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Economic Integration: Balancing Potential Gains with Harms


by David J. Armor - November 15, 2013

Most studies of economic integration, including some published by TCR, emphasize the potential educational benefits of economic integration while ignoring potential educational harms. This commentary illustrates these potential harms using a recent TCR article that looked only at benefits.

There is a substantial body of educational research showing that low SES students have higher achievement test scores when they attend higher SES schools, even when their individual SES is taken into account (Poverty & Race Research Action Council, 2011). This apparent benefit has led to growing support for school diversity in student socioeconomic status (SES), or economic integration, on the grounds that such policies will improve academic achievement of lower SES students while not harming achievement of higher SES students (Kahlenberg, 2013).  


A good example of this type of research is a recent study by Perry and McConney of Australian students using 2003 PISA data (2010).  Their study showed that low SES Australian students attending high SES schools received higher test scores than low SES students attending low SES schools.  They concluded, “Our findings suggest that schools with large concentrations of students with low-SES backgrounds should be discouraged.”  Their data also show that high SES students who attend lower SES schools score lower than high SES students who attend higher SES schools, but this aspect is not emphasized. This commentary explores that second relationship.


Perry and McConney acknowledge that they cannot infer a cause and effect relationships from cross-sectional data like the PISA surveys; they state, “nor are we here suggesting or testing a causal mechanism between SES, whether individual or school, and achievement.  Rather, our research questions are clearly descriptive.”  Yet this caveat does not prevent them from drawing a broad policy conclusion about discouraging schools with high concentrations of low SES students.   


Table 1 and Figure 1 reproduce their findings for math scores.  The rows of the table represent quintiles of a student’s individual SES score, the columns represent quintiles of school SES, and the entries are mean math scores in bold and N’s in italics.  School SES is measured by averaging individual SES scores for all students attending a given school.  


The table shows a strong relationship between school SES and test scores, controlling for individual SES.1  Considering any single row, math scores increase as school SES rises.  For example, students from the lowest individual SES quintile average 459 points in the lowest SES schools compared 475 in middle SES schools and 516 points in the highest SES schools.


Figure 1 (also from the paper) plots Table 1 means.  Because the relationship is approximately linear for each student quintile, Perry and McConney observe, “we can now say that, in Australia at least, all student SES groups are influenced relatively equally [by school SES].”  


Table 1. Mean Math Scores in 2003 Australia by Individual & School SES

 

School Quintile

 

 

 

 

 

 

 

Student Quintile

Lowest

(N*)

2nd

(N)

Middle

(N)

4th

(N)

Highest

(N)

Lowest

459

984

460

690

475

490

498

231

516

88

2nd

486

591

495

681

505

596

529

425

546

195

Middle

495

416

501

492

514

639

539

568

562

348

4th

522

213

521

377

531

516

555

682

575

693

Highest

543

99

535

199

546

362

571

602

600

1212

Source: Perry & McConney, 2010

Notes. *N is number of students


The slopes in Figure 1 appear steeper for higher SES schools and somewhat flatter for lower SES schools.  This has a potential implication that is not stressed in the paper: the potential losses from SES integration for high SES Australian students may be greater than the potential gains of low SES Australian students, at least in 2003.  


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How does one compare the potential gains with the potential losses, assuming an economic integration plan?  I am assuming (like the authors) that the potential gains for students in a given row are estimated by subtracting their scores in a higher SES column from their scores in a lower SES column.  That is, the effect of moving a group of students from one SES level to another is estimated by the difference in their scores between those two levels.  One does not have to assume that all of the difference is due to SES effects; some part of the difference might be due to other school factors, such as higher quality teachers, smaller class sizes, stronger curriculum, etc.


Assume that the scores in Table 1 are from the elementary schools in a single, reasonably large school district, with a total enrollment of just over 12,000 students (in 20 schools, four per quintile), and an economic integration plan is proposed to eliminate schools with high concentrations of poverty.   In constructing a plan, school officials recognize that the column differences might be due school SES in part, but also by different school resources, programs, and teacher characteristics.  


Accordingly, a “maximum” economic and resource integration plan is proposed, whereby the four lowest quintile schools are paired with the four highest quintile schools, and the four 2nd quartile schools are paired with the four 4th quartile schools, and any resource differences would be equalized.  This means that all schools would be in the middle quintile of SES, and all would share the same resources.     


Table 2 shows the potential estimated effect of this maximum SES (and resource) integration.  1st quintile students in the 1st quintile schools would gain 17 points, on average, by becoming middle quintile schools through pairing with 5th quintile schools.  In contrast, 5th quintile students in 5th quintile schools would lose 54 points by being enrolled in middle quintile schools.  The score gains and losses shown in each cell are derived by subtracting the score in the middle quintile of Table 1 from all of the scores in the same row; middle quintile schools are not affected and hence their scores are unchanged.    


Averaging across all schools, lowest quintile students would experience a net gain of 7 points, and students in the 2nd quintile would have an estimated net change of 0, both gains being offset by losses from those lower SES students who were attending higher SES schools before the integration plan.  All the other groups experience net losses, the largest being -31 for the highest SES students.  The average net effect over all five rows is -10 points.  In other words, based on the SES relationships found by Perry and McConney, maximum school SES integration for this hypothetical school population would reduce math scores by 10 points.  The reason is that the potential gains experienced by the low SES students in low SES schools are more than offset by the potential losses of students who were in high SES schools, both high SES and low SES.  


Table 2. Maximum SES Integration in 2003 Australia: All Schools become Middle Quintile Schools through Pairing

 

Math Changes

 

 

 

 

 

 

 

 

Average

Student Quintile

1st to 3rd

2nd to 3rd

No Change

4th to 3rd

5th to 3rd

Effectsa

Lowest

17

 

16

 

0

 

-23

 

-40

 

7

2nd

20

 

10

 

0

 

-24

 

-41

 

0

Middle

18

 

12

 

0

 

-25

 

-49

 

-7

4th

9

 

9

 

0

 

-24

 

-45

 

-17

Highest

3

 

11

 

0

 

-25

 

-54

 

-31

Total Effecta

 

 

 

 

 

 

 

 

 

 

-10

Notes. a Weighted by the number of students in each category.  Cell entries are estimated changes in test scores by moving to middle quintile


Although the estimates in Table 2 are based on a hypothetical integration plan using cross-sectional data, a longitudinal study of achievement in the U.S. found similar results.  “The present analysis suggests that gains in achievement to predominantly minority students moving from low-SES to middle-class schools would be less than the declines in achievement of White students moving from high-SES to middle-class schools. This suggests that integration would lower the achievement gap between Whites and Blacks, but it could also lower overall achievement levels (Rumberger & Palardy, 2005).”


An alternative explanation for the relationship found by Perry and McConnery is that the correlation between school SES and achievement is mostly spurious, generated by a self-selection process caused the economic segregation of housing and residential areas.  More affluent families tend to settle in suburbs or communities with more expensive housing, while most low income families reside in low cost housing areas (including public housing) in urban areas.  At least one rigorous longitudinal study, using a fixed effect model for North Carolina testing data, has come to the conclusion that classroom poverty levels have little effect on achievement, and therefore the observed relationship between school SES and achievement may be mostly spurious (Lauen & Gaddis, 2013).  


If the relationship between school SES and achievement is eventually shown to be spurious, then supporters of economic integration plans cannot use achievement gains for low SES students as a justification for the policy.  If the relationship is causal, as implied by Perry and McConnery, then the public debate about economic integration needs to weigh potential gains for low SES students against potential losses for high SES students.  At present, writings on SES integration do not even mention this possibility.2     


Notes


1.  The mean PISA math score for Australia in 2003 was approximately 524 with a standard deviation of 94.  

2.  Analyses similar to that in Table 2 for the U.S., Canada, and more recent Australia data are available from the author.  


References


Kahlenberg, R. (2013). From All Walks of Life: New Hope for School Integration.  American Educator, Winter 2012-13.


Lauen, D. & Gaddis, S. (2013). Exposure to Classroom Poverty and Test Score Achievement: Contextual Effects or Selection? American Journal of Sociology, 118(4). 943-979.


Perry, L. & McConney, A. (2010). Does the SES of the School Matter? An Examination of Socioeconomic Status and Student Achievement Using PISA 2003. Teachers College Record, 112(4). 1008–1037


Poverty & Race Research Action Council. (2011). Annotated Bibliography: The Impact of School-Based Poverty Concentration on Academic Achievement & Student Outcomes. Washington, DC: Author.


Rumberger, R. W. & Palardy, G. J. (2005). Does segregation still matter? The impact of student

composition on academic achievement in high school. Teachers College Record, 107. 1999–2045.




Cite This Article as: Teachers College Record, Date Published: November 15, 2013
https://www.tcrecord.org ID Number: 17324, Date Accessed: 5/24/2022 6:51:03 PM

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About the Author
  • David Armor
    George Mason University
    E-mail Author
    DAVID J. ARMOR is a Professor Emeritus of Public Policy, School of Public Policy, George Mason University. His research interests include education, school desegregation, military manpower, and quantitative research methods. His books include Maximizing Intelligence and Forced Justice: School Desegregation and the Law.
 
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