Spending Changes and Non-instructional Service Consolidation. Thomas A. DeLuca Educational Administration (K-12) Michigan State University

Spending Changes and Non-instructional Service Consolidation Thomas A. DeLuca Educational Administration (K-12) Michigan State University Presented at 2012 AEFP Annual Conference Boston, Massachusetts March 15-17, 2012 Contact: Thomas A. DeLuca, PhD Candidate Educational Administration (K-12) Michigan State University East Lansing, MI (586) 879-7111 delucath@msu.edu Abstract Educational policy makers continue to encourage or require districts to consolidate noninstructional services under the expectation that economies of scale will reduce operating expenditures. Unfortunately, there is essentially no empirical evidence on the size or source of cost savings associated with such measures. Using panel data from 2004-2010, this mixed methods study measures and finds few spending reductions associated with the consolidation of services from the local district to the Educational Service Agency (ESA). Although this is only one of several service consolidation models, (e.g., local districts sharing services with each other, local districts sharing services with municipalities, privatization of services), this study also finds no support for the contention that the consolidation of non-instructional services other than the Business Office results in a statistically significant increase in instructional spending. 1

In an era of sustained financial pressure on K-12 schools, policy makers and school officials continue to give serious consideration to significant changes in the way educational services are delivered in order to lower costs. One of the most prominent measures being advanced is the consolidation of multiple school districts into a single larger district, with the hope that the larger district will realize lower per-student costs through economies of scale. However, a related reform to achieve economies of scale currently enjoys far greater political appeal. Under this alternative, local school districts would remain intact but only consolidate selected services (e.g., business services, personnel, bus transportation, technology support, food service) with other local school districts or a regional Educational Service Agency (ESA) in the hope of reducing costs through economies of scale. (In Michigan, ESAs are called Intermediate School Districts (ISD) and typically conform to county or multi-county boundaries.) This paper analyzes local school district spending and focuses on a comparison of spending changes between local districts that consolidated non-instructional services to the ISD and districts that maintained these services within the local district. This study addresses three main issues. First, to what extent are Michigan school districts consolidating the provision of services to an ISD? Second, does this service consolidation result in spending reductions at the local school district, and if so, are these funds reallocated to increase instructional spending? Finally, what are the underlying sources of any spending changes (e.g., scale economies, staff reductions, changes in service quality, renegotiated collective bargaining agreements)? In order to address these issues, I used a mixed methods approach. First, I surveyed all of Michigan s ISD Chief Financial Officers (CFO) to identify service consolidation patterns and describe the characteristics of local districts that opted to consolidate services with the ISD. 2

Second, using panel data, I performed a series of statistical analyses 1 of local district per pupil spending for the provision of the selected services, comparing districts that consolidated services with those that did not. Finally, through interviews with ISD and local district officials, I described changes in service quality and the sources of local district spending changes, one of which appears to be economies of scale. Conceptual Background Economists have long studied the parallels between manufacturing and educational organizations. This theoretical tradition has been proposed to hold valuable insights for educational policymakers and researchers, especially when evaluating the existence of scale economies due to the consolidation of school districts, or more simply, consolidation for the provision of individual services. In his classic book Principles of Economics first published in 1890, Alfred Marshall (1961, p. 278) referred to the chief advantages of production on a large scale as economies of skill, economies of machinery, and economies of supplies. This basic description includes three key economic concepts that directly or indirectly reduce the average cost per unit (student) through an increase in overall production efficiency. Assuming that economies of scale exist in a manufacturing or educational environment, one key question is: What factor, or combination of factors, likely leads to a decrease in the average cost per unit (student) while maintaining a consistent quality when increasing the quantity of output? Although a small amount of K-12 educational literature relates to the consolidation of multiple school districts into a single larger district, even fewer studies describe the concept of the cross-district service consolidation as a method of reducing costs while maintaining a 1 Note: Statistical tables are found in Appendix A 3

consistent quality of service, though none of these studies offer empirical support for the concept. (Eggers, W., Snell, L., Wavra, R., & Moore, A., 2005; Hirsch, W. Z., 1960) When reviewing quantitative studies related to school district consolidation, the vast majority (83%) employed a cross-sectional approach as opposed to a longitudinal methodology. These cross-sectional studies suggest that increasing the size of small school districts through consolidation typically reduces the average cost per student (economies of scale). However, once a district size exceeds 4,000 students, average per-student costs typically begin to increase (diseconomies of scale). As an alternative methodology, Duncombe & Yinger (2002) and Slate & Jones (2005) suggest that longitudinal studies may provide the best evidence of scale economies when school districts consolidate. By using a longitudinal study to compare a single district to itself over time, the potential for bias due to unobserved district characteristics is reduced. The vast majority of studies leave unresolved the question of whether same district spending patterns change as the result of consolidating the provision of services to the ISD. Consequently, a significant gap exists in the literature based solely on research methodology. Research Questions This study answers the following research questions: 1) To what extent are local school districts in Michigan consolidating the provision of services to the ISD, and how does this activity vary by type of service and district characteristics? 2) To what extent does service consolidation change district operating expenditures? 3) How does service consolidation affect instructional spending? 4) What are the sources of change(s) in expenditures when school districts consolidate services? 5) What role do scale economies play when operating expenditures change due to school district service consolidation? 4

Data Collection Methods This study uses three types of data: 1) online survey responses from ISD CFOs, 2) panel data sets that include the merged survey results, highly disaggregated financial data, and administrative data such as student, district, and community characteristics, and 3) interview responses describing the source and context of district spending changes. I surveyed Michigan's ISD business officials (n=47) to identify local districts where consolidation for the provision of service(s) occurred, and more specifically, the year in which the consolidation began. This type of information is not available from any administrative source. Secondly, I merged these survey data with highly disaggregated financial data upon which I performed multiple statistical analyses using the per-pupil expenditure as the dependent variable, in order to measure and compare spending changes between districts that consolidated services and those that did not. These analyses include first-last comparisons, cross-sectional regression analysis for each year of available data, and finally, a fixed effects regression analysis, comparing annual spending within a specific district, thus holding constant most unobservable characteristics that typically appear in the unobserved error term of a cross-sectional regression. Finally, I interviewed local district superintendents and ISD business officials in order to identify the specific sources and context of spending changes as well as to compare and triangulate the statistical findings described above. What are the Trends and Patterns for Non-instructional Service Consolidation? By its nature, an ISD provides services for its constituent districts. However, this study focused on non-instructional service consolidations that moved services from the local district to the ISD (e.g., business services, personnel, bus transportation, technology support, food service). 5

Trends in service consolidation. Figure 1 shows the number of service consolidations from 2004-2011. I count each service separately so a single district that consolidated payroll, technology services and student transportation in a given year would count as three service consolidations. For the years 2004-2011, there is no apparent acceleration or deceleration in the pace of consolidations. It is interesting to note that local districts were actively pursuing service consolidation arrangements well before the recent intense interest by state and local policy makers. Figure 1. Number of New Service Consolidation Arrangements, 2004-2011 Number of services consolidated 80 70 60 50 40 30 20 10 46 51 37 53 49 72 53 50 0 2004 2005 2006 2007 2008 2009 2010 2011 Patterns in service consolidation by type. Although the annual number of consolidations remained relatively consistent from 2004-2011, the pace of consolidation varies sharply across service areas. Figure 2 shows the percent of districts that consolidated each of the given services through 2011. The three most commonly consolidated services are student transportation 2 (35 percent of local districts), payroll (20 percent), and technology services (20 percent). 2 Survey and interview comments indicated that the primary form of transportation consolidation relates to transporting special education students. 6

Figure 2. Percent of Local Districts Consolidating Given Services with Their ISDs, 2011 40% 35% 35% 30% 25% 20% 20% 20% 15% 14% 12% 10% 5% 0% 6% 8% Curr Dir AP/AR* Gen Acct Other Payroll Purch Ops & Maint * Accounts Payable / Receivable 5% 1% 5% Transport HR Tech Deconsolidation of services. While the number of service consolidation arrangements continued to increase, a small number of school districts (less than 4%) chose to deconsolidate or shift the provision of a consolidated service from the ISD back to the local district. In recent years, once districts consolidate services to the ISD, it is a relatively rare occurrence to move provision back to the local level. Service type and district characteristics. School districts that consolidated the provision of services ranged from remote rural districts to large urban districts with fund balances ranging from 18% of operating expenditures to districts with deficit fund balances. The context for initiating service consolidation ranged from an overall strategy to increase operational efficiency to the last-minute retirement of a local district's payroll clerk. In addition, Michigan recently approved legislation whereby local districts are encouraged via a best practices categorical grant to consolidate non-instructional services, leading local school boards and administrative policy makers to step up their interest in consolidation as one of several strategies to reduce spending. 7

What is the Financial Impact of Service Consolidation? For those districts that consolidated the provision of services, this strategy was typically part of a larger spending reduction plan that included school building closures, staffing reductions, program reductions, and spending reductions achieved through collective bargaining such as wage concessions and the transfer of health benefit costs from the district to its employees. Descriptive statistics. Table 1 shows that the mean of per pupil spending is consistently lower in districts that consolidated services compared to those that did not. Although interesting, this comparison does not control for a variety of other factors that may influence district spending aside from service consolidation. Cross-sectional estimation strategy. In order to control for some of these factors, I used a cross-sectional analysis for estimating the influence of consolidation on spending in each of six services in each of 7 years (2004-2010), with the model taking the following form: PPE i = α i + β 1 Con i + SDstructure i β 2 + µ i (1) where PPE i is the per pupil expenditure in district i. The focus variable of interest is the dummy variable Con i, which assumes the value 1 in districts that consolidated the respective service. SDstructure i is a vector of characteristics of district i that impact districts spending across different non-instructional functions. Monk, D. H. and Hussain, S. (2000, p. 21) focus on four district characteristics that impact resource allocation: per pupil expenditures, per pupil property wealth, percentage of students in the free and reduced lunch program, and school district size measured by enrollment. SDstructure i includes each of these variables. Also included is a quadratic term for district enrollment to capture scale effects on spending, (Andrews, Duncombe, & Yinger, 2002). Along with the student poverty rate, SDstructure i includes the percent of 8

residents 18 years and over with a high school diploma to further capture the influence of socioeconomic status on local residents needs and preferences for local services. Table 2 shows the results of my cross-sectional estimation for 2010. The variable of interest, Consolidated, appears to have a statistically significant influence on per pupil spending for two of the six services (i.e., Curriculum Director, Business Office). Although not shown, similar results are found using 2004-2009 expenditures, with only the Business Office showing a statistically significant spending reduction associated with service consolidation. Most of the control variables are statistically significant and have the expected signs. As might be expected, Per Pupil Revenue appears to be a strong indicator of spending changes in all of the services. Based on these cross-sectional estimates, the findings show that for the years measured, service consolidation is associated with statistically significant spending reductions in only one of six services (Business Office). Fixed effects estimation strategy. Where cross-sectional models measure time-specific across district spending changes, fixed effect models estimate within district spending changes over time. As described by Allison (2005), a fixed effect model has two key advantages over a cross-sectional model: 1) by measuring time-varying variables across time for school districts, each district becomes its own control group, and 2) this model controls for unobserved variables and stable characteristics (e.g., district location, political climate, community preferences) that may or may not be measurable. The following section estimates the independent influence of selected school district characteristics on per pupil service-specific spending using fixed effect models. These models take the following form: PPE it = β 1 Con it + SDstructure it β 2 + I i + Θ i + µ it (2) 9

PPE it = β 1 ConYrs it + SDstructure it β 2 + I i + Θ i + µ it (3) where PPE it is the per pupil expenditure in district i at time t. The focus variable in equation 2 is the dummy variable Con it, which assumes the value 1 if service consolidation arrangements exist in district i at time t. SDstructure it represents the same vector of school district characteristics described for equation 1. In equation 3, the variable of interest, ConYrs it that describes the number of years in which consolidation arrangements have been in existence (i.e., if consolidated in 2007, the number of years, 2007-2010, is 4). By using the number of years versus a dummy variable, I am able to estimate the influence of service consolidation duration on spending changes. Following the work of Arsen and Ni (2011, p. 14), I included a set of year dummy variables, I i, to capture "any systematic influence not accounted for by the observable inputs that vary over time but are common to all districts." Finally, in order to pick up any unobserved characteristics that are stable over time, or unobserved district fixed effects, I included Θ i while µ it represents the unobserved error. Findings. Table 3 shows that when using only a dummy indicator of consolidation, district spending changes were not statistically significant in any of the services. On the other hand, the other explanatory variables reflect the expected sign and, in most cases, are statistically significant. To pursue this further, I estimated equation 3 with the variable of interest, ConYrs it. As shown in Table 4, the results show a statistically significant spending reduction based on the number of years of consolidation for only one service, the Business Office. As with the prior estimate (Table 3), the control variables have the expected signs, with most being statistically significant. 10

Summary. Based on this evidence, there is no support for the contention that the consolidation of non-instructional services to the ISD, other than the Business Office, will generate statistically significant service-specific per-pupil spending reductions for local districts. How Does Service Consolidation Affect Instructional Spending? I next estimated the influence of service consolidation on instructional spending. For this model, instructional spending is limited to basic instruction only. Although the consolidation of individual services to the ISD may not generate statistically significant service-specific spending changes, the combination or interaction of multiple service consolidation arrangements may influence instructional spending. The following fixed effect models estimate the influence of service consolidation on instructional spending: PPInst it = β 1 Con it + SDstructure it β 2 + I i + Θ i + µ it (4) PPInst it = β 1 ConCount it + SDstructure it β 2 + I i + Θ i + µ it (5) where PPInstit is the per pupil basic instruction expenditure in district i at time t. The focus variable in model 4 is the dummy variable Con it, which assumes the value 1 if one or more (of six) service consolidation arrangements exist in district i at time t. The focus variable in equation 5, ConCount it, is a count of the number of services consolidated to the ISD in district i at time t. Again, SDstructure it represents the same vector of school district characteristics described for equation 1. Table 5 shows the estimates of the impact of any of 6 service consolidations on instructional spending (Equation 4). Much of the rhetoric put forth by policy makers suggests that service consolidation will increase the allocation of spending on instruction. However, with 11

the exception of Business Office consolidations to the ISD, these results provide no such evidence. Similarly, Table 6 displays estimates of the impact of service consolidations on instructional spending based on the number of services consolidated within a district (Equation 5). Again, with the exception of Business Office consolidations, these results offer no statistically significant evidence that multiple non-instructional service consolidations result in an increase in instructional spending. Discussion. As school districts across the country continue to face financial pressures, policy makers continue to look to the provision of non-instructional services as a source of spending reductions, with any savings then allocated to either instructional spending or the district's fund balance. One phrase frequently used by non-instructional service consolidation advocates is that "savings can be achieved through economies of scale". Based on this study's findings, economies of scale do, in fact, appear to be one potential source of savings when consolidating Business Services. However, other spending reduction initiatives will likely provide larger per-student spending reductions than economies of scale (i.e., school closings, staff reductions, collective bargaining concessions, elimination or reduction of services). From another perspective, one ISD CFO suggests that, spending is not always the deciding factor when implementing service consolidation arrangements. (D. Birkett, personal communication May 24, 2011) Even though maintaining consistent service quality is a primary expectation when consolidating services, improving service quality can also serve as an impetus for consolidation. In some cases, smaller districts consolidate business office services to the ISD where professional accountants and other data experts are able to manage multiple districts as opposed to each local district hiring a bookkeeper with limited training and experience. The 12

rationale for this type of decision appears to be especially critical given the growing demands of federal, state, and local accountability, as well as the public s interest and access to school district financial reports. Conclusions and Policy Implications. Michigan presents an interesting case where local school districts are being encouraged by policy makers to consolidate services with either the ISD or other local districts. Much of the rhetoric centers around the proposition that consolidating selected non-instructional services will generate spending reductions through economies of scale 3. In general, my empirical findings do not support the hypothesis that consolidating selected non-instructional services will reduce local district spending in a statistically significant way. Of the six services evaluated, only one (Business Office) generated statistically significant spending reductions after consolidation. In these financially stressed times, the consolidation of non-instructional services remains an area of focus for policy makers, school district administrators, and researchers. Although the context of this study was service consolidation within a Michigan ISD, research on other models of service consolidation (e.g., local districts sharing services with each other, local districts sharing services with municipalities, privatization of services) will further inform this conversation as policy makers continue to pursue alternative service delivery solutions in order to increase operational efficiency, improve service quality, and reallocate non-instructional spending into the classroom. 3 In fact, recent legislation in Michigan allocated categorical funds ($100 per pupil) for districts that subscribe to the best practice of consolidating non-instructional services to reduce school operating costs (MDE, 2011). 13

APPENDIX A Table 1. Descriptive Statistics for Per Pupil Expenditures, 2010 Curr Dir Business Office Ops & Maint Stu Trans H.R. Tech Consolidated Services a Mean 48.26 132.94 662.96 409.34 20.23 105.00 Std. Deviation 93.68 242.71 71.72 156.74 35.60 78.00 Number of districts b 24 92 3 142 19 67 Non-Consolidated Services Mean 89.95 163.16 908.37 510.98 33.00 119.94 Std. Deviation 82.80 214.28 383.52 1013.40 41.79 83.94 Number of districts b 300 332 438 296 270 307 a: Includes service consolidations prior to 2004. b: Not all districts reported expenditures for the listed functions. In some cases, the service is simply not provided. In other cases, multiple services may be delivered by a single person, with associated multiple-service expenditures reported under only one service. There is no precise way to distinguish between these two situations. 14

Table 2. District Per-Pupil Expenditure on Non-instructional Services, 2010 Variable Curr Dir Busines s Office Ops & Maint Transp ort H.R. Tech Consolidated 48.01 * -70.87** -174.68 56.07 5.66 2.72 (21.05) (16.41) (129.65) (45.14) (8.98) (11.51) LogEnroll -74.27 538.26** 79.68 308.11** -1.88 25.52 (48.67) (40.10) (52.45) (111.00) (25.72) (46.57) LogEnrollSq 6.18 33.68** -7.01-18.24* 1.59-2.77 (3.20) (2.88) (3.98) (8.25) (1.69) (3.14) %FRL 161.70 ** -53.46 168.40* -669.51** 51.23 * 22.40 (26.75) (42.87) (71.35) (140.56) (14.52)* (28.72) PPRev1000 17.54 ** 12.05** 64.97** 165.28** 6.30 * 4.05 ** (2.22) (2.04) (2.93) (6.07) (1.37)* (1.70) %HSGrad -176.64** 167.25-313.42* 1941.55** -56.41-135.72 * (63.70) (99.63) (158.35) (320.57) (33.54) (67.63) Constant 111.82 (195.97) 2114.85** 111.74 (157.41) (196.72) -2748.20** -110.87 (431.38) (103.50) 83.56 (186.04) #observations 310 410 441 436 284 364 R 2 0.42 0. 69 0.66 0.74 0.41 0.08 Note: Standard errors are in parentheses, *p<0.05, **p<0.01 15

Table 3. District Per Pupil Expenditure on Non-instructional Services: Fixed Effects Variable Curr Dir Business Office Ops & Maint Transport H.R. Tech Consolidated -0.75 (9.49) -7.94 (6.27) -86.56 (77.86) -11.47 (18.21) 8.99 (6.86) 3.52 (7.66) LogEnroll -107.43** -751.25** -175.03** -1061.07** -69.91** 60.23* (39.27) (31.36) (35.32) (87-17) (22.34) (27.81) LogEnrollSq 8.16 ** 49.75** 12.01** 78.49** 6.06 ** -4.62* (2.61) (2.32) (2.71) (6.76) (1.46) (1.90) %FRL 30.54 ** 11.99 23.20 110.11** -1.29-1.67 (7.99) (12.22) (29.71) (32.99) (6.49) (13.92) PPRev1000 8.72 ** 8.66** 70.48** 79.86** 4.28 ** 9.19** (1.01) (0.82) (1.80) (2.69) (0.78) (1.13) %HSGrad -45.86 181.20* -289.39** 643.71-20.88-81.58 (48.37) (84.65) (105.39) (308.55) (23.77) (42.58) #Observations 2081 2822 3045 3017 1737 2426 Note: Standard errors are in parentheses, *p<0.05, **p<0.01 16

Table 4. Per Pupil Expenditure, Number of Years Consolidated, Fixed Effects. Variable Curr Dir Business Office Ops & Maint Transport H.R. Tech #ConYrs 4.56-12.93 ** -27.66 7.10 1.32-0.90 (3.50) (3.44) (27.30) (7.79) (1.78) (1.37) LogEnroll -97.64 * -751.87 ** -175.28 ** -1071.10 ** -69.59 ** 61.68 * (39.79) (31.10) (35.34) (87.45) (22.44) (27.82) LogEnrollSq 7.57 ** 49.39 ** 12.04 ** 79.16 ** 6.04 ** -4.73 * (2.63) (2.29) (2.70) (6.78) (1.46) (1.90) %FRL 30.78 ** 11.21 24.34 110.90 ** -1.21-1.47 (7.78) (12.20) (29.71) (32.97) (6.50) (13.92) PPRev1000 8.82 ** 8.56 ** 70.46 ** 79.75 ** 4.29 ** 9.20 ** (1.01) (0.82) (1.80) (2.69) (0.78) (1.13) %HSGrad -48.43 187.03 * -288.17 ** 656.51 * -20.98-80.45 (48.31) (83.73) (105.49) (309.12) (28.80) (42.60) #Observations 2081 2822 3045 3017 1737 2426 Note: Standard errors are in parentheses, *p<0.05, **p<0.01 17

Table 5. Instructional Spending, Consolidated Dummy: Fixed Effects Variable Curr Dir Business Office Ops & Maint Transport H.R. Tech Consolidated -41.05 142.97 ** 125.63 46.21 27.30 74.74 (77.34) (38.48) (208.33) (37.22) (71.00) (38.63) LogEnroll 187.74-727.38 ** -999.57 ** -1041.40 ** 182.93-33.31 (268.06) (138.69) (109.31) (111.69) (247.66) (193.73) LogEnrollSq -17.79 40.17 ** 60.88 ** 65.00 ** -14.30-3.91 (17.76) (10.17) (8.44) (8.49) (106.04) (13.38) %FRL 123.76 99.69 219.85 ** 148.72 * -1.29 37.37 (65.77) (74.80) (80.48) (72.95) (66.27) (69.59) PPRev1000 455.00 ** 316.33 ** 323.47 ** 361.15 ** 495.07 ** 368.40 ** (8.09) (4.80) (4.89) (5.46) (8.02) (5.92) %HSGrad -567.07-1659.33 ** -1612.59 ** -1013.85 ** 52.99-684.86 * (323.80) (346.81) (333.73) (335.74) (263.28) (316.58) #Observations 2081 2822 3045 3017 1737 2426 Note: Standard errors are in parentheses, *p<0.05, **p<0.01 18

Table 6. Instructional Spending, # of Services Consolidated: Fixed Effects Variable Curr Dir Business Office Ops & Maint Transport H.R. Tech #ServicesCons -8.95 28.70 * 117.46 1.53-25.76 4.94 (23.42) (14.03) (88.21) (8.03) (19.88) (10.80) LogEnroll 180.59-714.49 ** -1000.33 ** -1036.80 ** 153.77-22.37 (272.11) (139.23) (109.31) (112.00) (248.29) (194.73) LogEnrollSq -17.37 39.50 ** 60.85 ** 64.69 ** -12.80-4.78 (17.98) (10.21) (8.44) (8.51) (16.18) (13.45) %FRL 121.88 102.66 216.95 ** 146.07 109.50 38.66 (65.73) (74.94) (80.45) (74.96) (66.24) (69.64) PPRev1000 454.86 ** 317.20 ** 323.45 ** 361.17 ** 495.05 ** 368.70 ** (8.11) (4.81) (4.89) (5.46) (8.01) (5.92) %HSGrad -563.23-1669.42 ** -1625.00 ** -1020.56 ** 45.36-690.20 * (324.22) (348.23) (333.89) (336.34) (263.22) (318.38) #Observations 2081 2822 3045 3017 1737 2426 Note: Standard errors are in parentheses, *p<0.05, **p<0.01 19

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