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The College Transfer and Articulation Network: How Are These Statewide Policies and Bilateral or Dyadic Partnerships Structured across the United States?

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Title: The College Transfer and Articulation Network: How Are These Statewide Policies and Bilateral or Dyadic Partnerships Structured across the United States?
Language: English
Authors: Manuel S. González Canché (ORCID 0000-0001-8926-7614), Jiayi Arthur Qiu, Kaiwen Zheng, Mingbo Gong, Chelsea Zhang
Source: Research in Higher Education. 2025 66(3).
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 46
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Transfer Students, Transfer Policy, Articulation (Education), State Policy, Partnerships in Education, Economic Factors, Proximity, College Credits, Intercollegiate Cooperation, Interstate Programs
DOI: 10.1007/s11162-024-09831-5
ISSN: 0361-0365
1573-188X
Abstract: Every academic year, millions of college students change institutions before degree completion, confronting the challenge of validating credits across colleges. Despite state-level efforts to legislate strategies for smoother transfers, actual credit recognition relies on non-state-regulated bilateral partnerships that are more (i.e., articulation agreements) or less (general transfer partnerships) specific as policies or guides to avoid credit loss. This study comprehensively sheds light on the USA nationwide structure of transfer and articulation agreements by focusing on statewide policies and in-state and out-of-state informal bilateral partnerships as units of analyses. The spatial configuration of both statewide policies and institutionally driven partnerships enabled testing for economic spillovers as well as measuring whether distance is a factor that may impact the formation of these partnerships. Data were retrieved from CollegeTransfer.Net (N = 18,260 partnerships and 1163 colleges), the Education Commission of the States (118 statewide policies), the IPEDs, and the US Census Bureau. Findings at the state-level revealed economic spillovers in two of four statewide policies, highlighting greater structure of program-specific articulation agreements over general transfer partnerships (i.e., agreements that do not require program continuation). Regarding institutionally driven agreements, the analyses indicated that general partnerships were the most prevalent form, which, compared to more structured articulation efforts, may be less effective in the avoidance of credit loss. We also found that shorter distances are a significant but impractical partnership-forming factor, for the average distance reduction among partnering colleges is 30 miles across models. Combining state and institutional datasets, we found that neither individual nor combined statewide policies actively predict institutional partnership formation. All databases and code created (statewide policies: https://cutt.ly/uwHyvkWQ, institutionally driven agreements: https://cutt.ly/7wHtPkEA, replication codes: https://cutt.ly/JwGRmVDu, https://cutt.ly/EwG1VbaW) may be used in future analyses to address questions of transfer effectiveness and transferring financial costs, which although important go beyond the scope of our study.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1462833
Database: ERIC
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  Value: <anid>AN0183779576;rhe15mar.25;2025Mar21.13:05;v2.2.500</anid> <title id="AN0183779576-1">The College Transfer and Articulation Network: How are These Statewide Policies and Bilateral or Dyadic Partnerships Structured Across the United States? </title> <sbt id="AN0183779576-2">Introduction</sbt> <p>Every academic year, millions of college students change institutions before degree completion, confronting the challenge of validating credits across colleges. Despite state-level efforts to legislate strategies for smoother transfers, actual credit recognition relies on non-state-regulated bilateral partnerships that are more (i.e., articulation agreements) or less (general transfer partnerships) specific as policies or guides to avoid credit loss. This study comprehensively sheds light on the USA nationwide structure of transfer and articulation agreements by focusing on statewide policies and in-state and out-of-state informal bilateral partnerships as units of analyses. The spatial configuration of both statewide policies and institutionally driven partnerships enabled testing for economic spillovers as well as measuring whether distance is a factor that may impact the formation of these partnerships. Data were retrieved from CollegeTransfer.Net (N = 18,260 partnerships and 1163 colleges), the Education Commission of the States (118 statewide policies), the IPEDs, and the US Census Bureau. Findings at the state-level revealed economic spillovers in two of four statewide policies, highlighting greater structure of program-specific articulation agreements over general transfer partnerships (i.e., agreements that do not require program continuation). Regarding institutionally driven agreements, the analyses indicated that general partnerships were the most prevalent form, which, compared to more structured articulation efforts, may be less effective in the avoidance of credit loss. We also found that shorter distances are a significant but impractical partnership-forming factor, for the average distance reduction among partnering colleges is 30 miles across models. Combining state and institutional datasets, we found that neither individual nor combined statewide policies actively predict institutional partnership formation. All databases and code created (statewide policies: , institutionally driven agreements: , replication codes: , ) may be used in future analyses to address questions of transfer effectiveness and transferring financial costs, which although important go beyond the scope of our study.</p> <p>Every academic year, about two million college students switch higher education institutions before they attain a degree or credential at their first institution (Causey et al., [<reflink idref="bib8" id="ref1">8</reflink>]; National Student Clearinghouse, [<reflink idref="bib48" id="ref2">48</reflink>]; Nietzel, [<reflink idref="bib49" id="ref3">49</reflink>]). And yet, remarkably, the transfer process across institutions that delineate how credits attained in one institution will be accounted for in the other, is neither smooth nor clearly defined and articulated (Anderson et al., [<reflink idref="bib1" id="ref4">1</reflink>]; Dougherty, [<reflink idref="bib17" id="ref5">17</reflink>]; Spencer, [<reflink idref="bib62" id="ref6">62</reflink>]). Despite state-level interest in passing legislation to ease the transfer and articulation processes among institutions in the same state (Whinnery & Peisach, [<reflink idref="bib69" id="ref7">69</reflink>]), at least two issues remain. First, there is no discussion <emph>of the existence</emph> of out-of-state articulation agreements, which limits our understanding of their scope and programs. Second, transfer agreements that recognize and honor credits attained at the original institution are typically based on informal or unregulated partnerships across participating colleges and universities (Jenkins et al., [<reflink idref="bib39" id="ref8">39</reflink>]; Root, [<reflink idref="bib52" id="ref9">52</reflink>]; Schudde et al., [<reflink idref="bib58" id="ref10">58</reflink>], [<reflink idref="bib57" id="ref11">57</reflink>]). Relatedly, although colleges and universities are federally mandated to provide information about their transfer policies on their websites, they are not required to publicly identify their partnering colleges. Additionally, the mere presence of a transfer policy on a college website does not mean that the institution has any articulation agreements or partnerships in place (United States Government Accountability Office, [<reflink idref="bib66" id="ref12">66</reflink>]). These issues can make it difficult for students to understand their transfer options.</p> <p>From this perspective, then, although institutional partnerships are vital for the smooth transition of credits across institutions, the bilateral and to some degree informal—in the sense of being faculty-driven and program-oriented—nature of transfer and articulation partnerships adds uncertainty to this already complex and challenging process. As Jenkins et al. ([<reflink idref="bib39" id="ref13">39</reflink>]) and Root ([<reflink idref="bib52" id="ref14">52</reflink>]) noted, students typically shoulder the burden of navigating the transfer process. Ideally, they would have received help from their advisors but, in reality, students are largely on their own because college advisors are stretched so thin. For example, estimates put the student-to-advisor ratio at 375 to 1 (Petracca, [<reflink idref="bib50" id="ref15">50</reflink>]) among all colleges and 1000 to 1 at public 2-year colleges, with about half of the students at 2-year colleges unaware that they have access to advising (Marcus, [<reflink idref="bib44" id="ref16">44</reflink>]).</p> <hd id="AN0183779576-3">Relevance</hd> <p>Considering that students (and, potentially, their advisors) are largely responsible for finding information about transfer and articulation agreements, a clear understanding of the most up-to-date agreements may "facilitate the transfer of credits and coursework between degree programs in different [colleges and universities] within [and out of] the state" (Holod et al., [<reflink idref="bib36" id="ref17">36</reflink>], p. ix). Notably, despite the existence of a network of institutionally driven agreements (18,260 across 1163 colleges and universities) and a dataset of statewide policies (118 policies) (CollegeTransfer.Net, [<reflink idref="bib10" id="ref18">10</reflink>]; Whinnery & Peisach, [<reflink idref="bib69" id="ref19">69</reflink>]), there has been no comprehensive analysis of the informal and formal transfer and articulation agreements across the continental United States.[<reflink idref="bib1" id="ref20">1</reflink>] Because an institution's location may influence their decision to enter into an agreement, we used spatial relational modeling (Barthélemy, [<reflink idref="bib4" id="ref21">4</reflink>]; Borgatti, [<reflink idref="bib6" id="ref22">6</reflink>]; Maroulis et al., [<reflink idref="bib45" id="ref23">45</reflink>]; Mitchell, [<reflink idref="bib46" id="ref24">46</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref25">68</reflink>]), to unveil the structure, spatial distribution, and prevalence of these agreements based on their program type and scope (i.e., within or out-of-state).</p> <p>Our research questions are designed to enhance our understanding of the transfer and articulation systems in the continental United States. As we discuss in our literature review and conceptual sections, the processes driving the adoption of statewide policies differ from those shaping bilateral partnerships, prompting different questions for each policy type. Our first two research questions focus on statewide policies, the next three address institutionally driven agreements, and the sixth question considers the relationship between the two types of policies.</p> <hd id="AN0183779576-4">Statewide Transfer Policies: Research Questions 1 and 2</hd> <p>The database published by the Education Commission of the States identifies four types of transfer policies, which we describe in the literature review. Considering that states are under no obligation to adopt any of these policies (Whinnery & Peisach, [<reflink idref="bib69" id="ref26">69</reflink>]), we pose the following questions:</p> <p></p> <ulist> <item> What are the most central statewide policies across the continental United States?</item> <p></p> <item> When considering the geographical distribution statewide policies by type, is there evidence of</item> <p></p> <item> economic spillovers, wherein neighboring states are more likely to implement the same statewide policies than non-neighboring states?</item> <p></p> <item> states clustering, wherein certain states may be grouped together based on their policies decisions?</item> </ulist> <hd id="AN0183779576-5">Institutionally Driven Transfer Policies: Research Questions 3–5</hd> <p>Unlike the statewide database, CollegeTransfer.net's database of institutional agreements does not classify transfer policies by type. We pose the following questions about these institutionally driven policies:</p> <p></p> <ulist> <item> What types of institutions (i.e., public and private, 4-year or less than 4-year institutions) participate in institutionally driven agreements, and how does this participation vary based on the scope of these agreements (i.e., within state or out-of-state)?</item> <p></p> <item> What are the most prominent forms of partnerships (i.e., discipline- and program-specific or general) and participating states and institutions in these agreements?</item> <p></p> <item> Is there any evidence suggesting that spatial proximity drives the establishment of articulation and transfer agreements in both in-state and out-of-state partnerships?</item> </ulist> <hd id="AN0183779576-6">When Combining Both Institutions and Statewide Policies: Research Question 6</hd> <p></p> <ulist> <item> Does the presence of different statewide policies impact the proportion of institutions implementing institutionally driven partnerships?</item> </ulist> <p>Together, the answers to these inquiries provide a comprehensive understanding of the complex landscape of transfer and articulation agreements in the United States. The study concludes by presenting findings organized by each research question and discusses potential future research and implications for college affordability.</p> <hd id="AN0183779576-7">Literature Review</hd> <p>The guiding question for our literature review was: <emph>What are college transfer or articulation policies and agreements, and why should they matter?</emph> We present this question because, based on our experience, one of the most challenging aspects of this study was formally conceptualizing the assortment of statewide policies and bilateral partnerships that constitute the transfer and articulation network.</p> <p>To address this challenge, we introduce Fig. 1, which represents our conceptualization of the landscape of college transfer and articulation agreements. As can be seen in this conceptualization, college transfer and articulation agreements may be classified as either statewide policies or institutionally driven bilateral agreements. Both types of agreements are meant to help students determine which credits will transfer and the process for validating them at the receiving institution (CollegeTransfer.Net, [<reflink idref="bib11" id="ref27">11</reflink>]).</p> <p>Graph: Fig. 1 College transfer and articulation conceptual framework</p> <hd id="AN0183779576-8">State-Level Transfer and Articulation Policies or Legislatures</hd> <p>Figure 1 visually outlines the statewide transfer and articulation policies, formalized through state legislatures, as documented by the Education Commission of the States (Whinnery & Peisach, [<reflink idref="bib69" id="ref28">69</reflink>]). These policies aim to improve transfer rates between 2-year and 4-year colleges (Anderson et al., [<reflink idref="bib1" id="ref29">1</reflink>]). This is an important differentiation point from institutionally driven partnerships, which are not limited to these two sectors.</p> <p>Figure 1 also highlights four distinct transfer and articulation policies identified by the Education Commission of the States across all 50 U.S. states (Whinnery & Peisach, [<reflink idref="bib69" id="ref30">69</reflink>]),[<reflink idref="bib2" id="ref31">2</reflink>] as described below:</p> <p></p> <ulist> <item> Transferable core of lower-division courses: A uniform numbering convention for lower-division courses adopted by all public postsecondary institutions.</item> <p></p> <item> Statewide common course numbering: A set of general education courses unanimously agreed upon by all public postsecondary institutions. Full transferability is ensured, with institutions offering a crosswalk for courses with different naming conventions.</item> <p></p> <item> Statewide guaranteed transfer of an associate's degree: Guarantees that students awarded an associate's degree before transferring to a 4-year institution can transfer all credits to the 4-year institution and enter at junior-level standing. Most state policies exempt students from further general education courses unless specified by their major.</item> <p></p> <item> Statewide reverse transfer: Allows all public institutions to retroactively grant an associate's degree to students transferring to a 4-year institution who have not completed associate's degree requirements.</item> </ulist> <hd id="AN0183779576-9">Institutional Transfer and Articulation Agreements</hd> <p>Institution-driven transfer and articulation agreements are formal agreements between sending and receiving institutions about how the receiving institution will articulate the coursework from the sending college to award course credit (CollegeTransfer.Net, [<reflink idref="bib11" id="ref32">11</reflink>]). Such agreements have also been conceptualized as "informal" (see Jenkins et al., [<reflink idref="bib39" id="ref33">39</reflink>]; Root, [<reflink idref="bib52" id="ref34">52</reflink>]; Schudde et al., [<reflink idref="bib58" id="ref35">58</reflink>], [<reflink idref="bib57" id="ref36">57</reflink>]) because they are initiated by institutions and their faculty as opposed to being state-regulated or accredited by external agencies.</p> <hd id="AN0183779576-10">Policy Types Based on Specificity</hd> <p>Both state-level and institutional-level agreements generally fall into two different categories based on their level of specificity. <emph>Articulation agreements</emph> establish <emph>specific</emph> pathways between two or more colleges and involve specific program continuation. Such agreements are typically published after a thorough review of curricula and instructional levels, involving collaboration among academic departments to draft and finalize the agreements (CollegeTransfer.Net, [<reflink idref="bib11" id="ref37">11</reflink>]). The first two statewide policies—concerning lower division courses and common course numbering—can be considered articulation agreements. <emph>Transfer agreements</emph> are more <emph>general</emph>; they can involve any program and do not require a specific pathway or continuity in course numbering. The second two statewide policies—concerning guaranteed and reverse transfer of associate's degrees—fall into this more general category. Given that, arguably, program continuation should result in less credit loss (González Canché, [<reflink idref="bib20" id="ref38">20</reflink>], [<reflink idref="bib21" id="ref39">21</reflink>], [<reflink idref="bib24" id="ref40">24</reflink>], [<reflink idref="bib25" id="ref41">25</reflink>]; González Canché et al., [<reflink idref="bib33" id="ref42">33</reflink>]), our analyses differentiate between <emph>general transfer agreements</emph> and more <emph>specific articulation agreements</emph>.</p> <hd id="AN0183779576-11">How Effective are These Agreements?</hd> <p>Studies indicate that transfer and articulation agreements have limited effectiveness in improving transfer rates for both 2- and 4-year students (Anderson et al., [<reflink idref="bib1" id="ref43">1</reflink>]; Baker, [<reflink idref="bib3" id="ref44">3</reflink>]; Roksa, [<reflink idref="bib51" id="ref45">51</reflink>]). A common barrier to degree or credential attainment concerns "transferring" or validating credits earned at one institution for use toward a degree or credential at another college or university (Holod et al., [<reflink idref="bib36" id="ref46">36</reflink>]). Indeed, credit loss is one of the biggest barriers that transfer students face when attempting to continue their education at a different institution (Holod et al., [<reflink idref="bib36" id="ref47">36</reflink>]; Monaghan & Attewell, [<reflink idref="bib47" id="ref48">47</reflink>]). While positive outcomes have been observed for some transfer students, such as increased associate's degree attainment in California (Baker, [<reflink idref="bib3" id="ref49">3</reflink>]), these benefits did not boost transfer rates (Anderson et al., [<reflink idref="bib1" id="ref50">1</reflink>]; Baker, [<reflink idref="bib3" id="ref51">3</reflink>]; Roksa, [<reflink idref="bib51" id="ref52">51</reflink>]). A study in Texas (Schudde et al., [<reflink idref="bib57" id="ref53">57</reflink>]) showed that adhering to the transferable core of lower-division courses up to 42 credit hours increased the likelihood of attaining a bachelor's degree; however, transferring more than 42 credits reduced that probability. Overall, despite positive associations with credit transfer degree attainment, statewide policies seem inadequate in addressing low transfer rates, raising questions about the effectiveness of transfer and articulation agreements (Shapiro et al., [<reflink idref="bib59" id="ref54">59</reflink>], [<reflink idref="bib60" id="ref55">60</reflink>]).</p> <p>Minimizing or avoiding credit loss is only one of the challenges associated with changing institutions. Transfer students also need to adapt to the new institutional environment (Dougherty, [<reflink idref="bib16" id="ref56">16</reflink>]) and are also likely to endure increases in their cost of attendance (González Canché, [<reflink idref="bib20" id="ref57">20</reflink>], [<reflink idref="bib21" id="ref58">21</reflink>], [<reflink idref="bib24" id="ref59">24</reflink>], [<reflink idref="bib25" id="ref60">25</reflink>], [<reflink idref="bib30" id="ref61">30</reflink>]). Nonetheless, when credit validation is completed successfully, that is with little to no credit loss, we would expect that transfer students' academic trajectories toward a degree or credential may continue virtually uninterrupted, at least in terms of program of study and satisfactory academic progress.</p> <p>Having said this, the challenges associated with college transfer are not uniformly distributed. A conceptually useful classification of transfer students is their status as <emph>continuing</emph> or <emph>returning</emph> students. <emph>Continuing</emph> students have no interruption in enrollment between institutions, whereas <emph>returning</emph> students dropped out of college temporarily, or interrupted their consecutive enrollment for at least one term (i.e., they were enrolled in the fall term but did not enroll in the subsequent spring term) (Causey et al., [<reflink idref="bib8" id="ref62">8</reflink>]). These classifications are noteworthy because about half of all transfer students are returning students (Causey et al., [<reflink idref="bib8" id="ref63">8</reflink>]). This prevalence means that around one million transfer students every year who have already dropped out (at least temporarily) of one institution decide to re-enroll in a different one (Hubbard, [<reflink idref="bib37" id="ref64">37</reflink>]). From this perspective then, about half of all transfer students (i.e., returning transfer students) may be considered at a higher risk of experiencing subsequent stopouts and thus significantly diminished prospects of graduating—see DesJardins et al., ([<reflink idref="bib15" id="ref65">15</reflink>], p. 588) for an analysis of students who re-enrolled <emph>at the same 4-year campus</emph> after temporarily stopping out. Thus, transfer students, and especially <emph>returning</emph> transfer students, may benefit more from participating in institutions with clear <emph>articulation agreements</emph> rather than general <emph>transfer partnerships</emph>.</p> <p>Our working premise is that a better understanding of the presence, type, and scope of these transfer and articulation programs may aid with the decision-making process that millions of students and their families endure every academic year. Our study analyzes both formal and informal policies and partnerships to understand the structure of 1,163 participating colleges and 18,260 agreements across the continental United States, as well as 44 states with at least one statewide policy, as we detail next in our data sources section.</p> <hd id="AN0183779576-12">Data Sources</hd> <p>In this section we describe our data sources. We begin with a discussion of the institutional data collected by CollegeTransfer.Net and then describe the data on statewide policies collected by the Education Commission of the States.</p> <hd id="AN0183779576-13">Institutionally Driven Transfer and Articulation Agreements</hd> <p>The Higher Education Opportunity Act mandates that schools participating in Title IV programs must publicly disclose their credit transfer policies (United States Government Accountability Office, [<reflink idref="bib66" id="ref66">66</reflink>]). Our main source of institutionally driven data, CollegeTransfer.Net ([<reflink idref="bib10" id="ref67">10</reflink>]), compiled these federally mandated agreements. In their words, CollegeTransfer.Net "ha[s] taken the time to collect, codify and publish transfer agreements by institution" (CollegeTransfer.Net, [<reflink idref="bib12" id="ref68">12</reflink>], para. 7). As the self-identified largest collection of transfer articulation agreements (CollegeTransfer.Net, [<reflink idref="bib10" id="ref69">10</reflink>]), CollegeTransfer.net's data can provide importance insights into institutionally driven bilateral partnerships initiated by interested institutions and faculty rather than external regulation.</p> <p>CollegeTransfer.Net ([<reflink idref="bib11" id="ref70">11</reflink>]) defines transfer agreements as agreements between sending and receiving institutions that specify how the receiving institution will articulate the coursework and award course credit. Notably, these agreements are not constrained to transfers that take place from a two-institution to a 4-year institution, which has long been the most prevalent transfer analysis practice (Garrison, [<reflink idref="bib19" id="ref71">19</reflink>]; Helland, [<reflink idref="bib35" id="ref72">35</reflink>]; Spindt, [<reflink idref="bib63" id="ref73">63</reflink>]; Vaughan, [<reflink idref="bib67" id="ref74">67</reflink>]).</p> <p>Figure 2 shows a snapshot of the data provided by CollegeTransfer.Net. As can be seen, this dataset provides participating institutions' college names without their Office of Postsecondary Education Identification number (OPE ID). To add institution-level attributes, we manually matched these institutions to their OPE IDs, as provided by the U.S. Department of Education's Integrated Postsecondary Education Data System's Survey Data files ([<reflink idref="bib65" id="ref75">65</reflink>]). The added attributes included sector, state, and latitude and longitude coordinates that enabled us to conduct spatial relational analyses (González Canché, [<reflink idref="bib26" id="ref76">26</reflink>], [<reflink idref="bib28" id="ref77">28</reflink>]).</p> <p>Graph: Fig. 2 Snapshot of transfer articulation agreements provided by CollegeTransfer.Net ([<reflink idref="bib10" id="ref78">10</reflink>])</p> <hd id="AN0183779576-14">State-Level Transfer and Articulation Policies or Legislation</hd> <p>Our main data source for the statewide policies is the Education Commission of the States (Whinnery & Peisach, [<reflink idref="bib69" id="ref79">69</reflink>]), a dataset documenting 118 state-level transfer and articulation policies and legislation across the 50 U.S. states. As explained earlier, this dataset classifies these statewide policies into four classes, which we use to test for spatial clustering across these classes. We merged this dataset with the United States Census Bureau's TIGER/Line shapefiles (https://<ulink href="http://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html">www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html</ulink>) to incorporate the states' locations to enable us to test for spatial relational processes, as discussed in the methods section.</p> <hd id="AN0183779576-15">Conceptual Framework: Spatial Relational Modeling and Economic Spillovers</hd> <p>The relational nature of these statewide policies and institution-driven agreements justified the application of spatial relational modeling to retrieve/visualize their structures. Our units of analyses—institutional agreements and statewide policies—may be operationalized as networks or systems, based on their bilateral or <emph>dyadic/relational natures</emph> (Maroulis et al., [<reflink idref="bib45" id="ref80">45</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref81">68</reflink>]). Relational data, also referred to as dyads, involve at least two linked units following a clear identifying rule (Kenny et al., [<reflink idref="bib42" id="ref82">42</reflink>]), such as friendship, co-authorship, or ascription to a given policy or political party. Relational thinking alone, however, does not account for the location of the units configuring the network (Maroulis et al., [<reflink idref="bib45" id="ref83">45</reflink>]; Mitchell, [<reflink idref="bib46" id="ref84">46</reflink>]). Location may not only help us gain more nuanced understandings of the phenomenon under study (González Canché, [<reflink idref="bib20" id="ref85">20</reflink>], [<reflink idref="bib22" id="ref86">22</reflink>], [<reflink idref="bib23" id="ref87">23</reflink>], [<reflink idref="bib28" id="ref88">28</reflink>], [<reflink idref="bib29" id="ref89">29</reflink>]; González Canché et al., [<reflink idref="bib33" id="ref90">33</reflink>]), but following economic spillovers mechanisms, the establishment of both statewide policies and partnerships may be impacted by proximity (Shipan & Volden, [<reflink idref="bib61" id="ref91">61</reflink>]). That is, economic spillovers may happen across neighboring states based on localized diffusion mechanisms, and the establishment of bilateral partnerships may be driven by shorter distances across participating institutions (Shipan & Volden, [<reflink idref="bib61" id="ref92">61</reflink>]). Because proximity might influence statewide policies and partnerships, we use <emph>spatial relational modeling</emph> (Barthélemy, [<reflink idref="bib4" id="ref93">4</reflink>]) as part of our analysis.</p> <hd id="AN0183779576-16">Spatial Relational Modeling and Complex Systems</hd> <p>A system comprises elements/units and relations/links (Maroulis et al., [<reflink idref="bib45" id="ref94">45</reflink>]; Mitchell, [<reflink idref="bib46" id="ref95">46</reflink>]; Zeng et al., [<reflink idref="bib70" id="ref96">70</reflink>]), which is why these systems can be conceptualized as networks. However, even in small settings, such as academic departments, units may occupy differing hierarchical levels, implying heterogeneous responsibilities and expectations (Maroulis et al., [<reflink idref="bib45" id="ref97">45</reflink>]). According to complex systems theory, as the units and relations of a network grow in number, new interactions organically emerge, yielding new levels of organization that vary in nonlinear ways (Mitchell, [<reflink idref="bib46" id="ref98">46</reflink>]; Zeng et al., [<reflink idref="bib70" id="ref99">70</reflink>]). Nonlinearity thus suggests that even if the number of elements does not change, the number of connections may vary, potentially impacting outcome variation or performance (Maroulis et al., [<reflink idref="bib45" id="ref100">45</reflink>]; Sanchez et al., [<reflink idref="bib53" id="ref101">53</reflink>]; Zeng et al., [<reflink idref="bib70" id="ref102">70</reflink>]).</p> <p>These concepts are useful for understanding statewide policies and bilateral agreements. As the number of programs involved increases, students form new and nonlinear connections across programs, institutions, and peers, thus impacting the success of these agreements. Nonetheless, nonlinearity implies that simply increasing programs without considering participants' needs may not necessarily improve outcomes. Students' expectations, personal circumstances, and connections with peers also impact partnerships success. Moreover, students' out-of-school experiences significantly impact their academic performance (González Canché, [<reflink idref="bib28" id="ref103">28</reflink>], [<reflink idref="bib29" id="ref104">29</reflink>], [<reflink idref="bib31" id="ref105">31</reflink>]), even with well-designed articulation agreements.</p> <p>In the case of statewide policies, the main actors are states and their enacted policies. The presence of these policies may impact institutions' incentives to establish these agreements. Institutions in states with robust policies may be more inclined to establish these partnerships—a supposition we consider in our sixth research question. This property and behavior of state policymaking aligns with spontaneous order and the emergence of new levels of organization in complex systems theory, drawing attention to the natural formation of subgroups influenced by the central organization (Maroulis et al., [<reflink idref="bib45" id="ref106">45</reflink>]; Sanchez et al., [<reflink idref="bib53" id="ref107">53</reflink>]), all of which may be impacted by the location of the units in the system (Barthélemy, [<reflink idref="bib4" id="ref108">4</reflink>]; Shipan & Volden, [<reflink idref="bib61" id="ref109">61</reflink>]).</p> <p>In sum, complex systems thinking is useful for analyzing diverse relationships and highlighting the structures embedded in the systems. Complex systems can be operationalized as complex adaptive networks, applicable to both statewide policies and institutionally driven agreements. Our next section details the steps undertaken to apply these complex systems thinking tools to networks (Wasserman & Faust, [<reflink idref="bib68" id="ref110">68</reflink>]) and spatial networks (Barthélemy, [<reflink idref="bib4" id="ref111">4</reflink>]), incorporating units' geocoded or georeferenced information for Earth's surface identification.</p> <hd id="AN0183779576-17">Operationalizing Networks: Construction and Modeling Strategies</hd> <p>A network is represented as</p> <p>1 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>N</mi><mo>=</mo><mo stretchy="false">(</mo><mrow><mi>V</mi><mo>,</mo><mi>E</mi></mrow><mo stretchy="false">)</mo><mo>,</mo></mrow></math> </ephtml></p> <p>with <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>V</mi></math> </ephtml> representing vertices or units and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>E</mi></math> </ephtml> representing edges or connections. When these agreements are used to operationalize statewide policies, the units are of two types: (a) states and (b) the four classes of policies discussed in statewide policies section above. In the case of institutional partnerships, the elements are colleges of different sectors (e.g., nonprofit, public, for-profit) and levels (e.g., 4-year, 2-year, less than 2-year).</p> <p>Incorporating location is denoted as</p> <p>2 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>N</mi><mo>=</mo><mfenced close=")" open="("><msub><mi>V</mi><mrow><mi mathvariant="italic">coords</mi></mrow></msub><mo>,</mo><mi>E</mi></mfenced><mo>,</mo></mrow></math> </ephtml></p> <p>In the case of institutions, the incorporation of location data consists of adding latitude and longitude coordinates retrieved from their physical addresses, referred to as point data. For states, these coordinates form polygons that capture the entire perimeter of each state.</p> <p>In both Eqs. (<reflink idref="bib1" id="ref112">1</reflink>) and (<reflink idref="bib2" id="ref113">2</reflink>), the formation of <emph>E</emph> implies that a bilateral agreement or a statewide policy has been identified. For institutions, represented as points, this requires intersecting these coordinates.</p> <hd id="AN0183779576-18">Statewide Policies Spatial Network Analyses (Questions 1)</hd> <p>The statewide policies network is configured by two types of units: states (<emph>S</emph><subs><emph>i</emph></subs>) and policies (<emph>P</emph><subs><emph>i</emph></subs>). The connections are represented as</p> <p>3 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>S</mi><mi>i</mi></msub><mo>-</mo><msub><mi>P</mi><mi>i</mi></msub></mrow></math> </ephtml></p> <p>The subscript <emph>i</emph> is used for both states and policies, creating a two-mode network (Breiger, [<reflink idref="bib7" id="ref114">7</reflink>]). States' locations in this case are measured as a polygon, or a collection of coordinate points that form borders (González Canché, [<reflink idref="bib27" id="ref115">27</reflink>], [<reflink idref="bib28" id="ref116">28</reflink>], [<reflink idref="bib29" id="ref117">29</reflink>]). For visualization purposes, policies can be strategically placed outside the continental United States. For example, in Fig. 4b(b), Alaska and Hawaii have been brought closer to the contiguous United States, and the four policies are represented north and south of the contiguous United States. The implementation of Eq. (<reflink idref="bib3" id="ref118">3</reflink>) makes it possible to test for the relevance of both states and policies via key actor analyses. Although we are describing statewide policies, key actor analyses can also be applied to institutions [see Eq. (<reflink idref="bib8" id="ref119">8</reflink>)]; accordingly, we present Figs. 3a and 4a for conceptual illustration purposes.</p> <p>Graph: Fig. 3 Conceptual and operational depictions of network thinking. HTML visualizations for b(a) https://cutt.ly/l2csDDh, b(b) https://cutt.ly/c2csRbr and b(c) https://cutt.ly/W2csz6x (Color figure online)</p> <p>Graph: Fig. 4 Conceptual and operational depictions of network thinking. HTML visualizations for b(a) https://cutt.ly/f2eVczs and b(b) https://cutt.ly/J2eBR2N (Color figure online)</p> <hd id="AN0183779576-19">Key Actor Analysis</hd> <p>Key actor analysis (Borgatti, [<reflink idref="bib6" id="ref120">6</reflink>]) assists in recognizing active units, connectors, and influential entities. This methodology may be applied to institutions, programs, states, and statewide policies. Key actor analysis encompasses degree and strength centrality (Borgatti, [<reflink idref="bib6" id="ref121">6</reflink>]), betweenness centrality (Wasserman & Faust, [<reflink idref="bib68" id="ref122">68</reflink>]), and eigenvector centrality (Borgatti, [<reflink idref="bib6" id="ref123">6</reflink>]; Csárdi & Nepusz, [<reflink idref="bib13" id="ref124">13</reflink>]; González Canché, [<reflink idref="bib26" id="ref125">26</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref126">68</reflink>]).</p> <p> <emph>Degree centrality</emph> counts the number of connections that a given actor or unit has established in the network (Borgatti, [<reflink idref="bib6" id="ref127">6</reflink>]), while <emph>strength centrality</emph> accounts for connection weight. Although degree and strength may vary when connections take values greater than 1 (i.e., multiple partnerships between two institutions), in cases when only presence or absence are measured, degree and strength render the same result. For example, in Fig. 4a Policy A (P. A) and Texas (TX) have the highest degree and strength values, with four connections each. In Fig. 3a, institutions B and C may have the same degree but differ in strength, with B having nine agreements whereas C has only one agreement.</p> <p> <emph>Betweenness centrality</emph> measures a unit's relevance in indirectly connecting two other units. For example, although Michigan and Texas are not directly connected in Fig. 4a, they both enacted policies C and D, which increases the possibility of them interacting, according to relational theory (Borgatti, [<reflink idref="bib6" id="ref128">6</reflink>]; Csárdi & Nepusz, [<reflink idref="bib13" id="ref129">13</reflink>]). This same rationale is applied to institutions. Figure 3a shows that institution A indirectly connects institutions B and C, thus potentially facilitating a direct connection between B and C in the future.</p> <p>Finally, <emph>eigenvector</emph> highlights units that are central as a function of being connected with active units in the network (Borgatti, [<reflink idref="bib6" id="ref130">6</reflink>]; Csárdi & Nepusz, [<reflink idref="bib13" id="ref131">13</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref132">68</reflink>]). In Fig. 3a, institution A, with the highest strength and betweenness, is the most eigenvector influential. In the two-mode network (Fig. 4a), Policy A's influence makes it the highest eigenvalue in this network. In addition to the interactive visualizations we present, Tables 3 and 6 aid in identifying key players among states and institutions, respectively.</p> <hd id="AN0183779576-20">Testing for Economic Spillovers [Question 2(a)]</hd> <p>Economic spillovers offer insights into policies and partnerships that were influenced by proximity. Negative effects may deter neighboring governments from adopting a policy, while positive effects may encourage emulation (Shipan & Volden, [<reflink idref="bib61" id="ref133">61</reflink>]). To test if a given state's implementation of a policy is impacted by whether its neighboring states have implemented such a policy (Research Question 2), we present Fig. 5(a), (b), (c), (d) and relied on Moran's <emph>I</emph> and join-count tests (Bivand et al., [<reflink idref="bib5" id="ref134">5</reflink>]) along with machine-driven k-medoids clustering (Kaufman & Rousseeuw, [<reflink idref="bib41" id="ref135">41</reflink>]; Schubert & Rousseeuw, [<reflink idref="bib55" id="ref136">55</reflink>], [<reflink idref="bib56" id="ref137">56</reflink>]) via tetrachoric correlations (Kirk, [<reflink idref="bib43" id="ref138">43</reflink>]) to explore economic spillovers.</p> <p>Graph: Fig. 5 Join-count analyses. All Monte-Carlo simulations are based on 100,000 resamplings</p> <p>Considering the diverse policy landscapes—ranging from no policies to multiple policies—we examine the status of individual policy implementation. Moreover, we explore whether a state's policy bundle aligns with that of its neighboring states, akin to a strength or concentration analysis. This approach posits that a state is likely to adopt a similar policy pattern as its neighbors with more policies (Shipan & Volden, [<reflink idref="bib61" id="ref139">61</reflink>]). The subsequent sections delineate the methodological steps involved in these assessments.</p> <hd id="AN0183779576-21">Join-Count Analyses</hd> <p>Join-count analyses assess whether a unit <emph>i</emph> and each of its neighbors <emph>j</emph> jointly have the same presence (1 and 1) or absence (0 and 0) of a policy. Thus, the first step to execute join counts is to identify neighbors (Table 1). Here, two states are neighbors if they share a border or touch in a given point along their state perimeters, also referred to as the <emph>queen's approach</emph> (Bivand et al., [<reflink idref="bib5" id="ref140">5</reflink>]).[<reflink idref="bib3" id="ref141">3</reflink>] To show how the queen's approach works, we present Table 2. If we select Arizona and want to identify its neighboring states using this queen's approach, Arizona will have five neighbors: California, Nevada, Utah, New Mexico, and Colorado. With Colorado not sharing a border but just touching in a point.</p> <p>Table 1 Moran's I intuition using Arizona* and its neighbors as an example</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>States</p></th><th align="left"><p>A. <math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>y</mi><mi>i</mi></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq98.gif" /></p></th><th align="left"><p>B. <math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>y</mi><mi>i</mi></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq97.gif" /> − mean</p></th><th align="left"><p>C. <math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>y</mi><mi>j</mi></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq96.gif" /></p></th><th align="left"><p>D. <math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>y</mi><mi>j</mi></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq95.gif" /> − mean</p></th><th align="left"><p>Cross product B*D</p></th><th align="left"><p>(<math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>y</mi><mi>i</mi></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq94.gif" /> − mean)<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq93.gif" /></p></th></tr><tr><th align="left" colspan="7"><p>Panel A. Outcome is presence of common course numbering (see Fig. 5c)</p></th></tr></thead><tbody><tr><td align="left"><p>Arizona</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.800</p></td><td align="left"><p>− 0.033</p></td><td align="left"><p>-0.006</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>California</p></td><td align="left"><p>0</p></td><td align="left"><p>− 0.833</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>-0.139</p></td><td align="left"><p>0.6944</p></td></tr><tr><td align="left"><p>Nevada</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.667</p></td><td align="left"><p>− 0.167</p></td><td align="left"><p>-0.028</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>Utah</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.028</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>Colorado</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.028</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>New Mexico</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>1</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.028</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>Overall Mean</p></td><td align="left"><p>0.833</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.089</p></td><td align="left"><p>0.8333</p></td></tr><tr><td align="left" colspan="7"><p>As per Eq. (6) Moran's <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>I</mi><mo>=</mo><mfrac><mrow><mo>-</mo><mn>0.089</mn></mrow><mrow><mn>0.8333</mn></mrow></mfrac><mo>=</mo><mo>-</mo><mn>0.1067</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq92.gif" /></p></td></tr></tbody></table> </ephtml> </p> <p>Table 1 Moran's I intuition using Arizona* and its neighbors as an example</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="7"><p>Panel B. Outcome is total number of statewide policies (see Fig. 6(a))</p></th></tr></thead><tbody><tr><td align="left"><p>Arizona</p></td><td align="left"><p>2</p></td><td align="left"><p>− 0.833</p></td><td align="left"><p>3</p></td><td align="left"><p>0.167</p></td><td align="left"><p>− 0.139</p></td><td align="left"><p>0.6944</p></td></tr><tr><td align="left"><p>California</p></td><td align="left"><p>1</p></td><td align="left"><p>− 1.833</p></td><td align="left"><p>3</p></td><td align="left"><p>0.167</p></td><td align="left"><p>− 0.306</p></td><td align="left"><p>3.3611</p></td></tr><tr><td align="left"><p>Nevada</p></td><td align="left"><p>4</p></td><td align="left"><p>1.167</p></td><td align="left"><p>2.000</p></td><td align="left"><p>− 0.833</p></td><td align="left"><p>− 0.972</p></td><td align="left"><p>1.3611</p></td></tr><tr><td align="left"><p>Utah</p></td><td align="left"><p>3</p></td><td align="left"><p>0.167</p></td><td align="left"><p>3.250</p></td><td align="left"><p>0.417</p></td><td align="left"><p>0.069</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>Colorado</p></td><td align="left"><p>4</p></td><td align="left"><p>1.167</p></td><td align="left"><p>2.667</p></td><td align="left"><p>− 0.167</p></td><td align="left"><p>− 0.194</p></td><td align="left"><p>1.3611</p></td></tr><tr><td align="left"><p>New Mexico</p></td><td align="left"><p>3</p></td><td align="left"><p>0.167</p></td><td align="left"><p>3</p></td><td align="left"><p>0.167</p></td><td align="left"><p>0.028</p></td><td align="left"><p>0.0278</p></td></tr><tr><td align="left"><p>Overall Mean</p></td><td align="left"><p>2.833</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 1.514</p></td><td align="left"><p>6.8333</p></td></tr></tbody></table> </ephtml> </p> <p>As per Eq. (<reflink idref="bib6" id="ref142">6</reflink>) Moran's <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>I</mi><mo>=</mo><mfrac><mrow><mo>-</mo><mn>1.514</mn></mrow><mrow><mn>6.8333</mn></mrow></mfrac><mo>=</mo><mo>-</mo><mn>0.2215</mn></mrow></math> </ephtml> *Network limited to Arizona and its neighbors based on the Queen's approach</p> <p>Table 2 Example of join count mechanisms using Arizona as an example and common–course numbering as shown in Fig. 5c(a)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2" /><th align="left" colspan="5"><p>Neighboring States</p></th><th align="left"><p>Join</p></th></tr><tr><th align="left"><p>California</p></th><th align="left"><p>Nevada</p></th><th align="left"><p>Utah</p></th><th align="left"><p>Colorado</p></th><th align="left"><p>New Mexico</p></th><th align="left"><p>Count</p></th></tr></thead><tbody><tr><td align="left"><p>Arizona<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq99.gif" /></p></td><td align="left"><p>(1*0)*1 = 0</p></td><td align="left"><p>(1*1)*1 = 1</p></td><td align="left"><p>(1*1)*1 = 1</p></td><td align="left"><p>(1*1)*1 = 1</p></td><td align="left"><p>(1*1)*1 = 1</p></td><td align="left"><p>4</p></td></tr><tr><td align="left"><p>Arizona<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq100.gif" /></p></td><td align="left"><p>([1 − 1]*[1 − 0])*1 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*1 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*1 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*1 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*1 = 0</p></td><td align="left"><p>0</p></td></tr></tbody></table> </ephtml> </p> <p>Table 2 Example of join count mechanisms using Arizona as an example and common–course numbering as shown in Fig. 5c(a)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2" /><th align="left" colspan="5"><p>Subset of Not Neighboring States</p></th><th align="left"><p>Join</p></th></tr><tr><th align="left"><p>Texas</p></th><th align="left"><p>Oregon</p></th><th align="left"><p>Washington</p></th><th align="left"><p>Florida</p></th><th align="left"><p>New York</p></th><th align="left"><p>Count</p></th></tr></thead><tbody><tr><td align="left"><p>Arizona<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq101.gif" /></p></td><td align="left"><p>(1*1)*0 = 0</p></td><td align="left"><p>(1*1)*0 = 0</p></td><td align="left"><p>(1*0)*0 = 0</p></td><td align="left"><p>(1*1)*0 = 0</p></td><td align="left"><p>(1*0)*0 = 0</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>Arizona<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq103.gif" /></p></td><td align="left"><p>([1 − 1]*[1 − 1])*0 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*0 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 0])*0 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 1])*0 = 0</p></td><td align="left"><p>([1 − 1]*[1 − 0])*0 = 0</p></td><td align="left"><p>0</p></td></tr></tbody></table> </ephtml> </p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mo>†</mo></mmultiscripts></math> </ephtml> Join Presence given <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><msub><mi>x</mi><mi>j</mi></msub><mo stretchy="false">)</mo></mrow><mrow /><mo>∗</mo><msub><mi>W</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub></mrow></math> </ephtml> , where join count is 1 if all values are 1, hence if <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo stretchy="false">(</mo><mn>1</mn><mrow /><mo>∗</mo><mn>1</mn><mo stretchy="false">)</mo><mrow /><mo>∗</mo><mn>1</mn></mrow></math> </ephtml> neighbor units have this policy in place <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math> </ephtml> Join Absence given <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mo stretchy="false">(</mo><mrow><mo stretchy="false">[</mo><mn>1</mn><mo>-</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">]</mo></mrow><mrow><mo stretchy="false">[</mo><mn>1</mn><mo>-</mo><msub><mi>x</mi><mi>j</mi></msub><mo stretchy="false">]</mo></mrow><mo stretchy="false">)</mo></mrow><mrow /><mo>∗</mo><msub><mi>W</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub></mrow></math> </ephtml> , where join count is 1 if attributes are 0 and units are neighbors For both <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mo>†</mo></mmultiscripts></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math> </ephtml> see Bivand et al. ([<reflink idref="bib5" id="ref143">5</reflink>])</p> <p>Join-count analyses render two coefficient estimates and probability values for each neighbor combination (i.e., dyad): one for join absence and one for join presence. Mathematically, this is expressed as:</p> <p>4 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mo>∑</mo><mi>j</mi></msub><mi>f</mi><mfenced close=")" open="("><msub><mi>x</mi><mi>i</mi></msub><mo>,</mo><msub><mi>x</mi><mi>j</mi></msub></mfenced><msub><mi>W</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub></mrow></math> </ephtml></p> <p>where join presences are <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>=</mo><mn>1</mn></mrow></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>x</mi><mi>j</mi></msub><mo>=</mo><mn>1</mn></mrow></math> </ephtml> and join absences imply <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>=</mo><mn>0</mn></mrow></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>x</mi><mi>j</mi></msub><mo>=</mo><mn>0</mn></mrow></math> </ephtml> . Since only one of these conditions can be true per attribute, the same two units cannot concurrently contribute a value of 1 for absence and a value of 1 for presence. These computations are constrained to neighbors as captured by <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>W</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub></math> </ephtml> , following the Arizona rationale just illustrated.</p> <p>Procedurally, we rely on products across dyads (two neighboring units at the time) where</p> <p>5 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mrow><mi mathvariant="bold-italic">x</mi></mrow><mrow><mi mathvariant="bold-italic">i</mi></mrow></msub><msub><mrow><mi mathvariant="bold-italic">x</mi></mrow><mrow><mi mathvariant="bold-italic">j</mi></mrow></msub><mo>=</mo><mn>1</mn><mtext>for join presence or</mtext><mfenced close=")" open="("><mn>1</mn><mo>-</mo><msub><mrow><mi mathvariant="bold-italic">x</mi></mrow><mrow><mi mathvariant="bold-italic">i</mi></mrow></msub><mo>=</mo><mn>1</mn></mfenced><mfenced close=")" open="("><mn>1</mn><mo>-</mo><msub><mrow><mi mathvariant="bold-italic">x</mi></mrow><mrow><mi mathvariant="bold-italic">j</mi></mrow></msub><mo>=</mo><mn>1</mn></mfenced><mo>=</mo><mn>1</mn><mtext>for join absence</mtext></mrow></math> </ephtml></p> <p>where, when at least one neighbor differs, the result will not add to 1. Finally, to determine if observed estimates are stronger than chance alone, we conduct Monte Carlo simulations of join-count statistics with 100,000 resamplings (Bivand et al., [<reflink idref="bib5" id="ref144">5</reflink>]), creating the same number of connections but across all units, not only true neighbors. The pseudo p-value is calculated based on the proportion of reshuffled estimates as or more extreme than the observed value, providing an empirical distribution rather than relying on distributional assumptions. This process is repeated for both join absence and join presence.</p> <hd id="AN0183779576-22">Moran's I</hd> <p>Moran's <emph>I</emph> is our second method for analyzing statewide policies, providing a global estimate of clustering in neighboring states' performances. The formula is expressed as:</p> <p>6 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>I</mi><mo>=</mo><mfrac><mi>n</mi><mrow><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msubsup><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msub><mi>w</mi><mrow><mi mathvariant="italic">ij</mi></mrow></msub></mrow></mfrac><mfrac><mrow><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msubsup><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msub><mi>w</mi><mrow><mi mathvariant="italic">ij</mi></mrow></msub><mfenced close=")" open="("><msub><mi>y</mi><mi>i</mi></msub><mo>-</mo><mover><mi>y</mi><mo>¯</mo></mover></mfenced><mfenced close=")" open="("><msub><mi>y</mi><mi>j</mi></msub><mo>-</mo><mover><mi>y</mi><mo>¯</mo></mover></mfenced></mrow><mrow><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msup><mrow><mfenced close=")" open="("><msub><mi>y</mi><mi>i</mi></msub><mo>-</mo><mover><mi>y</mi><mo>¯</mo></mover></mfenced></mrow><mn>2</mn></msup></mrow></mfrac></mrow></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>y</mi><mi>i</mi></msub></math> </ephtml> is the <emph>i</emph>th indicator of interest for unit<subs><emph>i</emph></subs>, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mover><mi>y</mi><mo>¯</mo></mover></math> </ephtml> is the mean of the indicator across the sample, and <emph>w</emph><subs><emph>ij</emph></subs> is the row-standardized spatial weight matrix <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>w</mi><mrow><mi mathvariant="italic">ij</mi></mrow></msub></math> </ephtml> . Moran's <emph>I</emph> relies on spatial lags computation, representing the mean values of neighboring units (see Table 2, using Arizona as an example). Spatial lags assess similarity in performance levels between units and their neighbors. The standardized Moran's <emph>I</emph> value ranges from <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>-</mo><mn>1</mn></mrow></math> </ephtml> to <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>+</mo><mn>1</mn></mrow></math> </ephtml> , with negative values indicating discrepancy and positive values indicating consistent performance. Again, we used Monte Carlo simulations to verify the strength of Moran's I estimates, reshuffling values across regions 100,000 times per outcome and presenting pseudo p-values.</p> <hd id="AN0183779576-23">Identifying States Clustering via Machine Learning [Research Question 2(b)]</hd> <p>The clustering methodology is rooted in the partition around medoids clustering (Schubert & Rousseeuw, [<reflink idref="bib55" id="ref145">55</reflink>], [<reflink idref="bib56" id="ref146">56</reflink>]). This method represents a cutting-edge approach to discern patterns in state policies. The tetrachoric correlation matrix for binary indicators—a critical precursor—is established before applying the clustering algorithms, which ensures accuracy in capturing relationships within binary indicators (Foldnes & Grønneberg, [<reflink idref="bib18" id="ref147">18</reflink>]; Kirk, [<reflink idref="bib43" id="ref148">43</reflink>]).</p> <p>Before initiating the clustering process, we systematically gauged the optimal number of clusters using the average silhouette method (Kassambara, [<reflink idref="bib40" id="ref149">40</reflink>]; Kaufman & Rousseeuw, [<reflink idref="bib41" id="ref150">41</reflink>]). This data-driven determination, which is illustrated in Fig. 6b(a), established that five clusters would yield the most insightful results. The subsequent application of the partition around medoids algorithm then facilitated the identification of these five clusters, each characterized by its unique policy configuration.</p> <p>Graph: Fig. 6 Innovative applications of machine learning and spatial analyses on statewide policy adoptions—all Monte-Carlo simulations based on 100,000 resamplings. See HTML version of b(b) at https://cutt.ly/ewG3Wrp4 (Color figure online)</p> <p>Figure 6b(b) visually maps these clusters, unveiling their spatial distribution and autocorrelation. The presentation of clusters in ascending order accentuates their concentration and policy dynamics, providing a comprehensive snapshot of the policy landscape across the United States. This innovative integration of machine learning enhances our ability to comprehend the intricate interplay of policies and their spatial arrangement (Schubert & Rousseeuw, [<reflink idref="bib55" id="ref151">55</reflink>], [<reflink idref="bib56" id="ref152">56</reflink>]; Ciaburro, [<reflink idref="bib9" id="ref153">9</reflink>]), offering valuable insights for policymakers and researchers alike.</p> <hd id="AN0183779576-24">Institution-to-Institution Spatial Network Analyses (Research Question 3)</hd> <p>The connections shown in Fig. 2, representing the dataset structure from CollegeTransfer.Net ([<reflink idref="bib10" id="ref154">10</reflink>]), form a dyadic or relational edgelist (Csárdi & Nepusz, [<reflink idref="bib13" id="ref155">13</reflink>]; González Canché, [<reflink idref="bib26" id="ref156">26</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref157">68</reflink>]). This network includes institutions connected by at least one transfer agreement. Here, entities in the "From College" column connect to those in the "To College" column as:</p> <p>7 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi><mi>r</mi><mi>o</mi><mi>m</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>i</mi><mo>,</mo><mi>s</mi><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi></mrow></msub><mo>-</mo><mi>T</mi><mi>o</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>j</mi><mo>,</mo><msup><mi>s</mi><mn>0</mn></msup><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi></mrow></msub></mrow></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>i</mi></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>j</mi></math> </ephtml> represent different colleges, and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup></math> </ephtml> may be the same state (if <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>s</mi><mo>=</mo><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup></mrow></math> </ephtml> ) or different (if <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>s</mi><mo>≠</mo><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup></mrow></math> </ephtml> ). <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="italic">coords</mi></mrow></math> </ephtml> capture these units' location. Although " <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi><mi>r</mi><mi>o</mi><mi>m</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi></mrow></math> </ephtml><emph>e</emph>" and " <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>T</mi><mi>o</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><mi>e</mi></mrow></math> </ephtml> " imply directionality, in reality, partnerships are reciprocal. Reciprocity means that if college A partners with college B, B also partners with A (Csárdi & Nepusz, [<reflink idref="bib13" id="ref158">13</reflink>]; Wasserman & Faust, [<reflink idref="bib68" id="ref159">68</reflink>]).</p> <p>To account for institutions with multiple agreements, the connections in Eq. (<reflink idref="bib7" id="ref160">7</reflink>) are collapsed:</p> <p>8 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi><mi>r</mi><mi>o</mi><mi>m</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>i</mi><mo>,</mo><mi>s</mi><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi></mrow></msub><mfrac><mi>X</mi><mrow /></mfrac><mi>T</mi><mi>o</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>j</mi><mo>,</mo><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi></mrow></msub></mrow></math> </ephtml></p> <p>where <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><mi>X</mi><mrow /></mfrac></math> </ephtml> counts the number of programs being implemented between colleges <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>i</mi></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>j</mi></math> </ephtml> in states <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup></math> </ephtml> . These agreements may involve multiple programs within the same or different states.</p> <p>Figure 3a shows this operationalization that allows accounting for institutional characteristics like sector, level, and state, all of which make it possible to address Research Question 3, regarding types of institutions participating in institutionally driven agreements. These relationships were also used to compute degree centrality (number of connections) and strength (total programs) for each institution. For instance, in Fig. 3a institution A has a degree centrality of 2 and a strength of 10, indicating two agreements with 10 participating programs, nine of which involve Institution B. Figure 3b(a) shows the network representation of these institutionally driven connections, with institutions' rankings shown in Table 3. Spatial network representations within-state and out-of-state are shown in Fig. 3b(b) and (c), respectively.</p> <p>Table 3 Top 50 most central institutions</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Name</p></th><th align="left"><p>Bet</p></th><th align="left"><p>Eig</p></th><th align="left"><p>Degree</p></th><th align="left"><p>Strength</p></th><th align="left"><p>Key_actors</p></th><th align="left"><p>Sect_level</p></th></tr></thead><tbody><tr><td align="left"><p>Montgomery County Community College</p></td><td align="left"><p>0.328</p></td><td align="left"><p>0.722</p></td><td align="left"><p>44</p></td><td align="left"><p>503</p></td><td align="left"><p>2.338</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Bucks County Community College</p></td><td align="left"><p>0.116</p></td><td align="left"><p>0.989</p></td><td align="left"><p>25</p></td><td align="left"><p>604</p></td><td align="left"><p>2.279</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Indiana University of Pennsylvania-Main Campus</p></td><td align="left"><p>0.042</p></td><td align="left"><p>0.929</p></td><td align="left"><p>23</p></td><td align="left"><p>641</p></td><td align="left"><p>2.171</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Community College of Philadelphia</p></td><td align="left"><p>0</p></td><td align="left"><p>0.987</p></td><td align="left"><p>21</p></td><td align="left"><p>604</p></td><td align="left"><p>2.108</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Purdue University Global-Davenport (formerly Kaplan University-Davenport)</p></td><td align="left"><p>0.69</p></td><td align="left"><p>0.023</p></td><td align="left"><p>61</p></td><td align="left"><p>376</p></td><td align="left"><p>2.05</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Shippensburg University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>1</p></td><td align="left"><p>17</p></td><td align="left"><p>549</p></td><td align="left"><p>1.992</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>University of San Francisco</p></td><td align="left"><p>0.831</p></td><td align="left"><p>0</p></td><td align="left"><p>75</p></td><td align="left"><p>75</p></td><td align="left"><p>1.935</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Edinboro University of Pennsylvania</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.798</p></td><td align="left"><p>29</p></td><td align="left"><p>448</p></td><td align="left"><p>1.929</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Clarion University of Pennsylvania</p></td><td align="left"><p>0.257</p></td><td align="left"><p>0.683</p></td><td align="left"><p>32</p></td><td align="left"><p>388</p></td><td align="left"><p>1.907</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Mansfield University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.925</p></td><td align="left"><p>18</p></td><td align="left"><p>523</p></td><td align="left"><p>1.893</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Harrisburg Area Community College-Harrisburg</p></td><td align="left"><p>0.004</p></td><td align="left"><p>0.838</p></td><td align="left"><p>23</p></td><td align="left"><p>518</p></td><td align="left"><p>1.87</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Slippery Rock University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.88</p></td><td align="left"><p>17</p></td><td align="left"><p>517</p></td><td align="left"><p>1.827</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Butler County Community College</p></td><td align="left"><p>0.012</p></td><td align="left"><p>0.838</p></td><td align="left"><p>20</p></td><td align="left"><p>498</p></td><td align="left"><p>1.811</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>University of Toledo</p></td><td align="left"><p>0.836</p></td><td align="left"><p>0</p></td><td align="left"><p>63</p></td><td align="left"><p>63</p></td><td align="left"><p>1.764</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Carlow University</p></td><td align="left"><p>0</p></td><td align="left"><p>0.838</p></td><td align="left"><p>17</p></td><td align="left"><p>479</p></td><td align="left"><p>1.732</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Drexel University</p></td><td align="left"><p>0.459</p></td><td align="left"><p>0.284</p></td><td align="left"><p>33</p></td><td align="left"><p>392</p></td><td align="left"><p>1.729</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Delaware County Community College</p></td><td align="left"><p>0.028</p></td><td align="left"><p>0.649</p></td><td align="left"><p>25</p></td><td align="left"><p>446</p></td><td align="left"><p>1.631</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Strayer University-District of Columbia</p></td><td align="left"><p>1</p></td><td align="left"><p>0.002</p></td><td align="left"><p>41</p></td><td align="left"><p>59</p></td><td align="left"><p>1.631</p></td><td align="left"><p>Priv_4-yr-Profit</p></td></tr><tr><td align="left"><p>Lehigh Carbon Community College</p></td><td align="left"><p>0.026</p></td><td align="left"><p>0.712</p></td><td align="left"><p>22</p></td><td align="left"><p>426</p></td><td align="left"><p>1.624</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Northern Virginia Community College</p></td><td align="left"><p>0.801</p></td><td align="left"><p>0.009</p></td><td align="left"><p>42</p></td><td align="left"><p>165</p></td><td align="left"><p>1.6</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Community College of Beaver County</p></td><td align="left"><p>0.038</p></td><td align="left"><p>0.693</p></td><td align="left"><p>20</p></td><td align="left"><p>412</p></td><td align="left"><p>1.572</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Community College of Allegheny County</p></td><td align="left"><p>0.022</p></td><td align="left"><p>0.667</p></td><td align="left"><p>21</p></td><td align="left"><p>411</p></td><td align="left"><p>1.541</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Northampton County Area Community College</p></td><td align="left"><p>0.016</p></td><td align="left"><p>0.625</p></td><td align="left"><p>22</p></td><td align="left"><p>397</p></td><td align="left"><p>1.487</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Jamestown Community College</p></td><td align="left"><p>0.747</p></td><td align="left"><p>0.006</p></td><td align="left"><p>31</p></td><td align="left"><p>227</p></td><td align="left"><p>1.482</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>SUNY College of Environmental Science and Forestry</p></td><td align="left"><p>0.029</p></td><td align="left"><p>0.039</p></td><td align="left"><p>34</p></td><td align="left"><p>688</p></td><td align="left"><p>1.479</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>University of North Carolina at Chapel Hill</p></td><td align="left"><p>0.582</p></td><td align="left"><p>0</p></td><td align="left"><p>58</p></td><td align="left"><p>58</p></td><td align="left"><p>1.436</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>George Mason University</p></td><td align="left"><p>0</p></td><td align="left"><p>0.002</p></td><td align="left"><p>24</p></td><td align="left"><p>718</p></td><td align="left"><p>1.322</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Western Kentucky University</p></td><td align="left"><p>0.888</p></td><td align="left"><p>0</p></td><td align="left"><p>23</p></td><td align="left"><p>75</p></td><td align="left"><p>1.299</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>California University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.577</p></td><td align="left"><p>18</p></td><td align="left"><p>332</p></td><td align="left"><p>1.279</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Westmoreland County Community College</p></td><td align="left"><p>0.005</p></td><td align="left"><p>0.501</p></td><td align="left"><p>22</p></td><td align="left"><p>335</p></td><td align="left"><p>1.266</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Seattle University</p></td><td align="left"><p>0.388</p></td><td align="left"><p>0</p></td><td align="left"><p>33</p></td><td align="left"><p>262</p></td><td align="left"><p>1.193</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Regis University</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>39</p></td><td align="left"><p>477</p></td><td align="left"><p>1.184</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>SUNY College at Oswego</p></td><td align="left"><p>0.801</p></td><td align="left"><p>0</p></td><td align="left"><p>23</p></td><td align="left"><p>46</p></td><td align="left"><p>1.172</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Azusa Pacific University</p></td><td align="left"><p>0.432</p></td><td align="left"><p>0</p></td><td align="left"><p>47</p></td><td align="left"><p>58</p></td><td align="left"><p>1.14</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Columbia College-Chicago</p></td><td align="left"><p>0.333</p></td><td align="left"><p>0</p></td><td align="left"><p>34</p></td><td align="left"><p>242</p></td><td align="left"><p>1.123</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>Kutztown University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.497</p></td><td align="left"><p>17</p></td><td align="left"><p>281</p></td><td align="left"><p>1.115</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Luzerne County Community College</p></td><td align="left"><p>0</p></td><td align="left"><p>0.486</p></td><td align="left"><p>17</p></td><td align="left"><p>285</p></td><td align="left"><p>1.11</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Lock Haven University</p></td><td align="left"><p>0.035</p></td><td align="left"><p>0.417</p></td><td align="left"><p>24</p></td><td align="left"><p>239</p></td><td align="left"><p>1.105</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Glendale Community College - California</p></td><td align="left"><p>0.994</p></td><td align="left"><p>0</p></td><td align="left"><p>7</p></td><td align="left"><p>7</p></td><td align="left"><p>1.097</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>University of Missouri-Columbia</p></td><td align="left"><p>0.384</p></td><td align="left"><p>0</p></td><td align="left"><p>21</p></td><td align="left"><p>305</p></td><td align="left"><p>1.089</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>SUNY Adirondack (formerly Adirondack Community College)</p></td><td align="left"><p>0.476</p></td><td align="left"><p>0.002</p></td><td align="left"><p>28</p></td><td align="left"><p>169</p></td><td align="left"><p>1.086</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Mountain Empire Community College</p></td><td align="left"><p>0.394</p></td><td align="left"><p>0</p></td><td align="left"><p>39</p></td><td align="left"><p>89</p></td><td align="left"><p>1.038</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>University of Kentucky</p></td><td align="left"><p>0.672</p></td><td align="left"><p>0</p></td><td align="left"><p>17</p></td><td align="left"><p>92</p></td><td align="left"><p>1.027</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Pennsylvania Highlands Community College</p></td><td align="left"><p>0.033</p></td><td align="left"><p>0.369</p></td><td align="left"><p>21</p></td><td align="left"><p>245</p></td><td align="left"><p>1.023</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>West Chester University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.432</p></td><td align="left"><p>17</p></td><td align="left"><p>244</p></td><td align="left"><p>0.999</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Texas Tech University</p></td><td align="left"><p>0.38</p></td><td align="left"><p>0</p></td><td align="left"><p>39</p></td><td align="left"><p>50</p></td><td align="left"><p>0.97</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Bluefield College</p></td><td align="left"><p>0.551</p></td><td align="left"><p>0</p></td><td align="left"><p>24</p></td><td align="left"><p>47</p></td><td align="left"><p>0.936</p></td><td align="left"><p>Priv_4-yr_No-prof</p></td></tr><tr><td align="left"><p>SUNY College of Agriculture and Technology at Cobleskill</p></td><td align="left"><p>0.361</p></td><td align="left"><p>0.003</p></td><td align="left"><p>26</p></td><td align="left"><p>160</p></td><td align="left"><p>0.934</p></td><td align="left"><p>Pub_4-yr</p></td></tr><tr><td align="left"><p>Bristol Community College</p></td><td align="left"><p>0.324</p></td><td align="left"><p>0</p></td><td align="left"><p>25</p></td><td align="left"><p>179</p></td><td align="left"><p>0.906</p></td><td align="left"><p>Pub_2-yr</p></td></tr><tr><td align="left"><p>Lincoln University of Pennsylvania</p></td><td align="left"><p>0</p></td><td align="left"><p>0.372</p></td><td align="left"><p>18</p></td><td align="left"><p>209</p></td><td align="left"><p>0.903</p></td><td align="left"><p>Pub_4-yr</p></td></tr></tbody></table> </ephtml> </p> <p>Access to complete dataset at https://cutt.ly/5wG3HuOE</p> <hd id="AN0183779576-25">State-to-State Analyses (Research Question 4)</hd> <p>With the information provided by CollegeTransfer.Net ([<reflink idref="bib10" id="ref161">10</reflink>]) (see Fig. 2), we can address our fourth research question regarding disciplines and programs as well as the states where those institutions are located<emph>.</emph> To do so, we need to modify our operationalization strategy.</p> <p>Equation (<reflink idref="bib8" id="ref162">8</reflink>) can be condensed to focus on states involved in the agreements, represented as</p> <p>9 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>s</mi><mi>i</mi></msub><mfrac><mi>X</mi><mrow /></mfrac><msubsup><mi>s</mi><mrow><mi>j</mi></mrow><mn>0</mn></msubsup></mrow></math> </ephtml></p> <p>This equation allows the estimation of the number of agreements as a function of the institutions' locations while considering their region in the USA. The result of this equation is visualized in Fig. 7b(a), with network representations based on within state and out-of-state statuses [Fig. 7b(b) and b(c)].</p> <p>Graph: Fig. 7 Both graphs reflect participation in bilateral "Informal" or institution- and faculty-driven transfer and articulation agreements (Color figure online)</p> <hd id="AN0183779576-26">Program-to-Program Analyses (Research Question 4)</hd> <p>Expanding on Fig. 2, relationships at the program level are expressed as program k:</p> <p>10 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>F</mi><mi>r</mi><mi>o</mi><mi>m</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>i</mi><mo>,</mo><mi>s</mi><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi><mo>,</mo><mrow /><mrow><mi mathvariant="bold-italic">p</mi></mrow><mrow><mi mathvariant="bold-italic">r</mi></mrow><mrow><mi mathvariant="bold-italic">o</mi></mrow><mrow><mi mathvariant="bold-italic">g</mi></mrow><mrow><mi mathvariant="bold-italic">r</mi></mrow><mrow><mi mathvariant="bold-italic">a</mi></mrow><msub><mrow><mi mathvariant="bold-italic">m</mi></mrow><mrow><mi mathvariant="bold-italic">k</mi></mrow></msub></mrow></msub><mo>-</mo><mi>T</mi><mi>o</mi><mo>.</mo><mi>C</mi><mi>o</mi><mi>l</mi><mi>l</mi><mi>e</mi><mi>g</mi><msub><mi>e</mi><mrow><mi>j</mi><mo>,</mo><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup><mo>,</mo><mi>c</mi><mi>o</mi><mi>o</mi><mi>r</mi><mi>d</mi><mi>s</mi><mo>,</mo><mrow><mi mathvariant="bold-italic">p</mi></mrow><mrow><mi mathvariant="bold-italic">r</mi></mrow><mrow><mi mathvariant="bold-italic">o</mi></mrow><mrow><mi mathvariant="bold-italic">g</mi></mrow><mrow><mi mathvariant="bold-italic">r</mi></mrow><mrow><mi mathvariant="bold-italic">a</mi></mrow><msub><mrow><mi mathvariant="bold-italic">m</mi></mrow><msup><mrow><mrow><mi mathvariant="bold-italic">k</mi></mrow></mrow><mn>0</mn></msup></msub></mrow></msub></mrow></math> </ephtml></p> <p>The <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>k</mi></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>k</mi></mrow><mn>0</mn></msup></math> </ephtml> indicate that the programs may be the same or different, as depicted in Fig. 2 under the columns "From Department" and "To Department." Transforming Eq. (<reflink idref="bib10" id="ref163">10</reflink>) to highlight the relevance of academic program while retaining state yields:</p> <p>11 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>P</mi><mi>r</mi><mi>o</mi><mi>g</mi><mi>r</mi><mi>a</mi><msub><mi>m</mi><mrow><mi>k</mi><mo>,</mo><mi>s</mi></mrow></msub><mfrac><mi>X</mi><mrow /></mfrac><mi>p</mi><mi>r</mi><mi>o</mi><mi>g</mi><mi>r</mi><mi>a</mi><msub><mi>m</mi><msup><mrow><msup><mrow><mi>k</mi></mrow><mn>0</mn></msup><mo>,</mo><mi>s</mi></mrow><mn>0</mn></msup></msub></mrow></math> </ephtml></p> <p>wherein states (same [ <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi></math> </ephtml> ] or different [ <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>s</mi></mrow><mn>0</mn></msup></math> </ephtml> ]) are now attributed of the program, which may also be the same ( <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>k</mi></math> </ephtml> ) or different ( <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>k</mi></mrow><mn>0</mn></msup></math> </ephtml> ). The results of these equations representing the program-to-program relationships are presented in Fig. 7a, demonstrating the significance of programs and disciplines in both within-state and out-of-state agreements. All these figures also have links to HTML versions that provide more details.</p> <hd id="AN0183779576-27">Distance as a Potential Agreement Formation Factor (Research Question 5)</hd> <p>Latitude and longitude coordinates were used to calculate 1,351,406 physical distances considering Earth's curvature among all 1163 institutions in our analytic sample. After excluding self-relations (i.e., <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>i</mi><mo>-</mo><mi>i</mi></mrow></math> </ephtml> ) and loops (i.e., only <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>i</mi><mo>-</mo><mi>j</mi></mrow></math> </ephtml> counts, whereas <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>j</mi><mo>-</mo><mi>i</mi></mrow></math> </ephtml> does not), a total of 338,522 connections were generated in the system (see full data here https://cutt.ly/7wHtPkEA). This number, however, includes institutions located in states where no agreements exist. That is, <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>i</mi><mrow><mi mathvariant="italic">stateA</mi></mrow></msub><mo>-</mo><msub><mi>j</mi><mrow><mi mathvariant="italic">stateK</mi></mrow></msub></mrow></math> </ephtml> are presented in the 338,522 connections even if no partnership exists between states A and K. Relationships like these may be a source of upward bias by inflating the distances across units located in states with no agreements. To address potential distance inflation, we refined our models by including only institutions located in states with at least one agreement. This refinement reduced the relationships to 237,033. Of these totals, 3241 connections involved actual agreements or partnerships. With these data, we investigated if institutions with partnerships are closer than those without.</p> <p>The replication code and partnership data shown in Table 4 may be accessed at https://cutt.ly/JwGRmVDu. Our analysis focused on assessing the impact of partnerships on the expected distance between institutions, distinguishing between in-state and out-of-state agreements. We also tested whether number of agreements held by an institution resulted in significant distance variations.</p> <p>Table 4 Data structure of institutions, number of agreements, and distances</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>inst_i</p></th><th align="left"><p>inst_j</p></th><th align="left"><p>No._agg</p></th><th align="left"><p>ST_inst_i</p></th><th align="left"><p>ST_inst_j</p></th><th align="left"><p>in_ST</p></th><th align="left"><p>long_i</p></th><th align="left"><p>lat_i</p></th><th align="left"><p>long_j</p></th><th align="left"><p>lat_j</p></th><th align="left"><p>Sect_inst_i</p></th><th align="left"><p>Sect_inst_j</p></th><th align="left"><p>Dist</p></th><th align="left"><p>has_aggrement</p></th></tr></thead><tbody><tr><td align="left"><p>217615</p></td><td align="left"><p>166018</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>MA</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 72.53</p></td><td align="left"><p>42.33</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Priv 4-yr</p></td><td align="left"><p>790.39</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>123961</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>CA</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 118.28</p></td><td align="left"><p>34.02</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Priv 4-yr</p></td><td align="left"><p>2086.46</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>236939</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>WA</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 117.16</p></td><td align="left"><p>46.73</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 4-yr</p></td><td align="left"><p>2057.10</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>200226</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>ND</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 97.32</p></td><td align="left"><p>47.51</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 4-yr</p></td><td align="left"><p>1257.62</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>171137</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>MI</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 83.23</p></td><td align="left"><p>42.32</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 4-yr</p></td><td align="left"><p>610.50</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>436599</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>MA</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 71.19</p></td><td align="left"><p>42.18</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Priv 2-yr</p></td><td align="left"><p>832.22</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>207236</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>OK</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 96.68</p></td><td align="left"><p>34.22</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>853.46</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>217615</p></td><td align="left"><p>244613</p></td><td align="left"><p>0</p></td><td align="left"><p>SC</p></td><td align="left"><p>IL</p></td><td align="left"><p>0</p></td><td align="left"><p>− 81.84</p></td><td align="left"><p>33.53</p></td><td align="left"><p>− 87.94</p></td><td align="left"><p>41.75</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Priv 2-yr Prof</p></td><td align="left"><p>657.33</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>200314</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>ND</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 100.44</p></td><td align="left"><p>48.83</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>619.16</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218140</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 79.00</p></td><td align="left"><p>33.80</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1487.88</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>200192</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>ND</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 98.87</p></td><td align="left"><p>48.13</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>607.44</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>200305</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>ND</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 96.61</p></td><td align="left"><p>46.27</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>576.20</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218894</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 80.03</p></td><td align="left"><p>32.93</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1461.42</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>200341</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>ND</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 103.61</p></td><td align="left"><p>48.16</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>538.05</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218672</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 80.79</p></td><td align="left"><p>34.74</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1368.13</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218681</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 81.30</p></td><td align="left"><p>33.01</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1393.47</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218690</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 80.37</p></td><td align="left"><p>33.93</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1412.65</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>218706</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 81.62</p></td><td align="left"><p>34.72</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>1325.25</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>367972</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>IL</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 89.63</p></td><td align="left"><p>39.72</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Pub less 2-yr</p></td><td align="left"><p>802.88</p></td><td align="left"><p>0</p></td></tr><tr><td align="left"><p>126207</p></td><td align="left"><p>217633</p></td><td align="left"><p>0</p></td><td align="left"><p>CO</p></td><td align="left"><p>SC</p></td><td align="left"><p>0</p></td><td align="left"><p>− 104.76</p></td><td align="left"><p>40.41</p></td><td align="left"><p>− 82.64</p></td><td align="left"><p>34.52</p></td><td align="left"><p>Pub 2-yr</p></td><td align="left"><p>Priv 4-yr</p></td><td align="left"><p>1278.21</p></td><td align="left"><p>0</p></td></tr></tbody></table> </ephtml> </p> <p>Full Data base with 338,522 rows at https://cutt.ly/7wHtPkEA, ST= state, No. agg = number of agreements, in_ST = in-state, Sect = sector</p> <p>The specific regression specifications were conducted as follows:</p> <ulist> <item>12 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mi>h</mi><mi>a</mi><mi>s</mi><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mrow /><mo>∗</mo><msub><mi>D</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub></mrow></math> </ephtml></item> <item>13 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mi>h</mi><mi>a</mi><mi>s</mi><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mrow /><mo>∗</mo><msub><mi>D</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mtext>in/out-state</mtext></mrow></mrow></math> </ephtml></item> <item>14 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mi>h</mi><mi>a</mi><mi>s</mi><mspace width="0.166667em" /><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mrow /><mo>∗</mo><msub><mi>D</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mtext>states</mtext><mspace width="0.166667em" /><mtext>exist</mtext></mrow></mrow></math> </ephtml></item> <item>15 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mi>h</mi><mi>a</mi><mi>s</mi><mspace width="0.166667em" /><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mrow /><mo>∗</mo><msub><mi>D</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mtext>in</mtext><mo stretchy="false">/</mo><mtext>out-state</mtext><mspace width="0.166667em" /><mo>&</mo><mspace width="0.166667em" /><mtext>states</mtext><mspace width="0.166667em" /><mtext>exist</mtext></mrow></mrow></math> </ephtml></item> <item>16 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mo>#</mo><mspace width="0.166667em" /><mi>o</mi><mi>f</mi><mspace width="0.166667em" /><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi><mrow /><mo>∗</mo><msub><mi>β</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub></mrow></math> </ephtml></item> <item>17 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mo>#</mo><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi><mrow /><mo>∗</mo><msub><mi>β</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mtext>in/out-state</mtext></mrow></mrow></math> </ephtml></item> <item>18 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mo>#</mo><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi><mrow /><mo>∗</mo><msub><mi>β</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mtext>states exist</mtext></mrow></mrow></math> </ephtml></item> <item>19 <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>D</mi><mi>i</mi><mi>s</mi><mi>t</mi><mo>=</mo><mi>α</mi><mo>+</mo><mo>#</mo><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi><mrow /><mo>∗</mo><msub><mi>β</mi><mn>1</mn></msub><mo>+</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><msub><mi>s</mi><mrow><mi>A</mi><mo>,</mo><mi>K</mi></mrow></msub><mrow /><mo>∗</mo><msub><mi>D</mi><mi>n</mi></msub><mo>+</mo><msub><mi>ϵ</mi><mi>i</mi></msub><mrow><mo stretchy="false">|</mo><mrow><mtext>in</mtext><mo stretchy="false">/</mo><mtext>out-state</mtext><mspace width="0.166667em" /><mo>&</mo><mspace width="0.166667em" /><mtext>states</mtext><mspace width="0.166667em" /><mtext>exist</mtext></mrow><mo>.</mo></mrow></mrow></math> </ephtml></item> </ulist> <p>The conditional forms of <emph>in/out-state</emph> indicate estimation based on in-state or out-of-state relationships. The conditional forms for <emph>states exist</emph> ensure that linked institutions are in states with at least one agreement (N = 237,033). Models (Eqs. 12 to 15) estimate the number of agreements ( <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>#</mo><mi>a</mi><mi>g</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi></mrow></math> </ephtml> ) as the main predictor. All models include fixed effects for states<subs><emph>A,K</emph></subs> to control for unobserved heterogeneity in state and institution location (Arellano, [<reflink idref="bib2" id="ref164">2</reflink>]; Stock et al., [<reflink idref="bib64" id="ref165">64</reflink>]). Regression results are detailed in Table 5.</p> <p>Table 5 Regression results of distance estimates</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>All</p></th><th align="left"><p>All in-state</p></th><th align="left"><p>All out-state</p></th><th align="left"><p>States A&K</p></th><th align="left"><p>States A&K in-state</p></th><th align="left"><p>States A&K out-state</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>(Intercept)</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq49.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>130</mn><mo>.</mo><mmultiscripts><mn>99</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq50.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq51.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq52.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>130</mn><mo>.</mo><mmultiscripts><mn>99</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq53.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq54.gif" /></p></td></tr><tr><td align="left"><p>(73.32)</p></td><td align="left"><p>(42.52)</p></td><td align="left"><p>(73.74)</p></td><td align="left"><p>(72.92)</p></td><td align="left"><p>(42.93)</p></td><td align="left"><p>(73.35)</p></td></tr><tr><td align="left" rowspan="2"><p>Has agreement?</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>30</mn><mo>.</mo><mmultiscripts><mn>81</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq55.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>30</mn><mo>.</mo><mmultiscripts><mn>25</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq56.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>33</mn><mo>.</mo><mmultiscripts><mn>05</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq57.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>32</mn><mo>.</mo><mmultiscripts><mn>08</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq58.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>31</mn><mo>.</mo><mmultiscripts><mn>44</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq59.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>34</mn><mo>.</mo><mmultiscripts><mn>37</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq60.gif" /></p></td></tr><tr><td align="left"><p>(2.29)</p></td><td align="left"><p>(2.10)</p></td><td align="left"><p>(5.14)</p></td><td align="left"><p>(2.40)</p></td><td align="left"><p>(2.26)</p></td><td align="left"><p>(5.16)</p></td></tr><tr><td align="left"><p>Fixed effects</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td></tr><tr><td align="left"><p>R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq61.gif" /></p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.99</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.99</p></td></tr><tr><td align="left"><p>Adj. R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq62.gif" /></p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.99</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.99</p></td></tr><tr><td align="left"><p>Num. obs.</p></td><td align="left"><p>338522</p></td><td align="left"><p>11397</p></td><td align="left"><p>327125</p></td><td align="left"><p>237033</p></td><td align="left"><p>8750</p></td><td align="left"><p>228283</p></td></tr><tr><td align="left"><p>RMSE</p></td><td align="left"><p>103.69</p></td><td align="left"><p>85.04</p></td><td align="left"><p>104.29</p></td><td align="left"><p>103.12</p></td><td align="left"><p>85.85</p></td><td align="left"><p>103.73</p></td></tr></tbody></table> </ephtml> </p> <p>Table 5 Regression results of distance estimates</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>All</p></th><th align="left"><p>All in-state</p></th><th align="left"><p>All out-state</p></th><th align="left"><p>States A&K</p></th><th align="left"><p>States A&K in-state</p></th><th align="left"><p>States A&K out-state</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>(Intercept)</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq63.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>130</mn><mo>.</mo><mmultiscripts><mn>99</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq64.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq65.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq66.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>130</mn><mo>.</mo><mmultiscripts><mn>99</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq67.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>3361</mn><mo>.</mo><mmultiscripts><mn>59</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq68.gif" /></p></td></tr><tr><td align="left"><p>(73.34)</p></td><td align="left"><p>(42.83)</p></td><td align="left"><p>(73.74)</p></td><td align="left"><p>(72.94)</p></td><td align="left"><p>(43.34)</p></td><td align="left"><p>(73.35)</p></td></tr><tr><td align="left" rowspan="2"><p>Numb. of agreement</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>1</mn><mo>.</mo><mmultiscripts><mn>11</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq69.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0</mn><mo>.</mo><mmultiscripts><mn>94</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq70.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>2</mn><mo>.</mo><mmultiscripts><mn>94</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq71.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0</mn><mo>.</mo><mmultiscripts><mn>98</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq72.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0</mn><mo>.</mo><mmultiscripts><mn>78</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq73.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>3</mn><mo>.</mo><mmultiscripts><mn>11</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq74.gif" /></p></td></tr><tr><td align="left"><p>(0.17)</p></td><td align="left"><p>(0.15)</p></td><td align="left"><p>(0.60)</p></td><td align="left"><p>(0.18)</p></td><td align="left"><p>(0.15)</p></td><td align="left"><p>(0.60)</p></td></tr><tr><td align="left"><p>Fixed effects</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Yes</p></td></tr><tr><td align="left"><p>R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq75.gif" /></p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.99</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.99</p></td></tr><tr><td align="left"><p>Adj. R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq76.gif" /></p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.98</p></td><td align="left"><p>0.99</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.99</p></td></tr><tr><td align="left"><p>Num. obs.</p></td><td align="left"><p>338522</p></td><td align="left"><p>11397</p></td><td align="left"><p>327125</p></td><td align="left"><p>237033</p></td><td align="left"><p>8750</p></td><td align="left"><p>228283</p></td></tr><tr><td align="left"><p>RMSE</p></td><td align="left"><p>103.72</p></td><td align="left"><p>85.66</p></td><td align="left"><p>104.29</p></td><td align="left"><p>103.15</p></td><td align="left"><p>86.68</p></td><td align="left"><p>103.73</p></td></tr></tbody></table> </ephtml> </p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.001</mn></mrow></math> </ephtml> ; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.01</mn></mrow></math> </ephtml> ; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></math> </ephtml> , states A&K only include states where at least one agreement was realized see page 24 Complete code and data to replicate these models available at https://cutt.ly/JwGRmVDu</p> <hd id="AN0183779576-28">The Influence of Statewide Policies on Partnership Formation (Research Question 6)</hd> <p>This question builds from all previous steps. Here, the outcome variable measures the proportion of institutions in a state that participate in at least one bilateral partnership. We selected this outcome because it accounts for both participating and not participating institutions. Moreover, using proportions as the outcome measure accounts for variations in college prevalence across states. Although other possibilities to address this question are the number of agreements per state and/or the number of agreements each participating institution has in a given state, both approaches ignore the presence of nonparticipating colleges.[<reflink idref="bib4" id="ref166">4</reflink>] Our outcome offers a more precise measure of institutional participation. For example, hovering over Arizona in Fig. 3b(b) displays a message showing that 11% of the institutions in that state participate in at least one of these agreements.</p> <p>Based on our interest in measuring the impact of statewide policies, we tested their impact based on an aggregated count of policy implementation, varying from 0 to 4, and then tested individual impacts as binary indicators per each policy. Finally, we relied on our <emph>States Clustering</emph> [see Fig. 6b(b)] to estimate the impact of the five classes identified as factors.[<reflink idref="bib5" id="ref167">5</reflink>] These results are shown in Table 9.</p> <hd id="AN0183779576-29">Findings</hd> <p>This section addresses our six research questions, beginning with findings related to statewide policies, followed by those related to institutionally driven agreements, and concluding with an examination of their intersection.</p> <hd id="AN0183779576-30">Relevant Statewide Policies and States (Research Question 1)</hd> <p>Key actor analyses enabled us to highlight the relevance of states and statewide policies following a traditional social networks graph [Fig. 4b(a)] that organically clusters units based on their common policy adoption. The spatial network depiction is complementary, highlighting potential clusters of relevance while also considering states' actual location [Fig. 4b(b)]. Actual values for more detailed analyses of Fig. 4b are provided in Table 6.</p> <p>Table 6 Most relevant states and policies RQ1</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="6"><p>States</p></th><th align="left" colspan="6"><p>Statewide policies</p></th></tr><tr><th align="left"><p>Name</p></th><th align="left"><p>Bet</p></th><th align="left"><p>Eig</p></th><th align="left"><p>Degree</p></th><th align="left"><p>Deg<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow><mi mathvariant="italic">norm</mi></mrow><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq113.gif" /></p></th><th align="left"><p>Key_actors<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq114.gif" /></p></th><th align="left"><p>Name</p></th><th align="left"><p>Bet</p></th><th align="left"><p>Eig</p></th><th align="left"><p>Degree</p></th><th align="left"><p>Deg<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow><mi mathvariant="italic">norm</mi></mrow><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq115.gif" /></p></th><th align="left"><p>Key_actors<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq116.gif" /></p></th></tr></thead><tbody><tr><td align="left"><p>Colorado</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left"><p>Core_lower_division</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>38</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td></tr><tr><td align="left"><p>Florida</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left"><p>Asocc_deg_guarateed</p></td><td align="left"><p>0.722</p></td><td align="left"><p>0.962</p></td><td align="left"><p>35</p></td><td align="left"><p>0.921</p></td><td align="left"><p>2.605</p></td></tr><tr><td align="left"><p>Kansas</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left"><p>Reverse_transfer</p></td><td align="left"><p>0.38</p></td><td align="left"><p>0.699</p></td><td align="left"><p>25</p></td><td align="left"><p>0.658</p></td><td align="left"><p>1.737</p></td></tr><tr><td align="left"><p>Louisiana</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left"><p>Common_numbering</p></td><td align="left"><p>0.269</p></td><td align="left"><p>0.548</p></td><td align="left"><p>20</p></td><td align="left"><p>0.526</p></td><td align="left"><p>1.343</p></td></tr><tr><td align="left"><p>Missouri</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Nevada</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Oregon</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Tennessee</p></td><td align="left"><p>1</p></td><td align="left"><p>1</p></td><td align="left"><p>4</p></td><td align="left"><p>1</p></td><td align="left"><p>3</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Texas</p></td><td align="left"><p>0.636</p></td><td align="left"><p>0.7</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>2.086</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Arkansas</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Idaho</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Indiana</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>New Mexico</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>North Carolina</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>South Dakota</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Utah</p></td><td align="left"><p>0.464</p></td><td align="left"><p>0.782</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.996</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Hawaii</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Illinois</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Maryland</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Massachusetts</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Mississippi</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>New Jersey</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Ohio</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Pennsylvania</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Washington</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>West Virginia</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wisconsin</p></td><td align="left"><p>0.367</p></td><td align="left"><p>0.829</p></td><td align="left"><p>3</p></td><td align="left"><p>0.75</p></td><td align="left"><p>1.946</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Alabama</p></td><td align="left"><p>0.184</p></td><td align="left"><p>0.529</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.213</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Georgia</p></td><td align="left"><p>0.085</p></td><td align="left"><p>0.611</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.196</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Kentucky</p></td><td align="left"><p>0.085</p></td><td align="left"><p>0.611</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.196</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Oklahoma</p></td><td align="left"><p>0.085</p></td><td align="left"><p>0.611</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.196</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>South Carolina</p></td><td align="left"><p>0.085</p></td><td align="left"><p>0.611</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.196</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Virginia</p></td><td align="left"><p>0.085</p></td><td align="left"><p>0.611</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.196</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Arizona</p></td><td align="left"><p>0.197</p></td><td align="left"><p>0.483</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.18</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>North Dakota</p></td><td align="left"><p>0.197</p></td><td align="left"><p>0.483</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.18</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wyoming</p></td><td align="left"><p>0.182</p></td><td align="left"><p>0.471</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.153</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Iowa</p></td><td align="left"><p>0.098</p></td><td align="left"><p>0.517</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.115</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Maine</p></td><td align="left"><p>0.098</p></td><td align="left"><p>0.517</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.115</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Rhode Island</p></td><td align="left"><p>0.098</p></td><td align="left"><p>0.517</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>1.115</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Alaska</p></td><td align="left"><p>0</p></td><td align="left"><p>0.312</p></td><td align="left"><p>1</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.562</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>California</p></td><td align="left"><p>0</p></td><td align="left"><p>0.312</p></td><td align="left"><p>1</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.562</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Montana</p></td><td align="left"><p>0</p></td><td align="left"><p>0.312</p></td><td align="left"><p>1</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.562</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Michigan</p></td><td align="left"><p>0</p></td><td align="left"><p>0.218</p></td><td align="left"><p>1</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.468</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Minnesota</p></td><td align="left"><p>0</p></td><td align="left"><p>0.171</p></td><td align="left"><p>1</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.421</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Connecticut</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Delaware</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Nebraska</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>New Hampshire</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>New York</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Vermont</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Complete dataset to replicate these models available at https://cutt.ly/5wHlcMcV <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mo>†</mo></mmultiscripts></math> </ephtml> Normalized to range between 0 and 1 <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mrow><mo>†</mo><mo>†</mo></mrow></mmultiscripts></math> </ephtml> Key actor is the sum of the normalized values for betweenness, eigenvector (influence) and degree</p> <p>Our analyses revealed that eight states emerge as the most active players, implementing all four policies (see the first eight rows in Table 6). This is visually represented in the eight yellow states in Fig. 4b(a). Among states enacting three policies, a common pattern includes guaranteed associate's degree, common numbering, and core lower division. Texas, highlighted in red, differs from this pattern by implementing reverse transfer, which enabled Texas to become indirectly connected to other states adopting this policy. Six states, detailed in the last six rows of Table 6, did not report implementing any of these policies, resulting in their disconnection from this network.</p> <p>Concerning statewide policies, <emph>core lower division</emph> emerges as the most relevant, with 38 states implementing it. Its high connectivity contributes to its role as a pivotal policy in the network (see explanation of Fig. 4a above). The second most important statewide policy is <emph>associate's degree guaranteed</emph>, present in 35 states. <emph>Reverse transfer</emph> is active in 25 states, while legislative frameworks on <emph>common course numbering</emph> are in place in 20 states.</p> <hd id="AN0183779576-31">Economic Spillovers [Research Question 2(a)]</hd> <p>Moran's <emph>I</emph> and join-count tests assessed neighboring states' likelihood to implement the same statewide policies compared to non-neighboring states. The findings of both tests are shown in Fig. 5a–d.</p> <p>Figure 5a shows the result of the analysis of core lower division courses, which is implemented by 38 states. The Moran's <emph>I</emph> indicates that there is evidence of positive autocorrelation, meaning that states neighboring states implementing this policy are themselves likely to implement this policy. Here, the join-count test corroborated that join-counts for the presence of this policy across the continental United States are based on structure, when compared to random draws of policy implementation in this system. Like in all Moran's <emph>I</emph> and join-count tests, the pseudo p-values are based on 100,000 Monte Carlo simulations. The join-count test for the join absence of this policy did not reach standard acceptable pseudo p-values—therefore bordering states with no implementation of this policy does not increase the no implementation of core lower division courses. In other words, these findings indicate that the Moran's <emph>I</emph> is being driven by the presence of these policies among neighboring states, and these dyadic counts (i.e., considering two neighboring state at the time) corroborate this join presence of the policy.</p> <p>The second policy analyzed is guaranteed transfer of associate's degree before transferring—implemented in 35 states (see Fig. 5b). Unlike core lower division, Moran's <emph>I</emph> results for this policy did not support the presence of economic spillovers across all neighboring units. The join-count test results for the join presence of guaranteed transfer, however, were better than random policy allocation draws, while the join absence results did not reach significance. These findings highlight the importance of dyadic counts in identifying structural patterns over the overall number of states implementing policies. These mixed results does not allow us to be as confident about the presence of economic spillovers as in the case of core lower division courses.</p> <p>Our next policy analyzed is common course numbering or uniform numbering conventions (Fig. 5c), currently implemented in 20 states. In this case, Moran's <emph>I</emph> and join presence tests indicate that states are more likely to have this policy in place if their neighbors do as well. This spatial influence appears more pronounced in the Northeast and South regions of the United States than elsewhere. In the case of the join absence, we could not reject the null hypothesis of no influence.</p> <p>Finally, Fig. 5d shows the findings associated with reverse transfer, consisting of retroactively granting associate's degrees in 25 states. Despite a broader adoption than common course numbering policies, both Moran's <emph>I</emph> and join-count tests reflected no evidence of spillover effects. That is, the distribution of states embracing this policy behaves as if this policy is randomly implemented across the continental United States.</p> <p>A final Moran's <emph>I</emph> tested whether intensity, measured as total number of policies implemented per state, yielded evidence of economic spillovers. The result of this analysis, shown in Fig. 6a, indicates that statewide policy intensity does not follow a spatial dependence or economic spillover pattern. That is, states are not likely to have similar policy implementation intensity levels as their neighboring states.</p> <hd id="AN0183779576-32">States Clustering [Research Question 2(b)]</hd> <p>The results of the states' partition around medoids clustering yielded an optimal classification of states into five categories or classes [see Fig. 6b(a)]. These classes are the following:</p> <p></p> <ulist> <item> Rev. trans. 10%, Assoc. deg. 0%, Core low 0%, Common # 10%, Avg = 0.2 (8 states)</item> <p></p> <item> Rev. trans. 0%, Assoc. deg. 60%, Core low 100%, Common # 0%, Avg = 1.6 (8 states)</item> <p></p> <item> Rev. trans. 100%, Assoc. deg. 90%, Core low 80%, Common # 0%, Avg = 2.7 (15 states)</item> <p></p> <item> Rev. trans. 0%, Assoc. deg. 80%, Core low 90%, Common # 100%, Avg = 2.7 (10 states)</item> <p></p> <item> Rev. trans. 100%, Assoc. deg. 90%, Core low 100%, Common # 100%, Avg = 3.9 (9 states)</item> </ulist> <p>The interactive version of Fig. 6b(b), available here https://cutt.ly/ewG3Wrp4, shows the states that belong to each category. The fifth class includes states with the highest implementation intensity, including the eight most active states and Texas [see Table 6 and states in red in Fig. 6b(b)]. Another set consisted of eight states with minimal policy implementation [in purple and class 1 in Fig. 6b(b)]; six of these states implemented zero policies and the remaining two implemented only reverse transfer and common numbering. Additionally, eight states (in class 2) focused on the implementation of core lower division courses (100%) and granting associate's degree (60%). Lastly, 25 states had a mean implementation of 2.7 policies, with 15 focusing on reverse transfer, associate's degrees guaranteed, and core lower division courses (class 3), and 10 focusing on common numbering, core lower, and associate's degree guaranteed.</p> <p>With these classes we tested whether the distribution of these resulting clusters exhibited spatial clustering using Moran's <emph>I</emph>. The results did not support rejecting the null hypothesis, thus indicating that these classes tend to behave as if they were randomly distributed across the continental United States. Although no economic spillovers were observed, future studies may test whether these classifications have predictive power to explain differences in transfer rates.</p> <hd id="AN0183779576-33">Institutional Participation in Bilateral Agreements (Research Question 3)</hd> <p>Table 7 presents all the partnerships in the network, categorized by institution type and whether they were within-state and out-of-state agreements. Approximately 45.3% of all partnerships take place across public 2-year and public 4-year colleges, followed by 35.5% between public 2-year and private not-for-profit 4-year colleges. When these agreements are analyzed by within-state status, 47.8% take place among public 2-year and public 4-year colleges. In the out-of-state sub-network, the most prevalent set of relationships (40.8%) involves public 2-year and private not-for-profit 4-year colleges. Public 2-year and public 4-year agreements account for 28.8% of out-of-state agreements. Notably, 13% of these out-of-state partnerships are among public 2-year and private for-profit 4-year institutions, contrasting with 1.9% of such partnerships in the within-state network.</p> <p>Table 7 Institutionally-driven partnerships by in- and out-state</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p>Partnerships across institutions</p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi mathvariant="bold">n</mi><mrow><mi mathvariant="normal">out</mi><mo>-</mo><mi mathvariant="normal">state</mi></mrow></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq88.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mo>%</mo><mrow><mi mathvariant="normal">out</mi><mo>-</mo><mi mathvariant="normal">state</mi></mrow></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq87.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi mathvariant="bold">n</mi><mrow><mi mathvariant="normal">in</mi><mo>-</mo><mi mathvariant="normal">state</mi></mrow></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq86.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mo>%</mo><mrow><mi mathvariant="normal">in</mi><mo>-</mo><mi mathvariant="normal">state</mi></mrow></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq85.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi mathvariant="bold">n</mi><mtext mathvariant="bold">total</mtext></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq84.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mo>%</mo><mtext mathvariant="bold">total</mtext></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq83.gif" /></p></th></tr></thead><tbody><tr><td align="left" rowspan="19"><p>sectors_ij</p></td><td align="left"><p>Priv 2-yr No-prof & Priv 4-yr No-prof</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>3</p></td><td align="left"><p>0.1</p></td><td align="left"><p>3</p></td><td align="left"><p>0.1</p></td></tr><tr><td align="left"><p>Priv 2-yr No-prof & Pub 4-yr</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Priv 2-yr Profit & Priv 4-yr-Profit</p></td><td align="left"><p>2</p></td><td align="left"><p>0.5</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>2</p></td><td align="left"><p>0.1</p></td></tr><tr><td align="left"><p>Priv 2-yr Profit & Pub 4-yr</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Priv 4-yr-Profit & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Priv 4-yr No-prof</p></td><td align="left"><p>6</p></td><td align="left"><p>1.4</p></td><td align="left"><p>16</p></td><td align="left"><p>0.6</p></td><td align="left"><p>22</p></td><td align="left"><p>0.7</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Pub 4-yr</p></td><td align="left"><p>7</p></td><td align="left"><p>1.6</p></td><td align="left"><p>67</p></td><td align="left"><p>2.4</p></td><td align="left"><p>74</p></td><td align="left"><p>2.3</p></td></tr><tr><td align="left"><p>Priv Less 2-yr Profit & Priv 4-yr No-prof</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 2-yr Profit</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr-Profit</p></td><td align="left"><p>56</p></td><td align="left"><p>12.9</p></td><td align="left"><p>54</p></td><td align="left"><p>1.9</p></td><td align="left"><p>110</p></td><td align="left"><p>3.4</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr No-prof</p></td><td align="left"><p>177</p></td><td align="left"><p>40.8</p></td><td align="left"><p>973</p></td><td align="left"><p>34.7</p></td><td align="left"><p>1150</p></td><td align="left"><p>35.5</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv Less 2-yr Profit</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 2-yr</p></td><td align="left"><p>6</p></td><td align="left"><p>1.4</p></td><td align="left"><p>69</p></td><td align="left"><p>2.5</p></td><td align="left"><p>75</p></td><td align="left"><p>2.3</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 4-yr</p></td><td align="left"><p>125</p></td><td align="left"><p>28.8</p></td><td align="left"><p>1343</p></td><td align="left"><p>47.8</p></td><td align="left"><p>1468</p></td><td align="left"><p>45.3</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub Less 2-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Pub 4-yr & Priv 4-yr No-prof</p></td><td align="left"><p>22</p></td><td align="left"><p>5.1</p></td><td align="left"><p>115</p></td><td align="left"><p>4.1</p></td><td align="left"><p>137</p></td><td align="left"><p>4.2</p></td></tr><tr><td align="left"><p>Pub 4-yr & Pub 4-yr</p></td><td align="left"><p>27</p></td><td align="left"><p>6.2</p></td><td align="left"><p>164</p></td><td align="left"><p>5.8</p></td><td align="left"><p>191</p></td><td align="left"><p>5.9</p></td></tr><tr><td align="left"><p>Pub Less 2-yr & Priv 4-yr No-prof</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p>Pub Less 2-yr & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>0.2</p></td><td align="left"><p>0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1</p></td><td align="left"><p>0.0</p></td></tr><tr><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>p</mi><mo><</mo><mn>0.0001</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq89.gif" /></p></td><td align="left"><p>All</p></td><td align="left"><p>434</p></td><td align="left"><p>100.0</p></td><td align="left"><p>2807</p></td><td align="left"><p>100.0</p></td><td align="left"><p>3241</p></td><td align="left"><p>100.0</p></td></tr></tbody></table> </ephtml> </p> <p>Complete dataset to replicate these distributions available at https://cutt.ly/7wHtPkEA</p> <p>Table 8 further explores the strength of partnerships, starting with number of agreements. In-state partnerships tend to have slightly more programs (5.7, SD = 10.4) than out-of-state partnerships (5.0, SD = 6.9), with 75% (third quartile or q3 in the table) involving up to five to seven programs in both within-state and out-of-state partnerships, respectively. None of these values are significantly different (p = 0.18).</p> <p>Table 8 Summary of distances and number of agreements across institutions by in- and out-state</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p>Levels</p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="bold" xmlns="">n</mi></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq39.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mover accent="true" xmlns=""><mrow><mi mathvariant="bold">x</mi></mrow><mrow><mo stretchy="false">¯</mo></mrow></mover></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq40.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="bold" xmlns="">s</mi></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq41.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi mathvariant="bold">q</mi><mn mathvariant="bold">1</mn></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq42.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mover accent="true" xmlns=""><mi mathvariant="bold">x</mi><mo stretchy="true">~</mo></mover></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq43.gif" /></p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi mathvariant="bold">q</mi><mn mathvariant="bold">3</mn></msub></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq44.gif" /></p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>Number of agreements</p></td><td align="left"><p>Out-state</p></td><td align="left"><p>434</p></td><td align="left"><p>5.0</p></td><td align="left"><p>6.9</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>7.0</p></td></tr><tr><td align="left"><p>In-state</p></td><td align="left"><p>2807</p></td><td align="left"><p>5.7</p></td><td align="left"><p>10.4</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>5.0</p></td></tr><tr><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>p</mi><mo>=</mo><mn>0.18</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq45.gif" /></p></td><td align="left"><p>Total</p></td><td align="left"><p>3241</p></td><td align="left"><p>5.6</p></td><td align="left"><p>10.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>6.0</p></td></tr><tr><td align="left" rowspan="2"><p>Distance in miles</p></td><td align="left"><p>Out-state</p></td><td align="left"><p>434</p></td><td align="left"><p>721.6</p></td><td align="left"><p>742.8</p></td><td align="left"><p>116.1</p></td><td align="left"><p>404.5</p></td><td align="left"><p>1192.7</p></td></tr><tr><td align="left"><p>In-state</p></td><td align="left"><p>2807</p></td><td align="left"><p>109.5</p></td><td align="left"><p>88.9</p></td><td align="left"><p>39.8</p></td><td align="left"><p>88.6</p></td><td align="left"><p>158.4</p></td></tr><tr><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>p</mi><mo><</mo><mn>0.0001</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq46.gif" /></p></td><td align="left"><p>Total</p></td><td align="left"><p>3241</p></td><td align="left"><p>191.4</p></td><td align="left"><p>352.2</p></td><td align="left"><p>43.6</p></td><td align="left"><p>98.8</p></td><td align="left"><p>183.3</p></td></tr><tr><td align="left"><p>Number of agreements</p></td><td align="left"><p>Priv 2-yr No-prof & Priv 4-yr No-prof</p></td><td align="left"><p>3</p></td><td align="left"><p>2.7</p></td><td align="left"><p>2.9</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>3.5</p></td></tr><tr><td align="left" rowspan="11"><p>Within-state</p></td><td align="left"><p>Priv 2-yr No-prof & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Priv 2-yr Profit & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Priv 4-yr No-prof</p></td><td align="left"><p>16</p></td><td align="left"><p>2.9</p></td><td align="left"><p>2.2</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td><td align="left"><p>4.2</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Pub 4-yr</p></td><td align="left"><p>67</p></td><td align="left"><p>2.2</p></td><td align="left"><p>3.1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 2-yr Profit</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr-Profit</p></td><td align="left"><p>54</p></td><td align="left"><p>1.9</p></td><td align="left"><p>1.6</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>3.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr No-prof</p></td><td align="left"><p>973</p></td><td align="left"><p>3.6</p></td><td align="left"><p>6.9</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 2-yr</p></td><td align="left"><p>69</p></td><td align="left"><p>1.0</p></td><td align="left"><p>0.1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 4-yr</p></td><td align="left"><p>1343</p></td><td align="left"><p>8.2</p></td><td align="left"><p>12.7</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td><td align="left"><p>11.0</p></td></tr><tr><td align="left"><p>Pub 4-yr & Priv 4-yr No-prof</p></td><td align="left"><p>115</p></td><td align="left"><p>3.4</p></td><td align="left"><p>4.1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>7.0</p></td></tr><tr><td align="left"><p>Pub 4-yr & Pub 4-yr</p></td><td align="left"><p>164</p></td><td align="left"><p>5.0</p></td><td align="left"><p>11.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>3.0</p></td></tr><tr><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>p</mi><mo><</mo><mn>0.0001</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq47.gif" /></p></td><td align="left"><p>Total</p></td><td align="left"><p>2807</p></td><td align="left"><p>5.7</p></td><td align="left"><p>10.4</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>5.0</p></td></tr><tr><td align="left"><p>Number of agreements</p></td><td align="left"><p>Priv 2-yr Profit & Priv 4-yr-Profit</p></td><td align="left"><p>2</p></td><td align="left"><p>1.0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left" rowspan="14"><p>Out-state</p></td><td align="left"><p>Priv 4-yr-Profit & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Priv 4-yr No-prof</p></td><td align="left"><p>6</p></td><td align="left"><p>2.0</p></td><td align="left"><p>1.5</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>3.2</p></td></tr><tr><td align="left"><p>Priv 4-yr No-prof & Pub 4-yr</p></td><td align="left"><p>7</p></td><td align="left"><p>2.4</p></td><td align="left"><p>2.4</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>3.5</p></td></tr><tr><td align="left"><p>Priv Less 2-yr Profit & Priv 4-yr No-prof</p></td><td align="left"><p>1</p></td><td align="left"><p>6.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>6.0</p></td><td align="left"><p>6.0</p></td><td align="left"><p>6.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr-Profit</p></td><td align="left"><p>56</p></td><td align="left"><p>2.0</p></td><td align="left"><p>3.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv 4-yr No-prof</p></td><td align="left"><p>177</p></td><td align="left"><p>6.1</p></td><td align="left"><p>7.4</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td><td align="left"><p>8.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Priv Less 2-yr Profit</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 2-yr</p></td><td align="left"><p>6</p></td><td align="left"><p>1.0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub 4-yr</p></td><td align="left"><p>125</p></td><td align="left"><p>5.5</p></td><td align="left"><p>7.5</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td><td align="left"><p>7.0</p></td></tr><tr><td align="left"><p>Pub 2-yr & Pub Less 2-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub 4-yr & Priv 4-yr No-prof</p></td><td align="left"><p>22</p></td><td align="left"><p>6.0</p></td><td align="left"><p>10.2</p></td><td align="left"><p>1.0</p></td><td align="left"><p>2.0</p></td><td align="left"><p>7.0</p></td></tr><tr><td align="left"><p>Pub 4-yr & Pub 4-yr</p></td><td align="left"><p>27</p></td><td align="left"><p>4.6</p></td><td align="left"><p>3.6</p></td><td align="left"><p>1.0</p></td><td align="left"><p>4.0</p></td><td align="left"><p>7.0</p></td></tr><tr><td align="left"><p>Pub Less 2-yr & Priv 4-yr No-prof</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p>Pub Less 2-yr & Pub 4-yr</p></td><td align="left"><p>1</p></td><td align="left"><p>1.0</p></td><td align="left"><p>NA</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td></tr><tr><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mi>p</mi><mo>=</mo><mn>0.07</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq117.gif" /></p></td><td align="left"><p>Total</p></td><td align="left"><p>434</p></td><td align="left"><p>5.0</p></td><td align="left"><p>6.9</p></td><td align="left"><p>1.0</p></td><td align="left"><p>1.0</p></td><td align="left"><p>7.0</p></td></tr></tbody></table> </ephtml> </p> <p>Complete dataset to replicate these descriptive statistics available at https://cutt.ly/7wHtPkEA</p> <p>When disaggregating these distributions by institution type, within-state agreements show the highest program strength between public 2-year and public 4-year colleges (mean = 8.2 programs, SD = 12.7). In the out-of-state context, the strongest partnerships occur between public 2-year colleges and private not-for-profit 4-year colleges (mean = 6.1 programs, SD = 7.4). Among the 110 partnerships taking place among public 2-year and private for-profit 4-year colleges, 50% took place within-state with the other 50% were realized out-of-state. Both cases have mean strengths of about two programs per partnership, on average (SDs = 1.6 for in-state and 3.0 for out-of-state).</p> <hd id="AN0183779576-34">Program Specificity in Partnerships (Research Question 4)</hd> <p>Figure 7a explores whether the partnerships are program- or discipline-specific or whether they were driven by general agreements, considering within-state and out-of-state scopes. Each sub-figure uses colors to represent program disciplines. In Fig. 7a(a), 40% of the 18,260 programs involved in partnerships <emph>lack specificity, indicating a focus on transfer rather than articulation agreements</emph>. This lack of specificity indicates that the partnerships align more with transfer than with articulation agreements. That is, if we understand articulation agreements as those where more careful curricular evaluation is needed across institutions to avoid credit loss, then we can conclude that 40% of these partnerships are not necessarily ensuring the level of specificity that may prevent credit loss. For context, Fig. 7a(a) also shows that the second largest group involved partnerships among Business/Finance programs with 15.1%.</p> <p>For out-of-state partnerships [Fig. 7a(c)], the general/not specific category accounted for about 50% of all agreements, while for within-state partnerships [Fig. 7a(b)] this category accounted for 39% of agreements. In within-state partnerships, business and finance is the second most represented discipline, followed by education (13.58%). In out-of-state partnerships, the second and third most represented disciplines were business/finance (13.92%) and architecture, design and computer and information science and software engineering (12.32%).</p> <p>Note that the interactive versions of Fig. 7a show the program counts for each type of partnership, such as within-program articulation agreements. For instance, hovering over the loop line in the interactive version reveals a message (dark rectangle) indicating 2662 partnerships in which institutions accept transfers from business, finance, and marketing programs, representing 14.58% of the total agreements (interactive version here https://cutt.ly/V2N6WSe).</p> <hd id="AN0183779576-35">States, Regions, and Institutions (Research Question 4)</hd> <p>Figure 7b contains similar analyses as Fig. 7a but focuses on identifying active states and regions. Pennsylvania stands out, contributing to 33% of all partnerships [Fig. 7b(a)]. Within-state agreements amplify this percentage, reaching 37.5%, with 6029 partnerships in PA alone [see Fig. 7b(b)]. With respect to regions, the Northeast dominates, with 58.33% of all 18,260 agreements [see Fig. 7b(a)]. For out-of-state relationships, the South emerges as the most active region, with about 40% of agreements [Fig. 7b(c)]. The Northeast is the second most active region for out-of-state relationships, contributing to over a third of these agreements.</p> <p>The spatial network representation of these institutionally driven agreements is shown in Fig. 3b(b) and (c). These figures also include a measure of the prevalence of institutionally driven agreements, calculated as the proportion of institutions in a given state that have at least one partnership. This indicator is our outcome of interest in the last set of regression models (Table 9) addressing our final research question. The connecting lines in these figures (HTML version here https://cutt.ly/c2csRbr) contain information about distance between each dyad and the number of programs participating in each partnership, following the conceptual framework in Fig. 3(a).</p> <hd id="AN0183779576-36">Institutions' Centralities</hd> <p>Figure 3b(a) enables us to identify the most central institutions within this network. Although Fig. 3b(a) enhances visibility through size (betweenness centrality) and color (eigenvector centrality), the network's vastness and complexity make identifying the most central institutions challenging (see HTML interactive version here https://cutt.ly/l2csDDh). To ease this process, we present Table 3, highlighting the 50 most central institutions in the network. The complete dataset, including all institutions (with satellite campuses) and centrality measures, can be accessed at https://cutt.ly/5wG3HuOE.</p> <p>These measures present both degree (number of agreements) and strength (number of programs), suggesting their importance in capturing the diversity of partnerships. For example, Montgomery County Community College, located in Blue Bell, PA, has 44 partnerships, representing a total strength of 503. As illustrated in Fig. 3a, this difference indicates that many of these partnerships involve more than one participating program. The lines in the interactive version of Fig. 3b(a) display the number of programs per partnership across all institutions.</p> <p>The second-ranking institution, Bucks County Community College, also in Pennsylvania, has 25 partnerships representing 604 programs, even more than Montgomery County. Following relational analyses principles, the centrality measures go beyond counting connections to also consider the activity levels of the partnering institutions. Overall, these findings align with state-level analyses, accentuating Pennsylvania's prominence in institutionally driven partnerships.</p> <hd id="AN0183779576-37">Importance of Distance in Partnership Formation (Research Question 5)</hd> <p>Table 8 shows the distance distribution by within-state and out-of-state partnerships. Corroborating the accuracy of our distance estimates, out-of-state partnerships have distances about seven times larger than within-state agreements (p < 0.001), with average distances of 721.6 miles (SD = 742.8) and 109.5 miles (SD = 88.9), respectively; median distances are 88.6 miles for within-state partnerships and 404.5 miles for out-of-state partnerships.</p> <p>The regression results in Table 5, whose specifications are shown in Eqs. (<reflink idref="bib12" id="ref168">12</reflink>) to (<reflink idref="bib19" id="ref169">19</reflink>), indicate that institutions with agreements are, on average, about 30 miles closer than institutions without agreements (p < 0.001). This estimate is consistent in aggregate and for within-state and out-of-state models, all accounting for state fixed effects. Further analysis constrained models to states with at least one agreement [Eqs. (<reflink idref="bib14" id="ref170">14</reflink>), (<reflink idref="bib15" id="ref171">15</reflink>), (<reflink idref="bib18" id="ref172">18</reflink>), and (<reflink idref="bib19" id="ref173">19</reflink>)]. Notably, we did not observe any meaningful change, which indicates that our original estimates were not upwardly biased by including states where no partnerships were observed.</p> <p>Our last set of models included the number of programs per agreement (i.e., strength) as the outcome variable as opposed to the presence or absence of these agreements. The significant results corroborate that, as the number of programs increased (i.e., strength), the distance across institutions holding these partnerships decreased. For within-state agreements, each additional program corresponds to an expected decrease in distance of 0.78 miles (p < 0.001). Similarly, in out-of-state partnerships, each additional program results in an expected decrease of 3.11 miles on average (p < 0.001).</p> <hd id="AN0183779576-38">Impact of Statewide Policies on Institutionally Driven Participation (Research Question 6)</hd> <p>In this closing findings section, we explore whether statewide policies affect the variability in institution participation in the transfer and articulation agreements network.</p> <p>Using the proportion of institutions in a given state participating in at least one partnership (i.e., number of colleges with at least one partnership divided by total number of colleges in that state), we tested the impact of each of the four statewide policies, the combined number of these policies, and their cluster classification (i.e., clustering based on medoids). The results, shown in Table 9, consistently demonstrate that none of these predictors yielded significant relationships. These results align with past literature suggesting that statewide policies may not influence participation in institutional and, to some extent, informal partnerships. A set of institution-level models not shown here, but available upon request, shows that these policies are not related to institutions' participation strength in these agreements.</p> <p>Table 9 Do statewide policies impact institutionally driven agreements?</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Model 1</p></th><th align="left"><p>Model 2</p></th><th align="left"><p>Model 3</p></th><th align="left"><p>Model 4</p></th><th align="left"><p>Model 5</p></th><th align="left"><p>Model 6</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>Intercept</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>0</mn><mo>.</mo><mmultiscripts><mn>30</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq137.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>0</mn><mo>.</mo><mmultiscripts><mn>30</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq138.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>0</mn><mo>.</mo><mmultiscripts><mn>23</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq131.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>0</mn><mo>.</mo><mmultiscripts><mn>22</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq132.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mn>0</mn><mo>.</mo><mmultiscripts><mn>28</mn><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq133.gif" /></p></td><td align="left"><p>0.14</p></td></tr><tr><td align="left"><p>(0.06)</p></td><td align="left"><p>(0.06)</p></td><td align="left"><p>(0.04)</p></td><td align="left"><p>(0.05)</p></td><td align="left"><p>(0.04)</p></td><td align="left"><p>(0.07)</p></td></tr><tr><td align="left" rowspan="2"><p>Number of agreements</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.03</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq136.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>(0.02)</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="2"><p>Core lower division</p></td><td align="left" /><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.09</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq134.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left"><p>(0.07)</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="2"><p>Common numbering</p></td><td align="left" /><td align="left" /><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.02</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq135.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>(0.06)</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="2"><p>Asocc deg guarateed</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.01</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.07)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="2"><p>Reverse transfer</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.10</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq139.gif" /></p></td><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.06)</p></td><td align="left" /></tr><tr><td align="left" rowspan="2"><p>Pam clust 2<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq140.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.12</p></td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.10)</p></td></tr><tr><td align="left" rowspan="2"><p>Pam clust 3<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq142.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.10</p></td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.10)</p></td></tr><tr><td align="left" rowspan="2"><p>Pam clust 4<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq141.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.06</p></td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.09)</p></td></tr><tr><td align="left" rowspan="2"><p>Pam clust 5<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mo>†</mo></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq143.gif" /></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.17</p></td></tr><tr><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>(0.10)</p></td></tr><tr><td align="left"><p>R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq129.gif" /></p></td><td align="left"><p>0.03</p></td><td align="left"><p>0.04</p></td><td align="left"><p>0.00</p></td><td align="left"><p>0.00</p></td><td align="left"><p>0.05</p></td><td align="left"><p>0.07</p></td></tr><tr><td align="left"><p>Adj. R<math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts xmlns=""><mrow /><mrow /><mn>2</mn></mmultiscripts></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq128.gif" /></p></td><td align="left"><p>0.01</p></td><td align="left"><p>0.02</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.02</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq127.gif" /></p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.02</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq126.gif" /></p></td><td align="left"><p>0.03</p></td><td align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>-</mo><mn>0.01</mn></mrow></math><inline-graphic mime-subtype="GIF" href="11162_2024_9831_Article_IEq125.gif" /></p></td></tr><tr><td align="left"><p>Num. obs.</p></td><td align="left"><p>50</p></td><td align="left"><p>50</p></td><td align="left"><p>50</p></td><td align="left"><p>50</p></td><td align="left"><p>50</p></td><td align="left"><p>50</p></td></tr></tbody></table> </ephtml> </p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.001</mn></mrow></math> </ephtml> ; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.01</mn></mrow></math> </ephtml> ; <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mmultiscripts><mrow /><mrow /><mrow><mrow /><mo>∗</mo></mrow></mmultiscripts><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></math> </ephtml> , Complete code and data to replicate these models available at https://cutt.ly/EwG1VbaW <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow /><mrow /><mo>†</mo></mmultiscripts></math> </ephtml> Comparison group is cluster 1, see meaning of clusters in Fig. 6b(b)</p> <hd id="AN0183779576-39">Limitations</hd> <p>This study relied on databases built to document institutionally driven and statewide transfer and articulation policies. We used these databases for descriptive analyses, but they are potentially valuable resources for studying transfer effectiveness and financial costs associated with transferring. Such analyses, however, require other sources of data that we do not have, such as individual-level or administrative records documenting transfer origin and destinations. Nonetheless, when other scholars have access to these data sources, our databases may be used for research on college transfer. Accordingly, an important goal of our study is to share all the data and analytic resources created to conduct the analyses presented in this manuscript; these resources can be found here https://cutt.ly/NwHr3c93.</p> <hd id="AN0183779576-40">Discussion and Recommendations</hd> <p>This study was designed with the purpose of revealing the structure of transfer and articulation agreements in the United States. Our structure-unveiling strategy relied on spatial and relational modeling to account for both dyadic links and the locations where those links happened. The inclusion of location served to assess whether spatial clustering and/or economic spillovers were characteristic in these networks, both of which were partially corroborated.</p> <p>An important conceptual consideration guiding our analyses was the differentiation between transfer (i.e., more general) and articulation (i.e., program- or discipline-specific) policies or agreements across the networks. In this respect and considering our two main sources of data (statewide policies and institutionally driven agreements), we found that statewide policies focused on <emph>general transfer agreements</emph> do not follow economic spillovers patterns (i.e., whether neighboring similarities in policy implementation are not due to random chance). Instead, <emph>general transfer policies</emph> appeared to be statistically and spatially randomly distributed across the continental United States. On the other hand, statewide policies that may be classified as <emph>articulation-focused</emph> (or program specific) consistently followed the framework of neighboring influence, or economic spillovers, in the Moran's <emph>I</emph> and join-count analyses. In this respect, it is worth noting that, as our empirical findings showed, the structure we found in our analysis of statewide policies was not explained by prevalence (or lack thereof) of policies alone. Specifically, the guaranteed associate's degree policy that is present in 35 states did not reach significance in the Moran's <emph>I</emph> model [see Fig. 5b(a)], whereas the policy concerning common-course numbering or uniform numbering convention, which is currently present in 20 states, did reach significance in both the Moran's <emph>I</emph> and the join-count tests. Having said this, our <emph>first takeaway</emph> is that states are more likely to implement <emph>articulation policies</emph> (i.e., more specific and well aligned) if their neighboring states have these policies in place, but this pattern does not hold true for <emph>general transfer statewide</emph> policies.</p> <p> <emph>Our second takeaway</emph> involves our institutionally driven analyses. Here, we found that the most prevalent partnership form does not involve any specific programs or disciplines. That is, we found that 40% of the 18,260 programs participating in these partnerships are general, involving any discipline or program, which means that agreements across institutions adhere more closely to <emph>general transfer</emph> rather than to <emph>specific articulation</emph> agreements. In this respect, a recommendation we offer is that future studies may want to estimate the impact of time-to-degree completion and credit loss associated with participation in <emph>transfer-focused</emph> partnerships and compare these results with outcomes associated with participation in <emph>articulation-oriented</emph> agreements. Given their different levels of specificity, transfer agreements may be <emph>less useful in serving as clear guidelines</emph> for transfer students compared to partnerships based on program articulations. To the extent that ambiguity in the requirements leads delays in course progression due to loss of credits, we expect that transfer programs may be associated with worsened student outcomes compared to the outcomes realized by transfer students participating in articulation partnerships. From this view, though more research is needed on this topic, a recommendation we raise for students and advisors (our <emph>third takeaway</emph>) is to select institutions implementing articulation agreements rather than institutions holding or focusing on transfer partnerships.</p> <p>On this note, let us highlight that for-profit institutions occupy the third most active type of institutions that have established a <emph>general transfer</emph> partnership with public 2-year colleges. This may be a strategy to capture a market of public 2-year students interested in a 4-year degree with flexibility and online-course-taking promises (González Canché, [<reflink idref="bib20" id="ref174">20</reflink>], [<reflink idref="bib21" id="ref175">21</reflink>], [<reflink idref="bib25" id="ref176">25</reflink>], [<reflink idref="bib30" id="ref177">30</reflink>]). Given widespread concerns about the for-profit sector engaging predatory practices targeting low-income and minoritized students (Deming et al., [<reflink idref="bib14" id="ref178">14</reflink>]; James, [<reflink idref="bib38" id="ref179">38</reflink>]; Schade, [<reflink idref="bib54" id="ref180">54</reflink>]), students in 2-year public colleges should be extra cautious about transfer partnerships with for-profit institutions. If students want to pursue this route, they should assess whether the receiving for-profit institution has a <emph>clearly delineated articulation agreement</emph> and some form of assurance that credit loss and time-to-degree will not be issues during this transfer process.</p> <p>Our <emph>final takeaway</emph> concerns the distinction between returning and continuing transfer students discussed in the literature review. Studies on transfer success could consider whether students are returning or continuing, for differences in their experiences may help us build a more nuanced understanding and develop plans of action that may address different needs.</p> <hd id="AN0183779576-41">Moving and Building Forward</hd> <p>Based on the relevance of the transfer process for millions of students, the overarching goal of our study is to reduce the uncertainty surrounding this process. We offer a comprehensive analysis of the structure of transfer and articulation agreements across the United States, with an emphasis on delineating participating institutions, programs, and states. Notably, however, we found that the presence of statewide policies was not enough to impact or motivate institution-level participation (see Table 9). This finding adds to the evidence on the inefficacy of statewide transfer policies intended to strengthen students' prospects of success. From this perspective, the field needs a better understanding of the specific processes that institutions follow to create "informal" or institutionally driven transfer and articulation partnerships.</p> <p>Despite this lack of association between state policy and partnership formation, tools like the datasets and interactive visualizations we provide in this article (see Fig. 3b(b) and (c), for example), along with the classification of states following machine learning principles (see Fig. 6b), may offer students and counselors more clarity on which institutions participate in within-state and out-of-state transfer agreements as well as a clearer understanding of states' priorities regarding these policies.</p> <p>Importantly, an understudied topic remains the costs associated with transferring. For example, consider how our findings concerning institutional distances has cost-related implications. We consistently observed that institutions with agreements tend to be physically closer than institutions without agreements. This finding is more relevant in within-state cases, where costs may be considerably lower in terms of tuition (particularly when transferring to public 4-year colleges; González Canché, [<reflink idref="bib20" id="ref181">20</reflink>], [<reflink idref="bib22" id="ref182">22</reflink>], [<reflink idref="bib23" id="ref183">23</reflink>], [<reflink idref="bib24" id="ref184">24</reflink>], [<reflink idref="bib28" id="ref185">28</reflink>], [<reflink idref="bib30" id="ref186">30</reflink>]) and transportation costs (based on the shorter distance estimates found in Table 5). For example, 30 miles may not be relevant if out-of-state partnering institutions are located 700 miles apart, but it may be quite important if institutions participating in within-state programs are located 50 miles apart (see Table 8 for these distance distributions and follow this link https://cutt.ly/7wHtPkEA to access all distances calculated across partnerships).</p> <p>Another cost-related area of study made possible by the datasets we provide concerns estimating the expected costs of college attendance associated with transfer. For example, considering that the most prevalent institution type involved in these partnerships is the public 2-year sector, the transfer process represents a considerable increase in the cost of college attendance—particularly when transferring from the public 2-year sector to 4-year institutions. Accordingly, future research could study expected changes in cost of attendance when moving across institutions and programs of study.</p> <p>Relatedly, we recommend paying careful attention to the language used by receiving 4-year colleges. For example, Regis University, a private not-for-profit college in Colorado, is highly active in out-of-state agreements with public 2-year colleges (N = 499). This college states that transfer students may validate up to <emph>90 credits</emph> and that these students may benefit from online or in-person courses that are flexible throughout the year.[<reflink idref="bib6" id="ref187">6</reflink>] Moreover, Regis states that <emph>there are no out-of-state tuition rates</emph>. Although this statement <emph>is technically true</emph>, one should also consider that private institutions charge a tuition premium (U.S. Department of Education, Integrated Postsecondary Education Data System, Survey Data files, [<reflink idref="bib65" id="ref188">65</reflink>]) and they typically do not differentiate between in-state and out-of-state tuition and fees (González Canché, [<reflink idref="bib20" id="ref189">20</reflink>], [<reflink idref="bib22" id="ref190">22</reflink>], [<reflink idref="bib28" id="ref191">28</reflink>]). Although Regis University's claim that it does not charge out-of-state tuition may sound appealing, it does not mean that transfer students will endure no changes in costs when participating in this transfer process.</p> <hd id="AN0183779576-42">What Incentives and Challenges Do Receiving Institutions Experience to Honor/Validate Previou...</hd> <p>Our conversation has been centered around the <emph>institutionally driven, bilateral nature</emph> of college transfer agreements. This bilateral nature assumes good intentions on both ends of the partnership. To our knowledge, the enactment of these transfer and articulation agreements seems to be based on an honor system, even though it may represent an important financial loss for the receiving college. That is, a smooth college transition, where no credits are lost, could be seen as forgone revenue from a captive student market for the receiving college. We explore this situation using Regis University as our example; however, we want to be clear that we have no evidence that this college employs the practice we describe next.</p> <p>As mentioned above, Regis University allows transfer students to validate up to 90 credit hours. Considering that a typical 4-year degree requires 120 credit hours, this means that, in theory, the minimum number of credits Regis University would offer to transfer students who transfer the maximum number of allowed credits is 30 (or 120–90). If we take the nationwide cost for one credit hour (i.e., $1492) charged by 4-year not-for-profit colleges (see Hanson, [<reflink idref="bib34" id="ref192">34</reflink>]), these 30 credit hours represent $44,760 in tuition revenue alone for each transfer student (i.e., this estimate does not consider room and board and other fees). However, if for any reason, the receiving college decides that it can accept only 60 credit hours for a given student, even if such a student is coming with 90 credit hours, then the expected revenue would be an extra $44,760 (or 30*1492) for a grand total of $98,472 for a single incoming transfer student with 60 credit hours. From this view, the incentive of honoring the 90 credit hours comes with an important loss of potential revenue.</p> <p>We are not arguing that receiving colleges impose extra hurdles on students to meet transfer agreements as a way to maximize revenue. Instead, we want to note that if colleges and universities are facing serious financial challenges, they might find it challenging to honor these agreements. From this view, although we celebrate the creation of bilateral partnerships, we also recognize the urgency of ensuring that these transitions are as smooth as possible for students. Accordingly, our intention with this closing discussion is to pose the following questions: What systems are in place to ensure that receiving institutions honor these agreements? Are any safeguards in place? Should there be any safeguards to protect student transfers?</p> <p>The prevalence of student transfers in the United States every year, the financial concerns that this process represents for receiving colleges, and, more importantly, the financial challenges it creates for transfer students, make it essential that policymakers establish formal, mandatory transfer guidelines for institutions. No student should have to pay—at double or triple the original costs—for the course credit hours they already attained at the sending institution. The federal government and accrediting agencies should develop strategies to formally protect the interests of this already vulnerable student population.</p> <hd id="AN0183779576-43">Funding</hd> <p>Research Funded by the National Academy of Education and the Spencer Foundation. Findings do not represent the standing of the funding agencies. All errors remain my own.</p> <hd id="AN0183779576-44">Data availability</hd> <p>The replication code and partnership data shown in Table 3 may be accessed at https://cutt.ly/JwGRmVDu. 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Physics Reports. 2017; 714: 1-73. 10.1016/j.physrep.2017.10.001</bibtext> </blist> </ref> <ref id="AN0183779576-47"> <title> Footnotes </title> <blist> <bibtext> In a related study, González Canché and Zhang ([32]) explored this network focusing on the public 2-year sector.</bibtext> </blist> <blist> <bibtext> Available at https://<ulink href="http://www.collegetransfer.net/Search/Search-for-Transfer-Articulation-Agreements">www.collegetransfer.net/Search/Search-for-Transfer-Articulation-Agreements</ulink>.</bibtext> </blist> <blist> <bibtext> In our main specification, Hawaii was linked to California and Alaska was linked to Washington. Note, however, that alternative specifications where Hawaii and Alaska remained disconnected rendered the same inferences, thus validating the findings presented in this study.</bibtext> </blist> <blist> <bibtext> Having said this, our findings regarding this last set of models are consistent, indicating, as we discuss below, that there is no relationship between statewide policies and agreement participation. Tables available upon request.</bibtext> </blist> <blist> <bibtext> Access to data and replication code may be found here https://cutt.ly/EwG1VbaW; the geographical distribution of these outcomes can be seen in Fig. 3b(b) and (c).</bibtext> </blist> <blist> <bibtext> See more information here https://<ulink href="http://www.regis.edu/admissions/transfer-students/index">www.regis.edu/admissions/transfer-students/index</ulink>.</bibtext> </blist> </ref> <aug> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib48" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib49" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib17" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib62" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib69" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib39" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib52" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib58" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib57" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib66" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib50" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib44" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib36" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib10" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib45" firstref="ref23"></nolink> <nolink nlid="nl16" bibid="bib46" firstref="ref24"></nolink> <nolink nlid="nl17" bibid="bib68" firstref="ref25"></nolink> <nolink nlid="nl18" bibid="bib11" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib20" firstref="ref38"></nolink> <nolink nlid="nl20" bibid="bib21" firstref="ref39"></nolink> <nolink nlid="nl21" bibid="bib24" firstref="ref40"></nolink> <nolink nlid="nl22" bibid="bib25" firstref="ref41"></nolink> <nolink nlid="nl23" bibid="bib33" firstref="ref42"></nolink> <nolink nlid="nl24" bibid="bib51" firstref="ref45"></nolink> <nolink nlid="nl25" bibid="bib47" firstref="ref48"></nolink> <nolink nlid="nl26" bibid="bib59" firstref="ref54"></nolink> <nolink nlid="nl27" bibid="bib60" firstref="ref55"></nolink> <nolink nlid="nl28" bibid="bib16" firstref="ref56"></nolink> <nolink nlid="nl29" bibid="bib30" firstref="ref61"></nolink> <nolink nlid="nl30" bibid="bib37" firstref="ref64"></nolink> <nolink nlid="nl31" bibid="bib15" firstref="ref65"></nolink> <nolink nlid="nl32" bibid="bib12" firstref="ref68"></nolink> <nolink nlid="nl33" bibid="bib19" firstref="ref71"></nolink> <nolink nlid="nl34" bibid="bib35" firstref="ref72"></nolink> <nolink nlid="nl35" bibid="bib63" firstref="ref73"></nolink> <nolink nlid="nl36" bibid="bib67" firstref="ref74"></nolink> <nolink nlid="nl37" bibid="bib65" firstref="ref75"></nolink> <nolink nlid="nl38" bibid="bib26" firstref="ref76"></nolink> <nolink nlid="nl39" bibid="bib28" firstref="ref77"></nolink> <nolink nlid="nl40" bibid="bib42" firstref="ref82"></nolink> <nolink nlid="nl41" bibid="bib22" firstref="ref86"></nolink> <nolink nlid="nl42" bibid="bib23" firstref="ref87"></nolink> <nolink nlid="nl43" bibid="bib29" firstref="ref89"></nolink> <nolink nlid="nl44" bibid="bib61" firstref="ref91"></nolink> <nolink nlid="nl45" bibid="bib70" firstref="ref96"></nolink> <nolink nlid="nl46" bibid="bib53" firstref="ref101"></nolink> <nolink nlid="nl47" bibid="bib31" firstref="ref105"></nolink> <nolink nlid="nl48" bibid="bib27" firstref="ref115"></nolink> <nolink nlid="nl49" bibid="bib13" firstref="ref124"></nolink> <nolink nlid="nl50" bibid="bib41" firstref="ref135"></nolink> <nolink nlid="nl51" bibid="bib55" firstref="ref136"></nolink> <nolink nlid="nl52" bibid="bib56" firstref="ref137"></nolink> <nolink nlid="nl53" bibid="bib43" firstref="ref138"></nolink> <nolink nlid="nl54" bibid="bib18" firstref="ref147"></nolink> <nolink nlid="nl55" bibid="bib40" firstref="ref149"></nolink> <nolink nlid="nl56" bibid="bib64" firstref="ref165"></nolink> <nolink nlid="nl57" bibid="bib14" firstref="ref170"></nolink> <nolink nlid="nl58" bibid="bib38" firstref="ref179"></nolink> <nolink nlid="nl59" bibid="bib54" firstref="ref180"></nolink> <nolink nlid="nl60" bibid="bib34" firstref="ref192"></nolink>
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  Data: The College Transfer and Articulation Network: How Are These Statewide Policies and Bilateral or Dyadic Partnerships Structured across the United States?
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Manuel+S%2E+González+Canché%22">Manuel S. González Canché</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-8926-7614">0000-0001-8926-7614</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jiayi+Arthur+Qiu%22">Jiayi Arthur Qiu</searchLink><br /><searchLink fieldCode="AR" term="%22Kaiwen+Zheng%22">Kaiwen Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Mingbo+Gong%22">Mingbo Gong</searchLink><br /><searchLink fieldCode="AR" term="%22Chelsea+Zhang%22">Chelsea Zhang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Research+in+Higher+Education%22"><i>Research in Higher Education</i></searchLink>. 2025 66(3).
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 46
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22College+Transfer+Students%22">College Transfer Students</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+Policy%22">Transfer Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Articulation+%28Education%29%22">Articulation (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22State+Policy%22">State Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Partnerships+in+Education%22">Partnerships in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+Factors%22">Economic Factors</searchLink><br /><searchLink fieldCode="DE" term="%22Proximity%22">Proximity</searchLink><br /><searchLink fieldCode="DE" term="%22College+Credits%22">College Credits</searchLink><br /><searchLink fieldCode="DE" term="%22Intercollegiate+Cooperation%22">Intercollegiate Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Interstate+Programs%22">Interstate Programs</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11162-024-09831-5
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0361-0365<br />1573-188X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Every academic year, millions of college students change institutions before degree completion, confronting the challenge of validating credits across colleges. Despite state-level efforts to legislate strategies for smoother transfers, actual credit recognition relies on non-state-regulated bilateral partnerships that are more (i.e., articulation agreements) or less (general transfer partnerships) specific as policies or guides to avoid credit loss. This study comprehensively sheds light on the USA nationwide structure of transfer and articulation agreements by focusing on statewide policies and in-state and out-of-state informal bilateral partnerships as units of analyses. The spatial configuration of both statewide policies and institutionally driven partnerships enabled testing for economic spillovers as well as measuring whether distance is a factor that may impact the formation of these partnerships. Data were retrieved from CollegeTransfer.Net (N = 18,260 partnerships and 1163 colleges), the Education Commission of the States (118 statewide policies), the IPEDs, and the US Census Bureau. Findings at the state-level revealed economic spillovers in two of four statewide policies, highlighting greater structure of program-specific articulation agreements over general transfer partnerships (i.e., agreements that do not require program continuation). Regarding institutionally driven agreements, the analyses indicated that general partnerships were the most prevalent form, which, compared to more structured articulation efforts, may be less effective in the avoidance of credit loss. We also found that shorter distances are a significant but impractical partnership-forming factor, for the average distance reduction among partnering colleges is 30 miles across models. Combining state and institutional datasets, we found that neither individual nor combined statewide policies actively predict institutional partnership formation. All databases and code created (statewide policies: https://cutt.ly/uwHyvkWQ, institutionally driven agreements: https://cutt.ly/7wHtPkEA, replication codes: https://cutt.ly/JwGRmVDu, https://cutt.ly/EwG1VbaW) may be used in future analyses to address questions of transfer effectiveness and transferring financial costs, which although important go beyond the scope of our study.
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  Data: As Provided
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  Data: 2025
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        Value: 10.1007/s11162-024-09831-5
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 46
    Subjects:
      – SubjectFull: College Transfer Students
        Type: general
      – SubjectFull: Transfer Policy
        Type: general
      – SubjectFull: Articulation (Education)
        Type: general
      – SubjectFull: State Policy
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      – SubjectFull: Partnerships in Education
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      – SubjectFull: Economic Factors
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      – SubjectFull: Proximity
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      – SubjectFull: College Credits
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      – SubjectFull: Intercollegiate Cooperation
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      – SubjectFull: Interstate Programs
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      – TitleFull: The College Transfer and Articulation Network: How Are These Statewide Policies and Bilateral or Dyadic Partnerships Structured across the United States?
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