Too Big, Too Small, or Just Right? Cost‐Efficiency of Environmental Inspection Services in Connecticut

Abstract

Objective

To assess optimal activity size/mix of Connecticut local public health jurisdictions, through estimating economies of scale/scope/specialization for environmental inspections/services.

Data Sources/Study Setting

Connecticut's 74 local health jurisdictions (LHJs) must provide environmental health services, but their efficiency or reasons for wide cost variation are unknown. The public health system is decentralized, with variation in organizational structure/size. We develop/compile a longitudinal dataset covering all 74 LHJs, annually from 2005 to 2012.

Study Design

We estimate a public health services/inspections cost function, where inputs are translated into outputs. We consider separate estimates of economies of scale/scope/specialization for four mandated inspection types.

Data Collection/Extraction Methods

We obtain data from Connecticut Department of Public Health databases, reports, and other publicly available sources. There has been no known previous utilization of this combined dataset.

Principal Findings

On average, regional districts, municipal departments, and part‐time LHJs are performing fewer than the efficient number of inspections. The full‐time municipal departments and regional districts are more efficient but still not at the minimum efficient scale. The regional districts' elasticities of scale are larger, implying they are more efficient than municipal health departments.

Conclusions

Local health jurisdictions may enhance efficiency by increasing inspections and/or sharing some services.

Environmental health inspections are mandated in Connecticut and are crucial to public health and safety. We focus on the cost‐efficiency of inspection services in all 74 local health jurisdictions (LHJs) in Connecticut, over the time period 2005–2012. Cost‐efficiency implies getting to the “right” level of services—and not providing too many or too few services. This can be examined in the form of the quantity of total inspection services performed, as well as by determining whether it is cost‐reducing for jurisdictions to perform different types of inspections together, or to specialize in a small number of inspections.

In Connecticut, LHJs may be full‐time1 or part‐time2 municipal health departments, or multi‐town regional health districts.3 While all Connecticut LHJs provide state‐mandated environmental services, there is no known research about the influence of organization structure and size on environmental services costs. The state's Department of Public Health had been compiling a longitudinal database on local public health services and costs for several years, however, there had not been any known studies utilizing this data for analyzing the determinants of costs. This approach was attractive because it was an alternative to research involving more time‐intensive and costly surveys of a subset of the LHJs. The diversity of and variation in organizational structure of local health in Connecticut make the state an ideal “petri dish” for evaluating the role of these variations on costs.

Previous research has shown that variations in public health systems performance depend on funding and staffing levels (Gordon, Gerzoff, and Richards 1997; Kennedy and Moore 2001) but can also be influenced by the population served (Turnock, Handler, and Miller 1998; Mays et al. 2004). As suggested by Santerre (2009), public health systems may be more cost‐efficient if they serve larger populations. Others examine whether consolidation of services into centralized departments is more/less efficient (Mukherjee, Santerre, and Zhang 2010), but more research is needed in this area. LHJs in Connecticut vary in jurisdictional type, funding levels, and staffing, and they serve a range of population sizes, which allows us a unique opportunity to further this research.

We analyze the scale/scope of the four required environmental health services provided in Connecticut4 and the differences in associated costs incurred by LHJs that may arise from differences in size and structure of LHJs. These services are as follows: food protection,5 private water wells,6 subsurface sewage disposal,7 and child lead poisoning prevention/control.8 ^, 9 Specifically, we address the question of whether or not local health departments could lower their average (i.e., unit) costs of providing these services by providing more or fewer inspections. Second, we examine how the incremental costs of a particular service are impacted by providing it together with another type of service. We examine differences in these effects for municipal health departments, regional health districts, and part‐time LHJs.10

We use regression analysis to estimate a semi‐translog total cost function for providing four types of public health inspection services. We compile longitudinal data for annual cost of providing inspection services, average wages of personnel, average price of physical capital, number of inspections, number of establishments, mix of inspection sites, and characteristics of various local health departments. Subsequently, we use the regression estimates to estimate economies of scale and scope/specialization for each LHJ in Connecticut during 2005–2012.

Our contribution in this research is severalfold: We estimate a cost function for local public health services with a model grounded in economic theory of the production process where inputs are translated into outputs; we consider separate estimates of economies of scale and scope for several categories of environmental inspections; and we leverage a comprehensive data set that we compile from various sources covering all 74 LHJs in Connecticut, annually from 2005 to 2012. To our knowledge, such an analysis of rich data using a rigorous economic framework is unique.

Background and Literature Review

In choosing input combinations to use in their production process, we assume LHJs compare the incremental benefits obtained from hiring another worker against the incremental benefits from renting or purchasing more physical capital (equipment, machines, real estate, etc.). If the extra “output” per dollar spent on workers is greater than (less than) the extra “output” per dollar spent on capital, the LHJ should choose to hire more (less) workers and use less (more) capital. With this balancing act, the LHJ will hire the efficient11 amount of both inputs when the extra output per dollar spent on each input is equal. The cost function we estimate for local public health services embodies this balancing process, and for this reason, it is an ideal tool for estimating economies of scale and scope as it assumes LHJs are doing their best in choosing inputs to balance the benefits of using all inputs.

Cost function analysis is a technique from the industrial organization literature in the field of economics, which has been applied to many different industry studies. It has been used for a variety of different sectors and industries, including transportation, manufacturing, and health care,12 among others,13 to aid in decisions of how many firms, how much of each input each firm should use, and what firm size is optimal in an industry. This approach can help with decisions of whether it is more efficient for many small firms to produce small amounts, or fewer large firms to produce large amounts, of a product/service. Cost functions have also been widely used to understand whether it is less costly for production of two or more distinct products or services with each occurring separately in different firms, or together in one firm. Underlying cost functions is the production process, where “inputs” are converted into “outputs.” A crucial point is that this approach helps determine how much of a product “entities” should produce, and what input combination they should use to produce it, in order to operate “efficiently.” When LHJs are not using the optimal input mix, this is inefficient and some people/groups may not get services they need. While it may seem like a trivial problem to solve, it is complex as there are many other variables affecting a LHJ's decision of how to produce its output(s). It is necessary to control for these factors with regression analysis.

In the literature on local public health services costs, Tilson et al. (2006) outline a process for analyzing the costs of public health services. This relatively comprehensive guide includes discussion of the need to identify “outcomes” for cost‐effectiveness studies. But cost‐effectiveness studies are different from our goals—that is, to assess the optimal size of LHJs. Our approach controls for other factors that affect costs, and it is a promising way to help LHJs analyze the scale/scope of services to provide.

Mays (2013) studies scale/scope economies for 20 public health services across 360 communities in 3 years (1998, 2006, 2012). He estimates a “semi‐translog” cost function, where “scale” represents the population size, “scope” represents availability of 20 public health services, and “quality” represents “perceived effectiveness of each activity.” The functional form is a semi‐translog opposed to a translog, because Mays includes linear and quadratic terms but omits interaction terms. He finds costs increase as scale rises; costs increase as scope rises; and costs decrease as perceived effectiveness increases.

Singh and Bernet (2014) analyze the costs of local public health services in Florida. They consider economies of scale and scope. Their ad hoc specification, with scale and scope variables similar to Mays (2013), is a contribution to the literature on costs of local public health services because it is among the very small number of studies in this emerging literature that have estimated a cost function.

Research Methodology and Approach

In order to examine economies of scale/scope for LHJs, the first step is to estimate a semi‐translog cost function of providing various types of public health inspection services using data from Connecticut's LHJs. As described above, the semi‐translog total cost function is a flexible functional form that allows for nonlinear relationships between the dependent variable (total costs) and the explanatory variables (the “input” prices, the “outputs” or services, and other shift factors). Our estimation of a total cost function is guided by neoclassical microeconomic cost theory. Details of the cost function approach are described in Appendix SA2. Figure S1 in Appendix SA3 graphically depicts the concept of economies of scale.

Data

A contribution of our research is our unique data synthesis from several state agency sources in the context of local public health services. Our focus on economies of scale and scope for environmental health services as the area of analysis reflects the reality of Connecticut's local public health system. Connecticut is a state with a population of 3.5 million with 169 towns. There is no county system in Connecticut, and only four municipalities with populations above 100,000. The 169 towns are served by 74 local health departments or regional health districts (and we have been referring to all 74 of these as “local health jurisdictions,” abbreviated as “LHJs”). The 21 regional health districts serve anywhere from 2 to 20 towns. The remainder of the state's residents is served by municipal departments, which can be either part‐time or full‐time. While part‐time municipal departments are decreasing, there are still 24 towns that do not have a full‐time Director of Health, and their health departments may be served by a single sanitarian. These communities account for only 6 percent of the Connecticut population. There are 29 towns with full‐time municipal health departments. Tables S1 and S2 in Appendix SA3 present the population ranges for each type of LHJ. Over the period of our analysis (2005–2012), there was one pair of towns that merged to form a regional health district, and therefore, we have omitted this district from our analysis. Otherwise, the LHJs were stable in size and type over the observation period.14

In our analysis, we are interested in determining how total costs for a LHJ change as inspections change—in other words, our objective is to answer the question: Are there economies of scale and/or scope for environmental health inspections services? Data limitations preclude an analysis exclusively focusing on the environmental health subsections of the LHJs as separate entities. While a substantial proportion of all CT LHJs place their focus on environmental health services, many full‐time municipal departments and regional districts provide a broader range of public health measures that were not captured in these data. Due to the limitations of the cost data available for the LHJs, only total budgetary costs were available for use, as opposed to an estimate of environmental cost centers. Therefore, the cost function could appear inflated for those municipal departments and regional districts providing a wider array of public health services, as we only had access to environmental health data.

Total LHJ expenses include personnel expenses, contractual expenses, legal expenses, operations expenses, and miscellaneous expenses. The latter two expenses categories encompass overhead costs. We deflated expenses with the Consumer Price Index from table B‐3 of the 2013 Economic Report of the President (and converted to a base year of 2005). Wages were calculated as personnel expenses divided by full‐time equivalents (these include all employees in the districts or municipalities; some municipalities and districts have primarily environmental health employees, while some other include other types of public health employees). Capital prices were obtained from the Capital Equipment Producer Price Index in table B‐65, 2013 Economic Report of the President.15

The outputs that we include in our regressions for equation (2) in Appendix SA2 are as follows:

1. Private water wells: the total number of private and public water well permits issued

2. Food Services: the total number of food establishments (Classes I–IV) inspections and temporary events

3. Septic Services: the total number of new permits, repair permits, lots tested, and B‐10016 application reviews

4. Lead: total number of childhood lead blood level investigations

Our approach of analyzing a Department of Public Health data set obtained from official reports is in contrast to that of some of the other ongoing cost studies that engage in primary data collection for a subset of jurisdictions through survey instruments. Our data are obtained from the 2005–2012 LHJ Annual Reports from the Connecticut Department of Public Health. Unfortunately, expenditure data from municipal departments were neither required nor consistently collected by the Connecticut Department of Public Health during the study period. This resulted in considerable missing expenditure data from municipalities. Substantial effort was expended to obtain expenditure data for all municipal LHJs. In some cases, financial information is available online on the town websites. In a few instances, we obtain expenditure data from the local health director and/or the finance director.17 We subsequently clean/merge the data.18 These data are combined with additional data from the publicly available State of Connecticut's childhood blood lead surveillance reports, to provide a rich data set for the purpose of estimating cost functions and economies of scale for the local health organizations. Table 1 includes summary statistics.

Table 1.

Costs, Wages, Environmental Health Inspections, and Rural–Urban Status by LHJ Type

Variable	Overall for All LHJs	Full‐Time Municipal Departments	Regional Districts	Part‐Time Jurisdictions
Total expenses
Mean	$1,541,909	$3,013,206	$1,173,964	$193,655
Median	$565,453	$846,184	$978,331	$ 44,291
Average annual salaries
Mean	$33,341	$41,327	$42,082	$17,585
Median	$36,832	$42,387	$41,537	$ 7,773
Number of wells inspected
Mean	40	20	84	29
Median	15	11	48	12
Lead inspections
Mean	22	46	11	4
Median	1	2	2	1
Food inspections
Mean	434	565	665	111
Median	269	398	562	47
Septic inspections
Mean	257	161	559	130
Median	140	112	459	82
Rural or urban
Mean	.835	.835	.820	.845

In addition to the variables discussed, we control for LHJ efforts and services outside of environmental health. Nurses and health educators are the most commonly employed health care workers by LHJs outside of environmental health personnel.19 We also control for urban/rural designation. In other words, this dummy variable equals 1 if a LHJ is in an urban or rural area, and 0 otherwise (e.g., in a suburban area; this enables us to distinguish suburban areas from other areas).20

The final semi‐translog model includes average wage, average capital price, food inspections, water inspections, lead inspections, sewer inspections, rural/urban dummy variable, nurse staff dummy, child cumulative lead blood level over 10, and dummies for whether or not the LHJ is a full‐time municipality or a district. The semi‐translog total cost function regression results (using White robust standard errors) are in Table 2.21

Table 2.

Least Squares Regression Results, Translog Total Cost Function (Equation (2))

	Coefficient
Variable Name	p‐Value
Constant	−49.1485
	.8353
LOG(WAGE)*LOG(CAPITAL PRICE)	0.0655
	.5623
LOG(WAGE)2	0.0043
	.0868
LOG(CAPITAL PRICE)2	0.6240
	.9423
(LOG(WATER INSPECTIONS))2	0.0375
	.1640
(LOG(LEAD INSPECTIONS))2	0.0178
	.2709
(LOG(FOOD INSPECTIONS))2	0.0179
	.0259
(LOG(SEPTIC INSPECTIONS))2	0.0375
	.0468
LOG(SEPTIC INSPECTIONS)*LOG(WATER INSPECTIONS)	−0.0624
	.1617
LOG(SEPTIC INSPECTIONS)*LOG(LEAD INSPECTIONS)	−0.0085
	.7215
LOG(SEPTIC INSPECTIONS)*LOG(FOOD INSPECTIONS)	−0.0231
	.1767
LOG(WATER INSPECTIONS)*LOG(FOOD INSPECTIONS)	0.0072
	.7510
LOG(WATER INSPECTIONS)*LOG(LEAD INSPECTIONS)	0.0482
	.1639
LOG(LEAD INSPECTIONS)*LOG(FOOD INSPECTIONS)	−0.0190
	.1735
DUMMY FOR NURSE(S) ON STAFF	0.4367
	.0000
DUMMY FOR RURAL OR URBAN JURISDICTION	−0.2969
	.0083
YEAR	0.0299
	.7997
DUMMY FOR MUNICIPAL HEALTH DEPARTMENTS	1.5681
	.0000
DUMMY FOR HEALTH DISTRICTS	1.5686
	.0000
CHILDREN WITH BLOOD LEAD CUMULATIVE STATS OVER 10	0.0134
	.0000
Included observations (N):	529
R‐squared:	0.6412
Adjusted R‐squared:	0.6278

Notes: Data are annual (2005–2012), for 74 jurisdictions (missing values reduces sample size to N = 529).

p‐Values calculated based on White heteroskedasticity‐consistent standard errors and covariance.

The “base” is part‐time districts and/or departments, for the two dummies for full‐time municipal departments and full‐time health districts.

The model is a reasonably good fit, with R‐squared of 0.64.22 Several inspections parameter estimates are statistically significant at the 5 percent or 10 percent levels, although many interaction terms are insignificant.23 Many other control variables are highly statistically significant, including whether nurses are on staff; whether the municipality is urban or rural (negative and significant effect on total costs, implying more money is spent in other municipalities—i.e., suburbs); and number of children tested with blood levels of at least 10. Also, regional districts and municipal health departments tend to spend more money than part‐time LHJs.24

It is noteworthy that a complete set of linear and quadratic and interaction terms are often included in cost function analyses. However, as described above, Mays (2013) is one example of a recent cost function study that omits some interaction terms. We chose to omit some terms here because the high degree of multicollinearity that inflates the standard errors led to fewer significant parameter estimates25 (as well as some differences in the parameter estimates when we included the full set of terms). Retaining the interaction and quadratic terms while eliminating the linear terms enables us to reduce multicollinearity while at the same time allowing for the possibility of curvature in the cost function.

In addition, there may be concerns about autocorrelation in longitudinal data. Autocorrelation affects the standard errors (and t‐statistics). This autocorrelation has no effect at all on the actual parameter estimates we use to calculate the elasticities (and, therefore, no effect on the elasticities), which we confirm with a heteroskedasticity and autocorrelation consistent (HAC) adjustment to our cost function regression. Accordingly, we present results from the HAC estimation procedure in order to avoid the autocorrelation concerns that can lead to statistical insignificant parameter estimates, and this does not alter the actual values of the parameter estimates.

Table 3 reports descriptive statistics for the economies of scale estimates. The largest value is 0.38, while the lowest is 0.025. The mean of all elasticities is 0.19. These categories demonstrate the number of LHJs with elasticities of scale in each of four arbitrarily chosen ranges, and they provide some details about the specifics of the elasticities. Among the 74 total LHJs, 47 had mean elasticities between 0.1 and 0.3. The mean for each LHJ is taken over the years 2005–2012. The standard deviations for each category are small relative to the mean of each category, implying a reasonable degree of confidence.

Table 3.

Descriptive Statistics for the 74 LHJ Elasticities of Scale Estimates

Elasticity of Scale	Mean	Max	Min.	SD	Obs.
[0, 0.1)	0.0611	0.0936	0.0246	0.0220	16
[0.1, 0.2)	0.1497	0.1930	0.1168	0.0235	26
[0.2, 0.3)	0.2577	0.2988	0.2030	0.0245	21
[0.3, 0.4)	0.3565	0.3887	0.3227	0.0247	11
All [0, 0.4)	0.1919	0.3887	0.0246	0.1012	74

Notes: Descriptive Statistics for Elasticity of Scale. The elasticity for each LHJ is evaluated at the mean of data for each LHJ over all of the years 2005–2012 (there are 74 LHJs total in our sample).

Categorized by values of Elasticity of Scale.

Included observations: 74.

We find that, on average, Connecticut's LHJs have elasticity of scale <1.0. When separating these into the various types of LHJs, we find the part‐timers have elasticity of scale estimates of closest to 0. This implies these part‐time departments may be performing too few inspections. In contrast, the full‐time municipal health departments and regional health districts are closer but still not at minimum efficient scale, as their elasticity of scale is greater than the part‐timers but still <1.0. The elasticities of scale for the districts are larger on average than for the full‐time municipal departments, implying the districts are closer to being efficient than the municipal health departments. A histogram of the 74 elasticities of scale estimates is shown in Figure S2 in Appendix SA3.26

Figure S2a shows the distribution of the 74 LHJs elasticity of scale estimates. Figure S2b–d break these out by whether they are a municipal, district, or part‐time.27 For the part‐timers, there are 20 with elasticities <0.20, while for the (full‐time) districts, there are 15 jurisdictions with elasticities >0.20. The municipal health departments have the mode economies of scale estimate, which is 0.26. As described above, many of these municipalities are concentrating on many activities in addition to environmental health, which can potentially explain the scattered observations across the low end of the economies of scale distribution. As can be seen in Figure S2, the elasticities are fairly uniformly distributed throughout for the full‐time municipal health departments and the part‐timers. The elasticities for the districts are skewed to the right, which implies that those elasticities were slightly closer to 1.0 (the minimum efficient scale). We explore graphically the relationships between economies of scale estimates and several other variables that are representative of the size of the LHJ. These size variables include population, full‐time equivalents, total cost, and total output.28 There is a positive relationship between economies of scale and each of these size variables, as can be seen in Figure 1.

Figure 1.

Plots of Various Variables against Elasticity of Scale Estimates. (a) Population of Local Health Jurisdictions versus Economies of Scale Estimates. (b) Full‐Time Equivalents of Local Health Jurisdictions versus Economies of Scale Estimates. (c) Total Costs (Real) versus Economies of Scale Estimates. (d) Total Output (all four inspection types) versus Economies of Scale Estimates

These positive relationships between economies of scale and each of the size variables imply that “larger” LHJs tend to have larger economies of scale estimates. In other words, smaller LHJs tend to be less cost efficient than the larger ones. Interestingly, the “smaller” LHJs tend to be part‐timers.

This notion of consolidation and shared services is closely related to, yet distinct, from the notion of economies of specialization and scope.29 Specifically, based on the results of our regression analysis in equation 2, we find economies of scope for four pairwise combinations of inspections. In other words, incremental costs fall with the production of water and septic inspections together; food and septic inspections together; food and lead inspections together; and lead and septic inspections together. On the other hand, we find economies of specialization for two pairwise combinations of inspections. That is, there are higher incremental costs when water and lead inspection services are performed separately; and when water and food inspections are done separately. These results are presented in Table 4.

Table 4.

Economies of Scope/Specialization Estimates

Inspection Pairs	Estimate
FOOD and LEAD	−0.018977
FOOD and SEPTIC	−0.023105
LEAD and SEPTIC	−0.008518
WATER and FOOD	0.007207
WATER and LEAD	0.048250
WATER and SEPTIC	−0.062385

Note: These estimates of economies of scope/specialization in Table 4 above are for the entire sample of 74 jurisdictions during the period of our sample (2005–2012). Due to the nature of the semi‐translog cost function, these estimates are equal across all LHJs. A negative value indicates economies of scope, while a positive number implies economies of specialization.

Limitations

There are a number of potential limitations of our study. The greatest are the data available to us. One major contribution is our development and use of the Connecticut Department of Public Health Annual Report data for each jurisdiction over a period of 8 years for a cost function estimation. We initially had assumed the data set was complete for all LHJs.30 But there are many data issues that we have detected. Some jurisdictions are missing values of some variables for one or more years, necessitating interpolation in a small number of instances.31 In a few cases, some data seem to be implausible, possibly a result of keystroke errors when the data were entered into the system with the initial Department of Public Health surveys. Other jurisdictions are simply missing data for some years, which we have determined when contacting Connecticut Department of Public Health to try and follow up.

Another potential limitation is the economies of scale policy implications. The interpretation of the economies of scale results is intended to apply to small changes in output. These estimates tell us how efficiency would change when there is a small change in output (number of inspections). If two reasonably large districts or municipalities are to share services, the significantly large jump in “output” might lead to unit costs that are too large because of the inefficiencies associated with a large organization. However, these results do not necessarily imply that a part‐time LHJ should not join a regional district, as there still may be cost savings for both.

In terms of economies of scope, our methodology allows for pairwise comparison of two types of inspections, whereas in reality, most jurisdictions perform more than two types of inspections. Therefore, we cannot address the question of whether or not it is less costly for one district to perform all three or four types of inspections, or if it is more efficient to have four different jurisdictions with each specializing and performing only one of these types of inspections.

Many full‐time municipal departments and regional health districts provide a broader range of public health services for which we do not have data.32 Therefore, as we control for the four environmental health outputs but the costs include all other types of outputs, some of the elasticities of scale may be understated.33 This implies that some LHJs are likely to be closer to the minimum efficient scale (i.e., elasticity of scale closer to 1.0) than we have estimated. For those LHJs that provide a diverse set of services (such as communicable diseases), the cost per service may be somewhat exaggerated.34 Nevertheless, in some situations, especially in larger LHJs, it may be difficult to distinguish how much of a particular employee's time is dedicated to environmental health inspections versus other activities, whereas their entire salary may be included in total operating expenses. This is an example of another reason why care should be taken in jumping to policy conclusions from these results, and why there should be a push to acquire and maintain more reliable data on environmental health costs and their components.

Finally, cost is not the only consideration in determining the appropriate local public health jurisdiction for the provision of environmental health services.35 As we discuss above, the mix of services is also different in the various types of LHJs, so there may not be much cost savings by merging the LHJs that provide very different types of services.36

Conclusion/Discussion

There are several potential policy implications of our research. First, analyses of scale and scope may be a valuable tool to determine efficiency of LHJ services and to evaluate the benefits of sharing specific services. Despite the data limitations, our methodology is a valid approach that is deserving of applications to Connecticut's and other states' Environmental Health costs.37 As noted above, two small, part‐time LHJs may elect to share some inspection services (or consider merging) to move closer to full utilization of environmental health staff and reduction of fixed costs. Specialization economies may imply that it is more efficient for some LHJs to focus on the services they can do best (e.g., a part‐time LHJ to contract with a municipal LHJ to provide lead services). On the other hand, some services are performed together quite naturally. For instance, water testing as an environmental service is relatively simple, lower cost, and involves minimal worker time to perform. It is the least complex of the four mandated services. It is also performed nearly in conjunction with septic work for new dwelling or repairs of existing septic systems, and it is reasonable that providing both services would imply economies of scope. For the same reasons, it follows that the combination of either water and lead or water and food would produce higher incremental costs, because of the complexity of the inspection process as well as the time involved in the inspection/investigation. On average, a food service inspection may require up to 2 hours to complete as well as the administrative time for completion of forms and reports. In terms of specialization, lead investigations require special training and perhaps certification, and these are very complex and may require weeks to months of follow‐up, if remediation is required.

Second, more research utilizing existing LHJ financial and service data deserves attention. These include the following: limitations in working with available LHJ service delivery data that may not be broken down to specific types and/or components; the lack of clear definitions for outputs (i.e., what we count) and whether a standard “routine” set of activities will be adopted for inclusion in economies of scale and scope analyses. Adoption of the appropriate outputs for analysis is critical.38

Third, developing and encouraging a national standard for financial data would strengthen this research. LHJs and states have essential roles in developing and executing more standardized data systems. States that provide funding to LHJs could establish required standardized report forms that incorporate the categories and types of information that would allow for analysis of data over time. A National Clearing House could also be established to gather and maintain state and local financial and service data, sponsored by organizations such as the Robert Wood Johnson Foundation or a federal agency. National associations, such as National Association of County & City Health Officials, and/or Association of State and Territorial Health Officials, could play a lead role or become this repository.39

Fourth, public health training for administrators in governmental agencies should include more on financial management and application of business models to the management of LHJ finances. Few, if any, have the ability or expertise to determine true unit costs for public health services. This can be addressed through a number of mechanisms. Modular, online courses, training through national associations, Public Health Training Centers, and other appropriate national organizations, and incorporation into existing public health school curriculums, are specific suggestions for how this additional education and training might occur.

Supporting information

Appendix SA1: Author Matrix.

Appendix SA2: Background on Cost Function Theory.

Appendix SA3: Supplemental Tables and Figures.

Table S1. Full‐Time and Part‐Time Local Health Jurisdictions in Connecticut, 2005–2012.

Table S2. Local Health Jurisdictions in Connecticut by Population, 2005–2012.

Figure S1. Economies of Scale.

Figure S2. Frequency Distributions of Elasticity of Scale Estimates.