Nitya Alen Joseph, B.A.

EXPLORING THE RELATIONSHIP BETWEEN EXTRACURRICULAR PARTICIPATION & PROBABILITY OF EMPLOYMENT FOR HIGH SCHOOL GRADUATES A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy By Nitya Alen Joseph, B.A. Washington, DC April 8, 2009

EXPLORING THE RELATIONSHIP BETWEEN EXTRACURRICULAR PARTICIPATION & PROBABILITY OF EMPLOYMENT FOR HIGH SCHOOL GRADUATES Nitya Alen Joseph, B.A. Thesis Advisor: Laura LoGerfo, PhD ABSTRACT Extracurricular activities are gaining more attention from educators and policy makers, as they are considered a means to increase academic achievement as well as to provide social and emotional enrichment. Research focused on participation in these activities and its possible link to various labor market outcomes tests whether extracurricular activities support one of the basic goals of education: to prepare a skilled labor force. This study examines the implications of participating in extracurricular activities during high school for a very specific population: high school graduates who do not immediately enroll in any type of post secondary education after high school. Within this population, extracurricular participants and non-participants are compared, on their likelihoods of being employed the year after finishing high school. This relationship is subdivided by five types of extracurricular activities a student participated in, specifically: athletics, leadership, arts, clubs, and academics activities. Of the five activities studied in this paper, participation in leadership extracurriculars is associated with the highest likelihood of being employed directly after completing high school for among these students. Participation in athletic extracurricular activities is negatively related to being employed directly after graduation for these students. ii

To Appa, Amma, Achachen, Kavitha, and all my family and friends who support me throughout my every endeavor. I especially want to give thanks to Laura LoGerfo for her valuable guidance throughout the research and writing of this thesis. I would also like to thank Eric Gardner, who helped me tremendously throughout this process. Finally, I would like to dedicate this to the amazing educators and colleagues who inspire me to work within the education sector. iii

TABLE OF CONTENTS Introduction... 1 Policy Question... 3 Literature Review... 4 Research Hypothesis... 13 Description of the Data... 14 Model and Methodology... 15 Empirical Results & Analysis... 35 Discussion of Findings... 40 Implications and Suggestions for Future Research... 41 Bibliography... 41 iv

INTRODUCTION Youth entering the work force directly out of high school face several challenges. This population commonly faces difficulty securing employment after high school due to a lack of marketable skills and social networks (Rosenbaum et al, 1990). Additionally, they often do not work consistently or maintain the same job for a long period of time. While more students are applying to higher education institutions after high school, there are still many who do not enroll in college directly after graduation. In the high school graduating class of 2007, about 33 percent of the students did not enroll in college for the following year, and of these students, approximately 76 percent participated in the labor force (Bureau of Labor Statistics, 2008). For many of these individuals, high school is one of the last institutions where they have the opportunity to formally learn academic and social skills. Therefore, the high school experience for these students especially influences their opportunities after they graduate from high school. Beyond providing formal learning experiences, high schools also provide opportunities for informal learning. Nearly every school in the country offers some type of extracurricular activity and the majority of students participate in some extracurricular activity during both primary and secondary school (US Department of Education, 1995 & 2006). The benefits of participation are therefore of particular interest to educators and policy makers who search for ways to enrich students academic, social, and emotional enrichment development. For students who do not enroll in college directly after high 1

school, participation in extracurricular activities can influence these students marketable skills and potential for employment. Several researchers have found that extracurricular participation is positively related to student academic performance and the development of social skills. Specifically, engagement in extracurricular activities is linked to increased academic achievement, lower rates of delinquent behavior, and lower drop out rates (Eccles et al, 1999; Dryfoos, 1999; Mahoney, 2000; Cosden et al, 2001; Kahne et al, 2001; Hollister, 2003; White & Gager, 2007). While these studies report that participating in extracurricular activities is significantly related to positive outcomes, many researchers find that the magnitude of the relationship is often small and inconsistent, varying by the type of extracurricular activity (Marsh, 1992; McNeal, 1995; Cooper et al, 1999). Indeed, empirical proof within the research is plagued by several issues, including sample selection bias and widespread variety with the structure and attendance of these activities (Holland et al, 1987; Eccles et al, 2002). Moreover, a few studies have shown diminishing returns to participation in extracurricular activities when students become involved in several at one time (Swanson, 2000; Schneider, 2003). But most of this research studies the benefits of participating in extracurricular activities for students who are still in school, who are college bound, or who graduated from high school and/or college several years ago. There is little in this field that examines if and how participation in these activities is related to outcomes among students who graduate from high school, but do not enroll directly in higher education. 2

Although the lower dropout rates associated with participating in extracurricular activities may benefit this population of students more than others, there may be other benefits to extracurricular participation for this population. For students who may not wish to pursue further education or cannot afford to do so immediately after high school, the skills with which they leave high school become especially important. Both educators and policy makers must understand what skills and knowledge bases are best supported during these students high school experiences in order to maximize their opportunities once they leave secondary school. POLICY QUESTION For students who do not enroll in post secondary education directly after high school, what is the relationship between participating in extracurricular activities and the probability of being employed? Specifically, how is the specific type of extracurricular activity differentially related to this probability? Previous research has examined the connections between participating in athletic and leadership activities and income levels for students (Barron et al, 2000; Kuhn et al, 2005); these studies find that over ten years after high school, there is a positive relationship between participating in these extracurricular activities and income levels. In contrast, research studying earnings for individuals who enter the work force directly after high school finds that participating in extracurricular activities has no association on income levels (Crawford et al, 1997). 3

While previous research has studied earnings, this paper examines if and how participation in extracurricular activities during high school is related to the probability of being employed among high school graduates who do not immediately enroll in post secondary education. Furthermore, this study aims to distinguish this relationship by the following types of extracurricular activities: athletics, leadership, arts, clubs, and academics. LITERATURE REVIEW In order to comprehend how participating in extracurricular activities is associated with being employed or unemployed for these students, it is essential first to define the field, then to examine who participates in extracurricular activities, and finally to turn to how, if at all, participation is related to positive academic, social, and emotional outcomes. Defining the field Defining extracurricular activities proves to be a difficult task due to the vast scope of the field. Extracurriculars cover a range of foci and structure (Huebner & Mancini, 2005). These activities can range from having a single focus, for example music, or can encompass a combination of program areas, such as academics and leadership development. Generally, programs are defined as those possessing structure, boundaries, and goals while extracurricular activities are those includ[ing] athletics, 4

lessons, and extracurricular activities that may or may not take place within the context of a well defined position (Wimer et al, 2008). Rather than focusing on structured programs, this study examines extracurricular activities, which are generally sponsored by schools and grounded in athletics, academics, arts, leadership, and clubs. Unlike programs, extracurricular activities are not commonly measured for success against quantifiable benchmarks or objectives. Activities include individualized lessons such as tutoring or mentoring sessions and group activities such team sports or leadership clubs. Participation ranges as well; students can participate in any number or combination of activities. One study found that over a period of six years, from sixth to twelfth grade, most students participated on average in between one and two activities consistently (Eccles et al, 2003). Another study reported a sample of eighth graders being involved in an average of 5.1 extracurricular activities in one year (Kahne et al, 2003). Many of these activities do not require participants to attend daily (Vandell et al, 2007). It is this voluntary and inconsistent nature of extracurricular participation which makes it difficult to gauge the quality of these various activities (Kahne et al, 2001), a feat this study will not attempt. Although there is diversity among extracurricular participation rates, there are clear trends of extracurricular participation among groups defined by race/ethnicity, gender, family factors, and other characteristics. 5

The Participants Much of the research in this field suggests that involvement in extracurricular activities depends on a variety of factors, including income, family, and individual characteristics (i.e.: gender and race/ethnicity) (Huebner & Mancini, 2003; Smolensky & Gootman, 2003; US Department of Education, 2007; White & Gager, 2007). Students from higher-income families are more likely to participate, although participation in extracurricular activities shows larger gains in academic and social development for students from lower-income backgrounds (Huebner & Mancini, 1999). One challenge to extracurricular activity participation for lower-income families is the cost of some activities (White & Gager, 2007). Renting musical instruments, traveling to debates, and purchasing athletic gear may pose an insurmountable challenge to participation among low-income students. Additionally, schools in low income areas may face an issue of inadequate infrastructure and resources that prevent schools from hosting a wide range of enrichment programs (Reed, 1975). Family endorsement and support for enrolling in extracurricular activities also predicts extracurricular participation (Smolensky & Gootman, 2003). The students more likely to participate in volunteering and extracurricular activities are those more likely to share supportive and stable relationships with their parents (Huebner & Mancini, 2003). Apart from socioeconomic and family characteristics, participation varies by gender and race/ethnicity, based on the type of extracurricular activity in question. Analyses of data from the U.S. Department of Education s nationally representative 6

Education Longitudinal Study of 2002 indicated that sophomore males were more likely to participate in extracurricular sports, while sophomore females were more likely to participate in all other types of extracurricular activities, such as performing arts, student government and leadership, and academic clubs (KewalRamani et al, 2007). Furthermore, within this same survey, white and black males and females were more likely to be engaged in sports than their Asian/Pacific Islander and Hispanic counterparts. Compared to students of other ethnic backgrounds, Asian/Pacific Islanders surveyed during their sophomore year were more likely to be involved in academic, hobby, and service clubs than any other race/ethnicity. Lastly, participation in extracurricular activities also varies by student age. Some research suggests that as children grow older and become more autonomous, they tend to participate less frequently in extracurricular activities (Wilmer et al, 2008). However, as the 2005 APSA survey reported, as children grow older they become more involved in extracurricular activities. There are increases in participation in service and academic clubs from middle school to high school, although participation in activities like scouting and athletics decline during these same years (Wimer et al, 2008). Older students may realize that participation in service or academic activities might boost their chances of selection into elite postsecondary institutions. At the same time, joining athletic teams becomes more exclusive as students grow older and varsity team participation simultaneously becomes more competitive. From elementary school to high school, participation in extracurricular activities tends to increase in general (Wimer et al, 2008). 7

Potential benefits from participation This section highlights the observed social, emotional, and academic benefits that students may gain from participating in extracurricular activities, and how these benefits vary by type of activity and intensity of participation. Regular participation in activities is the primary factor in determining how beneficial extracurricular participation is for students; the more consistently students participate in extracurricular activities, the more likely they are to experience the potential behavioral and academic advantages, which are described in detail below (Vandell et al, 2007; Zaff et al, 2003). Behavioral benefits Due to the supervision provided by extracurricular activities, many argue that risky behaviors such as early sexual interaction and substance abuse decrease when students are engaged in extracurricular activities (Scott-Little et al, 2001). More specifically, several studies have shown that participation in extracurricular activities is associated with lower rates of dropping out and delinquent behavior. Mahoney (1999; 2000) studied 392 participants who were tracked from elementary and middle school to early adulthood. To determine high school dropout rates and criminal rates, school records and personnel files were analyzed, as well as were records from the State Bureau of Investigation and the Electronic Clearinghouse Search Base. Using a cluster analysis method, Mahoney (2000) found that regardless of individual characteristics, students who participated in one or more extracurricular activities prior to the eleventh grade had lower rates of early dropout and criminal activity. Eccles et al (1999; 2003) performed a study 8

finding similar benefits related to specific types of extracurricular activities. Using a longitudinal survey, Eccles et al (1999; 2003) studied 1,259 sixth grade participants from southeastern Michigan, and tracked these students several times until 1996-7. Students involved in prosocial activities (i.e., volunteer and religiously affiliated activities), performing arts, and school-involved activities (i.e., leadership, cheerleading, etc.) had lower rates of substance abuse and were less likely to skip school. Involvement in team sports, however, was positively related to the likelihood of alcohol abuse. The type of extracurricular activity thus seems of great importance to the question of positive or negative outcomes. McNeal (1995) conducted a study that examined how the relationship between extracurricular participation and high school dropout rate varies by the type of extracurricular activity. Using a sample of 14,249 participants from the High School and Beyond survey, McNeal (1995) found that all else equal, students participating in athletics and fine arts extracurriculars were 1.7 and 1.2 times less likely to drop out of high school than non-participants, respectively. Participants in academic clubs were found to be 1.15 times less likely to drop out, but the finding was statistically not significant; further, McNeal (1995) reports that when these activities, as well as participation in vocational clubs, are taken into account at the same time, the only activity that retains a statistically significant relationship to lowering dropout rates is athletic extracurricular activities. Interestingly, athletics seems related to both positive behaviors (i.e., staying in school) and negative behaviors ( i.e., excess drinking), whereas other activities do not show nearly as much inconsistency across participation outcomes. 9

Researchers often find a major benefit from participating in extracurricular activities is the development of social capital. Because students participating in these activities often have the opportunity to network and develop strong ties with adults and community members, they may create valuable social networks of peers and adults who will provide support for future success (Dworkin et al, 2003; White & Gager, 2007). Developing social capital is extremely relevant to the population of graduating high school students who do not enroll in college and are in search of employment opportunities after graduation (Rosenbaum et al, 1990). It offers these students support from positive role models and the social networks they can draw upon for possible employment. Academic benefits Beyond the social benefits, several studies find that participating in extracurricular activities is linked to positive academic outcomes. In the same study cited earlier by Eccles et al (1999; 2003), students involved in any of the five studied activities (social clubs, team sports, academic, performing arts, and leadership activities) earned higher GPAs in the twelfth grade and were more likely to attend college by the age of 21 when compared to non-participants. Broh (2002) conducted a study using a sample of 12,578 respondents taken from the U.S. Department s National Education Longitudinal Study of 1988 (NELS: 88), to examine if and how academic achievement is associated with participation in extracurricular activities, including, but not limited to, interscholastic sports, intramural sports, student leadership, music, and drama. 10

Participation in interscholastic sports was positively and significantly correlated with both math and English grades, as well as math test scores. Only music-related extracurricular activities were as positively related to grades as interscholastic sports (Eccles et al, 2003). Participation in extracurriculars is considered high for those students who are motivated to attend college and consider extracurricular activities as resume builders during their high school years (Zaff et al, 2003; Swanson, 2002; Schneider, 2003). In Swanson s study (2002), he examined a sample of 8,211 individuals from the NELS: 88 survey, to find that participation in extracurricular activities is positively correlated with college matriculation; however, participating in more than four activities at one time created diminishing marginal benefits for the student (Swanson, 2002; Schneider, 2003). Students interested in attending college, whether from an innate desire or due to an intervention, may have the ambition but lack the direction needed to successfully matriculate into college. This is what Schneider (2003) refers to as the ambition paradox in which students who desire to go to college never reach the goal because they are not directed or guided in their efforts. As a result of this over-reaching and undirected ambition, over-enrollment in extracurricular activities may hurt academic performance. While the ambition paradox may be a concern for college-bound high school students, this is not an issue for students who are not planning to enroll in higher education directly after high school. Despite some disparity in results, the empirical 11

evidence showing the benefits associated with participating in extracurriculars suggests that these activities may offer additional support in developing academic and behavioral skills within students who do not plan to go to college or who are holding off on post secondary school. Connections to employment The main goals of education are to prepare students for further education and to increase marketability and employment opportunities for students. The majority of high school students participate in extracurricular activities, but the minority of students choose not to pursue postsecondary education directly following secondary education (US Department of Education, 1995 & 2006). These students are more frequently overlooked by researchers. The focus is so narrowly trained on college preparation that students who are not on the track for higher education are relatively neglected. For students who feel like school is not their main goal, extracurricular activities might not only engage them and keep them in school but also provide valuable skills that might contribute to a student s marketability and likelihood of being employed after graduating high school. Previous research explored how participating in athletic extracurricular activities relates to income levels. Barron et al (2000) used two nationally administered surveys, the National Longitudinal Survey of Youth (NLSY) and the National Longitudinal Study of the High School Class of 1972 (NLS), to learn that athletic activity participation is positively and significantly correlated with participants income levels several years after 12

completing high school. However, Barron et al (2000) found that most of this relationship is explained by individual differences in ability rather than in extracurricular participation. Nevertheless, the study found that this positive relationship exists if athletic extracurricular activities are chosen in place of other extracurricular activities. Crawford et al (1997) studied the labor market specifically for those students entering the work force directly after graduating from high school, using a sample of 3,043 individuals. Crawford et al (1997) found that participating in extracurricular activities was not related to income for this population of high school graduates. Whether or not these benefits translate into skills which are useful and recognizable in the work force and by employers is the direct focus of this study. RESEARCH HYPOTHESIS Based on past research, this study will examine if extracurricular participation is positively related to the employment for high school graduates who enter the work force rather than enroll in college directly after high school graduation. Athletic participation is expected to be positively related to the probability of being employed probability for this population. The confidence and team building that often is acquired by participating on team sports, as well as the social capital that is developed through students interacting very closely with adult coaches, adds to skills that may make these participants marketable in the work force. Past studies have shown that several years after high school, participation in extracurricular athletics has a positive association to income more 13

than other extracurricular activities; this relationship is expected to be the same in terms of the probability of employment for these individuals directly after graduating high school. DESCRIPTION OF THE DATA The National Education Longitudinal Study of 1988 (NELS: 88) is a nationally representative longitudinal survey of students who were surveyed as eighth graders in 1988, as tenth graders in 1990, as twelfth graders in 1992, and two years following the intended high school graduation date in 1994. A long-term follow-up survey was conducted in 2000. That data collection, as well as the 1988 data collection effort, are not part of this study. The U.S. Department of Education administered these surveys, gathering information from students, parents, teachers, and school administrators about individual student characteristics, family background, and transition of these students from middle school into adulthood. Teacher and school data were collected during the first three rounds of data collection, and parents of the student respondents were also surveyed during the 1990 and 1992 follow-up surveys. NELS: 88 data were gathered through surveys, administered on in-person or over the phone, and through student cognitive tests, which were taken in-person. Surveys gathered from school administrators and teachers were self-administered, as were parent surveys. Relevant to this study, the NELS database includes variables reporting extracurricular participation by type of activity; captured in the base year (1988), as well 14

as the first (1990), and second (1992) follow-up surveys. Additional variables of interest for this study include student socioeconomic status, race and ethnicity, and availability of extracurricular activities within a school. The outcome is drawn from the 1994 survey through which respondents reported employment status and employment information. MODEL AND METHODOLOGY The entire NELS: 88 study started with approximately 20,000 students but given attrition and non-response at each subsequent data collection, the dataset includes approximately 12,144 participants with information in 1988, 1990, 1992, and 1994. Participants who dropped out of high school or had no data in 1994 data collection were dropped from the sample and excluded as missing data. From the remaining respondents, the study sample was constructed with data from students who met the following criteria: 1) those who have data in the 1992 data collection (the year most of the survey respondents should have been seniors in high school); 2) those who had completed high school by 1994; and 3) those who were not enrolled in any kind of post secondary education in 1993. This results in a sample of 2,992 respondents. The excluded sample of 9,152 respondents encompasses students who completed high school by 1994 and reported enrollment in some type of post secondary instruction in 1993. This study uses a logistic regression model to estimate the possible relationship between extracurricular activity participation and the probability of being employed the year after high school completion. The logistic model is a binary response model, in 15

which the value of the outcome variable is restricted to either a zero or one value. Because fitted probabilities can often be less than zero or greater than one, the logistic model is useful because it ensures that the response probabilities are bounded between zero and one. For the purposes of this study, the outcome variable, employed, was constructed as a binary response variable. Gathered from the 1994 data collection, if the respondent reported any employment for any length of time during 1993, then employed=1; for all those who reported that they were either out of the work force or unemployed but not in school, employed was set equal to zero. In the logistic model estimating the link between employment and extracurricular activities, the focus is on the differences in the probability of employment the year after high school completion between extracurricular participants and non-participants. Further, five separate types of extracurricular activities- athletics, academics, arts, leadership, and general clubs- are examined separately, to find if the type of activity has a different relationship to the probability of being employed for these students. Further, because students who participate in an extracurricular activity may be intrinsically motivated to do so or may be encouraged by their family or school staff, sample selection bias is a serious issue with estimating the relationship between employment and extracurricular participation. The following discussion of variables aims to target the issues raised by sample selection bias; these independent variables are included in the model in an effort to control for at least some of the characteristics which 16

make participants fundamentally different from non-participants. The following variables are all weighted by the panel weight, F4F1PNWT, applied to data taken from the follow-up surveys administered from 1990 through 1994 and the last follow-up in 2000. The weight is used in an effort to base the results from the logistic regression models on the population figure instead of the sample size. Student characteristics Based on the literature, participation in extracurricular activities is related to several individual characteristics, including race/ethnicity, gender, and socioeconomic status. Using the data collected from the 1994 follow-up survey, these characteristics are reflected in the model used to estimate the relationship between extracurricular participation and the chances of being employed. Race/ethnicity. As discussed earlier, students extracurricular activity participation varies by their racial/ethnic background. White and Black students tend to participate more frequently in extracurricular activities than other racial groups, such as Asian/Pacific Islanders and Hispanics (KewalRamani et al, 2007). Asian and Pacific Islanders tend to be involved more often than other racial groups in academic, hobby, and service related extracurriculars. In order to account for these trends, the logistic regression model includes a series of dummy variables indicating whether a student is Black, Asian/Pacific Islander, or Hispanic. Because Native American students constitute such a small portion of the sample (about 1.3% of the total sample included in the third follow-up survey), these students are considered with the missing data. The reference 17

group for the model is white students. The majority of respondents included in the study sample, excluded sample, and overall NELS: 88 data are mostly white. However, there are key differences in the racial demographics between each of these groups of students. The study sample has 3.96 percent more Black respondents and 2.77 percent more Hispanic students than the excluded sample (Table 1). Gender. Males tend to be over-represented in athletic activities, whereas females show higher participation rates in most other activities, including academics, performing arts, school leadership/government, and other clubs (KewalRamani et al, 2007). These differences require the inclusion of gender into the logistic regression model, and as such, gender is represented by the dummy variable, female, where females are coded as 1 and males, coded as 0, are the reference group. The study sample represents more male than female respondents compared to both the NELS: 88 data and the excluded sample, which are both majority female (Table 1). Socioeconomic status. A student s socioeconomic status is also related to their access to and participation in extracurricular activities. Huebner and Mancini (1999) report that lower income students may experience disincentives to participate in extracurricular activities if there are any fees attached to the activity (i.e.: participation fees, paying for athletic equipment, music instruments, etc.). Students socioeconomic status is represented through a categorical variable split into SES quartiles. This variable is constructed from a composite SES variable, which includes information regarding 18

parental employment, family income, and highest parental education and occupation reported during 1992. The study sample represents many more students who are in the lower half of the SES distribution. Nearly 65 percent of respondents included in the study sample are in the lowest half of the SES distribution, compared to approximately 41 percent in the same quartile among the excluded sample and 47 percent in the entire NELS: 88 data (Table 1). Furthermore, approximately 9 percent of the study sample is from the highest SES quartile, while 25 percent of the respondents in the larger NELS: 88 data are from the highest quartile of the SES distribution. These findings mean that the study sample is generally poorer, more male dominant, and more ethnic-minority (i.e., Black and Hispanic) than the overall NELS:88 sample. These students also tend to face challenges in landing employment in the labor market, thus the outcome may be depressed and the relationships weaker than expected due to the characteristics of the sample. Motivation for College. As Swanson (2002) and Schneider (2003) discuss, some students engage in extracurricular activities because they want to improve their chances of college admissions. These students often participate in extracurricular activities to build their resumes for college. Since students included in the study sample did not enroll in college directly after high school, this reason for participating in extracurricular activities likely does not apply to the majority of these respondents. However, motivation for college may serve as a proxy for other ambitions that may set participants 19

apart from non-participants. Also, since the model only estimates the relationship between extracurricular participation and the likelihood of being employed the year after high school completion, some students may desire to enroll in some type of higher education later. The variables reflecting the respondents motivation for college derive from two separate questions asked in the 1990 and 1992 data collections. In 1990, when most participants were in 10 th grade, they were asked to assess how far they think they will get in college. The dummy variable included in this model indicates expect_college=1 if the participant answered that he/she expected to achieve any college education; expect_college=0 if the respondent answered that he/she did not expect to enroll in any higher education. In 1992, when most respondents were 12 th graders, they again assessed the chances that they would go to college. If the respondent answered that he/she was either unsure or it was unlikely that he/she would attend college, then prob_college=0. If the respondent reported high expectations of attending college, then prob_college=1. As expected, the study sample includes a greater proportion of respondents who did not expect to attend college than the excluded sample, in both the 1990 and 1992 data collections. When comparing the excluded sample and the overall NELS: 88 data, the excluded sample shows greater frequencies of respondents reporting higher expectations for college in both 1990 and 1992 than the entire NELS: 88 sample. 20

School-level characteristic Free and Reduced Price Lunch. In order to account for the general wealth of the school community, the percent of the student body eligible for free and/or reduced lunch programs is included in the model. Often school finance plans are tied directly to housing prices of the community the school serves; therefore schools that serve poorer populations often do not receive large amounts of funding. Reed (1975) writes that lowfunded schools do not have the means to offer a variety of extracurricular activities, limiting student access and engagement in this kind of programming. Students whose family incomes are 130 percent of the poverty level or below qualify for the federal free lunch program; family incomes between 130 percent and 185 percent qualify students for the reduced price lunch program (Institute for Education Sciences, 2007). The measure of students eligible for free and reduced lunch plans serves as a proxy for the school s funding and access to resources. This variable in constructed through four separate dummy variables indicating the percent of the school population eligible for the free and reduced lunch program: 0 percent; 1-10 percent; 11-50 percent; and 51-100 percent. The reference category indicates those schools with 0 percent of the student body eligible for the lunch program; this group comprises approximately 5 percent of the study sample. Coinciding with the previous distributions, the study sample shows more schools serving student populations with more than half of their student enrollments eligible for the lunch program. Approximately 4 percent more of the schools in the study sample than the excluded 21

sample serve student populations where 51-100 percent of the students qualify for the lunch program. In contrast, the excluded sample includes nearly 9 percent more than the study sample of schools that serve communities where 10 percent and below of the students qualify for the lunch program. Parental Involvement Discussion of 1) school activities, 2) grades, 3) college, and 4) job opportunities. Encouragement from families to get involved in extracurricular activities is one of the strongest predictors for participation (Smolensky & Gootman, 2003; Huebner & Mancini, 2003). Levels of parental involvement were captured in the 1992 data collection, through a series of questions asking students how often they discussed a variety of school and social issues with their parents. From this information, dummy variables were constructed to gauge whether or not students discussed the following with their parents: school activities, grades, college, and future job opportunities with their children. The excluded sample respondents discussed their school activities and options for college more often than the sample respondents. Respondents from the study sample discussed grades and opportunities for jobs with their parents more than those in the excluded sample, which seems to fit the post-high school plans of the respondents. Academic Achievement Standardized composite test scores. The model includes a measure for achievement in order to compare students who perform the same academically. The NELS: 88 data include the composite scores for standardized tests in math and English in 22

both 1990 and 1992. The estimated coefficients from this continuous variable should be interpreted in terms of standard deviations; this allows for a more meaningful interpretation and comparison of the test score estimates between the 1990 and 1992 standardized test (see Table 3). Extracurricular Participation High school extracurricular activity participation. A set of dummy variables was constructed for each of the five extracurricular activities that are studied in this paper: athletics; academics; arts; leadership; and clubs. These variables indicate participation at any point during high school. The indicator variable is coded as 1 if a student participated in the given activity and coded as 0 if the student did not participate in the activity at all during high school. Non-participation in an activity is the reference group. From the participation frequencies present in Table 2, it is evident that the excluded sample respondents participated in every activity at higher rates than the study sample. Academic extracurricular activities had the biggest difference in the rates of participation between those in the study and in the excluded sample. The largest differences in the rate of non-participation were in leadership and arts-related extracurricular activities, respectively. In addition to the comparisons of participation in the various types of activities between the study sample and the excluded sample, a number of logistic regression models estimated the relationships among the student, school, parental involvement, and academic achievement characteristics with the probability of participating in each of the 23

five activities. Those results, displayed in Table 4, show that the majority of student, school, parental involvement, and academic achievement variables are significantly correlated to either increasing or decreasing the probability of extracurricular participation in each of the five activities, for the respondents in the study, limited, and NELS: 88 samples. A few highlights from the results of these logistic regression models are discussed here in further detail. As expected, respondents from lower SES backgrounds in the study sample are less likely to participate in each of the activities than their more affluent counterparts. Within the study sample, students from the lowest SES quartile are 0.56 times less likely to participate in athletics than those the highest SES quartile; 0.91 less likely to participate in academics; 0.75 times less likely to participate in arts; and 0.90 times less likely to participate in clubs. Leadership is the only activity in which the poorest students were more likely than those in the highest SES quartile to participate in the activity (1.04 times more likely) (see Table 5). Females are significantly more likely than males to participate in every activity except for athletics. Interestingly, when comparing the likelihood of athletic participation for females across the samples, female respondents in the study sample are participating in athletics at higher rates than the females in the NELS: 88 data. Yet among the students who do not attend college directly after high school, females are 0.38 times less likely than males to participate in athletic extracurricular activities (see Table 5). A similar trend occurs comparing the probability in arts extracurricular activities for female versus 24

males across the three samples. Females in the study sample are 2.84 times more likely than males to participate in arts extracurriculars; in the excluded sample, females are 1.99 times more likely than males and in the NELS: 88 data, females are 2.11 times more likely than males to participate in arts activities (see Table 5). Within the study sample, Asian/Pacific Islanders are more likely than white respondents to participate in every extracurricular activity. This is especially true for leadership activities, where Asian/Pacific Islanders are 2.64 times more likely than white respondents within the sample to participate (see Table 5). Black respondents in the study sample also were more likely than white respondents to participate in most of the activities. Specifically, within this sample, Black students are 2.06 times more likely than whites to participate in athletic extracurricular activities, 1.14 times more likely to participate in arts, and 1.50 times more likely to participate in leadership activities. The only activity where Black students were significantly less likely than whites to participate is in academic activities, in which they are 0.70 times less likely to be involved than their white counterparts. The Hispanic students included in the study sample are less likely than white students to participate in every activity, except for leadership activities. In this activity, Hispanic students are 4.26 times more likely than white students to participate; this is the highest likelihood of participation in any extracurricular activity relative to white students, across all three of the samples (see Table 5). Predictably, respondents who reported in both 1990 and 1992 that they expected to attend college were more likely to participate in the activities than those who did not 25

expect to go to college. However, respondents who reported in 1992 high expectations to attend college are 0.97 and0.96 times less likely to participate in clubs than those who did not report high chances of attending college, in both the study and excluded samples respectively (see Table 5). Additionally, those in the excluded sample are 0.77 times less likely to participate in arts activities than those who did not report they were expecting to attend college (see Table 5). Considering that these lower likelihoods for participation in extracurricular activities occur when students were expected to be in the twelfth grade, perhaps students who are about to finish high school disengage in extracurricular activities since high school is almost over and they may either know they are going to college or have resigned to the idea that they will not go on to college directly after graduating. In 1990, when most respondents were in tenth grade, motivation for college is associated with higher likelihoods of extracurricular participation for every activity, within all three groups. Students who expect to enroll in higher education early on in high school are more likely to participate in extracurricular activities, probably to make themselves more desirable to colleges during the admissions process. Outcome Variable: Employment Probability of employment. During the 1994 data collection, respondents reported on their employment status. A binary variable was constructed from a categorical variable which measured the labor force status of all respondents in 1993. The employment opportunity variable is coded as 1 for those individuals who were employed in the labor force in 1993 for one to six plus months, and 0 for those who were 26

not enrolled in any school and were unemployed or were out of the work force during 1993. Students who reported that they were enrolled in any post secondary education, even if they were simultaneously working, are not included the study sample. Six Logistic models were run for each of the five extracurricular activities on the employment variable, and one model included all the extracurricular participation variables. All the models are weighted by the NELS: 88 variable that accounts for the 1990 through 1994 and 2000 data collections. 27

TABLE 1: Summary Statistics for Individual, School, and Parent Characteristics (%) Study Excluded NELS:88 Sample Sample N 2,992 9,152 12,144 Individual Characteristics Socioeconomic Status Quartile 1 34.31 19.99 23.59 Quartile 2 30.58 21.49 23.81 Quartile 3 22.31 26.20 24.50 Quartile 4 10.50 30.03 25.76 Missing 2.29 2.27 2.35 Sex Female 43.38 52.29 52.22 Male 56.62 47.71 47.02 Missing 0.00 0.00 0.77 Race Asian/Pacific Islander 2.19 4.31 7.01 Black 15.66 11.70 9.51 Hispanic 13.22 10.45 13.26 White 65.96 71.96 68.05 Missing 2.97 1.57 2.17 Motivation Chances R will to go to college: 1990 78.59 86.75 83.86 1992 47.39 67.20 66.52 School Characteristics Free & reduced price lunch 0% of school 3.56 9.98 10.50 1-10% 26.02 28.45 28.39 11-50% 41.18 32.48 35.30 51-100% 11.93 7.82 7.74 Missing 17.32 21.28 18.07 28

TABLE 2: Extracurricular Activity Participation, Divided by Sample (%) 29 Study Sample Excluded Sample NELS:88 N 2,992 9,152 12,144 Extracurricular Participation Athletics Participant 53.62 56.63 58.31 Non-participant 45.17 35.21 35.79 Missing 1.22 8.16 5.90 Academics Participant 35.00 44.90 45.41 Non-participant 65.00 55.10 54.59 Missing 0.00 0.00 0.00 Arts Participant 26.59 31.72 33.16 Non-participant 72.05 59.78 60.77 Missing 1.37 8.50 6.07 Leadership Participant 11.60 17.17 16.49 Non-participant 87.16 74.14 77.40 Missing 11.60 8.69 6.11 Clubs Participant 46.48 46.88 49.96 Non-participant 53.52 53.12 50.04 Missing 0.00 0.00 0.00 TABLE 3: Composite Scores for Reading & Math Standardized Tests N Mean Std. Dev. Min. Max Standardized Test Composite Score 1990 Study Sample 2699 1.17 e -14 1.00-1.88 2.82 Excluded Sample 8146 1.32 e -14 1.00-2.26 1.91 NELS:88 Sample 10845 1.66 e -15 1.00-2.11 2.05 1992 Study Sample 2304 7.29 e -15 1.00-2.09 2.79 Excluded Sample 6855 6.39 e -15 1.00-2.53 1.85 NELS:88 Sample 9159 7.98 e -15 1.00-2.38 1.98

TABLE 4: Logistic Regression Estimates for Extracurricular Participation within the (1) Study, (2) Excluded, & (3) NELS: 88 samples (N=2,992) Athletic EC Participtation Academic EC Participation Arts EC Participation (1) (2) (3) (1) (2) (3) (1) (2) (3) N 1553 4727 6280 1554 4727 6281 1552 4727 6279 Intercept 0.18** 0.23** 0.52** -1.39** -2.26** -0.31** -2.37** -2.06** -1.59** Individual Characteristics SES Quartile 1-0.58** 0.80** -0.83** -0.10** 0.00-0.15-0.29** -0.67** -0.39** SES Quartile 2-0.29** -0.47** -0.50** -0.03* -0.44** -0.12-0.29** -0.24** -0.25** SES Quartile 3-0.17** -0.33** -0.40** 0.05** -0.09** -0.11-0.29** -0.11** -0.24** Female -0.97** -0.78** -0.75** 0.20** 0.62** 0.43** 1.04** 0.69** 0.75** Asian/Pacific Islander 0.89** -0.21** -0.11 0.26** -0.08** 0.25* 0.63** -0.01-0.29** Black 0.72** -0.53** -0.26* -0.36** 0.23** 0.14 0.13** 0.47** 0.29** Hispanic -0.11** 0.15** -0.02-0.59** -0.04** -0.13-0.51** -0.22** -0.31** Motivation for college in 1990 0.65** 1.09** 0.74** 0.29** 1.51** 0.41** 0.17** 1.01** 0.44** in 1992 0.62** 0.63** 0.49** 0.20** 0.55** 0.32** 0.66** -0.26** 0.25** School Characteristics 1-11% on free/reduced lunch -0.38** 0.07** -0.33** -0.18** 0.14** 0.06 0.46** 0.04** -0.01 11-50 -0.18** -0.08** -0.31** -0.06** 0.49** 0.31** 0.88** 0.32** 0.25** 30 51-100 -0.75** -0.66** -0.64** 0.60** 0.75** 0.58** 0.59** 0.67** 0.41** Parental involvement School activities 0.42** 0.77** 0.72** 0.36** 0.35** 0.45** 0.14** 0.47** 0.34** Grades -0.32** -0.42** -0.42** 0.23** -0.24** -0.16 0.08** -0.18** -0.10 College 0.23** 0.01 0.21* 0.48** -0.10** 0.26* -0.32** 0.21** -0.02 Job possibilities -0.02-0.03** -0.06-0.41** 0.06** -0.03 0.06** 0.00-0.02 Academic Achievement Std. Test Composite 1990 0.01 0.21** 0.15* 0.22** 0.39** 0.34** -0.16** 0.15** 0.16* Std. Test Composite 1992-0.25** -0.27** -0.27** -0.07** 0.27** 0.22** 0.35** 0.01~ 0.05 Note: ~ Statistically significant to 0.10 level; * Statistically significant to 0.05 level; ** Statistically significant to 0.01 level

TABLE 4 (continued): Logistic Regression Estimates for Extracurricular Participation within the (1) Study, (2) Excluded, & (3) NELS: 88 samples Clubs EC Participation Leadership EC Participation (1) (2) (3) (1) (2) (3) N 1554 4727 6281 1554 4727 6281-0.76** -0.46** -0.14** Intercept -3.94** -3.36** -2.53** Individual Characteristics SES Quartile 1 0.04* -0.66** -0.50** -0.11** 0.00 0.01 SES Quartile 2-0.06** -0.52** -0.45** -0.55** -0.18** -0.24** SES Quartile 3 0.23** -0.08** -0.16~ -0.36** 0.04** -0.14~ Female 0.33** 0.27** 0.38** 0.55** 0.66** 0.64** Asian/Pacific Islander 0.97** -0.07** 0.17 0.27** -0.03** 0.11 Black 0.40** 0.38** 0.46** 0.01-0.43** -0.14 Hispanic 1.46** 0.28** 0.07-0.45** -0.13** -0.23* Motivation for college in 1990 0.54** 0.68** 0.19 0.19** 0.08** 0.09 in 1992 0.08** 0.65** 0.38* -0.03** -0.04** 0.09 School Characteristics 1-11% on free/reduced lunch 0.03 0.18** -0.11 0.16** -0.33** -0.45** 11-50 0.12** 0.36** 0.08 0.21** 0.00-0.16~ 51-100 -1.11** 0.65** 0.10 0.44** 0.31** 0.16 Parental involvement 31 School activities 0.76** 0.82** 0.65** 0.31** 0.68** 0.60** Grades -0.03-0.03* -0.20 0.25** -0.08** -0.14 College 0.39** -0.35** 0.15 0.35** 0.27** 0.02 Job possibilities 0.09** 0.22** 0.03-0.21** -0.10** -0.05 Academic Achievement Std. Test Composite 1990-0.14** 0.49** 0.36** 0.36** 0.08** 0.09 Std. Test Composite 1992 0.25** -0.25** -0.06-0.42** 0.07** 0.10 Note: ~ Statistically significant to 0.10 level; * Statistically significant to 0.05 level; ** Statistically significant to 0.01 level

TABLE 5: Odds Ratio Estimates for Logistic Regression Models of Extracurricular Participation within the (1) Study Sample, (2) Excluded Sample, & (3) NELS: 88 sample Athletics Academics Arts Leadership Clubs (1) (2) (3) (1) (2) (3) (1) (2) (3) (1) (2) (3) (1) (2) (3) Individual Characteristics SES Q1 0.56 0.45 0.44 0.91 1.00 0.86 0.75 0.51 0.68 1.04 0.52 0.61 0.90 1.01 1.01 SES Q2 0.75 0.62 0.61 0.97 0.65 0.88 0.75 0.79 0.78 0.95 0.60 0.63 0.58 0.84 0.79 SES Q3 0.84 0.72 0.67 1.05 0.91 0.90 0.74 0.90 0.79 1.25 0.92 0.85 0.70 1.04 0.87 Female 0.38 0.46 0.47 1.22 1.87 1.54 2.84 1.99 2.11 1.39 1.31 1.46 1.74 1.94 1.90 API 2.43 0.81 0.89 1.30 0.93 1.28 1.89 0.99 0.75 2.64 0.94 1.19 1.30 0.97 1.12 Black 2.06 0.59 0.77 0.70 1.26 1.15 1.14 1.60 1.33 1.50 1.46 1.58 1.01 0.65 0.87 Hispanic 0.90 1.16 0.98 0.56 0.96 0.88 0.60 0.80 0.74 4.29 1.33 1.07 0.64 0.87 0.79 Motivation for College in 1990 1.91 2.98 2.10 1.34 4.51 1.51 1.18 2.74 1.55 1.72 1.97 1.21 1.21 1.08 1.10 in 1992 1.87 1.88 1.64 1.22 1.73 1.37 1.94 0.77 1.29 1.09 1.92 1.46 0.97 0.96 1.10 School Characteristics 1-11% FRL 0.68 1.07 0.72 0.84 1.15 1.07 1.59 1.04 0.99 1.03 1.20 0.89 1.17 0.72 0.64 11-50% FRL 0.84 0.92 0.74 0.95 1.63 1.36 2.41 1.38 1.29 1.13 1.43 1.08 1.23 1.00 0.85 51-100% FRL 0.47 0.52 0.53 1.82 2.11 1.78 1.81 1.96 1.50 0.33 1.92 1.11 1.56 1.36 1.17 Parental Involvement discuss activities 1.52 2.16 2.05 1.43 1.42 1.57 1.15 1.59 1.40 2.14 2.27 1.92 1.37 1.97 1.83 discuss grades 0.72 0.66 0.65 1.26 0.78 0.85 1.09 0.84 0.91 0.97 0.97 0.82 1.28 0.92 0.87 discuss college 1.32 1.01 1.24 1.62 0.91 1.30 0.72 1.24 0.98 1.47 0.70 1.16 1.43 1.31 1.02 discuss job 0.98 0.98 0.94 0.66 1.06 0.97 1.07 1.00 0.98 1.10 1.25 1.03 0.81 0.91 0.95 Academic Achievement Score in 1990 1.01 1.24 1.16 1.25 1.47 1.04 0.85 1.16 1.18 0.86 1.64 1.44 1.44 1.08 1.10 Score in 1992 0.78 0.76 0.76 0.93 1.31 1.25 1.43 1.02 1.06 1.29 0.78 0.94 0.65 1.07 1.10 32

TABLE 6: Logistic Regression Coefficients for Models of Labor Force Participation in 1993 (N=2,992) Athletics Academics Arts Leadership Clubs All EC N 1553 1554 1552 1554 1554 1552 Intercept 2.72** 2.56** 2.58** 2.60** 2.53** 2.65** Individual Characteristics SES Quartile 1 0.13** 0.17** 0.17** 0.15** 0.17** 0.15** SES Quartile 2 0.36** 0.38** 0.38** 0.37** 0.41** 0.39** SES Quartile 3 0.62** 0.62** 0.64** 0.61** 0.66** 0.64** Female -0.81** -0.77** -0.83** -0.77** -0.79** -0.92** Asian/Pacific Islander -1.88** -1.93** -1.96** -1.98** -1.94** -1.99** Black -0.85** -0.87** -0.89** -0.90** -0.90** -0.85** Hispanic -0.16** -0.13** -0.11** -0.24** -0.13** -0.16** Motivation for colege in 1990-0.05** -0.07** -0.08** -0.08** -0.09** -0.10** in 1992 0.09** 0.06** 0.03* 0.07** 0.07** 0.06** School Characteristics 1-10% on free/reduced lunch 0.33** 0.35** 0.33** 0.35** 0.34** 0.32** 11-50 0.41** 0.41** 0.37** 0.41** 0.40** 0.35** 51-100 0.23** 0.25** 0.23** 0.32** 0.25** 0.18** Parental involvement School activities 0.63** 0.61** 0.61** 0.59** 0.62** 0.57** Grades -0.50** -0.51** -0.50** -0.50** -0.51** -0.54** College -0.10** -0.13** -0.10** -0.13** -0.15** -0.11** Job possibilities -0.05* -0.04* -0.04* -0.06** -0.05** -0.02 Academic Achievement Std. Test Composite 1990 0.04** 0.02 ~ 0.04** 0.04** 0.03* 0.03* Std. Test Composite 1992 0.29** 0.31** 0.30** 0.30** 0.32** 0.28** Extracurricular Activities Athletics -0.22** -- -- -- -- -0.28** Academics -- 0.20** -- -- -- 0.15** Arts -- -- 0.30** -- -- 0.29** Leadership -- -- -- 0.46** -- 0.42** Clubs -- -- -- -- 0.25** 0.25** Note:~ Statistically significant to the 0.10 level; ** Statistically significant to the 0.05 level; *** Statistically significant to the 0.01 level 33

TABLE 7: Odds Ratio Estimates for Logistic Regression Models of Labor Force Participation in 1993 (N=2,992) Athletics Academics Arts Leadership Clubs All EC Individual Characteristics SES Q1 1.14 1.18 1.19 1.17 1.18 1.16 SES Q2 1.43 1.46 1.47 1.45 1.51 1.48 SES Q3 1.85 1.86 1.89 1.84 1.93 1.89 Female 0.45 0.46 0.44 0.46 0.45 0.40 API 0.15 0.15 0.14 0.14 0.14 0.14 Black 0.43 0.42 0.41 0.41 0.41 0.43 Hispanic 0.85 0.88 0.89 0.79 0.88 0.85 Motivation for College in 1990 0.95 0.93 0.93 0.92 0.91 0.91 in 1992 1.10 1.06 1.03 1.07 1.07 1.06 School Characteristics 1-11% free/reduced lunch 1.40 1.42 1.40 1.41 1.40 1.37 11-50% FRL 1.50 1.51 1.45 1.50 1.48 1.42 51-100% FRL 1.26 1.29 1.25 1.38 1.29 1.20 Parental Involvement discuss activities 1.88 1.83 1.84 1.81 1.86 1.77 discuss grades 0.61 0.60 0.61 0.61 0.60 0.58 discuss college 0.91 0.88 0.90 0.88 0.86 0.89 discuss job 0.95 0.96 0.96 0.95 0.95 0.98 Academic Achievement Score in 1990 1.04 1.02 1.04 1.04 1.03 1.03 Score in 1992 1.34 1.37 1.35 1.35 1.38 1.33 Extracurricular Activities Athletics 0.8 -- -- -- -- 0.76 Academics -- 1.22 -- -- -- 1.16 Arts -- -- 1.35 -- -- 1.34 Leadership -- -- -- 1.59 -- 1.53 Clubs -- -- -- -- 1.29 1.28 Note: ~ Statistically significant to the 0.10 level; ** Statistically significant to the 0.05 level; *** Statistically significant to the 0.01 level 34

EMPIRICAL RESULTS & ANALYSIS The series of logistic regression models in Table 6 presents estimates for the probability of labor force participation by five different types of extracurricular activities, as well as an additional model estimating the link between participating in all five extracurricular activities and the probability of being employed the year after completing high school. As shown in the results from Table 6, the coefficients for each of the extracurricular participation variables in the six logistic models are statistically significant to the 0.01 level. Additionally, the odds ratio estimates for participation in academic, arts, leadership, and clubs extracurricular activities indicate a positive relationship between participating in any of these activities during high school and the probability of being employed, relative to non-participants, holding all else equal (see Table 7). Even when adjusting for differences in type of extracurricular participation (model 6), academic, arts, leadership, and general extracurricular activities are related to a greater likelihood of employment after high school completion. Compared to non-participants, those who participate in leadership extracurricular activities are 1.59 times more likely to be employed the year after high school is completed. Those who participate in arts related activities are 1.35 times more likely to be employed after graduation when compared with students who do not participate in arts. Participants in clubs and academic extracurricular activities are 1.29 and 1.22 times 35

more likely to be employed relative to those respondents who did not participate in these activities (see Table 7). Participation in athletics is the only activity that is negatively related to the likelihood of being employed directly after finishing high school. Directly juxtaposed from the research hypothesis, those in the study sample who participated in athletic extracurricular activities at any point during high school are 0.80 times less likely to be employed after high school than non-participants. Apart from the extracurricular participation variables, several individual, school, parental involvement, and academic achievement variables are related to the likelihood of employment. Among students who do not attend college directly after secondary school, lower income students are more likely to be employed directly after high school graduation (see Tables 6 & 7). Adjusting for differences in extracurricular participation, parental involvement, and academic achievement, lower income students are 1.14 to 1.19 times more likely to be employed the year after finishing high school than respondents from the top SES quartile (see Table 7). Depending on the type of extracurricular activity held constant, students in the lower middle class are 1.43 to 1.51 times more likely to be employed directly after high school than respondents belonging to the top socioeconomic status quartile. This range increases for those in the third SES quartile, who are 1.84 to 1.93 times more likely to be employed after high school than those in the fourth or most advantaged SES quartile (see Table 7). The odds ratio estimates comparing females to males on the employment variable are significant and negative for all models. For all six logistic regression models, females 36

range from being 0.40 to 0.46 times less likely than males to be employed directly after high school (see Table 7). In terms of race/ethnicity, race is negatively and significantly related to the likelihood of being employed relative to white respondents. This is especially true for Hispanics; compared to other ethnic groups, Hispanics ranged from being 0.79 to 0.89 times less likely than their white counterparts to be employed directly after high school (see Table 7). Blacks and Asian and Pacific Islanders are also less likely than their white counterparts to be employed in all six of the model estimates. Asian and Pacific Islanders are 0.14 to 0.15 times less likely to be employed than white students, while Black students are 0.41 to 0.43 times less likely to be employed than white students post high school graduation. Respondents motivation and expectation for college in 1990 and 1992 are positively connected to the likelihood of being employed after finishing high school. Students who in 1990 as 10 th graders expected to attend college were 1.02 to 1.04 times more likely to be employed than those who did not expect to move onto higher education (see Table 7). This likelihood increases for those who reported as 12 th graders that they expect to attend college after high school; these respondents are1.33 to 1.38 times more likely employed after high school than those who had no expectations of attending college in 1992 (see Table 7). The students who in 1992 report wanting to attend college might be motivated to seek employment post-graduation to save money to fund their college attendance, which may explain the increased likelihood of being employed from 1990 to 1992. 37

Schools with greater proportions of students eligible for free and reduced price lunch are positively linked to students chances of being employed after graduation (see Table 7). Respondents in schools with more than half of its student body eligible for the lunch program are 1.20 to 1.38 times more likely to be employed than respondents in school with zero percent of the population eligible for the lunch program (see Table 7). The likelihood of employment increases for respondents who attend schools with 11-50 percent of its student body eligible for the program. These students range from being 1.42 to 1.51 times more likely to be employed after high school than schools with zero percent of its student body eligible for the program (see Table 7). The likelihood again decreases for students who attend schools with 1-11 percent of its student body eligible for the lunch program. Students at these schools are 1.37 to 1.42 times more likely to be employed than students from schools with zero percent of the student population eligible for the program (see Table 7). These estimates suggest that students from the poorest communities are likely to work after high school, probably because of their need to support themselves and their families. College, a costly endeavor, may not be a feasible option immediately after high school for these students. The likelihood of employment increases for students in schools from relative wealthier communities. The necessity for these students to work after high school or to save funds so that they may eventually be able to afford college is the same. The lower likelihood of employment for the poorer community may be due to the challenges associated with growing up in poverty. Students from the poorest communities may face realities that make it difficult to secure employment relative to those in slightly wealthier communities. 38

Students who reported discussing school activities with their parents were 1.77 to 1.88 times more likely to be employed after high school than those who did not discuss these activities with their parents. Among students who discussed grades, college, and job opportunities with their parents, they were less likely to be employed after high school than those who did not discuss these subjects with their parents. More specifically, students who discussed grades with their parents were 0.58 to 0.61 times less likely to be employed the year after finishing high school than those who did not discuss grades with their parents. Those who discussed college with their parents were 0.86 to 0.91 times less likely to be employed after high school, and those who reported speaking about job possibilities with their parents were 0.95 to 0.98 times less likely to be employed (see Table 7). Perhaps none of these conversations came to fruition, or the students could not move from planning to finding employment, or, if students and parents were focusing on academics and college admission but the students were not able to attend college immediately after graduation for whatever reason, the students may have not been seeking work. Lastly, academic achievement measured by the composite standardized test scores in 1990 and 1992 were significantly and positive associated with the probability of being employed the year after high school. In 1990, respondents whose test scores increased by one standard deviation ranged from being 1.02 to 1.04 times more likely to be employed a year after finishing high school, than those who did not experience a rise in test scores (see Table 7). This likelihood increases with the 1992 standardized test scores. Respondents who increase their composite reading and math test score by one 39

standardized deviation are 1.33 to 1.38 times more likely to be employed post high school completion, than those who did not experience this increase in score (see Table 7). Students who performed academically better than others towards the end of high school likely maintain their high performance after leaving secondary school. This academic ability likely aids these students as they secure jobs. DISCUSSION OF FINDINGS The research hypothesis predicted that the relationship between participating in athletics and the probability of being employed would be positively related to the likelihood of post-high school employment. These findings, however, indicate the opposite outcome. Participating in athletics during high school is in fact the only activity of the five that is associated with a reduction in the likelihood of employment for students who do not enroll in any post secondary education after completing high school. Adjusting for differences in student background and achievement, parental involvement, and school resources, participants in leadership extracurricular activities, are more likely employed after high school. Those involved in arts, clubs, and academics also appear to have a positive relationship to this labor market outcome but to a lesser extent. Those involved in arts activities were 1.35 times more likely to be employed after high school than non-participants; those who participated in clubs were 1.29 more likely to be employed after high school than those who did not join clubs during high school; and those who participated in academics were 1.22 times more likely to be employed than those who did not participate (see Table 7). 40

Leadership activities ideally develop important life skills in participants, such as decision making, project management of school activities, running for school elections, and interacting with school administration. For students who do not enroll in college to further develop these skills, perhaps the skills associated with leadership extracurricular activities better prepares them for the work force after completing high school. These students potentially interact with school administration and peers through a leadership role. This could build social capital, or a supportive network of role models, for these students, which arguably aids them in securing employment after graduation relative to those who did not participate in leadership activities. However, the serious concerns with sample selection bias cannot be ignored, especially in the case of considering participants in leadership extracurricular activities. Given the rates of participation in leadership activities in Table 2, a small percentage of the study sample engaged in leadership activities. The students who decide to get involved in these activities are likely to have initiative and ambition that set them apart from other students. Therefore, while participation in leadership extracurricular activities is linked to the highest likelihood of being employed amongst all the extracurricular activities included in this study, causal inferences cannot be assumed between participating in any of these activities, especially leadership, and employment directly after high school. Implications and Suggestions for Future Research There were several limitations with this study, which could be addressed in continued research conducted in this field. More of the factors in a students life need to 41

be addressed in the model, so that the relationship between participation in extracurricular activities and the likelihood of being employed is clearer. Issues with sample selection bias would be better addressed with more sophisticated models, controlling for more variables which may explain why a student participated in a given activity. Additionally, future research could investigate the connections between the intensity of participation in these activities and its relationship to a variety of labor market outcomes. More importantly, the probability of being employed after high school is not necessarily the best measure for being actively engaged in the work force. For example, a student may be employed at a low skill job while another with a more sophisticated skill set may be unemployed because he/she is holding out for an appropriate job. This example is meant to highlight that there are a number of reasons why an individual may be unemployed for a period of time. Furthermore, this model only takes one year of employment status into consideration and does not consider the type or quality of employment. Studies conducted in the future could examine income levels, employment in various fields, and other related outcomes which will better illustrate the relationship, if any, extracurricular participation in high school may have on positive labor market outcomes. Although there are no definite causal relationships between participating in extracurricular activities and employment, research does suggest that there is some positive correlation between participation in some of these activities and employment, academic outcomes, or in behavior. This has particularly strong policy implications for 42

students who graduate from high school and do not enroll directly in postsecondary education. Students who disengage in education after completing high school are often overlooked in education policy. While they may receive services catering to their needs while they are still in primary and secondary school, for the students who complete high school but do not enroll in higher education, they are nearly forgotten. As mentioned previously, approximately 33 percent of the high school graduating class in 2007 did not go directly into college. For these students, high school is one of the last formal institutions where educators and policy makers can provide support systems to connect them with larger society. By creating school policies that support extracurricular activities, perhaps students will be better prepared for a productive life after high school, especially if college is not an immediate option. This is particularly relevant in today s economic climate. With the country facing serious financial challenges, anecdotal stories about families not being able to afford college for their children, and colleges not being able to provide financial support for its students are common. College may not be a feasible option for many of the young people graduating from secondary school today and in the next few years to come. If that is the case, the education students receive while in school must be holistic, developing not only academic competencies but behavioral skills as well. Extracurricular activities could be a possible tool to support this goal. 43

BIBLIOGRAPHY After-School Activities. (2003). Child Trends DataBank. Retrieved December 12, 2008, from http://www.childtrendsdatabank.org/indicators/86afterschoolactivities.cfm Afterschool Alliance. (2008). 21 st Century Community Learning Centers Federal Afterschool Initiative. Retrieved November 16, 2008, from http://www.afterschoolalliance.org/policy21stcclc.cfm Akiba, M., Apthorp, H., Lauer, P., Martin-Glenn, M., Snow, D., & Wilkerson, S. (2004). The Effectiveness of Out-of-School Time Strategies in Assisting Low-Achieving Students in Reading and Mathematics: A Research Synthesis. Aurora, CO: Mid- Continent Research for Education and Learning. Retrieved September 23, 2008, from http://www.eric.ed.gov/ericdocs/data/ericdocs2sql/content_storage_01/000009b /80/23/4b/4a.pdf. Barron, J.M., Ewing, B.T., & Waddell, G.R. (2000). The Effects of High School Athletic Participation on Education and Labor Market Outcomes. The Review of Economics and Statistics, 82(3). Beckett, M. & Bodilly, S. (2005). Making Out-of-School Time Matter: Evidence for an Action Agenda. Santa Monica, CA: RAND Corporation. Retrieved September 14, 2008, from http://www.wallacefoundation.org/nr/rdonlyres/d8d935c1-0708- 409C-B94F-5C86D9BBE2E0/0/MakingOutofSchoolTimeMatter.pdf Cooper, H., Valentine, J., Nye, B., & Lindsay, J. (1999). Relationships Between Five After-School Activities and Academic Achievement. Journal of Educational Psychology, 91(2). Crawford, D.L., Johnson, A.W, & Summer, A.A. (1997). Schools and Labor Market Outcomes. Economics of Education Review, 16(3). Dohm, A., & Wyatt, I. (2002). College at Work: Outlook and earnings for college graduates, 2000-10. Occupational Outlook Quarterly, 46(3). Dryfoos, J. G. (1999). The Role of School in Children s Out-of-School Time. The Future of Children: When School is Out, 9(2). Retrieved from Google Scholar database. 44

Dworkin, J. B., Larson, R., & Hansen, D. (2003). Adolescents Accounts of Growth Experiences in Youth Activities. Journal of Youth and Adolescence, 32(1). Eccles, J.S. & Barber, B. L. (1999). Student Council, Volunteering, Basketball, or Marching Band: What Kind of EC Involvement Matters? Journal of Adolescent Research, 14(1). Eccles, J., & Gootman, J.A. (2002). Community Programs to Promote Youth Development. Washington, DC: National Research Council, Institute of Medicine. Retrieved November 15, 2008, from ERIC database. Eccles, J & Templeton, J. (2002). Extracurricular and Other After-School Activities for Youth. Review of Research in Education, 26. Retrieved from JSTOR database. Eccles, J., Barber, B.L., Stone, M., Hunt, J. (2003). Extracurricular Activities and Adolescent Development. Journal of Social Issues, 59 (4). Gewertz, Catherine (2008, September). Consensus on Learning Time Builds: Interest in Expanding Hours for Students to Master Academic, Social, and Workplace Skills is Mounting. Education Week. http://www.edweek.org/ew/articles/2008/09/24/05narmain_ep.h28.html?tmp=614 205495. Holland, A.H. & Andre, T. (1987). Participation in EC Activities in Secondary School: What is Known, What Needs to be Known? Review of Educational Research, 57 (4). Retrieved from JSTOR database. Hollister, R. (2003). The Growth in After-School Programs and their Impact (The Brookings Institution publication). Retrieved from: http://www.brookings.edu/views/papers/sawhill/20030225.pdf Huebner, A. J., & Mancini, J. A. (2003). Shaping Structured Out-of-School Time Use Youth: The Effects of Self, Family, and Friend Systems. Journal of Youth and Adolescence, 32(6). Institute of Education Sciences: National Center for Education Statistics. (2007). NAEP Research e-center. Retrieved April 5, 2009 from http://nces.ed.gov/nationsreportcard/glossary.asp 45

Kahne, J., Nagaoka, J., Brown, A., O Brien, J., Quinn, T., & Thiede, K. (2001). Assessing After-School Programs as Contexts for Youth Development. Youth & Society, 32(4). Kane, T. J. (2004). The Impact of After-School Programs: Interpreting the Results of Four Recent Evaluations (William T. Grant Foundation Working Paper). Los Angeles, CA: University of California, Los Angeles. Retrieved on October 14, 2008, from http://www.wtgrantfoundation.org Kuhn, P.K. & Weinberger, C. (2005). Leadership Skills and Wages. Journal of Labor Economics, 23(3). James-Burdumy, S., Dynarski, M., & Deke, J. (2007). When Schools Stay Open Late: Results From the National Evaluation of the 21 st Century Learning Community Centers Program. Educational Evaluation and Policy Analysis, 29(4). doi: 0.3102/0162373707309077 Mahoney, J.L. & Cairns, R.B. (1997). Do Extracurricular Activities Protect Against Early School Dropout? Developmental Psychology, 33(2). Retrieved from Google Scholar database. Mahoney, J.L. (2000). School Extracurricular Activity Participation as a Moderator in the Development Antisocial Patterns. Child Development, 71(2). Retrieved from JSTOR database. Mahoney, J., Lord, H., & Carryl, E. (2005, July/August). Ecological Analysis of After- School Program Participation and the Development of Academic Performance and Motivational Attributes for Disadvantaged Children. Child Development, 76(4), 811-825. Retrieved from Project Muse database. Marsh, H. W. (1988). Extracurricular Activities: A Beneficial Extension of the Traditional Curriculum or a Subversion of Academic Goals. US Department of Education. Retrieved January 4, 2009, from Google Scholar database. McNeal, R.B. (1995). Extracurricular Activities and High School Dropouts. Sociology of Education, 68(1). Retrieved from JSTOR database. Posner, J., & Vandell, D. (1999). After-School Activities and the Development of Low- Income Urban Children: A Longitudinal Study. Developmental Psychology, 35(3). 46

Reed, R. (1975). Ethnicity, Social Class and Out-of-School Educational Opportunities. The Journal of Negro Education: The Continuing Crisis of Urban Education, 44(3), 316-334. Retrieved from JSTOR database. Schneider, B. (2003). Strategies for Success: High School and Beyond. Brookings Paper on Education Policy, 55-79. Retrieved from Project Muse database. Scott-Little, C., Hamann, M. S., & Jurs, S. G. (2002). Evaluations of After-School Programs: A Meta-Evaluation of Methodologies and Narrative Synthesis of Findings. American Journal of Education, 23(4), 387-419. Retrieved from Google Scholar database. Smolensky, E, & Gootman, J. A. (Eds.). (2003). Working Families and Growing Kids: Caring for Children and Adolescents. Washington, DC: The National Association Press. Swanson, C. B. (2002). Spending Time OR Investing Time? Involvement in High School Curricular and Extracurricular Activities as Strategic Action. Rationality and Society, 14(4), 431-471. Retrieved from Sage database. U.S. Department of Education (1995). Extracurricular Participation and Student Engagement. Washington, DC. Retrieved from http://nces.ed.gov/pubs95/web/95741.asp U.S. Department of Education. (2004). Before- and After- School Care, Programs, and Activities of Children in Kindergarten Through Eighth Grade: 2001. Washington, DC: Brian Kleiner, Mary Jo Nolin, & Chris Chapman. U.S. Department of Education. (2006). National Household Education Surveys Program of 2005 After-School Programs and Activities. Washington, DC: Carver, P & Iheoma, U. Retrieved from http://eric.ed.gov/ericdocs/data/ericdocs2sql/content_storage_01/0000019b/80/ 1b/d4/3b.pdf U.S. Department of Education (2007). Digest of Education Statistics: 2007. Washington, DC: Thomas D. Snyder & Valena Plisko. Retrieved from http://nces.ed.gov/programs/digest/d07/ch_3.asp U.S. Department of Education (2007). Status and Trends in the Education of Racial and Ethnic Minorities: 2007. Washington, DC: Angelina KewalRamani, Mary Ann Fox, & Stephen Provasnik. 47

U.S. Department of Education, National Center for Education Statistics (2008). The Condition of Education 2008 (NCES 2008 031), Retrieved from http://nces.ed.gov/fastfacts/display.asp?id=27 Vandell, D., Reisner, E., & Pierce, K. (2007). Outcomes Linked to High-Quality Afterschool Programs: Longitudinal Findings from the Study of Promising Afterschool Programs. Retrieved from Google Scholar database. Wimer, C., Simpkins, S. D., Dearing, E., Bouffard, S. M., Caronongan, P., & Weiss, H. B. (2008). Predicting Youth Out-of-School Time Participation: Multiple Risks and Developmental Differences. Merrill-Palmer Quarterly, 54(2), 179-207. White, A. M. & Gager, C. T. (2007). Idle Hands and Empty Pockets? Youth Involvement in Extracurricular Activities, Social Capital, and Economic Status. Youth and Society, 39(1), 75-111. Retrieved from Sage Publications database. Zaff, J. F., Moore, K. A., Papillo, A. R., & Williams, S. (2003). Implications of Extracurricular Activity Participation During Adolescence on Positive Outcomes. Journal of Adolescent Research, 18(6), 559-630. doi:10.1177/0743558403254779 48