Revisiting the relationship between wages and sleep duration: The role of insomnia

Accepted Manuscript Title: Revisiting the Relationship between Wages and Sleep Duration: The Role of Insomnia Author: Golnaz Sedigh Rose Anne Devlin Gilles Grenier Catherine Deri Armstrong PII: DOI: Reference:

S1570-677X(16)30216-7 http://dx.doi.org/doi:10.1016/j.ehb.2016.11.010 EHB 622

To appear in:

Economics and Human Biology

Received date: Revised date: Accepted date:

16-11-2015 24-11-2016 28-11-2016

Please cite this article as: Sedigh, Golnaz, Devlin, Rose Anne, Grenier, Gilles, Deri Armstrong, Catherine, Revisiting the Relationship between Wages and Sleep Duration: The Role of Insomnia.Economics and Human Biology http://dx.doi.org/10.1016/j.ehb.2016.11.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Revisiting the Relationship between Wages and Sleep Duration: The Role of Insomnia AUTHORS: EHB_EHB-D-15-00132 1) Golnaz Sedigh Lecturer Department of Economics University of Ottawa 120 University Private Ottawa, Ontario Canada K1N 6N5 [email protected] 2) Rose Anne Devlin Professor Department of Economics University of Ottawa 120 University Private Ottawa, Ontario Canada K1N 6N5 [email protected] 3) Gilles Grenier Professor Department of Economics University of Ottawa 120 University Private Ottawa, Ontario Canada K1N 6N5 [email protected] 4) Catherine Deri Armstrong Associate Professor 1

Department of Economics University of Ottawa 120 University Private Ottawa, Ontario Canada K1N 6N5 [email protected]

Highlights    

Workers reduce sleep time in response to wage rate increases. Sleep time responses depend upon worker’s sex, sleep problems, and economic context. Male insomnaics are the most responsive to wage rate hikes in an economic downturn. Sleep problems appear to be randomly distributed across the population of workers.

Abstract This paper uses the 2005 and 2010 Canadian General Social Surveys (Time Use) to investigate the effect of wages on the sleep duration of individuals in the labour force. The endogeneity of wages is taken into account with an instrumental variables approach; we find that the wage rate affects sleeping time in general, corroborating Biddle and Hamermesh’s (1990) main conclusion. A ten percent increase in the wage rate leads to an 11-12 minute decrease in sleep per week. But this number masks several effects. The responsiveness of sleep time to wage rate changes depends upon the sex of the individual, whether or not sleep problems are present and general economic conditions. By far the largest adjustment is found for insomniacs in 2010, a year of general economic downturn in Canada. We also investigate the non-randomness of insomnia in the population by using a Heckman procedure, and find that the sleep time of female non-insomniacs is even more responsive to wage rate changes once account is taken of this selection bias, but otherwise selection was not a problem in our samples. Key words: sleep duration, wages, insomnia JEL classifications: J22, I12

1.

Introduction 2

Individuals vary in the amount of time that they devote to sleep. Some average as little as four hours per night while others sleep more than ten hours. While part of this variation is biological, some of this variation has been shown to be related to choices influenced by economic incentives. If someone’s promotion, for example, is contingent on getting a report done in a timely fashion, then sleep may be sacrificed. To earn more money in situations where the opportunity presents itself, work may take the place of leisure or sleep. In a seminal article, Biddle and Hamermesh (1990, hereafter, BH) investigate empirically the extent to which individuals adjust sleep duration in response to economic stimuli. They demonstrate that socio-economic factors influencing the quantity of time an individual spends working, namely wage rate, non-labour income and level of educational attainment, also affect the quantity of time the individual spends sleeping. This result highlights the importance of recognizing the implied joint decisions determining an individual’s work, leisure and sleep. The literature examining how labour supply responds to wage rates (“labour-supply elasticities”) and how this response affects a wide-variety of policy questions is massive. The policy implications of labour supply elasticities are of as much importance today as they have been in the past.1 Several recent papers focus on labour-supply responses to policies affecting workers income, estimating for example how labour supply is influenced by income taxes (e.g., Ericson, Flood and Islam, 2015), or tax redistribution (Pestieau and Racionero, 2015); and even whether education should be subsidized (Li and Zhang, 2015). Adjusting one’s hours of sleep at the margin in response to wage changes, is another factor that may influence labour-supply elasticities and consequently affect policy prescriptions and their effectiveness. For example, a simple search of “labor-supply elasticities” on March 22, 2016 yielded 1,362 publications from 1966 – 2016 in the search engine ECONLIT. 1

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However, while in theory sleep duration may be a choice variable, in practice it is not always. Individuals who struggle getting to sleep or staying asleep may not be able to adjust sleep duration in response to economic incentives; others, who are just satisfying their sleeping needs, may be similarly insensitive. This paper examines whether economic incentives, namely wages, affect the sleep decisions of individuals taking account of sleep disorders (“insomnia”), helping to improve our understanding of how the labour supply responds to wages. Between a quarter and a third of people in developed countries suffer from some sort of sleep problems (Sutton et al., 2001; Doi et al., 2003; Morin et al., 2006; Stewart et al., 2006). The social importance of sleep disorders is far reaching, having been shown, for instance, to decrease productivity and increase absenteeism, to increase the frequency of accidents, alcohol consumption and depression (Daley et al., 2009) leading to large economic costs (Daley et al., 2009; Walsh and Engelhardt, 1999); chronic sleep problems are also linked to several, costly, health conditions (see discussion in Antillon, Lauderdale and Mullahy, 2014). In this paper, we use the 2005 and 2010 Canadian General Social Surveys to contribute to the small literature that examines the relationship between sleep duration and wages, and to examine whether sleep problems affect the previously documented relationship. We find that the presence of sleep disorders does matter when it comes to responding to changes in wages, but that their influence differs across sexes and the state of the economy in general. In 2005, for instance, when the Canadian economy was in a period of growth, individuals with insomnia did not adjust their sleep time to changes in wages, but this was not the case in 2010. In 2010, when the economy was still suffering from one of the worst recessions of recent decades (Cross and Bergevin, 2012), 2 males with sleep problems adjusted their sleep time in response to wages whereas others did not. Our findings suggest

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The severe recession of 2008-2009 came after 16 years of “uninterrupted expansion” (Cross and Bergevin, 2012, p.1).

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that taking account of sleep problems, sex, and economic conditions is important when estimating labour supply responses to wages.

2.

The Determinants of Sleep

2.1 Non-Economic Determinants: Insomnia Insomnia, defined as having “difficulty initiating and/or maintaining sleep or a non-restorative sleep for at least one month”3 is a pervasive condition. Treating insomnia is considered difficult for several reasons: for instance, it is often thought of as being “…benign, trivial, or something one should be able to cope with alone…” (Stinson et al., 2006, p. 1643). General practitioners and pharmacists may lack sufficient information about sleep problems to effectively assist in its treatment. Vincent and Lionberg (2001) find that most practitioners choose pharmacological treatments (like sleeping pills), while patients fearing side-effects prefer psychological ones (like counselling for anxieties). Although various treatments exist, in a review of some 37 studies on insomnia, Morin et al. (2006) conclude that neither psychological nor behavioural therapies nor pharmacological treatments can cure insomnia completely. Insomnia has been shown to be correlated with age, gender, marital status, education, income, work time, and unemployment (Ohayon and Zulley, 2001; Paine et al., 2004; Tjepkema, 2005; Xiang et al., 2008; Virtanen et al., 2009; Gu et al., 2010). Individuals with insomnia sleep less than those who do not suffer from the condition (Hurst, 2008; Brochu et al., 2012): Brochu et al. (2012) estimate that men and women who have sleep problems sleep on average 1.4 and 2.4 fewer hours in a week than those who do not. 3

This definition of insomnia is from the Diagnostic and Statistical Manual of Mental Disorders (DSM) [http://web4health.info/en/answers/sleep-insomnia-what.htm], the standard classification of mental disorders used in Canada (developed in the United States) [http://www.psych.org/mainmenu/research/dsmiv.aspx].

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2.2 Economic Determinants Becker (1965) presents sleep as one of the main activities to which people allocate their time in order to maximize utility. Modelling the wage rate as the (opportunity) cost of non-market time, including sleep, he observes that sleep is “required for efficiency” (Becker, 1965 p.498). Economic models usually take hours of sleep as given, and have individuals chose only between labour and leisure from the remaining time. Biddle and Hamermesh (BH) (1990) were the first develop a model in which individuals explicitly choose their number of hours of sleep; they predict that, all else equal, higher wages should be associated with less time devoted to sleep. This prediction is tested and confirmed empirically using the 1975-76 General Social Survey data from the United States. The key implication of this result is that ignoring sleep in models of labour supply may lead to incorrect estimates of the effect of wages on hours worked (the labour supply elasticity). Other papers have examined the relationship between a variety of economic variables and sleep. One paper closely related to BH is Szalontai (2006) who uses South African time-use data from 2001 to confirm the negative relationship between wages and sleep duration. Hurst (2008) employs Statistics Canada's General Social Survey (GSS 2005) data to provide a descriptive summary of the relationship between income and sleep, and finds that higher income individuals sleep less than those with lower incomes. Brochu et al. (2012) pool three cycles of the Canadian GSS (1992, 1998, and 2005) to examine whether economic conditions, as measured by the unemployment rate, affect sleep. Their results suggest that as economic conditions worsen (higher unemployment rates), and the opportunity cost of time falls, individuals respond by sleeping more. Antillon et al. (2014) corroborate these findings using US time-use data. In a related set of papers, Ásgeirsdóttir and Zoega (2011), Ásgeirsdóttir et al. (2014) and Ásgeirsdóttir and Olafsson (2015) find that the lower wages associated with economic downturns in Iceland led to increased sleep duration. In contrast, Nena et al. (2014) find

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that sleep duration fell during the Greek financial crisis, suggesting that the positive impact of a lower cost of sleep was overwhelmed by the stress associated with the crisis.

3. Methods and Data 3.1 Methods The main model estimated by BH and upon which this work is based is: 𝑆 𝑖 = 𝛽0 + 𝛽1 ln 𝑊 𝑖 + 𝛽2 𝐻 𝑖 + 𝛽3 𝑋 𝑖 + 𝛽4 ln 𝑀𝑖 + ε𝑖

(1)

where 𝑆 𝑖 is sleep duration measured in minutes per week by individual i; ln 𝑊 𝑖 is the logarithm of the wage rate per hour; 𝐻 𝑖 denotes a dummy variable representing the health of the individual; 𝑋 𝑖 is a vector of the following personal characteristics: such as age, marital status, gender, presence of children less than five years old, and religion; and ln 𝑀𝑖 is the logarithm of other income or non-labour income of individual i; ε𝑖 is the error term. Our estimated model can be considered as a “modified BH model” as we make a few additions/changes based on the differences in the nature of the available data as explained further below. All summary statistics and regressions are weighted using Statistics Canada’s personal weights. Equation (1) aims to capture the impact of the wage rate and other income on sleep duration. However, the factors that influence the amount of sleep undertaken by an individual may also affect the wage rate that the individual commands. Motivating the seminal work of BH was the notion that sleep and wages might be simultaneously determined. Unobserved factors such as motivation or ambition could be correlated with both the amount of sleep undertaken by an individual and the wage rate that individual commands. Further, sleep may respond to the same factors affecting market work and leisure, like measures of the opportunity cost of time such as wages (BH; Szalontai, 2006) or economic conditions (Brochu et al., 2012; Ageirsdottir et al., 2014), while at the same time sleep might directly

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affect wages via increased productivity, as found in Gibson and Shrader (2014).

In addition,

measurement error in the wage variable may attenuate the estimated effect of that variable on sleeping time. To address the problem of measurement error as well as both the possibility of omitted variable bias and reverse causality, we use a two-stage least squares instrumental variables approach using “instruments” correlated with wages but uncorrelated with the error in the sleep equation to help identify causal link between the wage rate and sleep duration. We also separate the sample into individuals with and without sleep problems (insomnia), and estimate equation (1) for these two groups to see if insomnia affects the impact of socio-economic variables on sleep duration. One issue that may arise is the possible non-randomness of insomnia in the population. If there are factors that affect both the wage rate and whether or not the individual reports being an insomniac, then the estimate of the effect of wages on sleep time for these two groups would be compromised. We use a “Heckman” approach (Heckman, 1979) to address this non-randomness issue. This approach entails modelling the factors influencing the likelihood that an individual is an insomniac (first stage), constructing a test statistic based on the estimates from this model (the Inverse Mills Ratio), and including this test statistic in the second stage model of the determinants of sleep duration. However, in this second stage we also account for the endogeneity of wages as before, using an instrumental variables two stage least squares approach (so, in effect, we employ a Heckman three stage process).

Insomnia is assumed to be a function of wage, marital status, age, gender, health,

having young children, religion, other income, educational attainment, time spent working, shift work, and stress. These last four factors are excluded from the second stage regression (sleep duration) and help to identify to which of the insomniac – non-insomniac groups the individual belongs.4

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The choice of determinants of insomnia is based on previous studies.See, for example: http://healthysleep.med.harvard.edu/healthy/science/how/external-factors

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3.2

Data This study uses the Canadian General Social Surveys (GSS) - Time Use, for 2005 and 2010,

which allows us to examine carefully how sleep decisions respond to wages within different economic contexts: one of growth and the other of recession. Respondents are asked to complete a diary listing all their activities over a 24-hour designated day beginning at 4:00 am. The benefit of using time use data is that individuals report their sleep duration in minutes, generating more variation in the data on sleep duration than is available in surveys that simply ask about usual hours of sleep. Lauderdale et al. (2008) and Robinson and Michelson (2010) report that surveys that ask about a single night of sleep duration are better for analyzing sleep than are surveys with self-reported usual hours of sleep. According to Lauderdale et al. (2008), the correlation between wrist-monitored sleep duration with self-reported usual sleep duration is 0.45; whereas, the correlation between wrist-monitored sleep duration with single night reported sleep duration is 0.60. This result suggests that single night sleep duration reflects better the real sleep time than self-reported usual sleep time. Our sleep variable is created based on the number of minutes the individual reports sleeping over the 24 hours in question, including naps. It is then multiplied by seven to obtain the weekly time spent sleeping and to render our results comparable to BH. One question arises about whether or not we keep respondents who report on a weekend day. On the one hand, to ensure that the given day is representative, we may want to remove all observations taken of individuals during the weekend. On the other hand, individuals may use weekend sleep to “catch-up” from a lack of weekday sleep, and by omitting those individual we would be biasing upwards the estimated effect of wages on sleep.5 We therefore include weekends and add a dummy variable to capture if the individual responded to the survey on a weekend.

5

We thank a reviewer for this observation.

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The variables included as regressors in our sleep equation follow those used in the literature. Standard variables included in BH and others (Brochu et al., 2012; Antillon et al., 2014; Asgeirsdottir and Olafsson, 2015) are marital status, age, gender, health status, presence of young children, wage, and other income. Married individuals tend to sleep more (although not always, and some gender differences have been observed (Brochu, et al., 2012)); age has a non-linear effect on sleep and hence a squared term is included; men and women have differing sleep needs; health status is important, with poor health often impinging upon sleep duration; the presence of young children often affects sleep time. Race is another factor that is usually included in US studies, with Black individuals being found to sleep less than Whites (e.g., BH; Asgeirsdottir and Olafsson, 2015). The data set used in our paper does not have a “race” variable, but it does include information on religious affiliation, which we use to proxy race and other cultural, traditional influences on sleep. We include indicator variables denoting if the individual identifies as one of: Roman Catholics, Protestant, “other” religions, and no religion (the reference category). In addition, we include a variable capturing the rurality of the individual’s residence: specifically we include a dichotomous indicator for living in a Census Metropolitan Area or CMA to capture if the individual lives in a city or its surrounding urban area as opposed to a rural one. The CMA indicator can help to pick up some unobservable factors associated with location that may influence sleep: for instance, fewer distractions (noise and light) in rural areas may contribute to better quality sleep (e.g., Um & Um, 2015). On the other hand, rural individuals may have longer commutes to work which could hamper sleep time. Dummy variables for the province of residence, and for month of the interview to control for seasonality in sleep (individuals sleeping more in winter months than summer ones) are included. The main variable of interest, hourly wage rate, is created from the annual personal income reported by respondents, divided by 12 months, divided again by 4.3 (weeks in a month) and then

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divided by the number of hours usually worked at all jobs in a week. We also create an “other income” variable by subtracting personal income from total household income. Unlike personal income, total household income is only available in 12 ranges. We estimate annual household income as the midpoint of each range with the exception of the highest income group (household income in excess of $100,000) where we follow others and estimate an income of $150,000 ($100,000 times 1.5) (Phipps et al., 2001; Brochu et al., 2012). In addition to estimating equation (1) for everyone in our sample, we divide the sample into two groups: those reporting regular sleep problems (insomniacs) and those who do not (non-insomniacs). The grouping is based on the answer to the question in the GSS 2005: “Do you regularly have trouble going to sleep or staying sleep?”, and based on the answer to the subtly different question in the GSS 2010: “Do you regularly have trouble falling asleep or staying asleep?” (in both cases, emphasis is added). Once these data are weighted, in 2005, 30% of the sample had sleep problems (25% of males and 35% of females), and in the latter survey, 31% of the sample had sleep problems (28% males, 35% females). We construct a binary variable which is zero for those who do not report having sleep problems and one for those who do. The definition of “insomnia” used in this paper is different than the clinical definition provided earlier, namely: “difficulty initiating and/or maintaining sleep or a non-restorative sleep for at least one month”. We do not know if the individual has regular difficulties with sleep or if that particular day was anomalous; by the same token, a “true” insomniac may have responded to this question on a good day, when sleep did not elude them. Chances are, however, that our measure of insomnia will overstate its prevalence when judged against the clinical definition.

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There are a total number of 19,597 observations in the GSS 2005 and 15,390 in the GSS 2010, but we need to make a number of sample restrictions. The most restrictive are that the respondents be of working age (23 to 65 years), reducing observations by 5,271 (2005) and 4,717 (2010), and that they report hours worked, reducing our samples by 3,879 and 3,039 observations, respectively. Missing information on non-wage income reduces the samples by 2,159 and 1,918, and on wages drops the sample sizes by 1,968 and 1,784; union status is missing for 1,794 and 1,361 individuals. In addition, we drop full-time students (359, 229), those who are missing information on religion (259, 169), on NAICS6 (303, 54), and a handful of other missing observations on marital status, health and education. Overall, we are left with a usable sample of 6,455 (4,443 non insomniacs and 2,012 insomniacs) in 2005 and of 4,668 (3,160 non insomniacs and 1,508 insomniacs) in 2010.7 Tables 1a and 1b report the mean values of all of the variables employed in our analyses weighted by the sample weights provided by Statistics Canada, for the full sample, by sex, and for insomniacs and non-insomniacs grouped by sex, by sample year. The means of these sub-samples are statistically different from each other, virtually across the board. 8 The symbols beside the entry tells us that that mean is statistically different than the mean of its opposite group (at different significance levels: *1%, †5%, ＊10%): for instance, the mean characteristics of the male sample are statistically different that those of the female sample (columns (2) and (3)); the mean characteristics of noninsomniacs, column (4), are statistically different from the insomniacs, column (7); and male

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The North American Industry Classification System (NAICS) is a standard way of classifying business establishments; we use the two-digit classification of which there are 24. 7 We determined the number of missing observations by systematically dropping variables from the list and comparing the number of resulting observations from the original sample. The problem with this method, is that it does not take into account that some respondents have missing information for several of our variables: in other words, when you sum up all of the missing variables it sums to a number larger than than would be the case if one were to simply take our final sample and subtract it from the orginal sample. 8 We applied a two-tailed t-test for the difference in means (http://onlinestatbook.com/2/tests_of_means/difference_means.html )

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insomniacs (non-insomniacs) are statistically different from female insomniacs (non-insomniacs). The means across the 2005 and 2010 samples are also statistically different from each other (not indicated in table 1) with few exceptions. However, it is important to note that statistical differences are not necessarily meaningful differences. Whereas the fact that 43.1% of our male respondents who are insomniacs have a diploma and 44.8% of the female insomniacs hold a diploma constitutes a statistical difference, it is not clear that this is a meaningful difference. Meaningful differences in tables 1a and 1b are found in the amount of sleep reported by group, in the marital status of some, the presence of young children, and in the proportion of the sample with sleep problems. (Tables 1a and 1b about here) While on average, people slept about 8 hours a night (3,380 minutes a week in 2005; 3,359 minutes in 2010), some clear differences are apparent across the various sub-samples: in 2005, women without sleep problems slept the most of all of the groups under study – at 3,466 minutes a week (8.25 hours a night); whereas in 2010, it was women with sleep problems who slept the most (3,459 minutes). The least amount of average sleep time was experienced by men with sleep problems in 2010: 3,260 minutes or 7.76 hours a night. Not surprisingly, insomniacs are generally less healthy than non-insomniacs. Before turning to the results, an important issue needs to be addressed, namely how to make the best use of the data contained in the two time use surveys. We estimated the model(s) using the two surveys separately and found big differences across the estimated coefficients, especially when it came to the main variable of interest (wages) and the various subgroups (males, females; insomniacs, noninsomniacs) under study.9 For this reason, we decided not to pool the two surveys. 10 Two possible

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Estimates for these regressions are reported in tables 2 and 3.

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explanations for such differences have already been discussed. First, as revealed in the summary statistics of tables 1a and 1b, average sleep times across different groups are statistically (and meaningfully) different from each other. For instance, in 2005, there was not much difference across the amount of time slept by male insomniacs (3,352 minutes per week) versus female insomniacs (3,342 minutes) while this difference was large in 2010, with male insomniacs sleeping, on average, 3,360 minutes, a full 199 minutes less than the average time slept by female insomniacs (3,459). Second, the economic context of the two surveys is quite different. Whereas in 2005 the Canadian economy was in a period of growth, in 2010 it remained shaken by and just starting to slowly recover from the severe recession of 2008-09. The literature previously mentioned is convincing on the fact that economic conditions affect sleep duration; the fact that in 2005, the national unemployment rate was 6.8% and that it rose to 8.1% in 2010, may be important when looking at sleep duration. Moreover, the unemployment rate in Canada’s most populous province, Ontario, increased from 6.6% to 8.7% over this period.11 The link between economic conditions and sleeping may well help us to understand better the observation that male insomniacs slept significantly less in 2010 when compared to 2005. Nena et al. (2014) found that during the recent severe economic crisis in Greece, people slept less not more (as found by, for instance, Brochu et al., 2012), if Canadian men were particularly ‘stressed’ by the recession, their sleep time might have been more negatively affected. In sum, we analyze these two surveys separately to better understand how individuals adjust sleep time to wage rate changes, and to better understand how this adjustment differs across males and females, across those with and without sleep problems, all within different economic contexts. We ran all of the regressions with the pooled data too – and we included a dummy variable for the presence of sleep problems and its estimated coefficient was usually negative but not always significant depending upon the sample. By and large, too much information was being netted out with pooling these two years together. 11 As reported by Statistics Canada and derived from the Labour Force Surveys: see http://www.stats.gov.nl.ca/Statistics/Labour/PDF/UnempRate.pdf 10

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4.

Empirical Results: Does the wage rate matter? Equation (1) is estimated using an instrumental variables procedure treating the wage rate as

endogenous. Finding appropriate instruments is often a difficult task. A good instrument should be highly correlated with the wage rate but uncorrelated with the error term of the sleep model. BH point to several candidates: education, union status, large CMA (metropolitan area), regions (provinces), and industry dummy variables (NAIC codes). We ran regressions that included all five potential instruments directly in the sleep equation and found that the CMAs and provincial dummy indicators had a direct impact on sleep time, whereas the other three did not. Living in an urban (CMA) area may be associated with less sleep because of, say, noise and light. But why would provincial dummy variables matter? One explanation is that the fixed effects captured by these variables reflect interprovincial differences, such as those found in unemployment rates. These rates differ quite significantly across provinces: for instance, in 2005, the unemployment rate was 4% in Alberta but 15.2% in Newfoundland and Labrador; in 2010, the lowest provincial unemployment rate was found in Saskatchewan (5.2%) while Newfoundland and Labrador continued to hold last place at 14.7%. Brochu et al. (2012) and Antillon et al. (2014), using a sample of employed and unemployed individuals, find that sleep increases with unemployment. As a result, we include CMA and provincial dummy variables in the main estimating equation and use union status, education and industry (NAICS) dummy variables, as instruments for the wage rate in the first stage (results reported in the appendix, tables A1 and A2). We test for the appropriateness of this list of instruments by, in the first stage, examining robust F-statistics for the joint significance of their estimated coefficients on wages, and found that they met the standard criterion of having an F of at least 10 (as reported at the end of tables A1 and A2) except for the insomniac male sample in 2010, where the instruments were found to be weak (F=6).

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The second condition that the instruments must be uncorrelated with the error term (in the second stage) is difficult to test directly; the validity of these exclusion (identifying) restrictions depends critically upon a priori justifications (van den Berg, 2008). Union status per se is unlikely to affect sleep time directly, but it will affect wages insofar as members of unions consistently earn higher wages than non-members; similarly, large differences in the average wages of individuals are reported by industry classification.12 We were inspired to use education as an instrument for wages in our sleep equation by the seminal article on this matter, BH, who argue that education, per se, does not affect an individual’s demand or taste for sleep. This point is reiterated more recently by Asgeirsdottir and Olafson (2015, p. 270), who write that: “Higher education hardly changes the taste for sleep but rather operates through wages …”, again to justify the use of education as an instrument for wages in the sleep equation.13 In other words, education affects the ‘price’ of sleep by influencing the wage rate. While a direct test of the exclusion restrictions is not possible, we can test whether the set of instruments is orthogonal to the residuals in the IV (second stage) regression using the Wooldridge score test chi-squared statistic (Wooldridge, 1995).14 These statistics are provided at the end of our two main results’ tables (2 and 3), along with their corresponding probability values. Tables 2 and 3 about here In table 2, using the 2005 data set, the Wooldridge score test for overidentifying restrictions indicates that we cannot reject the null hypothesis of no correlation between the instruments and the 12

For instance, according to Statistics Canada, average hourly wages for union employees in March 2015 was $28.72, as opposed to $23.37 for non-union members; individuals involved in management activities earned, on average, $39.53 an hour, as opposed to sales and retail at $16.51 per hour: see: http://statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/labr69aeng.htm. 13 We also estimated the model without education as an instrument to respond to a comment made by one reviewer. The estimated magitudes of the effects of wage on sleeping time are not affected very much, but the standard errors are larger, since we are using one fewer instrument. Overall, our preferred estimates are those that keep education among the instruments. 14 There are several overidentification tests that depend on the estimation method. This test is applicable when two-stage least squares is used with robust standard errors.

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error term, except for the three insomniac specifications reported in columns (7) to (9). Hence, our instruments are appropriately independent of the error process in our main regressions, except for the insomniac sample. However, when we use the 2010 data set, the situation is quite different. As reported in table 3, the Wooldridge score test cannot reject the null of no correlation in all but one of the specifications, suggesting that our instruments for this data set may be invalid. However, Angrist and Pischke (2009) point out that different instruments identify different compliers, which may help explain why our overidentification tests differ across these two sample years. Parente and Santos Silva (2012) warn that the relationship between the validity of overidentification restrictions and the instruments’ ability to identify a parameter of interest, here the estimated effect of wages on sleep time, is not straightforward. They demonstrate that tests for overidentfying restrictions are tests of the coherency of the instruments (assessing whether all instruments are identifying the same vector of parameters) and not the validity of the instruments themselves. Here our instruments: educational attainment, industry dummies and union status, might in fact have different relationships with wages in 2010 as compared to 2005 given the different points they represent in the business cycle and given that we include only employed individuals from each year in our models; consequently each instrument may be identifying different underlying parameters. We use caution when comparing estimated effects across the two years. Tables 2 and 3 present nine sets of regression results using the 2005 and 2010 surveys. Column (1) reports the basic model for our “full” samples (n=6,455 and 4,668); the next two columns present the basic model by males (n=3,047 and 2,147) and females (n=3,408, and 2,521), followed by the model estimated for non-insomniacs (n=4,443 and 3,160) and then by non-insomniacs and sex (males = 2,243 and 1,527; females = 2,200 and 1,633) and insomniacs (n=2,012 and 1,508) and insomniacs and sex (males = 804 and 620; females = 1,208 and 888). From the appendix tables A1 and A2 providing

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the corresponding first-stage regression results,

we see clearly that our instruments (education,

membership in a union, and industry classification) are correlated with the natural logarithm of the wage rate in an expected manner. For instance, relative to someone with a high-school education, those with less than high-school earn lower wages, and those with more schooling earn higher wages; membership in a union is associated with higher wages; and some industry classifications matter. The first important finding from tables 2 and 3 is that in the full sample (column (1) of both tables), wages matter: a ten percent increase in the wage rate leads to a 11 minute reduction in weekly sleep in 2005, and a 12 minute reduction in 2010, ceteris paribus. This result is in line with BH who report that a ten percent increase in hourly wages decreases sleep duration, on average, by 14 minutes a week. In 2005, men and women react similarly to a ten percent wage increase by decreasing sleep time by 12 and 13 minutes. But in 2010, it is only males who adjust sleep time in response to wage rate changes – with a ten percent increase in wages leading to a reduction of 18 minutes in male sleep time per week.15 The presence of sleep problems matter in general when it comes to adjusting sleep time to wages, but they matter differently across the two surveys. In 2005, we find that it is non-insomniacs who adjust their sleep time to wages, but insomniacs do not. This result suggested that insomniacs were inflexible in their time responses, perhaps because their sleep problems led to difficulty controlling the amount of time that they actually slept.16 However, in 2010, the situation is quite different. It is those with sleep problems who adjust their sleep time to wages, whereas those without such problems do not.

15

In all cases, we interprete a statistically insignificant estimated coefficient as having no impact on the dependent variable. However, there may well be an impact from the given regressor, but that our sample size is simply not large enough to capture it precisely. This point is particularly useful to bear in mind when we parse the sample both by sex and the presence of sleep disorderts. We thank a reviewer for making this important point. 16 Our 2005 findings are also consistent with the possibility that insomniacs are already starting from a lower bound on sleep, and hence cannot adjust sleep time. The 2010 findings, as discussed below, are not consistent with this possibility. We thank a reviewer for raising this explanation.

18

The fact that we are sampling workers (and not, for instance, the unemployed) may help to explain what is going on. The stress associated with working and maintaining employment in periods of economic downturn, may well lead both to sleep problems and to working as much as possible to ensure job security in precarious economic times. Differences across the sexes are apparent in both sample years. For instance, in 2005 when the sample is parsed by the presence of sleep problems; male non-insomniacs respond to a ten percent increase in the wage rate by reducing sleep time by 11 minutes, whereas women reduce theirs by 16 minutes. In 2010, male insomniacs respond to wage adjustments by the largest amount of all of the sub-groups, reducing their sleep time by 56 minutes a week in response to a ten percent hike in wage rates. However, at n=620, the sample size of our male insomniac group is quite small, although it far exceeds the 400 male observations used by BH. Moreover, the instruments for the wage rate for this group were weak. Female insomniacs, by comparison, do not adjust their sleep time in response to wages. To understand better how the presence of sleep disorders affects labour market responses, we employ a Heckman model to take account of the potential non-randomness of insomnia in the population. Table 4 presents the first stage results for the 2005 and 2010 data sets for the sample as a whole and for males and females separately.17 We begin by examining the four variables (instruments) that help to identify the likelihood of experiencing insomnia: time working, education, shift work and stress, inspired by the literature on this topic (e.g., Tjepkema, 2005 and Lallukka, et al., 2012). It is often difficult to justify exclusion restrictions. We could not include time worked in the sleeping 17

In the Heckman first stage table 4, the sample size is much larger than reported in the IV regressions of tables 2 and 3 because the sample restrictions are different: in the first stage of the Heckman, wages are exogenous so the fact that observations are missing for some instruments (especially union status) does not reduce sample size at this stage. We again employed a two-tailed t-test for the difference in means for the Heckman samples (n=7,800 and 5,601 for the two survey years) and compared them to the means for the “full” samples used for the IV procedures (n=6,455 and 4,668), and found most means to be statistically different from each other, but not at all meaningfully different. We do not report this table for space reasons.

19

equations because of collinearity (and endogeneity) concerns, but it is associated with sleep problems. More educated people, arguably, are more knowledgeable about the factors that trigger sleep problems, and hence can take measures to avoid them. Shift work and stress clearly impact aspects of sleep but whether it is insomnia, sleep time, or both, is not clear. The literature on the determinants of insomnia cites these factors as being important. We also included these variables in some specifications of sleep time in the IV models (reported in tables 2 and 3) and for the most part, they were statistically insignificant determinants of sleep duration. We find them to matter when it comes to reporting sleep problems. We see clearly that the more time spent working, the less likely that the individual has insomnia. Having a university degree has a negative impact on reporting sleep problems. Working night shifts and rotating shifts both increase the likelihood of sleep problems, whereas it is only in the 2005 survey that we find that being on call or having an irregular schedule increases this likelihood. Finally, across the board, the presence of stress has a big impact on the probability of reporting difficulties with sleep. Table 4 about here When we look at the extent to which wages affect the selection into insomnia, we see that, once again, males and females are different. In both samples, the selection of women into the insomnia group is affected by the wage rate: the higher the wage rate, the less likely that the female worker has insomnia, ceteris paribus; the impact of the wage rate on men is insignificant. Males are also less likely than females to report sleep disorders. As expected, good health reduces the likelihood of sleep problems when compared to poor health. While responding to the time-use surveys on a weekend is always associated with a longer sleep duration, it is not at all associated with the presence of sleep disorders – which is reasonable.

20

The second stage regression results, where the dependent variable is minutes of sleep a week by non-insomniacs, are reported in table 5; robust F-statistics are presented at the bottom of the table, and for all but the male sample in 2010 (F=6) the standard criterion of at least 10 is met. The Inverse Mills Ratio (IMR) is statistically significant for three of the six regressions: the female group in 2005 (column (3)), and for the full sample and the male group in 2010 (columns (4) and (5)). This means that we are detecting a selection bias in these groups. Men and women are thus different in terms of their non-random selection into having sleep problems, both within each survey year and across the survey years as well. In 2005, a ten percent increase in the wage rate leads to a 20 minute decrease in sleep time per week for women non-insomniacs once this bias is taken into account – larger than the 16 minute decrease revealed in table 2 (column (6)). In 2010, once account is taken of the selection bias, male non-insomniacs do not adjust their sleep time to wage changes, which is consistent with the IV results presented in table 3. Table 5 about here Of the other influences on minutes of sleeping per week, the estimated coefficients from the second stage Heckman procedure (table 5) point again to the importance of the sex and age of the respondent. Being female and aging is associated with less sleep except in the case of insomniacs. Males usually sleep less relative to their female counterparts; and people sleep more on the weekends. Interestingly, women systematically catch up on sleep on the weekends more than males, ceteris paribus; people, in general, report sleeping some 300 to 400 more minutes a week if they are surveyed on the weekend. Overall, the Heckman procedure was useful insofar as it identified a couple of cases where there was a non-random selection into those with and without sleep problems. But when we looked carefully at the impact of this non-randomness on the adjustment of sleep time in response to wage rate changes,

21

it did not make much difference. It is possible that we were simply unable to identify adequately this selection process: the justification of the exclusion restrictions was a bit weak. We are not relying on these results, and think that this is an area where future work would be useful. The IV results presented in tables 2 and 3 mostly hold, with the exception of the sleep duration of non-insomniac women in 2005. Finally, although we included a rich array of variables in our model – a much longer list than is often found in the literature, we may still have problems associated with missing variables. More and better data on several factors would solve this problem; a point to which we return below.

5. Discussion and Conclusions Motivated by the seminal work of Biddle and Hamermesh (1990), we investigate the impact of wages on sleep duration, taking careful account of the endogeneity of wages in the model. Using a sample of workers aged 23-65 from the 2005 and 2010 Canadian General Social Surveys, we confirm that, on average, individuals react to increases in wage rates by sleeping less: a ten percent increase in wages led, in 2005, to about a 11-12 minute decrease in sleeping per week, and in 2010 to a reduction of 18 minutes by males (no response by females). Our findings are comparable to the small number of other papers that have looked at sleep duration and wages: BH find that a 10 percent increase in hourly wages results in about a 14 minute a week adjustment; Szalontai (2006) finds a 12 minute a week adjustment. One might fruitfully ask about the economic importance of such a finding. There are several ways to respond to this question. The fact that sleep time responds to economic incentives (here, wages) implies that the classic labour-leisure choice is not simply a question of how to allocate an exogenously given number of daily hours (typically, 24 minus eight hours for sleeping), but, within the bounds of biological necessity, individuals can adjust at the margin their sleep. The labour supply,

22

therefore, may be more responsive than otherwise thought. Moreover, these responses can differ significantly across groups. For instance, in 2010, we found that men responded to wage rates much more than women – men decreasing sleep by 18 minutes a week in response to a 10 percent increase in their wage rate, while women did not respond in a statistically significant manner. Insomniac men in 2010 reacted much more than others in response to wage incentives, while insomniac women did not respond. These differences may contribute, for instance, to our understanding of earnings gaps. It is also the case that while the marginal impact on sleep time of an increase in wages for the average person is small, the impact on those at different parts of the earnings distribution may be much larger. As noted by Szalontai (2006), individuals with higher than average wage rates will experience a larger impact on sleep time than others. Finally, our results can be used to understand better short-run, inframarginal (individual) labour supply responses versus longer-run, extramarginal (group) labour supply responses. For instance, in growth sectors where demand is outstripping supply, wage incentives will push up time spent at work at the expense of sleep. The oil patch in its hey-day is replete with examples of 12 hour days and spectacularly high returns. Indeed, the Western Canada oil patch saw a five-fold increase in earnings when compared to other sectors, at a 21 percent growth in average earnings, over the 2001-2008 period.18 It is not only leisure time that is being sacrificed by longer work hours, it is also sleep time. Overall, the link between wages and sleep duration is more complicated than was, perhaps, first thought. It is sensitive to three factors: the sex of the individual; the presence of sleep disorders; and general economic conditions. We have uncovered some stark differences between male workers and female workers, and insomniacs and non-insomniacs when it comes to responding to wage changes. By far the largest impact of wage adjustments on sleep time is by insomniac men when the economy is not

18

http://www.cbc.ca/news/business/oil-patch-salaries-rise-5-times-as-fast-as-rest-of-canada-1.2494418

23

doing well, as was the case in 2010. Although it is hard to generalize on the basis of only two time periods, our results corroborate those found elsewhere – namely, that where the economy is in the business cycle matters when it comes to understanding the sleep duration of individuals of working age. In 2005, the Canadian economy was in the midst of a period of prolonged growth whereas in 2010, like in most other Western countries, it was still reeling from the worst recession in recent history. Average unemployment rates had risen from 6.8 to 8.1 over these five years. In periods of economic downturn, there are two competing influences on sleep: individuals sleep more because the opportunity cost of doing so is lower as a result of unemployment; but, economic uncertainty is one of the leading causes of individual stress, leading to sleep disorders. Our results suggest that it was male workers with sleep problems who adjusted their sleeping time the most in response to wage rate changes in 2010. Indeed, the sleep response of insomniacs in the 2010 sample to changes in the wage rate is much larger than was found across all samples in 2005. Because we are focusing on individuals in the labour market, we are potentially picking up the combined effect of pressure to “prove oneself” in times of layoffs (economic downturns) and stress leading to the reporting of sleep disorders. In periods of economic growth, we do not observe the sleep-problem group adjusting sleep time in response to wage rate changes. As we conclude, it is worth remembering that, by necessity, biological needs will prevail in the long run when it comes to sleep time. No matter how much someone earns per hour, a minimum amount of sleep is required –and while one may be able to skimp on this amount in the short-run, our physical need for an ‘optimal’ amount of sleep will ultimately dominate. Our results, like those of

24

others, suggest small, marginal adjustments of sleep occur in response to wage rate changes, but not enough to be unsustainable over time.19 Empirical work can always be improved with more and better data. More detailed information on sleep disorders that did not rely solely on self-reporting, would be useful. Better information on other potential influences on sleep, like physical activity, would also improve the analyses. 20 We were limited in our ability to run more granular analyses, for instance, pulling out light (and heavy) sleepers and seeing how they reacted to wage rate changes, by the lack of observations; more time-use surveys would attenuate this problem. Longitudinal data would be ideal for examining carefully the impact of the business cycle on work patterns, and how individuals adjust sleeping in response to economic incentives. Understanding better this link between wages, sleep time, and economic conditions would not only help inform public policies, but it could identify areas where such policies might usefully be placed in order to avoid the economic and social costs associated with sleep problems and the workplac

Acknowledgements. We thank Pierre Brochu, Jennifer Stewart and Tammy Schirle for comments on earlier versions of this paper; Wenzhuo Zhao created our tables. We also benefited from the astute comments of several referees and the editor of this journal, for which we are most grateful. This research was supported by funds to the Canadian Research Data Centre Network (CRDCN) from the Social Science and Humanities research Council (SSHRC), the Canadian Institute for Health Research (CIHR), the Canadian Foundation for Innovation (CFI) and Statistics Canada. Although the research and analysis are based on data from Statistics Canada, the opinions expressed do not represent the views of Statistics Canada or the Canadian Research Data Centre Network (CRDCN). R. A. Devlin acknowledges the financial support of SSHRC grant number 435-2012-0489.

19

We thank a reviewer for pointing out the fact that these sleep responses are not likely to cause individuals to have less than ‘optimal’ sleep. 20 We had some information on physical activity for the 2010 survey, but none for the 2005 survey. In 2010, we knew how many days a week an individual exercised for at least 30 minutes. Only when they exercised all seven days, did it have an impact. Having better data on this element would definitely be useful. Again, we thank a reviewer for this point.

25

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Heckman, James J. 1979. “Sample selection bias as a specification error.” Econometrica 47(1): 153– 161. Hurst, Matt. 2008. “Who gets any sleep these days- Sleep patterns of Canadians.” Component of Statistics Canada Catalogue No 11-008-X, Canadian Social Trends, http://www.statcan.ca/english/free pub/11-008-XIE/2008001/article/10553-en.pdf. Lallukka, Tea, Laura Sares-Jäske, Erkki Kronholm, Katri Sääksjärvi, Annamari Lundqvist, Timo Partonen,Ossi Rahkonen and Paul Knekt. 2012. “Sociodemographic and socioeconomic differences in sleep duration and insomnia-related symptoms in Finnish adults.” BMC Public Health, 12: 565. http://www.biomedcentral.com/1471-2458/12/565l Lauderdale, Diane S., Kristen L. Knutson, Lijing L. Yan, Kiang Liu, and Paul J. Rathouz. 2008. “Self-reported and measured sleep duration- how similar are they?” Epidemiology, 19(6): 838-845. Li, Bei and Jie Zhang. 2015. “Efficient education subsidization and the pay-as-you-use principle.” Journal of Public Economics, 129: 41–50. Morin, Charles M., Richard R. Bootzin, Daniel J. Buysse, Jack D. Edinger, Colin A. Espie, and Kenneth L. Lichstein. 2006. “Psychological and behavioral treatment of insomnia: update of the recent evidence (1998-2004).” Sleep, 29(11): 1398-1414. Nena Evangelia, Paschalis Steiropoulos, Nikolaos Papanas, D. Kougkas, P. Zarogoulidis, Tc Constantinidis 2014. “Greek financial crisis: From loss of money to loss of sleep?” Hippokratia, 18(2): 135-138. Ohayon Maurice M., and Jürgen Zulley. 2001. “Correlates of global sleep dissatisfaction in the German population.” Sleep, 24(7): 780-787. Paine, Sarah-Jane, Philippa H. Gander, Ricci Harris, and Papaarangi Reid. 2004. “Who reports insomnia? relationships with age, sex, ethnicity, and socioeconomic deprivation.” Sleep, 27(6): 11631169. Parente, Paulo MDC, and JMC Santos Silva. 2012. "A cautionary note on tests of overidentifying restrictions." Economics Letters 115(2): 314-317. Pestieau, Pierre, and Maria Racionero. 2015. “Tagging with leisure needs.” Social Choice and Welfare, 45: 687–706. Phipps, Shelley, Peter Burton, and Lynn Lethbridge. 2001. “In and out of the labour market: longterm income consequences of child-related interruptions to women's paid work.” Canadian Journal of Economics, 34(2): 411- 429.

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Robinson, John P. and William Michelson. 2010. “Sleep as a victim of the “time crunch” – a multinational analysis.” Electronic International Journal of Time Use Research, 7(1): 61-72. Statistics Canada. 2005. “General Social Survey, Cycle 19: Time use, user’s guide to the public use micro data file.” Statistics Canada, Ottawa. Stewart, Robert, Alain Besset, Paul Bebbington, Traolach Brugha, James Lindesay, Rachel Jenkins, Nicola Singleton, and Howard Meltzer. 2006. “Insomnia comorbidity and impact and hypnotic use by age group in a national survey population aged 16 to 74 years.” Sleep, 29(11): 13911397. Stinson, Kathleen, Nicole K. Y. Tang, and Allison G. Harvey. 2006. “Barriers to treatment seeking in primary insomnia in the United Kingdom: a cross-sectional perspective.” Sleep, 29(12): 1643-1646. Sutton, Deborah A., Harvey Moldofsky and Elizabeth M. Badley. 2001. “Insomnia and health problems in Canadians.” Sleep, 24(6): 665-670. Szalontai, Gabor. 2006. “The demand for sleep: a South African study.” Economic Modeling, 23(5): 854-874. Tjepkema, Michael. 2005. “Insomnia.” Health Reports, 17(1): 9-26. van den Berg, Gerard J. 2008. “An Economic Analysis of Exclusion Restrictions for Instrumental Variable Estimation” IZA DP No. 2585, Jan 2008. http://ftp.iza.org/dp2585.pdf. Vincent, Norah, and Carrie Lionberg. 2001. “Treatment preference and patient satisfaction in chronic insomnia.” Sleep, 24(4): 411-417. Virtanen, Marianna, Jane E. Ferrie, David Gimeno, Jussi Vahtera, Marko Elovainio, Archana Singh-Manoux, Michael G. Marmot, and Mika Kivimäki. 2009. “Long working hours and sleep disturbances: the whitehall II prospective cohort study.” Sleep, 32(6): 737-745. Walsh, JK and CL Engelhardt. 1999. “The direct economic costs of insomnia in the United States for 1995.” Sleep, 22(Supplement 2): S386-S393. Wooldridge, J. M. 1995. “Score diagnostics for linear models estimated by two stage least squares.” In Advances in Econometrics and Quantitative Economics: Essays in Honor of Professor C. R. Rao, ed. G. S. Maddala, P. C. B.Phillips, and T. N. Srinivasan, 66–87. Oxford: Blackwell. Xiang, Yu-Tao, Xin Ma, Zhuo-Ji Cai, Shu-Ran Li, Ying-Qiang Xiang, Hong-Li Guo, Ye-Zhi Hou, Zhen-Bo Li, Zhan-Jiang Li, Yu-Fen Tao, Wei-Min Dang, Xiao-Mei Wu, Jing Deng, Kelly Y. C. Lai, and Gabor S. Ungvari. 2008. “The prevalence of insomnia, its sociodemographic and clinical correlates, and treatment in rural and urban regions of Beijing, China: A general population-based survey.” Sleep, 31(12): 1655-1662.

28

variables sleep naps per week married/ common age male little schooling diploma/ certificate university degree union good health kids<5 Roman Catholic Protestant other religion CMA PEI NS NB QC ON MB SK AB BC Feb. Mar. Apr. May June July Aug. Sept.

Table 1a: Weighted Sample Means (2005) Modified BH Model non insomniacs All Men Women All Men Women (1) (2) (3) (4) (5) (6)

All (7)

insomniacs Men Women (8) (9)

3380

3344*

3423*

3394 *

3341＊

3466＊

3347*

3352*

3342*

0.713

0.750*

0.670*

0.735*

0.770*

0.688*

0.660*

0.690*

0.636*

41 0.538

41*

41*

41* 0.574*

41

40

41* 0.451*

40*

41*

0.082

0.096*

0.064*

0.080*

0.096*

0.058*

0.086*

0.097*

0.076*

0.454

0.445*

0.463*

0.438*

0.431＊

0.448＊

0.490*

0.490

0.491

0.313

0.298*

0.330*

0.332*

0.319*

0.350*

0.267*

0.235*

0.293*

0.341

0.328*

0.355*

0.340*

0.324*

0.361*

0.342*

0.340*

0.344*

0.898

0.905*

0.889*

0.928*

0.929

0.926

0.826*

0.833*

0.821*

0.133

0.156*

0.108*

0.149*

0.167*

0.124*

0.097*

0.121*

0.077*

0.413

0.403*

0.424*

0.424*

0.417＊

0.433＊

0.386*

0.359*

0.407*

0.311

0.299*

0.326*

0.307*

0.304

0.310

0.323*

0.283*

0.355*

0.061

0.064*

0.057*

0.062*

0.064*

0.060*

0.057*

0.063*

0.051*

0.820 0.004 0.031 0.023 0.244 0.394 0.039 0.027 0.103 0.120 0.092 0.078 0.084 0.086 0.112 0.112 0.106 0.091

0.815* 0.004* 0.031* 0.021* 0.261* 0.385* 0.037* 0.024* 0.101* 0.121* 0.094* 0.074* 0.086* 0.081* 0.115* 0.117* 0.103* 0.094*

0.827* 0.005* 0.032* 0.024* 0.225* 0.404* 0.041* 0.031* 0.105* 0.119* 0.090* 0.083* 0.082* 0.091* 0.107* 0.106* 0.111* 0.087*

0.821† 0.004* 0.030* 0.021* 0.257* 0.387* 0.036* 0.027* 0.104* 0.119* 0.093* 0.074* 0.083* 0.087* 0.111* 0.112* 0.108* 0.089*

0.817 0.004 0.030 0.019* 0.267* 0.387 0.035† 0.024* 0.101* 0.119 0.092 0.071* 0.086* 0.082* 0.113＊ 0.119* 0.109 0.092*

0.826 0.004 0.031 0.025* 0.243* 0.387 0.037† 0.031* 0.109* 0.118 0.093 0.078* 0.080* 0.094* 0.109＊ 0.103* 0.108 0.086*

0.820† 0.005* 0.034* 0.025* 0.214* 0.410* 0.045* 0.028* 0.100* 0.124* 0.090* 0.088* 0.088* 0.082* 0.112* 0.110* 0.102* 0.095*

0.808* 0.004* 0.033* 0.029* 0.241* 0.379* 0.043* 0.025* 0.103* 0.128* 0.099* 0.083* 0.088* 0.079* 0.122* 0.109* 0.086* 0.101*

0.830* 0.005* 0.035* 0.023* 0.192* 0.435* 0.047* 0.031* 0.097* 0.122* 0.083* 0.091* 0.087* 0.085* 0.104* 0.111* 0.116* 0.090*

29

Oct. 0.092 0.092† 0.092† 0.092* 0.092 0.092 0.091* 0.090* 0.092* Nov. 0.062 0.064* 0.061* 0.061* 0.060† 0.063† 0.064* 0.075* 0.056* Dec. 0.025 0.026* 0.025* 0.027* 0.027 0.027 0.022* 0.021* 0.022* sleep 0.297 0.249* 0.352* problem weekends 0.282 0.285* 0.278* 0.288* 0.287 0.288 0.267* 0.276* 0.260* Obs. 6,455 3,047 3,408 4,443 2,243 2,200 2,012 804 1,208 Significance level: *1%, †5%, ＊10%, using t test (two-sided tails) of difference in means across groups. .

30

Table 1b: Weighted Sample Means (2010) Modified BH Model variables sleep naps per week married/ common age male little/ no schooling diploma/ certificate university degree union good health kids<5 Roman Catholic Protestant other religion CMA PEI NS NB QC ON MB SK AB BC Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

non insomniacs

insomniacs

All (1)

Men (2)

Women (3)

All (4)

Men (5)

Women (6)

All (7)

Men (8)

Women (9)

3359

3290*

3438*

3357*

3302*

3426*

3364*

3260*

3459*

0.760

0.790*

0.727*

0.761*

0.793*

0.722*

0.758*

0.781*

0.738*

42 0.530

42*

42*

42* 0.554*

42*

42*

43* 0.478*

41*

44*

0.058

0.070*

0.045*

0.048*

0.056*

0.039*

0.080*

0.104*

0.057*

0.481

0.482*

0.480*

0.473*

0.480*

0.465*

0.496*

0.485*

0.507*

0.344

0.334*

0.356*

0.367*

0.354*

0.384*

0.295*

0.284*

0.305*

0.355

0.333*

0.379*

0.347*

0.324*

0.375*

0.372*

0.354*

0.387*

0.889

0.898*

0.880*

0.923*

0.934*

0.910*

0.816*

0.807*

0.824*

0.176

0.200*

0.148*

0.182*

0.199*

0.160*

0.162*

0.203*

0.124*

0.385

0.376*

0.394*

0.395*

0.391*

0.401*

0.361*

0.339*

0.381*

0.281

0.255*

0.311*

0.277*

0.254*

0.307*

0.290*

0.257*

0.319*

0.081

0.094*

0.066*

0.086*

0.096*

0.074*

0.069*

0.088*

0.051*

0.834 0.005 0.033 0.026 0.197 0.429 0.040 0.032 0.103 0.121 0.052 0.111 0.054 0.120 0.063 0.109 0.054 0.108 0.058 0.106 0.056

0.827* 0.004* 0.034* 0.027* 0.197* 0.425* 0.041* 0.030* 0.106* 0.122* 0.064* 0.107* 0.047* 0.123* 0.068* 0.105* 0.062* 0.097* 0.058 0.114* 0.061*

0.843* 0.006* 0.031* 0.024* 0.198* 0.433* 0.039* 0.033* 0.099* 0.120* 0.040* 0.115* 0.062* 0.117* 0.056* 0.114* 0.046* 0.122* 0.058 0.098* 0.052*

0.835* 0.005* 0.032* 0.024* 0.207* 0.422* 0.041* 0.032* 0.098* 0.122* 0.057* 0.108* 0.051* 0.120* 0.068* 0.103* 0.057* 0.109* 0.060* 0.105* 0.057*

0.825* 0.005* 0.032 0.024* 0.202* 0.422 0.042* 0.033* 0.100* 0.126* 0.070* 0.100* 0.048* 0.118* 0.070* 0.094* 0.062* 0.102* 0.063* 0.113* 0.063*

0.847* 0.006* 0.032 0.024* 0.213* 0.422 0.038* 0.031* 0.096* 0.117* 0.040* 0.117* 0.054* 0.123* 0.067* 0.114* 0.050* 0.119* 0.057* 0.095* 0.050*

0.833* 0.004* 0.033* 0.029* 0.176* 0.444* 0.038* 0.031* 0.113* 0.117* 0.043* 0.118* 0.061* 0.121* 0.050* 0.122* 0.049* 0.106* 0.052* 0.109* 0.055*

0.829* 0.004* 0.039* 0.034* 0.182* 0.433* 0.036* 0.024* 0.122* 0.109* 0.046* 0.125* 0.046* 0.136* 0.065* 0.131* 0.061* 0.083* 0.044* 0.116* 0.056*

0.836* 0.005* 0.028* 0.024* 0.170* 0.454* 0.039* 0.037* 0.105* 0.124* 0.039* 0.110* 0.075* 0.107* 0.037* 0.114* 0.038* 0.127* 0.059* 0.102* 0.054*

29

sleep 0.314 0.283* 0.349* problem weekends 0.286 0.300* 0.270* 0.285* 0.295* 0.272* 0.287* 0.311* 0.265* Obs. 4,668 2,147 2,521 3,160 1,527 1,633 1,508 620 888 Significance level: *1%, †5%, ＊10%, using t test (two-sided tails) of difference in means across groups.

30

Table 2: IV Regression Results (2005 Sample): Various Groups dependent variable (sleep minutes per week) ln wage married / common age age square male good health kids <5 Roman Catholic Protestant other religion ln other income CMA weekends province months interview constant

Modified BH Model

non insomniacs

insomniac

All (1)

Men (2)

Women (3)

All (4)

Men (5)

Women (6)

All (7)

Men (8)

Women (9)

-109.90 (50.50) 54.402 (30.70) -22.470 (9.63) 0.227 (0.11) -58.466 (25.32) 11.675 (39.40) -72.504 (37.06) -18.851 (36.25) -32.899 (35.57) 0.290 (63.63) 8.639 (5.86) -18.072 (30.03) 399.4 (28.15) Yes

-115.31 (62.88) 62.424 (45.20) -16.656 (13.38) 0.149 (0.15)

-131.43 (77.37) 46.111 (45.97) -29.699 (13.82) 0.327 (0.16)

-106.47 (73.05) 30.580 (54.08) -9.520 (15.15) 0.066 (0.17)

-161.88 (72.50) 45.265 (51.65) -30.239 (15.36) 0.342 (0.18)

-5.662 (152.13) 51.690 (84.95) -29.154 (26.13) 0.288 (0.30)

---

---

---

---

---

-53.347 (53.31) -97.564 (49.66) -39.586 (48.60) -24.197 (45.62) -40.048 (92.17) 4.002 (7.46) -32.413 (42.39) 334.2 (39.37) Yes

75.785 (56.50) -39.438 (53.16) 1.921 (55.99) -40.916 (56.00) 43.853 (83.55) 14.213 (9.97) 4.882 (40.90) 473.7 (39.00) Yes

-47.347 (65.82) -136.786 (58.24) 12.443 (56.70) 17.097 (54.62) -9.411 (115.23) -4.179 (8.53) -44.090 (49.67) 331.1 (46.68) Yes

-33.981 (84.29) -24.187 (58.20) 52.866 (56.29) -3.576 (58.75) 74.128 (93.73) 9.615 (10.19) -16.808 (47.30) 503.8 (45.39) Yes

-58.571 (101.30) 74.207 (54.11) -27.058 (19.02) 0.270 (0.21) -3.915 (49.17) 39.107 (59.46) -30.074 (76.25) -122.866 (71.78) -111.851 (67.61) -79.811 (98.34) 21.716 (11.57) 20.633 (55.27) 388.4 (49.37) Yes

-60.999 (123.47) 126.954 (79.16) -36.440 (27.09) 0.372 (0.30)

---

-114.62 (54.70) 40.355 (37.49) -18.463 (10.92) 0.183 (0.13) -97.492 (29.03) -42.994 (53.11) -94.501 (42.62) 33.431 (40.79) 9.703 (40.71) 38.649 (78.35) 1.713 (6.71) -36.939 (35.55) 405.2 (33.66) Yes

-80.928 (90.10) 36.452 (95.07) -180.332 (92.48) -114.748 (79.97) -98.925 (129.41) 21.932 (14.90) -12.824 (82.50) 348.5 (72.77) Yes

122.765 (78.37) -115.323 (117.61) -74.689 (120.87) -114.906 (112.07) -14.317 (153.30) 20.118 (19.67) 38.033 (72.01) 401.7 (65.86) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4,068 (226.60)

3,988 (293.18)

4,164 (344.68)

4,130 (237.65)

3,875 (336.86)

4,405 (318.20)

3,863 (466.94)

4,170 (571.66)

3,705 (723.57)

29

20

14

22

11

12

4,443

2,243

2,200

2,012

804

1,208

First Stage Robust F 51 31 23 Statistic Obs. 6,455 3,047 3,408 Robust standard errors in parentheses.

31

Table 3: IV Regression Results (2010 Sample): Various Groups dependent variable (sleep minutes per week) ln wage married/ common age age square male good health kids <5 Roman Catholic Protestant other religion ln other income CMA weekends province months interview constant

Modified BH Model

non insomniacs

insomniacs

All (1)

Men (2)

Women (3)

All (4)

Men (5)

Women (6)

All (7)

Men (8)

Women (9)

-120.17 (61.65) 48.306 (42.93) -31.548 (12.00) 0.322 (0.13) -130.33 (31.56) -25.135 (43.72) -87.470 (43.43) -17.043 (40.00) 1.297 (40.83) 18.702 (62.02) 1.853 (8.41) 1.325 (35.76) 396.2 (34.99) Yes

-178.90 (85.86) 30.355 (62.89) -18.332 (17.15) 0.185 (0.19)

-34.244 (86.29) 54.333 (56.07) -43.845 (16.44) 0.455 (0.18)

-36.385 (93.97) 40.873 (71.78) -41.347 (18.03) 0.410 (0.20)

-6.731 (92.60) 58.191 (68.88) -45.872 (19.36) 0.461 (0.22)

-99.129 (141.75) 31.770 (92.45) -37.502 (30.10) 0.409 (0.33)

---

---

---

---

---

-25.094 (66.87) -150.525 (62.10) 14.387 (55.58) 75.614 (62.38) 76.941 (85.96) -1.770 (11.99) 26.873 (53.73) 393.5 (48.78) Yes

-38.580 (57.60) 8.613 (60.47) -55.305 (57.54) -84.296 (52.32) -47.974 (87.61) 5.990 (10.57) -40.293 (44.96) 397.6 (49.85) Yes

-73.578 (82.62) -192.978 (70.64) 35.496 (68.54) 84.474 (65.09) 187.623 (101.20) -5.440 (13.95) -15.755 (53.64) 371.7 (51.81) Yes

-2.438 (74.68) -44.063 (73.94) 1.733 (72.72) 32.586 (66.12) -1.340 (110.10) 1.512 (12.83) -11.715 (51.29) 332.3 (58.13) Yes

-306.31 (115.32) 36.409 (84.00) 3.104 (25.97) -0.032 (0.28) -157.17 (61.27) -33.970 (68.82) 0.121 (85.75) -97.069 (68.12) -117.479 (81.52) -157.968 (104.20) 9.091 (16.31) 6.259 (76.73) 481.8 (71.34) Yes

-560.07 (151.86) 4.606 (136.77) 42.400 (44.48) -0.425 (0.48)

---

-31.622 (67.66) 45.441 (50.10) -44.754 (13.26) 0.450 (0.15) -116.24 (35.64) -30.847 (54.91) -122.665 (49.84) 22.678 (49.65) 61.900 (46.05) 101.184 (74.83) -0.473 (9.88) -11.254 (37.80) 356.4 (38.86) Yes

-33.433 (103.95) -109.127 (124.37) -37.268 (102.45) 60.899 (146.33) -198.002 (154.73) 11.645 (24.38) 110.479 (133.91) 435.5 (111.36) Yes

-54.254 (88.35) 166.973 (104.48) -144.649 (90.59) -281.505 (81.72) -128.995 (126.81) 21.544 (18.44) -95.303 (81.60) 522.0 (89.15) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4,381 (271.4)

4,207 (417.9)

4,378 (351.9)

4,398 (299.2)

4,435 (437.9)

4,210 (425.5)

4,231 (555.6)

3,819 (959.6)

4,665 (633.8)

15

10

13

14

6

12

3,160

1,527

1,633

1,508

620

888

First Stage 25 12 19 Robust F Statistics Obs. 4,668 2,147 2,521 Robust standard errors in parentheses.

32

Table 4: Heckman Regression Results (First Stage, 2005 and 2010 Samples) dependent 2005 2010 variable (sleep Full Full Men Women Men Women minutes per sample sample week) -0.014 0.003 -0.035 -0.026 -0.016 -0.034 ln wage (0.010) (0.013) (0.013) (0.012) (0.019) (0.017) married/ -0.040 -0.039 -0.037 -0.003 -0.018 0.034 common (0.016) (0.020) (0.025) (0.023) (0.032) (0.031) 0.001 0.002 -0.002 0.002 0.003 -0.002 age (0.005) (0.006) (0.007) (0.006) (0.009) (0.009) age -7.94e-06 -2.81e-05 4.69e-05 -2.11e-05 -5.72e-05 5.71e-05 square (5.56e-05) (7.16e-05) (8.66e-05) (7.06e-05) (9.85e-05) (1.01e-04) -0.077 -0.045 male --------(0.014) (0.017) good -0.169 -0.168 -0.178 -0.173 -0.221 -0.114 health (0.023) (0.031) (0.032) (0.028) (0.040) (0.036) -0.055 -0.028 -0.095 -0.035 -0.030 -0.044 kids <5 (0.019) (0.024) (0.028) (0.023) (0.030) (0.036) Roman -0.046 -0.070 -0.003 -0.043 -0.050 -0.024 Catholic (0.018) (0.021) (0.029) (0.021) (0.029) (0.031) -0.034 -0.069 0.020 -0.037 -0.044 -0.025 Protestant (0.018) (0.021) (0.028) (0.021) (0.029) (0.030) other -0.075 -0.086 -0.047 -0.064 -0.042 -0.102 religion (0.027) (0.030) (0.046) (0.034) (0.045) (0.047) ln other -0.001 -0.003 0.002 0.004 0.004 -0.000 income (0.003) (0.003) (0.005) (0.004) (0.005) (0.006) -0.002 0.011 -0.018 -0.006 -0.003 -0.010 weekends (0.014) (0.018) (0.021) (0.018) (0.024) (0.025) province Yes Yes Yes Yes Yes Yes months Yes Yes Yes Yes Yes Yes interview work time -2.55e-05 -1.98e-05 -3.85e-05 -4.19e-06 4.73e-06 -1.47e-05 per week (8.77e-06) (1.07e-05) (1.37e-05) (1.08e-05) (1.42e-05) (1.67e-05) little/ 0.005 -0.014 0.060 0.076 0.071 0.060 no schooling (0.027) (0.031) (0.050) (0.042) (0.052) (0.067) diploma / -0.002 -0.000 0.007 -0.026 -0.040 -0.013 certificate (0.019) (0.023) (0.030) (0.024) (0.033) (0.035) university -0.073 -0.093 -0.032 -0.065 -0.062 -0.065 degree (0.020) (0.024) (0.032) (0.026) (0.034) (0.038) regular 0.032 0.060 0.002 0.089 0.111 0.061 evening shift (0.039) (0.055) (0.053) (0.050) (0.065) (0.075) regular 0.155 0.141 0.174 0.173 0.320 -0.060 night shift (0.050) (0.062) (0.082) (0.064) (0.078) (0.077) rotating 0.122 0.150 0.074 0.126 0.134 0.115 33

shift (0.024) (0.033) a split -0.035 -0.014 shift (0.057) (0.075) a compressed 0.004 0.013 work week (0.083) (0.099) on call 0.153 0.206 or casual (0.060) (0.082) an irregular 0.117 0.146 schedule (0.027) (0.034) other type 0.067 0.074 of shift (0.069) (0.079) not very 0.072 0.038 stressful (0.035) (0.039) a bit 0.202 0.159 stressful (0.031) (0.036) a quite a bit 0.387 0.343 stressful (0.035) (0.045) extremely 0.505 0.442 stressful (0.040) (0.065) Obs. 7,800 3,891 Robust standard errors in parentheses.

(0.035) -0.063 (0.084) 0.021 (0.129) 0.088 (0.084) 0.074 (0.041) 0.114 (0.118) 0.140 (0.059) 0.280 (0.051) 0.466 (0.051) 0.563 (0.041) 3,909

34

(0.033) -0.027 (0.073) -0.060 (0.083) 0.093 (0.077) 0.042 (0.029) 0.011 (0.141) 0.053 (0.043) 0.159 (0.038) 0.317 (0.043) 0.344 (0.064) 5,601

(0.045) -0.005 (0.106) -0.004 (0.134) 0.076 (0.099) 0.065 (0.040) -0.163 (0.111) 0.025 (0.050) 0.133 (0.045) 0.261 (0.055) 0.270 (0.097) 2,701

(0.043) -0.097 (0.085) -0.113 (0.091) 0.099 (0.123) 0.016 (0.041) 0.463 (0.186) 0.145 (0.069) 0.233 (0.059) 0.425 (0.060) 0.437 (0.072) 2,900

Table 5: Heckman Regression Results (Second Stage, 2005 and 2010 Samples): Determinants of Sleep by Non-Insomniacs (IV Procedure) dependent 2005 2010 variable (sleep minutes Full sample Men Women Full sample Men Women per week) -131.849 -106.384 -197.310 -93.430 -92.751 -31.077 lnwage (55.677) (73.756) (73.899) (72.673) (97.594) (98.564) married / 35.177 31.258 27.474 41.731 32.463 63.492 common (37.458) (54.087) (51.627) (50.035) (72.364) (68.498) -16.015 -8.860 -26.336 -38.242 -33.225 -44.313 age (11.031) (15.356) (15.234) (13.428) (18.385) (19.242) age 0.156 0.057 0.310 0.383 0.316 0.451 square (0.128) (0.177) (0.179) (0.149) (0.204) (0.215) -113.544 -132.440 male --------(30.753) (36.519) good -81.169 -47.368 -156.409 -124.218 -196.768 -34.735 health (58.363) (74.897) (84.921) (62.750) (101.487) (82.682) -98.785 -137.031 -50.413 -129.085 -194.407 -55.891 kids <5 (42.926) (58.792) (58.325) (49.672) (70.596) (72.187) Roman 26.383 11.811 63.278 3.799 10.302 0.294 Catholic (41.212) (58.961) (56.077) (49.396) (67.692) (73.283) 5.291 17.806 11.359 48.221 66.174 33.078 Protestant (40.902) (56.107) (58.905) (46.462) (65.372) (66.913) other 21.922 -13.696 62.647 45.500 140.318 -35.713 religion (79.028) (117.291) (93.216) (76.723) (102.369) (113.086) ln other 1.426 -4.197 9.880 0.615 -3.953 -0.049 income (6.715) (8.593) (10.124) (9.866) (14.055) (12.857) -38.965 -42.317 -26.144 -11.735 -20.214 -9.851 CMA (35.527) (49.768) (47.190) (37.486) (53.301) (51.361) 406.760 331.982 497.267 350.218 364.475 327.680 weekends (33.655) (46.597) (45.405) (38.695) (51.374) (58.280) province Yes Yes Yes Yes Yes Yes months Yes Yes Yes Yes Yes Yes interview 4,241 3,862 4,683 4,711 4,775 4,354 constant (247.2) (351.9) (325.3) (318.7) (459.9) (461.5) 131.783 -0.903 363.132 340.348 371.256 169.496 IMR (80.961) (122.853) (97.476) (121.496) (167.699) (167.869) First Stage Robust F 30 20 13 14 8 11 Statistic Obs. 4,435 2,237 2,198 3,149 1,521 1,628

obust standard errors in parentheses.

35

APPENDIX Table A1: First Stage Regression Results Corresponding to Table 2 of Text dependent variable (ln wage) married / common law age age squared male good health kids <5 Roman Catholic Protestant other religion ln other income CMA weekend province months interview

Modified BH Model All Men Women (1) (2) (3)

All (4)

non insomniacs Men Women (5) (6)

All (7)

insomniacs Men (8)

Women (9)

0.1170 (0.0210)

0.1437 (0.0265)

0.0922 (0.0331)

0.1410 (0.0270)

0.1308 (0.0327)

0.1572 (0.0465)

0.0693 (0.0320)

0.1757 (0.0463)

-0.0201 (0.0424)

0.0439 (0.0067) -0.0004 (0.0001) 0.2057 (0.0181) 0.1111 (0.0266) 0.0525 (0.0249) -0.0044 (0.0234) 0.0065 (0.0224) -0.878 (0.0474) -0.0129 (0.0038) -0.0714 (0.0236) -0.0054 (0.0171) Yes

0.0424 (0.0082) -0.0004 (0.0001)

0.0464 (0.0106 -0.0004 (0.0001)

0.0404 (0.0095) -0.0003 (0.0001)

0.0443 (0.0142) -0.0003 (0.0001)

0.0464 (0.0139) -0.0004 (0.0001)

---

---

---

---

---

0.0861 (0.0301) 0.0374 (0.0302) 0.0141 (0.0302) 0.0091 (0.0285) -0.0660 (0.0545) -0.0116 (0.0049) 0.0679 (0.0341) 0.0059 (0.0216) Yes

0.1322 (0.0430) 0.0698 (0.0433) -0.02169 (0.0368) -0.0037 (0.0353) -0.1224 (0.0795) -0.0142 (0.0059) 0.0832 (0.0326) 0.0023 (0.0273) Yes

0.1153 (0.0449) 0.0678 (0.0327) 0.0210 (0.0358) 0.0240 (0.0343) -0.0706 (0.0596) -0.0124 (0.0054) 0.0558 (0.0426) 0.0237 (0.0257) Yes

0.0173 (0.0742) 0.0753 (0.0509) 0.0080 (0.0498) 0.0076 (0.0452) -0.1462 (0.1115) -0.0211 (0.0076) 0.1354 (0.0438) -0.0113 (0.0361) Yes

0.0460 (0.0104) -0.0004 (0.0001) 0.2222 (0.0310) 0.0846 (0.0351) -0.0319 (0.0529) -0.0502 (0.0370) -0.0153 (0.0378) -0.0640 (0.0708) -0.0053 (0.0073) 0.0324 (0.0327) -0.0355 (0.0294) Yes

0.0471 (0.0149) -0.0004 (0.0001)

---

0.0426 (0.0083) -0.0003 (0.0001) 0.1939 (0.0225) 0.1418 (0.0401) 0.0721 (0.0281) 0.0143 (0.0291) 0.0185 (0.0276) -0.0941 (0.0595) -0.0154 (0.0044) 0.0818 (0.0308) 0.0059 (0.0211) Yes

0.0567 (0.0467) -0.0852 (0.0696) -0.0039 (0.0529) -0.0104 (0.0483) -0.0097 (0.1135) -0.0076 (0.0102) 0.0833 (0.0456) -0.1060 (0.0431) Yes

0.1069 (0.0476) 0.0316 (0.0845) -0.0823 (0.0546) -0.0244 (0.0565) -0.1047 (0.0835) -0.0023 (0.0092) -0.0209 (0.0477) 0.0222 (0.0408) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

36

less than HS

diploma univ. degree union NAICS 21 NAICS 22 NAICS 23 NAICS 31 NAICS 32 NAICS 33 NAICS 41 NAICS 44 NAICS 45 NAICS 48 NAICS 49 NAICS 51 NAICS 52

-0.1283 (0.0442)

-0.1510 (0.0607)

-0.0932 (0.0622)

-0.1350 (0.0583)

-0.1658 (0.0775)

-0.0815 (0.0814)

-0.1057 (0.0601)

-0.0993 (0.0743)

-0.1237 (0.0957)

-0.1181 (0.0216) 0.3855 (0.0267) 0.1123 (0.0193) 0.3976 (0.1095) 0.4912 (0.1095) 0.1133 (0.1034) 0.0569 (0.1036) 0.2364 (0.1028) 0.1824 (0.1012) 0.1931 (0.1038) -0.0350 (0.1023) -0.2360 (0.1134) 0.0951 (0.1061) 0.0430 (0.1261) 0.2579 (0.1075) 0.3747

0.0944 (0.0293) 0.3682 (0.0365) 0.0707 (0.0230) 0.4414 (0.1001) 0.5330 (0.1006) 0.1717 (0.0895) 0.1342 (0.0922) 0.3117 (0.0898) 0.2288 (0.0874) 0.2384 (0.0928) 0.0189 (0.0910) -0.2602 (0.1352) 0.1261 (0.0922) -0.0485 (0.1287) 0.3277 (0.1004) 0.4561

0.0152 (0.0331) 0.4152 (0.0416) 0.1644 (0.0323) 0.1943 (0.3543) 0.4179 (0.3633) -0.1362 (0.3679) -0.1546 (0.3428) -0.01283 (0.3439) -0.0058 (0.3454) 0.0023 (0.3454) -0.2077 (0.3398) -0.3393 (0.3408) -0.0298 (0.3593) 0.0977 (0.3519) 0.0544 (0.3404) 0.1795

0.1098 (0.0266) 0.3767 (0.0327) 0.1225 (0.0234) 0.4767 (0.1208) 0.4939 (0.1227) 0.1584 (0.1158) 0.1081 (0.1162) 0.2610 (0.1149) 0.1776 (0.1137) 0.2221 (0.1153) -0.0345 (0.1145) -0.2193 (0.1350) 0.1020 (0.1165) 0.0367 (0.1548) 0.3151 (0.1214) 0.3882

0.0878 (0.0349) 0.3739 (0.0426) 0.0682 (0.0273) 0.4603 (0.1216) 0.5012 (0.1245) 0.1811 (0.1149) 0.1087 (0.1194) 0.2895 (0.1150) 0.1889 (0.1127) 0.2205 (0.1153) -0.0006 (0.1170) -0.3709 (0.1888) 0.0688 (0.1184) -0.1310 (0.1676) 0.2895 (0.1262) 0.4290

0.1430 (0.0419) 0.3862 (0.0543) 0.2022 (0.0406) 0.5132 (0.3935) 0.6521 (0.4298) 0.0633 (0.4215) 0.1422 (0.3664) 0.1860 (0.3730) 0.1850 (0.3798) 0.2285 (0.3740) -0.0326 (0.3647) -0.0931 (0.3693) 0.3112 (0.3703) 0.3513 (0.3810) 0.3376 (0.3695) 0.3814

0.1254 (0.0367) 0.4098 (0.0448) 0.0852 (0.0338) 0.2314 (0.2200) 0.5260 (0.2113) -0.0042 (0.2057) -0.0409 (0.2018) 0.1778 (0.2006) 0.2095 (0.1948) 0.1146 (0.2065) -0.0239 (0.1971) -0.2602 (0.2046) 0.0687 (0.2204) 0.0712 (0.2051) 0.1308 (0.2095) 0.3460

0.0935 (0.0521) 0.3543 (0.0703) 0.0767 (0.0431) 0.3509 (0.1882) 0.6102 (0.1667) 0.1341 (0.1330) 0.1963 (0.1357) 0.3584 (0.1305) 0.3426 (0.1226) 0.2594 (0.1567) 0.0736 (0.1295) -0.0825 (0.1579) 0.2907 (0.1349) 0.1312 (0.1597) 0.4189 (0.1619) 0.5116

0.1585 (0.0551) 0.4650 (0.0625) 0.0755 (0.0556) -0.1733 (0.6033) 0.2500 (0.6006) -0.3860 (0.6246) -0.4981 (0.5833) -0.2952 (0.5889) -0.1832 (0.5839) -0.3003 (0.5857) -0.3757 (0.5828) -0.6378 (0.5867) -0.5185 (0.6616) -0.1941 (0.5858) -0.2987 (0.5821) -0.0388

37

NAICS 53 NAICS 54 NAICS 55 NAICS 56 NAICS 61 NAICS 62 NAICS 71 NAICS 72 NAICS 81 NAICS 91 constant

(0.1034) 0.1095 (0.1239) 0.2348 (0.1112) -0.2995 (0.1082) -0.0701 (0.1117) 0.0784 (0.1034) 0.1704 (0.1018) 0.0656 (0.1192) -0.2099 (0.1069) -0.0238 (0.1059) 0.3467 (0.1000) 0.9939 (0.1726) 6,455

(0.0986) 0.1433 (0.1408) 0.2735 (0.1205) -0.2879 (0.0984) -0.0452 (0.1171) 0.1075 (0.0950) 0.1716 (0.1016) -0.1124 (0.1293) -0.1856 (0.1065) 0.0718 (0.0959) 0.3751 (0.0889) 1.2063 (0.1915) 3,047

(0.3372) -0.0581 (0.3479) 0.0593 (0.3395)

(0.1159) 0.0508 (0.1287) 0.2123 (0.1287)

(0.1225) 0.0946 (0.1773) 0.1902 (0.1515)

(0.3641) -0.0582 (0.3933) 0.2587 (0.3676)

---

---

---

---

-0.2156 (0.3441) -0.1016 (0.3380) 0.0015 (0.3365) 0.0753 (0.3448) -0.3635 (0.3416) -0.2603 (0.3431) 0.1932 (0.3367) 1.0809 (0.4046) 3,408

-0.0964 (0.1289) 0.0816 (0.1159) 0.1871 (0.1147) 0.1205 (0.1340) -0.1630 (0.1191) 0.0098 (0.1231) 0.3415 (0.1114) 0.9529 (0.2118) 4,443

-0.1630 (0.1544) 0.0730 (0.1190) 0.1727 (0.1305) -0.0680 (0.1448) -0.1890 (0.1283) 0.0922 (0.1258) 0.3311 (0.1135) 1.2589 (0.2328) 2,243

29

20

Obs. First Stage Robust F 51 31 23 Statistic Robust standard errors in parentheses.

38

(0.1489) 0.3221 (0.2050) 0.5078 (0.1361) -0.3079 (0.1385) 0.1093 (0.1624) 0.1879 (0.1561) 0.1852 (0.1437) -0.1964 (0.2625) -0.1953 (0.2267) 0.0188 (0.1266) 0.5159 (0.1257) 1.0529 (0.3373) 804

(0.5791) -0.1729 (0.5841) -0.1827 (0.5788)

-0.0189 (0.3719) 0.0945 (0.3652) 0.2047 (0.3620) 0.2977 (0.3770) -0.1201 (0.3664) -0.0945 (0.3765) 0.3844 (0.3618) 0.8028 (0.4722) 2,200

(0.1992) 0.1941 (0.2115) 0.2963 (0.1948) -0.4088 (0.2041) -0.0389 (0.2085) 0.0738 (0.1975) 0.1418 (0.1958) -0.0426 (0.2335) -0.2875 (0.2116) -0.0908 (0.1970) 0.3883 (0.1961) 1.0373 (0.2868) 2,012

14

22

11

12

---0.4429 (0.5941) -0.3077 (0.5764) -0.2163 (0.5788) -0.2044 (0.5878) -0.6281 (0.5860) -0.4610 (0.5814) -0.0049 (0,5790) 1.4489 (0.6568) 1,208

Table A2: First Stage Regression Results Corresponding to Table 3 of Text Modified BH Model non insomniacs dependent variable All Men Women All Men Women (ln wage) (1) (2) (3) (4) (5) (6) married / 0.0206 0.0401 -0.0201 0.0467 0.0516 0.0065 common (0.0289) (0.0395) (0.0422) (0.0347) (0.0485) (0.0470) age 0.0593 0.0534 0.0585 0.0554 0.0566 0.0492 (0.0079) (0.0116) (0.0104) (0.0097) (0.0135) (0.0131) age -0.0005 -0.0004 -0.0005 -0.0004 -0.0004 -0.0004 squared (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) male 0.1958 0.1897 --------(0.0219) (0.0266) good 0.0790 0.0496 0.0859 0.0966 0.0646 0.1020 health (0.0293) (0.0437) (0.0357) (0.0473) (0.0716) (0.0478) kids <5 0.1104 0.1570 0.0340 0.1142 0.1865 0.0015 (0.0287) (0.0362) (0.0433) (0.0353) (0.0436) (0.0529) Roman -0.0232 0.0239 -0.0668 -0.0057 0.0471 -0.0595 Catholic (0.0250) (0.0333) (0.0391) (0.0310) (0.0405) (0.0468) Protestant -0.0432 -0.0543 -0.0414 -0.0351 -0.0251 -0.0480 (0.0269) (0.0360) (0.0407) (0.0305) (0.0428) (0.0423) other -0.2553 -0.2765 -0.1915 -0.2586 -0.2459 -0.2203 religion (0.0495) (0.0676) (0.0608) (0.0618) (0.0842) (0.0733) ln other 0.0024 0.0014 0.0075 -0.0041 -0.0058 0.0016 income (0.0061) (0.0078) (0.0095) (0.0073) (0.0096) (0.0107) CMA 0.0403 0.0626 0.0072 0.0410 0.0419 0.0252 (0.0248 (0.0346) (0.0360) (0.0285) (0.0405) (0.0421) weekend 0.0342 0.0189 0.0437 0.0410 0.0182 0.0506 (0.0229) (0.0283) (0.0353) (0.0254) (0.0344) (0.0352) province Yes Yes Yes Yes Yes Yes months Yes Yes Yes Yes Yes Yes interview less than -0.1195 -0.1601 -0.1164 -0.1238 -0.1735 -0.0748 HS (0.0520) (0.0652) (0.0794) (0.0618) (0.0700) (0.1027) diploma 0.1637 0.1013 0.2186 0.1291 0.0926 0.1586 (0.0383) (0.0493) (0.0599) (0.0388) (0.0593) (0.0469) univ. 0.3903 0.2972 0.4602 0.3615 0.2933 0.4198 degree (0.0448) (0.0573) (0.0682) (0.0452) (0.0685) (0.0552) union 0.0576 0.0432 0.0692 0.0418 0.0425 0.0205 (0.0207) (0.0287) (0.0310) (0.0245) (0.0328) (0.0374) NAICS 21 0.4998 0.5367 0.3652 0.4983 0.4793 0.6673 (0.1258) (0.1361) (0.2612) (0.1568) (0.1705) (0.1787) NAICS 22 0.5805 0.5690 0.4693 0.6567 0.5867 0.8039 (0.1096) (0.1266) (0.2489) (0.1208) (0.1471) (0.1630) NAICS 23 0.2128 0.2603 0.0161 0.2878 0.2813 0.3942 (0.0973) (0.1065) (0.2569) (0.1119) (0.1308) (0.1831) NAICS 31 0.0119 -0.0608 0.0603 0.0661 -0.1023 0.4133 39

insomniacs Men (8) 0.0044 (0.0631) 0.0410 (0.0222) -0.0003 (0.0002)

Women (9) -0.0489 (0.0779 0.0785 (0.0161 -0.0007 (0.0001

---

---

-0.0133 (0.0546) 0.0880 (0.0612) -0.0167 (0.0607) -0.0887 (0.0713) -0.3036 (0.0895) 0.0223 (0.0130) 0.1048 (0.0711) 0.0257 (0.0522) Yes

0.06115 (0.0573 0.1532 (0.0738 -0.0524 (0.0638 -0.0362 (0.0846 -0.0671 (0.0995 0.0125 (0.0194 -0.0272 (0.0628 0.0111 (0.0704 Yes

Yes

Yes

Yes

-0.1017 (0.0841) 0.2104 (0.0786) 0.4146 (0.0851) 0.0698 (0.0374) 0.4846 (0.2122) 0.4226 (0.2305) 0.0176 (0.1895) -0.1377

-0.1252 (0.1214) 0.1130 (0.0794) 0.2934 (0.0944) 0.0034 (0.0569) 0.7091 (0.2217) 0.5564 (0.2436) 0.2236 (0.1908) 0.0626

-0.1408 (0.1152 0.2740 (0.1323 0.4914 (0.1373 0.1656 (0.0548 -0.0395 (0.5701 0.0729 (0.5594 -0.5378 (0.5714 -0.7999

All (7) -0.0462 (0.0511) 0.0670 (0.0134) -0.0006 (0.0001) 0.2046 (0.0384) 0.0352 (0.0379) 0.1274 (0.0496) -0.0463 (0.0413) -0.0633 (0.0527) -0.2196 (0.0496) 0.0161 (0.0114) 0.0452 (0.0481) 0.0278 (0.0446) Yes

NAICS 32 NAICS 33 NAICS 41 NAICS 44 NAICS 45 NAICS 48 NAICS 49 NAICS 51 NAICS 52 NAICS 53 NAICS 54 NAICS 55 NAICS 56 NAICS 61 NAICS 62 NAICS 71 NAICS 72 NAICS 81 NAICS 91

(0.1601) 0.2592 (0.1004) 0.2584 (0.0999) 0.3444 (0.0980) 0.0155 (0.0982) 0.0847 (0.1122) 0.2703 (0.0980) 0.1613 (0.1020) 0.3259 (0.1032) 0.3480 (0.1155) 0.2128 (0.1281) 0.3444 (0.0997) 0.0167 (0.1084) 0.2284 (0.0959) 0.2794 (0.0949) 0.0863 (0.1199) -0.1397 (0.1106) 0.1851 (0.1107) 0.4855 (0.0937) 0.7854 (0.2065) 4,668

(0.1711) 0.3069 (0.1132) 0.3062 (0.1106) 0.3857 (0.1116) 0.0386 (0.1199) 0.1551 (0.1486) 0.2613 (0.1092) 0.1735 (0.1214) 0.3841 (0.1290) 0.5463 (0.1261) 0.2952 (0.1325) 0.3930 (0.1168) 0.0180 (0.1391) 0.2694 (0.1081) 0.3043 (0.1211) 0.2951 (0.1268) -0.2046 (0.1713) 0.2516 (0.1154) 0.5364 (0.1051) 1.1338 (0.2915) 2,147

(0.3221) 0.0486 (0.2420) 0.0347 (0.2385) 0.1478 (0.2355) -0.1373 (0.2327) -0.0896 (0.2426) 0.1976 (0.2419) 0.0385 (0.2402) 0.1379 (0.2374) 0.0739 (0.2567) -0.0201 (0.2966) 0.1750 (0.2347) -0.0902 (0.2378) 0.0608 (0.2351) 0.0937 (0.2327) -0.2331 (0.2604) -0.2315 (0.2347) 0.0162 (0.2610) 0.2999 (0.2333) 0.9743 (0.3360) 2,521

Obs. First Stage Robust F 25 12 19 Statistics Robust standard errors in parentheses

(0.1940) 0.2926 (0.1097) 0.2781 (0.1132) 0.3966 (0.1114) 0.1408 (0.1120) 0.2046 (0.1311) 0.3465 (0.1110) 0.2701 (0.1145) 0.4353 (0.1177) 0.4924 (0.1198) 0.3146 (0.1471) 0.4383 (0.1132) 0.1032 (0.1298) 0.3107 (0.1076) 0.3187 (0.1070) 0.1561 (0.1408) -0.0601 (0.1371) 0.2718 (0.1129) 0.5481 (0.1050) 0.8261 (0.2610) 3,160

(0.2134) 0.3528 (0.1267) 0.2874 (0.1312) 0.3994 (0.1343) 0.1386 (0.1468) 0.1788 (0.1949) 0.3041 (0.1307) 0.1909 (0.1436) 0.5056 (0.1502) 0.5918 (0.1529) 0.4126 (0.1437) 0.4381 (0.1408) 0.0783 (0.1791) 0.2989 (0.1267) 0.3117 (0.1479) 0.2861 (0.1494) -0.1965 (0.2673) 0.2494 (0.1399) 0.5523 (0.1238) 1.0705 (0.3622) 1,527

(0.2884) 0.1281 (0.1630) 0.1894 (0.1704) 0.4211 (0.1409) 0.2108 (0.1363) 0.2895 (0.1525) 0.5220 (0.1669) 0.4312 (0.1510) 0.3938 (0.1570) 0.4653 (0.1531) 0.1849 (0.2834) 0.5003 (0.1389) 0.2222 (0.1468) 0.3982 (0.1408) 0.3752 (0.1354) 0.0732 (0.2055) 0.0636 (0.1452) 0.3848 (0.1423) 0.5861 (0.1401) 0.9510 (0.3254) 1,633

(0.1948) 0.1866 (0.2125) 0.1833 (0.1989) 0.1798 (0.1938) -0.2427 (0.1936) -0.1253 (0.2097) 0.0575 (0.1921) -0.0993 (0.2095) 0.1019 (0.2018) 0.0117 (0.2473) -0.0987 (0.2555) 0.1013 (0.1982) -0.2000 (0.1993) 0.0395 (0.1929) 0.1663 (0.1913) -0.1313 (0.2177) -0.3528 (0.1956) -0.0270 (0.2464) 0.3365 (0.1905) 0.7641 (0.1905) 1,508

(0.2001) 0.2056 (0.2482) 0.3754 (0.2101) 0.3510 (0.2021) -0.0992 (0.2236) 0.1551 (0.2453) 0.1838 (0.2047) 0.1697 (0.2349) 0.1071 (0.2327) 0.4847 (0.2332) -0.1536 (0.3136) 0.2874 (0.2077) -0.0451 (0.2039) 0.2351 (0.2082) 0.3307 (0.2245) 0.4017 (0.2393) -0.2540 (0.2371) 0.2860 (0.2100) 0.5506 (0.2045) 1.4179 (0.4699) 620

(0.5319 0.0299 (0.5344 -0.2291 (0.5299 -0.2195 (0.5426 -0.6355 (0.5303 -0.5615 (0.5408 -0.2291 (0.5310 -0.6268 (0.5437 -0.1375 (0.5343 -0.5034 (0.5705 -0.1993 (0.5526 -0.3163 (0.5387 -0.5376 (0.5484 -0.4318 (0.5298 -0.2949 (0.5278 -0.7076 (0.5457 -0.6504 (0.5295 -0.5302 (0.6039 -0.0854 (0.5289 0.8564 (0.6305 888

15

10

13

14

6

12

40