Attendance, schooling quality, and the demand for education of Mexican Americans, African Americans, and non-Hispanic whites

Economics of Education Review, Vol. 16, No. 4, pp. 407-418, 1997

~ Pergamon

© 1997 ElsevierScienceLtd All rights reserved. Printedin Great Britain 0272-7757/97 $17.00+0.00 Plh S0272-7757(96)00064-7

Attendance, Schooling Quality, and the Demand for Education of Mexican Americans, African Americans, and Non-Hispanic Whites MARIE T. MORA Department of Economics and International Business, New Mexico State University, Las Cruces, NM 88003-8001, U.S.A.

Abstract--This study examines the influence of the opportunity costs of school attendance, educational quality attributes, and household socioeconomic status on the educational demand of Mexican Americans, African Americans, and non-Hispanic whites using the 1988-1990 surveys of the National Education Longitudinal Study. Implementing a utility-maximizing framework, educational demand assumes the form of schools' average dally attendance rates and student dropout decisions between the 8th and 10th grades. The basic results suggest that a school's attendance rate is sensitive to educational quality and student characteristics. Moreover, the demand for education measured by student attrition inversely relates to unexplained school attendance, and is positively affected by household socioeconomic status. Some policy recommendations based on the results are discussed. [JEL I21] ©1997 Elsevier Science Ltd 1. I N T R O D U C T I O N FEW STUDIESIN the economics literature have empirically investigated the impact of schooling quality on a student's demand for education. Occasional exceptions exist: Orazem (1987) suggests that educational quality differentials in segregated Southern schools between the 1920's and 1930's generated parental differences in primary education demand, measured by attendance rates, for their children. Moreover, Becker (1990) contends that college quality affects college enrollment. Finally, Ehrenberg and Brewer (1994) propose that teacher and schooling attributes influence students' dropout decisions in secondary education. This study extends Orazem's (1987) work on the underlying preferences that determine attendance rates. I assume, however, that students maximize their o w n utility of attending school as opposed to Orazem's assumption that offspring school attendance belongs in their parents' utility function.t Within this conceptual framework, I advance the hypothesis that a student attends class when his or her maximum utility from attending exceeds the maximum utility of the next best alternative (say, skipping class that day). When schools exhibit high quality and offer "value", the hypothesis argues thai students receive greater benefits from attending class. Findings in the education literature support this claim. For example, previous research has established inverse relationships between attendance and various

negative educational attributes, such as student dislike of teachers or curriculum, student boredom, ineffectual and unresponsive teachers, and highly competitive classes (American Association of School Administrators, 1979; Education Research Service, 1977; Moos and Moos, 1978; Aspy and Roebuck, 1977; Wright, 1976). I use the 1988 and 1990 surveys of the National Longitudinal Education Study (NELS) to test the aforementioned hypothesis for Mexican Americans, African Americans, and non-Hispanic whites. Specifically, I discuss the influence of opportunity costs, schooling quality attributes, and. household socioeconomic status on educational demand, as measured by attendance rates and student dropout decisions. I focus on Mexican Americans and African Americans because they represent the largest ethnic and racial minority groups in this country, and are currently experiencing rapid population growths (Fullerton, 1991; Valencia, 1991). The importance of scrutinizing educational demand across racial and ethnic dimensions arises because Mexican Americans2 and African Americans consistently attain less education on average than non-Hispanic whites (Cattan, 1993; NCES, 1995a, b; Thomas and Hirsch, 1989;Valencia, 1991). 3 To this stylized fact, I add that the low schooling investments serve as one of the primary foundations of the African American/non-Hispanic white and the Mexican American/non-Hispanic white earnings gaps (McManus et al., 1983; Mora, 1996; Smith and

[Manuscript received 10 January 1995; revision accepted for publication 18 August 1996] 407

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Economics o f Education Review

Welch, 1989). Moreover, recent research suggests that the relative returns to higher education have been increasing over time (Bound and Johnson, 1992; Katz and Murphy, 1992; Murphy and Welch, 1989), implying that the low average educational attainment of these groups may lead to relatively lower earnings vis-fi-vis non-Hispanic whites. It follows that insight into the determinants of schooling demand for African Americans and Mexican Americans is becoming increasingly critical to facilitate a convergence of economic equality across racial and ethnic boundaries in the near future. 2. A T T E N D A N C E R A T E S AND S C H O O L I N G DEMAND Every day a student faces the decision to attend or not to attend school. Arguably, this decision depends on the maximum attainable utility from going to or skipping class. Although not directly observable, the student's decision of attending reveals his or her utility (Becker, 1990): Aij = 1 if U~j > U~; A~j = 0 otherwise,

(1)

where Aij denotes student i's attendance at school j, U~.~ represents the maximum utility that student i gains from attending school j, and U~jN depicts the maximum utility i receives from skipping a day of school. The difference between the two alternative utility levels reveals the net benefit of attending school on a given day. Benefits may be viewed as adding value to i's current human capital stock. Assuming that utility is linearly related to its inputs (Becker, 1990), then: Ui JA_ Ui JN:

Sjol I 4. Xijol2 .j. X j O l 3 _ f i j o l 4 _}_ Ei + ej,

(2) where Sj denotes a vector of school j ' s attributes, X~j denotes a vector of i's personal and socioeconomic characteristics (such as household income), x~ is a vector of peer and neighborhood effects measured by school j ' s average student characteristics,4 C~a stands for i's opportunity costs of attending school j, and ei and ej represent the individual and school-specific error terms, respectively. The opportunity costs, G j, may also be viewed as the daily price i pays in order to attend school. Combining Equation (1) and Equation (2), and assuming n individuals and m educational institutions, Equation (2) may be averaged across n individuals in a particular school j: n -1

n A

i

_

i

+ XjOd3 -- Cijod 4 "~- e i -1- ej),

or ADAj = Sj[31 + xff32-cff33 + ej.

(3)

A D A i represents school j ' s average attendance on any given day (average daily attendance) and cj depicts the average opportunity costs (i.e. the price) of attending j. The additional terms remain the same as in Equation (2). Note that the attendance rate embodies a demand function for school j, such that when educational quality, household, and peer effects are held constant, O(ADAj)/3(cj) = - [33<0.

(4)

Equation (3) also reveals a positive relationship between schooling quality and attendance. Moreover, an increase in household socioeconomic status, ceteris paribus, leads to an increase in ADAj, reflecting an income effect of attending school j. Finally, Equation (3) implies elasticities (negative in sign) between the opportunity costs of schooling with both household socioeconomic status and the schooling inputs. Student attrition may also be considered a form of educational demand. The impact of schooling quality and household attributes on student attrition should be considered when implementing policy to increase schooling attainment. To formally analyze dropout behavior, consider the following: Dropoutij = C i j g l - S j g 2 - X i j g 3 - x j g 4 + Pi + vj,

(5) where Dropout u represents a zero-one decision on whether i drops out of school after attending school j, vi and vj denote the individual and school-specific errors, respectively, and the remaining variables equal those in Equation (2). Note that schooling inputs negatively affect students' dropout decisions. This concurs with previous research studying the relationship between educational characteristics and dropout decisions (Ehrenberg and Brewer, 1994; Rumberger, 1983; Velez, 1989). In addition, research has examined the influence of households on dropout rates, and finds that household socioeconomic status (such as parental income) negatively relates to dropout decisions (Ehrenberg and Brewer, 1994; Rumberger, 1983; Velez, 1989). I further include xj as a proxy for peer and neighborhood effects (see Note 4); for example, Borjas (1995) notes that an individuals's peers and neighbors may affect human capital acquisition. Finally, Equation (5) indicates that high opportunity costs of schooling induce student attrition. In order to estimate Equation (5), a proxy for the educational opportunity cost of schooling should be implemented. When such factors as the average earnings and employment rates of high school dropouts within the local area are unobservable, I propose that unexplained school attendance [from Equation (3)] serves as a reliable proxy for the opportunity cost of schooling, as described in Equation (6):

Attendance, Schooling Quality, and Demand (cj-ej[33 I) = ( - ( A D A j - estimated ADAj)[33J). (6) That is, the difference between schoolj's estimated and actual ADAs describes unexplainable school attendance, which in turn captures the average student's opportunity costs of attending school j. When attendance rates are unexplained by the host of factors presented in Equation (3), Equation (6) represents a close proxy for i's opportunity costs, particularly if individual i closely parallels school j ' s average student and ej is relatively small. With this theoretical framework in mind, I now discuss the data that are utilized to empirically estimate Equation (3) and Equation (5). 3. T H E N A T I O N A L E D U C A T I O N L O N G I T U D I N A L SURVEY I use the 1988 and 1990 surveys of the National Education Longitudinal Study (NELS) to test the hypotheses discussed above. In 1988, the National Center for Education Statistics (NCES) sponsored the NELS to nationally represent over 1000 schools and around 25 000 8th-graders. Currently, two follow-up surveys are available (conducted in 1990 and 1992). NELS is suitable for addressing the issues discussed in this paper because the researcher may aggregate student data within a particular school. Furthermore, the use of the 8th-grade survey allows one to observe the individuals before they enter high school, providing additional insight into their outcomes. I extract the 24,246 8th-graders that have corresponding school surveys in 1988. These individuals are retained for the aggregation of student data within schools pertaining to the relative socioeconomic status and proportions of African Americans and Mexican Americans in schools. Further sample restrictions are discussed below. To estimate Equation (3) and Equation (5), variables in Sj include the school's estimated expenditures on beginning teachers' salaries per pupil (deflated by average beginning teacher salaries in the region), an adjusted pupil-per-teacher ratio, minutes of classes per year, total school enrollment, whether academic or vocational counseling exists, structured classroom environment, the departmentalization of schools at the 8th-grade level, whether a minimum grade-point average for students to participate in school activities is required, private vs public school status, and school location. Appendix A selectively lists variables and their construction. I include expenditures per pupil (EPP) on beginning teachers' salaries as a positive schooling quality attribute to proxy for the school's expenditures per student (Ehrenberg and Brewer, 1994). NELS provides beginning, rather than average, teacher salary; thus, estimated EPP represents a lower-bound because most teachers earn more than the entry-level salary. One criticism with using teachers' salaries

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arises because local labor market differences for college educated workers may exist, thus distorting the quality attribute of salary expenditures. Moreover, the use of salaries and expenditures in general may be misleading because of regional cost of living differences. To account for some of these potential problems, I deflate the beginning salaries by the average beginning teacher salary in the geographic region. Although a superior deflator would be the average wages within the same county or state, the public version of the NELS does not provide this information regarding the school's location. The regional deflator thus may not completely capture cost of living and labor market differences in these expenditures; however, it helps to reduce the potential bias associated with such differences. The adjusted pupil-per-teacher (PPT) ratio accounts for the teacher-student interaction intensity by approximating pupils in attendance, rather than pupils enrolled, per teacher. I use the adjusted ratio because schools with relatively fewer students present on any given day maintain higher interaction capabilities between teachers and the students in attendance. It should be noted that the estimated effect of PPT on schooling quality has been mixed. 5 A high PPT ratio implies lower individualized contact between teachers and students, which may be viewed as a negative quality attribute. At the same time, some researchers argue that a high PPT ratio may reflect a school-scale effect and capture teacher specialization, particularly in secondary institutions (D~ivila and Allahdad, 1996; Welch, 1966). 6 I include the minute-length of classes in the school year as a quality measure because the longer the time spent in class, the more time students are exposed to knowledge (Margo, 1986; Orazem, 1987). The measure of annual class-minutes is superior to the commonly used school term length expressed in days because of time variability within the school day. This measure may also reflect a school's attitude toward education, as schools emphasizing education presumably require students to spend more time in class. At the same time, the more minutes spent in class during the school term reduces available time for nonschool activities, increasing the opportunity costs of attending school. Student enrollment is another factor included in Si, which serves as a proxy for school size. Relatively larger schools may increase student competitiveness, which has been found to negatively affect attendance (Moos and Moos, 1978; Wright, 1976). Moreover, overcrowded schools may offer fewer per-pupil resources independent of teachers (such as capital per student), suggesting lower school quality. The offering of academic and vocational counseling to 8th-graders is also assumed to reflect educational quality. Schools with such counseling may provide students with additional insight into the variety of available educational investments. That is, academic and vocation counseling may offer the neces-

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sary information to match students with an optimal schooling bundle. Th~ lack of educational options has been cited by students as a reason for missing school (American Association of School Administrators. 1979). Si also consists of a categorical variable for the school administrator's evaluation of whether classes are structured. Institutions with structured classes may have the objective to create a serious learning environment, thus exhibiting positive quality. Extant research suggests that the degree to which schools maintain an orderly environment affects students' achievements (NCES, 1995a, b). The departmentalization of the 8th grade may also influence the learning environment. The NELS provides three possible categories of departmentalization: departmentalized, semi-departmentalized, or self-contained. These categories may have two conflicting effects on schooling quality. First, departmentalized schools may foster teacher specialization, leading to an increase in quality. Second, departmentalized schools may have fragmented policies between the departments, such that students do not receive homogeneous instruction and experience a lack of cohesiveness within the school. In this case, departmentalization may decrease quality. Another attribute in Sj is whether the institution mandates a minimum grade-point average (GPA) for students to participate in school activities. The effect of such a policy on schooling demand is three-fold. First, schools with a minimum GPA requirement may set higher academic standards than schools without the policy, therefore enhancing quality. Second, schools requiring a minimum GPA may have high student competitiveness, leading to a lower educational demand (Moos and Moos, 1978; Wright, 1976). Third, some low ability students may attend school only to participate in nonacademic activities. If their grades fail to meet a minimal requirement, certain students may decrease their demand for education because the relative benefits obtained from school fall below their opportunity costs. A categorical variable denoting private schools helps to control for differences in educational outcomes related to public vs private school status. Private schools may hire more effectual teachers and possess greater resources per pupil than public schools. Moreover, students enrolled in private schools may come from households where parents motivate their children. School location variables include categories for urban and suburban vs rural status, as well as the geographic region. The NCES (1995c) reports that schooling attributes differ between urban and rural areas, as well as between geographic regions. Here, the urban and suburban variables have expected negative coefficients because they may capture external labor market opportunities; urban areas most likely provide a wider array of nonschool activities, such as greater employment opportunities or entertainment

possibilities, than rural areas. The geographic variables should capture regional differences in schooling demand, possibly stemming from alternative options for nonschool activities. At the school level, the shares of African Americans and Mexican Americans of the 8th-grade student body, as well as the proportions of students in each socioeconomic (SES) quartile, compose xj.7 At the individual level, categorical variables for ethnicity and SES quartiles compose Xj. The racial and ethnic shares may capture unmeasurable quality attributes associated with segregated institutions. Extant research finds that minority students attending segregated schools acquire less education relative to students attending desegregated schools (Donato et al., 1991; Ehrenberg and Brewer, 1994). Segregated schools have also been criticized in that they tend to be overcrowded and underfinanced. Moreover, schools with relatively large shares of students with a high SES are expected to have the highest attendance rates because household SES and parental education have been found to positively relate to the educational attainment of youths (Becker, 1990; Borjas, 1995~; Ehrenberg and Brewer, 1994; Rumberger, 1983; Velez, 1989). With these variables in mind, I now proceed to estimate Equation (3) and Equation (5). Recall that Equation (3) utilizes the school-level variables and student characteristics, while Equation (5) incorporates schooling attributes with individuallevel components. 4. E M P I R I C A L RESULTS

4.1. Average Daily Attendance Rates ! employ the data from the 1988 NELS school and student surveys to empirically estimate Equation (3). 8 The NELS school survey includes a total of 1035 schools. Here, I delete 33 schools with missing data on average daily attendance rates or yearly classminutes, such that I retain 1002 schools. Aggregated student data for within schools (such as the proportion of 8th-graders in each SES quartile) were constructed using the entire sample of 24,246 students with both student and schooling surveys in 1988. Table 1 displays the empirical results from estimating Equation (3). Note that the salary EPP appears significantly related to a school's daily attendance rate. This result coincides with other findings that per pupil expenditures positively relate to school quality. Since this measure incorporates teachers' salaries in its calculation, it may further capture the quality of teachers in the school. The adjusted PPT ratio also reveals a positive and statistically significant effect, implying that students exposed to specialized teachers appear to attend school more often than students in schools with smaller classes. In other words, the teacher-specialization effect dominates the teacher-student interaction intensity in the 8th grade, reaffirming D~vila and Allahdad's (1996) hypothesis.

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411

Table 1. Regression results from estimating Equation (3) (dependent variable: 1988 school average daily attendance)

Variable

Coefficient

School characteristics Adjusted pupil-per-teacher ratio Teacher-salary-expenditures per pupil Minutes of class per year/1000 Total school enrollment School provides academic counseling School provides vocational ed. counseling Classroom environment is structured 8th-grade instruction is departmentalized 8th-grade instruction is semi-departmentalized Minimum GPA required for school activities Private school

0.244 0.275 0.053 -0.001 0.469 0.724 4.315 - 1.633 -2.747 -0.798 1.806

Standard Error

0.035 0.058 0.021 0.0006 0.347 0.317 0.882 0.501 0.503 0.301 0.424

School location category (base = rural; Northeastern U.S.)

Urban Suburban West North central South

- 1.448 -0.629 0.574 0.790 0.902

0.415 0.343 0.454 0.385 0.396

Proportion of African Americans Proportion of Mexican Americans Proportion in second SES quartile Proportion in third SES quartile Proportion in highest SES quartile

- 1.306 -0.502 2.399 1.195 2.811

0.694 1.041 1.175 1.011 0.784

Constant Adjusted R2 Number of observations

81.499

Student characteristics

1.839 0.168 1002

Source: 1988 survey of the National Education Longitudinal Study (NELS). Excluded from this analysis are 33 schools out of the initial 1035 reported in the NELS; the deleted schools had missing data pertaining to average daily attendance rates or minutes of class per day. Notes: Other variables in this regression not shown include the average of unknown socioeconomic status of students and unknown academic counseling. These variables are included to avoid deleting observations with missing data.

The school term length in minutes has a positive and statistically significant coefficient, indicating that schools with more minutes of classes during the school year have relatively higher quality, as found by Margo (1986). The positive effect of the quality and increased learning opportunities dominates its potential negative effect of the increased opportunity costs of students' time. The negative and statistically significant coefficient on school enrollment supports other research establishing a negative relationship between attendance and school size (Wright, 1976). Moreover, larger schools may have other unobservable negative quality aspects, such as reduced resources per pupil, that affect students' educational demand. Both academic and vocational education counseling have positive coefficients; vocational counseling is statistically significant. This finding agrees with extant research that suggests the lack of educational options to be a negative factor in school attendance (American Association of School Administrators, 1979). The significant coefficient on vocational counseling may indicate that students interested in nonacademic careers have superior information concerning nonacademic education, and are

thus more encouraged to attend school than their peers in schools without such counseling. Schools with structured classroom environments have significantly higher attendance rates, suggesting that order in the classroom relates to the demand for education. This finding coincides with other studies showing that the classroom atmosphere and curriculum affects student absences (American Association of School Administrators, 1979; Aspy and Roebuck, 1977; Education Research Service, 1977; Moos and Moos, 1978; Wright, 1976). The departmentalization of schools also appears to influence students' demand for schooling. Schools with both departmentalized and semi-departmentalized instruction have significantly lower attendance rates than self-contained schools. This finding may ensue from a lack of cohesion among departments of a school or because of instruction quality heterogeneity resulting from departmentalization. Schools requiring a minimum grade-point average for students to compete in student activities have significantly lower attendance rates than schools without the policy. Perhaps students who value nonacademic activities (e.g. sports) and who have difficulties meeting the GPA criteria find undertaking such activities

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Economics of Education Review

away from school preferable to attending class. Moreover, institutions with the minimum GPA requirement are more likely to foster student competitiveness, which has been found to negatively affect attendance (Moos and Moos, 1978; Wright, 1976). Private schools have significantly higher attendance rates than public schools, as expected. Students in private schools may face more pressure on average from households, and are more likely to attend schools with effectual teachers, thus motivating them to attend class (Education Research Service, 1977). That is, teachers in private schools may increase the "value-added" of attending school. Schools located in urban and suburban areas have significantly lower attendance rates. This coincides with the assumption that schools in rural areas may have fewer nonschool activities competing for students' time (e.g. employment opportunities or entertainment alternatives) than nonrural schoolsY Moreover, geographic differences exist between schools' attendance rates, possibly resulting from alternative nonschool activities and attitudes toward education in the different regions. Future research should incorporate such factors as the teenage unemployment rate and diversity of entertainment opportunities within the nonrural vs rural areas and geographic regions to fine-tune this relationship. Unfortunately the NELS does not provide such information. The student characteristics reveal that schools with a high share of African American students have significantly lower attendance rates, while the proportion of Mexican Americans does not appear to be statistically related to attendance rates? ° The difference in statistical significance may result from unmeasured negative quality attributes associated with African American segregated schools. This regression may already account for negative characteristics associated with predominately Mexican American schools, leading to the statistically insignificant coefficient on the Mexican American share. The composition of a school's average student socioeconomic status also appears to affect attendance rates, although this relationship is not strictly monotonic as expected. Nevertheless, schools with a large share of students in the highest SES quartile have the highest attendance rates. These findings support previous research that households, neighborhoods, and peers influence schooling demand (Becker, 1990; Borjas, 1995; Ehrenberg and Brewer, 1994; Rumberger, 1983; Velez, 1989). Moreover, the relationship between attendance and SES composition demonstrates the potential existence of an intergenerational schooling inertia for poor households and neighborhoods. That is, wealthy students living in upper class neighborhoods demand more education than poor students in lower class areas. The higher schooling levels translate into higher future earnings, thus perpetuating the cycle in the following generation. The past decade has witnessed an increase in the returns to higher education,

implying that the earnings gap between the educated and uneducated workers is growing (Bound and Johnson, 1992; Katz and Murphy, 1992; Murphy and Welch, 1989). If these trends continue, each additional cycle will further increase socioeconomic disparity. In sum, the results in Table 1 indicate that educational demand, as measured by attendance rates, is sensitive to schooling quality. Moreover, private schools and schools with large shares of students in the highest SES quartile have better attendance rates than public schools composed of students in the lowest SES quartile. These results were obtained without incorporating a specific proxy for the opportunity costs of attending school. I now employ these results to construct the opportunity-cost proxy given by Equation (6). 4.2. Student Attrition To examine educational demand at the individual level, I estimate Equation (5). The empirical results using the entire sample, as well as the racial/ethnic samples of African American, Mexican American, and non-Hispanic whites, are presented in Table 2. The dependent variable equals one when the student dropped out of school between the 8th grade (1988) and the NELS first follow-up survey (1990). The schooling variables pertain to the 1988 values because some students dropped out before completing the 8th grade. I do not use the second follow-up (1992) survey to observe dropouts because individuals may be exposed to additional factors that distort or diminish the effects of 8th-grade schools. Out of the 19 264 individuals who completed both the 1988 and 1990 NELS surveys, I exclude individuals with unknown dropout status in 1990. Furthermore, I omit individuals with missing data pertaining to the 1988 school's average daily attendance or class-minutes per year. Finally, I retain only those individuals whose self-reported race/ethnicity is Mexican American (including Mexican or Chicano), African American, or non-Hispanic white. In sum, I examine a total of 14 708 students. Observe that the proxy for the opportunity cost of education is positive and statistically significant at the 1% level for all groups except non-Hispanic whites. This finding supports the hypotheses presented in section 2, and indicates that minority students who attended 8th grade in schools with large unexplained attendance rates are more likely to drop out of school. One explanation is that African American and Mexican American individuals who face a relatively high opportunity cost (price) of education appear more likely to drop out of school than their otherwise similar peers. The coefficient on the adjusted EPP on teacher salaries is negative and statistically significant for all groups except for Mexican Americans, indicating that an increase in school expenditures per pupil may decrease the attrition of African Americans and non-

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Table 2. Logit results estimating Equation (5) (dependent variable: dropped out of school between 1988 and 1990) Variable

Entire sample

African American

M e x i c a n Non-Hispanic American White

Unexplained school attendance

0.041 (0.009)

0.113 (0.021 )

0.077 (0.029)

0.0001 (0.013)

-0.083 (0.015) -0.139 (0.027) 0.026 (0.008) 0.022 (0.014) -0.060 (0.154) -0.132 (0.083) -0.384 (0.194) -0.130 (0.246) 0.150 (0.260) 0.183 (0.102) -2.317 (0.384)

0.019 (0.044) -0.182 (0.082) 0.054 (0.019) 0.096 (0.036) -0.526 (0.541 ) 0.057 (0.247) -0.172 (0.440) -0.259 (0.619) -0.953 (0.750) 0.739 (0.340) -2.849 (0.967)

-0.065 (0.047) 0.108 (0.074) 0.006 (0.025) 0.031 (0.040) -0.663 (0.480) -0.487 (0.236) -0.418 (0.796) * 16.130 (0.348) -0.004 (0.370) -0.004 (1.097)

-0.107 (0.018) -0.113 (0.018) 0.001 (0.010) 0.036 (0.019) 0.037 (0.184) -0.205 (0.103) -0.246 (0.245) -0.121 (0.310) 0.196 (0.322) -0.056 (0.120) -1.983 (0.475)

0.587 (0.349) 0.541 (0.364) -3.406 (0.655) - 1.588 (0.445) - 1.070 (0.339)

0.465 (0.371) 0.303 (0.354) 0.520 (0.882) -0.545 (0.929) 1.995 (0.883)

0.410 (0.159) 0.45 l (0.113) 0.838 (0.206) 0.117 (0.158) 0.334 (0.162)

School characteristics

Adjusted pupil-per-teacher ratio Adjusted teacher-salary-expenditures per pupil Minutes of class per year/1000 School enrollment/100 Provides academic counseling Provides vocational ed. counseling Classroom environment is structured 8th-grade instruction is departmentalized 8th-grade instruction is semi-departmentalized Minimum GPA required for school activities Private school

School location category (base = rural; Northeastern U.S.)

Urban Suburban West North central South

0.682 (0.117) 0.421 (0.098) 0.081 (0.167) -0.268 (0.135) 0.213 (0.128)

Table - - continued overleaf

Hispanic whites. The adjusted PPT ratio appears to be significant for non-Hispanic whites, while Mexican Americans and African Americans have statistically insignificant coefficients on this variable. For non-Hispanic whites, the potential benefits associated teacher specialization appear to offset or overshadow the decreased student-teacher interaction associated with large PPT ratios. The length of the school term in minutes appears to significantly increase attrition for the entire sample and African Americans only; the dropout decision of both Mexican Americans and non-Hispanic whites does not appear affected by this variable. For African Americans, the positive coefficient may be interpreted as an increased opportunity cost associated with spending a large amount of time in school. The dropout decision of African Americans and non-Hispanic whites appears to be related to school size, as seen in the statistically significant coefficient on total school enrollment. The Mexican American

sample has a positive but statistically insignificant coefficient on this school size proxy. Mexican Americans and non-Hispanic whites who attended schools in 1988 that offered vocational counseling have significantly lower dropout rates than their peers who attended schools without such counseling. None of the samples have statistically significant coefficients on academic education counseling, although the coefficient is negative for the minority samples. The schooling demand of the entire sample appears to significantly respond to structured classes. As mentioned, an organized school environment has been cited as an ingredient for student achievement (NCES, 1995a, b)." At the same time, none of the ethnic/racial samples reveal statistically significant coefficients on these variables, suggesting the lack of robustness for this measure of schooling quality. The departmentalization of the 8th-grade schools does not appear to affect dropout decisions for the

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Table 2. Continued Variable

Student

Entire sample

Mexican American

Non-Hispanic White

0.994 (0.214) -0.844 (0.326) 0.881 (0.399) 0.285 (0.399) -0.739 (0.340)

1.208 (0.440) 4.341 (0.987) 0.877 (1.186) 0.191 (1.034) -0.702 (1.005)

2.743 (0.826) -2.384 (0.655) 0.366 (1.240) -0.043 (1.230) -0.323 (1.226)

1.243 (0.337) -1.636 (0.544) 0.388 (0.497) -0.852 (0.492) -1.217 (0.419)

- 1.351 (0.105) - 1.626 (0.121) -1.616 (0.140) -0.394 (0.130) 0.432

- 1.573 (0.343) -0.804 (0.290) 0.889 (0.309) ----

- 1.231 (0.329) -2.816 (0.730) *

- 1.387 (0.119) - 1.856 (0.142) -2.690 (0.210) ---

characteristics

Proportion of African Americans Proportion of Mexican Americans Proportion in second SES quartile Proportion in third SES quartile Proportion in highest SES quartile Individual

African American

characteristics

Second SES quartile Third SES quartile Highest SES quartile African American Mexican American

(0.173) Constant

Pseudo-R 2 Chi z Number of observationst

- 1.481 (0.664l 0.138 923.89 14 708

--

-5.453 (1.926) 0.221 241.63 1635

-----

- 16.819 (2.428) 0.224 201.16 1250

---

0.380 (0.836) 0.168 760.33 11 718

Notes: Standard errors are given in parentheses. Other variables in this regression not shown include unknown socioeconomic status and unknown academic counseling in the 1988 school. The variables accounting for missing data are included in the analyses to avoid deleting additional observations with missing data; *There are too few observations with this characteristic to be included in the logit analysis; tThe sample sizes in the following columns may not add up to the entire sample because some observations were dropped in the logit analyses when the combination of the variables predict failure perfectly. Source: 1988 and 1990 surveys of the National Education Longitudinal Study (NELS). Excluded from this analysis are individuals with missing data pertaining to school average daily attendance rates, minutes of class per year, or dropout status by 1990. See text for any additional sample restrictions.

samples, except for the effect of semi-departmentalization on M e x i c a n A m e r i c a n students. The inflated coefficient mostly likely results from the fact that the share of Mexican A m e r i c a n s in schools offering this type of departmentalization is relatively small. The entire and African A m e r i c a n samples have positive and statistically significant coefficients on whether the 8th-grade school required a m i n i m u m G P A for school activities. As such, it appears that the existence of a m i n i m u m G P A is associated with lower schooling d e m a n d by African Americans, possibly resulting from increased student competitiveness and higher opportunity costs. The coefficient on the private school variable is negative and statistically significant for all samples except Mexican Americans. Interestingly, Mexican A m e r i c a n s in private schools do not appear to have significantly different dropout rates than those students enrolled in public schools. This finding indicates that private school heterogeneity may exist for Mexican A m e r i c a n students, The remaining groups who attended private schools in the 8th grade appear

more likely to remain in school than their peers enrolled in public schools. With the exception o f Mexican Americans, the school location variables also appear significantly related to dropout behavior. African Americans and non-Hispanic whites living in rural areas have significantly lower dropout rates than their peers living in urban or suburban areas. As mentioned, urbanized areas may offer more e m p l o y m e n t and entertainment opportunities, increasing the likelihood that a student drops out of school. In terms of geography, African Americans and non-Hispanic whites living in the Western United States have a larger propensity to drop out than individuals in other regions. The s c h o o l ' s student characteristics also appear to affect dropout rates, implying a role of neighborhoods in an individual's schooling d e m a n d function. All samples reveal the larger the share of African Americans, the larger the likelihood of dropping out. The African A m e r i c a n share may reflect unmeasurable low school quality; recall that extant research suggests that relatively segregated schools tend to be

Attendance, Schooling Quality, and Demand overcrowded and underfinanced. Conversely, the entire, Mexican American, and non-Hispanic white samples reveal that the larger the share of Mexican Americans, the smaller the likelihood of dropping out. It is interesting to note that the share of Mexican American students relates to a relatively higher dropout probability for African Americans. Note that the share of students in the highest SES quartile negatively relates to dropout behavior. That is, students who attended schools with peers in relatively high SES positions were more likely to remain in school. This finding reflects a neighborhood or peer effect, where students who associate with upper class individuals "pick up" the positive attitude or desire to stay in school. All of the individual socioeconomic status categorical variables have statistically significant coefficients for the samples, although the highest SES quartile has an unexpected sign in the African American sample. The Mexican American and non-Hispanic white samples reveal that the likelihood of dropping out monotonically decreases with household SES. This finding concurs with previous findings that household SES influences youths' demand for education (Becket, 1990; Ehrenberg and Brewer, 1994; Rumberger, 1983; Velez, 1989). The African American sample reveals that individuals in the highest SES quartile have relatively higher dropout rates than their peers in lower quartiles; the reader should realize, however, that less than 11.5% of the entire African American sample falls into this SES quartile. In the entire sample, the individual ethnic categorical variables indicate that Mexican Americans are more likely, and African Americans are less likely, to drop out of school as their non-Hispanic white peers, ceteris paribus. The negative coefficient on the African American variable does not conform to previous findings; extant research has found that African Americans tend to be more likely to drop out of school than non-Hispanic whites. The results presented here were obtained by holding other factors constant, the reader should interpret these findings as such. For example, recall that the signs on the shares of the racial/ethnic minorities are opposite to these individual racial/ethnic coefficients. In all, this section provides empirical evidence that a student's demand for education as measured by student attrition is significantly related to unexplained schooling attendance, which includes opportunity costs of schooling (the price). In addition, school quality, households, peers, and neighborhoods appear to play a role in student educational demand. All of these effects should be considered when designing educational policy to increase schooling attainment. 5. C O N C L U D I N G R E M A R K S Mexican Americans (and Hispanics in general) and African Americans consistently attain less education on average than non-Hispanic whites. Given that edu-

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cation has become more valuable in the labor market (Bound and Johnson, 1992; Katz and Murphy, 1992; Murphy and Welch, 1989), and that African Americans and Hispanics are experiencing relatively high population growths (Fullerton, 1991; Valencia, 1991), research investigating educational demand for these groups is becoming increasingly important to facilitate economic equality convergence across racial and ethnic boundaries. This paper attempts to provide insight into the demand for education, as measured by school attendance rates and attrition for African Americans, Mexican Americans, and non-Hispanic whites. The whole picture of schooling demand, however, is far from being complete. The results provided here indicate that schools with high educational quality have relatively large average daily attendance rates. Moreover, minority students who attended schools with unexplained attendance were more likely to drop out of school. One interpretation of this particular finding is that Mexican American and African American students facing higher costs appear more likely to drop out of school between the 8th and 10th grades. Household socioeconomic status and neighborhoods also relate to students' educational demand. In fact, the extent to which neighborhoods influence school quality through voting and taxes suggests there are both direct and indirect effects of peers and neighborhoods on an individual's demand for education. As such, the findings presented here can be extrapolated into a neighborhood or public schooling demand framework. Future research should address this issue in more detail. The issues of school choice and the educational voucher system may be vehicles to increase schooling attainment in this country. Basically, students could select schools that provide the optimal bundle of education best suited to their preferences. The increased competitiveness resulting from school choice and vouchers should increase school quality overall. According to my results, the quality increase should positively affect students' educational demand. Moreover, the influence of households and neighborhoods suggests the presence of intergenerational inertia of schooling demand. Individuals from wealthy households in upper class neighborhoods appear more likely to demand higher levels of schooling than their peers from poor households in lower class areas. Because education has become more valuable in the labor market over the past decade due to a relative increase in the demand for skilled labor, the earnings gap between the higher educated and lower educated workers has been increasing (Bounc[ and Johnson, 1992; Katz and Murphy, 1992; Murphy and Welch, 1989). Essentially, workers with higher education are becoming wealthier and workers with low levels of education are becoming relatively poorer. Future generations will most likely follow the same pattern, such that the intergenerational schooling demand creates a

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cycle where the relative socioeconomic status o f the wealthy vs the poor diverges over time. Educational policy reforms may be able to nullify this intergenerational schooling demand cycle. Policymakers and school officials have direct control over schooling quality, and indirect control over students' opportunity costs (through student subsidies and increased schooling quality). If the labor market

demand for skilled labor continues to increase, these measures may be used as vehicles to increase the educational attainment and hence the future earnings o f minorities and individuals from low income households.

Aeknowledgements--I thank Alberto Dfivila, Stephen A. Hoenack, and two anonymous reviewers for their helpful and insightful comments. Any error in this paper is my own.

NOTES 1. I assume that students, rather than their parents, maximize utility of attending school because I will examine 8th-graders who presumably have enough independence to control their own attendance. Orazem (1987) examined elementary school students; younger students' attendance most likely depends on parental preferences. 2. The reader is cautioned that information presented for Hispanics may not necessarily be the same for Mexican Americans. However, Mexican Americans represented almost two-thirds of the Hispanic population in the United States during the 1980s, and comprise one of the fastest growing ethnic groups (Cattan, 1993). Hence, many educational and labor market trends observed for Hispanics reflect the trends for Mexican Americans. In terms of education, Mexican Americans experienced the highest dropout rates out of the Hispanic subgroups in the early 1990s (Cattan, 1993). 3. For example, 13.6% of all African Americans and 35.3% of all Hispanics age 16-24 yrs were high school dropouts in 1991, compared to 8.9% of non-Hispanic whites (NCES, 1993, Table 101). 4. Households, peers, and neighborhoods have been found to affect human capital acquisition (Borjas, 1995). I include average student characteristics within a school to proxy for peer and neighborhood effects under the assumption that schools reflect the composition of the surrounding neighborhoods. Although this assumption may not always hold in the case of mainstream busing, this measure at least accounts for the average student that individual i will encounter at school. 5. The extent to which PPT reveals schooling quality has been an unresolved debate ( Hanushek, 1986; Harbison and Hanushek, 1992). In a review pertaining to educational quality in the United States, Harbison and Hanushek (1992) find that out of 152 studies examining PPT, 59 infer positive effects, 48 conclude negative effects, and 45 studies were undetermined (p. 18, Table 2-1). Harbison and Hanushek, however, do not categorize these studies by educational level, which might yield more definitive conclusions. Recent evidence proposes that secondary schools with relatively large PPT-ratios have higher quality because larger schools allow more teacher specialization, whereas primary schools with relatively large PPT ratios have lower quality because of the reduced interaction between teachers and students for the transmission of basic skills (D~ivila and Allahdad, 1996). 6. Other criticisms of using PPT as a quality proxy include the possibility that equalization efforts in the United States may have standardized these ratios across schools over time. However, a quick perusal of the 1988 NELS data suggests variability in the PPT ratio exists, particularly when adjusted by the attendance rate. Another reservation placed on this proxy results from the fact that schools with teacherintensive programs (such as special education) may have low PPT ratios independent of educational quality. The reader should keep this criticism in mind. 7. NELS provides the SES quartiles, which account for parental education, income, and occupational status. The SES quartiles may be viewed as an overall summary of the effect of households on educational demand. Although the use of SES over individual measures of parental education, income, and occupation may restrict some broader comparisons between my study and others in the literature, the use of the three specific household measures introduces severe multicollinearity into the analyses, such that the effects of each individual variable may be difficult to interpret. Other studies have also used SES instead of specific parental attributes (Velez, 1989). 8. The NELS provides weights to be used in analyses that adjust for differential selection probabilities and response rates, such that the cross-sectional and longitudinal samples are nationally representative. All empirical analyses conducted here use these weights as analytic weights. 9. One could argue that rural students are more likely to face high commuting costs (i.e. riding the school bus) relative to nonrural students. In this case, the opportunity cost of attending rural schools is larger than attending nonrural schools; hence, rural students would have a lower educational demand than nonrural individuals. At the same time, the knowledge that the school bus will go out of its way to pick up a rural student may encourage attendance because the alternative involves facing an unhappy bus driver and passengers when the individual rides the bus the following day. 10. In an additional estimation of Equation (3) (not shown), I included the proportions of Other Hispanics, Native Americans, and Asian/Pacific Islanders; none of these variables were statistically different from zero. REFERENCES American Association of School Administrators (1979) Keeping Students in School: Problems and Solutions. Education News Services, Sacramento. Aspy, D. N. and Roebuck, F. N. (1977) Kids Don't Learn from People They Don't Like. Human Resource Development Press, Massachusetts.

Attendance, Schooling Quality, and D e m a n d Becker, W. E. (1990) The demand for higher education. In The Economics o f American Universities: Management, Operations, and Fiscal Environment, eds. S. A. Hoenack and E. L. Collins, pp. 155188. State University of New York Press, Albany. Borjas, G. J. (1995) Ethnicity, neighborhoods, and human-capital externalities. American Economic Review 85, 365-390. Bound, J. and Johnson, G. (1992) Changes in the structure of wages during the 1980s: An evaluation of alternative explanations. American Economic Review 82, 371-392. Cattan, P. (1993) The diversity of Hispanics in the U.S. work force. Monthly Labor Review 116, 3-15. D~ivila, A. and Allahdad, M. (1996) Home country schooling quality and immigrant earnings. Unpublished Manuscript, University of New Mexico (March). Donato, R., Menchaca, M. and Valencia, R. R. (1991) Segregation, desegregation, and integration of Chicano students: Problems and prospects. In Chicano School Failure and Success: Research and Policy Agendas for the 1990s, ed. R. R. Valencia, pp. 2 7 ~ 3 . The Falmer Press, New York. Education Research Service (1977) Student Absenteeism. Education Research Service, Arlington VA. Ehrenberg, R. G. and Brewer, D. J. (1994) Do school and teacher characteristics matter? Evidence from high school and beyond. Economics o f Education Review 13, 1-17. Fullerton, H. N. (1991) Labor force projections: The baby boom moves on. Monthly Labor Review 114, 31-44. Hanushek, E. A. (1986) The economics of schooling: Production and efficiency in public schools. Journal of Economic Literature 24, 1141-1177. Harbison, R. W. and Hanushek, E. A. (1992) Educational Performance of the Poor: Lessons from Rural Northeast Brazil. Oxford University Press, Washington, DC. Katz, L. F. and Murphy, K. M. (1992) Changes in relative wages, 1963-1987: Supply and demand factors. Quarterly Journal of Economics 107, 35-78. Moos, R. H. and Moos, B. S. (1978) Classroom social climate and student absences and grades. Journal of Educational Psychology 70, 263-269. Margo, R. A. (1986) Educational achievement in segregated school systems: The effects of separate-butequal. American Economic Review 76, 794-801. McManus, W. S., Gould, W. and Welch, F. (1983) Earnings of Hispanic men: The role of English language proficiency. Journal of Labor Economics 1(2), 101-130. Mora, M. T. (1996) English proficiency, bilingual education, and the earnings of Hispanic Americans. Unpublished Ph.D. dissertation, Texas A and M University. Murphy, K. M. and Welch, F. (1989) Wage premiums for college graduates: Recent growth and possible explanations. Educational Researcher, 18, 17-26. National Center for Education Statistics (NCES) (1993) Digest o f Education Statistics. U.S. Government Printing Office, Washington, DC. National Center for Education Statistics (NCES) (1995a). The Educational Progress of Black Students: Findings from the Condition of Education 1994. U.S. Department of Education, Office of Educational Research and Improvement, Washington, DC. National Center for Education Statistics (NCES) (1995b). The Educational Progress of Hispanic Students: Findings from the Condition o f Education 1994. U.S. Department of Education, Office of Educational Research and Improvement, Washington, DC. National Center for Education Statistics (NCES) (1995c). Disparities in Public School Spending 198990. U.S. Department of Education, Office of Educational Research and Improvement, Washington, DC. Orazem, P. F. (1987) Black-white differences in schooling investment and human capital production in segregated schools. American Economic Review 77, 714-723. Rumberger, R. W. (1983) Dropping out of high school: The influence of race, sex, and family backgroun d. American Educational Research Journal 20, 199-220. Smith, J. P. and Welch, F. R. (1989) Black economic progress after Myrdal. Journal of Economic Literature 27, 519-564. Thomas, G. E. and Hirsch, D. J. (1989) Blacks. In Shaping Higher Education's Future: Demographic Realities and Opportunities, 1990-2000, ed. A. Levine, pp. 62-86. Jossey-Bass, San Francisco. Valencia, R. R. (1991 ) The plight of Chicano students: An overview of schooling conditions and outcomes. In Chicano School Failure and Success: Research and Policy Agendas for the 1990s, ed. R. R. Valencia, pp. 3-26. The Falmer Press, New York. Velez, W. (1989) High school attrition among Hispanic and non-Hispanic youth. Sociology of Education 62, 119-133. Welch, F. (1966) Measurement of the quality of schooling. American Economic Review 56, 379-392. Wright, J. S. (1976) Factors in school attendance. Phi Delta Kappan 58, 358-359.

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Economics of Education Review APPENDIX

A

Construction of selected variables Variable

Construction

Unexplained school attendance (Proxy for opportunity costs)

-[ADAj-(estimated ADA)], where the estimated ADA is the predicted value from estimating Equation (3); see Equation (6) for theoretical detail.

School characteristics Adjusted PPT ratio Adjusted teacher-salary-expenditures per pupil*

Private school

(Pupil-per-teacher ratio)xADA [(Beginning teacher salary deflated by average beginning teacher salary in geographic region)x(number of teachers)+(student enrollment)]. (Minutes in each class)x(totai number of 8th-grade classes per day)x(estimated number of days in school year). = 174 if in category "130-174"; = 175 if in category "175"; = 178 if in category "176-179"; = 180 if in category "180"; = 181 if in category "181+". Mean of enrollment categories in NELS. = 1 if school offers academic counseling; = 0 otherwise. = 1 if school offers voc. ed. counseling; = 0 otherwise. = 1 if administrator responds "3", "4", or "5" in school survey (5= "very much accurate"; 1= "not at all accurate"); = 0 otherwise. = 1 if major school organization for 8th-graders is departmentalized; = 0 otherwise. = 1 if major school organization for 8th-graders is semidepartmentalized; = 0 otherwise. = 1 if school requires students to maintain a minimum GPA to participate in school activities; = 0 otherwise. = 1 if private school; = 0 otherwise.

School location category Urban Suburban

= 1 if school located in urban area; = 0 otherwise. = 1 if school located in suburban area; = 0 otherwise.

Minutes of class per year* Estimated number of days in school year Student enrollment* Academic counseling Vocational ed. counseling Classroom environment is structured 8th-grade instruction is departmentalized 8th-grade instruction is semi-departmentahzed Minimum GPA required for school activities

* The NELS reports many variables in categories rather than continuously; these variables are estimated using the mean values of the categories.