Consumer financial vulnerability: review, synthesis, and future research agenda

DOI: 10.1111/joes.12573 ARTICLE Consumer financial vulnerability: Review, synthesis, and future research agenda Sara Fernández-López1,2Marcos Álvarez-Espiño1 Lucía Rey-Ares1Sandra Castro-González3 1Research Group "Valoración Financiera Aplicada (VALFINAP)", Department of Financial Economics and Accounting, Universidade de Santiago de Compostela, Santiago de Compostela, Spain Email: [email protected]; marcos.alv[email protected] 2Interuniversity Research Center ECOBAS (Economics and Business Administration for Society), Vigo, Spain 3Department of Business Organization and Commercialization, Universidade de Santiago de Compostela, Lugo, Spain Email: sandra.castr[email protected] Correspondence Lucía Rey-Ares, Faculty of Economics and Business Studies, Ave. do Burgo, s/n. PC 15782 – Santiago de Compostela, A Coruña, Spain. Email: lucia.rey[email protected] Funding information Ministerio de Universidades, Grant/Award Number: FPU21/03287 Abstract Research on consumer financial vulnerability (CFV) was especially encouraged in the aftermath of the 2007– 2008 financial crisis, becoming a phenomenon of global interest to academics and policy makers. This paper reviews the existing academic literature on CFV with the aim of mapping out four research fields (i.e., the concept, the measures, the methods, and the drivers of this phenomenon) and providing a roadmap for a future research agenda for this field. To this end, we review the last two decades of academic research on households’ financial vulnerability, thereby presenting a hybrid literature review. Evidence from a comprehensive analysis of 98 academic papers suggests that this is still an emerging and highly fragmented field and, therefore, a comprehensive future research agenda is offered, including concrete suggestions for research designs and measurements. The proposal of a homogeneous definition of financial vulnerability, the consideration of measures that include subjective aspects of the phenomenon, the use of qualitative methods, and the development of more detailed theoretical and empirical research on the drivers of CFV, are among the authors’ recommendations for future research. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. Journal of Economic Surveys published by John Wiley & Sons Ltd. JEconSurv.2023;1–40. wileyonlinelibrary.com/journal/joes 1 2SARA et al. KEYWORDS financial distress, financial fragility, household financial vulnerability, over-indebtedness, systematic review 1 INTRODUCTION Financial vulnerability has been broadly defined as the situation experienced by households or consumers who have high debt relative to income, especially when it comes to consumer debt and unsecured loans (Anderloni et al., 2012; Kim et al., 2016), and/or limited ability to repay (Kim et al., 2016). In contrast to this “narrow” conceptualization that associates consumer financial vulnerability (CFV) with high indebtedness, other authors qualify households as fragile if they cannot afford to do their favorite activities such as eating out with family and friends, taking a holiday or doing leisure pursuits (Worthington, 2006). Non-indebted consumers can also be considered fragile when they are unable to meet an unexpected expense (Demertzis et al., 2020), make ends meet, or pay their utility bills (Bridges & Disney, 2004). Moreover, the possible exposure to adverse economic shocks such as changes in interest rates, correction of asset prices (e.g., housing or retirement accounts), or sudden unemployment (Kim et al., 2016) might leave households in a financially vulnerable position (Ali et al., 2020a). Thus, the term “financial vulnerability” is very multifaceted, complex, dynamic and elusive (Ali et al., 2020a). Since the 2007–2008 financial crisis, policy makers have been paying more attention to household financial fragility (Azzopardi et al., 2019) due to the increasing levels of indebtedness in Western households (Angel & Heitzmann, 2015; Livshits, 2015). In 2018, Demertzis et al. (2020) reported that more than 30% of EU households could be considered financially fragile. Such a high percentage in a period of moderate growth, and prior to the post-COVID-19 period, explains why the financial vulnerability of consumers has become a growing concern for governments and society. Nevertheless, it is not the only reason. This critical state affects consumption levels and, given that the largest share of wealth in developed economies is held by households (Ampudia et al., 2016), it has an impact on the economy (Ali et al., 2020a; Ampudia et al., 2016). The stability of the financial system also depends on families’ ability to meet their financial obligations (Azzopardi et al., 2019; Noerhidajati et al., 2020). Moreover, financially fragile households tend to experience deprivation, which may eventually lead to absenteeism and low productivity at work, as well as financial exclusion (Daud et al., 2019). Research on household financial vulnerability has increased due to the fact that this issue has become a major concern for governments and for the financial industry. Nevertheless, we should note that the existing literature is highly fragmented in terms of theoretical and empirical approaches. More specifically, the term “financial vulnerability” has been used interchangeably with others such as “financial fragility” and “financial distress” (Ali et al., 2020a;Daudetal.,2019). Common empirical CFV measurements are lacking. Moreover, while a non-negligible number of studies address the influence of demographic and socioeconomic driving forces on CFV, only a few consider other behavioral drivers. On a more practical level, these shortcomings make it difficult to use the findings of the current literature to design interventions aimed at strengthening the financial resilience of households. This paper has a threefold objective: to review the existing academic literature on CFV, to synthesize prior research findings and to identify gaps that could open new and exciting directions for future research. To this end, we pose the following research question: How is CFV investigated (i.e., 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 3 in terms of conceptualization, measurements, and determinants used)? To answer this question, we have conducted a hybrid review (Paul & Criado, 2020). This study aims to contribute to the existing literature in four ways. Firstly, it is the first attempt to identify, explore, and systematize the priority areas prevailing in CFV by way of a systematic review of 98 academic papers in the field of CFV published in the last two decades (from January 2000 to July 2022) with the help of bibliometric analyses. Secondly, it further enhances the literature on financial vulnerability by identifying four main lines of research, namely, the conceptualization of CFV, its different measures, the empirical methods used in its analysis and its main drivers (Daud et al., 2019). Thirdly, it provides the basis for progress in the field by organizing the findings as described above, while it allows us to provide a detailed future research agenda. Finally, it helps governments and practitioners to design interventions aimed at reducing the risk of domestic financial fragility as it presents recent findings on the driving forces of CFV. This paper proceeds as follows: Section 2presents the methodology; Section 3details the descriptive results; Section 4discusses the findings on the conceptualization and measurement of CFV, as well as on its driving forces, thus leading to the future research agenda, which is discussed in Section 5; the conclusions are presented in Section 6. 2THE REVIEW PROCESS: OBJECTIVE AND METHODOLOGY To investigate the state of the art in CFV, a systematic literature review (SLR) has been conducted. This method carefully identifies and synthesizes relevant research on a specific topic, increasing methodological rigor and providing objective, replicable, and reliable results (Moher et al., 2009) from which to draw conclusions (Tranfield et al., 2003). As previously stated, this paper conducts a hybrid literature review on CFV. Figure 1depicts the systematic review flow diagram, consisting of the following five stages: 1. Search. Existing research has contributed to the literature on CFV at both macro and microlevels of study. Whereas the former has focused on the resilience of economies and financial systems during specific periods of financial distress, the latter has placed households at the center of the analysis. This systematic review focuses on the second level. Financial vulnerability has been used in the literature interchangeably alongside financial fragility, financial distress, financial debt burden, and over-indebtedness, to name but a few (Ali et al., 2020a;Daudetal.,2019). Therefore, a literature search has been conducted using the following search strings in the title, abstract and keywords: [“financial vulnerab*” OR “financial distress” OR “financial stabil*” OR “indebt*” OR “financial debt burden” OR “over-indebt*” OR “financial fragil*”]AND[“household*”]. Initially, two databases (i.e., Web of Science (WoS)andScopus) have been used for the systematic search of academic papers. The searches in Scopus have yielded on average 20% more results than those in WoS. This second database, as Van der Have and Rubalcaba (2016) acknowledged, does not include less formal, but significant resources in the field of household finances (e.g., the Social Science Research Network), which can guide the discussion in an emerging field of knowledge such as CFV. More specifically, the searches have yielded a total of 989 studies in WoS and 1150 in Scopus. All studies considered were written in English and published between January 2000 and July 2022, covering, therefore, at least a bare minimum period of 10 years (Paul & Criado, 2020). A number of inclusion and exclusion criteria have been considered. In this step, studies in areas 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4SARA et al. FIGURE 1 Flow diagram of the systematic review. Source: Authors’ own elaboration. [Colour figure can be viewed at wileyonlinelibrary.com] of knowledge with little or no relation to finance (e.g., arts, medicine, physics, natural science, and astronomy) had been discarded through the advanced filtering options of the WoS and Scopus databases. By doing so, 405 publications have been rejected in WoS,and476inScopus. After filtering out duplicates, an initial sample of 878 remains. 2. Title analysis. The titles of the remaining publications (878 studies) have been screened to discard any studies analyzing CFV through aggregate variables, such as the country’s level of consumption or savings, as we consider that these refer to the macro-level of study. At this stage, 383 papers that do not fit the aim of the research have been excluded. 3. Abstract analysis. The abstracts of a total of 495 publications have been read to eliminate any that, according to the pre-defined criteria of relevance to the study (Calabrò et al., 2019), are beyond our scope. More specifically, the publications measuring financial fragility through non-financial factors, such as consumption habits (e.g., alcohol, tobacco, and car purchases), chronic diseases and difficulties in accessing the health care system, have been considered off-topic entries. After discarding a total of 304 publications, this screening results in 191 publications for further consideration. 4. Full-text assessment. A detailed reading of the 191 resulting publications has allowed for the selection of papers that fit the aim of the research. In doing so, studies that have addressed financial vulnerability at the individual (household) level, either theoretically (1) or empirically, whether it be by measuring financial vulnerability (2) or by exploring its determinants (3), have been selected. On the other hand, studies referring to a sampling unit larger than households (e.g., neighborhood, district, or ward) have been discarded. A total of 82 publications have been admitted to the next stage. 5. Hand searching. Finally, via citation tracking, 16 additional publications have been identified, resulting in a final sample of 98 publications. 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 5 The final sample has been analysed using an Excel spreadsheet (Calabrò et al., 2019)anda pre-defined coding scheme (Lock, 2019) has been shared with a scholar from the field in order to reachan agreement on the relevant information to be included. All authors of the paper have participated in the data collection and analysis in a collaborative manner. Besides formal variables (e.g., authors, journal, and citation), a range of previously coded variables (e.g., datasets used, methodology, and econometric techniques) and the main results and findings have been gathered for each item. The information collected has been organized and synthesized to provide a complete picture of the state of art in CFV literature. Moreover, VOSviewer software (Van Eck & Waltman, 2010), aimed at constructing and viewing bibliometric maps (e.g., co-citation maps, thematic country maps, and keyword concurrence), has been used to process the papers, visualize, and analyze the main information. This software allows the in-depth review of the maps thanks to its functionalities and it is particularly helpful for any which contain at least a moderately large number of items, such as is the case in our literature review. 3 DESCRIPTIVE RESULTS Before discussing the main findings, it is worth providing some descriptive elements of the selected publications, as this will also allow us to draw some conclusions. It should be noted that some of the papers analyzed provide results for different subsamples (e.g., countries, age, and groups) and, in these cases, the results by subsamples have been treated individually, meaning that in some of the parameters analyzed, the total number of observations may be greater than the number of papers (i.e., 98). 3.1 The emerging topic of CFV The number of publications on CFV displays an upward trend over the period considered (Figure 2). Specifically, three stages can be identified in this evolution. The first, which spans the years 2000–2007 and comprises 3% of the studies, is characterized by the scarcity of publications. This situation could be for two reasons: either the limited interest of the authors in dealing with the subject of analysis or the lack of databases that would have allowed them to be researched at an empirical level. The second stage began in 2008. The outbreak of the financial crisis and the subsequent recession marked a turning point in the study of this topic and from that year onwards, research on CFV became more constant. The interest probably lay in the social and economic situation at that time. Not in vain, the 13 papers published in the year 2016, when the number of publications at this stage peaked, attempted to analyze CFV in the years immediately following the 2007–2008 financial crisis. The third stage, starting in 2017 and finishing in 20221, which consists of 56% of the studies, is characterized by a steady growth in the number of publications2. The 17 that were published in 2021 reached the maximum quantity for the whole period analyzed. This made up almost a third of all the papers published in this last stage, which might be due to the COVID-19 outbreak. The most recent studies have sought to analyze more recent data. Thus, there is a peak in data from 2020 onwards, which might be related to the dissemination of the first surveys analyzing the situation of households after the pandemic began. This temporal sequencing in the appearance of CFV publications may be for several reasons. Firstly, there used to be a lack of databases on household finances. It was not until the 1980s and 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6SARA et al. 0 5 10 15 20 25 30 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Papers published in each year Number of papers with data referring to the year FIGURE 2 Publications by year of publication and year of study (1990–2022). Source: Authors’ own elaboration. Note: Publications analyzing empirical data referring to more than 1 year have been computed for all the years for which they have data. This SLR analyses papers published up to July 2022. [Colour figure can be viewed at wileyonlinelibrary.com] 1990s that these became widespread in developed countries. Secondly, many CFV studies have used public databases, sources that tend to accumulate a considerable time lag. This has resulted in a delay between the publish date of papers and the period to which their data refers. As Figure 2 shows, studies on CFV began to be published in 2002, but the earliest ones referred to data from 1990. Thus, 84% of the sample publications have been based on data obtained from public surveys, with an average delay of 7.21 years between the date of the study’s publication and the year from which the data were taken. However, the studies that have used self-reported surveys (16%) have tended to have considerably shorter time gaps (3.66 years). Working with more up-to-date data may partly explain why this second group of papers has been published in highly ranked journals (50% of which have been indexed in WoS and 44% in Scopus). The percentage of publications in highly ranked journals has been slightly lower (89%) for papers using official surveys. Thirdly, the publication of papers on CFV is related to the economic cycle. Thus, beyond the consolidation of the topic as a global trend (see Figure 2), the most notable peaks for publications (in 2016 and 2021) have been associated with the study of the two major economic crises of this century, namely, the 2007–2008 global financial crisis and its subsequent recession, and the COVID-19 crisis. Today’s households must cope with an increasingly complex and unstable environment, facing different challenges such as high levels of inflation and loss of confidence. These factors make us expect an even more pronounced trend in the number of publications on this topic, reaching a new high in the next few years (or even sooner, perhaps in 2024 or 2025). 3.2 Countries’ characteristics Figure 3shows the 15 most analyzed countries, together with the databases where the journals that published the articles have been indexed. By and large, CFV has been analyzed in the world’s 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 7 12 16 7677 46665454 1 5 2 4 221 31112216 51 3 53 232222222 0 5 10 15 20 yrtnuocehtgnisylanasrepapforebmuN WoS Scopus Other FIGURE 3 The 15 most analyzed countries by database. Note: Whenever the papers analyze data from more than one country, these studies are individually recorded for each of the countries analyzed. Comparative analyses of regional areas are not included. At the geographic level, data are included for countries that are analyzed by a minimum of four studies. The category “Other” includes articles integrated into non-indexed journals, working papers, and studies in other formats (e.g., books, book chapters, or dissertations). [Colour figure can be viewed at wileyonlinelibrary.com] most economically developed countries, that is, in Europe and the USA. Italy (with 23% of the sample publications), the USA (19%), Spain (14%), the UK (13%), Portugal (12%), and Germany (10%) are the countries that have constituted the geographical focus of most papers. Thus, within Europe, CFV has been most widely studied in Southern countries (i.e., Italy, Portugal, and Spain) and in the UK. Papers on different countries have displayed different levels of academic impact according to the ranking of the journal. Thus, despite coming top of the most analyzed countries, papers focused on Italy, Spain, the UK, and Portugal have not had as much academic influence when it comes to publication in indexed journals. Nevertheless, those focused on Austria, Belgium, Denmark, and France, fewer in number, have been published mostly in WoS-indexed journals. Among the most published and best indexed ones, however, are focused on the USA. Furthermore, the motivation to analyze CFV has seemed to differ across countries. We should note that studies focusing on the USA have tended to concentrate on specific issues, such as the situation of racial minorities, while those focusing on Europe have usually had a broader perspective, as many of them have analyzed the countries most severely affected by the 2007–2008 financial crisis. 3.3 The performance of publications 3.3.1 Journals’ performance Of the 98 papers analyzed in this literature review, 86 are articles published in journals indexed either in the WoS or Scopus databases, which confirms, once again, the intense interest in this topic. These publications are scattered among 69 indexed journals, of which 12 have published at 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8SARA et al. TABLE 1 Top 12 journals with at least two publications on CFV. Journal Number of publications Database Journal of Family and Economic Issues 5WoS Social Indicators Research 4WoS International Journal of Social Economics 3Scopus Journal of Consumer Policy 3WoS Applied Financial Economics 2Scopus Economia Politica 2WoS International Journal of Bank Marketing 2WoS Journal of Business Research 2WoS Journal of Economic Studies 2WoS JournalofSocialSciences 2Scopus Review of Financial Studies 2WoS Risks 2WoS least two papers on CFV (see Table 1), accounting for 35% of the articles published (i.e., 30 articles). Namely, The Journal of Family and Economic Issues and Social Indicators Research are the most productive journals (five and four papers each, respectively), closely followed by The International Journal of Social Economics and The Journal of Consumer Policy (three papers each). Other highly ranked journals, such as The International Journal of Bank Marketing,The Journal of Economics Studies and The Review of Financial Studies have published at least two papers each. 3.3.2 Citation analysis Figure 4shows the number of citations, in absolute terms, automatically extracted from the Scopus database and supplemented by a manual search on Google Scholar. It also includes the number of citations in relative terms calculated as the quotient between the number of citations and the difference between the year 2022 and the year of publication. The total number of citations follows the same pattern as the number of studies published each year (Figure 2), that is, the higher the number of publications, the higher the number of citations. However, publications from 2011, 2016, and 2021 stand out for their high number of citations in relative terms. While the publications from 2011 to 2016 have analyzed the financial aftermath of the 2007–2008 financial crisis, those from 2021 have been contextualized in the COVID-19 crisis and its destabilizing consequences. Although traditional citation dynamics has acknowledged that papers take roughly 2–3 years after publication to reach their peak in citations (Wang, 2013), the relative ones obtained by 2021’s publications have only surpassed by those from 2016. This result confirms a growing scientific interest in this research topic. The 98 sample publications have scored, on average, 18 citations according to Scopus whereas Google Scholar calculates the figure as 51. Analyzing a topic such as CFV, currently under development, leads to a relatively low median number of citations (6.5 in Scopus’s opinion while Google Scholar believes it to be 15.5). In addition, the Scopus database says that a total of nine papers (9% of the total) have not received any citation, compared to Google Scholar at 5%. If we look at the publication dates of these papers, the conclusions are clear; 67% of uncited papers were published 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 9 0 20 40 60 80 100 120 140 160 180 0 200 400 600 800 1000 1200 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Citations/Year Number of citations Scopus Citations Google Scholar Citations C/Y Scopus C/Y Google Scholar FIGURE 4 Temporal analysis of the citations of CFV studies. Note: The ratio for the number of citations by year (C/Y) has been calculated as the quotient between the number of citations obtained according to each database (Scopus or Google Scholar) and the difference between the year 2022 and the year of publication. The citations found correspond to July 28, 2022. [Colour figure can be viewed at wileyonlinelibrary.com] in 2021 and 2022 while in 2020, it was just 22%, claim Scopus;Google Scholar states that these figures were 80% and 20%, respectively. As expected, the scientific impact of the papers according to Google Scholar is greater than what Scopus calculates. We may note that an increase in Scopus’s citations leads to a more than proportional increase in Google Scholar’s. As previously acknowledged, articles published more recently and barely cited should not be considered to have lower scientific quality because if their number of citations is adjusted for the time elapsed since they were published, those from 2020, very scarcely cited in absolute terms, are of greater scientific interest than any from 2007, 2008, or 2009, for instance. The most highly cited papers are those that have analyzed empirical data from the USA, the UK, and Italy, as Figure 5depicts. However, this conclusion should be treated with caution as it could be related to the high number of papers analyzing CFV in these countries. In relative terms, papers focused on the USA and the UK are much more cited than those referring to Southern European countries such as Italy, Spain, and Portugal. Overall, the most cited papers have analysed Northern European countries such as Ireland (in first place), Finland, and the Netherlands. 3.3.3 Articles and authors’ performance Table 2lists the publications with more than 60 citations on Google Scholar. Among them, the most cited article has been by Lusardi et al. (2011), published in Brookings Papers on Economic Activity and cited 628 times until 2022. This is followed by the article by Allgood and Walstad (2016), published in Economic Inquiry and cited 485 times, which has also been the most annually cited article. 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 SARA et al. FIGURE 7 (a) Map of keyword concurrence. Source: Authors’ own elaboration using VOSviewer.(b) Co-citation map. Source: Authors’ own elaboration using VOSviewer.Note: The bibliometric networks include the 15 papers of Table 2that have been published in indexed journals. Some terms have been omitted to make the maps clearer. [Colour figure can be viewed at wileyonlinelibrary.com] et al., 2019). Figure 8depicts the references clustered according to the term they have mostly used to refer to CFV. As can be seen, over-indebtedness has been the term most frequently used in the references considered in this systematic review (34.7% of the references), followed by financial vulnerability (26.5%). What is more, the two phrases have been the most used terms in the latest references, that is, those published in 2021 and 2022 (34.7% and 26.1% of the references, respectively). Similarly, the analysis of keyword concurrence considering all sample publications (Figure 9) has revealed the existence of five interrelated clusters. While four of them have been associated with the general terms used in the search process (i.e., financial vulnerability, financial distress, financial fragility, and debt-burden or over-indebtedness), the fifth cluster has grouped the papers related to COVID-19. The clusters referring to financial distress (the green cluster) and financial vulnerability (the red cluster) seem to be the most influential ones, as they have exhibited a higher 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 17 FIGURE 8 Classification of references according to the main term used to refer to CFV. [Colour figure can be viewed at wileyonlinelibrary.com] number of nodes and more intense relationships. The word “debt” has emerged as the backbone term of the studies analyzing CFV, with this relationship being particularly strong between the terms in the financial distress and over-indebtedness clusters. Thus, consumers’ financial vulnerability has most often been identified with a situation of high debt burden and/or overindebtedness. However, the presence of other scattered and interrelated clusters suggests that to date there is no widely accepted concept in the literature to refer to financial vulnerability. 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 18 SARA et al. FIGURE 9 Map of keyword concurrence. Note: Only those keywords that have been considered by a minimum of three papers have been included. Some terms have been omitted to make the map clearer. [Colour figure can be viewed at wileyonlinelibrary.com] Figure 9also reveals the presence of other keywords that could be relevant in future research on CFV. These refer to financial behavior or situations considered as appropriate proxy variables. Thus, the terms related to financial vulnerability (the red cluster) have been linked to objective aspects of household financial management (e.g., household income or expenditure), while the terms associated with over-indebtedness (the blue cluster) and financial distress (the green cluster) have been mainly related to poverty, financial markets, and financial crises. Authors addressing financial fragility (the purple cluster) have often related it to emergency savings and financial literacy. Although this group has been the least extensive in terms of nodes, it has occupied a central position on the bibliometric map, functioning as a link between the other clusters. Finally, authors mentioning COVID-19 (the yellow cluster) have referred to household income and wealth for the most part. This is a peripheral cluster, under-developed and barely related to the over-indebtedness and financial distress clusters, but with great potential for growth. 4.2 Financial vulnerability: Measures The lack of a common concept may partly explain the large number of CFV measures identified in the literature, which can be classified using four criteria. The first distinguishes between measures based exclusively on household debt-related issues and those that consider other financial aspects (mainly savings and consumption). There is a larger number of papers within the latter category. However, there are only a few references that have used both measures, which are based on indebtedness and on saving and consumption capacity (e.g., Anderloni et al., 2012;Disney& Gathergood, 2011; Yusof et al., 2015). The second classification criterion refers to CFV measures, which can be classified as either objective or subjective: the former are factors external to individuals that may be reported by them 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 19 or obtained from an outside source (e.g., credit history or financial assets); the latter are factors internal to individuals that cannot be assessed independently of them (O’Connor et al., 2019). It is mostly objective measures which have been used in the literature (i.e., in up to 67% of the sample documents), especially in studies identifying CFV with indebtedness. Furthermore, its use has gradually increased, as Figure 10(a) shows. Nevertheless, further consideration of subjective measures enriches the analysis for two main reasons: firstly, in doing so, the extent to which individuals’ own perceptions of their financial circumstances are aligned with objective measures can be checked (O’Connor et al., 2019); secondly, individuals’ financial behavior is affected by the perception of their financial situation (Brüggen et al., 2017). In this respect, Figure 10(b) shows that while in Southern European countries (i.e., Italy, Portugal, and Spain), the authors have mainly opted for objective measures to approximate CFV (around 80%), in Northern countries (i.e., Denmark, Ireland, Finland, and the UK), subjective measures have been more frequent (around 35%). This disparity may be due to cultural differences between countries. The third classification criterion refers to the number of items on which the CFV measure is based. Some studies have used measures constructed from a single item (e.g., the ability to cover living costs or unexpected expenses). By doing it like this, they have understood CFV as a debt or savings/consumption issue, which may be objective or subjective. In contrast, other papers have utilized one measure calculated from several items (e.g., the debt-to-income ratio or net disposable income after debt payment and living expenses) or several CFV measures each based on a single item (Disney & Gathergood, 2011; McCarthy, 2011). Following these paths, the studies have often given a fragmented view of CFV. To date, only Anderloni et al. (2012)andDaudetal.(2019)have combined several items into a CFV index, drawing on nonlinear principal component analysis, in an attempt to construct a measure more in accordance with the multidimensional nature of CFV. The fourth, and perhaps most important, classification criterion concerns whether CFV is understood as a binary or a graded phenomenon. Up to now, most research has operationalized CFV on the assumption that a household either “is or is not” financially fragile rather than assuming that it may experience different degrees of financial vulnerability (O’Connor et al., 2019). This empirical choice has been largely determined by the information available to measure CFV; that is, when it shows whether or not a household is able to raise $2000 to deal with an unforeseen expense in the following month (Lusardi et al., 2011; Friedline & West, 2016; West & Mottola, 2016; Philippas & Avdoulas, 2020), CFV is measured as a binary phenomenon. However, many papers that have had continuous variables (e.g., debt-to-income ratio), have often turned them into binary variables (based on a certain threshold) in order to classify households as financially fragile or the opposite. 4.3 Financial vulnerability: Methods The methodological approach of the sample studies has been dominated by quantitative methods (98% of the publications), whereas about 2% of the reviewed literature could be labelled as “theoretical”. The predominant choice of quantitative methods has been consistent with both the type of research question posed by the papers (i.e., identifying the drivers of CFV) and the data used to answer it. Thus, a large number of studies have used official surveys (85% of the sample studies) carried out by organizations such as the Bank of Italy, the Federal Reserve System,the University of Essex, Eurostat, the FINRA Foundation, the European Central Bank, the German Research Foundation, and the Pakistan Bureau of Statistics (Table 3). 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 20 SARA et al. 1111 22 4 21 10 3 6 4 6 13 9 11 11 11 1 3 1 2 4 1 1 2 2 1 1 1 3 3 0 2 4 6 8 10 12 14 16 18 2002 2005 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Number of papers Objective Subjective Mixed 17 13 11 6 8766667 5445 5 4 3 4 3 243332 3 32 2 2 31 1 1 0 5 10 15 20 Number of papers Objective Subjective Mixed (a) (b) FIGURE 10 Objective vs. subjective measures. (a) Temporal analysis (2002–2022). Source: Authors’ own elaboration. Note: The temporal distribution is due to the year of publication of each paper. (b) Geographical analysis. Source: Authors’ own elaboration. Note: Whenever papers have studied data from more than one country, these investigations have been registered for all the countries analyzed. Comparative analyses of regional areas have been omitted. This figure lists the countries analyzed by a minimum of four investigations. [Colour figure can be viewed at wileyonlinelibrary.com] 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 21 TABLE 3 Official data sources used in two or more publications. Survey Entity Number of articles Survey on Household Income and Wealth (SHIW) Bank of Italy 8 Household Finance Consumption Survey (HFCS) European Central Bank 8 Survey of Consumer Finances (SCF) Federal Reserve System 7 European Union Statistics on Income and Living Condition (EU-SILC) Eurostat 5 British Household Panel Survey (BHPS) University of Essex 4 China Household Finance Survey (CHFS) Southwestern University of Finance and Economics 4 National Financial Capability Study (NFCS) FINRA Foundation 3 Household Integrated Economic Survey (HIES) Pakistan Bureau of Statistics 3 European Community Household Panel (ECHP) Eurostat 3 Vulnerability in Southeast Asia German Research Foundation 2 Survey of Health, Ageing and Retirement in Europe (SHARE) Munich Center for the Economics of Aging 2 One of the advantages of using official data sources is the sheer size of the samples, in order, the largest coming from Peking University in China (n=61,046), the Bank of Italy (n=52,793), and the FINRA Foundation in the United States (n=25,509). The sample size of the studies using customized surveys has been smaller, where, for example, there have been 1647 British households (Kempson, 2002), 1300 Polish households (Kurowski, 2021), and 1006 Zimbabwean households (Chamboko & Chamboko, 2020). In contrast, customized surveys have not only tended to have more up-to-date data, as noted previously, but have also tended to consider more qualitative variables (i.e., capturing the perceptions of individuals) than official surveys have done. Another potential advantage of official surveys is their regularity over time, which makes it possible to take advantage of the longitudinal nature of the data. However, the analyzed papers relying on official surveys have used either a single wave of data (49%) or several waves as crosssectional data (i.e., pooled), which makes it difficult to obtain causal relationships (see, e.g., Baugh and Correia (2022), and Emmons and Noeth (2013)). This review also reveals the extent to which quantitative methods have been used for research on CFV, despite the fact that they are less likely to identify perceptions of financial circumstances. Among the quantitative methods on offer, probit or logit binary methods have been used in 34% of the publications, OLS regressions in 17%, and probit or logit ordered regressions in 10%. Less common have been papers using the ANOVA test or K-means clustering algorithm (5%), those using tobit models (3%), and those applying more recent quantitative techniques related to microsimulation and machine learning (3%). 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 22 SARA et al. 4.4 The drivers of financial vulnerability From the review of the literature, different driving forces of CFV have been identified. Table 4 summarizes these main drivers, as well as the references that have included these variables in their analyses. Most of these factors refer to household or respondent level (i.e., the micro-level of analysis), although a few references have also considered variables connected to neighborhoods (i.e., the mezzo level) and to the country or region in which the household is located (i.e., the macro level). Macro-levelvariablesclassifylarge population groups accordingtospecific characteristics,their scant ability to discriminate being the reason for their anecdotal presence in the sample publications. These are mostly categorical variables relating to a range of geographical areas (countries and regions) in studies that have disaggregated and compared at a spatial level. Evidence points to a reduction in financial vulnerability in the most economically developed areas. Micro-level variables, the ones considered in this systematic review, can be divided into socio-demographic, economic, and to a lesser extent, behavioral variables. Some of the most relevant variables identified in the literature on CFV will hereinafter be presented. Among the socio-demographic drivers, the following are the most common: 1. Age. Most references within this systematic review have included this driver (81% of the analyses2to be precise) and just over 50% of the papers that have found a statistically significant relationship have proven that it is a negative one. This scenario has been seen to a greater extent when age interacts with other demographic characteristics, such as being female or living alone. 2. Gender. This is a relatively common variable, included in 69% of the analyses, even though most of the estimates have failed to obtain statistically significant results. Among those that have found a statistically significant effect, estimates that positively relate financial vulnerability to males (23%) have slightly outnumbered those that have associated it with females (21%). 3. Educational attainment. Overall, the longer spent in education, the lower the probability of becoming financially vulnerable, as corroborated by 46% of the analyses that have included this variable. This relationship has most often been confirmed for higher education, while the effect of basic or intermediate education has, in most cases, not been statistically significant. In this regard, most of the references have analyzed countries belonging to The Organization for Economic Co-operation and Development (OECD), where basic and intermediate education is widespread, and does not act as a differentiating factor. The underlying argument is that higher education leads individuals to better paid and more stable jobs and/or to the development of cognitive skills that allow a better understanding of households’ financial management to be gained. 4. Marital status. Being married or living with a partner reduces the risk of financial vulnerability, especially when it is measured through subjective variables, unlike objective ones; with the latter, it has been found that couples tend to suffer from a higher level of indebtedness (especially if they have children), although their ability to make ends meet is higher than for single people. Marital status, rather than being a stand-alone variable, is often studied as a moderating variable for others, such as household size. 5. Household size. The greater the number of dependent children, the greater the level of indebtedness and financial stress, as noted by 43% of the analyses that have considered this 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 23 TABLE 4 Main drivers of CFV and references that have considered them. Driver Studies Age Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Bertocchi et al. (2023), Betti et al. (2007), Bricker and Thompson (2016), Brown and Taylor (2008), Brown (2015), Bruce et al. (2022), Brunetti et al. (2016), Camões and Vale (2020), Cao-Alvira et al. (2021), Cavalletti et al. (2020), Chamboko and Chamboko (2020), Chen and Jin (2017), Chhatwani and Mishra (2021), Chichaibelu and Waibel (2017), Chichaibelu and Waibel (2018), Chotewattanakul et al. (2019), Cifuentes et al. (2020), Comelli (2021), Coste et al. (2020), Daud et al. (2019), Del-Río and Young (2008), Disney et al. (2008), Disney and Gathergood (2011), Dorsey-Palmateer (2020), Emmons and Noeth (2013), Fasianos et al. (2014), Fernández-López et al. (2022), French and McKillop (2016), Georgarakos et al. (2010), Georgarakos et al. (2014), Giannetti et al. (2014), Giarda (2013), Haq et al. (2018), Kempson (2002), Kim and Wilmarth (2016), Koulischer et al. (2022), Kurowski, 2021,LaCavaandSimon(2005), Lee and Mori (2021), Loke (2016, 2017), Lusardi et al. (2011), Marsellou and Bassiakos (2016), McCarthy (2011), Midões and Seré (2022), McCloud and Dwyer (2011), Mutsonziwa and Fanta (2019), Noerhidajati et al. (2020), Ntsalaze and Ikhide (2016), Philippas and Avdoulas (2020), Ray et al. (2019), Russell et al. (2013), Sánchez-Martínez et al. (2016), Smith et al. (2012), Taylor (2011), Togba (2012), West and Mottola (2016), Wiedemann (2022), Yue et al. (2022), Yusof (2019), Zhou (2022). Age squared Allgood and Walstad (2016), Aristei and Gallo (2016), Baldini et al. (2020), Bertocchi et al. (2023), Brunetti et al. (2016), Chotewattanakul et al. (2019), Cifuentes et al. (2020), Coste et al. (2020), Fasianos et al. (2014), Koulischer et al. (2022), McCarthy (2011), McCloud and Dwyer (2011), Mutsonziwa and Fanta (2019), Sánchez-Martínez et al. (2016), Taylor (2011), Togba (2012), Yue et al. (2022). Education level Ali et al. (2020a), Ali et al. (2020b), Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Bertocchi et al. (2023), Bricker and Thompson (2016), Brown and Taylor (2008), Brown (2015), Bruce et al. (2022), Brunetti et al. (2016), Camões and Vale (2020), Cao-Alvira et al. (2021), Cavalletti et al. (2020), Chamboko and Chamboko (2020), Chen and Jin (2017), Chhatwani and Mishra (2021), Chichaibelu and Waibel (2017), Chichaibelu and Waibel (2018), Cifuentes et al. (2020), Comelli (2021), Coste et al. (2020), Daud et al. (2019), Del-Río and Young (2008), Disney and Gathergood (2011), Disney et al. (2008), Dorsey-Palmateer (2020), Emmons and Noeth (2013), Fasianos et al. (2014), Fernández-López et al. (2022), French and McKillop (2016), Friedline and West (2016), Georgarakos et al. (2010, 2014), Giannetti et al. (2014), Giarda (2013), Haq et al. (2018), Keese (2012), Kim and Wilmarth (2016), Koulischer et al. (2022), Kuhnen and Melzer (2018), Kurowski, 2021,Loke(2016, 2017), Lusardi et al. (2011), McCarthy (2011), McCloud and Dwyer (2011), Midões and Seré (2022), Mussida and Parisi (2021), Mutsonziwa and Fanta (2019), Ntsalaze and Ikhide (2016), Parise and Peijnenburg (2019), Philippas and Avdoulas (2020), Ray et al. (2019), Russell et al. (2013), Sánchez-Martínez et al. (2016), Smith et al. (2012), Šubová et al. (2021), Taylor (2011), Togba (2012), West and Mottola (2016), Wiedemann (2022), Yue et al. (2022), Yusof et al. (2015), Yusof (2019), Zhou (2022). (Continues) 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 24 SARA et al. TABLE 4 (Continued) Driver Studies Employment status Ali et al. (2020a), Ali et al. (2020b), Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Bertocchi et al. (2023), Bricker and Thompson (2016), Brown and Taylor (2008), Brown (2015), Bruce et al. (2022), Brunetti et al. (2016), Camões and Vale (2020), Cao-Alvira et al. (2021), Cavalletti et al. (2020), Chamboko and Chamboko (2020), Chhatwani and Mishra (2021), Chen and Jin (2017), Chichaibelu and Waibel (2017), Chichaibelu and Waibel (2018), Chotewattanakul et al. (2019), Cifuentes et al. (2020), Comelli (2021), Czech and Puszer (2021), Del-Río and Young (2008), Disney et al. (2008), Fasianos et al. (2014), Fernández-López et al. (2022), Friedline and West (2016), Georgarakos et al. (2010, 2014), Giannetti et al. (2014), Giarda (2013), Haq et al. (2018), Jappelli et al. (2013), Keese (2012), Kempson (2002), Kim and Wilmarth (2016), Koulischer et al. (2022), Krumer-Nevo et al. (2017), La Cava and Simon (2005), Lusardi et al. (2011), Marsellou and Bassiakos (2016), McCarthy (2011), McCloud and Dwyer (2011), Midões and Seré (2022), Mussida and Parisi (2021), Mutsonziwa and Fanta (2019), Ntsalaze and Ikhide (2016), Parise and Peijnenburg (2019), Philippas and Avdoulas (2020), Russell et al. (2013), Sánchez-Martínez et al. (2016), Smith et al. (2012), Šubová et al. (2021), Taylor (2011), Togba (2012), West and Mottola (2016), Yusof (2019), Zhou (2022). Ethnicity Allgood and Walstad (2016), Azzopardi et al. (2019), Bertocchi et al. (2023), Bricker and Thompson (2016), Brown and Taylor (2008), Bruce et al. (2022), Chen and Jin (2017), Chhatwani and Mishra (2021), Chichaibelu and Waibel (2018), Coste et al. (2020), Emmons and Noeth (2013), Friedline and West (2016), Georgarakos et al. (2010), Kim and Wilmarth (2016), Loke (2016, 2017), Lusardi et al. (2011), McCloud and Dwyer (2011), Ntsalaze and Ikhide (2016), Philippas and Avdoulas (202020), Smith et al. (2012), Togba (2012), West and Mottola (2016), Wiedemann (2022), Yusof et al. (2015), Yusof (2019). Financial literacy Allgood and Walstad (2016), Anderloni et al. (2012), Cao-Alvira et al. (2021), Chhatwani and Mishra (2021), Chotewattanakul et al. (2019), Daud et al. (2019), Disney and Gathergood (2011), Friedline and West (2016), Giannetti et al. (2014), Kurowski, 2021,Loke(2016, 2017), Lusardi et al. (2011), McCarthy (2011), Mutsonziwa and Fanta (2019), Parise and Peijnenburg (2019), Philippas and Avdoulas (2020), Ray et al. (2019), West and Mottola (2016), Yusof et al. (2015). Gender Ali et al. (2020a), Ali et al. (2020b), Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Baldini et al. (2020), Bertocchi et al. (2023), Brown and Taylor (2008), Brown (2015), Bruce et al. (2022), Brunetti et al. (2016), Cao-Alvira et al. (2021), Chamboko and Chamboko (2020), Chen and Jin (2017), Chhatwani and Mishra (2021), Chichaibelu and Waibel (2017, 2018), Cifuentes et al. (2020), Coste et al. (2020), Daud et al. (2019), Disney et al. (2008), Fasianos et al. (2014), Fernández-López et al. (2022), French and McKillop (2016), Friedline and West (2016), Georgarakos et al. (2014), Giannetti et al. (2014), Giarda (2013), Keese (2012), Kurowski, 2021,LaCava and Simon (2005), Lee and Mori (2021), Loke (2016, 2017), Long (2018), Lusardi et al. (2011), Magli et al. (2020), Marsellou and Bassiakos (2016), McCarthy (2011), McCloud and Dwyer (2011), Midões and Seré (2022), Mussida and Parisi (2021), Mutsonziwa and Fanta (2019), Ntsalaze and Ikhide (2016), Philippas and Avdoulas (2020), Sánchez-Martínez et al. (2016), Smith et al. (2012), Šubová et al. (2021), Togba (2012), West and Mottola (2016), Wiedemann (2022), Yue et al. (2022), Yusof et al. (2015), Yusof (2019), Zhou (2022). (Continues) 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 25 TABLE 4 (Continued) Driver Studies Household size Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Bertocchi et al. (2023), Bricker and Thompson (2016), Brown and Taylor (2008), Brown (2015), Bruce et al. (2022), Brunetti et al. (2016), Camões and Vale (2020), Cao-Alvira et al. (2021), Chichaibelu and Waibel (2017, 2018), Cifuentes et al. (2020), Disney et al. (2008), Dorsey-Palmateer (2020), Emmons and Noeth (2013), Fasianos et al. (2014), Fernández-López et al. (2022), French and McKillop (2016), Friedline and West (2016), Georgarakos et al. (2010, 2014), Giannetti et al. (2014), Giarda (2013), Haq et al. (2018), Keese (2012), Kempson (2002), Koulischer et al. (2022), La Cava and Simon (2005), Loke (2016), Loke (2017), Lusardi et al. (2011), Marsellou and Bassiakos (2016), McCarthy (2011), Midões and Seré (2022), Mussida and Parisi (2021), Mutsonziwa and Fanta (2019), Ray et al. (2019), Russell et al. (2013), Smith et al. (2012), Šubová et al. (2021), Taylor (2011), Togba (2012), West and Mottola (2016), Wiedemann (2022), Yusof et al. (2015), Zhou (2022). Income Ali et al. (2020b), Allgood and Walstad (2016), Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Baugh and Correia (2022), Bertocchi et al. (2023), Betti et al. (2007), Bricker and Thompson (2016), Brown and Taylor (2008), Brunetti et al. (2016), Camões and Vale (2020), Cao-Alvira et al. (2021), Cavalletti et al. (2020), Chamboko and Chamboko (2020), Chhatwani and Mishra (2021), Chen and Jin (2017), Chichaibelu and Waibel (2017, 2018), Chotewattanakul et al. (2019), Comelli (2021), Coste et al. (2020), Daud et al. (2019), Del-Río and Young (2008), Disney et al. (2008), Dorsey-Palmateer (2020), Emmons and Noeth (2013), Fasianos et al. (2014), Fernández-López et al. (2022), French and McKillop (2016), Friedline and West (2016), Georgarakos et al. (2010, 2014), Giannetti et al. (2014), Giarda (2013), Haq et al. (2018), Kempson (2002), Koulischer et al. (2022), Kuhnen and Melzer (2018), Kurowski, 2021, La Cava and Simon (2005), Lee and Mori (2021), Loke (2016, 2017), Long (2018), Lusardi et al. (2011), Magli et al. (2020), McCloud and Dwyer (2011), Marsellou and Bassiakos (2016), McCarthy (2011), Mussida and Parisi (2021), Mutsonziwa and Fanta (2019), Noerhidajati et al. (2020), Ntsalaze and Ikhide (2016), Parise and Peijnenburg (2019), Philippas and Avdoulas (2020), Ray et al. (2019), Russell et al. (2013),Togba (2012), West and Mottola (2016), Wiedemann (2022), Yusof et al. (2015), Yusof (2019). Indebtedness Anderloni et al. (2012), Aristei and Gallo (2016), Azzopardi et al. (2019), Baldini et al. (2020), Bricker and Thompson (2016), Brunetti et al. (2016), Camões and Vale (2020), Chichaibelu and Waibel (2017, 2018), Chotewattanakul et al. (2019), Cifuentes et al. (2020), Coste et al. (2020), Daud et al. (2019), Del-Río and Young (2008), Dorsey-Palmateer (2020), Emmons and Noeth (2013), Fasianos et al. (2014), Fatoki (2015), Georgarakos et al. (2010), Gerth et al. (2021), Giannetti et al. (2014), Giarda (2013), Jappelli et al. (2013), Kurowski, 2021,LaCavaandSimon(2005), McCarthy (2011), Noerhidajati et al. (2020), Philippas and Avdoulas (2020), Russell et al. (2013), Sánchez-Martínez et al. (2016), Smith et al. (2012), Šubová et al. (2021), West and Mottola (2016). (Continues) 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 32 SARA et al. effect (Georgarakos et al., 2014), the influence of religion (Renneboog & Spaenjers, 2012) and the social stigma attached to high indebtedness (Mewse et al., 2010). These aspects, along with others such as unexpected life events, medical problems and political preferences, should be considered in future research as local culture and norms have been shown to strongly influence stigma perceptions about over-indebtedness. It might be beneficial for future research to obtain information (through questionnaires and/or qualitative methods) that allows these types of variables belonging to the sociological and/or cultural domain to be constructed. The incorporation of macroeconomic and sociological/cultural factors opens up a new line of research at the methodological level. Thus, the literature on CFV has mostly focused on a single level of analysis, either at the macroeconomic level, when studying aggregate indicators by country, or at the microeconomic or household level. However, in practice, CFV is likely to be a result of forces at all levels of analysis. Future studies could consider more than one level of analysis; for instance, financial vulnerability can be negatively related to consumers’ financial literacy at the micro level, and positively related to the existence of a large informal credit market (e.g., moneylenders and pawnbrokers) at the country level. Using multilevel modelling in empirical analyses would facilitate the exploration of effects across levels. This methodology would also allow different theoretical perspectives to be applied, thus complementing the view of the CFV phenomenon. Consequently, while the life-cycle hypothesis of consumer saving and consumption behavior looks at the household (or individual) level, the institutional theory points to the influence of norms and regulations at the institutional level (e.g., interest rates and credit ratings for individuals). In addition, the determinants of CFV could be studied separately, distinguishing between different subsamples. To date, most studies have considered global samples of the adult population and only some of them have focused on specific subsamples (Friedline & West, 2016; Philippas & Avdoulas, 2020; Smith et al., 2012). This latter group of studies has mainly used demographic criteria to construct the subsamples, although it would be enriching to apply other criteria. Thus, given that social norms and values can affect stigma perceptions associated with debt (Mewse et al., 2010) and that these aspects are shared by different groups, it would also be worthwhile to explore the degree to which the drivers identified for the general population still hold across religions or generations. With regard to the latter, it has been documented that millennials tend to be highly consumption-oriented (Burnsed & Bickle, 2015), which could affect their financial vulnerability. 6 CONCLUSIONS Although the financial vulnerability of households has been a concern since the mid-1990s (Russell et al., 2013), much more attention has been given to it in the last few decades. This is due to the dire consequences of the 2007–2008 financial crisis for household finances (Azzopardi et al., 2019), the subsequent instability of the financial system and the economy, and most recently in the wake of COVID-19. Over the course of two decades, academic research has attempted to provide a more complete picture of CFV and, particularly, of its determinants. Despite these valuable contributions, CFV is still an emerging and highly fragmented field, yet “financial vulnerability” remains an ambiguous concept for its plurality of terms, which are used interchangeably to refer to it. To date, no systematic reviewonCFV has been published. Therefore,thispaperaimstopromote a greater understanding of the CFV phenomenon and provide a roadmap for future research on 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 33 this challenging field. We have attempted to do so by basing the study on bibliometric analyses and systematic review methodologies, thus creating a hybrid review in accordance with Paul and Criado (2020). When it comes what the results have revealed, we have observed several points. Firstly, research on CFV is relatively recent in the academic literature, but it is attracting the interest of researchers from different fields of knowledge, whose academic papers are increasingly being published in international indexed journals. This interest initially spiked in Western countries following the increase in household indebtedness in the aftermath of the 2007–2008 financial crisis, which exacerbated financial stress for many households. More recently, the interest on the topic has been fueled by concerns about what the consequences of COVID-19 will be for household finances. The effects of high inflation since 2021, especially for resources such as energy, suggest that interest in CFV will increase by the end of this decade. Secondly, most of the current literature refers to economically developed countries. Fundamentally, three large countries/regions can be highlighted: the USA, the UK and the European Union countries with Italy at the forefront. Moreover, in the EU, a significant percentage of publications refer to Southern economies, which have been hardest hit by the Great Recession of the 21st century. Based on these first two conclusions we can infer that the growing interest in CFV is highly context-dependent. Thirdly, the authors’ review shows that CFV is quite an unexplored field, even by the most experienced authors who specialize in the topic of household finances. Authors have tended to publish in teams of two or more, and have not usually collaborated with each other or cited their colleagues’ research in their own. Evidence also reveals that authors have tended to focus on the empirical reality of their home countries. These features confirm therefore that CFV is still a highly fragmented field of research. Fourthly, CFV refers to a wide range of terms (e.g., financial fragility, financial distress, debtburden, over-indebtedness and so on), but there has been neither debate nor agreement about what the “financial vulnerability” means. In this sense, it is essential to establish a definition of this concept that takes into account that any household can be financially vulnerable to some degree and that does not just consider them in financial or indebtedness terms. Such a conceptualization needs to be context-dependent and regularly reviewed (i.e., the symptoms of financial fragility associated with the 2007–2008 financial crisis may not be the same as those associated with COVID-19 or high levels of inflation). Fifthly, this systematic review reveals that there are some important drawbacks to CFV measures, one of which is that a large number of studies have used those constructed from just one item, meaning that they have captured a single dimension of CFV (either debt or savings/consumption, and either an objective or subjective perspective). Even when they have been calculated from two or more items, they have often reported only one dimension of CFV in binary terms (i.e., either a household is financially vulnerable or it is not). It might be beneficial for future research to construct continuous CFV measures that include subjective and objective aspects based on debt and consumption as well as savings-related issues. Sixthly, many studies have used quantitative methods and relatively large samples often obtained from official surveys. However, these data sources have significant shortcomings, including outdated information due to how long it takes for it to be released, lack of data for many less developed economies and shortage of subjective characteristic details that would provide a more comprehensive view of the concept of CFV. Those responsible for these official surveys should strive to ensure that, together with quantitative (objective) information, subjective information be captured by way of questionnaires, and that the information be up-to-date and comparable with 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 34 SARA et al. similar questionnaires. Furthermore, researchers should, whenever possible, take advantage of the longitudinal nature of the data included in such surveys to test causal relationships. Qualitative methods such as interviews, observations or experiments are needed so that the research can advance. Finally, the determinants of CFV have been extensively investigated at the micro level; although the socio-demographic and economic drivers have received wide attention in the literature, the study on behavioral drivers has been rather limited. This, as previously stated, has probably been due to a lack of information on these aspects in the datasets used (mostly official surveys). In the previous section of this paper, many new and promising research paths to enrich the study of these determinants have been proposed. This is undoubtedly an ongoing process. Despite the efforts to embrace much of the research on financial vulnerability at the household level, we recognize that this review could not be completely inclusive bearing in mind the research that is currently available. It is the research method in particular which has been applied in the sample selection that has its limitations. For instance, there may be relevant contributions that have been published in languages other than English. Furthermore, the need to balance scope, scale, and depth has led us to discard studies in which financial vulnerability has “worked” as an independent variable, especially in the fields of health economics and labor economics. In this regard, future review studies should connect these different fields of research on CFV together and analyze how they relate to the results in this study. The drivers of CFV lack a discussion of methodological issues such as potential endogeneity problems that could be addressed with future research. Another limitation is that our findingsmaynotholdinthefutureasCFVissucharapidly growing field of knowledge, especially fueled by the financial stress experienced by households during and after economic shocks. As previously noted, a growing number of studies analyzing the consequences of the COVID-19 crisis or high inflation rates for household finances could be expected in the near future. Despite these limitations, we believe that this paper makes an interesting contribution to the CFV literature. Through our review, we have mapped the different terms, methods, and empirical measures used to proxy for the CFV, while also explaining how socio-demographic and economic households’ characteristics may impact financial vulnerability. Based on a non-negligible stock of publications in the field, we have developed a research agenda suggesting unexplored directions for future research. We also hope that the findings related to the drivers of CFV provide policy makers and practitioners with valuable insights to support households in their quest to adapt their financial behavior in order to improve their financial resilience in the face of unpredictable economic and non-economic shocks. ORCID SaraFernández-López https://orcid.org/0000-0003-2496-4333 MarcosÁlvarez-Espiño https://orcid.org/0000-0002-9514-7544 LucíaRey-Ares https://orcid.org/0000-0002-5165-742X SandraCastro-González https://orcid.org/0000-0002-8206-1776 ENDNOTES 1Four papers published in this time period (viz, Bertocchi et al., 2023; Funke et al., 2022; Noerhidajati et al., 2021; Philippas and Avdoulas, 2020; and Wałęga and Wałęga, 2021) have been analysed as if they had been published in the year prior to their publication, as they were only published online at the time of conducting this SLR. 2As previously explained, in the case of the references that include separate estimations for different countries or regions, all of them have been considered in the review, as in many cases, the effect of the drivers varies depending 14676419, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/joes.12573 by Universidade de Santiago de Compostela, Wiley Online Library on [20/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License SARA et al. 35 on the country or region studied. Therefore, we have accounted for 98 papers in this systematic literature review even though the number of estimations is higher, which, in this section, we refer to as analyses. 3This SLR analyses papers published up to July 2022. ACKNOWLEDGMENTS Marcos Álvarez-Espiño acknowledges financial support from the Spanish Ministry of Universities through the FPU grant (Ayudas para la Formación del Profesorado Universitario) [FPU grant: FPU21/03287]. The authors acknowledge the support of the Consellería de Cultura, Educación e Universidade through the “Axudas para a consolidación e estruturación de unidades de investigacióncompetitivas”. The authors acknowledge the funding of the Universidade de Santiago de Compostela/CISUG for open access charge. 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