An Empirical Study of the Consolidation of Local Public Health Services in Connecticut

Laurie J. Bates

Bryant University Department of Economics

1130 Douglas Pike Smithfield, RI 02917 Phone: 401-232-6459 Fax: 401-232-6319

Email: lbates@bryant.edu

Becky A. Lafrancois Syracuse University

Department of Economics 110 Eggers Hall

Syracuse, NY 13244 Phone: 315-443-9067 Fax: 315-443-1075

Email: balafran@maxwell.syr.edu

Rexford E. Santerre University of Connecticut

Department of Finance 2100 Hillside Avenue, Unit 1041

Storrs, CT 06269 Phone: 860-486-6422 Fax: 860-486-0634

Email: rsanterre@business.uconn.edu

Abstract: Only a few studies, mostly in the case of school districts, have empirically examined the factors affecting municipal consolidations. This study contributes to the literature by empirically examining the decision of Connecticut communities to consolidate the delivery of public health services. As theory suggests, the prospect of scale economies is found empirically to increase the likelihood that a community consolidates public health services. In addition, differences across communities are found to inhibit the consolidation of public health services. Overall, the results imply that financial incentives may be necessary to encourage more regional districts because localities may underestimate the true minimum efficient scale for public health services and because heterogeneity among jurisdictions impedes regional cooperation. Keywords: public health, consolidation, JEL Codes: H41; H7; I18

An Empirical Study of the Consolidation of Local Public Health Services in Connecticut

1. INTRODUCTION Special districts in the United States have increased nearly three-fold over the last 50

years or so.1 Special districts provide water, fire protection, sanitation, and public health,

among other important collective services. Economic theory suggests the number of

special districts may have grown over time as individual local governments consolidated

specific municipal functions to benefit from scale economies. Economic theory also

suggests that some local governments may not have merged similar municipal functions

because of the real or perceived political externality costs, transaction costs, and principal

agent problems associated with the consolidation of larger and more heterogeneous

jurisdictions.

While theory seems to offer a good explanation as to why local governments may

or may not consolidate municipal functions, few studies have directly subjected the

theory to empirical testing (Bates and Santerre, 2008; Brasington, 1999 and 2003; and

Gordon and Knight, 2006). Moreover, these empirical studies are mostly limited to the

consolidation of school districts. Yet, it would be beneficial to know the specific factors

driving the creation of other types of special districts, especially if the consolidation of

specific municipal functions is viewed favorably by higher levels of government.

Given the paucity of empirical studies devoted to the consolidation issue, this

paper examines the decision of Connecticut communities to enter into a public health

district relationship. More specifically, this study empirically investigates whether scale

economies and community differences influence the regional consolidation of local

1 Statistical Abstract of the U.S. (2007).

2

public health services. The results may be important because many experts believe that

public health will take on an increasing role in the future given the threat of bioterrorism

attacks, concerns over emerging diseases such as avian flu and SARS, and the seemingly

growing burden from natural disasters such as Katrina (Tilson and Berkowitz, 2006). Yet,

concern has been expressed in Connecticut, the focus of the forthcoming empirical

investigation, and elsewhere, that small, independent departments may lack the necessary

resources to produce public health services cost-effectively (e.g., Hicks, 2004; Hartford

Courant, 2009; and Penny, 2009). Sixty-two percent of all local health departments in the

U.S. fall into the small category (NAACHO, 2006). Many public health policy-makers

argue that regional consolidation offers a solution to this problem. In particular, policy-

makers point out that regional public health departments provide more efficient

administration, broader financial resources, improved personnel management, less

duplication of resources, and improved reporting (Turnock, 2004). If so, from a public

policy perspective it may be important to know why some localities choose to offer

public health services on an independent rather than a consolidated basis.

The next section of this paper develops the conceptual framework behind the

empirical model of regional consolidation. Section III describes the sample and data used

in the empirical test and section IV reports on the findings. A summary and some policy

implications are offered in the final section.

3

2. PREVIOUS STUDIES, CONCEPTUAL FRAMEWORK, AND EMPIRICAL MODEL Numerous studies, such as Adelaja and Racevskis (2005) Alesina and Spolaore (1997),

Alesina, Baqir, and Hoxby (2004), Borck (1998), Brasington (1999, 2003, and 2004),

Ellingsen (1998), Feiock (2007), Gerber and Gibson (2005), Krueger and McGuire

(2005), and Sorensen (2006), have investigated the political economics of regional

cooperation and consolidation. While these studies take different approaches and/or

address slightly different issues, they all share two things in common. First, they agree

that trade-offs are involved when local governments consider cooperating or

consolidating municipal functions with other local governments in the region.2 Second,

all of these studies agree that this trade-off can be couched in terms of costs and benefits.

The benefits of cooperating or consolidating include any cost-savings from scale

economies in production and the internalization of any externality problems in the region.

In this regard, smaller-sized communities have more to gain from cooperating or

consolidating than larger jurisdictions do. Santerre (2009) finds that local public health

costs per capita continue to fall with population until a minimum efficient scale (MES) of

100,000 people is served by a local public health department. However, Martin and

McKenzie (1975) argue that consolidation may not offer tax savings for consumer-voters

because bureaucrats siphon them off in the form of nonmonetary gains. It should be

pointed out that Martin and McKenzie are referring to the consolidation of general

2 Local governments may also turn to contracting-out as an alternative method of delivering services. For relatively recent studies on the contracting-out decision see Boyne (1998), Brown and Potoski (2003), Ferris and Grady (1994), Joassart-Marcelli and Musso (2005) and Nelson (1997) .

4

purpose governments such as cities or counties and not to the consolidation of a specific

municipal function such as the delivery of public health services which is studied below.3

Principal/agent problems stemming from greater centralization, transaction costs

relating to inter-local negotiations and agreement, and the costs associated with losing

control over decision-making make up the cost side of the cooperating and consolidating

calculus. The degree of heterogeneity among communities weighs importantly in

determining the size of these costs. Greater heterogeneity among communities potentially

raises both transaction and decision making costs. In fact, Brasington (2004) finds

empirically that the loss of control over local public school services, because of

consolidation, reduces house values by slightly over $2,900 or 3.5 percent assuming all

other factors remain constant.

The few existing empirical studies model the decision to consolidate municipal

responsibilities as a function of a community’s economic and demographic factors as

well as the difference between the community’s and potential merger partner’s

characteristics (Bates and Santerre, 2008; Brasington, 1999 and 2003; and Gordon and

Knight, 2006). In addition, Brasington (1999 and 2003), Ferris and Graddy (1988), and

Bates and Santerre (2008) allow for the possibility that an inverted-U relationship may

hold between the population in a community and the internal production of municipal

services. Ferris and Grady find that medium-sized communities are more likely to retain

internal control for the provision of public health services, whereas both small and large

local governments contract out to a greater degree. Brasington, (1999, 2003) determines

empirically that medium-sized schools are less likely than small or large schools to form

3 See Honadale (1995, 1998) for a couple of case studies focusing on political economy aspects of general government consolidations.

5

a regional school district with surrounding communities. In contrast, Bates and Santerre

find that medium-sized communities are more likely to consolidate public health

activities. Brasington (1999 and 2003) and Gordon and Knight (2006) find evidence that

consolidation of local public education services involves a trade-off between scale

economies and loss of decision-making. Bates and Santerre (2008) show empirically that

differences among communities inhibit the regional consolidation of public health

services but do not study the effect of scale economies.

To model the decision to consolidate public health activities, we employ the

median-voter framework adopted by Bates and Santerre (2008). A large number of

studies have found the median-voter model represents a useful conceptual device when

examining collective decision-making, particularly at the local level of government (e.g.,

Borcherding and Deacon, 1972; Bergstrom and Goodman, 1973; and Santerre, 1985). In

any case, Fischer (2007) points out similar results are obtained if a dominant party model

is employed where the majority party maximizes the utility of the average voter.

Based on fairly normal assumptions, public choice theory predicts that the median

demand dominates over all other demands when collective political outcomes are decided

by a simple majority voting rule in a direct democratic setting (Downs, 1957).

Interestingly, many communities in Connecticut, the observations used in the

forthcoming empirical analysis, have retained the open-town meeting which is a direct

democratic form of local government. But even in a mayor-council or council-manager

form of government the median-voter model may hold because politicians in a

representative democracy gravitate towards the middle of the preference distribution to

maximize their number of votes (Downs 1957). It follows under these two conditions

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that the median-voter, as the swing-voter in each community, decides either directly or

indirectly if that particular community should enter into a district relationship with

another community or other communities for the provision of a specific public service.

The median-voter in a particular jurisdiction decides in favor of consolidation if

she expects her utility to increase upon joining a district. Utility increases if the expected

benefits from any economies of scale and internalization of spillovers,  , conditioned

upon her current tax-share or price, Pi, income, Ii, and tastes and preferences for a

particular public service, Ti, exceed the expected cost, , associated with consolidation.

As discussed above, expected costs consider the loss of political control, any transaction

costs associated with negotiating with partners, and the potential principal/agent

problems that may arise in larger organizations. She realizes that these “political

externality costs” are likely to be higher when the characteristics or attributes of the

community in which she resides differ significantly from those of potential district

partners, . Political externality costs are higher because heterogeneity may result

in the consolidated entity not providing the collective good at a level demanded by that

particular median-voter. Consequently, she votes in favor of consolidation or merging

i , are positive, or: with potential partners, M , if the expected net benefits,

;   ,  ,   ;   0. (1)

From th s expression, a re uced-f

, , , . (2)

i d orm version can be written as:

The relationship between each of the independent variables in equation 2 and net

benefits is fairly straightforward. A direct relationship can be expected between the

median-voter’s tax-share, P, and her propensity towards consolidation. Assuming all

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other factors remain constant, it stands to reason that the median-voter has less to gain

from consolidation when her tax-share for public health services is relatively low.

Oppositely, when the tax-share for public health services is relatively high, consolidation

offers a greater chance of reducing her overall tax burden.

As far as income is concerned, Bates and Santerre (2008) find that public health

services represent a normal good which means the demand for public health services rises

with income. If that finding can be generalized, then the influence of income, I, on the

decision to consolidate depends on whether the median-voter perceives the output of

public health services will rise or fall upon consolidating services. If she believes public

health services will rise, then wealthier individuals will be more likely to vote in favor of

regional consolidation. With respect to tastes, the median-voter favors consolidation

when she has a strong preference for public health services and consolidation improves

its delivery when c pared to independent production. om

Of course, , the median-voter’s net benefit from consolidation is not directly

observable. However, we do observe some information about that will allow us to

determine how P, I, T, or  individually influences the median-voter’s choice

behavior because we know or how that median-voter actually voted. Thus while we

cannot distinguish between a strong and weak yes, we do observe if = 1 or = 0. If

we let represent the vector of independent variables in equation (2), our regression

e ten as: mod l can be writ

  μ (3)

8

with reflecting the vector of parameters to be estimated and μ capturing the random

error term. The related observable variables are 1 if 0 and 0 if

0.  

3. OBSERVATIONS, VARIABLES, AND DATA

With observations for a variety of communities and relevant data, probit analysis can be

used to estimate equation 3.4 All 169 towns and cities in Connecticut serve as the set of

observations used in the empirical test and community level data for 2004 are used in the

cross-sectional analyses. Connecticut municipalities have the option to operate their own

independent health department or form or join a unified health district. In 2004, 92

Connecticut municipalities voluntarily participated in 18 unified health departments. The

number of communities in a district health department ranges from 2 to 19. While this

decentralized structure makes Connecticut relatively unique (NAACHO, 2006) and

therefore the findings may be non-generalizable, the more centralized public health

infrastructures in other states do not allow one to observe the decision to voluntarily join

a local public health district. Hence, the underlying demand for consolidation cannot be

estimated in those other areas. 4 We also experimented with a bivariate probit model because consolidation involves both political entities jointly voting in favor of it and not just one. However, the bivariate probit model would not converge using either the LIMDEP or STATA statistical packages. In any case, we are not totally convinced that a bivariate probit model is necessary in this case given that both equations specify the same right-hand side variables. For example, suppose 2 communities, i and k are deciding to combine into a district. The probability of community i voting to join a district can be written as  . Similarly, the probability of community k voting to join a district equals  . The probability of each community deciding to join a distinct is a function of its own characteristics,  or , the difference between the two sets of characteristics, D, and the potential for scale economies among the two, S. Therefore , , and  , , . Of course, we never observe the individual probabilities; we only observe the joint probability or or , , , . Assuming the partial effects of  and  on are equal (and why not) then , , where X = either or  . Thus, the estimation of a bivariate probit equation is unnecessary in this case much like it is unnecessary to use seemingly unrelated regression for a system of equations with the same right hand side variables.

9

In the towns and cities of Connecticut, the authorized legislative body (open-town

meeting or town-council) must vote to form or join a district with neighboring

communities. A community can also decide legislatively prior to the beginning of any

year to withdraw from a district if that community has been a member for at least two

years. While an existing district must approve membership, it receives $2.43 per capita

from the state for any town with population under 5,000 and $2.08 per capita for any

town with 5,000 or more people. The district board also levies a head tax on all of the

participating communities. The amount of the head tax varies widely across the public

health districts in Connecticut. We should also note that the Connecticut State

Department of Public health mandates several services, such as restaurant and septic

system inspections, which are mainly funded by fees. Districts also apply for grants from

the state and federal governments and other private sources to cover the costs of

providing additional public health services.

To implement the test, we need some method of matching up communities for

consolidation purposes. Thus, it is assumed that each community views its potential

merging partner or partners in one of two ways. First, our “single-model” supposes the

median-voter in each community considers the consolidation of public health services

individually with each of its adjacent communities. For this matching model, the

characteristics  are simply those of the adjacent community. This matching assumption

results in 878 matches because the typical community faces 5 to 6 adjacent communities

on average. If two adjacent communities were in the same public health district in 2004,

the dependent variable in equation 3 takes on the value of one. Otherwise, the dependent

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variable takes on the value of zero. About 25 percent of the 878 matches reflect two

communities in the same public health district.

One advantage of this single-model is that all Connecticut communities can be

used as observations in the empirical test. Another advantage of this approach is that a

community may actually consider just the characteristics of a nearby community, and not

the characteristics of all of its potential partners, when deciding to consolidate public

health services. This method of deciding might economize on decision-making costs. For

example, median-voter i may think joining a district would be beneficial because

community j, which shares many similar traits, already belongs to it. The last advantage

of this approach is that a community can choose to form a district with any one

community or all of its neighboring communities.

The alternative “group-model” uses only those communities who currently belong

to a public health district or are located adjacent to an existing public health district.

Thus, each community is matched up with a nearby district or the district to which it

currently belongs. In this case, characteristics are determined by calculating the

weighted averages of all of the characteristics of the communities belonging to a

particular public health district exclusive of the selecting community if it also belongs to

that district. This group model results in 268 observations with roughly 34 percent of the

matches involving a district relationship. Notice that, in effect, the group-model

questions if a particular town will join an existing district whereas the single-model

examines if a community will form a district with a neighboring community.5

5 The results may differ because of the greater fixed costs associated with forming a new district rather than joining an existing one.

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The equalized or effective mill rate in each community serves as the tax-price of

the median voter. Total property values represents the relevant tax base because property

taxes provide the main source of funding in Connecticut communities and tax rates are

constant across different types of property (e.g., residential, commercial, and industrial).

Following studies on the demands for local public goods such as Borcherding and

Deacon (1972) and Bergstrom and Goodman (1973), the median-voter is assumed to

possess the median level of income in each community. The tastes and preferences of the

median-voter regarding public health services are assumed to be shaped by the

demographic and physical composition of the community in which she resides.

Therefore, the proportion of the population that is elderly (65 years of age and older), the

percentage of the population that is white, population size and land area of the

community are specified as taste variables in the estimation equation. While the rest of

the taste variables are specified in linear form, community population enters the probit

equation in quadratic form because the net benefits from consolidation may be nonlinear

with respect to community size as discussed previously.

According to the conceptual model, differences between communities may

impose political externality costs and thereby impede consolidation. In the empirical

analysis, the absolute differences of several characteristics are specified: tax-price,

median income, population, percentage of the population that is white, and land area. A

negative coefficient estimate is expected on each of these absolute difference variables.

Given that some public health services are devoted to environmental issues such as water

quality and sanitation, the difference in land area is included because a geographically

small community may believe that a geographically larger community will draw more

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attention and resources from the district public health department.6 To capture the

potential for scale economies, the population of each community is added to the

population of each of its adjacent communities or the district and the resulting combined

population is specified in the estimation equation in quadratic form. An inverted-U

relationship seems appropriate between the likelihood of consolidation and combined

population because economies of scale may at first encourage consolidation but

diseconomies may set in after some level, at least in a perceived sense.

Finally, the residual term in equation (3) may be influenced by spatial

autocorrelation. More specifically, the choice that one community makes might affect the

choices that other communities make with regard to joining a public health district. If so,

the error term in equation (3) will be correlated across observations and would therefore

bias the estimated coefficients in some unpredictable way. One way of dealing with a

spatially correlated error term is to include location variables for each community in the

regression equation (e.g., Pace, Barry, and Sirmans, 1998). We experimented with five

location variables: distance to the nearest central city in Connecticut, distance to New

York City, distance to Boston, distance to Hartford, and distance to the Connecticut

coastline. Experimentation showed statistically that the latter two location variables are

the most meaningful and robust in explaining the consolidation decision.

Data for the market value of all taxable property and population are obtained from

Municipal Fiscal Indicators, which can be accessed on-line at the Connecticut State

Office of Policy and Management website. The Connecticut Department of Economic

and Community Development publishes an on-line version of Connecticut Town Profiles

6 Conversations with J. Robert Galvin, Connecticut State Commissioner of Public Health, and Pamela Kilbey-Fox, Branch Chief of the Local Health Administration Board in Connecticut confirmed that these differences among communities could potentially impede consolidation.

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which lists the rest of the necessary data including median household income, percentage

of the population that is white, land area, distance to Hartford, and the percent of

population 65 years and older. Table 1 provides descriptive statistics for all of the

variables used in the empirical tests.

4. EMPIRICAL FINDINGS

The results for the probit analysis are reported in Table 2 where the coefficient estimates

and corresponding z-statistics, based on heteroskedasticity-consistent standard errors, are

shown opposite each explanatory variable. Results are displayed for both the single-

model and group-model methods of matching up communities. Focusing first on the

single model, the estimated positive coefficient on tax-price suggests that the median-

voter typically views consolidation as lowering tax burden when her tax share is

relatively high. Alternatively, it may be the case that the median-voter views

consideration as providing better services and thus reducing her quality-adjusted tax-

price.

The estimated negative coefficient on income implies that higher income

communities are less likely to form a district with a neighboring community, ceteris

paribus. That inverse relationship may hold for a number of reasons.7 First, the median-

voter in a wealthy community may be concerned that forming a district with a lower-

income community may result in a lower level of public health services supplied than she

desires. This is particularly true if public health services represent a normal good as

shown empirically by Bates and Santerre (2008). Second, higher-income communities

may be better able to afford the provision of local public health services on an 7 We thank the anonymous referees of this journal for pointing out two of the reasons.

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independent basis than poorer communities. Third, assuming some substitutability

between private medical care and public health services in the production of health,

wealthier individuals may choose the more expensive option of purchasing their health

services in the private marketplace. Finally, higher-income communities may face a more

difficult time finding a potential merger partner with similarly high income.

Four of the five estimated coefficients on the absolute difference variables possess

negative signs, as expected. A Wald test indicates that the estimated coefficients on the

absolute difference variables are collectively significant at the 0.0054 level with an F-

statistic of 3.34. Moreover, on an individual basis, the findings suggest that differences in

median income and land area matter more at influencing the decision to consolidate on a

statistical basis. Thus, the empirical results support the hypothesis that differences across

communities inhibit consolidation.8

The results for the single-model also show that community population does not

influence the decision to form a public health district. In contrast, the combined

population of the community and partner town does affect consolidation in an inverted-U

fashion. Its inverted U-shape reflects that perceived economies and diseconomies hold

with respect to forming larger public health districts. By taking the first derivative of the

single-model specification with respect to combined population and setting the resulting

expression equal to zero, we can solve for the population at which diseconomies set in

with respect to producing public health services. The calculation indicates that perceived

diseconomies set in at a combined population of roughly 47,000 people, which is slightly

8 Note that both the percentage of the population that is white and absolute difference of the percent white have no statistical impact on the decision to form a district. That result may hold because the percentage of nonwhite population is relatively small at about 9 to 10 percent on average in Connecticut, particularly after controlling for population and population differences because nonwhites live disproportionately in the larger cities.

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more than twice the size of the typical Connecticut community. As mentioned previously,

Santerre (2009) finds empirically that the MES of a public health department occurs at

roughly 100,000 people. Interestingly, Gordon and Knight (2006) report a relatively low

perceived MES for local public education services in Iowa.

Estimating the marginal effects of the various independent variables may help

determine if differences across municipalities or scale economies matter more at the

margin in terms of influencing the decision to remain independent. Point estimates drawn

from a probit function are highly sensitive to specification so Studenmund (2006)

recommends a rough approximation by multiplying each estimated probit coefficient by

0.4. Following through with his recommendation indicates the marginal effects are

economically tiny. This may mean that any single factor does not, in isolation,

significantly influence the decision to consolidate. Moreover, while collectively the

various factors matter, the relative inertia to a single factor may explain why so many

municipalities continue to operate independent public health departments.

Turning to the results for the group-model matching scheme in Table 2, a

different story is portrayed. Similar to the single-model matching scheme, joining a

public health district is more likely when the tax share is higher, median income is lower,

and more elderly people reside in the community. However, in the case of joining a

district, community population does affect the decision to consolidate. In particular,

compared to an otherwise similar small or large community, the results imply that a

medium-sized community is more likely to join an existing health district. Indeed,

according to the estimated equation, a community with 34,000 people is the most likely

to join a health district, ceteris paribus.

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Four of the five difference variables possess negative coefficient estimates but

only land area possesses an estimated coefficient that is different from zero at

conventional levels of statistical significance. Thus, community differences do not seem

to matter as much when joining an existing health district as compared to forming one

with a neighboring community.

A noticeable difference between these and the single-model results is the

estimated U-shaped relationship between combined population and the probability of

joining a health district. One possibility is that the U-shaped relationship reflects that

relatively small and large public health districts are more successful at attracting new

member towns than medium-sized districts. Small health districts may mean less losing

out on political decisions whereas large districts may offer huge scale economies. Solving

for the district size at which the probability of joining a health district is minimized

results in a population of 203,000. With only 6 of the 268 matches resulting in combined

populations greater than 203,000, a more prudent interpretation is that an independent

community is less likely to join larger districts, ceteris paribus. Recall that the single-

model estimates the perceived MES occurs at roughly 47,000 people. Combined

population lies below 47,000 for only 33 of the 269 group-model observations and the

average level is nearly 94,000 people (see Table 1). Consequently, this observed inverse

relationship between combined population and the probability of joining a public health

district may simply reflect that independent communities are less likely to join larger

districts because most consolidations result in combined populations beyond the MES.

While a few communities belonged to public health districts as far back as the late

1960s, any community can choose to withdraw from a district in Connecticut after a two

17

year membership period. In fact, three communities withdrew from existing health

districts over the last 12 years. Another district dissolved itself two year prior to the year

under observation (2004) with one of the two formerly participating communities joining

another district and the other remaining independent. Yet, since 2004, another district

has been formed and eleven communities have joined existing districts. The implication

is that the net benefit calculus of belonging to a district, as summarized in equation 1, is

continuously made by each and every community and current rather than past values of

the independent variables are relevant.9

Nevertheless, we also test if “path dependent choices” influence the results by

restricting the sample to communities that consolidated after 1995. A cut-off period after

1995 results in too few observations for pairs of communities belonging to a public health

district. The probit regression results are shown in Table 3 for the two different matching

methods. Notice that the multiple regression results are very similar to those in Table 2

and suggest that the regression results are not greatly affected because the decision to join

or form a health district had been made many years earlier.10

9 One of the authors of this paper served on the board of finance of a town participating in a public health district. It was not unusual for discussion to take place about withdrawing from the district during annual budget deliberations. Discussion involved the current costs and benefits of remaining in the district. 10 Both of the estimated coefficients on the location variables are consistently positive and statistically significant for three out of four specifications. These results suggest that towns and cities located at a greater distance from both Hartford and the coast are more likely to consolidate their public health services, ceteris paribus. A glance at a Connecticut map of independent and consolidated districts shows that is definitely the case with few exceptions. For reasons, other than those already captured in the regression equations, towns and cities in the northeast and northwest corners of the state are more likely to cooperate or coordinate decisions regarding public health services.

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5. SUMMARY

This paper offers some information regarding the factors influencing the regional

consolidation of public health services. Only Bates and Santerre (2008) empirically

examine the consolidation decision regarding public health services but they did not

consider if communities merge public health activities because of scale economies. This

study finds that a community considers both the potential for scale economies and

political externality costs when forming a public health district with a neighboring

community. In fact, the empirical results suggest that perceived diseconomies set in at

relatively low range of population. This may mean that consumer-voters underestimate

the true scale economies associated with the consolidation of public health services. Also

similar to Bates and Santerre, this study finds, not surprisingly, that towns lean more

towards the regional consolidation of public health services when their tax rates are

relatively high and the people in the town are relatively poor.

This paper also analyzes the decision of a community to join an existing public

health district. The empirical results indicate that the decision to join an existing district

is also directly related to tax share and inversely related to income. Moreover, the

findings indicate that medium-sized communities are more likely than small or large

communities to join an existing health district. Finally, empirical results for both the

single- and group-models, taken together, imply that jurisdictional heterogeneity matters

more for the decision to form a health district with an adjacent community than the

decision to join an existing health district. It may be that the averaging or smoothing of

characteristics across the towns and cities in a district makes a district more appealing

than any one individual town.

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One interesting aspect of the results is the general similarity of the empirical

findings for both the local public school and public health department consolidation

decision. Both sets of research find that potential scale economies motivate and

heterogeneity hinders consolidation. The decision to consolidate public schools is

politically a much hotter issue than the consolidation of local health departments. More

people expect to benefit from their children being educated in the local school than they

expect to benefit from a local public health initiative such as a bioterrorism threat or

communicable disease outbreak. In addition, a much greater amount of money is spent on

local schools than local public health. Recall the small head tax for public health services

in Connecticut. In addition, Santerre (2009) reports that local public health departments

in the U.S. spent only $45 per person on average in 2005. The similarity of the empirical

findings may attest to the general applicability and richness of the underlying public

choice model.

While the empirical analysis in this paper is limited to health departments

Connecticut, the results may shed some light on the relatively small-sized local public

health departments, particularly in other New England states where county governments

do not exist, but also in other areas of the U.S. As noted earlier, Santerre (2009) finds that

the optimal size of a local public health department is around 100,000 people in terms of

minimizing public health costs per capita. However, NACCHO (2006) reports that 77

percent of all local public health departments, containing about 18 percent of the U.S.

population, operate below this efficient level of population. Various factors have

evidently prevented these small-sized local health departments from consolidating with

20

others to reap the cost-savings from greater size. Clearly, the results of this paper have

relevance beyond the state of Connecticut.

In the case of local public health departments, which are studied here, the

conventional wisdom is: “If you’ve seen one local health department, you’ve seen one

local health department” That is, local governments, lacking a national template

regarding an effective structure, organize their local public health departments in

countless ways (Tilson and Berkowitz, 2006). The myriad of organizational forms may

create challenges for the coordination of the entire national system particularly in a time

period facing bioterrorist threats and natural disasters such as Hurricane Katrina. If so,

and if national uniformity is valued by society, regional consolidation may provide one

way of moving towards that goal. However, the results from this paper suggest voluntary

movement may be limited because of differences across communities and a relatively low

perceived minimum efficient scale for public health services. The implication is that

higher levels of government might want to use financial incentives to induce more local

governments to form regional public health districts.

21

ACKNOWLEDGEMENTS

We thank the discussants and participants at the 2008 annual meeting of the Eastern Economics Association and the anonymous referees of this journal for their helpful comments. REFERENCES

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Table 1: Descriptive Statistics Variable Single-Model

(N=878) Group-Model (N= 268)

Mean Value (standard deviation)

Consolidated 0.246 (0.43)

0.340 (0.47)

Tax-Price 16.34 (4.18)

17.02 (4.24)

Median Household Income 64167 (19493)

62376 (17571)

Fraction Old 0.133 (0.04)

0.133 (0.04)

Fraction White 0.907 (0.11)

0.904 (0.11)

Land Area (square miles) 29.44 (12.3)

28.29 (12.1)

Distance to Hartford 28.03 (13.0)

25.85 (12.4)

Distance to Coastline 25.10 (16.5)

27.40 (16.2)

Community Population 20212 (24018)

20902 (16.17)

Absolute Difference of Tax-Price 3.309 (3.4)

2.932 (2.9)

Absolute Difference of Median Income 14999 (14217)

13621 (11332)

Absolute Difference of Population 18119 (23561)

15883 (18640)

Absolute Difference of Fraction White 0.075 (0.11)

0.064 (0.09)

Absolute Difference of Land Area 12.457 (9.7)

9.983 (7.6)

Combined Population 40601 (38023)

93721 (43926)

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Table 2: Results from the Probit Estimation (Dependent Variable = 1 if Consolidated; 0 if not) Single-Model Group-Model Estimated Coefficient

(Absolute Value of Z-statistic) Constant -4.162*

(3.37) 0.097 (0.04)

Tax-Price 0.083* (3.79)

0.069* (1.69)

Median Household Income -1.18E-05* (2.61)

-1.53E-05* (2.13)

Fraction Old 4.385* (2.85)

7.756* (2.65)

Fraction White 0.534 (0.486)

-0.631 (0.34)

Land Area 0.0007 (0.15)

-0.002 (0.18)

Distance to the Hartford

0.024* (3.88)

0.036* (3.33)

Distance to the Coastline 0.035* (8.39)

0.041* (4.80)

Community Population -1.70E-05 (1.11)

7.10E-05* (2.80)

Square of Community Population 9.48E-11 (0.39)

-1.05E-09* (2.42)

Absolute Difference of Tax-Prices 0.006 (0.22)

-0.070 (1.28)

Absolute Difference of Median Incomes

-1.95E-05* (3.00)

-2.14E-05 (0.17)

Absolute Difference of Populations -4.78E-06 (0.67)

2.01E-06 (0.14)

Absolute Difference of Fractions White -1.308 (1.49)

-1.480 (0.69)

Absolute Difference of Land Areas -0.011* (2.06)

-0.029* (2.17)

Combined Population 5.68E-05* (4.40)

-5.17E-05* (3.73)

Square of Combined Population -5.96E-10* (4.82)

1.27E-10* (1.83)

Number of Observations 878 268 McFadden R-Squared 0.234 0.332 * statistically significant at the 10 percent level or better.

26

27

Table 3: Results from the Logit Estimation (Dependent Variable = 1 if Consolidated; 0 if not) Single-Model

Consolidations After 1995

Group-Model Consolidations After 1995

Estimated Coefficient (Absolute Value of Z-statistic)

Constant -2.589 (1.37)

-1.710 (0.50)

Median Tax-Price 0.064* (2.00)

0.139* (1.98)

Median Household Income -1.44E-05* (2.36)

-3.37E-05* (3.38)

Fraction Old -2.318 (0.92)

4.996 (0.96)

Fraction White 0.287 (0.22)

3.114 (0.98)

Land Area 0.013* (2.10)

0.017 (1.22)

Distance to Hartford 0.002

(0.22) 0.043* (2.26)

Distance to Coastline 0.017* (3.53)

0.030* (2.16)

Community Population -1.20E-05 (0.53)

0.0001* (2.58)

Square of Community Population -1.65E-10 (0.72)

-2.69E-09* (2.02)

Absolute Difference of Tax-Prices 0.0106* (2.78)

0.009 (0.09)

Absolute Difference of Median Incomes -3.81E-05* (3.78)

-2.26E-05 (1.03)

Absolute Difference of Populations -3.30E-06* (3.40)

-3.05E-05 (1.25)

Absolute Difference of Fractions White -0.446 (0.35)

3.353 (1.04)

Absolute Difference of Land Areas -0.019* (2.35)

-0.040* (2.03)

Combined Population 6.55E-05* (3.88)

-0.0001* (4.93)

Square of Combined Population -6.13E-10* (3.88)

4.78E-10* (4.31)

Number of Observations 720 201 Number of Consolidating Pairs 58 24 McFadden R-Squared 0.230 0.435 * statistically significant at the 10 percent level or better.