Sage Journals: Discover world-class research

Abstract

Career and technical education (CTE) programs aim to prepare students for college and careers in a wide range of occupations and industries. However, it is necessary to examine how existing inequalities in the K–12 education system structure access to and participation in different types of CTE. Using a non-parametric clustering approach to categorize CTE programs, I demonstrate that CTE can be reduced to two types—career-focused or college-focused. These two types of CTE offer participants divergent postsecondary opportunities. I then use regression analyses to show that there is a positive association between school district income level and access to college-focused CTE, and inequality in access shapes inequality in participation. However, school districts are similarly likely to offer career-focused CTE, but students in higher-income districts are less likely to participate. These findings highlight how income inequality between school districts influences CTE access and participation.

Keywords

educational policy high schools social stratification vocational education disparities descriptive analysis regression analyses secondary data analysis career and technical education nonparametric analysis

Policymakers and researchers alike have called for more robust postsecondary pathways that prepare students for a wide range of opportunities (J. E. Rosenbaum, 2001; J. Rosenbaum et al., 2015). These calls have contributed to movement toward a “college and career readiness” focus in secondary schools (Conley, 2012). Career and technical education (CTE), an updated approach to vocational education, has emerged as an integral piece of the “career and college readiness” framework. While previous models of vocational education tracked students out of academic coursework and into career-specific courses, CTE aims to combine academic rigor with career-relevant skills to prepare students for additional education and higher-skilled employment in a variety of industries (Holzer et al., 2013). However, as CTE expands, increased attention to how various inequalities in the education system shape students’ CTE experiences is necessary. A recent report states that the “history of differences in experience and access to CTE based on race, class, and gender make it incredibly important for studies of CTE impacts to include explicit checks on whether access and impacts are equitable” (Dougherty et al., 2020). A growing body of literature focuses on how students’ individual sociodemographic attributes shape access to and participation in CTE, but inequality between school districts can also play a role in shaping educational inequality. The current study explicitly analyzes how access to and participation in CTE is shaped by existing forms of socioeconomic inequality between districts in the K–12 education system.

There are two primary reasons access to and participation in CTE might vary across school districts: growing heterogeneity in CTE programs and rising inequality between school districts. First, CTE programs have diverse foci. CTE programs aim to prepare students for future education and employment and span 16 federally recognized “clusters” that roughly map to industries ranging from culinary arts to information technology to healthcare. As the variety of CTE programs grows, opportunities associated with CTE will vary. In fact, recent work finds substantial heterogeneity in participation in and returns to different CTE programs (Ecton & Dougherty, 2023). Therefore, inequality in CTE access depends both on whether school districts provide CTE and what types of CTE programs schools provide.

Second, inequality between school districts may shape variation in CTE access and participation. Vocational education mainly contributed to inequality between students within the same schools (Gamoran, 1989). However, increased income inequality and segregation has contributed to divergence between school districts that serve low-income and high-income families (Owens, 2018). Access to and participation in CTE may become more stratified if school districts offer different types of programs. We should consider access to CTE (and to different types of CTE) and inequality in participation in the context of segregation and place-based inequality.

In this paper, I investigate the relationship between school district income levels and access to and participation in CTE programs in Michigan. Because of the wide range of programs under the CTE umbrella, I use a non-parametric clustering approach to categorize CTE programs by the types of postsecondary opportunities they provide. I then examine the association between school district income levels and (1) the probability of offering different types of CTE and (2) the shares of students participating in different types of CTE.

My analyses reveal two distinct types of CTE: programs focused on career preparation and programs focused on college preparation. Rather than promoting college and career readiness, CTE programs seem to prepare students for either college or a career. The bifurcation of CTE programs may further distance career-focused and college-focused secondary education rather than providing a necessary middle ground. Further, high-income school districts are significantly more likely to offer college-focused CTE programs, which contributes to higher participation rates among students. Districts are similarly likely, regardless of income levels, to offer career-focused CTE programs, but students in higher-income districts are less likely to participate.

Ultimately, the opportunities students have in secondary school are consequential for their postsecondary education and labor market outcomes. My findings indicate that the varied implementation of CTE between school districts may exacerbate existing inequities in the K–12 education system and contribute to growing gaps between students from high-income and low-income districts.

Understanding the Relationship Between School District Inequality and CTE

In the past few decades, career and technical education (CTE) has expanded to offer a wider variety of opportunities (Imperatore & Hyslop, 2017). During the same time period, income inequality between school districts increased (Reardon & Owens, 2014). These two trends—rising heterogeneity in CTE programs and growing inequality between school districts—may work together to shape inequality in access to and participation in CTE in Michigan.

Rising Heterogeneity in Career and Technical Education Programs

Career and technical education (CTE) represents a revised approach to vocational education, which declined in popularity in the 1980s and 1990s. Vocational education was delivered via tracking; students in the vocational track completed job-specific training in place of academic coursework. Students in vocational tracks experienced worse educational outcomes compared to students on the college track (Gamoran, 1989; Gamoran & Mare, 1989), and vocational education became tantamount to a less rigorous path targeted at disadvantaged students (Lynch, 2000). Further, employers’ needs changed as the growth of low-skilled and high-skilled occupations squeezed out many middle-skilled or routine task-intensive jobs (Autor & Dorn, 2009). As enrollment in vocational education declined (Levesque et al., 2008), CTE emerged as a response to shifting educational and economic contexts in the United States (Imperatore & Hyslop, 2017).

CTE contrasts with vocational education in at least two ways: its dual focus on career and college preparation and its wide coverage of occupations. CTE aims to prepare all students for college and careers in various occupations and industries (Kim et al., 2021) by emphasizing general skills, such as computer skills, verbal communication, writing, and teamwork, in addition to job-specific technical skills (T. Lewis & Cheng, 2006). CTE is also organized around broad “clusters” related to a wide range of related occupations rather than linked to specific jobs (Castellano et al., 2003). These broad clusters span many occupations and industries, from STEM fields to healthcare to service-related occupations.

Given these shifts in CTE, there has been substantial interest in investigating whether CTE programs are associated with postsecondary success. Overall, CTE appears to be beneficial for students. CTE participants have positive academic outcomes—participants are more likely to take AP and STEM courses in high school (Castellano et al., 2012), less likely to drop out of high school compared to students who do not take CTE courses (Dougherty, 2016; Giani, 2019; Plank et al., 2008), and more likely to attain postsecondary education (Dougherty et al., 2019; Giani, 2019). Beyond educational outcomes, students who completed upper-level CTE courses also experienced wage increases (Kreisman & Stange, 2018). CTE is a pathway to increased educational and occupational attainment for many students.

However, the bulk of this research analyzes CTE as a singular program. Examining CTE’s benefits by program type or focus reveals substantial heterogeneity. Some CTE programs, such as those in education, information technology (IT), and healthcare, are associated with better postsecondary educational outcomes compared to programs in construction or transportation (Ecton & Dougherty, 2023). Economic outcomes also vary by type of CTE. For example, postsecondary CTE programs in health and technical fields result in large economic returns, whereas programs in several other fields had no effects on earnings (Bahr et al., 2015; Stevens et al., 2019; Xu & Trimble, 2016). Clearly, different programs offer different opportunities.

Given the wide range of opportunities associated with different CTE programs, it is critical to examine whether access to and participation in different types of CTE are equitable. Research investigating how student-level characteristics shape participation in CTE is mixed. For example, a typical CTE student in California is more likely to be disadvantaged compared to a non-CTE student (Kim et al., 2021). Free or reduced-price lunch students are overrepresented in CTE in Massachusetts, but especially so in transportation, hospitality, and healthcare (Ecton & Dougherty, 2023). However, other types of CTE are more likely to enroll advantaged students. White students are more likely to participate in STEM CTE programs compared to students from all other racialized/ethnic groups (Leu & Arbeit, 2020). Although researchers relate participation gaps to “barriers in access” (Kim et al., 2021), few, if any, studies attempt to measure or describe how existing inequality between school districts may create barriers to students’ CTE access and participation.

Income Inequality Between School Districts

Inequality between school districts is important in understanding both access to and participation in CTE. School district boundaries are often drawn in ways that reinforce economic and social cleavages (Cooperstock, 2023). This is in part because school segregation is closely related to residential segregation. Historic and contemporary trends, such as redlining and ongoing housing discrimination, contribute to residential segregation by both race/ethnicity and socioeconomic status (Frankenberg, 2013). Further, growing income inequality may exacerbate inequalities between neighborhoods and school districts. Income segregation, or the concentration of households with similar incomes, has increased since the 1970s (Reardon & Bischoff, 2011) and has been most substantial among families with children (Owens, 2016). Today, students in schools are more segregated by income than they were during the 1990s (Owens et al., 2016; Reardon & Owens, 2014). Income inequality between school districts has consequences for students. Districts in richer areas have more resources than districts in poorer areas due to links between school funding and local property taxes (Darling-Hammond, 2013). Indeed, income segregation is linked to increased achievement gaps between rich and poor students (Owens, 2018).

Income inequality between districts may also lead to differential access to CTE for three reasons. First, high- and low-income school districts have implemented curricular policies differently. For example, in the 1980s and 1990s, districts serving low-income students were more likely to discontinue tracking practices, whereas more affluent schools maintained tracking (Loveless, 2011). Second, it is possible that as new forms of CTE become popular, they may also become more exclusive. CTE access may follow the path of college-preparatory courses, which are more likely to be found in high-income, predominantly White schools (Rodriguez & McGuire, 2019). Finally, school districts cater their offerings to attract and appease middle-class, White, suburban families (Cucchiara, 2008; Wilson & Carlsen, 2016), and this may translate to inequality in CTE access between school districts. Parents and families play a large role in shaping school contexts (A. E. Lewis & Diamond, 2015), as schools often depend on financial support from upper- and middle-class families (Posey-Maddox, 2014). Previous research shows that predominantly White schools offered more business-focused vocational programs in the 1980s, whereas schools serving non-White students offered programs in less-rewarding industries (Oakes, 2005). However, research on inequality in access to contemporary CTE is scant.

CTE offerings may also be unequal across districts because CTE is inherently linked to local economic contexts. School administrators strive to offer courses that meet the perceived needs of the labor market (Oakes & Guiton, 1995; Roscigno et al., 2006), and courses may be adopted as a way of “training the future local labor pool” (Ainsworth & Roscigno, 2005, p. 266). This trend seems to continue with respect to CTE. For example, schools located in labor markets with more sub-baccalaureate jobs offer more CTE courses and fewer college-preparatory courses compared to schools in labor markets with fewer sub-baccalaureate jobs (Sutton, 2017; Sutton et al., 2016). Further, local economic contexts may shape the resources districts have to offer CTE. There is a long-standing shortage of CTE teachers (Hasselquist & Graves, 2020), which may be related to the pool of potential teachers with relevant career experiences. Aligning CTE offerings relevant to local economic contexts can reinforce inequality in access to different types of CTE between districts situated in different and unequal economic contexts.

CTE in Michigan

In this study, I focus on CTE access and participation in Michigan, where CTE is popular. In 2019–20, the Michigan Department of Education recognized nearly 50 distinct CTE programs. School districts across the state offered over 2,000 instances of these CTE programs.¹ CTE programs can either be offered within districts or via intermediate school districts (ISDs) or consortia.² When programs are offered through ISDs or consortia, multiple districts share resources and operational costs, and students in multiple districts have access. Programs operated by a single district represent 72% of all CTE programs in Michigan, whereas 28% are offered through consortia or ISDs.

Widespread CTE offerings yield high CTE participation rates in Michigan. Approximately half of Michigan’s high school graduates between 2010 and 2018 took at least one CTE course (Jacob & Guardiola, 2020). Male students and White students were overrepresented among CTE participants, while economically disadvantaged students were underrepresented (Jacob & Guardiola, 2020). However, two-thirds of Black-White and Hispanic-White gaps in CTE program completion are due to White students and students of color attending different schools (Carruthers et al., 2020), indicating that is it is especially important to consider how inequality between school districts may structure access to and participation in CTE.

Analytic Approach

This paper aims to analyze the relationship between school district income levels and access to and participation in different kinds of CTE. I first identify distinct types of CTE via cluster analysis. I then conduct regression-based analyses to describe the association between school district income level and access to and participation in the different types of CTE identified via cluster analysis.

Categorizing CTE Using Cluster Analysis

CTE programs are wide-ranging, but most analyses of CTE treat it as a singular educational program or examine differences by broadly defined CTE focus areas. In contrast, I employ a non-parametric clustering approach to identify groups of similar CTE programs based on the types of postsecondary opportunities they offer. Cluster analysis is a classification technique to discover groups (clusters) with similar characteristics. Clustering allows for the incorporation of multiple dimensions of difference between CTE programs, rather than centering the analysis on a singular characteristic, such as industry. I create clusters of CTE programs using characteristics widely seen as indicators of future postsecondary opportunities.

Data on the potential opportunities CTE programs offer students come from multiple sources. The Michigan Department of Education (MIDOE)’s Office of Career and Technical Education provides a list of all CTE programs in the state by school that were offered in the 2019–2020 school year. I use a crosswalk of CTE programs and related occupations provided by the U.S. Department of Education’s Office of Vocational and Adult Education (U.S. Department of Education, 2007) to associate each CTE program with the types of employment and educational opportunities it may provide for students. The crosswalk identifies each CTE program with a Classification of Instructional Program (CIP) code and links CIP codes to relevant O*NET and SOC occupational codes. I average the characteristics of all linked occupations for each CTE program, as CIP codes are typically associated with multiple occupations (mean = 3.4). Averaged characteristics are unweighted, but additional analyses weighting by occupational size yielded similar results, in part because CTE programs link to closely related occupations.

I create measures for each CTE program based on their linked occupations. These measures include: occupation-specific projected growth rates and wage distributions at the national and state level from the Bureau of Labor Statistics (U.S. Bureau of Labor Statistics, 2021a), required skills, education, and job training requirements from the O*NET database (U.S. Bureau of Labor Statistics, 2021b), occupational prestige scores from the General Social Survey, and demographic information on occupations (% employees who are male, female, White, Black, Hispanic, or another race in a given occupation), as well as the share of individuals who work full-time, hold multiple jobs, and are self-employed from the American Community Survey (ACS) five-year estimates (Ruggles et al., 2021). Measures about job openings and growth indicate current and future employment opportunities associated with CTE programs, and measures created from the O*NET database approximate the skill levels of occupations. Prestige and demographic measures may indicate occupations with high barriers to entry or levels of exclusivity. Measures related to job tenure and number of jobs held are proxies for job quality. Descriptives of variables used in the cluster analysis are reported in Appendix Table A1.

Cluster analysis requires researchers to choose an algorithm, a distance measure, and a number of clusters (k). My preferred clustering results are based on the kmeans algorithm, using Manhattan (city-block) distance and two clusters (k = 2). The kmeans clustering algorithm makes no assumptions about data structure or how units are related; it iterates between computing k (k is chosen by the researcher) cluster centroids by minimizing the within-cluster variance and updating classifications (Hastie et al., 2009). Because clustering is sensitive to the scale of variables, I dichotomize all characteristics at the median and use Manhattan distance as a similarity measure. Manhattan distance is well-suited to deal with binary data (Garip, 2012). I produced several diagnostic statistics to determine the value of k that produces the clearest separation between clusters. When k = 2, the Dunn Index, Goodman-Kruksal Gamma, and the Herbert Gamma statistics are all relatively high, while the within-to-between ratio is at a lower point, which is ideal (Appendix Figure A1). These are commonly used diagnostic statistics used to evaluate and choose ideal clusters (Garip, 2012).

Perhaps most importantly, the k-means clustering with two clusters approach yielded a sensible interpretation. Based on the characteristics of each cluster, I refer to the two resulting groupings of CTE as “college-focused” and “career-focused” clusters. The occupations associated with college-focused CTE typically require a postsecondary credential, whereas the occupations associated with career-focused CTE often allow for a direct transition from secondary school to the workforce. I expand on the characteristics of college-focused and career-focused CTE in the results section.

Because of the sensitivity of clustering to analytic choices, I compared results across many different combinations of algorithms, distance measures, treatment of variables (i.e., raw or standardized vs. median-dichotomized), and number of clusters, which I detail in Appendix A. My preferred approach outperformed most other specifications on multiple metrics, with a few exceptions. Diagnostic statistics (shown in Appendix Figure A1) were slightly better when using a hierarchical clustering approach with 4 or 5 clusters than when using kmeans clustering with two clusters. However, the hierarchical clustering algorithm imposes assumptions about how clustering units are related (i.e., in a hierarchical or nested structure). I was tolerant of slightly lower diagnostic statistics because I prefer the fewer assumptions that come with the kmeans algorithm. Further, the hierarchical clustering approach with a larger k value still yielded two clusters that were similar to the college- and career-focused clusters I identified in my preferred specification. Additional clusters were made of 2–3 CTE programs that “split off” from the career- and college-focused clusters rather than forming a new cluster of programs that I would describe in a substantively distinct way.

Analyzing Access to Different Types of CTE

I measure access to college-focused and career-focused CTE by creating dichotomous indicators of whether a school district offers any college-focused or career-focused CTE programs within its own buildings. I focus on programs within the district, as opposed to programs offered jointly through school district consortia or intermediate school districts. Students likely have readier access to offerings within their own district buildings due to barriers such as transportation, scheduling, or competition for enrollment with students from other districts in externally housed programs.

I estimate the relationship between school district income and access to each type of CTE using logistic regression models with the following form:

L o g i t (y_{i}) = β_{0} + β_{1} {I n c o m e}_{i} + \sum_{j} β_{j} Z_{j i} + \sum_{k} β_{k} W_{k i} + ε_{i}

(1)

where $Logit (y_{i})$ represents the probability of a school district i offering a specific type (career-focused or college-focused) of CTE. Income_i represents the logged median income level of the school district’s catchment area, which comes from aggregated American Community Survey (ACS) data at the school district level (National Center for Education Statistics, 2021b). I focus on school district income level because of growing income segregation between school districts (Owens, 2016). Because of the skewed nature of income, I log the median household income of each school district’s associated catchment area.

Other factors may also influence a school district’s likelihood of offering different types of CTE. Therefore, Z is a set of j covariates related to school district characteristics, and W is a set of k covariates related to neighborhood characteristics. Control variables related to school district characteristics come from the National Center for Educational Statistics and include measures of school district size (measured as the total number of students in the district), the share of students who have an individualized education plan or special needs, the share of students who are White, the student-teacher ratio, and the district’s logged expenditure per student (National Center for Education Statistics 2021a). These covariates are related to both the populations that school districts serve and the levels and types of resources to which districts have access.

Control variables related to neighborhood contexts include the urbanicity of the school district (urban, suburban or rural), the share of the population in the neighborhood area with a BA or more advanced degree, the racial composition of the catchment area (percent Black, White, Hispanic, and all other races), and the local dominant industry (defined as the industry that employs the largest share of workers in the county containing most of the school district). Urbanicity and demographic indicators come from the ACS data, while data on the strength of specific industries come from the U.S. Census County Business Patterns data (U.S. Census Bureau, 2019).

Analyzing Participation in Different Types of CTE

To measure participation in CTE, I calculate the share of secondary students in a district who enroll in college-focused or career-focused CTE. I create these variables using publicly available counts of students in grades 9–12 who enrolled in CTE programs of different types in the 2019–20 school year. I divide the number of enrolled students by the total number of enrolled 9th- through 12th-grade students in the same school year.³

I analyze the relationship between the percentage of students enrolling in college-focused and career-focused CTE and school district income levels using a two-part regression approach. Two-part models are well-suited for mixed discrete-continuous outcomes with non-negative values and a mass point at zero (Belotti et al., 2015). The two-part model first estimates the likelihood of observing a positive value (i.e., whether any student participated in CTE)⁴ and then estimates the relationship between predictors and positive values (i.e., participation rates). Unlike other approaches that model discrete-continuous outcomes (i.e., a Heckman selection model), the two-part model makes no assumptions about the correlation of standard errors between the binary and continuous equations and assumes zeroes to be true zeros rather than censored values (Belotti et al., 2015).

A key advantage of the two-part model approach is that predicted values are constructed by multiplying predictions from both models, which is preferable to estimating models for zero values and positive models and producing predicted values separately. I employ a logit model in the first part of the model (for zeroes/positive values) and OLS regression in the second part (for positive values only), although results using probit regression in the first model and GLM in the second model are similar and lead to the same conclusions. The first-stage model takes the following form:

l o g i t (p_{i} > 0) = β_{0} + β_{1} {I n c o m e}_{i} + \sum_{j} β_{j} Z_{j i} + \sum_{k} β_{k} W_{k i} + ε_{i}

(2)

And the OLS model takes the following form:

p_{i} = β_{0} + β_{1} {I n c o m e}_{i} + \sum_{j} β_{j} Z_{j i} + \sum_{k} β_{k} W_{k i} + ε_{i}

(3)

where $p_{i}$ is the share of students participating in a given type of CTE in school district i. The two-part models use the same independent variables and covariates as the logistic models described in the previous section.

Key independent variables, including income levels, are only available at the district level, which precludes a school-level analyses. However, the majority of school districts in Michigan have only one high school (54%). Further, 86% of districts have one “regular” high school, as defined by NCES. I repeat these analyses limited to districts with one high school and one regular high school, and I find them to be consistent with the main results. These results are reported in Appendix B. Still, my results may mask within district heterogeneity. Appendix B also includes results from additional model specifications, all of which yield fairly consistent findings to demonstrate the robustness of results.

Results

Categorizing CTE

Cluster analysis yielded two distinct types of CTE, which I call college-focused CTE and career-focused CTE. Table 1 lists CTE programs within each cluster. Table 2 summarizes the average characteristics of CTE programs in each cluster. A defining feature of the college-focused cluster is that the majority of occupations linked to these CTE programs (70%) require at least a bachelor’s degree; an additional 10% require some college or a sub-baccalaureate credential (see Table A1). In comparison, only 4% of occupations linked to the career-focused cluster require a bachelor’s degree or more education, while the majority (88%) require no college credential, indicating that participants could enter related occupations soon after secondary school. Despite the “college and career ready” ethos, it appears that CTE programs prepare students for college or careers.⁵

Table 1

List of CTE programs in the career-focused clusters and college-focused clusters identified via cluster analysis

Career-focused Cluster	College-focused Cluster
Aerospace science & technology	Agriculture, agricultural operations, and related programs
Airframe Technology	Animal health and veterinary science
Automobile technician (ASE certified)	Applied horticulture & horticultural operations
Avionics maintenance technology	Biotechnology
Collision repair technician (ASE certified)	Biotechnology medical sciences
Construction trades	Business admin mgt & operations
Cooking & related culinary arts	Computer and information systems
Cosmetology	Computer programming/programmer
Electricity/power trans installer	Computer systems networking & tele-communications
Electrical/electronics equipment installation and repair	Diagnostic services
Graphics communications	Digital/multimedia & information resources design
Health sciences	Drafting/design technology
Heating, AC, & refrigeration	Education
Heavy/industrial equipment maintenance technologies	Engineering technology
Lineworker	Family & consumer sciences
Machine tool technology/machinist	Fashion design
Marketing sales and services	Finance & financial management services
Mechanical drafting	Home furnishing equipment installation & construction
Medium/heavy truck technician	Insurance
Plumbing technology	Mechatronics
Power plant technology (aircraft)	Natural resources and conservation
Public safety and protective services	Systems administration/administrator
Radio & TV broadcasting technology
Small engine & related equipment repair
Welding, brazing, and soldering
Woodworking

Table 2

Average characteristics of occupations associated with CTE programs in the career-focused and college-focused clusters. Data sources include BLS (wages and job growth), GSS (occupational prestige), O*NET (educational and skill requirements), and ACS (demographics). Note that skills are measured on a 0–6 scale. Reported estimates are averages across CTE programs. Estimates are unweighted, but weighting by occupational employment does not substantively change results

Characteristic	College-focused CTE	Career-focused CTE
Required Education Levels
HS or less	8%	53%
Some college	12%	35%
Sub-BA credential	9%	7%
BA+	70%	4%
Social and Economic Measures
Average annual wages	$76,900	$45,849
% Projected change in number of jobs	6.08%	0.03%
Occupational prestige	72.5%	38.7%
% Female	42.2%	17.8%
% White	74.6%	63.9%
Occupational Characteristics
% STEM programs	57.7%	0.00%
% Manual occupations	5.44%	89.5%
Average verbal skill required	3.79	2.97
Average quantitative skill required	2.62	1.94
Average analytic skill required	3.32	2.75
Number of technological tools used	46.8	13.7

Beyond required education levels, the characteristics of the opportunities associated with college-focused CTE are often the characteristics we associate with jobs held by highly educated workers. For example, the average annual wages of occupations associated with college-focused CTE programs are $77,000 per year compared to $46,000 per year for the career-focused programs. Occupations linked to the programs in the college-focused cluster are projected to grow over time, while occupations linked to the programs in the career-focused cluster are predicted to remain stagnant. Occupations linked to the college-focused cluster have prestige scores nearly twice as large as the prestige scores of occupations linked to the career-focused cluster (72.5 versus 38.7). All STEM programs fall into the college-focused programs, while 90% of occupations linked to the career-focused cluster are manual occupations. Finally, the skill levels associated with occupations in the career-focused cluster are lower than in the college-focused cluster. Scatterplots show little overlap in the distributions of select characteristics between the two CTE types (see Appendix Figure A3). Clearly, these two clusters indicate that different CTE programs offer distinct postsecondary opportunities for students.

Between-District Inequality and Access to CTE

The majority of Michigan’s school districts offer CTE, with many offering multiple programs in the career- and college-focused CTE clusters. Out of 512 districts, 57% offer at least one CTE program, 53% offer college-focused CTE, and 29% offer career-focused CTE (Table 3). College-focused CTE programs appear to be more available to students, which may reflect the general movement of CTE away from trades or traditional vocational fields in the context of an increasingly knowledge-based economy. However, it is unclear how economic inequality between districts shapes differential access to CTE among students.

Table 3

Average access to and participation in CTE, overall and by type of CTE, across school districts in Michigan (n = 512). The average number of programs presented are conditional on districts offering any CTE programs. MIDOE data

	Any CTE	College-focused CTE	Career-focused CTE
Access
% of districts offering CTE	56.3	52.5	29.3
% of districts offering CTE (consortia included)	92.5	89.6	86.7
Average number of programs	3.26	2.41	0.85
Average number of programs (consortia incl.)	18.6	10.3	8.3
Participation
Average % of students participating in CTE	15.9	12.1	3.8
Average % of students participating in CTE, conditional on any students participating	28.8	23.3	12.8

Regression results show that there is a strong, positive relationship between district income level and access to college-focused CTE and a weak, but still positive, relationship between district income level and access to career-focused CTE. Controlling for school and neighborhood characteristics, the predicted probability of a school district with a median household income of $40,000 offering a college-focused CTE program is 0.51 (95% CI: 0.47, 0.56), and 0.93 (95% CI: 0.80, 1.05) for a district with an average income level of $120,000 (Figure 1, gray line). The relationship between income level and the likelihood of offering career-focused CTE is also positive, increasing from 0.29 to 0.35 as district income levels increase from $40,000 to $120,000, but confidence intervals substantially overlap (Figure 1, black line). However, the positive association between district income level and offering college-focused CTE along with the weaker, positive relationship between district income level and career-focused CTE may be indicative of students in high-income districts having more resources than students in low-income districts rather than suggesting that students and high- and low-income districts are being pushed into different career interests. Regression coefficients are reported in Appendix Table B1.

Figure 1.

Predicted probabilities of school districts offering CTE courses by type of CTE and school district income level. MIDOE, ACS, and NCES data. Bars represent 95% confidence intervals of the predicted values.

Several school and neighborhood characteristics beyond income level are also associated with the probability that a school district offers CTE of a particular type. School district size is positively associated with CTE access; districts with more students are more likely to offer both career-focused and college-focused CTE programs. Compared to rural schools, urban and suburban schools are more likely to offer both types of CTE courses. These findings are also suggestive of schools with more resources offering more CTE.

Notably, there is a small but significant negative association between the proportion of adults in a school district catchment area with a BA degree or higher. A one-percentage-point increase in the proportion of the population with a BA degree is associated with a 7% decrease in the odds of a district offering a college-focused CTE program and a 4% decrease in the odds of a district offering a career-focused CTE program, conditional on the districts being otherwise equal. While this relationship may seem counterintuitive, it seems plausible that districts with similar income levels but different educational profiles may have different curricular preferences. For example, if district A and district B are both middle-income districts, but district B has more college-educated adults in its catchment area, parents may be more inclined to advocate for AP or traditional college preparatory courses instead of CTE.

Additional results, shown in Appendix Table B2, indicate that students in high-income districts also have access to more college-focused CTE programs. As the average income of a school district increases from $40,000 to $120,000, the predicted number of college-focused CTE programs increases from 4 to 33, and the number of career-focused CTE programs increases from 1.5 to 2. Access to CTE programs, and particularly access to college-focused CTE, appears to be structured by existing economic inequality between school districts.

Between-District Inequality and Participation in CTE

Access to CTE varies by school district income levels, which may shape inequality in students’ participation in CTE. Indeed, participation in CTE does vary across school districts. On average, 4% of high school students in a district enrolled in career-focused CTE, and 12% of high school in a district enrolled in college-focused CTE in the 2019–2020 school year. In districts with non-zero participation, an average of 13% and 23% of students in a district participated in career-focused and college-focused CTE, respectively (Table 3).

Regression results show that school district income levels are differentially associated with participation in college-focused and career-focused CTE. For college-focused CTE, there is a strong, positive association (p-value < 0.001) between district income level and the probability of any students participating in college-focused CTE. However, conditional on any students participating, the relationship between income level and percentage of students participating in college-focused CTE is insignificant. The positive gradient in participation (Figure 2) is driven by income-based inequality in access to college-focused CTE. For career-focused CTE, the association between income level and any participation in career-focused CTE is positive but indistinguishable from zero (p = 0.11). Although school districts, regardless of income level, are similarly likely to offer career-focused CTE, participation in career-focused CTE decreases as income levels increase. A 1% increase in school district level is associated with a 14% decrease in the participation rate (p = 0.046). School district income levels appear to shape who has access to college-focused CTE but who participates in career-focused CTE.

Figure 2.

Predicted percent of students participating in CTE in a school district, by type of CTE and school district income level. MIDOE, ACS, and NCES data. Bars represent 95% confidence intervals of the predicted values.

Additional covariates contribute to access to and participation in CTE in different ways. Similar to the models predicting access to CTE, being an urban school (compared to rural) and school district size are positively associated with any students participating in either type of CTE, while the share of the adult population with a BA in the school catchment area is negatively associated (p < 0.05). However, the share of White students within a district is predictive of increased participation in both college-focused and career-focused CTE, and the student-teacher ratio is predictive of participation in college-focused CTE (p < 0.05). While access to CTE is associated with school district income level, participation appears to be more strongly associated with districts’ demographics and resources. This suggests that the dynamics that lead to school districts adopting CTE and students within districts participating in CTE may be distinct. The full model results are available in Appendix B.

Discussion

CTE may fill a void in the “bachelor’s or bust” debate, as it can provide students with multiple postsecondary opportunities. Career and technical education (CTE) programs aim to prepare students for both highly skilled work and further education across many fields and industries. The many programs under the moniker of CTE make CTE appealing to a wide range of students, but this variation may create inequality in how much students benefit from CTE. Given the variable benefits of different CTE programs, examining who has access to different types of CTE and how access is structured by existing inequalities in the K–12 education system provides insights into whether CTE can create new opportunities or reinforce inequalities between students. Specifically, inequality between school districts arising from income-based segregation may be associated with access to different kinds of CTE, as districts have different resources and preferences when implementing new programs and operate within different local economic contexts. Any association between school district income levels and CTE offerings can shape inequalities in access, participation, and future benefits between advantaged and disadvantaged students.

I demonstrate that the CTE programs offered in Michigan can be reduced to two types and that school district income level is indeed associated with access to and participation in different types of CTE. Using a non-parametric clustering approach, I identify two clusters of CTE programs: one that is made up of primarily “career-focused” programs, which prepare students for occupations they could transition to immediately after secondary school, and the other made up of “college-focused” programs, which are linked to occupations that require a college credential. While CTE seems to be distinct from previous versions of vocational education, it appears to fall short of its directive to prepare students for college and career. Rather, it is more likely that CTE programs present participants with disparate opportunities. I also show that high-income districts are more likely to offer CTE, especially college-focused CTE courses. Differences in participation in college-focused CTE appear to be driven by inequality in access. The associations for career-focused CTE are different. While there is virtually no relationship between district income level and access to career-focused CTE, students in high-income districts are less likely to participate. Career-focused CTE participation patterns may mirror the participation patterns of earlier versions of vocational education, which were concentrated among disadvantaged students. Ultimately, income inequality between districts contributes to inequality in access to and participation in different types of CTE in distinct ways.

The implications of these findings are two-fold. First, conceptualizing CTE as a collection of programs that offer a wide range of opportunities for students rather than a singular educational intervention is critical. There is a growing body of research on the heterogeneous impacts of CTE programs (Ecton & Dougherty, 2023), yet few schematics exist to distinguish types of CTE beyond looking at individual programs or grouping programs into a dozen plus industries or career pathways. I demonstrate that reducing CTE courses to a career vs. college focus is one helpful schematic in analyzing inequality across school districts. This dichotomy could be used in future research on CTE, especially when considering variation in outcomes related to work and education. Of course, this is certainly not the only way to distinguish different types of CTE, and researchers should consider whether measuring CTE in the aggregate or separating out different kinds of CTE is appropriate given their research motivations. The addition of new data, such as CTE offerings in other states or CTE characteristics related to curricula, could also result in different or additional clusters or clusters that are most separated by characteristics not analyzed here.

Second, my analyses highlight how existing forms of inequality, such as income segregation between school districts, continue to shape educational inequalities between advantaged and disadvantaged students. School district income levels are positively associated with access to college-focused CTE, and there is a weak, but still positive, association between district income level and career-focused CTE. Together, these results suggest that higher income districts have resources to expand their curricula and increasingly focus their curricula on college-preparatory programs. Indeed, patterns of access to and participation in CTE mirror the stratification associated with other types of advanced coursework, such as AP and other college preparatory courses (Rodriguez & McGuire, 2019). Inequality in access to CTE is a clear example of how educational inequality, particularly at the K–12 level, is inextricably linked to neighborhood and housing inequality, as students in high-income districts have more access to programs that offer the largest rewards. As CTE has expanded to encompass more fields and a dual college-career focus, an unintended consequence may be that it contributes to the accumulation of advantages and disadvantages among students in different school contexts.

My findings also motivate at least four directions for future research. First, additional work showcasing variation in CTE programs is essential in describing both the heterogeneity and inequality associated with CTE. Several recent studies aim to further understand heterogeneity in CTE by program type and have found substantial variation (Ecton & Dougherty, 2023). Future evaluations of CTE programs can investigate whether participation patterns and student outcomes vary in career-focused CTE programs versus college-focused CTE programs or develop their own dimensions of difference between CTE. Furthermore, my clustering approach incorporates rich data that links CTE programs of study to occupations via existing crosswalks and a wide range of measures, including occupational outlook, prestige, and education requirements associated with those occupations. This linked data could be a valuable tool for constructing portraits of CTE programming and participation by CTE program or researcher-defined grouping or by district, school, or student characteristics. This may be a useful resource for policymakers, educators, parents, and students.

Second, analyzing the mechanisms through which school district income levels and CTE access and participation are related may refine our understanding of the relationship between income inequality between districts and educational opportunities and outcomes. While researchers have called attention to the low CTE participation rates of disadvantaged students (see Jacob & Guardiola, 2020), policymakers and researchers have not yet focused on the roles that school districts and between-district inequality play in contributing to inequality in access. There are many ways that districts’ income levels may shape CTE offerings and participation. High-income districts may have resources, both material and nonmaterial, to implement new programs and curricula. Or the economic conditions that contribute to divergences between high- and low-income districts may also shape how schools implement CTE. Researchers could also investigate why some programs are more prevalent than others. For example, I find that schools are nearly three times as likely to offer college-focused CTE. Investigating what drives the association between school district income levels and CTE offerings may provide guidance on how to make access to CTE more equitable. For example, partnerships with local community colleges that successfully deliver CTE could be one avenue to increase access and compensate for inequality between districts.

Third, additional research on participation in CTE may reveal important insights about how inequality between and within schools contributes to inequality in students’ CTE course-taking. Conditional on access to career- and college-focused CTE, high district income levels did not contribute to more participation in CTE. Future work could examine what additional factors shape CTE participation. A large body of work examines how students select secondary courses—families (Crosnoe & Mueller, 2014), counselors and teachers (Francis et al., 2019; Irizarry, 2021), school contexts (Legewie & DiPrete, 2014; Sutton, 2017), students’ expectations (Domina et al., 2011), and peers (Francis & Darity, 2021) all shape students’ educational decisions. These factors could also be associated with participation in different types of CTE. Relatedly, investigating heterogeneity in the reputations of and perceptions of different types of CTE could also help explain participation patterns. Qualitative research may be particularly helpful in understanding students’ CTE course-taking patterns.

Relatedly, my findings also indicate that participation in college-focused CTE is greater in schools with a larger share of White students, even controlling for district income levels. Both the concentration of college-focused CTE in high income districts and the higher participation rates in Whiter schools may be an example of “opportunity hoarding” (Tilly, 1998), in which high-income, predominantly White families accumulate advantages, even when policies and programs aim to mitigate inequality (A. E. Lewis & Diamond, 2015). The present study has largely focused on economic inequality between districts; however, other forms of inequality, including racial inequality, may also significantly influence access to and participation in CTE.

Finally, my analyses focus on Michigan, but CTE is at the forefront of secondary and postsecondary education policy across the United States. In 2022, 36 states enacted 123 policy actions related to career and technical education (Advance CTE, 2023). Expanding research to consider variation in CTE across multiple states can provide important insights into the relationship between variation in CTE and inequality in different contexts.

CTE is a popular policy that continues to expand at both the secondary and postsecondary levels, making research on its relationship to inequality essential. If the goal of career and technical education is to increase students’ opportunities and pathways, we must pay more attention to the types of opportunities CTE programs can provide and how those opportunities are distributed in the context of existing forms of inequality, such as income inequality between school districts. Understanding heterogeneity in access, participation, and outcomes is crucial in understanding how CTE can mitigate or exacerbate inequality between and within school districts, as well as contribute to future educational and economic disparities among students.

Supplemental Material

sj-docx-1-ero-10.1177_23328584251334378 – Supplemental material for College or Career Ready, But Not Both? Heterogeneity of Career and Technical Education (CTE) Programs and Income-Based Inequality in Access and Participation

Supplemental material, sj-docx-1-ero-10.1177_23328584251334378 for College or Career Ready, But Not Both? Heterogeneity of Career and Technical Education (CTE) Programs and Income-Based Inequality in Access and Participation by Jane Furey in AERA Open

Footnotes

Declaration of Conflicting Interests

The author declares no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the National Science Foundation Graduate Research Fellowship Program (DGE 2241144), the National Academy of Education/Spencer Dissertation Fellowship, and an NICHD training grant to the Population Studies Center at the University of Michigan (T32HD007339).

Open Practices

The data and analysis files for this article can be found at

ORCID iD

Jane Furey

Note: This article was accepted under the editorship of Kara Finnigan.

Notes

Author

JANE FUREY is PhD candidate in public policy and sociology at the University of Michigan, 500 S. State Street, Ann Arbor, MI, 48109; email: jlfurey@umich.edu. Her research focuses on how variation in access to and returns to education is associated with socioeconomic inequality.

References

Advance CTE. (2023). State policies impacting CTE: 2022 year in review. https://careertech.org/event/state-policies-impacting-cte-2022-year-in-review/

Ainsworth

J. W.

Roscigno

V. J.

(2005). Stratification, school-work linkages and vocational education. Social Forces, 84(1), 257–284. https://doi.org/10.1353/sof.2005.0087

Autor

Dorn

(2009). This job is ‘getting old’: Measuring changes in job opportunities using occupational age structure. American Economic Review, 99(2), 45–51. https://doi.org/10.1257/aer.99.2.45

Bahr

P. R.

Dynarski

Jacob

Kreisman

Sosa

Wiederspan

(2015). Labor market returns to community college awards: Evidence from Michigan (A CAPSEE Working Paper). Center for Analysis of Postsecondary Education and Employment. https://eric.ed.gov/?id=ED557080

Belotti

Deb

Manning

W. G.

Norton

E. C.

(2015). Twopm: Two-part models. The Stata Journal, 15(1), 3–20. https://doi.org/10.1177/1536867X1501500102

Carruthers

Kreisman

Dougherty

Martin

(2020). A multi-state analysis of trends in career and technical education: Massachusetts, Michigan, and Tennessee (Vol. 6). Georgia Policy Labs. https://scholarworks.gsu.edu/gpl_reports/6

Castellano

Stringfield

Stone

J. R.

(2003). Secondary career and technical education and comprehensive school reform: Implications for research and practice. Review of Educational Research, 73(2), 231–272. https://doi.org/10.3102/00346543073002231

Castellano

Sundell

Overman

L. T.

Aliaga

O. A.

(2012). Do career and technical education programs of study improve student achievement? Preliminary analyses from a rigorous longitudinal study. International Journal of Educational Reform, 21(2), 98–118. https://doi.org/10.1177/105678791202100202

Conley

D. T.

(2012). A complete definition of college and career readiness. Educational Policy Improvement Center. https://eric.ed.gov/?id=ED537876

10.

Cooperstock

(2023). The demographics of school district secession. Social Forces, 101(4), 1976–2012. https://doi.org/10.1093/sf/soac069

11.

Cowen

J. M.

Creed

(2017). Public school choice and student achievement: Evidence from Michigan’s interdistrict open enrollment system. AERA Open, 3(3): 1–12.

12.

Crosnoe

Muller

(2014). Family socioeconomic status, peers, and the path to college. Social Problems, 61(4), 602–624. https://doi.org/10.1525/sp.2014.12255

13.

Cucchiara

(2008). Re-branding urban schools: Urban revitalization, social status, and marketing public schools to the upper middle class. Journal of Education Policy, 23(2), 165–179. https://doi.org/10.1080/02680930701853088

14.

Darling-Hammond

(2013). Inequality and school resources: What it will take to close the opportunity gap. In Carter

P. L.

Welner

K. G.

(Eds.), Closing the opportunity gap: What America must do to give every child an even chance (pp. 77–98). Oxford University Press.

15.

Domina

Conley

A. M.

Farkas

(2011). The link between educational expectations and effort in the college-for-all era. Sociology of Education, 84(2), 93–112. https://doi.org/10.1177/1941406411401808

16.

Dougherty

S. M.

(2016). The effect of career and technical education on human capital accumulation: Causal evidence from Massachusetts. Education Finance and Policy, 13(2), 119–148. https://doi.org/10.1162/edfp_a_00224

17.

Dougherty

S. M.

Gottfried

M. A.

Sublett

(2019). Does increasing career and technical education coursework in high school boost educational attainment and labor market outcomes?” Journal of Education Finance, 44(4), 423–447. https://doi.org/10.1353/jef.2019.a738163

18.

Dougherty

S. M.

Kamin

Klein

(2020). Improving measurement in career and technical education to support rigorous research. American Institutes for Research, Career and Technical Education Research Network. https://cteresearchnetwork.org/resources/improving-measurement-cte

19.

Ecton

W. G.

Dougherty

S. M.

(2023). Heterogeneity in high school career and technical education outcomes. Educational Evaluation and Policy Analysis, 45(1), 157–181. https://doi.org/10.3102/01623737221103842

20.

Francis

D. V.

Darity

W. A.

(2021). Separate and unequal under one roof: How the legacy of racialized tracking perpetuates within-school segregation. RSF: The Russell Sage Foundation Journal of the Social Sciences, 7(1), 187–202. https://doi.org/10.7758/RSF.2021.7.1.11

21.

Francis

D. V.

de Oliveira

A. C. M.

Dimmitt

(2019). Do school counselors exhibit bias in recommending students for advanced coursework? The B.E. Journal of Economic Analysis & Policy, 19(4), 20180189. https://doi.org/10.1515/bejeap-2018-0189

22.

Frankenberg

(2013). The role of residential segregation in contemporary school segregation. Education and Urban Society, 45(5), 548–570. https://doi.org/10.1177/0013124513486288

23.

Gamoran

(1989). Measuring curriculum differentiation. American Journal of Education, 97(2), 129–143. https://doi.org/10.1086/443918

24.

Gamoran

Mare

R. D.

(1989). Secondary school tracking and educational inequality: Compensation, reinforcement, or neutrality? American Journal of Sociology, 94(5), 1146–1183. https://doi.org/10.1086/229114

25.

Garip

(2012). Discovering diverse mechanisms of migration: The Mexico–US stream 1970–2000. Population and Development Review, 38(3), 393–433. https://doi.org/10.1111/j.1728-4457.2012.00510.x

26.

Giani

M. S.

(2019). Does vocational still imply tracking? Examining the evolution of career and technical education curricular policy in Texas. Educational Policy, 33(7), 1002–1046. https://doi.org/10.1177/0895904817745375

27.

Hasselquist

Graves

N. A.

(2020). CTE teacher retention: Lessons learned from mid-career teachers. Career and Technical Education Research, 45(1), 3–16. https://doi.org/10.5328/cter45.1.3

28.

Hastie

Tibshirani

Friedman

(2009). Unsupervised learning. In Hastie

Tibshirani

Friedman

(Eds.), The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics (pp. 485–585). Springer. https://doi.org/10.1007/978-0-387-84858-7_14

29.

Holzer

H. J.

Linn

Monthey

(2013). The promise of high-quality career and technical education: Improving outcomes for students, firms and the economy. The College Board.

30.

Imperatore

Hyslop

(2017). CTE policy past, present, and future: Driving forces behind the evolution of federal priorities. Peabody Journal of Education, 92(2), 275–289. https://doi.org/10.1080/0161956X.2017.1302221

31.

Irizarry

(2021). On track or derailed? Race, advanced math, and the transition to high school. Socius, 7, 2378023120980293. https://doi.org/10.1177/2378023120980293

32.

Jacob

Guardiola

(2020). Career and technical education in Michigan: Access and participation. University of Michigan Youth Policy Lab.

33.

Kim

E. H.

Flack

C. B.

Parham

Wohlstetter

(2021). Equity in secondary career and technical education in the United States: A theoretical framework and systematic literature review. Review of Educational Research, 91(3), 356–396. https://doi.org/10.3102/0034654321995243

34.

Kreisman

Stange

(2018). Vocational and career tech education in American high schools: The value of depth over breadth. Education Finance and Policy, 15(1), 11–44. https://doi.org/10.1162/edfp_a_00266

35.

Legewie

DiPrete

T. A.

(2014). The high school environment and the gender gap in science and engineering. Sociology of Education, 87(4), 259–280. https://doi.org/10.1177/0038040714547770

36.

Leu

K. B.

Arbeit

C. A.

(2020). Differences in high school CTE coursetaking by gender and race/ethnicity. Career & Technical Education Research, 45(1), 33–61. https://doi.org/10.5328/cter45.1.33

37.

Levesque

Laird

Hensley

Choy

S. P.

Cataldi

E. F.

Hudson

(2008). Career and technical education in the United States: 1990 to 2005. Statistical analysis report. NCES 2008-035. National Center for Education Statistics. https://eric.ed.gov/?id=ED502109

38.

Lewis

A. E.

Diamond

J. B.

(2015). Despite the best intentions: How racial inequality thrives in good schools. Oxford University Press.

39.

Lewis

Cheng

S.-Y

. (2006). Tracking, expectations, and the transformation of vocational education. American Journal of Education, 113(1), 67–99. https://doi.org/10.1086/506494

40.

Loveless

(2011). The tracking wars: State reform meets school policy. Brookings Institution Press.

41.

Lynch

(2000). High school career and technical education for the first decade of the 21st century. Journal of Vocational Education Research, 25(2), 155–198. Available from: https://search-ebscohost-com.proxy.lib.umich.edu/login.aspx?direct=true&db=eue&AN=507682403&site=ehost-live&scope=site

42.

National Center for Education Statistics. (2021a). American community survey – Education tabulation (ACS-ED) [dataset]. https://nces.ed.gov/programs/edge/demographic/acs

43.

National Center for Education Statistics. (2021b). Local education agency (School District) Universe survey data, v.2a [dataset]. https://nces.ed.gov/ccd/files.asp

44.

Oakes

(2005). Keeping track: How schools structure inequality. Yale University Press.

45.

Oakes

Guiton

(1995). Matchmaking: The dynamics of high school tracking decisions. American Educational Research Journal, 32(1), 3–33. https://doi.org/10.3102/00028312032001003

46.

Owens

(2016). Inequality in children’s contexts: Income segregation of households with and without children. American Sociological Review, 81(3), 549–574. https://doi.org/10.1177/0003122416642430

47.

Owens

(2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education, 91(1), 1–27. https://doi.org/10.1177/0038040717741180

48.

Owens

Reardon

S. F.

Jencks

(2016). Income segregation between schools and school districts. American Educational Research Journal, 53(4), 1159–1197. https://doi.org/10.3102/0002831216652722

49.

Plank

S. B.

DeLuca

Estacion

(2008). High school dropout and the role of career and technical education: A survival analysis of surviving high school. Sociology of Education, 81(4), 345–370. https://doi.org/10.1177/003804070808100402

50.

Posey-Maddox

(2014). When middle-class parents choose urban schools: Class, race, and the challenge of equity in public education. When middle-class parents choose urban schools. University of Chicago Press. https://doi.org/10.7208/9780226120355

51.

Reardon

S. F.

Bischoff

(2011). Income inequality and income segregation. American Journal of Sociology, 116(4), 1092–1153. https://doi.org/10.1086/657114

52.

Reardon

S. F.

Owens

(2014). 60 years after Brown: Trends and consequences of school segregation. Annual Review of Sociology, 40(1), 199–218. https://doi.org/10.1146/annurev-soc-071913-043152

53.

Rodriguez

McGuire

K. M.

(2019). More classes, more access? Understanding the effects of course offerings on Black-White gaps in advanced placement course-taking. The Review of Higher Education, 42(2), 641–679. https://doi.org/10.1353/rhe.2019.0010

54.

Roscigno

V. J.

Tomaskovic-Devey

Crowley

(2006). Education and the inequalities of place. Social Forces, 84(4), 2121–2145. https://doi.org/10.1353/sof.2006.0108

55.

Rosenbaum

Ahearn

Becker

Rosenbaum

(2015). The new forgotten half and research directions to support them. William T. Grant Foundation.

56.

Rosenbaum

J. E.

(2001). Beyond college for all: Career paths for the forgotten half. Russell Sage Foundation.

57.

Ruggles

Flood

Foster

Goeken

Pacas

Schouweiler

Sobek

(2021). IPUMS USA: Version 11.0 [dataset]. IPUMS. https://doi.org/10.18128/D010.V11.0

58.

Stevens

A. H.

Kurlaender

Grosz

(2019). Career technical education and labor market outcomes evidence from California community colleges. Journal of Human Resources, 54(4), 986–1036. https://doi.org/10.3368/jhr.54.4.1015.7449R2

59.

Sutton

(2017). Preparing for local labor: Curricular stratification across local economies in the United States. Sociology of Education, 90(2), 172–196. https://doi.org/10.1177/0038040717703447

60.

Sutton

Bosky

Muller

(2016). Manufacturing gender inequality in the new economy: High school training for work in Blue-collar communities. American Sociological Review, 81(4), 720–748. https://doi.org/10.1177/0003122416648189

61.

Tilly

(1998). Durable inequality. Durable inequality. University of California Press. https://doi.org/10.1525/9780520924222

62.

U.S. Bureau of Labor Statistics. (2021a). Occupational employment and wage statistics, 2017 [dataset]. https://www.bls.gov/oes/tables.htm

63.

US. Bureau of Labor Statistics. (2021b). O*NET 26.1 database [dataset]. https://www.onetcenter.org/database.html

64.

U.S. Census Bureau. (2019). County business patterns: 2017 [dataset]. https://www.census.gov/data/datasets/2017/econ/cbp/2017-cbp.html

65.

U.S. Department of Education, Office of Vocational and Adult Education (OVAE). (2007). Perkins IV crosswalks [dataset]. https://careertech.org/crosswalks

66.

Wilson

T. S.

Carlsen

R. L.

(2016). School marketing as a sorting mechanism: A critical discourse analysis of charter school websites. Peabody Journal of Education, 91(1), 24–46. https://doi.org/10.1080/0161956X.2016.1119564

67.

Trimble

(2016). What about certificates? Evidence on the labor market returns to nondegree community college awards in two states. Educational Evaluation and Policy Analysis, 38(2), 272–292. https://doi.org/10.3102/0162373715617827

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.25 MB

College or Career Ready,But Not Both? Heterogeneity of Career and Technical Education (CTE) Programs and Income-Based Inequality in Access and Participation

Abstract

Keywords

Understanding the Relationship Between School District Inequality and CTE

Rising Heterogeneity in Career and Technical Education Programs

Income Inequality Between School Districts

CTE in Michigan

Analytic Approach

Categorizing CTE Using Cluster Analysis

Analyzing Access to Different Types of CTE

Analyzing Participation in Different Types of CTE

Results

Categorizing CTE

Between-District Inequality and Access to CTE

Between-District Inequality and Participation in CTE

Discussion

Supplemental Material

sj-docx-1-ero-10.1177_23328584251334378 – Supplemental material for College or Career Ready, But Not Both? Heterogeneity of Career and Technical Education (CTE) Programs and Income-Based Inequality in Access and Participation

Footnotes

Declaration of Conflicting Interests

Funding

Open Practices

ORCID iD

Notes

Author

References

Supplementary Material