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The Growth of Community Colleges in the American States: An Application of Count Models to Institutional Growth

by William R. Doyle & Alexander V. Gorbunov - 2011

Background/Context: The establishment of community colleges in the American states stands as one of the most unique features of our system of postsecondary education. Four possible explanations have been suggested for the growth of community colleges. An economic perspective argues that the development of community colleges came about as a result of increased demand. The sociological perspective argues that these institutions were developed as a result of broader social forces. The political science literature focuses on the role of lobbying and constituent demands. The organizational ecology literature suggests that community colleges fill a unique niche.

Purpose/Objective/Research Question/Focus of Study: We seek to understand why certain states created large systems of community colleges, whereas other states developed few or none of these institutions.

Research Design: We test theories from the literature using a multilevel Poisson regression model, using Bayesian methods for estimation and inference.

Data Collection and Analysis: The data for this study come from a unique data set, compiled from a variety of sources of state-level data. The data cover all 50 states for the years 1969–2002. We estimate four models: a complete-pooling model with no unit-specific controls, a no-pooling model with controls for each state, a partial pooling model that allows state effects to vary, and a partial pooling model with a state-specific time trend.

Findings/Results: Our results indicate support for the idea that community colleges grew in response to changes in state populations and that states with a large number of other types of institutions of higher education saw slower growth. Little support is found for theories regarding community colleges as engines of stratification.

Conclusions/Recommendations: This study provides support for the idea that the supply of higher education institutions is responsive to demand. Little support is found for the role of social stratification in the development of new institutions. Political forces do appear to play at least a small role in the expansion of institutions. Existing institutions may slow the growth of newer forms of postsecondary education.

The establishment of community colleges in the American states stands as one of the most unique features of our system of postsecondary education. Community colleges constitute 35% of total enrollment in American higher education, and the colleges themselves represent around 25% of all institutions (U.S. Department of Education, 2001). The size and scope of the community college system in the United States reflects both a desire for educational opportunity and a structure that can contribute to stratification.

Despite the voluminous literature on community colleges and their importance to the American system of higher education, one major question about these institutions remains unanswered: Why did certain states create large systems of community colleges, whereas other states developed few or none of these institutions?

The simplest answer is that large states created more community colleges. However, size alone does not predict the number of community colleges across states. This study attempts to determine characteristics of states, other than just population size, that are associated with the development of community colleges. The creation of community colleges within the states has been characterized as an effort to both respond to increasing levels of student demand and create more differentiation in systems of higher education (Dougherty, 1988b). To better understand how states might respond in the future to these two pressures, this study attempts to refine our understanding of what may have driven past changes in policy.


The literature review looks to past research to better understand the following question: What explanations have been offered for the differential growth rates of community colleges in the American states?

This literature review focuses on four possible explanations for the growth of community colleges. First, researchers operating from within the economic literature have argued that the development of community colleges came about as a result of increased demand for higher education, coupled with business leaders’ interests in developing more young people with specific vocational skills that did not necessarily require a bachelor’s degree. Second, many researchers from within the sociological literature have argued that these institutions were created as a result of broader social forces, although they disagree about both the nature of those forces and the function of community colleges themselves. The class-reproduction perspective argues that community college development had more to do with broader societal forces that push toward stratification and inequality in educational opportunity. In contrast, the functionalists within the sociological framework argue that community colleges were part of democratization and expansion in opportunity (Dougherty, 1988b). The third strand of research describes the development of these institutions in explicitly political terms, as the result of a series of decisions made by policy makers in response to specific demands from constituents and important lobbies. Last, the organizational ecology literature speaks to the development of community colleges within states in terms of organizational founders’ ability to fill a particular “niche,” in which they would not compete for resources with other, more established institutions.

We describe each of the four strands next, with a particular emphasis on those studies that seek to provide explanations for the development of these institutions, as opposed to characterizing their internal operation and campus environments. Each of the four approaches suggests specific, testable propositions. We develop these propositions in our conceptual framework.


The first area of research to be considered focuses on explanations of community college expansion that deal primarily with economic considerations. According to this literature, community colleges were developed as a result of both (1) labor market conditions that created increased demand for a trained workforce, which accompanied a demand for new careers and retraining, especially during economic crises (Betts & Farland, 1995; Griffith & Connor, 1994), and (2) consumer demands (Blau, McVeigh, & Land, 1994). The common view is that vocational education and training are instrumental to making a nation more competitive; thus, community colleges, among other institutions, are directed to serve the needs of capital through provision of a trained workforce (Levin, 2001). Their critical role in workforce preparation, preservice training, in-service training, and assistance for small business development is greatly influenced by purposive state policies (Dougherty, 2001). These government policies resulted from the economic concerns: They emphasized vocationalism, workforce development, commercialism, and state competitiveness, and encouraged colleges to become more efficient, productive, accountable to governments, and responsive to business needs (Levin, 2001).

Brint and Karabel (1989) attributed the community college vocationalization to the fiscal crises of the 1970s. During that time, community colleges adapted by providing more educational services to businesses. Many authors mention the demographic factor as one of the leading causes of the development of public two-year colleges. Demographic pressures have been accompanied by the growing demand for educational opportunity and additional years of schooling (Baker & Dudziak, 1994; Blau, McVeigh, & Land, 2000; Cohen & Brawer, 2003; Witt, Wattenbarger, Gollattscheck, & Suppiger, 1994) and by changing educational needs such as lifelong learning (Cain, 1999).

At the same time, some believe that recent increases in technology and changing educational needs of the nation have complicated the many-functions-at-one-address idea. Most important is the observation that in the pursuit of economic goals, community colleges altered their missions and moved away from social needs of local communities toward local market needs, abandoning their earlier goals of accessibility, personal and social development, and general education (Levin, 2001).

The economic perspective on the development of community colleges traces their founding back to a set of specific demands from the workplace. These institutions arose in the marketplace to meet that demand, ensuring that citizens within each state would have access to the appropriate level of training required for the changing economies of states. In contrast, social theorists see these institutions as being part of broader social forces, and not necessarily as an outgrowth of particular market needs.


The next strand of literature describes the development of community colleges as reflecting broader societal forces rather than the specific supply-demand relationships posited by economists. Within this literature, there are two opposite ideological camps. The first is the class-reproduction school, which, according to Dougherty (1988b), theorizes that “the community college’s fundamental social role is to reproduce the class structure of capitalist society” (p. 354). A second group, described by Dougherty (1988b) as “functionalists,” extols the role of community colleges as democratizers of access (Pedersen, 1997; Witt et al., 1994).

The elitists of the barrier function promote one overarching idea: Two-year institutions have developed as a means of social stratification and served to keep the undesirable students out of “genuine” institutions of higher learning. Performing their “cooling out” function (Clark, 1960), community colleges help softly deny students of certain backgrounds and aspirations the entry into the higher levels of educational hierarchy. Although Clark’s perspective has been widely repeated, few studies have attempted to empirically verify his claims.

Taking Clark’s (1960) argument one step further, the proponents of the social stratification idea posit that community colleges perform a contradictory task: They simultaneously provide educational opportunities and serve to divert numerous students from four-year institutions (Brint & Karabel, 1989). They believe that community college vocationalization poses a grave threat to equal educational opportunity and the dream of social upward mobility (Pincus, 1980). Meeting the need to select and sort students destined to occupy different positions in the job structure of a capitalist economy, junior colleges were designed as a buffer between a high school and a four-year college and were never meant to provide more than a terminal education for the majority of its students. In many states, they were originally conceived as inferior institutions aimed at removing students from universities and providing for exclusion (Cain, 1999; Griffith & Connor, 1994). Brint and Karabel posited that it was the junior college expansion of the 1950s and 1960s that made it possible for state higher education institutions to become more exclusive.

On the other hand, the populists—the proponents of the educational opportunity idea—believe that the major role of public two-year institutions is providing broader access to, and less expensive entry into, higher education (Pedersen, 1997; Witt et al., 1994). They claim that the primary purpose of community colleges is greater inclusion and trace back the ongoing expansion of community colleges to the junior college movement, which arose out of local citizens’ demands (Griffith & Connor, 1994). In the study of the educational opportunity context, Keith (1996) sought to explain the legitimation of a contextual framework within which community college students get opportunities to transfer to four-year institutions. He found that the transfer opportunities depend on the way the community colleges are integrated into a state’s higher education system.

Social theorists generally agree that community colleges expanded as part of broader trends at play in society, while disagreeing about what those trends might be and how they specifically resulted in the kinds of institutions we see today. The political theorists described next emphasize the role of individuals’ self-interest and desire to maximize influence and power, particularly within a government setting.


The growth of community colleges in the states can also be viewed as a result of policy makers and citizens acting in their own interests to create these institutions. Political theorists share a common perspective in suggesting that the motivation of many involved had less to do with specific educational or demographic pressures, and more to do with specific groups seeking to maximize their influence and power through government actions. Although they may differ on which groups were most important and how these groups interacted to create institutions, they share a common interest in development of political power through the creation of these new institutions of higher education.

Cohen and Brawer (2003) noted that the overarching reason for community college growth could have been simply an increasing number of demands placed on schools at every level. Many believe that the original proponents of the idea were research universities seeking to move away from the liberal arts education and toward more research specialization. These lobbying efforts were supported by local citizens motivated by a number of desires (Dougherty, 1988b; Griffith & Connor, 1994). There is evidence that in comparison with the federal government and local authorities, state governments have been the most influential constraining force for community colleges: They accredited colleges, supplied most of their resources, prevented them from becoming four-year institutions, and pressured them to expand vocational training (Brint & Karabel, 1989; Cohen, 2001). In the face of these views, Pedersen (1997, 2005) posited that junior colleges were often their communities’ last choice (after failing to secure traditional institutions or trying to sustain the community status after a private college closure), and not the outcome of a university lobbying, advocacy of a school superintendent, or willingness to help a remote university cope with the influx of students.

Political theorists suggest that these institutions were not the result of specific labor market demands, nor part of broader social forces, but instead the result of efforts by individuals to use public policy for their own ends. The last perspective, described next, suggests that these institutions may have been developed to fill a particular organizational niche that existing institutions could not or would not fill.


Organizational ecology as a theoretical approach seeks to understand the development of organizations from within a given context. In particular, organizational ecologists focus on the ways in which overall organizational competition for resources and functional differentiation (and subsequent organizational legitimation) affect both the birth and death of specific organizations. This perspective may have much to offer in terms of understanding the differential growth rate of community colleges in the states, yet few studies have used this perspective to understand why and under what circumstances community college systems might be expanded.1

Competition for scarce resources in the organizational ecology literature is tied to the concept of density, the number of organizational units at any one time within a defined type of organizational population (Baum & Oliver, 1996). Density has implications for two key aspects of organizational founding: legitimation and competition. As Hannan, Carroll, Dundon, and Torres (1995) stated,

According to the theory, as density increases, legitimation increases at a decreasing rate and competition increases at an increasing rate. Thus, growth in density from zero mainly legitimates an organizational form, but continued growth eventually generates enough competition to overwhelm the effect of legitimation. (p. 510)

Thus, the creation of organizations is generally thought to follow a U-shaped curve, increasing from very few, but then decreasing after competition becomes too fierce.

Another key insight from this field is the concept of resource differentiation. This concept explains the coexistence of related organizations without competition. Under circumstances of differentiation, organizations specialize in some specific aspect of their expertise rather than competing broadly with one another. As organizations differentiate, they can develop into either complementary or symbiotic partners in a resource area (Barnett & Carroll, 1987; Baum & Oliver, 1996).

Much of the literature in organizational ecology has to do with for-profit firms, which will, of course, face different pressures for survival and different modes of competition than nonprofit entities such as community colleges. However, as Baum and Oliver (1996) argued in their study of the founding of childcare centers, nonprofit organizations face more challenges in the area of legitimacy; without strong social legitimacy, these organizations are much less likely to form and persist, but with social legitimacy, these organizations are formidable competitors.

The literature has several implications for the growth of community colleges. First, the concept of density is key—the size and scope of the system of higher education in states should play a role in the number of community colleges formed in any year. Second, the competing pressures of legitimacy and competition will also most likely play a role. This implies that other similar organizations and previously existing community colleges may play a role in the expansion of the system, to include additional institutions. Last, the high levels of social legitimacy that this literature says are required for nonprofit firms may serve to dampen competition between them and instead encourage some form of complementary relationship.

The preceding review deals primarily with studies that explore reasons for the expansion of community colleges. However, recent research has explored four vital areas that may affect the development of these institutions in the near future. These include the transfer function; community colleges offering bachelor’s degrees; state policy and public two-year institutions; and the role of community colleges in educating recent immigrants.

First, the transfer function has become increasingly important as increased demand for more highly educated students, combined with increasing costs, has driven more students who seek a bachelor’s degree to attend a community college. Several studies have found that beginning at a community college decreases a student’s probability of completing a bachelor’s degree (Bowen, Chingos, & McPherson, 2009; Doyle, 2009; Long & Kurlaender, 2009; Reynolds, 2009; Rouse, 1995). However, an important exception is Melguizo (2009), who found little institutional effect of community colleges on the probability of Hispanic students receiving a bachelor’s degree. One compelling result from this literature is that students who successfully transfer from a community college to a four-year institution appear to have nearly the same probability of degree completion as so-called native students (Bowen et al.; Melguizo & Dowd, 2009). The continued use of community colleges as the first step toward a bachelor’s degree indicates the need for both more research and evolving policy responses to help students navigate this transition (Kolesnikova, 2009).

Second, many community colleges have undergone a change in mission, transitioning to offer bachelor’s degrees (Floyd, Skolnik, & Walker, 2005; Floyd & Walker, 2009). Not everyone supports this change (Eaton, 2005). Studies have found that this change is likely to bring new students, faculty, and organizational forms to community colleges (Levin, 2004; Skolnik, 2009). At least 11 states have now authorized some form of bachelor’s degree to be awarded at some community colleges, with many others implementing or pondering similar programs (Floyd & Walker). The effect of these new programs on the success rates of students is not yet well understood.

Many authors have continued to study the role that state policy plays in community colleges. Of particular importance to these studies are the design of community college governance and the structure of articulation agreements. Wellman (2002) argued that both structural and academic policies at the state level can affect transfer rates and success of transfer students at four-year institutions. Anderson, Sun, and Alfonso (2006) found no effect of state articulation agreements on the success rates of transfer students from community colleges. Roksa and Keith (2008) found a similar result. Roksa, in a later article, argued that the state of data and knowledge in this area is too weak to support any policy recommendations at this time (Roksa, 2009). In a case study of California, Shulock and Moore (2007) described how state policies can hinder students’ degree completion.

Last, community colleges play an important role in access for immigrant students. Using data from City University of New York community colleges, Bailey and Weininger (2002) found that foreign-born students who attended U.S. high schools were more likely than their native-born peers to attend a bachelor’s degree program, whereas foreign-born students who did not attend U.S. high schools were no more likely than their peers to attend a bachelor’s degree program. Similar results hold for associate’s degree completion and credit attainment, but not for bachelor’s degree completion (Bailey & Weininger). Conway (2009), writing about a large urban community college system, found that “while immigrants may need additional help at the onset of their higher education journey, they quickly become acclimated and succeed at rates outpacing those of native students as measured by credits attained and grade point average” (p. 341).

This recent research has important implications, particularly thinking about the development of community colleges in the future. States with effective transfer policies may see growth in community college enrollment and more demand for community college campuses. Similarly, an increased role for community colleges in the awarding of bachelor’s degrees may also increase the number of these institutions. States that face an influx of new immigrants may choose to use community colleges as a point of initial postsecondary attendance. As these studies show, each of these trends may have implications for the growth rate of community colleges in the 21st century.


Two studies on the expansion of community colleges merit special attention, both of which were cited earlier because of their overlapping approaches to the study of community college development. Dougherty (1988a, 1988b, 1994) found that business and student demand was too weak to account for the extent of community college expansion and the degree of responsiveness to their interests. His explanation is that community colleges as a policy response emerged out of a combination of demands from the interest groups and relatively autonomous actions of government officials. The officials could act on their own in pursuit of their self-interests; however, their actions favored student and business interests. Thus, the main initiative of establishing community colleges lay with the government officials. Although limiting his analysis to only five states and relying exclusively on interviews and document examination, Dougherty concluded that the community college expansion is generally characterized by homogeneity across the states.

Blau et al. (1994, 2000) conducted a quantitative study of community college foundings in 28 states in the period of their most rapid growth (1942–1979) to test several hypotheses accounting for their establishment. Using a dynamic, multilevel model, the researchers concluded that the community college expansion was not directly related to demand; a diverse economy is important in their expansion, with the large manufacturing sectors and diverse industrial sectors translating their need for trained labor into educational policies; manufacturing elites play an important role if there is a surplus of high school graduates; and the states with Democratic traditions tend to have higher community college founding rates than states with Republican traditions.

The Blau study, although an important contribution, has several shortcomings. First, its panel is limited to four 10-year segments, with just 28 states included as part of the sample. Second, although their model does incorporate a multilevel approach, the authors did not employ the types of estimation techniques that allowed them to simultaneously estimate first- and second-level parameters. Last, their model does not incorporate state-specific time effects, which, as will be shown later, constitute an important facet of the differential development of community colleges across states.


Our conceptual framework develops on the literature reviewed to posit hypotheses directly related to each of the theoretical areas outlined previously. We first hypothesize a simple supply and demand relationship, which would suggest that overall population size, as well as the size of the young population, should account for the expansion of community colleges. We then explore the social stratification literature, which would suggest that in states where pressures for social stratification are large, we would expect to see the creation of more community colleges. The political explanation for the expansion of community colleges would suggest that states with a more liberal ideology would be more likely to create more community colleges because these institutions are the types of solutions that favor equity over an approach that emphasizes funding for more inegalitarian institutions. Organizational ecology would suggest that the existence of resources and the presence of other competitors in an organizational niche may influence the rate of founding of new institutions.

Following (Dougherty, 1988b) we look first to the most straightforward explanation of the expansion of community colleges: States with high levels of population growth expanded their community college systems to provide educational opportunities for more of their citizens. This includes the young population—it is reasonable to expect that states with a higher proportion of young people would have been more likely to build more community colleges.

For the purposes of this study, we examine the extent to which growth in the young population is related to the establishment of community colleges in the states. Although other populations might also be part of the demand for more community colleges, we posit that this group should be particularly important for state policy makers and organizational leaders in their thinking about creating more community colleges.

In general, the social stratification theory would suggest that states with more inequality between classes would have more of a stake in reproducing the existing order. This could also extend to states with more heterogeneous populations, in which those populations that traditionally held advantages sought to maintain their status at the expense of other populations.

Based on this conception of the social stratification literature, we posit two possible relationships based on the stratification theory. First, as more of a state’s young people are non-White, this theory would suggest that the state would be more likely to expand its community college system. This follows from the preceding theory, in that privileged groups in the state would attempt to divert young people from other racial or ethnic groups from four-year colleges by creating a system of community colleges. In addition, according to many stratification theorists, one of the important roles of community college is to separate students by race or ethnicity (Brint & Karabel, 1989).

We expect to see more community colleges in states with higher levels of class inequality, as the class-reproduction literature suggests. States with higher levels of income inequality should therefore be more likely to put community college systems in place as the better off reserve the four-year institutions for themselves.

We then turn to political explanations. In general, the expansion of community colleges has been tied to the general policy liberalism that was prevalent in the early 1970s (Klingman & Lammers, 1984). Like other state and federal projects, community colleges represented a large government effort to ensure equality of opportunity among all citizens (Dougherty, 1988b). It is possible, too, that conservative politicians may have favored the development of community colleges, given that they typically are a more efficient alternative to their four-year counterparts (Kane & Rouse, 1999).

The organizational ecology literature also has several important implications for this study. The first is the recognition of the influence of existing institutions on the creation of new institutions. It seems likely that existing institutions might view the development of a community college system as a threat (Dougherty, 1988b, 1994). Again, following Dougherty, we hypothesize that states with a more competitive postsecondary environment will be less likely to develop a community college system because existing institutions will lobby the legislature to slow or even stop the development of community colleges. Thus, four-year institutions in these states would seek to deprive these institutions of the legitimacy that they would need to be founded in larger numbers (Baum & Oliver, 1996).

We also hypothesize that population density will be an important predictor of the number of community colleges in any year. Many states have an explicit policy that all citizens should be within a certain range of a community college. If this policy were followed explicitly, we should find that states with lower population densities created more community colleges, after controlling for population levels. This also follows from the organizational ecology literature, which would suggest that in areas where organizations are less likely to face heavy competition, foundings are more likely.

The organizational ecology literature also emphasizes the importance of the wealth of a given environment for organizational founding. In any state, available resources will serve as a constraint on the types of actions that can be undertaken. We include income in the state as an important predictor of this state policy because legislators cannot develop any policy if they cannot draw from a resource base to fund it.


This article introduces a new data set compiled from a variety of sources that describes a long period of development of community colleges in the states. Second, this section describes the models to be estimated and the Bayesian framework for estimation and inference used for this study. Both the data and the methods employed are uniquely well suited to test the hypotheses developed in the conceptual framework.


The data for this study come from a unique data set compiled from a variety of sources of state-level data. The data cover all 50 states for the years 1969–2002.2 During this time period, the total number of community colleges increased from 633 to 1,101—nearly 42% of all community colleges were opened in the years covered in this study. Data were drawn from a variety of sources. Descriptive statistics for the variables in this analysis can be found

in Table 1.

Table 1. Descriptive Statistics for Variables in Analysis















Community Colleges









Percent of Population 20–24 years old









Percent of Young Population Non-White









Income Inequality









Legislative Ideology









ln (Population Density)









Per Capita Income (1000s)









Public 4 Years









Private 4 Years









The dependent variable in this study is the number of community colleges in each state in each year. This variable was collected from the Office of Education and Welfare, and later, the Department of Education statistical compendia, which eventually became the Digest of Education Statistics (U.S. Department of Education, 2001). The same source is also used for the number of public and private four-year institutions in each state.

Population data by age and race are available from the National Cancer Institute Surveillance, Epidemiology and End Results program (National Cancer Institute, 2007). Young population is defined as the proportion of state population aged 20–24 years, and the proportion of the young population that is non-White is defined as all non-White individuals as a proportion of the same (20–24) age bracket.

Income inequality comes from the University of Texas’ income inequality project (Galbraith & Hale, 2006). The researchers compute this measure by predicting the Gini coefficient for family income inequality from state-level between-industry wage inequality data from the Bureau of Economic Analysis. Between-industry pay inequality is computed using Theil’s t statistic. The resulting measure correlates highly with more infrequently observed inequality data from the Census Bureau.

Legislative ideology as a concept captures to what extent elected officials in the state are more or less liberal. This measure, as developed by Berry, is based on the voting record of the congressional delegation from the state (Berry, Ringquist, Fording, & Hanson, 1998). The ideological positions of state legislators and governors from each party are assumed to be the same as those of the members from their party who are sent to the U.S. Congress. Higher levels on the ideology index indicate a more liberal outlook among those elected to state government.

Income and population data are drawn from the Bureau of Economic Analysis, and state land area (the denominator for population density) is available from the Census Bureau.


The dependent variable in this study takes on a limited number of positive integer values, suggesting that the Poisson distribution best describes the underlying data-generating process. The probability density function for the Poisson model in a panel data setting is:


where yst is the number of community colleges in state s at time t. The variable λ is also known as the “rate.” The Poisson distribution has only the single parameter λ, which is equal to both the expectation (E(yist)) and the variance. Failure to meet this assumption is known as overdispersion. Overdispersion can be handled either through specifying another distributional link, such as the negative binomial, or by modeling. We take the latter approach in this article.

The likelihood function for a Poisson regression model is as follows:


where λ = Xβ.

The plan of analysis for this article is to estimate the underlying model using four different functional forms: a complete-pooling model, which assumes a common intercept for all states; a no-pooling model, which estimates a separate intercept for all 50 states; a partial pooling model, which pools information to estimate separate intercepts; and a partial pooling model with a state-specific time effect.3 Each model is evaluated relative to its overall fit with the data. As we will show, the partial pooling model with a state-specific time effect provides the best fit to the data.

This study uses a Bayesian framework for inference and estimation. The critical difference between this approach and a standard (frequentist) approach is that Bayesian statistics assume that the population parameter under study is a random variable with some distribution, rather than a fixed point. Although a full description of the Bayesian approach is beyond the scope of this article, the results are based on posterior density estimates of population parameters, which are proportional to a prior (a distribution for the parameters to be estimated postulated by the analyst) times a likelihood. Estimates are therefore described in distributional terms, not as point estimates with standard errors. Most of the inferences are drawn using the median, central 50%, and central 95% of posterior distributions, which refer to quantiles drawn from the thinned Markov chains for the parameter or prediction of interest. Given the state of knowledge in this area, noninformative priors are provided for all parameters of interest.

We use a Bayesian framework for estimation and inference for two reasons. First, we are concerned with estimating models and coefficients for population as opposed to sample data. As Gill (2001) explained, “in population models variance exists around coefficients, but this variance is not an indication of estimator reliability. Rather it measures the variability of the observed effect size (ß) subject to model misspecification” (p. 340). Given that the coefficients to be reported cannot be subjected to traditional null hypothesis significance testing, reporting their point estimates and standard errors does not provide enough information to judge the reliability of the estimates. Instead, taking the Bayesian perspective that the population parameters are themselves random variables and providing a series of probability statements regarding posterior estimates of these population parameters provide more useful information than a frequentist approach.

A second motivation for our use of Bayesian methods comes from Gelman and Hill (2006), who stated that the Bayesian approach for estimation and inference “averages over the uncertainty in all the parameters in the model” (p. 345). This is particularly helpful, they noted, when the number of groups in a multilevel model is relatively small, and the model itself is relatively complex. Given some of the state-specific effects we wish to estimate, we believe that this approach will provide better estimates of the population characteristics under study.

We provide two ways to interpret the results from our models. First, we discuss the estimates of the parameters in the Poisson regression. With each, we provide a 95% credible interval and a median of the posterior distribution. These are very similar to the 95% confidence interval and point estimate provided by standard (frequentist) methods of estimation. The interpretation of a 95% credible interval is that there is a 95% probability that the parameter lies within the range reported.

Our second means of interpretation is based on prediction. We use the results from each model to predict the number of community colleges in each state in each year, again with a credible interval around each prediction. Model fit is evaluated by the extent to which our intervals include the true number of community colleges in a given state.


The complete-pooling model is the simplest of the models to be considered. Given the likelihood described in the preceding section, the rate λ is estimated via:


with the following prior distributions:

α ~ N(0,100)

βk ~ N(0,10)

This model in effect assumes that most states’ processes for creating community colleges can be reliably estimated using a common intercept, along with covariates.


The no-pooling model involves estimating a separate intercept αs for all 50 states, with a noninformative prior distribution. This is equivalent to a fixed effects model in econometrics (Greene, 2003).

In the no-pooling (fixed effects) model, the rate λ is estimated using the following specification:


with the following prior distributions

αi ~ N(0,10,000)

βk ~ N(0,10)

As with fixed effects, the no-pooling model does not allow for cross-state inferences because all the unit-specific heterogeneity has been absorbed by the αs term. This type of specification is common in studies in which states are the unit of analysis because a correlation between state characteristics and predictors can bias results (Greene, 2003; Hausman, 1978).


The no-pooling model, although providing a reasonable fit to the data, has several drawbacks. First, it is inefficient, which is not a major concern for this study. Second, it prevents the analyst from making any cross-unit inferences, because any systematic differences are absorbed by the state-specific intercepts. Another approach is to estimate a partially pooled model, so called because cross-state differences are partially pooled when estimating separate intercepts for each state. This is commonly referred to as a “random effects” model in the econometric literature (Greene, 2003; Hausman, Hall, & Griliches, 1984). This model has been shown to have superior efficiency in the estimation of models for count data (Hausman et al.).

To overcome the problem of group effects that correlate with predictors, we use the within-state means of all predictors to estimate the partially pooled intercept terms. This is the approach described by Bafumi and Gelman (2006). This approach removes the state-level heterogeneity that is of concern by controlling for different within-state means in the pooled estimation of state-specific parameters.

The partial pooling model, like the no-pooling model, estimates a separate intercept for each state. However, unlike the no-pooling model, which has an uninformative prior, the partial pooling model estimates the intercept from the data itself, with a noninformative prior on the precision of this estimate:


The pooled intercept term αi is modeled using state means of all covariates [39_16174.htm_g/00012.jpg]


with the following prior distributions

βk ~ N(0,10)

bk ~ N(0,10)

σ ~ U(0, 5)

The partial pooling model provides a compromise between the two extremes of the complete-pooling and no-pooling models. Whereas the complete-pooling model ignores important unit-specific heterogeneity in the data, the no-pooling model absorbs all the heterogeneity in an intercept term, preventing the analyst from making any cross-state inferences. Traditionally, no-pooling models are used when group effects and predictors are correlated (Bafumi & Gelman, 2006). Controlling for within-state means obviates the need for the no-pooling model, which allows for cross-state inferences.

The emphasis in this study is on the characteristics of states that made them more likely than other states to develop a system of community colleges. This implies that we must use cross-state, as opposed to within-state, inferences, which are only possible in the partial pooling models. For this reason, we employ this model—to make cross-state inferences possible.


The partial pooling with time effects model is identical to the partial pooling model, with an additional state-specific coefficient—γ— for time:


The state-specific time coefficient has a noninformative prior of the form:

γs ~ N(0,100)

The effect of adding a state-specific covariate on time is to allow each state to have its own linear growth pattern, which can be modeled directly from the data.4 The interpretation of β changes to allow for the impact of covariates beyond what would have been expected under normal state-level conditions for growth.

With these four models described, we turn to our estimation techniques.


All the above models were estimated using Markov chain Monte Carlo techniques, specifically a Gibbs sampler. For each model, three separate chains of 100,000 iterations each were run. The first 50,000 iterations were discarded, and the second 50,000 were thinned by 50, resulting in 1,000 draws from the estimated posterior density from each chain for each unknown parameter. Missing data were multiply imputed using the same techniques.5


Results are presented for each of the models. We investigate first whether the functional form for the model adequately fits the data, then proceed to inferences regarding the predicted impact of specific covariates.


The pooled model estimates a standard Poisson regression, with a common intercept term for all states. This model provides a poor fit to the data. Figure 1 shows the 95% central interval for the value exp( lst )for all states in all years, plotted against the actual number of institutions in that year. As the figure shows, the model is relatively good at predicting values for states in the middle of the distribution but provides a poor fit for very large states such as California or very small states such as Delaware. For the pooled model, the 50% interval includes the true value of community colleges 17% of the time, and the 95% interval includes the true value of community colleges 19% of the time. This indicates relatively poor model fit for the complete-pooling model.

Figure 1. Posterior predictive densities and actual values for all states, pooled model


Note. Points indicate actual values, and lines indicate 50% and 95% posterior prediction intervals.


The no-pooling model, as described earlier, estimates a separate intercept for every state, thus removing any time-constant differences among states from the model. Interpretation of this model is thus limited to within-state changes. For the no-pooling model, the actual number of community colleges lies within the central 50% prediction interval 30% of the time, and the 95% central prediction interval captures the true value 54% of the time. Although an improvement on the pooled model, this method of estimation also does not fit the data well.


The partial pooling model, as described before, estimates separate intercepts for each state but generates these estimates from a common distribution. Following Gelman and Hill (2006), the pooling model understates the variation across units, whereas the no-pooling model may overstate the variation. The partial pooling model represents a compromise between these two extremes.

The partial pooling model does not fit the data any better than the no-pooling model. In this model, the true value of the number of community colleges lies within the 50% interval 29% of the time, and within the 95% interval 54% of the time.


The last model to be considered is essentially the same as the partial pooling model, with the important addition of a state-specific time effect. This allows the estimates in each state to be conditional on an underlying time trend that is estimated separately for each state.

The partial pooling model with time effects provides a superior fit to the data. The true value lies within the 50% predictive interval 43% of the time. The 95% interval contains the true value 74% of the time. This model works better in capturing the data than the other models considered in this article.6 Figure 2 shows that this model is better at handling the kind of shocks that occurred in specific states, such as Louisiana, Georgia, and Minnesota, particularly late in the sample.

Figure 2. Posterior predictive densities and actual values for all states, partial pooling with time effects model


Notes. Points indicate actual values, lines indicate 50% and 95% posterior prediction intervals.

Because it provides a better fit to the data, we will consider the results of the partial pooling with time effects model in the most detail. As mentioned, posterior density estimates for the coefficients in the form of medians and 50% and 95% intervals are plotted for all models in Figure 3.

Figure 3. Posterior estimates of coefficients, all models


Note. Points represent medians, black lines indicate central 50%, gray lines indicate central 95% of posterior estimate.

We next turn to tests of each of the specific theoretical areas described in the conceptual framework. For each of the theories, we use the information from the modeling process to describe the level of support we observe in the data for the underlying concept.


In the conceptual framework, we hypothesize that demand in the form of a large young population would be a key antecedent of the development of community colleges. As Figure 3 shows, the posterior density of the coefficient for the young population in the state is positive in all model specifications. As the proportion of the population aged 20–24 years increased, in most states, so did the number of community colleges. Figure 4 shows the predicted impact of an increase in the proportion of the population aged 20–24 for four states in 1984.

Figure 4. Predicted impact of young population on number of community colleges


Note. Black lines indicate lower value of young population, gray lines indicate upper. 50th and 75th percentile are represented by dotted lines, medians by solid lines.

This figure plots the probability of any number of community colleges from 0 to 100 for each of four states: Illinois, New York, North Carolina, and Washington.7 In each state, the 25th, 50th, and 75th percentile of the posterior predictive density is plotted for each of the two hypothetical scenarios: Under the lower value, the proportion of young people is set to one standard deviation below the mean, whereas under the higher value, the proportion of young people is set to one standard deviation above the mean. The predicted probability of a given number of colleges m is calculated for every m from 0 to 100 via the following:


where μ = exp( lst ). This prediction is carried out for values of lst at each of the prespecified quartiles of the posterior predictive density. The predictions generated show the likely number of colleges in each state, based on plausible changes in the proportion of the population that is young.

The first panel in Figure 4 shows the impact of increasing the proportion of young people on the expected number of community colleges in Illinois. As the figure shows, the median number of colleges centers on 40 in the lower hypothetical scenario and on 60 in the higher hypothetical scenario. Although the size of the difference varies, the impact of an increasing proportion of young people can be observed in the remaining states as well. This finding provides support for the demand-side hypotheses from the conceptual framework. In short, we find that states that had rapid increases in their young population were the most likely to see rapid increases in the number of community colleges. This result conforms with much of the economic literature on this topic.


Our next set of hypotheses concerned the class-reproduction theories. We expected to see that in states with more heterogeneous young populations, more community colleges would be created. We found mixed evidence on this question. In the no-pooling and partial pooling models, the coefficient for this variable is positive. However, once we include a parameter for time, the 95% central density estimate for this coefficient overlaps with 0. We therefore have a substantial amount of uncertainty about this result.

As Figure 3 shows, the predicted impact of the proportion of the young population that is non-White is positive for most draws from the posterior distribution, but is highly variable. Figure 5 plots the impact of an increase in the size of the proportion of the population that is non-White on the predicted number of community colleges. As the figure shows, in all three states, the impact is substantively large, but highly uncertain. The dotted gray lines indicate the 25th and 75th percentiles of the posterior predictive distribution for lst . In each state, these range considerably from the median estimate. This suggests that although the impact of this variable has been estimated with some uncertainty, its practical effect may be quite large.

Figure 5.  Predicted impact of non-White young population on number of community colleges


Note. Black lines indicate lower value of non-White young population, gray lines indicate upper. 50th and 75th percentile are represented by dotted lines, medians by solid lines.

We did not find support for the class-reproduction theory in terms of income inequality in the states. In the no-pooling, partial-pooling, and time effects models, the central density estimates for the coefficient on the non-White young population are all centered on 0. We have no evidence from this analysis that income inequality is associated with the growth of community colleges during this time period.


Political characteristics were also hypothesized to have an impact on the growth of community colleges. In our conceptual framework, we posit that states with more liberal policy makers should be more likely to create more community colleges. Figure 3 shows that liberal ideology has a small but negative relationship with the number of community colleges across states. Figure 6 shows the predicted impact of an increase in liberal ideology, using the same method as that used previously. As the figure shows, even fairly large changes in ideology (from one standard deviation above the mean to one standard deviation below) have a very small impact on the predicted number of community colleges.

Figure 6. Predicted impact of liberal ideology on number of community colleges


Note. Black lines indicate lower value of non-White young population, gray lines indicate upper. 50th and 75th percentile are represented by dotted lines, medians by solid lines.


The organizational ecology theories considered in our conceptual framework lead us to suspect that competition from other institutions would slow the growth of community colleges, as would higher levels of population density. State resources should increase the rate of development of community colleges. In the no-pooling and partial pooling model, the coefficient for the number of both private and public institutions is estimated to be negative. However, in the final specification, the central posterior density estimates for the private institutions center on 0, whereas the 50% central density estimate for public institutions is negative. Figure 7 shows the impact of increasing the number of four-year public institutions on the growth of community colleges in the states. This finding provides partial support for the idea that competition from public institutions may have slowed the growth of community colleges within states.

Figure 7. Predicted impact of public four-year institutions on number of community colleges


Note. Black lines indicate lower value of non-White young population, gray lines indicate upper. 50th and 75th percentile are represented by dotted lines, medians by solid lines.


This article has attempted to model in a comprehensive fashion the development of community colleges in the American states. We approach this conclusion by noting at the outset that the enormous complexity of such a phenomenon cannot be entirely modeled through the use of only a handful of variables. Yet we also find that using a relatively parsimonious but flexible model, we can accurately predict the range of community colleges most likely to be found within states.

We find, first of all, that community colleges did indeed appear to grow as a direct result of the rapidly increasing proportion of young people in states that occurred, particularly during the early part of the years in our study. This provides support for the economic perspective examined in the literature review, which suggested that these institutions grew in response to increased demand for their services. Second, we find some evidence that the proportion of the population that is non-White is also associated with the growth of these institutions, but we cannot accurately estimate the substantive importance of this effect. We find a statistically significant and negative, but substantively small, relationship between the level of state liberalism and growth in community colleges. Last, we also find evidence that states with more four-year colleges may have seen slower growth in their community college systems.

We derive several implications for the community college literature. First, there is evidence that states were being directly responsive to their own needs, and less evidence that factors like income inequality or political factors such as ideology impacted the expansion of these institutions. However, politics surely could be at work in the lobbying efforts of public four-year colleges. Much of the qualitative literature points to resistance from these institutions in many states to the formation of community colleges (Dougherty, 1988b). The result of this could have been the slower growth of community colleges in states where four-year institutions predominated, as observed in the results.

Based on these specific findings, we suggest the following possible implications for the theoretical frameworks described in our literature review. The results as stated do provide some evidence for the propositions derived from the economic framework, but clearly more work needs to be done in this area. In particular, the specific demands of local labor markets have not been tied to the development of these institutions. Future research could examine the link between increased labor market demands for students with some postsecondary education and the development of community colleges.

The evidence from this study does not provide conclusive proof regarding the social theories we examined earlier. We did not find any evidence that income inequality can be tied to the development of these institutions. The findings on racial heterogeneity are not as clear and suggest the need for a finer grained approach to understanding how forces for social stratification may have played a role in the development of these institutions.

Future research in this area could contribute to this discussion by going beyond our work to analyze the factors that lead to stratification by income across sectors of higher education in the states. National data suggest that low-income students are more likely to enroll in community colleges, whereas their higher income peers are more likely to enroll in four-year institutions (Dowd & Melguizo, 2008; Horn & Nevill, 2006). Exploring the degree to which this pattern differs across states could give important clues about how postsecondary structures lead to different enrollment patterns.

Our evidence regarding the role of political ideology goes against what we expected, based on extant theories. Theories regarding the politics of higher education are still very much under development (McLendon, 2003). It appears that the role of political liberalism did extend to the development of these institutions, although the substantive impact of this variable is quite small in our analysis.

Last, our findings regarding the impact of four-year institutions on the development of community colleges are intriguing. One of the key questions to be answered about more recent developments in higher education is the role of existing institutions in the development of for-profit institutions of higher education. Given the role of existing institutions during the time period in this study, we would expect to see that in many states, these organizations would seek to limit the development of alternatives, particularly those that would impact existing organizational niches.

As we move into the 21st century, new organizational forms for higher education that are as yet unknown but that may be as large in impact as the community college could develop. This study, by providing some insight into the development of one of the largest higher education policy innovations in the 20th century, also suggests conditions that may favor the development of the next new thing in higher education.


1. Again, Blau et al. (2000) is a notable exception.

2. The first year of this study was chosen because it was the first year that reliable data across the dependent variable and all the covariates were available. The last year was chosen because more current information was not available at the time of writing.

3. We follow Gelman and Hill (2006) in running models in this order.

4. This follows the approach suggested by Singer and Willett (2003) in their work on longitudinal data analysis.

5. Data for this analysis, along with Bugs code for all models and R code for all pre- and postestimation transformations of data and results, can be obtained from the authors on request.

6. Posterior estimates of the deviance information criterion (DIC) were also calculated from all samples. The DIC for the partial pooling with time effects has the lowest DIC, indicating the best out-of-sample predictive accuracy of all the models (Gelman, Carlin, Stern, & Rubin, 2004).

7. These states were chosen because, as Figure 1 shows, they have either much higher or much lower numbers of community colleges than one might expect from a simpler model.


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Cite This Article as: Teachers College Record Volume 113 Number 8, 2011, p. 1794-1826
https://www.tcrecord.org ID Number: 16174, Date Accessed: 1/25/2022 3:57:04 AM

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About the Author
  • William Doyle
    Vanderbilt University
    E-mail Author
    WILLIAM R. DOYLE is an assistant professor of higher education in the department of Leadership, Policy and Organizations at Peabody College of Vanderbilt University. His research concerns the antecedents and outcomes of higher education policy at the federal and state levels. Prior to joining the faculty at Vanderbilt, he was Senior Policy Analyst at the National Center for Public Policy and Higher Education. His recent publications include a study of academic intensity on transfer rates to be published in Economics of Education Review, and a study of institutional aid to be published in Research in Higher Education.
  • Alexander Gorbunov
    Vanderbilt University
    ALEXANDER V. GORBUNOV is a doctoral student at the department of Leadership, Policy, and Organizations at Vanderbilt University and a research assistant at Tennessee Higher Education Commission. Mr. Gorbunov holds a Candidate of Pedagogical Sciences postgraduate degree from Buryat State University, Russia, and M.Ed. in Higher Education Administration from Vanderbilt University. His research interests lie in the area of antecedents and impacts of state higher education policies. His publications include: 1) Hearn, J.C., & Gorbunov, A.V. (2005). Funding the core: Understanding the financial contexts of academic departments in the humanities. Tracking changes in the humanities (pp. 1-45). Cambridge, MA: American Academy of Arts and Sciences; 2) Gorbunov, A.V. (2002). A course on decision making in the classical university. Vestnyk Buryatskogo Universiteta, 8(8), 84–93. Ulan-Ude, Russia: Buryat State University; and 3) Gorbunov, A.V. (2000). Written English: Textbook for students of departments of foreign language. Ulan-Ude, Russia: Buryat State University.
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