Many environmental policies have clear public health impacts and are designed to improve health outcomes either by reducing the environmental health risks individuals encounter in their daily lives, or by encouraging more healthy lifestyles. One way of testing the effectiveness of these policies is to examine the behavioral changes they induce. In this dissertation, I use the American Time Use Survey (ATUS) to estimate behavioral responses to several environmental policies by examining how individuals shift the amount of time they spend in various activities during the day.
The ATUS is a nationally representative, federally administered survey on time use in the United States. The survey collects information on all activities performed by respondents during a designated 24-hour period. It was first administered in 2003 and has continued throughout every year since, allowing me to collect responses for an 8-year period, 2003-2010. Because each respondent provides detailed information on his/her activities during the designated 24-hour period, I am able to determine how much time each person spends in various morning, afternoon and, evening activities that may be affected by the policies of interest.
Although the ATUS has been in existence for 9 years, it has been under utilized in the economic literature. Researchers have traditionally focused primarily on the budget constraint faced by individuals and households, ignoring the time constraint. Examining how time use is affected by exogenous policy changes has the potential to shed light on many economic questions. For example, the literature has found that as gas prices increase consumption decreases, however; at a very inelastic rate. Analysis of time-use data could add to these findings by examining what behaviors are most affected. Do the higher prices cause individuals to carpool or take public transit to work, or do they contribute to fewer recreational excursions? Do the higher prices make commutes longer or shorter? Does this affect the amount of time spent working during the day? Time use data sets such as the ATUS can be used to lend insights to many of the behavioral questions we are concerned about in economics.
This dissertation consists of three essays that use the ATUS to examine individual responses to different environmental policies with a particular focus on the behavioral responses that may affect health. In the first essay, I investigate whether individuals respond to publicly provided information on air quality by reducing their vigorous outdoor activities, and thus minimizing their exposure to dangerous concentrations of pollutants. In the second essay, I estimate behavioral responses to Daylight Savings Time (DST) by examining how individuals shift the amount of time they spend in activities that may affect residential energy use. Finally, in the third essay I investigate how DST affects the time individuals spend in exercise and other aerobic activities to determine if it can be used as a low cost policy to promote public health.
Despite considerable improvements in air quality over the past few decades, there is still concern that the health risks from air pollution are too high. This has lead the EPA and others to push for even stricter emissions and ambient air-quality standards. However, there are concerns that the marginal benefits of additional abatement regulations no longer exceed their increasing marginal costs and that alternative approaches are needed to reduce the health risks from air pollution. Essay 1 investigates the effectiveness of one alternative policy -- demand-side episodic programs that attempt to reduce exposure on high-pollution days by increasing averting behavior. If effective, these policies offer a lower cost alternative to tighter standards and other supply side policies. Specifically, I study whether individuals respond to daily information provided on air-quality levels, and whether they respond particularly to air quality alerts issued during periods of high pollution. While controlling for individual responses to actual air quality index levels, my results show that individuals engage in averting behavior on alert days by reducing the time they spend in vigorous outdoor activities by 18 percent or 21 minutes on average.
With few exceptions, previous DST studies have relied on simulation models to estimate and extrapolate energy savings under different policy programs. Although these studies have found a range of energy savings, Kellogg and Wolf (2007) found that the most sophisticated simulation model available in the literature significantly overstated electricity savings when it was applied to Australian data. This suggests that individuals are not operating completely off the clock (as assumed in previous DST studies), but instead that the time of sunrise and sunset affects their daily behaviors. Essay 2 uses the ATUS to estimate behavioral responses to DST by examining how individuals shift the amount of time they spend sleeping, awake at home, and awake away from home during the day for a time period immediately surrounding a change in the DST regime. Aggregating activities into these three broad categories allows for a simple and clear analysis of how changes in time use due to DST may affect residential energy consumption.
Sunrise occurs one hour later in the morning due to DST, meaning that mornings are darker and cooler than they would be on ST. During the cooler months in the spring and fall especially, this may cause individuals to use more lighting and heating electricity regardless of behavioral/time-use adjustments. Similarly, DST causes the late afternoons and early evenings to be warmer and brighter. This should reduce lighting electricity, but likely lead to increased air conditioning use, making the overall impact on afternoon and evening energy consumption ambiguous. Most simulation models suggest that afternoon energy savings do result from DST, and those savings more than offset the increased use in the morning, making DST an energy-reducing policy. However, one cannot accurately draw such conclusions without information on how behaviors change on DST. My results suggest that the DST time shift has the largest impact in the spring, and that individuals are getting up earlier in the morning and spending the additional time at home. This would use additional energy beyond what traditional simulation models predict. Additionally, there is also evidence that individuals are spending less time at home in the evenings, which may reduce energy consumption.
Several states have recently discussed legislation to either observe standard time year around or DST year around. These new proposals are interesting because they mark a clear shift in the motivation for DST away from residential energy conservation. Proposals now cite other economic and social costs of DST, whereas the literature to date has focused primarily on energy effects. To address the need for additional research into the other possible behavioral impacts of DST, Essay 3 uses ATUS data to investigate how DST affects the time individuals spend in exercise and other aerobic activities. For example, an additional hour of after-work daylight can be used for biking, jogging, playing golf or tennis, walking, or other aerobic activities. If DST leads to increases in physical activity, there may be a public health argument for adopting year long DST or even double DST. In its 2011 Annual Report on Health Statistics, the Center for Disease Control (CDC) found that only 20 percent of adult Americans meet the federal Physical Activity Guidelines. Given these disappointing numbers and America's obesity problem, DST may prove to be a low cost policy tool to improve health outcomes. In fact, my results suggest that adding an additional hour of evening daylight in the spring results in an additional 16 minutes of exercise on average.
In all three essays of this dissertation, the previous literature was narrow in scope, focusing on small geographic regions, and used incomplete outcome measures. Thus, each essay makes a unique contribution to the literature by using a nationally representative data set over a long period of time with detailed data on time use. The results from all three essays also have important policy implications that are discussed further in their respective conclusions.