The Nearshoring Loop: A Review of Triggers, Location Choice, and Captured Outcomes

Abstract

Background: Nearshoring has risen after shocks and policy shifts. We synthesize evidence in a compact loop linking triggers (trade frictions, supply-chain risk, new agreements) to location choices mediated by multidimensional proximity (geographic, institutional, organizational, social, cognitive, functional) to components (manufacturing footprint, Foreign Direct Investment (FDI), employment) and outcomes (spillovers, productivity, innovation) conditioned by absorptive capacity and institutions. Methods: We conducted a literature review using major bibliographic databases. A staged screening pipeline (deduplication, pre-eligibility, and title–abstract screening) preceded full-text coding aligned with the review framework (triggers, proximity, components, outcomes, mediators). Studies were appraised with a five-criterion checklist, and themes were consolidated with basic bibliometric checks. Results: Evidence is North Atlantic and manufacturing-centric. Supply-chain disruptions dominate triggers; non-geographic proximity strongly moderates relocation. FDI anchors ecosystems, while employment effects are lagged and compositional. Strong capability and policy mixes yield broader spillovers; otherwise, benefits remain enclave-like. Sustainability and transformative outcomes are rarely assessed. Conclusions: The loop clarifies feedback from outcomes to future siting. Firms should build proximity beyond geography and pair early FDI with supplier and skills upgrading; policymakers should align instruments to governance, capability formation, and logistics. Research should expand Global South coverage and integrate environmental and inclusion metrics.

1. Introduction

A new wave of regionalization has brought nearshoring to the forefront of firm strategy and public policy. The immediate backdrop is the sequence of global shocks and logistics frictions that exposed the fragility of long, thin supply chains. Recent indicators such as the New York Fed’s Global Supply Chain Pressure Index (GSCPI) and container freight benchmarks show that supply-chain stress peaked in 2021–2022 and has only partially normalized, with elevated costs and volatile lead times persisting on major trade lanes [1,2,3]. These disruptions had already brought nearshoring to the forefront of business and policy debates by the time our review protocol was implemented, and subsequent data releases have further reinforced its relevance.

At the same time, the North American economy has deepened intraregional trade ties. In 2024, U.S. total goods trade with Mexico reached $839.6 billion, while near-zero industrial real estate vacancies in key Mexican corridors signal rapid absorption and capacity constraints consistent with nearshoring demand [4,5].

In this context, we define nearshoring as the reconfiguration of production footprints and sourcing networks toward proximate, rule-aligned locations within a broader regional bloc. We argue that observed relocations are best understood through a compact loop: (i) triggers (disruptions, policy realignments) feed into a (ii) modeling and location decision moderated by multidimensional proximity (geographic, institutional, organizational, social, cognitive, functional), which yields (iii) direct components (manufacturing output, FDI commitments, employment) and (iv) outcomes (spillovers, productivity, innovation). Critically, the extent to which outcomes are captured depends on absorptive capacity and institutional mechanisms; captured outcomes then upgrade capabilities and reshape proximity profiles, influencing subsequent location and sourcing decisions.

Public policy also operates as a trigger and an enabler of capture. In the United States, federal initiatives to reshore and regionalize advanced manufacturing—most visibly in batteries—combine substantial funding with demand-side incentives and sustained support for regional value chains [6,7].

In this context, our review contributes three elements. First, it synthesizes dispersed evidence into a decision-oriented framework—the nearshoring loop—that foregrounds proximity and absorptive capacity as levers for turning shocks into durable regional gains. Second, it clarifies the role of FDI as the commitment mechanism that anchors supplier ecosystems and explains why employment effects are often lagged and skill-biased. Third, it documents thematic and geographic imbalances in the literature (North Atlantic and manufacturing bias) and the scarcity of work on transformative and environmental outcomes, motivating a research agenda that couples site selection with mission-oriented, multilevel policy design.

2. Theoretical Background and Conceptual Framework

This section sets out the conceptual scaffolding for analyzing nearshoring. Section 2.1 outlines the theoretical foundations behind the shift from globalization to deeper regional integration and its implications for supply chains and organizational strategy, while Section 2.2 decomposes nearshoring into triggers and core components, incorporating multidimensional proximity and its operationalization across methods. Together, these elements provide a framework for assessing the costs, risks, capabilities and governance arrangements that shape location decisions.

2.1. Theoretical Background

Nearshoring can be interpreted as a response to the broader turn from neoliberal globalization toward denser forms of regional integration. Whereas offshoring was largely justified by global cost arbitrage and scale-driven efficiencies, current dynamics privilege intraregional coordination, the exploitation of trade agreements, and reduced exposure to external shocks [8,9,10]. Within this setting, geographic proximity shortens transit times and lowers transport costs, enables the capture of preferential tariffs, and is often associated with more predictable exchange-rate environments. Taken together, these factors mitigate financial risk relative to distant relocation alternatives [8,9]. As summarized in Figure 1, three analytical lenses structure this perspective and their cross-cutting interactions (cost–risk; governance–capability; policy–trade).

From economics and operations, supply chain management (SCM) advances a holistic approach to optimizing flows, coordination and logistical performance, while transaction cost economics clarifies location and outsourcing choices by weighing coordination costs against the benefits of internalization [11,12,13,14,15]. Formal treatments of “closeness”, including near set theory, offer mathematical tools for operationalizing proximity in regionalized supply chains [16,17].

At the organizational and strategic level, stakeholder theory [18] highlights the management of complex relations with governments, communities, and other actors. In operations and project settings, applied treatments show how political and environmental uncertainty shape these relationships [19], so that shifts toward regional footprints also respond to institutional and sustainability pressures [20,21,22]. Strategy process perspectives, in turn, emphasize that strategies emerge from the interplay of rationality, learning, politics, and social forces rather than from linear planning, yielding gradual and contingent nearshoring trajectories [21].

Finally, established theories of the firm—behavioral, transaction cost, property rights, agency, and resource-based views—explain why certain activities are internalized while others are outsourced, combining cost calculations with aspirations for control and capability building [23,24]. Behavioral strategy adds that organizational context—routines, biases, and incentive structures—conditions both decision making and the implementation of relocation moves [25].

In sum, nearshoring is best understood as the confluence of three lenses: the economic (costs, tariffs, exchange rate), the supply chain lens (coordination, resilience, proximity), and the organizational–strategic lens (stakeholder governance, capabilities, and the strategy process). Integrating these perspectives enables a more complete assessment of what to relocate, where, and under which contractual and governance arrangements [8,10].

2.2. Decomposition and Modeling of Components

Nearshoring can be decomposed into triggers and core components with observable effects. The principal triggers include trade conflicts, supply-chain disruptions, and the enactment of new trade agreements, such as the United States–Mexico–Canada Agreement (USMCA), that reconfigure tariff and regulatory incentives. On the components side, the most salient dimensions are manufacturing output, foreign direct investment (FDI), and employment; the latter may be limited or lagged depending on technology intensity and capital–labor substitution [26]. As depicted in Figure 2, these elements are organized around a central modeling and location decision that is moderated by multidimensional proximity [27].

To account for cooperation and performance in these reconfigurations, proximity is conceptualized as multidimensional (geographic, cognitive, organizational, social, institutional, and functional) rather than purely spatial [28]. The evidence indicates that geographic proximity alone is insufficient to sustain knowledge flows and collaborative advantages; cognitive affinities, organizational compatibility, and institutional arrangements that reduce coordination costs and ambiguity are often decisive [29,30,31,32]. In this framework, proximity moderates the trigger–decision link (what gets relocated, where, and under which governance) and mediates the translation of components into outcomes (spillovers, learning, productivity, innovation).

Operationally, these forms of proximity can be measured and embedded in location analyses using qualitative, quantitative, or mixed approaches, together with costs, market access, and risk variables; applications include discrete choice models; gravity and market-access models; network and optimization models; and multi-criteria evaluations, as well as qualitative and comparative designs [33]. Local absorptive capacity and institutional mechanisms (technology transfer offices, consortia, governance rules) condition the extent to which knowledge spillovers are captured, shaping productivity and interfirm learning [29,30,34]. Crucially, as shown by the feedback arrow in Figure 2, captured outcomes strengthen absorptive capacity and, through policy and learning, reshape proximity profiles; this in turn influences subsequent modeling and location decisions, initiating further rounds of components and outcomes. Where proximity and absorptive mechanisms are aligned, effects cumulate; where they are weak, benefits are thin or enclave-like even under short physical distances [31,32].

In sum, decomposing nearshoring into triggers and components, modeling proximity as a multidimensional construct, and recognizing the intertemporal feedback from outcomes to capacity and future decisions together provide a rigorous basis for understanding location choices, interorganizational cooperation, and the heterogeneous regional trajectories observed in the literature [26,28].

3. Methodology

The aim of this review is to provide a clear, replicable account of how nearshoring has been studied and to relate the evidence to a structured framework of triggers, proximity, components, outcomes, and mediators. We combine a targeted search, a staged screening pipeline, and a transparent coding protocol. In doing so, the review departs from a purely narrative style and adopts elements of systematic reviews: we disclose search strings, counts at each filter, and the criteria used in every step.

The literature search was run on 13 February 2025, in Scopus and the Web of Science Core Collection using the following strings: TITLE-ABS-KEY("nearshoring") in Scopus and "nearshoring" (Topic) in Web of Science (WoS). This date corresponds to the final update of the search protocol prior to coding and analysis. We deliberately restricted the search to these two multidisciplinary, citation-indexed databases because they concentrate the bulk of peer-reviewed research in logistics, supply chain management and regional development, and because their shared metadata structure is highly compatible with the bibliometric procedures applied in this review. No year or language limits were imposed. By design, this strategy targets contributions that explicitly self-identify with nearshoring; related work framed primarily in terms of “regionalization”, “friend-shoring” or other labels without using the term nearshoring may therefore be underrepresented in the initial corpus. The search yielded 144 Scopus and 95 WoS records, which were merged into 239 items.

Figure 3 summarizes the flow and the criteria applied at each filter. We map three explicit criteria (C1–C3) to the blocks shown in the scheme.

C1

Deduplication. Duplicates were removed first by title (67) and then by DOI (12); for Scopus/WoS overlaps, the record with the most complete metadata was retained. The set was reduced from 239 to 160. Additional cross-checks were supported by standardized titles.

C2

Pre-eligibility screening. Incomplete or non-research records were excluded (13 in total): items lacking essential metadata (abstract, keywords, or author data) and items labeled as editorials, letters, or “conference review”. For WoS-only items not available in Scopus, minimal metadata were imputed when inclusion requirements were met. The set was reduced from 160 to 147.

C3

Title–abstract screening. Titles and abstracts of the 147 records underwent independent duplicate screening by the authors for a substantive nearshoring focus (as a core topic or clearly situated within Global Value Chains (GVCs), relocation, or regional development). Forty items that did not meet this relevance condition were excluded, leaving 107 articles for full-text review and coding.

The 107 articles underwent full-text assessment and structured coding. A coding template aligned with the nearshoring loop (triggers, proximity dimensions, components, outcomes and mediators) was first developed and piloted on a small subset of studies; both authors participated in this pilot phase and iteratively refined the codebook. A subset of articles was double-coded to check consistency in the interpretation of categories, and any discrepancies were discussed until agreement was reached, with resulting clarifications incorporated into the coding manual.

Quality appraisal followed a five-criterion checklist (clear objectives; transparent and justified methods; discussion of limitations; empirical support; coherence between results and conclusions). Each criterion was rated Yes/No/Partial/Not determined, an overall score (1–5) was assigned, and disagreements were resolved by consensus.

With respect to the nearshoring framework, full-text bibliographic fields were extracted and grouped into the following contexts:

• Triggers were inferred from the research objectives, the salience of nearshoring (direct, indirect, marginal), and explicit mentions of trade disputes, supply-chain disruptions, or policy shifts (e.g., USMCA).
• Proximity was derived from geographic focus and territorial scale and complemented with markers of geographic, cognitive, organizational, social, institutional, and functional proximity; when available, operational measures (e.g., distance thresholds, interfirm networks) were noted.
• Components were standardized using North American Industry Classification System (NAICS) sector codes (2–3 digits), an official classification system [35,36]. When reported, we also recorded quantitative indicators of manufacturing output, foreign direct investment, and employment.
• Outcomes were distilled from the main findings, emphasizing knowledge spillovers, productivity, and innovation; sustainability effects were retained through an environmental flag.
• Mediators were captured through the type of innovation and the institutional mechanisms discussed, together with policy notes indicative of absorptive capacity and governance arrangements.

Method class (qualitative, quantitative, mixed, case study, review) and specific techniques (e.g., interviews, econometrics, simulation, network analysis) were also recorded to support design-based synthesis.

Microsoft Excel 2016 (64-bit) served as the master repository (metadata, coding, quality, audit log). Harmonization, residual deduplication, and visualizations were performed in R 4.4.2 with Bibliometrix [37]. Controlled vocabularies (NAICS, methods, theoretical tags) and a versioned change log supported reproducibility.

The review relies on Scopus and WoS; relevant work outside these databases may be missed, particularly contributions indexed only in regional or specialized databases, practitioner outlets or gray literature, including studies on nearshoring dynamics in under-represented regions. Despite careful deduplication and harmonization, some subjectivity is inherent in coding and thematic classification, particularly when abstracts or keywords lack detail. Although we attempted minimal metadata recovery and documented imputation rules, items failing essential completeness were excluded at pre-eligibility. Consensus-based coding mitigates, but does not eliminate, interpretive bias. We explicitly acknowledge this coverage choice as a limitation and encourage future reviews to complement Scopus and WoS with additional regional and non-indexed sources, even if this implies trading some homogeneity of metadata for broader substantive coverage.

A complete summary of the reviewed articles and their coverage across the five coded dimensions of the nearshoring loop is provided in Appendix A (

Table A1. Summary of reviewed articles across key dimensions of the nearshoring loop. Columns use compact mnemonics: TRG_SD (supply/demand trigger), PROX_INST (institutional proximity), COMP_FDI (FDI as component), OUT_SPILL (spillover outcomes), ABS_CAP (absorptive capacity). Cells indicate whether each topic is clearly addressed (✓) in the reviewed articles, based on our coding of abstracts and main text. Rows marked with * do not explicitly engage any of the five coded dimensions but are retained for completeness.

Reference	TRG_SD	PROX_INST	COMP_FDI	OUT_SPILL	ABS_CAP
Alvarez M. [102]	✓	✓	✓		✓
Ashby [146] *
Asp et al. [108] *
Baraldi et al. [119]	✓
Ben et al. [81]		✓	✓
Bock [88] *					✓
Boden et al. [105] *
Borodina et al. [117]	✓	✓
Bárcia de Mattos et al. [99]	✓				✓
Brodzicki [109]		✓
Budzyńska [92]			✓
Butollo and Staritz [41]	✓
Camacho-Vallejo et al. [136]	✓
Camacho-Vallejo et al. [69] *
Capello and Dellisanti [84]		✓
Carmel and Abbott [89]				✓
Carrasco [120]		✓			✓
Casciani [135]	✓				✓
Cattafi and Papp [42]	✓
Cedillo-Campos et al. [43]	✓
Costantino et al. [86] *
Cruz Ake et al. [133]			✓
Dabrowski [44]	✓
Dai and Tang [129]	✓
de la Mora [148] *
Della Posta [118]		✓
de Lucio et al. [45]	✓
Di Berardino et al. [125]	✓
Di Stefano et al. [151] *
Elia et al. [131]	✓				✓
Ellram et al. [46]	✓
Ersahin et al. [132]	✓
Estreal and Ramirez [71] *
Fernández-Miguel et al. [47]	✓
Freund et al. [48]	✓
Freund et al. [112]		✓
Gadde and Jonsson [128]					✓
García-Alaminos et al. [49]	✓	✓
García G. and Márquez M. [50]	✓		✓	✓
García R and Mendez [90]			✓
García-Weil [51]	✓	✓
Gaytán A. and Martínez H. [100] *
Gómez-Rocha et al. [68] *
Guedes and Pereira [113]			✓
Hahn et al. [116]			✓
Hartman et al. [75] *
Hasbum et al. [98]	✓
Helmold et al. [94] *
Hilmola et al. [115]	✓		✓
Hoek [52]	✓
Hoàng [53]	✓		✓	✓
Huq et al. [95]					✓
Johansson et al. [85] *
Jurakovaite and Gaigaliene [150]	✓		✓
Kainuma et al. [73] *
Kalotay [110]			✓
Kamann and Van Nieulande [106] *
Kazancoglu et al. [54]	✓
Keller and Zoller-Rydzek [149] *
Khazaei et al. [122]			✓
Khorana et al. [55]	✓
Lacity et al. [74]				✓	✓
Lábaj and Majzlíková [123]	✓	✓
Lieb and Lieb [72] *
Lostal Martínez [67]		✓	✓	✓	✓
Arambari et al. [97]					✓
Malos [127]				✓
Marciniak [87] *
Martinez and Terrazas S. [56]	✓	✓	✓	✓	✓
Montiel M. and Muzzio [57]		✓		✓
Morales-Contreras et al. [58]	✓	✓
Morales F. and Franzoni [59]	✓	✓	✓
Pantea [126]					✓
Parés Olguín et al. [147]		✓
Perez B. and Trevino [96]	✓
Piña B. et al. [60]	✓	✓
Piatanesi and Arauzo C. [80] *
Ponce et al. [142]				✓
Prechelt [77] *
Puraeng et al. [145]		✓
Rainnie [124]	✓			✓
Romero Aguilar [111]	✓		✓
Rosič et al. [61]	✓
Roza et al. [79] *
Ruivo et al. [137] *
Santillán Luna et al. [8]	✓	✓	✓		✓
Šarkanová and Krištofík [91] *
Schlegelmilch [27]		✓	✓
Schott et al. [141] *
Shin and Shin [62]	✓	✓
Ramírez S. et al. [26]	✓	✓	✓		✓
Simachev et al. [10]	✓
Sim et al. [63]	✓
Simonović and Kostić [121]			✓
Slepniov et al. [93] *
Swain-Oropeza et al. [70]	✓			✓
Tam and Lung [64]	✓		✓
van Hassel et al. [65]	✓
von Stetten et al. [114]	✓
von Stetten et al. [78]				✓
Wang et al. [66]	✓
Wawryk et al. [76] *
Wiener et al. [107] *
Yakovlev [9]	✓		✓	✓
Yücesan [130] *
Zeraati Foukolaei et al. [101]	✓
Zieris and Salinger [134] *

).

4. Results

The reviewed literature exhibits a range of objectives but a clear thematic focus on nearshoring’s effects on global value chains, industrial reorganization, and regional development. Most studies analyze how nearshoring reshapes supply chains, manufacturing locations, or trade relationships amid economic and geopolitical changes, exploring both the drivers and conditions that enable regions to emerge as nearshoring destinations.

All 107 articles maintain a substantive link to nearshoring, either directly or as part of broader discussions on reshoring, production reorganization, or competitiveness. While nearshoring is sometimes a peripheral topic, it frequently appears as a strategic consequence or policy implication in these works.

Most articles take a neutral or cautiously positive stance, recognizing both opportunities and risks. Commonly cited benefits include enhanced supply chain resilience and regional development, though limitations such as uneven territorial impacts and persistent dependencies are noted. Explicitly critical perspectives are rare.

Research design quality is generally strong: almost all studies define clear objectives, and most justify their methodological choices. However, explicit discussion of study limitations and the strength of empirical evidence are less consistent across the literature, as shown in Figure 4. These patterns highlight both the progress and remaining gaps in the field’s capacity to inform policy and practice.

In particular, the sizeable share of studies that do not fully discuss limitations or only partially justify the strength of their empirical support suggests that some reported nearshoring effects may be overstated or insufficiently qualified. In several cases, estimates are based on narrow samples, short observation windows or strong identification assumptions, yet conclusions are formulated in broad terms with limited sensitivity analysis. This reduces transparency and makes it harder for policymakers and practitioners to gauge how robust specific findings are to alternative specifications or data constraints. Systematic reporting of assumptions, data limitations and robustness checks would therefore enhance both the interpretability and the policy relevance of future nearshoring research.

4.1. Bibliometric Analysis

Figure 5 presents a thematic map generated through co-word analysis of the nearshoring literature, where research themes are positioned according to their relevance (centrality) and development (density). The four resulting quadrants are: Motor Themes, Basic Themes, Emerging or Declining Themes, and Niche Themes; together they offer a structured view of the field’s conceptual landscape.

To construct the map, bigrams were extracted from document abstracts, with word stemming and synonym merging applied to standardize terminology and enhance thematic clarity [37,38,39]. The preprocessing phase included removal of extraneous terms, such as publisher statements, using a custom stopword list to prevent semantic distortion. The thematic network, based on the 250 most frequent bigrams, was clustered using the Leiden algorithm, which optimizes modularity and identifies robust communities within bibliometric data [39,40].

Thematic clusters, visually distinguished by color and spatial arrangement, clarify which concepts are foundational, emerging, or peripheral within nearshoring research. Thematic mapping highlights both the intellectual structure and the evolving priorities of the field, with major research themes grouped into four quadrants.

Motor Themes (upper right) represent topics that are both central and well developed, forming the intellectual engine of the literature. These include digital technologies, outsourcing services, nearshoring strategies, and the strategic focus on Latin America and the American market, as well as global value chains and the impact of recent global shocks. The proximity and interconnection of these clusters reflect the dynamic interplay between technological, strategic, and structural issues at the core of nearshoring studies.

Basic Themes (lower right) are highly central but less developed, featuring traditional drivers such as the manufacturing sector, foreign investments, labor costs, and the experience of Eastern Europe. Their spatial closeness to Motor Themes suggests a convergence of traditional and emerging perspectives, indicating that foundational economic factors remain essential to understanding production relocation.

Emerging or Declining Themes (lower left) capture areas gaining or losing momentum. In this case, the quadrant highlights the rise of sustainability (clean energy, critical minerals), new governance and policy concerns (supply management, trade policies, location decisions), and the influence of technological innovation and sectoral shifts (artificial intelligence, fashion industry, social implications). Their proximity to more central quadrants signals potential for future growth and integration.

Niche Themes (upper left) consist of specialized clusters, such as European Union, empirical evidence, and firm size, that, while well developed, remain relatively isolated from the main research currents. These topics add granularity to the literature but exert limited influence on broader debates.

Overall, the thematic map reveals a dynamic and interconnected field, with digital transformation, strategic relocation, and global value chains anchoring the literature, while emerging issues related to sustainability, technological innovation, and regional experiences promise to shape future research directions.

4.2. Triggers

Across the reviewed corpus, nearshoring is set in motion by discrete shocks and institutional realignments that increase the expected costs and risks of distant sourcing while raising the relative attractiveness of regional configurations. We classify these triggers into three canonical types: (i) trade conflicts, (ii) supply-chain disruptions, and (iii) new trade agreements, together with two cross-cutting amplifiers (cost/market shifts; technology–security–sustainability mandates). In the reviewed set, 28 studies explicitly address nearshoring triggers; within this subset, supply-chain disruptions dominate (96%), while roughly a quarter engage new trade agreements or cost/market forces, and about one fifth discuss technology–security or environmental mandates; many studies stack more than one trigger (see

Table 1. Nearshoring triggers addressed in the literature.

References	Triggers
	Trade Conflicts	Supply Chain Disruptions	New Trade Agreements	Cost/Market	Tech/Security/ Sustainability
Butollo and Staritz [41]				✓
Cattafi and Papp [42]					✓
Cedillo-Campos et al. [43]		✓
Dabrowski [44]		✓	✓
de Lucio et al. [45]		✓
Ellram et al. [46]				✓
Fernández-Miguel et al. [47]		✓
Freund et al. [48]		✓
García-Alaminos et al. [49]		✓
García G. and Márquez M. [50]	✓	✓		✓
García-Weil [51]		✓		✓
Hoek [52]		✓
Hoàng [53]		✓			✓
Kazancoglu et al. [54]		✓			✓
Khorana et al. [55]		✓
Martinez and Terrazas S. [56]					✓
Montiel M. and Muzzio [57]		✓
Morales-Contreras et al. [58]		✓
Morales F. and Franzoni [59]		✓
Piña B. et al. [60]			✓	✓
Rosič et al. [61]				✓
Santillán Luna et al. [8]	✓	✓	✓
Shin and Shin [62]	✓				✓
Ramírez S. et al. [26]	✓	✓	✓
Sim et al. [63]		✓			✓
Tam and Lung [64]	✓	✓
van Hassel et al. [65]			✓	✓
Wang et al. [66]		✓

).

Tariffs, export controls, sanctions, and tightened rules of origin erode the cost arbitrage that supported offshoring and segment markets into partially incompatible regulatory zones. Firms respond by repositioning production within regional blocs to preserve market access, reduce compliance uncertainty, and protect service levels [26,50,62].

Pandemic shutdowns, port congestion, armed conflict, and logistics bottlenecks expose long lead times and thin inventories as sources of recurrent fragility. The operational response prioritizes reliability over unit cost: shortening chains, dual or multi-sourcing, and building regional redundancy to compress lead-time variance and inventory-carrying risk [26,49,50].

Agreement entry into force and modernization (e.g., revised rules of origin, cumulation, local-content thresholds) alter the payoff matrix of location choices. Preferential access and clearer standards reward re-embedding activities within the region, especially in integrated systems like automotive and electronics [26,60,65].

Two forces frequently amplify or sequence with the core triggers. First, cost and market shifts—energy and freight spikes, narrowing wage gaps, exchange-rate volatility, and near-market demand pressures—tilt decisions toward locations that stabilize delivered-cost risk [41,60,65]. Second, technology, security, and sustainability mandates—critical minerals and battery value chains, semiconductor controls, data-sovereignty requirements, and carbon constraints—create compliance and ecosystem dependencies that favor proximate, rule-aligned production nodes [42,62,63]. In practice, “trigger stacking” is common: a policy shock coincides with a disruption under a new agreement, jointly tipping firms toward regional relocation.

Triggers reset constraints and incentives that feed the central modeling and location decision. Their effects are then mediated by multidimensional proximity and downstream absorptive capacity, explaining heterogeneous regional outcomes even under seemingly similar shocks.

4.3. Proximity and Location Decision

The reviewed literature exhibits a clear geographic concentration, with a predominance of studies focusing on Mexico, which appears explicitly in 28 documents (see, e.g., [8,67,68,69,70,71,72]). This reflects both Mexico’s strategic role in North American manufacturing and its growing prominence in nearshoring discussions under frameworks such as the United States–Mexico–Canada Agreement (USMCA). Additionally, China is referenced in 17 articles (e.g., [65,73,74]), often as a benchmark or point of comparison due to its centrality in global value chains.

Other frequently analyzed regions include the United States ([46,75], among others), Germany (e.g., [76,77,78]), the United Kingdom (such as [54,79]), and France (see, e.g., [80,81]), underscoring the dominance of Global North perspectives in the academic discourse on nearshoring.

Figure 6 provides an UpSet plot [82,83] of the main (sub)continents addressed in the reviewed literature, highlighting both the frequency and overlap of territorial foci. The most commonly examined regions are Western Europe (59 articles), North America (49), and Eastern Europe (33), followed by South America (18) and Central America (13). In Asia, East Asia is most prominent (28 articles), while South Asia and Southeast Asia are less frequently addressed (15 and 14, respectively). Africa (14) and Oceania (7) remain marginal in the scholarly discussion.

When aggregated by continent, Europe stands out as the dominant focus (92 articles), followed by the Americas (80), Asia (57), Africa (14), and Oceania (7). This quantitative pattern underscores a North Atlantic and Eurocentric bias [84]. This quantitative pattern underscores a clear North Atlantic and Eurocentric bias, reflecting the predominance of case studies and empirical investigations from the Global North. In contrast, coverage of the Global South, particularly Africa and parts of Asia and Latin America, remains limited, despite these regions’ growing relevance as sites of supply chain reconfiguration and nearshoring strategies.

This concentration of attention on Europe and the Americas, and the relative neglect of other world regions, raises important questions about the generalizability of current findings and the need for more place-based research in underrepresented contexts.

A significant number of studies adopt a global or multi-country perspective, referencing diverse contexts such as the European Union (see, e.g., [78,85,86,87]), Asia-Pacific (such as [88,89]), Latin America and the Caribbean (e.g., [9,90]), and in some cases, more targeted areas like the Visegrád Group (Poland, Hungary, Czech Republic, and Slovakia) [91,92] or the Baltic and Scandinavian States [93]. These studies often frame nearshoring as part of broader processes of supply chain reconfiguration and industrial relocation.

Despite occasional references to Africa [94], Southeast Asia (e.g., [74,85,95]), and Central America [96], the overall representation of the Global South remains limited. Only a handful of documents explore countries such as Peru [97], Costa Rica [98], or Bangladesh [99], and most of these are discussed from the perspective of their role as suppliers in global production networks rather than as regions with active policy agency.

In terms of territorial scale, national-level analyses are the most common, followed by regional or subnational case studies, particularly in the case of Mexico, where several papers focus on border states and industrial corridors (e.g., Nuevo León, Coahuila, Guanajuato) [100,101,102]. A smaller set of studies adopt a transnational or supranational lens, examining nearshoring within the context of trade blocs (e.g., EU, USMCA) [60] or firm strategies across multiple jurisdictions.

Overall, the literature displays a strong orientation toward North American and European contexts, with limited attention to the territorial specificities and institutional capacities of underrepresented regions. This geographic bias has implications for the generalizability of findings and highlights the need for more place-based research in the Global South.

Beyond geographic distance, the literature emphasizes institutional, organizational, social, cognitive, and functional proximities as independent levers shaping site selection [103,104]. Institutional proximity, via agreement membership and rules of origin, aligns incentives and reduces uncertainty, thereby increasing the probability of locating within the same trade bloc [26,59,102]. Organizational proximity, reflected in prior supplier ties, standardization, and governance compatibility, lowers coordination and ramp-up costs in modular chains [93,105,106,107]. Social proximity, including language, cultural distance, and trust, affects day-to-day integration across sites [76,89,108]. Cognitive and functional proximities—skills/knowledge relatedness and value-chain fit—mediate whether relocation yields reliable operations and upgrading within existing clusters [67,80,106,109].

While many contributions are qualitative, some explicitly combine multiple proximity criteria in structured evaluations (e.g., multi-criteria/AHP for plant siting and supplier reconfiguration) [101]. Taken together, this evidence suggests that proximity dimensions moderate the effect of triggers (trade conflicts, disruptions, new agreements): where institutional and organizational proximities are high (USMCA + dense supplier bases), shocks translate more readily into regional relocation; where social/cognitive/functional distances remain large, firms either delay, stage the move, or choose hybrid footprints.

4.4. Components

Figure 7 illustrates the distribution and co-occurrence of industrial sectors addressed in the reviewed literature, classified using NAICS 2-digit codes. The analysis reveals that the most frequently studied sectors include manufacturing, particularly the automotive (see, e.g., [10,66,110,111]), electronics ([10,48,112], among others), software (e.g., [76,113,114]), and textile and apparel industries (such as [99,115]). In nearshore software development, risk profiles and service characteristics condition integration and performance outcomes [116]. These findings are consistent with broader narratives that position these sectors as prime candidates for nearshoring due to their reliance on global supply chains, modular production processes, and sensitivity to lead times and trade policy changes.

The automotive industry stands out with strong coverage, often linked to electric-vehicle production and regional supplier integration. Similarly, the electronics sector appears prominently, reflecting concerns over supply-chain resilience for semiconductors and advanced components. The textile and apparel sector is also frequently analyzed, typically in connection with labor conditions and cost-optimization dynamics in global sourcing.

In contrast, sectors such as energy (see, e.g., [117,118]), healthcare, and social services [58,119] are either absent or highly underrepresented. This suggests that nearshoring is still conceptualized primarily within a manufacturing and logistics framework, with limited exploration of its implications for service sectors or sustainability transitions.

Co-occurrence patterns indicate that a considerable number of studies adopt multisectoral perspectives, addressing intersections among manufacturing, logistics, and digital services. The plot reveals frequent overlaps between industrial production (NAICS 31–33; see, e.g., [54,65,73,120]) and transportation and warehousing (NAICS 48–49; [43,60,63,121], among others), highlighting the importance of infrastructure and supply chain coordination in nearshoring strategies.

The emergence of terms such as manufacturing, services, global value chains, and industrial development in the analysis of sectoral descriptions confirms a dominant analytical focus on globalized industrial reorganization, often framed around efficiency, risk mitigation, and competitiveness. However, relatively few articles link these sectoral shifts to broader development goals such as decarbonization, circularity, or social inclusion.

Overall, the literature privileges high-tech and export-oriented manufacturing sectors, offering limited insight into the potential for nearshoring to transform other parts of the economy or to drive inclusive regional development beyond industrial hubs.

Beyond sectoral shifts in output, several studies position FDI, particularly greenfield announcements and reinvestment by existing plants, as the concrete commitment mechanism through which nearshoring materializes in place. FDI shapes the depth and composition of local supplier networks, mediates technology transfer, and anchors compliance with rules of origin and local-content thresholds where these apply [26,50,102]. The literature also cautions that the geography and “type” of FDI matter for spillovers: greenfield projects tend to densify regional ecosystems (e.g., Electric Vehicles/batteries, electronics), whereas reinvestment and capacity debottlenecking strengthen existing clusters but may deliver narrower knowledge externalities. Moreover, “indirect” FDI routed via third-country affiliates complicates attribution and local capture of benefits, blurring how much production and capability actually relocate [90,110]. Sector-targeted policies and agreement provisions (e.g., cumulation, origin rules) tilt the FDI portfolio toward activities that are most complementary to regional supply bases [53,122].

Evidence on employment underscores a nonlinear and often lagged response relative to output and investment. Headcount effects are conditioned by technology intensity and capital–labor substitution; automation and digitalization can raise productivity without proportionate job creation, while the composition of employment shifts toward higher-skill occupations in logistics, quality, maintenance, and digital operations [99,123,124,125]. Regulatory and institutional contexts shape job quality and retention (wages, formality, safety), and the ability to meet skill requirements hinges on training pipelines and firm–education coordination [126,127]. Spatially, employment gains concentrate where supplier density and connectivity are already high, reinforcing corridor dynamics unless complementary policies expand workforce development and intermodal access to second-tier regions [26,102].

Conceptually, FDI and employment function as distinct yet linked components: investment announcements and reinvestment plans are early indicators of relocation commitments, while realized employment crystallizes later and depends on the technological and organizational choices embedded in those projects. Studies emphasize the need to track both the composition of FDI (greenfield vs. reinvestment; sector mix; ownership structures) and the quality of jobs (skills, formality, wages) to avoid over-inferring regional development from output metrics alone [50,110,125]. This sequencing helps explain heterogeneous local outcomes under similar sectoral expansions: where absorptive capacity and proximity profiles are stronger, the same unit of investment yields denser supplier linkages and more durable employment effects; where they are weaker, gains remain thin or enclave-like.

4.5. Outcomes

We distinguish structural outcomes (reallocations in production, trade, and employment) from transformative outcomes (systemic shifts aligned with sustainability, inclusion, or mission-oriented transitions). In the reviewed corpus, the former are far more common than the latter.

Most studies report structural change through GVC and input–output lenses, documenting altered specialization, reindustrialization trajectories, and the relocation of manufacturing within regional blocs (e.g., [64,69,124,125,128,129]). These shifts entail a spatial redistribution of industry and employment with direct implications for national and regional development strategies [55,130,131,132]. Empirically, structural outcomes are operationalized using trade and FDI reorientation, value-added decompositions, location quotients, and employment elasticities, often with identification around shocks or regime onsets.

Explicit treatments of transformation (decarbonization, smart specialization, socioecological resilience, and inclusive development) are comparatively rare [43,56,71,122,133,134]. Where present, they are grounded in transformative innovation policy, the Sustainable Development Goals, or post-neoliberal critiques [57,134,135]. However, these approaches remain marginal relative to the dominant focus on competitiveness, efficiency, and logistical resilience [51,125,128].

Innovation appears predominantly as organizational and technological change: process reengineering, chain governance redesign, automation, and Industry 4.0, rather than as social or institutional innovation [47,101,121,136,137]. We adopt the standard taxonomy (technological, organizational, social, institutional) to map pathways of modernization and transformation [138,139,140]. In the reviewed evidence, the first two dominate, while the latter two are underrepresented [53].

Agency is concentrated in firms (notably multinationals), which initiate relocation, reconfigure supply chain governance, and undertake technological upgrading [121,130,132,141]. National governments and supranational entities create enabling conditions through agreements and industrial policy frameworks [42,49,102,129]. Less frequent are co-directed, innovation-oriented arrangements with regions, universities, and consortia [57,96,122,142]. Civil society and labor actors rarely appear, limiting the reading of outcomes in terms of inclusion and distributive justice.

Structural outcomes are typically measured via (i) metrics: manufacturing value added and employment; export/FDI reorientation; supplier density and network centrality; lead-time and inventory risk; (ii) designs: structural Input–Output, differences-in-differences around policy onsets, synthetic controls, and multiplex network analysis; and (iii) traceability of sourcing footprints. Transformative outcomes are less systematically identified, relying more on case studies, qualitative evidence, and normative constructs, as systematized in

Table 2. Outcome families, typical metrics, and salient mediators in the reviewed literature.

Outcome Family	Typical Metrics/Identification	Salient Mediators (Absorptive)	Representative References
Structural (reindustrialization, specialization shifts, relocation)	Manufacturing Value Added and employment; export/FDI reorientation; Input–Output decompositions; difference-in-differences around policy onsets	Supplier density; logistics/connectivity; rules-of-origin alignment	[55,125,129,130]
Organizational innovation (governance, process, chain design)	Time-to-market; lead-time variance; inventory turns; network centrality; process tracing	Cluster governance; firm capabilities; standardization/quality assurance	[121,136]
Technological innovation (automation, digitalization, Industry 4.0)	Capital Expenditures/retrofit; adoption indexes; productivity and defect rates	Workforce skills; vendor ecosystems; digital infrastructure	[47,101]
Social & institutional innovation (inclusion, rules, coordination)	Participation/representation; new governance rules; program uptake	Multi-level coordination; Regional Innovation Systems (RISs) platforms; mission-oriented agendas	[57,134,135]
Sustainability-oriented transformation (decarbonization, resilience)	Energy/emissions intensity; water stress; circularity proxies; green procurement	Environmental regulation; technology access; financing instruments	[43,56,122]

.

Direct components (manufacturing, FDI, employment) feed into broader outcomes (knowledge spillovers, productivity, learning, innovation). Their magnitude and direction depend on multidimensional proximity and absorptive mechanisms. Where supplier density, multilevel coordination, and capability formation are strong, effects extend beyond the plant perimeter; where they are weak, outcomes tend to be enclave-like.

Part of the scarcity of work on transformative outcomes can be traced to measurement and design constraints: many nearshoring studies rely on relatively short panels or event windows and on readily available trade, FDI and employment statistics, which are better suited to tracking structural shifts than long-term social or environmental transitions. In addition, data on inclusion, distributive impacts, or decarbonization along regionalized supply chains are often fragmented or proprietary, and funding and policy agendas have historically prioritized competitiveness and resilience indicators. As a result, transformative effects are more frequently discussed normatively or through qualitative case evidence than captured through systematic, comparable metrics.

4.6. Absorptive Capacity and Institutional Mechanisms

We treat policy and governance as mediators that condition whether trigger–decision dynamics translate into durable regional gains. Following established policy-mix perspectives, instruments can be grouped into four families: fiscal, regulatory, territorial, and innovation support; each acts on specific absorptive-capacity levers (coordination and governance; capability formation; infrastructure and logistics) and shapes multidimensional proximity (institutional, organizational, cognitive, social, geographic, functional) [143,144], as summarized in

Table 3. Policy instruments as absorptive-capacity levers and proximity enhancers.

Instrument Family	Main Absorptive Lever	Proximity Primarily Affected	Typical Mechanisms/Examples
Fiscal (tax incentives, subsidies)	Capability formation; supplier densification	Institutional, organizational	Lower hurdle rates for retooling and supplier upgrading; anchor tenants with local-content targets [8,62].
Regulatory (standards, rules of origin, environmental rules)	Coordination; compliance predictability	Institutional, functional	Rules of Origin/cumulation align regional sourcing; streamlined permits lower coordination costs; green conditionalities steer upgrading [26,49,145].
Territorial (Special Economic Zones, clusters, spatial planning)	Logistics & infrastructure; agglomeration	Geographic, organizational, social	Park infrastructure, intermodal nodes, supplier parks; brokerage and governance of clusters [66,136].
Innovation support (Research and Development, training, platforms)	Skills & knowledge flows; firm capabilities	Cognitive, organizational, social	Technology Transfer Offices, Public–Private Partnerships platforms, curricula co-design; digital infrastructure for upgrading [109,144,146].

.

The literature engages policy unevenly. Most discussions sit at the national level: industrial policy frameworks, fiscal incentives, and trade agreements such as the USMCA serve as levers to support nearshoring (e.g., [26,49]). Subnational governance appears mainly in case-based work (notably Mexico), referencing asymmetries across states, productive specializations, and localized initiatives (industrial parks, regional innovation programs) [26,147]. Few contributions, however, conceptualize regional governance as an autonomous policy space with distinct instruments and planning logic, keeping analysis framed by national or global architectures.

Frequently cited tools include tax incentives, FDI promotion, infrastructure investment, workforce training, and regulatory facilitation [8,62,66,115,121,136]. Logistics and connectivity investments are acknowledged as pivotal to unlock regional benefits [66,136]. More sophisticated instruments include public–private innovation platforms, digital infrastructure for upgrading, and environmental conditionalities; these are rarer and typically confined to sustainability-oriented pieces [98,109,145,146].

Across regions, persistent gaps in productive capabilities (industrial density, supplier bases, GVC integration), institutional coordination, and skills formation limit benefit capture [42,58,100,117,148]. These often interact with geographical frictions (distance to ports, network quality, peripheral isolation), reducing feasibility outside major corridors [100,148]. Socioeducational constraints (skills shortages, talent retention, education–industry mismatch) are recurrent [91,100,142,149].

Environmental considerations are comparatively marginal. Land use, emissions, water stress, and industrial pollution are seldom treated explicitly [49,50,63,147]. Policy horizons skew toward the short to medium term (business cycles, investment windows, electoral timing), with fewer long-term, transition-oriented agendas [50,57,70,96,109,147]. Normatively, many studies are propositional or diagnostic; some offer prescriptive menus (attraction, incentives, workforce, infrastructure) [62,133], while critical strands warn against repeating past patterns or deepening inequalities [42,57,96,98,121]. Neutral, descriptive analyses contribute empirics but seldom reframe norms [48,63,129,150,151].

Absorptive capacity is the hinge between nearshoring’s direct components (manufacturing, FDI, employment) and downstream outcomes (spillovers, productivity, learning, innovation). Where policy mixes align instrument families with the right levers (governance coordination, capability formation, and logistics and infrastructure), proximity profiles improve and spillovers are amplified; where mixes are misaligned or thin, benefits remain narrow or enclave-like. This points to the value of mission-oriented, multilevel governance and RIS-informed designs that explicitly target institutional, organizational, and cognitive proximities alongside geographic ones.

5. Discussion

The evidence reviewed is consistent with the nearshoring loop articulated in this article (Section 2.2 and Figure 2). Shocks and institutional realignments act as triggers, multidimensional proximity moderates modeling and location decisions, components (manufacturing footprint, FDI, employment) generate outcomes (spillovers, productivity, innovation), and absorptive capacity and institutional mechanisms condition both their capture and the feedback into subsequent rounds of location and sourcing decisions.

Three boundary conditions recur across studies and help explain heterogeneous trajectories. First, capacity and proximity profiles vary markedly across regions; identical shocks therefore translate into different relocation intensities and timing. Second, sectoral technology intensity shapes both FDI composition and employment responses, with capital- and knowledge-intensive activities producing thinner headcount effects and stronger compositional shifts. Third, policy regimes and the depth of coordination determine whether gains remain plant perimeter and enclave-like or propagate through supplier densification, skills formation, and platform-based innovation.

While the reviewed studies rarely estimate the relative weight of each proximity dimension in a formal sense, consistent patterns emerge. In high-tech and knowledge-intensive activities, cognitive proximity—similarity in skills, R&D capabilities and technological trajectories—and institutional proximity around intellectual-property regimes, standards and regulatory predictability appear more critical than social proximity alone. In more standardized manufacturing and assembly, organizational and functional proximity—compatibility of production systems, supplier bases and process architectures—tend to dominate, with geographic and logistics proximity acting as necessary but insufficient conditions. Social and cultural proximity, including trust and language, becomes particularly salient in relational supply chains and SME-dominated ecosystems, yet it typically reinforces rather than substitutes institutional and organizational alignment. These sectoral variations suggest that location choices should be informed by tailored “proximity bundles” rather than by geography or any single dimension in isolation.

Methodologically, the field blends qualitative and quantitative approaches with uneven attention to limitations. The strongest contributions tie identification strategies to policy onsets or disruption windows and track not only first-round effects but also ecosystem variables—supplier centrality, workforce pipelines, and governance changes—that signal capture and potential feedback before the next siting round. This reinforces the value of explicitly distinguishing between decision moderators (proximity profiles) and outcome mediators (absorptive capacity) when interpreting results. The quality gaps documented in Figure 4 further underline the need for more explicit treatment of data constraints, identification assumptions and robustness when drawing policy conclusions from nearshoring case studies and empirical evaluations.

A practical translation of the nearshoring loop for policy is to design mixes that act on three levers at distinct stages of the cycle. At the trigger and modeling stage, governance and coordination instruments (clarity on rules of origin, streamlined permits, cluster brokerage) shape perceived risk and the feasibility of regional footprints. Between components and outcomes, capability-formation tools (training pipelines, supplier upgrading, collaborative Research and Development and testing) raise the probability that new manufacturing, FDI and jobs translate into knowledge spillovers, productivity gains and innovation. In the feedback from captured outcomes to subsequent decisions, investments in logistics and infrastructure (intermodal nodes, industrial real estate, reliable energy and digital networks) consolidate proximity profiles and reduce the cost of expanding or deepening regional footprints. Targeting these levers improves institutional, organizational and cognitive proximities—the very dimensions that moderate decisions and mediate outcomes—thus strengthening the feedback from captured outcomes to the next decision cycle.

Implications follow for key constituencies along the loop. For firms, at the modeling and location decision node, the priority is to model delivered-cost risk with explicit proximity profiles and to plan staged footprints where social and cognitive distances are large. For policymakers, at the components-to-outcomes and feedback nodes, sequencing instruments so that early FDI anchors are matched by supplier and skills upgrading within 12–24 months increases the conversion of components into captured outcomes. For researchers, focusing on capture and feedback—rather than only on initial relocations—calls for designs that can identify how specific policy and proximity configurations shape the dynamics of the loop over time.

Limitations in the corpus (North Atlantic and manufacturing biases, sparse coverage of transformative and environmental outcomes, and uneven transparency about constraints) suggest a future agenda that expands Global South evidence, integrates sustainability metrics into siting and evaluation, and connects regional industrial policy to mission-oriented, multilevel governance.

6. Conclusions

Nearshoring is neither a panacea nor a mere logistics fix. It is a cycle: triggers reprice risk and market access; proximity profiles moderate the siting decision; components generate outcomes whose capture depends on absorptive capacity; captured outcomes, in turn, upgrade capacity and reshape proximity, influencing subsequent decisions. Whether this loop delivers transformation rather than enclave gains hinges on intentional policy mixes and coordinated capability formation.

The synthesis presented here clarifies two recurrent patterns. First, FDI operates as the commitment mechanism that anchors ecosystems, with distinct implications for greenfield investment versus reinvestment and debottlenecking. Second, employment effects are typically lagged and compositional, shaped by technology intensity and skills, which means that headline output or export gains do not automatically translate into broad-based job creation or upgrading.

For practice, the message is straightforward. Engineer proximity beyond geography by improving institutional, organizational, and cognitive dimensions; invest early in absorption through skills, supplier upgrading, and governance platforms; and measure feedback, not only first-round effects, so that regionalization compounds instead of stalling. For research, broaden geographic coverage, bring environmental and inclusion metrics into standard evaluations, and test feedback explicitly with designs that can separate immediate relocation from second-round capacity gains.

Promising avenues include longer-horizon panel and event-study designs that track regional trajectories beyond the initial siting decision; mixed-method approaches that combine firm- and territory-level indicators of decarbonization, resilience and inclusion; and comparative case studies or quasi-experiments that embed nearshoring episodes in mission-oriented policy frameworks. Such designs are better suited to capturing whether nearshoring contributes to structural transformation rather than merely redistributing activity.

Taken together, these practice and research recommendations map directly onto the nodes and feedbacks of the nearshoring loop: shaping triggers and modeling choices through proximity-oriented policies, enhancing the conversion of components into outcomes via absorptive-capacity investments, and tracking how captured outcomes feed back into subsequent siting and sourcing decisions.

Ultimately, the impact of nearshoring will depend on how decisively firms and governments align triggers, decisions, and capture mechanisms within place-based strategies. When embedded in coherent policy mixes and supported by multilevel coordination, the nearshoring loop can drive industrial upgrading, learning, and innovation aligned with longer-term regional development goals.