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Student Poverty, School Accountability, andPostsecondary Enrollment: A Challenge for EducationalReform in KentuckyDerek V. Price a & Edward B. Reeves ba Lumina Foundation for Education , Indianapolis, INb Center for Educational Research and Leadership, Morehead State UniversityPublished online: 17 Oct 2008.
To cite this article: Derek V. Price & Edward B. Reeves (2003) Student Poverty, School Accountability, and PostsecondaryEnrollment: A Challenge for Educational Reform in Kentucky, Journal of Poverty, 7:4, 21-35, DOI: 10.1300/J134v07n04_02
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Student Poverty, School Accountability,and Postsecondary Enrollment:
A Challenge for Educational Reformin Kentucky
Derek V. PriceEdward B. Reeves
ABSTRACT. The Kentucky Educational Reform Act of 1990 promisedimproved academic performance by public schools and greater equity ineducational outcomes. The present study relates postsecondary enrollmentto school accountability-testing and student poverty. It concludes thatnearly a decade after reform began, high schools with large numbers ofpoor students send a smaller proportion of their graduates to college thando high schools with fewer poor students. Further, impoverished highschools that manage to perform well on the accountability tests lag wellbehind like-performing affluent schools in postsecondary enrollment. The
Derek V. Price is Director of Higher Education Research at Lumina Foundation forEducation in Indianapolis, IN.
Edward B. Reeves is Professor of Sociology and Director of the Center for Educa-tional Research and Leadership at Morehead State University.
The authors thank Jill Wohlford, Julie Singer and Hui-Wen Lee for providing re-search assistance. They offer a special thank you to David Powell for his editorial ex-pertise, and to three anonymous reviewers for their helpful suggestions.
An earlier version of this paper, with a different title, was presented at the AnnualMeeting of the American Educational Research Association, New Orleans, LA, inApril 2002.
The content of this article does not necessarily represent the official position orviews of all Lumina Foundation employees or members of its board of directors.
Journal of Poverty, Vol. 7(4) 2003http://www.haworthpress.com/store/product.asp?sku=J134
2003 by The Haworth Press, Inc. All rights reserved.10.1300/J134v07n04_02 21
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harmful effects of poverty on education are felt throughout the state andundercut the goals that are embraced by Kentucky educational reform.[Article copies available for a fee from The Haworth Document Delivery Ser-vice: 1-800-HAWORTH. E-mail address: <[email protected]>Website: <http://www.HaworthPress.com> © 2003 by The Haworth Press, Inc.All rights reserved.]
KEYWORDS. Student poverty, educational reform, school account-ability, postsecondary enrollment
INTRODUCTION
During the past decade, the nationwide movement to achieve greater ac-countability in public education through the implementation of state-mandatedtesting and assessment programs has expanded (Ladd, 1996; Federal ReserveBank of New York, 1998). Many states, including Kentucky (Guskey, 1994),have placed a high priority on rewarding schools that boosted the academicachievement of their students. Recently, the federal government provided fur-ther impetus to this accountability movement. The 2001 Reauthorization of theElementary and Secondary Education Act mandates annual testing for studentsin grades 3-8. Despite the praise heaped on the movement’s philosophy andgoals, judging schools according to standardized measures of student perfor-mance runs counter to the findings of empirical studies that have shown schoolshave a relatively small influence on students’ acquisition of cognitive skills.
The much celebrated, and criticized, “Coleman Report” (Coleman et al.,1966) was the first study to explore this issue, and numerous other studies fol-lowed in its wake (for an overview, see Gamoran, 2001). Achievement testscores were shown to be influenced, not by schools, but by the social, cultural,and financial capital possessed, first, by students’ families (Conley, 2001; Is-rael, Beaulieu, & Hartless, 2001; Schneider & Coleman, 1993), and, second,by the communities where the students and schools were located (Garner &Raudenbush, 1991; Israel, Beaulieu, & Hartless, 2001; Reeves, 2000; Roscigno &Crowley, 2001). These studies and many similar ones call into questionwhether schools have the capacity to substantially increase the academicachievement of their students independent of the influences of family andcommunity.
A more critical approach informed by Bowles and Gintis (1976) andMichelson and Carnoy (1975) suggests that schooling in capitalist societies func-tions to sort students according to their social backgrounds; thus schools do not
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promote achievement and meritocracy for all students. Karen’s (2002) recentanalysis of college enrollment patterns through the early 1990s found persis-tent direct effects of family income, race and gender, even after controlling foracademic factors. Because inequities in college access according to ascriptivecharacteristics of students remain, his data undermine arguments that schoolsequalize opportunities for people of color, women and low-income families.
The prevailing view of school accountability assumes a close link betweenthe accountability criteria and career outcomes in adult life. However, regard-less of how students acquire their cognitive skills (whether through school orthrough family and community influences), these skills do not appear to be theprimary key to occupational success. Recent research has found that the totalyears of schooling, or “seat time,” educational credentials, sociability and will-ingness to accept a work regimen are all better predictors of occupational suc-cess than cognitive skills (Bowles & Gintis, 2002; Kerckhoff, Raudenbush, &Glennie, 2001).
Nevertheless, cognitive skills learned in primary and secondary school mayhave an indirect relationship to occupational outcomes. They influence thelikelihood of continued schooling beyond high school and the acquisition ofpostsecondary credentials. Therefore, accountability testing of high schoolsmay be justified if:
a. the results are not significantly compromised by factors over whichschool personnel have little control, such as the characteristics and com-position of the student body, and
b. the results are used to alter policies and practices and thereby redress theinequities in postsecondary enrollment.
This study examines school characteristics that predict postsecondary en-rollment rates of high school graduates in Kentucky. Unlike the recent studyby Beattie (2002) that explores the factors that contribute to an individual’s op-portunity to attend college, our analysis uses aggregated school-level data.This approach is complementary to Beattie’s but is specifically intended tomake a contribution to the debate about the design of school accountabilitysystems (Ladd, 1996). In order to provide an empirical basis to inform discus-sion about the benefits of educational reform in Kentucky, this paper answersfive important questions for educators and policy makers:
1. Controlling for percent minority, what is the effect of school poverty onthe postsecondary enrollment rate of high schools?
2. What is the effect on postsecondary enrollment rates if the student bodyis high-percent minority as well as poor?
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3. Are low postsecondary enrollment rates confined to particular geo-graphic localities?
4. Does the school accountability test score predict the postsecondary en-rollment rate, and what is the effect of the dropout rate?
5. What is the effect on the postsecondary enrollment rate of schools thatare poor and that have high test scores, or high dropout rates?
BACKGROUND: EDUCATIONAL REFORM IN KENTUCKY
The Kentucky Education Reform Act (KERA) of 1990 was intended to cre-ate “equal educational opportunities for all of Kentucky’s children regardlessof the property wealth of the district in which they lived.” One catalyst for thislegislation was the smaller number of college-educated Kentucky residentscompared to the nation at large. According to the 1990 U.S. Census, 13.6 per-cent of Kentucky’s population 25 years and older had at least a bachelor’s de-gree. This figure compared poorly with the national average of 20 percent in1990 and was also lower than several bordering states, including Indiana, Ten-nessee, Ohio, and Virginia. The Current Population Survey from the U.S. Cen-sus provides an estimate that 20 percent of Kentuckians 25 years and older hadat least a bachelor’s degree in 1998. By this measure, the percentage of Ken-tucky residents with at least a college degree improved 6 percentage points be-tween 1990 and 1998, which is important because the Kentucky Council onPostsecondary Education has publicly stated its goal to increase college enroll-ment by 80,000 individuals over the next two decades. However, it remainsunclear if these improvements are shared across all demographic groups.
The National Center for Public Policy and Higher Education (2000) gaveKentucky a “C” in preparation for college and a “D” in participation, whichfurther underscores the importance of investigating the progress of educationalreform in Kentucky. More recently, a report issued by Lumina Foundation forEducation, Unequal opportunity: Disparities in college access among the 50states (Kipp, Price, & Wohlford, 2002), found that all of Kentucky’s publictwo- and four-year colleges and universities were affordable to low- and me-dian-income dependent students, although most four-year colleges requiredthese students to borrow to attend. Thus the price of college is not necessarily abarrier for low-income students who graduate from Kentucky high schools.
During the last decade, the implementation of KERA focused on improvingthe Accountability Indices intended to measure academic preparedness. Theseindices have been shown to vary considerably according to student povertyand geographic location (Reeves, 2000). In fact, the difference of accountabil-ity scores between advantaged and disadvantaged Kentucky schools increased
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dramatically from 1993 to 1999 (Roeder, 2000). Economic inequality isstrongly marked in Kentucky and has a clear geographic component. In1989-90, per pupil property wealth ranged from more than $38,000 inMcCreary County to almost $343,000 in Fayette County. That is, the poorestdistrict had per pupil property wealth equal to 11 percent of the richest districtbefore KERA legislation. In 1999-2000, the comparable ratio improved to 16percent (Office of Educational Accountability, 2000). Similarly, the medianincome of the most rural counties (i.e., counties whose largest town has a pop-ulation less than 2,500) barely reached half of the median income that is foundin metropolitan counties (Reeves, 2000).
Standardized test scores seemed to confirm the underachievement of Ken-tucky students. In 1989-90, the composite SAT scores of Kentucky test-takerswere almost 10 percent lower than the national average, and ACT scores inEnglish, math, reading, and science were at or below the national average. In1992, Grades 4 and 8 National Assessment of Educational Progress (NAEP)math scores in Kentucky ranked 23rd and 21st, respectively, and had declinedfurther by 1996. Since 1986-87, Kentucky public high school graduation rateshave remained lower than the national average. The data from the KentuckyDepartment of Education from 1990-91 show that 71 percent of 9th gradersgraduated from high school within four years; however, by 1998-99, only 65.9percent of 9th graders graduated from high school within four years. In addi-tion to this declining high school graduation rate, Innes (2000) has cautionedthat a larger number of Kentucky students are dropping out of high school.This concern is difficult to test because the Kentucky Department of Educationchanged the formula used to calculate the dropout rate three times between1990-91 and 1998-99. Still, it raises the troubling possibility that increasedpostsecondary enrollment may come at the expense of more students droppingout of high school.
DATA SOURCES AND VARIABLE DEFINITIONS
The data for all of the variables described below, except for geographic lo-cation, were acquired from the Kentucky Department of Education. Descrip-tive statistics for the variables used in this study are shown in Table 1.School-level variables were used exclusively; and, unless otherwise indicated,all data were for the 1998 school year. The dependent variable is the percent ofstudents enrolling in any kind of postsecondary educational institution. Thestatewide mean for this variable is 55.5 percent with a range between 14.1 and95.9 percent. There are eight independent variables. The percent of studentsenrolled in the free or reduced lunch program is a common proxy for school
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poverty level (see, for instance, Weston, 2000) and was used in this study. Themean percentage of students participating in the free or reduced lunch programis 38.2 with a range between 2.3 and 84.7 percent.
The percent of minority students was chosen to determine if this exogenouscharacteristic of schools also affected postsecondary enrollment. In Kentucky,minority students are overwhelmingly African-American. Thus, as a practicalmatter, this variable can be used to test the effect of the proportion of Afri-can-Americans in the student body. In our sample, the proportion of minoritystudents in high school averaged 8.8 percent, and ranged between 0 and 57.5percent. The variable’s large standard deviation of 12.2 indicates a non-normaldistribution. The majority of schools had very few, if any, minority students. Inpreliminary regression analyses, we attempted to correct the non-normality ofthis variable using various transformations. The results of these trials differedlittle from those obtained with the untransformed variable. Therefore, we pre-sent the regression results in this paper with the percentage minority variableleft untransformed.
The geographic locations of schools are indicated by their rural-metropoli-tan differences. We wanted to include variables denoting geographic locationin order to control for the fact that the distribution of student poverty is spa-tially skewed. For our school locale variables, we used a modification of theUrban Influence Codes that were developed by the Economic Research Ser-vice, U.S. Department of Agriculture. This set of scaled geographic categorieshas been used previously in research on school outcomes in Kentucky(Reeves, 2000). The reference category for the effects of geographic locationis “metropolitan area.” The means for the dummy variables denoting geo-
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Table 1. Descriptive Statistics for Variables Used in the Study
Variable Mean Std. Dev. Min. Max.
% postsecondary enrollment 55.5 15.5 14.1 95.9
% free/reduced lunch 38.2 18.3 2.3 84.7
% minority students 8.8 12.2 0.0 57.5
Math accountability index 65.4 12.3 36.4 110.4
Dropout rate (1997) 4.5 2.4 0.0 13.6
Metro (reference category) 0.34 – 0 1
Next to metro area 0.19 – 0 1
Rural: Town < 2,500 0.13 – 0 1
Rural: Town 2,500-9,999 0.24 – 0 1
Rural: Town � 10,000 0.10 – 0 1
N = 233 high schools
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graphic location shown in Table 1 reveal the percentage of cases in the sampleof 233 high schools.
The accountability tests used in Kentucky yield school performance scoresin a variety of subject areas, including reading, writing, mathematics, scienceand social studies. The scores are strongly interrelated (generally speaking, r >0.90), but the Math Accountability Index score was used because it is most in-dicative of preparation for postsecondary education: Research shows that mathability is the lead indicator of cognitive skill development in high school(Lucas, 1999). In order to control for the effect that the dropout rate may haveon the postsecondary enrollment rate, data on school dropouts from the 1997school year were used.
METHODOLOGY
The analysis took the form of a series of block entry regression models thatwere used to determine the cumulative effect of adding various groups of inde-pendent variables. To reduce the likelihood of multicollinearity in the regres-sion equations that have interaction terms, all of the continuous variablesshown in Table 1 were centered prior to performing the multivariate analyses.The independent variables were entered into the regression analyses in fivestages in order to ascertain their significance and relative importance. Diag-nostics were examined for each of the regression models, and in no instancewas multicollinearity found to be problematic.
The first regression model (Model 1) examined the results of predicting thepercentage of postsecondary enrollment with two exogenous variables: Theproportion of students participating in the free or reduced lunch program andthe percentage of minority students. The second regression model (Model 2)examined the interaction of these measures of school poverty and racial diver-sity. Model 3 tested whether the preceding findings were confounded by geo-graphic location. The fourth regression model (Model 4) examined the effectsof school accountability test scores and high school dropout rates. The final re-gression model (Model 5) represents the full model and includes the interac-tions between school accountability test scores and school poverty.
RESULTS
The results of the regression analyses are presented in Table 2. Model 1found that school poverty and racial diversity explained 18 percent of the totalvariance in postsecondary enrollments. High schools with higher proportions
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of students in poverty enrolled a smaller proportion of graduates inpostsecondary institutions. By contrast, having a large percentage of minoritystudents in the school had a positive, but statistically nonsignificant effect onpostsecondary enrollment in this model. When the interaction effect betweenthese two variables was added (see Model 2), an additional 8 percent of varia-tion was explained. The results of this model show that in schools having acombination of higher proportions of minority students and more students inpoverty, postsecondary enrollment rates were reduced dramatically. Net ofthis interaction effect, high-minority schools showed a somewhat higher thanaverage enrollment rate, whereas high poverty schools continued to show alower enrollment rate. The extent of school poverty and the percentage of mi-nority students, along with their interaction, explained 26 percent of the totalvariance in 1998 Kentucky postsecondary enrollment rates.
While the proportion of students participating in the free or reduced lunchprogram is skewed toward the more rural locations, the percentage of minoritystudents is skewed toward metropolitan locations as well as rural localitieswhere the town has 10,000 inhabitants or more (see the Appendix). We addedmeasures of geographic locale to test for possible spatial effects of school pov-erty and racial diversity (Model 3), but geographic location explained only 2percent of the total variance in postsecondary enrollments. In fact, there was
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TABLE 2. Between-School Effects on 1998 Kentucky Postsecondary Enroll-ment Rates
Independent Variable Model 1 Model 2 Model 3 Model 4 Model 5
% free/reduced lunch �.425*** �.483*** �.546*** �.233** �.304**
% minority students .085 .193** .219** .206** .204**
Free/reduced lunch × Minority �.307*** �.297*** �.203** �.237***
Next to metro area �.088 �.092 �.045
Rural: Town < 2,500 .049 .025 .052
Rural: Town 2,500-9,999 .156* .150* .185**
Rural: Town � 10,000 .050 .015 .047
Math accountability index .389*** .320***
Dropout rate �.050 �.087
Free/reduced lunch × Math �.125*
Free/reduced lunch × Dropout .117
Adjusted R² .182 .259 .282 .355 .388
Adjusted R² .182*** .077*** .023* .073*** .033**
N = 233
Standardized coefficients: ~p < .10; *p < .05; **p < .01; ***p < .001.
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no significant difference in the enrollment rates across four of the five geo-graphic categories. It is only in rural counties where the largest town has a pop-ulation between 2,500 and 9,999 inhabitants that high schools had a higherthan average postsecondary enrollment rate. Importantly, the effects of schoolpoverty and percentage of minority students increased when geographic loca-tion was controlled, while the interaction effect of the two variables declinedonly slightly. The exogenous effects of school poverty and the percentage ofminority students on postsecondary enrollment are not explainable by spatialskewing at all. Instead, the geographic locations of high schools have theirown separate and quite modest effects on enrollment rates. (For insight into thegeographic effects, see Marino, 1995; McGrath et al., 2001.)
The addition of the Math Accountability Index and dropout rate to the re-gression equation (Model 4) raised the explained total variance in enrollmentrates to nearly 36 percent, an increase of 7 percentage points over the previousmodel. Whereas the math score had a strong positive influence on the enroll-ment rate, the effect of the dropout rate was not significant, although its influ-ence tilted toward the negative. Another noteworthy result of this model is thatthe school poverty coefficient was reduced by greater than 50 percent. This re-sult clearly signals the common variance explained between the school pov-erty measure (the proportion of students participating in the free or reducedlunch program) and the Math Accountability Index score, with possible in-volvement of the dropout rate. To decompose the common variance explainedby these variables, their interaction effects were examined in Model 5.
The final model explained nearly 39 percent of the variance in 1998 Ken-tucky postsecondary enrollment rates. We found that high schools with ele-vated Math Accountability Index scores had lower postsecondary enrollmentrates if the school poverty level was also elevated. Once the shared variance ofthis interaction was controlled, the negative main effect of school poverty wasstrengthened, while the positive main effect of the Math Accountability Indexdeclined. The interaction of the proportion of students participating in the freeor reduced lunch program and the dropout rate was positively related to enroll-ment although the significance level was marginal.
DISCUSSION
The findings of this study impart the salience of exogenous factors in influ-encing the rate at which high school graduates enroll in postsecondary institu-tions. These findings should not be construed to mean that within-schoolefforts such as guidance counseling and creative interventions do not makesome difference (see, for example, Smith-Mello, 2002). But policymakers
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cannot ignore the negative influence of concentrated poverty in high schoolsand other factors over which schools have little control. The findings from thisresearch demonstrate the powerful impact school poverty has on post-secondary opportunity for Kentucky high school graduates. School poverty in-hibits postsecondary enrollment even after controlling for geographic locationand accountability test scores. For instance, we extrapolate from Model 5 thata one standard deviation increase in the percentage of students on free and re-duced lunch lowers postsecondary enrollment rates by nearly 5 percentagepoints.
This study also shows that larger minority student populations and greatermath accountability scores are associated with higher enrollment rates, espe-cially when the schools have fewer students on free or reduced lunch. Extrapo-lating in a similar manner from Model 5, we find that a one standard deviationincrease in the percentage of minority students would result in a 3 percentagepoint increase in enrollment rates, so long as this increased minority enroll-ment was not concentrated in high-poverty schools. The explanation for thiseffect does not appear to be that high-minority populations in Kentucky areconcentrated in metropolitan areas that could be said to offer greater culturalcapital resources or higher education access, because the effects of geographiclocation have been controlled in this model. This finding might be the conse-quence of improved achievement for minority students who attend diverseschools (see Entwisle & Alexander, 1992); however, this conclusion is onlyspeculative. In any case, the advantage that a school with higher minority en-rollment may have is negated when the school is poor. Model 5 informs us thata one standard deviation increase in the percentage of minority students ac-companied by a one standard deviation increase in school poverty is associatedwith nearly a 4 percent decline in postsecondary enrollment.
Kentucky’s Math Accountability Index does predict postsecondary enroll-ment, but its usefulness is seriously compromised when the high school has alarge proportion of poor students. Holding student poverty constant, increas-ing the math score by 12 points would increase the postsecondary enrollmentrate by 5 percentage points. However, when the school is also poorer–say, forinstance, the proportion of students on free or reduced lunch is increased byone standard deviation–postsecondary enrollment declines by 2 percent. Thus,in this hypothetical example, changing from a high-performing school withaverage poverty to a like-performing school with above-average poverty re-sults in a net loss in postsecondary enrollment of 7 percentage points. Al-though the math score strongly predicts postsecondary enrollment, this effectis contingent upon the school not having a large percentage of impoverishedstudents. These findings demonstrate that poverty compromises the usefulnessof the Accountability Index for predicting postsecondary enrollment.
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There is little evidence that the dropout rate of a school influences the pro-portion of its graduates that attend postsecondary institutions. But once again,the extent of school poverty exerts an influence by its contingent effect on thedropout rate. Poor schools with high dropout rates appear to send a larger pro-portion of their graduates to postsecondary institutions. For example, whenschool poverty increases by one standard deviation and the dropout rate in-creases by one standard deviation, postsecondary enrollment may increase by1.8 percentage points. To explain this seemingly anomalous phenomenon, weneed to inquire into the reasons why students drop out of high school. The is-sue is complex, of course (see, for example, Rossi & Montgomery, 1994), buttwo schools of thought are worth mentioning here. First, McNeal (1997) sug-gests that students may be “pulled” out of high school to pursue full-time jobopportunities. This outcome is far more likely if the student’s family back-ground is impoverished. Second, Riehl (1999) has proposed that some stu-dents are “pushed,” or discharged, from high school by teachers and schoolstaff who have given up trying to teach them.
McNeal’s explanation fits the facts at hand. In high-poverty schools signifi-cant numbers of students may drop out before their senior year to join the laborforce, leaving a graduating class that has a slightly higher propensity to pursuepostsecondary education. Riehl’s explanation also applies in some instances.High-poverty schools are likely to contain more students who perform poorlyin their classes and therefore feel that they do not belong there. Dischargingthese disenchanted students would also have the effect of boosting the propor-tion of graduates who enroll in postsecondary institutions.
CONCLUSIONS
Three key findings from this analysis of between-school effects on 1998postsecondary enrollment rates in Kentucky illustrate that students inhigh-poverty schools may not benefit from school-accountability policies in-tended to improve academic achievement and college participation:
1. Eight years after KERA (1990), high-poverty schools still sent fewer oftheir graduates to postsecondary institutions than did more affluent highschools. High schools in which the student population had above-aver-age proportions of both poor and minority students were particularly un-likely to send students to postsecondary institutions.
2. Despite the positive association between the Math Accountability Indexscores and postsecondary enrollment rates in Kentucky, high-povertyschools that improved average math scores had postsecondary enroll-
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ment rates that lagged well behind schools where the students were rela-tively affluent.
3. Impoverished schools with high dropout rates may have increased theircollege enrollment rates. This surprising outcome could be the result ofpush and/or pull factors that cause students to drop out, thereby chang-ing the composition of the graduating class. In such instances, the posi-tive effect on the enrollment rate is artificial and misleading.
The evidence from this study touches a fundamental education policy con-cern in Kentucky: How to achieve educational excellence in an equitable man-ner. The findings echo the conclusions of Mussoline and Shouse (2001), whofound that school-restructuring effects are significantly negative for thoseschools that mainly serve disadvantaged students. In the context of this study,the evidence suggests that focusing exclusively on school accountability mea-sures is unlikely to generate “equal educational opportunities for all of Ken-tucky’s children” due to the strong negative influence of school poverty onpostsecondary enrollments.
State policymakers and educators who are committed to more equitablehigher education opportunity and who want solutions to the issues raised bythis analysis might seek answers to the following questions:
1. Does a tradeoff exist between the goal of improving aggregate academicperformance in schools and the goal of increasing the college participa-tion rate for Kentucky high schools?
2. Does the focus on raising school accountability scores put pressure ondisadvantaged schools to direct resources away from high-risk students,which results in higher college enrollment rates among a smaller num-ber of high school graduates?
3. Because the concentration of poverty in high schools remains the pri-mary obstacle to college enrollment, what new programs can addressthe segregation of poor students in Kentucky high schools?
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34 JOURNAL OF POVERTY
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