The Effects of Active and Passive Leisure on Cognition in Children: Evidence from Exogenous Variation in Weather

The Effects of Active and Passive Leisure on Cognition in Children: Evidence from Exogenous... Abstract Leisure time activity is often positioned as a key factor in child development, yet we know relatively little about the causal significance of various specific activities or the magnitude of their effects. Here, we couple individual fixed effects and instrumental variable approaches in trying to determine whether specific forms of leisure contribute to gains in test performance over time. We merge a restricted access version of the Panel Study of Income Dynamics (PSID) Child Development Supplement (CDS), longitudinally collected from 1997 to 2007, with a database of over three million county-day observations of sunlight. We use this proxy for weather to instrument for the variation in physical, outdoor, sedentary, and screen-time behaviors based on CDS time diaries. We find evidence that physical and outdoor activity positively influence math performance, while sedentary behavior and screen time exhibit the opposite effect. Moreover, the effect sizes range from a fifth to more than half a standard deviation per additional daily hour of activity, rendering them meaningful in a real-world sense. Our stratified results indicate that children from less educated mothers and girls seem to be most sensitive to the effects of active and passive forms of leisure. We conclude with a descriptive examination of the trend lines between our data and the new 2014 CDS cohort, providing relevant contemporary context for our findings. Time use patterns among children have long been a popular empirical concern in light of their intuitively influential contributions to developmental trajectory. In the social sciences, work tends to focus on time use as outcome, tacitly positioning specific behaviors as meaningfully contributing to the mechanics of social reproduction and individual life chances (Gershenson 2013; Kalil et al. 2016; Weininger, Lareau, and Conley 2015). Other work in the realm of public health customarily considers time use as a predictor, focusing on the associations between specific behaviors and phenomena like obesity or attention deficit disorder (Falbe et al. 2013; Nikkelen et al. 2014). Unfortunately, because of the practical difficulties of inferring causal relationships using observational data, much of what we suspect to be involved in altering developmental trajectory is ultimately based on intuition and theory rather than robust empirical evidence. While some work does offer experimental evidence of the effects of select activities using randomized control trials (RCT)—for example, Loprinzi and Kane (2015)—it is difficult to infer how routinized behaviors affect outcomes of interest solely based on interventions among carefully targeted respondent populations. That is, RCT studies are by definition unable to reproduce the real-world conditions in which behaviors emerge and unfold, while their limited scale prevents inferences on how effects vary among subgroups. Moreover, the paucity of plausibly causal estimates based on observational data hamstrings our ability to approximate the magnitude of effects and thus the real-world significance of certain behaviors, whether intuitively or associatively positive or negative. Here, we exploit the robust association between weather patterns and a select subset of leisure activities in attempting to draw a clearer connection between behavior and cognition, a crucially important factor in child development and achievement. We link a database of more than three million county-day measures of sunlight compiled by the US government to corresponding time use diary and cognitive assessment records longitudinally collected in the first Child Development Supplement (CDS) module of the Panel Study of Income Dynamics (PSID). By exploiting the plausibly exogenous variation in weather on the day of measured activity, we can deliver estimates of how more “active” and “passive” forms of leisure—which are particularly sensitive to weather and sunlight—contribute to shaping cognitive assessment over time. We find that physically active and outdoor leisure activity (for American children, largely synonymous) positively contribute to growth in math skills, while sedentary activity and screen time in its various manifestations exhibit the opposite effect. These effects are meaningfully large in a real-world sense, ranging from a rise or fall of a fifth to more than half a standard deviation in math scores per additional daily hour spent on the specific activity. We then present stratified results, with the clearest differences based on caregiver’s education (in our data, almost always mothers)—with strong effects of active and passive leisure on cognition evident among the children of high school graduates, and smaller, statistically insignificant relationships among those with college-educated parents. We highlight relevant systematic differences in time use among families in our data along with other factors that may contribute to some of the patterns found in our stratified results, and that may also relate to achievement gaps among children more generally. We conclude by descriptively examining time use among a comparably aged subset of the new CDS cohort in 2014 to adolescents in our main data, offering suggestive evidence of recent trend lines in behavior among US children, and providing relevant contemporary context for our findings. Childhood Inequality, Time Use, and Cognitive Growth Much of the literature on child development is animated by long-standing gaps in academic achievement and cognitive assessment. Baseline differences in test performance (i.e., before entering kindergarten) between white and black children, to take one example, are often found to be as large as a full standard deviation (Bond and Lang 2013). Differences patterned on race are significantly narrower than they were in the mid-twentieth century by any reasonable estimate, yet there is evidence that this convergence has stagnated since the 1990s (Neal 2006), and may be attributable to cohort-specific gains that manifested in the 1970s and 1980s (Chay, Guryan, and Mazumder 2009). These differences in measured cognition reverberate through the life course, and have been implicated in reduced intergenerational mobility and flatter wage trajectories among African Americans, controlling for other significant factors in achievement like grades and attitudinal traits (Bhattacharya and Mazumder 2011; Hall and Farkas 2011). The differences between high- and low-socioeconomic-status (SES) children are even starker. Despite some evidence of a modest convergence in baseline readiness between 1998 and 2010, differences in aptitude between the richest and poorest children have increased by over a third since 1970s-born cohorts and are currently about twice as large as the black-white achievement gap (Reardon and Portilla 2015). Class inequalities have primarily been driven by gains among high-SES daughters compared to poorer girls and boys of all backgrounds, highlighting an emerging and interrelated inequality based on sex (Bailey and Dynarski 2011). These initial advantages extend into college enrollment and degree attainment, with sex-based achievement gaps continuing to widen both within and between birth cohorts (Buchmann and DiPrete 2006). While the etiology of comparative disadvantage is difficult to disentangle, significant differences in cognition before kindergarten and the tendency for gaps to widen during the summer months highlight the crucial importance of the home environment (Alexander, Entwisle, and Olson 2007; Condron 2009). Significant baseline differences among high- and low-SES toddlers have been found as early as 18 months (Fernald, Marchman, and Wiesleder 2013), and indeed may be large enough in magnitude to largely account for the lag in US educational assessment compared to similar industrialized countries (Merry 2013). While institutional and geographic factors are doubtlessly crucial to the formation of intellectual capital, these findings suggest that variations in home activity (i.e., outside formal school or childcare settings) may have cumulative effects on development that rival or even exceed those of school or neighborhood quality (Potter and Roksa 2013). Research in the theoretical tradition of Bourdieu has long stressed the importance of activities geared toward curating a disposition suitable for succeeding in school and navigating institutions later in life, like engagement with high art, music, and literature (DiMaggio 1982). More recently, the theory of “concerted cultivation” has been developed as an extension of this work to describe how the child-rearing practices of parents and time uses of children systematically differ by race and social class. Using extensive qualitative evidence, Lareau (2000, 2002) describes how the leisure time of children in middle-class families is highly structured and organized compared to the more informal, family-oriented working-class home environment. Quantitative work generally supports the notion that material and cultural resources are predictive of enrollment in more formalized activity (Weininger, Lareau, and Conley 2015), and that these “cultivated” time uses positively predict school achievement and test scores (Gaddis 2013; Jæger 2011). Meanwhile, public health research in this vein is largely concerned with the division between active and sedentary forms of leisure time expenditure, rather than the formal-informal distinction. Interestingly, many of the same systematic differences between classes that characterize enrollment in music lessons or museum attendance are mirrored in sedentary activity and media consumption. Recent reviews have illustrated that low-SES children are significantly more likely to engage in sedentary behavior than their peers in the United States and other high-income countries (Leech, McNaughton, and Timperio 2014; Mielke et al. 2017). Related research also highlights a growing SES disparity in adolescent obesity prevalence that is masked by a recent trend of overall stabilization, consistent with gaps in relevant time use (Frederick, Snellman, and Putnam 2014). Systematic reviews generally find substantial positive associations between physical activity and cognitive well-being across the life course, but particularly among children and seniors (Esteban-Cornejo et al. 2015; Prakash et al. 2015). Aside from cross-sectional correlates, which are suggestive of better overall health profiles and the long-run lifestyle differences they reflect, other work finds that even relatively modest short-term interventions in physical activity can manifest in changes in measured cognitive function. Usually, these consist of interventions that try to capture the “acute” effects of exercise (e.g., studies estimating the immediate effects on cognitive performance of 30 minutes of moderate-intensity exercise) or more medium-run behavioral modification (e.g., engaging in physical activity for 20 minutes a day over two weeks, and comparing pre- and post-treatment cognitive assessment). Meta-analyses of research on the effects of acute exercise intervention suggest a positive effect on executive functioning (i.e., skills that allow for planning, multitasking, self-control, etc.), particularly in children (Ludyga et al. 2016). Because executive function seems especially sensitive to interventions in activity, this suggests that test performance could be affected by, for instance, impulse inhibition more so than improvements in working memory or reaction time. Other reviews of medium-term interventions find more general positive cognitive effects—that is, across domains that include academic skills—but effect sizes differ widely among the studies (Lees and Hopkins 2013). These experimental results illustrate that the benefits of exercise may partly reflect a direct dose-response relationship that can manifest in the short or medium term, rather than simply reflecting long-term differences in well-being. In addition to differences in cognitive performance, some cross-sectional and RCT studies also find physical, structural differences in brain integrity between more and less fit children (e.g., white or gray matter volume) (Erickson, Hillman, and Kramer 2015). The extant evidence illustrates that activity (and inactivity) may be related both to physical changes in the brain itself, along with cognitive improvements that can positively affect test performance through numerous channels—including those that relate to behavior (e.g., ability to focus) rather than intellectual skill per se. These observations are nevertheless preliminary and provisional, as researchers have tended to focus on the effects of physical behavior on cognition later in the life course (i.e., older adults) rather than in childhood (Prakash et al. 2015). On the other end of the time use spectrum, research has often focused on the effects of screen time, as it constitutes an outsized proportion of American children’s leisure activity. Still, because screen time and sedentary behavior are so intertwined in the American context, much of this work focuses on physical outcomes like body mass index, and extant findings on cognition are rather limited and equivocal. Several longitudinal studies have found early exposure to media and television in both infants and toddlers to predict worse cognitive outcomes later (Tomopoulos et al. 2010; Zimmerman and Christakis 2005). Yet other research which specifically examines the impact of educational programming (e.g., Sesame Street) finds positive and significant effects, signaling the importance of content (Mares and Pan 2013). Quasi-experimental studies on the cognitive effects of computer use have found that effects are largely null (Fairlie and Robinson 2013), or negative but modest in magnitude (Vigdor, Ladd, and Martinez 2014). With respect to video games, Suziedelyte (2015) uses a fixed-effects research design and finds that gaming (both console and computer based) is positively associated with math ability. Small-scale experimental studies have also shown that gaming may positively affect spatial reasoning and executive functioning (Kühn et al. 2014). Yet other experimental research has found that even very short exposures (i.e., minutes, not hours) to high-intensity media can depress the executive functioning in children (Lillard and Peterson 2011), negatively mirroring related RCT study findings in physical activity. Cognition: Practical Significance and Empirical Measurement Establishing the real-world significance and construct validity of cognition as it is commonly measured in aptitude tests is less straightforward than it may seem. For instance, some have provocatively argued that the primary driver of labor market success and attendant social reproduction is not cognition or skills per se but the attitudes and personality traits that are inculcated in the higher social strata (Bowles and Gintis 2002). Other researchers have similarly argued for the primacy of noncognitive skills in fostering long-run outcomes in educational attainment (Heckman and Kautz 2013). While recent work that attempts to econometrically isolate the causes of achievement supports the importance of intangibles like attitude and disposition, cognition still appears to be the primary determinant of economic success on both individual and national scales (Hanushek 2013; Lundborg, Nystedt, and Rooth 2014). There is also the related question of what cognition as it is customarily rendered in the social sciences signifies. Tests that measure aptitude are only imperfect proxies of cognitive ability, and necessarily signal other noncognitive factors like motivation or goal orientation (Heckman and Kautz 2013). This is why, as Heckman and colleagues point out, achievement tests are better predictors of future success than fluid IQ, as they are capturing these intangibles which help mediate the effects of ability on outcomes. While we recognize that our measures of cognition are almost certainly capturing other dimensions of overall ability like motivation, these tests are purer reflections of aptitude than grades, to take one example, and are the closest we can reasonably hope to get in capturing intellectual performance using secondary data (Borghans et al. 2011). Moreover, even if behaviors are affecting cognitive performance through a more nebulous channel than raw intellectual ability (i.e., through more robust executive function and impulse control), ultimately they still produce measurable effects on assessment and can be considered net positive or negative. Methods and Research Design Dataset and Variables Our core data come from the original CDS module of the PSID (Panel Study of Income Dynamics 2017). The PSID is a nationally representative, longitudinal study, with respondents surveyed annually from 1968 to 1997, and biennially thereafter up to the present. In 1997, the CDS component collected additional data on children (newborns to 12-year-olds) and their parents or guardians. The second and third waves followed up on eligible minors in 2002 and 2007, respectively, with any child 18 or over transferring to the Transition to Adulthood (TA) supplement, which consists of a more limited array of data that do not include time diaries or cognitive assessments. A major strength of the CDS is the detailed measurement of daily behavior offered by the time diary module. Parents and children listed the child’s activities over 24 hours in an open-ended diary with no predetermined minimum time increment on a randomly selected day during the school week and weekend in each CDS wave. These activities were then coded by PSID researchers, and in their raw form may be aggregated to obtain a detailed snapshot of how and where children spent their time. While we aggregated many individual activities into cohesive categories in the earlier stages of our analyses, we ultimately include only those that exhibit a robust first-stage association with our weather measures in the full specifications. These consist of the total time on the diary day doing: 1) Any leisurely physical activity that is not formal or organized (e.g., walking or hiking, but not baseball practice); 2) Any outside activity (including most forms of physical activity); 3) Any passive sedentary activity (“hanging out,” relaxing, or media consumption, but not reading or homework); 4) Television shows or movies across any platform; 5) Video games across any platform; and 6) Total screen time (media, gaming, and any additional non-educational computer or handheld use). A full table of our time use variables and their stratified descriptive statistics across the three waves is located in the online appendix (appendix table S1). We use weekday measures of time use, and necessarily exclude summer vacation as the CDS was administered only during the school year. The CDS also collected assessments of math and verbal ability using the Woodcock Johnson Revised (WJ-R) tests, which are well-established, age-standardized metrics of reasoning ability in children as young as three years old (see: Woodcock and Johnson [1989] for more detail). We use both applied problems (open-ended math word problems) and broad reading (reading, writing, and oral exercises) age-standardized scores as outcomes, but present the results for the latter in the online appendix (appendix table S2) because our time use variables of interest fail to significantly predict verbal assessment. Analytic Strategy We begin by leveraging the longitudinal aspect of the data and employ an individual fixed-effects (FE) approach, which zeros out any time-invariant factors. In the FE methodological framework, each respondent effectively acts as their own control by only considering within-child variation in behaviors, covariates, and test scores over time, and as a matter of course accounts for stable characteristics like race, birthweight, or sex. Still, even if we assume that time-varying unobservables were not biasing results, the potential for reverse causation could complicate our interpretation of them. That is, parents may deliberately modify their child’s behavior because they are not performing as well in school in a way that is reflected in cognitive tests. Specific activities would then incorrectly be identified as aiding or impeding development when the causal directionality is actually reversed. To better address issues of endogeneity, we use a combined fixed-effects instrumental variable (FE-IV) approach, where we exploit the variation in weather to estimate the effect of time use on cognition across waves. Consider we have a variable Z that is associated with an endogenous variable X, but has no direct causal relationship with the outcome Y except through its effect on X. The variable Z—the instrument—could then be used to estimate the causal effect of X on Y because Z would only affect Y through the mediator. In the first stage, the endogenous variable of interest—here, a given time use measure—is regressed on the instrument (sunlight) and any relevant covariates: Xi=β0+β1Zi+β2Ki+ε1 (1) where Z is the instrumental variable, K is a suite of covariates, and ε is the error term. In the second stage, the parameter Xi*—which represents the first stage effect of Z on Xi, adjusting for covariates—is then incorporated into the second-stage equation, which captures the effect of the instrumented endogenous variable on the outcome: Yi=β0+β1Xi*+β2Ki+ε2 (2) These second-stage estimates provide the effect sizes of time use on cognition, but only through the influence of the exogenous instrument sunlight. For our estimates to be unbiased, however, it is crucial that our instrument meets the exclusion restriction criteria of not having a direct effect on cognition; that is, that there is no relationship between sunlight or shade and cognition except through how the intervening behavior is influenced. Challenges to this assumption include the possibility that season itself is predictive of cognitive performance. Recent research suggests that while there does appear to be a seasonal pattern to cognitive processing presumably based in circadian rhythms, these do not manifest in actual differences in measurable ability (Meyer et al. 2016). That is, the brain seems to work harder in certain seasons than it does in others to exhibit stable performance, with the only difference being in energy expenditure. Another possibility is that restrictions in sunlight depress cognitive performance through the channel of negative mood or depression, popularized in the psychological literature as Seasonal Affective Disorder (SAD). Recent work finds that rainfall seems to depress subjective life satisfaction in cross-sectional but not longitudinal models, with effect sizes in the former that are trivial compared to other determinants of mood like economic status (Barrington-Leigh and Behzadnejad 2017). Other work finds that temperatures above 70°F tend to induce negative emotions, while even particularly cold weather (<20°F) is associated with higher subjective well-being (Noelke et al. 2016). In the only large-scale study that directly examines the effect of weather on cognition that we are aware of, Kent et al. (2009) find that among adults over 45, sunlight is positively associated with cognitive assessment, but only among individuals suffering from depression. In their small-scale experimental study, Keller et al. (2005) found that time outdoors moderates the relationship between weather and affective and cognitive outcomes, which suggests that behavior (i.e., going outside, rather than general abstract satisfaction with positive weather conditions) is the linchpin in the causal process. Taken together, these findings suggest that the impact of the weather on mood is rather ambiguous, and that sunlight seems to affect outcomes chiefly through the act of being in- or outdoors—that is, through activity and behavior. There is also the possibility that our results are driven by differences in the bioavailability of vitamin D, which aside from specific foods like fortified milk is largely endogenously produced via sunlight. Though vitamin D is integral to skeletal health, it does play a role in neurological development and its deficiency tends to be associated with neuropsychiatric disorders (Eyles et al. 2009). Conceptually, we do not necessarily see this as a violation of the exclusion restriction because in order to get the benefits of sunlight on cognitive performance via the vitamin D pathway, children do still need to go outside and expose their skin to the sun, as even conventional window glass blocks most ultraviolet rays. This would merely alter the causal pathway, such that physiologically endogenous vitamin synthesis acts as the causal mechanism between behavior and performance instead of through some other physical or psychological channel. Another consideration is the effect sizes of behavior on assessment we find among African Americans compared to whites, which could reflect differential vitamin D absorption because of the protective effects of melanin (Matsuoka et al. 1991). Recent research, however, suggests that vitamin D deficiency among blacks may be an artifact of conventional testing methods, and that levels of bioavailable 25-hydroxyvitamin D are similar to whites due to the presence of a related hormone marker that is not customarily measured (Powe et al. 2013). There are also other more clinically significant sun-related factors that work in the opposite direction, and would bias our estimates downward. Cutaneous exposure to ultraviolet light has long been recognized as degrading folic acid—or B9, an essential vitamin that acts as a precursor to DNA methylation—in both clinical trials and in vivo (Off et al. 2005). Indeed, the degradation of folate induced by ultraviolet light has been positioned as a prominent factor in the evolution of melanin content in humans because of its role in fetal neural tube development and related reproductive success (Jablonski and Chaplin 2000). Recent reviews have argued that the consumption of folate in particular and other essential B vitamins in general are important to cognitive performance and brain health at every stage of the life cycle, in addition to during pregnancy (McGarel et al. 2015). Sunlight also plays a key role in the formation of ozone (O3) from the environmental precursors of volatile organic compounds (VOC), carbon dioxide (CO), and nitrogen dioxide (NO2), and these chemical reactions are accelerated in higher temperatures. The links between pollution and cognitive development are rather clear and well established empirically, with prenatal exposures being particularly damaging (Peterson et al. 2015). While more immediate effects are difficult to estimate, extant work tends to show that childhood exposure to these compounds is associated with reduced academic achievement and cognitive functioning, as well as with accelerated decline later in the life course (Clifford et al. 2016). Researchers have also illustrated related links between seasonal warm-weather allergens and decreased performance on high-stakes exams (Bensnes 2016). Because time diaries were assigned based on randomly selected dates, we assume no systematic relationship between when behavior was measured and any relevant demographic correlates. Additionally, we include a suite of household shocks which are linked in the literature to either childhood development or other fundamentals like family SES status, including indicators of an additional birth (Sandberg and Rafail 2014), the departure of a parent from the household (Tach 2015), and whether the family changed residence from a prior wave (Jelleyman and Spencer 2008). We also include a five-year rolling average of real family income preceding the measurement year, and the poverty rate of the home census tract as a proxy for neighborhood conditions. We found parental education to be so stable across waves as to function as time invariant, and thus dropped it from the specifications. Finally, we control for “educational time,” consisting of the total time in school and doing homework summed together. Children experience systematic differences in their exposure to formal academic instruction due to the geographic variation in the minimum number of hours that states and other localities mandate students receive. We found no noticeable difference in results by excising homework time and confining the measure to the length of the school day alone. To capture the effect of sunlight on behavior, we use daily historical records of insolation (sunlight measured in kilojoules per square meter at the county level), collected as part of the North America Land Data Assimilation System (NLDAS) and disseminated by the Centers for Disease Control (CDC) Wonder database (Centers for Disease Control 2017). Raw insolation values were calculated by the CDC on grid cells and aggregated up to the county level, and then quality-checked before being publicly released. We then coupled these daily county-level values of sunlight to the PSID CDS based on the individual days when the time diaries were completed and the places children lived using a restricted access version of the core data. Because the NLDAS measures sunlight at the ground level, in practice it also acts as a proxy for broader weather patterns, picking up precipitation, overcast skies, and so on. In figure 1, we compare the sunniest (Arizona) and cloudiest (Vermont) places to the national average in daily insolation over the same time periods, and over the course of the year. We also show the variation in average annual insolation at the county level across the United States from 1997 to 2008 graphically in figure 2. Figure 1. View largeDownload slide Extremes in sunlight compared to national average (average daily sunlight values in kilojoules/m2, 1997–2008) Figure 1. View largeDownload slide Extremes in sunlight compared to national average (average daily sunlight values in kilojoules/m2, 1997–2008) Figure 2. View largeDownload slide Geographic variation in average sunlight (kilojoules/m2), 1997–2008 Figure 2. View largeDownload slide Geographic variation in average sunlight (kilojoules/m2), 1997–2008 There are three possible sources of variation in sunlight from wave to wave in our data: 1) the effect of children moving to a qualitatively different climate across waves (e.g., testing in May 1997 and 2002, but having moved from Vermont to Arizona in the interim); 2) the broader changes in weather due to taking the test in different seasonal conditions (e.g., testing in December 1997 and June 2002); and 3) the random weather perturbations that the jagged edges of the curves in figure 1 represent (e.g., engaging in activity and testing during a seasonably sunny few days in May 1997, and then again during a frost in May 2002). The first two represent the most obvious potential sources of bias. For instance, there could in theory be some character trait that prompts a simultaneous migration to the Sunbelt and an outsized growth in cognition, which would bias our estimates upward. Yet because we found that only a small minority of families move across waves (about 10 percent of the sample over the 10-year span), and even then mostly to geographically proximate locales, we feel this is an unrealistic candidate confounder. Further, the “whether moved” control is insignificant in every one of our specifications, while restricting the sample to “never-movers” produces substantively similar results, with slightly larger coefficients. The prospect of a seasonally based confounder is more problematic considering the realities of the data. Estimates based on a convergence in active and sedentary time use as the school year progresses and the weather gets sunnier would be biased if the real driver of improving test scores is simply having had a longer time in school since the prior summer within each wave. For instance, we may find that children who have their time use and cognitive performance measured in December in the first wave and June in the second exhibit an increase in active behavior and a co-occurring growth in cognitive performance. Yet these cognitive improvements may be attributable merely to the timing of the test further along in the school year in the second wave, and the substantial accumulation of knowledge children exhibit by virtue of the additive gains they experience as the school year progresses (Fitzpatrick, Grissmer, and Hastedt 2011). We control for the cumulative gains realized by the progression of the school year by including a “time in school” variable backdated to the day after Labor Day (largely but not universally coincident with the start of the school year in the United States) from the date of the WJ-R test in each CDS wave. As we will discuss in the Results section, time in school was a significant predictor in the verbal but not math specifications. We also perform a robustness check whereby we restrict our sample to sunlight outliers in an attempt to capture the influence of more random as opposed to seasonally based weather, finding substantively similar results—with the exception of gaming, which is no longer significantly predictive (see table S3 in the online appendix). Conceptually, we intuit that short- and medium-run changes in sunlight result in consequent reductions or increases in specific forms of leisure activity over the same time period, which then may have short- and medium-run cognitive effects. For instance, we might expect less sunlight during a storm spell to result in more sedentary time at home, and less time engaging in physical activity outdoors. Prior work on leisure time expenditure expectedly illustrates a positive association between sunlight and physical and outdoor activity, with the opposite for sedentary behavior and screen time (Lee, Gino, and Staats 2014; Zivin and Neidell 2014). Others have also leveraged the random occurrence of weather shocks to explore electoral outcomes (Madestam et al. 2013), criminal behavior (Jacob, Lefgren, and Moretti 2007), and civil conflict (Miguel, Satyanath, and Sergenti 2004), to name a few. Though we match sunlight data to the single time diary day, we recognize that these discrete daily averages will usually reflect the time of year they take place in. Thus while we instrument with the average sunlight in KJ/m2 over the 24-hour period consistent with a time diary recorded on, say, Wednesday, June 5, we recognize by virtue of seasonality that we will in many cases be approximating sunlight on Tuesday the fourth, Thursday the sixth, and so on. Because we view this in theory as more or less a dose-response relationship in the short and medium term—like a milder cousin to other exposures that induce changes in cognitive assessment, like neighborhood violence (e.g., Sharkey 2010)—we exclude observations where the WJ-R test was administered before the time diary day, and cap the lag time at nine days so as to avoid an unrealistic coupling of exposure and outcome. About 16 percent of our observations were cases where children either took the test on the same or following day as the time diary was recorded, while 80 percent were lagged by less than 10 days. Specifications that were not bounded by lag time restrictions produced substantively similar results, but with smaller coefficients and greater precision, consistent with the growth in sample size and reduced signal after including cases where testing substantially lags or precedes treatment. Missing Data and Survey Weights Another crucially important potential source of bias arises from missing data. Even when attrition is properly accounted for, estimates will be biased if missingness is not randomly determined, and involve a factor that also affects cognitive performance. Data missingness is not a trivial concern using PSID-CDS data; in the inaugural 1997 wave, about 20 percent of families otherwise eligible and contributing to the study did not complete time diaries. Here, we use multiple imputation (MI) to handle missing cases, which involves an iterative process where observed data are used to predict missing values multiple times, resulting in a pre-specified number of imputed datasets that are then combined to obtain optimal parameter estimates (McKnight et al. 2007). We follow the best practices suggested by Graham, Olchowski, and Gilreath (2007) and conservatively use 150 imputations. We also include the dependent variables in our imputation procedure, as suggested by Young and Johnson (2010). We present imputed results alongside our main estimates, as well as conventional OLS and FE specifications. We find that weighting our data do not affect our estimates. Results and Discussion In table 1, we present our core results on the effects of physical, outdoor, and sedentary activity on math scores. In our FE-IV models, each additional daily hour of physical and outdoor activity produced a nearly seven-point gain in math scores, or about 40 percent of a standard deviation (pooled SD = 17.07). The negative effect of sedentary behavior is less substantial, but still accounts for a reduction of about a fifth of a standard deviation with each additional hour. We also find substantial effect sizes for television and gaming, with each additional hour resulting in a loss of 38 and 63 percent of a standard deviation, respectively. For total screen time, the effect size is a more modest reduction of about one-fifth of a standard deviation with each additional daily hour. The effects of sedentary behavior and screen time are particularly meaningful, given that by the 2007 wave, the adolescents in our data spent over 2.5 hours each day performing these passive leisure activities. This suggests that even halving the time spent engaging in sedentary behavior or consuming electronic media could manifest in significant math gains. Table 1. Behaviors and Math Outcomes Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Sample sizes decline slightly in our FE-IV specifications because of a small number of observations with missing geocodes. Table 1. Behaviors and Math Outcomes Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Sample sizes decline slightly in our FE-IV specifications because of a small number of observations with missing geocodes. Standard OLS estimates (pooled across waves) do evince significant, negative relationships between sedentary behavior, television, total screen time, and math scores, but with noticeably smaller coefficients. Our conventional FE estimates, however, render null effects across the board (though removing the “education” control—length of school day plus homework—produces significant results for sedentary behavior and screen time in the expected direction [estimates not shown]). This raises the possibility of a selection bias mechanism whereby higher-ability children spend more time sedentary or consuming media. Another possibility is that the effects we find in FE-IV specifications reflect relationships among “compliers” in our natural experiment that are not common across the CDS sample (more on this later in the results). Our imputed specifications in table 1 produce substantively similar estimates, which suggest that data missingness is not systematically biasing our core results. Among the stratified results, the clearest differences are among the college- and non-college-educated caregiver subsamples (table 2). For each of our time use variables, coefficients are about five to six times as large for the children of non-college-educated mothers compared to their peers, and the null results for the latter are likely at least partly due to more modest first-stage associations, which are near or under commonly proposed weak-instrument thresholds. (While there is no clear consensus value, specialists generally recommend first-stage F statistics of the excluded instrument above 10 [Stock, Wright, and Yogo 2002].) That is, sunlight is a noticeably poorer predictor of behavior among children with college-educated mothers compared to their peers, and this results in higher standard errors (but cannot account for the lower point estimates). Table 2. FE-IV Results Stratified by Primary Caregiver’s Education Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 2. FE-IV Results Stratified by Primary Caregiver’s Education Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Further, as a matter of course, we cannot produce estimates for non-compliers who, say, mostly stay inside regardless of weather conditions. We suspect behavior may be less sensitive to weather conditions among the children of college-educated caregivers because they are engaged or enrolled in more formalized activities that would be more likely to take place “rain or shine” (Lareau 2000, 2002; Weininger, Lareau, and Conley 2015). Intuitively, one would suspect that children who have unstructured free time would be more sensitive to weather conditions compared to those taking piano or violin lessons, for example. This leaves open the possibility that a different estimation strategy may uncover significant relationships between time use and cognition among the children of college-educated parents that we do not observe here. We also see variation in effect sizes stratified by sex and race. (Stratifying by sex or race does not reveal differences in the responsiveness of behavior to weather conditions, unlike the caregiver college/no college estimates.) Effect sizes are at least twice as large for girls compared to boys, with all activities exhibiting significant relationships with math assessment except for video games (table 3). For whites, the effect of physical and outdoor activity was greater in magnitude than for African Americans, with the opposite true of sedentary behavior and screen time—though the latter two are only significantly predictive at the 10 percent level for the black subsample (table 4). Our stratified results based on imputed data, however, deliver larger coefficients and smaller p values for the black subsample, and effect sizes are about 30–60 percent larger compared to whites in those specifications depending on the behavior (online appendix table S6). Because other imputed stratified estimates (see tables S4–S5 in the online appendix) deliver similar or more conservative estimates compared to those using listwise deletion, it is possible that our main race-stratified results are downwardly biased due to nonresponse. One possibility in explaining the differences in effect sizes (or whether behaviors are significantly predictive) stratified by race or sex is the presence of a threshold effect, whereby the marginal consumption of a specific behavior becomes either more or less impactful past a certain point. For instance, girls are over three times more sensitive to the effects of screen time than boys, yet the latter consume 25–40 percent more depending on the wave. Boys may thus reach a saturation point after which each additional unit of electronic media consumption no longer appreciably affects performance. Table 3. FE-IV Results Stratified by Sex Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 3. FE-IV Results Stratified by Sex Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 4. FE-IV Results Stratified by Race Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 4. FE-IV Results Stratified by Race Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). The differences in the effects of physical and outdoor activity are more difficult to speculate on because all children exhibit relatively low levels of either behavior. If children are customarily indoors and relatively inactive whatever their background, why might we see such a difference in effect sizes? One reason could be a systematic difference in opportunity cost structures, such that some children are substituting active behavior for less productive time use. While we seek to determine the effects of qualitatively orthogonal behaviors—that is, active versus sedentary—we do not code these in such broad ways as to render them in zero-sum opposition. That is, because we deliberately exclude many kinds of behavior that could fall under the outside/active (e.g., walking to or from school) or sedentary (e.g., doing homework on the couch) categories, reductions in one do not necessarily lead to increases in the other. Thus some children may be substituting physically active behavior for, say, watching television, while for others that same active leisure may be impinging on studying or practicing violin. With respect to screen-time behaviors, we also cannot account for the content of what children were watching or what games they were playing, which could also influence our results. (The CDS does include general information on the genre of television programs children consumed, but only for the 1997 and 2002 waves.) If higher-SES children are watching National Geographic and their peers are watching daytime talk shows, one might expect a qualitatively different effect. Another consideration is why we did not see similar effects using verbal outcomes (see online appendix table S2 for broad reading results). First, fewer children were eligible to take the reading tests because of different age restrictions. Still, we suspect the reason may lie not in the structure of the data but in how math and verbal ability change over the course of the school year. In our verbal outcome specifications, time in school is highly significant in each. Verbal ability may thus be more sensitive to the additive cognitive gains children experience as the school year progresses, while math may be more “sticky” in comparison. Our results also have implications for achievement gaps documented in the social science literature. In our data, black-white and caregiver college/no college math score gaps are about four-fifths and two-thirds of a standard deviation, respectively, which is broadly consistent with recent estimates of school readiness differences by race and income among similarly aged cohorts (Reardon and Portilla 2015). Extrapolating from effect sizes and average time use/math scores across waves, back-of-the-envelope calculations suggest that differences in behavior may account for nontrivial proportions of the black/white and caregiver college/no college gaps in assessment. For instance, differences in sedentary behavior alone account for about 15 and 10 percent of the black/white and caregiver college/no college assessment gaps in our data. (We use sedentary behavior as an example because it is an “umbrella” category that includes screen time, television, etc.) Thus for certain subpopulations, narrowing time use differences among children may aid in helping shrink gaps in assessment and academic achievement more generally. Strategies that decrease sedentary time and promote physical activity in its place may have a particularly robust impact on confronting these pernicious inequalities of achievement. In figure 3, we compare time use in the original CDS cohort we use in this analysis (1997–2007) to the newer 2014 module. We restricted the 2014 CDS descriptive estimates to children above 10 so they are comparable in age to the 2007 wave (when the youngest children in our sample were about 10 years old). Somewhat encouragingly, we did not see any dramatic overall changes from 2007 to 2014, particularly in screen time or sedentary behavior. We actually see a small reduction in total screen time, most likely because we code these behaviors as primary rather than secondary activities. With the emergence of smartphones and tablets, many children may be consuming media while they are primarily waiting for their parents at school, being driven home, and so forth. In terms of the trends in behavioral and assessment gaps among adolescents from 2007 to 2014, some results are modestly encouraging (see online appendix table S1). For instance, black-white and college-no college gaps in math performance and sedentary behavior shrank from 2007 to 2014. On the other hand, gaps in screen time largely persisted or grew. Sex-based gaps also increased noticeably, as girls were less sedentary and consumed less media in 2014 compared to 2007, while boys’ behavior was stable or only trivially declined. Whether the convergence in time use among subgroups or positive trends in behavior compared to earlier cohorts manifest in any measurable effect on test performance gaps either in the PSID or more broadly is an open question. One conclusion is rather clear: in 2014, American children are still engaging in far more passive than active leisure, whatever their background. Figure 3. View largeDownload slide Weekday time use, 1997–2014 Figure 3. View largeDownload slide Weekday time use, 1997–2014 Our research suffers from various limitations that render our results provisional. For instance, we do not capture the qualitative details of what kinds of physical, sedentary, and so on, behavior children are engaging in. We may see differences among our subsamples because, for example, higher-SES kids are engaging in relatively productive or cognitively active sedentary behaviors like conversing with their siblings as opposed to sitting alone watching television. Using the weather as an instrument, as we have described, also has its pitfalls and potential biases. Just as sunnier weather may allow us more time outside and the opportunity to be physically active, it varies systematically with the school year, produces a critically important secosteroid in humans, and so on. While we make every attempt to account for these factors conceptually and empirically, because one cannot “prove” that the exclusion criteria are met, the possibility remains that there is some latent bias that drives our results. It is also important to remember that our findings consist of local average treatment effects, where only children that actually accepted the treatment—“compliers,” who are induced to go outside in sunny weather but who otherwise might not, for example—contribute to our estimates. It may be the case that population-level estimates (i.e., average treatment effects) are lower than what we find here, if, for example, there are declining marginal returns to going outside for “outdoor-oriented” children, or adaptations to staying indoors for their “indoor-oriented” counterparts. The signal between behavior and cognitive achievement would be weakened in these subgroups, and thus perhaps not broadly relevant in terms of policy because changing behaviors would only affect children whose behavior is sensitive to environmental conditions. Finally, due to the realities of the data we use, we cannot detail the mechanisms of the causal process we are trying to capture. That is, we cannot distinguish whether, for example, the connection between physical activity and cognitive performance is more straightforwardly physiological (e.g., through concrete channels like brain structure or enhanced executive functioning) or psychological (by being beneficial in themselves or substituting for other behaviors that may offer negative psychic and cognitive value). Conclusion In this paper we use individual-level fixed effects coupled with a quasi-experimental research design, instrumenting active and sedentary behaviors with sunlight in trying to determine the cognitive effects of time use. We find robustly positive effects of physical activity and outdoor activity on math scores, with the opposite true for sedentary behavior and screen time. We also find that sedentary and screen time use exhibit more substantial effects among children with less educated caregivers and girls, and speculate that time use gaps, opportunity cost structures (i.e., the range of alternative activities), and the differential sensitivity of behavior to weather conditions among subgroups may play a role in the stratified results. In the realm of public health, small-scale RCT and other experimental studies could help uncover what mechanisms are at play in deconstructing causal pathways, while other research using observational data and quasi-experimental methodology can lend support to or undermine our findings. While it is only in its earliest stages, the new PSID CDS will be a powerful tool in the future for researchers who seek to understand how behavior affects assessment and achievement. Other longitudinal observational studies like the National Longitudinal Study of Adolescent to Adult Health (Add Health) can also be leveraged to examine these relationships, though each dataset has its relative weaknesses (e.g., the inability for researchers to obtain geocoded data in Add Health). For practical purposes, we believe our findings lend further support to the general notion that, ceteris paribus, physical activity seems to generally be a positive factor in child development, while sedentary and screen behavior seem to evince the opposite effect. Whether this is more purely physiological in origin or a reflection of the different psychological effects certain behaviors have on development is an open question, and fertile ground for future research. Somewhat encouragingly, we did not see an overall uptick in total screen time, television consumption, or sedentary activity when comparing adolescents in our module to the new 2014 CDS cohort. Still, children today seem to be exhibiting similarly low levels of both physical and outdoor activities as their peers did in 2007. Gaps in more passive leisure behaviors patterned on race, sex, and particularly SES as captured by mother’s education persist, but seem to have attenuated somewhat. Still, even with modest convergence, adolescents with college-educated caregivers in 2014 spend more than a half hour less engaging in sedentary behavior per day than their peers. Future work in the social sciences could integrate these differences in active and passive leisure into theoretical models that seek to explain why life chances so noticeably diverge among children. In this sense, playing outside or watching television can be seen as extensions of the formal-informal dichotomy many sociologists sketch out in examining patterns in achievement and childhood trajectory. The social and economic background of families influences not only the level of formalization but often whether activities are active or passive, and our findings are provisional evidence that these behaviors are significant in terms of not only weight gain or physical health but cognitive performance as well, implicating them in broader trends in childhood inequality in achievement. Supplementary Material Supplementary material is available at Social Forces online. About the Authors Tom Laidley is a PhD candidate in sociology at New York University. His current research focuses on how urban spatial structure affects health and well-being and contributes to stratification and life chances. His other interests include housing affordability and the growth and development of cities and the regions they anchor. Dalton Conley is the Henry Putnam University Professor in Sociology and a faculty affiliate at the Office of Population Research and the Center for Health and Wellbeing. His current work applies econometric methods for causal inference—namely, a natural experiment framework—to genome-wide data available in social surveys to model gene-by-environment interaction effects. He is also interested in mapping the genetic architecture of phenotypic plasticity, and characterizing social and genetic sorting as distinct processes. References Alexander , Karl L. , Doris R. Entwisle , and Linda S. Olson . 2007 . “ Lasting Consequences of the Summer Learning Gap .” American Sociological Review 72 : 167 – 80 . Google Scholar CrossRef Search ADS Bailey , Martha J. , and Susan M. Dynarski . 2011 . “Gains and Gaps: Changing Inequality in U.S. College Entry and Completion.” NBER Working Paper No. 17633. Barrington-Leigh , Christopher , and Fatemeh Behzadnejad . 2017 . “ The Impact of Daily Weather Conditions on Life Satisfaction: Evidence from Cross-Sectional and Panel Data .” Journal of Economic Psychology 59 : 145 – 63 . Google Scholar CrossRef Search ADS Bensnes , Simon Søbstad . 2016 . “ You Sneeze, You Lose: The Impact of Pollen Exposure on Cognitive Performance During High-Stakes High School Exams .” Journal of Health Economics 49 : 1 – 13 . Google Scholar CrossRef Search ADS Bhattacharya , Debopam , and Bhashkar Mazumder . 2011 . “ A Nonparametric Analysis of Black-White Differences in Intergenerational Income Mobility in the United States .” Quantitative Economics 2 : 335 – 79 . Google Scholar CrossRef Search ADS Bond , Timothy N. , and Kevin Lang . 2013 . “ The Evolution of the Black-White Test Score Gap in Grades K–3: The Fragility of Results .” Review of Economics and Statistics 95 : 1468 – 79 . Google Scholar CrossRef Search ADS Borghans , Lex , Bart H H Golsteyn , James Heckman , and John E. Humphries . 2011 . “ Identification Problems in Personality Psychology .” Personality and Individual Differences 51 : 315 – 20 . Google Scholar CrossRef Search ADS Bowles , Samuel , and Herbert Gintis . 2002 . “ Schooling in Capitalist America Revisited .” Sociology of Education 75 : 1 – 18 . Google Scholar CrossRef Search ADS Buchmann , Claudia , and Thomas A. DiPrete . 2006 . “ The Growing Female Advantage in College Completion: The Role of Family Background and Academic Achievement .” American Sociological Review 71 : 515 – 41 . Google Scholar CrossRef Search ADS Centers for Disease Control . 2017 . CDC Wide-Ranging Online Data for Epidemioligic Research (WONDER). http://wonder.cdc.gov/ (accessed June 1, 2017). Chay , Kenneth Y. , Jonathan Guryan , and Bhashkar Mazumder . 2009 . “Birth Cohort and the Black-White Achievement Gap: The Roles of Access and Health Soon After Birth.” NBER Working Paper No. 15078. Clifford , Angela , Linda Lang , Ruoling Chen , Kaarin J. Anstey , and Anthony Seaton . 2016 . “ Exposure to Air Pollution and Cognitive Functioning Across the Course—A Systematic Literature Review .” Environmental Research 147 : 383 – 98 . Google Scholar CrossRef Search ADS Condron , Dennis J. 2009 . “ Social Class, School and Non-School Environments, and Black/White Inequalities in Children’s Learning .” American Sociological Review 74 : 683 – 708 . Google Scholar CrossRef Search ADS DiMaggio , Paul . 1982 . “ Cultural Capital and School Success: The Impact of Status Culture Participation on the Grades of U.S. High School Students .” American Sociological Review 47 : 189 – 201 . Google Scholar CrossRef Search ADS Erickson , Kirk I. , Charles H. Hillman , and Arthur F. Kramer . 2015 . “ Physical Activity, Brain, and Cognition .” Behavioral Sciences 4 : 27 – 32 . Esteban-Cornejo , Irene , Carlos Tejero-Gonzalez , James F. Sallis , and Oscar L. Veiga . 2015 . “ Physical Activity and Cognition in Adolescents: A Systematic Review .” Journal of Science and Medicine in Sport 18 : 534 – 39 . Google Scholar CrossRef Search ADS Eyles , D. W. , F. Feron , X. Cui , J. P. Kesby , L. H. Harms , P. Ko , J. J. McGrath , and T. H. J. Burne . 2009 . “ Developmental Vitamin D Deficiency Causes Abnormal Brain Development .” Psychoneuroendocrinology 34 : S247 – 57 . Google Scholar CrossRef Search ADS Fairlie , Robert W. , and Jonathan Robinson . 2013 . “Experimental Evidence on the Effects of Home Computers on Academic Achievement Among Schoolchildren.” NBER Working Paper No. 19060. Falbe , Jennifer , Bernard Rosner , Walter C. Willett , Kendrin R. Sonneville , Frank B. Hu , and Alison E. Field . 2013 . “ Adiposity and Different Types of Screen Time .” Pediatrics 132 : 1497 . Google Scholar CrossRef Search ADS Fernald , Anne , Virginia A. Marchman , and Adriana Wiesleder . 2013 . “ SES Differences in Language Processing Skill and Vocabulary Are Evident at 18 Months .” Developmental Science 16 : 234 – 48 . Google Scholar CrossRef Search ADS Fitzpatrick , Maria D. , David Grissmer , and Sarah Hastedt . 2011 . “ What a Difference a Day Makes: Estimating Daily Learning Gains During Kindergarten and First Grade Using a Natural Experiment .” Economics of Education Review 30 : 269 – 79 . Google Scholar CrossRef Search ADS Frederick , Carl B. , Kaisa Snellman , and Robert D. Putnam . 2014 . “ Increasing Socioeconomic Disparities in Adolescent Obesity .” Proceedings of the National Academy of Sciences 111 : 1338 – 42 . Google Scholar CrossRef Search ADS Gaddis , S. Michael . 2013 . “ The Influence of Habitus in the Relationship Between Cultural Capital and Academic Achievement .” Social Science Research 42 : 1 – 13 . Google Scholar CrossRef Search ADS Gershenson , Seth . 2013 . “ Do Summer Time-Use Gaps Vary by Socioeconomic Status? ” American Educational Research Journal 50 : 1219 – 48 . Google Scholar CrossRef Search ADS Graham , J. W. , Allison E. Olchowski , and Tamika D. Gilreath . 2007 . “ How Many Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory .” Prevention Science 8 ( 3 ): 206 – 13 . Google Scholar CrossRef Search ADS Hall , Matthew , and George Farkas . 2011 . “ Adolescent Cognitive Skills, Attitudinal/Behavioral Traits and Career Wages .” Social Forces 89 : 1261 – 86 . Google Scholar CrossRef Search ADS Hanushek , Eric A. 2013 . “ Economic Growth in Developing Countries: The Role of Human Capital .” Economics of Education Review 37 : 204 – 12 . Google Scholar CrossRef Search ADS Heckman , James J. , and Tim Kautz . 2013 . “Fostering and Measuring Skills: Interventions That Improve Character and Cognition.” NBER Working Paper No. 19656. Jablonski , Nina G. , and George Chaplin . 2000 . “ The Evolution of Human Skin Coloration .” Journal of Human Evolution 39 : 57 – 106 . Google Scholar CrossRef Search ADS Jacob , Brian , Lars Lefgren , and Enrico Moretti . 2007 . “ The Dynamics of Criminal Behavior: Evidence from Weather Shocks .” Journal of Human Resources 42 : 489 – 527 . Google Scholar CrossRef Search ADS Jæger , Mads Meier . 2011 . “ Does Cultural Capital Really Affect Academic Achievement? New Evidence from Combined Sibling and Panel Data .” Sociology of Education 84 : 281 – 98 . Google Scholar CrossRef Search ADS Jelleyman , Tim , and N. Spencer . 2008 . “ Residential Mobility in Childhood and Health Outcomes: A Systematic Review .” Journal of Epidemiology and Community Health 62 : 584 – 92 . Google Scholar CrossRef Search ADS Kalil , Ariel , Kathleen M. Ziol-Guest , Rebecca M. Ryan , and Anna J. Markowitz . 2016 . “ Changes in Income-Based Gaps in Parent Activities with Young Children from 1988 to 2012 .” AERA Open 2 : 1 – 17 . Google Scholar CrossRef Search ADS Keller , Matthew C. , Barbara L. Frederickson , Oscar Ybarra , Stéphane Côté , Kareem Johnson , Joe Mikels , Anne Conway , and Tor Wager . 2005 . “ A Warm Heart and a Clear Head: The Contingent Effects of Weather on Mood and Cognition .” Pyschological Science 16 : 724 – 31 . Kent , Shia T. , Leslie A. McClure , William L. Crosson , Donna K. Arnett , Virginia G. Wadley , and Nalini Sathiakumar . 2009 . “ Effect of Sunlight Exposure on Cognitive Function Among Depressed and Non-Depressed Participants: A REGARDS Cross-Sectional Study .” Environmental Health 8 : 34 – 48 . Google Scholar CrossRef Search ADS Kühn , Simone , Tobias Gleich , Robert C. Lorenz , U. Lindenberger , and Jurgen Gallinat . 2014 . “ Playing Super Mario Induces Structural Brain Plasticity: Gray Matter Changes Resulting from Training with a Commercial Video Game .” Molecular Psychiatry 19 : 265 – 71 . Google Scholar CrossRef Search ADS Lareau , Annette . 2000 . “ Social Class and the Daily Lives of Children: A Study from the United States .” Childhood 7 : 155 – 71 . Google Scholar CrossRef Search ADS ——— . 2002 . “ Invisible Inequality: Social Class and Childrearing in Black Families and White Families .” American Sociological Review 67 : 747 – 76 . Google Scholar CrossRef Search ADS Lee , Jooa J. , Francesca Gino , and Bradley R. Staats . 2014 . “ Rainmakers: Why Bad Weather Means Good Productivity .” Journal of Applied Psychology 99 : 504 – 13 . Google Scholar CrossRef Search ADS Leech , Rebecca M. , Sarah McNaughton , and Anna Timperio . 2014 . “ The Clustering of Diet, Physical Activity and Sedentary Behavior in Children and Adolescents: A Review .” International Journal of Behavior Nutrition and Physical Activity 11 : 4 . Google Scholar CrossRef Search ADS Lees , Caitlin , and Jessica Hopkins . 2013 . “ Effect of Aerobic Exercise on Cognition, Academic Achievement, and Psychosocial Function in Children: A Systematic Review of Randomized Control Trials .” Preventing Chronic Disease 10 : 1300 – 1310 . Google Scholar CrossRef Search ADS Lillard , Angeline S. , and Jennifer Peterson . 2011 . “ The Immediate Impact of Different Types of Television on Young Children’s Executive Function .” Pediatrics 128 : 644 – 49 . Google Scholar CrossRef Search ADS Loprinzi , Paul D. , and Christy J. Kane . 2015 . “ Exercise and Cognitive Function: A Randomized Controlled Trial Examining Acute Exercise and Free-Living Physical Activity and Sedentary Effects .” Mayo Clinic Proceedings 90 : 450 – 60 . Google Scholar CrossRef Search ADS Ludyga , Sebastian , Markus Gerber , Serge Brand , Edith Holsboer-Trachsler , and Uwe Pühse . 2016 . “ Acute Effects of Moderate Aerobic Exercise on Specific Aspects of Executive Function in Different Age and Fitness Groups: A Meta-Analysis .” Psychophysiology 53 : 1611 – 26 . Google Scholar CrossRef Search ADS Lundborg , Petter , Paul Nystedt , and Dan-Olof Rooth . 2014 . “ Height and Earnings: The Role of Cognitive and Noncognitive Skills .” Journal of Human Resources 49 : 141 – 66 . Google Scholar CrossRef Search ADS Madestam , Andreas , Daniel Shoag , Stan Veuger , and David Yanagizawa-Drott . 2013 . “ Do Political Protests Matter? Evidence from the Tea Party Movement .” Quarterly Journal of Economics 128 : 1633 – 85 . Google Scholar CrossRef Search ADS Mares , Marie-Louise , and Zhongdang Pan . 2013 . “ Effects of Sesame Street: A Meta-Analysis of Children’s Learning in 15 Countries .” Journal of Applied Developmental Psychology 34 : 140 – 51 . Google Scholar CrossRef Search ADS Matsuoka , Lois Y. , Jacobo Wortsman , John G. Haddad , Paul Kolm , and Bruce W. Hollis . 1991 . “ Racial Pigmentation and the Cutaneous Synthesis of Vitamin D .” Archives of Dermatology 127 : 536 – 38 . Google Scholar CrossRef Search ADS McGarel , C. , J. J. Strain , Kristina Pentieva , and H. McNulty . 2015 . “ Emerging Roles for Folate and Related B-Vitamins in Brain Health Across the Lifecycle .” Proceedings of the Nutrition Society 74 : 46 – 55 . Google Scholar CrossRef Search ADS McKnight , P. E. , K. M. McKnight , S. Sidani , and A. J. Figueredo . 2007 . Missing Data: A Gentle Introduction . New York : Guilford Press . Merry , Joseph J. 2013 . “ Tracing the U.S. Deficit in PISA Reading Skills to Early Childhood: Evidence from the United States and Canada .” Sociology of Education 86 : 234 – 52 . Google Scholar CrossRef Search ADS Meyer , Christelle , Vincenzo Muto , Mathieu Jaspar , Caroline Kussé , Erik Lambot , Sarah L. Chellappa , Christian Degueldre , Evelyne Balteau , André Luxen , Benita Middleton , Simon N. Archer , Fabienne Collette , Derk-Jan Dijk , Pierre Maquet , and Gilles Vandewalle . 2016 . “ Seasonality in Human Cognitive Brain Responses .” Proceedings of the National Academy of Sciences 113 : 3066 – 71 . Google Scholar CrossRef Search ADS Mielke , Gregore I. , Wendy J. Brown , Bruno P. Nunes , Inacio C M Silva , and Pedro C. Hillal . 2017 . “ Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis .” Sports Medicine 47 : 61 – 75 . Google Scholar CrossRef Search ADS Miguel , Edward , Shanker Satyanath , and Ernest Sergenti . 2004 . “ Economic Shocks and Civil Conflict: An Instrumental Variables Approach .” Journal of Political Economy 112 : 725 – 53 . Google Scholar CrossRef Search ADS Neal , Derek . 2006 . “Why Has the Black-White Skill Convergence Stopped?” In Handbook of the Economics of Education , edited by Eric A. Hanushek , Stephen Machin , and Ludger Woessmann , pp. 511 – 76 . Boston : Elsevier . Nikkelen , Sanne W C , Patti M. Valkenburg , Mariette Huizinga , and Brad J. Bushman . 2014 . “ Media Use and ADHD-Related Behaviors in Children and Adolescents: A Meta-Analysis .” Developmental Psychology 50 : 2228 – 41 . Google Scholar CrossRef Search ADS Noelke , Clemens , Mark McGovern , Daniel J. Corsi , Marcia P. Jiminez , Ari Stern , Ian Sue Wing , and Lisa Berkman . 2016 . “ Increasing Ambient Temperature Reduces Emotional Wellbeing .” Environmental Research 151 : 124 – 29 . Google Scholar CrossRef Search ADS Off , Morten K. , Arnfinn E. Steindal , Alina C. Porojnicu , Asta Juzeniene , Alexander Vorobey , Anders Johnsson , and Johan Moan . 2005 . “ Ultraviolet Photodegradation of Folic Acid .” Journal of Photochemistry and Photobiology B: Biology 80 : 47 – 55 . Google Scholar CrossRef Search ADS Panel Study of Income Dynamics . 2017 . P ublic and restricted use datasets. Produced and distributed by the Survey Research Center, Institute for Social Research, University of Michigan, Ann Abor. Peterson , Bradley S. , Virginia A. Rauh , Ravi Bansal , Xuejun Hao , Zachary Toth , Giancarlo Nati , Kirwan Walsh , Rachel L. Miller , Franchesca Arias , David Semanek , and Frederica Perera . 2015 . “ Effects of Prenatal Exposure to Air Pollutants (Polycyclic Aromatic Hydrocarbons) on the Development of Brain White Matter, Cognition, and Behavior in Later Childhood .” JAMA Psychiatry 72 : 531 – 40 . Google Scholar CrossRef Search ADS Potter , Daniel , and Josipa Roksa . 2013 . “ Accumulating Advantages over Time: Family Experiences and Social Class Inequality in Academic Achievement .” Social Science Research 42 : 1018 – 32 . Google Scholar CrossRef Search ADS Powe , Camille E. , Michele K. Evans , Julia Wenger , Alan B. Zonderman , Anders H. Berg , Michael Nalls , Hector Tamez , Dongsheng Zhang , Ishir Bhan , Ananth Karumanchi , Neil R. Powe , and Ravi Thadhani . 2013 . “ Vitamin D-Binding Protein and Vitamin D Status of Black Americans and White Americans .” New England Journal of Medicine 369 : 1991 – 2000 . Google Scholar CrossRef Search ADS Prakash , Ruchika Shaurya , Michelle W. Voss , Kirk I. Erickson , and Arthur F. Kramer . 2015 . “ Physical Activity and Cognitive Vitality .” Annual Review of Psychology 66 : 769 – 97 . Google Scholar CrossRef Search ADS Reardon , Sean F. , and Ximena A. Portilla . 2015 . “Recent Trends in Socioeconomic and Racial School Readiness Gaps at Kindergarten Entry.” Center for Education Policy Analysis Working Paper No. 15-02. Stanford Center for Education Policy Analysis. Sandberg , John , and Patrick Rafail . 2014 . “ Family Size, Cognitive Outcomes, and Familial Interaction in Stable, Two-Parent Families: United States, 1997–2002 .” Demography 51 : 1895 – 1931 . Google Scholar CrossRef Search ADS Sharkey , Patrick . 2010 . “ The Acute Effect of Local Homicides on Children’s Cognitive Performance .” Proceedings of the National Academy of Sciences 107 : 11733 – 38 . Google Scholar CrossRef Search ADS Stock , James H. , Jonathan H. Wright , and Motohiro Yogo . 2002 . “ A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments .” Journal of Business and Economic Statistics 20 : 518 – 29 . Google Scholar CrossRef Search ADS Suziedelyte , Agne . 2015 . “ The Effects of Old and New Media on Children’s Weight .” Applied Economics 47 : 1008 – 18 . Google Scholar CrossRef Search ADS Tach , Laura . 2015 . “ Social Mobility in an Era of Family Instability and Complexity .” ANNALS of the American Academy of Political and Social Science 657 : 83 – 96 . Google Scholar CrossRef Search ADS Tomopoulos , Suzy , Bernard P. Dreyer , Samantha Berkule , Arthur H. Fierman , Carolyn Brockmeyer , and Alan L. Mendelsohn . 2010 . “ Infant Media Exposure and Toddler Development .” Archives of Pediatrics and Adolescent Medicine 164 : 1105 – 11 . Google Scholar CrossRef Search ADS Vigdor , Jacob L. , Helen F. Ladd , and Erika Martinez . 2014 . “ Scaling the Digital Divide: Home Computer Technology and Student Achievement .” Economic Inquiry 52 : 1103 – 19 . Google Scholar CrossRef Search ADS Weininger , Elliott B. , Annette Lareau , and Dalton Conley . 2015 . “ What Money Doesn’t Buy: Class Resources and Children’s Participation in Organized Extracurricular Activities .” Social Forces 94 : 479 – 503 . Google Scholar CrossRef Search ADS Woodcock , Richard W. , and Mary B. Johnson . 1989 . Tests of Achievement, Standard Battery . Chicago : Riverside . Young , R. , and D. R. Johnson . 2010 . “ Imputing the Missing Y’s: Implications for Survey Producers and Survey Users .” AAPOR Conference Abstracts 2010 : 6242 – 48 . Zimmerman , Frederick J. , and Dimitri A. Christakis . 2005 . “ Children’s Television Viewing and Cognitive Outcomes .” Archives of Pediatrics and Adolescent Medicine 159 : 619 – 25 . Google Scholar CrossRef Search ADS Zivin , Joshua G. , and Matthew Neidell . 2014 . “ Temperature and the Allocation of Time: Implications for Climate Change .” Journal of Labor Economics 32 : 1 – 26 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Forces Oxford University Press

The Effects of Active and Passive Leisure on Cognition in Children: Evidence from Exogenous Variation in Weather

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press.
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0037-7732
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1534-7605
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10.1093/sf/soy020
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Abstract

Abstract Leisure time activity is often positioned as a key factor in child development, yet we know relatively little about the causal significance of various specific activities or the magnitude of their effects. Here, we couple individual fixed effects and instrumental variable approaches in trying to determine whether specific forms of leisure contribute to gains in test performance over time. We merge a restricted access version of the Panel Study of Income Dynamics (PSID) Child Development Supplement (CDS), longitudinally collected from 1997 to 2007, with a database of over three million county-day observations of sunlight. We use this proxy for weather to instrument for the variation in physical, outdoor, sedentary, and screen-time behaviors based on CDS time diaries. We find evidence that physical and outdoor activity positively influence math performance, while sedentary behavior and screen time exhibit the opposite effect. Moreover, the effect sizes range from a fifth to more than half a standard deviation per additional daily hour of activity, rendering them meaningful in a real-world sense. Our stratified results indicate that children from less educated mothers and girls seem to be most sensitive to the effects of active and passive forms of leisure. We conclude with a descriptive examination of the trend lines between our data and the new 2014 CDS cohort, providing relevant contemporary context for our findings. Time use patterns among children have long been a popular empirical concern in light of their intuitively influential contributions to developmental trajectory. In the social sciences, work tends to focus on time use as outcome, tacitly positioning specific behaviors as meaningfully contributing to the mechanics of social reproduction and individual life chances (Gershenson 2013; Kalil et al. 2016; Weininger, Lareau, and Conley 2015). Other work in the realm of public health customarily considers time use as a predictor, focusing on the associations between specific behaviors and phenomena like obesity or attention deficit disorder (Falbe et al. 2013; Nikkelen et al. 2014). Unfortunately, because of the practical difficulties of inferring causal relationships using observational data, much of what we suspect to be involved in altering developmental trajectory is ultimately based on intuition and theory rather than robust empirical evidence. While some work does offer experimental evidence of the effects of select activities using randomized control trials (RCT)—for example, Loprinzi and Kane (2015)—it is difficult to infer how routinized behaviors affect outcomes of interest solely based on interventions among carefully targeted respondent populations. That is, RCT studies are by definition unable to reproduce the real-world conditions in which behaviors emerge and unfold, while their limited scale prevents inferences on how effects vary among subgroups. Moreover, the paucity of plausibly causal estimates based on observational data hamstrings our ability to approximate the magnitude of effects and thus the real-world significance of certain behaviors, whether intuitively or associatively positive or negative. Here, we exploit the robust association between weather patterns and a select subset of leisure activities in attempting to draw a clearer connection between behavior and cognition, a crucially important factor in child development and achievement. We link a database of more than three million county-day measures of sunlight compiled by the US government to corresponding time use diary and cognitive assessment records longitudinally collected in the first Child Development Supplement (CDS) module of the Panel Study of Income Dynamics (PSID). By exploiting the plausibly exogenous variation in weather on the day of measured activity, we can deliver estimates of how more “active” and “passive” forms of leisure—which are particularly sensitive to weather and sunlight—contribute to shaping cognitive assessment over time. We find that physically active and outdoor leisure activity (for American children, largely synonymous) positively contribute to growth in math skills, while sedentary activity and screen time in its various manifestations exhibit the opposite effect. These effects are meaningfully large in a real-world sense, ranging from a rise or fall of a fifth to more than half a standard deviation in math scores per additional daily hour spent on the specific activity. We then present stratified results, with the clearest differences based on caregiver’s education (in our data, almost always mothers)—with strong effects of active and passive leisure on cognition evident among the children of high school graduates, and smaller, statistically insignificant relationships among those with college-educated parents. We highlight relevant systematic differences in time use among families in our data along with other factors that may contribute to some of the patterns found in our stratified results, and that may also relate to achievement gaps among children more generally. We conclude by descriptively examining time use among a comparably aged subset of the new CDS cohort in 2014 to adolescents in our main data, offering suggestive evidence of recent trend lines in behavior among US children, and providing relevant contemporary context for our findings. Childhood Inequality, Time Use, and Cognitive Growth Much of the literature on child development is animated by long-standing gaps in academic achievement and cognitive assessment. Baseline differences in test performance (i.e., before entering kindergarten) between white and black children, to take one example, are often found to be as large as a full standard deviation (Bond and Lang 2013). Differences patterned on race are significantly narrower than they were in the mid-twentieth century by any reasonable estimate, yet there is evidence that this convergence has stagnated since the 1990s (Neal 2006), and may be attributable to cohort-specific gains that manifested in the 1970s and 1980s (Chay, Guryan, and Mazumder 2009). These differences in measured cognition reverberate through the life course, and have been implicated in reduced intergenerational mobility and flatter wage trajectories among African Americans, controlling for other significant factors in achievement like grades and attitudinal traits (Bhattacharya and Mazumder 2011; Hall and Farkas 2011). The differences between high- and low-socioeconomic-status (SES) children are even starker. Despite some evidence of a modest convergence in baseline readiness between 1998 and 2010, differences in aptitude between the richest and poorest children have increased by over a third since 1970s-born cohorts and are currently about twice as large as the black-white achievement gap (Reardon and Portilla 2015). Class inequalities have primarily been driven by gains among high-SES daughters compared to poorer girls and boys of all backgrounds, highlighting an emerging and interrelated inequality based on sex (Bailey and Dynarski 2011). These initial advantages extend into college enrollment and degree attainment, with sex-based achievement gaps continuing to widen both within and between birth cohorts (Buchmann and DiPrete 2006). While the etiology of comparative disadvantage is difficult to disentangle, significant differences in cognition before kindergarten and the tendency for gaps to widen during the summer months highlight the crucial importance of the home environment (Alexander, Entwisle, and Olson 2007; Condron 2009). Significant baseline differences among high- and low-SES toddlers have been found as early as 18 months (Fernald, Marchman, and Wiesleder 2013), and indeed may be large enough in magnitude to largely account for the lag in US educational assessment compared to similar industrialized countries (Merry 2013). While institutional and geographic factors are doubtlessly crucial to the formation of intellectual capital, these findings suggest that variations in home activity (i.e., outside formal school or childcare settings) may have cumulative effects on development that rival or even exceed those of school or neighborhood quality (Potter and Roksa 2013). Research in the theoretical tradition of Bourdieu has long stressed the importance of activities geared toward curating a disposition suitable for succeeding in school and navigating institutions later in life, like engagement with high art, music, and literature (DiMaggio 1982). More recently, the theory of “concerted cultivation” has been developed as an extension of this work to describe how the child-rearing practices of parents and time uses of children systematically differ by race and social class. Using extensive qualitative evidence, Lareau (2000, 2002) describes how the leisure time of children in middle-class families is highly structured and organized compared to the more informal, family-oriented working-class home environment. Quantitative work generally supports the notion that material and cultural resources are predictive of enrollment in more formalized activity (Weininger, Lareau, and Conley 2015), and that these “cultivated” time uses positively predict school achievement and test scores (Gaddis 2013; Jæger 2011). Meanwhile, public health research in this vein is largely concerned with the division between active and sedentary forms of leisure time expenditure, rather than the formal-informal distinction. Interestingly, many of the same systematic differences between classes that characterize enrollment in music lessons or museum attendance are mirrored in sedentary activity and media consumption. Recent reviews have illustrated that low-SES children are significantly more likely to engage in sedentary behavior than their peers in the United States and other high-income countries (Leech, McNaughton, and Timperio 2014; Mielke et al. 2017). Related research also highlights a growing SES disparity in adolescent obesity prevalence that is masked by a recent trend of overall stabilization, consistent with gaps in relevant time use (Frederick, Snellman, and Putnam 2014). Systematic reviews generally find substantial positive associations between physical activity and cognitive well-being across the life course, but particularly among children and seniors (Esteban-Cornejo et al. 2015; Prakash et al. 2015). Aside from cross-sectional correlates, which are suggestive of better overall health profiles and the long-run lifestyle differences they reflect, other work finds that even relatively modest short-term interventions in physical activity can manifest in changes in measured cognitive function. Usually, these consist of interventions that try to capture the “acute” effects of exercise (e.g., studies estimating the immediate effects on cognitive performance of 30 minutes of moderate-intensity exercise) or more medium-run behavioral modification (e.g., engaging in physical activity for 20 minutes a day over two weeks, and comparing pre- and post-treatment cognitive assessment). Meta-analyses of research on the effects of acute exercise intervention suggest a positive effect on executive functioning (i.e., skills that allow for planning, multitasking, self-control, etc.), particularly in children (Ludyga et al. 2016). Because executive function seems especially sensitive to interventions in activity, this suggests that test performance could be affected by, for instance, impulse inhibition more so than improvements in working memory or reaction time. Other reviews of medium-term interventions find more general positive cognitive effects—that is, across domains that include academic skills—but effect sizes differ widely among the studies (Lees and Hopkins 2013). These experimental results illustrate that the benefits of exercise may partly reflect a direct dose-response relationship that can manifest in the short or medium term, rather than simply reflecting long-term differences in well-being. In addition to differences in cognitive performance, some cross-sectional and RCT studies also find physical, structural differences in brain integrity between more and less fit children (e.g., white or gray matter volume) (Erickson, Hillman, and Kramer 2015). The extant evidence illustrates that activity (and inactivity) may be related both to physical changes in the brain itself, along with cognitive improvements that can positively affect test performance through numerous channels—including those that relate to behavior (e.g., ability to focus) rather than intellectual skill per se. These observations are nevertheless preliminary and provisional, as researchers have tended to focus on the effects of physical behavior on cognition later in the life course (i.e., older adults) rather than in childhood (Prakash et al. 2015). On the other end of the time use spectrum, research has often focused on the effects of screen time, as it constitutes an outsized proportion of American children’s leisure activity. Still, because screen time and sedentary behavior are so intertwined in the American context, much of this work focuses on physical outcomes like body mass index, and extant findings on cognition are rather limited and equivocal. Several longitudinal studies have found early exposure to media and television in both infants and toddlers to predict worse cognitive outcomes later (Tomopoulos et al. 2010; Zimmerman and Christakis 2005). Yet other research which specifically examines the impact of educational programming (e.g., Sesame Street) finds positive and significant effects, signaling the importance of content (Mares and Pan 2013). Quasi-experimental studies on the cognitive effects of computer use have found that effects are largely null (Fairlie and Robinson 2013), or negative but modest in magnitude (Vigdor, Ladd, and Martinez 2014). With respect to video games, Suziedelyte (2015) uses a fixed-effects research design and finds that gaming (both console and computer based) is positively associated with math ability. Small-scale experimental studies have also shown that gaming may positively affect spatial reasoning and executive functioning (Kühn et al. 2014). Yet other experimental research has found that even very short exposures (i.e., minutes, not hours) to high-intensity media can depress the executive functioning in children (Lillard and Peterson 2011), negatively mirroring related RCT study findings in physical activity. Cognition: Practical Significance and Empirical Measurement Establishing the real-world significance and construct validity of cognition as it is commonly measured in aptitude tests is less straightforward than it may seem. For instance, some have provocatively argued that the primary driver of labor market success and attendant social reproduction is not cognition or skills per se but the attitudes and personality traits that are inculcated in the higher social strata (Bowles and Gintis 2002). Other researchers have similarly argued for the primacy of noncognitive skills in fostering long-run outcomes in educational attainment (Heckman and Kautz 2013). While recent work that attempts to econometrically isolate the causes of achievement supports the importance of intangibles like attitude and disposition, cognition still appears to be the primary determinant of economic success on both individual and national scales (Hanushek 2013; Lundborg, Nystedt, and Rooth 2014). There is also the related question of what cognition as it is customarily rendered in the social sciences signifies. Tests that measure aptitude are only imperfect proxies of cognitive ability, and necessarily signal other noncognitive factors like motivation or goal orientation (Heckman and Kautz 2013). This is why, as Heckman and colleagues point out, achievement tests are better predictors of future success than fluid IQ, as they are capturing these intangibles which help mediate the effects of ability on outcomes. While we recognize that our measures of cognition are almost certainly capturing other dimensions of overall ability like motivation, these tests are purer reflections of aptitude than grades, to take one example, and are the closest we can reasonably hope to get in capturing intellectual performance using secondary data (Borghans et al. 2011). Moreover, even if behaviors are affecting cognitive performance through a more nebulous channel than raw intellectual ability (i.e., through more robust executive function and impulse control), ultimately they still produce measurable effects on assessment and can be considered net positive or negative. Methods and Research Design Dataset and Variables Our core data come from the original CDS module of the PSID (Panel Study of Income Dynamics 2017). The PSID is a nationally representative, longitudinal study, with respondents surveyed annually from 1968 to 1997, and biennially thereafter up to the present. In 1997, the CDS component collected additional data on children (newborns to 12-year-olds) and their parents or guardians. The second and third waves followed up on eligible minors in 2002 and 2007, respectively, with any child 18 or over transferring to the Transition to Adulthood (TA) supplement, which consists of a more limited array of data that do not include time diaries or cognitive assessments. A major strength of the CDS is the detailed measurement of daily behavior offered by the time diary module. Parents and children listed the child’s activities over 24 hours in an open-ended diary with no predetermined minimum time increment on a randomly selected day during the school week and weekend in each CDS wave. These activities were then coded by PSID researchers, and in their raw form may be aggregated to obtain a detailed snapshot of how and where children spent their time. While we aggregated many individual activities into cohesive categories in the earlier stages of our analyses, we ultimately include only those that exhibit a robust first-stage association with our weather measures in the full specifications. These consist of the total time on the diary day doing: 1) Any leisurely physical activity that is not formal or organized (e.g., walking or hiking, but not baseball practice); 2) Any outside activity (including most forms of physical activity); 3) Any passive sedentary activity (“hanging out,” relaxing, or media consumption, but not reading or homework); 4) Television shows or movies across any platform; 5) Video games across any platform; and 6) Total screen time (media, gaming, and any additional non-educational computer or handheld use). A full table of our time use variables and their stratified descriptive statistics across the three waves is located in the online appendix (appendix table S1). We use weekday measures of time use, and necessarily exclude summer vacation as the CDS was administered only during the school year. The CDS also collected assessments of math and verbal ability using the Woodcock Johnson Revised (WJ-R) tests, which are well-established, age-standardized metrics of reasoning ability in children as young as three years old (see: Woodcock and Johnson [1989] for more detail). We use both applied problems (open-ended math word problems) and broad reading (reading, writing, and oral exercises) age-standardized scores as outcomes, but present the results for the latter in the online appendix (appendix table S2) because our time use variables of interest fail to significantly predict verbal assessment. Analytic Strategy We begin by leveraging the longitudinal aspect of the data and employ an individual fixed-effects (FE) approach, which zeros out any time-invariant factors. In the FE methodological framework, each respondent effectively acts as their own control by only considering within-child variation in behaviors, covariates, and test scores over time, and as a matter of course accounts for stable characteristics like race, birthweight, or sex. Still, even if we assume that time-varying unobservables were not biasing results, the potential for reverse causation could complicate our interpretation of them. That is, parents may deliberately modify their child’s behavior because they are not performing as well in school in a way that is reflected in cognitive tests. Specific activities would then incorrectly be identified as aiding or impeding development when the causal directionality is actually reversed. To better address issues of endogeneity, we use a combined fixed-effects instrumental variable (FE-IV) approach, where we exploit the variation in weather to estimate the effect of time use on cognition across waves. Consider we have a variable Z that is associated with an endogenous variable X, but has no direct causal relationship with the outcome Y except through its effect on X. The variable Z—the instrument—could then be used to estimate the causal effect of X on Y because Z would only affect Y through the mediator. In the first stage, the endogenous variable of interest—here, a given time use measure—is regressed on the instrument (sunlight) and any relevant covariates: Xi=β0+β1Zi+β2Ki+ε1 (1) where Z is the instrumental variable, K is a suite of covariates, and ε is the error term. In the second stage, the parameter Xi*—which represents the first stage effect of Z on Xi, adjusting for covariates—is then incorporated into the second-stage equation, which captures the effect of the instrumented endogenous variable on the outcome: Yi=β0+β1Xi*+β2Ki+ε2 (2) These second-stage estimates provide the effect sizes of time use on cognition, but only through the influence of the exogenous instrument sunlight. For our estimates to be unbiased, however, it is crucial that our instrument meets the exclusion restriction criteria of not having a direct effect on cognition; that is, that there is no relationship between sunlight or shade and cognition except through how the intervening behavior is influenced. Challenges to this assumption include the possibility that season itself is predictive of cognitive performance. Recent research suggests that while there does appear to be a seasonal pattern to cognitive processing presumably based in circadian rhythms, these do not manifest in actual differences in measurable ability (Meyer et al. 2016). That is, the brain seems to work harder in certain seasons than it does in others to exhibit stable performance, with the only difference being in energy expenditure. Another possibility is that restrictions in sunlight depress cognitive performance through the channel of negative mood or depression, popularized in the psychological literature as Seasonal Affective Disorder (SAD). Recent work finds that rainfall seems to depress subjective life satisfaction in cross-sectional but not longitudinal models, with effect sizes in the former that are trivial compared to other determinants of mood like economic status (Barrington-Leigh and Behzadnejad 2017). Other work finds that temperatures above 70°F tend to induce negative emotions, while even particularly cold weather (<20°F) is associated with higher subjective well-being (Noelke et al. 2016). In the only large-scale study that directly examines the effect of weather on cognition that we are aware of, Kent et al. (2009) find that among adults over 45, sunlight is positively associated with cognitive assessment, but only among individuals suffering from depression. In their small-scale experimental study, Keller et al. (2005) found that time outdoors moderates the relationship between weather and affective and cognitive outcomes, which suggests that behavior (i.e., going outside, rather than general abstract satisfaction with positive weather conditions) is the linchpin in the causal process. Taken together, these findings suggest that the impact of the weather on mood is rather ambiguous, and that sunlight seems to affect outcomes chiefly through the act of being in- or outdoors—that is, through activity and behavior. There is also the possibility that our results are driven by differences in the bioavailability of vitamin D, which aside from specific foods like fortified milk is largely endogenously produced via sunlight. Though vitamin D is integral to skeletal health, it does play a role in neurological development and its deficiency tends to be associated with neuropsychiatric disorders (Eyles et al. 2009). Conceptually, we do not necessarily see this as a violation of the exclusion restriction because in order to get the benefits of sunlight on cognitive performance via the vitamin D pathway, children do still need to go outside and expose their skin to the sun, as even conventional window glass blocks most ultraviolet rays. This would merely alter the causal pathway, such that physiologically endogenous vitamin synthesis acts as the causal mechanism between behavior and performance instead of through some other physical or psychological channel. Another consideration is the effect sizes of behavior on assessment we find among African Americans compared to whites, which could reflect differential vitamin D absorption because of the protective effects of melanin (Matsuoka et al. 1991). Recent research, however, suggests that vitamin D deficiency among blacks may be an artifact of conventional testing methods, and that levels of bioavailable 25-hydroxyvitamin D are similar to whites due to the presence of a related hormone marker that is not customarily measured (Powe et al. 2013). There are also other more clinically significant sun-related factors that work in the opposite direction, and would bias our estimates downward. Cutaneous exposure to ultraviolet light has long been recognized as degrading folic acid—or B9, an essential vitamin that acts as a precursor to DNA methylation—in both clinical trials and in vivo (Off et al. 2005). Indeed, the degradation of folate induced by ultraviolet light has been positioned as a prominent factor in the evolution of melanin content in humans because of its role in fetal neural tube development and related reproductive success (Jablonski and Chaplin 2000). Recent reviews have argued that the consumption of folate in particular and other essential B vitamins in general are important to cognitive performance and brain health at every stage of the life cycle, in addition to during pregnancy (McGarel et al. 2015). Sunlight also plays a key role in the formation of ozone (O3) from the environmental precursors of volatile organic compounds (VOC), carbon dioxide (CO), and nitrogen dioxide (NO2), and these chemical reactions are accelerated in higher temperatures. The links between pollution and cognitive development are rather clear and well established empirically, with prenatal exposures being particularly damaging (Peterson et al. 2015). While more immediate effects are difficult to estimate, extant work tends to show that childhood exposure to these compounds is associated with reduced academic achievement and cognitive functioning, as well as with accelerated decline later in the life course (Clifford et al. 2016). Researchers have also illustrated related links between seasonal warm-weather allergens and decreased performance on high-stakes exams (Bensnes 2016). Because time diaries were assigned based on randomly selected dates, we assume no systematic relationship between when behavior was measured and any relevant demographic correlates. Additionally, we include a suite of household shocks which are linked in the literature to either childhood development or other fundamentals like family SES status, including indicators of an additional birth (Sandberg and Rafail 2014), the departure of a parent from the household (Tach 2015), and whether the family changed residence from a prior wave (Jelleyman and Spencer 2008). We also include a five-year rolling average of real family income preceding the measurement year, and the poverty rate of the home census tract as a proxy for neighborhood conditions. We found parental education to be so stable across waves as to function as time invariant, and thus dropped it from the specifications. Finally, we control for “educational time,” consisting of the total time in school and doing homework summed together. Children experience systematic differences in their exposure to formal academic instruction due to the geographic variation in the minimum number of hours that states and other localities mandate students receive. We found no noticeable difference in results by excising homework time and confining the measure to the length of the school day alone. To capture the effect of sunlight on behavior, we use daily historical records of insolation (sunlight measured in kilojoules per square meter at the county level), collected as part of the North America Land Data Assimilation System (NLDAS) and disseminated by the Centers for Disease Control (CDC) Wonder database (Centers for Disease Control 2017). Raw insolation values were calculated by the CDC on grid cells and aggregated up to the county level, and then quality-checked before being publicly released. We then coupled these daily county-level values of sunlight to the PSID CDS based on the individual days when the time diaries were completed and the places children lived using a restricted access version of the core data. Because the NLDAS measures sunlight at the ground level, in practice it also acts as a proxy for broader weather patterns, picking up precipitation, overcast skies, and so on. In figure 1, we compare the sunniest (Arizona) and cloudiest (Vermont) places to the national average in daily insolation over the same time periods, and over the course of the year. We also show the variation in average annual insolation at the county level across the United States from 1997 to 2008 graphically in figure 2. Figure 1. View largeDownload slide Extremes in sunlight compared to national average (average daily sunlight values in kilojoules/m2, 1997–2008) Figure 1. View largeDownload slide Extremes in sunlight compared to national average (average daily sunlight values in kilojoules/m2, 1997–2008) Figure 2. View largeDownload slide Geographic variation in average sunlight (kilojoules/m2), 1997–2008 Figure 2. View largeDownload slide Geographic variation in average sunlight (kilojoules/m2), 1997–2008 There are three possible sources of variation in sunlight from wave to wave in our data: 1) the effect of children moving to a qualitatively different climate across waves (e.g., testing in May 1997 and 2002, but having moved from Vermont to Arizona in the interim); 2) the broader changes in weather due to taking the test in different seasonal conditions (e.g., testing in December 1997 and June 2002); and 3) the random weather perturbations that the jagged edges of the curves in figure 1 represent (e.g., engaging in activity and testing during a seasonably sunny few days in May 1997, and then again during a frost in May 2002). The first two represent the most obvious potential sources of bias. For instance, there could in theory be some character trait that prompts a simultaneous migration to the Sunbelt and an outsized growth in cognition, which would bias our estimates upward. Yet because we found that only a small minority of families move across waves (about 10 percent of the sample over the 10-year span), and even then mostly to geographically proximate locales, we feel this is an unrealistic candidate confounder. Further, the “whether moved” control is insignificant in every one of our specifications, while restricting the sample to “never-movers” produces substantively similar results, with slightly larger coefficients. The prospect of a seasonally based confounder is more problematic considering the realities of the data. Estimates based on a convergence in active and sedentary time use as the school year progresses and the weather gets sunnier would be biased if the real driver of improving test scores is simply having had a longer time in school since the prior summer within each wave. For instance, we may find that children who have their time use and cognitive performance measured in December in the first wave and June in the second exhibit an increase in active behavior and a co-occurring growth in cognitive performance. Yet these cognitive improvements may be attributable merely to the timing of the test further along in the school year in the second wave, and the substantial accumulation of knowledge children exhibit by virtue of the additive gains they experience as the school year progresses (Fitzpatrick, Grissmer, and Hastedt 2011). We control for the cumulative gains realized by the progression of the school year by including a “time in school” variable backdated to the day after Labor Day (largely but not universally coincident with the start of the school year in the United States) from the date of the WJ-R test in each CDS wave. As we will discuss in the Results section, time in school was a significant predictor in the verbal but not math specifications. We also perform a robustness check whereby we restrict our sample to sunlight outliers in an attempt to capture the influence of more random as opposed to seasonally based weather, finding substantively similar results—with the exception of gaming, which is no longer significantly predictive (see table S3 in the online appendix). Conceptually, we intuit that short- and medium-run changes in sunlight result in consequent reductions or increases in specific forms of leisure activity over the same time period, which then may have short- and medium-run cognitive effects. For instance, we might expect less sunlight during a storm spell to result in more sedentary time at home, and less time engaging in physical activity outdoors. Prior work on leisure time expenditure expectedly illustrates a positive association between sunlight and physical and outdoor activity, with the opposite for sedentary behavior and screen time (Lee, Gino, and Staats 2014; Zivin and Neidell 2014). Others have also leveraged the random occurrence of weather shocks to explore electoral outcomes (Madestam et al. 2013), criminal behavior (Jacob, Lefgren, and Moretti 2007), and civil conflict (Miguel, Satyanath, and Sergenti 2004), to name a few. Though we match sunlight data to the single time diary day, we recognize that these discrete daily averages will usually reflect the time of year they take place in. Thus while we instrument with the average sunlight in KJ/m2 over the 24-hour period consistent with a time diary recorded on, say, Wednesday, June 5, we recognize by virtue of seasonality that we will in many cases be approximating sunlight on Tuesday the fourth, Thursday the sixth, and so on. Because we view this in theory as more or less a dose-response relationship in the short and medium term—like a milder cousin to other exposures that induce changes in cognitive assessment, like neighborhood violence (e.g., Sharkey 2010)—we exclude observations where the WJ-R test was administered before the time diary day, and cap the lag time at nine days so as to avoid an unrealistic coupling of exposure and outcome. About 16 percent of our observations were cases where children either took the test on the same or following day as the time diary was recorded, while 80 percent were lagged by less than 10 days. Specifications that were not bounded by lag time restrictions produced substantively similar results, but with smaller coefficients and greater precision, consistent with the growth in sample size and reduced signal after including cases where testing substantially lags or precedes treatment. Missing Data and Survey Weights Another crucially important potential source of bias arises from missing data. Even when attrition is properly accounted for, estimates will be biased if missingness is not randomly determined, and involve a factor that also affects cognitive performance. Data missingness is not a trivial concern using PSID-CDS data; in the inaugural 1997 wave, about 20 percent of families otherwise eligible and contributing to the study did not complete time diaries. Here, we use multiple imputation (MI) to handle missing cases, which involves an iterative process where observed data are used to predict missing values multiple times, resulting in a pre-specified number of imputed datasets that are then combined to obtain optimal parameter estimates (McKnight et al. 2007). We follow the best practices suggested by Graham, Olchowski, and Gilreath (2007) and conservatively use 150 imputations. We also include the dependent variables in our imputation procedure, as suggested by Young and Johnson (2010). We present imputed results alongside our main estimates, as well as conventional OLS and FE specifications. We find that weighting our data do not affect our estimates. Results and Discussion In table 1, we present our core results on the effects of physical, outdoor, and sedentary activity on math scores. In our FE-IV models, each additional daily hour of physical and outdoor activity produced a nearly seven-point gain in math scores, or about 40 percent of a standard deviation (pooled SD = 17.07). The negative effect of sedentary behavior is less substantial, but still accounts for a reduction of about a fifth of a standard deviation with each additional hour. We also find substantial effect sizes for television and gaming, with each additional hour resulting in a loss of 38 and 63 percent of a standard deviation, respectively. For total screen time, the effect size is a more modest reduction of about one-fifth of a standard deviation with each additional daily hour. The effects of sedentary behavior and screen time are particularly meaningful, given that by the 2007 wave, the adolescents in our data spent over 2.5 hours each day performing these passive leisure activities. This suggests that even halving the time spent engaging in sedentary behavior or consuming electronic media could manifest in significant math gains. Table 1. Behaviors and Math Outcomes Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Sample sizes decline slightly in our FE-IV specifications because of a small number of observations with missing geocodes. Table 1. Behaviors and Math Outcomes Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day OLS FE FE-IV Imputed OLS FE FE-IV Imputed OLS FE FE-IV Imputed Educational time .457*** .501*** .819*** .905*** .459*** .506*** .870*** .930*** .296** .445*** −.457 −.200 (.093) (.104) (.158) (.166) (.093) (.104) (.174) (.174) (.100) (.117) (.345) (.257) Days in school .008 .010* −.004 −.003 .008 .010* −.004 −.003 .007 .010* −.007 <.001 (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.008) (.005) Physical activity .171 .376 6.587** 6.927** (.317) (.321) (2.116) (2.280) Outdoor activity .186 .434 6.854** 6.766** (.311) (.308) (2.240) (2.231) Sedentary activity −.550*** −.127 −3.269** −2.876** (.133) (.142) (1.090) (.927) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 52.46*** 4.41*** 4.41*** 4.91*** 52.63*** 4.48*** 4.17*** 4.81*** 54.69*** 4.48*** 4.40*** 5.26*** First-stage F statistic — — 36.26 — — — 31.88 — — — 30.00 — Educational time .326** .500*** −.741 −.735 .457*** .487*** −.101 −.040 .362*** .489*** −.410 −.181 (.096) (.114) (.534) (.533) (.095) (.107) (.298) (.250) (.099) (.116) (.340) (.263) Days in school .008* .011* .007 .006 .008* .011* −.002 .002 .008 .011* −.004 .001 (.004) (.005) (.006) (.006) (.004) (.005) (.007) (.006) (.004) (.005) (.007) (.005) Television −.643*** .097 −6.419* −6.951* (.175) (.188) (2.621) (2.880) Video games .192 .097 −10.735* −11.340** (.282) (.282) (4.508) (4.235) Total screen time −.344* .030 −3.651** −3.208** (.144) (.157) (1.250) (1.049) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 3,646 3,646 2,210 6,693 F 54.14*** 4.11*** 2.80** 2.81** 51.89*** 4.15*** 2.69** 3.28** 52.63*** 4.15*** 4.16*** 5.09*** First-stage F statistic — — 11.54 — — — 10.69 — — — 26.49 — Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Sample sizes decline slightly in our FE-IV specifications because of a small number of observations with missing geocodes. Standard OLS estimates (pooled across waves) do evince significant, negative relationships between sedentary behavior, television, total screen time, and math scores, but with noticeably smaller coefficients. Our conventional FE estimates, however, render null effects across the board (though removing the “education” control—length of school day plus homework—produces significant results for sedentary behavior and screen time in the expected direction [estimates not shown]). This raises the possibility of a selection bias mechanism whereby higher-ability children spend more time sedentary or consuming media. Another possibility is that the effects we find in FE-IV specifications reflect relationships among “compliers” in our natural experiment that are not common across the CDS sample (more on this later in the results). Our imputed specifications in table 1 produce substantively similar estimates, which suggest that data missingness is not systematically biasing our core results. Among the stratified results, the clearest differences are among the college- and non-college-educated caregiver subsamples (table 2). For each of our time use variables, coefficients are about five to six times as large for the children of non-college-educated mothers compared to their peers, and the null results for the latter are likely at least partly due to more modest first-stage associations, which are near or under commonly proposed weak-instrument thresholds. (While there is no clear consensus value, specialists generally recommend first-stage F statistics of the excluded instrument above 10 [Stock, Wright, and Yogo 2002].) That is, sunlight is a noticeably poorer predictor of behavior among children with college-educated mothers compared to their peers, and this results in higher standard errors (but cannot account for the lower point estimates). Table 2. FE-IV Results Stratified by Primary Caregiver’s Education Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 2. FE-IV Results Stratified by Primary Caregiver’s Education Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day College-educated primary caregiver Non-college-educated primary caregiver Educational time 1.130*** 1.171 .783 .396 .913 .818 .818 .862 −.782 −1.251 −.213 −.761 (.288) (.384) (.571) (1.500) (.385) (.525) (.200) (.213) (.499) (.884) (.344) (.506) Days in school .017 .017 .018 .023 .019 .018 −.010 −.011 −.014 .002 −.002 −.009 (.019) (.019) (.017) (.014) (.016) (.017) (.009) (.009) (.010) (.009) (.008) (.009) Physical activity 2.413 8.286** (4.760) (3.043) Outdoor activity 2.782 8.370** (4.956) (3.093) Sedentary activity −1.047 −4.059* (1.803) (1.572) Television −3.922 −8.268* (8.283) (4.187) Video games −2.224 −12.130* (3.748) (5.591) Total screen time −1.107 −4.634* (1.896) (1.858) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 634 634 634 634 634 634 2,779 2,779 2,779 2,779 2,779 2,779 F 2.76** 2.62* 3.29** 2.54* 3.20** 3.32** 2.78** 2.72** 2.54** 1.42 1.81 2.32* First-stage F statistic 7.80 4.46 7.77 .77 9.39 6.88 19.60 18.86 18.05 6.24 9.08 15.50 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Further, as a matter of course, we cannot produce estimates for non-compliers who, say, mostly stay inside regardless of weather conditions. We suspect behavior may be less sensitive to weather conditions among the children of college-educated caregivers because they are engaged or enrolled in more formalized activities that would be more likely to take place “rain or shine” (Lareau 2000, 2002; Weininger, Lareau, and Conley 2015). Intuitively, one would suspect that children who have unstructured free time would be more sensitive to weather conditions compared to those taking piano or violin lessons, for example. This leaves open the possibility that a different estimation strategy may uncover significant relationships between time use and cognition among the children of college-educated parents that we do not observe here. We also see variation in effect sizes stratified by sex and race. (Stratifying by sex or race does not reveal differences in the responsiveness of behavior to weather conditions, unlike the caregiver college/no college estimates.) Effect sizes are at least twice as large for girls compared to boys, with all activities exhibiting significant relationships with math assessment except for video games (table 3). For whites, the effect of physical and outdoor activity was greater in magnitude than for African Americans, with the opposite true of sedentary behavior and screen time—though the latter two are only significantly predictive at the 10 percent level for the black subsample (table 4). Our stratified results based on imputed data, however, deliver larger coefficients and smaller p values for the black subsample, and effect sizes are about 30–60 percent larger compared to whites in those specifications depending on the behavior (online appendix table S6). Because other imputed stratified estimates (see tables S4–S5 in the online appendix) deliver similar or more conservative estimates compared to those using listwise deletion, it is possible that our main race-stratified results are downwardly biased due to nonresponse. One possibility in explaining the differences in effect sizes (or whether behaviors are significantly predictive) stratified by race or sex is the presence of a threshold effect, whereby the marginal consumption of a specific behavior becomes either more or less impactful past a certain point. For instance, girls are over three times more sensitive to the effects of screen time than boys, yet the latter consume 25–40 percent more depending on the wave. Boys may thus reach a saturation point after which each additional unit of electronic media consumption no longer appreciably affects performance. Table 3. FE-IV Results Stratified by Sex Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 3. FE-IV Results Stratified by Sex Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day Girls Boys Educational time .787** .821** −1.115 −1.422 −.387 −1.070 .813*** .853*** .163 −.012 .267 .185 (.240) (.250) (.586) (.875) (.471) (.618) (.212) (.237) (.418) (.606) (.371) (.402) Days in school −.011 −.011 −.018 .004 −.002 −.011 .003 .003 .002 .011 .002 .003 (.008) (.010) (.013) (.010) (.013) (.012) (.009) (.009) (.009) (.008) (.010) (.009) Physical activity 10.403** 3.149 (3.418) (2.555) Outdoor activity 10.494** 3.348 (3.491) (2.733) Sedentary activity −4.876** −1.727 (1.731) (1.421) Television −8.264* −4.052 (3.692) (3.695) Video games −30.782 −3.931 (18.233) (18.233) Total screen time −5.795** −1.846 (2.227) (1.523) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,807 1,807 1,807 1,807 1,807 1,807 1,815 1,815 1,815 1,815 1,815 1,815 F 2.30* 2.24* 2.68** 1.68 .93 2.22* 3.37*** 3.31** 3.15** 2.33* 3.07** 3.14** First-stage F statistic 21.58 20.53 17.45 8.04 3.79 13.63 19.96 16.77 13.70 4.08 7.00 13.07 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 4. FE-IV Results Stratified by Race Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). Table 4. FE-IV Results Stratified by Race Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Outcome: Applied problems standardized scores (math word problems); IV: Sunlight (KJ/m2) on activity day White subsample Black subsample Educational time .967*** 1.055** −.262 −.662 .132 −.213 .636** .667** −.580 −.539 −1.087 −.674 (.256) (.306) (.437) (.754) (.276) (.429) (.242) (.252) (.524) (.542) (1.948) (.603) Days in school −.001 −.002 −.004 .008 .001 −.003 −.001 −.001 −.002 .008 .003 .003 (.011) (.011) (.012) (.010) (.011) (.012) (.009) (.009) (.009) (.009) (.016) (.009) Physical activity 7.115* 5.849* (3.389) (2.797) Outdoor activity 7.754* 5.916* (3.837) (2.836) Sedentary activity −2.786* −3.031 (1.342) (1.562) Television −5.717 −4.869 (3.325) (2.766) Video games −9.281 −20.456 (4.814) (25.129) Total screen time −3.028* −3.951 (1.495) (2.164) Controls/Shocks Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 1,755 1,755 1,755 1,755 1,755 1,755 1,460 1,460 1,460 1,460 1,460 1,460 F 2.71** 2.45* 3.38*** 2.35* 2.14* 3.25** 1.49 1.47 1.46 1.03 .45 1.25 First-stage F statistic 15.69 12.11 19.42 7.18 11.14 16.69 15.17 14.63 10.85 6.61 .75 7.83 Note: Cluster-robust standard errors are in parentheses. * p < .05 ** p < .01 *** p < .001 (two-tailed tests). The differences in the effects of physical and outdoor activity are more difficult to speculate on because all children exhibit relatively low levels of either behavior. If children are customarily indoors and relatively inactive whatever their background, why might we see such a difference in effect sizes? One reason could be a systematic difference in opportunity cost structures, such that some children are substituting active behavior for less productive time use. While we seek to determine the effects of qualitatively orthogonal behaviors—that is, active versus sedentary—we do not code these in such broad ways as to render them in zero-sum opposition. That is, because we deliberately exclude many kinds of behavior that could fall under the outside/active (e.g., walking to or from school) or sedentary (e.g., doing homework on the couch) categories, reductions in one do not necessarily lead to increases in the other. Thus some children may be substituting physically active behavior for, say, watching television, while for others that same active leisure may be impinging on studying or practicing violin. With respect to screen-time behaviors, we also cannot account for the content of what children were watching or what games they were playing, which could also influence our results. (The CDS does include general information on the genre of television programs children consumed, but only for the 1997 and 2002 waves.) If higher-SES children are watching National Geographic and their peers are watching daytime talk shows, one might expect a qualitatively different effect. Another consideration is why we did not see similar effects using verbal outcomes (see online appendix table S2 for broad reading results). First, fewer children were eligible to take the reading tests because of different age restrictions. Still, we suspect the reason may lie not in the structure of the data but in how math and verbal ability change over the course of the school year. In our verbal outcome specifications, time in school is highly significant in each. Verbal ability may thus be more sensitive to the additive cognitive gains children experience as the school year progresses, while math may be more “sticky” in comparison. Our results also have implications for achievement gaps documented in the social science literature. In our data, black-white and caregiver college/no college math score gaps are about four-fifths and two-thirds of a standard deviation, respectively, which is broadly consistent with recent estimates of school readiness differences by race and income among similarly aged cohorts (Reardon and Portilla 2015). Extrapolating from effect sizes and average time use/math scores across waves, back-of-the-envelope calculations suggest that differences in behavior may account for nontrivial proportions of the black/white and caregiver college/no college gaps in assessment. For instance, differences in sedentary behavior alone account for about 15 and 10 percent of the black/white and caregiver college/no college assessment gaps in our data. (We use sedentary behavior as an example because it is an “umbrella” category that includes screen time, television, etc.) Thus for certain subpopulations, narrowing time use differences among children may aid in helping shrink gaps in assessment and academic achievement more generally. Strategies that decrease sedentary time and promote physical activity in its place may have a particularly robust impact on confronting these pernicious inequalities of achievement. In figure 3, we compare time use in the original CDS cohort we use in this analysis (1997–2007) to the newer 2014 module. We restricted the 2014 CDS descriptive estimates to children above 10 so they are comparable in age to the 2007 wave (when the youngest children in our sample were about 10 years old). Somewhat encouragingly, we did not see any dramatic overall changes from 2007 to 2014, particularly in screen time or sedentary behavior. We actually see a small reduction in total screen time, most likely because we code these behaviors as primary rather than secondary activities. With the emergence of smartphones and tablets, many children may be consuming media while they are primarily waiting for their parents at school, being driven home, and so forth. In terms of the trends in behavioral and assessment gaps among adolescents from 2007 to 2014, some results are modestly encouraging (see online appendix table S1). For instance, black-white and college-no college gaps in math performance and sedentary behavior shrank from 2007 to 2014. On the other hand, gaps in screen time largely persisted or grew. Sex-based gaps also increased noticeably, as girls were less sedentary and consumed less media in 2014 compared to 2007, while boys’ behavior was stable or only trivially declined. Whether the convergence in time use among subgroups or positive trends in behavior compared to earlier cohorts manifest in any measurable effect on test performance gaps either in the PSID or more broadly is an open question. One conclusion is rather clear: in 2014, American children are still engaging in far more passive than active leisure, whatever their background. Figure 3. View largeDownload slide Weekday time use, 1997–2014 Figure 3. View largeDownload slide Weekday time use, 1997–2014 Our research suffers from various limitations that render our results provisional. For instance, we do not capture the qualitative details of what kinds of physical, sedentary, and so on, behavior children are engaging in. We may see differences among our subsamples because, for example, higher-SES kids are engaging in relatively productive or cognitively active sedentary behaviors like conversing with their siblings as opposed to sitting alone watching television. Using the weather as an instrument, as we have described, also has its pitfalls and potential biases. Just as sunnier weather may allow us more time outside and the opportunity to be physically active, it varies systematically with the school year, produces a critically important secosteroid in humans, and so on. While we make every attempt to account for these factors conceptually and empirically, because one cannot “prove” that the exclusion criteria are met, the possibility remains that there is some latent bias that drives our results. It is also important to remember that our findings consist of local average treatment effects, where only children that actually accepted the treatment—“compliers,” who are induced to go outside in sunny weather but who otherwise might not, for example—contribute to our estimates. It may be the case that population-level estimates (i.e., average treatment effects) are lower than what we find here, if, for example, there are declining marginal returns to going outside for “outdoor-oriented” children, or adaptations to staying indoors for their “indoor-oriented” counterparts. The signal between behavior and cognitive achievement would be weakened in these subgroups, and thus perhaps not broadly relevant in terms of policy because changing behaviors would only affect children whose behavior is sensitive to environmental conditions. Finally, due to the realities of the data we use, we cannot detail the mechanisms of the causal process we are trying to capture. That is, we cannot distinguish whether, for example, the connection between physical activity and cognitive performance is more straightforwardly physiological (e.g., through concrete channels like brain structure or enhanced executive functioning) or psychological (by being beneficial in themselves or substituting for other behaviors that may offer negative psychic and cognitive value). Conclusion In this paper we use individual-level fixed effects coupled with a quasi-experimental research design, instrumenting active and sedentary behaviors with sunlight in trying to determine the cognitive effects of time use. We find robustly positive effects of physical activity and outdoor activity on math scores, with the opposite true for sedentary behavior and screen time. We also find that sedentary and screen time use exhibit more substantial effects among children with less educated caregivers and girls, and speculate that time use gaps, opportunity cost structures (i.e., the range of alternative activities), and the differential sensitivity of behavior to weather conditions among subgroups may play a role in the stratified results. In the realm of public health, small-scale RCT and other experimental studies could help uncover what mechanisms are at play in deconstructing causal pathways, while other research using observational data and quasi-experimental methodology can lend support to or undermine our findings. While it is only in its earliest stages, the new PSID CDS will be a powerful tool in the future for researchers who seek to understand how behavior affects assessment and achievement. Other longitudinal observational studies like the National Longitudinal Study of Adolescent to Adult Health (Add Health) can also be leveraged to examine these relationships, though each dataset has its relative weaknesses (e.g., the inability for researchers to obtain geocoded data in Add Health). For practical purposes, we believe our findings lend further support to the general notion that, ceteris paribus, physical activity seems to generally be a positive factor in child development, while sedentary and screen behavior seem to evince the opposite effect. Whether this is more purely physiological in origin or a reflection of the different psychological effects certain behaviors have on development is an open question, and fertile ground for future research. Somewhat encouragingly, we did not see an overall uptick in total screen time, television consumption, or sedentary activity when comparing adolescents in our module to the new 2014 CDS cohort. Still, children today seem to be exhibiting similarly low levels of both physical and outdoor activities as their peers did in 2007. Gaps in more passive leisure behaviors patterned on race, sex, and particularly SES as captured by mother’s education persist, but seem to have attenuated somewhat. Still, even with modest convergence, adolescents with college-educated caregivers in 2014 spend more than a half hour less engaging in sedentary behavior per day than their peers. Future work in the social sciences could integrate these differences in active and passive leisure into theoretical models that seek to explain why life chances so noticeably diverge among children. In this sense, playing outside or watching television can be seen as extensions of the formal-informal dichotomy many sociologists sketch out in examining patterns in achievement and childhood trajectory. The social and economic background of families influences not only the level of formalization but often whether activities are active or passive, and our findings are provisional evidence that these behaviors are significant in terms of not only weight gain or physical health but cognitive performance as well, implicating them in broader trends in childhood inequality in achievement. Supplementary Material Supplementary material is available at Social Forces online. About the Authors Tom Laidley is a PhD candidate in sociology at New York University. His current research focuses on how urban spatial structure affects health and well-being and contributes to stratification and life chances. His other interests include housing affordability and the growth and development of cities and the regions they anchor. Dalton Conley is the Henry Putnam University Professor in Sociology and a faculty affiliate at the Office of Population Research and the Center for Health and Wellbeing. His current work applies econometric methods for causal inference—namely, a natural experiment framework—to genome-wide data available in social surveys to model gene-by-environment interaction effects. He is also interested in mapping the genetic architecture of phenotypic plasticity, and characterizing social and genetic sorting as distinct processes. References Alexander , Karl L. , Doris R. Entwisle , and Linda S. Olson . 2007 . “ Lasting Consequences of the Summer Learning Gap .” American Sociological Review 72 : 167 – 80 . Google Scholar CrossRef Search ADS Bailey , Martha J. , and Susan M. Dynarski . 2011 . “Gains and Gaps: Changing Inequality in U.S. College Entry and Completion.” NBER Working Paper No. 17633. Barrington-Leigh , Christopher , and Fatemeh Behzadnejad . 2017 . “ The Impact of Daily Weather Conditions on Life Satisfaction: Evidence from Cross-Sectional and Panel Data .” Journal of Economic Psychology 59 : 145 – 63 . Google Scholar CrossRef Search ADS Bensnes , Simon Søbstad . 2016 . “ You Sneeze, You Lose: The Impact of Pollen Exposure on Cognitive Performance During High-Stakes High School Exams .” Journal of Health Economics 49 : 1 – 13 . Google Scholar CrossRef Search ADS Bhattacharya , Debopam , and Bhashkar Mazumder . 2011 . “ A Nonparametric Analysis of Black-White Differences in Intergenerational Income Mobility in the United States .” Quantitative Economics 2 : 335 – 79 . Google Scholar CrossRef Search ADS Bond , Timothy N. , and Kevin Lang . 2013 . “ The Evolution of the Black-White Test Score Gap in Grades K–3: The Fragility of Results .” Review of Economics and Statistics 95 : 1468 – 79 . Google Scholar CrossRef Search ADS Borghans , Lex , Bart H H Golsteyn , James Heckman , and John E. Humphries . 2011 . “ Identification Problems in Personality Psychology .” Personality and Individual Differences 51 : 315 – 20 . Google Scholar CrossRef Search ADS Bowles , Samuel , and Herbert Gintis . 2002 . “ Schooling in Capitalist America Revisited .” Sociology of Education 75 : 1 – 18 . Google Scholar CrossRef Search ADS Buchmann , Claudia , and Thomas A. DiPrete . 2006 . “ The Growing Female Advantage in College Completion: The Role of Family Background and Academic Achievement .” American Sociological Review 71 : 515 – 41 . Google Scholar CrossRef Search ADS Centers for Disease Control . 2017 . CDC Wide-Ranging Online Data for Epidemioligic Research (WONDER). http://wonder.cdc.gov/ (accessed June 1, 2017). Chay , Kenneth Y. , Jonathan Guryan , and Bhashkar Mazumder . 2009 . “Birth Cohort and the Black-White Achievement Gap: The Roles of Access and Health Soon After Birth.” NBER Working Paper No. 15078. Clifford , Angela , Linda Lang , Ruoling Chen , Kaarin J. Anstey , and Anthony Seaton . 2016 . “ Exposure to Air Pollution and Cognitive Functioning Across the Course—A Systematic Literature Review .” Environmental Research 147 : 383 – 98 . Google Scholar CrossRef Search ADS Condron , Dennis J. 2009 . “ Social Class, School and Non-School Environments, and Black/White Inequalities in Children’s Learning .” American Sociological Review 74 : 683 – 708 . Google Scholar CrossRef Search ADS DiMaggio , Paul . 1982 . “ Cultural Capital and School Success: The Impact of Status Culture Participation on the Grades of U.S. High School Students .” American Sociological Review 47 : 189 – 201 . Google Scholar CrossRef Search ADS Erickson , Kirk I. , Charles H. Hillman , and Arthur F. Kramer . 2015 . “ Physical Activity, Brain, and Cognition .” Behavioral Sciences 4 : 27 – 32 . Esteban-Cornejo , Irene , Carlos Tejero-Gonzalez , James F. Sallis , and Oscar L. Veiga . 2015 . “ Physical Activity and Cognition in Adolescents: A Systematic Review .” Journal of Science and Medicine in Sport 18 : 534 – 39 . Google Scholar CrossRef Search ADS Eyles , D. W. , F. Feron , X. Cui , J. P. Kesby , L. H. Harms , P. Ko , J. J. McGrath , and T. H. J. Burne . 2009 . “ Developmental Vitamin D Deficiency Causes Abnormal Brain Development .” Psychoneuroendocrinology 34 : S247 – 57 . Google Scholar CrossRef Search ADS Fairlie , Robert W. , and Jonathan Robinson . 2013 . “Experimental Evidence on the Effects of Home Computers on Academic Achievement Among Schoolchildren.” NBER Working Paper No. 19060. Falbe , Jennifer , Bernard Rosner , Walter C. Willett , Kendrin R. Sonneville , Frank B. Hu , and Alison E. Field . 2013 . “ Adiposity and Different Types of Screen Time .” Pediatrics 132 : 1497 . Google Scholar CrossRef Search ADS Fernald , Anne , Virginia A. Marchman , and Adriana Wiesleder . 2013 . “ SES Differences in Language Processing Skill and Vocabulary Are Evident at 18 Months .” Developmental Science 16 : 234 – 48 . Google Scholar CrossRef Search ADS Fitzpatrick , Maria D. , David Grissmer , and Sarah Hastedt . 2011 . “ What a Difference a Day Makes: Estimating Daily Learning Gains During Kindergarten and First Grade Using a Natural Experiment .” Economics of Education Review 30 : 269 – 79 . Google Scholar CrossRef Search ADS Frederick , Carl B. , Kaisa Snellman , and Robert D. Putnam . 2014 . “ Increasing Socioeconomic Disparities in Adolescent Obesity .” Proceedings of the National Academy of Sciences 111 : 1338 – 42 . Google Scholar CrossRef Search ADS Gaddis , S. Michael . 2013 . “ The Influence of Habitus in the Relationship Between Cultural Capital and Academic Achievement .” Social Science Research 42 : 1 – 13 . Google Scholar CrossRef Search ADS Gershenson , Seth . 2013 . “ Do Summer Time-Use Gaps Vary by Socioeconomic Status? ” American Educational Research Journal 50 : 1219 – 48 . Google Scholar CrossRef Search ADS Graham , J. W. , Allison E. Olchowski , and Tamika D. Gilreath . 2007 . “ How Many Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory .” Prevention Science 8 ( 3 ): 206 – 13 . Google Scholar CrossRef Search ADS Hall , Matthew , and George Farkas . 2011 . “ Adolescent Cognitive Skills, Attitudinal/Behavioral Traits and Career Wages .” Social Forces 89 : 1261 – 86 . Google Scholar CrossRef Search ADS Hanushek , Eric A. 2013 . “ Economic Growth in Developing Countries: The Role of Human Capital .” Economics of Education Review 37 : 204 – 12 . Google Scholar CrossRef Search ADS Heckman , James J. , and Tim Kautz . 2013 . “Fostering and Measuring Skills: Interventions That Improve Character and Cognition.” NBER Working Paper No. 19656. Jablonski , Nina G. , and George Chaplin . 2000 . “ The Evolution of Human Skin Coloration .” Journal of Human Evolution 39 : 57 – 106 . Google Scholar CrossRef Search ADS Jacob , Brian , Lars Lefgren , and Enrico Moretti . 2007 . “ The Dynamics of Criminal Behavior: Evidence from Weather Shocks .” Journal of Human Resources 42 : 489 – 527 . Google Scholar CrossRef Search ADS Jæger , Mads Meier . 2011 . “ Does Cultural Capital Really Affect Academic Achievement? New Evidence from Combined Sibling and Panel Data .” Sociology of Education 84 : 281 – 98 . Google Scholar CrossRef Search ADS Jelleyman , Tim , and N. Spencer . 2008 . “ Residential Mobility in Childhood and Health Outcomes: A Systematic Review .” Journal of Epidemiology and Community Health 62 : 584 – 92 . Google Scholar CrossRef Search ADS Kalil , Ariel , Kathleen M. Ziol-Guest , Rebecca M. Ryan , and Anna J. Markowitz . 2016 . “ Changes in Income-Based Gaps in Parent Activities with Young Children from 1988 to 2012 .” AERA Open 2 : 1 – 17 . Google Scholar CrossRef Search ADS Keller , Matthew C. , Barbara L. Frederickson , Oscar Ybarra , Stéphane Côté , Kareem Johnson , Joe Mikels , Anne Conway , and Tor Wager . 2005 . “ A Warm Heart and a Clear Head: The Contingent Effects of Weather on Mood and Cognition .” Pyschological Science 16 : 724 – 31 . Kent , Shia T. , Leslie A. McClure , William L. Crosson , Donna K. Arnett , Virginia G. Wadley , and Nalini Sathiakumar . 2009 . “ Effect of Sunlight Exposure on Cognitive Function Among Depressed and Non-Depressed Participants: A REGARDS Cross-Sectional Study .” Environmental Health 8 : 34 – 48 . Google Scholar CrossRef Search ADS Kühn , Simone , Tobias Gleich , Robert C. Lorenz , U. Lindenberger , and Jurgen Gallinat . 2014 . “ Playing Super Mario Induces Structural Brain Plasticity: Gray Matter Changes Resulting from Training with a Commercial Video Game .” Molecular Psychiatry 19 : 265 – 71 . Google Scholar CrossRef Search ADS Lareau , Annette . 2000 . “ Social Class and the Daily Lives of Children: A Study from the United States .” Childhood 7 : 155 – 71 . Google Scholar CrossRef Search ADS ——— . 2002 . “ Invisible Inequality: Social Class and Childrearing in Black Families and White Families .” American Sociological Review 67 : 747 – 76 . Google Scholar CrossRef Search ADS Lee , Jooa J. , Francesca Gino , and Bradley R. Staats . 2014 . “ Rainmakers: Why Bad Weather Means Good Productivity .” Journal of Applied Psychology 99 : 504 – 13 . Google Scholar CrossRef Search ADS Leech , Rebecca M. , Sarah McNaughton , and Anna Timperio . 2014 . “ The Clustering of Diet, Physical Activity and Sedentary Behavior in Children and Adolescents: A Review .” International Journal of Behavior Nutrition and Physical Activity 11 : 4 . Google Scholar CrossRef Search ADS Lees , Caitlin , and Jessica Hopkins . 2013 . “ Effect of Aerobic Exercise on Cognition, Academic Achievement, and Psychosocial Function in Children: A Systematic Review of Randomized Control Trials .” Preventing Chronic Disease 10 : 1300 – 1310 . Google Scholar CrossRef Search ADS Lillard , Angeline S. , and Jennifer Peterson . 2011 . “ The Immediate Impact of Different Types of Television on Young Children’s Executive Function .” Pediatrics 128 : 644 – 49 . Google Scholar CrossRef Search ADS Loprinzi , Paul D. , and Christy J. Kane . 2015 . “ Exercise and Cognitive Function: A Randomized Controlled Trial Examining Acute Exercise and Free-Living Physical Activity and Sedentary Effects .” Mayo Clinic Proceedings 90 : 450 – 60 . Google Scholar CrossRef Search ADS Ludyga , Sebastian , Markus Gerber , Serge Brand , Edith Holsboer-Trachsler , and Uwe Pühse . 2016 . “ Acute Effects of Moderate Aerobic Exercise on Specific Aspects of Executive Function in Different Age and Fitness Groups: A Meta-Analysis .” Psychophysiology 53 : 1611 – 26 . Google Scholar CrossRef Search ADS Lundborg , Petter , Paul Nystedt , and Dan-Olof Rooth . 2014 . “ Height and Earnings: The Role of Cognitive and Noncognitive Skills .” Journal of Human Resources 49 : 141 – 66 . Google Scholar CrossRef Search ADS Madestam , Andreas , Daniel Shoag , Stan Veuger , and David Yanagizawa-Drott . 2013 . “ Do Political Protests Matter? Evidence from the Tea Party Movement .” Quarterly Journal of Economics 128 : 1633 – 85 . Google Scholar CrossRef Search ADS Mares , Marie-Louise , and Zhongdang Pan . 2013 . “ Effects of Sesame Street: A Meta-Analysis of Children’s Learning in 15 Countries .” Journal of Applied Developmental Psychology 34 : 140 – 51 . Google Scholar CrossRef Search ADS Matsuoka , Lois Y. , Jacobo Wortsman , John G. Haddad , Paul Kolm , and Bruce W. Hollis . 1991 . “ Racial Pigmentation and the Cutaneous Synthesis of Vitamin D .” Archives of Dermatology 127 : 536 – 38 . Google Scholar CrossRef Search ADS McGarel , C. , J. J. Strain , Kristina Pentieva , and H. McNulty . 2015 . “ Emerging Roles for Folate and Related B-Vitamins in Brain Health Across the Lifecycle .” Proceedings of the Nutrition Society 74 : 46 – 55 . Google Scholar CrossRef Search ADS McKnight , P. E. , K. M. McKnight , S. Sidani , and A. J. Figueredo . 2007 . Missing Data: A Gentle Introduction . New York : Guilford Press . Merry , Joseph J. 2013 . “ Tracing the U.S. Deficit in PISA Reading Skills to Early Childhood: Evidence from the United States and Canada .” Sociology of Education 86 : 234 – 52 . Google Scholar CrossRef Search ADS Meyer , Christelle , Vincenzo Muto , Mathieu Jaspar , Caroline Kussé , Erik Lambot , Sarah L. Chellappa , Christian Degueldre , Evelyne Balteau , André Luxen , Benita Middleton , Simon N. Archer , Fabienne Collette , Derk-Jan Dijk , Pierre Maquet , and Gilles Vandewalle . 2016 . “ Seasonality in Human Cognitive Brain Responses .” Proceedings of the National Academy of Sciences 113 : 3066 – 71 . Google Scholar CrossRef Search ADS Mielke , Gregore I. , Wendy J. Brown , Bruno P. Nunes , Inacio C M Silva , and Pedro C. Hillal . 2017 . “ Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis .” Sports Medicine 47 : 61 – 75 . Google Scholar CrossRef Search ADS Miguel , Edward , Shanker Satyanath , and Ernest Sergenti . 2004 . “ Economic Shocks and Civil Conflict: An Instrumental Variables Approach .” Journal of Political Economy 112 : 725 – 53 . Google Scholar CrossRef Search ADS Neal , Derek . 2006 . “Why Has the Black-White Skill Convergence Stopped?” In Handbook of the Economics of Education , edited by Eric A. Hanushek , Stephen Machin , and Ludger Woessmann , pp. 511 – 76 . Boston : Elsevier . Nikkelen , Sanne W C , Patti M. Valkenburg , Mariette Huizinga , and Brad J. Bushman . 2014 . “ Media Use and ADHD-Related Behaviors in Children and Adolescents: A Meta-Analysis .” Developmental Psychology 50 : 2228 – 41 . Google Scholar CrossRef Search ADS Noelke , Clemens , Mark McGovern , Daniel J. Corsi , Marcia P. Jiminez , Ari Stern , Ian Sue Wing , and Lisa Berkman . 2016 . “ Increasing Ambient Temperature Reduces Emotional Wellbeing .” Environmental Research 151 : 124 – 29 . Google Scholar CrossRef Search ADS Off , Morten K. , Arnfinn E. Steindal , Alina C. Porojnicu , Asta Juzeniene , Alexander Vorobey , Anders Johnsson , and Johan Moan . 2005 . “ Ultraviolet Photodegradation of Folic Acid .” Journal of Photochemistry and Photobiology B: Biology 80 : 47 – 55 . Google Scholar CrossRef Search ADS Panel Study of Income Dynamics . 2017 . P ublic and restricted use datasets. Produced and distributed by the Survey Research Center, Institute for Social Research, University of Michigan, Ann Abor. Peterson , Bradley S. , Virginia A. Rauh , Ravi Bansal , Xuejun Hao , Zachary Toth , Giancarlo Nati , Kirwan Walsh , Rachel L. Miller , Franchesca Arias , David Semanek , and Frederica Perera . 2015 . “ Effects of Prenatal Exposure to Air Pollutants (Polycyclic Aromatic Hydrocarbons) on the Development of Brain White Matter, Cognition, and Behavior in Later Childhood .” JAMA Psychiatry 72 : 531 – 40 . Google Scholar CrossRef Search ADS Potter , Daniel , and Josipa Roksa . 2013 . “ Accumulating Advantages over Time: Family Experiences and Social Class Inequality in Academic Achievement .” Social Science Research 42 : 1018 – 32 . Google Scholar CrossRef Search ADS Powe , Camille E. , Michele K. Evans , Julia Wenger , Alan B. Zonderman , Anders H. Berg , Michael Nalls , Hector Tamez , Dongsheng Zhang , Ishir Bhan , Ananth Karumanchi , Neil R. Powe , and Ravi Thadhani . 2013 . “ Vitamin D-Binding Protein and Vitamin D Status of Black Americans and White Americans .” New England Journal of Medicine 369 : 1991 – 2000 . Google Scholar CrossRef Search ADS Prakash , Ruchika Shaurya , Michelle W. Voss , Kirk I. Erickson , and Arthur F. Kramer . 2015 . “ Physical Activity and Cognitive Vitality .” Annual Review of Psychology 66 : 769 – 97 . Google Scholar CrossRef Search ADS Reardon , Sean F. , and Ximena A. Portilla . 2015 . “Recent Trends in Socioeconomic and Racial School Readiness Gaps at Kindergarten Entry.” Center for Education Policy Analysis Working Paper No. 15-02. Stanford Center for Education Policy Analysis. Sandberg , John , and Patrick Rafail . 2014 . “ Family Size, Cognitive Outcomes, and Familial Interaction in Stable, Two-Parent Families: United States, 1997–2002 .” Demography 51 : 1895 – 1931 . Google Scholar CrossRef Search ADS Sharkey , Patrick . 2010 . “ The Acute Effect of Local Homicides on Children’s Cognitive Performance .” Proceedings of the National Academy of Sciences 107 : 11733 – 38 . Google Scholar CrossRef Search ADS Stock , James H. , Jonathan H. Wright , and Motohiro Yogo . 2002 . “ A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments .” Journal of Business and Economic Statistics 20 : 518 – 29 . Google Scholar CrossRef Search ADS Suziedelyte , Agne . 2015 . “ The Effects of Old and New Media on Children’s Weight .” Applied Economics 47 : 1008 – 18 . Google Scholar CrossRef Search ADS Tach , Laura . 2015 . “ Social Mobility in an Era of Family Instability and Complexity .” ANNALS of the American Academy of Political and Social Science 657 : 83 – 96 . Google Scholar CrossRef Search ADS Tomopoulos , Suzy , Bernard P. Dreyer , Samantha Berkule , Arthur H. Fierman , Carolyn Brockmeyer , and Alan L. Mendelsohn . 2010 . “ Infant Media Exposure and Toddler Development .” Archives of Pediatrics and Adolescent Medicine 164 : 1105 – 11 . Google Scholar CrossRef Search ADS Vigdor , Jacob L. , Helen F. Ladd , and Erika Martinez . 2014 . “ Scaling the Digital Divide: Home Computer Technology and Student Achievement .” Economic Inquiry 52 : 1103 – 19 . Google Scholar CrossRef Search ADS Weininger , Elliott B. , Annette Lareau , and Dalton Conley . 2015 . “ What Money Doesn’t Buy: Class Resources and Children’s Participation in Organized Extracurricular Activities .” Social Forces 94 : 479 – 503 . Google Scholar CrossRef Search ADS Woodcock , Richard W. , and Mary B. Johnson . 1989 . Tests of Achievement, Standard Battery . Chicago : Riverside . Young , R. , and D. R. Johnson . 2010 . “ Imputing the Missing Y’s: Implications for Survey Producers and Survey Users .” AAPOR Conference Abstracts 2010 : 6242 – 48 . Zimmerman , Frederick J. , and Dimitri A. Christakis . 2005 . “ Children’s Television Viewing and Cognitive Outcomes .” Archives of Pediatrics and Adolescent Medicine 159 : 619 – 25 . Google Scholar CrossRef Search ADS Zivin , Joshua G. , and Matthew Neidell . 2014 . “ Temperature and the Allocation of Time: Implications for Climate Change .” Journal of Labor Economics 32 : 1 – 26 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Social ForcesOxford University Press

Published: Apr 4, 2018

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