Couch‐Locked With the Munchies: Effects of Recreational Marijuana Laws on Exercise and Nutrition

In a wrongful death case1 the task of the jury, if liability is found, is to award those pecuniary and non-pecuniary damages to the decedent’s survivor(s) that would make them whole. Steven Shavell (2004) defines pecuniary and non-pecuniary losses as follows: Pecuniary losses are those that either are monetary or are losses of goods that can be purchased in markets, in which case the measure of the losses comprises the replacement costs. Non-pecuniary losses correspond to the losses in utility suffered when irreplaceable things have been destroyed, such as family portraits or other unique objects. Making the survivors of a decedent whole, of course, is impossible and, in practice, testimony by a forensic economist on wrongful death damages is usually limited to calculations of lost earnings support and household work services the decedent could have provided survivors. There are other compensable pecuniary damages suffered by survivors of a decedent such as the advice, counsel, instruction, supervision, and care a decedent may have provided survivors for which a replacement cost might be calculated. Finally there are the usual non-pecuniary losses of pure companionship, consortia, love, affection, the loss of the enjoyment of the decedent’s life and bereavement suffered by survivors which are usually categorized as intangibles and left to a jury to decide or are prescribed by statute. The fundamental difference between pecuniary and non-pecuniary damages is that pecuniary damages are associated with a productive use of time (such as work, offering advice and counsel, or giving care, support, or assistance) while non-pecuniary damages derive from the pure utility of relationships alone. Schwartz and Thornton (1989) suggest that economists should approach damages from a utility base which would incorporate both the tangible (pecuniary) and intangible (non-pecuniary) losses suffered by survivors of a decedent. Such a holistic approach to damages is beyond the capacity of traditional means of calculating wrongful death damages by economists. A number of states, over the past two decades, have attempted to

This paper describes an evaluation of the US Bureau of Labor Statistics' American Time Use Survey (ATUS) for potential use in modeling human exposures to environmental pollutants. The ATUS is a large, on-going, cross-sectional survey of where Americans spend time and what activities they undertake in those locations. The data are reported as a series of sequential activities over a 24-h time period--a "diary day"--starting at 0400 hours. Between 12,000 and 13,000 surveys are obtained each year and the Bureau has plans to continue ATUS for the foreseeable future. The ATUS already has about 73,000 diary days of data, more than twice as many as that which currently exists in the US Environmental Protection Agency's (EPA) "Consolidated Human Activity Database" (CHAD) that the Agency uses for exposure modeling purposes. There are limitations for using ATUS in modeling human exposures to environmental pollutants. The ATUS does not report the location for a number of activities regarded as "personal." For 2006, personal activities with missing location information totaled 572 min/day, on average, for survey participants: about 40% of their day. Another limitation is that ATUS does not distinguish between indoor and outdoor activities at home, two of the traditional locational demarcations used in human exposure modeling. This lack of information affects exposure estimates to both indoor and outdoor air pollutants and potentially affects non-dietary ingestion estimates for children, which can vary widely depending on whether or not a child is indoors. Finally, a detailed analysis of the work travel activity in a subsample from ATUS 2006 indicates that the coding scheme is not fully consistent with a CHAD-based exposure modeling approach. For ATUS respondents in this subsample who reported work as an activity, roughly 48% of their days were missing work travel at one or both ends of the work shift or reported within work-shift travel inconsistently. An extensive effort would be needed to recode work travel data from ATUS for EPA's exposure modeling purposes.

This article examines nonresponse in a large government survey, the American Time Use Survey (ATUS), which interviews persons in households previously interviewed in the Current Population Survey. The response rate for the ATUS has been below 60 percent for the first two years of its existence, raising questions about whether the results can be generalized to the target population. The article begins with an analysis of the types of nonresponse encountered in the ATUS. Noncontact accounts for roughly 60 percent of ATUS nonresponse, with refusals accounting for roughly 40 percent. We find little support for the hypothesis that busy people are less likely to respond to the ATUS but find considerable support for the hypothesis that people who are weakly integrated into their communities are less likely to respond, mostly because they are less likely to be contacted. When we compare aggregate estimates of time use calculated using the ATUS base weights without any adjustment for nonresponse, estimates calculated using the ATUS final weights with a nonresponse adjustment, and estimates cal- culated using weights that incorporate our own nonresponse adjustment based on a propensity model, we find some modest differences, but the three sets of estimates are broadly similar. The article ends with sugges- tions for further research and analysis.

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual's activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

Unemployment insurance (UI) reduces the opportunity cost of leisure, but it is unknown whether this additional leisure time is physically active. To obtain unbiased estimates of the effect of UI on physically active leisure participation, I exploit changes in UI program legislation across US states and time. Using nationally representative monthly data between 2003 and 2010 from the Behavioral Risk Factor Surveillance System (BRFSS) and the American Time Use Survey (ATUS), I find evidence that both state UI eligibility expansions and increases in maximum allowable state UI benefits coincide with greater probability of physical activity among the recently unemployed. Based on point estimates, state UI eligibility expansions increased the probability of physical activity participation by 8–10 percentage points among the unemployed with less than a high school education, while a 10% increase in the maximum allowable state UI benefit increased the probability of physical activity by 0.3 to 0.6 percentage points among the unemployed who have completed high school or some college.

Public health experts caution that legalization of recreational marijuana may normalize smoking and undermine the decades-long achievements of tobacco control policy. However, very little is known about the impact of recreational marijuana laws (RMLs) on adult tobacco use. Using information from four national datasets (National Survey of Drug Use and Health, Behavioral Risk Factor Surveillance System, Current Population Survey-Tobacco Use Supplements, and Population Assessment of Tobacco and Health) and dynamic difference-in-differences and discrete-time hazard approaches, we find little support for the hypothesis that RML enactment leads to increases in adult tobacco use. In the short-run, RMLs have no effect on tobacco use and in the medium-run, we find some evidence of a lagged reduction in tobacco use on the order of approximately 0.5 to 2 percentage points. This finding generally persists across cigarettes and ecigarettes, is robust to event-study analyses generated from estimators designed to expunge bias due to heterogeneous dynamic treatment effects, and persists in panel data-based survival analyses that account for consumption dynamics. We conclude that fears of adult tobacco-related public health costs from RMLs are, at least in the short-and medium-run, unfounded; instead, there may be important public health benefits from reductions in adult tobacco use.

This paper examines non-response in a large government survey. The response rate for the American Time Use Survey (ATUS) has been below 60 percent for the first two years of its existence, raising questions about whether the results can be generalized to the target population. The paper begins with an analysis of the types of non-response encountered in the ATUS. We find that non-contact accounts for roughly 60 percent of ATUS non-response, with refusals accounting for roughly 40 percent. Next, we examine two hypotheses about the causes of this non-response. We find little support for the hypothesis that busy people are less likely to respond to the ATUS, but considerable support for the hypothesis that people who are weakly integrated into their communities are less likely to respond, mostly because they are less likely to be contacted. Finally, we compare aggregate estimates of time use calculated using the ATUS base weights without any adjustment for non-response to estimates calculated using the ATUS final weights with a non-response adjustment and to estimates calculated using weights that incorporate our own non-response adjustments based on a propensity model. While there are some modest differences, the three sets of estimates are broadly similar. The paper ends with a discussion of survey design features, their effect on the types and level of non-response, and the tradeoffs associated with different design choices.

In 2003, the data from the American Time Use Survey (ATUS) began being published and made available to researchers by the Bureau of Labor Statistics. Data from that survey have been published ever since. Beginning in 2003, ATUS was immediately adopted by Dr. Kurt Krueger as his data source for his annual series Dollar Value of a Day (DVD); the series is published by Expectancy Data. The DVD series was designed to provide data in a manner convenient for use by forensic economists and has been widely cited and used in forensic economic reports for that purpose. This paper looks at problems posed by the ATUS survey itself, by the use of ATUS in DVD, and at a specific criticism leveled at DVD in a paper by Dr. Terrence Clauretie (2010). Clauretie proposes an alternative use of ATUS that he argues is superior. This paper argues that the Clauretie criticism and proposed alternative use of ATUS are without merit and that DVD provides a much more useful basis for valuing loss of household services than the proposed Clauretie method. There are three problems with ATUS and DVD that make that survey and its interpretation in DVD less than perfect instruments for measuring household services of an individual who has been injured or killed. The first problem is that time use in ATUS is based on the age of the youngest child in a family, regardless of the number of children in a household. The second problem is that time use after retirement is not a good proxy for the output of household services by retired persons. The third problem is that DVD shows comprehensive time use for an average person, not just the amount of household services provided by an average person in a demographic category. As such, there may be evidence in the record of a case that shows ways that an individual's time use is not typical of the average that must be accounted for.

This paper describes IPUMS ATUS, which simplifies the use of time diary data by disseminating a harmonized and enhanced version of the American Time Use Survey (ATUS). The ATUS time diary data capture the detailed activities over a 24 h period for thousands of respondents along with their sociodemographic characteristics. The ability to measure, at a population level, how people spend their time provides nearly endless possibilities for examining questions that hinge on understanding human behavior. The flexible data format can be used to estimate time use as captured by stylized survey questions (e.g., sleep duration, work hours), but it also allows for the study of activity sequencing and the context of time use (e.g., where it happens, who else is present). However, wrangling the complex, hierarchical record structure of the data requires advanced programming skills. To address these challenges, IPUMS ATUS harmonizes the ATUS data and provides customization tools that allow researchers to (i) combine data from multiple original ATUS files and (ii) easily create and save custom variables that summarize time use utilizing the full array of contextual information spread across the complex record structure of the ATUS.

The Bureau of Labor Statistics' American Time Use Survey (ATUS), launched in 2003, offers the first comprehensive look at how individuals spend their time. Health services researchers can use it to study time spent on a variety of health-related activities. We explain the survey's structure and provide an overview of the health-related activities reported by 34,693 respondents in 2003-2004. For the ATUS, computer-assisted phone interviewers ask respondents age 15 or older to report their activities during the day before the call (their "designated day"), including where they were and who was with them. Activities are assigned 6-digit codes, grouped into 17 major categories. Associated waiting and travel time have separate codes. Certain household types are oversampled to ensure reliable estimates. In 2003-2004, 11.3% of American adults reported spending time (mean, 108 minutes) on activities related to health on their designated day. Some 5.6% reported personal health self-care (86 minutes); 3.4% reported medical and care services (123 minutes); and about 1% each reported activities related to the health of household children, household adults, and nonhousehold adults (78-115 minutes). The prevalence of health-care related activities rose with age. Sports, exercise, and recreation were reported by 17.6% of respondents (114 minutes), with men more likely than women to report these activities. The ATUS, a new publicly available resource, allows researchers to explore factors that influence time devoted to health-related activities, and the relationships among them and other activities, in a nationally representative sample.

The American Time Use Survey (ATUS) collects information on how Americans spend their time. Sponsored by the Bureau of Labor Statistics (BLS) and conducted by the U.S. Census Bureau, the ATUS is a continuous, monthly survey that started in January 2003. Estimates from the Survey show the range of detailed activities performed daily, how much time is spent in each activity, and how time is allocated by demographic group, labor force status, and weekday versus weekend. Decisions individuals make on how they use their 24 hours a day have short-run and long-run implications for income and earnings, health, and other aspects of well-being. The Food Economics Division of Economic Research Service (ERS) is particularly interested in understanding individuals’ time use patterns as they are related to eating patterns, grocery shopping, and meal preparation, as well as understanding whether the time constraints that food assistance program participants face are the same or are different than those faced by other households. In October 2005, the Food & Eating Module was added to the ATUS. Because the ATUS only collects time diary information on the respondent’s primary activity, researchers at the U.S. Department of Agriculture Economic Research Service (ERS) knew that all eating occurrences would not be reported. As a consequence, the Module contains questions on eating as a secondary activity, that is, eating while engaged in other activities, such as while watching TV or while driving. In addition, the Module includes questions on height and weight, which can be used to compute the Body Mass Index (BMI); participation in the Food Stamp Program and school meals program; grocery shopping and meal preparation; and household income. The Module was developed by ERS and is funded by ERS and the National Institutes of Health, National Cancer Institute. The data from the Module questions, along with the ATUS time diary, labor force, and demographic information, will offer a unique opportunity to answer important research questions in the research areas of: ▪ Eating patterns, nutrition, and obesity Are certain patterns of eating or of time use associated with obesity? How does meal preparation time affect other indicators of well-being? ▪ Food assistance programs Are food assistance recipients more time pressured than other low-income individuals? What are the time benefits of school meals? ▪ Grocery shopping and meal preparation Are grocery shopping and meal preparation times different across different types of households? What are the travel times to grocery shopping for various demographic and geographic groups? October, November, and December 2005 were the dress rehearsal months for the Module. The 2006 Food & Eating Module data are scheduled to be released in Fall 2007, and BLS will distribute the data on its website along with the 2006 ATUS data. The Module is scheduled to run through December 2007.

Feasibility of Using the American Time Use Survey to Study the Impact of Sickle Cell Disease and Hematopoietic Cell Transplant on the Daily Lives of Patients and Their Caregivers: A Stellar Study

Application of American time use survey (ATUS) in modelling energy-related occupant-building interactions: A comprehensive review

Research into the relationships between commuting and other activities has been hampered by the lack of suitably comprehensive datasets. This paper identifies a possible source of detailed information for such studies, the American Time Use Survey (ATUS). This paper surveys approaches used by researchers to analyze commuting in the ATUS and outlines a method of measuring commuting in a clear and consistent way. This analysis details the advantages of this method over other approaches. Commuting measured in the ATUS using this methodology is shown to be consistent with commuting measures in other large, nationally representative studies. The proposed methodology makes possible a range of analyses exploiting the unique information in the ATUS.

To gain some insight into how various behavioral (lifestyle) factors influence sleep duration, by investigation of the relationship of sleep time to waking activities using the American Time Use Survey (ATUS). Cross-sectional data from ATUS, an annual telephone survey of a population sample of US citizens who are interviewed regarding how they spent their time during a 24-hour period between 04:00 on the previous day and 04:00 on the interview day. Data were pooled from the 2003, 2004, and 2005 ATUS databases involving N=47,731 respondents older than 14 years of age. N/A. Adjusted multiple linear regression models showed that the largest reciprocal relationship to sleep was found for work time, followed by travel time, which included commute time. Only shorter than average sleepers (<7.5 h) spent more time socializing, relaxing, and engaging in leisure activities, while both short (<5.5 h) and long sleepers (> or =8.5 h) watched more TV than the average sleeper. The extent to which sleep time was exchanged for waking activities was also shown to depend on age and gender. Sleep time was minimal while work time was maximal in the age group 45-54 yr, and sleep time increased both with lower and higher age. Work time, travel time, and time for socializing, relaxing, and leisure are the primary activities reciprocally related to sleep time among Americans. These activities may be confounding the frequently observed association between short and long sleep on one hand and morbidity and mortality on the other hand and should be controlled for in future studies.