UBRAhY
Michigan State
University

 

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2/05 c:/CIRCIDateDuo.indd-p. 15

 

mu‘i’apet” .. ' -
P oflotsky, Greg F. 1998

The Effect of Educational Expenditures
on Crime & Juvenile Arrest Rates
in Michigan Cities

Plan “B” Thesis
by
Greg F. Orlofsky
Submitted to
Michigan State University

in partial fulfillment of the
requirements for the degree of

Master in Urban & Regional Planning - Urban Studies

Spring 1998

ACKNOWLEDGMENTS

I wish to extend my gratitude to the following groups and individuals for their assistance in
compiling and analyzing the necessary data: Greg Winter; Christina Dejon g, Ph.D;; Glenda Rader,
Michigan Department of Education, Office of Administrative Services; Bernadette Scarborough,
Michigan Department of State Police, Central Records Division (Uniform Crime Reporting); and the
Michigan Information Center. 7 . 'j‘ ”1;.

Special thanks to Zenia Kotval, AICP, PhD. and James Mertes, PhD. for their help in preparing
this document. And finally, I wish to express my sincerest appreciation to my friends and family for their

continuing support.

TABLE OF CONTENTS

EXECUTIVE SUMMARY

I. Introduction

11. Purpose

III. Methodology

IV. Background on “Proposal A”

V. Education, the Community and Crime

VI.
VII.

VIII.

IX.

A. Educational Indicators

B. Education and Economic Activity in States
C. Involving the Public More in Education

D. Education as a Deterrent to Criminal Activity
Data Collection

Data Adjustment

Correlations

A. Results from Correlations

B. Discussion
Regressions

A. Results from Regressions

B. Discussion

Conclusions

Page Nmber

11
11
11
12
13
17
21
30

33

53
55

56

59

60

Bibliography

APPENDIX A - list of Variables
APPENDIX B — Population Estimates
APPENDIX C - Descriptive Statistics
APPENDIX D - Distributions
APPENDIX E - Correlation Output

APPENDIX F - Regression Output

Page Number
61

A1

A4
A6
A16

A32

EXECUTIVE SUMMARY

In 1994 the Michigan legislature passed “Proposal A” which abolished property taxes as
the primary source of funding for public education and raised the State Sales Tax by 2%. The
change in the tax structure means that all consumers are now contributing to the generation of
funds for public education. The purpose of this paper was to discover whether or not
communities, in general, benefit from increased levels of spending on local education through a
reduction in crime and juvenile arrest rates.

It has been argued that changes in educational spending per student is not an adequate
means of measuring student performance and that testing scores (i.e. ACT, SAT, etc.) would be
better indicators, however, for the purpose of this paper, spending per student is the appropriate
variable, as student performance is not in question. Previous efforts to show linkages between
crime and education have been fairly unsuccessful, still, no attempts have been made to link the
actual amount of spending on education to crime. The unit of analysis in past studies seems to
have always been the individual, rather than the community. Moreover, recent studies do imply
that education increases the productivity of workers and that productivity can, in turn, lead to a
reduction in crime.

Data for the study was obtained from the Michigan Department of Education and
Michigan Department of State Police. Crime/juvenile arrest variables were adjusted using
population estimates to develop rates. Expenditure data was adjusted for inflation. There were
five variables used in the study. Current Operating Expenditures per Pupil was the independent
variable. Index Crime Rate, Non-Index Crime Rate, Total Crime Rate and Juvenile Arrest Rate
represented the dependent variables.

Scatterplots accompanied by a fit—line provided a visual indication as to whether or not
these variables were, in fact, related. These inferences were confirmed using statistical analysis
(i.e. Pearson’s correlation coefficient r and a t-test). The level of significance for the null
hypothesis to be rejected was < .05 (95% confident that there was not a Type I error). Analysis
for those correlations found to be statistically significant, proceeded further with regressions to
determine the extent of the relationship between the two variables (coefficient of determination r’)
and the regression equation to see exactly how crime rates vary with increases in operating
expenditures. .

The results seemed to indicate that communities with large amounts of expenditures are
just as susceptible to crime and juvenile arrests as are communities with relatively low
expenditures on education. Comparing % change in expenditures with % change in crime/juvenile
arrest rates also produced insignificant results. When a “five-year lag” was incorporated into
expenditures there appeared to be a clear relationship between the two variables. Although, still
statistically insignificant, the likelihood of a Type I error had fell considerably. Finally, when a
“ten-year lag’ was incorporated there were three statistically significant correlations between %
change in current operating expenditures and % change in the index crime rate, non-index crime

rate and total crime rate. This implies that there is a relationship between educational
expenditures and crime/juvenile arrest rates, but only in the long term.

There was no relationship between changes in the juvenile arrest rate and expenditures,
even with regard to the longest lagged correlation. This was attributed to the fact that increased
expenditures help students to form a more law-abiding community when they are older. After all,
juveniles, would cease to be juveniles after ten years. The fact that there was no relationship in
the non-lagged correlations would seem to indicate that a community doesn’t benefit from
increased expenditures on education until juveniles have grown and become part of the adult
community.

Changes in the Non-Index Crime Rate appears to be affected most by changes in
expenditures, followed by the Total Crime Rate and finally the Index Crime Rate. The coefficient
of determination (r’) for the Non-Index Crime Rate was 16.6% (i.e. explained variation). While
this may not seem significant , the goal here was not to “explain” crime rates. The purpose was to
determine whether or not, educational expenditures alone can have any impact on crime rates.
The results from the regressions indicate that there is, in fact, a relationship between the two.

Whether the relationship between the variables found to be related is truly a causal one
may remain suspect. Since the unit of observation had to be consistent for the study, the sample
was in effect, non-random. It may be inappropriate to generalize the findings of this study to
cities that did not meet the criteria for the sample. Nonetheless, the very fact that a relationship
was found to exist between the variables, even if only in lagged correlations, serves as a step in
the right direction with regard to showing the way in which public education is, in fact, a benefit
to all.

I. Introduction

Traditionally, local property owners have contributed the most revenue to public
education. In 1994 the Michigan legislature passed “Proposal A” which abolished property taxes
as the primary source of funding for public education instead raised the State Sales Tax by 2%.
This was done to equalize both the amount of revenue that is going to each of the school systems
in the State and the way in Which revenue is generated (provide property tax relief and make
revenue generation more equitable). With the addition of the 2% increase in the Sales Tax, which
goes towards the public schools, all consumers are in effect contributing to the generation of
funds for public education. While education has, for the most part, stayed out of the planning
arena, this Legislative act has implications for not only students and parents, but for the rest of the
community as well.

The purpose of education, as most see it, is to provide children with an adequate
knowledge base so that they may become willfully employed. It seems safe to assume that the
benefits parents derive in paying for the school system is that their children are educated. Very
often non-parent property tax-payers contend that they derive no benefit from having other
people’s children educated and therefore should not be held responsible for funding schools. With
the introduction of Proposal A, all members of the community are now contributing, to a certain
extent, revenue for the public school system. It is my contention that there does exist a benefit to

the community from public education.

1]. Purpose

The purpose of the paper is to discover whether or not communities, in general, benefit
from increased levels of spending on local education through a reduction in crime and juvenile
arrest rates. Crime data will be correlated with figures on educational expenditures to determine
which, if any, are correlated with one another, if the relationship is positive or negative, and finally
which indicator is most affected by changes in spending per pupil. For those variables that do
exhibit a correlation, regressions will be run to determine to what extent they are related and their
regression equation.

There are many implications for this research. If, for instance, education is found to
contribute to the rest of the community through a reduction in crime rates, then equity issues
about the way in which school’s are funded (i.e. whether there should be less of a reliance on
property owners) could be addressed by future legislation. Although the focus here is the benefit
to a community from public education, if there is in fact a relationship between expenditures and

crime rates, that variable could be used in modeling crime rates for multiple regressions.

III. Methodology

The study began with a more in-depth investigation into .“Proposal A” followed by a
review of existing literature to demonstrate the linkages between education, the community and
crime. Next, data on school expenditures, crimes and juvenile arrests were collected from various
state agencies. Once compiled, crime data was adjusted using population estimates so that
“rates” could be developed. Educational expenditures data was also adjusted to reflect inflation.
Finally, the data was aggregated into five-year time periods so that “lags” could be incorporated
into the study.

Analysis of the data began with correlations between crime/juvenile arrest rates and
inflation adjusted expenditures. The following general hypotheses were tested:

Ho: The crime/juvenile arrest variable (independent) and the variable on educational
expenditures (dependent) are INDEPENDENT events (crime/juvenile arrests (10—th
depend on the educational expenditures).

HA: The crime/juvenile arrest variable (independent) and the variable on educational
expenditures (dependent) are INDEPENDENT events (crime/juvenile arrests d_o depend
on the educational expenditures).

Hypotheses were “non-directional” to account for the possibility of a positive relationship

between the variables.

Scatterplots accompanied by a fit-line provided a visual indication as to whether or not the
variables were, in fact, related. These inferences were confirmed using statistical analysis (i.e.

Pearson’s correlation coefficient rand a t-test). The level of significance for the null hypothesis to

be rejected was < .05 (95% confident that there was not a Type I error).

Analysis for those correlations found to be statistically significant, proceeded further with
regressions to determine the extent of the relationship between the two variables (coefficient of
determination r’) and the regression equation to see exactly how crime rates vary with increases in
operating expenditures. The final section suggests directions for future research.

The methodology and remaining sections of the paper are then ordered as follows:

“Proposal A”

L
Education, the Community and Crime
L
Data Collection
L
Data Adjustment
L
Correlations
(discussion)

L
Regressions
(discussion)

L

Conclusions
(future research)

IV. Background on “Proposal A”

Prior to 1973, Michigan used a “Minimum Foundation Approach” in which the State
guaranteed a per pupil cost to local school districts which depended upon the local districts
levying the state-determined tax rate. The State paid the difference between what was generated
under this tax rate and the guaranteed per-pupil cost. If they levied less that the State tax rate,
then local districts received less than the guaranteed amount.

In 1973, the State switched to the “District Power Equalization Approach” which provided a
guaranteed revenue yield and paid each district below this yield the difference in the form of State
formula aid payments. If a district’s local revenue per pupil per mill exceeded the State’s
guaranteed revenue under this formula, the district was “out-of-formula” and thus received no
state aid. The benefit of this system was that school districts had the power to raise more revenue
than was guaranteed by the State by levying a higher millage rate. This gave school districts more
“control” over the amount of funds that their schools received, and the only disadvantage to them
was that they were unable to receive state aid. The drawback of this system, however, was that it
produced great wealth disparities between districts. Wealthier districts that enjoyed a high
property tax base could raise large revenues with a low property tax rate, while poorer districts
with a low tax base raised less money even after levying high rates. In other words, where a
family lived largely determined the quality of its children’s education.

Between 1972-1987, Michigan voters rejected nine of ten proposals to alter taxes, three of
which involved education. One election in particular in 1981 involving a plan called Proposal A—

a property tax cut tied to a sales tax increase was defeated by a 3-1 margin. For fifteen years

people were debating about property taxes and education and yet nothing had been resolved
(Christoff, 9/27/87).

On February 10, 1987 the Michigan Citizens PrOperty Tax Commission released a 38 page
report recommending long-term changes in the property tax system, especially with regard to the
financing of elementary and secondary education. It recommended an increase in the sales tax to
6% and advocated that the state should guarantee a $3,000—per—pupil minimum (similar to the
recommendations made in 1981). The report said that the minimum amount would ensure that all
students in Michigan receive a basic quality education regardless of theeconomic wealth of their
jurisdiction. The report suggested that local property taxes in support of schools be lowered from
the current average of 32 mills to an average of less than 20 mills (1/ 1000 of the taxable property
value) (Jones, 2/10/87).

The purpose of this proposed legislation was not only to reduce property taxes for
homeowners and business owners but to reduce the enormous gap in funding that existed between
school districts. Whitefish School District in Chippewa County had the highest level of current
operating expenditures per pupil for the 1985-86 school year with $6,208. Kingsley Area School
District in Grand Traverse County ranked lowest (525“) with $2,107. This amounted to almost a
3-1 ratio between the highest and lowest funded schools.

A key recommendation in the Commissions’ report was the increase in the statewide Sales
Tax from 4% to 6%. The increase in sales tax revenue would be pulled and redistributed to all
school districts by the state so that wealthier districts would no longer be able to create such

disparities (DFP, 9/24/87).

Wealthier districts were against the proposal, arguing that their schools were being
punished just because they had a natural advantage. State Treasurer Robert Bowman also
objected to the proposal, saying it could cause a huge flow of tax revenues to Washington, since
property taxes are deductible on federal income tax returns and sales taxes are not. Others
claimed that sales taxes are regressive, and consequently, the poor would suffer the greatest
burden. Since many of the poor do not own their own homes, property tax cuts do not provide
any kind of relief, conversely the poor do spend a relatively large amount on consumer goods that
are charged sales tax (Stroud, 5/9/93).

Proposal A, first voted on in 1981, reemerged as a special issue twelve years later in 1993
and was defeated. In July 1993, without regard to the consequences, the Michigan State House
and Senate overwhelmingly approved a $5.6 Billion property tax cut (PA 145 of 1993) without
identifying replacement funds for the school system (Andrews, 7/23/93). Many schools were left
wondering how they would be able to function the following school year. In Eaton Rapids,
property taxes made up about 58% of the school budget—approximately $7.8 million. In East
Lansing, 94% or nearly $23 million of the school budget was generated through property taxes all
of which was reduced as a result of the cuts (Iorio, 7/23/93). On March 15, 1994, voters were
asked once again to choose between alternative plans to fill the hole in funding left by the
property tax cut eight months earlier, Proposal A was passed by a 69-31margin (Kearney, 1994).

The following, outlines the specific changes made in the taxing system as a result of

Proposal A:

Sales Tax

Income Tax

Property tax (mills):
Homestead
Second homes
Comm. & Ind.
Enhancement
ISD’s

Assessment cap

Property transfer tax

Single business tax

Cigarette tax

Out-of-state calls

Personal income

Tax exemption

Pre-propgsal A

4%
4.6%

34 (average)
34 (average)
34 (average)
N/A

3 (average)
N/A
.0011%
2.35%

25 cents

4%

$2,100

Proposal A

6%
4.4%

6
24
24
3

3 (average)
5% or CPI

2.0%
2.35%
75 cents
6%

$2,100

Source: A Primer on Michigan School Finance, C. Philip Kearney

10

V. Education, the Community and Crime

A. Educational Indicators

It could be argued that changes in educational spending per student is not an adequate
means of measuring student performance and that testing scores (i.e. ACI', SAT, etc.) would be
better indicators when trying to establish a correlation between education and other variables (in
this case crime and juvenile arrest rates). Still, many schools are often accused of excluding low
scoring children from testing and focusing instruction on the skills measured by tests only
('Murname, 1988). This would make the level of spending a more attractive variable. For the
purpose of this paper, spending per student is the appropriate variable, as student performance is
not in question. Students are not the unit of observation, rather it is communities and the benefit
each derives from the level of spending. If spending levels do not mirror performance, then
students, in turn, will not be productive in the future, which will only prove that spending on
education has no positive effect on communities.

Despite the comments of Former U.S. Secretary of Education , William Bennett who
contended that there was not a strong correlation between school spending and achievement
referring to earlier research in an article “The Economics of Schooling: Production and Efficiency
in Schools” (Hanushek, 1986); more recent findings suggest the relationship between spending
and achievement was much higher than previously thought. Educational levels not “gains” were

the focus of the earlier study which did not address what students had learned (Baker, 1991).

B. Education and Economic Activity in States
According to one study (Quan et al., 1987), the level of educational services may affect

economic growth in a state in two ways. Potential migrants may choose to locate in states that

11

have a higher quality of education. This, in turn, contributes to a growing population, increasing
both the supply of labor and the demand for local goods. They have termed this “parental
migration effect” (Quan, 1987, 361). And secondly, as noted by other studies (Mandi, 1981,
Teng, 1991, and Psacharoopoulos, 1993) with regard to the effects of education on nations,
education may increase the productivity of workers. This productivity effect should raise wage
rates in a state, however the studies revealed that increases (or decreases) in the quality of
education can only be expected to have an impact on nations after 4 to 9 years and used lags in
their regression to adjust for this contingency.

Their findings seemed to indicate that the effects of eduCational expenditures on the levels
of wages and employment differ in the Northeast and the Sunbelt. Education expenditures have
positive and significant effects on the levels of wage and employment in the Northeast, while the
reverse is true in the Sunbelt. These “Northeast states” included, among others, Illinois, Ohio and

Wisconsin, but not Michigan (Quan, 1987).

C. Involving the public more in Education

There is a fear that schools are becoming disconnected from the public. One of the main
criticisms of Proposal A is that it has taken away “local control” of the schools and put it in the
hands of the State. State dollars now provide 75% of the revenue schools receive with local
revenues making up approximately 19% and the remaining 6% from the national government
(Kearney, 1994). As part of Proposal A’s mission, wealthier districts are less capable of raising
their millage rates and increasing the amount of local funds available to schools.

For some, problems with schools seem to be a question of “legitimacy” rather than

effectiveness of the public schools. There are a great many people who don’t believe that the

12

public schools are their agents, who don’t believe that the public schools are responsive to their
concerns. Consequently, the public needs to get more involved (Danzberger et al., 1994). Social
problems that affect student achievement can be addressed only if schools, families, and
communities work together. And yet budget battles and voucher movements attest to the public’s
growing disenchantment with the public school system. The issue of legitimacy lies in the fact
that people don’t recognize that schools serve a public purpose, that everyone benefits not just
parents. The solutions they suggested involve focus groups and town meetings to discuss the
state of local education (Mathews, 1997).

If linkages between community and spending on public education exist, it could be a
catalyst in increasing the amount of community involvement the authors above believe are
necessary to increase student achievement because communities will have a proven stake in the

effectiveness or “legitimacy” of schools.

D. Education as a deterrent to criminal activity

For years, the widespread assumption about the role of schools is that they function as a
positive form of social control and act as a deterrent to delinquent behavior. Schools provide an
important environment in which children learn to be law-abiding, and acquire the qualifications
that give them the opportunity to have a stake in society (Gilling, 1997). Those who do not
perform well, and perhaps even drop out, are presumed to be less likely to obtain employment,
which, in turn, could lead to criminal behavior. Assumptions, such as these, have led to policy
making to increase educational opportunities for young people and encourage those who have
already “dropped out” to return to school, all in an effort to reduce delinquency and crime

(Wolfgang et al., 1987).

13

Increasing amounts of delinquent behavior, both in and outside of schools, has been
attributed by some to impersonal atmosphere created there as a result of budget crunches which
have led to the consolidation of school districts, large classes, and other factors which have
reduced opportunities for positive social interaction between school personnel and students
(Kratcoski, 1990).

The majority of young men and young women participate in some kind of delinquent acts
during their juvenile years. As one researcher noted, “By the age of 18 possibly over 90 percent
of young males have participated in delinquent acts. . .50 to 60 percent of young females have
been involved in delinquent acts by the time they are 18” (W itte, 1997, 219). Still, for most
individuals, criminal activity is usually restricted to the teen years and those individuals who began
offending as juveniles have usually stopped by their mid-twenties.

According to the National Crime Survey, the level of crime today is lower than it was in
the late 1970’s and early 1980’s for crimes such as rape, aggravated assault, burglary, and
larceny, as well as less-serious (non-index) offenses (W itte, 1997). Juvenile arrests, however,
have been on the rise, particularly in Michigan. Between 1990 and 1994 juvenile arrests in the
state increased by nearly 8%. Of these approximately 47% were index crimes1 (Stoetzer et al.,
1997).

In 1996, the Institute for Public Policy and Social Research (IPPSR) at Michigan State
University conducted its State of the State Survey (SOSS) of adult residents in Michigan. One of

its goals was to gather information on perceptions of the causes of juvenile delinquency”.

 

A listing of index crimes appears on page 19.

2 “Juvenile” for this study meant persons under the age of 18.

14

Irresponsible Parents
Gangs

No Punishment
Crinind Parents
Poor Justice System
Poor Schools

Not Enough Jobs

Single Parents

Factors which Michigan residents believe contribute “a great deal” or “quite a bit” to why

Perceived Causes of Delinquency

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0%

_, 79%
70%
'* 53%
50%
. -— 53%
- «.1 53%
20% 40% 50% 80%

.A Great Deal .Quite a Bit

 

some teenagers are juvenile delinquents.

MSU State of the State Survey (SOSS)
Spring 1996; N = 1133; Sampling Error = 2.9%
Michigan State University, lPPSR

As the figure shows, people seemed to believe that the principle reasons for delinquency
were irresponsible parents (79%) and gangs (70%) (these factors contributed “a great deal” or

“quite a bit” ). While it was not at the top of the list, “poor schools” tied for fifth as a perceived

influence of juvenile delinquency (53%) (Stoetzer et al., 1997).

Despite all the assumptions and perceived influences, there is little evidence that education
and criminal activity are related. Education has apparently not been analyzed in any great detail in
correlational studies of crime. When studies did include education as a variable (measured in

terms of grade completed, possession of a high school diploma, or scores on tests), researchers

15

 

concluded there was so significant relationship to crime. Some correlational studies, however, did
find a statistically significant inverse relationship between the amount of time spent and the level
of criminality. . These results have been interpreted as indicating the importance keeping young
people in school and off the streets (Witte, 1997).

While efforts to show linkages between crime and education have been less than startling,
it appears that no attempts have been made to link the actual amount of spending on education to
crime. Moreover, the unit of analysis in past studies seem to have always been the individual,
rather than the community.

Recent studies do imply that education increases the productivity of workers and that

productivity can, in turn, lead to a reduction in crime.

 

 

 

Increases Reduction
Education “'9 Productivity _) in Crime

 

 

 

 

 

 

 

It is with this assertion, that this study shall continue.

16

VI. Data Collection

The purpose of the study is to compare data on school expenditures per student with data
on various crimes and juvenile arrests. Crime/juvenile arrest data is available on an annual basis at
the city level; while annual data on expenditures per student is available only for each school
district. School districts may cut across city boundaries and may include more than one city.
Cities may, in fact, have more than one school district operating within their city limits. The unit
of analysis must remain consistent across variables, therefore cities were chosen based on the
following criteria:

1) Current Operating Expenditures per Student (COES) data exists for each city/school

district selected for years 1974-93 (i.e. the school district has operated since at least
1974).

2) There is only one school district for each city.

3) The school district entirely surrounds the city boundaries so that the figures to which it
will be compared (crime rates) are consistent with the data on expenditures.

4) Crime data is available for cities for years 1982-1993.

While this does represent a non-probability (purposive) sample, and therefore has certain
drawbacks with regard to the generalization of the results, using a probability sample (i.e. simple
random sample, systematic random sample, stratified random sample, etc.) would undoubtedly
produce cities in the sample population that would not fit the criteria and not be consistent with
the unit of analysis.

The data had to be available for the range of years mentioned above so that time-periods
and lags could be developed. This will be outlined in greater detail below. The following is a list

of thirty-nine cities with their corresponding school district chosen for the study:

17

CITY

Allen Park
Alpena1
Benton Harbor
Berkley
Birmingham
Cadillac‘
Clawson
Dearbom

East Lansing
Ecorse
Escanaba1
Femdale
Garden City
Hamtramck
Harper Woods
Hazel Park
Highland Park
Kentwood
Lincoln Park
Menominee‘
Mt. Clemens
Muskegon Heights
Norton Shores
Novi

Oak Park
Owosso
Portage
Romulus
Roseville
Royal Oak
Southfield
Southgate
Sterling Heights
Taylor
Traverse City1
Trenton

Troy

‘ Westland
Wyandotte

 

1

DIST'RI NAME

Allen Park Public Schools

Alpena Public Schools

Benton Harbor Area Schools
Berkley School District
Birmingham City School District
Cadillac Area Public Schools
Clawson City School District
Dearbom City School District
East Lansing School District
Ecorse Public School District
Escanaba Area Public Schools
Femdale City School District
Garden City School District
Hamtramck Public Schools

City of Harper Woods Schools
Hazel Park City School District
Highland Park City Schools
Kentwood Public Schools

Lincoln Park Public Schools
Menominee Area Public Schools
Mt. Clemens Community Schools
Muskegon Heights School District
Mona Shores Public School District
Novi Community School District
Oak Park City School District
Owosso Public Schools

Portage Public Schools

Romulus Community Schools
Roseville Community Schools
School District City of Royal Oak
Southfield Public School District
Southgate Community School District
Utica Community Schools

Taylor School District

Traverse City Area Public Schools
Trenton Public Schools

Troy School District
Wayne-Westland Community School District
Wyandotte City School District

An intermediate school district is also located in these cities which directs expenditures for other services

(special education, etc.). All public schools have intermediate school districts like these that direct these

operations for a region.

18

Data on school expenditures was obtained from the Michigan Department of Education’s,
“Ranking of Michigan Public School Districts by Selected Financial Data: Bulletin 1012” for
years 1974-75 through 1992-93. The publication includes several general fund expenditure
categories for each of the school districts in the state. The category chosen for this study was
Current Operating Expenditures per Pupil (COEP). This category represents the closest
approximation of the dollar amount devoted to each pupil in each school district and does not
include payments for community services and capital outlay.

Crime and Juvenile arrest variables were taken from the Michigan Department of State
Police “Crime in Michigan: Uniform Crime Reports” for the years 1982-93. Four variables were
used to compare to educational expenditures: Index Crimes, Non-Index Crimes, Grand Total

Crimes, and Juvenile Arrests.

Index crimes include the following:

Murder & Non-negligent Manslaughter
Rape

Robbery

Burglary

larceny

Motor Vehicle Theft

Arson

 

‘ In 1980 the subheading of the publication was changed from Bulletin 1012 to Bulletin 1014.

19

Non-index crimes include the following:

Negligent Manslaughter

Assault (Non-aggravated)

Forgery & Counterfeiting

Fraud

Embezzlement

Stolen Property

Vandalism

Weapons (carry, possession, etc.)
Prostitution & Common Law Vice

Sex Offenses (except rape & prostitution)
Narcotic Laws

Gambling

Family & Children

Driving Under Influence Alcohol or Narcotics
Liquor Laws

Disorderly Conduct

All Other (includes drunkenness & vagrancy)

Total crimes are the sum of all index and non-index crimes and juvenile arrests includes arrests

of all individuals ages 16 & under.

VII. Data Adjustment

Expenditure data was adjusted for inflation using annual % change rates from a Consumer

Price Index (CPI) for all urban consumers.

 

 

 

CONSUMER PRICES - ALL URBAN CONSUMERS
1970 THROUGH 1998
(1982-84 a 100)
CALENDEFI u.s. PERCENT
YEAR CONSUMER CHANGE
PRICE INDEX
1970 38.8 5 9
1971 40.5 4 3
1972 41.8 3 3
1973 44.4 8 2
1974 49.3 11 o
1975 53.8 9 1
1978 58.9 5 8
1977 80.8 8 5
1978 85.2 7 8
1979 72.8 11 4
1980 82.4 13 5
1981 90.9 103
1982 98.5 82
1983 99.8 3 2
1984 108.9 43
1985 107.8 3 8
1988 109.8 1 9
1987 113.8 3 8
1988 118.3 4 1
1989 124.0 4 8
1990 130.7 5 4
1991 138.2 4 2
1992 140.3 3 o
1993 144.5 3 0
1994 148.2 2.8
1995 152.4 2.8
1998 158.9 3.0

 

 

 

Source: U.S.DeparunentotComrnerce,BweeuothorStatlstlca

21

Formulas for adjustment of the data are shown below:

Adjusted cores 1975 = (COEP 1975)/(1 + % Change in CPI from 1974 to 1975)

= (COEP1975)/(1+0.091) -.- (COEP1976)/1.091

Adjusted COES 1976 = (COEP1976)/{(1.091)*(1 + 0.058)}

Adjusted C0E51977 = (COEP1977)/{(1.091)*(1.058)*(1 + 0.085)}

And so forth, through 1993 in which the adjusted figure is:

Adjusted COEP1993 = (COEP1998)/{ (1.091)*(1.058)*(1.065)*(1.076)*(1.114)*
(1 .135)*(1 .103)*(1 .062)*(1.032)*(1 .043)*
(1 .036)*(1 .041 )*(1 .048)*(1.054)*(1 .042)*
(1 .030)*(1 .030) }

There is a significant difference between adjusted and unadjusted expenditures. Below is

the average expenditures for years 1974-93 for the thirty-nine cities Chosen for study.

22

 

Current Operating Expenditures per Pupil 1974-1993

 

 

—— Man-Adjust
— Adjust

8 Level
i

 

 

 

 

1974 1979 1984 1989 1994

Yes

 

 

 

 

 

 

Only inflation adjusted expenditures will be used in the study. Graphing average inflation
adjusted expenditures alone enables us assess the amount of volatility that exists over the 20 year

span.

 

Inflation Adjusted Current Operating Expenditures
per Pupil 1974-1993

 

1800
161114?
1400 ’
12m 1. , .
11!!) t ‘ . : '

1974 1979 1984 1989 1994

3 Level

 

 

 

 

Year

 

 

 

23

Crime/juvenile arrest variables were also adjusted with regard to the population of the city
to develop a rate per 100,000 people. Census figures can be used to adjust for population,
however, the decennial census would provide only one change in population over the twelve-year
span. To adjust the data more accurately, population estimates were obtained from the Michigan
Information Center. ‘ Population estimates were available annually at the sub-county level for the
year 1990-93 (1990 = Census data). For the years 1982-89, estimates were only available on a

biannual basis (even years), therefore, uneven years were adjusted using the most recent estimate.

 

' A complete listing of population estimates for each of the thirty-nine cities appears in Appendix B.

Example using the City of Alpena:

Crime/Juvenile Arrest Rate per 100,000 = {(Annual # of Index Crimes)/(Population)} * 100.000

1984 1985 1988 1987
Index Index Index Index
Crime Crime Crime Crime

Total Total Total Total

City of Alpena 537 567 621 601

Population Population Population Population
Estimate Estimate Estimate Census
1984 1988 1988 1990

CityofAlpena 11535 11290 11350 11354

1984 Alpena Index Crime Rate
per 100,000 people

= {(537)/(11535)}*100000

= £5.15

1989 Alpena Index Crime Rate
per 100,000 people

= {(606)/(11350)}*100000

5332.21

1988
Index
Crime
Total

680

1989
Index
Crime
Total

606

1990
Index
Crime
Total

607

{(1984 # of Index Crimes)/(1984 Population» * 100,000

{(1989 11 of Index Crimes)/(1988 Population» * 100,000

Rates were developed for non-index crimes, total crimes and juvenile arrests in the same fashion.

The following graphs show average trends for the thirty-nine cities with regard to these rates.

 

 

Rate per 100,000 people

Average Index Crime Rate 1982-1993

7700
7500
7300
7100
6900
6700

6500 t t '
1982 1986 1990 1994

 

 

 

 

 

Year

 

 

 

Rate per 100,000 people

Ave rage Non-Index Crim e-Rate
1982-1993

 

 

 

 

 

Year

 

26

 

 

 

 

Average Total Crime Rate 1982-1993

 

18000..
17000..
16°00 —9 _ i
15000 _ ,
14000 I f f
1982 1986 1990 1994

 

 

 

 

Rate per 100,000 people

Year

 

 

 

Rate per100,000 people

Average Juvenile Arrest Rate
1982-1993

 

880
640 ;4 ' w
620

 

6°° 1 “
58o '

 

 

560 . . . .. _, _ .‘ ,
1982 1986 1990 1994

 

Year

 

27

 

 

There is a lot more volatility in the average crime/juvenile arrest rates than in inflation
adjusted expenditures. This proved difficult in finding a relationship between each of the
variables, particularly the juvenile arrest rate; however, since the graphs above only show the
“average” trends, they say nothing about the way in which inflation adjusted expenditures and
crime/juvenile arrest rates are correlated with one another for each individual city.

In order to correlate the variables, data on expenditures and crime/juvenile arrest rates

were placed into four time periods for each of the cities in the study. The four periods are as

follows:
Period Years Data required
Period 1 1974-1978 Expenditure data only
Period 2 1979-1983 Expenditure data only
Period 3 1984-1988 Expenditure & Crime data
Period 4 1989-1993 Expenditure & Crime data

In order to make use of “lags” in the study, percentage change rates were developed using the
procedure below:
1) Sum the inflation adjusted expenditures and crime/juvenile arrest rates for each of the
applicable periods to develop four five-year time periods.

2) Develop a % change rate for expenditure data between the first and second period, the
second and third period and the third and fourth period.

3) Develop a % change rate for crime/juvenile arrest rates between the third and fourth
periods.

Current Operating Expenditure per Pupil (COEP) data actually cut across two different
years (as does the school year) so the latter year was arbitrarily Chosen as the year to which

crime/juvenile arrest rates will be compared.

Example using the City of Alpena:

1974 1975 1976 1977 1978
Adjusted Adjusted Adjusted Adjusted
COEP 73-74 COEP 75-76 COEP 75-76 COEP 76-77 COEP 77-78

Alpena 978 1018 1090 1062 1054

Alpena Adjusted COEP for Period 1 (1974-78) = 5202

This represents the entire amount of dollars spent on each student for the years-1974
through 1978 in the City of Alpena. Figures were computed for each of the other three periods

and a % change computed from one period to the next.

Alpena Adjusted COEP for Period 2 (1979-83) 5150

% Change in COEP from Period 1 to Period 2 {(Adjusted COEP for Period 2 -
Adjusted COEP for Period 1 ) /

(Adjusted COEP for Period 1 )} * 100

= {(5150 -5202)/5202}* 100

0.999616

Crime/juvenile arrest rates were adjusted in the same fashion to develop % change rates

for the applicable periods. ‘

 

‘ A complete listing of all variables is located in Appendix A.

29

VIII. Correlations

There were twenty individual correlations of variables (4 sets). Below are a list of the

independent and dependent variables in each as well as a description of what each was to

accomplish.
Set 1
Dependent variable Independent variable
Crime/Juvenile Arrest Expenditures Period 3
Rates for Period 3: Index

Non-Index

Total

Arrest
Crime/Juvenile Arrest Expenditures Period 4
Rates for Period 4: Index

Non-Index

Total

Arrest

The eight correlations above will only indicate whether or not cities that have higher
expenditures on education have correspondingly lower (or higher) crime rates. It would say
nothing about whether or not a change in expenditures would potentially change crime rates. To
find out whether or not a change in the level of expenditures has any effect on crime/juvenile

arrest rates, data on the % change from one period to the next must be utilized.

30

Set 2

Dependent variable Independent variable
% A in Crime/Juvenile % A in Expenditures from
Arrest Rates from Period 3 to Period 4
Period 3 to Period 4: Index
Non-Index
Total
Arrest

The four c0rrelations above will show whether or not the change in expenditures from the

third period to fourth produced any effect on the change in crime/juvenile arrest rates from the

third period to the fourth.
Set 3
Dependent variable Independent variable
% A in Crime/Juvenile % A in Expenditures from
Arrest Rates from Period 2 to Period 3
Period 3 to Period 4: Index
Non-Index
Total
Arrest

These next four correlations allow a five-year “lag” in the comparison of variables by
comparing the change in expenditures from the second to third period with change in

crime/juvenile arrest rates from the third to fourth period.

31

Set 4

Demndent variable Indemndent variable
% A in Crime/Juvenile % A in Expenditures from
Arrest Rates from Period 1 to Period 2
Period 3 to Period 4: Index
Non-Index
Total
Arrest

The final four correlations compare the change in expenditures from the first to the second
period with the change in crime/juvenile arrest rates from the third to the fourth period to allow a
longer (IO-year) “lag” to be used. Data from each of the four sets of correlations was analyzed to

determine whether or not the relationship between the variables is statistically significant. ‘

 

' Data was analyzed using Statistical Programming for Social Science (SPSS) software. Correlation output with
matrices with Pearson’s (r), two-tailed significance and number of cases are located in Appendix E.

32

A. Results from Correlations
Set 1

H0: Current Operating Expenditures per Pupil for period 3 (1984-88) and Index Crime Rate
for Period 3 are independent events (r is equal to 0).

Ha: Current Operating Expenditures per Pupil for period 3 (1984-88) and Index Crime Rate
for Period 3 are related. (r is not equal to 0).

 

 

 

 

 

100000
0
D
80000 .
D
D
m D
3 80000 -
' D
E a °
E I: a :1
I
6 40000 E o .—. a n a
D D
D D
D D ch a
D c1 :1
Q: B u I:
20000 1 o
D
D D D
o H I r ’r
4000 8000 8000 10000 12000 14000

COEP for Period 3

The data points are dispersed evenly about the graph. The slope of the fit line is nearly
horizontal, perhaps even positive, indicating there is almost no relationship between these two
variables.

Statistical Analysis
Pearson’s Correlation (r) = .002

Significance (2-tailed) = .992

As expected, the correlation is not significant (.992 > .05) and therefore the null hypothesis can
not be rejected.

33

Ho: Current Operating Expenditures per Pupil for period 3 (1984-88) and Non-Index Crime
Rate for Period 3 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 3 (1984-88) and Non-Index Crime
Rate for Period 3 are related (r is not equal to 0).

 

120000

100000‘ D

80000. D

60000' a a

NICRforPen'od3

40000 . '3

20000I a a

 

 

 

TIF’T TITT’ - I
4000 6000 8000 10000 12000 14000

COEP for Period 3

The data points again seem to have a good dispersion. The slope of the fit line is negative,
however it doesn’t seem great enough to be significant.

Statistical Analysis

Pearson’s Correlation (r) = -. 148
Significance (2-tailed) = .369

The correlation is not significant (.369 > .05) and therefore the null hypothesis is accepted.

34

H0: Current Operating Expenditures per Pupil for period 3 (1984-88) and Total Crime Rate
for Period 3 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 3 (1984-88) and Total Crime Rate
for Period 3 are related (r is not equal to 0).

180000

 

160000I

140000'l 0

120000“

100000! a

80000! a

TCFtbrPaiod3
0

60000! a D D

40000' a

 

20000 r 4 _ r
4000 8000 8000 10000 12000 14000

 

 

COEP for Period 3

There is a wide dispersion of data points on both sides of the fit line. A negative slope
indicates a modest negative relationship, but most likely one that is statistically insignificant.

Statistical Analysis
Pearson’s Correlation (r) = -.088

Significance (2-tailed) = .594

The relationship is statistically insignificant (.594 > .05) and the null hypothesis is accepted.

35

H0: Current Operating Expenditures per Pupil for period 3 (1984-88) and Juvenile Arrest Rate
for Period 3 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 3 (1984-88) and Juvenile Arrest Rate
for Period 3 are related (r is not equal to 0).

 

 

 

 

 

20000
a
a I:
ca 10000 I
i
0.. a r: a
5 n a n U D l I:
u D fl D u a D
g 1:: I: :13 D
O I a I: an m0 u a a a
'1 000° 1 I T r
4000 6000 8000 10000 12000 14000

COEP for Period 3

The data points are much more grouped around the fit line than in previous correlations,
with the exception of three outliers. Apparently there is a much lower variance for the cities
studied with regard to Juvenile Arrest Rate for Period 3. The fit line has a modest downward
slope, but there is clearly no significant relationship between these two variables.

Statistical Analysis
Pearson’s Correlation (r) = -.042

Significance (2-tailed) = .800

The relationship is indeed insignificant (.800 > .05) and the null hypothesis is accepted.

36

H0: Current Operating Expenditures per Pupil for period 4 (1989-93) and Index Crime Rate
for Period 3 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 4 (1989-93) and Index Crime Rate
for Period 3 are related (r is not equal to 0).

 

 

 

 

 

120000
0
100000 I
80000 - r3
‘- D
8 a ..
'5
E 80000 I
E 5’ I: D
D
40000 1 u g, D o
a D
D
can on Dan D n D D
20000 I a I31:: 9 P c,
D
D I:
o t i I 1 i
4000 6000 8000 10000 12000 14000 18000

COEP for Period 4

The data points are scattered nicely about the fit line with one notable outlier (Benton
Harbor ICR = 111,211.0). Apparently there is a very slight negative relationship, but nothing
significant.

Statistical Analysis

Pearson’s Correlation (r) = -.057
Significance (2-tailed) = .729

The relationship is not significant (.729 > .05), therefore the null hypothesis can not be rejected.

37

Ho: Current Operating Expenditures per Pupil for period 4 (1989-93) and Non-Index Crime
Rate for Period 4 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 4 (1989-93) and Non-Index Crime
Rate for Period 4 are related (r is not equal to 0).

 

 

 

 

160000
I:
140000 1
120000 "
V' a
'8 100000 ‘ n a
E
a 80000 i
D
0 DD
§ 60000 ' an” 1::
an
'3 :1
c: I:
40000 . Tb ° on c, ° a
an a: ”D
20000 - 8" °
0 Ifi t 1 - t
4000 6000 8000 1 0000 1 2000 14000 1 6000

COEP for Period 4

The data points are nicely dispersed with one notable outlier (Mt. Clemens NICR =
155,038.1). The slope of the fit line is slightly negative, but obviously insignificant.

Statistical Analysis
Pearson’s Correlation (r) = -.095

Significance (2-tailed) = .564

The relationship is indwd insignificant (.564 > .05) and the null hypothesis is accepted.

38

H0: Current Operating Expenditures per Pupil for period 4 (1989-93) and Total Crime Rate
for Period 4 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 4 (1989-93) and Total Crime Rate
for Period 4 are related (r is not equal to 0).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

300000
13
* 200000 I o
'8 o
'5
n- D
a , e
a j . . ;
100000 __ c, e D
U D
c: a U a
db D D D DO U D
D DE D U
D :0 n
o I_ r F, T
4000 6000 8000 10000 12000 14000 16000

COEP for Period 4

The data points are dispersed nicely with no major outliers. The slope of the fit line is
negative, but the relationship is insignificant.

Statistical Analysis

Pearson’s Correlation (r) = -.088
Significance (2-tailed) = .593

The correlation does not produce a significant relationship (.593 > .05), therefore the null
hypothesis can not be rejected.

39

H0: Current Operating Expenditures per Pupil for period 4 (1989-93) and Juvenile Arrest Rate
for Period 4 are independent events (r is equal to 0).

HA: Current Operating Expenditures per Pupil for period 4 (1989-93) and Juvenile Arrest Rate
for Period 4 are related (r is not equal to 0).

 

 

 

 

 

30000
20000 A a
*
.3
5
‘3- 10000 n
5 u
g I: a 81:1 I: a
1:
:5 a ‘5 on n U :1" u
0 I, a 1:1 1:1 dE'I no ”a D D
4000 6000 8000 10000 12000 14000 16000

COEP for Period 4

The data points are grouped around the fit line producing a slightly negative relationship
with one notable outlier (Harper Woods JAR = 20,137.51). The relationship between these two
variables is obviously insignificant.

Statistical Analysis
Pearson’s Correlation (r) = -.177

Significance (2-tailed) = .480

Once again the relationship is statistically insignificant (.480 > .05) and the null hypothesis is
accepted.

40

Set 2

H0: % change in Current Operating Expenditures per Pupil from period 3 (1984-88) to
period 4 (1989-93) and % change in Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA: % change in Current Operating Expenditures per Pupil fi'om period 3 (1984-88) to
period 4 (1989-93) and % change in Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

 

 

 

 

20
‘- u D D D
I:
8
'5 10I
0. I:
O
(‘0 D D
.8 0‘ D D D D
E 1:1 1:1 :1 1:1 1:1
:1
i . .
'3 D
1:1 I:
g -10'l 1:1 1:1
c:
.E I: D
a c1
c:
E -20' a
a a a
33
D D
30 i i t
-10 0 10 20 30

% Change in COEP from Period 3 to Period 4

This is the first correlation to show percentage change rates between periods and has
perhaps the greatest dispersion of data points thus far. This represents a high variance between
both variables. It appears that several Cities actually fell in the amount of expenditures allocated
fiom period 3 to period 4 which proves that adjusting for inflation was, indeed, a worthwhile
effort. The slope of the fit line is negative, but the relationship is most likely insignificant.

Statistical Analysis
Pearson’s Correlation (r) = -.131

Significance (2-tailed) = .427
The relationship is insignificant (.427 > .05) and the null hypothesis is accepted.

41

H0: % Change in Current Operating Expenditures per Pupil from period 3 (1984-88) to
period 4 (1989-93) and % change in Non-Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA: % change in Current Operating Expenditures per Pupil from period 3 (1984-88) to
period 4 (1989-93) and % change in Non-Index Crime Rate fi'om period 3 (1984-88) to

period 4 (1989-93) are related (r is not equal to O).

80

 

601

40I

20‘

DD

 

 

.20 l

%ChmgeinNCRiomPeriod3to Period4

 

40

 

 

-10

6

10

20

% Change in COEP from Period 3 to Period 4

30

Unlike the former correlations, this one Clearly exhibits a positive relationship; however is
it still appears to be statistically insignificant. Once city, in particular, seemed to have both a
reduction in COEP and NICR (City of Allen Park -7 .4466 and -23.6005 respectively).

Statistical Analysis

Pearson’s Correlation (r) = .059

Significance (2-tailed) = .721

The relationship is not significant (.721 > .05) and therefore the null hypothesis can not be

rejected.

42

Ho: % change in Current Operating Expenditures per Pupil from period 3 (1984-88) to
period 4 (1989-93) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

Ha: % change in Current Operating Expenditures per Pupil from period 3 (1984-88) to
period 4 (1989-93) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

 

 

 

 

 

60
I:
V
B
i
8 4O . a
(0
i .. °
:1
O. a a
E 20 I
a 1:1
5 ° °
1:
D n
.E U D 1:: D 1:
a D :1 c1

a D “.5

5’ c:
a
.20 D i 1' fit
-10 0 10 20 30

% Change in COEP from Period 3 to Period 4

There appears to many cities from the sample that exhibited both a positive % change in
COEP and a negative % change in TCR and yet, the fit line is almost horizontal, perhaps even
positive.

Statistical Analysis

Pearson’s Correlation (r) = .006

Significance (2-tailed) = .959

The relationship is, in fact, positive, but nonetheless insignificant (.969 > .05), therefore the null
hypothesis must be accepted.

43

Ho: % change in Current Operating Expenditures per Pupil fiom period 3 (1984-88) to
period 4 (1989-93) and % change in Juvenile Arrest Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are independent events (I is equal to 0).

11.4: % change in Current Operating Expenditures per Pupil fi'om period 3 (1984-88) to

period 4 (1989-93) and % change in Juvenile Arrest Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

3000

 

 

 

 

 

 

 

 

V‘
.‘3
i
2 2000- D
t")
E
s 1000-
.E :1 °
:9 U .0

E 0' '3 5p 58:11:: ch agaumcb EDIE—“5* u -
a?

-1000 F— l r

-10 0 10 20 30

% Change in COEP from Period 3 to Period 4

The data points appear to be Situated closely around the fit line, however, this is most
likely a result of the wide range required for the graph to include the one major outlier which
represents the City of Norton Shores. This city displayed a more than 2000% increase in the
juvenile arrest rate fi'om period 3 to period 4 (period 3 = 14.04; period 4 = 248.68). The
negative slope of the fit line appears to be insignificant.

Statistigfl Aggysis
Pearson’s Correlation (r) = -. 121
Significance (2-tailed) = .463

The relationship is insignificant (.463 > .05) and the null hypothesis is accepted.

Set 3

H0: % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to
period 3 (1984-88) and % change in Index Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA: % change in Current Operating Expenditures per Pupil fiom period 2 (1979-83) to
period 3 (1984-88) and % change in Index Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

 

20

 

 

 

 

a D g
g e
'5 10'l
0. c1
.9.
4"!
.3
8
O.
.E
e\°
a D
-30 T r I I
-10 0 10 20 30 40

% Change in COEP from Period 2 to Period 3

Clearly, a negative relationship exists between these two variables. The data points are
dispersed nicely and it appears that the relationship could be Significant.

Statistical Analysis
Pearson’s Correlation (r) = -.220

Significance (2-tailed) = .178

Despite the downward slope of the fit line, the chances of making a type I error are still
too great (, 17 8 > .05). Therefore the null hypothesis cannot comfortably be rejected.

45

H0: % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to
period 3 (1984-88) and % change in Non-Index Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA2 % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to

period 3 (1984-88) and % change in Non-Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

80

 

60I a

40' DD

 

%CImgeinNICRIromPeIiod3toPeriod4

 

 

 

a D
.20 I
D
.40 i F ‘- ‘r
-10 0 10 20 30 40

% Change in COEP from Period 2 to Period 3

Again there seems to be a Clear negative relationship between the two variables and the
data points are dispersed evenly about the graph. Judging by the last correlation, however, the
relationship here would appear to be insignificant as well.

Statistical Analysis
Pearson’s Correlation (r) = -.175

Significance (2-tailed) = .288

The relationship is not Significant enough to comfortably reject the null hypothesis (.288 > .05)
therefore it must be accepted.

Ho: % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to
period 3 (1984-88) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

Ha: % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to
period 3 (1984-88) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are related (I is not equal to O).

 

 

 

 

 

so
D
1’
B
E
2 40- a
('0
3 ° a
5 :1
o. a D
g 201
h
.s
39 a
D D
.20 _ _ fi r
~10 o 10 20 30 4o

% Change in COEP from Period 2 to Period 3

The downward slope of the fit line indicates a negative relationship between the two
variables. The data points are grouped around the center of the graph with a few exceptions.

Statistical Analysis
Pearson’s Correlation (r) = -.207

Significance (2-tailed) = .205

Although there is a clear negative relationship, it is not significant enough to comfortably reject
the null hypothesis (.205 > .05).

47

Ho: % change in Current Operating Expenditures per Pupil fi'om period 2 (1979-83) to
period 3 (1984-88) and % change in Juvenile Arrest Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA: % change in Current Operating Expenditures per Pupil from period 2 (1979-83) to
period 3 (1984-88) and % change in Juvenile Arrest Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to O).

 

 

 

 

 

 

3000
1-
T8
8
% 2000 I» a
co.-
co
%
0..
g 1000 I
a.
.E c: r:

n D n n g,

§ 0 ‘ g D D % D #0” Bu 0 D U U u
a?

-1000 _ _ I

40

Ol
tn.
0
N
O
r»
O

-10

% Change in COEP from Period 2 to Period 3

Again the data points appear to be closely positioned around the fit line because of the
graphs wide y-axis range to include the large outlier (Norton Shores). There appears to be a
slight negative relationship, but nothing significant.

Statistical Analysis
Pearson’s Correlation (r) = -.114

Significance (2-tailed) = .489

The relationship is, indeed, insignificant (.489 > .05) and therefore the null hypothesis is accepted.

48

Ho: % change in Current Operating Expenditures per Pupil from period 1 (1974-7 8) to
period 2 (1979-83) and % change in Index Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

Ha: % change in Current Operating Expenditures per Pupil from period 1 (1974-78) to
period 2 (1979-83) and % change in Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

 

 

 

 

 

20
a 5’:
D
E a
"5 10*
0.
2
(0
E
8
5 -10-
.E
E -20‘ I:
D a D
Q
0
D D
-20 -10 o 10 20 30 4o

% Change in COEP from Period 1 to Period 2

The first of the correlations in the longest time lag produced a nice scattering of data
points about the graph and a clear negative relationship between the two variables. The
relationship appears as though it could be a significant one.

StatisticalAnalysis
Pearson’s Correlation (r) = -.385
Significance (2-tailed) = .015

The relationship between the two variables is, in fact, statistically significant (.015 < .05). This is
the first correlation in which the null hypothesis can comfortably be rejected.

49

Ho: % change in Current Operating Expenditures per Pupil fiom period 1 (1974-78) to
period 2 (1979-83) and % change in Non-Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

HA: % change in Current Operating Expenditures per Pupil fi'om period 1 (1974-78) to

period 2 (1979-83) and % change in Non-Index Crime Rate fi'om period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

80

 

 

%GimgeinNiCRimmPeriod3toPeriod4

 

 

.40 i t I t
-20 -10 '0 10 20 30 40

 

% Change in COEP from Period 1 to Period 2

Again there is a nice scattering of the data points and a clear negative relationship between
these two variables.

Statistical Analysis
Pearson’s Correlation (r) = -.407

Significance (2-tailed) = .010

This correlation is statistically significant as well (.010 < .05), therefore the null hypothesis can be
rejected for its alternative.

50

Ho: % change in Current Operating Expenditures per Pupil from period 1 (1974-78) to
period 2 (1979-83) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

Ha: % change in Current Operating Expenditures per Pupil from period 1 (1974-78) to
period 2 (1979-83) and % change in Total Crime Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to 0).

 

 

 

 

 

60
a

1'
.‘3
E
2 40 I D
m
g
g 20 1
oh
.5
E o I
$

'20 r I - f I— i

-20 -10 0 1O 20 30 40

% Change in COEP from Period 1 to Period 2

This correlation, the third in the set of longest lags, produces another fit line with a
negative slope which implies that these two variables are inversely related as well.

Statistfisal Analysis
Pearson’s Correlation (r) = -.400
Significance (2-tailed) = .012

This correlation, like the latter two, is significant (.012 < .05) and the null hypothesis is rejected.

51

H0: % change in Current Operating Expenditures per Pupil from period 1 (1974-78) to
period 2 (1979-83) and % change in Juvenile Arrest Rate from period 3 (1984-88) to
period 4 (1989-93) are independent events (r is equal to 0).

Ha: % change in Current Operating Expenditures per Pupil from period 1 (1974-78) to
period 2 (1979-83) and % change in Juvenile Arrest Rate from period 3 (1984-88) to
period 4 (1989-93) are related (r is not equal to O).

 

 

 

 

 

 

3000
V
B
8
% 2000 1» D
0-0 .
0')
E
a i
§ 10000
oh
.9: c: a
g 0- a e a DB 3‘5 go‘bé’b 5; guess :1 “a...
39

'1000 - I— - - I -

-20 -10 0 10 20 30 40

% Change in COEP from Period 1 to Period 2

The fourth correlation, in the longest lagged series, does not appear to have a significant
relationship. It resembles the two previous correlations that used 3 change in JAR from period 3
to period 4 as its y-axis.

Statistical Analysis

Pearson’s Correlation (r) = -.133
Significance (2-tailed) = .419

The final correlation is not a significant one (-419 > .05) and therefore there is no other choice but
to accept the null hypothesis.

52

B. Discussion

While the ultimate goal of this study is to determine whether or not w in school
expenditures have any effect on crime/juvenile arrest rates, the aim of the first set of correlations
was only to show whether or not cities with higher expenditures on education enjoy lower
crime/juvenile arrest rates, but said nothing about increases or decreases. The correlations for
both period 3 and period 4 produced no statistically significant results. This would seem to
indicate that communities with large amounts of expenditures are just as susceptible to crime and
juvenile arrests as are communities with relatively low expenditures on education.

The second set of correlations used the % change in expenditures from period 3 to period
4 for both expenditures and crime/juvenile arrest rates as dependent and independent variables,
respectively. Again there was so significant relationship to be found between these variables. It
was not until the third set of correlations in which the “five-year lag” was incorporated into
expenditures that there appeared to be a clear relationship between the two variables. Although,
still statistically insignificant, the likelihood of a Type I error had fell considerably.

It was in the fourth set (IO-year lag) that there were three statistically significant
correlations between % change in current operating expenditures per pupil from period 1 to
period 2 and % change variables from period 3 to period 4 (i.e. index crime rate, non-index crime
rate and total crime rate). This implies that there is a relationship between educational
expenditures and crime/juvenile arrest rates, but only in the long term. Surprisingly enough, there
was no relationship between changes in the juvenile arrest rate and expenditures, even with regard
to the longest lagged correlation. This could be attributed to the enormous volatility in the

juvenile arrest rate as shown in the graph on page 27, or perhaps it was the more than 2000%

53

increase in the City of Norton Shores juvenile arrest rate that skewed the data. There is, perhaps,
another explanation. Educational expenditures were, in fact, found to be related to crime rates,
but only in the long term. This could suggest that increased expenditures help students to form a
more law-abiding community when they are older. After all, juveniles, would cease to be
juveniles after ten years. The fact that there was no relationship in the non-lagged correlations
would seem to indicate that a community doesn’t benefit from increased expenditures on

education until juveniles have grown and become part of the adult community.

54

IX. Regressions
The three sets of correlated variables found to be statistically significant were further

analyzed using Ordinary Least Squares (OLS) regressions‘:

Y=b0+b1Xr+e

where Y = dependent variable
X1 = independent variable
b0 = constant (y-intercept)
bi = x-coefficient (slope)
e = standard error term

Regressional analysis determines the extent to which the variables are related (i.e. explained
variation in the dependent variable). It will also indicate how the independent variable
(crime/juvenile arrest rate) responds to per-unit changes in the independent variable (educational
expenditures). Assumptions in linear regression are as follows:
1) The relationship between x (the independent variable) and y (the dependent variable) is
linear.
2) The values of x are fixed (y varies as a function of x).
3) The data points are evenly distributed about the line:
a. Error term has constant variance across values of x (homoskedasticity).
b. The errors are uncorrelated across observations.

c. The error is normally distributed.

 

' Data was analyzed using Statistical Programming for Social Science (SPSS) software. Regression output with

model summaries, AN OVA and coefficients appears in Appendix F.

55

A. Results from Regressions

The three regressions were carried out are as follows:

Dgpendent variable Indeggndgnt variable
1. % A in Index Crime Rate (ICR) % A in Current Operating
from period 3 (1984-88) to Expenditures per Pupil (COEP)
period 4 (1989-93) from period 1 (1974-78) to
period 2 (1979-83)

(ICR % A from period 3 to period 4) = b0 + br(% A in COEP from period 1 to period 2) + e

Statistical Analysis
Coefficient of Determination (r’) = .148

Standard Error of the Estimate = .1127
Constant (B) = -3.6E-03 = -0.0036
X-coefficient = -.452

The regression equation that describes the relationship between the two variables:
(ICR % A from period 3 to period 4) = -.0036 - (.452)(% A in COEP from period 1 to period 2)

The proportion of squared deviations from the mean that are “explained” by the regression
equation and the degree to which a change in expenditures can affect a change in the Index Crime
Rate in a city in the next ten years 14.8%.

For each 1% increase in Current Operating Expenditures per Pupil from period 1 to period
2 there is a corresponding decrease in the Index Crime Rate from the third to fourth period by
0.452%.

 

 

56

Deeendent variable Indemndent variable

2. % A in Non-Index Crime Rate % A in Current Operating
from period 3 (1984-88) to Expenditures per Pupil from
period 4 (1989-93) period 1 (1974-78) to period 2
(1979—83)

(NICR % A from period 1 to period 2) = bo + br(% A in COEP from period 1 to period 2) + e

Statistical Analysis
Coefficient of Determination (r’) = .166

Standard Error of the Estimate = .2068 ’
Constant (B) = .268
X-coefficient = -.884

The regression equation that describes the relationship between the two variables:

(NICR % A from period 1 to period 2) = .268 - (.884)(% A in COEP from period 1 to period 2)

The proportion of squared deviations from the mean that are “explained” by the regression
equation and the degree to which a change in expenditures can affect the Non-Index Crime rate in
a city in the next ten years is 16.6%.

For each 1% increase in Current Operating Expenditures per Pupil from period 1 to period
2 there is a corresponding degease in the Non-Index Crime Rate from the third to fourth period
by 0.884%.

 

57

Qegendent variable Indemndent variable

3. % A in Total Crime Rate % A in Current Operating
from period 3 (1984-88) to Expenditures per Pupil from
period 4 (1989-93) period 1 (1974-78) to period 2
(1979-83)

(TCR % A from period 1 to period 2) = b0 + br(% A in COEP from period 1 to period 2) + e

Statistical Analysis
Coefficient of Determination (r1) = .160

Standard Error of the Estimate = .1425
Constant (8) = .129
X-coefficient = -.597

The regression equation that describes the relationship between the two variables:

(TCR % A from period 1 to period 2) = .129 - (.597)(% A in COEP from period 1 to period 2)
The proportion of squared deviations from the mean that are “explained” by the regression

equation and the degree to which a change in current operating expenditures per pupil can affect

the Total Crime Rate in a city in the next ten years is 16.0%.

For each 1% increase in Current Operating Expenditures per Pupil from period 1 to period
2 there is a corresponding decrease in the Total Crime Rate from the third to fourth period by
0.597%.

 

 

58

B. Discussion

Changes in the Non-Index Crime Rate appears to be affected most by changes in
expenditures, followed by the Total Crime Rate and finally the Index Crime Rate. While 16.6%
explained variation may not seem significant , the goal here was not to “explain” crime rates. If
that were the case, then adding additional independent variables, already proven to be related to
crime, to a multiple regression would undoubtedly produce a more significant explanation. The
purpose here, however was to determine whether or not, educational expenditures alone can have
any impact on crime rates. The results from the regressions indicate that there is, in fact, a
relationship between the two.

Whether the relationship between the variables found to be related is truly a causal one
may remain suspect. Since the unit of observation had to be consistent for the study, the sample
was in effect, non-random. It may be inappropriate to generalize the findings of this study to
cities that did not meet the criteria for the sample.

Nonetheless, the very fact that a relationship was found to exist between the variables,
even if only in lagged correlations, serves as a step in the right direction with regard to showing

the way in which public education is, in fact, a benefit to all.

59

X. Conclusion

A reduction in crime is but one of many potential ways in which changes in educational
expenditures could affect communities. Originally, it was my intention to include other dependent
variables in this study, in addition to crime/juvenile arrest rates, to determine whether or not they
exhibited any relationship to changes in educational expenditures. The potential variables to be
studied include median income, wages and salaries and unemployment rates. Unfortunately, this
data was not available annually at the sub-county level.

Proposal A in Michigan, which raised the sales tax by 2% for educational expenditures,
prompted this study. The thought being that if non-property owner, non-parent citizens of
Michigan are expected to pay for provision of education, then research should demonstrate that
these citizens benefit from public education.

For future study, the inclusion of the other potential dependent variables would be a
worthwhile effort. That is, of course if those variables could be obtained. Perhaps other states in
the US do have access to this information annually. The fact that this study was, in essence,
directed by the availability of the data, makes it difficult to generalize the results. Developing a
method by which cities with more than one school district could be added to the model, could
make the study more representative and add to the strength of the results. If this were done, a

similar study could even take place using samples from various states.

Bibliography
Anderson, Robert C., “The Interorganizational Community,” Lewiston, NY, Oueenston, Ontario
and Lapeter, Wales, The Edwin Mellen Press, 1993.

Andrews, Chris and Greg J. Borowski, “State slashes tax, no plan for schools,” Lansing State
Journal, pp. 1A, July 23, 1993.

Andrews, Chris, “Citizens’ commission proposes property tax levy cut, sales tax hike,” Lansing
State Journal, pp. 3A, Feb. 10, 1987.

Babbie, Earl, “The Practice of Social Research,” Wadsworth Publishing Company, 1995.

Bailey, Kenneth D., “Methods of Social Research,” New York: Free Press; Toronto: Maxwell
Macmillan Canada; New York: Macmillan International, 1994.

Baker, Keith, “Yes, Throw Money at Schools,” Phi Delta Kappan, April 1991, v72, n8, pp. 628-
31.

Christoff, Chris, “School tax reform plan may face rocky road,” Detroit Free Press, September
27, 1987.

Clark, W . A. V. and P. L. Hosking, “Statistical Methods for Geographers,” New York, John
Wiley & Sons, Inc., 1986.

Danzberger, Jacqueline P. and Michael D. Usdan, “Local education governance: perspectives on
problems and strategies for change,” Phi Delta Kappan, Jan. 1994, v75, n5, p. 366.

“Data Collection and Analysis,” London; Thousand Oaks, CA, Sage in association with Open
University, 1996.

Decker, Paul T., “Findings From Education and the Economy: An Indicators report,”
Washington, DC: National Center for Education Statistics, US Dept. of Education, Office
of Educational Research and Improvement.

Gilling, Daniel, “Crime Prevention: Theory, Policy and Politics,” London and Bristol, PA, UCL
Press, 1997.

“GOP calls property tax funding for schools unfair, wants changes: Republican leaders say that
less affluent districts are losing out,” Detroit Free Press, February 3, 1987.

Iorio, Mary E., “Schools stunned by revenue loss,” Lansing State Journal, July 23, 1993.

61

Jones, Tim, “Report says sales tax of 6% would aid schools,” Detroit Free Press, February 10,
1987.

Kearney, C Philip, “A Primer on Michigan School Finance Third Edition,” The University of
Michigan, 1994.

Kratcoski, Peter C. and Lucille Dunn Kratcoski, “Juvenile Delinquency: Third Edition,”
Englewood Cliffs, NJ, Prentice-Hall, Inc., 1990.

Mandi, Peter, “Education and Economic Growth in the Developing Countries,” Budapest,
Hungary, Akademiai, 1981.

Mathews, David, “The Lack of Public for Public Schools,” Phi Delta Kappan, June 1997, v78,
n10, pp. 740-55.

Mumane, Richard J. and Edward W. Pauly, “Lessons from Comparing Educational and Economic
Indicators,” Phi Delta Kappan, March 1988, , v69, n7, pp. 509-13.

Nation, Jack R., “Research Methods,” Upper Saddle River, NJ, Prentice Hall, 1997.

Pierce, Hank, “Fiscal Focus: Proposal A and Pupil Equity,” prepared for the House Fiscal
Agency, December 1996.

Psacharopoulous, George and Eduardo Velez, “Educational quality and labor market outcomes:
evidence from Bogota, Colombia,” Sociology of Education, April 1993, v66, n2, pp. 130-
45.

Psacharopoulous, George assisted by Keith Hinchliffe, “Returns to Education: An International
Comparison,” San Francisco; Washington, Jossey-Bass Inc., 1973.

Quan, Nguyen T. and John H. Beck, “Public Education Expenditures and State Economic
Growth: Northeast and Sunbelt Regions,” Southern Economic Journal, Oct. 1987, v54,
n2, pp. 361-66.

“Questions, answers on plan to overhaul school finance,” Detroit Free Press, September 24,
1987.

Stroud, Joe H., “Prop A offers meaningful improvements,” Detroit Free Press, May 9, 1993.

Stoetzer, Karin E. and Merry Morash, “Juvenile Crime in Michigan: Evidence and Public
Perceptions,” East Lansing, MI, Institute for Public Policy & Social Research, 1997.

Teng, Teng, “Education Spurs Economic and Social Progress,” Beijing Review, Dec. 2-8, 1991,
v72, n8, pp. 28-31.

62

Whorton, Joseph W. Jr. and David R. Morgan, “Measuring Community Performance: A
Handbook of Indicators,” Bureau of Government Research, 1975.

Witte, Ann Dryden, “Crime,” from The Social Benefits of Education, ed. Jere R. Behrman and
Nevzer Stacey, Ann Arbor, MI, The University of Michigan Press, 1997.

Wolfgang, Marvin E., Terence P. Thomberry and Robert M. Figlio, “From Boy to Man, from
Delinquency to Crime,” Chicago and London, The University of Chicago Press, 1987.

63

IPPENDIX A — List of Variables

DONOW&UN-|

«0..—00. 0.050.

200.8 0:2.0 «0.60.0

0030: 1032 )3. «0.60.0
00030.8 )3. 0:2.0 00:00.0
43.330 05 >30 0:2.0 00300.0
000. P0323 00.60. 0.0.10.
003000 0:2.0 00:00.0

x3250. 0:28 00.60..

i. 0.030:- 0033334 00.60..
3002.... 00335.2 00300.0
5.00 0033334 «0.60.0
{0:03.300 )3. 0:2.0 «0.60.0
35.600: 10.030 00:00. 0.030.
20.... 0.630 0:2.0 00.60. 0.050.
02:04 00.80. 0.050.
0.43.3033 0.? 90:02 0.0.10.
03530: 03. 00:00. 0.0.10.
0033.0 0.2 00.60. 0.0.10.
132 00:. 0.2 00.52 0.0.4.0.
Zo<. 00333.? 00.62 0.0.10.
00.. 0o} 0.? 00:02 0.030.
00:00. 0.0.10. 0.2 0. 3040. Cox
M3530... 0:20 00.60. 0.0.10.
434 00.60. 0.0.10.

020000 0:2.0 «0300.0

2.0... 0!... 0:2.0 00:00.0
002.003 0.... 00.60. 0.030.
00030 0:2.0 «0.50. 0.050.
0030: 0:4 00.60. 0.0.10.
103.330.. 0:2.0 00.60..

0.... 0. 10304 5.0000 00:00.0
1.03.02. 02.. 0.2 00:00.:
2:00.: 02.. 0:2.0 00:00.0
0032:- 003353 00:00..
00:303. 00335.2 «0.60. 0.0.10.
43.2 «0.60. 0.3.0.

4330: 0:26 00:00..
$0395.33... 00335.2 «0.60. 0.0.10.
5.42.0030 0.2 00.60. 0.0.4.0.
02....00 >30 0:2.0 00:00.0

OOM0

00m...

00m0

00m-

0030... 00:00» 003000 00:00.

u»0»
000.
.00»
.400
4»uu
008
0.»..
0000
0.00
0040

0.00
0040
00.»
.000
4...
0400
0.00
00.0
0000
$0
00.0

0400

40.0
0400

0004
00.0

0.00
40».
0000
040.
.0».4

OOM0

OOM0

8M0

.03

#03500 $03300 #0330. 02.000
.04...30.040..§.8f.8¢.§..08 00:00.8» 03000.00 02.2.0.0. .8...8¢

000008.00
0.00.0800.
0.00.3»00.

0.3040
0.80.4»40»
0.3mm»u400

0.83000.
0.. 30000.0
0.. 00.00440
0.00004»:
0.80400»:

0.80. 00»04
0.8..00000
00.33000
0.0800430
0.. .4008. .
0..»...0400

0.00.0430
0.». .»uu». .
0.04. 0000. 0
0.0.000»!
0.»000.4000
0.00uu.»u.
0.8»..0000

00.80.00
0.. 380000
0.00». 4040
0.».004.4»0

0.0000. 440. .
0.0. .8008 .
.0. 30400.4 .
98.404000 .
0.0000000. .
0.. 40800»4 .
0.00.0800 .

0.0.».0000
0.0.0.0004
0.»0.8».»m

0.308»...

0.049.000.
0.0003000
0..u.».»u.0
0...900».u.
0.49.0200
0.4.4008“
0.3040000.
Pig.“
0.8»0004. 4
0.»000008.
0.. 44.04004
0.4002004
0.30.0000»

0.2.30.3 .

00000.88
0.0.0». 00
0.0000030
0...04004»4
0.. 43400.0
0.00.380
0.»..4»000o
0.»00.»0.04
0.8.80.0.
0.. . 4»0»w.u
0.0.000».
0.80.0000»
004000040»

0.8048.»
0..»..0.»00
0...».4.0u

0.. 3.30.0
0.830000
0.0080000.

0.00.40».
0.40.0044.

9:804:
0.0500000
0.. .8384
0..»uuo»»»o
0.84.0000
0.00.880»

0.. u.0..0$
0.». 04»». 40
0.0.0». .00»
00048.2.
0..0.00.»0.
0.00». .0..4
0.. .04004».
0.0.0.8.
0.»4.084.»
0.88.0.0
0.049.000.
0.0»0000400
0.»..80..u

0..04048»u
0.80.00.“
0.300083
0.0.»00000
0.03.04

0.04. .400»
0.80.0034
0.. 5004.».

»800.40

Al

DONOGbUN-I

882388288388

03

2......
00:30:03.!

00000000
43330 0.2
000. .0013
003000
.9330...

3. 0.03000
0009...?
0.00.00 10.0...-
2303.300
5.0.800: 10.0...-
228: 0.630
0010.!
0.3.3.33
0.02000
10300.0
I000. 00}
20¢.

00.. 00:.
30.0. 00..
09050.0
43..

020000

2.00 00....
000303
00030
00.00: 05
103.330..
10.02 .2800
1.02000 00:.
2:00.: 00...
003200

005.60.-
4010.
4830:
5000000
5.100030
000...00

00:22 $0.58. 0380 0322
.8938 02.808. .8..38.80..08 02.808. 330838.88 028008. .8308.80..80 03808..

»4000.0» 0.00.04.00
...».. 0.0034».
».040.00 .0.»...00»00
»4404.00 .0.»o»004000
»..00.40 0..0000.40
»0..0.»0 0.4.00.40.
»000..0» b.00..8»00
..3».0. .0.0.44..00
.000.»» 0.0»».0..00
»0..0.00 .0.0.0»00..0
»430.0. 0.0440..04
400.4. 0.0004000.
3.44.00 0.04»»040
.0400.00 0.00040».0.
.0440». .0.»40»00...
.00»..0 0.000.084.
04404.»0 00.040030
.0000.00 0.40.4»040
»0..0.»0 .0..00.»00.0
0403.0» 0.38.0.
»044000 0.340.000
00.0.0. 0.»404.0040
30.04 .0.»000.004»
»0».0.04 .0....0400.

»800 0.30.30
3000.4. 000000.03
00.4.00 0.3.0.004
».0...04 00040.0.0
00»...00 0.00.0300
40.000 0.000.40..0
4000..»0 b.0..80»00
0.00.04 .0..»00...0»
30.0 .0.0.»00.0
800.00 0.04.0.080
030.00 0.0..000.0.
.3400. 0.0.0000
»0000.4. 0.0000000»4
.00»0.0 .0.»»00.0»40
0030..» 0.0004030

0»0»0.00

4.004..

00... .0
4000. .0.
00.00.04
». 3.00
».0»0.00
30000.0
0»0...0»
.. 30.00
0.4.0.04
00.0.04

3.4»
.8.0».0

.300» .

0»004.00
0.400.»»
0030.40
»0.0. .04

0.04000
.00». .00

$030300 004.000 0010...

0.0. .00. 0. 0
b.0»».»0. .»
0... 400004»
0.. 4000. .00
0.8000404.
0.»00400400
0.»8000400
0.40. 0»04. »
P30400000
0.004. 300
0.. ..40.. .4
0.0003000
0.00. 004. 0
0.»000. .00.
0.».400»4.»
0.30. 00.
0.». .00000.
0.300.. »04
0.32.0.
00000003
0.0.0. 0000.
93003.00
0.00»000».0
0.»04040.00
.0. 30000.0
0.00. 0. .00
0.300000. .
0.04. »»04. 0
0.00.. 0.00

. 0.»0000..40

0.04.004. .0
0.000040»
0.0.3000.»
b.0..0»00»0

403

004. 0.00
.0400..0
04000.4.
.000. 0.0
0. 040.00
.0»0..00
0. 80.8
.0008.
0000.00
0.0400»
00000.04
..0.04.0
.0040. 4
..0000»
4800...
00000.».
4.00. .0.
30040.0
000. 0.00
00.0. .00
00»00.04

3.00.0
4.3.00
3000.00
0»000.0»
40400.04
44»04.0.
00000.0»
. .0000.
...000.»
3000..»
0840.00
..00.4.0
43.0.00
40000.. .
0000» .4.
3000.4.
03..»0
.004.0.0

401408

3.00.0. 0.»0.40. 00.

». .0400 0.»0.0300
4080.00 0.0... 00. 4.
00000... .0. 0. 30400
0300.0» 0.0040000»4
3040.0. 0.0440000.»
.00.0..0 0.0003». 4»
.00000. 0.»00»00.00
.00444 .0 0.0300000
0.044.40 .000. 0000»»
4.0.0.4. 0.. 44000000

.40004 0.»»..00»00
443030 000000000.

...00.0 00.008000

040.0». 0.000.433
01.00.40 0.0400. 08»
4.00004 0.0040000.4
04040.00 0040000000
00000.00 0.0000000.0
30.0.0. 0...00440»4
030»... 0.0...»400»
0.»0».00 004000400
38034 .0.. »0000044
00004 .. 4 0.0000.»»»
.0000... 0.040000. 0

44004.0 0.0. »000.00
3.0000 0.0. .»0.0.0
000...00 0.0.0.0400»
. .00. 0.. 0.004..0440
30000.0 00.00030
..0000.0 0.. 4.0.0000
0008.00 .0000. 3». »
..»004.0 0.300.000
4.000.04 0.3008400
00000.00 0.0.04.0..
00040.00 no.0»».00»40
00000.. 0.30. 04000

00000.00 0.8404430
.0.404.. 0.0000.»4.0

0))

$030300 08.000

300.»
.040.»4
00.0.0

004...

...;
000.00.

33.0
3».»»
».00..»
». 4. .00
.0300
000. .0»
.400.00
000.40
0000.00

...0.0 .
.4800 .

300.04
»00.00
000...
040.00

.»00.»4

..44.0.

»000.».

334.0

»400.00
44. .0.
.0000

»000.».
.0..00

0»0..

3.0.
.0004

.000.00

».00.»4

»0.04.0.
0.00.04
0.0. .»4

»08

»000.04

040. .00
».. .40

»040.4.

000.0
0000.0

0)..
#090000

0.004040..0
0.044004».
0.00000. 00.
... 404»44»0
0.0. .000. .»
...4».. 0040
900000800
0.0. ..0».04
.94. 000.040

0.34.. 040
»0.»4000.»4
».000000.0.

.0...0400»00
0.0400.»;
0000000040
0.000.340
..0..00..40.0
0.. .3300
000000800
.0.. 330»
0.04.0008»
0.34000
000000000»
0004400000
0.0040030
0.00.0000.
0.0030000
0.»..»00N0.
.0.»0.. 0. ».0
0.0020003
0.00»00000.
003400.00
0.00.40000
b.0.00000»0
.0.»00»000.0
0.00.0080
.»008300

APPENDIX B — Population Estimates

SBBfiBBBBBBSEGififi36”°““m““”‘

883882889888

1962 538.
16226 to}
£38.: to}
.3355
«9.598
43.2
4:58:
58.33

3:53.
03...!"

t§t§§t§t§t§v§§
0328 mango manage macs-3 macs-8

mg mango macs-8

.8.

..Wm
.33

.0:

..N8
3.8
.8
.85
8.8
g
848
.3
9V8
...g
88
.590
”.30
..33
M88
.38
803
MOMS
NS
9.8
8.8

.00.

£3
.QB
.88
.83
Q3
$3
$3
.83
fig
:§8
38
E8
fig
.da
.88
.38
$8
33
Qfiu
QB.
38

. V. 03

Bus
.mg
g
8.8
.MAS
8.8
.38
.520
g
bug
3
R80
4.8
N63
33
830
.03

.8:

38¢
.83
.g
5.8

.00.

....3
.3.

3.8
.58
.34
2%

8.3
93.0

83A
.0. ma

.0.»

.03

..m8
3.8
.§
.98

.0:

APPENDIX C - Descriptive Statistics

 

Range

Minimum

Maximum

Variance

 

 

%
Change
in
COEP
from
Period 1
to
Period 2

96
Change
in
COEP
from
Period 2
to
Period 3

%
Change
in
COEP
from
Period 3
to
Period 4

COEP for

Period 1

COEP for

Period 2

COEP for

Period 3

COEP for

Period 4

%
Change
in ICR
from
Period 3
to
Period 4

ICR for
Period 3
iCR for
Period 4

 

39

39

39

39

39

39

39

39

39

39

 

.47

.40

.37

4512.00

4739.00

7077.00

8424.00

.45

81203.88

98937.99

 

-.12

-.07

-.07

4411.00

4895.00

5444.00

6075.00

-.28

14643.65

12273.01

 

.35

.33

.29

8923.00

9634.00

12521.00

14499.00

.17

95847.53

111211.0

 

.1113

.1489

.1168

6053.82

6735.79

7753.38

8645.10

-5.E-02

37524.4

35805.3

 

1.1E-02

6.1 E—03

6.4E-03

10281 52

1779146

2997836

3946804

1 .5E-02

3.6E+08

4.2E+08

 

A4

 

 

Range

Minimum

Maximum

Variance

 

 

%
Change
In JAR
from
Period 3
to
Period 4

JAR for
Period 3

JAR for
Period 4

%
Change
in NICR
from
Period 3
to
Period 4

NICR
for Period
3

NICR
for Period
4

%
Change
in TCR
from
Period 3
to
Period 4

TCR for
Period 3
TCR for
Period 4
Valid N
(listwise)

 

39

39

39

39

39

39

39

39

39

39

 

20.99

16570.85

1 9895.78

1.00

94897.81

135517

.73

1 30972

1 76097

 

-.72

14.04

241.73

-.24

14468.05

19521.59

-.17

36382.71

35576.35

 

20.27

16584.89

20137.51

.76

109365.9

1 55038.1

.57

167354.6

211673.3

 

.8105

3217.25

3302.96

.1701

44658.5

51927.0

6.3E-02

82182.9

87732.3

 

10.964

1 .2E+07

1.2E+07

5.0E-02

4.2E+08

6.8E+08

2.4E-02

1 .ZE+09

1 .7E+09

 

 

APPENDIX D — Distributions

 

12

101

81

4|

 

an MW
=ms
szm

 

 

0
£00 5110 no mo mo
31110 mo mm amo mo

CIB’b’Fbia‘H

 

 

 

 

31110 81130 mo 8113.0 amo
sano mo 75110 no mo

CIE’thich

A6

 

 

  
   

 

 

qukmgsfifiso

 

 

 

Wmswsmm

mumm4

A7

 

 

 

 

-.10 2(5 .0) .6 .10 .15 Z) .5 .fl .5

*Omhmmw1bw2

 

81

4d

 

 

      

205405.fi.075.1fi.17532755
awwmmmimmzom

%Omhmmm2bm3

A8

 

 

 

 

    

-_075 {m .025 .075 .15 .175 .E .275

%mmmm9mmmmmmm4

 

 

 

amassmsmmsm

ooooooooooooooooo
imumma

A9

 

 

SiDFMQ
”8&3
o N=$CD
101110 311110 sumo mo 911110 1101110

211110 mo 811110 811110 mmo

 

 

KRb'FBiaM

 

 

 

 

a!) -5 -Z) -.15 -.10 ..05 .CD .(5 .10 .15

%GamhmiunPakxi3tthm4

A10

 

 

 

 

o _ 7 , , ,. I
ma armo sumo mno sumo 11111110
211110 moo mo mo mo

NCRUWS

 

 

 

wwsmwmé

All

 

21

 

 

sum/=2
this-.17

0 M30)

 

-Z -.13 am .13 z .3 .5) .3 .75
-.19 ~13 .(B .19 .31 .44 .$ .$

%Gmhhmiunm3tom4

 

6|

 

 

 

-$CD

sasszasgmm.

A12

 

 

 

8018410475
“18877.23
0 N830
mo 811110 1211110 mo ammo

mo mmo 1mm m0 m0

 

 

‘KRb’PBiuM

 

 

 

 

*O'I'ph'lmiImWSbmli

A13

 

 

at

  
    
    

 

4

2

 

8 0183013

 

 

Wkkfi'bfi3fi%%§%%§%
m I! W3

 

- JN-m

no mo mnfirzfno'umo mo
mm mm mo mo mo

 

mm4

A14

 

 

 

3)

Z)

10
8:01:33!
has

0 imam

00 20 40 6'0 50 100150110 150 15.0 210

 

%Olwhmmm3bm4

A15

 

\PPENDIX E - Correlation Output

Correlations

 

COEP
for iCR for
Period 3 Period 3

 

Pearson CEEP

Correlation for 1.000 .002
Period 3

ICR for

Period 3 .002 1.000

 

Sig. COEP

(2-talled) for . .992
Period 3
iCR for

Period 3 '992

 

N COEP
for 39 39
Period 3

ICR for
Period 3 39 39

 

 

 

 

 

 

Correlations

 

COEP NICR
for for
Period 3 Period 3

 

Pearson 55§P

Correlation for 1.000 -.148
Period 3
NICR

for -.148 1.000
Period 3

 

Sig. COEP

(2-talled) for . .369
Period 3
NICR

for .369
Period 3

 

N COEP

for 39 39
Period 3

NICR

for 39 39
Period 3

 

 

 

 

 

A16

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

COEP
for TCR for
Period 3 Period 3
Pearson 056P
Correlation for 1.000 -.088
Period 3
TCR for
P erlod 3 -.088 1.000
Sig. COEP
(2-tailed) for .594
Period 3
TCR for
Period 3 '59“
N COEP
for 39 39
Period 3
TCR for
Period 3 39 39
Correlations
COEP
for JAR for
Period 3 Period 3
Pearson 0551r
Correlation for 1.000 -.042
Period 3
JAR l'or
P erlod 3 -.042 1.000
Sig. COEP
(2-talled) for .800
Period 3
JAR for
Period 3 30°
N COEP
for 39 39
Period 3
JAR for
Period 3 39 39

 

A17

 

 

 

Correlations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

COEP
for iCR for
Period 4 Period 4
Pearson COEP
Correlation for 1.000 -.057
Period 4
ICR for
Period 4 -.057 1.000
Sig. COEP
(24ailed) for .729
Period 4
iCR for
Period 4 '729
N COEP
for 39 39
Period 4
iCR for
Period 4 39 39
Correlations
COEP NICR
for for
Period 4 Period 4
Pearson CCEP
Correlation for 1.000 -.O95
Period 4
NICR
for -.095 1.000
Period 4
Sig. COEP
(2-tailed) for .564
Period 4
NICR
for .564
Period 4
N COEP
for 39 39
Period 4
NICR
for 39 39
Period 4

 

A18

 

 

Correlations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

COEP
for TCR for
Period 4 Period 4
Pearson COEP
Correlation for 1.000 -.088
Period 4
TCR for
Period 4 -.088 1.000
Sig. COEP
(2-tailed) for .593
Period 4 ’
TCR for
Period 4 '593
N COEP
for 39 39
Period 4
TCR for
Period 4 39 39
Correlations
COEP
for JAR for
Period 4 Period 4
Pearson COEP
Correlation for 1.000 -.117
Period 4
JAR for
P eriod 4 -.117 1.000
Sig. COEP
(2-tailed) for .480
Period 4
JAR for
Period 4 "80
N COEP
for 39 39
Period 4
JAR for
Period 4 39 39

 

A19

 

 

Cbnehmows

 

Change

COEP
hem
Pmmm
3k)
Pakd4

cmmme
hHCR
flom
Paku
3hr
Ruhd4

 

1fimnwn

Cbmfldhn

96
cmmme
m
COEP
flom
Paw”
3k)
lfiuhd4

‘%
cmmme
hHCR
flom
Pmmm
3k:
Huhd4

1xmo

-J31

-J31

rxmo

 

Sig.
(2-taiied)

96
Chane
m
COEP
ham
Pmmm
3k)
me04

‘%
Change
uHCR
flom
Pawn
3k)
Puhd4

427

A27

 

 

cmmme

COEP
flom
Pmmm
3k)
Rmhd4

cmmme
hHCR
flmm
Pumm
3n:
Rmhd4

39

39

 

39

39

 

A20

 

 

Correlations

 

Change

COEP
from
Period
3 to
Period 4

%
Change
in NICR

from
Period
3 to
Period 4

 

earson

Correlation

%
Change
in
COEP
from
Period

3 to
Period 4

%
Change
in NICR
from
Period

3 to
Period 4

1.000

.059

.059

1 .000

 

 

Sig.
(2-talied)

%
Change
in
COEP
from
Period

3 to
Period 4

%
Change
in NICR
from
Period
3 to
Period 4

.721

.721

 

 

%
Change
in
COEP
from
Period
3 to
Period 4

96
Change
in NICR
from
Period
3 to
Period 4

 

39

39

 

39

39

 

 

1
-i

Correlations

 

%
Change
in
COEP
from
Period
3 to
Period 4

Change
in TCR
from
Period
3 to
Period 4

 

'Pearson

Correlation

%
Change
in
COEP
from
Period

3 to
Period 4

%
Change
in TCR
from
Period

3 to
Period 4

1.000

.006

1.000

 

Sig.
(2-tailed)

%
Change
in
COEP
from
Period

3 to
Period 4

%
Change
in TCR
from
Period

3 to
Period 4

.969

.969

 

 

%
Change
in
COEP
from
Period

3 to
Period 4

96
Change
in TCR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

A22

 

 

Correlations

 

Change

COEP
from
Period
3 to
Period 4

Change
in JAR

from
Period
3 to
Period 4

 

58318011

Correlation

%
Change

COEP
from
Period

3 to
Period 4

Change
in JAR
from
Period

3 to
Period 4

1.000

-.121

-.121

1 .000

 

Sig.
(2-taiied)

%
Change

COEP
from
Period

3 to
Period 4

Change
in JAR
from
Period

3 to
Period 4

.463

.463

 

 

Change

COEP
from
Period

3 to
Period 4

Change
in JAR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

A23

 

 

Correlations

 

Change

COEP
from
Period
2 to
Period 3

96
Change
in ICR
from
Period
3 to
Period 4

 

'Pearson

Correlation

96
Change
in
COEP
from
Period

2 to
Period 3

96
Change
in iCR
from
Period

3 to
Period 4

1.000

-.220

-.220

1.000

 

Sig.
(2-talled)

96
Change
in
COEP
from
Period
2 to
Period 3

96
Change
in iCR
from
Period

3 to
Period 4

.178

.178

 

 

96
Change
in
COEP
from
Period
2 to
Period 3

96
Change
in iCR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

A24

 

Correlations

 

96
Change
in
COEP
from
Period
2 to
Period 3

96
Change
in NICR

from
Period
3 to
Period 4

 

'Pearson

Correlation

96
Change
in
COEP
from
Period

2 to
Period 3

96
Change
in NICR
from
Period

3 to
Period 4

1.000

-.175

-.175

-1.000

 

Sig.
(2-talled)

96
Change
in
COEP
from
Period
2 to
Period 3

96
Change
in NICR
from
Period

3 to
Period 4

.288

.288

 

 

96
Change
in
COEP
from
Period
2 to
Period 3

96
Change
in NICR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

 

iJthfl'“-q
1 . v a I

1...“?
L

Conehfiows

 

‘%
Change
m
COEP
hom
Pmku
2k:
Puwd3

cmmme
lnTCR
hem
Pamm
3h)
Pakd4

 

lflmmmn

Correlation

96
CMmme
m
COEP
flom
Pawn
2h)
Fauna

96
Change
lnTCR
flom
Faun
3k)
lfiubd4

10m)

-207

-207

11m0

 

Sig.
(2-taiied)

'%
Change
m
COEP
flom
Pmkm
2k)
Puwd3

‘%
Chane
lnTCR
hem
Pmkm
3k)
med4

205

205

 

 

96
CNmme
m
COEP
flom
Pmkfl
2k)
lfinbd3

96
cmmme
lnTCR
flom
Fahd
3k)
Pmmd4

39

39

 

 

39

39

 

A26

 

Correlations

 

Change

COEP
from
Period
2 to
Period 3

Change
in JAR
from
Period
3 to
Period 4

 

‘Pearson

Correlation

96
Change
in
COEP
from
Period
2 to
Period 3

%
Change
in JAR
from
Period

3 to
Period 4

1.000

-.114

-.114

1 .000

 

Sig.
(2—tailed)

96
Change
in
COEP
from
Period

2 to
Period 3

96
Change
in JAR
from
Period
3 to
Period 4

.489

.489

 

 

96
Change

39

39

 

 

39

39

 

A27

 

 

Correlations

 

Change

COEP
from
Period
1 to
Period 2

Change
in lCR
from
Period
3 to
Period 4

 

Pearson
Correlation

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in iCR
from
Period

3 to
Period 4

1.000

-.385'

-.385"

1.000

 

Sig.
(2-taiied)

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in iCR
from
Period

3 to
Period 4

.015

.015

 

 

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in iCR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

'. Correlation is significant at the 0.05 level (2-tailed).

A28

 

 

Correlations

 

96
Change
in
COEP
from
Period
1 to
Period 2

96
Change
in NICR

from
Period
3 to
Period 4

 

'Pearson
Correlation

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in NICR
from
Period

3 to
Period 4

1.000

-.407"

-.407'

1.000

 

Sig.
(2-taiied)

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in NICR
from
Period
3 to
Period 4

.010

.010

 

 

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in NICR
from
Period
3 to
Period 4

39

39

 

 

39

39

 

'. Correlation is significant at the 0.05 level (2-taiied).

A29

 

Correlations

 

Change

COEP
from
Period
1 to
Period 2

Change
lnTCR
from
Period
3 to
Period 4

 

'Pearson
Correlation

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in TCR
from
Period

3 to
Period 4

1 .000

-.400*

-.400"

1 .000

 

Sig.
(2—talied)

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
lnTCR
from
Period

3 to
Period 4

.012

.012

 

 

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in TCR
from
Period

3 to
Period 4

39

39

 

39

39

 

*. Correlation is Significant at the 0.05 level (2-tailed).

A30

 

 

 

Correlations

 

Change

COEP
from
Period
1 to
Period 2

Change
in JAR
from
Period
3 to
Period 4

 

'Pearson

Correlation

96
Change
in
COEP
from
Period

1 to
Period 2
96
Change
in JAR
from
Period

3 to
Period 4

1.000

-.133

-.133

1.000

 

Sig.
(2-tailed)

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in JAR
from
Period

3 to
Period 4

.419

.419

 

 

96
Change
in
COEP
from
Period

1 to
Period 2

96
Change
in JAR
from
Period

3 to
Period 4

39

39

 

 

39

39

 

 

APPENDIX F - Regression Output

Model Summary”

 

Model

Variables

 

Entered

Removed

R Square

Adjusted
R Square

Std. Error
of the
Estimate

 

 

96
Change
in
COEP
from
Period

1 to

 

 

Period 2°"

 

.385

 

.148

 

.125

 

.1127

 

 

a. Dependent Variable: 96 Change in iCR from Period 3 to Period 4
b. Method: Enter

c. independent Variables: (Constant), 96 Change in COEP from Period 1 to Period 2

d. All requested variables entered.

 

 

 

 

 

 

ANOVA‘
Sum of Mean
Model Squares df Square F SE.
1 Regression 8.2E-02 1 825-02 6.444 .015
Residual .470 37 1.3E-02
Total .552 38

 

 

 

 

a. Dependent Variable: 96 Change in iCR from Period 3 to Period 4
b. independent Variables: (Constant), 96 Change in COEP from Period 1 to Period 2

 

 

 

 

 

 

 

 

 

 

Coefficients'
Standar
dized
Unstandardized Coefilci
Coefficients ents
Std.
Model 8 Error Beta t Sig.
1 ( onstant) -3.6E-03 .027 -.136 .893
96
Change
in COEP
from -.452 .178 -.385 -2.538 .015
Period 1 to
Period 2

 

 

a. Dependent Variable: 96 Change in iCR from Period 3 to Period 4

A32

 

Model Summary“

 

 

 

 

 

 

 

Std. Error
Variables Adjusted or the

Model Entered Removed R R Square R Square Estimate
1 96

Change

in

$05? .407 .166 .143 .2068

rom

Period

1 to c ‘

Period g '

 

 

 

 

 

a. Dependent Variable: 96 Change in NICR from Period 3 to Period 4
b. Method: Enter

6. Independent Variables: (Constant). 96 Change in COEP from Period 1 to Period 2

d. All requested variables entered.

 

 

ANOVA'
Sum of Mean
Model Sgares dt Square F Sig.
1 Pegression .314 1 .314 7.343 .010
Residual 1.582 37 4.3E-02
Total 1.896 38

 

 

 

 

 

 

 

 

a. Dependent Variable: 96 Change in NICR from Period 3 to Period 4
b. independent Variables: (Constant), 96 Change in COEP from Period 1 to Period 2

 

 

 

Coefficients‘I
Standar
dized
Unstandardized Coefflci
Coefficients ' ents
Std.
Model 8 Error Beta 1 Sig.
1 (Constant) .268 .049 5.463 .000
96
Change
in COEP
from -.884 .326 -.407 -2.710 .010
Period 1 to
Period 2

 

 

 

 

 

 

 

 

 

a. Dependent Variable

: 96 Change in NICR from Period 3 to Period 4

A33

 

 

 

 

 

Model Summary“
Std. Error
Variables Adjusted of the

Model Entered Removed R R Square R Square Estimate
1 96

Change

in

COEP .400 .160 .137 .1425

from

Period

1to 6 ‘

Period 2 '

 

 

 

 

 

 

 

 

a. Dependent Variable: 96 Change in TCR from Period 3 to Period 4
b. Method: Enter

c. independent Variables: (Constant). 96 Change in COEP from Period 1 to Period 2
d. All requested variables entered.

 

 

 

ANOVA'
Sum of Mean
Model Squares dt Square F SE.
1 Regression .143 1 .143 7.052 .012
Residual ' .751 37 2.0E-02
Total .894 38

 

 

 

 

 

 

 

a. Dependent Variable: 96 Change in TCR from Period 3 to Period 4

b. independent Variables: (Constant). 96 Change in COEP from Period 1 to Period 2

 

 

 

Coefficients‘
Standar
dized
Unstandardlzed Coeffici
Coefficients ents
Std.
Model B Error Beta t Sig.
1 (Constant) .129 .034 3.809 .001
96
Change
in COEP
from -.597 .225 -.400 -2.655 .012
Period 1 to
Period 2

 

 

 

 

 

 

 

 

 

a. Dependent Variable: 96 Change in TCR from Period 3 to Period 4

A34

 

 

 

31293 02638 2527