High school career academies and post-secondary outcomes

Economics of Education Review 21 (2002) 137–152 www.elsevier.com/locate/econedurev

High school career academies and post-secondary outcomes Nan L. Maxwell a

a,*

, Victor Rubin

b

Department of Economics and Human Investment Research and Education (HIRE) Center, California State University, Hayward, Hayward, CA 94542, USA b PolicyLink, 101 Broadway, Oakland, CA 94607, USA Received 27 August 1999; accepted 23 March 2000

Abstract This paper focuses on the outcomes associated with one type of school-to-work program, the career academy. By comparing the outcomes from career academy programs with those from more traditional programs, we evaluate their potential for improving the post-secondary experiences over students from more traditional curriculum programs. Using both single-district and national (across-district) databases, we show that the career academy has the potential for increasing education levels to those of students describing themselves as having followed an academic program. However, we show that the career academy may not be equally effective for all students, and other studies have shown that they carry relatively high marginal costs over more traditional programs. Thus, it may be that career academies should be offered as part of an array of high school programs to meet the educational needs of diverse student bodies and cost constraints of administrators.  2002 Elsevier Science Ltd. All rights reserved. JEL classification: I2 Keywords: Educational economics; Human capital; Economic development

1. Introduction Parents, educators, politicians and communities in general are concerned about the failure of the public schools in our cities to provide an adequate education for our children. Although many of today’s public, comprehensive high schools were designed to promote unity through their inclusiveness and to allow for individual development through program differentiation (i.e., academic, general and vocational curricula), many schools fail to meet these challenges.1 High schools are routinely * Corresponding author. Tel.: +1-510-885-3191; fax: +1510-885-2602. E-mail addresses: [email protected] (N.L. Maxwell), [email protected] (V. Rubin). 1 See Conant (1967) for a discussion of the comprehensive high school and Natriello, McDill, and Pallas (1990), Louis and Miles (1990), or Gamoran (1996) for a discussion of problems in urban, public high schools.

criticized for their failure to provide students with the knowledge and skills necessary to succeed in either postsecondary education or in the labor market. In response, current educational reform efforts blur the century-old idea of program differentiation between academic and vocational education, for both motivational and educational reasons. While there are many approaches to integrating existing academic and vocational curricula, virtually all of these approaches have at their core the underlying philosophy of using an occupational focus and workplace context to stimulate academic learning. These “school-to-work” reforms strive to help high school students achieve academically, while providing them with marketable, work-based skills. Desired outcomes include both clear pathways to post-secondary education and productive employment. One of the best-established of these school-to-work models is the career academy, a “school-within-a-school” that uses an integrative curriculum focused on a specific career theme.

0272-7757/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 ( 0 0 ) 0 0 0 4 6 - 7

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This study addresses the potential for the career academy to increase post-secondary success within the urban, public school environment. Specifically, we answer the question “How do post-secondary outcomes for students from career academy programs compare with those of students from academic and traditional vocational programs?” By analyzing both a detailed data set that contains post-secondary information for general, academic, vocational and career academy students from a single school district and the urban, public school students in the National Education Longitudinal Study (NELS), we show that students from career academies attend twoand four-year colleges at the same level as students describing themselves as having had an academic track program. Because career academies often draw students from backgrounds that typically define them as “at-risk” of school failure, these gains are realized for individuals who are often considered less likely to achieve post-secondary success. The results also suggest that career academies may not be equally effective at increasing education for all demographic groups. If this is the case, their increased cost over more traditional programs cautions universal adoption.

2. Framework School-to-work reforms blur the distinction between traditional academic and vocational curriculum by integrating an occupational focus into academic courses. Advocates of these programs do not see them as simply a revamping of traditional vocational education or as limited to students who would otherwise be in those programs. Instead, most advocates argue that these programs can meet the need for increased academic rigor that was set forth in A Nation at Risk (National Commission on Excellence in Education, 1983). In fact, many programs place a particular emphasis on establishing connections between high school and community college, and many are designed to keep university attendance open as an option for students, even as they pursue technical training at community colleges. As such, school-to-work programs seek to develop academic and work-based skills for all segments of the student population. If successful, these programs would increase the academic abilities of students who are not in academic programs and the work-based abilities of students who are not in vocational programs. The career academy is arguably the most well developed school-to-work program model. The career academy builds a “school-within-a-school” and coordinates curriculum and activities around a single occupation, profession or industry that is in demand in the local labor market. Core academic subjects are integrated with vocational/technical laboratory courses and emphasize the relationship between academics and the

work place. Although students do not earn formal occupational skill credentials, they often work in the industry of focus during the summer after their junior year. Employers are actively involved in building curriculum and in donating time as mentors and work place supervisors (e.g., Stern, Raby, & Dayton, 1992). Research on career academy programs illustrates their effectiveness (e.g., Kemple, 1997; Kemple & Rock, 1996; Kemple & Snipes, 2000; Maxwell & Rubin, 2000), including their ability to reduce high school dropout rates (Stern, Dayton, Paik, & Weisberg, 1988) and improve job performance and work attendance (e.g., Linnehan, 1996). Their relative newness has precluded a thorough assessment of their influence on post-secondary activities, however. Previous evaluations of career academies have used comparison or control groups in assessing outcomes but have not contrasted academies specifically with other curriculum programs. As a result, the merits of the more integrative program as compared with more traditional academic and vocational programs are unknown. This study therefore adds to the literature by comparing outcomes from career academy programs with those from other high school programs as well as by extending an assessment of their influence into post-secondary activities. Specifically, we compare the relative post-secondary outcomes from the career academy to each of the three self-defined traditional curriculum programs (general, academic and vocational) with respect to educational achievement (high school graduation, two-year college attendance and four-year college attendance) and selfassessed education and work skills obtained from high school. To estimate the outcomes that are associated with each high school program, we use an educational production function such that: PstOut⫽a⫹a1CAREER⫹a2VOC ⫹a3ACADEMIC⫹

冘

aj Schoolj ⫹

冘

(1)

aiStuChari,

where: PstOut a vector of post-secondary outcomes CAREER a binary variable indicating a student who was enrolled in a career academy VOC a binary variable indicating a student who was in enrolled in a vocational program ACADEMIC a binary variable indicating a student who was in enrolled in an academic program School a vector of variables measuring characteristics of the school StuChar a vector of variables measuring the student’s characteristics. Because the general program is the omitted program variable, a1, a2 and a3 (career academy, vocational, academic) estimate the associations between each high

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

school program and post-secondary outcomes as compared with the general program. Positive and significant coefficients on the program variables (a1, a2 and a3⬎0) suggest that the specific program increases outcomes as compared with the general program. The remaining variables provide statistical controls for both school and individual characteristics. Unfortunately, data limitations preclude specifying a value-added function which controls for knowledge and skill levels at high school entrance (e.g., Hanushek, 1986). Because skills at high school entrance are often correlated with high school program, their omission as control measures may overstate the impact of high school programs on skills acquired in high school (e.g., Meyer, 1996). To examine for this potential, we analyze the impact of knowledge and skills at high school entrance on program selection such that: HsProg⫽b⫹

冘

biK&Si,

(2)

where: HsProg the student’s high school program (career academy, academic, vocational) K&S a vector of variables measuring the knowledge and skills that the student brings to high school. This equation models the observable correlates of program enrolment and academic abilities of students at program entrance in order to examine for the possible “creaming” of higher achieving students into the career academy. Should career academy enrollment be positively correlated with knowledge and skills at program entrance (bi⬎0), students enrolled in the program would have an above-average probability of program outcomes before entering high school. If this is the case, coefficient estimates (a1) in Eq. (1) overstate the impact of the career academy because positive outcomes are attributed to program intervention when, in fact, the participants would have a higher probability of success without intervention (e.g., Hanushek, 1997). Should career academy enrolment be negatively correlated with knowledge and skills at program entrance (bi⬍0), students enrolled in the program had a below-average probability of program outcomes before entering high school. Estimates from Eq. (1) would understate the program’s impact because students who entered the program had a below-average probability of success.2 We note that all measured correlates of high school 2 Two types of test for sample selection were undertaken. Heckman’s λ was constructed and inserted into Eq. (1) with little change in results. Eq. (1) was also stratified by knowledge and skills at program entrance, which indirectly controls for “unobservables”. Results of these estimations suggest that the positive results presented here may not hold for students entering high school with the lowest levels of academic achievement.

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programs (i.e., knowledge and skills) do not capture the unmeasured aspects of individual heterogeneity that undoubtedly affect selection of high school program (e.g., motivation). If these observed correlates capture unobservable components, our models would be correctly specified. This might be the case if, for example, achievement test scores at program entrance, as a measure of knowledge and skills, reflected both observable and unobservable components by fully capturing all characteristics that produce knowledge and skills. Thus, the extent of misspecification bias depends on the strength of the knowledge and skill measure. 3. Data We use both single-district and national (acrossdistrict) databases of former students in urban, public high schools to compare outcomes from high school programs. In both databases we restricted our sample to individuals who identified their high school program, attended a comprehensive high school, and were not part of “special education”. The district from which we draw our career academy data is one of the largest in a populous West Coast state. It services over 50,000 students in a core central city of a large metropolitan area, and contains six comprehensive high schools.3 Over 90% of the district’s students are ethnic “minorities” (Tables 1 and 2), over one-quarter have limited English proficiency (LEP), nearly 40% receive free lunches, and the average daily attendance (percentage of days that a student came to school) is only slightly over 80%. The student–teacher ratio stands around 28 in the comprehensive high schools. In other words, the district in many ways epitomizes the challenges and difficulties faced by large, inner city school districts throughout the nation. In contrast, students in our national database of urban, public high schools are more white (44.7%), less impoverished (28% receive free lunches), more proficient in English (14% LEP), and have higher attendance (91%) and smaller classes (student–teacher ratio of under 20). Of note, a greater percentage of students in the single-district database graduated from high school and attended post-secondary education. This, no doubt, reflects the fact that the district is part of a metropolitan area that requires a highly skilled workforce and contains three world-class research universities, four comprehensive state universities, and a large number of community colleges and private universities.4 3 The district also contains two alternative high schools, two centers for redirection and one continuation high school. 4 These high rates of attending post-secondary education do not translate into high levels of completing college (Maxwell, 1999). In fact, over two-thirds of individuals from career academies in this district needed remediation before college-level

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Table 1 Individual student characteristics: the single-district and national (across-district) data Individual characteristics High schools A (%) B (%) C (%) D (%) E (%) F (%) Student characteristics Mom and dad at 14 (%) English first language (%) Take achievement test (sophomore year) (%) High school attendance (%) Program Career academy (%) Academic (%) Vocational (%) General (%) Entrance Assigned to program (%) Talked to teacher/counselor (%) Talked to parents (%) Talked to friends (%) Education High school graduate (%) Attend 2- or 4-year college (%) Attend 4-year college (%)

Districta high schools

Singleb-district sample

Nationalc sample

15.5 17.4 7.2 19.4 18.8 21.7

9.2 18.8 3.8 24.6 15.0 27.6

– – – – – –

– – – –

61.2 55.3 52.4 82.5

61.2 74.7 84.6 –

– – – –

25.4 20.6 3.9 50.1

– 37.3 15.9 46.8

– – – –

40.0 27.4 13.3 13.6

40.3 42.8 33.0 21.4

– – –

84.5 75.3 41.0

79.9 62.7 29.7

a

All district numbers are reported for the 1995–1996 school year and are taken from the district’s School District Information Summary, 1995–1996 and School Profiles. Only information for students in comprehensive high schools is reported in the district high school information in our table. This excludes the 750 students who attended alternative high schools in 1995–1996. A comprehensive high school is defined as one that offers a curriculum that fulfils entrance requirements into the university system. b Our single-district sample is restricted to students who responded to the post-graduation survey with an identifiable high school program and were not in a special education program. c The national sample is restricted to sophomores who attended urban, public high schools, were not in a special education program, and were reinterviewed in the third follow-up. Schools that were used in subsequent analysis may differ from those presented here because nearly 1000 students have missing data on ADA, which may also explain the large differences between the national database and our sample.

The 10,102 students who attended district schools as sophomores from 1990–1993 are the population base for this study. Post-high school data on this population were obtained from a survey that was mailed to each former student. Data about high school activities (e.g., test scores) were taken from district records.5 A total of 1257 coursework could begin. The location of the district in a county with two four-year universities and three community college districts may increase attendance in post-secondary education over the national sample. However, it does not necessarily suggest that there will be differences in degree completion. 5 The post-high school survey was mailed from May to October (with follow-ups lingering through December) of 1996, when the former students were between one and three years out of high school with an on-time graduation. Only one respondent was still in high school at the time of survey administration. Each non-respondent was contacted at least twice for follow-up.

of these students were enrolled in a career academy. The program of the non-academy students was not available in district records; however, students were asked to identify their program on the post-high school survey.6 In total 1223 surveys were returned. The survey was structured to gather four types of data: post-high school educational and labor market out-

6

We note that our response rates (about 11% non-academy and 20% academy) are extremely low. Part of the non-response, undoubtedly, reflects use of addresses that are one to five years old for an extremely mobile population. A discussion of differences between respondents and non-respondents is given in Section 5.

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Table 2 Individual school characteristics: the single-district and national (across-district) data High school characteristics Demographic Male (%) Race/ethnicity African–American (%) Latino/Hispanic (%) Asian (%) White (%) Filipino (%) Pacific Islander (%) Native American (%) “Other” in NELS (%) High school characteristics Average daily attendance Limited English proficiency (%) Receiving free lunch (%) Student–teacher ratio (mean) Number of students (mean) N

District high schools

Single-district sample

National sample

National high schools

49.0

34.0

48.6

–

54.5 17.6 21.3 3.8 1.4 1.0 0.2

37.7 16.4 33.1 9.9 1.6 0.8 0.4

19.9 27.2 11.5 40.3 – – – 1.2

24.9 23.4 – 44.7 – – – 6.9

82.0 26.2 38.2 28.4 1460 8760

comes;7 expectations about education, labor market and the community; assessment of the high school and program; and general demographic information. Survey design maintained as much overlap with the NELS (the national data set used here) as possible, with over half of the survey questions drawn directly from questions in NELS. This district has operated career academy programs since 1985. During the years of this study, it operated nine career academies that were scattered across each of the six comprehensive high schools. The career academy model for this district includes both school-based and work-based components. The school-based learning component calls for students to take four academy classes per grade — three academic courses and a laboratory class — to be integrated through both interrelated curricula and through incorporation of material from and about the industry or profession. Students take these courses as a group starting in the 10th grade. The workbased learning component includes an internship for many of the academy students after their junior year, and a host of other job visits and opportunities to experience

7 Since the former students were only one to three years out of high school, labor market information is probably not predictive of ultimate labor market outcomes, especially since many individuals were still enrolled in post-secondary education. As a result, the focus of this study centers on post-secondary education, even though building labor market skills in a career field of interest is a primary goal of many school-to-work programs.

– – – – – 1087

– – – – – 2845

91.0 14.0 28.3 18.1 1914 –

the work world and to learn about the educational pathways to achievement of a skilled position. The district also established policies for the career academies so that they would reflect a heterogeneous group of students from all levels of prior academic achievement and would avoid creaming only high achievers or, conversely, taking only those with academic problems. The district did not subscribe to the notion, often used in school-to-work programs elsewhere, that career academies should serve principally or exclusively “the forgotten half” of high school students, those deemed not college-bound. Table 3 describes students and their outcomes from each high school program, including the career academies. From this description we see that educational outcomes are remarkably similar for students from career academy programs and those defining themselves as from academic programs. However, differences between the two groups exist in school and student characteristics and the reason the student entered the program. A higher proportion of students from the career academy program than defining an academic program are female and attended schools A, B, C and E, the schools that serve the more impoverished areas of the city. This suggests that career academies enrolled students from a more disadvantaged background. Students from career academy programs are less likely than other students to come from a home where English is the native language (mostly Asian families in this district) and less likely to have enrolled in a high school program based on parental conversations or assignment.

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Table 3 Determinants of high school program: multinominal logit estimation Characteristic

Estimated using student characteristicsa Demographic Male African–American Latino Asian Background Take achievement test Mom and dad at 14 English first language Entrance Assigned to program Talked to teacher/counselor Talked to parents Talked to friends Intercept N Estimated using test scoresb Math, sophomore year Reading, sophomore year Language, sophomore year (single-district) History, sophomore year (national) Intercept N

Single-district sample

National sample

Career academy Academic

Vocational

Academic

Vocational

⫺0.207 0.845 1.017 ⫺0.106

⫺0.429 ⫺0.545 ⫺0.315 ⫺0.837

ⴚ0.521 0.689 0.053 ⫺0.478

⫺0.049 ⴚ0.752 ⫺0.353 0.674

ⴚ0.309 ⫺0.067 ⴚ0.431 ⫺0.372

0.302 0.237 ⴚ0.705

0.607 0.808 0.245

0.237 0.371 ⫺0.209

0.317 0.161 0.269

0.259 0.101 ⫺0.088

ⴚ1.44 ⫺0.367 ⴚ0.871 1.106 ⫺0.460

0.186* 0.843* 0.146 1.076 ⫺1.282 973

ⴚ1.60* 0.118 ⫺0.319 0.953 0.459

0.379 0.049 0.464 ⫺0.066 0.368

0.003 ⴚ0.014 0.007

0.013 ⫺0.001 0.058*

0.002 0.008* ⫺0.001

0.030 0.086

⫺0.251 527

ⴚ0.961

0.534

0.038 ⫺6.528

ⴚ0.474 0.273 0.408 0.327 ⫺0.055 1409 0.018 0.053 0.034 ⫺5.196 1331

a

Numbers are multinominal logit coefficients estimated from text [Eqs. (2) and (2a)]. Coefficients in boldface are significant at Pⱕ0.05 and coefficients in italics are significant at Pⱕ0.10. An asterisk (*) in the single-district analysis indicates that the t-test for coefficient differences shows significant difference (Pⱕ0.05) from the career academy coefficient. See Appendix A for variable definitions. b The table contains two separate estimations. Results of the extended estimation are located in the top portion of the table and results of the estimation containing only test scores are located in the bottom portion of the table. While both the single-district and national databases contain differing tests to measure math and reading, only the single-district database contains a language examination and only the national database contains a history examination.

This single-district database allows us to compare outcomes from the four high school programs. Because data from the single district reflect a relatively homogeneous environment (i.e., pupil–teacher ratios, per pupil expenditures, class size, and policies and procedures are all relatively constant), the estimated associations between programs and outcomes are less likely to reflect spurious correlation than would estimates derived from acrossdistrict databases. Because these unmeasured environmental factors bias the estimates of program impact on education (Meyer, 1996), the single-district coefficients may estimate associations better than the across-district coefficients generated with the national database. However, the potential limitation of these data may be in generalizing results from its analysis. Because all local districts are unique, estimated relationships between high school program and post-secondary outcomes may reflect idiosyncratic practices rather than nationally con-

sistent patterns. We use the national database to ensure that the estimated associations and patterns between high school programs and post-secondary outcomes from the single district are similar to those from national sources. Should estimated correlations be similar between the samples, we assume that associations obtained from our single-district database might be generalizable outside the district. Our national (across-district) data are drawn from the first and third follow-up surveys in the National Education Longitudinal Study (NELS). The first follow-up to the NELS sample was conducted in the spring of 1990, when most of the sample was in 10th grade.8 This

8 For a discussion, see National Center for Education Statistics (1996). The first follow-up included a freshened sample over the base year (1988, when most students were in 8th

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

round of surveys included about 18,000 students from over 1000 schools. The third follow-up to NELS interviewed 16,000 students and began on September 30, 1994, when most individuals had been out of high school about two years. We restricted the NELS sample to students who were in urban, public schools. Both data sets have identical measures of educational achievement that are used to quantify the educational outcomes outlined in Eq. (1): high school graduation, two-year college attendance, or four-year college attendance.9 These measures capture only one program outcome: education. Because career academies also set as their goal to increase workplace skills and broad-based educational skills that facilitate lifelong learning, we want to assess program outcomes along these lines. Unfortunately, career academy programs are too new to be the subject of the longitudinal data collection that would be needed to assess career-based work outcomes and lifelong learning skills. Both of these outcomes can be evaluated only after sufficient time has elapsed since high school. While our single-district database is unique in its ability to follow a large number of career academy students after high school, sufficient time has not elapsed to accurately assess success in the labor market or the ability and propensity to engage in lifelong learning. Instead, we rely on measures from the single-district database of education and workplace skills that students believe they took from high school. In the post-high school survey, respondents were asked to “check the box that indicates how well your high school education helped you to…” obtain 13 different work and education-related types of knowledge and skills. Respondents were given four options with which to rate their high school program: a great deal, somewhat, a little, or not at all. These measures were specifically designed to capture three sets of qualities: “workplace” skills, “education” skills and “school-to-work” skills. While many items are relevant to both education and workplace settings, this grouping eases interpretation of results because traditional vocational education programs were designed to facilitate “workplace skills”, traditional academic programs were designed to facilitate “education skills”, and the career academy was designed to facilitate all three sets of skills. High school program, the dependent variable in Eq. (2) and the key independent variable in Eq. (1), is self-

grade), which was added to achieve a representative sample of the nation’s sophomores. 9 A small percentage (between 3 and 4%) of both samples report more than one highest grade attended because dropouts are defined as individuals who did not graduate on time. These individuals are counted as both a dropout and attending a postsecondary college. Our single-district analysis includes 26 such individuals.

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defined in both databases with respondents selecting their high school program (general high school program; college prep or academic; (specific) vocational, technical or business). Career academy students (in the single-district database) were identified by the district office and were asked only to name their academy.10

4. Empirical methods Coefficients from all equations are interpreted in two ways. First, because the general high school program is the omitted variable in all estimations, the significance tests for career academy, academic and vocational coefficients are used to compare associations to the general program. Should career academy coefficients be positive and significantly different from zero, we conclude that the career academy is associated with increased program outcomes as compared with the general program. Second, to compare program outcomes of students from the career academy program and those of students defining themselves as from academic and vocational programs, we perform t-tests for coefficient differences. Should program coefficients from more traditional programs significantly differ from coefficients from academies, we would conclude that students from academy programs have different outcomes from students in vocational or academic programs. Coefficients that are estimated from Eqs. (1) and (2) are based on a structural modeling of factors that are correlated with post-secondary education/self-assessed skills [Eq. (1)] or program selection [Eq. (2)]. Unfortunately, data limitations preclude estimating fully specified equations and, as a result, four potential complications arise. First, because factors at the school (e.g., school climate) or individual (e.g., ethnicity) level could influence post-secondary outcomes, we must control for as many differences between schools and individuals as possible. In the single-district database, controlling for school-level influences is fairly straightforward because we can insert binary variables for each school into the estimation. The national database has school characteristics that proxy for school-level influences (average

10 We recognize that the student’s definition of high school program may not fully reflect the curriculum undertaken. Instead, it represents a general notion of the student’s high school program. If there is switching between programs in high school, the self-definition allows the student to select the program that (most probably) had the strongest influence on their post-secondary activities. Because we use the district’s definition to identify students who are in career academies, there may be students who we define as career academy students for whom the program had minimal influence (e.g., students who were in the program only one semester). The inclusion of such students will understate the program’s impact.

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daily attendance, student demographic composition, size and pupil–teacher ratio).11 The individual-level control variables available in both databases include basic information on demographics (gender, race/ethnicity) and background (native English speaker, family structure at age 14). The second potential complication is the binary nature of the measures of educational benchmarks [Eq. (1)]. With a binary dependent variable, estimations with ordinary least squares (OLS) will produce inefficient estimates of program associations. We therefore estimate a series of probit analyses for each educational outcome (e.g., high school graduation, two-year college, four-year college) and use estimated probit coefficients12 to compute the marginal probability of achieving each level of education for students enrolled in each high school program.13 We use OLS to estimate the relative program influences (a1, a2, a3) on the self-assessed skill outcomes and to make direct comparisons between the programs using the standardized (for unit variance) coefficients. The third potential complication is that accurate and widely available measures of knowledge and skills do not exist to estimate students’ placement in different programs, Eq. (2). Remember that the primary advantage of this estimation is to examine for program selection along lines that might bias career academy coefficient estimates in Eq. (1) (a1, a2, a3).14 Unfortunately, accurate 11

Unless both sets of controls capture school-level effects, differences could exist between the single-district and national databases in explanatory power and factors that are captured in the error term. As long as these differences are not correlated with high school program, coefficient estimates on high school program are not biased. 12 An ordered probit is not appropriate because individuals can be included on multiple outcomes. We feel that these multiple outcomes are more appropriate measures of education for students who are only one to three years out of high school because they show progression toward a goal. In contrast, highest grade completed suggests a sense of attainment at a point in a youth’s life where education is not static. 13 The probability is obtained by converting the z-score computed from the estimated probit for each educational benchmark. The z-value is computed using the mean values on all independent variables and 1 on the relevant program variable (zero on all program variables for students in the general track). The marginal probability is computed by subtracting the predicted probability for students in each program from the predicted probability of students in a general program. 14 For the single-district database, the coefficient estimates of the association between career academy enrolment and postsecondary outcomes have been rigorously evaluated for potential biases from sample selection. This evaluation includes explicit tests for sample selection using Heckman’s l (which controls for observables), stratification of estimations by entering test scores (which better control for unobserved heterogeneity that is correlated with academic achievement) and esti-

measures of knowledge and skills are not available and their proxies — achievement test scores — often have a large number of cases with missing values. In fact, in our single-district database only about half of the students took the district-mandated, state-generated achievement tests in their sophomore year. This greatly reduced the sample available for estimating Eq. (2). To retain sample cases, we use variables that may be associated with selecting a high school program: HSProg⫽ba⫹

冘

bi,aHSEntrancei⫹

冘

bk,aBkgdk,

(2a)

where: HSEntrance a vector of variables indicating the sources of advice and information used to select high school program Bkgd a vector of variables indicating student characteristics. We also estimate Eq. (2) with the smaller sample of respondents who took achievement tests at high school entrance. By examining results of both estimations, we can begin to assess whether some observables, academic achievement at program entrance [Eq. (2)] or demographics, background and choice mechanism [Eq. (2a)], are associated with career academy enrolment. Should coefficient estimates be significant with career academy enrolment, program selection may systematically occur along lines that are observable. In both estimations [Eqs. (2) and (2a)], the categorical nature of the dependent variable dictates that a multinominal logit maximum likelihood estimation be used.15 The fourth potential complication is that, because estimations exclude cases if data are missing, our estimations are performed on a sampling from each database. This raises concerns that: (1) the reduced sample may not reflect the students in the school; and (2) the sample may not reflect the proportion of students in each program (if missing data are correlated with high school mations with inclusion of entering test scores as controls (which explicitly control for academic ability at program entrance). In all of these estimations, the coefficients of the career academy remain significant in the aggregate, although differences in the estimates exist across environmental lines — much like the variation along demographic lines in this study. 15 One assumption of the multinominal logit is that the choice of high school program is independent of irrelevant alternatives. With respect to the choice of the career academy program, our database allows for an implicit examination of this assumption because the career academy is added as a third alternative among structured high school programs. Since estimates are similar for the within-district (with three structured programs) and the national (with two structured programs) databases, we find support for the independence assumption that underlies this technique.

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

program). We examined for potential problems along these lines by using t-tests to uncover significant differences between the population of students in the singledistrict and the national databases and the samples used in our estimations. We found that, like most empirical studies, differences exist between the individuals in the databases and the samples used in estimations. In the single-district database, a larger percentage of the survey respondents were from career academies and were located in schools that have higher socio-economic attendance areas. Survey respondents were more likely to be white and Asian and less likely to be African– American than the population of students in the schools. These differences did not bias coefficient estimates. The NELS sample used in estimations contained a greater proportion of individuals who might be higher achievers,16 consistent with the potential non-response biases in the single-district database.

5. Results We first examine the potential of creaming, or selecting more adept students into the academy program, to assess whether our structural estimation of outcomes is potentially biased. Results strongly suggest that creaming does not exist in the career academies in this district.17 In fact, results presented in Table 3 suggest that career academies draw a disproportionate number of students who have academic difficulties. When “all else is equal”, the career academy program enrolled a disproportionate number of African–Americans, Latinos and students who do not have English as their native language (usually Asians) as compared with the general program. Differences also exist between students from 16 The sample used in the NELS estimations had increased numbers in academic programs, in two-parent households, and with English as a native language and with increased educational attainment and high school GPA and with decreased numbers of Latinos. 17 Of course, “creaming” into the career academy could easily exist along lines that are not captured here and which may be unobservable. For example, the relative newness of the career academies may attract students who are disillusioned with traditional programs. Alternatively, the empowerment gained from students selecting their high school programs (with friends instead of parents or school assignment) may motivate students to excel in the career academy environment. More rigorous testing along these lines (Maxwell & Rubin, 2000) suggests that academy programs are associated with increased positive success even with statistical controls for many of these unmeasured factors. Thus, results suggest that career academies enrol students with below-average probabilities of educational success and, for whatever reason, raise their marginal probability of achieving educational benchmarks to a level achieved by students in the academic program.

145

career academies and other programs with respect to the reason why they selected their high school program.18 Perhaps most telling is the negative correlation between (reading) test score in the sophomore year and enrolment into the career academy program. This suggests that students who enrolled in the career academy program were less academically prepared upon entering high school than were students from the general program. Furthermore, significant differences between coefficients suggest that students from career academies were less academically inclined as compared with students from vocational and academic programs. The magnitude of the differences among students in different programs can be seen in Table 4, which shows the mean differences in student characteristics from each program. We next examine results from estimation of Eq. (1), which assesses the outcomes associated with each high school program. Results support the proposition that students from career academies are more likely to continue their education than students from general and vocational programs (Table 5). The insignificant coefficient differences between educational outcomes for career academy students and students from the academic program confirms the descriptive finding in Table 4 that students from career academy have educational outcomes similar to those of students from the academic program, which is designed to facilitate post-secondary education. Of course, these results are more powerful because they hold constant many important influences that could account for the academy’s measured influence. As Table 5 shows, the “average” career academy student has an 8.7 percentage point increase in the probability of graduating from high school, a 11.6 percentage point increase in attending a two- or four-year post-secondary institution, and a 17.9 percentage point increase in attending a four-year post-secondary institution (i.e., coefficients significantly differ from 0). Career academy students are also more likely than students from vocational education to undertake each level of higher education (i.e., t-tests for coefficient differences between the two programs are significant). t-Tests for coefficient differences between the single-district and national district estimates were not significant (Pⱕ0.05), suggesting that program patterns hold outside the district, all else equal.19 18 Students from career academies are less likely than students from other programs to have been assigned to their program or to have enrolled based on conversations with their parents, and are more likely to have chosen it after talking with friends. 19 Interestingly, the estimated probabilities of attending either a two- or four-year college or a four-year college differ significantly between the single-district and national samples, in large part because the standard errors on the coefficients are relatively small. Results are available from the authors.

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N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

Table 4 Characteristics of students by high school program: the single-district sample Characteristica Education High school graduate (%) Attend 2- or 4-year college (%) Attend 4-year college (%) Demographic Male (%) African–American (%) Latino (%) White (%) Asian (%) High school Attends school A (%) Attends school B (%) Attends school C (%) Attends school D (%) Attends school E (%) Attends school F (%) Student characteristics Mom and dad at 14 (%) English first language (%) Take test (%) Classes attended in high school (%) Program Assigned to program (%) Talked to teacher/counselor (%) Talked to parents (%) Talked to friends (%) N

Career academy

Academic

Vocational

General

92.0 82.2 51.8

92.9 83.5 52.2

69.0* 59.5* 9.5*

78.3* 69.7* 33.4*

27.6 42.7 18.8 8.0 28.6

32.4 44.6 8.5* 16.0* 27.2

35.7 45.2 14.3 2.4* 35.7

37.8* 31.7* 18.5 8.9 37.6

12.0 17.4 11.6 8.7 35.5 14.9

7.6 13.8 0.9* 25.0* 7.6* 44.2*

2.4 45.2* 2.4 23.8* 16.7* 9.5

9.0 19.4 1.1* 32.5* 7.5* 28.6*

61.1 55.6 59.4 87.6

58.3 69.2* 51.3 82.7*

47.6 52.4 42.9* 75.7*

63.6 49.6 50.1* 80.3*

16.5 32.6 13.3 23.2 276

42.9* 35.3 22.3* 10.7* 224

23.8 19.0 14.3 11.9 42

51.9* 22.2* 9.7 10.1* 545

a

See Appendix A for a definition of the variables. Numbers are mean values for the single-district sample. Subsequent tables and Appendix A present data based on a sample that excludes individuals with missing data on estimation variables (87% of this sample). An asterisk (*) indicates that the t-test for mean differences shows significant difference (Pⱕ0.05) from the career academy mean.

Table 6 shows the standardized OLS coefficients estimated from Eq. (1) with each of the 13 self-assessed knowledge and skill measures as the dependent variables. In general, students from career academies who attend post-secondary education rate their high school program as more favorable than do others at providing education-appropriate knowledge and skills. High school program is not correlated with a more favorable selfassessment for students who end education at high school (columns 2–4).20 However, former career academy and vocational students who attend a two-year college rate (some) education and school-to-work skills higher than students from the general program (column 5 and 7), while students from academic programs rate their knowledge and skills about the same as students in 20 We are, of course, assuming that the student’s rating reflects the program’s ability to impact skills. That is, that errors in student assessment are randomly distributed (within and between programs) with a mean of zero.

the general program (column 6). These results are consistent with the emphasis on two-year technical training in vocational and career academy programs. Students from career academies who attend four-year universities rate their work, education and school-to-work knowledge and skills higher than do equivalent students from the general program (columns 8 and 9), consistent with their academic program emphasis. We note that, with one exception, estimated correlations between the programs and self-assessed skills do not differ significantly from each other. This suggests that students at two-year colleges who say they are from vocational and career academy programs and students at four-year universities who say they are from career academy and academic programs have similar assessments of the ability of their program to impact knowledge and skills. Thus far, we have shown that students from career academy programs are more likely to continue education and to believe that their high school provided them with more education and workplace skills as compared with

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

147

Table 5 Probability of achieving each level of education

On-time HS graduateb Attend 2- or 4-year college Attend 4-year college

Single-district analysis

National (across-district) analysis

Probability for:a

Percentage point change in probability:

Probability for:

Percentage point change in probability:

General track

Career academy

Academic

Vocational

General track

Academic

89.7 76.1 33.7

8.7 11.6 17.9

7.2 8.7 15.9

⫺7.1* ⫺7.3* ⴚ21.2*

93.5 61.4 23.6

3.0 12.8* 13.1*

Vocational 0.37 ⫺0.77 ⴚ1.8

a The estimated probability for a student in the general track to advance to each level of education is located in the second and sixth columns of the table. Numbers in the remaining columns are the change in the probability of the event occurring for students in each program. Probabilities are multiplied by 100. The probability to advance is computed by converting the z-score computed from the estimated probit for each level. The z-value is computed using the mean values on all independent variables and 1 on the relevant track (0 on all track variables for students in the general track). The probability for general track students is reported here. The change in probability is computed by subtracting the predicted probability for students in a general track from the predicted probability of the relevant track. Full results of the probit analysis [estimation of Eq. (1)], on which the probabilities are computed, are available for the authors. Appendix A provides a full definition of the variables. b Numbers in boldface indicate that the probit coefficient is significant at Pⱕ0.05 and numbers in italics indicate that the probit coefficient is significant at Pⱕ0.10. An asterisk (*) in the single-district analysis indicates that the t-test for coefficient differences shows significant difference (Pⱕ0.05) from the career academy coefficient. An asterisk (*) in the national across-district analysis indicates that the t-test for coefficient differences shows significant difference (Pⱕ0.05) from the equivalent coefficient in the singledistrict analysis (i.e., academic program coefficients between the national and single-district data sets differ).

students from the general program. Education and skill assessment of individuals in the four-year university increase to levels equivalent to those achieved by students who were in academic programs, and skill assessment increases for individuals in two-year colleges to levels equivalent to those by students who were in vocational programs. However, results presented in Table 7 suggest that these outcomes may not be uniform across all demographic groups. Table 7 shows the marginal probabilities of achieving different levels of education for different demographic groups. This is akin to the aggregate probabilities presented in Table 5. For example, the first row of this table presents the marginal probabilities for male students who are “average” on all other characteristics. This table shows that females, African–Americans and (native) English speakers from the career academies are more likely to have increased educational probabilities over comparable students who define themselves as from general and vocational programs. The career academy program may have less positive outcomes for males, Latinos and non-native English speakers (usually Asian). No significant differences exist between career academy enrolment and educational outcomes for males and the only statistically significant increase for non-native English speakers is in attending four-year college. For Latinos, career academy attendance is consistently less likely to increase post-secondary educational enrolment than attending the “academic”

track.21 Unfortunately, data in this study do not permit analysis of the reasons underlying these differentials, but they do suggest that curriculum and demographics may interact.

6. Summary and conclusions Virtually all of our analysis supports the proposition that career academy programs are associated with increased education for urban, public school students, at least in this district. Results of this study show that students from career academies have a (marginal) probability of attending college equal to that of students from the academic program. Career academy students more often report that their high school program provided knowledge and skills that facilitate chosen educational pathways. Furthermore career academies, at least in the district from which we drew our data, enrol students that may be more “at-risk” than other students. One rather large caveat is in order before endorsement is given to the career academy approach as a universal

21 Differential enrolment in schools cannot explain this result. Latinos are disproportionately enrolled in schools in the middle of the socio-economic status service areas with near district average of students attending post-secondary education.

0.083 0.013 0.024 0.026 0.118 0.093 0.050 0.085 0.095 0.194 0.072 0.101 0.031 239

0.076 0.033 0.083 0.028 0.115 ⫺0.001 0.016 ⫺0.024 0.029 0.155 0.038 0.012 0.023

0.076 0.045 0.039 0.086

0.022

0.048

0.042 0.064 0.083

0.014 0.000 0.100 0.085

0.127 0.148 0.082 0.097

0.155

0.124

0.163 0.116 0.016

0.060 0.020 0.076 0.115

0.145 0.095 0.038 0.137 340

0.088

0.047

0.047 0.042 0.098

0.086 0.122 0.108 0.095

0.145 0.152 0.092 0.181*

0.094

0.088

0.183 0.071 0.130

0.073 0.073 0.064 0.151

Vocational

0.252 0.207 0.169 0.248

0.177

0.129

0.310 0.151 0.133

0.159 0.104 0.117 0.193

Career academy

0.114 0.097 0.064 0.139 420

0.192

0.086

0.250 0.164 0.081

0.079 0.057 0.055 0.119

Academic

Attend 4-year university

0.036 0.021 0.001 0.010

0.100

0.016

0.010 0.006 0.042

0.012 0.020 0.018 0.020

Vocational

a Columns include only individuals whose highest grade attended was high school (including dropouts and graduates, columns 2–4), two-year college (columns 5–7), or fouryear university (columns 8–10). Twenty-six individuals who dropped out of high school also attended a post-secondary college. These individuals are included in both analyses. b Numbers are standardized regression coefficients estimated from text [Eq. (1)] with knowledge and skill measures as the dependent variable. Full results of the estimation are available from the authors. Appendix A provides a full definition of all variables. Coefficients in boldface are significant at Pⱕ0.05 and coefficients in italics are significant at Pⱕ0.10. An asterisk (*) indicates that the t-test for coefficient differences shows significant difference (Pⱕ0.05) from the career academy coefficient. c The education and work skill variables reflect a respondent’s rating of how well their “high school education” helped them to achieve the listed skills. The rating scale ranges from 1 (a great deal) to 4 (not at all). The signs on the coefficients were reversed so that a positive standardized coefficient indicates a more positive rating.

Work focusc Meet work dead ines Communicate with others Be punctual/on-time Be self-motivated Education focus Think critically Improve in basic skills Obtain good study habits Maintain a positive attitude towards education/training Prepare for your current education/training School-to-work focus Become aware of what is required for success Gain confidence about your abilities Understand the link between school-to-work Set future goals N

Academic

Career academy

Vocational

Career academyb Academic

Attend 2-year college only

High school onlya

Table 6 Attainment of skills and awareness about work and education: standardized regression coefficients

148 N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

c

b

a

6.8 8.6 17.0 7.2 13.6 20.0 12.3 10.3 15.8 4.0 15.8 15.0 12.9 15.7 16.0 2.9 4.1 19.7

89.1 75.5 29.5 91.5 76.7 35.6 83.4 74.5 27.4 82.6 59.1 12.1 84.1 73.6 28.4 94.1 80.6 42.1

4.3 6.4 9.1

9.7 10.1 18.4

15.1 32.2 27.3

8.8 4.9 15.0

6.9 11.4 17.2

2.9 4.7 14.2

94.3 63.7 24.2 94.6 47.6 20.9

⫺13.6* ⫺6.2* ⫺0.7* ⫺2.6 ⫺6.9 ⫺18.5*

91.9 86.9 22.4

⫺12.9* ⫺2.3 ⫺22.2*

88.5 54.0 11.7

93.8 70.5 27.1

⫺0.8* ⫺7.2* ⴚ25.9*

0.0 ⫺13.5 ⫺4.8

93.9 66.6 20.0

⫺19.6* ⫺5.0 ⫺14.1

1.9 12.3 11.7*

3.0* 13.1* 14.0*

3.6 13.0 11.0

3.4 4.0 9.6

3.8 11.6 15.0*

2.1 11.3* 11.2

Academic

0.1 ⫺2.0 ⫺1.1

0.5 ⫺0.4 ⴚ2.1

0.4 0.0 ⫺0.4

1.4 ⫺1.3 0.0

0.5 ⫺1.4 ⴚ3.2

0.1 ⫺0.0 ⫺0.6

Vocational

Percentage point change in probability:

Sample size limitations prohibited probit analysis for Asians. See notes for Table 3. Full results are available from the author. The first n is the sample size for the single-district analysis and the second n is the second sample size for the national (across-district) analysis.

Males (n=328, n=651)c HS graduate Attend 2- or 4-year college Attend 4-year college Females (n=645, n=758) HS graduate Attend 2- or 4-year college Attend 4-year college African–American (n=360, n=280) HS graduate Attend 2- or 4-year college Attend 4-year college Latino/Hispanic (n=151, n=321) HS graduate Attend 2- or 4-year college Attend 4-year college English (n=530, n=1109) HS graduate Attend 2- or 4-year college Attend 4-year college Non-native English (n=443, n=300) HS graduate Attend 2- or 4-year college Attend 4-year college

Vocational

General track

Academic

General track

Career academy

Probability of:

National (across-district) sample

Probability of:b Percentage point change in probability:

Single-district sample

Table 7 Demographic differences in achieving each level of educationa

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152 149

Educational benchmarks HS graduate (on-time) Attend 2- or 4-year university Attend a 4-year university

Talked to friends

Talked to parents

Talked to teacher/counselor

English first language Take achievement test (sophomore year) High school attendance Program Career academy Academic Vocational Entrance Assigned to program

Mom and dad at 14

Asian High school A B C D E Student characteristics

Latino/Hispanic

Demographic Male African–American

Individual characteristic

binary binary binary binary binary

with with with with with

1 1 1 1 1

indicating indicating indicating indicating indicating

a a a a a

respondent respondent respondent respondent respondent

who who who who who

attended attended attended attended attended

A high school B high school C high school D school E high school

A 0, 1 binary variable with 1 indicating a respondent who graduated from high school on time A 0, 1 binary variable with 1 indicating a respondent who attended a college or university A 0, 1 binary variable with 1 indicating a respondent who attended a 4-year college or university

a respondent who talked to his/her friends before selecting

a respondent who talked to his/her parents before selecting

0.215 (0.411)

0.345 (0.476)

0.888 (0.327) 0.946 (0.226) 0.779 (0.415) 0.732 (0.443) 0.432 (0.496) 0.370 (0.483) (continued on next page)

0.143 (0.350)

0.134 (0.340)

0.442 (0.497)

0.386 (0.487) 0.282 (0.450)

0.386 (0.487)

A 0, 1 binary variable with 1 indicating A 0, 1 binary variable with 1 indicating selecting a high school program A 0, 1 binary variable with 1 indicating a high school program A 0, 1 binary variable with 1 indicating a high school program

a respondent who was assigned to a high school program a respondent who talked to a teacher or counselor before

– 0.447 (0.497) 0.152 (0.359)

0.658 (0.475)

– – – – –

A 0, 1 binary variable with 1 indicating a respondent who attended an academy 0.263 (0.441) A 0, 1 binary variable with 1 indicating a respondent who was in an academic high school program 0.208 (0.406) A 0, 1 binary variable with 1 indicating a respondent who was in a vocational high school program 0.041 (0.199)

0.625 (0.484)

(0.290) (0.381) (0.189) (0.432) (0.360)

0.787 (0.410) 0.940 (0.237) –

variable with 1 indicating a respondent for whom English is the native language variable with 1 indicating a respondent who took the achievement test the final fall quarter semester spent in class

variable with 1 indicating a respondent who lived with his/her mother and father at

variable variable variable variable variable

0.114 (0.318)

0.228 (0.420)

0.462 (0.499) 0.198 (0.399)

National

0.545 (0.498) 0.542 (0.499) 82.6 (21.0)

A 0, 1 binary age 14 A 0, 1 binary A 0, 1 binary Percentage of

1 1 1 1 1

0.092 0.176 0.037 0.248 0.153

0, 0, 0, 0, 0,

A A A A A

0.155 (0.362) 0.345 (0.476)

0.333 (0.473) 0.370 (0.483)

Single-districta

A 0, 1 binary variable with 1 indicating a male respondent A 0, 1 binary variable with 1 indicating an African–American respondent A 0, 1 binary variable with 1 indicating a Latino (Mexican or Latino) respondent in the singledistrict sample and an Hispanic respondent in the national sample A 0, 1 binary variable with 1 indicating an Asian respondent

Description

Table 8 Description of variables: means and standard deviations

150 N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

to

to

to

to

to

to

to

to

to

to

to

to

to

Percentage average daily attendance of students Percentage of students in the student’s class (10th grade) who are African–American Percentage of students in the student’s class (10th grade) who are Hispanic Percentage of students in the student’s class (10th grade) who are white Percentage of students in the student’s school who have limited English proficiency Percentage of students in the student’s school who are part of the free lunch program Student–teacher ratio. Computed using the number of full-time teachers at the school divided by size (defined below) An estimate of the number of students in the school, which uses a categorical variable (1–9) midpoints of each range as the estimated number of students

A 1–4 categorical variable indicating how well the high school program helped the respondent meet work deadlines (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent communicate with others (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent be punctual/on-time (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent be self-motivated (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent think critically (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent improve in basic skills (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent obtain good study habits (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent maintain a positive attitude towards education/training (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent prepare for current education/training (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent become aware of what is required for success (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent gain confidence about abilities (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent understand the link between school and the “real world” (1=a great deal; 4=not at all) A 1–4 categorical variable indicating how well the high school program helped the respondent set future goals (1=a great deal; 4=not at all)

Description

973

–

–

– – – – – –

2.15 (1.03)

2.34 (1.09)

2.10 (0.982)

2.14 (0.991)

2.24 (0.999)

2.06 (0.969)

2.40 (0.979)

1.83 (0.836)

2.03 (0.913)

2.03 (0.974)

2.00 (0.950)

1.92 (0.886)

2.10 (0.949)

Single-districta

(4.45) (27.0) (28.4) (32.7) (19.0) (21.7)

1409

1862 (690)

17.6 (4.0)

90.9 23.7 21.2 49.6 13.9 27.3

–

–

–

–

–

–

–

–

–

–

–

–

–

National

a The mean values on these variables differ from those in those presented in Tables 1, 2 and 4, which are based on all individuals in the relevant databases who were from comprehensive, urban, public high schools and for whom a high school program that was not special education could be identified. The number of missing values changes for each variable. The characteristics presented in this table are based on a (constant) sample of individuals who were used in the estimations (results of which are presented in Tables 5 and 6). This sample is reduced by virtue of missing data on (any) variable used in the analysis.

N

Size

SFR

School characteristics ADA Percent African–American Percent Hispanic Percent White Percent LEP Percent free lunch

Set future goals

Understand the link between school-towork

Gain confidence about abilities

Become aware of what is required for success

Prepare for current education/training

Maintain a positive attitude towards education/training

Obtain good study habits

Improve basic skills

Think critically

Be self-motivated

Be punctual/on-time

Communicate with others

Meet work deadlines

Skills and awareness

Individual characteristic

Table 8 (continued)

N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152 151

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N.L. Maxwell, V. Rubin / Economics of Education Review 21 (2002) 137–152

strategy for high school students. All students may not benefit equally from the program, at least for the educational outcomes analyzed here. While most groups analyzed here generally did as well as students who described their program as academic, the increased cost of the career academy (e.g., Maxwell & Rubin, 2000; Stern et al., 1988) would not warrant their universal implementation unless significant gains could be shown. These gains were realized only for some groups of students. The reasons for these differences lie beyond the data presented here, and dictate further research. Our findings therefore suggest that the academy program may be of great utility in increasing the educational attainment of at-risk students. However, this does not necessarily suggest that it should be the only option in a high school serving a cross-section of the population, especially in light of their increased cost over more traditional programs. It may be that their benefit depends on the voluntary nature of enrolment and that forcing students into their programs would reduce their effectiveness. Other individual students might not thrive in the same sort of academy-style learning environment. Because public school student bodies are so diverse, identification of the factors that stimulate learning for different types of students will be essential to maintaining an equitable educational system. Of course, this analysis is well beyond the scope of our study and is left for future research. Acknowledgements An earlier version of this paper was presented at the 1997 Western Economic Association Meetings. The authors wish to thank Ronald D’Amico, Mary King and journal reviewers for comments. This research was partially funded by the W.E. Upjohn Institute for Employment Research and was written, in part, while Rubin was an associate researcher and Maxwell was a visiting scholar at the Institute of Urban and Regional Development, University of California, Berkeley. The views and interpretations contained within are those of the authors and may not reflect those of sponsoring institutions. Appendix A See Table 8. References Conant, J. B. (1967). The comprehensive high school. New York: McGraw Hill.

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