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Journal of Educational AdministrationSpending on school infrastructure: does money matter?Faith E. Crampton
Article information:To cite this document:Faith E. Crampton, (2009),"Spending on school infrastructure: does money matter?", Journal of EducationalAdministration, Vol. 47 Iss 3 pp. 305 - 322Permanent link to this document:http://dx.doi.org/10.1108/09578230910955755
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Spending on schoolinfrastructure: does money
matter?Faith E. Crampton
Department of Administrative Leadership,University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
Abstract
Purpose – The purpose of this study is to further develop an emerging thread of quantitativeresearch that grounds investment in school infrastructure in a unified theoretical framework ofinvestment in human, social, and physical capital.
Design/methodology/approach – To answer the research question, what is the impact ofinvestment in human, social, and physical capital on student achievement, the author uses canonicalanalysis, a multivariate statistical approach that allows for multiple independent and dependentvariables. The null hypothesis is selected given the limited body of research on this question, and thestate is selected as the unit of analysis. Level of student poverty is added as a control variable given anextensive body of research that supports its negative impact on achievement. Descriptive statistics aregenerated as well as a Pearson product moment correlation matrix to diagnose and address potentialissues of multicollinearity and simultaneity. Three national databases are used: United States CensusBureau, US Department of Education’s National Assessment of Educational Progress test scoredata, and the US Department of Education’s Common Core of Data. Years analyzed are 2003, 2005,and 2007.
Findings – The results of the canonical analysis are robust, statistically significant, andconsistent over time. Investment in human, social, and physical capital accounts for between 55.8and 77.2 percent of the variation in student achievement in fourth and eighth grade Reading andMathematics. Investment in human capital is consistently the largest influence on studentachievement followed by social and physical capital. The null hypothesis is rejected.
Originality/value – This study advances the use of theory in explaining the impact of investmentin school infrastructure on student achievement, a feature which distinguishes it from much previousresearch in this domain. The use of a theory also addresses a major weakness of traditional,atheoretical education production function research. In addition, utilization of canonical analysisrather than multiple regression to operationalize the theoretical model and analyze thedata represents an advance in research design and statistical analysis for this type of researchquestion.
Keywords Schools, School buildings, Students, Expenditure, Human capital, United States of America
Paper type Research paper
At a time of severely constrained resources in the United States, coupled withheightened expectations for student achievement and administrator accountability,prudent allocation of education funds is necessary to maximize the impact of everydollar. Under normal economic conditions, public elementary and secondary educationcompetes for funding at every level of government against other public programs andservices whose functions are also critical to the well-being of communities, states, andthe nation. In times of fiscal exigency when the pool of available resources shrinks orstagnates, the competition is fiercer. At such times, spending on school facilities and
The current issue and full text archive of this journal is available at
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Spendingon school
infrastructure
305
Received January 2009Revised February 2009
Accepted February 2009
Journal of EducationalAdministration
Vol. 47 No. 3, 2009pp. 305-322
q Emerald Group Publishing Limited0957-8234
DOI 10.1108/09578230910955755
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maintenance may be accorded low priority, based upon the belief that infrastructurehas no educational impact. Here, research can provide important insights forpolicymakers and practitioners. This study follows an emerging thread of quantitativeresearch that seeks to ground investment in school infrastructure in a unifiedtheoretical framework of investment in human, social, and physical capital (Crampton,2007). The paper begins with a background section offering a brief history of researchinterest in the relationship between student outcomes and school infrastructure, andthe evolution of research methods to study it. The second section presents thetheoretical model used in this study, a model that contextualizes investment in schoolinfrastructure with investment in other research- and theory-based variables, andcontrasts this model with more traditional educational productivity research. In thethird section, this model is operationalized in a multivariate statistical approach thatcaptures multiple inputs and outputs, the latter being measures of studentachievement. The fourth, and final, section presents results of the analysis,conclusions, and implications.
Background of the studyEducational researchers have long held an interest in the impact of the physicalenvironment of schools, here referred to as school infrastructure, on student learning(Holy, 1932, 1935, 1938a, b), but until recently they lacked the sophisticatedquantitative tools and large databases to study it systematically. Nor has interest inthis relationship been limited to educational researchers. In Weinstein’s (1979) reviewof research, she noted related research from disciplines such as architecture, sociology,and psychology. Further interest in this topic has been evidenced by studies in anumber of other disciplines where researchers have analyzed the effects ofinfrastructure-related variables, such as lighting, color, thermal environment, and airquality (Heschong Mahone Group, 1999; Ross and Walker, 1999). Most recently, studiesfrom a range of disciplines have analyzed the impact of environmentally sound or“green” approaches to building and maintaining school facilities (National ResearchCouncil, 2007).
The aforementioned lines of inquiry have employed a broad range of quantitativeand qualitative methodological approaches. However, in the 1980s, there occurred asignificant intersection in methodological approaches between school-infrastructure-related studies (McGuffey, 1982) and broader studies of therelationship between educational inputs and student achievement commonlyreferred to as “production function” research (Benson, 1978). Although theunderlying conceptual framework for production function research usually drawsupon the concepts of efficiency or productivity as derived from the disciplines ofeconomics and public finance, it should be noted that one of the earliest, and oft-cited,production function studies, Equality of Educational Opportunity, was conducted bysociologist Coleman et al. (1966). Still, beginning in the mid-1960s, this methodologicalapproach has been employed most widely by researchers interested in the economics ofeducation (Benson, 1978).
The major goal of production function research is to determine how to maximizestudent achievement with a given set of inputs, which have often included types ofeducational expenditures (Cohn and Geske, 1990; Monk, 1990; Thompson et al., 1994).Over the course of almost four decades, the range of sophistication of the research
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analyses used in these studies has varied greatly, with the most primitive correlatingtypes of education expenditure with a measure of student outcomes, e.g. a standardizedtest or state assessment score (Stinson and Krahmer, 1969; Sharp, 1993). A largerproportion of studies have sought to disaggregate total educational expenditures, forexample, to focus on the impact of instructional expenditures or teacher salaries andrelated variables such as experience and education level. More sophisticated studieshave sought to control for variables outside the direct control of schools, such asstudent poverty. These latter two groups of studies most commonly have used amultivariate statistical approach such as multiple regression analysis to quantify therelationship.
Multiple regression allows analysis of the impact of multiple independent variableson a single dependent variable; and therein lies its strength and weakness. Overall, itsanalytical capabilities are far superior to other approaches like simple correlation.On one side of the equation, researchers are permitted to include an unlimited numberof variables that they hypothesize impact student learning. However, on the other sideof the equation, they can select only one student outcome. Realistically, studentachievement is multidimensional. For example, achievement scores on Reading andMathematics at a particular grade level do not occur independent of one another, and,as such, multiple regression represents a less than optimal methodological approachfor this type of research question.
Second, the origin of the concept of production function lies with a private sectormanufacturing or factory model; as such, its application to education and studentachievement raises concerns about its appropriateness (Burkhead, 1973). A thirdshortcoming in this body of research lies with its atheoretical approach to the selectionof “production factors” or independent variables. Without a strong theory base to guideselection, results of education production function research over time have been unevenand inconsistent (Hanushek, 1989; Hedges et al., 1994). Still, a number of educationalresearchers interested in determining quantitatively the impact of school infrastructureon student outcomes have adopted this approach (Crampton, 2003). Here too, theresults have been uneven – for the same reasons. The challenges describedabove provided a powerful incentive for the author to engage in theory building and toexplore other multivariate statistical approaches that might be more suited to thisresearch question.
Theoretical frameworkWhy is a theoretical framework necessary for a study that seeks to determine theimpact of investment in school infrastructure on student achievement? Pedhazur (1997)offers a succinct rationale: “[. . .] [M]ethods mean little unless they are integrated withina theoretical framework” (p. 1). Specifically, a theoretical framework guides researchersin the selection of appropriate variables. Without such guidance, researchers cannot beconfident that the set of variables they have chosen is complete or relevant; and,as such, the results of the statistical analysis may be flawed. A theoretical frameworkalso provides guidance for the generation and testing of hypotheses; and theacceptance, rejection, and modification of these hypotheses over time furthersknowledge in and understanding of a particular research problem.
As noted in the previous section, much production function research in general andin the field of school infrastructure specifically has been atheoretical, which in turn has
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led to inconsistent results over time (See, for example, PricewaterhouseCoopers, 2003,2001; Picus et al., 2005; Jones and Zimmer, 2001; Earthman, 1998; Earthman andLeMasters, 1997). These inconsistencies pointed to the need for a unifying theory thatcontextualizes investment in school infrastructure with other factors that impactacademic achievement (Crampton, 2007). The resulting theory linked investment inschool infrastructure to investment in human and social capital. The corollary to thistheoretical proposition is: investment in school infrastructure represents physicalcapital that works in tandem with investments in human and social capital to buildcapacity for increased student achievement.
Recent research on the development of education capacity for reform and greaterstudent success dovetails theoretically with human, social, and physical capitaldevelopment. The construct of educational capacity dates back to the 1970s butre-emerged in the 1990s as a strategy to support education reforms that targetedstudent achievement through the establishment of statewide standards, school anddistrict accountability measures, and high-stakes testing (Firestone and Pennell, 1997;Gilley, 2000; Massell, 1998). Although early research on education capacity buildingtended to focus on professional development activities for classroom teachers, theconcept has matured to acknowledge the role of human, physical, and fiscal resourcesand their inter-relatedness. Although the importance of investments in human andsocial capital in education is well-established in qualitative research (Corcoran andGoertz, 1995; Spillane and Thompson, 1997; Massell, 1998), there is little quantitativeresearch, with the exception of the author’s earlier cited study, that incorporates these.Less well-established in qualitative research is the role of physical capital. Researcherssuch as Spillane and Thompson (1997) have mentioned physical capital as part of thisemerging paradigm. Although their discussion was limited to fiscal resources, physicalcapital generally is defined more broadly as physical inputs, of which infrastructure isone (Cohn and Geske, 1990, p. 161).
Based upon Coleman’s 1988 work, Spillane and Thompson defined human capitaldevelopment as follows: “[. . .] [H]uman capital is created by changes in persons thatbring about skills and capabilities that make them able to act in new ways” (p. 190).In building education capacity for reform, they operationalized this definition to referto teachers’ and administrators’ individual commitment to reform and their dispositionto learn new instructional strategies to improve student achievement. Social capitalreferred to the creation and maintenance of professional networks and collegialrelations that supported instructional reforms to improve student outcomes.In grounding education capacity in human, social, and physical capital development,they also noted their interactive nature. As a result, the author developed and tested aunified theory to underpin the analysis of factors which contribute to studentachievement. The next step was to operationalize the theory in a research design thataddressed the limitations of current methodological approaches.
Research design and methodsThe research question that guided this study follows from the theory posited in theintroduction and developed in the previous section. As noted, this theory wasdeveloped and initially tested by the author in an earlier study (Crampton, 2007).The extension of that study to include another year of student achievement data wasthe primary goal of this study in order to begin to create a longitudinal line of inquiry
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that would strengthen the inferences that can be drawn from results. Investments inhuman, social, and physical capital were operationalized as quantitatively measuredindependent variables to determine their impact, on multiple measures of studentachievement. The primary research question was as follows: What is the impact ofinvestment in human, social, and physical capital on student achievement?
Data sourcesData for the study were drawn from national databases maintained by the Institute forEducation Sciences (2008a, b) and the US Census Bureau (2008a, b). Specific databasesutilized in the study were the Common Core of Data (CCD) and the NationalAssessment of Educational Progress (NAEP); both are databases of the Institute forEducation Sciences. From the US Census Bureau (2008b), the public elementary-secondary education finance database was utilized.
Although all of the databases accessed for this study were longitudinal in nature,shortcomings in the NAEP database limited the number of years available for analysis.Even though NAEP test results date back to 1990, state-by-state data for studentachievement for fourth and eighth graders were available for only 2003 and 2005 whenthe previous study was conducted. For this study, 2007 NAEP data were available. Assuch, the reporting of results covers 2003, 2005, and 2007 NAEP scores for Reading andMathematics at the fourth and eight grades.
Definition of variablesIn elementary and secondary education, human capital is often represented by thequality of classroom teachers as evidenced by licensure in area of teaching, advanceddegrees, and years of experience (Plecki and Monk, 2003). However, such data on astate-by-state basis were not available at the national level. Because level ofcompensation, teacher experience, and advanced degrees are highly correlated, thelevel of school district expenditure on instructional compensation from the US CensusBureau public elementary-secondary database, was used as a proxy. Compensationincludes salary, wages, and benefits.
Social capital refers to professional networking and growth opportunities providedto teachers, often through professional development activities. Investment in socialcapital was measured by school district expenditure on instructional support. Data forthis variable were drawn from the category of expenditures labeled “instructional staffsupport”, in the US Census Bureau’s public elementary and secondary database, anddefined as follows: “Expenditure for supervision of instruction service improvements,curriculum development, instructional staff training, and media, library, audiovisual,television, and computer-assisted instruction services” (US Census Bureau 2008a, A-5).
Physical capital refers to the condition and adequacy of the physical environment ofschools. Investment in physical capital was defined as the level of school districtexpenditure on school infrastructure and its maintenance from US Census Bureau’spublic elementary and secondary database. School infrastructure is defined by the USCensus Bureau (2008a, A-1) as “capital outlay” and includes school construction (new,renovations, additions) and land acquisition. This definition is consistent with theauthor’s definition of the components of school infrastructure (Table I). Interest on debtassociated with capital outlay was also included.
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Student achievement was measured using scores from fourth and eighth gradeReading and Mathematics NAEP assessments for 2003, 2005, and 2007. NAEP can bedescribed as follows:
The NAEP is the only nationally representative and continuing assessment of whatAmerica’s students know and can do in various subject areas. Assessments are conductedperiodically in Mathematics, Reading, Science, Writing, the Arts, Civics, Economics,Geography, and US History. Since NAEP assessments are administered uniformly using thesame sets of test booklets across the nation, NAEP results serve as a common metric forall states and selected urban districts. The assessment stays essentially the same from year toyear, with only carefully documented changes. This permits NAEP to provide a clearpicture of student academic progress over time (Institute for Education Sciences, 2008b).
See Appendix 1 for a description of NAEP Reading performance scores and Appendix2 for a description of NAEP Mathematics performance scores.
Student poverty, defined as the percentage of students eligible for free or reduced-price lunch, was added as a control variable because of its historically strong negativerelationship with student achievement. The data source for this variable was the CCD.
Deferred maintenance Deferred maintenance refers to maintenance necessary to bring aschool facility up to good condition; that is, a condition where onlyroutine maintenance is required. If a facility is in such poorcondition that it cannot be brought up to good condition, or if itwould cost more to do so than to construct a new facility, deferredmaintenance can refer to replacement of an existing facility
New construction New construction may be a response to current overcrowding; tofederal, state, or local mandates that require additional facilities,such as class-size reduction measures; or to projected enrollmentgrowth. The construction of a new facility includes the building(s);grounds (purchase, landscaping, and paving); and fixtures, majorequipment, and furniture necessary to furnish it
Renovation Renovation of an existing facility includes renovations for health,safety, and accessibility for the disabled. Renovation may alsoinclude renovations necessary to accommodate mandatededucational programs
Retrofitting Retrofitting of an existing facility applies to such areas as energyconservation (for example, installation of insulation orenergy-efficient windows) and technology readiness (for example,electrical wiring, phone lines, and fiber optic cables)
Additions to existing facilities Additions to existing facilities may be necessary to relieveovercrowding; to meet federal, state, or local mandates, such asclass size reduction measures; or to accommodate projectedenrollment growth. The cost of additions usually includes thefixtures, major equipment, and furniture necessary to furnish them
Major improvements to grounds Such as landscaping and paving
Notes: Some states use the term capital outlay rather than school infrastructure. Capital outlay is anolder and more traditional term. In some states, the definition of capital outlay may be broader thanthat of school infrastructure. For example, capital outlay may include major equipment or equipmentabove a certain purchase price. Depending on the definition of capital outlay in a particular state, awide range of equipment might be included – from school buses to computersSources: Crampton et al. (2001) and Thompson et al. (2008)
Table I.Comprehensive definitionof school infrastructure
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HypothesisBecause the study tested a newly emerging theory, a conservative approach waswarranted in the form of a null hypothesis: There will be no statistically significantrelationship between the independent variables of human, social, and physical capitaland the dependent variable of student achievement. This newly emerging theory, asmentioned in the introduction, postulates that investment in human, social, andphysical capital work in tandem to enhance academic achievement. However, it isimportant to note that the earlier study rejected the null hypothesis finding thatthe independent variables had a statistically significant impact on studentachievement.
Unit of analysisThe unit of analysis for this study was the state where state data represented averagesfor the school districts within their boundaries. The state was selected as the unit ofanalysis because, constitutionally, funding of education is a state responsibility in theUnited States (Thompson et al., 2008).
MethodologyThe study utilized descriptive statistics and multivariate analysis to answer theresearch question. Descriptive statistics, calculated for the independent and dependentvariables, primarily provided context for the multivariate analysis and includedstandard measures such as the mean, median, mode, minimum, maximum, variance,and standard deviation. For the multivariate analysis, canonical analysis was utilizedrather than multiple regression. The primary advantage of canonical analysis is itsability to utilize multiple dependent variables whereas multiple regression, as notedearlier, is limited to a single dependent variable (Levine, 1977; Pedhazur, 1997). Becauseof potential issues with multicollinearity and simultaneity, student poverty, defined asthe percentage of students eligible for free or reduced-price lunch, was converted to anordinal variable where: 1, low percentage of student poverty; 2, moderate percentage ofstudent poverty; 3, high percentage of student poverty; and 4, very high percentage ofstudent poverty.
The canonical analysis equation was formulated as follows:
Y 1t þ Y 2t þ Y 3t þ Y 4t ¼ b1X1t þ b2X2t þ b3X3t þ b4X4t;
where Y1t, fourth grade Reading scores for state i for year t; Y1t, fourth gradeMathematics scores for state i for year t; Y3t, eighth grade Reading scores for state i foryear t; Y4t, eighth Mathematics scores for state i for year t; b1-b4, structure coefficients;X1t, per pupil expenditure on instructional compensation in state i for year t; X2t, perpupil expenditure on instructional support in state i for year t; X3t, per pupilexpenditure on infrastructure in state i for year t; and X4t, student poverty in state i foryear t.
Results of the statistical analysisTables II and III provide descriptive statistics for the independent and dependentvariables. In 2003, states and local school districts spent approximately $236.0 billionin instructional compensation, representing investment in human capital.Expenditures ranged from $3,122 per pupil in Utah to $8,608 per pupil in New York.
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(a)
Ind
epen
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tv
aria
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s:20
03(b
)D
epen
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tv
aria
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s:20
03N
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Psc
ale
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com
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sati
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per
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pil
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Inst
ruct
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alsu
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ort
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il)
Infr
astr
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exp
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itu
re($
per
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tud
ent
pov
erty
(%)
Rea
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rad
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ath
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(fou
rth
gra
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Rea
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g(e
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ath
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(eig
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Min
imu
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1.13
748.
3212
.620
322
325
126
1M
axim
um
8,60
8.68
687.
323,
227.
4965
.722
824
327
329
1R
ang
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485.
7955
6.18
2,47
9.17
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2520
2230
Med
ian
4,65
0.30
326.
081,
656.
6632
.621
923
626
427
9M
ean
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0.75
353.
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698.
5936
.121
823
426
327
7S
D1,
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189
113.
5250
8.27
11.7
6.54
75.
414
5.93
57.
451
N50
5050
49a
5050
5050
Note
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Table II.Descriptive statistics forindependent anddependent variables
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(a)
Ind
epen
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($p
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Infr
astr
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ure
exp
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itu
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(%)
Rea
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5713
1.75
1,03
3.01
16.6
204
224
249
262
Max
imu
m9,
120.
8770
1.43
3,37
5.71
64.4
231
247
274
292
Ran
ge
5,93
4.29
569.
682,
342.
7047
.827
2325
30M
edia
n4,
745.
1734
5.34
1,67
9.98
34.6
220
239
264
281
Mea
n5,
052.
0736
1.36
1,77
6.64
37.8
219
238
262
278
SD
1,21
9.34
109.
8549
9.24
10.4
6.45
85.
692
6.26
67.
284
N50
5050
47a
5050
5050
Note
:aK
entu
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,N
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ork
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Table III.Descriptive statistics for
independent anddependent variables
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Hawaii was closest to the mean per pupil instructional compensation of $4,850,and California and Montana were closest to the median of $4,650. For instructionalsupport, which represented investment in social capital, states and local schooldistricts spent approximately $18.6 billion. Expenditures ranged from $131 per pupil inDelaware to $687 per pupil in Massachusetts. Colorado was closest to the mean perpupil expenditure of $353, and Missouri was closest to the median per pupilexpenditure of $326. In 2003, states and local school districts spent approximately$82.8 billion for school infrastructure which represented investment in physical capital.Expenditures ranged from $748 per pupil in Hawaii to $3,227 per pupil inAlaska. Massachusetts was closest to the mean expenditure per pupil of $1,698 whileGeorgia and Maine were closest to the median per pupil expenditure of $1,656. Theincidence of student poverty, measured as the percentage of students eligible forfree and reduced price lunch, ranged from 16.8 percent in New Hampshire to65.7 percent in Kentucky. Illinois was closest to the mean incidence of studentpoverty at 36.1 percent, and Indiana represented the median incidence at 32.6 percent(Table II).
Table II presents descriptive statistics for fourth and eighth grade NAEP test scoreson Reading and Mathematics in 2003. For fourth grade Reading, scores ranged from223 in New Mexico to 228 in Connecticut, Massachusetts, and New Hampshire. Florida,Idaho, and Oregon scored at the mean of 218 while six states – Kentucky, Maryland,Michigan, Pennsylvania, Utah, and West Virginia – scored at the median of 219. Forfourth grade Mathematics, scores ranged from 223 in Alabama, Mississippi, and NewMexico to 243 in New Hampshire. Florida scored at the mean of 234 with the followingeight states scoring at the median of 236: Delaware, Michigan, Montana, Nebraska,New York, Oregon, Pennsylvania, and South Carolina. For eighth grade Reading,scores ranged from 251 in California and Hawaii to 273 in Massachusetts. No statescored at the mean of 263 although six states – Idaho, Michigan, Oregon,Pennsylvania, Utah, and Washington – scored at the median of 264. For eighth gradeMathematics, test scores ranged from 261 Mississippi to 291 in Minnesota. Delaware,Illinois, South Carolina, and Texas scored at the mean of 277 while Alaska, Missouri,and Pennsylvania scored at the median, 279.
Table III provides descriptive statistics for the independent variables in 2004. Stateand local school districts spent approximately $245.2 billion in instructionalcompensation with expenditures ranging from $3,186 per pupil in Utah to $9,120 inNew York. Virginia was closest to the mean of $5,052 with Montana closest to themedian of $4,745. Instructional support spending totaled approximately $20 billionnationwide and ranged from $131 per pupil in Delaware to $701 in Massachusetts.Kentucky was closest to the mean per pupil expenditure of $362, and Louisiana wasclosest to the median of $345. Infrastructure expenditures, which totaled $94.7 billion,ranged from $1,033 in Hawaii to $3,375 in Alaska. Georgia was closest to the meanper pupil expenditure of $1,777 while Vermont was closest to the median of $1,680.The incidence of students in poverty ranged from 16.6 percent in New Hampshire to64.4 percent in Mississippi. Missouri was closest to the mean percentage of 37.8, andIndiana had the median percentage of 34.6.
Table III presents descriptive statistics for the dependent variables in 2005. Forfourth grade Reading, scores ranged from 204 in Mississippi to 231 in Massachusetts.Florida and Texas scored at the mean, 219, while Kansas, Kentucky, and Maryland
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scored at the median, 220. For fourth grade Mathematics, scores ranged from 224 inNew Mexico to 247 in Massachusetts. Six states – Maryland, Michigan,Nebraska, New York, Oregon and South Carolina – scored at the mean of 238 whilethree states – Colorado, Florida, and Utah – scored at the median, 239. For eighthgrade Reading, scores ranged from 249 in Hawaii to 274 in Massachusetts. Utah scoredat the mean of 262 while Connecticut, Idaho, Illinois, and Kentucky scored at themedian of 264. For eighth grade Mathematics, scores ranged from 262 in Alabama andMississippi to 292 in Massachusetts. Illinois and Maryland scored at the mean, 278,with eight states scoring at the median of 281: Colorado, Connecticut, Delaware, Idaho,Maine, Pennsylvania, South Carolina, and Texas.
Table IV provides descriptive statistics for the independent variables in 2006.Instructional compensation ranged from $3,443 per pupil in Utah to $10,285 in NewYork for a total of $271.8 billion. Nebraska was closest to the mean of $5,512 perpupil with Montana closest to the median of $5,200. Instructional support expenditurestotaled approximately $21.7 billion, ranging from $152 per pupil in Delaware to $733in Massachusetts. Connecticut was closest to the mean per pupil expenditure of $412,and Iowa was closest to the median of $404. In 2006, states and local school districtsspent $107.5 billion on school infrastructure. Expenditures ranged from $967per pupil in Hawaii to $3,472 in Alaska. New Hampshire was closest to the mean perpupil expenditure of $2001 while Georgia and New Mexico were closest to themedian of $1,853. The incidence of students in poverty ranged from 17.1 percentin New Hampshire to 69.5 percent in Mississippi. Missouri was closest tothe mean percentage of 39.4, and Washington was closest to the median percentageof 34.6.
Table IV presents descriptive statistics for the dependent variables in 2007. Forfourth grade Reading, scores ranged from 207 in Louisiana to 236 in Massachusetts.Utah scored closest to the mean of 219, while Nebraska scored at the median, 223. Forfourth grade Mathematics, scores ranged from 228 in Mississippi and New Mexico to252 in Massachusetts. Colorado scored closest to the mean of 240 while Idaho scoredclosest at the median, 239. For eighth grade Reading, scores ranged from 250 inMississippi to 273 in Massachusetts and Vermont. Kentucky scored at the mean of 262while Indiana scored at the median of 264. For eighth grade Mathematics, scoresranged from 265 in Mississippi to 298 in Massachusetts. Utah scored at the mean, 281,while Delaware scored at the median of 283.
Results of the canonical analysis are reported in Table V. These results were robustand consistent over time. Overall, investment in human, social, and physical capitalaccounted for between 55.8 and 77.2 percent of the variation in student achievement.Specifically, in 2003, investment in human, social, and physical capital amountedfor 57.4 percent of the variation while in 2005, it accounted for 55.8 percent. Thispercentage increased to 77.4 percent in 2007. An F-ratio and Bartlett x-square wereutilized to test the robustness of the model; both were statistically significant at the0.001 level for the three years. The structure coefficient for the student poverty factor,used as a control variable, was negative as expected, ranging from 20.464 to 20.947.Of great concern was the large and growing negative impact of poverty on studentachievement. The structure coefficients[1] for the independent variables representinginvestment in human, social, and physical capital were positive for all years studied,indicating that instructional compensation, instructional support expenditures,
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(a)
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7352
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264
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262
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1,31
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124.
1557
2.03
10.3
6.30
5.78
6.24
7.46
N50
5050
5050
5050
50
Table IV.Descriptive statistics forindependent anddependent variables
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and infrastructure expenditures had a positive impact on student achievement,although the magnitude of their individual impact varied over years studied. Forexample, although investment in human capital had, by far, the strongest impact onstudent achievement over time, the structure coefficients declined from 0.890 in 2003 to0.648 in 2007. Over the same time period, the structure coefficients for social capitalincreased from 0.158 to 0.299 although it dipped slightly in 2005. The structurecoefficients for school infrastructure were somewhat inconsistent dipping from 0.236 in2003 to 0.049 in 2005. However, in 2007 the structure coefficient rose to approximatelythe same level as in 2003. This indicated that there was nearly a one to one relationshipbetween increases in investment in human capital and higher student achievement.Investment in physical capital was second with a structure coefficient of 0.236. Thiscoefficient is consistent with the highest coefficients found in previous research withregard to the impact of school infrastructure on student achievement. Interestingly, in2003, the impact of investment in social capital, with a structure coefficient of 0.158,was not as strong as that of infrastructure expenditure. Nonetheless, instructionalsupport expenditures did have a positive influence on student achievement. Given theresults described above, the null hypothesis was rejected.
Conclusions and implicationsThe purpose of this study was to determine if fiscal investment in school infrastructureaffected student achievement. School infrastructure was defined broadly to include notonly construction, renovation, and retrofitting of facilities but also maintenance. Thispaper did not attempt to do justice to the rich mix of disciplines and methodologiesnow evident in the study of school infrastructure and its potential impact on studentlearning. Rather, the study began with an examination of the implications of anintersection of interest between school-infrastructure-related studies and broaderstudies of the relationship between educational inputs and student achievementevidenced in one methodological approach, the production function. This study wasdesigned to overcome some of the widely acknowledged limitations of this approachby couching the analysis in a theoretical framework and using a multivariatestatistical approach more suited to educational achievement. The theoreticalframework posited that investment in human, social, and physical capital enhancedstudent achievement.
Structure coefficients by yearIndependent variables 2003 2005 2007
Instructional compensation 0.890 0.732 0.648Instructional support 0.158 0.148 0.299Infrastructure expenditure 0.236 0.049 0.234Student poverty 20.464 20.643 20.947
R 2 0.574 0.558 0.772F-ratio (probability) 2.536 (0.002) 2.294 (0.006) 3.803 (0.000)Rc 0.615 0.697 0.829Bartlett x2 (probability) 37.124 (0.002) 33.892 (0.006) 0.762 (0.000)N 49 47 50
Table V.Canonical estimates of
the impact of investmentof human, social, and
physical capital onstudent achievement
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Using national databases and the state as the unit of analysis, the ensuing analysissupported this proposition. In total, these investments explained betweenapproximately one-half to two-thirds of the variations in student achievement onfourth and eighth grade Mathematics and Reading assessments. A variablerepresenting student poverty was entered into the analysis as a control. Although itserved an important purpose statistically, its large, negative coefficient raisespragmatic concerns for policymakers and practitioners with regard to barriers povertypresents to student achievement.
Controlling for poverty, investment in human capital, defined as instructionalexpenditures, was consistently the largest influence on student achievement followedby social capital, which was defined as expenditures for instructional support.However, the impact of investment in physical capital, or school infrastructure asdefined in the study, was also a significant contributor. To conclude, spending onschool infrastructure does matter when it comes to student achievement. However, theresults of this study indicate that the impact of these investments is maximized whenthey are made in tandem. The implications of these findings for policymakers andpractitioners are straightforward. Prudent investment in education to maximizestudents’ academic success is a balancing act including all three forms of capital,as opposed to a zero sum game where greater investment in one form of capital comesat the expense of another.
Note
1. Note that canonical analysis interprets structure coefficients in a manner similar toregression coefficients in multiple regression.
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Appendix 1. NAEP Reading performance-level descriptionsLevel 350: learn from specialized reading materialsReaders at this level can extend and restructure the ideas presented in specialized and complextexts. Examples include scientific materials, literary essays, and historical documents. Readersare also able to understand the links between ideas, even when those links are not explicitlystated, and to make appropriate generalizations. Performance at this level suggests the ability tosynthesize and learn from specialized reading materials.
Level 300: understand complicated informationReaders at this level can understand complicated literary and informational passages, includingmaterial about topics they study at school. They can also analyze and integrate less familiarmaterial about topics they study at school as well as provide reactions to and explanations of thetext as a whole. Performance at this level suggests the ability to find, understand, summarize,and explain relatively complicated information.
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Level 250: interrelate ideas and make generalizationsReaders at this level use intermediate skills and strategies to search for, locate, and organize theinformation they find in relatively lengthy passages and can recognize paraphrases of what theyhave read. They can also make inferences and reach generalizations about main ideas andauthor’s purpose from passages dealing with literature, science, and social studies. Performanceat this level suggests the ability to search for specific information, interrelate ideas, and makegeneralizations.
Level 200: demonstrate partially developed skills and understandingReaders at this level can locate and identify facts from simple informational paragraphs, stories,and news articles. In addition, they can combine ideas and make inferences based on short,uncomplicated passages. Performance at this level suggests the ability to understand specific orsequentially related information.
Level 150: carry out simple, discrete reading tasksReaders at this level can follow brief written directions. They can also select words, phrases,or sentences to describe a simple picture and can interpret simple written clues to identify acommon object. Performance at this level suggests the ability to carry out simple, discretereading tasks in both subject areas.
Source: Perie et al. (2005, p. 16)
Appendix 2. NAEP Mathematics performance-level descriptionsLevel 350: multistep problem solving and algebraStudents at this level can apply a range of reasoning skills to solve multistep problems. They cansolve routine problems involving fractions and percents, recognize properties of basic geometricfigures, and work with exponents and square roots. They can solve a variety of two-stepproblems using variables, identify equivalent algebraic expressions, and solve linear equationsand inequalities. They are developing an understanding of functions and coordinate systems.
Level 300: moderately complex procedures and reasoningStudents at this level are developing an understanding of number systems. They can computewith decimals, simple fractions, and commonly encountered percents. They can identifygeometric figures, measure lengths and angles, and calculate areas of rectangles. These studentsare also able to interpret simple inequalities, evaluate formulas, and solve simple linearequations. They can find averages, make decisions based on information drawn from graphs,and use logical reasoning to solve problems. They are developing the skills to operate withsigned numbers, exponents, and square roots.
Level 250: numerical operations and beginning problem solvingStudents at this level have an initial understanding of the four basic operations. They are able toapply whole number addition and subtraction skills to one-step word problems and moneysituations. In multiplication, they can find the product of a two-digit and a one-digit number.They can also compare information from graphs and charts and are developing an ability toanalyze simple logical relations.
Level 200: beginning skills and understandingsStudents at this level have considerable understanding of two-digit numbers. They can addtwo-digit numbers but are still developing an ability to regroup in subtraction. They know somebasic multiplication and division facts, recognize relations among coins, can read informationfrom charts and graphs, and use simple measurement instruments. They are developing somereasoning skills.
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Level 150: simple arithmetic factsStudents at this level know some basic addition and subtraction facts, and most can addtwo-digit numbers without regrouping. They recognize simple situations in which addition andsubtraction apply. They also are developing rudimentary classification skills.
Source: Perie et al. (2005, p. 21)
About the authorFaith E. Crampton’s professional career has spanned public education, senior administrativepositions in state government, senior research and policy positions in national education andlegislative organizations, and graduate faculty positions in public and private researchuniversities. Her current position is Associate Professor in the Department of AdministrativeLeadership at the University of Wisconsin-Milwaukee. She has written extensively on schoolinfrastructure funding and education finance related issues. She served as principal investigatorof the first 50-state study of unmet funding needs for school infrastructure in 2001, and morerecently published an update, with support from the American Federation of Teachers. In 2003,she published with Dr David C. Thompson the landmark book, Saving America’s SchoolInfrastructure, with a foreword by The Honorable Senator Edward M. Kennedy. In 2007, asprincipal investigator she authored a research monograph on the results of a major study,“Investment in school infrastructure as a critical educational capacity issue: a national study,”with support from the Council of Educational Facilities Planners International. She holds a PhDin Educational Policy and Leadership with an emphasis in public finance from The Ohio StateUniversity. Faith E. Crampton can be contacted at: [email protected]
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