Developing a predictive metric to assess school viability.
James, John T. ; Tichy, Karen L. ; Collins, Alan 等
This article examines a wide range of parish school indicators that
can be used to predict long-term viability.
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Catholic elementary school enrollment peaked in 1965, when 4.491
million students were educated in over 10,000 Catholic schools across
the country (United States Catholic Conference, 1976). Since then,
Catholic elementary school enrollment and the number of schools have
dropped back to 1920 levels (see Table 1). Numerous reasons for this
decline have been offered. McLellan (2000) identified the lack of
leadership on the part of the Church hierarchy and their unwillingness
to make critical changes in governance and administration, the
diminished value and utility placed on Catholic schools by Catholic
parents who had entered the American economic and cultural mainstream
after World War II, theological shifts regarding the purpose and
effectiveness of Catholic education in the period immediately following
Vatican II, organizational changes, such as the declining number of
vowed women religious and the hiring of lay teachers, and demographic
shifts as reasons for the decline.
CHANGING DEMOGRAPHY
During the later part of the 20th century, Catholics participated
in the mass migration of the White middle class from the city to the
suburbs (Convey, 1992; Greeley, 1959; McGreevy, 1996). As the White
middle class moved out, African Americans, who are proportionately far
less likely to be Catholic, moved into the urban areas.
Catholics had abandoned the inner-city parish-school
infrastructure, moving to the suburbs en masse, thereby greatly
expanding the population in areas lacking parish schools. The Diocese of
Cleveland provides a vivid example of what has happened in many urban
centers. The City of Cleveland lost 409,192 residents between 1950 and
1990, while Cuyohoga County grew by 432,000 (Harris, 1996). In 1950,
average parish membership in the city and in the suburbs was 2,668 and
2,488 respectively; by 1990, the city parishes had dropped in membership
to an average of 1,666, while the suburban parish average membership had
more than doubled to 5,617 (Harris, 1996).
More recent quantitative research supports the contention that
changing demography has contributed to the closure of Catholic schools.
McLellan (2000) found that the 20 dioceses with the largest Catholic
school enrollment (the Top 20) accounted for 62% of the national
Catholic elementary school population in 1940, but only 42% of the
national Catholic elementary population in 1990. Furthermore, Top 20
enrollment declines from 1960 to 1990 were related to White population
declines in their central cities. Interestingly enough, McLellan (2000)
found that the more urban dioceses seemed to be more successful in
stemming the decline in the proportion of parishes with schools. This
paradoxical finding is reconciled when consideration is given to the
Herculean efforts made by large urban dioceses to keep urban parish
schools open even while the White Catholic population vacated the parish
boundaries.
A recent study by the Center for Applied Research in the Apostolate (CARA; 2006) found that "some schools reached critical tipping
points during this period [2000-2005] as the demographic changes that
had been taking place for more than five decades caught up with the most
vulnerable campuses" (p. 1). The study found that in 2005, 22% of
Catholic elementary schools were located in counties that had a loss in
Catholic population or very low growth since the 1950s: "The
current and emerging geographic centers for potential Catholic
elementary students in the 21st century no longer closely overlap as
well with the Catholic elementary school system that was primarily
designed and built in the early 20th century" (p. 2).
FINANCIAL ISSUES
The number of vowed religious serving in Catholic schools dropped
precipitously during the 1960s. By 2005, religious teachers in Catholic
schools comprised 5% of the total teaching staff, down from the slightly
greater than 90% common prior to 1950 (see Table 2).
Consequently, parish schools had to replace the religious with lay
faculty at considerably higher cost, since the religious were often paid
very meager stipends far below market value.
Unfortunately, the capacity for the parish to accommodate these
financial demands diminished during this same time period. Harris (1996)
notes that "Catholic giving as a proportion of household income
declined sharply between 1965 and 1984" (p. 2). Parishes responded
by cutting back on increases to the subsidy given to the parish school.
The percentage of parish school income from the parish dropped from 63%
in 1969 (Bredeweg, 1980) to less than 25% at present, while tuition grew
to account for over 60% of parish school income (Bimonte, 2004). The
large increases in tuition, the diminished value and utility placed on
Catholic schools by Catholic parents, and the changing demographics, led
to smaller enrollments, further exacerbating the precarious financial
position of many Catholic elementary schools. Commenting on these trends
Harris (2000) rightly concludes, "Catholic schools have evolved
from a Church-funded endeavor managed by professed religious to a system
of largely parent-funded programs for a diminishing portion of the
Catholic school population" (p. 56).
Lundy (1999) attempted to identify differences between
"survivor" and "non-survivor" parish elementary
schools from the Archdiocese of Chicago for the period spanning
1991-1994. "Survivor" schools were those Catholic elementary
schools that were sponsored by a single parish as of June 1994, and were
not scheduled for closure or consolidation the following fall.
"Non-survivor" schools were defined as those that had been
closed or consolidated between 1991 and 1994. Tests for correlations
were performed between survivor status and a number of financial
variables, and the best independent predictors were then used in a
discriminant function analysis to predict survivor status.
Lundy (1999) found that the sum of two ratios, one indicating the
financial solvency of the parish and the other indicating the financial
solvency of the parish school, was the single best predictor of survivor
status, correctly classifying 85.9% of the schools. The sum of the two
ratios was termed the "keyratio" and defined as the sum of
parish compensation expenditures divided by parish-collection income and
school compensation expenditures divided by tuition-and-fees revenue
(see Figure 1).
Figure 1. Definition of keyratio
Keyratio = Parish compensation expenditures/Parish collection income
+ School compensation expenditures/School tuition and fees revenue
Lundy (1999) found that most parishes with a keyratio of 1.75 or
below had balanced budgets, while most above that level were in deficit
spending. Other strong independent predictors of survivor status
included the percentage of school income spent on compensation (80.2%
correct classification), school compensation costs divided by tuition
and fee revenue (79.7% correct classification), and parish total balance
(76.7% correct classification). Two demographic variables also served as
strong predictors of survivor status: K8 enrollment (62.6%) and Catholic
enrollment (65.6%).
STUDY
This study explored the relationship between demographic variables,
financial variables, and parish grade school closures in the Archdiocese
of Saint Louis. Specifically, this study investigated whether
statistically significant and substantively meaningful differences exist
between open and closed schools on selected demographic and financial
variables. Discriminant function analysis was utilized to create a model
for predicting parish school viability. The final portion of this study
describes the translation of this discriminant function analysis into a
diagnostic tool that could be used as an early warning system to assess
school viability on an ongoing basis.
METHODOLOGICAL CONSIDERATIONS
This study was limited to the quantitative analysis of select
demographic and financial variables. It does not investigate the other
qualitative reasons for school closure cited by McLellan (2000), namely,
the influence of the Church hierarchy, the attitudes of Catholic parents
toward Catholic schools, or theological shifts.
The first part of the study provided a general profile of both open
and closed parish schools through a comparison of means of 21
demographic and financial variables using independent t tests. Due to
the large number of t tests conducted, the alpha required for
significance was lowered to .002 to control for type 1 errors (.002 * 21
= .042, below the standard threshold of .05). The study then utilized
discriminant function analysis to create a model for predicting parish
school viability. Discriminant function analysis was selected for use in
this study because of its ability to classify each case (school) into a
dichotomous category (open or closed).
Twenty-one variables thought to potentially capture aspects of the
demographic changes and financial issues were selected for inclusion.
While a more sophisticated methodology (such as factor analysis) would
uncover latent characteristics behind these variables (e.g., the factor
"demographic decline" characterized by small parish
populations, low baptismal rates, etc.), this study employed only
readily accessible variables with the explicit intent of creating a
diagnostic tool that could be used as an early warning system to assess
school viability on an ongoing basis.
Parish boundaries were coded into a geographic information system (GIS) that enabled United States census data to be linked directly to
the parish boundaries. The demographic and financial variables were
entered into an Excel spreadsheet and then transferred into SPSS for
statistical analysis.
Using census data disaggregated by parish boundaries assumes that
parishioners and school parents have the same demographic and financial
characteristics as the general population that resides within the parish
boundaries. However, Catholics routinely cross parish boundaries in
selecting their parish. Therefore, a threshold for including a parish in
the study needed to be created that would maximize the inclusion of
parish schools while ensuring the integrity of data attributed to
parishes. The Annual Catholic Appeal provided the best available record
of the addresses of every household within the archdiocese. These
addresses were coded into GIS and provided data for every parish on the
percentage of parishioners who actually reside within the parish
boundaries. The mean for the archdiocese was determined to be 67%, and a
threshold of 50% within-parish residency of parishioners was established
for the inclusion of a parish school in this study. This criterion
eliminated only a handful of parish schools and none of the closed
parish schools.
The years spanning 2000-2005 were examined as a group in order to
generate a sufficient number of closed schools to make possible a
quantitative statistical analysis. Doing so created the need for a sound
methodology for making comparisons among schools with data from
different years. Comparison year (CY) refers to the most recent year of
operation of a particular school; that is the 2004-2005 school year for
the open schools and the last year of operation for the closed schools.
Fiscal year (FY) refers to the fiscal year for the Archdiocese of St.
Louis which runs from July 1 of the prior calendar year to June 30 of
the year represented. Adjusted median household income and the variables
using adjusted income were calculated using the urban consumer price
index (CPI-U) for St. Louis (Bureau of Labor Statistics, 2007). In
constructing this variable, the CPI-U from the spring prior to the
academic year was used. The logic for this choice was that parents are
often informed of tuition and are required to pay a pre-registration fee
in the spring of the year preceding the school year. Thus the
calculation of affordability on the part of the parents is most likely
based on income in the spring preceding the academic year. Only schools
that closed after the 1999-2000 school year used the actual median
adjusted income from the 2000 United States census (1999 annual; United
States Census Bureau, 2007) without any adjustment. Other variables were
not adjusted unless otherwise noted in their definitions in Table 3.
SAMPLE
Open schools were defined as Catholic elementary schools sponsored
by a single parish in the Archdiocese of St. Louis that were not
scheduled for closure for the 2005-2006 school year. Closed schools were
defined as Catholic elementary schools that had closed in the spring of
2000 through the spring of 2004 or were not scheduled to re-open in the
fall of 2005. Parishes without parish schools, parishes that sponsored
consolidated schools, and parish schools that did not meet the threshold
level of parishioners living within parish boundaries were not included
in this study. Ultimately 138 schools were used in the analysis (99 open
schools and 39 closed schools).
DATA
Financial and demographic data for parish schools and their
parishes were obtained from the Archdiocese of St. Louis for the period
of 2000 through 2005. Data were obtained from the Archdiocesan Finance
Office, the Status Animarum, and the school data form. The Status
Animarum is an annual report completed each year by pastors that
contains data on both the school and the parish. The school data form is
an instrument that is sent out every summer to every school to be
completed by the administrator. The school data form requests data on
enrollment, finances, staffing, programs, and resources. The financial
data from the school data form were cross referenced against financial
data obtained from the Archdiocesan Finance Office for accuracy.
Additional financial data such as median household income were
collected from the 2000 Census (United States Census Bureau, 2007).
Parish membership data were obtained from the records of the
Archdiocesan Annual Catholic Appeal. All data are for the comparison
year (CY) unless otherwise stipulated. Ultimately 21 variables were used
that are described below in Table 3.
RESULTS: INDEPENDENT T TESTS
A series of independent t tests comparing open and closed schools
were run. Eighteen of the 21 comparisons of means between open and
closed schools were statistically significant at the .002 level (see
Table 4).
Among the variables that had means with no statistically
significant difference were tuition ($2,591 for open and $2,740 for
closed schools), and giving as a percentage of median household income
(1.22% for open and 1.29% for closed schools). The means for giving as a
percentage of median household income were remarkably similar to the
1.2% giving percentage of Catholics reported in a recent national study
(Zech, 2000). The most surprising finding was that there was no
statistically significant difference in the keyratio between open and
closed schools, and that the open schools actually had a higher average
keyratio. While Lundy (1999) found parishes with non-survivor schools
spent 77.4% of their income on compensation and parishes with survivor
schools spent only 53.2% of theirs on compensation, the present study
found that parishes with closed schools spent 48% of their income on
compensation and the open ones spent 57% of their income on
compensation. Clearly, the keyratio variable that captured the dynamic
operative in Chicago in the early 1990s was not correlated with school
closures in St. Louis in the early 2000s.
The effect size (d) displayed in Table 4 was calculated using
Cohen's (1988) d, defined as the difference between means divided
by the pooled standard deviation: d = ([M.sub.1] - [M.sub.2])/[square
root of ([([SD.sub.1]).sup.2]] + [([SD.sub.2]).sub.2]]/2). Nearly all
the effect size differences were large (.8 and above), with only a few
that could be classified as medium (.5) and none that could be
classified as small (.2).
The most substantively meaningful differences between open and
closed schools as measured by the effect size involved changes in
enrollment and total enrollment (pchgsch-1, pchgsch-3, pchgsch-5, and
nsch-1), parish finances (ptotinc04, adjhseinc, hseinc00), and parish
demographics (npar, totbapt, nhshlds). A line graph comparison of open
and closed schools' average enrollment for the 5 years prior to the
comparison year can be found in Figure 2.
[FIGURE 2 OMITTED]
TIPPING POINTS
The CARA study (2006) suggests that the recent school closures were
the result of schools reaching "critical tipping points that
quickened between 2000 and 2005" (p. 1). Large effect size
differences between open and closed schools in the areas of enrollment
(in years 1, 2, 3, and 5 prior to CY), as well as the precipitous
decline in the mean enrollment of closed schools in the 3 years prior to
closure led the investigators to think in terms of a tipping point.
Lundy (1999) found that the average enrollment of survivor schools
(345) was nearly twice as large as non-survivor schools (175). The
present study found that open schools had an average enrollment one year
prior to CY of 284, while closed schools had an average enrollment of
134.
Could there be a tipping point, a threshold enrollment, where once
broken, changes in enrollment become very significant for reasons of
efficiency, capacity, and per pupil cost, not to mention marketability?
In order to investigate this possibility, the variable
"above200" was created, a dichotomous variable that
distinguished the schools based upon this threshold enrollment of being
either above 200 students or not. The rationale for 200 as the threshold
is suggested from the data illustrated in Table 4. The number also
approximates school enrollment with a kindergarten through eighth grade
school with one class per grade and 25 students per class.
RESULTS: DISCRIMINANT FUNCTION ANALYSES
The 18 variables with significant differences between open and
closed schools and the newly created "above200" variable were
included in a discriminant function analysis predicting status.
Variables that were highly correlated with one another were removed from
the analysis. The discriminant function analysis took the remaining
variables ([x.sub.0], [x.sub.1], [x.sub.2], ...) that were reliably
correlated with another variable (status) and created an equation. The
analysis generated coefficients ([a.sub.0], [a.sub.1], [a.sub.2], ...)
for the variables ([x.sub.0], [x.sub.1], [x.sub.2], ...) and a constant
(C) that maximized the difference between the quotients of open and
closed parish schools: DF = [a.sub.0][x.sub.0] + [a.sub.1][x.sub.1] +
[a.sub.2][x.sub.2].... + C. When the variables are entered into this
equation, a viability score is produced that can then be compared to the
average scores for the open and closed parish schools. The discriminant
function analysis then calculated the probabilities of a particular
quotient being a member of either the open group or the closed group. It
assigned the parish school to either the open group or the closed group,
and then checked the accuracy of its prediction based upon the known
outcome.
The analysis included 124 of the 138 cases; 14 cases were not
included due to missing variables (7 open and 7 closed). Three variables
generated a 93% overall correct classification (see Tables 5 and 6), and
an 87.5% correct classification of closed schools, a very important
attribute for an early warning system. The mean discriminant function
score for open schools was .597, while the mean discriminant function
score for the closed schools was 1.716.
APPLICATIONS
This study employed only readily accessible variables with the
explicit intent of creating a diagnostic tool that could be used as an
early warning system to assess school viability on an ongoing basis. The
discriminant function analysis produced an equation (see Figure 3) that
demonstrated a 93% overall correct classification of open and closed
schools based upon three variables (see Tables 5 and 6).
Figure 3. Equation produced by discriminant function analysis
Viability Score = .686 + 1.241(above 200) + 8.106(pchgsch1) -
17.290(tuitpctadjinc)
The viability score for an individual school can be calculated by
entering the appropriate values into the equation and performing the
calculation. If a school has an enrollment of above 200, a 1 is placed
in the parentheses "above200," if not, a zero is placed in the
parentheses. The percentage change in enrollment from the previous year
(either positive or negative) is placed in the parentheses designated
"pchgsch1," and the tuition as a percentage of adjusted median
household income is placed in the parentheses designated
"tuitpctadjinc."
The tuition as a percentage of median adjusted household income is
the tuition charged for one child in the school divided by the adjusted
median household income. This adjustment is done by multiplying the
median household income from the 2000 United States Census (1999 annual;
United States Census Bureau, 2007) for a particular parish boundary by a
ratio of numbers obtained from the CPI-U St. Louis, a statistic produced
by the Bureau of Labor Statistics (see Table 7). An income of $50,000
for the year 1999, adjusted forward to the spring of 2006, would be
$59,898 ($50,000 * [188.8 / 157.6] = $59,898).
The tuition as a function of adjusted median household income is
then calculated by dividing the tuition charged for one child in the
school ($4,275) by the adjusted median household income ($59,898). The
value, .07137, is then placed in the parentheses designated
"tuitpctadjinc" completing the equation.
AN EARLY WARNING SYSTEM FOR THE ARCHDIOCESE
A frequency analysis of the viability scores for all the parish
schools included in this study were run (see Figure 4). Basic
descriptive statistics were also run on the distribution of these
viability scores. The standard deviation for open schools was found to
be approximately 1 (see Table 8).
[FIGURE 4 OMITTED]
The discriminant function analysis, in addition to producing a
viability score, also calculated the probability of a school being in
either the open or closed category. A careful examination of the scores
that approach equal probability provides a method for interpolating the
threshold viability score for predicting closure. This threshold
viability score for predicting closure was found to be approximately
-1.0.
The descriptors Red, Orange, Yellow, and Green, analogous to
traffic light colors, were chosen as the descriptor of school viability.
Additionally, since there will be movement from year to year based upon
whether the change in K-8 enrollment is going up or down, whether
tuition increases are above or below the CPI-U growth for St. Louis, and
whether the school's enrollment lies above 200 or below, a
directional component (+ or -) was added to the descriptor. The division
line between Green and Yellow lies one standard deviation (1.0) below
the mean viability score for open schools (approximately .6) which is
-.4, the division line between Yellow and Orange lies at the threshold line for predicting closure (-1.0), and the division line between Orange
and Red lies two standard deviations below the mean viability score for
open schools (-1.4). Green represents viability scores of -.4 or higher,
Yellow represents scores of -.4 to -1.0, Orange represents scores of
-1.0 to -1.4, and Red is anything lower than -1.4. The change in value
from one year to the next provides the directional sign. A school that
has a score of .55 one year, but one of .43 the next would be
"Green -." A school that has a score of -.84 one year, but has
a score of -.75 the next would be "Yellow +" (see Figure 5).
[FIGURE 5 OMITTED]
ST. ELSEWHERE ELEMENTARY SCHOOL
A parish school named St. Elsewhere will serve as an example for
the viability score calculations. St. Elsewhere has a median household
income of $50,000 for the year 1999. St. Elsewhere had a precipitous
decline in enrollment from 1999 to 2004, but has recently experienced
modest growth. The tuition increased only 6% per year over the entire
period as the parish subsidized the heavy losses incurred by the school
when the enrollment plunged. The enrollment, the percentage change in
enrollment, the tuition charged for one child, and the adjusted median
household income are all shown in Table 9.
Placing the appropriate values into the viability score equation
produced the following viability scores and descriptors for St.
Elsewhere (1999-2007; see Table 10).
St. Elsewhere's drop in enrollment of five students from
1999-2000 to 2000-2001 yielded only a minus sign on an overall healthy
(Green) school profile; a viability score of .74 is above the
archdiocesan average of .60 for open schools in the study. Another drop
in 10 students puts the viability score below the archdiocesan average
and merits another "Green -" rating. A loss of 15 students and
a drop in enrollment below the 200 threshold merits a "Yellow
-" rating. A loss of 20 more students the following year puts St.
Elsewhere into the "Orange -" category. At this point, the
model indicates that St. Elsewhere's profile is more like the
schools that eventually closed than those that remained open. However,
the following year, the enrollment decline reverses itself with a modest
increase of 5 students, enough to merit a "Green +" rating.
Another increase in students generates a slight decline in the viability
score generates a "Green -" A very slight decline in
enrollment the following year puts St. Elsewhere back into the
"Yellow -" zone. An explanation and interpretation of these
changes follows below.
The example provides some insight into the mechanics of the
viability score. As long as tuition rises above the rate of the CPI-U
increases, the "tuitpctadjinc" term of the equation will
provide a negative drag on the viability score. If enrollment drops
below 200, a tipping point has been passed, and tuition as a function of
adjusted median household income and changes in enrollment (either
positive or negative) become very significant in the viability score;
this explains why an increase of five students in one year and a loss of
two in another can launch St. Elsewhere from "Orange -" to
"Green +" and from "Green -" back to "Yellow
-" respectively.
How did the addition of five students in two successive years take
St. Elsewhere from "Orange-" to "Green +" in one
year and from "Green +" to "Green-" the next? The
answer lies in understanding the viability score as both an assessment
of general health ("above200" and "tuitpctadjinc")
and an indicator of the most recent trend ("pctchgsch1"). When
St. Elsewhere reversed the enrollment decline, the
"pctchgsch1" term of the equation went from a negative of
substantial magnitude to a positive. This greatly improved the viability
score. Should the addition of 5 students warrant such an extreme
reversal from "Orange -" to "Green +" in one year?
It could be argued that taking a school out of what appeared to be the
prototypical "death spiral" demonstrated graphically in Table
5 by reversing rapid enrollment declines and holding the line on tuition
is also very extreme and significant. So why does the school go to
"Green -" the following year by adding the exact same number
of students? The answer lies in both the "pctchgsch1" term and
the "tuitpctadjinc" term of the equation. The former, while
positive, was a slightly smaller percentage increase over the last year,
and the latter, as stated earlier, is a negative drag on the viability
score whenever the tuition rises above the increase in the CPI-U St.
Louis.
A complicating issue not addressed in the St. Elsewhere example is
the calculation of the viability scores when less than 50% of the
students' parents reside within the parish boundaries or
multi-parish grade schools. A logical fix would be a weighted adjusted
income based upon the zip codes of the students actually attending the
school. These and other issues will be monitored over time as the model
is tested against reality.
CONCLUSIONS
A comparison of means between open and closed schools revealed many
statistically significant and substantively meaningful differences.
Subsequent discriminant function analyses identified three variables
that were utilized to correctly classify 93% of parish schools as either
open or closed. The model achieved a correct classification of closed
schools of 87.5%, a very important attribute for an early warning
system, and is significantly higher than the 70% cited by Lundy (1999).
It also achieved the same overall predictive value of Lundy's model
using far fewer variables (3 variables instead of 14). Therefore, the
viability score provides a framework and useful guide for understanding
the variables that affect parish school viability
This study has produced an equation that can generate a credible
viability score for parish grade schools within the Archdiocese of St.
Louis. The model was also quickly validated in reality. A parish school
that was open during the period of this study, but had the lowest
viability score in the Archdiocese of St. Louis, closed soon after the
completion of this study. The viability score captures the key drivers
that have led to the closure of schools and therefore holds great
promise as a component of an early warning system for parish schools.
The viability score and the direction of its change has been integrated
into an early warning system for the Archdiocese of St. Louis.
The obvious strengths of such a viability score also reveal its
weaknesses. While the utility of a viability score has obvious appeal,
parish school viability decisions should not be dependent upon the
calculation of a quotient. Catholic schools are indispensable to the
evangelizing mission of the Church. The contribution of Catholic schools
to the educational mission of the Church cannot be reduced to a single
number.
The viability score does not capture important elements such as
mission, impact of the school on its geographic community, stakeholder satisfaction levels, quality of curriculum and instruction, Catholic
identity, and quality of religious education and formation that are
critically important in crafting a comprehensive early warning system.
However, coupled with best practice program evaluation systems such as
the Archdiocese of St. Louis' Instructional Program Review and
AdvancEd's Quality Assurance Review, a comprehensive quantitative
and qualitative instructional assessment of quality and viability can be
performed.
In addition, as noted above, the model captures a significant
proportion of the variance between schools that closed and schools that
remain open. However, just as persons who score well on health risk
assessments are not immune from health problems, and just as persons who
score poorly may beat the odds, it is essential that the viability score
not be treated as a self-fulfilling prophecy nor as a cause for
complacency.
The viability score was developed from data in the Archdiocese of
St. Louis, and while this research has demonstrated that the key terms
of the viability quotient capture key drivers of school closure, these
may not have the same magnitude of importance in other dioceses.
Lundy's research (1999) regarding closures in Chicago in the early
1990s found the keyratio, a measure of the aggregate parish-parish
school financial picture, as the single strongest key driver, whereas
the present research found enrollment, enrollment changes, and family
costs as the key drivers of school closure. Some of these variables,
which attempt to capture universal drivers, might have greater or lesser
impact in other dioceses.
The viability score might serve as one of several variables within
a comprehensive early warning system that takes a balanced scorecard approach (Kaplan & Norton, 2007). A balanced scorecard approach,
taking first and foremost into account the Church's mission in a
particular geographic area, that includes quantitative data such as the
viability score, might provide the impetus for regional strategic
planning. Such an approach by a diocese might actually require regional
strategic planning and systems-thinking based upon the mission of the
Church in a particular region when a parish or collection of parishes
reaches critical thresholds. This approach prevents a parish-by-parish
Darwinian survival of the fittest that has heretofore allowed for
wholesale loss of schools in large swaths of a particular region without
regard to overall mission. Given the financial costs of a school, the
demographics required to supply the school with students, and the
ability of Catholic schools to attract parishioners into a parish,
changes in a parish school viability score might well serve as a key
leading indicator of not only an individual parish school's
viability but that of the parish and other parishes in the Deanery; as
go the parish schools, so go the parishes.
RECOMMENDATIONS
In light of what has been stated earlier, large archdioceses ought
to seriously consider a similar study, based upon their own historical
data, to determine the drivers and magnitude of such drivers for school
closures. Smaller dioceses without sufficient data to warrant an
independent study, might consider the use of multiple measures of
viability (e.g., the Lundy score as well as the viability score derived
from the present research). As was stated earlier, these viability
scores ought not be the determinant in school closures. Rather, these
scores become individual measures of a comprehensive balanced scorecard
approach that gives due consideration to questions of mission, regional
impact, program quality, and systemic approaches to mission.
Within the Archdiocese of St. Louis, additional research needed to
further refine the model includes:
* monitoring the predictive effectiveness of the model over time
* examining potential differences in the model's effectiveness
for various types of schools (single parish, multiparish) and for
schools in various locations (urban, suburban, rural)
* determining further ways to address the median household income
factor in the cases where more than half of the school families do not
live within parish boundaries
* repeating the study when 2010 Census data become available.
The balanced scorecard approach lends itself to systems-thinking
(Senge, 1990) regarding the importance and impact of the various Church
structures like parishes and schools, and presupposes a thoughtful
deliberation regarding mission. The questions of mission and how the
Church ought to utilize its resources at any given point in time are
perennial questions that must be revisited in light of changing
circumstance. These are signs of a healthy, thoughtful, and evangelizing
Church.
Such approaches may serve as the first steps in the development of
an information architecture for the diocese that helps inform key
decisions regarding mission. Information architecture, as the name
implies, is real-time information, data, and data analysis that are made
available in a timely and useful manner to all those engaged in the
mission of the Church. Tools such as geographic information systems
(GIS) that link any conceivable diocesan database to geographical
location (parish, deanery, city, diocese, etc.) and statistical packages
for the analysis of data (SPSS) that were utilized in this research and
have historically been the province of city planners and academicians
respectively, ought to be routinely employed by dioceses to make
data-driven decisions in the service of the mission of the Church. Such
tools will be the catalyst for "action research" by individual
dioceses and may provide avenues for further research.
An example of just such an avenue of inquiry comes from a
peripheral finding not explored in this article. There are indications
from the data gathered that parishes with schools have a greater ability
to attract families into the parish (James, 2008). These families are
presumably younger families with children. This research coupled with
that of Zech (2000) regarding the differential giving of Catholic school
parents, may indeed reconfirm the original thesis of Greeley (1977) that
Catholic schools are not only an effective mechanism in the evangelical
mission of the Church, but actually are profit-making enterprises over
the lifetime of the students.
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secondary schools 2004-2005: The annual statistical report on schools,
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McGreevy, J. T. (1996). Parish boundaries: The Catholic encounter
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McLellan, J. A. (2000). Rise, fall, and reasons why: U.S. Catholic
elementary education, 19401995. In J. Youniss & J. J. Convey (Eds.),
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17-32). New York: Teachers College Press.
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million? A statistical survey of Catholic elementary and secondary
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JOHN T. JAMES
Saint Louis University
KAREN L. TICHY
Archdiocese of St. Louis
ALAN COLLINS
JOHN SCHWOB
Archdiocesan Board of Catholic Education, Archdiocese of St. Louis
John T. James is Assistant Professor in the Department of
Educational Leadership and Higher Education and Director of the Catholic
Leadership Program at Saint Louis University. Karen L. Tichy is
Associate Superintendent for Instruction for the Archdiocese of St.
Louis. Alan Collins and John Schwob are on the Archdiocesan Board of
Catholic Education for the Archdiocese of St. Louis. Correspondence
concerning this article should be sent to Dr. John T. James, Saint Louis
University, Department of Educational Leadership and Higher Education,
Suite 113, McGannon Hall, 3750 Lindell Blvd., St. Louis, MO 63108.
Table 1
Catholic Elementary School Enrollment (in Millions) and Number of
Schools 1920-2005
Year Enrollment Schools
1920 1.796 6,551
1930 2.223 7,923
1940 2.035 7,944
1950 2.561 8,589
1960 4.373 10,501
1970 3.359 9,366
1980 2.293 8,100
1990 1.983 7,395
2000 2.013 6,923
2005 1.780 6,574
Note. Source: McDonald (2005).
Table 2
Number of Religious in Catholic Schools and their Percentages in
Schools (1920-2005)
Year Number Percentage
1920 45,563 92.0
1930 65,601 90.4
1940 73,960 91.2
1950 84,925 90.1
1960 112,029 73.8
1970 80,615 48.4
1980 42,732 29.0
1990 20,020 14.6
2000 11,011 7.0
2005 7,990 5.0
Note. Source: McDonald (2005).
Table 3
Variables Used in the Analysis
Parish Demographics
nhshlds The number of households within the parish in 2004.
npar The number of parishioners in the parish in 2004.
totbapt The total number of baptisms in the parish performed
in the 3 years prior to the comparison year.
School Demographics
nsch-1 The number of students in the parish school 1 year
prior to the CY.
nsch-2 The number of students in the parish school 2 years
prior to the CY.
nsch-3 The number of students in the parish school 3 years
prior to the CY.
nsch-5 The number of students in the parish school 5 years
prior to the CY.
pchgsch1 The percent change in enrollment for the parish school
between 2 years prior to the CY and 1 year prior to
the CY.
pchgsch3 The percent change in enrollment for the parish school
between 3 years prior to the CY and 1 year prior to
the CY.
pchgsch5 The percent change in enrollment for the parish school
between 5 years prior to the CY and 1 year prior to
the CY.
Parish Finances
ptotinc04 The parish total income for FY 2004.
givpctinc The giving per household in the parish as a percentage
of median household income defined as total parish
income divided by the number of households in the
parish, then divided by the median household income
for the parish.
hseinc00 The median household income for the parish derived
from the 2000 United States census data.
adjhseinc The median household income within the parish
boundaries adjusted to the CY using the CPI-U
(St. Louis).
School Finances
tuition The tuition charged for one child in the school for
the CY.
schttlinc The parish school's total income.
schttlexp The parish school's total operating expense.
schtuitinc The parish school's total income from tuition.
perpupilcost The total operating expenses divided by the enrollment
for one year prior to the CY.
Parish and School Finances
keyratio The sum of two ratios: the ratio of school salary and
benefit expenses to school income from tuition plus
the ratio of the parish's total income minus the
parish's subsidy given to the school to the parish's
total income.
Parent Finances
tuitpctadjinc The tuition charged for one child in the school for
the comparison year divided by the median household
income adjusted to the comparison year using the
CPI-U (St. Louis).
Table 4
A Comparison of Means of Open and Closed Catholic Grade Schools
Variable n M SD SE p d
pchgsch-1 open 92 -.0002 .08674 .00904 .000 1.61
closed 32 -.1526 .10173 .01798
pchgsch-3 open 92 -.024 .1472 .0153 .000 1.60
closed 37 -.244 .1273 .0209
pchgsch-5 open 92 -.020 .2643 .0276 .000 1.36
closed 37 -.306 .1350 .0222
ptotinc04 open 99 722028 485984 48843 .000 1.19
closed 33 295602 141189 24578
adjhseinc open 98 56825 17928 1811 .000 1.18
closed 39 40082 8915 1428
nsch-1 open 97 284.4 176.7 17.95 .000 1.15
closed 39 134.4 53.3 8.54
hseinc00 open 98 51647 16294 1646 .000 1.12
closed 39 37300 8026 1285
npar open 92 3802 2771 288.9 .000 1.05
closed 37 1665 799 131.5
totbapt open 92 160.1 131.9 13.75 .000 1.03
closed 37 60.1 37.2 6.12
schttlexp open 98 998032 496030 50107 .000 0.98
closed 32 617634 233230 41230
schttlinc open 94 998244 495913 51150 .000 0.96
closed 31 625145 233437 41926
nhshlds open 92 1301 $853.6 88.99 .000 0.96
closed 37 688 $300.3 49.37
tuitpctadjinc open 97 .0499 .02408 .00244 .000 0.95
closed 31 .0705 .01921 .00345
nsch-2 open 99 287.4 176.4 17.73 .000 0.93
closed 34 163.8 64.3 11.03
perpupilcost open 88 3794 905 96.49 .000 0.88
closed 29 4714 1172 217.6
nsch-3 open 97 292.5 177.1 17.98 .000 0.84
closed 39 178.7 73.3 11.74
schtuitinc open 94 563751 378715 39061 .000 0.84
closed 31 320527 157209 28236
nsch-5 open 97 292.8 173.0 17.57 .000 0.74
closed 39 194.4 74.7 11.96
givpctinc open 92 .01219 .004981 5.19 E-4 .471 --
closed 31 .01294 .004888 8.78 E-4
tuition open 99 2591 1000 100.5 .210 --
closed 34 2740 359 61.6
keyratio open 93 2.4487 1.96998 .20428 .390 --
closed 25 2.1605 1.24126 .24825
Table 5
Canonical Discriminant Function Unstandardized Coefficients
Function
1
above200 1.241
pchgsch1 8.106
tuitpctadjinc -17.290
(Constant) .686
Table 6
Discriminant Function Analysis Classification Results
Predicted group
Status membership Total
Open Closed
Original Count Open 87 5.00 92
Closed 4 28 32
Percentage Open 94.6 5.4 100.0
Closed 12.5 87.5 100.0
Cross-validated (a) Count Open 87 5 92
Closed 4 28 32
Percentage Open 94.6 5.4 100.0
Closed 12.5 87.5 100.0
(a) Cross validation is done only for those cases in the analysis. In
cross validation, each case is classified by the functions derived
from all cases other than that case.
(b) 92.7% of original grouped cases correctly classified.
(c) 92.7% of cross-validated grouped cases correctly classified.
Table 7
Consumer Price Index--All Urban Consumers--All Items--St. Louis,
Missouri
Year Annual First half Second half
1999 157.6 156.4 158.8
2000 163.1 162.1 164.0
2001 167.3 167.5 167.1
2002 169.1 167.8 170.3
2003 173.4 172.3 174.5
2004 180.3 179.1 181.6
2005 186.2 185.0 187.4
2006 189.5 188.8 190.3
Note. Source: Bureau of Labor Statistics (2007).
Table 8
Descriptive Statistics on Viability Scores for Parish Schools
Included in the Study
Status N M SD SE
Open 92 .5968 1.030 .1074
Closed 32 -1.716 .9067 .1603
Table 9
Data for St. Elsewhere Parish Elementary School
Enrollment Change Tuition AMHI Tuition/AMHI
1999-2000 225 (1)
2000-2001 220 (1) -.02222 $3,000 $51,428 .05833
2001-2002 210 (1) -.04546 $3,180 $53,141 .05984
2002-2003 195 (0) -.07143 $3,375 $53,236 .06340
2003-2004 175 (0) -.1026 $3,575 $54,664 .06540
2004-2005 180 (0) .02857 $3,790 $56,821 .06670
2005-2006 185 (0) .02778 $4.025 $58,693 .06858
2006-2007 183 (0) -.01081 $4.275 $59,899 .07137
Table 10
Enrollment, Viability Score and Descriptor for St. Elsewhere Parish
School 1999-2007
Enrollment Score Descriptor
2000-2001 220 .74 Green -
2001-2002 210 .52 Green -
2002-2003 195 -.99 Yellow -
2003-2004 175 -1.28 Orange -
2004-2005 180 -.24 Green +
2005-2006 185 -.27 Green -
2006-2007 183 -.64 Yellow -
