322 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 1 Characteristics of the ‘all-Regents’ case study sites
Urban Suburban
Rural Eastern
2 Northern
1 1
Western 1
1 Central
1 Southern Hudson
2 Long Island
1 Total
2 6
2
3. Results
Table 2 reports basic descriptive statistics that are use- ful to keep in mind. During the 1992–93 school year, on
average, 65 of the students enrolled in grades 9–12 in New York State high schools excluding the Big 5 city
districts were taking Regents examinations. The inci- dence of Regents test taking increased significantly over
the period we studied and by the 1996–97 school year it had reached close to 76, better than a 10 percentage
point gain. A particularly large percentage increase can be observed for New York City in Table 2. Regents par-
ticipation increased in New York City by more than 30 percentage points during the four year period we studied.
Part of this increase can be explained by a dramatic increase in the participation rate in mathematics i.e. it
increased by 55 percentage points.
8
On the other hand, the Big 4 districts Buffalo, Rochester, Syracuse, and
Yonkers did not register similar increases. In fact, we found an average decline for this group. A closer exam-
ination of the Big 5 districts revealed that the partici-
Table 2 Mean participation rates for regents achievement examinations
a
with standard deviations in Whole state NYC n
= 1
Big 4 n =
4 n
= 580
1992 Average 65.27
30.50 55.19
participation 12.88
– 15.58
1996 Average 75.97
63.75 49.00
participation 15.22
– 16.92
Ave change in 10.70
33.25 26.19
particip 92–96 14.12
– 3.70
a
Figures are averages of the percentage of students in grades 9–12 taking Regents achievement examinations in Course I
mathematics, English, Global Studies, and US History.
8
As well, the participation rates in other Regents examin- ations i.e. English, Global Studies, and US History increased
an average of 25 percentage points.
pation rate for New York City in 1992 was much lower compared to the Big 4 and hence the dramatic increase
over the next four years brought the participation rate closer to its counterpart city districts. We also note that
the average decline in participation for the Big 4 is mostly explained by declines in participation for Buffalo
and Rochester both decreased in the neighborhood of 9 percentage points. We were unable to correlate any of
the structural features of these city districts to the changes in participation. With regards to the implications
of these changes for the Big 5 districts, we found that the percentage of test takers passing Regents examinations
increased for the Big 4 by approximately 5 percentage points while this statistic declined by 12.75 percentage
points for New York City. Also, it is interesting to note that the academic staff per 1000 district pupils for the
Big 4 remained intact during this time period while New York City experienced a decrease in the academic staff.
The standard deviations in Table 2 warrant comment. There is a significant degree of variation around the
means that we report. Indeed, for the State as a whole in 1992–93 the districts ranged from a low participation
rate of 20.3 to a high of over 100.
9
In 1996–97, the districts ranged between a low of 20.5 and a high of
152.8. We also found that while the average rate of participation grew over the period, participation rates in
no fewer than 107 districts declined. Fig. 1 provides an illustration of how the districts are distributed across dif-
ferent magnitudes of change in participation during the period. The figure makes it quite clear that some districts
have been moving in a direction that runs counter to the State’s current efforts to increase Regents examination
participation rates.
Question 1: What explains the willingness or ability of school districts to increase Regents examination
participation rates?
Fig. 1. Changes in percentage of 9–12 enrolments taking
Regents’ exams 1992–96.
9
The percentage can rise above 100 because the enrollment count is restricted to four grade levels. The exams themselves
are not restricted to these four grade levels.
323 D.H. Monk et al. Economics of Education Review 19 2000 319–331
3.1. Results from the statewide data As we realized how varied New York districts are with
respect to both the level of participation in the Regents exam program and their inclination to increase these par-
ticipation rates, we became interested in seeing if we could identify structural characteristics that are associa-
ted with these phenomena. In particular, we wanted to know if there are some clear distinguishing character-
istics of the districts that increased the percentage of stu- dents who participated in the Regents testing program,
and we thought it would be useful to look at relationships between the structural features we identified district
type, property wealth, incidence of poverty, and size and both the incidence of test taking in 1992 and changes in
test taking between 1992 and 1996.
Our regression results suggest that many different types of districts are represented among those who
increased their Regents participation rates during the per- iod we studied. In other words, we found no evidence
suggesting that increasing districts were more likely to be urban rather than suburban or rural, large rather than
small, or wealthy in terms of property wealth rather than poor. One notable exception to this involves our measure
of the incidence of poverty where we did find that higher levels of poverty were associated with both lower levels
of Regents participation in 1992 and smaller increases in participation between 1992 and 1996. More specifi-
cally, we found that a 10 higher incidence of poverty in 1992 was associated with a 7.34 decline in the rate
of participation over the next four years see Table 3.
10
Another exception is the positive correlation between increases in full value per pupil and participation in
Regents’ examinations between 1992 and 1996. We also found that districts reporting high partici-
pation rates in 1992 reported smaller increases in partici- pation over the period we studied. We interpret this as
a ceiling effect in the sense that if the district already has a high rate of participation, it becomes more difficult
to increase the rate further. The marginal cost of increas- ing participation rates must surely rise with the percent-
age of students participating at the outset, and the nega- tive relationship we are finding in our regression
analyses between 1992 participation rates and sub- sequent increases in participation is consistent with this
expectation.
10
This marginal effect is determined by calculating the elas- ticity at the mean values and then multiplying it by 10. The
New York State average measures for incidence of poverty in 1992 and change in participation are 0.28 and 10.70 respect-
ively. Hence, the elasticity is dydxxy =
227.90.2810.7 =
20.734.
3.2. Results from the case studies The case studies reveal a number of ‘critical factors’
that appear to have important influences on school dis- tricts’ willingness or ability to move forward with an
‘all-Regents’ reform agenda. Table 4 provides a list of these characteristics. We hasten to point out that we can-
not generalize from this list. Nor is it the case that all of these phenomena were found in each of the ten case
study sites. The list provides a sampling of the kinds of things respondents mentioned when they were asked
about what gave rise to the reform.
We have several observations to make about the list. First, some of the items are not easily transferred from
one place to another. For example, ‘amicable relation- ships with bargaining units’ can be elusive, and the case
studies do not offer much insight into what needs to be done to increase Regents participation rates in places
where labor relations are less than good.
Second, and in contrast, some of the items look like they are relatively easy to transfer to other sites. For
example, we were impressed with how the maintenance of some kind of upper-level honors program seemed to
make it easier to raise Regents participation rates.
And third, we were struck by the fact that a number of the items require a willingness of leaders at the local
level to take real risks. We think the last two items on the list fall into this category. It takes considerable self-
confidence to convince teachers and parents that the necessary support will be provided when the uncertainty
surrounds the source, magnitude and nature of this sup- port.
Question 2: What have been the effects on student performance?
We dealt explicitly with two aspects of student per- formance: changes in test score results and changes in
drop-out rates over the period. These findings all come from the statewide data, and we discuss our results for
each aspect of student performance in turn.
3.2.1. Changes in the percentage of students passing We found that for the State as a whole, the passing
rate for the non-Big 5 districts went up by 2.68 percent- age points. The increase was the largest for the districts
that decreased participation. It went up by better than eight points for these districts. The magnitude of the
average increase went down as the magnitude of the change in participation rate increased and turned nega-
tive for the districts that increased their participation the most.
We can also gain insight into the nature of this relationship by looking at the scatter plot that appears in
Fig. 2. The plot portrays each of the districts in terms of its change in participation and its change in student
324 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 3 Predictors of initial percentage of students in grades 9–12 taking Regents’ examinations in 1992 and predictors of changes in the
percentage of students in grades 9–12 taking Regents’ achievement examinations
a
between 1992 and 1996
b,c,d
Participation ’92 Change in participation
OLS WLS
OLS WLS
Urban 20.4309 1.864
0.189 1.319 20.326 2.192
21.79 1.692 Rural
1.143 1.234 1.070 1.441
0.147 1.449 20.515 1.787
ln FV ’92pupil 0.926 0.717
20.911 0.723 23.02
e
0.914 21.87 1.100
Poverty ’92 231.75 3.684
241.60 3.158 229.84 4.64
227.90 4.345 ln size ’92
0.233 0.633 0.0227 0.593
21.413 0.7811 20.892 0.7323
DFull valuepupil –
– 6.19E-06 4.04E-06
1.20E-05 4.87E-06 DPoverty
– –
211.12 8.79 216.04 9.07
Dsize –
– 4.23E-04 0.0029
29.46E-04 0.0017 Participation ’92
– –
20.501 0.047 20.4217 0.049
Constant 60.230
87.28 100.496
77.26 Adjusted R
2
0.196 0.312
0.193 0.145
Sample size 580
559 Mean value
65.27 10.755
a
The dependent variable is an average for Course 1 mathematics, English, Global Studies, and US History.
b
The sample does not include the Big 5 districts and the districts for which participation and performance information is not avail- able.
c
The WLS has been weighted by 8–12 enrolments.
d
Regression coefficients with standard errors in .
e
P ,0.1, P,0.05, P,0.01.
Table 4 Precursors and precipitators of movement toward ‘all-Regents’
programs. Findings from the ten case studies Administrative turnover
A cooperative internal unit e.g. a department within a high school
Ability and willingness to block alternatives to the regents exams e.g. requirement of a superintendent’s hearing
Amicable relationships with bargaining units Commitment to the maintenance of an honors track
Maintenance of data and the monitoring of progress Willingness to view the reform as k12 rather than secondary
school only Perception in the community that necessary support services
will be available Perception among teachers that appropriate inservice
programs will be available
performance. The change in participation is on the hori- zontal axis and the change in performance is on the verti-
cal axis. There are several things to notice in the scatterplot.
First, the drift of the points is negative suggesting that higher rates of increase in participation are associated
with smaller increases in performance. Second, most of the points are above the horizontal line, suggesting that
in most of the districts there have been increases in pass- ing rates. And third, there is a more even distribution of
cases above and below the horizontal line for the districts with increasing compared to decreasing participation
rates. Next, we turn to our regression models to examine the
effects of changes in participation rates when controlling for a variety of characteristics such as structural features
of districts, initial performance of districts in 1992, and the baseline participation rate in 1992. We find that the
negative correlation between changes in participation and performance between 1992 and 1996 continues to
hold. For instance, according to the WLS regression model, a 10 increase in the change in participation
variable is associated with a 9.9 decrease in the change in performance variable
11
see Table 5. We expected to find that the higher the initial partici-
pation rate, the more difficult it would be to maintain or
11
Note that the comparative statics imply that the marginal effect of change in participation variable is not too large. To
see this, consider a school district with a total of 100 students in grades 9–12. Assume that this hypothetical district has par-
ticipation and performance statistics that are identical to the state average. Specifically, assume that the participation rates
are 65.27 and 75.97 in 1992 and 1996 respectively and the performance rates are 85.14 and 87.90 in 1992 and 1996
respectively. Then it can be shown that a 10 increase in change in participation variable implies a 18.03 increase in
the baseline participation rate i.e. participation rate in 1992. Similarly, the resulting 9.9 decrease in change in passing vari-
able reflects a modest 2.7 decrease in the baseline perform- ance rate.
325 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Fig. 2. Changes in performance and participation without the Big 5 districts 1992–96.
Table 5 Predictors of changes in the percentage of test takers who passed Regents’ achievement examinations
a
1992–96 and predictors of changes in the drop-out rate between 1992 and 1996
b,c,d
D in percentage of test takers who passed D in percentage of total enrolments that
regents achievement examinations dropped out between 1992 and 1996
OLS WLS
OLS WLS
Urban 1.483 1.184
1.253 0.870 0.025 0.265
0.302 0.217 Rural
1.794 0.782 1.303 0.915
20.053 0.173 0.152 0.222
ln FV ’92pupil 20.708 0.498
1.021
e
0.561 20.140 0.110
20.323 0.138 Poverty ’92
219.658 2.830 215.465 2.618
4.072 0.600 3.673 0.604
ln size ’92 20.700 0.422
20.965 0.374 0.226 0.096
0.238 0.093 DFull valuepupil
1.421E-06 6.88E-06
23.306E-07 22.416E-07
2.186E-06 2.494E-06
4.825E-07 6.071E-07
DPoverty 217.503 4.777
217.743 4.662 0.335 1.050
21.341 1.131 DSize
0.001 0.001 2.193E-04 8.934E-04
23.695E-05 3.558E- 1.415E-04 2.200E-04
04 Participation ’92
20.156 0.028 20.125 0.027
20.011 0.006 20.008 0.006
DParticipation 20.309 0.023
20.248 0.021 20.012 0.005
20.013 0.005 Performance ’92 columns 1, 2
20.677 20.573
20.792 20.797
Dropout rates 92 columns 3, 4 0.043
0.048 0.043
0.046 Constant
93.638 61.576
1.665 3.72
Adjusted R
2
0.452 0.346
0.381 0.368
Sample size 559
559 Mean value
2.681 20.076
a
The dependent variable is based on the average for Course 1 mathematics, English, Global Studies, and US History.
b
The sample does not include the Big 5 and the districts for which participation and performance information is not available.
c
The WLS has been weighted by 8–12 enrolments.
d
Regression coefficients with standard errors in .
e
P ,0.1, P,0.05, P,0.01.
326 D.H. Monk et al. Economics of Education Review 19 2000 319–331
increase performance rates. We reasoned that students with greater educational needs would be entering the
testing program as the participation rates reached higher levels and that these students would place increasing
demands on the resource base that was available within the school. Our estimates from the regression models
12
do not reveal such a tendency. Specifically, we found that the marginal negative effect of the change in partici-
pation variable decreases in magnitude with the baseline participation rate. In other words, the relative size of the
negative effect depends upon where on the scale the dis- trict began. These results suggest that large increases in
participation pose greater challenges for districts at lower starting points. Table 5 columns 1 and 2 also shows
that the districts with low starting points experienced less difficulty in their efforts to maintain and increase pass-
ing rates.
3.2.2. Changes in the percentage of students dropping- out
Critics of increased high school graduation require- ments often express concern about the potential for
higher requirements to discourage persistence in school and thereby increase drop-out rates. Therefore, we look
to see if changes in Regents participation rates between 1992 and 1996 were related to changes in drop-out rates,
as calculated by the State Education Department. We realize that changes in Regents participation rates were
not directly linked to high school graduation require- ments during the period we studied, but we believe these
analyses are relevant since the districts that increased participation were signaling increased expectations for
academic performance. We were interested in seeing if these increased expectations translated into significant
changes in the rates at which students left high school.
The last two columns of Table 5 report the effects of predictor variables on changes in the percentage of drop
out rates. Here we can see that districts with larger increases in participation did not report increases in their
drop-out rates over the period. In fact, the regression models suggest that the relationship was actually nega-
tive, and this has led us to the conclusion that these data reveal no evidence that increases in participation led to
increases in drop-out rates during this period.
12
To see whether the effects of increases in participation were dfferent across districts with different initial participation
rates, we included in our basic additive effects model an inter- action term between initial participation rate and changes in
participation between 1992 and 1996. This term allows the effect of change in participation to differ according to the level
of the initial participation. The coefficient on this term was sig- nificant and positive. The results from these regression models
are not reported in Table 5 and are available from authors upon request.
Question 3: What have been the changes in resource allocation behavior?
We used the statewide data to examine changes in dis- trict resource allocation behavior at several levels,
including overall spending levels and more narrowly focused measures of staffing allocations. We also took
advantage of the case study interviews to ask questions about how the ten districts changed their resource allo-
cation behavior. We begin with a report on what we learned from the statewide data, and then turn to the
results from our case studies.
3.3. Results from the statewide data 3.3.1. Changes in spending per pupil
Efforts by the Regents to raise graduation standards generate concerns about the adequacy of the underlying
resource base. Questions are commonly asked about whether the State will be able to afford the costs associa-
ted with fulfilling the higher expectations. Answering such questions is difficult, and the difficulty is com-
pounded by the absence of clear information about how much it will cost to realize the reformers’ goals.
We hope to contribute fruitfully to this debate by examining what happened to spending levels in the dis-
tricts that increased their Regents participation rates rela- tive to others. On average, the non-Big 5 city districts
in the State increased their spending by 1240 between 1992 and 1996. In order to examine the determinants of
the change in spending levels, we turned to our regression models. Table 6 reports what we found and
follows the same format that we used for the previous analyses. Changes in full value per pupil property
wealth per pupil and incidence of poverty were the only variables that significantly explain change in spending
between 1992 and 1996. It thus appears that the magni- tude of the change in participation has little to do with
changes in spending levels.
Table 6 also examines the impact of the initial partici- pation rate on subsequent spending changes. Our think-
ing was that marginal costs ought to be higher in places that are increasing participation rates from already high
levels and that this might manifest itself in the form of larger expenditure increases in those districts that are
increasing participation from already high levels.
13
Our analyses show no evidence of larger expenditure
increases in the districts that are moving their partici-
13
Again, we examined the interaction between initial partici- pation and changes in participation on the change in spending
variable while controlling for other influences on spending. The interaction term was not significantly different from zero.
The results are not reported in this paper but are available upon request.
327 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 6 Predictors of change in spending levels per pupil between 1992
and 1996
a,b,c
OLS WLS
Urban 227.947
139.144 204.07
189.450 Rural
32.567 229.475
135.777 198.056
ln FV ’92pupil 173.639
349.501
d
129.227 173.584
Poverty ’92 8.218
25.225 489.848
573.733 ln size ’92
25.088 72.660 70.805 80.439 DFull valuepupil
0.003 0.003
3.801E-04 5.740E-04
DPoverty 1639.913
1464.335 825.073
998.208 DSize
20.536
d
20.393 0.278
0.194 Participation ’92
20.464 4.889 27.97 5.828 DParticipation
21.298 3.959 22.433 4.659 Performance ’92
6.233 29.388
7.548 10.379
Spending per pupil ’92 0.039
20.100 0.037
0.050 Constant
21877.742 21440.208
Adjusted R
2
0.180 0.141
Sample size 559
Mean value 1244.235
a
The sample does not include the Big 5 and the districts for which participation and performance information is not avail-
able.
b
The WLS has been weighted by 8–12 enrolments.
c
Regression coefficients with standard errors in .
d
P ,0.1, P,0.05, P,0.01.
pation levels into the upper reaches of the range. It is clear that districts, during this period, did not accommo-
date the needs of the new students entering their Regents programs by spending additional dollars.
3.3.2. Changes in staffing levels per pupil We turn now to some alternative measures of staffing.
We used BEDS data to construct measures of how the time of professional personnel is allocated across differ-
ent areas of the instructional program. In making these calculations, we used a methodology developed pre-
viously in conjunction with a study of microlevel resource allocation for education Monk, Roelke
Brent, 1996. We began with the total FTE count of pro- fessional personnel and then worked our way toward
more narrowly drawn categories of use.
On average, the staffing level per 1000 pupils in the State increased by 2.26 professional i.e. certificated
personnel for the non-Big 5 districts between 1992 and 1996. It is interesting to notice that the increase tended
to be larger in the districts that increased their Regents participation rates the most. Table 7 provides the now
familiar determinants with the focus on changes in the number of professional staff, academic staff, and second-
ary special education staff. According to our WLS regression model, a 10 point increase in the change in
Regents participation variable is associated with a 1.23 increase in the change in the total number of professional
staff per 1000 pupils. It appears that while districts with large increases in their Regents participation rates have
not been spending more dollars, they have been careful to protect the numbers of professional staff that are
working with pupils.
As for resource allocation effects in places that started from the higher starting points, we found mixed results.
In particular, districts with both low and high starting points did not show decreases in personnel per pupil.
14
Perhaps the best conclusion to draw is that large increases in participation, regardless of the starting point,
occasioned significant reallocations of resources. Next, we provide an analysis of resources that are allo-
cated into the academic portion of the curriculum between 1992 and 1996. We defined ‘academic’ to
include courses in the following subject areas: English, foreign language, mathematics, music and art, physical
education, science, and social studies. Overall the staffing levels in the academic area of the curriculum
experienced an overall decline of 0.43 personnel per 1000 pupils between 1992 and 1996. Recall that the state
experienced an overall increase in professional personnel during the period. It thus appears that academic subject
areas’ share of resources within districts experienced a slight decline. Further analysis revealed that growth
occurred for special education personnel. In particular, we found that for the State as a whole, the investment
in special education increased on average by 2.67 pro- fessional personnel per 1000 pupils between 1992 and
1996.
15
While there has been an overall decline in New York State in the average investment of personnel resources
in the academic portion of the instructional program between 1992 and 1996, the positive direction of the
WLS regression coefficient for the change in partici- pation variable column 4 in Table 7 suggests that that
14
This was determined by adding a second order interaction term between initial participation rate and change in the partici-
pation in the model. The coefficient on this term was not statisti- cally different from zero. We have not included this equation
in Table 7, however, the results are available from authors upon request.
15
The relative level of investment of resources in subject area teacher in contrast to special education forms of teaching has
been the subject of recent scholarly research. For examples, see Lankford and Wyckoff 1995 and Miles 1995 and Miles
1995.
328 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 7 Predictors of changes in professional staffing levels
a
per 1000 pupils between 1992 and 1996
b,c,d
D in total staff1000 pupils D in academic staff1000 pupils
D in second. special educ. staff 1000 pupils
OLS WLS
OLS WLS
OLS WLS
Urban 20.361 0.860 0.349 0.573
20.005 3.94 0.109 0.250
0.094 0.238 20.195 0.168
Rural 20.682 0.563 20.240 0.598 0.227 0.259
0.361 0.264 20.261 0.160 20.259 0.178
ln FV ’92pupil 1.836
e
1.872 0.680
0.425 0.568
0.363 0.482
0.458 0.202
0.184 0.104
0.114 Poverty ’92
5.304 5.528
0.972 0.354
0.078 0.628
2.044 1.724
0.937 0.753
0.571 0.510
ln size ’92 20.197
20.795 20.628
20.664 20.011
0.129 0.352
0.263 0.164
0.120 0.088
0.074 DFull valuepupil
1.42E-05 1.02E-05
6.46E-06 4.31E-06
6.79E-8 24.93E-7
1.612E-06 1.63E-06
7.40E-07 7.186E-07
4.397E-7 4.28E-7
DPoverty 8.281
1.137 21.876
22.486 3.372
2.06 3.455
3.046 1.586
1.340 0.959
0.902 D size
20.066 20.004
20.002 20.001
28.06E-4 28.63E-4
0.001 5.996E-04
5.357E-04 2.619E-04
3.248E-4 1.73E-4
Participation ’92 0.011
0.027 0.014
0.021 20.008
20.002 0.020
0.017 0.009
0.007 0.005
0.005 DParticipation
0.010 0.026
0.005 0.014
20.007 20.001
0.016 0.014
0.007 0.006
0.004 0.004
Performance ’92 0.022 0.031
0.051 0.031 0.011 0.014
0.028 0.014 0.012 0.008 0.016 0.009
Total staff ’92 columns 20.062
20.158 20.186
20.252 20.283
20.231 1, 2 Academic staff ’92
0.026 0.028
0.024 0.026
0.060 0.053
colums 3, 4 Sec. Sp. Ed. staff ’92 columns 5, 6
Constant 217.544
210.022 20.995
2.211 24.369
23.857 Adjusted R
2
0.207 0.223
0.235 0.302
0.107 0.083
Sample size 559
559 559
Mean value 22.332
20.358 2.651
a
The total staff is a sum of FTE instructional and administrative staff1000 pupils. The instructional staff is an aggregate of elementary, vocational, academic, and special education professional staff. The academic staff is a a sum of secondary staff1000
pupils in math, English, social studies, science, foreign language, physical education, music, and art.
b
The sample does not include the Big 5 and the districts for which participation and performance information is not available.
c
The WLS has been weighted by 8–12 enrolments.
d
Regression coefficients with standard errors in .
e
P ,0.1, P,0.05, P,0.01.
districts with the greatest increases in Regents partici- pation are the most inclined to ‘protect’ their investments
in academic subject area teaching. Specifically, the WLS model suggests that an increase of 10 percentage points
in the change in participation variable was associated with a 4.32 increase in the change in academic
staffing ratio.
The effects of the starting points in 1992 are also inter- esting. Specifically, we found that the districts with
higher starting points actually increased their invest- ments in the staffing of academic courses. Indeed, the
largest increase in staffing can be seen for the highest participation gain districts that started from the highest
initial level of participation.
The last two columns of Table 7 reveal a weak nega- tive association between change in participation rate and
change in special education staff. However, it is important to note that this negative relationship is insig-
nificant according to the WLS estimation technique. Finally, in Table 8, we refine our analyses and focus
on investments of professional staff resources across advanced, ‘regular,’ and remedial course offerings within
the academic portion of the curriculum.
16
16
Courses were considered ‘advanced’ if their titles were modified by terms like advanced, advanced placement, college,
or honors. Courses were considered ‘remedial’ if their titles were modified by terms like basic or remedial. ‘Regular’
courses constituted the residual category. Courses in music and art and physical education were excluded from these analyses
since there was no distinction drawn among remedial, regular, and advanced versions of the courses.
329 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 8 Predictors of changes in professional staffing levels
a
per 1000 pupils within the academic program between 1992 to 1996
b,c,d
D in total advanced staff per D in total regular staff per 1000 D in total remedial staff per
1000 pupils pupils
1000 pupils OLS
WLS OLS
WLS OLS
WLS Urban
20.028 0.081 20.048 0.06 0.196 0.283
0.275 0.189 0.009 0.146
20.038 0.083 Rural
20.011 0.053 20.017 0.063 0.313 0.186 0.367
e
0.199 20.043 0.096 0.003 0.087
ln FV ’92pupil 0.148
0.183 0.361
0.017 0.038
20.025 0.038
0.044 0.141
0.131 0.061
0.053 Poverty ’92
20.167 0.195 0.078 0.18 0.384 0.673
20.309 0.568 0.390 0.354 0.505 0.252
ln size ’92 0.124
0.097 20.569
20.661 20.239
20.134 0.032
0.028 0.118
0.092 0.051
0.036 DFull valuepupil
23.824E-07 9.15E-8
4.15E-6 2.37E-6
8.52E-07 1.76E-07
1.503E-07 1.727E-07
.5.37E-7 5.412E-7
2.695E-07 2.369E-07
DPoverty 20.240
20.240 21.87 1.14 22.386
0.849 0.424
0.328 0.322
1.01 0.593
0.442 DSize
23.48E204 22.55E-04 20.001
24.15E-4 23.399E-04
21.91E-04 1.12E-04
6.484E-05 3.84E-4
1.95E-4 1.988E-04
8.487E-05 Participation ’92
0.002 0.003
0.007 0.01
20.001 23.78E-4
0.002 0.001
0.006 0.005
0.003 0.002
DParticipation 6.81E-4
0.002 0.012
0.012 20.01
20.005 0.001
0.001 0.005
0.004 0.002
0.002 Performance ’92
24.9E24 0.001
0.003 0.013
0.002 0.006
0.003 0.003
0.01 0.01
0.005 0.004
Advanced staff ’92 20.238
20.153 20.227
20.280 20.383
20.350 columns 1, 2 Regular
0.030 0.026
0.023 0.027
0.034 0.029
staff 92 colums 3, 4 Remedial staff 92
columns 5, 6 Constant
22.467 22.817
2.863 7.79
1.707 1.055
Adjusted R
2
0.103 0.057
0.279 0.310
0.210 0.225
Sample size 559
559 559
Mean value 0.076
20.306 20.341
a
The dependent variables include math, science, English, social studies, and foreign language.
b
The sample does not include the Big 5 and the districts for which participation and performance information is not available.
c
The WLS has been weighted by 8–12 enrolments.
d
Regression coefficients with standard errors in .
e
P ,0.1, P,0.05, P,0.01.
Table 8
reveals some
noteworthy relationships
between increases in Regents participation rates and the distribution of teacher resources among advanced, reg-
ular, and remedial academic course offerings. In parti- cular we found rather striking positive relationships
between the magnitude of the increase in participation and the investment of resources in regular versions of
academic courses. It is clear that districts with the high- est increases in Regents participation rates have been
shifting resources away from remedial courses and toward
regular courses.
According to
our WLS
regression results, a 10 increase in the change in Regents participation variable is associated with a 4.63
increase in the change in staffing ratio for regular aca- demic courses and a 1.63 reduction in the change in
staffing of remedial academic courses. 3.4. Results from the case study data
The case studies provide information about how the ten sites changed resource allocation practices as they
made efforts to move toward an ‘all-Regents’ program. Table 9 provides a list of the different devices the
respondents talked about utilizing. These can be grouped into three broad categories. The first involves efforts to
provide additional class time for students needing the extra assistance. These efforts involved doing things like:
moving to a nine period day; reducing the use of study halls; making it possible for a student to take a Regents
course over more than two semesters; and making it possible for students to attend extra periods of instruction
while they were enrolled in Regents classes.
The second category involves efforts to provide more
330 D.H. Monk et al. Economics of Education Review 19 2000 319–331
Table 9 Resource allocation responses to ‘all-Regents’ reforms. Find-
ings from the ten case studies Timingscheduling
Develop a 9 period day Schedule students needing assistance for additional regular
instructional periods: Increase the number of classes during a semester; andor
Increase the number of semesters of instruction typically three or four rather than two
Reduce the use of study halls and focus any remaining study halls on academic work
Make greater use of ‘unconventional’ time and methods for instruction and extra help
Use time between the close of school and the start of practices and club activities, evenings, and summers.
Use innovative technologies: Web pages and telephone hot lines for help
Utilization of professional staff Increase the use of teaching assistants
Upgrade the curricula through increased professional development
Reduce class size Shift teachers from study hall and other ‘duty’ periods into
instructional roles Establish academic help centers
Provide regents review sessions
instructional opportunities outside of regular school hours. These changes included: providing Regents
review sessions in the evenings; establishing a telephone hot line for students with questions about their Regents
courses; making use of new technologies like Web pages to provide support for students; and using time between
the end of the school day and the start of extra curricular activities for some additional study time.
The third category involved making changes in the type of instruction that was being offered. Here we heard
reports of districts: making greater use of teaching assist- ants to supplement what classroom teachers were able to
provide; taking steps to reduce class size; and increasing the quantity as well as the quality of professional devel-
opment for teachers.
We also asked the case study districts about how they paid for all these changes. Our respondents confirmed
what we found using the statewide data: It is not the case that these districts made increases in their spending lev-
els per pupil. The story coming from these districts is much more a story of making efforts to re-allocate exist-
ing resources.
Table 10 provides a list of the strategies the case study respondents talked about as they reflected upon their
efforts to pursue reform. In particular respondents in these districts talked about: limiting growth in salaries
so that the savings could be used to hire new staff; using
Table 10 Resource allocation strategies. Findings from the ten case stud-
ies Limit the growth of salaries and use the savings to support
the hiring of new staff Use savings realized from the breakage associated with
retirements to support teaching assistants and other types of new staff
Rely on the willingness of teachers and other professionals to increase their effort without direct or immediate
compensation Rely on other donated resources e.g. the time and energy of
National Honor Society members and parents Take advantage of resources that become available as special
education students are included in regular classroom programs
a portion of the ‘breakage’ savings that are realized from retirements to support teaching assistants and smaller
class sizes; relying on the willingness of teachers to increase their effort without direct or immediate extra
compensation; relying on donated resources such as tutoring help from parents and honor society students;
and taking advantage of resources that become available as part of inclusion programs.
4. Discussion
