What drives special district dissolution in the areas of fire protection, housing and community development, natural resources, and water supply?

Motivation

  • Special districts dissolve often
    • Approximately 6% of special districts, in any functional area, were dissolved in any 5-year block between 1977 and 2012
  • Special districts are highly specialized
    • Some functional areas are dynamic, and others are relatively static
    • Previous work suggests analyzing all types of special district function together is inappropriate (Foster 1997)
  • Little to no research on special district dissolution
    • Exceptions: Bauroth (2010), Moldogaziev, Scott, and Greer (2019), Goodman and Leland (2024)

Motivations

  • Two approaches to examining dissolutions
    • Individual level analyses
    • Systems level analyses
  • Potentially important factors
    • An area’s prior experience with local government reorganization (obsolescence)
    • Local demand for services
    • State limitations on general purpose local governments
    • Boundary change entrepreneurs

Empirical Model

  • Time period: 1977-2017
  • Unit of analysis: all and metropolitan counties
  • Four largest functional areas
    • Fire Protection; Housing and Community Development; Natural Resources; Water Services
  • Data sources:
    • Census of Governments
    • Census Bureau
    • Bureau of Economic Analysis
    • CDC

Coverage

Year Total Districts Total Fire Protection Housing and Community Development Natural Resources Water Supply
1977 11,911 48.78% 17.31% 9.16% 11.69% 10.62%
1982 13,075 48.73% 17.34% 10.40% 10.42% 10.57%
1987 13,613 49.12% 17.85% 10.60% 10.32% 10.35%
1992 14,615 46.71% 17.00% 9.74% 9.72% 10.24%
1997 15,561 44.35% 16.70% 8.84% 9.24% 9.58%
2002 15,640 44.12% 16.94% 8.61% 9.20% 9.37%
2007 16,449 42.03% 15.89% 8.16% 9.02% 8.96%
2012 17,209 40.18% 15.11% 7.69% 8.87% 8.51%
2017 17,524 39.57% 15.10% 7.46% 8.61% 8.40%

Model Specification

\[ XR_{ijt} = \alpha + \beta ER_{ijt-1} + \gamma X_{it} + \delta I_{it} + \rho E_{it} + \phi_i + \tau_t + \varepsilon_{ijt} \]

  • where,
    • \(ER_{ijt-1}\) is the special district entry rate in the previous period for the same functional area
    • \(X\) is a vector of service demand related variables
    • \(I\) is a vector of state limitation on local governments
    • \(E\) is a vector of variables related to the presence of boundary change entrepreneurs
    • \(\phi_i\) is county fixed effects, \(\tau_t\) is time fixed effects, and \(\varepsilon\) is the usual composite error term

Entry & Exit

Exit Rate

\[ XR_{it-1}=\frac{NX_{it-1}}{NT_{it-1}} \]

Entry Rate

\[ ER_{it}=\frac{NE_{it}}{NT_{it-1}} \]

  • \(NX_{it-1}\) is the number of special districts that dissolved in the previous period
  • \(NE_{it}\) is the number of new special districts in the current period
  • \(NT_{it-1}\) is the total number of special districts in the previous period “at risk”

Independent Variables

  • Entry rate (by function) in previous period
  • Municipal TEL
  • County TEL
  • Municipal debt limit
  • County debt limit
  • Municipal functional home rule
  • County functional home rule
  • Location quotient (4 infrastructure/real estate related industries)
  • Personal Income
  • Population (level, growth, density)
  • Employment
  • Age/Racial variation
  • Change in the number of cities
  • Usage of Towns

Methodology

  • Two way (county and year) fixed effects regression
  • Cluster SE on state
Mean SD
Exit rate, Fire Protection 0.011 0.082
Exit rate, Housing and Community Development 0.023 0.133
Exit rate, Natural Resources 0.024 0.120
Exit rate, Water Supply Utility 0.023 0.124
Entry rate, Fire Protection 0.065 0.429
Entry rate, Housing and Community Development 0.061 0.285
Entry rate, Natural Resources 0.043 0.244
Entry rate, Water Supply Utility 0.073 0.445

Fire Protection Housing and Community Development Natural Resources Water Supply
* p < 0.05, ** p < 0.01
Standard errors are clustered on the state
Entry rate, same type (t-1) 0.008 0.037** 0.058** 0.022
(0.005) (0.010) (0.016) (0.012)
N 6,409 6,409 6,409 6,409

Fire Protection Housing and Community Development Natural Resources Water Supply
* p < 0.05, ** p < 0.01
Standard errors are clustered on the state
Municipal TEL -0.041** 0.007 0.023** -0.017
(0.009) (0.017) (0.007) (0.018)
County TEL 0.025** -0.012 -0.025 0.009
(0.008) (0.017) (0.015) (0.021)
Municipal debt limit 0.058** 0.009 0.079** 0.069**
(0.013) (0.011) (0.012) (0.008)
County debt limit -0.009 0.006 0.020 -0.004
(0.008) (0.016) (0.010) (0.012)
Municipal functional home rule 0.019** 0.067** 0.039** 0.022
(0.006) (0.020) (0.008) (0.011)
County functional home rule 0.006 0.021 0.032 0.012
(0.005) (0.012) (0.017) (0.009)
N 6,409 6,409 6,409 6,409

Fire Protection Housing and Community Development Natural Resources Water Supply
* p < 0.05, ** p < 0.01
Standard errors are clustered on the state
NAICS 236 = Construction of Buildings; NAICS 237 = Heavy and Civil Engineering Construction; NAICS 238 = Specialty Trade Contractors; NAICS 531 = Real Estate.
Location quotient, NAICS 236 0.000 0.003 0.003 0.002
(0.002) (0.005) (0.004) (0.004)
Location quotient, NAICS 237 -0.001 0.000 -0.002 -0.001
(0.001) (0.001) (0.001) (0.001)
Location quotient, NAICS 238 0.001 0.000 -0.002 -0.001
(0.004) (0.008) (0.004) (0.005)
Location quotient, NAICS 531 0.005 0.010* 0.001 0.003
(0.008) (0.005) (0.004) (0.005)
N 6,409 6,409 6,409 6,409

Fire Protection Housing and Community Development Natural Resources Water Supply
* p < 0.05, ** p < 0.01
Standard errors are clustered on the state
Personal income, per capita 0.000 0.000 0.000 0.001
(0.001) (0.001) (0.001) (0.001)
Population (1000s) 0.000 0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000)
Population density 0.000 0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000)
Employment ratio -0.032 -0.058 -0.067 -0.053
(0.036) (0.111) (0.051) (0.064)
Proportion between age 5 and 19 -0.405 -0.173 0.096 0.012
(0.227) (0.478) (0.300) (0.301)
Proportion age 65 and older -0.047 -0.032 0.070 0.108
(0.168) (0.142) (0.147) (0.225)
Ethnic fractionalization 0.070 0.099 0.043 0.050
(0.038) (0.086) (0.064) (0.073)
Change in cities 0.000 0.005 -0.001 0.011
(0.002) (0.003) (0.003) (0.007)
Use of towns (1=yes) -0.001 -0.028 -0.001 -0.079
(0.009) (0.025) (0.024) (0.058)
N 6,409 6,409 6,409 6,409

Conclusions

  • Prior experience with local government reorganization is associated with increased special district exit
    • Only for housing and community development districts and natural resource districts
  • Restrictions on general purpose local governments are important
    • Most consistently, granting service delivery powers to cities tends to increase exits
    • TELs and debt limits are also important but in a less consistent manner
  • Boundary change entrepreneurs do not appear influential
  • Demand for services is not a consistent predictor of special district dissolution

Thank you

References

Bauroth, Nicholas G. 2010. “The Strange Case of the Disappearing Special Districts: Toward a Theory of Dissolution.” American Review of Public Administration 40 (5): 568–92. https://doi.org/10.1177/0275074009352511.
Foster, Kathryn A. 1997. The Political Economy of Special-Purpose Government. Washington, DC: Georgetown University Press.
Goodman, Christopher B., and Suzanne M. Leland. 2024. “What Lies Beneath These Creatures of the State: Understanding the Death of Specialised Governments in the U.S.” Local Government Studies, May, 1–24. https://doi.org/10.1080/03003930.2024.2360559.
Moldogaziev, Tima T., Tyler A. Scott, and Robert A. Greer. 2019. “Organizational Dissolutions in the Public Sector: An Empirical Analysis of Municipal Utility Water Districts.” Journal of Public Administration Research and Theory 29 (4): 535–55. https://doi.org/10.1093/jopart/muy081.