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The effects of depressed mood on academic outcomes in adolescents and young adults

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Title:
The effects of depressed mood on academic outcomes in adolescents and young adults
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English
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Jones, Robert Christopher
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University of South Florida
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Economics
Health
Depression
Grades
Human capital
Dissertations, Academic -- Economics -- Doctoral -- USF   ( lcsh )
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Abstract:
ABSTRACT: The following dissertation investigates the relationship between depressed mood and academic performance (measured in terms of grade point average) in U.S. middle and high schools. Utilizing data from AddHealth, the dissertation establishes Ordinary Least Squares, Two-Stage Least Squares (2SLS), and individual and sibling fixed effect regressions that attempt to control for confounding factors, including student motivation, personality characteristics, and parental inputs that are unobserved but may influence both mental health and achievement. Study findings indicate that students who report feeling depressed do not perform as well academically as non-depressed students. Additionally, the degree of GPA impact increases with the severity of reported depression. Students reporting either depressed feelings "most or all of the time" - or symptoms consistent with major depression suffer GPA reductions of 0.06 to 0.84 grade points. In addition, middle schoolers and certain minority groups are hardest hit by depression, and persistent depression has a negative impact on grades.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
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by Robert Christopher Jones.
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Title from PDF of title page.
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Document formatted into pages; contains 107 pages.
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Includes vita.

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oclc - 319435617
usfldc doi - E14-SFE0002551
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ABSTRACT: The following dissertation investigates the relationship between depressed mood and academic performance (measured in terms of grade point average) in U.S. middle and high schools. Utilizing data from AddHealth, the dissertation establishes Ordinary Least Squares, Two-Stage Least Squares (2SLS), and individual and sibling fixed effect regressions that attempt to control for confounding factors, including student motivation, personality characteristics, and parental inputs that are unobserved but may influence both mental health and achievement. Study findings indicate that students who report feeling depressed do not perform as well academically as non-depressed students. Additionally, the degree of GPA impact increases with the severity of reported depression. Students reporting either depressed feelings "most or all of the time" or symptoms consistent with major depression suffer GPA reductions of 0.06 to 0.84 grade points. In addition, middle schoolers and certain minority groups are hardest hit by depression, and persistent depression has a negative impact on grades.
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The Effects of Depressed Mood on Academic Outcomes in Adolescents and Young Adults by Robert Christopher Jones A dissertation submitted in partial fulfillment of the requirement s for the degree of Doctor of Philosophy Department of Economics College of Business Administration University of South Florida Major Professor: Gabriel Picone, Ph.D. Jeffrey DeSimone, Ph.D. John Robst, Ph.D. Murat Munkin, Ph.D. Don Bellante, Ph.D. Date of Approval: May 30, 2008 Keywords: economics, health, depression, grades, human capital Copyright 2008, Robert Christopher Jones

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i Table of Contents List of Tables iv Abstract vi Chapter 1: Introduction 1.1 What is Depression? 1 1.2 The Issue of Mental Health Disorders and Human Capital Formation 3 1.3 Study Purpose 6 Chapter 2: Literature Review 2.1 Early Work Linking Mental Disorders to Human Capital Formation 8 2.2 Specific Mental Disorder s and Labor Market Outcomes 9 2.3 Recent Works on Mental Disorders and Achievement in Young People 10 Chapter 3: Data 3.1 Data Source 12 3.2 Creation of the Depression Variables 14 3.3 Variables Addressing Pe rsistent Depression 17 3.4 Description of Outcom e (Dependent) Variables 18 3.5 Description of Instrumental Variable Candidates 19 3.6 Description of Other Model Variables 22 Chapter 4: Methodology 4.1 Methodology Introduction 27 4.2 Ordinary Least Squares – Proxy Variable Approach 27 4.3 First Differencing 30 4.4 School Fixed Effects 31 4.5 Sibling Fixed Effects 31 4.6 Two-Stage Least Squares/In strumental Variables 32 4.7 Synopsis of Model Runs 4.7.1 OLS Regression of GPA on Depression and Exogenous Variables, by Progressive Depression Severity 36 4.7.2 OLS Regression of GPA on Depression and Exogenous Variables, for Major Depression Only 38

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ii 4.7.3 OLS Regression of GPA on Depression, Exogenous Variables, and Motivation Variables, By Progressive Depression Severity 38 4.7.4 OLS Regression of GPA on Depression, Exogenous Variables, and Motivation Variables, for Major Depression Only 39 4.7.5 OLS Regression of GPA on Depression, Exogenous Variables, and Ability Vari ables, by Progressive Depression Severity 39 4.7.6 OLS Regression of GPA on Depression, Exogenous Variables, and Ability Variables, for Major Depression Only 40 4.7.7 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, by Progressive Depression Severity 40 4.7.8 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, for Major Depression Only 40 4.7.9 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, by Grade 41 4.7.10 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, by Gender 41 4.7.11 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, by Race/Ethnicity 42 4.7.12 OLS Regression of GPA on Depression, Exogenous Variables, Mobility Variables, and Ability Variables, for Persistent Depression 42 4.7.13 OLS Regression – School Fixed Effects 42 4.7.14 OLS Regression – Si bling Fixed Effects 43 4.7.15 OLS Regression – First Differencing 44 4.7.16 IV/2SLS Regression 45 4.8 Summary of Advantages & Disadvantages of Model Alternatives 46 Chapter 5: Results 5.1 Summary Statistics of Key Variables 50 5.2 OLS Regression of GPA on Depression and Exogenous Variables 53 5.3 OLS Regression of GPA on Depression, Exogenous Variables, and Motivation Proxies 54 5.4 OLS Regression of GPA on Depression, Exogenous Variables, and Ability Proxies 55

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iii 5.5 OLS Regression of GPA on Depression, Exogenous Variables, Motivation Prox ies, and Ability Proxies 57 5.6 OLS Regression – School Fixed Effects 58 5.7 OLS Regression – Results by Grade 59 5.8 OLS Regression – Results by Gender 64 5.9 OLS Regression – Results by Race/Ethnicity 66 5.10 OLS Regression – Persist ence Regression Results 71 5.11 First Differencing Results 72 5.12 Sibling Fixed Effects Results 73 5.13 Two-Stage Least Squares Estimation Results 75 5.14 Concluding Remarks on Study Results 79 Chapter 6: Study Conclusions 6.1 Study Implications 84 6.2 Study Limitations 87 6.3 Further Research 87 References 89 Appendices Appendix A: Output Detail, OLS-Proxy Equation, Progressive Depression 92 Appendix B: Output Detail, OLS-Proxy Equation, Major Depression 95 Appendix C: Output Detail, OLS-Proxy Equation, Persistence Depression 98 Appendix D: Output Detail, 2SLS (Major Depression). 2nd Stage 101 Appendix E: U.S. S enate Proposal, FY 09 ESSCP Funding Increase 104 About the Author End Page

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iv List of Tables Table 1: Summary Statistics –D epression Impacts on GPA 51 Table 2: OLS Regression of GPA on Depression and Exogenous Variables Only 54 Table 3: OLS Regression of GPA on Depression, Exogenous Variables, and Motivation Proxy Vector 55 Table 4: OLS Regression of GPA on Depression, Exogenous Variables, and Ability Proxy Vector 56 Table 5: OLS Regression of GPA on Depression, Exogenous Variables, Motivation Proxy Vector, and Ability Proxy Vector 57 Table 6: OLS-School Fixed Effects Analysis 58 Table 7: OLS-GPA Impacts by Grade (Grades 7 & 8) 60 Table 8: OLS-GPA Impacts by Grade (Grades 9 through 12) 61 Table 9: OLS-GPA Impacts by Grade (Grade 7) 62 Table 10: OLS-GPA Impacts by Grade (Grade 8) 62 Table 11: OLS-GPA Impacts by Grade (Grade 9) 63 Table 12: OLS-GPA Impacts by Grade (Grade 10) 63 Table 13: OLS-GPA Impacts by Grade (Grade 11) 64 Table 14: OLS-GPA Impacts by Grade (Grade 12) 64 Table 15: OLS-GPA Impacts by Sex (Female) 65 Table 16: OLS-GPA Impacts by Sex (Male) 66 Table 17: OLS-GPA Impacts by Race/Ethnicity (White) 67

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v Table 18: OLS-GPA Impacts by Race/Ethnicity (Non-White) 68 Table 19: OLS-GPA Impacts by Race/Ethnicity (Black) 68 Table 20: OLS-GPA Impacts by Race/Ethnicity (Hispanic) 69 Table 21: OLS-GPA Impacts by Race /Ethnicity (Native American) 69 Table 22: OLS-GPA Impacts by Race/E thnicity (Asian/Pacific Islander) 70 Table 23: OLS-GPA Impacts by Ra ce/Ethnicity (Other Races) 71 Table 24: OLS-Persistence Depression Effects on GPA 71 Table 25: First Differencing of Responses for Students Reporting Both in Wave I and Wave II 72 Table 26: Sibling Fixed Effects – Wave I 73 Table 27: Sibling Fixed Effects – Wave II 74 Table 28: Two-Stage Least Squar es, First Stage Regressions 75 Table 29: Two-Stage Least Squares, Inst ruments for Major Depression 76 Table 30: Two-Stage Least Squares Overidentification Tests 77 Table 31: Summary of Coefficients for Severely Depressed Mood 81 Table 32: Summary of Coefficients for Severely Depressed Mood 82

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vi The Effects of Depressed Mood on Academic Outcomes in Adolescents and Young Adults Robert Christopher Jones ABSTRACT The following dissertation investigat es the relationship between depressed mood and academic performanc e (measured in terms of grade point average) in U.S. middle and high schools. Utilizing data from AddH ealth, the dissertation es tablishes Ordinary Least Squares, Two-Stage Least Squares (2SL S), and individual a nd sibling fixed effect regressions that attempt to cont rol for confounding factors, including student motivation, personality characte ristics, and parental inputs that are unobserved but may influence both mental health and achievement. Study findings indicate that student s who report feeling depressed do not perform as well academically as non-depr essed students. Additionally, the degree of GPA impact increases with the severity of reported depression. Students reporting either depre ssed feelings “most or all of the time” or symptoms consistent with major depressi on suffer GPA reductions of 0.06 to 0.84 grade points. In addition, middle sc hoolers and certain minority groups are hardest hit by depression, and persistent depression has a negative impact on grades.

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1 Chapter 1 Introduction 1.1 What is Depression? In the field of mental health, the term depression is generally characterized as a feeling of sadness or unhappiness. Most individuals ex perience depressed feelings sometime in life for short peri ods, often as the result of negative or unhealthy life events. Th is, however, does not thoroughly define the relevance of depressed mood for human behavior, nor does it convey the potential consequences of depression for other facets of human performance. Mental health researchers and practi tioners have come to recognize that depression exists in many forms, with va riations in origin and severity. The American Psychiatric Association (APA), in its Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (D SM-IV), identifies depressive behavior in the context of Mood Episodes and Mood Dis orders. Mood episodes are in effect individual mood events, and serve as the bu ilding blocks for disorder diagnoses. Depending on their frequency and depth, such episodes may reveal a clinical disorder that has far-reaching impacts on an individual’s mental health and overall functioning. The DSM-IV classifies mood disorders in three categories: Depressive Disorders, Bipolar Disorders, and “Other” Mood Disorders. Depressive disorders

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2 include Major Depressive Disorder, Dysthymic Disorder, and Depressive Disorders Not Otherwise Specified. De tailed explanations of these depressive disorders are as follows: Major Depressive Disorder is a clinical course that is characterized by one or more major depressive episodes, wit hout a history of other mood episodes (e.g. manic or bipolar). The essential f eature of the major depr essive episode is a period of at least two weeks during whic h there is either a depressed mood or a loss of interest in nearly all activities. In addition, four of the following additional symptoms must be experienced by the individual: (1) Changes in appetite, weight, sleep, and psychomotor activity; (2) decreased energy; (3) feelings of worthlessness or guilt; (4) difficulty th inking, concentrating, or making decisions; (5) recurrent thoughts of death or suici de; (6) suicide plans or attempts. Dysthymic Disorder is characterized by at least 2 years of depressed mood for more days than not, accompanied by at least two of the following symptoms: (1) poor appetite or overeat ing; (2) insomnia or hypersomnia (excessive sleeping); (3) low energy or fa tigue; (4) low self-esteem; (5) poor concentration or difficulty making decisions ; (6) feelings of hopelessness. For children and adolescents, dysthymic disord er requires only 1 year of depressed mood, or can be triggered by a pattern of long-term (1+ year s) irritability. Depressive Disorder Not Otherwise Specified includes disorders with depressive features that do not meet criteria for the pr eceding disorders. Major examples include: Premenstrual Dysphoric Disorder (e.g. PMS)

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3 Minor Depressive Disorder: Episodes of at least 2 weeks of depressive symptoms but with fewer than the 5 it ems required for Major Depressive Disorder Recurrent Brief Depressive Disorder: Depressive episodes lasting 2 days up to 2 weeks, occurring at leas t once a month for 12 months Postpsychotic Depressive Disorder of Schizophrenia Major Depressive Episode superimposed on Delusional Disorder Other mood disorders t hat reveal depressive behavior, such as bipolar disorder and mood disorders induced by s ubstance intake or medical conditions, are not classified by the APA as depressive disorders. 1.2 Mental Health Disorder s and Human Capital Formation Throughout much of recorded history, the subject of mental illness was addressed in the context of dealing wit h individuals who suffered the most extreme symptoms and displayed the greatest difficulties functioning in society. Many subjects studied in early mental health research were institutionalized, either in asylums or prisons. As recently as the early twentieth century, research emphasized gaining an understanding of why the mentally ill were afflicted; little was done to ascertain whether or not their disorders could be treated, or what the individual and societal impacts were from mental illness. The latter half of the twentieth cent ury saw a change in the approach to the study of mental illness. Evolutionary changes in t he evaluation and diagnosis of neuropsychological conditions, along with innovations in technology and

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4 medicine, began to reveal that a great er percentage of the population suffered from mental disorders than previously suspected. These discoveries brought to light the notion that society has many “w alking wounded”: i ndividuals who suffer from mental disorders, but fi ght to maintain a functional existence. An increased interest emerged in treating, as opposed to simply identifying, the mentally ill, and efforts were undertaken to assess the im pacts of mental illness on society. During the past two decades, various health economists have estimated the impacts of mental disorder s on the formation of human capital. According to human capital theory, individuals invest in themselves through education, training, and health to increase their earni ngs. Based on the pr emise that mental health is a component of the overall hea lth input (along with physical health), those suffering from mental disorders may achieve substandard labor market outcomes relative to those who do not, other things being equal. To provide a better understanding of why issues related to an individual’s mental health are important in economics, Grossman’ s (1972, 1975) theoretical constructs of the demand for health capi tal and the linkages between health and schooling are summarized. The consumer ’s intertemporal utility function is U = U( tHt,Zt), t = 0,1,….n (1) where Ht is the stock of health at age t, t is the service flow per unit stock (so tHt, is the total consumption of “health services”), and Zt is the consumption of another commodity. Net investment in the stock of health (Ht+1 – Ht) equals gross investment (It) minus depreciation ( tHt):

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5 Ht+1 – Ht = IttHt (2) Consumers produce gross investment in health and other commodities in the utility function according to a se t of household production functions: It = It (Mt, THt; Et) (3) Zt = Zt (Xt, Tt; Et) (4) In these functions, Mt and Xt are vectors of goods pur chased in the market that contribute to gross investments in health (It) and other commodities (Zt), THt and Tt are time inputs, and Et is the consumer’s st ock of knowledge or human capital of exclusive of health capital at time t. The specified equation for E depends on the amount of formal schooling (S) completed and a vector of variables (C) that include the current or “inherited” stock of human capital as well as dete rminants of the typical quantity of new knowledge produced per year of school attendance. Ei = Si + Ci (5) It is this stock of education that co ntributes to the ef ficiency of producing adult health and other commodities. Grossman’s model demonstrates t hat education is an investment commodity which can lead to increases in consumption of not only “hard” commodities (money, goods, services), but al so health itself. Health also serves as a human capital input to education, along with schooling (Equation 5). These equations demonstrate that t he consumption of health and other commodities is dependent upon education, while also recognizing that health is an input to education. Grossman’s wo rk supports the notion that health,

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6 including mental health, impacts educatio nal attainment and is relevant to consumer theory. Empirical work over the previous 20 years supports the hypothesis that mental health is an input to labor mark et outcomes. Bartel and Taubman (1986) estimated that the presence of mental illness in workers reduced earnings by double digit percentages for significant perio ds of their working careers. Ettner, Frank, and Kessler (1997) show that psych iatric disorders reduce employment and earnings among women and men. Curr ie and Madrian (1999) and Savoca and Rosenheck (2000) conclude that the l abor market consequences of mental health problems are large when compar ed to the consequences of physical health problems. Currie and Stabile (2006) note that many adult mental health conditions arise in childhood, so in addition to their direct effe cts, mental health disorders may reduce adult earnings and employment by inhibiting earlier accumulation of human capital. 1.3 Study Purpose The limited body of work in the fiel ds of health and labor economics on the impacts of mental diso rders on human capital formation has largely been generalized to include all mental disorder s. These include cognitive, psychotic, anxiety, somatoform, substance abuse, di ssociative, adjustment, and personality disorders, in addition to mood disorders. In addition, few res earchers in the field of health economics have conducted in-depth research on the im pacts of mental health disorders as they pertain to academic achievement.

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7 This research effort will examine t he experience of adolescents and young adults in the United States who report t hat they have experi enced feelings and moods consistent with depressive disor ders. The World Health Organization (2004) reports that depressive disorders ar e the leading cause of disability in the United States for persons aged 15 – 44. This dissertation, which attempts to isolate impacts on achievement from depressive disorders alone adds to the existing literature in health economics of the impact on achievement of more generalized mental illness. It attempts to establish the causal effects that depressed mood has on self-reported G PA in, English, mathematics, history/social studies, and science. The remainder of the dissertation is st ructured as follows: Chapter 2 offers an overview of the relevant literature in this field, from the disciplines of sociology, psychology, and labor and health economics. Chapter 3 specifies the data and variables that will be utilized for th is study. Chapter 4 explains the research methodology employed to obtai n estimates that represent causal effects of depression on G PA. Chapter 5 presents the estimation results. Chapter 6 concludes with a discussion of study implications, limitations, and suggestions for future research.

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8 Chapter 2 Literature Review 2.1 Early Work Linking Mental Di sorders to Human Capital Formation The literature review begins with an overview of studies that address the broader linkages between ment al disorders and human capital accumulation. Most of this work has focused on the association between mental illness and labor market outcomes in adults. Ba rtel and Taubman (1986) studied 1951-74 employee earnings data from a National Academy of Science-National Research Council twins sample. A Tobit model show ed that the presenc e of mental illness in workers reduces their annual earnings by approximately 12 percent, with a duration of impact lasting as long as 15 years. Ettner, Fr ank and Kessler (1997) used 1990 and 1992 National Comorbidity Survey data to develop OLS and probit models that found the presence of a mental disorder reduced the probability of gaining employ ment by approximately 11 percentage points, and reduced the earnings of t hose employed by 13 to 18 percent. The study was unable to draw conclusions on the severity of the impact rela tive to differing diagnoses (major depression, schizophren ia, phobias, etc.), because of the imprecise nature of the es timates generated by this stratified modeling. French and Zarkin (1998) surveyed workers at a la rge U.S. manufacturing facility and collected information on absenteeism, ear nings, health, emotional problems, and

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9 use of illicit substances. Results fr om OLS, logistic, and count data models indicated that employees who report sym ptoms of emotional and psychological problems are nearly 3 times as likely to be absent, with earnings of 13 percent less than workers who do not report these problems. 2.2 Specific Mental Disorders and Labor Market Outcomes Research at the beginning of this decade began to focus on the impacts of specific mental disorders on labor ma rket outcomes. Savoca and Rosenheck (2000) analyzed data from the National Surv ey of the Vietnam Generation in order to ascertain the labor market impac ts of post-traumatic stress disorder (PTSD) and major depression on Vietnam-e ra veterans. Using OLS & probit models, they found that veterans with a lifetime diagnosis of PTSD are 8.6 percentage points less likely to be empl oyed than those who did not have the disorder. Results were similar for majo r depression. In addition, vets suffering from major depression earn wages that are 45 percent lower than unafflicted vets, while PTSD sufferers experience a sm aller (16 percent) wage penalty. The study also concluded that these mental disorder s have greater impacts on employment and wages than chronic physica l conditions. Slade and Salkever (2001) focused on the employment impacts of schizophrenia, constructing a multinomial probit model that estima tes changes in employment rates for schizophrenics based on percentage reductions in their symptom levels resulting from drug therapy. The findings indicate that a 20 percent reduction in patient symptoms increased the aggregate employm ent rate by 5.2 percentage points.

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10 2.3 Recent Works on Mental Disorders and Achievement in Young People Recent efforts by health economists and psychologists focus on the impacts of specific mental disorders on hum an capital accumulation and academic achievement in children and young adults. Haines, Norris, and Kashy (1996) assessed college students on measures of depression, concentration, and academic performance. Using an OLS model that controls for age, sex, education, and verbal and abstract reasoni ng skills, they concluded that an inverse relationship exists between G PA and depressive symptoms. Currie and Stabile (2006) examine North American children wit h symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Using OLS and IV/2SLS modeling techniques, they find that schoolaged children with ADHD symptoms have significantly lower scores in math and reading than non-ADHD children, and ADHD children have a greater likelihood of being placed in special education classes. Currie and Stabile also found that the negative impact of ADHD on children’s math and reading performance wa s twice as large as the impact of a chronic physical condition (asthma). Wolfe and Fletcher ( 2007) studied ADHD impacts on older youth. Using the AddHealth database, Wolfe and Fletcher conducted OLS and fixed-effects mode ling for respondents who reported past ADHD symptoms in their childhood. T he results indicated that children with ADHD symptoms face long term educational problems, including lower grades, increases in suspension and expulsions, and fewer completed years of schooling. Few of these results, however were robust to the inclusion of family fixed effects. Fredrik sen et. al. (2004) studied 19951997 longitudinal data on

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11 Illinois middle-school students in an effort to estimate the e ffects of diminished sleep on grades. This work is relevant to the current analysis, because it evaluates a similar age group and academic performance measure, and implies that insufficient sleep can reduce self esteem and academic performance, and lead to depression. The study concludes that depression is an endogenous variable that is result, but not the cause, of reduced sleep.

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12 Chapter 3 Data 3.1 Data Source The dissertation analyzes data from Wave I and Wave II of AddHealth: The National Longitudinal Study of Adolescent Health (http://www.cpc.unc. edu/projects/addhealth) published by the Carolina Population Center at the Un iversity of North Caroli na-Chapel Hill. AddHealth commences with an in-school questionna ire administered to a nationally representative sample of students in gr ades 7 through 12, then follows up with a extensive in-home interviews of st udents approximately one and two years later1. The Wave I in-school questionnaire and corresponding in-home interview were administered during September 1994 – Dece mber 1995. The Wave II in-home interview was administered during April – August 1996. AddHealth examines the forces that may influence adolescents' behavior, particularly personality traits, families, fr iendships, romantic relationships, peer groups, schools, neighborhoods, and communities. The first stage of Wave I was a random sample of US high schools that included an 11th grade and at least 30 st udents. A feeder school, i.e. a school that sent graduates to the high school, that included a 7t h grade, was also 1 A third wave of the AddHealth study was condu cted six years after the administration of the original in-school questionnaire, but differs signi ficantly in the types of questions asked when compared to the first two waves, and thus is not used here.

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13 recruited from the communi ty. A total of 90,118 students completed in-school questionnaires. The second stage of Wave I involved an in-home sample of 20,700 adolescents, drawn from a core sample from each surveyed community plus selected special oversamples, elig ibility for which was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. In addition, parents were asked to complete a questionnaire about family and relationships The breakdown of Wave I in-home interviews by sample is as follows: Core Sample: 12,105 adolescents in grades 7–12 during the 1994–1995 school year Saturated schools: 2,559 adolescents (in addition to 200 core sample students) from schools in which all st udents were selected for the in-home sample Disabled: 471 adolescents who reported having a limb disability Ethnic/Racial Oversamples: (Afric an American, Chinese, Cuban, Puerto Rican)—2,259 adolescents Adolescents residing t ogether — 3,139 adolescents Full sibling, not t win — 1,251 adolescents Half sibling, not twin — 442 adolescents Non-related adolescent—415 adolescents Twin siblings — 784 adolescents The Wave II sample is the same as the Wave I in-home interview sample, with a few exceptions, mainly dealing wi th the omission of questions on time-

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14 invariant information (i.e. race sex, etc.). In addition, school administrators were contacted by telephone to update sc hool information. Information about neighborhoods/communities was gathered fr om a variety of previously published databases. Approximately 14,700 in-hom e interviews were administered in Wave II of the survey. 3.2 Creation of the Depression Variables As specified in Sect ion 1.1, the DSM-IV diagnos tic criteria for Major Depressive Disorder indica te that the primary condi tion of most recognized depressive disorders is a prolonged period (at least two weeks) of a depressed mood or loss of interest in nearly all acti vities. In addition, at least four of the following criteria must accompany the pr imary condition to prompt a diagnosis of major depressive disorder: Changes in appetite, weight, sl eep, and psychomotor activity Decreased energy Feelings of worthlessness or guilt Difficulty thinking, concentrating, or making decisions Recurrent thoughts of death or suicide Suicide plans or attempts These additional symptom s must also be prolonged, and they must have recently occurred or worsened. The nature of the AddHealth data pres ents challenges in the creation of a fully representative proxy variable for major depression. The self-reported data

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15 on student feelings does not ask specifically about feel ings over the two week period prior to the survey. The time c ontext of the survey questions dealing with student feelings is either “past week”, “pas t month”, or “past year ”. In addition, the AddHealth variables that reflect the other symptoms that must be present for a diagnosis of major depression are not perfect matches with the actual major depression diagnosis criteria. As a result, two different approaches for defining the depression variable are used in the study. The first uses only the primary depression conditions as a variable of study. In the “ feelings ” section of the Add Health in-home questionnaire, students are asked whether “ You felt depressed during t he last week/seven days .” (Wave I, Section 10, Question 6, Variable Name HIFS6; Wave II, Section 10, Question 6, Variable Name H2FS6). The four re sponse alternatives are progressive in intensity: “ never or rarely ”, “ sometimes ”, “ a lot of the time ”, or “ most or all of the time ”. Three binary depression variables we re constructed from this single AddHealth question, each representing a gr eater frequency of depressed mood. The first binary depression variable is coded as a “1” for all responses of “ sometimes ”. The second depression variable is coded as “1” for all responses of “ a lot of the time ”. The third depression variable is coded with a “1” for all responses of “ most of or all of the time ”. In the two in-home questionnaires, previous week depressed mood wa s reported with a frequency of “ sometimes ” by

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16 29.9 percent of the respondents, “ a lot of the time ” by 7.2 percent of respondents, and “ most or all of the time ” by 2.9 percent of respondents. The rationale for constructing the depr ession variables in this manner is two-fold. First, it is of interest to es tablish whether or not the existence of any sustained depression, regardless of frequency, has an impact on student achievement. If so, then it would also be of interest to assess whether or not achievement is progressively impact ed based on the frequency of the depressed mood. The second approach is an attempt to construct a proxy for major depression diagnosis as closely as possi ble. Although Section 3.2 notes that AddHealth does not allow for an exact replication of the major depression diagnosis, several major depression sympt om variables do exist within the dataset, each having similar reporting charac teristics, including a past week time frame and frequency choices of including “ never or rarely ”, “ sometimes ”, “ a lot of the time ”, or “ most of the time or all of the time ”. These additional variables and their DSM-IV symptom counterparts include: You felt depressed (e.g., DSM-IV “depressed mood” symptom) (Wave I, Section 10, Question 6, Variable Na me HIFS6; Wave II, Section 10, Question 6, Variable Name H2FS6). You didn’t feel like eating, your appetite was poor (e.g., DSM-IV “changes in appetite” symptom) (Wave I, Section 10, Question 2, Variable Name HIFS2; Wave II, Se ction 10, Question 2, Variable Name H2FS2).

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17 You had trouble keeping your mind on what you were doing (e.g., DSM-IV “difficulty thinking or concentrating” symptom) (Wave I, Section 10, Question 5, Variable Name HIFS5; Wave II, Section 10, Question 2, Variable Name H2FS5). You felt like you were too tired to do things (e.g., DSM-IV “decreased energy” symptom) (Wave I, Section 10, Ques tion 7, Variable Name HIFS7; Wave II, Section 10, Ques tion 7, Variable Name H2FS7). You thought your life had been a fa ilure (e.g., DSM-IV “feelings of worthlessness or guilt” symptom) (Wave I, Section 10, Question 9, Variable Name HIFS9; Wave II, Se ction 10, Question 9, Variable Name H2FS9). Using these questions, a major depre ssion binary variable was coded as a “1” for all respondents who answered someth ing other than “never or rarely” for the first depression indicator and each of the other four variables listed above. Thus, respondents responding to all five questions with a frequency of at least “some of the time” are cat egorized as suffering from ma jor depressive disorder. Approximately 6.8 percent of Wave I and II survey were categorized as having major depression, based on these criteria. 3.3 Variables Addressing Persistent Depression Another consideration in the analysis of depression how impacts grades is whether or not prolonged depression creates additional negative impacts. To address this issue, a third set of depre ssion variables was developed. Because

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18 AddHealth obtains student feedback on depre ssed mood at three separate points in time (the In-school Wave I, and Wave II surveys) over a two-year period, it is possible to identify whether students repor t depressed feelings on a persistent basis. Binary indicators serving as pro xy variables for persistent depression include the following: No persistent depression : Student does not report depressed mood for any of the in-school, Wave I, or Wave II surveys. Persistent depression : Student reports depressed mood for the in-school survey as further documented in Sect ion 3.5, and “some of the time” or more frequently in either the Wave I or Wave II surveys. Onset depression : Student does not report depr essed mood for the inschool survey, but does report depressi on of “some of the time” or more frequently in either of the Wave I or Wave II surveys. Remittance depression : Student reports depress ed mood for the in-school survey, but does not report depression of “some of the time” or more frequently for either the Wave I or Wave II survey. 3.4 Outcome (Dependent) Academic Performance Variables The variables presented below ar e the primary academic performance measures from Wave I and Wave II of AddHealth that serve as dependent variables in the analysis. The question asked was, “ at the most recent grading period, what was your grade in ___ ? ” Choice options are “ A ”, “ B ”, “ C ”, and “ D

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19 or lower ”. English or Language Arts? (Wave I, Section 5, Question 11, Variable Name H1ED11; Wave II, Section 6, Question 7, Variable Name H2ED7) Mathematics? (Wave I, Section 5, Questions 12, Variable Name H1ED12; Wave II, Section 6, Question 8, Variable Name H2ED8) History or Social Studies? (Wave I, Section 5, Question 13, Variable Name H1ED13; Wave II, Section 6, Question 9, Variable Name H2ED9) Science? (Wave I, Section 5, Question 14, Variable Name H1ED14; Wave II, Section 6, Question 10, Variable Name H2ED10) Student responses were recoded into a numeric grade for each course, based on a 4-point grade system, with “A” = 4, “B” = 3, “C” = 2, and “D or lower” = 1. In addition, an “Overall GPA” va riable was constructed by averaging the numeric grade from all subjects, for st udents who provided a grade response for all four courses. 3.5 Description of Instrumental Variable Candidates Numerous variables were initially ident ified as possible instrumental variable (IV) candidates for 2SLS modeling. T he majority were ultimately judged as failing to meet the two necessary conditions for serving as instruments; which are that the variable is correlated wit h depression, and uncorrelated with all unobserved determinants of academic per formance. Sections 4.6 and 5.12 provide further descriptions of both these conditions and the variables that ended up being used as instruments; this subsection provides an overview of all

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20 considered variables: How many hours of sleep do you usually get? (Wave I, Section 3, Question 51, Variable Name H1GH51; Wave II, Section 3, Question 45, Variable Name H2GH45): As previously mentioned, Fredriksen et al. (2004) concludes that insufficient sleep in young people can lead to depression as well as lower self esteem and academic performance. Under the assumption that reduces sleep causes depression rather than vice versa, this variable potentially influences depressi on without directly affecting GPA. However, it was ultimately rejected for final analysis. Other health variables deali ng with ailments/conditions: In the DSM-IV definitions of depressive disorders outlined in Section 1.1, there is recognition that depression might arise from and/or be asso ciated with other health conditions. Students were ask ed a series of questions in the health section of Waves I and II regarding t heir past year frequency of suffering from various ailments and/or conditi ons. Seven variables from these questions were tested as possible instruments: (1) Poor appetite (2) Trouble falling or staying asleep (3) Trouble relaxing (4) Moodiness (5) Frequent Crying (6) Fearfulness (7) Feeling very tired for no reason

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21 Frequency response alternatives include “ never ”, “ just a few times ”, “ about once a week ”, “ almost every day ”, and “ every day ”. For each of these questions, a binary variable was cons tructed to indicate a reported frequency of “ about once a week ” or higher. “Moodi ness”, “fearfulness”, and “frequent crying” were ultimately selected as instruments, with each noted in the DSM-IV as asso ciated features of a ma jor depressive episode. Depression variables from in-school survey : These are binary variables constructed from data provided in the Wave I in-school questionnaire. The variables are similar to the aforement ioned depression indicators developed from responses in the in-home surve ys, except the questions in the inschool surveys pertain to the past 30 days. The base depression question within the in-school survey, asked of students approximately one year prior to the “past week” depression question in the Wave I in-home survey, is: o In the last month, did y ou feel depressed or blue? (In-school questionnaire, variable name S60K). This question is similar to the analogous question from the in-home surveys, except that the time frame is the previous month not week. Potential responses include “ never ”, “ rarely ”, “ occasionally ”, “ often ”, and “ everyday ”. Binary variables were created to reflect reporting of depression (1) “ occasionally ”, (2) “ often ”, and (3) “ everyday ”. This is very similar in nature to the primary past week depression binary variables of “ sometimes ”, “ a lot of the time ”, and “ most of or all of the time ”. In addition, a major depression IV proxy is developed from the in-home survey responses. The

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22 variable is similar in to the afor ementioned “major depression” indicator developed from responses in the in-home surveys. The primary “symptom” indicator includes the question just discu ssed, plus the following questions. “In the last month, did you ____ ?” : o Wake up feeling tired? (In-school questionnaire, variable name S60B). o Have trouble eating, or a poor appetite? (In-school questionnaire, variable name S60I). o Have trouble falling asleep or staying asleep? (In-school questionnaire, variable name S60J). Affirmative responses (“ occasionally ”, “ often ”, or “ everyday ”) to all three questions are required to m eet the criteria for the major depression binary IV. These were the in-school survey questions being most similar to the corresponding earlier-outlined questions from the in-home questionnaires. These variable created from these ques tions, however, was ultimately not used in the final instrumentation procedures. 3.6 Description of Other Variables Chapter 4 provides a description of how the OLS and IV models that estimate the relationship between depre ssion and grades are selected. These models control for a wide range of potentia lly confounding variables, including: Sex (Wave I, Section A, Variable Name BIO_SEX; Wave II, Section A, Variable Name BIO_SEX2). This va riable is represented in the models as

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23 a binary indicator for being female. Month of year interview completed (Wave I, Section A, Variable Name IMONTH; Wave II, Section A, Variabl e Name IMONTH2). Manifested as a vector of binary month indi ctors, this variable accounts for seasonal factors that may affect student performance, in cluding the existenc e of the seasonal affective disorder (SAD) condition. Wave indicator variable: Because data from both survey waves are utilized in the OLS models, a binary wave indicator is included as a covariate School indicator variable: T o test for possible school fixed effects, school indicators (Wave I, Section A, Vari able Name SCID; Wave 2, Section A, Variable Name SCID2) are utilized in the modeling process. (Age) What is your birthdate? (Wave I, Section 1, Question 1, Variable Name H1GI1Y; Wave 2, Section 1, Q uestion 1, Variable Name H2GI1Y). Used in conjunction with information on the date of the survey, this is converted to a vector of age binary variables. (Grade) What grade are you in? (Wave I, Section 1, Question 20, Variable Name H1GI20; Wave 2, Section 1, Question 9, Variable Name H2GI9). This is converted to a binary variabl e for each grade level in the survey. The next two AddHealth variables were converted to a vector of binary variables for race/ethnicity: (Race/Ethnicity) Are you of Hispanic or Latino Origin? (Wave I, Section 1, Question 6, Variable Name H1GI16; Not asked in Wave II).

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24 (Race/Ethnicity) What is your race? (Wave I, Section 1, Question 6, Variable Name H1GI16; Not asked in Wave II). Choices include White, Black, Native American, Asi an/Pacific Islander, and Other. A vector of variables is included in the models to control for student ability: Have you ever skipped a grade? (Wave I, Section 5, Question 3, Variable Name H1ED3; Not asked in Wave II) A binary variable was created to recognize students who have skipped a gr ade, which often results from a student’s high academic ability. AddHealth Picture Vocabulary Test Score: (Wave I, Sect ion A, Variable Name AH_PVT; Not administered in Wave II). As part of the Wave I inhome questionnaire, AddHealth adminis tered an image-based vocabulary and comprehension exam to survey parti cipants, The variable is the actual score achieved by students, with a maximum score of 124. Reported GPA from in-school survey : (In-school survey, Questions S10A through S10D). Students are ask ed to report their mo st recent period grades in English/Language Ar ts, Mathematics, History /Social Studies, and Science, in identical fashion to t he grading questions asked during in-home survey waves I and II, previ ously noted in Section 3.4 The next three variables deal with attendance patterns and long term academic motivation of the students. (Absenteeism) During this school year, how many times were you absent from school for a full day with an excuse – for example, because you were sick or out of town? (Wave I, Section 5, Question 1,

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25 Variable Name H1ED1; Wave 2, Se ction 6, Question 1, Variable Name H2ED1). Choices included “never”, “1 or 2 times”, “3 to 10 times”, “or more than 10 times”. A binary variable was developed for each of these response categories. (Absenteeism) During this school year, how many times have you skipped school for a full day without an excuse? (Wave I, Section 5, Question 2, Variable Name H1ED2; Wave 2, Section 6, Question 2, Variable Name H2ED2). Students reported an open-ended response, their actual estimate of t he number of days skipped. (Desire to Attend College) On a scale of 1 to 5, where 1 is low and 5 is high, how much do you wa nt to go to college? (Wave I, Section 38, Question 1, Variable Name H1EE1; Wave 2, Section 37, Question 1, Variable Name H2EE1). A vector of binary variables was developed for student responses. The following three variables control for parental inputs and potential hereditary factors relevant to student achievement. Two-Parent Household: Constructed from repor ted data in Section 11 (Household Roster) of Waves I and II, a binary variable was created for children of two parent households. Educational Attainment of Biological Parent: In Sections 12 through 15 of Wave I, question number 5 asks about the educational attainment of the biological parent. The parent could be a non-resident biological mother (S.12), resident biological mother (S.14), non-resid ent biological father

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26 (S.13), or resident biological father (S. 15). The question is “how far in school did your parent go?” The choices include: o 8th grade or less o Beyond 8th grade but did not graduate high school o High school graduate o Completed GED o Went to business, trade, or vocational school after high school o Went to college but did not graduate o Graduated from a college or university o Post-graduate training Binary variables were established for each category referenced above. Disabled Biological Parent: In Sections 12 through 15 of Wave I, question number 5 asks about the disability status of the bi ological parent. The parent could again be a non-resident biological mother (S.12), resident biological mother (S.14), non-resident biological fat her (S.13), or resident biological father (S. 15). The question is “Is/was your parent mentally or physically disabled?”

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27 Chapter 4 Methodology 4.1 Methodology Introduction The purpose of the dissertation is to investigate w hether depressed mood among adolescents and young adults causally influences academic achievement. The modeling techniques employed to study this relationship include the following: Ordinary least squares (OLS), addressing omitted variable bias by including additional variables to account for unobserved factors Fixed-effects modeling o School fixed effects o Sibling fixed effects First Differencing Two stage least squares/instrumental variables 4.2 Ordinary Least Squares – Proxy Variable Approach Consider an OLS linear regression of achievement (A) on depression (D) and a vector of exogenous variables ( X ). (1) A = 0 + 1D + X 2 +

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28 “A” represents the dependent variable, achievement, measured in terms of grade point average for the following s ubjects: English, mathematics, history/social studies, and science. “D” represents the depression explanator y variable, as previously defined in Section 3.2. X denotes a vector of exo genous variables (described in Section 3.6) that deal with considerations of student age, sex, grade, ethnicity, ti me of year, family environment, and parental inputs that could influence achievement or depression. 0, 1, and 2 are the parameters to be estimated and is the error term. If unobservable factors exist that are re lated to both depression and grades, one can not assume that there is no correlation between the error term ( ) and depression (D), which is a necessary condi tion for OLS to cons istently estimate the causal effect of depression on achiev ement. If the depre ssion indicator and error term are in fact corre lated, OLS suffers from om itted variable bias. The proxy variables approach to attempts to address the omi tted variable issue within the context of OLS. Unobs ervable factors like motivati on and ability are likely to impact student achievement, and might al so be correlated with experiencing depression. In equation (1), these unobservable factors are omitted and therefore subsumed by the error term The result is omitted variable bias. One method for dealing with omitted variabl e bias is to directly address it by adding proxies for unobserved factors such as those listed above. To do this, The following OLS model is estimated:

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29 (2) A = 0 + 1D + X 2 + M 3 + P 4 + M denotes a vector of th ree student motivation vari ables that reflect the prevalence of absenteeism in the st udent and the student’s desire to attend college. It is conceivable that these variables are in some way affected by depressed mood, so their inclusion impart downward bias (towards zero) in the estimated effect of depression on academic achievement, if depression reduces grades partially by decreasing motivation. P denotes a vector of variables that atte mpt to control for a student’s ability. They would not necessarily be impacted by the presence of current depressed mood because they reflect outcomes t hat occurred before the current period corresponding to the depression indicator. These variables, identified in Section 3.6, include (1) whether or not the student has ever skipped a grade, (2) the student’s score on the AddHealth picture vocabulary test (PVT), and (3) the student’s reported grade from the initial in-school survey for each of the major subjects of study (English, Math, Scienc e, and History/Social Studies). Although determined prior to current depression, t hese variables might be related to past or persistent depression, so they coul d again impart downward bias in the estimated depression effect. For ex ample, if academic performance was affected by past depression, then student s who display persistent depressed mood might also have lower test scores and lower probability of skipping a grade. The addition of the M and P vector s to the regression equation should alleviate issues related to bias from omi tting any variables that affect grades as a

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30 result of a student’s ability or motivation to do well in sc hool. It is important to further recognize that while a student suffering from depression may feel less motivated to achieve, depression does not have to exist in order for the student to be academically unmotivated. 4.3 First Differencing A primary econometric use of panel data is to allow for the presence of timeinvariant unobserved effects that are corre lated with the explanatory variables. In this study, many unmeasured factors that affect GPA and might be correlated with depression could be const ant over time. Some ex amples include hereditary factors and family status. In a twoperiod panel, time-invariant unmeasured factors, or unobserved het erogeneity, can be addressed through the process of first differencing The first difference is the change in the value of a variable from the first period of the panel to the se cond. This is a natural setup in this case, in which the difference in student responses between Wave I and Wave II, for those who have responded in both survey waves, can be constructed. The equation for a first-differ enced model is denoted as (3) A = 0 + 1D + X 2 + M 3 + P 4 + Where denotes the change from t = 1 to t = 2. In this analysis, the first differencing procedure eliminates unobserved, time invariant factors that may affect student achievement. First differencing across waves is conducted for the responses of each individual that is surveyed in both Wave I and Wave II. T he OLS estimator of the effect of the change in

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31 depression on the change in GPA is referred to as the first-differenced estimator of depression on GPA. In a first differenced equation, any m easurement that does not change over time (for example, the sex or race of a student) will be “differenced away”. Therefore, the results of the FD analysis will estima te the relationship between changes in the dependent variable (grades ) and changes in depression status, holding constant other expl anatory variables that can vary over time. 4.4 School Fixed Effects With 144 U.S. middle and high schools included in the AddHealth Wave I and Wave II surveys, an opportunity exis ts to evaluate effects on academic performance attributable to particular sc hools. The survey schools could have wide variation in the relative standards of their respective curriculums, in addition to socioeconomic and demograph ic disparities. School fixed effects estimation was performed to eliminate cross-school heterogeneity by isolating the “withinschool” variation. This simply entails adding a binary variable for each survey school (except one), which equals 1 if t he student attends the school and zero otherwise, to equation 4.2. The estimates from this regression are purged of bias from school-specific elements that cont ribute to both academic achievement and depression incidence. 4.5 Sibling Fixed Effects Section 2.3 of the disse rtation noted that Wolfe and Fletcher (2007), found

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32 that the estimated ADHD impacts on achievem ent were not robust to controls for unobserved sibling effects. This outco me underscores the importance in this study of attempting an analogous method. If siblings with different depression status have correspondingly different ac ademic achievement, this would provide further evidence that any depression effe cts estimated in the OLD, FD, and school FE models do not merely reflec t spurious correlation induced by unobserved factors that simultaneously determine depression and achievement. AddHealth does not report sibling achi evement or mental health, but as detailed earlier, did intent ionally survey groups of siblings from the same households. Identifiers within the AddHealth dete rmine which respondents are siblings. To control for sibling effects a vector of fixed effects, i. e. binary variables that equal 1 if the respondent is a member of a specific sibling group and 0 other wise, is included in the regression equat ion for each sibling pair responding to Waves I and II. This procedure controls for unobserved family-specific factors that are correlated with both achievement and depression. 4.6 Two Stage Least Squar es/Instrumental Variables Section 4.2 discussed the implementat ion of a proxy variable approach to address omitted variable bias. The prox y variable approach, however, does not deal with the other two problems that create endogeneity, measurement error and reverse causation. This section discusses a methodology that addresses these issues as well as omitted variable bias, known as the instrumental

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33 variables (IV) approach. If we consider the scenario in wh ich depression responds to changes in grades, e.g. a student becomes depressed because of receiving poor grades, then shocks to the error term will ci rculate to depression through the achievement (dependent) variable. This is called the simultaneity, or reverse causation, problem. The most common solution to the addr ess the aforementioned problems is the two-stage least squares (2SLS)/instr umental variable (IV) approach, which produces consistent estimates even in the presence of endogeneity. The 2SLS/IV approach requires one or more in strumental variables. Wooldridge (2003) explains that appropriate IV’s must satisfy two conditions: The instrument must be uncorrelated with the error term and it must be correlated with the suspected endogenous variable; in this case, the depression explanatory variable D. In simpler terms, at leas t one variable must be identified that is correlated with depression but is ot herwise uncorrelated with academic performance. Sections 3.5 and 3.6 present a series of AddHealth “candidate” variables considered for implementation as inst ruments. The first candidate variable, hours of sleep, might meet the first IV criterion, as Fredriksen et. al. indicates that insufficient sleep leads to depressed mood. That study also finds, however, that insufficient sleep negatively impacts GPA in middle school students, which calls into question whether this variable fully satisfies the second IV criterion, that insufficient sleep is not otherwise re lated to academic performance.

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34 The next series of IV candida tes address whether students experienced the following conditions within the last 12 months: Poor appetite; Trouble falling asleep or staying asleep; Trouble re laxing; Moodiness; Frequent Crying; Fearfulness; Feeling very tired, for no reason. Each of these health variables has a potentially significant correlation wi th depressed mood, but not necessarily grades, other than the sleep and tiredness variables as just discussed. The final series of IV candidates are the binary variables for depression (including major depression) created from the Wave I in-school survey. These variables, are presumably highly co rrelated with subsequent depression as reported in the in-home su rveys, but have the potentia l to separately impact achievement if persistent or prolonged depression is relevant. An argument for possibly considering t he parental disability variable noted in Section 3.5 is that conditioning on par ental education in t he GPA equation may eliminate the potential c onnection between parental disability and respondent achievement, thus leaving this variabl e as one that would have a possible correlation with depressed mood in students (I V criterion #1) but not achievement (IV criterion #2). The 2SLS modeling procedur e in this case commences with a “first stage” OLS regression of depression on the instru ment(s) as well as all exogenous and explanatory variables. A significant t-st atistic on the candidate variable suggests that it may be an effective in strument for use in 2SLS. The fitted values from this regression are obtained for use in the second stage, which is simply an OLS regression of the structural equation in Section 4.1, substituting the depression

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35 variable with the fitted values from the first stage regression. Using more than one instrument necessitates testing for over identifying restrictions To test for overidentifying restrictions, the Davidson -Mackinnon (1993) test is performed. This procedure involves obtaining the residuals from 2SLS modeling and performing an auxiliary regressi on. More specifically: (1) Estimate the GPA equation by 2SLS and obtain the residuals. (2) Regress the residuals on all ex ogenous variables, including the instruments, and obtain the R-squared from this regression (call it R2*) (3) Under the null hypothesis t hat the overidentifying IV’s are uncorrelated with the 2SLS residu als, the test statistic is nR2*, with a 2 q distribution, where q is the number of IV’s minus the number of endogenous explanatory variables. If nR2* exceeds the 5 percent critical value in the 2 q distribution, we reject the null hypothesis of inst rument exogeneity and conclude that at least one of the IV’s is separately correlated with achievement. Two other methodological points ar e of note. First, although 2SLS estimates are consistent if instrum ent strength and exogeneity conditions are satisfied, they are inefficient relative to OLS if it turns out that depression is truly exogenous with respect to achievemen t. Even strong instruments generate larger standard 2SLS errors than those from OLS regr essions. As a result, endogeneity testing using the Hausm an (1978) method of comparing the statistical significance of the differ ences between 2SLS and OLS estimates can

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36 be implemented. Another advantage of 2SLS, as previ ously mentioned, is that it also addresses the issue of errors in the m easurement of the depr ession variable, which likely exist to some degree because the AddHealth data used are almost entirely self-reported. To summarize, 2SLS/IV will produce c onsistent estimates of the causal effect of depression on academic achiev ement in the presence of endogeneity, if valid instrument variables are used and all remaining classical linear regression model (CLRM) assumptions are met. 4.7 Synopsis of Model Runs The following presents a sequential out line of all OLS and 2SLS models developed and estimated fo r this dissertation: 4.7.1 OLS Regression of GP A on Depression and Exogenous Variables, by Progre ssive Depression Severity Model: A = 0 + 1D + X 2 + The dependent variable in this eq uation (A) is grade point average. Five separate equations are necessary to estimate each GPA-depression relationship, including one for Englis h GPA, one for math GPA, and one each for social studies GPA, sci ence GPA, and overall GPA. The independent variables in the equat ion include the following: “Depressed some of the time” binary variable (D) “Depressed a lot of the time” binary variable (D)

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37 “Depressed most or all of the time” binary variable (D) Binary variable for each month of su rvey administration, from January through November (Dec ember omitted) (X) Binary variables of student age by year, from “under 12” through “age 19” (“age greater than 19” omitted) (X) Binary variables of student grade by year, from “grade 7” through “grade 11” (“grade 12” omitted) (X) Binary variables of student race, in cluding “white”, “Hispanic”, “black”, “Native American”, and “Asian/Pacific Is lander” (“other races” category omitted) (X) Binary variable for identifying whet her or not the student comes from a 2-parent household (X) Binary variables for parental disability (X) Binary variables for academic achi evement of each parent, including the categories “beyond 8th grade-no high school”, “vocational school instead of high school”, ”high school graduate”, “GED”, “vocational school after high school”, “att ended college but did not graduate”, “college graduate”, and “pos t-graduate training” (“8th grade or lower” education category omitted (X)) The results of this model run ar e discussed in Section 5.2 of the dissertation, and Table 2.

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38 4.7.2 OLS Regression of GP A on Depression and Exogenous Variables, for Major Depression Only Model: A = 0 + 1D + X 2 + This equation is identical to the one discussed in Section 4.7.1, with one exception. Instead of including the three progressive states of depression in a single equation (“some of th e time”, “a lot of the time”, “most or all of the time”), only the major depr ession binary variable is included as a depression variable. It was necessa ry to separately estimate major depression because of identification ov erlaps between those meeting major depression criteria and those in t he progressive depression severity categories. The results of this model scenario can also be found in Section 5.2 and Table 2. 4.7.3 OLS Regression of GPA on De pression, Exogenous Variables, and Motivation Variables, by Progressive Depression Severity Model: A = 0 + 1D + X 2 + M 3 + This model adds the vector of motivation proxy va riables to the equation profiled in Section 4.7.1. These variables include: Binary variables for number of excused absences in school year, including the categories “1 to 2 times”, “3 to 10 times”, and “more than 10 times” (“never” response omitted). Number of unexcused abs ences in school year Binary variables for desire to go to college, with the categories “very low”, “low”, “medium”, and “hi gh” (“very high” omitted).

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39 All other estimation procedures are identical to that identified in Section 4.7.1. The results of this model run can be found in Section 5.3 of the dissertation, and Table 3. 4.7.4 OLS Regression of GPA on De pression, Exogenous Variables, and Motivation Variables, for Major Depression Only Model: A = 0 + 1D + X 2 + M 3 + In identical fashion to that descr ibed in Section 4.7.2, this equation replaces the progressive depression variables in 4.7.3 with the major depression variable, to estimate the impacts of major depression on GPA when motivation proxies are added. T hese results are also located in Section 5.3 and Table 3 of the dissertation. 4.7.5 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, by Progressive Depression Severity Model: A = 0 + 1D + X 2 + P 4 + This model adds the vector of abi lity proxy variables to the equation in Section 4.7.1. These variables include: Binary variable that acknowledges whether or not the student has ever skipped a grade AddHealth Picture Vo cabulary Test Score Reported GPA from initial in-school survey Estimation of the model is identical to that described in Section 4.7.1. The results of this model run can be found in Section 5.4 and Table 4 of the dissertation.

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40 4.7.6 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, fo r Major Depression Only Model: A = 0 + 1D + X 2 + P 4 + Again, the equation repl aces the progressive depression variables in 4.7.5 with the major depression binary va riable, to estimate the impacts of major depression on GPA when ability prox ies are included. These results are also seen in Section 5.4 and Table 4. 4.7.7 OLS Regression of GPA on De pression, Exogenous Variables, Motivation Variables, and Abilit y Variables, by Progressive Depression Severity Model: A = 0 + 1D + X 2 + M 3 + P 4 + This equation includes the depression measures and exogenous variables noted in 4.7.1, in addition to both the motivation va riables (4.7.3) and ability variables (4.7.5). This represents the “base” equation of explanatory variables from which all other analyses are conducted. Estimation of the model is identical to that described in Sect ion 4.7.1, 4.7.3, and 4.7.5. The results of this m odel run can be found in Section 5.5 and Table 5 of the dissertation. 4.7.8 OLS Regression of GPA on De pression, Exogenous Variables, Motivation Variables, and Ability Variables, for Major Depression Only Model: A = 0 + 1D + X 2 + M 3 + P 4 + The major depression binary variabl e replaces the three progressive depression variables in 4.7.7, with re sults also shown in Section 5.5 and Table 5.

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41 4.7.9 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, by Grade Model: A = 0 + 1D + X 2 + M 3 + P 4 + (for each grade 7 – 12) The equation and model procedures discussed in sections 4.7.7 and 4.7.8 were used to run OLS analyses by grade level, from grade 7 through grade 12. This exercise allows us to see differentials in depression impacts across grades, and determine whether or students in certain middle or high school grades are suffering greater achievement impacts from depressed mood. This grade-based OLS modeli ng is done for the progressive depression measures in a single equation, and major depression in a separate equation. The results of th is modeling are presented in Section 5.7 and Tables 7 through 14 of the dissertation. 4.7.10 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, by Gender Model: A = 0 + 1D + X 2 + M 3 + P 4 + (for males & females) The equations and models presented in sections 4.7.7 and 4.7.8 were also used to create gender-specific OLS regressions. This procedure helps to identify if there is a diffe rence in depression effects on grade performance between male and female students. These analyses are again conducted for the progressive depressi on measures in a single equation, and major depression in a separate equatio n. Model results are presented in Section 5.8 and Tables 15 16 of the dissertation.

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42 4.7.11 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, by Race/Ethnicity Model: A = 0 + 1D + X 2 + M 3 + P 4 + (by race/ethnicity) The final series of stratified OL S models were developed to compare depression impacts amongst various et hnic segments. These equations and models continue to be consistent wit h that presented in sections 4.7.7 and 4.7.8. The race-bas ed models also evaluate progressive depression measures in a single equation, and major depression in a separate equation. Model results are presented in Section 5.9 and Tables 17 through 23 of the dissertation. 4.7.12 OLS Regression of GPA on De pression, Exogenous Variables, and Ability Variables, for Persistent Depression Model: A = 0 + 1D + X 2 + M 3 + P 4 + For this equation, the binary depression persistence measures discussed in Section 3.3 ( persistent depression, onset depression, remittance depression ) replace the three progressive depression variables of “some of the time”, “a lot of the time”, and “most or all of the time”. No other changes are made to the base OLS equation. The results of the OLS persistence depression analysis are f ound in Section 5.10 and Table 24. 4.7.13 OLS Regression – School Fixed Effects Model: A = 0 + 1D + X 2 + M 3 + P 4 + S 5 + A school-based fixed effects analysis was conducted In an attempt to determine if any effects on academic performance are attributable to particular schools in the AddHealth su rvey. The rationale behind this

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43 analysis is based on consideration of the fact that particular schools may have divergent qualities in educational curriculum, as well as locationspecific socioeconomic considerations that may impact students’ learning capabilities. A vector of binary variables (S) identifying each of the 144 middle and high schools, save one, was added to the base OLS equation noted in Section 4.7.7 for this exercise. Impacts related to progressive states of depression severity, majo r depression, and depression persistence were modeled. A dummy variable regre ssion is employed, to control for the factors discussed in Section 4.4 of t he dissertation. Results of the school FE analysis are presented in Section 5.6 and Table 6 of the dissertation. 4.7.14 OLS Regression – Si bling Fixed Effects Model: A = 0 + 1D + X 2 + M 3 + P 4 + F 6 + To control for student achievem ent considerations that may be influenced by siblings, each full sibling pai r in the survey was identified, and a corresponding binary variable was assign ed to that pair. OLS regressions for Wave I and Wave II were conducted specifically on this group, with addition of the sibling bi nary vector (F) to the base OLS equation noted in Section 4.7.7. Impacts re lated to progressive states of depression severity and major depression were analyzed. Once again, a dummy variable regression is employed, in order to control for family-specific factors discussed in Section 4.5 of the disse rtation. Sibli ng FE results are presented in Section 5.12 and Tables 26 and 27 of the dissertation.

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44 4.7.15 OLS Regression – First Differencing Model: A = 0 + 1D + X 2 + M 3 + P 4 + The first differencing analysis is intended to measure changes in survey responses for students who ans wered questions in both the Wave I and Wave II surveys. For the near ly 15,000 students who responded in both survey waves, the difference in their individual responses between Wave I and II was calculated, and t he OLS model from Section 4.7.7 was used on this dataset to see whether or not depression continued to have a practically and statistically significant impact on grades. If the impacts do not remain statistically significant or change in practical significance by a large amount, it may be an indication t hat time factors (which may include depression persistence) are having an impact on the depression-GPA relationship. Of course, the chall enge in dealing with multiple binary variables that represent severity, or “degrees” of depr ession, can create challenges for effective analysis using a first-differencing methodology. The results of this analysis should demons trate the strength of the depressionGPA relationship, after unobserved time factors have been accounted for. As standard practice, impacts related to progressive states of depression severity and major depression were ev aluated. Results of the first differencing analysis and further discussi on of FD limitations are addressed in Section 5.11 and Table 25 of the dissertation.

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45 4.7.16 Instrumental Variables/Tw o Stage Least Squares (2SLS) Regression The following criteria was used to evaluate candidate instruments for major depression: Plausible argument that instrume nt is correlated with depression yet does not directly affect academic performance Significant t-statistics on candidat e variable in first-stage regression 2SLS analysis of instrument yields stat istically significant robust t-statistic in second-stage regression Sign of instrument is the same as the suspected endogenous variable, and the magnitude of the coefficient is reasonably similar (in this case, less than 0.5) R-squared of first stage regression is maximized If multiple instruments are us ed, the instruments must pass overidentification tests Initial testing on the following candidate instruments for major depression noted in Section 3.5 and 4.6 re sulted in their rejection for final tests of validity. Failures included st atistically insignificant t-statistics on first-stage regressions of the depression instrument at a 5 percent level of significance; or a second stage instrum ent coefficient with incorrect sign, insignificant t-statistic, or magnitude that exceeded a full grade point (1.0). As a result, they were eliminat ed from further validity testing. Poor appetite

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46 Hours of sleep Trouble falling asleep Trouble relaxing Feeling tired for no reason Parental disability Depression variables from initial in-school survey Instrument candidates that pa ssed initial testi ng and could be evaluated for further criteria (e.g. ov eridentification testing) included the following variables: Frequent crying within the prev ious 12 months, for no apparent reason (“crying12”) Moodiness within the previous 12 months (“moody12”) Fearfulness within the prev ious 12 months (“fearful 12”) Section 5.13 and Tabl es 28 and 29 of the dissertation offer the results of the two-stage least squa res modeling and ov eridentification testing for these candidate instruments. 4.8 Summary of Advantages & Disa dvantages of Model Alternatives Ordinary Least Squares/Pr oxy Variable Model (4.2) : The commonly recognized theoretical advantage of Ordinary Least Squares (OLS) regression analysis, is that has been shown to be the best method of satisfying the GaussMarkov theorem, where errors have ex pectation zero and have equal variances.

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47 Under the assumptions of linearity in parameters, random sampling, zero conditional mean, no perfect collinearity, and unbiasedness, the OLS estimator is the best linear unbiased estimator The primary disadvantage of using this approach is that, even with the inclusi on of proxy vectors to control for unobserved factors which may impact gr ade performance, om itted variables within the OLS equation(s) may exist. Omitted variable bias causes OLS estimators to be biased. First Differencing (4.3) : The principal benefit from employing first differencing (FD) in this analysis is that it controls for time-invariant factors related to student achievement, and allows for the effect of time-related issues not considered by the OLS model to be c onsidered in the analysis. The principal disadvantage of using the FD approach fo r this study primarily deals with the nature of the data. Consider th e following: The base OLS equation of progressive depression has three binary vari ables representing varying, mutually exclusive degrees (severity) of self-r eported depressed mood in students. The FD analysis, on its own, cannot determi ne if a change in one depression state (depressed some of the time, a lot of the time, most or all of the time), is resulting in an increase or decrease in depressed mood, from one wave to the next. For example: Consider a student who reports depressed mood of “a lot of the time” in Wave I. That student r eports no depressed mood of “a lot of the time” in Wave II. Did the student have an increase, or a decrease, in depressed mood from Wave I to Wave II? T he binary variables indicating the other two depression severity levels (some of the time, most or all of the time), may display this

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48 change, but the FD procedure falls short of being able to explai n the direction of this change. Therefore, the results of the FD analysis may not provide relevant information to account for the direction of such a change. Fixed Effects (School FE {4.4} and Sibling FE {4.5}) : The advantage of using fixed effects models is that they can control for individual differences that affect achievement which are unobserv able in the base OLS model. In this study, performance differences which may be attributable to individual schools, or differences that arise from family (sib ling) factors, are a ccounted for by the use of FE models. The disadvantage of usi ng these FE estimators varies based on the type of estimator used. In the case of schools, sufficient information does not exist to make a determination as to w hether or not educational or demographic standards vary across the 144 surveyed schools, so it is difficult to establish the full meaning of employing a school FE mode l for this analysis. In the case of sibling FE, there does not exist a co mprehensive profile of the social, psychological, and physical background of each student and their corresponding sibling. Therefore, it is difficult to accurately surmise all of the relevant sibling/family factors, if any, that may be attributabl e to the academic performance of the surveyed student(s). Two Stage Least Squares/Instru mental Variables (4.6) : Two-stage least squares regression is beneficial to employ when there is concern of endogeneity. If we believe that depression may be a result of grade performance (e.g. reverse causation), or if measurem ent error may exist, then 2SLS can produce consistent estimates in most forms of this endogen eity. Disadvantages of employing 2SLS

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49 arise in finding variables which satisf y the necessary criteria required for an effective instrument, which are noted in Section 4.6, and discussed in later sections of the analysis.

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50 Chapter 5 Results 5.1 Summary Statisti cs for Key Variables Table 1 presents summary statistics on grade point average, demographic characteristics, family background, moti vation, and ability for Wave I and Wave 2 survey respondents. The statistics ar e presented by “cat egory” of depressed mood for the student responde nts (no depressed mood, depressed some of the time, depressed a lot of t he time, depressed most or all of the time, major depression). The sample of respondents with “major d epression” characteristics is estimated at 6.8 percent. This co mpares with reported 12-month prevalence rates of 8.3 percent for U.S. adolescent s, and 10.3 percent in the general U.S. population, as reported by Birmaher, et al. (1996). Students who reported depressed mood of “some of the time” have GPA’s of 0.108 to 0.177 grade poi nts lower than students who report no depressed mood. For students with depre ssed mood “a lot the time ”, GPA’s were reported to be 0.203 to 0.271 grade points lowe r than those students with no depressed mood. Students who report depressed mood “most or all of the time” reported averages of 0.345 to 0.462 grade point s lower than students reporting no depression. Finally, students identified with “major depression” characteristics reported averages of 0.359 to 0.434 grade points lower than non-depressed students. This shows a progressive im pact in GPA decline, depending upon the

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51 severity (frequency) of the reported depressed mood, and the grade impacts appear to be more significant in social studies and science than English and math. Depression prevalence also increases with age. Table 1 Summary Statistics Depression Impacts on GP A CATEGORIES OF DEPRESSION FREQUENCY No Depressed Some of A lot of Most or all of Major Mood the Time the Time the Time Depression navg.navg.navg.navg.navg. GP A English19,8242.8699,6902.7622,2792.6668942.5252,0832.510 Math18,7192.7289,0202.5762,1232.4738262.3371,9322.335 Social Studies17,6692.9388,6102.7612,0262.6668052.5111,8852.505 Science17,6592.8748,4362.7051,9642.6037762.4121,7892.475 Overall15,1422.8847,1852.7271,6522.6386502.4521,5282.492 FEMALE 21,3160.45510,5770.5622,5550.6581,0350.6932,3960.654 AGE Less than 1221,3160.00110,5770.0002,5550.0001,0350.0002,3960.000 age1221,3160.01710,5770.0132,5550.0111,0350.0042,3960.008 age1321,3160.09210,5770.0682,5550.0471,0350.0402,3960.042 age1421,3160.14310,5770.1142,5550.1001,0350.1232,3960.096 age1521,3160.17210,5770.1602,5550.1641,0350.1712,3960.162 age1621,3160.19610,5770.2052,5550.2141,0350.2082,3960.220 age1721,3160.19110,5770.2172,5550.2301,0350.2202,3960.218 age1821,3160.14710,5770.1702,5550.1771,0350.1732,3960.181 age1921,3160.03510,5770.0422,5550.0481,0350.0522,3960.057 >1921,3160.00610,5770.0102,5550.0091,0350.0102,3960.015 GRADE grade721,3160.08910,5770.0742,5550.0541,0350.0432,3960.054 grade821,3160.15010,5770.1202,5550.1061,0350.1162,3960.101 grade921,3160.17110,5770.1552,5550.1591,0350.1692,3960.158 grade1021,3160.18510,5770.1872,5550.1941,0350.1922,3960.194 grade1121,3160.18010,5770.2102,5550.2171,0350.1902,3960.205 grade1221,3160.16610,5770.1832,5550.1781,0350.1732,3960.174 RACE/ETH. Hispanic21,3160.16410,5770.1792,5550.1761,0350.1862,3960.203 White21,3160.62310,5770.5992,5550.5971,0350.6012,3960.548 Black21,3160.23110,5770.2342,5550.2381,0350.2502,3960.234 Native American21,3160.03310,5770.0382,5550.0391,0350.0462,3960.045 Asian/Pacific Islander21,3160.07210,5770.0852,5550.0771,0350.0712,3960.106 Other Races21,3160.09210,5770.0992,5550.1051,0350.0902,3960.125 SKIP GRADE 21,3160.02710,5770.0302,5550.0311,0350.0432,3960.041 AH PVT SCORE 20,259100.54010,06898.6012,41497.78599496.6532,27395.939

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52Table 1 (continued) Summary Statistics Depression Impacts on GP A CATEGORIES OF DEPRESSION FREQUENCY No Depressed Some of A lot of Most or all of Major Mood the Time the Time the Time Depression navg.navg.navg.navg.navg. EXCUSED ABSENCES 021,3160.12010,5770.0952,5550.0841,0350.0882,3960.084 1 to 221,3160.30810,5770.2762,5550.2381,0350.1962,3960.214 3 to 1021,3160.42210,5770.4292,5550.3961,0350.3862,3960.381 11 or more21,3160.10610,5770.1442,5550.2071,0350.2312,3960.221 UNEXCUSED ABSENCE 20,3771.5669,9772.3552,3563.6299315.0412,1534.237 DESIRE FOR COLLEGE very low21,3160.03510,5770.0442,5550.0591,0350.0912,3960.069 low21,3160.02610,5770.0352,5550.0441,0350.0462,3960.062 medium21,3160.09210,5770.1162,5550.1501,0350.1382,3960.161 high21,3160.13110,5770.1412,5550.1371,0350.1272,3960.157 very high21,3160.69510,5770.6462,5550.5941,0350.5812,3960.535 2 PARENT HH 21,3160.65410,5770.6022,5550.5501,0350.5132,3960.528 MOTHER DISABLED 21,3160.04910,5770.0582,5550.0631,0350.0782,3960.067 FATHER DISABLED 21,3160.06510,5770.0742,5550.0731,0350.0922,3960.091 MOTHER'S EDUCATION 8th grade or less21,3160.05510,5770.0662,5550.0621,0350.0712,3960.074 9th grade, no hs 21,3160.10110,5770.1212,5550.1441,0350.1392,3960.162 Vocational, no hs21,3160.00810,5770.0082,5550.0091,0350.0122,3960.009 High school grad21,3160.30910,5770.3052,5550.2921,0350.3152,3960.291 GED21,3160.03710,5770.0432,5550.0461,0350.0482,3960.045 Vocational after hs21,3160.06510,5770.0642,5550.0711,0350.0592,3960.061 Some college, not finish21,3160.13210,5770.1252,5550.1261,0350.1242,3960.122 4 year college degree21,3160.19510,5770.1802,5550.1751,0350.1412,3960.162 Post-graduate work21,3160.08010,5770.0702,5550.0581,0350.0622,3960.053 FATHER'S EDUCATION 8th grade or less21,3160.05510,5770.0682,5550.0691,0350.0782,3960.075 9th grade, no hs 21,3160.08910,5770.0982,5550.1191,0350.1142,3960.122 Vocational, no hs21,3160.00710,5770.0082,5550.0071,0350.0062,3960.010 High school grad21,3160.28610,5770.2972,5550.2861,0350.2952,3960.288 GED21,3160.02810,5770.0292,5550.0281,0350.0262,3960.027 Vocational after hs21,3160.05610,5770.0532,5550.0511,0350.0602,3960.051 Some college, not finish21,3160.10910,5770.1022,5550.1041,0350.0852,3960.094 4 year college degree21,3160.18710,5770.1662,5550.1551,0350.1452,3960.142 Post-graduate work21,3160.09510,5770.0852,5550.0761,0350.0652,3960.061 Females comprise the majority of respondents reporting depressed mood (56.2 percent of “depress ed some of the time” res pondents, to 69.3 percent of “depressed most or all of the time re spondents”). Whether this suggests that females are more likely than males to be depressed during this period of life, to accurately self-report their feelings of depression, is an issue that will be discussed later in the paper.

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53 Regarding ethnicity, whites make up the largest share of survey respondents for all depression categorie s, including no depressed mood. However, as the severity of depressi on increases, whites make up a lower overall share of the re spondents. The percentage drops from 62.3 percent reporting no depressed mood, to 60 percent reporting depressi on of most or all of the time, and only 54.8 percent repor ting symptoms consistent with major depression. Ethnic groups with larger shares of the “more depressed” respondent base include Hispanics, Asians, and Native Americans. The share of black respondents remained relatively cons tant across all depression categories. Other summary statistics observations include the following; respondents who have skipped grades make up a slight ly higher share of the more frequently depressed groups than the non-depressed group. Respondents with collegeeducated parents make up a smaller shar e of the frequently depressed groups than the non-depressed group. In additi on, the more depressed respondent groups have lower standardized test scores, higher rates of absenteeism, lower desire to attend college, and are more likel y to live in a single-parent household with a disabled parent. Again, these im pacts also appear to be progressive, based on the severity of reported depressed mood. 5.2 OLS Regression of GPA on De pression and Exogenous Variables Table 2 provides results from t he OLS regression of GPA on depression and exogenous variables. We see the ex pected negative relationship between

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54 depressed mood and GPA, as well as the pr ogressive nature of the impact that more severe depressive states have on grades. Table 2: Results OLS Regression of GPA on Depression and Exogenous Variables Only Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables Only Depressed Some of the Time -0.150-14.700 -0.123-10.760-0.135-10.240-0.169-13.160-0.161-12.360 Depressed a Lot of the Time -0.231-12.530 -0.230-11.290-0.223-9.520-0.257-11.250-0.254-10.830 Depressed Most or All of the Time -0.406-14.300 -0.361-11.490-0.350-9.670-0.401-11.480-0.432-11.990 Major Depression -0.305-16.200 -0.326-15.600-0.299-12.410-0.338-14.560-0.303-12.560 For students reporting depre ssed mood of “some of t he time”, overall GPA falls by 0.15 grade points. Students reporting depressed mood “a lot of the time” have an overall GPA reduction of 0.231 grade points. Depressed feelings “most or all of the time” results in a 0.406 overall grade point reduction. Those with characteristics consistent with ma jor depression suffer a 0.305 grade point decline. When individual subjects are evaluated, results vary somewhat, based on the type of depressive mood reported. In the regression with the categorical depression variable, the lar gest grade impacts are consist ently in social studies and science. GPA is most affected in so cial studies, with English second. As illustrated in Table 1, all depression coe fficients display very high levels of statistical significance. 5.3 OLS Regression of GPA on Depr ession, Exogenous Variables and Motivation Proxies Table 3 displays the results when the motivation proxy variables are added to the base OLS model as discussed in sections 3.6 and 4.7.3. Although

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55 depression is only one of many pot ential reasons for a lesser degree of motivation, including these motivation prox ies in the OLS equation should help to mitigate omitted variable bias. Table 3: Results OLS Regression of GPA on Depression, Exogenous Variables, and Motivation Proxy Vector Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies Depressed Some of the Time -0.112-11.560 -0.083-7.480-0.099-7.700-0.127-10.250-0.122-9.570 Depressed a Lot of the Time -0.145-8.300 -0.134-6.760-0.142-6.170-0.158-7.120-0.165-7.220 Depressed Most or All of the Time -0.272-10.100 -0.228-7.490-0.232-6.500-0.262-7.720-0.294-8.370 Major Depression -0.200-11.170 -0.216-10.640-0.197-8.290-0.225-9.950-0.190-8.080 As expected, the inclusion of the motivation proxies reduces the overall negative impacts of depressed mood on G PA. Coefficient magnitudes generally fall by about one-third. St udents remain more impacted in social studies and science courses than in math and E nglish when depression is measured categorically, while those with major depr ession characteristics see the largest GPA impacts in social studies and Englis h. The depression coefficients remain very highly statistically significant. 5.4 OLS Regression of GPA on Depr ession, Exogenous Variables and Ability Proxies For the next OLS model, the ability prox y variables are substituted for the motivation proxies in the regression equat ion. This allows for comparative assessment of the impact s of the ability and mo tivation vectors on the GPA/depression relationship. The ability proxies, noted in Section 4.7.5, attempt to control for a student’s natural intelligenc e and/or aptitude. Again, inclusion of

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56 these variables is intended to at least partially address the issue of omitted variable bias. Table 4: Results OLS Regression of GPA on Depression, Exogenous Variables, and Ability Proxy Vector Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Ability Proxies Depressed Some of the Time -0.056-5.160 -0.064-4.920-0.063-4.220-0.086-4.700-0.089-5.790 Depressed a Lot of the Time -0.075-3.640 -0.138-5.850-0.113-4.150-0.116-2.870-0.130-4.560 Depressed Most or All of the Time -0.220-6.800 -0.213-5.700-0.238-5.540-0.143-5.840-0.343-7.700 Major Depression -0.128-5.790 -0.190-7.620-0.215-7.460-0.137-4.730-0.167-5.470 The results of Table 4 suggest that controlling for student ability generally has a more substantial mitigating effect on the depression/GPA relationship than controlling for motivation. While t he relationship between GPA and depression remains consistently negative and highly significant, the impac ts of depression on grades are typically less than that seen when the motivation proxies are added, although this varies by depression category and subject. The depressed “some” and “a lot” of th e time coefficients fall by 25-50 percent, except in one case (English) the latter actually increases slightly. Effects of “most or all of the time” and major depression are generally less impacted, with the math and science coefficients either rising or falling only slightly, but decline considerably for social studies. The net result is that science GPA now experiences the largest effect for the categorical depressi on measure, while major depression has the biggest impact on math.

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57 5.5 OLS Regression of GPA on De pression, Exogenous Variables, Motivation Proxies, and Ability Proxies This model includes both the motivation and ability pr oxies, in an attempt to maximally control for factors that may influence student grades, in addition to depressed mood. Table 5 presents the results. Table 5: Results OLS Regression of GPA on Depression, Exogenous Variables, Motivation Vector, and Ability Vector Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies Depressed Some of the Time -0.045-4.290 -0.044-3.470-0.046-3.090-0.068-4.580-0.071-4.700 Depressed a Lot of the Time -0.040-1.990 -0.080-3.440-0.066-2.420-0.066-2.430-0.081-2.870 Depressed Most or All of the Time -0.159-5.000 -0.125-3.400-0.166-3.890-0.061-1.430-0.258-5.840 Major Depression -0.087-4.030 -0.127-5.160-0.157-5.470-0.081-2.850-0.105-3.470 The impact of depression on grades is further reduced. Students with depressed mood “some of the time” have a 0.045 grade point reduction in overall GPA. Students reporting depressed mood “a lot of the time” are negatively impacted overall by 0.040 grade points. T hose with depressed feelings “most or all of the time” have a 0.159 overall grade point reduction. Students in the major depression category suffer a 0.087 grade poi nt drop. The coursework most significantly affected in this model re mains largely unchanged from the “ability vector only” model (Table 4). Table 5 indicates that all but one depression coefficient (“depressed most or all of the time” – social studies) remains statistically significant at 5 percent. It is also conceivable t hat the inclusion of these motivation and ability variables may be capturing some of the effects of depressed mood on grades; thus the results may be conservative.

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58 5.6 OLS Regression – School Fixed Effects Section 5.7 will present results fo r various grades in school, from 7th through 12th grade. Before these results are di scussed, the study assesses whether the results hold within schools or are partially caused by variation across schools in unobserved factors. Binary indicators for each school were created, and added to the base OLS model, in an attempt to determine whether controlling for variation across schools would further mitigate the impacts of depression on GPA. Table 6: Results OLS-School Fixed Effects Analysis Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (School FE) Depressed Some of the Time -0.043-4.050 -0.041-3.190-0.041-2.750-0.061-4.110-0.064-4.260 Depressed a Lot of the Time -0.035-1.750 -0.072-3.090-0.055-2.050-0.066-2.410-0.076-2.710 Depressed Most or All of the Time -0.156-4.960 -0.121-3.310-0.166-3.920-0.058-1.360-0.241-5.510 Major Depression -0.080-3.740 -0.121-4.950-0.139-4.870-0.079-2.770-0.096-3.190 Persistence Depression -0.038-2.870 -0.025-1.600-0.089-4.850-0.050-2.680-0.065-3.420 Onset Depression -0.065-5.210 -0.064-4.240-0.045-2.560-0.087-4.960-0.093-5.160 Remittance Depression -0.021-1.500 0.0191.090-0.064-3.200-0.034-1.740-0.012-0.590 Table 6 provides the results of this analysis. In summary, none of the depression coefficients changed by more than 0.017, and most changed by less than 0.01 of a grade point from Table 5 when school fixed effects were included. These small differentials between Tables 5 and 6 s uggest that, even within schools, the depression impacts previously estimated hold. It does not appear that more depressed students are a ttending schools that have omitted characteristics that are correlated with both lower grades and depressed mood

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59 (i.e. more disadvantaged socioeconom ic status, poor teaching, discipline problems, etc.). Table 6 also reports results of the school FE analysis using the persistence depression variables. The results, exc ept for math in which even remittance depression is harmful and has the strongest effect, suggest that grades do not suffer significantly from depression that is not current and that the onset of depression symptoms hurts grades as much or more than per sistent depression that has carried over from the baseline survey. These will be further discussed in Section 5.10. 5.7 OLS Regression – Results by Grade Tables 7 through 14 present the results of OLS regressions that include the motivation and ability proxies, but exclude th e school fixed effects, stratified by grade level. These regression equations do not differ structurally from those discussed in Sections 4.7.7 and 5.5, except that they include only respondents in specific grade levels. School fixed effe cts are omitted because they take up substantial degrees of freedom but were obs erved in Table 6 to have no tangible impact on the estimates. The presentation commences with a discussion of depression coefficients for two larger groups, students in middl e school (grades 7-8) and high school (grades 9-12), with follow-up discussions fo r grade-level specific samples. Table 7 profiles results of for respondents in grades 7 and 8.

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60Table 7: Results OLS-GPA Impacts by Grade (Grades 7 & 8) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 7-8) Depressed Some of the Time -0.068-3.230 -0.048-1.670-0.066-2.080-0.071-2.320-0.123-3.900 Depressed a Lot of the Time -0.045-1.010 -0.045-0.760-0.102-1.580-0.094-1.490-0.063-0.970 Depressed Most or All of the Time -0.350-5.360 -0.372-4.330-0.440-4.610-0.186-2.010-0.410-4.210 Major Depression -0.162-3.320 -0.181-2.930-0.244-3.520-0.061-0.900-0.222-3.130 The main difference between these resu lts, and those for the full sample in Table 5, are for the most severe cat egories of depression, the “depressed most or all of the time” and “major depression” categories. Overall GPA for middle school students in the “depresse d most or all of the time category” is reduced by 0.35 grade points, while st udents suffering from major depression have a GPA that is 0.162 grade points lower than those reporting no depression. These results show approximately twice t he depression effect among middle school students than the overall sample demonstr ates. In addition, middle schoolers hardest hit by depression are impacted subst antially in the subjects of math and science, where GPA falls from one quarte r to one-half of a grade point. Perhaps surprisingly, none of the depression coeffici ents for “depressed a lot of the time” are statistically significant at 5 perc ent, whereas for “depressed some of the time”, only the coefficient for the English GPA regressi on is insignificant at 5 percent. Also, compared to the coefficient for “most or all of the time”, that for major depression is never mu ch more than half the size, and is as little as onethird the size (and highly insignific ant) in the case of social studies.

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61 The results for high school students (grades 9 through 12) are presented in Table 8. The differences in depressi on impacts on GPA between middle school and high school students can be easily seen by comparing the coefficients with those from Table 7. Depression has a more modest impact on the GPA of high school students. Table 8: Results OLS-GPA Impacts by Grade (Grades 9 through 12) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 9-12) Depressed Some of the Time -0.034-2.810 -0.044-3.070-0.040-2.350-0.064-3.790-0.052-3.000 Depressed a Lot of the Time -0.040-1.780 -0.088-3.440-0.057-1.900-0.063-2.070-0.083-2.620 Depressed Most or All of the Time-0.083-2.300 -0.070-1.710-0.100-2.080-0.008-0.160-0.215-4.310 Major Depression -0.061-2.570 -0.074-2.180-0.138-4.340-0.081-2.540-0.074-2.180 High school students who are the most severely depressed (“most or all of the time”, major depression) have gr ade impacts of roughly one-third the magnitude of middle school students. Students depresse d “most or all of the time” see an overall GPA decline of 0.083 grade points, while major depression drops GPA by 0.061 grade points. Math scores suffer the most for those with major depression (-0.138), while those depre ssed “most or all of the time” are hard hit in science (-0.215). The coeffici ents for “depressed most or all of the time” are not statistically significant at 5 percent LOS, in the subjects of English and social studies. The remaining “severe depression” coefficients are statistically significant. In terestingly, unlike for middl e school students, for high school students major depressi on hurts GPA more than being depressed most or all of the time in all subjects except science, and has similar impacts on overall GPA.

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62 Tables 9 through 13 display OLS models estimated for each grade level. Table 9: Results OLS-GPA Impacts by Grade (Grade 7) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 7) Depressed Some of the Time -0.038-1.050 0.0230.500-0.052-0.9800.0350.660-0.108-2.070 Depressed a lot of the Time -0.175-2.180 -0.187-1.820-0.262-2.3400.0150.140-0.102-0.900 Depressed Most or All of the Time -0.200-1.710 -0.256-1.710-0.170-0.9800.1540.910-0.399-2.320 Major Depression -0.174-2.130 -0.197-1.940-0.288-2.4800.0760.690-0.225-1.920 Table 9 suggests that even moderate levels of depression appear to have sizable negative effects on the GPA of 7th graders, with frequent and major depression having particularly large effects on science GPA. Table 10: Results OLS-GPA Impacts by Grade (Grade 8) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 8) Depressed Some of the Time -0.083-3.130 -0.085-2.330-0.071-1.770-0.116-3.040-0.128-3.230 Depressed a Lot of the Time 0.0240.440 0.0230.320-0.018-0.230-0.133-1.740-0.046-0.580 Depressed Most or All of the Time -0.425-5.380 -0.429-4.060-0.534-4.630-0.335-3.010-0.437-3.670 Major Depression -0.148-2.390 -0.172-2.180-0.213-2.440-0.115-1.340-0.227-2.520 Being depressed most or all of th e time appears to negatively impact the performance of 8th graders more than any other gr ade level. Table 10 shows that 8th grade students who are depressed “most or all of the time” see a 0.425 overall GPA reduction. On a subject leve l, the impacts range from one-third to one-half grade point, with math performance suffering the most (-0.534). Yet, the effect of major depression, though signi ficant, is no larger than for 7th graders, and being depressed “a lot of the time” has little impact, except in the subject of social studies.

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63Table 11: Results OLS-GPA Impacts by Grade (Grade 9) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 9) Depressed Some of the Time -0.037-1.460 -0.004-0.110-0.056-1.540-0.048-1.280-0.053-1.480 Depressed a Lot of the Time -0.097-2.010 -0.124-2.100-0.015-0.230-0.110-1.620-0.123-1.900 Depressed Most or All of the Time -0.113-1.570 -0.178-1.960-0.039-0.400-0.012-0.120-0.209-2.100 Major Depression -0.045-0.910 -0.144-2.350-0.106-1.580-0.034-0.490-0.174-2.550 High school freshmen depressed at least “a lot of the time” struggle in the areas of science and English, with grade dec lines in the courses ranging from one-eighth to one-fifth of a gr ade point. The results in Table 11 also suggest little grade impact in math, social studies or overall. Table 12: Results OLS-GPA Impacts by Grade (Grade 10) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 10) Depressed Some of the Time -0.020-0.880 -0.002-0.060-0.019-0.600-0.091-2.600-0.046-1.390 Depressed a Lot of the Time -0.002-0.050 -0.054-1.0800.0380.6800.0270.420-0.111-1.870 Depressed Most or All of the Time -0.024-0.330 0.0590.730-0.036-0.4000.0330.330-0.207-2.190 Major Depression -0.096-2.140 -0.126-2.360-0.136-2.270-0.109-1.650-0.068-1.050 The results for sophomores show t hat depression coefficients are not statistically significant at low to moder ate levels of depressed mood. Table 12 also shows that major depression is si gnificant for all grades except social studies, whereas being depressed “most or a ll of the time” is significant only for science. For those depressed “most or all of the time”, science grades drop by one-fifth of a grade point. For student s having characteristics of major depression, math and English scores are af fected by one-eighth of a grade point.

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64Table 13: Results OLS-GPA Impacts by Grade (Grade 11) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 11) Depressed Some of the Time -0.048-2.150 -0.062-2.320-0.042-1.350-0.030-0.980-0.054-0.880 Depressed a Lot of the Time -0.017-0.410 -0.094-2.000-0.053-0.960-0.039-0.720-0.235-2.410 Depressed Most or All of the Time -0.118-1.710 -0.080-1.010-0.221-2.390-0.024-0.270-0.004-0.130 Major Depression -0.045-1.050 -0.083-1.690-0.170-2.870-0.030-0.5300.0100.160 In Table 13, OLS regressions su ggest that severely depressed mood impacts a junior’s math average by roughl y two-tenths of a grade point. Beyond that, depression impacts are eith er practically small, or statistically insignificant. Table 14: Results OLS-GPA Impacts by Grade (Grade 12) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Grade 12) Depressed Some of the Time -0.025-0.870 -0.084-3.000-0.044-1.170-0.097-2.880-0.036-0.930 Depressed a Lot of the Time -0.045-0.830 -0.068-1.330-0.245-3.490-0.113-1.810-0.018-0.250 Depressed Most or All of the Time -0.071-0.870 -0.105-1.310-0.084-0.790-0.063-0.670-0.162-1.430 Major Depression -0.063-1.060 -0.116-2.130-0.124-1.710-0.157-2.390-0.092-1.220 Table 14 results suggest that high school seniors appear to experience noticeable negative affects fr om depressed mood in Eng lish and social studies, even at lower levels of reported depression. GPA declines in both subjects are roughly one-tenth of a grade point. However, this dr op in performance rises only modestly as the severity of depressed mood increases. 5.8 OLS Regression – Results by Gender Table 15 presents the OLS model resu lts for survey females. The data suggests that depressed mood negatively af fects the GPA of females, even at

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65 relatively modest frequency. In addition, with increasing frequency of depression, females’ grade performance slips even further, with “technical” subjects seeing the greatest decline. Table 15: Results OLS-GPA Impacts by Sex (Female) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Female) Depressed Some of the Time -0.056-4.070 -0.070-4.160-0.063-3.180-0.084-4.330-0.075-3.750 Depressed a Lot of the Time -0.058-2.440 -0.125-4.530-0.069-2.090-0.072-2.220-0.096-2.870 Depressed Most or All of the Time -0.200-5.610 -0.185-4.310-0.232-4.580-0.107-2.170-0.306-5.970 Major Depression -0.080-3.140 -0.134-4.580-0.182-5.240-0.075-2.230-0.087-2.420 Females who report being depressed “s ome of the time” see a decline in overall GPA of 0.056 grade points, with science being the most affected subject (-0.075). Those reporting depression “a lo t of the time” experience a drop in overall GPA of 0.058 grade points, with English performance being affected the most (-0.125). Female students with depr essed mood “most or all of the time” suffer a 0.20 overall grade point decline, including setbacks of 0.306 GPA in science and 0.232 in math. When major depression characteristics are present in females, their overall GPA declines by 0.08 grade points, with math being the most heavily affected subject (-0.182). All depression coefficients for females are statistically significant at 5 percent. The results for depression frequency among male students in Table 16 tell a different story. The impacts are consider ably smaller in magnitude and are rarely statistically significant. C oefficients are mixed in thei r statistical significance.

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66Table 16: Results OLS-GPA Impacts by Sex (Male) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Male) Depressed Some of the Time -0.034-2.050 -0.017-0.850-0.029-1.290-0.048-2.100-0.067-2.850 Depressed a Lot of the Time -0.012-0.330 -0.010-0.240-0.081-1.680-0.071-1.450-0.054-1.040 Depressed Most or All of the Time -0.061-0.940 -0.021-0.290-0.021-0.2700.0300.370-0.174-2.070 Major Depression -0.103-2.630 -0.121-2.730-0.115-2.260-0.097-1.870-0.148-2.720 Beyond the lowest level of depression, only science course grades show a statistically significant negative impact (-0.1 74). On the other hand, except for math, the GPA reduction induced by major depression is similar or greater for males than females. Males in the ma jor depression category see an overall GPA decline of 0.103 points, again with science seeing the largest drop (-0.148) The differences seen in the results of the OLS model r uns between males and females generates questions as to w hether females’ grade performance is truly more impacted by depression, or whet her the results reflect differences in self-reporting of depression and grades bet ween the sexes. Nicholson (1984) points out that young males display a gr eater tendency than females to distort facts related to achievement. 5.9 OLS Regression – Resu lts by Race/Ethnicity The analysis of depression impacts on grades by race suggests that Caucasian students suffering from depr ession have similar academic performance issues when compared over all to non-Caucasian students. However, when each racial cohort is assess ed individually, ethnic distinctions in the GPA gap become more apparent.

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67Table 17: Results OLS-GPA Impacts by Race/Ethnicity (White) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (White) Depressed Some of the Time -0.052-3.860 -0.052-3.220-0.044-2.350-0.085-4.610-0.074-3.880 Depressed a Lot of the Time -0.039-1.530 -0.097-3.290-0.062-1.780-0.056-1.650-0.103-2.940 Depressed Most or All of the Time -0.169-4.220 -0.172-3.700-0.153-2.870-0.060-1.120-0.260-4.790 Major Depression -0.057-1.950 -0.131-3.950-0.131-3.350-0.069-1.820-0.100-2.460 Table 17 provides a profile of the OLS regression results for Caucasian students. Grade performanc e is impacted even at moderate levels of depression. For students that report depres sed mood “some of the time”, overall GPA falls by 0.052 grade points, with so cial studies being the most affected subject. Although statistical significance is mixed for coefficients of depressed mood “a lot of the time”, t hose subjects that pass signif icance testing at 5 percent indicate a 1/10 grade point negative impact (English, science). At more severe levels of depression, the impacts to G PA increase. Overall GPA falls by 0.169 grade points for students reporting depressed mood “most or all of the time”, with science grades seeing the la rgest decline (-0.260). C aucasian students who met the major depression criteria realized dec lines in English and math GPA of 0.13 grade points, as well as a 1/10 grade point drop in science. When all other races are evaluated as a single group, GPA impacts from depressed mood do not appear to differ dram atically from Caucasian students. Table 18 shows that non-whites depress ed “some of the time” see an overall GPA decline of 0.037 grade points, with social st udies and science grades affected similarly at 1/20 of a point. No c oefficients are statistically significant for the depression category “a lot of the time”.

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68Table 18: Results OLS-GPA Impacts by Race/Ethnicity (Non-White) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (All Non-White) Depressed Some of the Time -0.037-2.390 -0.023-1.140-0.046-2.000-0.051-2.180-0.056-2.330 Depressed a Lot of the Time -0.027-0.910 -0.041-1.120-0.041-0.970-0.048-1.100-0.060-1.320 Depressed Most or All of the Time -0.121-2.580 -0.046-0.810-0.159-2.380-0.063-0.930-0.252-3.500 Major Depression -0.094-3.240 -0.116-3.320-0.174-4.260-0.060-1.450-0.124-2.810 Non-white students with depr ession “most or all of the time” experience an overall negative GPA impac t of 0.121 points, with science grades suffering the most (-0.252). Those who have major depr ession characteristics see an overall GPA drop of slightly less than 1/10 of a point, with math performance being most affected (-0.174). Tables 19 through 24 display the resu lts for each individual non-Caucasian race/ethnic group. Table 19: Results OLS-GPA Impacts by Race/Ethnicity (Black) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Black) Depressed Some of the Time -0.030-1.370 -0.036-1.290-0.045-1.450-0.060-1.870-0.061-1.860 Depressed a Lot of the Time -0.041-0.970 -0.067-1.2900.000-0.010-0.073-1.210-0.037-0.590 Depressed Most or All of the Time -0.095-1.420 -0.055-0.690-0.139-1.560-0.076-0.810-0.212-2.190 Major Depression -0.159-3.590 -0.181-3.480-0.117-2.000-0.121-1.950-0.162-2.540 Table 19 suggests that black students with major depression are impacted much more substantially than whites, wit h an overall GPA drop of 0.159 points. At other levels of reported depression, it is not clear that blacks suffer a greater GPA impact. Many coefficients are not st atistically significant in these other

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69 categories, and most are lower than for the Caucasian segment. This may be attributable to different ials in self-reporting. Table 20: Results OLS-GPA Impacts by Race/Ethnicity (Hispanic) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Hispanic) Depressed Some of the Time -0.044-1.550 -0.059-1.810-0.087-2.270-0.052-1.360-0.058-1.430 Depressed a Lot of the Time -0.060-1.110 -0.053-0.890-0.063-0.900-0.071-1.000-0.182-2.410 Depressed Most or All of the Time -0.104-1.190 -0.027-0.290-0.177-1.6200.0080.070-0.137-1.150 Major Depression -0.048-0.920 -0.017-0.300-0.191-2.820-0.056-0.830-0.086-1.170 Table 20 shows that most of the depression coefficients for Hispanic students are not statistically significant at 5 percent LOS under any depression frequency scenario. Hispanic students su ffering from major depression characteristics have larger GPA impacts in the subject of math (-0.191) than whites or blacks. It is interesting to note that science GPA dr ops by 0.182 grade points at a more modest depression fr equency of “a lot of the time”. Table 21: Results OLS-GPA Impacts by Race/Ethnicity (Native American) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Native American) Depressed Some of the Time -0.112-1.700 -0.053-0.740-0.152-1.760-0.058-0.700-0.028-0.320 Depressed a Lot of the Time -0.091-0.800 -0.175-1.460-0.114-0.7800.1731.270-0.257-1.760 Depressed Most or All of the Time -0.042-0.240 -0.147-0.810-0.318-1.3600.3271.520-0.544-2.110 Major Depression -0.083-0.710 -0.034-0.270-0.394-2.6800.3262.430-0.167-1.080 The OLS results for Native American students in Table 21 are similar to the results for the Hispanic group, with limited statistical significance of coefficients in most scenarios and subjects, and large GPA impacts for the few subjects where

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70 statistical significance is met. Native American students having characteristics of major depression see a 0.394 drop in Math GPA, the largest performance drop for this subject among all racial groups Native American students reporting depression “most or all of the time” su ffer a science GPA decline of more than one-half of a grade point (-0.544), the lar gest subject-specific performance drop of any ethnic group. The results for Asian students in T able 22 also show few statistically significant depression coefficients at 5 percent LOS ( only two of twenty), including none for overall GPA. St udents with major depression suffer a 0.151 grade point decline in English, while t hose reporting mild depression (“some of the time”) have a 0.109 lower social studies GPA. Table 22: Results OLS-GPA Impacts by Race/Ethnicity (Asian/Pacific Islander) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Asian/PI) Depressed Some of the Time -0.053-1.460 -0.030-0.7200.0350.690-0.109-2.170-0.056-1.090 Depressed a Lot of the Time 0.0540.770 0.0410.520-0.072-0.760-0.036-0.3700.0390.390 Depressed Most or All of the Time -0.126-1.110 -0.036-0.3000.0350.220-0.002-0.010-0.290-1.820 Major Depression -0.010-0.150 -0.151-2.220-0.108-1.3200.0310.380-0.087-1.000 In Table 23, major depression is the only depression category where a statistically significant result is found for ethnic groups other than those previously defined. In math, students having major depression see their GPA fall by 0.271 grade points.

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71Table 23: Results OLS-GPA Impacts by Race/Ethnicity (Other Races) Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Other Races) Depressed Some of the Time -0.039-0.990 -0.001-0.020-0.080-1.510-0.027-0.520-0.083-1.460 Depressed a Lot of the Time -0.002-0.030 0.0370.480-0.087-0.940-0.076-0.830-0.104-1.020 Depressed Most or All of the Time-0.072-0.630 0.0720.550-0.269-1.750-0.113-0.780-0.100-0.590 Major Depression -0.068-1.040 0.0720.990-0.271-3.080-0.088-1.020-0.104-1.060 5.10 OLS Regression – Persistence Depression Results In sections 3.3 and 4.7.12 of the dissert ation, we discuss the interest in and methodology for evaluating student grade impac ts based on the persistent nature (or lack thereof) of depressed mood. T able 24 provides the results of this analysis. For those students experiencin g persistent depression, overall GPA falls by 0.038 grade points. Math is the most affected subject (-0.085) for this group. For students displaying “onset depression”, overall GPA is 0.071 grade points lower than for those who have never reported depressed mood. Table 24: Results OLS-Persistence Depression Effects on GP A Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-stat Persistence Depression + Exogenous Variables + Motivation Proxies + Ability Proxies Persistence Depression -0.038-2.790 -0.029-1.800-0.085-4.600-0.052-2.830-0.062-3.240 Onset Depression -0.071-5.640 -0.067-4.400-0.056-3.150-0.092-5.280-0.103-5.690 Remittance Depression -0.020-1.380 0.0241.430-0.054-2.700-0.028-1.4300.0020.080 Those with “remittance depression” charac teristics only show a statistically significant impact in the subject of ma th, where GPA falls by 1/20 of a grade point. Overall, the negative influenc e of depression on student grades does

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72 seem to increase with its persistence, potentially enhancing t he already observed effects on GPA. 5.11 First Differencing Results Table 25 presents the results of first di fferencing in the primary OLS model. The first differences were taken fr om responses of the 14,736 students who participated in both the Wave 1 and Wave 2 surveys. Table 25: Results First Differencing of Responses for Students Reporting in Both Wave I and Wave II Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Wave FD) Depressed Some of the Time -0.018-1.280 -0.024-1.290-0.027-1.2500.0020.080-0.024-1.030 Depressed a Lot of the Time 0.0140.580 -0.009-0.280-0.023-0.6100.0430.9600.0471.140 Depressed Most or All of the Time 0.0130.340 0.0050.100-0.077-1.3800.0931.3900.0300.490 Major Depression -0.021-0.840 -0.041-1.240-0.051-1.3600.0471.050-0.040-0.960 The first differencing results are relatively small, mixed in sign across various depression and subj ect scenarios, and never are statistically significant at 5 percent LOS. A number of positive coefficients are generated for severity of “ most of or all of the time ”. Two plausible arguments ex ist. Either time-invariant heterogeneity controlled for by first diffe rencing dominates, and is not controlled for by the other methods, or the first differencing method is not reliable because of time-related issues in survey reporting. These time issues include a relatively short period between the in-school (basel ine) survey and the Wave 1 and Wave 2 surveys, and possibility that FD may be eliminating some cross-respondent

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73 variation attributable to changes resulting from a wider variety of disorders that include depressed mood (e.g. dysthymic disorder). With the “major depression” variable because bi-directional changes in depression severity do not exist, the re sults can be interpreted in a more straightforward manner. Not withstanding, the results suggest that, once timeinvariant factors are controll ed for, a statistically signif icant relationship between major depression and GPA does not exist. 5.12 Sibling Fixed Effects Results Wave-specific results when controlling for sibling effects are presented in Tables 26 and 27. The sample size vari es from 1,448 to 2,129 in Wave I, and 984 to 1,718 in Wave II. The sample size for each reported GPA variable differs, based on number of student s who reported a grade. Wave I results are presented in T able 26. When sibling effects are controlled for, overall GPA is still negatively impacted by depressed mood, although the categorical effects are somewhat tempered relative to the results of the base OLS-proxy equation model presented in Section 5.5 and Table 5. For major depression, GPA impacts remain sizeable, even with a smaller sample. Table 26: Results Sibling Fixed Effects Wave I Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Sibs FE Wave I) Depressed Some of the Time -0.061-1.370 -0.049-0.960-0.064-1.060-0.033-0.560-0.102-1.670 Depressed a Lot of the Time -0.038-0.420 -0.112-1.210-0.002-0.020-0.185-1.720-0.006-0.050 Depressed Most or All of the Time-0.049-0.360 0.3112.0600.0160.1000.0030.0200.0450.240 Major Depression -0.095-1.120 -0.162-1.670-0.148-1.340-0.074-0.690-0.099-0.860

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74 Wave I overall GPA coefficients do not display statistical significance at 5 percent LOS, which again is likely a resu lt of smaller sample size. Only the English GPA impact, at a depr ession frequency of “most or all of the time”, is significant at 5 percent LOS, and this coefficient GPA has an unexpected positive sign. Table 27: Results Sibling Fixed Effects Wave II Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-statCoeff.t-stat Depression + Exogenous Variables + Motivation Proxies + Ability Proxies (Sibs FE Wave II) Depressed Some of the Time -0.172-2.510 -0.057-0.860-0.183-2.080-0.040-0.4400.0840.990 Depressed a Lot of the Time -0.043-0.330 -0.181-1.430-0.160-1.000-0.249-1.430-0.068-0.410 Depressed Most or All of the Time-0.389-2.410 -0.224-1.210-0.245-1.080-0.839-3.500-0.444-2.100 Major Depression -0.025-0.170 -0.174-1.370-0.162-0.990-0.405-2.320-0.186-1.060 The Wave II sibling FE results show much greater (and more statistically significant) GPA impacts from depression. Overall GPA for students depressed “most or all of the time” falls by 0.389 grade points, although those suffering from major depression have only a -0.025 over all grade impact. Save the latter coefficient, not only are these results larger in magnitude than in Wave I, they are in several cases larger than the over all GPA impacts for the base OLS-proxy equation discussed in Section 5.5 and T able 5. The explanation could be persistence depression effects, given t hat the base model includes data from both survey waves. As in the case of first differencing, we cannot ignore the potential issues that arise from interpre ting the directional changes in depression frequency (some of time, a lot of the time, mo st or all of the time) across siblings.

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75 Regardless, the results of this analysis indicate that the negative impacts of depression on GPA hold amongst the sibling groups. 5.13 Two-Stage Least Squa res Estimation Results As section 4.7.16 notes, three candidat e instruments were selected for final evaluation in the two-stage least squar es models: “moody12”, “crying12”, and “fearful12”. Combinations of these th ree variables were used as instruments for the “major depression” proxy in OLS modeling. Table 28 displays the first-stage regression results. Table 28: Results Two-Stage Least Squares, First Stage Regressions InstrumentsOverall GP A English GP A Math GP A SS GP A Sci. GP A moody 12 + fearful12 + crying 12 Coefficients moody 120.0380.0410.0390.0400.039 fearful 120.0810.0890.0850.0890.080 crying 120.1360.1490.1460.1480.133 t-statistics moody 128.99011.54010.83010.37010.410 fearful 129.29012.42011.71011.45010.380 crying 1215.76021.32020.44019.23017.800 F-statstic17.29029.61027.16024.23022.230 moody 12 + fearful12 Coefficients moody 120.0500.0530.0520.0530.050 fearful 120.1130.1270.1210.1250.112 t-statistics moody 1211.82015.19014.33013.69013.470 fearful 1213.11018.01016.85016.32014.730 F-statstic13.30022.24020.40018.24017.150 fearful12 + crying 12 Coefficients fearful 120.0910.0990.0950.0990.089 crying 120.1490.1630.1600.1620.147 t-statistics fearful 1210.42013.81013.07012.72011.660 crying 1217.55023.53022.52021.23019.760 F-statstic16.16027.75025.55022.74020.710

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76Table 28 (continued): Results Two-Stage Least Squares, First Stage Regressions InstrumentsOverall GP A English GP A Math GP A SS GP A Sci. GP A moody 12 + crying12 Coefficients moody 120.0430.0460.0440.0450.043 crying 120.1540.1700.1660.1690.151 t-statistics moody 1210.15013.03012.28011.76011.690 crying 1218.31025.05023.81022.52020.680 F-statstic16.07027.38025.20022.33020.720 With significant coefficient t-statisti cs and joint F-statistics, all four of the instrument combinatio ns meet initial IV validity criteria. Table 29 provides a summary of the 2SLS output for each of the second stage depression coefficients. Table 29: Results Two-Stage Least Squares, Effects of Major Depression Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A Depression VariableCoeff.t-statCoeff. t-statCoeff.t-statCoeff. t-statCoeff.t-stat 2SLS Major Depression Instruments: "moody12 + fearful12 + crying 12" -0.358-3.420 -0.324-3.000-0.385-2.900-0.372-2.900-0.462-3.110 Instruments: "moody12 + fearful12" -0.544-3.930 -0.300-2.160-0.497-2.910-0.562-3.320-0.737-3.840 Instruments: "fearful12 + crying12" -0.290-2.610 -0.303-2.630-0.328-2.320-0.330-2.410-0.329-2.070 Instruments: "moody12 + crying12" -0.318-2.810 -0.358-3.050-0.382-2.660-0.306-2.250-0.430-2.710 When all three instruments are used, ov erall GPA declines by 0.358 grade points. English GPA falls by 0.324 gr ade points, math GPA lowers by 0.385 grade points, social studies GPA drops by 0.372 grade points, and science GPA realizes a 0.462 grade point reduction. Using only the “moody12” and “fearful12” combination of instruments, we see that the depression IV coefficients fo r all but one GPA category exceed 0.5 in absolute value, which suggests too great of a change between the 2SLS

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77 coefficients and the corresponding OLS coe fficients ( -0.087 for overall, -0.127 for English, -0.157 for math, -0.081 for so cial studies, and -0.1 05 for science). With the “fearful12” and “crying12” pair of instruments, overall GPA declines by 0.290 grade points. English GPA dr ops by 0.303 grade points, math GPA falls by 0.328 grade points, social studies GPA is lowered by 0.330 grade points, and science GPA is reduced by 0.329 grade points. This group of 2SLS instruments generates coefficient results that are closer in magnitude to OLS coefficients than any of the ot her instrument combination. The final pair of instrument s, “moody12” and “crying12”, generate coefficients that very similar to those in the “fearful12”/”crying12” IV scenario, and are also kept as a potentially viable instrumentation set, leading into the overidentification testing. Table 30: Results Two-Stage Least Squares Overidentification Tests Depression Variable Overall GP A English GP A Math GP A Soc.Studies GP A Science GP A 2SLS Major Depression, Overidentifcation Tests moody12+fearful12+crying12 n (# of observations)12,31419,53618,34015,96716,387 R-squared of residual reg.0.00050.00000.00010.00020.0005 n R-squared6.160.001.833.198.19 Chi-Sq. CV, 5% LOS, 2 df5.995.995.995.995.99 Pass/Fail Overid test FAIL PASSPASSPASS FAIL moody12+fearful12 n (# of observations)12,31419,53618,34015,96716,387 R-squared of residual reg.0.00040.00000.00000.00000.0001 n R-squared4.930.000.000.001.64 Chi-Sq. CV, 5% LOS, 1 df3.843.843.843.843.84 Pass/Fail Overid test FAIL PASSPASSPASSPASS moody12+crying12 n (# of observations)12,31419,53618,34015,96716,387 R-squared of residual reg.0.00000.00000.00010.00010.0004 n R-squared0.000.001.831.606.55 Chi-Sq. CV, 5% LOS, 1 df3.843.843.843.843.84 Pass/Fail Overid test PASSPASSPASSPASS FAIL fearful12+crying12 n (# of observations)12,31419,53618,34015,96716,387 R-squared of residual reg.0.00020.00000.00000.00020.0001 n R-squared2.460.000.003.191.64 Chi-Sq. CV, 5% LOS, 1 df3.843.843.843.843.84 Pass/Fail Overid test PASSPASSPASSPASSPASS

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78 All four instrument combinations were tested for overidentification, although only three of the IV scenarios were consi dered to be viable at this juncture. The results of the overidentification tests, di splayed in Table 30, indicate that the “fearful12”/”crying12” IV pair was the only one to pass overidentification tests in each of the five GPA categories (overall, English, math, social studies, and science). To make the a fi nal determination of consist ency for the 2SLS IV pair “fearful12”/”crying12”, Using this, a H ausman test of endogeneity was conducted for the major depression variable, adding the residuals from the first stage equation to the structural equation (for overall GPA on major depression, all exogenous variables). The robust t-statis tic for the residual variable was 1.92, indicating moderate evidence that the major depression variable is endogenous with respect to GPA. Although the “fearful12/ crying12” IV pair passed all of the criteria established in Section 4.7.16 for a viabl e 2SLS analysis of ma jor depression on GPA, we cannot ignore the fact that 2S LS coefficients for major depression are approximately three times as large as t he OLS coefficients. It may be that factors related to measurement error a ccount for this difference, with 2SLS estimates being correct and OLS estimate s biased towards zero due to this measurement error. This brings back in to relevance the discussion from Section 5.8 on differences between male and female coefficients due to self-reporting. In order to address this issue, a separate anal ysis of the differences in 2SLS results of males and females was conducted, assessing overall GPA impacts of

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79 depression. A t-test of the 2SLS gender differences was performed, using the following formula: (|male coefficient| – |female coeffi cient|)/(Var male – Var female)^0.5 The null hypothesis for this test is that the 2SLS results between males and females are similar. A t-statistic ex ceeding 1.96 at 5 percent rejects the hypothesis, and indicates significant diffe rences in the 2SLS results between males and females. The results of this test are shown below: (0.704 – 0.280)/(0.345 – 0.114)^0.5 = 2.174 The analysis indicates significant di fferences in the 2SLS results between males and females. Considering as well the difference in magnitude between OLS and 2SLS coefficients for males and females (males -0.103 OLS, -0.704 2SLS, -0.601 difference; females -0.080 OLS, -0.280 2SLS, -0.200 difference) 2SLS may be having a larger impact on ma les than females, measurement (selfreporting) error may be bias ing the OLS results towards zero for male students. In this case, we would expe ct the 2SLS results to be la rger in magnitude than the OLS results. This provides additional sup port for the validity of the model results. 5.14 Concluding Remarks on Study Results The various OLS and 2SLS analyse s offer results which support the hypothesis that depression has a negative impact on grade performance amongst middle and high school students. T he magnitude of this grade impact increases as the severity/frequency of the reported depression increases. The

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80 results have held when controlling for mult iple confounding factors that may also contribute to lower academic performance. The base OLS-proxy model output (discussed in Section 5.5 and Table 5) indicates that students who report depressed mood most or all of the time suffer an overall GPA reduction of 0.159 grade points. On a conventional four-point grade scale, using a plus-minus system, a student depressed most or all of the time would potentially see their grade s lip by one “mark” (e.g. a B-plus student may fall to a B, or a B student may fall to a B-minus student). On an individual subject level, this severity of depre ssion results in a 0.125 grade point drop in English, a 0.166 decline in math, a 0. 061 reduction in social studies, and 0.258 grade point lowering in science GPA. Th is model also suggests that those suffering from symptoms consistent with major depression see a 0.087 grade point decline in their overall GPA. E nglish GPA falls by 0.127 grade points, math by 0.157 grade points, social studies by 0.081 grade points, and science by 0.105 grade points. These changes are not large enough to alter the letter grade of a student who has a mid-to-high num eric score within a given letter grade range. However, they would reduce grades for students at the lower margin of each range. Also of importance are the outcomes of OLS-proxy model ing for specific subcategories of the surveyed students. As Table 31 illustrates, 8th graders clearly appear to be the most profoundl y impacted subgroup of any studied. Severe depression impacts this group from up to three times more than the

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81 overall student sample, with GPA’s sli pping in some subjects by a half-grade point or more. Table 31 Summary of OLS Coefficients for Severely Depressed Mood Comparions of Base Model vs. Most Significantly Impacted Sub-Groups Depressed Most or All of the Time Major Depression Coefficientt-statisticSourceCoefficientt-statisticSource Overall GPA Base OLS/Proxy-0.159-5.000Base Model-0.087-4.030Base Model Largest Magnitude-0.425-5.3808th Graders-0.174-2.1307th Graders 2nd Largest Magnitude-0.389-2.410Sibs FE, Wave 2-0.159-3.590Blacks 3rd Largest Magnitude-0.200-5.610Females-0.148-2.3908th Graders 4th Largest Magnitude-0.169-4.220Caucasians-0.103-2.630Males English GPA Base OLS/Proxy-0.125-3.400Base Model-0.127-5.160Base Model Largest Magnitude-0.429-4.0608th Graders-0.181-3.480Blacks 2nd Largest Magnitude-0.185-4.310Females-0.172-2.1808th Graders 3rd Largest Magnitude-0.178-1.9609th Graders-0.151-2.220Asians/PI 4th Largest Magnitude-0.172-3.700Caucasians-0.144-2.3509th Graders Math GPA Base OLS/Proxy-0.166-3.890Base Model-0.157-5.470Base Model Largest Magnitude-0.534-4.6308th Graders-0.394-2.680Native Americans 2nd Largest Magnitude-0.232-4.580Females-0.288-2.4807th Graders 3rd Largest Magnitude-0.221-2.39011th Graders-0.213-2.4408th Graders 4th Largest Magnitude-0.166-3.920School FE Result-0.191-2.820Hispanics Social Studies GPA Base OLS/Proxy-0.061-1.430Base Model-0.081-2.850Base Model Largest Magnitude-0.839-3.500Sibs FE, Wave 2-0.405-2.320Sibs FE, Wave 2 2nd Largest Magnitude-0.335-3.0108th Graders-0.157-2.39012th Graders 3rd Largest Magnitude-0.107-2.170Females-0.079-2.770School FE Result 4th Largest Magnituden/an/a-0.075-2.230Females Science GPA Base OLS/Proxy-0.258-5.840Base Model-0.105-3.470Base Model Largest Magnitude-0.544-2.110Native Americans-0.227-2.5208th Graders 2nd Largest Magnitude-0.444-2.100Sibs FE, Wave 2-0.186-1.060Sibs FE, Wave 2 3rd Largest Magnitude-0.437-3.6708th Graders-0.174-2.5509th Graders 4th Largest Magnitude-0.399-2.3207th Graders-0.162-2.540Blacks 7th Graders and Black students also demonstrate widespread above average declines in GPA as a result of severe depression. Female students also display greater than normal GPA decli nes, possibly because of measurement error, with males possibly being less likely to reveal their true depressed feelings or grade performance. Native American students appear to be particularly hard

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82 hit by severe depression in the “technica l” subjects of science and math, with grade declines of more than twice the norm. Further results suggest that the persistence of depression over time c ontributes to declines in grade performance. The data indicates t hat those who suffer from prolonged depressed mood will have lower overall GPA’s than those who do not, and in some subjects the difference could approach 1/10th of a grade point. Also, the sibling fixed effects analysis for Wave II shows much greater negative impact on GPA than for Wave I, which could also be suggestive of depression persistence creating larger than normal impacts. Table 32 Summary of OLS Coefficients for Severely Depressed Mood Based on Key Model Outcomes Depr. Most/All of Time Major Depression Coefficientt-statisticCoefficientt-statistic First Differencing Overall GPA0.0130.340-0.021-0.840 English GPA0.0050.100-0.041-1.240 Math GPA-0.077-1.380-0.051-1.360 Social Studies GPA0.0931.3900.0471.050 Science GPA0.0300.490-0.040-0.960 Sibling FE, Wave I Overall GPA-0.049-0.360-0.095-1.120 English GPA0.3112.060-0.162-1.670 Math GPA0.0160.100-0.148-1.340 Social Studies GPA0.0030.020-0.074-0.690 Science GPA0.0450.240-0.099-0.860 Sibling FE, Wave II Overall GPA-0.389-2.410-0.025-0.170 English GPA-0.224-1.210-0.174-1.370 Math GPA-0.245-1.080-0.162-0.990 Social Studies GPA-0.839-3.500-0.405-2.320 Science GPA-0.444-2.100-0.186-1.060 Finally, the 2SLS-IV analysis also generates results that support the hypothesis of a negative relationship bet ween severe depression and GPA.

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83 Instrumenting for major depression generates coefficients that are larger in magnitude than the base OLS c oefficients. The instrumental variables selected pass overidentification tests, and their larger magnitude relative to OLS can likely be explained, at least in part, by self -reporting measurement error issues, where OLS modeling would bias results (par ticularly for males) towards zero.

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84 Chapter 6 Study Conclusions 6.1 Study Implications This research has built upon past efforts in the field of social science that investigate the relation ship between academic achievement and depression in young people. The limited inventory of prev ious literature on this subject stops at the simple recognition of a negative rela tionship, but does not go on to address the magnitude, specific sub-groups who ma y suffer greater impacts from severe depression, or causality. The dissertation advances the under standing of the depression-academic performance relationship, as it mo re clearly and thoroughly addresses the relative magnitude that depression has on GPA outcomes of middle and high school students. In addition, this wo rk identifies specific sub-groups of youngsters who may be at greater risk of significant academic difficulties from severe depression. In par ticular, these “at risk” sub groups include 7th and 8th graders, Blacks, Native Americans, fe males, and students suffering from prolonged depressed mood. The results of this analysis indica te that depression, even severe depression, does not turn an A student into an F student. Nor is it likely to turn a B student into a D student. But, this research clearly shows that depression

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85 hurts the academic performance of young people, and it could push certain students down a letter grade in their course (s), depending on where they stand in a given numeric grade range. The subject of mental illness and sc hooling has received considerable attention recently in the mainstream media2, and is now being emphasized at the highest levels of Federal government. A prevailing issue involves the role and responsibility of educational instituti ons to offer adequate student mental health counseling resources, in addition to t he standard instructional curriculum. At the collegiate level of education, officials are reporting that student demands for on-campus psychological servic es are on the rise, and insufficient numbers of trained professionals exist within the collegiate structure to deal with the increased demand. Anecdotal evidenc e from college counselors points to mental health problems as a ma jor cause of student drop-outs3. For primary levels of education (K-12), si milar, if not more significant, issues regarding mental healt h support services exist. The American School Counselor Association recommends a ratio of one sc hool counselor be available for every 250 enrolled students. However, t he most recently reported ratio4 indicates that nationally, the ratio of student s to counselor is 479 to 1. The deficiency at the pre-high school level is even more pr onounced. At the K8 grade level, the 2 Recent articles on the subject published in U.S. Newspapers include USA Today ( Reaching out to students 12/6/2004), the Universi ty of Michigan Record ( Increase in student counseling leads to plans for new cente r, 3/6/06), the Tampa Tribune ( University counseling centers feel strain 2/11/2007), and the Seattl e Post-Intelligencer ( College students seek therapy in record numbers 2/23/2007), 3 Based on data from the 2005 National Surv ey of Counseling Center Directors. 4 Taken from NCES Common Core Data (CCD) “State Nonfiscal Survey of Public Elementary/Secondary Education: 2004-2005 Sch ool Year”, National Center for Education Statistics, U.S. Dept. of Education.

PAGE 93

86 national ratio is 882 to 1. The research results in this study would seem to support the notion that deficiencies in t he pre-high school mental health support structure exist, and student academic perfo rmance may be suffering as a result of these deficiencies. S pecifically, the study result s indicate greater academic performance issues exist amongst mi ddle school students suffering from depressed mood than hi gh school students. On April 4, 2008, 11 United Stat es Senators proposed legislation5 that would provide increased appropriations in Fiscal Year 2009 for the Elementary and Secondary School Counse ling Program. As part of this proposal, the Senators specifically not ed the deficiencies in school counseling services nationwide, and stressed the need for additional funding in this area to improve student achievement. Possible solutions to address the i ssue of student depression and academic performance outside of the school environm ent are easy to identify, but very difficult to implement, because they deal with individual families’ abilities and willingness to address their children’s pr oblems and take appropriate corrective measures. In a society of substantial individual freedoms, government cannot legislate parents’ choices regarding the m ental health of their children. Ideally, the findings of this study will provide important new information on mental health and schooling, and draw more attenti on to the issue of depression and education. 5 A copy of the Senators’ proposal is included as an appendix to this dissertation

PAGE 94

87 6.2 Study Limitations The work presented in this dissertation carries with it an im portant limitation, that a clear identification of depression effects on grade performance is not fully achieved. There are three key factors involved this principal limitation, all relating to the data source utilized (AddHea lth longitudinal database). Factor 1is the absence of a perfectly representat ive measure for depression or major depression, as it is defined in the APA-DS M IV. While the DSM-IV measures of major depression include a period of at l east two weeks of depressed mood, the depression measures in A ddHealth in-home surveys only ask about “past week” feelings. Factor 2 involves the fact t hat all AddHealth data on the student is selfreported, thus creating meas urement error issues, particu larly as they relate to the self reporting of depression and gr ades between the sexes. Finally, the AddHealth database lacks an abundance of high quality instruments to utilize in the 2SLS-IV modeling procedure. This is further complicated by the fact that confidentiality requirements and subsequent security practices related to the AddHealth database make it very difficu lt, if not almost impossible, to add variables from outside the database. It should be noted, however, that at least one combination of instrument s used in 2SLS-IV for this study met the criteria necessary for a valid instrument. 6.3 Further Research Suggestions for future res earch into this subject would include investigation of labor market impacts as some of t he students surveyed in Add Health Wave 1

PAGE 95

88 and Wave 2 graduate, and participate in the l abor force. There does exist a third wave of the AddHealth survey; unfortunat ely, many of the Wave 1 and Wave 2 students (grades 7 – 12) had not been in the l abor force long enough, if it all, to quantify tangible labor market impacts from depression. UNC – Chapel Hill is currently in the process of conducting Wa ve 4 of the AddHealth survey. This wave should provide a richer inventory of responses from those young adults who were initially surveyed as students, but who are now graduates with some degree of labor market tenure. The goals of analyzing of this later wave of survey data would include the discover y of further trends in academic performance, as these students move th rough their academic careers, and the employment/wage outcomes of affect ed versus non-affected individuals.

PAGE 96

89 References American Psychiatric Associat ion. 2000. Mood Disorders. Diagnostic and Statistical Manual of Mental Disorders fourth edition, text revision, pp. 369-382. Bartel, Ann and Paul Taubman. 1986. Some Economic and Demographic Consequences of Mental Illness. Journal of Labor Economics 4(2), pp.243-256. Birmaher B, Ryan ND, Williamson DE, et al. 1996. Childhood and adolescent depression: a review of the past 10 years. Part I. Journal of the American Academy of Child and Adolescent Psychiatry 35(11), pp. 1427-1439. Currie, Janet and Mark Stabile. 2006. Child Mental Health and Human Capital Accumulation: The Case of ADHD. Journal of Health Economics, 25(6), pp. 1094-1118. Currie, Janet and Brigitte Madrian. 1999. Health, Health Insurance and the Labor Market, The Handbook of Labor Economics volume 3c, Card and Ashenfelter (eds.) (Amsterdam: North Holland), pp. 3309-3407. Davidson, R., and J. MacKinnon. 1993. Estimation and Inference in Econometrics New York: Oxford University Press. Ettner, Susan, Richard Frank, and R onald Kessler. 1997. The Impact of Psychiatric Disorders on Labor Market Outcomes. Industrial and Labor Relations Review 51(1), (Cornell University), pp. 64-76 Fredriksen, Katia, Jean Rhodes, R anjini Reddy, and Niobe Way. 2004. Sleepless in Chicago: Tracking the Effe cts of Adolescent Sleep Loss During the Middle School Years. Child Development 75(1), pp. 84-95 French, M. T., and Zarkin, G.A. 1998. Mental Health, Absenteeism and Earnings at a Large Manufacturing Worksite. Journal of Mental Health Policy and Economics 1, pp. 161-172. Gallagher, Robert, P. 2005. Survey Highlights. National Survey of Counseling Center Directors, University of Pittsburgh, pp. 3-6. Grossman, M. 1972. On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), pp. 223-255.

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90 Grossman, M. 1975. The correlation between health and schooling. In Household Production and Consumption, ed. Nestor Terleckyj, New York: Columbia University Press, pp. 147-211. Haines, Mary E., Deborah Kashy, and Mar garet Norris. 1996. The Effects of Depressed Mood on Academic Perfo rmance in College Students. Journal of College Student Development 37(5), pp. 519-526 Hausman, J. 1978. Specificat ion tests in econometrics. Econometrica 46(6), pp. 1251-1271. Kessler RC, McGonagle KA, and Zhao S, et al. 1994. Lifetime and 12month prevalence of DSM-III-R psychiatric disorders in the United States. Archives of General Psychiatry, 51, pp. 8-19 Nicholson, John. 1984. Men and Women: how different are they?. Oxford University Press, p. 172 Savoca, Elizabeth and Robert Rosenheck. 2000. The Civilian Labor Market Experiences of Vietnam-Era Veterans: T he Influence of Psychiatric Disorders. Journal of Mental Health Policy and Economics 3, pp. 199-207 Slade, Eric and David Salkever. 2001. Sy mptom Effects of Employment in a Structural Model of Ment al Illness and Treatment: Analysis of Patients with Schizophrenia. Journal of Mental Health Policy and Economics 4, pp. 25-34 Wolfe, Barbara and Jason Fletcher. 2007. Child Mental Health and Human Capital Accumulation: The Case of ADHD Revisited. National Bureau of Economic Research Working Paper Series 13474, pp. 1-29 Wooldridge, Jeffrey. 2003. Instrum ental Variables Estimation and Two Stage least Squares. Introductory Econometrics: A Modern Approach, 2nd Edition (Thomson South-West ern Publishing), pp. 484-524 World Health Organization. 2004. Bur den of disease in DALYs by cause, sex, and mortality stratum in WH O regions, estimates for 2002. The World Health Report 2004: Changing History Annex Table 3

PAGE 98

Appendices

PAGE 99

92 Appendix A: Output Detail, OLS-Prox y Equation, Progressive Depression English GP A Math GP A Source | SS df MS Number of obs = 19536 Source | SS df MS Number of obs = 18340 -------------+-----------------------------F( 63, 19472) = 140.56-------------+-----------------------------F( 63, 18276) = 110.86 Model | 5402.77529 63 85.7583379 Prob > F = 0.0000 Model | 5391.52278 63 85.5797267 Prob > F = 0.0000 Residual | 11880.6186 19472 .610138589 R-squared = 0.3126 Residual | 14108.4938 18276 .771968363 R-squared = 0.2765 -------------+-----------------------------Adj R-squared = 0.3104-------------+-----------------------------Adj R-squared = 0.2740 Total | 17283.3939 19535 .884739897 Root MSE = .78111 Total | 19500.0166 18339 1.06330861 Root MSE = .87862 ----------------------------------------------------------------------------------------------------------------------------------------------------------enggpa|Coef.Std. Err.tP>|t|[95% Conf Interval]matgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------dep7smon|-0.0442090.012757-3.470.001-0.069215-0.019204dep7smon|-0.0459310.01484-3.090.002-0.07502-0.016842 dep7lton|-0.0801110.023317-3.440.001-0.125815-0.034407dep7lton|-0.0656190.027109-2.420.016-0.118755-0.012483 dep7alon|-0.1254750.03688-3.40.001-0.197764-0.053187dep7alon|-0.1660130.042671-3.890-0.249651-0.082374 wave1|-0.0013120.012854-0.10.919-0.0265060.023882wave1|0.0016880.0150010.110.91-0.0277150.031092 female|0.2301440.01167419.7100.2072610.253026female|0.0868080.0133846.4900.0605740.113043 jan|-0.0561370.361558-0.160.877-0.7648220.652548jan|-0.0712750.409597-0.170.862-0.8741230.731574 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.3473540.15324-2.270.023-0.647718-0.04699apr|-0.2435440.179191-1.360.174-0.5947750.107686 may|-0.2779820.151105-1.840.066-0.5741620.018197may|-0.19210.17668-1.090.277-0.5384110.15421 june|-0.2673190.151011-1.770.077-0.5633130.028675june|-0.2233070.176567-1.260.206-0.5693940.122781 july|-0.2738790.151247-1.810.07-0.5703350.022578july|-0.2420380.176849-1.370.171-0.5886780.104602 aug|-0.2905160.151571-1.920.055-0.5876080.006577aug|-0.1854210.177215-1.050.295-0.532780.161937 sep|-0.2393330.153253-1.560.118-0.5397220.061056sep|-0.1662050.179088-0.930.353-0.5172350.184825 oct|-0.233780.158948-1.470.141-0.5453310.077772oct|-0.2327040.185787-1.250.21-0.5968630.131455 nov|-0.2036090.189655-1.070.283-0.5753490.168132nov|-0.1345780.217886-0.620.537-0.5616550.2925 agelt12|0.3747170.4792770.780.434-0.5647061.314141agelt12|0.0523450.5408970.10.923-1.0078641.112553 age12|0.3478850.1286012.710.0070.0958170.599954age12|0.1726410.1508471.140.252-0.1230320.468315 age13|0.2951380.1174042.510.0120.0650160.525261age13|0.1374660.1390750.990.323-0.1351340.410066 age14|0.2820060.1139892.470.0130.0585780.505435age14|0.0930730.1354370.690.492-0.1723970.358543 age15|0.2580330.1115272.310.0210.0394310.476635age15|0.1222090.1328480.920.358-0.1381850.382604 age16|0.2196080.10976520.0450.004460.434755age16|0.0801380.1309850.610.541-0.1766050.33688 age17|0.1849610.1083081.710.088-0.0273330.397254age17|0.0291860.1294420.230.822-0.2245330.282905 age18|0.1691860.1075751.570.116-0.041670.380042age18|0.0517980.1285690.40.687-0.200210.303805 age19|0.1267320.1127771.120.261-0.0943210.347785age19|0.0723380.1360210.530.595-0.1942760.338953 grade7|-0.2051570.051504-3.980-0.306108-0.104205grade7|-0.1428890.058899-2.430.015-0.258337-0.027441 grade8|-0.1958960.041297-4.740-0.276842-0.114951grade8|-0.0808930.04768-1.70.09-0.1743490.012564 grade9|-0.2549980.034175-7.460-0.321982-0.188013grade9|-0.1321470.039958-3.310.001-0.210468-0.053826 grade10|-0.1668520.028111-5.940-0.221951-0.111753grade10|-0.1807030.033489-5.40-0.246346-0.115061 grade11|-0.0887980.022136-4.010-0.132185-0.04541grade11|-0.0905180.027175-3.330.001-0.143784-0.037252 hisp_lat|-0.0283170.018776-1.510.132-0.065120.008486hisp_lat|-0.1007010.021951-4.590-0.143726-0.057676 white|-0.0171170.020877-0.820.412-0.0580370.023804white|0.0008210.0242510.030.973-0.0467140.048355 black|-0.0762280.023508-3.240.001-0.122306-0.030151black|-0.0776610.027311-2.840.004-0.131193-0.024129 nat_am|-0.0702450.031124-2.260.024-0.131251-0.009239nat_am|-0.007310.0362-0.20.84-0.0782650.063645 asian_pi|0.0027670.0272760.10.919-0.0506960.056229asian_pi|0.0088330.0314690.280.779-0.0528490.070514 twoparent|0.0712390.0123655.7600.0470020.095476twoparent|0.0874330.0144216.0600.0591660.115699 momdis|0.0059550.0264170.230.822-0.0458250.057734momdis|-0.0019480.030782-0.060.95-0.0622830.058387 daddis|-0.0437850.02297-1.910.057-0.0888080.001239daddis|-0.0053230.026737-0.20.842-0.057730.047084 mo9_nohs|-0.0307920.023578-1.310.192-0.0770070.015424mo9_nohs|0.0209380.0275190.760.447-0.0330030.074878 movocnoh s |-0.0571810.064722-0.880.377-0.1840410.06968movocnoh s |-0.1426840.073608-1.940.053-0.2869620.001594 mohsgrad|0.0058820.0198030.30.766-0.0329330.044697mohsgrad|-0.0160290.023068-0.690.487-0.0612440.029187 moged|-0.0012680.033281-0.040.97-0.0665020.063966moged|0.0749750.0388151.930.053-0.0011050.151056 movocafhs|0.0374970.0276031.360.174-0.0166070.091601movocafhs|0.0199150.0320990.620.535-0.0430020.082833 mocolnogr|-0.0081210.023128-0.350.726-0.0534540.037213mocolnogr|-0.0075920.026916-0.280.778-0.0603490.045165 mocol4yr|0.000380.0221660.020.986-0.0430670.043827mocol4yr|0.015950.0257860.620.536-0.0345930.066493 mopostgr|0.0469320.0280031.680.094-0.0079570.10182mopostgr|0.0734770.0325162.260.0240.0097420.137212 fa9_nohs|-0.0208350.022394-0.930.352-0.064730.023059fa9_nohs|-0.0033620.026097-0.130.897-0.0545140.047789 favocnohs|0.0559290.0656450.850.394-0.072740.184599favocnohs|-0.0636010.077612-0.820.413-0.2157270.088526 fahsgrad|0.0030990.0167140.190.853-0.0296620.03586fahsgrad|-0.0009020.019394-0.050.963-0.0389160.037113 faged|-0.0012540.03637-0.030.972-0.0725420.070034faged|-0.0590870.042284-1.40.162-0.1419680.023794 favocafhs|-0.0440590.026874-1.640.101-0.0967340.008617favocafhs|-0.0020930.031218-0.070.947-0.0632840.059098 facolnogr|0.011780.0215790.550.585-0.0305180.054077facolnogr|0.0064150.0250410.260.798-0.0426680.055499 facol4yr|0.0440960.0192282.290.0220.0064090.081784facol4yr|0.0196790.0223560.880.379-0.024140.063498 fapostgr|0.0425870.0248451.710.087-0.0061120.091286 fapostgr|0.0326130.0287491.130.257-0.023738 0.088964 abex_1_2|-0.0849930.019225-4.420-0.122676-0.047309abex_1_2|-0.0844660.022119-3.820-0.127822-0.041111 abex_3_1 0 |-0.1456730.018531-7.860-0.181995-0.109352abex_3_1 0 |-0.1489190.021365-6.970-0.190797-0.107041 abex_11pl|-0.246270.023448-10.50-0.292229-0.200311abex_11pl|-0.2131410.027304-7.810-0.26666-0.159623 unexab|-0.0122790.00104-11.810-0.014317-0.01024unexab|-0.0114960.001281-8.970-0.014008-0.008984 col_vl|-0.3305690.034856-9.480-0.398891-0.262247col_vl|-0.1779210.042655-4.170-0.261529-0.094313 col_low|-0.3112460.038039-8.180-0.385805-0.236687col_low|-0.2781010.044232-6.290-0.364799-0.191403 col_med|-0.3067730.020672-14.840-0.347292-0.266254col_med|-0.2929660.024439-11.990-0.340868-0.245063 col_hi|-0.1820750.016986-10.720-0.215369-0.14878 col_hi|-0.1750.019734-8.870-0.21368 -0.13632 skipgrde|0.0371780.0360351.030.302-0.0334540.107809skipgrde|0.0073310.042110.170.862-0.0752090.089871 adhltpvt|0.0024430.000435.6800.00160.003287adhltpvt|0.0019120.0004993.8300.0009350.00289 enggrd_is|0.412910.0061567.1400.4008570.424964 matgrd_is|0.448590.00668567.1100.435488 0.461693 _cons|1.7321160.1927238.9901.3543632.109869_cons|1.6283860.2264167.1901.1845892.072182

PAGE 100

93 Appendix A (Continued) Social Studies GP A Science GP A Source | SS df MS Number of obs = 15967 Source | SS df MS Numbe r of obs = 16387 -------------+-----------------------------F( 63, 15903) = 111.06-------------+-----------------------------F( 63, 16323) = 95.63 Model | 4628.40915 63 73.4668119 Prob > F = 0.0000 Model | 4318.01241 63 68.5398795 Prob > F = 0.0000 Residual | 10520.0056 15903 .661510758 R-squared = 0.3055 Residual | 11698.9537 16323 .716715905 R-squared = 0.2696 -------------+-----------------------------Adj R-squared = 0.3028-------------+-----------------------------Adj R-squared = 0.2668 Total | 15148.4147 15966 .948792104 Root MSE = .81333 Total | 16016.9661 16386 .977478709 Root MSE = .84659 ----------------------------------------------------------------------------------------------------------------------------------------------------------socsgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval] scigpa | Coef. Std. Err. t P >|t| [95% Conf. Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------dep7smon|-0.0675690.014756-4.580-0.096492-0.038646dep7smon|-0.0713170.015168-4.70-0.101048-0.041586 dep7lton|-0.0664730.027374-2.430.015-0.120129-0.012816dep7lton|-0.0812520.028304-2.870.004-0.13673-0.025773 dep7alon|-0.0608350.04267-1.430.154-0.1444730.022802dep7alon|-0.2583170.044252-5.840-0.345057-0.171578 wave1|-0.0235290.014911-1.580.115-0.0527570.005699wave1|-0.0116070.015371-0.760.45-0.0417360.018522 female|0.1154080.0133038.6800.0893340.141483female|0.1429580.01366610.4600.116170.169745 jan|-0.2486280.382215-0.650.515-0.9978130.500557jan|0.4139090.3962831.040.296-0.362851.190667 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.2392820.173192-1.380.167-0.5787580.100194apr|-0.0149590.176416-0.080.932-0.3607550.330836 may|-0.2220490.170558-1.30.193-0.5563630.112264may|0.0195380.1737820.110.91-0.3210940.360169 june|-0.2253170.170454-1.320.186-0.5594260.108791june|0.003990.1736740.020.982-0.336430.344409 july|-0.217490.170751-1.270.203-0.552180.117201july|-0.0130770.173967-0.080.94-0.3540710.327918 aug|-0.2142140.171073-1.250.211-0.5495360.121109aug|0.0357470.1743760.210.838-0.3060480.377543 sep|-0.2267630.173061-1.310.19-0.5659820.112456sep|0.0339270.1763380.190.847-0.3117150.379569 oct|-0.1105040.180195-0.610.54-0.4637070.242699oct|-0.0054870.183767-0.030.976-0.3656910.354718 nov|-0.207790.213266-0.970.33-0.6258150.210235nov|-0.0128430.219023-0.060.953-0.4421520.416466 agelt12|1.3073060.5047662.590.010.3179072.296706agelt12|1.0027690.5258641.910.057-0.0279832.03352 age12|0.5089340.1534443.320.0010.2081670.809702age12|0.523180.1614343.240.0010.2067520.839608 age13|0.4649690.1434173.240.0010.1838550.746082age13|0.4502450.1508812.980.0030.1545020.745989 age14|0.4188090.1404142.980.0030.1435820.694037age14|0.403750.1475492.740.0060.1145380.692962 age15|0.3856520.1380322.790.0050.1150940.65621age15|0.3786660.1451532.610.0090.094150.663182 age16|0.2887980.1359472.120.0340.0223260.555271age16|0.3298810.1434322.30.0210.0487380.611024 age17|0.251160.1344021.870.062-0.0122830.514603age17|0.248430.1418581.750.08-0.0296280.526488 age18|0.2395530.1335591.790.073-0.0222380.501345age18|0.2574080.1410581.820.068-0.0190810.533897 age19|0.0524370.1399060.370.708-0.2217950.326669age19|0.2114630.1491451.420.156-0.0808770.503802 grade7|-0.4075240.057405-7.10-0.520045-0.295004grade7|-0.2214140.059508-3.720-0.338056-0.104772 grade8|-0.294350.04742-6.210-0.3873-0.201401grade8|-0.1969570.048639-4.050-0.292295-0.101618 grade9|-0.2879760.040272-7.150-0.366914-0.209037grade9|-0.2323150.041192-5.640-0.313056-0.151574 grade10|-0.2529570.033342-7.590-0.318311-0.187603grade10|-0.1685240.034905-4.830-0.236943-0.100106 grade11|-0.1215470.026218-4.640-0.172937-0.070156grade11|-0.142680.028516-50-0.198575-0.086786 hisp_lat|-0.0324480.022077-1.470.142-0.0757210.010826hisp_lat|0.0109660.0225320.490.626-0.0331990.05513 white|-0.0084820.024324-0.350.727-0.0561610.039196white|0.048110.0251221.920.056-0.0011330.097352 black|-0.0682340.027321-2.50.013-0.121787-0.014682black|-0.0224580.028146-0.80.425-0.0776270.032712 nat_am|-0.016520.035312-0.470.64-0.0857360.052695nat_am|0.0423310.0370411.140.253-0.0302740.114936 asian_pi|0.0116040.0319480.360.716-0.0510180.074225asian_pi|0.052640.0327751.610.108-0.0116030.116883 twoparent|0.0581360.0143314.0600.0300450.086227twoparent|0.0614750.0147644.1600.0325350.090414 momdis|0.0046090.0302080.150.879-0.0546030.06382momdis|-0.0191680.031146-0.620.538-0.0802160.041881 daddis|-0.0389180.026448-1.470.141-0.090760.012924daddis|-0.0311630.027299-1.140.254-0.0846710.022345 mo9_nohs|-0.0154820.027369-0.570.572-0.0691290.038165mo9_nohs|-0.0665970.0281-2.370.018-0.121677-0.011518 movocnoh s |-0.0305150.076808-0.40.691-0.1810660.120037movocnoh s |0.0291390.076770.380.704-0.1213380.179616 mohsgrad|0.0157920.0228760.690.49-0.0290470.060631mohsgrad|-0.0547580.023642-2.320.021-0.1011-0.008417 moged|0.026990.0386990.70.486-0.0488640.102843moged|-0.065430.039611-1.650.099-0.1430710.012212 movocafhs|0.0505730.0316121.60.11-0.011390.112535movocafhs|-0.0212220.032822-0.650.518-0.0855570.043114 mocolnogr|-0.0003670.026705-0.010.989-0.0527130.051978mocolnogr|-0.0121180.027444-0.440.659-0.0659120.041676 mocol4yr|0.0125870.0256050.490.623-0.0376020.062776mocol4yr|0.0110660.0262680.420.674-0.0404210.062553 mopostgr|0.0508130.0323791.570.117-0.0126530.114279mopostgr|0.0384990.0331451.160.245-0.0264690.103468 fa9_nohs|0.0064730.0258650.250.802-0.0442250.057172fa9_nohs|0.0249580.0266210.940.348-0.0272220.077139 favocnohs|0.0324670.076040.430.669-0.116580.181514favocnohs|0.0826790.0764741.080.28-0.0672190.232576 fahsgrad|0.0005720.0192110.030.976-0.0370830.038228fahsgrad|0.0167860.0198560.850.398-0.0221340.055705 faged|-0.0495170.042927-1.150.249-0.1336580.034624faged|-0.0223730.043551-0.510.607-0.1077380.062992 favocafhs|-0.0113360.0309-0.370.714-0.0719040.049232favocafhs|-0.007870.03196-0.250.806-0.0705150.054776 facolnogr|0.006340.0249480.250.799-0.0425620.055241facolnogr|-0.024790.025525-0.970.331-0.0748210.025242 facol4yr|0.0228450.0220861.030.301-0.0204460.066137facol4yr|0.0275920.0227771.210.226-0.0170530.072238 fapostgr|0.0547590.0286271.910.056-0.0013520.110871fapostgr|0.0432390.0292191.480.139-0.0140330.10051 abex_1_2|-0.0606110.022127-2.740.006-0.103981-0.01724abex_1_2|-0.0980780.022443-4.370-0.142069-0.054087 abex_3_1 0 |-0.1255480.021385-5.870-0.167465-0.083632abex_3_1 0 |-0.1847730.021677-8.520-0.227263-0.142283 abex_11pl|-0.2037830.026991-7.550-0.256688-0.150877abex_11pl|-0.2653360.027884-9.520-0.319991-0.210681 unexab|-0.0133850.001273-10.510-0.01588-0.01089unexab|-0.0105070.00127-8.270-0.012997-0.008017 col_vl|-0.3678640.041468-8.870-0.449145-0.286582col_vl|-0.2891880.045261-6.390-0.377905-0.200471 col_low|-0.3361510.043332-7.760-0.421086-0.251216col_low|-0.3591340.046566-7.710-0.450409-0.267859 col_med|-0.2905060.024145-12.030-0.337833-0.243179col_med|-0.2520230.02533-9.950-0.301671-0.202374 col_hi|-0.1847430.019679-9.390-0.223316-0.14617col_hi|-0.1899520.020449-9.290-0.230033-0.14987 skipgrde|0.0115230.0407670.280.777-0.0683840.09143skipgrde|0.1132240.0423812.670.0080.0301530.196294 adhltpvt|0.0036770.0004997.3700.00270.004655adhltpvt|0.0030190.0005135.8900.0020140.004024 socgrd_is|0.4235850.00683561.9800.4101890.436981scigrd_is|0.3966630.00697356.8900.3829950.41033 _cons|1.582830.2243967.0501.142992.022671_cons|1.3598010.2314915.8700.9060531.813549

PAGE 101

94 Appendix A (Continued) Overall GP A Source | SS df MS Number of obs = 12314 Source | SS df MS Nu mber of obs = 12314 -------------+-----------------------------F( 63, 12250) = 209.53 -------------+-----------------------------F( 63, 12250) = 209.53 Model | 3430.16933 63 54.4471321 Prob > F = 0.0000 Model | 3430.16933 63 54.4471321 Prob > F = 0.0000 Residual | 3183.25761 12250 .259857764 R-squared = 0.5187 Residual | 3183.25761 12250 .259857764 R-squared = 0.5187 -------------+-----------------------------Adj R-squared = 0.51 62-------------+-----------------------------Adj R-squared = 0.5162 Total | 6613.42693 12313 .53710931 Root MSE = .50976 Total | 6613.42693 12313 .53710931 Root MSE = .50976 ----------------------------------------------------------------------------------------------------------------------------------------------------------overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------dep7smon|-0.0454290.010595-4.290-0.066196-0.024662dep7smon|-0.0454290.010595-4.290-0.066196-0.024662 dep7lton|-0.0403220.020234-1.990.046-0.079984-0.000659dep7lton|-0.0403220.020234-1.990.046-0.079984-0.000659 dep7alon|-0.1586320.031716-50-0.220799-0.096464dep7alon|-0.1586320.031716-50-0.220799-0.096464 wave1|-0.0051340.01081-0.470.635-0.0263230.016056wave1|-0.0051340.01081-0.470.635-0.0263230.016056 female|0.1205580.00954812.6300.1018410.139274female|0.1205580.00954812.6300.1018410.139274 jan|-0.0290340.243326-0.120.905-0.5059910.447925jan|-0.0290340.243326-0.120.905-0.5059910.447925 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.19730.11681-1.690.091-0.4262660.031667apr|-0.19730.11681-1.690.091-0.4262660.031667 may|-0.1413620.114787-1.230.218-0.3663620.083638may|-0.1413620.114787-1.230.218-0.3663620.083638 june|-0.1645440.114698-1.430.151-0.389370.060283june|-0.1645440.114698-1.430.151-0.389370.060283 july|-0.1608330.114924-1.40.162-0.3861020.064436july|-0.1608330.114924-1.40.162-0.3861020.064436 aug|-0.1398920.115184-1.210.225-0.3656710.085887aug|-0.1398920.115184-1.210.225-0.3656710.085887 sep|-0.1321980.116608-1.130.257-0.3607680.096373sep|-0.1321980.116608-1.130.257-0.3607680.096373 oct|-0.1551530.122333-1.270.205-0.3949450.084639oct|-0.1551530.122333-1.270.205-0.3949450.084639 nov|-0.1400320.143284-0.980.328-0.4208920.140827nov|-0.1400320.143284-0.980.328-0.4208920.140827 agelt12|0.7357130.3248362.260.0240.0989841.372441agelt12|0.7357130.3248362.260.0240.0989841.372441 age12|0.3532740.1217792.90.0040.1145680.591979age12|0.3532740.1217792.90.0040.1145680.591979 age13|0.3074940.1160682.650.0080.0799830.535005age13|0.3074940.1160682.650.0080.0799830.535005 age14|0.2866660.1143282.510.0120.0625640.510767age14|0.2866660.1143282.510.0120.0625640.510767 age15|0.2915350.1129552.580.010.0701250.512944age15|0.2915350.1129552.580.010.0701250.512944 age16|0.2418940.1117932.160.0310.0227630.461025age16|0.2418940.1117932.160.0310.0227630.461025 age17|0.2003250.1108191.810.071-0.0168980.417548age17|0.2003250.1108191.810.071-0.0168980.417548 age18|0.226180.1102952.050.040.0099830.442376age18|0.226180.1102952.050.040.0099830.442376 age19|0.2103720.117281.790.073-0.0195150.44026age19|0.2103720.117281.790.073-0.0195150.44026 grade7|-0.1880.040864-4.60-0.268101-0.1079grade7|-0.1880.040864-4.60-0.268101-0.1079 grade8|-0.1481190.034476-4.30-0.215696-0.080542grade8|-0.1481190.034476-4.30-0.215696-0.080542 grade9|-0.187030.030065-6.220-0.245963-0.128097grade9|-0.187030.030065-6.220-0.245963-0.128097 grade10|-0.1515660.025753-5.890-0.202046-0.101086grade10|-0.1515660.025753-5.890-0.202046-0.101086 grade11|-0.0938990.021177-4.430-0.135409-0.05239grade11|-0.0938990.021177-4.430-0.135409-0.05239 hisp_lat|-0.0167850.016151-1.040.299-0.0484440.014873hisp_lat|-0.0167850.016151-1.040.299-0.0484440.014873 white|-0.0063670.017849-0.360.721-0.0413540.028621white|-0.0063670.017849-0.360.721-0.0413540.028621 black|-0.0567330.019903-2.850.004-0.095747-0.01772black|-0.0567330.019903-2.850.004-0.095747-0.01772 nat_am|-0.0129130.026017-0.50.62-0.063910.038084nat_am|-0.0129130.026017-0.50.62-0.063910.038084 asian_pi|-0.0022380.023347-0.10.924-0.0480020.043527asian_pi|-0.0022380.023347-0.10.924-0.0480020.043527 twoparent|0.0597990.010385.7600.0394540.080145twoparent|0.0597990.010385.7600.0394540.080145 momdis|-0.0047960.021888-0.220.827-0.0476990.038108momdis|-0.0047960.021888-0.220.827-0.0476990.038108 daddis|-0.032520.019137-1.70.089-0.070030.004991daddis|-0.032520.019137-1.70.089-0.070030.004991 mo9_nohs|-0.0479890.019954-2.40.016-0.087103-0.008875mo9_nohs|-0.0479890.019954-2.40.016-0.087103-0.008875 movocnoh s |-0.0181070.055069-0.330.742-0.1260510.089837movocnoh s |-0.0181070.055069-0.330.742-0.1260510.089837 mohsgrad|-0.0126580.016562-0.760.445-0.0451210.019805mohsgrad|-0.0126580.016562-0.760.445-0.0451210.019805 moged|0.0169460.0279160.610.544-0.0377750.071666moged|0.0169460.0279160.610.544-0.0377750.071666 movocafhs|0.0112590.0229320.490.623-0.0336920.05621movocafhs|0.0112590.0229320.490.623-0.0336920.05621 mocolnogr|-0.0113230.019243-0.590.556-0.0490420.026397mocolnogr|-0.0113230.019243-0.590.556-0.0490420.026397 mocol4yr|0.0139290.018430.760.45-0.0221970.050055mocol4yr|0.0139290.018430.760.45-0.0221970.050055 mopostgr|0.0438610.0230791.90.057-0.0013780.0891mopostgr|0.0438610.0230791.90.057-0.0013780.0891 fa9_nohs|0.0075490.0188060.40.688-0.0293130.044411fa9_nohs|0.0075490.0188060.40.688-0.0293130.044411 favocnohs|0.0665910.055111.210.227-0.0414320.174615favocnohs|0.0665910.055111.210.227-0.0414320.174615 fahsgrad|0.0202370.0138571.460.144-0.0069260.047399fahsgrad|0.0202370.0138571.460.144-0.0069260.047399 faged|-0.0255660.030438-0.840.401-0.0852280.034096faged|-0.0255660.030438-0.840.401-0.0852280.034096 favocafhs|-0.0093520.022272-0.420.675-0.0530080.034304favocafhs|-0.0093520.022272-0.420.675-0.0530080.034304 facolnogr|0.0036750.0177890.210.836-0.0311940.038544facolnogr|0.0036750.0177890.210.836-0.0311940.038544 facol4yr|0.0293370.0158081.860.064-0.001650.060324facol4yr|0.0293370.0158081.860.064-0.001650.060324 fapostgr|0.0590190.0202612.910.0040.0193040.098734fapostgr|0.0590190.0202612.910.0040.0193040.098734 abex_1_2|-0.0810790.015406-5.260-0.111276-0.050881abex_1_2|-0.0810790.015406-5.260-0.111276-0.050881 abex_3_1 0 |-0.1299140.014933-8.70-0.159186-0.100643abex_3_1 0 |-0.1299140.014933-8.70-0.159186-0.100643 abex_11pl|-0.2045140.019409-10.540-0.242559-0.166469abex_11pl|-0.2045140.019409-10.540-0.242559-0.166469 unexab|-0.0097330.000989-9.840-0.011672-0.007794unexab|-0.0097330.000989-9.840-0.011672-0.007794 col_vl|-0.2063770.032547-6.340-0.270174-0.14258col_vl|-0.2063770.032547-6.340-0.270174-0.14258 col_low|-0.2054040.033054-6.210-0.270196-0.140612col_low|-0.2054040.033054-6.210-0.270196-0.140612 col_med|-0.2532520.018301-13.840-0.289124-0.217379col_med|-0.2532520.018301-13.840-0.289124-0.217379 col_hi|-0.156850.01443-10.870-0.185135-0.128565col_hi|-0.156850.01443-10.870-0.185135-0.128565 skipgrde|0.0191520.0299910.640.523-0.0396350.07794skipgrde|0.0191520.0299910.640.523-0.0396350.07794 adhltpvt|0.001780.0003584.9700.0010780.002482adhltpvt|0.001780.0003584.9700.0010780.002482 overallgpa ~ |0.5610290.00649286.4100.5483030.573755overallgpa ~ |0.5610290.00649286.4100.5483030.573755 _cons|1.2178040.1644997.400.8953611.540247_cons|1.2178040.1644997.400.8953611.540247

PAGE 102

95 Appendix B: Output Detail, OLSProxy Equation, Major Depression English GP A Math GP A Source | SS df MS Number of obs = 19536 Source | SS df MS Num ber of obs = 18340 -------------+-----------------------------F( 61, 19474) = 145.15-------------+-----------------------------F( 61, 18278) = 114.63 Model | 5402.07135 61 88.5585467 Prob > F = 0.0000 Model | 5395.57025 61 88.4519712 Prob > F = 0.0000 Residual | 11881.3225 19474 .610112074 R-squared = 0.3126 Residual | 14104.4463 18278 .771662454 R-squared = 0.2767 -------------+-----------------------------Adj R-squared = 0.3104-------------+-----------------------------Adj R-squared = 0.2743 Total | 17283.3939 19535 .884739897 Root MSE = .7811 Total | 19500.0166 18339 1.06330861 Root MSE = .87844 ----------------------------------------------------------------------------------------------------------------------------------------------------------enggpa|Coef.Std. Err.tP>|t|[95% Conf Interval]matgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.1266640.024544-5.160-0.174773-0.078555majdep7|-0.1569070.028689-5.470-0.21314-0.100675 wave1|-0.0030690.012845-0.240.811-0.0282460.022107wave1|-6.42E-050.01498800.997-0.0294420.029313 female|0.2258120.01157419.5100.2031260.248498female|0.0833750.0132726.2800.0573610.109389 jan|-0.0567360.361513-0.160.875-0.7653330.65186jan|-0.072390.409476-0.180.86-0.8750010.73022 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.3493610.153231-2.280.023-0.649706-0.049015apr|-0.2474650.17915-1.380.167-0.5986150.103685 may|-0.2794520.151096-1.850.064-0.5756130.01671may|-0.1962530.17664-1.110.267-0.5424830.149978 june|-0.267710.151-1.770.076-0.5636840.028263june|-0.2258310.176525-1.280.201-0.5718360.120174 july|-0.2738810.151234-1.810.07-0.5703140.022551july|-0.2448190.176805-1.380.166-0.5913730.101735 aug|-0.2903030.151561-1.920.055-0.5873750.006769aug|-0.1872550.177173-1.060.291-0.5345310.160022 sep|-0.2398290.153243-1.570.118-0.5401980.060539sep|-0.1685890.179047-0.940.346-0.5195380.182361 oct|-0.2339060.158942-1.470.141-0.5454470.077634oct|-0.2374290.18575-1.280.201-0.6015160.126657 nov|-0.2137520.18964-1.130.26-0.5854640.157959nov|-0.1457290.217837-0.670.504-0.5727090.281251 agelt12|0.3722510.4792640.780.437-0.5671481.31165agelt12|0.0353150.54080.070.948-1.0247031.095333 age12|0.3511110.1285692.730.0060.0991050.603118age12|0.163480.1508311.080.278-0.1321640.459124 age13|0.2972650.1173782.530.0110.0671940.527336age13|0.1265380.1390740.910.363-0.146060.399135 age14|0.2828030.1139662.480.0130.0594190.506187age14|0.0807810.1354410.60.551-0.1846950.346258 age15|0.2594920.1115062.330.020.0409310.478054age15|0.110820.132850.830.404-0.1495790.371219 age16|0.2216380.1097452.020.0430.0065290.436748age16|0.0703630.1309820.540.591-0.1863750.327101 age17|0.1867520.1082911.720.085-0.0255090.399012age17|0.0186360.1294450.140.886-0.2350880.272359 age18|0.1715050.107561.590.111-0.0393210.382331age18|0.0423860.128570.330.742-0.2096240.294394 age19|0.1306220.1127611.160.247-0.09040.351643age19|0.0637340.1360120.470.639-0.2028620.330329 grade7|-0.2008290.051523-3.90-0.301819-0.099839grade7|-0.138340.058898-2.350.019-0.253786-0.022893 grade8|-0.1929260.04131-4.670-0.273896-0.111955grade8|-0.0772630.047678-1.620.105-0.1707150.01619 grade9|-0.2518390.034188-7.370-0.31885-0.184828grade9|-0.1281590.03996-3.210.001-0.206483-0.049835 grade10|-0.1659350.028113-5.90-0.221039-0.110832grade10|-0.1795450.03348-5.360-0.245169-0.11392 grade11|-0.0884850.022134-40-0.13187-0.0451grade11|-0.0901470.027162-3.320.001-0.143387-0.036907 hisp_lat|-0.0271730.018774-1.450.148-0.0639710.009625hisp_lat|-0.0998230.021944-4.550-0.142835-0.056812 white|-0.0182210.020878-0.870.383-0.0591430.0227white|-0.0017840.02425-0.070.941-0.0493150.045748 black|-0.0762480.023505-3.240.001-0.12232-0.030176black|-0.0787030.027306-2.880.004-0.132225-0.025181 nat_am|-0.0718170.03112-2.310.021-0.132815-0.010819nat_am|-0.007650.03619-0.210.833-0.0785860.063286 asian_pi|0.0033610.0272690.120.902-0.0500890.056811asian_pi|0.0102320.0314560.330.745-0.0514250.071889 twoparent|0.0726370.0123575.8800.0484150.096858twoparent|0.0885930.0144086.1500.0603530.116834 momdis|0.0049160.0264150.190.852-0.046860.056692momdis|-0.0020960.030775-0.070.946-0.0624180.058225 daddis|-0.0444920.022967-1.940.053-0.0895080.000525daddis|-0.005060.026731-0.190.85-0.0574550.047336 mo9_nohs|-0.0290870.023581-1.230.217-0.0753070.017134mo9_nohs|0.0225010.0275150.820.414-0.0314310.076432 movocnoh s |-0.0596030.064721-0.920.357-0.1864620.067256movocnoh s |-0.1441280.073591-1.960.05-0.2883740.000118 mohsgrad|0.0063590.0198010.320.748-0.0324540.045171mohsgrad|-0.0156740.023062-0.680.497-0.0608770.02953 moged|-0.0022690.03328-0.070.946-0.0674990.062962moged|0.073620.0388071.90.058-0.0024450.149685 movocafhs|0.0365410.0276021.320.186-0.0175610.090643movocafhs|0.0195790.0320930.610.542-0.0433260.082484 mocolnogr|-0.006780.023128-0.290.769-0.0521120.038553mocolnogr|-0.0061250.026911-0.230.82-0.0588720.046623 mocol4yr|0.0009950.0221650.040.964-0.042450.044439mocol4yr|0.0165830.0257790.640.52-0.0339470.067113 mopostgr|0.0482850.0281.720.085-0.0065970.103168mopostgr|0.0744120.0325062.290.0220.0106980.138126 fa9_nohs|-0.0210320.022392-0.940.348-0.0649220.022858fa9_nohs|-0.0031110.026089-0.120.905-0.0542470.048025 favocnohs|0.0597530.0656450.910.363-0.0689160.188422favocnohs|-0.0589510.077593-0.760.447-0.211040.093139 fahsgrad|0.002230.0167150.130.894-0.0305320.034993fahsgrad|-0.0014480.019391-0.070.94-0.0394560.036561 faged|-0.0026120.03637-0.070.943-0.0739010.068677faged|-0.0593270.042275-1.40.161-0.142190.023536 favocafhs|-0.0432780.026873-1.610.107-0.0959510.009395favocafhs|-0.0013420.03121-0.040.966-0.0625170.059833 facolnogr|0.0111050.0215790.510.607-0.0311910.053401facolnogr|0.006170.0250360.250.805-0.0429020.055242 facol4yr|0.0443780.0192262.310.0210.0066940.082061facol4yr|0.0205950.0223460.920.357-0.0232050.064395 fapostgr|0.041980.0248451.690.091-0.0067190.090678fapostgr|0.0319460.0287441.110.266-0.0243940.088286 abex_1_2|-0.0863590.019225-4.490-0.124041-0.048677abex_1_2|-0.0860710.022113-3.890-0.129415-0.042728 abex_3_1 0 |-0.1474310.018526-7.960-0.183743-0.111119abex_3_1 0 |-0.1503240.021354-7.040-0.19218-0.108468 abex_11pl|-0.2496150.023415-10.660-0.29551-0.20372abex_11pl|-0.2152380.027261-7.90-0.268673-0.161803 unexab|-0.0123920.001038-11.940-0.014427-0.010357unexab|-0.0115920.001279-9.060-0.014099-0.009085 col_vl|-0.3356530.034805-9.640-0.403874-0.267433col_vl|-0.1831670.042585-4.30-0.266638-0.099696 col_low|-0.3109850.038039-8.180-0.385544-0.236426col_low|-0.2768720.044224-6.260-0.363554-0.19019 col_med|-0.3059290.020674-14.80-0.346451-0.265407col_med|-0.2912170.024435-11.920-0.339111-0.243323 col_hi|-0.1812530.016989-10.670-0.214552-0.147954col_hi|-0.1736060.019735-8.80-0.212288-0.134925 skipgrde|0.0378080.0360271.050.294-0.0328080.108423skipgrde|0.0061660.0420930.150.884-0.0763410.088673 adhltpvt|0.0024440.000435.6800.0016010.003287adhltpvt|0.0019110.0004993.8300.0009340.002888 enggrd_is|0.4131860.00614767.2200.4011370.425234matgrd_is|0.4489020.00668167.1900.4358070.461997 _cons|1.719080.192578.9301.3416262.096533_cons|1.630920.2262647.2101.1874212.074418

PAGE 103

96 Appendix B (Continued) Social Studies GP A Science GP A Source | SS df MS Number of obs = 15967 Source | SS df MS Num ber of obs = 16387 -------------+-----------------------------F( 61, 15905) = 114.34-------------+-----------------------------F( 61, 16325) = 97.86 Model | 4617.92077 61 75.7036192 Prob > F = 0.0000 Model | 4288.61426 61 70.3051517 Prob > F = 0.0000 Residual | 10530.494 15905 .662087014 R-squared = 0.3048 Residual | 11728.3519 16325 .718428905 R-squared = 0.2678 -------------+-----------------------------Adj R-squared = 0.3022-------------+-----------------------------Adj R-squared = 0.2650 Total | 15148.4147 15966 .948792104 Root MSE = .81369 Total | 16016.9661 16386 .977478709 Root MSE = .8476 ----------------------------------------------------------------------------------------------------------------------------------------------------------socsgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]scigpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.0814780.028611-2.850.004-0.137559-0.025397majdep7|-0.1049110.030257-3.470.001-0.164218-0.045605 wave1|-0.0258830.014906-1.740.083-0.0551010.003335wave1|-0.0146350.015379-0.950.341-0.0447790.01551 female|0.1096420.0131988.3100.0837730.13551female|0.1336440.0135779.8400.1070320.160256 jan|-0.2342310.382322-0.610.54-0.9836250.515164jan|0.4251840.3967081.070.284-0.3524071.202774 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.2348410.173257-1.360.175-0.5744430.104762apr|-0.0176490.176621-0.10.92-0.3638440.328547 may|-0.2173030.170623-1.270.203-0.5517440.117138may|0.017670.1739810.10.919-0.3233520.358692 june|-0.2185760.170516-1.280.2-0.5528060.115654june|0.0045950.1738710.030.979-0.3362120.345401 july|-0.2092130.170807-1.220.221-0.5440150.125589july|-0.0114780.174162-0.070.947-0.3528530.329898 aug|-0.206570.171134-1.210.227-0.5420130.128873aug|0.0381920.1745720.220.827-0.3039880.380373 sep|-0.2189140.17312-1.260.206-0.558250.120421sep|0.0355770.1765380.20.84-0.3104570.381611 oct|-0.1059670.180271-0.590.557-0.4593180.247384oct|-0.0046640.18398-0.030.98-0.3652850.355958 nov|-0.2094110.213324-0.980.326-0.627550.208729nov|-0.0239640.219266-0.110.913-0.453750.405821 agelt12|1.3104530.504972.60.0090.3206552.300252agelt12|0.9954730.5264961.890.059-0.0365172.027463 age12|0.5214250.1534733.40.0010.2206020.822249age12|0.5241770.1616313.240.0010.2073630.84099 age13|0.4745460.1434473.310.0010.1933740.755718age13|0.44750.1510712.960.0030.1513850.743615 age14|0.4264170.1404513.040.0020.1511170.701717age14|0.3970720.1477322.690.0070.1075020.686642 age15|0.392090.1380682.840.0050.121460.662719age15|0.3711370.1453342.550.0110.0862660.656007 age16|0.2950480.1359842.170.030.0285050.561591age16|0.322570.1436112.250.0250.0410780.604063 age17|0.2565810.1344361.910.056-0.0069280.52009age17|0.2408680.1420371.70.09-0.037540.519277 age18|0.2444270.1335931.830.067-0.0174310.506285age18|0.2505430.141231.770.076-0.0262820.527369 age19|0.0541320.1399530.390.699-0.2201910.328456age19|0.2030580.1493241.360.174-0.0896340.495749 grade7|-0.4071780.057448-7.090-0.519782-0.294574grade7|-0.2191210.059591-3.680-0.335926-0.102316 grade8|-0.2944150.047449-6.20-0.38742-0.201409grade8|-0.1945560.048706-3.990-0.290025-0.099087 grade9|-0.2869870.040302-7.120-0.365982-0.207991grade9|-0.2289930.041252-5.550-0.309852-0.148134 grade10|-0.2531580.03336-7.590-0.318547-0.187769grade10|-0.1674190.034949-4.790-0.235923-0.098915 grade11|-0.1232630.026226-4.70-0.174669-0.071857grade11|-0.1430040.028552-5.010-0.198968-0.08704 hisp_lat|-0.0319420.022085-1.450.148-0.0752310.011348hisp_lat|0.0127160.0225570.560.573-0.0314970.05693 white|-0.0086030.024338-0.350.724-0.0563080.039103white|0.0471970.0251571.880.061-0.0021130.096507 black|-0.0686250.027335-2.510.012-0.122204-0.015046black|-0.0223710.028181-0.790.427-0.0776080.032866 nat_am|-0.0170130.035326-0.480.63-0.0862550.052229nat_am|0.0407980.0370791.10.271-0.0318820.113477 asian_pi|0.0097420.0319580.30.76-0.05290.072384asian_pi|0.0507530.032811.550.122-0.0135580.115065 twoparent|0.0598740.0143284.1800.0317890.087959twoparent|0.0642340.0147754.3500.0352750.093194 momdis|0.003160.0302190.10.917-0.0560730.062393momdis|-0.0193320.031181-0.620.535-0.080450.041787 daddis|-0.0388770.026458-1.470.142-0.0907370.012984daddis|-0.0336980.027328-1.230.218-0.0872640.019868 mo9_nohs|-0.0144860.027381-0.530.597-0.0681560.039184mo9_nohs|-0.0652340.028136-2.320.02-0.120383-0.010084 movocnoh s |-0.0294620.07684-0.380.701-0.1800770.121153movocnoh s |0.0278170.0768610.360.717-0.1228390.178473 mohsgrad|0.0176560.0228820.770.44-0.0271950.062507mohsgrad|-0.0522090.023666-2.210.027-0.098598-0.005821 moged|0.0254540.0387140.660.511-0.050430.101337moged|-0.0663130.039658-1.670.095-0.1440460.011421 movocafhs|0.0501380.0316251.590.113-0.011850.112126movocafhs|-0.021530.032861-0.660.512-0.0859420.042881 mocolnogr|0.0017370.0267150.070.948-0.0506270.054102mocolnogr|-0.0103980.027478-0.380.705-0.0642580.043461 mocol4yr|0.0143180.0256140.560.576-0.0358870.064524mocol4yr|0.0125940.0262970.480.632-0.0389520.06414 mopostgr|0.0525180.032391.620.105-0.0109710.116007mopostgr|0.0413110.033181.250.213-0.0237250.106347 fa9_nohs|0.0059980.0258750.230.817-0.0447190.056715fa9_nohs|0.0249930.0266480.940.348-0.0272390.077225 favocnohs|0.0344530.0760710.450.651-0.1146550.183561favocnohs|0.0869790.0765631.140.256-0.0630930.237051 fahsgrad|-0.0002730.019221-0.010.989-0.0379490.037403fahsgrad|0.0164310.0198820.830.409-0.022540.055401 faged|-0.0505220.042946-1.180.239-0.1347020.033658faged|-0.022990.043608-0.530.598-0.1084660.062487 favocafhs|-0.0109030.030911-0.350.724-0.0714920.049686favocafhs|-0.0066530.031997-0.210.835-0.069370.056064 facolnogr|0.0058010.0249610.230.816-0.0431260.054727facolnogr|-0.0232460.025555-0.910.363-0.0733350.026844 facol4yr|0.0233930.0220951.060.29-0.0199150.066701facol4yr|0.0287530.02281.260.207-0.0159380.073444 fapostgr|0.0542090.0286411.890.058-0.001930.110347fapostgr|0.0428870.0292541.470.143-0.0144540.100228 abex_1_2|-0.061790.022136-2.790.005-0.105178-0.018401abex_1_2|-0.0992320.022468-4.420-0.143272-0.055191 abex_3_1 0 |-0.1276290.021388-5.970-0.169552-0.085706abex_3_1 0 |-0.1880070.021696-8.670-0.230532-0.145481 abex_11pl|-0.208130.026962-7.720-0.26098-0.155281abex_11pl|-0.2726150.027883-9.780-0.327268-0.217962 unexab|-0.013480.001271-10.60-0.015971-0.010988unexab|-0.0109030.00127-8.590-0.013393-0.008414 col_vl|-0.3719980.041434-8.980-0.453213-0.290783col_vl|-0.3035390.045249-6.710-0.392231-0.214846 col_low|-0.3389330.043347-7.820-0.423897-0.253969col_low|-0.3625710.046624-7.780-0.453959-0.271184 col_med|-0.2914240.024155-12.060-0.33877-0.244078col_med|-0.2540880.025358-10.020-0.303793-0.204383 col_hi|-0.1839930.019692-9.340-0.222592-0.145393col_hi|-0.191070.020478-9.330-0.231209-0.150931 skipgrde|0.0134660.0407740.330.741-0.0664550.093387skipgrde|0.1114420.0424262.630.0090.0282830.194602 adhltpvt|0.0037130.0004997.4400.0027350.004691adhltpvt|0.0030530.0005135.9500.0020470.004059 socgrd_is|0.4245050.00683362.1200.4111120.437899scigrd_is|0.3984750.00697457.1400.3848050.412145 _cons|1.5479080.2243126.901.1082311.987584_cons|1.3379820.231635.7800.8839621.792003

PAGE 104

97 Appendix B (Continued) Overall GP A Source | SS df MS Number of obs = 12314 Source | SS df MS Numb er of obs = 12314 -------------+-----------------------------F( 61, 12252) = 215.64 -------------+-----------------------------F( 61, 12252) = 215.64 Model | 3424.08775 61 56.1325861 Prob > F = 0.0000 Model | 3424.08775 61 56.1325861 Prob > F = 0.0000 Residual | 3189.33918 12252 .260311719 R-squared = 0.5177 Residual | 3189.33918 12252 .260311719 R-squared = 0.5177 -------------+-----------------------------Adj R-squared = 0.51 53-------------+-----------------------------Adj R-squared = 0.5153 Total | 6613.42693 12313 .53710931 Root MSE = .51021 Total | 6613.42693 12313 .53710931 Root MSE = .51021 ----------------------------------------------------------------------------------------------------------------------------------------------------------overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.0870740.021581-4.030-0.129375-0.044773majdep7|-0.0870740.021581-4.030-0.129375-0.044773 wave1|-0.0067770.010814-0.630.531-0.0279740.01442wave1|-0.0067770.010814-0.630.531-0.0279740.01442 female|0.1150860.00947112.1500.0965220.13365female|0.1150860.00947112.1500.0965220.13365 jan|-0.0204430.243494-0.080.933-0.4977290.456843jan|-0.0204430.243494-0.080.933-0.4977290.456843 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.1982430.116907-1.70.09-0.4273990.030914apr|-0.1982430.116907-1.70.09-0.4273990.030914 may|-0.1420070.114881-1.240.216-0.3671920.083177may|-0.1420070.114881-1.240.216-0.3671920.083177 june|-0.163330.114792-1.420.155-0.388340.06168june|-0.163330.114792-1.420.155-0.388340.06168 july|-0.1598220.115015-1.390.165-0.3852690.065626july|-0.1598220.115015-1.390.165-0.3852690.065626 aug|-0.1379040.115276-1.20.232-0.3638640.088055aug|-0.1379040.115276-1.20.232-0.3638640.088055 sep|-0.130340.116702-1.120.264-0.3590940.098414sep|-0.130340.116702-1.120.264-0.3590940.098414 oct|-0.1555820.122438-1.270.204-0.3955790.084415oct|-0.1555820.122438-1.270.204-0.3955790.084415 nov|-0.144950.143382-1.010.312-0.4260010.136102nov|-0.144950.143382-1.010.312-0.4260010.136102 agelt12|0.7220380.3251232.220.0260.0847451.359331agelt12|0.7220380.3251232.220.0260.0847451.359331 age12|0.3466930.1219052.840.0040.1077390.585646age12|0.3466930.1219052.840.0040.1077390.585646 age13|0.298740.1161892.570.010.0709920.526488age13|0.298740.1161892.570.010.0709920.526488 age14|0.2755960.1144452.410.0160.0512660.499925age14|0.2755960.1144452.410.0160.0512660.499925 age15|0.2812160.1130672.490.0130.0595860.502846age15|0.2812160.1130672.490.0130.0595860.502846 age16|0.2324970.1118982.080.0380.013160.451835age16|0.2324970.1118982.080.0380.013160.451835 age17|0.1907820.1109221.720.085-0.0266430.408206age17|0.1907820.1109221.720.085-0.0266430.408206 age18|0.2173560.1103911.970.0490.0009730.433739age18|0.2173560.1103911.970.0490.0009730.433739 age19|0.2007730.1173921.710.087-0.0293330.430879age19|0.2007730.1173921.710.087-0.0293330.430879 grade7|-0.1840070.040921-4.50-0.264218-0.103795grade7|-0.1840070.040921-4.50-0.264218-0.103795 grade8|-0.1440240.034522-4.170-0.211692-0.076355grade8|-0.1440240.034522-4.170-0.211692-0.076355 grade9|-0.1830040.030107-6.080-0.242019-0.12399grade9|-0.1830040.030107-6.080-0.242019-0.12399 grade10|-0.1490180.025781-5.780-0.199553-0.098483grade10|-0.1490180.025781-5.780-0.199553-0.098483 grade11|-0.0924720.021199-4.360-0.134024-0.050919grade11|-0.0924720.021199-4.360-0.134024-0.050919 hisp_lat|-0.0149550.016162-0.930.355-0.0466350.016726hisp_lat|-0.0149550.016162-0.930.355-0.0466350.016726 white|-0.006560.017867-0.370.714-0.0415820.028462white|-0.006560.017867-0.370.714-0.0415820.028462 black|-0.0567990.019922-2.850.004-0.095849-0.017748black|-0.0567990.019922-2.850.004-0.095849-0.017748 nat_am|-0.0138050.026037-0.530.596-0.064840.037231nat_am|-0.0138050.026037-0.530.596-0.064840.037231 asian_pi|-0.0026040.023365-0.110.911-0.0484030.043196asian_pi|-0.0026040.023365-0.110.911-0.0484030.043196 twoparent|0.0615970.0103815.9300.041250.081945twoparent|0.0615970.0103815.9300.041250.081945 momdis|-0.0067710.021902-0.310.757-0.0497020.03616momdis|-0.0067710.021902-0.310.757-0.0497020.03616 daddis|-0.0333090.019152-1.740.082-0.0708510.004232daddis|-0.0333090.019152-1.740.082-0.0708510.004232 mo9_nohs|-0.0459650.019974-2.30.021-0.085117-0.006813mo9_nohs|-0.0459650.019974-2.30.021-0.085117-0.006813 movocnoh s |-0.0193410.055119-0.350.726-0.1273830.0887movocnoh s |-0.0193410.055119-0.350.726-0.1273830.0887 mohsgrad|-0.0108940.016572-0.660.511-0.0433780.02159mohsgrad|-0.0108940.016572-0.660.511-0.0433780.02159 moged|0.0162670.0279390.580.56-0.0384980.071032moged|0.0162670.0279390.580.56-0.0384980.071032 movocafhs|0.0123940.0229510.540.589-0.0325930.057381movocafhs|0.0123940.0229510.540.589-0.0325930.057381 mocolnogr|-0.009440.01926-0.490.624-0.0471920.028312mocolnogr|-0.009440.01926-0.490.624-0.0471920.028312 mocol4yr|0.0157240.0184430.850.394-0.0204270.051874mocol4yr|0.0157240.0184430.850.394-0.0204270.051874 mopostgr|0.0458250.0230971.980.0470.0005510.091099mopostgr|0.0458250.0230971.980.0470.0005510.091099 fa9_nohs|0.008130.0188170.430.666-0.0287550.045014fa9_nohs|0.008130.0188170.430.666-0.0287550.045014 favocnohs|0.0688950.0551551.250.212-0.0392170.177007favocnohs|0.0688950.0551551.250.212-0.0392170.177007 fahsgrad|0.0200740.0138721.450.148-0.0071170.047264fahsgrad|0.0200740.0138721.450.148-0.0071170.047264 faged|-0.0267160.030467-0.880.381-0.0864350.033004faged|-0.0267160.030467-0.880.381-0.0864350.033004 favocafhs|-0.0095840.022289-0.430.667-0.0532740.034106favocafhs|-0.0095840.022289-0.430.667-0.0532740.034106 facolnogr|0.0036390.0178070.20.838-0.0312650.038543facolnogr|0.0036390.0178070.20.838-0.0312650.038543 facol4yr|0.029590.0158191.870.061-0.0014180.060598facol4yr|0.029590.0158191.870.061-0.0014180.060598 fapostgr|0.0579990.0202792.860.0040.0182480.097749fapostgr|0.0579990.0202792.860.0040.0182480.097749 abex_1_2|-0.0813610.015418-5.280-0.111583 -0.051139 abex_1_2|-0.0813610.015418-5.280-0.111583 -0.051139 abex_3_1 0 |-0.1311250.014941-8.780-0.16041 -0.101839 abex_3_1 0 |-0.1311250.014941-8.780-0.16041 -0.101839 abex_11pl|-0.207850.019395-10.720-0.245867 -0.169834 abex_11pl|-0.207850.019395-10.720-0.245867 -0.169834 unexab|-0.0099670.000988-10.090-0.011903 -0.00803 unexab|-0.0099670.000988-10.090-0.011903 -0.00803 col_vl|-0.2123270.032542-6.520-0.276113 -0.14854 col_vl|-0.2123270.032542-6.520-0.276113 -0.14854 col_low|-0.2078240.033077-6.280-0.272659 -0.142988 col_low|-0.2078240.033077-6.280-0.272659 -0.142988 col_med|-0.2530610.018318-13.810-0.288968 -0.217155 col_med|-0.2530610.018318-13.810-0.288968 -0.217155 col_hi|-0.1569070.014445-10.860-0.185222 -0.128592 col_hi|-0.1569070.014445-10.860-0.185222 -0.128592 skipgrde|0.0173990.0300110.58 0.562-0.0414280.076225 skipgrde|0.0173990.0300110.58 0.562-0.0414280.076225 adhltpvt|0.0017890.0003584.99 00.0010870.002491 adhltpvt|0.0017890.0003584.99 00.0010870.002491 overallgpa ~ |0.5624870.00648886.7 00.5497690.575204 overallgpa ~ |0.5624870.00648886.7 00.5497690.575204 _cons|1.2074430.1645447.3400.8849111.529975_cons|1.2074430.1645447.3400.8849111.529975

PAGE 105

98 Appendix C: Output Detail, OLS-Prox y Equation, Persistence Depression English GP A Math GP A Source | SS df MS Number of obs = 19535 Source | SS df MS Numb er of obs = 18339 -------------+-----------------------------F( 63 19471) = 140.56 -------------+-----------------------------F( 63, 18275) = 110.88 Model | 5402.97019 63 85.7614317 Prob > F = 0.0000 Model | 5392.44995 63 85.5944437 Prob > F = 0.0000 Residual | 11880.4099 19471 .610159206 R-squared = 0.3126 Residual | 14107.4785 18275 .771955048 R-squared = 0.2765 -------------+-----------------------------Adj R-squared = 0.3104-------------+-----------------------------Adj R-squared = 0.2740 Total | 17283.3801 19534 .884784483 Root MSE = .78113 Total | 19499.9285 18338 1.06336179 Root MSE = .87861 ----------------------------------------------------------------------------------------------------------------------------------------------------------enggpa|Coef.Std. Err.tP>|t|[95% Conf Interval]matgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------perdep|-0.0286840.015931-1.80.072-0.0599110.002543perdep|-0.085120.018522-4.60-0.121426-0.048815 onsetdep|-0.0669940.015211-4.40-0.096808-0.037179onsetdep|-0.055530.017649-3.150.002-0.090124-0.020936 remitdep|0.0243760.0170861.430.154-0.0091140.057866remitdep|-0.0540410.019995-2.70.007-0.093234-0.014849 wave1|-0.0027010.012861-0.210.834-0.0279090.022507wave1|0.0025510.015010.170.865-0.0268710.031972 female|0.2238780.0117861900.2007760.246979female|0.0908130.0135086.7200.0643360.117291 jan|-0.0520240.361579-0.140.886-0.7607490.656701jan|-0.0973370.409622-0.240.812-0.9002350.705561 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.3442350.153265-2.250.025-0.644647-0.043824apr|-0.2583890.179228-1.440.149-0.6096930.092915 may|-0.2741920.15113-1.810.07-0.5704190.022035may|-0.2079350.176721-1.180.239-0.5543240.138454 june|-0.2635880.151035-1.750.081-0.5596290.032452june|-0.2387840.176605-1.350.176-0.5849470.107379 july|-0.2701740.151271-1.790.074-0.5666780.02633july|-0.2578120.176886-1.460.145-0.6045260.088901 aug|-0.2862310.151597-1.890.059-0.5833740.010911aug|-0.2001170.177255-1.130.259-0.5475540.147319 sep|-0.2349860.153279-1.530.125-0.5354260.065453sep|-0.1809150.179131-1.010.313-0.5320280.170199 oct|-0.2252730.158989-1.420.157-0.5369050.086359oct|-0.2483980.18585-1.340.181-0.6126820.115886 nov|-0.2047570.189668-1.080.28-0.5765220.167009nov|-0.1502750.217912-0.690.49-0.5774020.276853 agelt12|0.3794990.4792720.790.428-0.5599151.318914agelt12|0.0555550.5408760.10.918-1.0046121.115723 age12|0.3462180.1286082.690.0070.0941360.5983age12|0.1765080.1508531.170.242-0.1191780.472194 age13|0.2929810.1174122.50.0130.0628440.523119age13|0.1415380.1390851.020.309-0.1310820.414158 age14|0.2783030.1139982.440.0150.0548580.501748age14|0.0965950.1354470.710.476-0.1688930.362083 age15|0.2547540.1115342.280.0220.0361370.473371age15|0.1255180.1328560.940.345-0.1348920.385929 age16|0.2170370.109771.980.0480.0018780.432196age16|0.0834780.1309910.640.524-0.1732780.340233 age17|0.1832070.1083111.690.091-0.0290910.395505age17|0.0324220.1294430.250.802-0.2212980.286142 age18|0.1670840.107581.550.12-0.0437820.37795age18|0.0557620.1285720.430.665-0.196250.307774 age19|0.126120.1127781.120.263-0.0949360.347175age19|0.0735770.1360230.540.589-0.193040.340195 grade7|-0.1990940.051567-3.860-0.300169-0.098019grade7|-0.1496310.058981-2.540.011-0.26524-0.034021 grade8|-0.1907250.041353-4.610-0.27178-0.10967grade8|-0.0866570.047747-1.810.07-0.1802450.006932 grade9|-0.252490.034194-7.380-0.319513-0.185468grade9|-0.135150.039983-3.380.001-0.213519-0.05678 grade10|-0.1651110.028122-5.870-0.220233-0.10999grade10|-0.1823710.033505-5.440-0.248043-0.116699 grade11|-0.0878910.022138-3.970-0.131284-0.044498grade11|-0.0909920.027178-3.350.001-0.144264-0.037721 hisp_lat|-0.0269650.018778-1.440.151-0.0637720.009842hisp_lat|-0.1014520.021952-4.620-0.14448-0.058424 white|-0.0172140.020876-0.820.41-0.0581330.023705white|0.0008930.024250.040.971-0.0466380.048425 black|-0.0747420.02351-3.180.001-0.120824-0.028661black|-0.0786420.027312-2.880.004-0.132177-0.025108 nat_am|-0.0722740.031128-2.320.02-0.133287-0.011261nat_am|-0.0055740.036205-0.150.878-0.0765390.065391 asian_pi|0.0042360.0272750.160.877-0.0492260.057697asian_pi|0.0094140.0314690.30.765-0.0522680.071095 twoparent|0.0719130.0123665.8200.0476750.09615twoparent|0.0877530.0144226.0800.0594840.116021 momdis|0.0064270.0264190.240.808-0.0453570.058211momdis|-0.0031890.030785-0.10.918-0.063530.057153 daddis|-0.044520.022968-1.940.053-0.0895390.000499daddis|-0.0067320.026735-0.250.801-0.0591350.045671 mo9_nohs|-0.0321490.023587-1.360.173-0.0783820.014084mo9_nohs|0.0214980.0275280.780.435-0.0324590.075455 movocnoh s |-0.0554080.064728-0.860.392-0.1822820.071465movocnoh s |-0.1455990.073612-1.980.048-0.289886-0.001313 mohsgrad|0.005520.0198060.280.78-0.0333010.044342mohsgrad|-0.0160130.023072-0.690.488-0.0612360.02921 moged|-0.0023420.033288-0.070.944-0.0675890.062904moged|0.0763850.0388191.970.0490.0002950.152475 movocafhs|0.0361760.027611.310.19-0.0179410.090294movocafhs|0.0214970.0321080.670.503-0.0414370.084432 mocolnogr|-0.008790.023133-0.380.704-0.0541330.036554mocolnogr|-0.0069580.026922-0.260.796-0.0597270.045811 mocol4yr|-0.0001670.02217-0.010.994-0.0436230.043289mocol4yr|0.0167790.025790.650.515-0.0337720.06733 mopostgr|0.0463830.0280061.660.098-0.0085110.101276mopostgr|0.0742240.0325172.280.0220.0104880.13796 fa9_nohs|-0.0215920.022394-0.960.335-0.0654870.022303fa9_nohs|-0.002610.026095-0.10.92-0.0537580.048539 favocnohs|0.0550780.065650.840.401-0.0736010.183758favocnohs|-0.0605890.077616-0.780.435-0.2127240.091547 fahsgrad|0.0039770.0167160.240.812-0.0287870.036741fahsgrad|-0.0010420.019396-0.050.957-0.039060.036976 faged|0.0006140.0363730.020.987-0.070680.071908faged|-0.0589590.042285-1.390.163-0.1418410.023923 favocafhs|-0.0433860.026876-1.610.106-0.0960650.009293favocafhs|-0.0029550.031219-0.090.925-0.0641470.058238 facolnogr|0.012260.021580.570.57-0.0300390.054559facolnogr|0.0060080.0250450.240.81-0.0430820.055099 facol4yr|0.0440.0192282.290.0220.0063110.081688facol4yr|0.018680.0223560.840.403-0.0251390.062499 fapostgr|0.0429480.0248471.730.084-0.0057530.091649fapostgr|0.0315710.0287511.10.272-0.0247840.087925 skipgrde|0.0366190.0360291.020.309-0.0340.107238skipgrde|0.0047210.0421030.110.911-0.0778050.087247 adhltpvt|0.0023840.0004315.5300.0015390.003228adhltpvt|0.0019990.0005400.0010190.002978 abex_1_2|-0.0845530.019226-4.40-0.122238-0.046868abex_1_2|-0.0833030.022117-3.770-0.126655-0.039951 abex_3_1 0 |-0.1467460.018537-7.920-0.183081-0.110412abex_3_1 0 |-0.1468380.021372-6.870-0.188729-0.104947 abex_11pl|-0.2506760.023452-10.690-0.296643-0.204708abex_11pl|-0.2114210.027312-7.740-0.264955-0.157888 unexab|-0.0124830.001038-12.020-0.014518-0.010448unexab|-0.0116490.001279-9.110-0.014156-0.009142 col_vl|-0.3341980.034819-9.60-0.402447-0.265949col_vl|-0.1832840.04261-4.30-0.266803-0.099765 col_low|-0.3125790.038042-8.220-0.387144-0.238014col_low|-0.276860.044236-6.260-0.363566-0.190154 col_med|-0.3082330.02067-14.910-0.348748-0.267719col_med|-0.2924420.024433-120-0.340333-0.244551 col_hi|-0.1830730.016988-10.780-0.21637-0.149776col_hi|-0.1748550.019734-8.860-0.213534-0.136175 enggrd_is|0.4131250.0061567.1700.401070.425181matgrd_is|0.4485470.00668667.0900.4354420.461651 _cons|1.7328780.1927828.9901.3550092.110748_cons|1.6427580.2264877.2501.1988222.086694

PAGE 106

99 Appendix C (Continued) Social Studies GP A Science GP A Source | SS df MS Number of obs = 15967 Source | SS df MS Numb er of obs = 16386 -------------+-----------------------------F( 63, 15903) = 111.19-------------+-----------------------------F( 63, 16322) = 95.31 Model | 4632.1283 63 73.525846 Prob > F = 0.0000 Model | 4307.13734 63 68.3672594 Prob > F = 0.0000 Residual | 10516.2864 15903 .661276893 R-squared = 0.3058 Residual | 11708.5681 16322 .717348859 R-squared = 0.2689 -------------+-----------------------------Adj R-squared = 0.3030-------------+-----------------------------Adj R-squared = 0.2661 Total | 15148.4147 15966 .948792104 Root MSE = .81319 Total | 16015.7054 16385 .977461423 Root MSE = .84696 ----------------------------------------------------------------------------------------------------------------------------------------------------------socsgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]scigpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------perdep|-0.0524980.018534-2.830.005-0.088827-0.016169perdep|-0.0618940.019093-3.240.001-0.099319-0.02447 onsetdep|-0.0923270.017496-5.280-0.126621-0.058033onsetdep|-0.1027140.018046-5.690-0.138086-0.067342 remitdep|-0.0281230.019711-1.430.154-0.0667580.010512remitdep|0.0016870.0201790.080.933-0.0378670.04124 wave1|-0.0241420.014916-1.620.106-0.0533790.005096wave1|-0.0130080.015384-0.850.398-0.0431630.017147 female|0.1152160.0134098.5900.0889320.141499female|0.1379760.0137961000.1109340.165019 jan|-0.2509990.382138-0.660.511-1.0000320.498034jan|0.4144610.3964741.050.296-0.3626711.191593 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.239240.173165-1.380.167-0.5786630.100184apr|-0.0175060.176522-0.10.921-0.3635090.328497 may|-0.2218620.170535-1.30.193-0.556130.112407may|0.0173780.1738840.10.92-0.3234540.35821 june|-0.224910.170429-1.320.187-0.5589710.10915june|0.0022590.1737750.010.99-0.338360.342877 july|-0.2168780.170724-1.270.204-0.5515150.11776july|-0.0154690.174067-0.090.929-0.3566590.325722 aug|-0.2138380.171049-1.250.211-0.5491140.121437aug|0.034360.1744780.20.844-0.3076360.376357 sep|-0.2257070.173035-1.30.192-0.5648740.113461sep|0.0333330.1764450.190.85-0.3125190.379185 oct|-0.1106220.180197-0.610.539-0.4638280.242584oct|-0.003740.183886-0.020.984-0.3641770.356697 nov|-0.2133750.213219-10.317-0.6313090.204559nov|-0.0188220.219127-0.090.932-0.4483350.41069 agelt12|1.3055940.5046412.590.010.3164412.294748agelt12|0.9968580.5260761.890.058-0.0343092.028025 age12|0.5062530.1534073.30.0010.2055570.806949age12|0.513350.1615033.180.0010.1967870.829914 age13|0.4617250.1433713.220.0010.1807010.74275age13|0.4385160.1509432.910.0040.1426510.73438 age14|0.4162440.1403742.970.0030.1410960.691391age14|0.3900570.1476052.640.0080.1007360.679378 age15|0.3833970.1379922.780.0050.1129180.653877age15|0.3666350.145212.520.0120.0820090.651261 age16|0.2869090.1359122.110.0350.0205060.553311age16|0.3196290.1434892.230.0260.0383750.600883 age17|0.2500930.1343561.860.063-0.0132610.513446age17|0.2396510.1419131.690.091-0.0385140.517815 age18|0.2381490.133521.780.075-0.0235650.499862age18|0.2487790.1411191.760.078-0.0278290.525386 age19|0.0509470.1398580.360.716-0.2231910.325085age19|0.2038660.1492041.370.172-0.0885910.496322 grade7|-0.4051450.057459-7.050-0.51777-0.292519grade7|-0.2146210.059613-3.60-0.331469-0.097773 grade8|-0.2929140.047465-6.170-0.385951-0.199876grade8|-0.1904460.048728-3.910-0.285958-0.094934 grade9|-0.2873560.04028-7.130-0.36631-0.208403grade9|-0.2283420.041235-5.540-0.309166-0.147518 grade10|-0.2526460.033348-7.580-0.318012-0.18728grade10|-0.1659310.034935-4.750-0.234408-0.097454 grade11|-0.1215840.026217-4.640-0.172971-0.070196grade11|-0.1411430.028535-4.950-0.197074-0.085212 hisp_lat|-0.0322650.022074-1.460.144-0.0755320.011003hisp_lat|0.0117070.0225430.520.604-0.0324790.055894 white|-0.008640.02432-0.360.722-0.056310.03903white|0.0471170.0251351.870.061-0.002150.096384 black|-0.0680760.027319-2.490.013-0.121625-0.014528black|-0.0218750.02816-0.780.437-0.0770720.033321 nat_am|-0.017040.035309-0.480.629-0.086250.05217nat_am|0.04180.0370611.130.259-0.0308430.114443 asian_pi|0.0124760.0319420.390.696-0.0501330.075086asian_pi|0.0538370.032791.640.101-0.0104340.118108 twoparent|0.057440.014334.0100.0293530.085528twoparent|0.062040.0147734.200.0330830.090997 momdis|0.0055280.0302060.180.855-0.053680.064736momdis|-0.0178870.031161-0.570.566-0.0789650.043191 daddis|-0.0386730.026439-1.460.144-0.0904960.01315daddis|-0.0326910.027307-1.20.231-0.0862160.020834 mo9_nohs|-0.0164130.027369-0.60.549-0.0700590.037234mo9_nohs|-0.0677650.028124-2.410.016-0.122891-0.012639 movocnoh s |-0.0304410.076801-0.40.692-0.180980.120099movocnoh s |0.0309680.0768070.40.687-0.1195820.181519 mohsgrad|0.0156010.0228720.680.495-0.029230.060432mohsgrad|-0.054750.023657-2.310.021-0.101121-0.008379 moged|0.0271280.0386970.70.483-0.0487210.102978moged|-0.0658250.039638-1.660.097-0.1435190.01187 movocafhs|0.0506240.031611.60.109-0.0113340.112582movocafhs|-0.0225140.032846-0.690.493-0.0868960.041868 mocolnogr|-0.0007750.026702-0.030.977-0.0531140.051563mocolnogr|-0.0129950.027464-0.470.636-0.0668260.040837 mocol4yr|0.0125620.0256020.490.624-0.037620.062744mocol4yr|0.0109040.0262850.410.678-0.0406190.062426 mopostgr|0.05070.0323731.570.117-0.0127550.114154mopostgr|0.0378850.0331621.140.253-0.0271170.102886 fa9_nohs|0.0061730.025860.240.811-0.0445160.056862fa9_nohs|0.0241660.0266310.910.364-0.0280340.076365 favocnohs|0.0293680.0760370.390.699-0.1196730.178408favocnohs|0.0818620.0765141.070.285-0.0681140.231837 fahsgrad|0.0006320.0192070.030.974-0.0370150.038279fahsgrad|0.0179740.0198670.90.366-0.0209680.056916 faged|-0.0485710.042926-1.130.258-0.1327110.035568faged|-0.0200940.043573-0.460.645-0.1055010.065313 favocafhs|-0.0112270.030893-0.360.716-0.071780.049326favocafhs|-0.0077920.031975-0.240.807-0.0704660.054882 facolnogr|0.0068980.0249450.280.782-0.0419970.055793facolnogr|-0.0229950.025538-0.90.368-0.0730520.027063 facol4yr|0.0226750.0220821.030.305-0.0206080.065957facol4yr|0.0269570.0227881.180.237-0.0177110.071624 fapostgr|0.0550730.0286221.920.054-0.001030.111175fapostgr|0.0436920.0292331.490.135-0.0136080.100992 skipgrde|0.0118840.0407520.290.771-0.0679950.091763skipgrde|0.1112350.0423952.620.0090.0281360.194334 adhltpvt|0.0036460.0004997.300.0026670.004625adhltpvt|0.0029660.0005145.7700.0019580.003973 abex_1_2|-0.0601780.022123-2.720.007-0.10354-0.016815abex_1_2|-0.0967110.022454-4.310-0.140723-0.052698 abex_3_1 0 |-0.1250710.02139-5.850-0.166997-0.083145abex_3_1 0 |-0.1852840.021696-8.540-0.227811-0.142758 abex_11pl|-0.2041990.026996-7.560-0.257115-0.151283abex_11pl|-0.2688630.027898-9.640-0.323545-0.214181 unexab|-0.0133940.001271-10.50-0.015884-0.010904unexab|-0.0108630.001269-8.560-0.01335-0.008377 col_vl|-0.3672920.041422-8.870-0.448483-0.286101col_vl|-0.2969790.045237-6.560-0.385648-0.208309 col_low|-0.3356480.043328-7.750-0.420576-0.25072col_low|-0.3597270.046592-7.720-0.451053-0.2684 col_med|-0.2900160.024141-120-0.337335-0.242698col_med|-0.2536190.025337-10.010-0.303283-0.203955 col_hi|-0.1845860.019676-9.380-0.223154-0.146018col_hi|-0.1915940.020457-9.370-0.231692-0.151496 socgrd_is|0.4234690.00683261.9800.4100770.436861scigrd_is|0.3969190.00697756.8900.3832430.410595 _cons|1.5943130.2243997.101.1544642.034161_cons|1.3776610.2316235.9500.9236541.831668

PAGE 107

100 Appendix C (Continued) Overall GP A Source | SS df MS Number of obs = 12314 Source | SS df MS Nu mber of obs = 12314 -------------+-----------------------------F( 63, 12250) = 209.32 -------------+-----------------------------F( 63, 12250) = 209.32 Model | 3428.54238 63 54.4213076 Prob > F = 0.0000 Model | 3428.54238 63 54.4213076 Prob > F = 0.0000 Residual | 3184.88455 12250 .259990576 R-squared = 0.5184 Residual | 3184.88455 12250 .259990576 R-squared = 0.5184 -------------+-----------------------------Adj R-sq uared = 0.5159-------------+----------------------------Adj R-squared = 0.5159 Total | 6613.42693 12313 .53710931 Root MSE = .50989 Total | 6613.42693 12313 .53710931 Root MSE = .50989 ----------------------------------------------------------------------------------------------------------------------------------------------------------overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------perdep|-0.0375910.013466-2.790.005-0.063986-0.011196perdep|-0.0375910.013466-2.790.005-0.063986-0.011196 onsetdep|-0.0706230.012521-5.640-0.095167-0.046079onsetdep|-0.0706230.012521-5.640-0.095167-0.046079 remitdep|-0.0196280.014193-1.380.167-0.0474480.008191remitdep|-0.0196280.014193-1.380.167-0.0474480.008191 wave1|-0.0058340.010822-0.540.59-0.0270460.015378wave1|-0.0058340.010822-0.540.59-0.0270460.015378 female|0.11860.00962712.3200.099730.137469female|0.11860.00962712.3200.099730.137469 jan|-0.03050.243391-0.130.9-0.5075840.446585jan|-0.03050.243391-0.130.9-0.5075840.446585 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.1995090.116852-1.710.088-0.4285570.029539apr|-0.1995090.116852-1.710.088-0.4285570.029539 may|-0.1443740.114832-1.260.209-0.3694630.080715may|-0.1443740.114832-1.260.209-0.3694630.080715 june|-0.1666180.114742-1.450.146-0.3915290.058294june|-0.1666180.114742-1.450.146-0.3915290.058294 july|-0.1635960.114966-1.420.155-0.3889480.061757july|-0.1635960.114966-1.420.155-0.3889480.061757 aug|-0.1423930.115227-1.240.217-0.3682560.083471aug|-0.1423930.115227-1.240.217-0.3682560.083471 sep|-0.1339790.116655-1.150.251-0.362640.094683sep|-0.1339790.116655-1.150.251-0.362640.094683 oct|-0.1568680.122405-1.280.2-0.3968010.083065oct|-0.1568680.122405-1.280.2-0.3968010.083065 nov|-0.1478950.143321-1.030.302-0.4288280.133037nov|-0.1478950.143321-1.030.302-0.4288280.133037 agelt12|0.7297140.3248932.250.0250.0928731.366554agelt12|0.7297140.3248932.250.0250.0928731.366554 age12|0.3467570.1217972.850.0040.1080150.585498age12|0.3467570.1217972.850.0040.1080150.585498 age13|0.2997850.1160782.580.010.0722530.527317age13|0.2997850.1160782.580.010.0722530.527317 age14|0.2780580.1143352.430.0150.0539440.502172age14|0.2780580.1143352.430.0150.0539440.502172 age15|0.2841970.1129662.520.0120.0627670.505627age15|0.2841970.1129662.520.0120.0627670.505627 age16|0.235010.1118052.10.0360.0158540.454166age16|0.235010.1118052.10.0360.0158540.454166 age17|0.1941240.1108281.750.08-0.0231170.411365age17|0.1941240.1108281.750.08-0.0231170.411365 age18|0.2203180.11030820.0460.0040970.436538age18|0.2203180.11030820.0460.0040970.436538 age19|0.2062270.1172981.760.079-0.0236960.43615age19|0.2062270.1172981.760.079-0.0236960.43615 grade7|-0.1849820.040921-4.520-0.265194-0.10477grade7|-0.1849820.040921-4.520-0.265194-0.10477 grade8|-0.145250.034518-4.210-0.212911-0.07759grade8|-0.145250.034518-4.210-0.212911-0.07759 grade9|-0.1853520.030082-6.160-0.244316-0.126387grade9|-0.1853520.030082-6.160-0.244316-0.126387 grade10|-0.1499420.025764-5.820-0.200443-0.099441grade10|-0.1499420.025764-5.820-0.200443-0.099441 grade11|-0.0924370.021183-4.360-0.133959-0.050915grade11|-0.0924370.021183-4.360-0.133959-0.050915 hisp_lat|-0.0161820.016154-10.316-0.0478470.015483hisp_lat|-0.0161820.016154-10.316-0.0478470.015483 white|-0.0065920.017856-0.370.712-0.0415910.028408white|-0.0065920.017856-0.370.712-0.0415910.028408 black|-0.0560970.019909-2.820.005-0.095122-0.017073black|-0.0560970.019909-2.820.005-0.095122-0.017073 nat_am|-0.0137390.026028-0.530.598-0.0647570.037279nat_am|-0.0137390.026028-0.530.598-0.0647570.037279 asian_pi|-0.0012060.023353-0.050.959-0.0469820.044569asian_pi|-0.0012060.023353-0.050.959-0.0469820.044569 twoparent|0.0595790.0103845.7400.0392240.079933twoparent|0.0595790.0103845.7400.0392240.079933 momdis|-0.0041290.021897-0.190.85-0.047050.038793momdis|-0.0041290.021897-0.190.85-0.047050.038793 daddis|-0.0342220.019136-1.790.074-0.071730.003287daddis|-0.0342220.019136-1.790.074-0.071730.003287 mo9_nohs|-0.0488210.019966-2.450.014-0.087958-0.009684mo9_nohs|-0.0488210.019966-2.450.014-0.087958-0.009684 movocnoh s |-0.0187780.055086-0.340.733-0.1267560.089201movocnoh s |-0.0187780.055086-0.340.733-0.1267560.089201 mohsgrad|-0.0130990.016568-0.790.429-0.0455750.019376mohsgrad|-0.0130990.016568-0.790.429-0.0455750.019376 moged|0.0163670.0279310.590.558-0.0383820.071117moged|0.0163670.0279310.590.558-0.0383820.071117 movocafhs|0.0117690.0229430.510.608-0.0332030.05674movocafhs|0.0117690.0229430.510.608-0.0332030.05674 mocolnogr|-0.0121580.019253-0.630.528-0.0498980.025582mocolnogr|-0.0121580.019253-0.630.528-0.0498980.025582 mocol4yr|0.0141340.0184380.770.443-0.0220070.050275mocol4yr|0.0141340.0184380.770.443-0.0220070.050275 mopostgr|0.0437140.0230861.890.058-0.0015390.088967mopostgr|0.0437140.0230861.890.058-0.0015390.088967 fa9_nohs|0.0080590.0188060.430.668-0.0288030.044921fa9_nohs|0.0080590.0188060.430.668-0.0288030.044921 favocnohs|0.0643310.0551341.170.243-0.0437410.172403favocnohs|0.0643310.0551341.170.243-0.0437410.172403 fahsgrad|0.0211120.0138631.520.128-0.0060630.048286fahsgrad|0.0211120.0138631.520.128-0.0060630.048286 faged|-0.0237070.030456-0.780.436-0.0834050.035991faged|-0.0237070.030456-0.780.436-0.0834050.035991 favocafhs|-0.0095960.022277-0.430.667-0.0532620.03407favocafhs|-0.0095960.022277-0.430.667-0.0532620.03407 facolnogr|0.0049740.0177970.280.78-0.029910.039858facolnogr|0.0049740.0177970.280.78-0.029910.039858 facol4yr|0.0287470.0158121.820.069-0.0022470.05974facol4yr|0.0287470.0158121.820.069-0.0022470.05974 fapostgr|0.0591940.0202682.920.0040.0194650.098923fapostgr|0.0591940.0202682.920.0040.0194650.098923 abex_1_2|-0.0797250.01541-5.170-0.109931-0.049518abex_1_2|-0.0797250.01541-5.170-0.109931-0.049518 abex_3_1 0 |-0.1292560.01494-8.650-0.158542-0.099971abex_3_1 0 |-0.1292560.01494-8.650-0.158542-0.099971 abex_11pl|-0.2058860.019407-10.610-0.243927-0.167845abex_11pl|-0.2058860.019407-10.610-0.243927-0.167845 unexab|-0.0099910.000988-10.120-0.011926-0.008055unexab|-0.0099910.000988-10.120-0.011926-0.008055 col_vl|-0.2098940.032528-6.450-0.273654-0.146134col_vl|-0.2098940.032528-6.450-0.273654-0.146134 col_low|-0.2053850.03307-6.210-0.270208-0.140562col_low|-0.2053850.03307-6.210-0.270208-0.140562 col_med|-0.2532860.018306-13.840-0.289168-0.217403col_med|-0.2532860.018306-13.840-0.289168-0.217403 col_hi|-0.1576090.014433-10.920-0.1859-0.129318col_hi|-0.1576090.014433-10.920-0.1859-0.129318 skipgrde|0.0172670.0299930.580.565-0.0415240.076059skipgrde|0.0172670.0299930.580.565-0.0415240.076059 adhltpvt|0.001750.0003594.8800.0010470.002454adhltpvt|0.001750.0003594.8800.0010470.002454 overallgpa ~ |0.5610880.00649586.3900.5483560.573819overallgpa ~ |0.5610880.00649586.3900.5483560.573819 _cons|1.2339370.1645737.500.9113491.556525_cons|1.2339370.1645737.500.9113491.556525

PAGE 108

101 Appendix D: Output Detail, 2SLS (Major Depression), 2nd Stage English GPA "fearful 12 + crying 12"Math GPA "fearful 12 + crying 12" Second-stage regressionsSecond-stage regressions IV (2SLS) regression with robust std. errors Number of obs = 19536IV (2SLS) regression with robust std. errors Nu mber of obs = 18340 F( 61, 19474) = 152.18 F( 61, 18278) = 121.74 Prob > F = 0.0000 Prob > F = 0.0000 R-squared = 0.3107 R-squared = 0.2753 Root MSE = .78213 Root MSE = .87929 ----------------------------------------------------------------------------------------------------------------------------------------------------------| Robust | Robust enggpa|Coef.Std. Err.tP>|t|[95% Conf Interval]matgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.3032940.115429-2.630.009-0.529545-0.077044majdep7|-0.3275880.14129-2.320.02-0.604529-0.050647 wave1|-0.0019840.013207-0.150.881-0.0278710.023904wave1|0.0009010.0153950.060.953-0.0292740.031076 female|0.2330280.01254318.5800.2084420.257614female|0.0898060.0141686.3400.0620360.117577 jan|-0.0664730.268527-0.250.804-0.5928090.459864jan|-0.0828040.303932-0.270.785-0.6785390.512932 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.3523140.167307-2.110.035-0.68025-0.024377apr|-0.2509850.146785-1.710.087-0.5386980.036727 may|-0.2807930.164877-1.70.089-0.6039660.04238may|-0.1989430.143069-1.390.164-0.4793720.081486 june|-0.2701340.164773-1.640.101-0.5931040.052835june|-0.2289360.142954-1.60.109-0.509140.051267 july|-0.2774570.164954-1.680.093-0.6007820.045867july|-0.2488240.143344-1.740.083-0.5297920.032144 aug|-0.2953090.165254-1.790.074-0.6192210.028603aug|-0.192310.143705-1.340.181-0.4739840.089364 sep|-0.2447640.16643-1.470.141-0.570980.081452sep|-0.1740290.145593-1.20.232-0.4594040.111346 oct|-0.2377970.170454-1.40.163-0.5719010.096307oct|-0.2445050.152037-1.610.108-0.5425110.053502 nov|-0.2207170.194429-1.140.256-0.6018150.16038nov|-0.1545790.197964-0.780.435-0.5426070.23345 agelt12|0.3376120.5393080.630.531-0.7194791.394702agelt12|-0.0057060.838847-0.010.995-1.6499241.638512 age12|0.3221290.1285632.510.0120.0701340.574125age12|0.1276810.1596410.80.424-0.185230.440592 age13|0.269840.1194562.260.0240.0356960.503985age13|0.0919570.1491040.620.537-0.2003010.384216 age14|0.2581410.1158652.230.0260.0310360.485246age14|0.0485360.1454940.330.739-0.2366460.333718 age15|0.2396980.1126592.130.0330.0188780.460519age15|0.0823330.142240.580.563-0.1964710.361138 age16|0.2045730.1104011.850.064-0.0118220.420968age16|0.0444520.1401030.320.751-0.2301640.319067 age17|0.1708470.1085421.570.115-0.0419050.383599age17|-0.0065010.138398-0.050.963-0.2777740.264772 age18|0.158450.1074581.470.14-0.0521780.369078age18|0.0199490.1371470.150.884-0.2488710.288769 age19|0.1223470.1121781.090.275-0.0975320.342226age19|0.0454990.1439310.320.752-0.236620.327617 grade7|-0.1882610.053684-3.510-0.293487-0.083035grade7|-0.1289150.060061-2.150.032-0.246639-0.011191 grade8|-0.1831430.043713-4.190-0.268823-0.097462grade8|-0.0698060.04946-1.410.158-0.1667520.02714 grade9|-0.2436120.035971-6.770-0.314118-0.173107grade9|-0.1213250.041203-2.940.003-0.202086-0.040564 grade10|-0.1609240.029008-5.550-0.217782-0.104065grade10|-0.1757720.034195-5.140-0.242797-0.108748 grade11|-0.0840760.022314-3.770-0.127813-0.040339grade11|-0.0869770.02748-3.170.002-0.14084-0.033114 hisp_lat|-0.0266810.019448-1.370.17-0.06480.011438hisp_lat|-0.0998240.022413-4.450-0.143756-0.055892 white|-0.020840.021725-0.960.337-0.0634230.021743white|-0.0049780.025582-0.190.846-0.0551220.045166 black|-0.0779190.024476-3.180.001-0.125893-0.029945black|-0.0807930.028743-2.810.005-0.137133-0.024453 nat_am|-0.0702850.031427-2.240.025-0.131884-0.008686nat_am|-0.0054780.037876-0.140.885-0.0797180.068762 asian_pi|0.0083540.0272030.310.759-0.0449650.061673asian_pi|0.0157060.0326720.480.631-0.0483350.079747 twoparent|0.0709470.0127585.5600.0459420.095953twoparent|0.0864860.014915.800.0572610.115711 momdis|0.0054480.0272820.20.842-0.0480280.058924momdis|-0.0003180.032372-0.010.992-0.0637710.063135 daddis|-0.0429410.02387-1.80.072-0.0897290.003846daddis|-0.002970.027374-0.110.914-0.0566260.050685 mo9_nohs|-0.0262420.024655-1.060.287-0.0745680.022084mo9_nohs|0.0242960.0284060.860.392-0.0313820.079974 movocnoh s |-0.0619660.06978-0.890.375-0.1987410.074808movocnoh s |-0.1456810.074749-1.950.051-0.2921970.000835 mohsgrad|0.0058940.0203750.290.772-0.0340430.045831mohsgrad|-0.0162890.023696-0.690.492-0.0627350.030157 moged|-0.0024620.034322-0.070.943-0.0697370.064812moged|0.0728560.0407431.790.074-0.0070040.152716 movocafhs|0.0362020.0278961.30.194-0.0184760.09088movocafhs|0.0194250.0325020.60.55-0.0442830.083132 mocolnogr|-0.0055460.023657-0.230.815-0.0519160.040823mocolnogr|-0.0047840.027356-0.170.861-0.0584050.048836 mocol4yr|0.0005250.0222710.020.981-0.0431290.044178mocol4yr|0.0160580.0259290.620.536-0.0347650.066882 mopostgr|0.0485640.0273751.770.076-0.0050940.102222mopostgr|0.0742220.0324792.290.0220.0105610.137883 fa9_nohs|-0.0208240.023126-0.90.368-0.0661540.024506fa9_nohs|-0.003310.02677-0.120.902-0.0557810.049161 favocnohs|0.0636350.0604471.050.292-0.0548450.182116favocnohs|-0.0577920.087178-0.660.507-0.2286680.113084 fahsgrad|0.000550.0172590.030.975-0.0332790.034378fahsgrad|-0.0031610.019968-0.160.874-0.04230.035977 faged|-0.0055490.037569-0.150.883-0.0791880.06809faged|-0.0611350.044868-1.360.173-0.149080.026811 favocafhs|-0.043580.026884-1.620.105-0.0962750.009115favocafhs|-0.0019530.031615-0.060.951-0.063920.060015 facolnogr|0.0091550.0220880.410.679-0.034140.052449facolnogr|0.0043420.025410.170.864-0.0454640.054148 facol4yr|0.0436870.0193072.260.0240.0058430.081531facol4yr|0.0202140.0226110.890.371-0.0241060.064533 fapostgr|0.0409250.0245291.670.095-0.0071530.089004fapostgr|0.0307820.0286161.080.282-0.0253090.086873 skipgrde|0.0390270.0376631.040.3-0.0347970.11285skipgrde|0.0055910.041890.130.894-0.0765170.087699 adhltpvt|0.0023520.0004445.300.0014830.003222adhltpvt|0.0018290.0005093.5900.0008310.002826 abex_1_2|-0.0877910.018434-4.760-0.123923-0.05166abex_1_2|-0.0878420.02093-4.20-0.128867-0.046817 abex_3_1 0 |-0.147080.017846-8.240-0.182059-0.112101abex_3_1 0 |-0.1499360.020342-7.370-0.189808-0.110064 abex_11pl|-0.2437440.02428-100-0.291334-0.196153abex_11pl|-0.2097820.028146-7.450-0.264951-0.154612 unexab|-0.0119950.001329-9.020-0.014601-0.009389unexab|-0.0111690.001536-7.270-0.014179-0.008158 col_vl|-0.3279560.042108-7.790-0.410491-0.24542col_vl|-0.1753580.049165-3.570-0.271726-0.07899 col_low|-0.3041590.041965-7.250-0.386414-0.221904col_low|-0.2692790.048156-5.590-0.363669-0.17489 col_med|-0.3001380.022898-13.10-0.34502-0.255256col_med|-0.2851730.026619-10.710-0.337349-0.232997 col_hi|-0.178270.017855-9.980-0.213267-0.143273col_hi|-0.1701310.020633-8.250-0.210574-0.129687 enggrd_is|0.4116620.00663862.0200.3986520.424673matgrd_is|0.4479840.00701163.900.4342420.461727 _cons|1.7546590.2050058.5601.3528312.156487_cons|1.6756210.2090648.0101.2658372.085406 ----------------------------------------------------------------------------------------------------------------------------------------------------------Instrumented: majdep7Instrumented: majdep7

PAGE 109

102 Appendix D (Continued) Social Studies GPA "fearful 12 + crying 12"Science GPA "fearful 12 + crying 12" Second-stage regressionsSecond-stage regressions IV (2SLS) regression with robust std. errors Number of obs = 15967IV (2SLS) regression with robust std. errors Nu mber of obs = 16387 F( 61, 15905) = 115.23 F( 61, 16325) = 97.67 Prob > F = 0.0000 Prob > F = 0.0000 R-squared = 0.3016 R-squared = 0.2653 Root MSE = .81561 Root MSE = .84903 ----------------------------------------------------------------------------------------------------------------------------------------------------------| Robust | Robust socsgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]scigpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.3297440.137018-2.410.016-0.598315-0.061172majdep7|-0.3291270.159051-2.070.039-0.640885-0.017368 wave1|-0.0249580.015219-1.640.101-0.0547890.004874wave1|-0.0129580.015777-0.820.411-0.0438820.017966 female|0.1194490.0141678.4300.0916810.147217female|0.1416070.0145629.7200.1130640.170149 jan|-0.2387810.348238-0.690.493-0.9213670.443805jan|0.4101920.2875391.430.154-0.1534160.9738 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.231260.186075-1.240.214-0.5959870.133467apr|-0.0242060.168273-0.140.886-0.3540390.305628 may|-0.2118640.183169-1.160.247-0.5708950.147168may|0.012470.1649050.080.94-0.3107610.335701 june|-0.2143660.18306-1.170.242-0.5731850.144453june|-0.0020910.164807-0.010.99-0.3251310.320949 july|-0.2063260.183323-1.130.26-0.565660.153009july|-0.0186250.165166-0.110.91-0.3423690.305119 aug|-0.2052730.183578-1.120.264-0.5651060.15456aug|0.0287320.1654320.170.862-0.2955320.352996 sep|-0.2170140.185459-1.170.242-0.5805350.146507sep|0.0276440.1675430.160.869-0.3007580.356047 oct|-0.1047210.193293-0.540.588-0.4835980.274156oct|-0.0112310.176006-0.060.949-0.3562220.33376 nov|-0.2110560.227375-0.930.353-0.6567370.234626nov|-0.0335110.204332-0.160.87-0.4340250.367002 agelt12|1.2552940.3577543.5100.5540551.956533agelt12|0.9519030.3444482.760.0060.2767461.627059 age12|0.4762170.1346123.5400.2123620.740072age12|0.4915140.1588283.090.0020.1801930.802835 age13|0.4292170.1240183.460.0010.1861270.672307age13|0.4136320.1493442.770.0060.1209010.706363 age14|0.3851060.1200423.210.0010.149810.620402age14|0.3649050.1455232.510.0120.0796640.650145 age15|0.3569180.1162923.070.0020.1289730.584862age15|0.3438970.1423292.420.0160.0649170.622877 age16|0.2639280.1130822.330.020.0422750.485582age16|0.2986610.1399882.130.0330.0242690.573053 age17|0.2242840.111042.020.0430.0066330.441934age17|0.2172020.1379451.570.115-0.0531840.487589 age18|0.2174330.1094631.990.0470.0028740.431992age18|0.2316660.136871.690.091-0.0366150.499946 age19|0.0294940.118190.250.803-0.2021710.261159age19|0.1886960.145291.30.194-0.0960880.47348 grade7|-0.3925440.060876-6.450-0.511868-0.273221grade7|-0.2088450.062495-3.340.001-0.331343-0.086347 grade8|-0.2845920.05009-5.680-0.382774-0.186411grade8|-0.1855490.051791-3.580-0.287065-0.084033 grade9|-0.2773360.042581-6.510-0.360801-0.193872grade9|-0.2207690.04398-5.020-0.306975-0.134563 grade10|-0.2476590.034494-7.180-0.315272-0.180047grade10|-0.1631470.036496-4.470-0.234683-0.09161 grade11|-0.1176710.026456-4.450-0.169528-0.065813grade11|-0.1386230.029064-4.770-0.195592-0.081654 hisp_lat|-0.0322520.022582-1.430.153-0.0765160.012012hisp_lat|0.0126370.0231780.550.586-0.0327950.058069 white|-0.0127830.025166-0.510.612-0.062110.036545white|0.043140.0260211.660.097-0.0078640.094143 black|-0.072320.028331-2.550.011-0.127853-0.016788black|-0.0250380.028967-0.860.387-0.0818160.03174 nat_am|-0.0127410.03631-0.350.726-0.0839110.05843nat_am|0.0434440.0379351.150.252-0.0309120.117801 asian_pi|0.0171480.0320830.530.593-0.0457380.080034asian_pi|0.0566360.0330541.710.087-0.0081530.121425 twoparent|0.0576670.0150643.8300.028140.087194twoparent|0.0618370.0154184.0100.0316160.092057 momdis|0.0065310.0324310.20.84-0.0570380.070101momdis|-0.0174920.033137-0.530.598-0.0824440.047461 daddis|-0.0343480.028501-1.210.228-0.0902130.021517daddis|-0.0305670.027519-1.110.267-0.0845060.023373 mo9_nohs|-0.0121260.028731-0.420.673-0.0684420.04419mo9_nohs|-0.0624480.0296-2.110.035-0.120467-0.004429 movocnoh s |-0.0356760.079018-0.450.652-0.1905610.119208movocnoh s |0.0267770.0757070.350.724-0.1216170.17517 mohsgrad|0.0171440.0234540.730.465-0.0288280.063116mohsgrad|-0.0529690.024251-2.180.029-0.100503-0.005434 moged|0.0248260.0394250.630.529-0.0524520.102104moged|-0.0666670.041898-1.590.112-0.1487910.015458 movocafhs|0.0500280.0319141.570.117-0.0125270.112584movocafhs|-0.0228070.034075-0.670.503-0.0895980.043985 mocolnogr|0.003150.0269130.120.907-0.0496030.055903mocolnogr|-0.0084040.027654-0.30.761-0.0626090.0458 mocol4yr|0.0138380.0257420.540.591-0.0366190.064296mocol4yr|0.0117240.026530.440.659-0.0402780.063726 mopostgr|0.053390.0317341.680.093-0.0088120.115593mopostgr|0.0423860.0323351.310.19-0.0209950.105766 fa9_nohs|0.0054390.0267380.20.839-0.046970.057848fa9_nohs|0.0244250.027140.90.368-0.0287720.077622 favocnohs|0.0360860.0789060.460.647-0.1185790.190751favocnohs|0.0887490.0807431.10.272-0.0695160.247014 fahsgrad|-0.0032140.01994-0.160.872-0.0422990.035871fahsgrad|0.0139740.0202020.690.489-0.0256240.053573 faged|-0.0543410.045424-1.20.232-0.1433770.034696faged|-0.0288590.044592-0.650.518-0.1162640.058547 favocafhs|-0.011460.031478-0.360.716-0.0731610.050241favocafhs|-0.0084220.03257-0.260.796-0.0722640.055419 facolnogr|0.0026690.0249980.110.915-0.0463310.051668facolnogr|-0.026090.026082-10.317-0.0772130.025034 facol4yr|0.0221620.0223210.990.321-0.021590.065915facol4yr|0.0270760.0228541.180.236-0.0177190.071872 fapostgr|0.0514280.027751.850.064-0.0029640.105821fapostgr|0.0409620.028791.420.155-0.0154690.097393 skipgrde|0.013690.041880.330.744-0.06840.09578skipgrde|0.1111690.0450642.470.0140.0228390.199498 adhltpvt|0.0035980.0005196.9400.0025810.004614adhltpvt|0.0029730.0005295.6200.0019350.00401 abex_1_2|-0.0638520.020954-3.050.002-0.104924-0.02278abex_1_2|-0.1008290.021657-4.660-0.143279-0.058379 abex_3_1 0 |-0.1265510.020485-6.180-0.166704-0.086398abex_3_1 0 |-0.1868650.021164-8.830-0.228348-0.145381 abex_11pl|-0.2002240.027999-7.150-0.255106-0.145342abex_11pl|-0.2655870.029132-9.120-0.322688-0.208485 unexab|-0.0129860.001589-8.170-0.016099-0.009872unexab|-0.0103950.001958-5.310-0.014233-0.006558 col_vl|-0.3605920.048214-7.480-0.455097-0.266087col_vl|-0.2952520.052228-5.650-0.397624-0.192881 col_low|-0.3305110.048465-6.820-0.425509-0.235514col_low|-0.3534320.052235-6.770-0.455817-0.251046 col_med|-0.2848410.02661-10.70-0.336999-0.232682col_med|-0.2473230.028363-8.720-0.302917-0.19173 col_hi|-0.1795330.02112-8.50-0.220929-0.138136col_hi|-0.1861350.021467-8.670-0.228213-0.144057 socgrd_is|0.4218770.00739457.0600.4073850.43637scigrd_is|0.3965030.00752852.6700.3817480.411258 _cons|1.6013960.2242387.1401.1618642.040927_cons|1.3878410.2251596.1600.9465041.829178 ----------------------------------------------------------------------------------------------------------------------------------------------------------Instrumented: majdep7Instrumented: majdep7

PAGE 110

103 Appendix D (Continued) Overall GPA "fearful 12 + crying 12" Second-stage regressionsSecond-stage regressions IV (2SLS) regression with robust std. errors Number of obs = 12314IV (2SLS) regression with robust std. errors Nu mber of obs = 12314 F( 61, 12252) = 218.42 F( 61, 12252) = 218.42 Prob > F = 0.0000 Prob > F = 0.0000 R-squared = 0.5143 R-squared = 0.5143 Root MSE = .51204 Root MSE = .51204 ----------------------------------------------------------------------------------------------------------------------------------------------------------| Robust | Robust overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval]overallgpa|Coef.Std. Err.tP>|t|[95% Conf Interval] -------------+-----------------------------------------------------------------------------+--------------------------------------------------------------majdep7|-0.2898610.111164-2.610.009-0.50776-0.071961majdep7|-0.2898610.111164-2.610.009-0.50776-0.071961 wave1|-0.0061170.011207-0.550.585-0.0280840.015849wave1|-0.0061170.011207-0.550.585-0.0280840.015849 female|0.1222710.01027611.900.1021280.142414female|0.1222710.01027611.900.1021280.142414 jan|-0.0262760.178219-0.150.883-0.3756150.323062jan|-0.0262760.178219-0.150.883-0.3756150.323062 feb|(dropped)feb|(dropped) mar|(dropped)mar|(dropped) apr|-0.1977340.131386-1.50.132-0.455270.059803apr|-0.1977340.131386-1.50.132-0.455270.059803 may|-0.1397210.129222-1.080.28-0.3930170.113575may|-0.1397210.129222-1.080.28-0.3930170.113575 june|-0.1624210.129148-1.260.209-0.4155720.09073june|-0.1624210.129148-1.260.209-0.4155720.09073 july|-0.1603280.129385-1.240.215-0.4139440.093287july|-0.1603280.129385-1.240.215-0.4139440.093287 aug|-0.1393790.129483-1.080.282-0.3931860.114428aug|-0.1393790.129483-1.080.282-0.3931860.114428 sep|-0.1303030.130749-10.319-0.3865910.125985sep|-0.1303030.130749-10.319-0.3865910.125985 oct|-0.1601790.135534-1.180.237-0.4258470.105488oct|-0.1601790.135534-1.180.237-0.4258470.105488 nov|-0.1460770.156629-0.930.351-0.4530940.160941nov|-0.1460770.156629-0.930.351-0.4530940.160941 agelt12|0.6753090.3458881.950.051-0.0026861.353304agelt12|0.6753090.3458881.950.051-0.0026861.353304 age12|0.3086870.1323532.330.020.0492540.568119age12|0.3086870.1323532.330.020.0492540.568119 age13|0.2603710.1270352.050.040.0113630.509379age13|0.2603710.1270352.050.040.0113630.509379 age14|0.2391150.1251961.910.056-0.0062880.484518age14|0.2391150.1251961.910.056-0.0062880.484518 age15|0.2500010.1234022.030.0430.0081140.491888age15|0.2500010.1234022.030.0430.0081140.491888 age16|0.2056680.1219251.690.092-0.0333240.444661age16|0.2056680.1219251.690.092-0.0333240.444661 age17|0.1638990.1210551.350.176-0.0733890.401186age17|0.1638990.1210551.350.176-0.0733890.401186 age18|0.1965040.1202341.630.102-0.0391740.432182age18|0.1965040.1202341.630.102-0.0391740.432182 age19|0.1787680.126821.410.159-0.0698210.427356age19|0.1787680.126821.410.159-0.0698210.427356 grade7|-0.1708650.042147-4.050-0.25348-0.08825grade7|-0.1708650.042147-4.050-0.25348-0.08825 grade8|-0.1331350.035479-3.750-0.202679-0.063592grade8|-0.1331350.035479-3.750-0.202679-0.063592 grade9|-0.1736710.03067-5.660-0.233789-0.113553grade9|-0.1736710.03067-5.660-0.233789-0.113553 grade10|-0.1431980.025308-5.660-0.192804-0.093591grade10|-0.1431980.025308-5.660-0.192804-0.093591 grade11|-0.0870810.020512-4.250-0.127288-0.046874grade11|-0.0870810.020512-4.250-0.127288-0.046874 hisp_lat|-0.0151020.016396-0.920.357-0.047240.017037hisp_lat|-0.0151020.016396-0.920.357-0.047240.017037 white|-0.0098270.01839-0.530.593-0.0458730.02622white|-0.0098270.01839-0.530.593-0.0458730.02622 black|-0.0597470.020602-2.90.004-0.100129-0.019364black|-0.0597470.020602-2.90.004-0.100129-0.019364 nat_am|-0.0120460.027188-0.440.658-0.0653390.041247nat_am|-0.0120460.027188-0.440.658-0.0653390.041247 asian_pi|0.0037830.0231090.160.87-0.0415140.049079asian_pi|0.0037830.0231090.160.87-0.0415140.049079 twoparent|0.0597910.0109665.4500.0382950.081286twoparent|0.0597910.0109665.4500.0382950.081286 mo9_nohs|-0.0429540.021123-2.030.042-0.084358-0.00155mo9_nohs|-0.0429540.021123-2.030.042-0.084358-0.00155 movocnoh s |-0.0244940.05757-0.430.671-0.137340.088352movocnoh s |-0.0244940.05757-0.430.671-0.137340.088352 mohsgrad|-0.0113780.017022-0.670.504-0.0447430.021988mohsgrad|-0.0113780.017022-0.670.504-0.0447430.021988 moged|0.0167040.0292910.570.569-0.0407120.07412moged|0.0167040.0292910.570.569-0.0407120.07412 movocafhs|0.0117450.0238450.490.622-0.0349950.058486movocafhs|0.0117450.0238450.490.622-0.0349950.058486 mocolnogr|-0.0076420.019314-0.40.692-0.0455010.030217mocolnogr|-0.0076420.019314-0.40.692-0.0455010.030217 mocol4yr|0.015130.0185270.820.414-0.0211860.051446mocol4yr|0.015130.0185270.820.414-0.0211860.051446 mopostgr|0.0470290.0229552.050.0410.0020350.092024mopostgr|0.0470290.0229552.050.0410.0020350.092024 fa9_nohs|0.0066920.0196160.340.733-0.0317580.045142fa9_nohs|0.0066920.0196160.340.733-0.0317580.045142 favocnohs|0.0668420.06663910.316-0.0637810.197464favocnohs|0.0668420.06663910.316-0.0637810.197464 fahsgrad|0.0172190.0143231.20.229-0.0108560.045294fahsgrad|0.0172190.0143231.20.229-0.0108560.045294 faged|-0.0310680.030085-1.030.302-0.090040.027905faged|-0.0310680.030085-1.030.302-0.090040.027905 favocafhs|-0.0114010.022559-0.510.613-0.0556190.032818favocafhs|-0.0114010.022559-0.510.613-0.0556190.032818 facolnogr|-6.73E-050.01800100.997-0.0353520.035218facolnogr|-6.73E-050.01800100.997-0.0353520.035218 facol4yr|0.0277660.0159761.740.082-0.003550.059081facol4yr|0.0277660.0159761.740.082-0.003550.059081 fapostgr|0.0558410.0204092.740.0060.0158360.095846fapostgr|0.0558410.0204092.740.0060.0158360.095846 momdis|-0.0054240.024341-0.220.824-0.0531370.04229momdis|-0.0054240.024341-0.220.824-0.0531370.04229 daddis|-0.0287210.020558-1.40.162-0.0690180.011576daddis|-0.0287210.020558-1.40.162-0.0690180.011576 col_vl|-0.2044550.03869-5.280-0.280294-0.128616col_vl|-0.2044550.03869-5.280-0.280294-0.128616 col_low|-0.2029550.038171-5.320-0.277777-0.128134col_low|-0.2029550.038171-5.320-0.277777-0.128134 col_med|-0.2477190.02044-12.120-0.287784-0.207655col_med|-0.2477190.02044-12.120-0.287784-0.207655 col_hi|-0.1528580.015312-9.980-0.182872-0.122844col_hi|-0.1528580.015312-9.980-0.182872-0.122844 abex_1_2|-0.0825610.014844-5.560-0.111656-0.053465abex_1_2|-0.0825610.014844-5.560-0.111656-0.053465 abex_3_1 0 |-0.1300750.014643-8.880-0.158778-0.101372abex_3_1 0 |-0.1300750.014643-8.880-0.158778-0.101372 abex_11pl|-0.2019020.02067-9.770-0.242419-0.161385abex_11pl|-0.2019020.02067-9.770-0.242419-0.161385 unexab|-0.0096080.001574-6.110-0.012693-0.006524unexab|-0.0096080.001574-6.110-0.012693-0.006524 skipgrde|0.0164820.0324610.510.612-0.0471480.080111skipgrde|0.0164820.0324610.510.612-0.0471480.080111 adhltpvt|0.0017120.0003744.5800.000980.002444adhltpvt|0.0017120.0003744.5800.000980.002444 overallgpa ~ |0.5599250.00744875.1800.5453260.574523overallgpa ~ |0.5599250.00744875.1800.5453260.574523 _cons|1.2532760.1842866.800.8920461.614506_cons|1.2532760.1842866.800.8920461.614506 ----------------------------------------------------------------------------------------------------------------------------------------------------------Instrumented: majdep7Instrumented: majdep7

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104 Appendix E: U.S. Senate Pr oposal, FY 09 ESSCP Funding Increase

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105 Appendix E (Continued)

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106 Appendix E (Continued)

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107 About the Author Chris Jones received a bachelor’s degree in Food & Resource Economics from the University of Florida in 1990, and a Master’s Degree in Business Administration from Rollins College in 1992. He began his career as a consulting economist with the firm of Fishkind & Associ ates, Inc. in Orlando, Florida. He has spent his entire 16-year professional career as a regiona l and real estate economist, including positions as Directo r of Economics for MSCW, Inc. in Orlando, Chief Economist for the City of Orlando, and now as the President of Florida Economic Advisors, LLC in Valrico. While in the Economics Ph.D. program at the University of South Florida, Mr. Jones earned his M.A. in Busine ss Economics (2005), and has broadened his scope of research interest to include t he field of mental health economics. He has also taught the Principles of Ma croeconomics course to USF undergraduate students and business majors.