The Social Structure of School Resource Disparities: How Social Capital and Interorganizational Relationships Matter for Educational Equity

Abstract

One potentially underestimated aspect of resource inequity in U.S. public schools is access to social capital in external organizational environments. This research examines partnerships among 211 New York City high schools and 918 partner organizations from 2001 to 2005 as sources of external school social capital providing resources that can strengthen organizational capacity to improve educational opportunities and outcomes. The findings, based on an innovative analysis combining content analysis, social network analysis, and multilevel modeling, demonstrate that four partnership characteristics are important in this context: (1) how long partnerships last versus how many there are, (2) partners concentrating resources in a particular area versus across diverse complementary areas, (3) partners being densely connected to other schools and partners rather than being central in the overall school–partner network, and (4) partners conveying instructional resources versus other kinds of resources. Hence, educational research and policy should more broadly conceptualize how schools’ external organizational environments matter for educational equity and the role particular kinds of partnerships can play.

Keywords

social capital organizational environments high school partnerships network analysis content analysis

Disparities in educational opportunity are built into the structure of U.S. public schooling. Schools in different geographic locales receive unequal funding (Baker 2021). Differences in financial capital are compounded by differences in cultural and social capital, providing greater advantages to some students (Horvat, Weininger, and Lareau 2003; Lee and Bowen 2006). While the important role of resources in educational opportunity is well recognized (Hanushek 2020), this role may be underestimated because current research often overlooks potentially important resources related to school social capital.

Social capital is the set of actual and potential resources—cognitive, social, and material—made available through actors’ relationships with others (Bourdieu 1986; Coleman 1988; Lin 2001; Portes 1998). Social capital accrues not only to individuals but also to what Coleman (1988:S98) calls “corporate actors” (i.e., a body or group of actors), namely organizations, given the connections organizations have with other organizations in their external environments. Hence, external school social capital (Tsang 2009) is the set of actual or potential cognitive, social, or material resources available to schools given their interorganizational relationships.

This includes relationships with community-based organizations, universities, social and health service agencies, foundations, faith-based groups, for-profit vendors, and a host of other intermediary organizations (Honig 2004). Resources from interorganizational relationships could further educational opportunities by addressing resource inequity. Indeed, this is the premise of education policy prescriptions advocating school partnerships, dating back to the 1988 Educational Partnership Act and continuing through to the 2015 Every Student Succeeds Act, which explicitly includes a section on school partnerships.

The existing research on school partnerships (e.g., Coburn and Penuel 2016; Sanders 2006; Smith and Wohlstetter 2006) typically does not consider the diverse forms of social capital resources involved. And little research examines how underlying patterns in interorganizational relationships might be another source of structural inequity. Hence, taking an ontological stance from organizational sociology, we ask two questions:

How do the characteristics of schools’ interorganizational relationships give rise to social capital providing access to different kinds of resources?

How does schools’ access to social capital resources relate to educational outcomes?

We answer these questions by studying the school–partner network characteristics of 211 New York City high schools and 918 partners from the 2001–2002 to 2004–2005 school years. The findings show how access to social capital resources and, in turn, educational outcomes relate to (1) the length versus number of partnerships, (2) the concentration versus complementarity of partnerships, (3) the density versus centrality ofpartnerships, and (4) the types of resources, ornetwork content (Burt 1997), conveyed in partnerships.¹

In the remainder of this article, we first discuss how school partnerships convey social capital. We then argue that different network configurations relate to social capital and school outcomes. The methods detail our longitudinal, mixed-methods historical case study, which novelly employs content analysis, network analysis, and multilevel modeling. We conclude with our findings and then discuss implications for partnership policy, practice, and future research.

Educational Opportunity, School Partnerships, and Social Capital Resources

Studies of educational opportunities and school resources typically focus on financial resources given federal, state, and local government spending (Baker 2021; Hanushek 2020; Jackson, Johnson, and Persico 2016). Some research focuses on educational “inputs,” such as teacher ability, the work staff do, or goals contributing to school quality and the capacity to address achievement barriers (Greenwald, Hedges, and Laine 1996; Jennings et al. 2015). Financial resources might be used to acquire educational inputs, but the direct source of inputs is a host of diverse organizations in schools’ external environments.

Universities and nonprofits, for example, provide professional development to strengthen teachers’ and school leaders’ expertise. Health and social service agencies provide “wraparound supports” for students and families. Recreation centers and arts agencies provide co-curricular learning opportunities. Hence, one mechanism involved in school operations and outcomes is the set of resources conveyed through schools’ relationships with organizations in their external environments. These resources on which schools depend (Pfeffer and Salancick 1979) are critical forms of social capital.

“Social capital constitutes a particular kind of resource available to an actor . . . [and] is defined by its function” (Coleman 1988:S98). For example, social capital can provide actors with three forms of resources—cognitive, material, and social (Portes 1998). Social capital provides cognitive resources, such as information, advice, or “know-how.” Research-practice partnerships, for instance, can convey cognitive resources in the form of information about how to reduce students’ delinquent behaviors so they are more “ready to learn,” and some professional development partnerships are a cognitive resource providing expertise to improve science instruction and assessment. Both examples of cognitive resources have been linked to increased student learning (Hawkins etal. 2008; Yarnall, Shechtman, and Penuel 2006).

Social capital also provides material resources, such as funding, personnel, supplies, activities, and services, all of which can support student learning. For example, some criticisms notwithstanding (Feuerstein 2001), business partnerships have helped schools obtain new equipment and technology, the appropriate use of which has been linked to student achievement (Lei and Zhao 2007). Research on community-based organizations providing health and social service supports and extended learning opportunities also has found positive effects on student attendance and achievement (Covelli, Engberg, and Opper 2022).

Finally, social capital provides social resources, such as trust and legitimacy. Research on trust in schools, such as that garnered by forging community relationships, shows trust is a key resource for school improvement (Bryk and Schneider 2002). Likewise, prior work on schools’ institutional environments has established that legitimacy is a critical resource that can help schools gain access to or prevent the loss of more tangible cognitive and material resources (Bridwell-Mitchell 2018; Dacin, Oliver, and Roy 2007; Meyer and Rowan 1977). Indeed, 25-plus years of coercive isomorphic pressures (DiMaggio and Powell 1983), from federal policy advocating that schools form partnerships, means one way schools can gain the social resource of legitimacy is by acquiring partners.

In summary, we argue that external school social capital from partnerships can provide schools with cognitive, material, and social resources (Koka and Prescott 2002). These resources— from professional development to health and social services to trust and support from families —increase schools’ organizational capacity for removing teaching and learning barriers and supportingstudent achievement. However, the extent of such benefits may depend on the specificnetwork configuration of schools’ partner relationships.

The Network Embeddedness of External School Social Capital

Network Configuration 1: having more relationships for longer to address unmet needs

One essential network configuration for social capital—indeed, the configuration that is the necessary, if not entirely sufficient, condition for social capital—is the existence of a relationship. In other words, to reap any social capital benefits from partnerships, schools must first have partnerships. To the extent that such partnerships provide needed resources, more partnerships suggest more access to resources and so greater capacity to address barriers to teaching and learning. Schools with longer partnerships likely benefit from the accumulation of resources to further improve outcomes.

Consider Simons and Curtis's (2007) description of partnerships at Nailor Elementary in Mississippi.The school partnered with Chevron-Texaco, Delta State University, a vocational-technical high school, and various other communityorganizations that provided literacy materials, teacher professional development, and volunteers to run tutoring and after-school programming. If Nailor students received literacy materials and tutoring over many years, they had more opportunities to read from more diverse reading materials. This could reinforce their fluency and comprehension skills and support their achievement (Allington and McGill-Franzen 2021). So, having partnerships over some period of time is a precondition for social capital benefits, yet the kind of relationships schools have with partners may also be important.

Network Configuration 2: having diverse, deep, or tailored relationships

Research on interorganizational relationships highlights the importance of having partners with the right resources (Gulati 1998; Koka and Prescott 2002). For example, Sampson (2007) shows that organizations with moderately diverse partners— versus too diverse or not diverse enough—perform better. Better outcomes may also relate to partners having resources that are uniquely complementary “in a way that [resources] can be combined to create greater value”; alternatively, it may be important to have partners that “create value because they are similar” and can thus concentrate resources on a given problem (Mitsuhashi and Greve 2009:977). For example, professional development has been linked to improved teaching and learning (Sun et al. 2013), so complementary professional development offerings, or their concentration in an area of greatest instructional need, might support improvement.

Having the right resources might also mean having specific partners. Some schools may need partners with resources that support classroom instruction, such as mathematics manipulatives, which have been linked to student achievement (Sowell 1989). Other schools may need partner resources to support leaderships’ organizational management skills and ability to foster school environments that minimize teaching and learning distractions, both of which are positively associated with student achievement (Branch, Hanushek, and Rivkin 2013; Grissom and Loeb 2011). The right resources could come from partners to which schools are directly connected, but they could also come indirectly from schools’ partners’ other connections.

Network Configuration 3: having relationships with well-connected organizations

When schools have partners who are partnered with other schools and these other schools also have other partners, this creates a pattern of interconnected relationships among schools and partners. In this case, resources may flow through direct and indirect partner connections. For example, in a conversation with their partner, a principal might learn about an innovative managerial practice used at another school with which the partner has a relationship. Likewise, the partner might tell the principal about a new funding opportunity— perhaps for student mental health supports supporting improved achievement (Covelli et al. 2022)—available from another organization to which the partner is connected given common work at another school.

These broader sets of connections among schools and partners can matter for both partnership formation and outcomes (Bridwell-Mitchell 2017; Sabatini 2009). If schools are embedded in a dense pattern of overlapping relationships, then it might be easier to coordinate partnership activities, including fostering a strong culture and norms among cooperating schools and partners, as advocated by popular partnership initiatives such as Networked Improvement Communities (Russell et al. 2017). Relatedly, some popular partnership initiatives advocate partners serve as “backbone support organizations” (Edmondson and Zimpher 2014). Partners that are more central in the overall school-partner network have greater access to other partners and their schools and so might better provide backbone support. We examine these arguments and the other network configuration arguments using the methods described below.

Methodology

Setting and Sample

Our sample comes from the historical administrative data set of New York City (NYC) Annual School Reports (ASRs) documenting 6,536 partnerships between 211 unique high schools listed in the ASRs and 918 unique partner organizations from the 2001–2002 to 2004–2005 school years, the final year ASRs were published. These partnerships do not advance one specific reform or intervention but provide different support given a partner's industry and a school's particular improvement focus. Based on how partner names and activities mapped onto the 2012 two-digit North American Industry Classification System, 30.64 percent of partners in our sample operated in the educational service industry; 21 percent in industries providing “other services,” including churches and community development organizations; 17.55 percent were in health care and social assistance; and 11.25 percent were in the arts, entertainment, and recreation industry. The remaining 19.55 percent of partners operated in many different industries, including finance and technology.

Across the study years, 2 to 5 percent of schools did not submit an ASR, so we have no data on their partnerships, and these schools are excluded from the analysis. A nonresponse analysis indicated that persistently underperforming schools assigned to the chancellor's oversight district were significantly less likely to submit ASRs compared to schools in the Manhattan Superintendency district ( $\hat{β}$ = –8.45, p≤.001). This means the study findings may differ for the very lowest performing schools.

Notably, the data cover the period directly following the 2001 No Child Left Behind Act, which precipitated a steep increase in partnerships given its explicit language encouraging them. The data also cover a reform period set into motion by NYC School Chancellor Harold Levy (2000–2002) and continued under Chancellor Joel Klein (2002–2011), during which there was increased spending for literacy programs to help students meet tougher promotion standards (Goodnough 2003; Herszenhorn 2003).

NYC high school partnership patterns may have changed since our study period, but these large-scale, longitudinal data help establish potentially important network characteristics imprinted on NYC high school partnerships up through today. Nevertheless, the results may not generalize to elementary or middle schools, which differ from high schools in size and structure, or to schools in smaller, less urbanized locales.

Data Collection

In addition to qualitative data listing schools’ partners, ASRs also provide quantitative data on school-level characteristics, including enrollment, student demographics, and passing rates on state exams. We identified partner names and ties from ASRs, but we collected qualitative data on partners’ activities with schools and resources conveyed, mainly from text extracted from partner websites (for further details, see Parts A and B in the online supplement for the article). These archival data cannot provide rich, fine-grained detail about how partnerships were managed, as would be the case with some qualitative case study research.

However, one strength of these large-scale data is being able to draw inferences that take into account numerous possible counterfactuals given different dimensions of variation in school and partner characteristics. Additionally, administrative data are generated unobtrusively so there are fewer concerns about observer bias (Webb et al. 1999). Still, administrative data are limited, including the possibility that the assumptions involved in generating the data do not match those of the research, resulting in invalid indicators of key constructs. We detail our data validation strategy, including informant interviews, in Part C in the online supplement for the article.

Measures

Dependent variable

While partnerships could have consequences for many school outcomes, from attendance to discipline, we focus on academic outcomes, namely the proportion of students passing the New York State (NYS) Regents exam. Passing the Regents with a score of 65 or better is required for graduation; a score of 85 or better is considered passing with distinction. We report results for both outcomes because the social capital resources needed to get students across a minimum passing threshold could differ from resources that help students excel. Because we are less interested in subject-related variation in outcomes, we focus on English language arts (ELA) achievement, consistent with the literacy-focused education reforms in NYC during the study period. For comparison purposes, we also examine mathematics outcomes; the results are largely consistent (see Appendix Table A1), and we discuss differences in the Discussion section.

Independent variables: partnership network characteristics

The two main predictors of interest are the structure and content (i.e., resources) of schools’ egocentric partnership networks, or partner egonets. We constructed school egonets from the whole network matrix, M_ijt, of all schools i and partners j in a given year t. We use the row values for each school i to construct the egonets, where 1 in the intersecting cell for partner j indicates the partner is in the school's egonet; 0 indicates the partner is not in the school's egonet. All partnership variables were constructed as aggregate characteristics for the set of ij = 1 partners in a school's egonet, with the exception of two network variables described below. The whole network data make it possible to construct the complete egocentric network of ties not only from schools to their partners but also between partners and between schools (Borgatti, Everett, and Freeman 2002; Wasserman and Faust 1994). This reveals underlyingresource patterns, as illustrated in Figures 1 and 2 in the Findings section and described in more detail in Part D in the online supplement for the article.

Figure 1.

2003–2004 partnership network for Beacon High School, Manhattan (Ego S1479).

Figure 2.

2003–2004 partnership network for the Institute for Collaborative Education (Ego S1407).

Partnership resources

We determined partner resources by conducting a content analysis of partners’ website text. To determine which resources partners emphasized in their work with schools, we used the qualitative analysis software Linguistic Inquiry and Word Count (LIWC; Pennebaker, Francis, and Booth 2001) to calculate the proportion of text on websites matching terms from a researcher-developed dictionary for school resources/inputs identified from the literature (e.g., Berne 1994; Caldas 1993; Figlio 1999; Greenwald et al. 1996; Hanushek 1997; Jackson et al. 2016; see Parts A and B in the online supplement for the article) and shown in Table 1.² Given moderate reliability (Landis and Koch 1977), for the 884 initially identified terms, only the 546 terms on which two raters reached complete agreement were included in the final dictionary.

Table 1.

Partner Resource Categories and Dictionary Terms with Factor Loadings Given Schools’ Partners’ Resources.

Resource category	Sample dictionary terms	Factor loadings: instructional resources	Factor loadings: student support resources	Factor loadings: administrative resources
Teacher expertise (cognitive social capital): Refers to teachers as inputs/resources for schools. This includes teachers’ education background and level, additional training, professional development, cultivating networks or community with other teachers, overall expertise, and experience, as well as factors affecting their overall motivation to deliver high-quality instruction.	Development for teachers, in-service teachers, instructional leaders, professional development, teacher education, teacher preparation, teacher workshop, training for teachers	0.7399	0.1680	0.2108
Learning environment-core curriculum (material social capital): Refers to resources/inputs related to the development and quality of the school curriculum. This includes the availability of advanced programs, opportunities for students to deepen and extend their classroom learning, specifically as this relates to additional time and activities devoted to core subjects such as English, math, science, and social studies and opportunities to develop analytic and critical thinking skills.	Academic instruction, basic skills, critical thinking, effective learning, exam preparation, individualized math, literacy programs, math and reading, math and science, Regents preparation, skills in science, social studies, study skills	0.7247	0.3453	0.1326
Learning environment-extracurricular (material social capital): Refers to resources/inputs related to the development and quality of the school curriculum. This includes availability of advanced programs, opportunities for students to deepen and extend their classroom learning. This relates specifically to additional time and activities outside of the classroom devoted to extracurricular activities such as the arts, sports, student government. This category also refers to offering field trips that are primarily recreational rather than related to the core curriculum.	After school, arts and crafts, classical theater, community service, cultural exchange, enrichment programs, financial literacy, introduction to music, modern dance, outdoor activities, physical education, recreational and enrichment, theater arts, visual arts, world music, youth media	0.6604	0.0421	0.1759
Instructional materials (material social capital): Refers to inputs/resources related to materials necessary for classroom instruction, including but not limited to notebooks, textbooks, and technology, such as computers.	Access to computers, books and supplies, educational resources, donations, educational software, information resources, manipulatives, much-needed, technology access, textbooks are free	0.6575	0.0747	−0.0639
Learning environment-careers and higher ed (material social capital): Refers to resources/inputs related to the development and quality of the school curriculum, the availability of advanced programs, opportunities for students to deepen and extend their classroom learning. This relates specifically to additional time and activities that are devoted to exposure to college, career opportunities, vocational training, and introduction to the professions and the real world of work.	Advancing into postsecondary, career academy, career counseling, college advisement, college preparatory, dual-enrollment, employability skills, GED preparation, higher education, job readiness, life planning, pre-college program, preparation for the PSAT, pre-vocational skills, technical education, work readiness	0.5414	0.5866	−0.0145
Support to disadvantaged populations (material social capital): Refers to inputs/resources that may be required to provide instruction to students with greater instructional needs or who are considered “high risk,” due to their family background related to socioeconomic status, race, country of origin, neighborhood effects, or learning disabilities or other special education needs.	At-risk youth, disadvantaged backgrounds, English language learners, ethnic and racial disparities, financially in need, former dropouts, homeless high school students, juvenile offenders, low income, low performing, pregnant and parenting students, students with disabilities, underrepresented minorities, underserved youth	−0.1015	0.7909	0.2200
Health and social services (material social capital): Refers to inputs/resources related to meeting students’ psycho-social, emotional, and health needs rather than their academic or instructional needs. This includes physical and mental health services as well as social services delivered directly to the student, families, or communities.	Behavioral health, bullying and violence, conflict resolution, counseling services, crisis intervention, emotional well-being, health and wellness, healthy eating, manage anger, nutrition and fitness, personal health, prevention programs, psychological services, safety and health, sex education, social services, therapeutic programs, wellness resources	0.2207	0.6269	0.0462
Noninstructional material (material social capital): Refers to inputs/resources related to the overall school building and facilities. This includes the availability of appropriate space, well-maintained equipment, façade, and grounds. It also includes provision and maintenance of equipment, including transportation.	Access to library, enhance the infrastructure, improving the facilities, lawn and garden, maintenance, purchases, water quality, weekly space	0.3877	0.5841	−0.2729
Administrative and managerial effectiveness (cognitive social capital): Refers to inputs/resources related to school administration and management, as overseen by the principal, vice principal, other administrators, and related administrative support staff. Resources related to administrative/managerial effectiveness include developing the expertise of the administrative staff, their leadership capabilities, and their decision-making and problem-solving skills. It also includes the effectiveness of managerial routines, the ability to meet mandates for accountability and high standards, and the ability to create a healthy school culture and environment.	Capacity in schools, leadership training, mentor principal, organizational politics, practicum for principals, school change, school culture, school leadership, school morale, school operational, school reforms, student achievement, team building, transform existing schools, trust, and community	0.0782	−0.0629	0.8560
General monetary support (material social capital): Refers to inputs/resources that are strictly monetary/financial in nature. These funds might be used for a variety of purposes but are generally targeted at instructional improvements.	Annual scholarship, donation to the school, financial services, funding for public schools, impactful giving, invest in projects, offset expenses, operating costs, seed funding, student fees	0.2567	0.3553	0.5507

Note: Parentheses indicate observable forms of social capital; partners providing the indicated resources may also provide social resources, such as legitimacy, which is not captured in the resource typology. Bold values in Columns 3, 4, and 5 indicate highest loading for each category and resource categories that loaded together on a given factor.

To determine how much websites emphasized a given resource, we summed the proportions of words matching a given resource category across all of a school's partners in a given time period, $\sum_{i}^{j} x_{jrt}$ , where x is the proportion of words in one resource category r for partner j at time t. These calculations result in each school having a score for each of 10 resources in each time period. We used multilevel factor analysis (Finch and Bolin 2017) to reduce the number of variables and explore underlying resource patterns perhaps related to different forms of social capital or resource types in prior research (see Smith and Wohlstetter 2006). This reduced the 10 resource categories to 3, the validation of which is an important area for future research. We name the three factors instructional resources, student support resources, and administrative resources based on the factor loadings in Table 1, all of which are above the 0.4 threshold widely used for the substantive interpretations of factors (Child 2006; Stevens 2012). The three measures for school partnership resources are thus factor scores for given partner resources during a given period.

One limitation of this measure is that the dictionary terms do not reflect all possible terms indicating resources. Although the measure conservatively estimates resources, there is a greater chance of Type 2 errors or finding no association when there is one. Another arguable limitation is that we do not directly measure partner resources, such as hours of volunteer time, square feet of space provided, or dollars of funding provided. Future research using such measures would be informative. Nevertheless, our content analytic approach of organizational communications measuring the resources partners emphasize on their websites is consistent with long-standing research on the “attention-based view of the firm” (Ocasio 1997) and existing content analyses of organizational activities relying on website text (e.g., Davis et al. 2019; Smith et al. 2016; Windscheid et al. 2018). Additional details about the approach are in Part C in the online supplement for the article.

Partnership structure

In addition to resources or network content, we are also interested in the structure of schools’ partnerships. We construct network structure measures for each school egonet, identified using the aforementioned row values of the M_ijt matrix and calculated using UCINET (Borgatti et al. 2002), unless otherwise indicated. We utilized two common sociometric measures for the partners schools had and for how long: egonet size, or the number of partners, and tie strength, or prior years being partnered (Marsden and Campbell 1984).

The three aforementioned resource factor scores indicate how tailored partnerships are. We created an additional set of measures examining how diverse and deep partnership resources are. We measure how diverse a school's partnerships are using Mitsuhashi and Greve's (2009) resource complementarity measure. The measure is the count of new or unique resources partners add to school egonets relative to the total number of unique resource categories met by all of a school's partners: $| A \cup B - A \cap B | / A \cup B$ , where A is the set of resource categories provided by one partner and B is the set of resources provided by another partner.³ We measure how deep partnerships are using industry concentration, constructed as the percent of a school's partners in a given industry, squared and summed across all industries (Rosenbluth 1955).

A third set of measures captures how well connected schools’ partners are. As detailed in Part D in the online supplement for the article, we construct an egonet density measure from the ties between schools and all other schools in the school-partner network given their common ties to partners, which is to say the one-mode (school) transformation of M_ijt matrix, M_iit. Thus, egonet density is the proportion of other schools to which a focal school is (indirectly) connected given all the schools to which it could be connected. Likewise, we construct a measure for the centrality of schools’ partners in the overall network by using the one-mode (partner) transformation of M_ijt matrix, M_jjt, to calculate the proportion of other partners each partner can reach within two degrees or steps and then calculate the mean of this two-step reach centrality for partners in a school's egonet.

Independent variables: school characteristics

All models include the prior year's proportion of students at a school passing the Regents exam because prior performance is a well-known explanation for current performance. The models also include schools’ district, but rather than include every district, we construct three indicators for district type: (1) one of six traditional districts roughly corresponding with NYC boroughs; (2) one of four alternative districts, including well-known District 2, headed by then district superintendent Anthony Alvarado; and (3) the specially designated Chancellor's oversight district, which assembled persistently underperforming schools and to which all analyses are relative.

In preliminary analyses assessing model fit, the proportion of students receiving free and reduced-price lunch (FRPL), proportion of English language learners (ELL), and student enrollment provided the greatest explanatory power. With parsimony as an aim, we include these demographic variables in the models rather than all possible covariates, such as teacher characteristics or students’ ethnic/racial background. Still, it is important to understand the role of race in our study. We thus note that in all preliminary models where the proportion of White students was included instead of the proportion of FRPL and ELL students, there was a significant association with ELA achievement, similar in size to the FRPL and ELL coefficients, but positive rather than negative. The broad pattern of association for key network variables was unchanged. We conducted additional follow-up analyses examining schools’ racial composition as a predictor of the type of partner resources schools had. The results showed that having a larger proportion of White students was significantly associated with schools having greater instructional resources from their partners, in contrast to social support or administrative resources, which were not associated with the proportion of White students at a school.

Models and analysis

The panel data are 805 repeated observations for schools’ partnership egonets across four periods with repeated observations nested within schools. These data are appropriately modeled with a two-level, multilevel model (Rabe-Hesketh and Skrondal 2012; Raudenbush and Bryk 2002). Because there may be variation in the time it takes for additional resources to increase organizational capacity supporting teaching, learning, and achievement (Malen et al. 2015), we estimate the models using lagged predictor terms; this also allows us to model the argued direction of association. Some models include lags offset from the outcome by two years (indicated by t−2 in tables in the Findings section), making it possible to examine the accumulating effects of predictors.

The conceptual model of interest is specified below using composite, mixed-model notation. It examines the association between the proportion of students passing the NYS ELA Regents exam and characteristics of schools’ egocentric partnership networks, controlling for school demographics, district, and potential endogeneity, given the possibility that some schools might be more likely to have partnerships (see below). The model is fit using maximum likelihood estimation with unstructured covariance and with Level 1 random effects assumed to be independent and normally distributed:

\begin{matrix} Y {(Regents passing rate)}_{s}_{[t]} = β_{00} + β_{1} \\ {(Heckman)}_{s}_{[t - n]} + β_{2 - 5} \\ {(school demographics)}_{s}_{[t - n]} \\ + β_{6 - 14} {(partnership network characteristics)}_{s}_{[t - n]} \\ + γ_{1 - 2} {(district covariates)}_{s} + r_{s} + e_{st}, \end{matrix}

where the Level-1 components are: Y _s_[t], the proportion of students passing the ELA Regents exam with a score of 65 and better or 85 and better for school s at time t; β₀₀, the overall model intercept when all explanatory variables are set to zero; β₁, the effects for a term correcting for potential endogeneity from selection bias from some schools potentially being more likely to have partnerships; β_2–5, the degree of association between the proportion of students passing the ELA Regents exam and the variables for the unique observation of school demographics for school s at time t;β_6–14, the degree of association between the proportion of students passing the ELA Regents exam and the variables for the unique observations of partnership characteristics for school s at time t; and e_st, the random variance component for unique observations of school s at time t.

The Level 2 components are: γ_1–2, the association between the proportion of students passing the ELA Regents exam and schools being located in one of two types of districts, relative to the reference category; and r_s is the random variance component for fixed effects for school s.

Our research design cannot establish causality, but we are concerned with ruling out alternative explanations. So, we include a Heckman two-stage correction term in the models to address sample selection bias (Heckman 1979) by controlling for the probability that a school would have a given partner.⁴ As a robustness check for potential bias given common history, we conducted sensitivity analyses using period fixed effects. The pattern of results is consistent with the main results. To check for robustness against alternative model specifications, we estimated a set of ordered probit models (Borooah 2002; Rabe-Hesketh and Skrondal 2012) using disaggregated, student-level data constructed from the aggregated passing rates and number of enrolled students at each school. The pattern of results is consistent with the main results and illustrated in Appendix Table B1.

Findings

School Partnership Characteristics

We argue that schools’ capacity to address fundamental resource inequities may be related to their external school social capital, given the structure of interorganizational relationships. Figures 1 and 2 illustrate such patterns in the 2003–2004 school year for two focal schools selected to illustratethe diversity of schools’ partnership characteristics. Beacon High School (BHS; S1479 in Figure 1) had eight partners, including New York University, Joyce Theater, and the Museum of Natural History, and the Institute for Collaborative Education (CEHS; S1407 in Figure 2) had five partners, including the Greenwich Village Youth Council, Borough of Manhattan Community College, and the Police Athletic League. To the extent the number of partners matters for access to resources, BHS had greater access than CEHS. The weight or thickness of the lines in the figures indicates how long schools had a partner relationship; the total prior partnership years across all partners was 23 years for BHS and 13 for CEHS.

Resource differences based on the number and length of partnerships are amplified when considering how partner resources are distributed. Often, a single partner emphasizes multiple resources equally, as illustrated by the gray symbols in Figures 1 and 2, but partners sometimes emphasize one resource over others. For example, BHS's partners emphasize resources related to instruction and student support, including resources for extracurricular activities, core curriculum, postsecondary exposure, and health and social services. CEHS's partners, in contrast, emphasize resources mainly for teacher expertise and extracurricular activities—resources related to the instructional resources factor.

We also see differences in the two schools’ indirect access to resources. Because of its connection to partner 0586, Columbia University, BHS is indirectly connected to partner 0336, Albert Einstein College of Medicine. The overlapping patterns in schools’ partner connections are reflected in an egonet density of 70.12 percent for BHS and 44.67 percent for CEHS. So, BHS potentially has access to a greater flow of resources from a broader set of other schools and partners. Table 2 shows central tendencies for partnership characteristics in the full sample. On average, schools have 8.12 partners, partners collectively worked with schools for 19.85 prior years, and partners are central enough in the overall school-partner network to reach any other partner in an average of 2.55 steps or degrees of separation.

Table 2.

Descriptive Statistics.

	Observations	M	SD	Minimum	Maximum	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. Passing ELA Regents	785	59.82	22.84	0.00	100.00	1
2. Passing with distinction	785	16.19	17.42	0.00	99.20	0.72***	1
3. School enrollment	805	1341.68	1196.07	4.00	4769.00	0.196***	0.201***	1
4 % Free-reduced lunch	805	58.91	24.27	2.90	100.00	−0.448***	−0.556***	−0.327***	1
5. % ELL	805	8.28	14.10	0.00	99.39	−0.14**	−0.161***	0.036	0.259***	1
6. Number of partners	805	8.12	3.73	1.00	17.00	0.107*	0.033	0.306***	0.003	0.046	1
7. Years prior partnerships	805	19.85	14.70	0.00	77.00	0.189***	0.128**	0.363***	−0.059	0.043	0.677***	1
8. Industry concentration	805	43.75	20.54	14.00	100.00	0.031	0.03	−0.108**	−0.109**	−0.031	−0.479***	−0.333***	1
9. Partnership complementarity	805	14.85	21.20	0.00	91.67	−0.119**	−0.059	−0.266***	0.105**	−0.077*	−0.654***	−0.442***	0.414***	1
10. Partnership centrality	805	2.55	1.30	0.00	6.09	0.132**	0.065	0.373***	−0.054	0.042	0.974***	0.696***	−0.424***	−0.656***	1
11. Partnership density	805	48.59	23.73	0.00	100.00	0.06	0.081*	0.049	−0.045	0.021	−0.104**	−0.027	−0.103**	−0.124***	−0.046	1
12. Instructional resources	805	0.00	1.00	−2.24	4.07	0.2***	0.182***	0.189***	−0.115**	0.013	0.686***	0.45***	−0.284***	−0.486***	0.709***	−0.02	1
13. Student support resources	805	0.00	1.00	−1.83	5.24	−0.099*	−0.063	0.215***	0.037	0.072*	0.474***	0.345***	−0.191***	−0.388***	0.47***	−0.028	0.000	1
14. Administrative resources	805	0.00	1.00	−2.16	4.95	−0.007	−0.041	0.008	−0.827	−0.018	0.045	0.21***	0.103**	−0.21***	−0.185***	0.174***	0.005	0.000	0.000	1
15. Traditional districts	805	0.70	—	0.00	1.00
16. Alternative districts	805	0.08	—	0.00	1.00
17. Chancellor’s district	805	0.22	—	0.00	1.00

Note: Correlations are constructed from deviations from school means to minimize autocorrelation effects from repeated observations; all other summary statistics use raw measures. Means for categorical variables are frequencies. ELA = English language arts; ELL = English language learner.

p < .05. **p < .01. ***p < .001.

Because partner resource factor scores are standardized with a mean of 0 and standard deviation of 1 and were obtained using an orthogonal rotation method minimizing correlation between factors, their central tendencies are less informative. Yet raw scores (not shown in Table 2) indicate the greatest proportion of partner activity text is related to instructional resources (M = 7.51, SD = 5.22). This includes activities such as those provided by BHS's partner, the Ackerman Institute for Family Therapy (p0347 in Figure 1), which emphasized resources not only for extracurricular activities but also for health and social services through courses “suitable for educators and other professionals interested in learning the techniques and theory of family therapy.”

The next largest proportion of partner activity text is related to student support (M = 5.99, SD = 4.78) and then administrative (M = 0.89, SD = 1.03) resources. As an example of the first, CEHS's partner, the Greenwich Village Youth Council (p0780 in Figure 2), offered multiple resources, including support to disadvantaged students and health and social services. As described on their website, this partner “engage[s] at-risk young people in activities, mentoring, and counseling in a warm and supportive community in order to help them avoid substance abuse, delinquency, and other risky behaviors.” CEHS's partner, the Police Athletic League (p0333), offered not only extracurricular resources but also resources for administrative and managerial effectiveness. This included providing school leaders with training “where they will learn policy and procedures, prevention curriculum, safety policy . . . and the logistics of setting up and providing a Teen Impact Program.” A key question is to what extent such resources or other partnership characteristics play a role in educational outcomes.

Partnership Characteristics and Student Outcomes

The contribution of partnership characteristics (model fit)

Table 3 shows how much partnership characteristics are associated with the proportion of students passing the NYS ELA Regents exam. Columns 1, 2, and 3 show results comparing the models for school characteristics only (Model 1) and then including partnership structure variables (Model 2) and partnership resource variables (Model 3). The log-likelihood statistics (Model 1: −2,224.34; Model 2: −2,210.43; Model 3: −2,205.53) show that models with partnership characteristics do a better job explaining variation in students passing the Regents exam. A log-likelihood ratio test indicates the differences between the pairs of models are statistically significant: Model 1 > Model 2, χ²(6) = 27.90, p < .001; Model 2 > Model 3, χ²(3) = 9.81, p = .020.

Table 3.

Multilevel Regression Models Predicting the Association between Partnership Characteristics and Students Passing the ELA Regents Exam.

	(1) ELA passing	(2) ELA passing	(3) ELA passing	(4) ELA passing (t–2)	(5) ELA distinction	(6) ELA distinction (t–2)
Heckman selection factor	−27.613^†	−21.117	−20.459	−20.005	−5.045	6.566
	(14.243)	(17.508)	(17.413)	(25.010)	(14.580)	(19.623)
Enrollment	0.000	−0.001	−0.001	−0.002	0.000	0.000
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
% Free-reduced lunch	−0.035	−0.030	−0.024	−0.198***	−0.143***	−0.223***
	(0.030)	(0.031)	(0.030)	(0.042)	(0.026)	(0.033)
% ELL	−0.067	−0.072^†	−0.072^†	−0.042	−0.025	−0.019
	(0.041)	(0.041)	(0.040)	(0.057)	(0.029)	(0.047)
ELA prior year performance	0.686***	0.658***	0.649***	0.274***	0.292***	0.284***
	(0.029)	(0.029)	(0.029)	(0.041)	(0.023)	(0.032)
Number of partners		−0.873	−0.690	1.341	−0.359	−0.271
		(0.894)	(0.901)	(1.273)	(0.864)	(0.988)
Years prior partnerships		0.115^†	0.120*	−0.238^†	0.168***	0.039
		(0.061)	(0.061)	(0.130)	(0.044)	(0.104)
Partner complementarity		−0.054	−0.072	−0.073	−0.005	0.024
		(0.047)	(0.048)	(0.062)	(0.039)	(0.050)
Industry concentration		0.114**	0.127**	0.137*	0.037	−0.021
		(0.042)	(0.042)	(0.059)	(0.039)	(0.046)
Partner centrality		0.003	0.003	0.000	−0.001	0.000
		(0.002)	(0.002)	(0.003)	(0.002)	(0.003)
Partner density		0.075*	0.077*	0.115**	0.038	0.058^†
		(0.031)	(0.030)	(0.041)	(0.027)	(0.032)
Instructional resources			−0.157	−0.195	2.414*	2.890*
			(1.177)	(1.715)	(1.180)	(1.324)
Student support resources			−2.451*	−2.922*	0.041	0.633
			(0.950)	(1.390)	(0.966)	(1.069)
Administrative resources			0.351	−0.108	0.790	0.476
			(0.725)	(1.069)	(0.727)	(0.825)
Traditional districts	4.265*	4.968*	4.983*	9.272**	1.181	1.053
	(2.004)	(2.042)	(2.023)	(3.025)	(2.159)	(2.311)
Alternative districts	5.850*	6.205*	5.717*	8.279^†	6.080^†	7.583*
	(2.906)	(2.929)	(2.909)	(4.335)	(3.113)	(3.310)
Constant	28.482**	18.455**	15.667*	40.383**	8.265	18.208**
	(3.019)	(4.921)	(5.306)	(7.427)	(5.065)	(5.770)
Variance components
School	55.790	57.843	56.008	146.224	93.872	79.109
	(11.929)	(12.178)	(11.835)	(29.279)	(12.789)	(16.417)
Residual	107.805	100.347	99.029	100.920	41.113	69.737
	(8.573)	(8.110)	(7.985)	(13.473)	(3.235)	(8.881)
Log-likelihood	–2,224.38	–2,210.43	–2,205.53	–1,545.81	–2,058.58	–1,458.71
Observations	568	568	568	380	568	380

Note: In Models 1, 2, 3, and 5, all terms are lagged one year prior to the outcome (t−1); in Models 4 and 6, terms are lagged two years prior to the outcome (t−2) to examine how accumulating effects over time, or “dosage,” for partnership characteristics may relate to the outcome. ELA = English language arts; ELL = English language learner.

†p < .1. *p < .05. **p < .01. ***p < .001.

This means, for example, if one wanted to predict educational outcomes for BHS, one would make a better prediction taking into account the school's partnerships with organizations, such as MOUSE (p0989 in Figure 1). MOUSE, as described in its website text, “is one of several partners participating in the NYC Connected Learning Initiative, focused on improving student achievement and strengthening the connection between school and home-based learning in high-need schools across the city.” Based on its website text, MOUSE provides resources related to almost all the resource categories in Table 1. However, MOUSE most emphasized resources related to the core curriculum aspects of school learning environments, such as providing activities through which “students learn an approach to problem solving.” Such partner supports are, as we show, related to student achievement. Importantly, this association is not due entirely to endogenous selection effects from some schools simply being more likely to have partnerships since the Heckman selection coefficient is not significant in most models in Table 3.

Having more resources for longer

One might expect that having more partnerships would provide a school more resources to help improve outcomes. Yet our results show it is not the number of partners providing resources that plays a role in outcomes, but how long partners provide resources. Number of partners is not significant in any model in Table 3. In contrast, Models 3 and 5 show the proportion of students passing the ELA Regents exam and passing with distinction is significantly associated with increases in years prior partnerships (Model 3: β = 0.120, p ≤ .05; Model 5: β = 0.168, p ≤ .001).

However, the potential benefits of having partners longer may be limited given that in Models 4 and 6, estimating two-year lag terms, the results were marginally or not at all significant. This may indicate declining marginal benefits of longer partnerships. Although the extensiveness of resources over time may have waning benefits, the extensiveness of resources in terms of their depth in a particular area may have more lasting benefits, as we show in the next section.

Having diverse, deep, or tailored partnerships

As shown by the non-statistically significant coefficients for partner complementarity across all models, there is no support for the argument that uniquely complementary or diverse partner resources play a role in educational outcomes. Yet Models 3 and 4 in Table 3 show that industry concentration, reflecting the depth of partnerships, has a positive and significant association with students passing the ELA Regents exam when considering partnerships one year (Model 3: β = 0.127, p ≤ .01) and two years (Model 4: β = 0, 137, p ≤ .05) prior. However, industry concentration is associated with students passing the ELA Regents exam but not passing with distinction (Models 5 and 6).

Models 5 and 6 show results for specific types of resources, or tailoring of partnerships. Instructional resources help students pass the ELA Regents exam with distinction for partnerships held one year (Model 5: β = 2.414, p ≤ .01) and two years (Model 6: β = 2.890, p ≤ .01) prior. Instructional resources are not related to merely passing the exam. Resources for student support are related to passing the Regents exam, but the association is negative. This negative association holds for partnerships one year (Model 3: β = −2.451, p ≤ .01) and two years (Model 4: β = −2.922, p ≤ .01) prior.

Having well-connected partners

The resources schools receive from partners might be further amplified by the relationships partners have with each other and with other schools. Model 3 in Table 3 shows that partnership density, given schools’ overlapping partner relationships and thus indirect connections with each other, has a significant positive association with students passing the ELA Regents exam (Model 3: β = 0.077, p ≤ .05). This association is even stronger for relationships two years prior (Model 4: β = 0.115, p ≤ .01). In contrast, partner centrality is not statistically significant in any of the models.

Discussion

Existing research shows that using key resources appropriately plays an important role in educational opportunities and outcomes (e.g., Hanushek 2020; Jennings et al. 2015). Here, we argue that these resources include cognitive, material, and social resources from external school social capital, and we show that this matters for student achievement, given schools’ increased capacity to address teaching and learning barriers. Although we do not directly measure increased organization capacity (see Malen et al. 2015) or reduced teaching and learning barriers (see Hawkins et al. 2008) as the mechanisms for the observed association, we do show, and will explain further, the role of four specific partnership characteristics in the likely variation of both and also school outcomes.

Why Long-Lasting Partnerships Matter

One way to interpret federal education policy encouraging partnerships is that schools should have many partnerships. Indeed, some schools in our study go so far as to explicitly state their total partnerships on their ASRs (e.g., “150 Collaborative Organizations with Community Service”). But we find it is not how many partners schools have that matters, but how long schools have had partners. Long-lasting partnerships may help schools and partners cultivate the quality relationships that some researchers advocate (Sanders 2006), develop the continuous communication advocated by others (Edmondson and Zimpher 2014), or utilize fully the accumulating resources partners provide.

Yet holding on to partners too long may have decreasing benefits, given that the association we observe carries forward only one school year. Our follow-up analyses show that the squared term for years of prior partnership is negative, indicating an inverted U-shaped relationship between partnership length and student outcomes. One implication for partnership policy and practice is that while it may be important, on average, to establish long-lasting quality relationships, it is also important to regularly reevaluate which partnershipsare worth holding onto because their contributions to organizational capacity may wane.

The association between partnership length and outcomes is also illustrated in our supplemental analysis of mathematics achievement (see Appendix Table A1). As with ELA, longer lasting partnerships are associated with increased mathematics achievement over the near term (Model 3: β = 0.471, p ≤ .001) and the longer two-year term (Model 4: β = 0.522, p ≤ .001). In contrast to ELA and in contrast to the positive association for merely passing the mathematics Regents, the association for passing the mathematics Regent with distinction is negative (Model 5: β = –0.239, p ≤ .001; Model 6: β = –0.172, p ≤ .001). Hence, longer partnerships may help students get over an initial threshold of mathematics achievement but not necessarily to excel, and longer lasting partnerships might exist to support students with lower mathematics performance.

Why Concentrated Partnerships Matter

To the extent that having more numerous partners also means more diverse partners, then current partnership policy may inadvertently encourage another counterproductive partnership strategy. Our findings show the concentration of partners in a given industry plays a more important role in student achievement than does partners’ ability to provide uniquely complementary, and thus diverse, resources. This finding holds for our main ELA analysis and for supplemental analyses of mathematics.

Being concentrated in a given industry may amplify partners’ shared expertise, enabling them to go deeper on a given problem. If so, then industry concentration is an important focus for popular school partnership initiatives calling for partners to have a common agenda (Edmondson and Zimpher 2014) or shared practice problem (Russell et al. 2017). Importantly, however, although diversity does not play a role in educational outcomes in our study, prior research showing the benefits of diverse partners focused on innovation (Sampson 2007). Future research might thus examine the role partnership diversity plays in outcomes such as schools’ organizational learning (Farrell, Coburn, and Chong 2019).

In considering the role of partner industry, some researchers have pointed out potentially negativeconsequences (Feuerstein 2001). For example, the largest proportion of partners in our sample were in the educational services industry, yet 10 percent were in finance and insurance; professional, scientific, and technical services; information; and other related industries. One could imagine a scenario in which the aims of partners from certain industries compete with the fundamental goals of schooling and this becoming more problematic with an increasing concentration of partners in industries outside of educational services. Indeed, our findings suggest there is a particularly important role for partners in educational services.

Why Instructional Resources in Partnerships Matter

Perhaps unsurprisingly given the importance of teaching and learning, we find that partners providing instructional support resources is associated with students’ performance on the ELA Regents exam. Notably, in our preliminary analyses, we found that schools with greater instructional resources also had a greater proportion of White students. Still, it is not clear how helpful instructional resources are for schools seeking to help students right below the bubble (see Booher-Jennings 2005). In our research context, instructional resources do not help students simply pass the Regents exam but to pass with distinction. This pattern holds for ELA and mathematics achievement (see Appendix Table A1).

When it comes to students passing the ELA Regents, student support resources have a significant negative association with achievement. In our follow-up analyses exploring possible moderating factors for this negative association, only models including both school suspension and attendance rates as well as proportion of FERPL and ELL students mitigated the negative association between student support resources and outcomes. One speculative explanation for the negative association is that student support resources are targeted to students with discipline issues or low attendance, who also tend to perform worse on the Regents exam. So, even with models including lag terms to predict future outcomes from prior school and partnership characteristics, this does not fully address prior school conditions that prompt administrators to seek social support resources from partners in the first place. Wefind no statistically significant association for student support resources and mathematics achievement.

Why Partnership Density Matters

One final key finding from our research is that the density, but not the centrality, of partner relationships is associated with educational outcomes. If partnership policies intend to encourage a robust flow of resources among schools and their partners, it would thus be preferable to have resources flow among a set of partners highly connected to each other given overlapping relationships with the same set of schools rather than among partners with a broad reach across a large set of partners. Interestingly, we find no significant association between partnership density and mathematics achievement (see Appendix Table A1). While our data cannot speak directly to why such differences between ELA and mathematics exist, one possibility is that during our study period, education reforms in NYC focused on literacy. Thus, the density measure could be constrained if there are not enough partners in the data set supporting mathematics instruction.

Conclusion

In the best-case scenario, partnership policies would address structural barriers to educational equity that are built into U.S. public schooling. Our research shows these structural barriers include the network structure of interorganizational relationships in schools’ external environments. In other words, even if funds are directly targeted to schools and spent in appropriate ways (Hanushek 2020), it is not clear this would alleviate resource inequities because important social capital resources flow through the relationships schools have with their partners. To further equity of educational opportunities and outcomes, our findings suggest it is important for schools to have longer lasting partnerships concentrated in a given industry with many interconnected relationships among partners emphasizing instructional resources.

Our work expands existing research on school resources to include a focus on resources in schools’ external environments, and we establish those resources as forms of social capital. One prevalent research approach focusing on schools’ external environments emphasizes the importance of legitimacy as a resource, but this work suggests partnerships serve mainly ceremonial purposes (Meyer and Rowan 1977). In contrast, we show that partnerships, in the right configuration, can convey more tangible resources and play a more technical role in school outcomes. Thus, it is important to conceptualize more broadly the nature and role of schools’ external organizational environments, including how they matter for educational equity.

Finally, our findings suggest that 25-plus years of education policy advocating partnerships is simply not enough. It is essential to also provide technical assistance helping schools cultivate partnerships with particular characteristics. This might include helping school staff better conceptualize the characteristics of organizations in the partner landscape (Tuma 2020) to determine which partners might best provide instructional resources through long-lasting relationships. Likewise, it would be important to determine which combination of partners could convey resources concentrated in a given industry and connect schools to existing dense networks of other partners and schools. Otherwise, any potential payoff from partnerships accrues to students and schools that are already advantaged (Bridwell-Mitchell 2017). In this case, partnership policies can have the unfortunate unintended consequence of reinforcing the very structural inequities they are intended to address.

Supplemental Material

sj-docx-1-soe-10.1177_00380407231176541 – Supplemental material for The Social Structure of School Resource Disparities: How Social Capital and Interorganizational Relationships Matter for Educational Equity

Supplemental material, sj-docx-1-soe-10.1177_00380407231176541 for The Social Structure of School Resource Disparities: How Social Capital and Interorganizational Relationships Matter for Educational Equity by E. N. Bridwell-Mitchell, James Jack and Joshua Childs in Sociology of Education

Footnotes

Appendices

Acknowledgements

We gratefully acknowledge the feedback of colleagues who provided thoughtful comments on earlier drafts of this article, including Peter Blair, Ain Grooms, Rebecca Horowitz, Jennifer Lin Russell, Katherine Strunk, and Eric Taylor. Any errors are our own.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a National Science Foundation grant for the study of human and social dynamics (SES-0433280).

Research Ethics

This research relies primarily on publicly available archival data. Any non-exempt research on human subjects involved in this research was approved by the overseeing internal review board.

ORCID iD

E. N. Bridwell-Mitchell

Supplemental material

Supplemental material for this article is available online.

Notes

Author Biographies

E. N. Bridwell-Mitchell is the Herbert A. Simon Professor in Education, Management, and Organizational Behavior at the Harvard University Graduate School Education. Bridwell-Mitchell’s research integrates research from organizational management and theory, public policy, and education to explain the organizational and institutional factors constraining and enabling the implementation of U.S. public school reform policies.

James Jack is a PhD candidate in education at Harvard’s Graduate School of Education. His research focuses on institutional change and continuity in formal learning spaces to contribute to ongoing projects aimed at transforming learning into a humanizing and liberatory practice.

Joshua Childs, PhD, is an assistant professor in the Educational Policy and Planning Program in the Department of Educational Leadership and Policy in the College of Education at the University of Texas at Austin. His research examines collaborative approaches that involve community organizations and stakeholders that have the potential to improve academic achievement and reduce opportunity gaps for students in urban and rural schools.

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