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Unveiling the intellectual structure of informality: Insights from the socioeconomic literature [1]

['Nelson Alfonso Gómez-Cruz', 'Innovation Center', 'School Of Management', 'Universidad Del Rosario', 'Bogotá', 'Colegio De Estudios Superiores De Administración', 'Cesa', 'David Anzola', 'School Of Sociology', 'University College Dublin']

Date: 2024-02

Abstract In the socioeconomic sphere, the concept of informality has been used to address issues pertaining to economic dynamics, institutions, work, poverty, settlements, the use of space, development, and sustainability, among others. This thematic range has given way to multiple discourses, definitions and approaches that mostly focus on a single phenomenon and conform to traditional disciplinary lines, making it difficult to fully understand informality and adequately inform policymaking. In this article, we carried out a multilevel co-word analysis with the purpose of unveiling the intellectual structure of socioeconomic informality. Co-occurring document keywords were used, initially, to delimit the scope of the socioeconomic dimension of informality (macro level) and, later, to identify its main concepts, themes (meso level) and sub-themes (micro level). Our results show that there is a corpus of research on socioeconomic informality that is sufficiently differentiable from other types of informality. This corpus, at the same time, can be divided into six major themes and 31 sub-themes related, more prominently, to the informal economy, informal settlements and informal institutions. Looking forward, the analysis suggests, an increasing focus on context and on the experience of multiple ‘informalities’ has the potential, on the one hand, to reveal links that help unify this historically fragmented corpus and, on the other hand, to give informality a meaning and identity that go beyond the traditional formal-informal dualism.

Citation: Gómez-Cruz NA, Anzola D, Batz Liñeiro A (2024) Unveiling the intellectual structure of informality: Insights from the socioeconomic literature. PLoS ONE 19(1): e0297577. https://doi.org/10.1371/journal.pone.0297577 Editor: Yue Gong, Wuhan University, CHINA Received: March 6, 2023; Accepted: January 8, 2024; Published: January 31, 2024 Copyright: © 2024 Gómez-Cruz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This research has been developed in the framework of the Colombia Cientifica – “Alianza EFI” Research Program, with code 60185 and contract number FP44842-220-2018, funded by The World Bank through the call Scientific Ecosystems, and managed by the Colombian Ministry of Science, Technology and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

1. Introduction Socioeconomic informality is a complex, multidimensional and context-dependent phenomenon in which social, economic, political, and geographical variables intervene [1]. The composition and impact of informality vary across economies and regions [2]. In emerging and developing countries, informal work accounts for around 30–35% of GDP and 70% of the total employment [3]. Globally, informal employment reaches 61.2% [4], which represents close to 2 billion people. Similarly, it is estimated that one in eight people in the world lives in informal settlements under precarious conditions [5]. In urban areas, informal settlements shelter 25% of the population [6]. Consequently, informality has profound implications for global development and sustainability. Research on informality has been characterized by its compartmentalized development, with a predominant focus on specific themes such as informal settlements, the informal economy, urban informality, the informal labor market, informal employment, informal institutions, and others [1, 7]. Unfortunately, this fragmented approach has resulted in a lack of interconnectedness between these themes, obscuring their potential linkages at the core of various socioeconomic issues. As a result, several authors have attempted to develop taxonomies or classifications of informality, aiming to improve the understanding of the primary factors contributing to it. However, these efforts have arisen from diverse thematic perspectives. For example, it is common to find general analyzes on the informal economy [1, 2] or on informal settlements [8]. Informality, however, has consistently been approached from a singular, narrowly framed perspective. Boanada-Fuchs and Boanada [7] concentrated their study on publications related to ‘informal economy’, ‘housing’, ‘land tenure’ and ‘urban planning’, while Venerandi and Mottelson [9] proposed a taxonomy exclusively focused on categorizing ‘informal settlements’. Furthermore, Dovey et al. [10] acknowledge that certain patterns lead to the formation of informal settlements and influence the emergent urban design or informal morphogenesis. Alsayyad [11] discusses various patterns of urban informality, such as invasion, survival, assimilation, adaptation, and cooperation. Roy [12] delves into the epistemology of public policies employed in urban planning and presents a characterization of these policies. Although most approaches have traditionally centered on the topic of informal settlements, Fernandez and Villar [13] put forward a taxonomy to better understand the informal labor market by analyzing three characteristics: choice, productivity, and barriers. However, efforts to establish general frameworks are scarce and are often focused on ad hoc definitions or on dimensions that fall within the domain of expertise of the authors [1, 7]. This lack of thematic unification makes it difficult to (i) clarify informality’s thematic boundaries, dimensions, and scope, (ii) achieve a basic conceptual understanding, (iii) offer satisfactory measurement alternatives and, in general, (iv) sufficiently integrate the research areas for which the concept is important. Hence, this paper endeavors to address the question: what is the intellectual structure of informality research? To address this question, we employed a multilevel variant of co-word analysis [14], which enables us to minimize bias in the development and classification of topics addressed in the literature. By using this approach, we aim to reveal the central research problems related to socioeconomic informality. Co-word analysis is a quantitative content analysis technique [15, 16] that allows mapping the intellectual structure of a research field through the relationships between concepts (co-occurrences) within a corpus of literature. In this text, we use the method at three different levels of detail to (i) determine the frontiers of research on socioeconomic informality and its differences with other forms of informality (macro level), (ii) identify the main concepts, themes (meso level) and sub-themes (micro level) addressed in informality studies, (iii) explore the relationships between the various sub-themes and (iv) assess, from a general perspective, the relative importance and level of development of the various sub-themes. To achieve this goal, the paper is organized as follows. The Methodology section provides an overview of the methodological framework. The Results section presents the main outcomes of the multilevel co-word analysis. Initially, it establishes the boundaries of socioeconomic informality (macro level). Subsequently, its main themes and sub-themes are identified and characterized (meso and micro levels). The Discussion section introduces a taxonomy that succinctly captures the intellectual structure of socioeconomic informality and delves into its evolution and future opportunities. Lastly, final remarks are provided in the Conclusions section.

2. Methodology This research employs a multilevel co-word analysis to identify and analyze the intellectual structure of socioeconomic informality. Co-word analysis is a content analysis and scientific mapping technique [15, 17] based on the co-occurrence of concepts found in the scientific literature. This approach allows to (re)construct and visualize, in a comprehensive and systematic way, the intellectual structure of a field as a weighted network of interrelated concepts. In this article, the concepts were extracted from the ‘author keywords’ of the literature indexed in Scopus. Those concepts that appear close within the network usually have a thematic relationship [17]. Therefore, a set of concepts that are densely connected to each other, but loosely connected to other concepts in the network, form a cluster that, in turn, can be interpreted as a research theme. The relevance and level of development of each theme can be captured through network metrics in a strategic diagram. Co-word analysis is used in this research on three levels. At the macro level, the general literature on informality is mapped to avoid bias on the delimitation of socioeconomic informality. The meso level extracts, in an explicit way, the literature on socioeconomic informality, characterizes it statistically and identifies its main research themes (meso clusters). At the micro level, each theme is divided into sub-themes (micro clusters). The degree of development and the importance of each sub-theme within its theme, that is, its relative strategic position, is analyzed in the section 4 Discussion. Fig 1 summarizes the methodological approach used. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Methodological framework. https://doi.org/10.1371/journal.pone.0297577.g001 2.1. Data collection This study used academic literature on informality indexed in Elsevier Scopus, one of the largest curated scientific databases in the world [18]. Scopus data has been extensively used in bibliometric studies and co-word analysis [19, 20]. This database was chosen over alternatives such as Web of Science (WoS) or Google Scholar due to its coverage and academic rigor. Scopus has a broader coverage than WoS, especially in social sciences [21]. Recent research [22] indicates that 99.11% of journals indexed in WoS are also included in Scopus. In contrast, only 33.93% of journals indexed in Scopus are found in WoS. Google Scholar, in turn, has broader coverage than Scopus, but faces significant issues regarding transparency and reproducibility [23, 24]. Firstly, it lacks a curated catalog, allowing the inclusion of non-peer-reviewed literature. Furthermore, search results are not consistently reproducible, and it does not support the use of boolean operators. Consequently, thanks to its balance between coverage and rigor, Scopus ensures that our analysis encompasses a broad spectrum of relevant literature (containing most of the WoS content) without compromising on the quality of data. Two search strategies were considered: a general one (macro) and one restricted to the socioeconomic domain (meso). Both searches employed the ‘title’ and ‘author keywords’ fields. The strategy employed at the macro level captures the various uses of the term ‘informal’ and its variations (informality, informalization), according to the following statement: (TITLE (informal*) OR AUTHKEY (informal*)) (1) 29,546 documents were retrieved, of which 21,463 contained author keywords (S1 File). The latter were used in the macro co-word analysis (see subsection 3.1 Macro level: A general view of informality research). After analyzing this first set of data and its co-occurrence network, the keywords that (i) belonged to the socioeconomic domain, (ii) had 50 or more occurrences, and (iii) included the term informal or informality, were included in the construction of the second search strategy. The resulting statement was: (TITLE (informality OR "informal econom*" OR "informal settlement*" OR "informal sector*" OR "informal institution*" OR "informal employment" OR "informal network*" OR "informal work*" OR "informal social control" OR "informal payment*" OR "informal market*" OR "informal housing" OR "informal recycling" OR "informal control" OR "informal labor" OR "informal finance" OR "informal governance" OR "informal entrepreneur*" OR "informal trade") OR AUTHKEY (informality OR "informal econom*" OR "informal settlement*" OR "informal sector*" OR "informal institution*" OR "informal employment" OR "informal network*" OR "informal work*" OR "informal social control" OR "informal payment*" OR "informal market*" OR "informal housing" OR "informal recycling" OR "informal control" OR "informal labor" OR "informal finance" OR "informal governance" OR "informal entrepreneur*" OR "informal trade")) (2) From this search, 10,236 documents were obtained and used for the statistical analysis of the literature on socioeconomic informality (see S2 File and subsection 3.2.1 Statistical analysis of literature). Of these, only 7,814 documents that contained keywords were used to create the co-word network presented in subsection 3.2.2 Co-word network of socioeconomic informality. Both searches were carried out on 17/11/2021 and included all document types available in Scopus. The records obtained for each search were stored in a CSV file for later analysis. The networks elaborated at the micro level were developed from the clusters identified at the meso level, so they were indirectly based on the same data set retrieved with search statement (2). 2.2. Keyword selection A thesaurus (controlled vocabulary) was built to filter each data set (macro and meso), according to the following rules [25]: (i) standardization of singular and plural words (e.g., settlement into settlements); (ii) combination of equivalent expressions in American and British English (e.g., urbanization into urbanization); (iii) integration of acronyms with their corresponding full names (e.g., non-governmental organizations into ngos); (iv) normalization of hyphenated expressions (e.g., neoliberalism into neo-liberalism); (v) reduction of synonymous expressions (e.g., small enterprise into small firm); (vi) stemming of derived words in justified cases (e.g., flooding into flood), and (vii) elimination of references to the territory (e.g., Latin America, India), research methodologies (e.g., case study, ethnography), and irrelevant words (e.g. informality, informal). For the macro data set, 45,308 keywords were found in the raw data. After preprocessing, 44,874 unique terms were retrieved. Last, 496 terms that had 20 or more occurrences were included in the macro network. At the meso level, meanwhile, 17,005 keywords were extracted from the raw data. After the application of the thesaurus, 16,613 unique terms were found. Finally, 759 terms that had 5 or more occurrences were included in the meso network. The networks analyzed at the micro level were extracted directly from the meso level network data. 2.3. Network visualization Once the keywords were preprocessed, the co-occurrence matrices for the macro and meso levels were calculated and mapped using the VOSviewer software [26]. In a co-occurrence network, the nodes represent the keywords and the edges the co-occurrences between them. The number of occurrences of a keyword within the corpus is represented by the size of its node. The larger a node is, the more frequent the keyword it represents. The thickness of an edge, for its part, indicates the number of documents in which the two connected keywords appear together. Once the network is built, the colors represent the thematic affinity between groups of nodes (clusters). Each cluster, therefore, can be explained from the nodes and edges it comprises. Co-occurrence networks at the macro, meso, and micro levels were mapped using the LinLog normalization algorithm [27] included in VOSviewer. The attraction and repulsion parameters for the macro and meso networks were set to 2 and 0, respectively. For the micro-level networks, in contrast, different attraction and repulsion values were used according to their specificities. Clusters detection was performed with the clustering technique that is incorporated by default in VOSviewer, which is a variant of the modularity-based clustering methods developed in physics [28]. The VOS clustering technique includes a resolution parameter that determines the number of partitions that the algorithm will produce. The higher its value, the higher the number of clusters generated. The resolution of the macro network was set to 0.80 to convincingly highlight the socioeconomic cluster. In all other cases, this parameter was set to its natural value of 1.00. VOS viewer, unlike other tools, uses a unified paradigm that integrates normalization (visualization) and clusterization techniques [29]. For this reason, the parameters must be calibrated iteratively. Finally, the data corresponding to each meso cluster were extracted to build sub-theme networks at the micro level. Each network, therefore, was clustered in such a way that it was possible to obtain a set of relevant themes and sub-themes for research on socioeconomic informality. 2.4. Analysis The co-occurrence network at the macro level was characterized in a general way, with the purpose of establishing clear demarcation criteria between socioeconomic informality and other forms of informality. A count of the occurrences of the socioeconomic informality keywords was performed to construct search statement (2). The meso-level corpus of literature, in turn, was statistically described and the network data were extracted for the purpose of reconstructing micro-level networks. At the micro level, the networks were described from their structure and from the review of documents (for example, highly cited) contained in the corpus. For each of the networks at this level, a strategic diagram that contrasts the network metrics of density and centrality of the micro-clusters was constructed (see section 4 Discussion). For a given micro cluster, the density captures its level of internal coherence, while the centrality indicates its importance within the meso cluster to which it belongs. According to Pourhatami et al. [30], the density and centrality of a cluster can be calculated using the following equations: where D L represents the density of the cluster L, while N and E indicate the total number of nodes and edges in L, respectively. where C L is the centrality of cluster L and M is the set of other clusters that make up the network in the micro level. i denotes the nodes of L and j the nodes belonging to the clusters of M. e ij can take the value of 0 if there is no edge between i and j, and 1 otherwise. Finally, w ij indicates the weight of the edge between i and j. A strategic diagram (Fig 2) allows the clusters of a network to be classified into four quadrants, according to their relative values of density and centrality. Within the diagram, the vertical axis represents centrality and the horizontal axis density. Its origin will be given by the average of the centralities and densities of the clusters that make up the network under analysis. The importance and maturity of each cluster will be defined according to the quadrant to which they belong. Clusters in quadrant I are relevant to the network and have a high level of internal cohesion (mainstream). Clusters in quadrant 2 have a high level of internal cohesion, but are isolated (ivory tower). Quadrant III includes underdeveloped and isolated clusters (chaos/unstructured). These clusters may, depending on the case, be emerging or in decline. The clusters in quadrant IV generally involve important concepts for the network that, however, are not sufficiently cohesive among themselves (bandwagon). PPT PowerPoint slide

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TIFF original image Download: Fig 2. Strategic diagram structure. https://doi.org/10.1371/journal.pone.0297577.g002

4. Discussion Fig 13 introduces a concise depiction of the intellectual structure of the literature on socioeconomic informality that explicitly links higher- and lower-level clusters at the micro, meso and macro levels. It positions the 31 micro clusters within the six meso clusters that belong to the socioeconomic informality macro cluster. It offers, to our knowledge, the most comprehensive and synthesizing account of a corpus that is decidedly fragmented, because it receives inputs from a multiplicity of communities working separately. This taxonomy has the advantage that the resulting thematic categories emerge directly from the co-word analysis, instead of being defined a priori, as with most previous reviews (which tend to emphasize the economic and urban dimensions of informality). PPT PowerPoint slide

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TIFF original image Download: Fig 13. A taxonomy of socioeconomic informality. https://doi.org/10.1371/journal.pone.0297577.g013 Fig 14 presents the strategic diagrams for the six meso clusters. In the figure, the 31 micro clusters are classified according to their centrality and density. This adds to the understanding of the literature on socioeconomic informality by offering an account of the relative importance of each micro cluster in the current literature and their potential to (re)shape how socioeconomic informality is studied. The classification of the micro clusters according to their location in the strategic diagrams is presented in Table 3. Values for their size, connectedness, centrality and density are included. PPT PowerPoint slide

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TIFF original image Download: Fig 14. Strategic diagrams for the 6 clusters of the meso level. https://doi.org/10.1371/journal.pone.0297577.g014 PPT PowerPoint slide

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TIFF original image Download: Table 3. Strategic diagram metrics and micro cluster classification. https://doi.org/10.1371/journal.pone.0297577.t003 Seven micro clusters are in QI (Motor themes) (Table 3). These themes have become mainstream, for they have achieved high levels of internal cohesion and are linked in several ways to other themes in the network. Their influence on the overall understanding of socioeconomic informality, however, is not justified for the same reasons. Two differences that are worth mentioning are scope and level. Regarding the former, the mainstream status of vulnerabilities (C1-5) appears to be driven by highly popular and transversal nodes, such as ‘poverty’ and ‘covid-19’, both strategically positioned at the meso-level border with C3 and C4. Even though it is part of C1, this micro cluster provides a thematically broad contribution to the understanding of socioeconomic informality. In contrast, the informal networks (C2-2) micro cluster has a more local orientation, being central to the institutional approach to informality. In terms of level, shadow economy (C5-4) and crime (C6-3), for instance, group thematically general nodes that are relevant for the macro-level characterization of the informal economy and informal social control, respectively. Comparatively, labor inequalities (C3-3) and labor precarity (C3-4) include nodes that mostly address micro-contextual determinants of socioeconomic informality. Seven micro clusters are in QII (Ivory tower) (Table 3). The micro clusters in this quadrant have a high level of internal development, but do not have a significant thematic impact on the meso network. Many of these clusters share a common feature: they developed independently of the literature on informality and remain peripheral because they only partially overlap. Occupational health (C4-4) and economic development (C5-6) are topics with an overarching scope that are nonetheless important for informality. The former, for instance, is relevant because of the increased environmental health risks and hazards in some informal activities, such as waste picking (C4-2), and because of the limited access that informal workers have to health services (C4-3). An additional reason for the location of some of these micro clusters is that they may simultaneously account for alternative approaches to informality. The governance micro cluster (C2-4), for instance, addresses different dynamics of governance in the informal market, but accounts, as well, for informal or soft governance at the international level. Similarly, the control enforcement micro cluster (C6-4), as mentioned above, accounts for dynamics of informal (social) control in several social settings, which affect the criteria used to determine whether these forms of control are informal. Eight micro clusters are in QIII (chaos/unstructured) (Table 3). Because of their low density and centrality, clusters in this quadrant are believed to be either emerging or declining. While the literature on socioeconomic informality is continuously increasing, two micro clusters could be declining: urbanization (C1-4) and corruption (C2-5). They are both dominated by relatively large and old nodes, ‘corruption’/‘informal payments’ and ‘urbanization’/‘cities’, respectively. Despite their relevance, the micro clusters are unstructured because, around these nodes, there is a constellation of smaller nodes with a narrower thematic focus and scope. In the corruption micro cluster, for example, the remaining nodes tackle specific types and contexts of corruption and informal payments. A narrower focus and scope is, at the same time, the reason the others micro clusters in the quadrant are better classified as emerging. They are, initially, highly contextual, highlighting the multiple dimensions of informality and how these dynamics hinge upon specific combinations of social, cultural, and economic variables. More important, though, they bring agency to the foreground: how individuals consciously and deliberately accumulate and mobilize individual and social resources to navigate their conditions of informality. Nine micro clusters are in QIV (Bandwagon) (Table 3). This is the most populated quadrant, and, perhaps, the one with the most interesting dynamics. It groups clusters that are transversal to the literature, but not entirely cohesive internally. Similarly to declining themes in QIII, the location of some micro clusters in QIV e.g., informal employment (C3-1) and informality in developing countries (C4-1), may be determined by the behavior of high-occurrence nodes. What makes these clusters transversal, instead of declining, is that these high-occurrence nodes remain current or are more strongly connected to a diversity of other nodes. The location of other micro clusters e.g., formal institutions (C2-3) and entrepreneurial entry (C5-1), is, instead, more likely attributable to the existence of second-tier nodes that are comparatively newer or with higher frequency, and also link out more significantly to other micro clusters in the meso network. Overall, the strategic diagrams offer some insights into the thematic relevance and influence of the 31 micro clusters and, to a certain extent, provide a rationale for the current state of the meso network. While the current location of the micro clusters can be attributed to a few common reasons, a structural change in the meso network should be expected in the future given that micro clusters located in the same quadrant for, arguably, the same reason, may behave differently in the future. For example, entrepreneurial entry (C5-1) could easily gain centrality and move to QI due to the increasing interest in informal entrepreneurship, its overall contribution to economic development and its potential for growth and innovation. Conversely, formal institutions (C2-3) is more likely to remain in QIV, or even move to QIII, since its central nodes focus on very general and standard institutional dynamics. The future structure of the meso network is also likely to change due to some thematic concerns that are downplayed by its current node composition. Sustainability, for instance, is progressively becoming important for every dimension of socioeconomic informality. In the current network structure, however, this contemporary relevance is not as evident, for it is split among seven nodes in different micro clusters. Since some aspects of sustainability are transversal to the multiple contexts of socioeconomic informality, it is possible that a structural unification occurs in the future, driven, initially, by the ‘sustainability’ node, which is currently positioned between C4 and C1. A more general and interesting dynamic with the potential to reshape the meso network is the increasing awareness about, and thematic accommodation of, the effects and relevance of micro and local aspects of socioeconomic informality. Several of the most novel nodes, some of the micro clusters in QIII and, arguably, the entire C3 meso cluster bring to the foreground contextual traits of actors, occupations and dynamics traditionally considered informal. As the acknowledgement that informality has a distinctive thematic identity beyond its relationship (or contrasts) with formality becomes widespread, a change in the thematic structure of the corpus should be expected. This change may be driven, first, by an increasing research output emphasizing the diverse contextual traits of informality, second, by the potential development of more specialized terminology (starting with the popularization of additional terms with the prefix “informal”) and, third, by the conceptual clarification of the several socioeconomic informalities (e.g., those pertaining to governance). Finally, future changes in the meso network could also be anticipated by paying attention to some nodes that, while not entirely general (e.g., ‘poverty’), seem to be able to thematically link or branch out to several micro and meso clusters. ‘Street vending’, for example, connects the economic aspects of the occupation (spread among the three clusters with economic underpinnings) with the spatial and institutional aspects linked to its dependence on the urban public space. These nodes or constellations of nodes might be used not only to anticipate the future state of the corpus, but also to identify areas of common work between the different communities interested in socioeconomic informality.

5. Conclusion Despite its high incidence and prevalence, especially in emerging and developing countries, the current understanding of informality is severely fragmented, in part, due to the variety of phenomena to which this label applies and the multiple disciplinary communities that study them. Although various authors have attempted to develop taxonomies and classifications that better grasp informality’s scope and nature, these efforts hinge on predefined frameworks and criteria that render only a partial characterization of socioeconomic informality. In this study, we avoided any frame of reference in the delimitation of what counts as socioeconomic informality, letting it, instead, to emerge from a co-word analysis of the literature available in Scopus from the 1960s until 2021. The analysis showed that, at the macro level, there are three sufficiently separated themes that respond to alternative conceptualizations and understandings of informality and the formal-informal dichotomy: (i) informal education, (ii) informal care, and (iii) socioeconomic informality. In this third macro cluster, six meso-themes were identified: (1) informal settlements, (2) informal sector, (3) informal economy, (4) informal institutions, (5) informal employment enablers, and (6) social control. The six meso clusters, in turn, were decomposed into 31 micro clusters that provide the most comprehensive characterization of informality in the socioeconomic dimension. In general, informality is about individuals, communities and institutions using alternative means and mechanisms to experience and navigate a variety of social settings. The analysis, however, shows that these alternative means and mechanisms are not homogeneous, nor share an identity. What makes them ‘informal’, the micro clusters suggest, is, rather, that they are not ‘formal’—and not being formal manifests in multiple ways e.g., being non-official, non-standard, non-traditional, or non-sanctioned. Since there is more than one way in which phenomena can be informal, socioeconomic informality can have positive and negative effects. The node composition of the micro clusters supports previous findings that link informality to higher vulnerabilities and inequalities. For instance, issues of infrastructure, exposure to natural disasters and poverty, among others, are prominent in the six micro clusters in the “informal settlements” meso cluster. Similarly, problems with social protection and working arrangements and conditions are noticeable in the three meso clusters addressing economic informality. Multiple positive effects of informality are equally identifiable, though. Informal finance, for example, partially counters the negative effects of financial exclusion. In turn, informal social networks facilitate the accumulation and mobilization of various sorts of capital. Finally, informal social control deters crime and victimization in contexts where the state’s capacity is limited. A recent trend in the literature that could help provide a more nuanced understanding of these various informalities is the increasing interest in the contextual determinants and effects of informality. This more contextual orientation in the analysis of informality, visible both in the node composition of the micro and meso clusters and the strategic diagram, might prove useful, first, because, in practice, formal and informal are not neatly separated and, second, because, for most phenomena of interest, ‘informal’ is too wide of an umbrella concept. Since most individuals experience informality in more than one way, accounting more explicitly for context should also offer means to bridge the literatures and communities working on socioeconomic informality. It would not be hard to find individuals, for example, that work informally, live in informal settlements, use informal transport, rely on informal finance, and make informal payments to access some services. Although connections between the different literatures could be established in a variety of ways, large nodes at the intersection between meso clusters (e.g., ’gender’, ’employment’, ’education’, ‘migration’, ’poverty’, ‘corruption’, and ’governance’), as well as themes that are currently fragmented in multiple nodes (e.g., sustainability) could pave the way for integration.

Supporting information S1 File. Records extracted from Scopus for the search statement (I). This file lists the 29546 documents used for the macro analysis. https://doi.org/10.1371/journal.pone.0297577.s001 (XLSX) S2 File. Records extracted from Scopus for the search statement (II). This file lists the 29546 documents used for the meso and micro analyzes. https://doi.org/10.1371/journal.pone.0297577.s002 (XLSX)

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