Abstract
Using recent census data, this research examines municipal incorporations. The research is guided by the Tiebout model and a reorganization framework: it posits that wider income heterogeneity among county areas raises the probability of incorporation for higher-income communities, and incentives for incorporation include revenue and growth control. Empirically, change in unincorporated places is examined to determine how their income heterogeneity influences initiatives for incorporation, through subsamples of revenue and growth control. Main findings are that income heterogeneity raised the probability of incorporation, particularly where municipal per capita revenue is greater, land-use regulation is nonrestrictive, and population growth is rising.
Keywords
Introduction
Income segregation has widened in metropolitan areas over the last two decades (Reardon and Bischoff 2011). Income segregation may reproduce uneven taxes and services—unevenness affecting levels of public services among communities (Boustan 2013). Segregation does correlate with uneven provision of public safety, education, and quality of life (Boustan et al. 2013). Those uneven impacts are of growing concern and motivate our research on incentives for municipal incorporation. How do income and policies motivate incorporation among unincorporated communities?
The present research analyzes this question for new municipal incorporations. It presents the following contributions. First, it presents recent data for unincorporated and incorporated places (communities) from the latest two Censuses of Population. The data set normalizes boundaries of unincorporated places for 2000 and 2010: using these normalized data, the probability of incorporation among unincorporated places is estimated using longitudinal methods. 1 Second, this research analyzes how socioeconomic segregation, among unincorporated communities, relates to policy incentives for incorporation. Contexts of policy are analyzed through subsamples of revenue, growth, and growth regulation. This focus allows us to specify incentives for incorporation among unincorporated places. (The terms place and community are used for equivalent intents, as places are the Census Bureau’s unit of observation for unincorporated communities.) 2
The research is guided by two literatures: metropolitan segregation and local government reorganization. The literature on residential segregation has developed partly around ideas introduced by Tiebout (1956; see also Miller 1981; Dowding, John, and Biggs 1994; Henderson 1985; Ostrom, Tiebout, and Warren 1961; Reardon and Bischoff 2011). This literature examines trends in residential segregation and its various dimensions. It documents that segregation patterns are indeed changing. It also postulates that residential segregation shapes tax–service packages provided by municipalities (Ross and Yinger 1999). Guided by this literature, we examine how socioeconomic heterogeneity and policy incentives may result in municipal incorporations. We focus on the distinct (though not mutually exclusive or exhaustive) incentives of revenue control and growth control. This focus allows us to specify conditions under which unincorporated communities will seek policies out of incorporation, and socioeconomic segregation is related as an influential condition (Rice, Waldner, and Smith 2014).
While making progress, the literature has yet to specify how socioeconomic segregation relates to policy initiatives for municipal incorporation. For its power to change policies, incorporating a new municipality is seen as one way to change the distribution of services. Furthermore, municipalities often have broader policy powers than other governments (Briffault 1996). Growth control is a policy power generally precluded, for instance, from special districts. While other governments may supply services, they cannot generally substitute for municipalities.
Second, and primarily, this research builds on the literature on local reorganization. This literature proposes, among other explanations, that demand for services and policies motivates incorporation. Policy powers thus encourage unincorporated residents to opt for incorporation. However, problems of collective action pervade incorporation (Feiock and Carr 2001). An initiative for incorporation, for instance, raises the problem of how to draw a new municipality’s boundaries so that a majority of residents does support that initiative. This problem suggests the possibility of boundaries could be drawn strategically to exclude certain neighborhoods. Those incentives could result in islands of unincorporated places, later turning difficult to serve (Waldner and Smith 2015). Incorporation is, in short, a reorganization framework of nontrivial consequences.
Related Literature
Martin and Wagner (1978) examine the legal framework of municipal incorporation, focusing on local agency formation commissions in California. Subsequently, Miller (1981) examines incorporations under the Lakewood Plan in California; he concludes that Lakewood’s city–county service contracting enabled incorporations. Linking to the Tiebout model, Miller finds that socioeconomic segregation and incorporation were hand in hand in postwar Los Angeles county. Likewise, Musso (2001) frames her study of incorporations in Southern California as a test of Tiebout. She reports a significant association between higher income and the probability of incorporation. Recently, Hogen-Esch (2011) examines the impact of California’s state–local legislation. He concludes that recent legislation, restrictive of local autonomy, has discouraged municipal incorporation.
Fleischmann (1986) outlines strategies used by entrepreneurs and interest groups in change of local boundaries. Similarly, Burns (1994) examines how certain groups are able to solve the collective action problem pervading the formation of local governments, and she also discusses the influence of class divisions. Building on Burns, Feiock and Carr (2001) analyze additional actors, collective action problems, and incentives for boundary change. Carr (2004) highlights the role of state laws in incentives for boundary change. Carr and Feiock (2004), and contributors, survey the larger field in light of consolidation debates.
Recently, Smith and colleagues have made several contributions. Smith (2011) finds that large annexations and population growth have encouraged municipal incorporation in North Carolina. Smith and Debbage (2011) document that state factors, such as laws on boundary change, explain the geographic distribution of new municipalities. Waldner, Rice, and Smith (2013) find a decreasing frequency of incorporations over the last fifty years. They conclude that less unincorporated places and slowing suburbanization discourage incorporation. Rice, Waldner, and Smith (2014) review the literature and media articles on incorporation and conclude with a typology of factors explaining incorporation. Waldner and Smith (2015) analyze how new city clusters are formed to avoid county policies. They conclude that incorporations have various outcomes, including a reduction in the county’s fiscal capacity and its ability to serve unincorporated communities.
This research continues the related agenda in order to build knowledge around municipal incorporation and its consequences for metropolitan governance. Consequences of municipal incorporation could include uneven taxes and services, uneven land-use patterns, and islands of unincorporated places. Yet municipal incorporation also allows for provision of services and matching of resident preferences with tax–service policies in developing communities. Although associated with mixed consequences, incorporation may be therefore a response to a vacuum of governance and policies for unincorporated communities. Incorporation may also reflect an inability of state and existing local governments to provide unincorporated communities with demanded policies (Parks and Oakerson 1989; Waldner and Smith 2015).
Income and Policies of Influence
The literature focuses on income, ethnicity, and race as sources of urban heterogeneity—a focus that reflects the historical sources of urban divisions in the United States (Alesina, Baqir, and Hoxby 2004). Recently, ethnic and racial segregation appear to be lessening (Massey, Rothwell, and Domina 2009). Yet socioeconomic (income) segregation has widened over recent decades (Reardon and Bischoff 2011). Widening income segregation is increasingly of concern, as it can affect communities’ ability to support provision of services. Socioeconomic structure underlies both need of and demand for services (Alesina, Baqir, and Hoxby 2004; Cutler, Elmendorf, and Zeckhauser 1993). Income structure, among unincorporated communities, will therefore shape their preferences for policies. If other local governments do not meet those demands, incentives for incorporation may arise (Waldner, Rice, and Smith 2013; Waldner and Smith 2015).
Incorporation will be intended for a municipality’s general policies—that is, policies granted generally to municipal jurisdictions. Therefore, revenue control and growth control are primary policies motivating incorporation (Leon-Moreta 2015; Miller 1981; Rice, Waldner, and Smith 2014). Revenue control and growth control are not mutually exclusive policies: an unincorporated community may well seek both out of incorporation. Our general hypothesis is consequently that unincorporated communities will incorporate to control revenue and growth policies. These policies, however, are distinct powers that should be assessed independently (Burns 1994; Rice, Waldner, and Smith 2014). Underlying are hypotheses that income-segregated communities will value those policies differently (Alesina, Baqir, and Hoxby 2004; Ross and Yinger 1999).
Income heterogeneity thus shapes policy incentives for incorporation. On one hand, revenue control matters because own-source revenue is the largest source for municipalities (U.S. Census Bureau 1987-2007). Own-source revenue thus plays a key role in financing services. On the other hand, growth control is a key incentive for incorporation because that regulatory power is granted to municipalities by law (Briffault 1996). By contrast, special districts cannot generally regulate growth. Counties may or may not regulate growth depending on state law. Greater powers of revenue and growth control thus distinguish municipalities from other governments with specialized functions (Briffault 1996; Foster 1997). Incorporation will be a reorganization framework of choice, if an unincorporated community intends to control revenue and growth policies (Carr 2004; Waldner and Smith 2015).
Policy incentives for incorporation should be thus specified in light of income heterogeneity. On one hand, lower-income communities may favor land-use policies that promote employment and economic development (Fischel 2004). They may also favor redistributive tax–service policies. If those incentives hold, lower-income communities may not benefit from incorporation—provided the county government satisfies those needs reasonably. On the other hand, higher-income communities may favor growth controls in order to protect their property base. By incorporation, they may prevent development that potentially devalues their property base: examples are lower-income housing or industrial activities that bring about nuisances (Fennell 2006; Fischel 2001). Furthermore, higher-income communities may favor exclusive tax–service policies. If those incentives hold, higher-income communities may seek incorporation in order to reshape those policies (Waldner and Smith 2015). Consequently,
Theoretically, unincorporated communities will opt for incorporation if expected benefits exceed costs (Alesina, Baqir, and Hoxby 2004; Brierly 2004). Expected benefits relate to control of policy powers. However, forming a municipality creates costs: ex ante costs of the incorporation process itself and ex post costs of sustaining a municipality’s functions over time. Those costs could be prohibitive for lower-income communities. Yet higher-income communities will be better situated to support those costs and eventually benefit from incorporation (Leon-Moreta 2015).
Revenue Control
Resident income supports a municipality’s ability to raise own-source revenue for services. However, revenue policies distribute costs and benefits of services differently. Tax policies can impose uneven burdens on residents. If differentiated by income, some unincorporated communities could bear different tax burdens compared to other communities. Different tax burdens could also arise if the county raises differential revenues for services provided to unincorporated communities. Some state laws allow the county to raise differential revenues from unincorporated residents. 3 While supporting services, differential revenues could nonetheless raise concerns of uneven burdens on unincorporated communities (Lazega and Fletcher 1997). Thus, incorporation may allow a once-unincorporated community to avoid county governance and reshape revenue policies (Waldner and Smith 2015).
Own-source revenue comprises the largest source of revenue for municipalities (Alm, Buschman, and Sjoquist 2011; Census of Governments 2007). Municipal revenue includes tax and nontax sources. These sources support a municipality’s ability to provide services. Own-source revenues are greater for municipalities than for other governments: over 80 percent for municipalities, as compared to 60 percent for counties (Census of Governments 2007). Two implications follow. First, revenue policies can impose differential burdens on unincorporated residents (Boustan 2013). Second, municipalities may reallocate revenues toward alternative purposes (Schneider 1989).
Broader revenues also allow municipalities to provide more services, as compared to other governments. Where revenue is available for counties or special districts, however, those governments may supply services to unincorporated residents in lieu of municipalities. Still, state laws typically restrict counties and special districts; these governments play minimal or no functions at all in some states (Krane, Rigos, and Hill 2001). Even if functional, counties and special districts depend on narrower revenues, such as intergovernmental aid or charges for services (Census of Governments 2007). Own-source revenue thus represents a more fundamental power for municipalities.
The Tiebout model posits that residents shop around the metropolitan area for a municipality “which best satisfies [their] preference pattern for public goods” (Tiebout 1956, 418) and “with appropriate fiscal patterns: that is, low taxes and a low level of redistribution” (Miller 1981, 146). Consequently, revenue policies shape the distribution of revenue burdens on residents. Whereas lower-income communities may favor revenue policy for redistributive services, higher-income communities may favor policy that limits revenue burdens (Waldner and Smith 2015). Unincorporated residents may particularly opt for incorporation, if county revenue policy conflicts with residents’ preferences (Waldner and Smith 2015). Incorporation will then be intended to avoid revenue burdens that the county could raise for redistributive spending. As the county makes revenue policies for unincorporated communities, these communities’ ability to influence countywide decisions tends to be diminished. Their ability to reshape revenue policies will increase by incorporation (Rice, Waldner, and Smith 2014; Waldner and Smith 2015). By incorporation, communities can therefore control revenue and its spending.
Low tax rates may not affect services in higher-income municipalities, as their greater property base allows them to lower tax rates while supporting exclusive services (Musso 2001; Rice, Waldner, and Smith 2014). Most municipal services are excludable from nonresidents, unless required otherwise by law; some state laws do allow municipalities to extend services to unincorporated residents by extraterritorial authority (Krane, Rigos, and Hill 2001). In most cases, however, a municipality’s boundaries define its service boundaries. By incorporation, unincorporated residents will redefine boundaries and reshape tax–service policies toward resident preferences.
Higher-income communities have a property base to support ex ante and ex post costs of a municipal incorporation. Also, some state laws require a minimum property base for incorporation (Krane, Rigos, and Hill 2001; Leon-Moreta 2015). This legal requirement further raises the role of revenue control in initiatives for incorporation. Where that requirement applies, unincorporated communities have to demonstrate that their property base will be sufficient to support a municipality and its functions over time. Where required, however, a minimum property base discourages incorporation. Where not required, incorporations will be more likely but so will dysfunctional incorporations or disincorporations as a consequence of low revenue capacity (Anderson 2012).
If revenue incentives hold, incorporation may be intended to control revenue policies. Incorporation will allow unincorporated communities to reshape those policies around their preferences. Thus, research has to assess revenue for municipalities and other local governments, as an incentive for incorporation among income differentiated communities (Rice, Waldner, and Smith 2014). Consequently,
Growth Control
Preferences for community environs underlie residential mobility; this mobility underlies the formation of unincorporated communities by socioeconomic class (Mieszkowski and Mills 1993; Ross and Yinger 1999). Still, formation of unincorporated communities and growth around them are insufficient to explain incorporation. Mismatch of policy preferences among communities should arise for a community to opt for incorporation (Alesina, Baqir, and Hoxby 2004; Miller 1981; Rice, Waldner, and Smith 2014). This section analyzes how mismatch of preferences for growth control may influence initiatives for incorporation.
Growth in and around unincorporated communities may raise an incentive for incorporation. This incentive is related to the socioeconomic composition of communities, as growth can affect that socioeconomic composition. If lower-income residents pay less taxes yet demand more services, development of lower-income housing could affect property values (Fischel 2001). If the county favors growth, an unincorporated community may well seek growth regulations out of incorporation. Similar incentives will work against industrial development, particularly for industries that produce nuisances (Fennell 2006; Fischel 2001). These incentives may be even more pronounced if a community’s environment is that of rising growth yet nonrestrictive regulation. Going hand in hand, the county’s nonrestrictive regulation and rising growth will then raise incentives for incorporation. By incorporation, a community would adopt regulations that are more restrictive than those of the county (Waldner and Smith 2015).
The record of municipal incorporations suggests that growth control has been a critical policy incentive. State law has granted this policy power to municipalities, and the U.S. Supreme Court’s decision on Village of Euclid v. Ambler Realty Co. reaffirmed it. Zoning has then turned out to be a powerful incentive for incorporation (Teaford 1979). Aiming for this policy, “20th century incorporations were most often motivated by residents’ desire to control land use” (Fischel 2004, 326).
While state laws empower municipalities’ regulation of land use, state governments are recently active in this area (Fischel 2004; Gyourko, Saiz, and Summers 2008). States restrain land-use regulation by restraining municipalities or by direct regulation. The states are increasingly active by either or both means (Gyourko, Saiz, and Summers 2008). However, state activism could make the regulatory environment more uniform throughout a state’s localities. State activism could also make the regulatory environment more restrictive (Fischel 2001). Thus, growth regulation is a policy context in which multiple governments are active. This activism could turn critical wherever municipalities, counties, and the state have conflicting preferences for land use. Often, the state and county governments favor denser development, whereas municipalities oppose development (Fischel 2001). By incorporation, municipalities may thus adopt more restrictive regulations (Waldner and Smith 2015). 4
The policy power of land-use regulation distinguishes municipalities from special districts lacking that power and from counties having that power depending on state law. For example, counties in most Northeastern states are deprived of functional powers. In that context, county policy may not be an incentive for boundary changes in most Northeastern states. 5 In other states, counties do have certain functional powers, including that of land-use regulation (Krane, Rigos, and Hill 2001; Fischel 2001).
Municipalities regulate land for different uses, and socioeconomically differentiated municipalities will regulate land use for different aims. Whereas lower-income communities may favor land use to attract employment and economic development, higher-income communities may oppose development in order to protect their property base (Fischel 2001). By incorporation, higher-income communities may zone land to prevent lower-income housing or industries that produce nuisances (Fennell 2006). The power of land-use regulation is so general that municipalities often employ it for exclusionary purposes (Fennell 2006; Ross and Yinger 1999). The literature singles out as exclusionary that zoning intended to prevent development of lower-income housing in particular. Typically, exclusionary zoning is intended to protect a community’s property values (Fennell 2006; Fischel 2004).
Land-use regulation is central to theory for municipal incorporation, as municipalities are empowered with that general regulatory power. It distinguishes municipalities from other local governments having limited or no regulatory role in that area. The county can be a relevant government depending on state law, whereas the state has turned into a relevant government by its recent activism. If these influences hold, incorporation may be intended to control growth. By incorporation, unincorporated residents may be able to reshape growth policies around their preferences. Consequently,
Empirical Framework
Data
This section describes the procedures followed in the construction of the data set. The unit of analysis is the place as defined by the Census Bureau for data reporting of unincorporated and incorporated places. Observing incorporation through census places builds on prior research. Musso (2001) originally introduces the census place as the unit of analysis in her study of incorporations in Southern California; Leon-Moreta (2015) uses the census place in a study of incorporations for the United States. One hurdle is, however, that the Census Bureau changes the boundaries of unincorporated places every decade. To ensure data comparability, our 2000 census data are normalized into 2010 census boundaries, as normalized by GeoLytics Inc. (2014). These data allow us to follow places over time, based on consistent boundaries. Thus, 2010 census boundaries are used to track unincorporated places over the last two decades. This section summarizes the data (see Table 1 for detailed definitions and sources). 6
Data.
Note: The table presents definition and sources of the data. The Boundary and Annexation Survey is for the annual surveys of 1990 through 2010. The Census of Population is for 2010 and for 2000 normalized on 2010 boundaries (GeoLytics 2014). The Intercensal Estimates are from 1990 to 2010. The Census of Governments is for 1997 and 2007. The American Community Survey is for the 2010 five-year estimates. Unless noted otherwise, the unit of observation is the place as defined by the U.S. Census for unincorporated and incorporated places. Except for growth regulation and incorporation requirements, all data are time-variant.
The data set identifies unincorporated places at risk of incorporation—that is, unincorporated places that may incorporate. Unincorporated places otherwise not suitable are removed from this analysis. One criterion, for including an unincorporated place in the data analysis, is whether or not the place has minimum population for incorporation as required by law. Additional criteria are whether general law allows for incorporation proceedings and whether any new municipality has incorporated in the state. For example, although having laws for incorporation, the state of Nebraska has no incorporations over recent years. Because new municipalities from the same state are necessary for comparability, then Nebraska unincorporated places are removed. Similar criteria are followed for other states.
Focusing on new municipal incorporations from 1990 to 2010, data from these decades are necessary to construct the dependent and independent variables of interest. To estimate the probability of incorporation, the dependent variable classifies places on whether they are unincorporated or incorporated by 2000 and 2010. A binary variable (0/1) thus classifies unincorporated and incorporated places from the preceding decade. The source of the dependent variable—incorporation—is the Census Bureau’s Boundary and Annexation Surveys (U.S. Census Bureau 1990-2010).
The first set of independent variables is for place–county income heterogeneity. Two variables are included for income. First, median household income controls for income level in a place. Second, the ratio of median income in a place to median income in the county area measures heterogeneity. These measures are derived from the median voter model (Meltzer and Scott 1981). The first variable is important because income level influences demand for services. The second variable captures income heterogeneity among communities. Sources for these variables are the 2006–2010 American Community Survey, the 2010 Census, and the 2000 Census normalized into 2010 boundaries.
Two variables control for racial and Hispanic-ethnic heterogeneity, based on the information theory measure of segregation developed by Reardon and Firebaugh (2002). This section summarizes how these measures are constructed (see Reardon and Firebaugh for an extensive discussion). In general, measures of segregation use a metropolitan area as the composite area and tracts as components. Then traditional measures of segregation estimate the unevenness with which ethnic groups are distributed through the tracts making up a metropolitan area. This research calculates measures following a similar logic. For consistency with our focus, the county is the composite area and places are the components. Although using place and county data, these measures are unique for every place. This is possible because, when estimating a measure for a place, data are disaggregated for the place and for the rest of its county area (after isolating the place). This procedure, in short, amounts to a partition of the county into a place of interest and the rest of its county area, for every case a segregation measure is computed. The heterogeneity variables thus vary by place, as every place is compared to its unique rest-of-county area. One variable is computed for racial heterogeneity and another for ethnic heterogeneity.
Growth and its regulation are independent variables of interest. The growth variable is an average of annual population growth in county areas. It accounts for growth rates for the county area in which an unincorporated community is placed. Sources for this variable are the 2010 and 2000 Censuses as well as the Census Bureau’s Intercensal Estimates since 1990. The growth regulation variable is based on Gyourko, Saiz, and Summers (2008). This variable is an index of executive and legislative regulations for a state.
Own-source revenue per capita is collected for municipalities. It accounts for one of the research hypothesis—that incorporation is intended to control revenue policy. These data are collected from the 1997 and 2007 Censuses of Governments, the nearest ones to the 2000 and 2010 Censuses of Population at the time of this writing. This variable is an average of revenue for municipalities in the county area. Additionally, revenue per capita is collected for counties and special districts. County and special-district revenues control for their role in financing services in lieu of municipalities. All revenue variables are measured in their natural log to account for their right-skewed distribution.
A control is added for the number of annexations over the prior decade in county areas. The literature suggests that threats of annexation are one incentive for incorporation—although recent work suggests that annexation may not be a generalized incentive (Waldner and Smith 2015). Data for this control variable—annexations—are collected from the Boundary and Annexation Surveys of 1990 through 2010. Additionally, the baseline model controls for legal requirements for incorporation. This variable is an average of twelve requirements if applicable in the state. While places with population below the minimum are removed, one of the twelve requirements does identify a minimum population requirement for places with that requirement in the data set (Krane, Rigos, and Hill 2001).
The baseline model also controls for factors underlying formation and scale of communities (Alesina, Baqir, and Hoxby 2004; Brierly 2004; Cutler, Elmendorf, and Zeckhauser 1993). These controls include population, its square, and density in a community. Additionally, total population in the county controls for county-area scale. Two control variables are added for the fraction of population aged under eighteen years and the fraction of population aged sixty-five years and over. These variables control for demand for services by those population groups (Cutler, Elmendorf, and Zeckhauser 1993). Last, a control is added for the fraction of manufacturing employment in a community.
Method
The above data allow for the testing of hypotheses through panel data methods. To isolate effects of the independent variables, two controls are added as possible in a panel data framework. First, fixed effects control for (unobserved) time-invariant factors. Fixed effects thus hold constant long-run features unique to every place. 7 Second, time effects control for (unobserved) time-variant factors, operationalized by year-specific binary variables. Using these model specifications, effects of the independent variables can be estimated under flexible conditions (Wooldridge 2010, chap. 11).
Baseline results are presented under two alternative methods: (1) pooled probit and (2) fixed effects ordinary least squares. The first method is a maximum likelihood estimator for binary dependent variables. It estimates baseline results that can be compared to subsequent analyses. The second method will be useful to isolate change in variables through time (Woolridge 2010). 8 For statistical significance of the results, clustering observations by state will ensure standard errors robust to serial correlation and heteroscedasticity.
Findings
Main Findings
Online Supplemental Table 1 reports summary statistics and main patterns of incorporation in the states. Broadly, Supplemental Table 1 shows a decrease in the number of incorporations over the last two decades. Approximately, the number of incorporations decreases by 38 percent from the 1990–2000 decade to the 2000–2010 decade. Nevertheless, the frequency of incorporations varies among states. Northeastern states have almost no incorporations, with a few exceptions. Midwestern states have varying frequencies of incorporation. Generally, Western and Southern states account for most incorporations over the last two decades. These are the broadest interstate patterns, although Supplemental Table 1 does reveal intraregional differences. Within a region, some states have more incorporations than others.
In Table 2, model (1) estimates average effects of the independent variables from pooled probit. This first model provides a baseline for subsequent analyses. This baseline yields preliminary evidence for factors influencing the probability of incorporation: income heterogeneity, higher per capita revenue for municipalities, growth regulation, and growth in county areas. These preliminary results appear consistent with prior expectations. Next, model (2) isolates the effect of change in variables through time. This second model estimates partial effects of independent variables by controlling for fixed effects, therefore absorbing time-invariant variables (Wooldridge 2010). In both models, income heterogeneity raises the probability of incorporation, significantly at 5 percent. This finding is consistent with Hypothesis 1 predicting a higher probability of incorporation as income heterogeneity widens.
What Factors Affect the Probability of Incorporation?
Source: See Table 1.
Note: The table reports average effects and, in parentheses, robust standard errors clustered by state. Columns (1) and (2) report χ2 and F statistics for joint significance of controls. The number of observations (n) is equal to the number of places × the number of decades. OLS = ordinary least squares.
***p < .01.
**p < .05.
*p < .10.
Revenue control is one of the policy factors of interest. In model (1), average own-source revenue for municipalities raises the probability of incorporation, significantly at 1 percent. This finding is consistent with Hypothesis 2 predicting a higher probability of incorporation as revenue per capita rises. In model (2), average revenue also raises the probability of incorporation, although losing significance. These findings thus give mixed evidence toward revenue incentives for incorporation. As for control variables, special district and county revenues are not significant under either model (1) or model (2). Taken together, these findings suggest that revenue for municipalities may be a more direct incentive for incorporation than revenue for counties or special districts.
Growth in county areas and growth regulation are the second set of context factors of influence. Their average effects are reported at the bottom of Table 2. In model (1), growth in county areas raises the probability of incorporation, significantly at 1 percent. This finding is consistent with Hypothesis 3 predicting a higher probability of incorporation as growth in county areas rises. Restrictive growth regulations lower the probability of incorporations, significantly at 1 percent. This finding is consistent with Hypothesis 4 predicting a lower probability of incorporation where preexisting regulations are restrictive.
Analysis of Subsamples
This section examines whether income heterogeneity influences the probability of incorporation in contexts. Table 3 reports first effects of income heterogeneity in subsamples of own-source revenue for municipalities. Models (3) and (4) examine whether income heterogeneity raises the probability of incorporation by levels of per capita revenue. Two subsamples are defined: one for below-median revenue and another for above-median revenue per capita. The subsamples thus classify the places for analysis by levels of revenue available for municipalities.
Partial Effects of Income Heterogeneity in Subsamples.
Source: See Table 1.
Note: The table reports partial effects and, in parentheses, robust standard errors clustered by state. It follows fixed effects methods as in model (2). F statistics show the joint significance of other independent variables and control variables. Medians are calculated from the full sample of incorporated places. The total number of observations (n) is equal to the number of places × number of decades.
***p < .01.
**p < .05.
*p < .10.
In models (3) and (4), income heterogeneity raises the probability of incorporation, significantly at 10 percent. Nevertheless, the partial effect turns greater in the subsample model (4). It suggests that income heterogeneity plays a greater role, particularly where own-source revenues are greater. This partial effect thus supports the Hypothesis 2A predicting a higher probability of incorporation if a higher-income community is placed in an environment of higher per capita revenues.
Models (5) and (6) examine whether income heterogeneity raises the probability of incorporation by rates of growth in county areas. Two subsamples are defined: one for below-median rates and another for above-median rates. They classify places for analysis by rates of growth in their county areas. In both models, income heterogeneity raises the probability of incorporation. Note nonetheless that income heterogeneity turns stronger and significant at 5 percent in model (6). This stronger effect suggests that income heterogeneity plays more of a role, where growth is rising. It thus supports the Hypothesis 3A predicting a higher probability of incorporation if a higher-income community is placed in a county of rising growth. By incorporation, higher-income communities may opt for incorporation in order to insulate themselves from rising growth.
Models (7) and (8) examine whether income heterogeneity raises the probability of incorporation by restrictiveness of growth regulations. Again, two subsamples are defined: one for below-median restrictiveness and another for above-median restrictiveness. These subsamples thus classify places for analysis by regulation restrictiveness. In both models, income heterogeneity raises the probability of incorporation. Note nonetheless that income heterogeneity is stronger and significant at 5 percent under model (7). This stronger effect suggests that income heterogeneity is more of an influence if preexisting regulations are nonrestrictive. It thus supports the Hypothesis 4A raising the probability of incorporation wherever higher-income communities are placed in a nonrestrictive regulatory environment. By incorporation, higher-income communities may opt for incorporation in order to introduce more restrictive regulation.
Other Independent Variables
This penultimate section summarizes findings for other independent variables, briefly returning to Table 2. While our focus is on income and policy factors of influence, other independent variables may be of interest, as they relate to alternative explanations for incorporation. First, prior research suggests that ethnicity and race have influenced boundary changes (Alesina, Baqir, and Hoxby 2004; Burns 1994). However, models (1) and (2) indicate that ethnicity and race are not necessarily factors in recent incorporations.
These findings suggest that, while socioeconomic heterogeneity influences recent incorporations, alternative heterogeneity factors such as ethnicity and race seem no longer significant. One interpretation may be that civil rights legislation has worked; antidiscrimination laws in housing could have resulted in ethnic and racial integration in metropolitan areas. That integration also seems to work through municipal jurisdictions, as race and ethnicity do not differentiate incorporated from unincorporated places. While more ethnic and racially integrated, however, new municipalities tend to be differentiated from other places by income.
Restrictive legal requirements for incorporation lower the probability of incorporation. This result is consistent with prior expectations, although not significantly at a conventional level. In models (1) and (2), the frequency of annexations also has a positive effect. Note nonetheless that the effect of annexation falls significantly in model (2). Thus, the annexation results should be interpreted cautiously. More research seems necessary to evaluate the plausibility, if any, of annexation activity on incorporations in specific contexts. Finally, the χ2 and F tests show that other control variables are jointly significant.
Discussion and Conclusions
This research presents results from an empirical analysis of new incorporations in the United States. It also presents boundaries-normalized data for unincorporated and incorporated places based on the 2000 and 2010 Censuses. These data allow us to examine how income heterogeneity among unincorporated communities influences initiatives for incorporation.
The main findings are that income heterogeneity, own-source revenue per capita for municipalities, rising growth, and nonrestrictive regulation raise the probability of incorporation. Income heterogeneity raises the probability of incorporation in context, as the effects of income are not uniform. These effects vary differentially in subsamples of revenues, growth, and regulation. Income appears more of an influence where own-source revenues are greater, growth is rising, and regulation is nonrestrictive. Unincorporated communities are more likely to opt for incorporation under those influences.
In particular, growth and land-use regulation appear contextual factors underlying a community’s probability of incorporation. Rising growth in county areas predates growth in and around an unincorporated community. Yet that growth could create uncertainties concerning growth’s impact on the community. In that context, incorporation may be motivated to control growth policies. These hypotheses are generally consistent with the research findings. Therefore, population growth and growth regulation are distinct yet related incentives influencing incorporations over the last two decades.
There are several areas still open for future research. One area relates to how boundaries are determined before initiatives for incorporation, as that strategic determination could explain the islands of unincorporated places. Another area relates to questions of municipal scale. Municipalities differ dramatically in size: new municipalities are smaller than older municipalities in general. Causes and consequences of differences in municipal size are open questions. And an additional question relates to the policies a municipality adopts after incorporation. A before and after-incorporation analysis may be useful to identify policy adoptions by new municipalities. Work on these questions will broaden knowledge around frameworks for local reorganization, and incorporation, in the states.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: The author gratefully acknowledges research grants by the University of New Mexico, the Florida State University, and the FSU Center for Disaster Risk Policy.
Notes
References
Supplementary Material
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