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									The Social Justice Implications of Contemporary
       School Finance Theory and Policy:
    The Efficacy of Relative Economic Efficiency Measures
        in Determining School Improvement Factors




                        Anthony Rolle, Ph.D.
             State of Texas Education Research Center
                       Texas A&M University
                   Harrington Tower – MS 4226
                 College Station, TX 77843-4226
                   INTRODUCTION

        The question revisited in this research is:

How should educational productivity be measured
 within the context provided by the principles of social
 justice?

For this discussion, social justice is defined as the ability
  to pursue and acquire desired economic, political, and
  social opportunities without limitation or benefit from
  observations of – or interpretations of – stereotyped
  individual or group characteristics.
                  BACKGROUND

It is important to acknowledge that public school
   spending is conducted in sociopolitical environments
   where both organizations and individuals struggle for
   legitimacy and the capacity to distribute scarce
   resources.

More specifically, as public organizations charged with
 meeting the multiple needs and demands of diverse
 constituents, the ability to negotiate and compromise
 becomes an essential asset for all actors.
              BACKGROUND

Add to this atmosphere the broader equity and
 social justice principles of advocates seeking
 to influence the distribution of educational
 resources and opportunities, and the resulting
 complexity appears to threaten the assumed
 rationality of long-established educational
 productivity theories.
               BACKGROUND

In fact, it seems inappropriate for a public
  school’s level of productivity to be measured
  as a pursuit of what could be the unattainable:
  An absolute mathematical representation of a
  legislative process that determines and
  distributes resources to administrators who
  may or may not direct resources toward
  mandated organization or policy goals.
                   BACKGROUND

Given that differing social justice principles are pursued
  within various educational contexts, the measurement
  of efficiency in public schools should be
  reconceptualized to focus on what the highest student
  learners achieve compared to the lowest student
  learners—not the best average performances
  predicted by traditional production function analyses.
That is, the measures used should focus on relative
  comparisons of the best observed performers to the
  worst.
                     Outline for Presentation

I.     Educational Productivity and Its Measurement

II.    Results from Typical Educational Productivity Studies

III.   Challenges to Typical Educational Productivity Studies

IV.    A Discussion on Relative Measures of Economic Efficiency

V.     Re-Thinking Educational Productivity and Its Measurement

VI.    Comments and Questions
       Educational Productivity and Its Measurement

Technical efficiency: Attempts to maximize student learning and
  organizational policy outcomes while utilizing given sets of
  financial and human resource inputs;

Allocative efficiency: Attempts to maximize student learning and
   organizational policy outcomes, given prices for inputs and the
   effectiveness of management strategies, while utilizing
   financial and human resources in optimal proportions; and,

Total economic efficiency: Attempts to maximize student learning
  and organizational policy outcomes while pursuing allocative
  and technical efficiency simultaneously
     Educational Productivity and Its Measurement

A generalized expression for a basic economic algorithm – a
cost function (or a production function in the dual sense) – that
is designed to predict levels of student learning costs looks
like:

 Student Learning Costs = A Combination of (C, H, I, P, S)

where,
    C represents community characteristics,
    H represents household characteristics,
    I represents individual student characteristics,
    P represents peer influence characteristics, and
    S represents school resource characteristics
         Educational Productivity and Its Measurement

  Mathematically, the algorithm described above can be
  represented as:

                         C =  +  BY + u

where,
         C represents student learning costs,
          represents a computational constant,
         B represents the direction and degree to which Y
            influences student learning outcomes,
         Y represents numerous characteristics that influence
            student learning costs, and
         u represents measurement error.
    Results from Typical Educational Productivity Studies
   (Monk, 1990; Hoenack, 1994; King and MacPhail-Wilcox, 1994; Porter, 2003)



Fiscal and Physical Capacity: Adequate per pupil expenditures;
   high teacher salaries, and contemporary buildings and
   facilities;
Administrative Policies: Appropriate levels of collaborative
  management, low student-teacher ratios, and small class sizes;
Teacher Characteristics: Appropriate levels of teacher training,
  verbal ability, years of experience, and cultural diversity; and,
Classroom and Curriculum Content: Appropriate pre-school
  preparation, student ability groupings, and instructional
  interventions for student at-risk of failure.
        Challenges to Educational Productivity Studies

• There is an assumption that all students, teachers, and
  administrators are performing optimally; but, no universally
  accepted determination of this optimality – or its measurement
  – exists for student effort, teacher effectiveness, or education
  management.
• There is a casualness that surrounds the construction of
  statistical models used to estimate student learning costs; but,
  no universally accepted pedagogical or curricular – and
  therefore no statistical – structure exists for the educational
  production process.
• There is education policy research that refers to the significant
  influences of community, household, and peer characteristics;
  but, no universally accepted definitions for these
  characteristics – or their measurement.
     Challenges to Educational Productivity Studies

In addition, researchers from Mises (1944) to Levin (1976) to
Barnett (1994) to Rolle (2005; 2007) assert that educational
organizations are structured for bureaucratic management –
not for efficient management – strategies which are supported
by centralized authorities, hierarchical rule orientations, and
external (i.e., economic, political, and social) influences.

Put simply, research developing economic theories for
bureaucratic organizations – and research regarding the
behavior of public sector administrators – indicate that it is
highly implausible that district and school administrators act to
minimize costs.
  An Alternative Conceptual Perspective on Efficiency

As a matter of fact, trends in education organizations seem to
be exemplified by continued increases in size, fiscal resources,
and constancy – or decreases – in educational outcomes. As a
result, applying economic efficiency measures designed to
incorporate the behavior of private industries seem to be
inappropriate for public schools.

This brief historical and contemporary economic literature
review supports assertions that public managers tend to pursue
increased budgets; and, the research findings establish the
necessity for exploring alternative concepts of educational
productivity using relative measures of economic efficiency.
          Discussing Relative Measures of
               Economic Efficiency
In the remainder of the presentation, economic efficiency will
be discussed and measured relative to the best performers in
the sample using:
              1. Stochastic Frontier Analysis
             2. Modified Quadriform Analysis
               3. Distance Function Analysis
Before looking at individual relative measures of efficiency,
let us first review a graphical representation of traditional
regression analysis.
                 Traditional Regression Analyses
                 Graphical example of linear regression analysis for Schools A
                                             and C




                 A


$$/Outcome
             *           *
                     *             * *
                                     *     *        *
                                                *            * *
                 Regression Line
                                                              *         * *
                                                                    *
                                                                           C



        O
                                               $$/Input(x)
               Stochastic Frontier Analyses
Stochastic frontier analysis (SFA) fits an efficiency frontier to a
   data set in order to measures levels of relative efficiency
   (Barrow, 1991; Fare, Grosskopf, and Lovell, 1994; Farrell,
   1957; Kumbhakar and Lovell, 2000).

The focus of SFA also lies in determining statistically the best-
  performing organization(s). If the statistically determined best-
  performing organization has lower costs than the remaining
  organizations, the residual organizations are labeled as
  inefficient producers relative comparison to the best-
  performing organization(s) in its comparison group.

Instead of a mathematical example, let’s consider a graphical one.
                 Stochastic Frontier Analyses
                   Graphical example of frontier regression analysis for
                                    Schools B and D


                    Cost Efficiency Frontier


                                         B

                    A



$$/Outcome


                                                                     D




                                                                 C


             O
                                               $$/Input(x)
MODIFIED QUADRIFORM ANALYSIS

       A Basic Quadriform Diagram



   Quadrant 1:                Quadrant 2:
    Efficient                  Effective

Low Input - High Output   High Input - High Output



   Quadrant 3:                Quadrant 4:
    Ineffective                Inefficient

Low Input - Low Output    High Input - Low Output
                 Distance Function Analysis




                 Cost Efficiency Frontier



                 Y                             A




$$/Outcome

                         A*




                                                               B
                                                   B*
                                                           Z


             O                              $$/Outcome 2
             Rethinking Educational Productivity
                    and Its Measurement

Even if current cost and production function frameworks (i.e.,
  combinations of inputs and processes generate output) are
  correct, scholars still need to conduct research that:

Model actual relationships between human resources allocation,
  organizational incentives, and individual preferences;
Improve statistical relationships between purchased schooling
  inputs and student learning outcomes;
Determine the influence of non-purchased inputs and student
  learning outcomes; and,
Create incentives that transfer organizational and individual
  productivity efforts into pursuits of organizational outcomes.
           Rethinking Educational Productivity
                    and Its Measurement
If current cost and production function research assumptions
are challenged, scholars still need to create improvements that
question:
     Assumptions regarding that a general normative process
for education exists;
     Assumptions regarding that schools allocate resources to
maximize student output;
     Assumptions regarding that curricula, teaching methods,
and student learning outcomes are aligned to maximize student
output;
     Assumptions regarding that teacher characteristics and
credentials are proxies for quality of teaching;
     Assumptions regarding that teachers and students work at
capacity
       Rethinking Educational Productivity
              and Its Measurement
If current cost and production function research assumptions
are proved lacking, scholars need to:
** Expand the traditional two-stage production function
relationship to acknowledge the complexity of educational
production processes; that is:
             Inputs + Process=Outcomes becomes
   Dollars + Personnel + Quality-of-Personnel + Services +
   Quality-of-Services + I/E-Environment + Student Effort =
                        Student Outcome
** Examine non-linear, hierarchical, and time series statistical
relationships to acknowledge the complexity of educational
production processes
         Rethinking Educational Productivity
                  and Its Measurement
Given the acknowledged complexity of student
  groupings examined by educational production
  functions, entertain the notion that one general
  productions functions may be insufficient to analyze
  individual groups;
Given that schools produce multiple outputs, it is
  necessary to examine theoretical and statistical
  relationships that use multiple output regression
  statistics;
Given that public choice economic assumptions may be
  more appropriate, consider alternative measures of
  educational productivity such as modified
  quadriform, stochastic frontier, distance function, and
  integral ratio analyses.
Comments and Questions

								
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