90 days use were no longer significant. Significant indirect effects remained for the motives of conformity and pain with symptoms of depression. For each of these indirect effects, a 95% bootstrap confidence interval based on 10,000 bootstraps did not contain zero . The completely standardized effect for the motive of pain was of -0.26 and of 0.22 for the motive of conformity. Evidence of a direct effect remained for the motive of social anxiety with symptoms of depression but not for the availability motive. The a path from motive of conformity to past 90 days marijuana use was negative, indicating that the more use is driven by conformity , the less days one is likely to use. However, for motive of pain the association was positive, indicating that the more use is driven by this motive, the more days of use is reported. Motives of use accounted for 19% of the variance of past 90 days marijuana use. Past 90 days of marijuana use was significantly, yet negatively, associated with symptoms of depression. However, although significant, the magnitude of the b coefficient here was almost 0. For each of these indirect effects, a 95% bootstrap confidence interval based on 10,000 bootstraps did not contain zero . Table 4.44 presents the multiple linear regression estimates without and with control variables.

Participants were recruited to the study upon entry to the residential treatment program. Inclusion criteria were MA dependence, English proficiency, age 18 to either 45 years for men or 55 years for women , and the ability to attend exercise or health education sessions. Individuals were excluded if they exhibited medical impairment that compromised their safety as a participant, met criteria for opiate dependence, or had a psychiatric impairment that warranted hospitalization or primary treatment . On-site research staff met with interested MA-dependent participants in a study office to conduct screening and enrollment procedures. Eligible participants signed consent for study participation and completed all baseline measures. A randomized block design approach was used to assign participants to one of two study conditions: exercise intervention or health education control . Randomization to study groups was stratified by gender and severity of baseline MA use . In previous clinical outcome studies with MAdependent clients, the median number of days of use has ranged from 16–20. Therefore, we define “low severity” as using MA for 18 or fewer days in the previous month, and “high severity” as using for 19 or more days in the past month. The study’s data management center maintained the urn randomization program and the records that linked participant identification numbers to study condition. See Figure 1 Consort Diagram for study flow. The exercise intervention consisted of a progressive aerobic and resistance exercise training program that was conducted with participants three days a week during the 8-week trial .

Exercise sessions were scheduled throughout the day at convenient times for participants . Exercise sessions were about 55 minutes in length,vertical grow system structured as follows: 5-minute warm-up, 30 minutes of aerobic activity on a treadmill, 15 minutes of weight training for the major muscle groups— and a 5-minute cool-down with stretching. Specific exercise maneuvers engaged in during the weight training included chest press, front pull down, leg press, reverse lunges, calf raises, lateral raises, bicep curls, and triceps press. Each session was individual-based, guided and monitored by a study staff exercise physiologist. Using heart rate monitors, the exercise physiologist worked closely with each individual participant on training days to increase treadmill speed/slope to maintain a heart rate between 60% and 80% of maximum for 30 minutes. Once a participant was able to complete two sets of 15 repetitions of any given exercise, weight was incrementally increased. The control group consisted of structured health education sessions given to participants three days a week during the 8-week trial . Health education sessions were 55 minutes in length and consisted of various health topics, including stress reduction, health screening, healthy relationships, and sexually transmitted diseases. The sessions were scheduled at a convenient time for clients to attend and were conducted by a trained health educator in a room at the treatment facility in a “group format” . Given that the main hypothesis of this study was testing the impact of the 8-week exercise intervention on reducing mood symptoms among MA participants , the two primary outcome measures included depression and anxiety symptoms. For this, we used data collected on these measures at baseline , weekly , and at study discharge .

Participants voluntarily completed baseline measures and were compensated with $10 gift cards per session for each exercise or education session they attended, once randomized. Depression symptoms were assessed at the end of each week using the Beck Depression Inventory , a 21-item self-report questionnaire . The BDI total score ranges from 0 to 63, with scores of 0 to 13 indicating minimal depression symptoms, 14 to 19 indicating mild depression symptoms, 20 to 28 referring to moderate depression symptoms, and 29 to 63 indicating severe depression symptomatology . Anxiety symptoms were also assessed at the end of each week using the Beck Anxiety Inventory . Similar to the BDI, the BAI is a 21-item measure that assesses for symptoms of anxiety using the same total scoring and symptom range breakdown . For analyses purposes, we used the total mean weekly scores for each of the mood measures. Secondarily, we also examined the potential effects of a dose response on changes in mood symptoms, as research indicates that greater exercise adherence is associated with better mental health outcomes than less exercise adherence . For this study, dose response was measured by session adherence for both study conditions using sign-in attendance checklists throughout the 8-week trial. Hence, the total number of sessions attended was computed and scored from 1 to 24 for each participant. It should be noted that because all participants in the study were concurrently enrolled in residential treatment for MA dependence, the facility policy was drug abstinence verified by random urine drug screens conducted at least weekly during treatment. If participants tested positive, they were immediately discharged from the facility. Hence MA participants in this study were assumed to be abstinent as verified by the random drug screens used during treatment. According to treatment records, two participants, one in each group, were discharged from the treatment facility prior to study completion for positive drug tests.

These participants were not included in analyses.MA use induces complex neurobiological and physiological changes in the brain and body that are associated with numerous physical and mental impairments, including depression and anxiety symptoms . Increasingly, exercise interventions have been embraced in health care as a promising approach for populations suffering from an array of health issues . This study extends the utility of a structured exercise intervention in mitigating symptoms of depression and anxiety in a group of MA-dependent participants in residential treatment . Particular attention is given to depression and anxiety since these are problematic in early-abstinent MA users and aerobic exercise has led to improvements in such symptoms in a variety of clinical populations . Consistent with previous studies, we found evidence that an 8-week structured program of exercise produces positive effects by reducing mood-related symptoms of depression and anxiety among MA-abstinent individuals in treatment. We also found a significant dose effect on mood outcomes for the exercise condition, such that those who participated in more exercise sessions during the 8-week trial had greater symptom reduction in depression and anxiety compared to those who participated in fewer sessions. This relationship did not occur for participants in the education control group. These study findings can be useful to treatment providers interested in addressing depression and anxiety symptoms commonly exhibited among MA-dependent individuals in early abstinence. Specifically, treatment providers can encourage MA users to engage in the type of exercise used in this study to help them deal with problematic anxiety and depression symptoms that are linked to relapse and early treatment termination . The beneficial effects of the 8-week exercise intervention on reducing depression and anxiety symptoms among MA-dependent individuals in treatment should be viewed in the context of other benefits reported from previous work specific to this study. Specifically,indoor vertical garden systems we have found that the exercise intervention also has led to significant improvements in physical fitness indices such as aerobic performance and muscle strength , as well as increases in heart rate variability, a validated index of autonomic nervous system control among the MA-dependent patient sample. Future studies are needed to further explore the specific neurobiological processes that contribute to reductions in symptoms of depression and anxiety as a result of aerobic exercise. Limitations of this study should be noted. The present sample is based on a treatment involved clinical sample that participated in a RCT of an 8-week exercise intervention trial while in residential treatment; hence, findings may not be generalizable to MA-dependent individuals in other treatment settings or to those who are not seeking treatment. This study only examined anxiety and depression symptoms via self-reported BDI and BAI measures. Participants in the health education session were exposed to sessions around general health topics, including stress.

This may be limiting to the outcomes of this study since stress education may have an impact on anxiety symptoms. It should be noted that this issue is not anticipated given that the educational sessions were about stress in general and not tied to how to reduce stress specific to anxiety symptoms. Lastly, the study sample was predominately male , which reduces the generalizability of the results to both sexes. In spite of these limitations, findings in this study provide valuable information with regard to the potential benefits of exercise within a treatment population who experience dysphoric mood states. As of January 2018, in California, all individuals ages 18 and over have access to some form of marijuana . Increasing perceived approval of use and decreasing perceived risk of use coincided with an increase in daily consumption of marijuana, especially among young adults . Young adults have the highest lifetime, past year and past month prevalence of marijuana use . They also have high rates of affective disorders, including anxiety and depression . Experiencing such disorders in young adulthood can have devastating long-term consequences for the development of individuals as they may hinder or delay developmental goals associated with the transition to adulthood. Although depression and anxiety are often comorbid, they manifest differently. Whereas depression can be characterized by emotions such as despair, anger, sadness and hopelessness, anxiety can be characterized by overwhelming worry or fear. Both depression and anxiety in young adulthood can be complicated by alcohol and drug use . There is a lack of consensus as to whether marijuana plays a causal role in the development of affective disorders but marijuana does appear to increase the risk of developing symptoms of affective disorders in the long term . Yet, this contradicts individuals who report benefiting from marijuana use as it alleviates their symptoms of depression and symptoms of anxiety . However, these contradictions might be resolved by viewing individuals who use marijuana as being heterogeneous. As I argue below, the reasons why people use marijuana might inform whether marijuana improves or worsens mental health. Furthermore, gender needs to be considered when examining the association between marijuana use and mental health outcomes as depressive and anxious disorders are more common in women compare to men, whereas substance use disorders are more common in men than women . It has also been demonstrated that women experience a telescoping effect whereas they progress from initiation of marijuana use to problematic use more quickly than men do . Thus, the association between marijuana use and mental health may differ by gender. Given that marijuana use is most prevalent among young people aged 18 to 25 and that marijuana is the most widely used substance among individuals with depressive and anxious symptomatology and disorders , it is imperative to understand the associations between marijuana use and symptoms of mental health. Motives, hereby conceptualized as a cognitive explanation for a behavior , drive marijuana use. Previous work has established that motives of alcohol use are related to different patterns of alcohol use and associated outcomes . Therefore, when motives of use are not considered in the association between marijuana use and mental health or other associated outcomes, it is assumed that use behavior is the same, regardless of why an individual uses marijuana. However, as indicated in the literature on alcohol motives of use, why people use lead to different use behaviors, which are driven by different needs with potentially different associated outcomes. Furthermore, in a study of cannabis using adolescents , changes in motives of use were associated with changes in patterns of use and a reduction of problematic outcomes. This reinforces not only the notion that different motives of marijuana use engender different use behaviors but also that motives of use may be an avenue of intervention in the association between marijuana use and mental health outcomes of young adults. The literature on the topic of motives of marijuana use and mental health outcomes however fails to address certain gaps, namely: marijuana use in a context where medical marijuana is legal, validated instruments that combine both recreational and medical motives of use, gender differences in motives of use and associated mental health outcomes, and a focus on symptoms of but not diagnoses of depression and anxiety as mental health outcomes. Therefore, the purpose of this dissertation to understand the associations between motives of marijuana use and mental health among young adults who use marijuana, and to examine whether these associations vary by gender. This work will be guided by Cooper’s Motivational Model of Alcohol Use .