literature review on investment analysis

The social return on investment model: a systematic literature review

Meditari Accountancy Research

ISSN : 2049-372X

Article publication date: 10 March 2022

Issue publication date: 19 December 2022

Social return on investment (SROI) has received increasing attention, both academically and professionally, since it was initially developed by the Roberts Enterprise Development Fund in the USA in the mid-1990s. Based on a systematic review of the literature that highlights the potential and limitations related to the academic and professional development of the SROI model, the purpose of this study is to systematize the academic debate and contribute to the future research agenda of blended value accounting.

Design/methodology/approach

Relying on the preferred reporting items for systematic reviews and meta-analyses approach, this study endeavors to provide reliable academic insights into the factors driving the usage of the SROI model and its further development.

A systematic literature review produced a final data set of 284 studies. The results reveal that despite the procedural accuracy characterizing the description of the model, bias-driven methodological implications, availability of resources and sector specificities can influence the type of approach taken by scholars and practitioners.

Research limitations/implications

To dispel the conceptual and practical haze, this study discusses the results found, especially regarding the potential solutions offered to overcome the SROI limitations presented, as well as offers suggestions for future research.

Originality/value

This study aims to fill a gap in the literature and enhance a conceptual debate on the future of accounting when it concerns a blended value proposition.

Social return on investment
Social impact assessment
Literature review
Impact of accounting

Corvo, L. , Pastore, L. , Mastrodascio, M. and Cepiku, D. (2022), "The social return on investment model: a systematic literature review", Meditari Accountancy Research , Vol. 30 No. 7, pp. 49-86. https://doi.org/10.1108/MEDAR-05-2021-1307

Emerald Publishing Limited

Published by Emerald Publishing Limited. This is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

The world has recently witnessed the decline and fragmentation of established bureaucracies in the face of a progressively more complex system involving the public, private and third sectors ( Mintzberg, 2015 ; Osborne, 2006 ). New geographies of value creation, including social and environmental dimensions, have emerged and merged into the economic value defining the concept of blended value ( Emerson, 2003 ). Specifically, while economic value is created when there is a financial return on an investment, social value is produced when people’s lives are improved owing to the successful combination of resources, input and processes. Further, environmental value regards the preservation of natural capital and its continuous regeneration ( Stiglitz et al. , 2009 ).

The combination and integration of the three dimensions of value has increasingly appealed different types of organizations within the private, public and third sectors.

Since the 2000s, the number of methodologies for assessing economic, social and environmental value has been rapidly increasing, innovating the field of policy, management and accounting ( Corvo et al. , 2021a ).

To satisfy the need for assessing the blended value, different approaches have been used over the years, including cost-effectiveness analysis, cost–utility analysis and cost–benefit analysis ( Layard and Glaister, 1994 ). Among these approaches, a methodology known as social return on investment (SROI) was first developed and promoted in the nonprofit sector by the Roberts Enterprise Development Fund (REDF) in 1996 and concurrently in academia as a social impact assessment tool. During the past decades of the 21st century, the nonprofit sector underwent a period of deep change, being challenged by the paradigm of social innovation and social entrepreneurship ( Portales, 2019 ) and being increasingly involved in multistakeholder relations with profit organizations, public administrations and financial actors. Consequently, the public and private funders (such as the REDF Foundation), businesses and national governments have also become interested in assessing the blended value created, through which public interventions are selected, therefore assuring, the maximization of the “value for money” and, concurrently, an efficient and economic allocation of public resources.

The SROI is one of the most well-known social impact methods ( Farr and Cressey, 2019 ) which represents “the nearest to a current industry standard for project or organizational level social impact reporting” ( Nicholls and Emerson, 2015 , p. 21). Therefore, using the SROI methodology, socioeconomic value is measured by quantifying the elements comprising an activity’s social value and subsequently monetizing them ( Emerson, 2003 ; Gair, 2002 ). Although this social impact measurement (SIA) model has been given increasing prominence both academically and professionally, it has been highly questioned in the academic field with regard to its practical and conceptual limitations ( Farr and Cressey, 2019 ; Green, 2019; Maier et al. , 2015 ), which have blurred its efficacy, resulting in fluctuating academic production. Based on a systematic literature review that highlights the potential and limitations related to the academic and professional development of the SROI model, this study aims to systematize the academic debate and contribute to the future research agenda of blended value accounting.

The remainder of this paper is organized as follows. The next section highlights the theoretical roots of SROI and how it has developed throughout the years. After providing an account of the research method implemented to conduct this systematic literature review, the results of the analysis are presented. The last section includes a discussion and concluding remarks on future research paths and the limitations of the study.

Theoretical background

The SROI model was initially developed by the REDF in 1996 as a tool to evaluate capital grant requests made by organizations belonging to the REDF’s philanthropic portfolio. The rationale behind REDF’s willingness to assess their resources’ impact was to evaluate how much people’s lives were improving in reality ( Gair, 2002 ) and simultaneously, broaden the traditional concept of financial return by enclosing “who” the return was linked to and including all the elements contributing to the production of the return.

Since its first application, SROI has been gradually modified and integrated with principles and processes usually applied to economic and financial assessments (e.g. return on investment). It aims to assess an intervention under social, economic and environmental points of view, known as the triple bottom line ( Norman and MacDonald, 2004 ), as each investment has to yield social, economic and environmental returns to create blended value ( Emerson, 2003 ). Moreover, REDF tried to overcome the barriers that nonprofit organizations had experienced for years in assessing nonmonetizable social results, by translating social results into dollars by developing the index of return , which is expressed as the ratio of the estimated value of benefits to the value of the estimated investment required to generate those benefits ( Gair, 2002 ).

The SROI model was developed and structured as a specific blended value-accounting method . First promoted in the nonprofit world and second in academia as an impact assessment tool, it has been defined as a framework for measuring and accounting for the much broader concept of value. It seeks to reduce inequality and environmental degradation and improve well-being by incorporating social, environmental and economic costs and benefits ( Nicholls and Murdock, 2012 ). The first step of the SROI process includes the calculation of the SROI ratio, which is the numerical relationship between monetized benefits and investments ( Faivel et al. , 2012 ) or according to Klemelä, a “pseudo-financial parameter” (2016, p. 387). For instance, a ratio of 4:1 indicates that an investment of $1 produces a social value of $4. However, even though the pursuit of a single digit as a summary of the social value created has been overemphasized among the potential results obtained from the SROI analysis, this model is not intended to be one sided and reductive ( Klemelä, 2016 ). Table 1 provides an executive summary of an SROI report to better present the information analyzed through this methodology.

The SROI analysis can target either a specific program or an entire organization in two different directions: evaluative and forecasting. While the former is conducted retrospectively on outcomes that have already occurred, whose information comes from the organization’s management systems, the latter aims to predict how much social value will be created if the activities meet their intended outcomes with information that should stem from an estimation of the organization’s experience, data from previous years and research based on other people’s experiences ( Mook et al. , 2015 ; Nicholls et al. , 2012 , p. 8).

In pursuing the twofold aim of proving and improving ( Arvidson et al. , 2010 ), SROI has spread worldwide since its first discovery in the USA, with different improvements occurring throughout the years. As part of an ongoing process, further advancements of the SROI model were proposed by the New Economics Foundation (NEF) in London and a new version was applied to social enterprises (SEs) throughout the UK with support from the Hadley Trust. One of the guiding principles that drove the expansion of the SROI model was to extend its usage as much as possible by including organizations with limited resources and integrating SROI with social accounting methods. Consequently, two reports were produced by the NEF as guidelines for the correct implementation of the SROI model ( NEF (New Economic Foundation) , 2007, 2008 ). In the second edition of the guidelines, Measuring Real Value: a DIY guide to Social Return on Investment , NEF decreased the number of procedural steps, standardized them, provided more knowledge through increasing assistance for organizations willing to implement the model and developed reliable impact indicators and financial proxies whose scarce availability is highlighted as one of the crucial barriers in the SROI analysis ( Higham et al. , 2018 ; Lophongpanit et al. , 2019 ; Murzaliyeva et al. , 2018 ; Ribeiro et al. , 2018 ; Willis et al. , 2018 ) leading to what Cooney defines as the “illusion of precision” (2017). Consequently, the choice of financial proxies becomes entirely subjective, compromising the reliability of the model ( Gibbon and Dey, 2011 ; Lingane and Olsen, 2004 ; Mook et al. , 2015 ).

In 2008, owing to the continuing growth of interest in SIA, a British government-funded program, conducted by the consortium of the SROI network, comprising the NEF, Charities Evaluation Services, the National Council for Voluntary Organizations and New Philanthropy Capital, aimed at measuring social value. Consequently, A Guide to Social Return on Investment was published by the Office of the Third Sector in the Cabinet Office in 2009 ( Nicholls et al. , 2009 ) and a revised version was published three years later ( Nicholls et al. , 2012 ). Furthermore, to support practitioners in the implementation of the SROI model, the Social Impact Analysis Association was established in the UK in 2011, and four years later, it merged with the SROI Network in a new organization known as Social Value International (SVI), which represented the governing body of the UK national networks. SVI developed a principle-based approach comprising seven principles of social value ( The SROI Network, 2015 ): involve stakeholders, understand change, do not overclaim, only include what is material, value what matters, be transparent and verify the result . The implementation of these guiding principles requires the undertaking of six stages: establishing scope and identifying stakeholders, mapping outcomes, evidencing outcomes and then availability, establishing impact, calculating the SROI and reporting and embedding ( Nicholls et al. , 2012 ). More specifically, SROI represents “a special case of implementation of these principles where valuation of the outcomes uses financial proxies to monetize outcomes” ( Nicholls, 2017 , p. 128). Therefore, SROI cannot be simplified by showing a single digit that is not able to fully explain how much value has been created, as SROI is “a story about change” ( Nicholls et al. , 2012 , p. 8).

Although the first application of the SROI model was registered in the USA and later in the UK, the model has not remained geographically confined to them. In fact, SROI has become an international product applied by a wide range of organizations in Europe and Asia. Interestingly, in addition to the surge of practical guidelines and handbooks produced by organizations and practitioners over the past 30 years, skepticism seems to dominate the academic field regarding the practical implications of SROI implementation. More specifically, while the SROI’s benefits have been widely explained ( Arvidson and Lyon, 2014 ), its limitations have received less attention, resulting in vague academic explanations. Therefore, starting from the analysis of the limitations hindering both the academic and professional development of the SROI model, this study aims to explore the levers that could maximize the potential of this measurement system.

The systematic literature review has some distinctive conceptual and methodological peculiarities. For instance, in addition to the review provided by Manetti (2014) on the usage of SROI by SEs, the review by Maier et al. (2015) concerning the merits and limitations of SROI as a method for evaluation research and the review conducted by Watson and Whitley (2017) on the different social impact assessment methods.[AQ3]

Several aspects differentiate this review from the others. First, although scholars have attempted to review the literature on SROI, none of the reviews available seem to provide a comprehensive approach by investigating the usage of SROI in all sectors. The majority of reviews have primarily focused on one sector, such as health ( Banke-Thomas et al. , 2015 ; Hutchinson et al. , 2019 ), environment ( Higham et al. , 2018 ) and sports ( Gosselin et al. , 2020 ; Keane et al. , 2019 ). Second, this review endeavors to retrieve as many academic contributions as possible by analyzing four databases: Web of Science , Scopus, EBSCO and JSTOR . Third, additional aspects highlighted in this review include the academic production distributed geographically and over the years and, more importantly, the approach taken (cautionary vs optimistic) by the authors toward implementing the SROI. The results presented stem from the data crossing of the different aspects of the SROI analyzed throughout the study.

The next section details the methodological model used to carry out this systematic literature review.

Methodological approach

The understanding of a specific topic requires a review of the existing literature concerning what has already been researched to reveal its main drivers, issues and future research paths (Hart, 1998). Particularly, Hart claims that a literature review involves:

[…] the selection on the topic, which contains information, ideas, data and evidence written from a particular standpoint to fulfill certain aims or express certain views on the nature of the topic (1998, p. 13; 2018).

However, this selection process requires qualitative researchers to implement “methodological pluralism and diversity rather than unitary or simplistic perspectives” ( Parker, 2005 , 2014 , p. 14). Therefore, to systematically review the literature within a specific stream of research, different strategies must be used to detect appropriate studies to guarantee the analysis of only the significant literature ( Wirtz and Daiser, 2018 ).

The first step of this process involves the selection of some common criteria to classify all available studies. This requires denominating and aggregating specific peculiarities to particular concepts and blending them into clusters, thus providing a clear picture of the research field investigated. However, loss of information is unavoidable, but it is compensated by the transparency of the knowledge acquired ( Webster and Watson, 2002 ).

Several scholars such as Braadbaart and Yusnandarshah (2008) and Arduini and Zanfei (2014) claim that confining the analysis to scientific journals as the main sources of current research represents a valid approach to retrieve high-quality literature ( Norris and Lloyd, 2006 ; Webster and Watson, 2002 ). However, as SROI analysis was originally revealed through a report edited by a private charitable foundation (REDF), both books and grey literature were analyzed in the theoretical framework of this review. This secondary source of knowledge led to the discovery of further publications in academic journals, which were included in the final results. These contributions provided strong inputs to the academic debate regarding the nature of the limitations in the usage of the SROI model and what drives these restrictions to explore the levers that would maximize the potential of this model. However, only academic articles were included in the final data set. Books and grey literature were merely used to understand the foundations of the SROI model.

Grant and Booth (2009) identified 14 different methods for reviewing the literature on a specific topic. However, although scholars have shown compelling examples of reviews such as structured literature review ( Cuozzo et al. , 2017 ), scoping review ( Ashton et al. , 2020 ) and critical review ( Keane et al. , 2019 ), this study implements a systematic literature review as it “seeks to systematically search for, appraise and synthesis research evidence, often adhering to guidelines on the conduct of a review” ( Grant and Booth, 2009 , p. 94). Additionally, as Gosselin et al. (2020) performed in their systematic literature review on SROI in sports, this study also follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement ( Moher et al. , 2009 ). More specifically, this method has been selected for its extensive replicability and transparency compared to traditional literature reviews, as it implies that scholars should follow simple and explicit steps to identify all potentially appropriate articles ( Moher et al. , 2009 ).

Figure 1 shows the flowchart of the PRISMA model followed for this review.

The first step involved the identification of all records related to the research topic investigated (SROI). This process required the analysis of the four databases: Web of Science, Scopus, JSTOR and EBSCO , which comprise several additional databases such as GreenFILE, Academic Search Complete, Business Source Complete and EconLit. This search strategy allowed the examination of a broad spectrum of peer-reviewed academic publications. For each database, either the term “Social Return on Investment” or simply “SROI” had to be included in the search fields such as article title, abstract, keywords, keywords plus and full text. The time frame chosen was 30 years (1990–2020). This initial search led to an unfiltered total batch of 589 peer-reviewed academic English-language publications (114 in EBSCO , 179 in JSTOR , 167 in Web of Science and 129 in Scopus ).

The second step involved screening for the relevance of the initial 589 articles included in the data set. All titles and abstracts were fully screened, 174 duplicates were removed from the data set and in case of ambiguity, papers were fully read by all the authors, leading to the exclusion of 63 additional studies and a data set of 352 articles. Considering the high number of scientific journals currently available, it is likely that we may have missed some significant publications. However, all the authors of this study feel confident that the developed set of articles should represent a concrete basis for this and further analysis.

field – articles ought to regard the usage of SROI in any sector;

topic – studies should contain either the words “Social return on Investment” or “SROI” in any part of the article, such as title, abstract, keyword or full text;

study design – only peer-reviewed academic journals were sought to fully acknowledge the validity of our findings;

year of publication – only studies published between January 1990 and December 2020 were included in the data set;

language – only papers written in English were included to avoid conceptual misunderstandings; and

publication status – only international peer-reviewed academic journal articles were included in the analysis.

Further, testing articles for eligibility resulted in 77 papers that did not meet the eligibility criteria. The full text of each article was carefully read by each author, which allowed the addition of nine papers, reaching a final number of 284 studies included in the data set analyzed ( Appendix ).

Although there is some debate in the social sciences concerning whether to elucidate the differences between applied and theoretical studies ( Ritchie et al. , 2013 ), a clarification of this ought to be made at this point. While theoretical researchers mainly aim at testing, generating or improving thinking within a particular discipline to “generate new theories or test existing theories” ( Patton, 2002 , p. 215), “applied research is concerned with using the knowledge acquired through research to contribute directly to the understanding or resolution of a contemporary issue” ( Ritchie et al. , 2013 , p. 24). In this research context, applied papers refer to studies that mainly focus on a practical calculation of the SROI to verify its suitability in different contexts. However, theoretical studies refer to the deepening of the understanding of either the reliability of the SROI calculation through the application of the SROI algorithm or, more generally, the validity of the results found in comparison with other SIAs. Both types of studies have contributed to strengthening the SROI theoretical foundations.

Legitimation function : The reliability of the SROI stems from its capacity to legitimize the existence and functioning of the projects and organizations primarily aimed at generating social and environmental value ( Klemelä, 2016 ; Luke et al. , 2013 ; Maier et al. , 2015 ; Manetti, 2014 ).

Strengthening function : This mainly refers to either an organization, association or a project that is financially sustainable. In this case, the SROI aims to highlight to the stakeholders the other dimensions of the blended value created, the environmental and/or social, respectively ( Daems et al. , 2014 ; Watson, 2018 ).

Managerial and communicative function : SROI can increase the internal managerial awareness and the external reputation about the relevance of the impact yielded beyond the generic financial terms on which activities, projects and, more generally, organizations are usually assessed ( Hervieux and Voltan, 2019 ).

In the next section, the results obtained from the analysis of the 284 papers included in the final data set are summarized and presented.

To assess the academic production expressed in terms of the number of articles published on SROI, Figure 2 shows the temporal academic production that occurred between 1996 (SROI’s first development by REDF) and 2020.

Even though SROI was first introduced in 1996, interestingly, for more than a decade (12 years) the number of articles related to SROI was no more than three per year, with no academic studies published between 1996 and 2001. However, it is worth noting that between 1996 and 2008, the methodological foundations of the SROI were laid through the publication of the most important reports ( Chun et al. , 2001 ; Emerson et al. , 2000 ; Emerson and Twersky, 1996 ; Jones and Tuan, 2000 ). Moreover, from 2009 to 2013, the number of articles published per year increased slightly, reaching a peak of 48 articles in 2015 and 2020. Unsurprisingly, from 2017 to 2020, academia produced no fewer than 30 articles per year, showing a constantly increasing interest in SROI.

This section presents the main results obtained from the data extraction performed on the 284 articles included in the final data set.

Table 2 shows three main aspects characterizing the evolution of SROI, the type of paper (theoretical vs applied), the approach taken by the author/s (cautionary vs optimistic) and the country of origin. In addition, the transversal approach has been taken to assess the validity and relevance of the results found.

The optimistic approach toward SROI prevails substantially. In fact, 226 out of 284 articles (80%) showed an encouraging attitude toward the results obtained from the application of SROI, of which 123 were from Anglo Saxon countries. Further, the cautionary approach prevails among theoretical studies (34 out of 58) and is mainly published in the UK (29%).

The theoretical approach toward SROI is manifested in less than one-third of the data set (79 out of 284 = 28%), of which almost half (43%) manifested a cautionary vision of SROI. The main drawbacks highlighted by the cautionary scholars refer to the “reductionism” critique, that is the excessive attempt to single out a number able to express the complexity of the impact assessment process ( Moody et al. , 2015 ; Mook et al. , 2015 ). A more philosophical rather than methodological approach is offered by Maier et al. (2015) , who mainly criticize the SROI approach regarding its usefulness, comparability and, more interestingly, its position and role within the branch of social sciences. In this regard, Farr and Farr and Cressey claim that “SROI reduces social complexity to an economic ratio” ( 2019 , p. 240). Furthermore, several scholars firmly reprimand the alleged and unrealistic ability of reducing social complexity based on a positivist and linear approach that can explain unexpected social events ( Maier et al. , 2015 ; Mook et al. , 2015 ; Neil, 2014). Other critiques are based on the effective usage of the results offered by the SROI. For instance, according to Neil, “consultants evaluating social value and impact have limited control over how SROI reports are subsequently used by their client, commissioners or service managers” (2014, p. 157).

Overall, the theoretical doubts regarding the implementation of SROI seem to be weakened by the 205 applied papers included in the data set categorized into legitimation, strengthening, managerial and communicative functions. Applied papers outnumbered theoretical studies considering that, through the practical applications of the SROI, the latter can be further advanced, allowing researchers to lay stronger theoretical foundations for social impact assessment.

In the sample analyzed, the legitimation and strengthening functions are predominant over the managerial and communicative ones. In particular, the legitimation function has proved to be relevant in case of financial difficulties encountered by the target organization where the SROI is still able to identify the social value created in the medium and long term. The SROI methodology shows a clear advantage, either for the donor or the target organization, in maintaining the provision of services because of its potential social and economic value. The health sector is particularly prone to highlight the relevance of this SROI function ( Bhaumik et al. , 2013 ; Laing and Moules, 2017 ; Millar and Hall, 2013 ). Regarding the 91 papers demonstrating the legitimation function of SROI, 60 studies were published in Anglo Saxon countries, of which 22 were published in the UK ( Iafrati, 2015 ; Parks and Brownlee, 2014 ), 16 in Canada ( Akingbola et al. , 2015 ; Shi et al. , 2019 ) and 13 in the USA ( Kousky et al. , 2019 ; Ramon et al. , 2018 ). Interestingly, the sector legitimized by the SROI is mainly the welfare sector, particularly health, with 31 studies ( Aguilar-Agudo et al. , 2019 ; Goudet et al. , 2018 ; Searles et al. , 2016 ), social inclusion counting 13 studies ( Hoffmann et al. , 2014 ; Mihalopoulos et al. , 2020 ) and other sectors such as well-being, education and justice (Akingbola et al. , 2015; Lund, 2015; Ravulo et al. , 2020 ).

The strengthening function requires the direct and active involvement of the stakeholders most affected by the impact created through the activities carried out. The objective of actively involving stakeholders in the SROI analysis is twofold: first, understanding what is important and therefore including it in the SROI analysis ( Nicholls et al. , 2012 ), and second, consolidating the relationship among stakeholders who are usually not part of the assessment process. A total of 68 applied papers were identified. There is a lack of a predominantly investigated sector; however, health, environmental, infrastructure and rural development are the primary sectors of interest ( Barber and Jackson, 2017 ; Vargas et al. , 2019 ; Venezia and Pizzutilo, 2018 ). Even for the strengthening function of SROI, Anglo Saxon countries dominate worldwide in terms of publication, particularly the UK ( Arvidson et al. , 2014 ; Everard et al. , 2017 ).

Lastly, the managerial and communicative function increases awareness of the multiple dimensions of value and enables the innovation of the organizational communication strategy for the internal environment, linking and motivating the internal stakeholders toward socially relevant goal achievements, and the external environment, shaping a more effective accountability for the engagement of the most crucial stakeholders (donors, funders, users, media partners).This function focuses on the communicative power of the SROI regarding the multiple dimensions of the value created through either an organization or approach. This was shown in 46 out of the 226 applied studies included in the data set. Almost half (22 papers) were focused on Anglo Saxon countries and 13 on European countries ( Hirunsalee et al. , 2013 ; Meza-Bolaños et al. , 2019 ; Stuermer and Dapp, 2016 ; Tulla et al. , 2020 ). It has been demonstrated in a variety of sectors, particularly in the health and finance sectors ( Murzaliyeva et al. , 2018 ; Moral Torres et al. , 2020 ). It is also noticeable in culture ( Refki et al. , 2020 ; Viganó and Lombardo, 2019 ; Whelan, 2015), sports ( Davies et al. , 2020 ; Osokin et al. , 2018 ) and health sectors ( Laing and Moules, 2017 ; Moral Torres et al. , 2020 ).

However, regardless of the sectors and functions exerted through the SROI, emerging countries seemed to have contributed to the advancement of the SROI assessment process to a minor extent, as the majority of studies, both theoretical and applied, stem from an Anglo-Saxon context.

Social return on investment limitations and levers for improvement

The ability of SROI to validly convey reliable social and environmental impact does not go unchallenged ( Klemelä, 2016 ), leading to an increase in doubts both within the academia ( Arvidson and Lyon, 2014 ; Luke et al. , 2013 ; Pathak and Dattani, 2014 ) and among practitioners ( Fujiwara, 2015 ; Higham et al. , 2018 ). Consequently, a deeper investigation of these barriers is required and a better understanding of ways to overcome them is necessary to further enhance this methodology and render it more reliable ( Arvidson and Lyon, 2014 ; Banke-Thomas et al. , 2015 ; Nicholls and Murdock, 2012 ).

Two sets of limitations were identified in the order of recurrence within the data set analyzed.

The first set of limitations is related to the lack of standardization, which represents the most recurrent limitations found in the implementation of the SROI methodology. The high subjectivity characterizing the choice of financial proxies ( Goudet et al. , 2018 ; Walk et al. , 2015 ) especially regarding the “soft outcomes” [ 1 ] such as well-being and self-esteem ( Willis et al. , 2018 ), deadweight and displacement, attribution and drop-off percentage ( Farr and Cressey, 2019 ) can make the entire process extremely subjective and, therefore, hardly comparable across similar organizations, programs and interventions ( Banke-Thomas et al. , 2015 ; Jones et al. , 2020a , 2020b ; King, 2014 ; Mook et al. , 2015 ; Vaileanu, 2017 ).

Studies have shown that different individuals working on the same data can produce different final SROI ratios ( Cooney and Lynch-Cerullo, 2014 ). Although the procedural implementation of SROI requires the running of a sensitivity analysis to assess the robustness of the SROI ratio by testing the effect of certain variables, such as the magnitude of discounting factors and the valuation given to important outcomes ( Weston et al. , 2015 ), subjectivity is still a factor that blurs the clarity of this method. However, according to Gosselin et al. (2020) , robust financial valuation can only partly improve the robustness of the SROI if outcomes are not properly measured and if deadweight is not robustly established using a proper study design. The lack of standardization still remains the main obstacle to the implementation of the SROI model, and it is principally because of the absence of benchmark data, metrics and social performance indicators, which inevitably leads to a condition of “information asymmetry” ( Hazenberg et al. , 2015 ) and limited comparability ( Hervieux and Voltan, 2019 ; Maier et al. , 2015 ).

The second set of limitations refers to the lack of resources . SROI analysis is a costly and time-consuming process that requires highly skilled human resources ( Hummels, 2012 ; Millar and Hall, 2013 ; Watson and Whitley, 2017 ). Carrying out a comprehensive SROI analysis involves considerable cost implications in terms of the resources required for training and labor ( Wood and Leighton, 2010 ). Moreover, the lack of financial and human resources is strictly linked to the lack of standardization of the SROI implementation process ( Jackson and McManus, 2019 ; Yates and Marra, 2017 ), which makes the entire process even more resource consuming and subjective ( Rangan et al. , 2011 ; Serrano-Cinca and Gutiérrez-Nieto, 2013 ) than it would usually be in the case of an abundance of resources. Furthermore, the availability of resources is usually directly proportional to the dimensions of the organization or program. Therefore, in the case of small organizations or programs with no standardized procedure to follow, the implementation of the SROI analysis can lead to an incomplete or untruthful analysis of the social impact generated by the activities carried out.

With regard to this first group of limitations, different solutions are offered as levers for improvement. For instance, Chandoevwit et al. (2014) recommend that the value of outcome indicators should be nationally and internationally collected in a systematic way and, therefore, available for any SROI analysis. Nicholls (2017) adds that a clearer normative approach would be beneficial for SROI analysis. Regarding comparability, Bosco et al. (2019) claim that the SROI methodology is highly sensitive to the context in which it is implemented, therefore the findings are difficult to generalize, while Maier et al. affirm that “a SROI analysis that is objective, in the sense of avoiding value judgments, is impossible” (2015, p. 1819). Klemelä (2016) proposed a different perspective to tackle the subjectivity issue, claiming that SROI should be considered as a multidimensional, discursive, legitimating means to manage organizations and prove that they are able to do valuable things ( Nicholls et al. , 2012 ). Therefore, the subjectivity obstacle can be reduced, but not completely removed.

Referring to the second set of limitations, among the academic solutions provided, Jackson and McManus (2019) recommend the provision of training courses for organizations’ stakeholders and SROI analysts to overcome the lack of skills and, consequently, to maximize the potential of the SROI model. On the one hand, the route of acquiring knowledge should be followed by organizations willing to be decisive about overcoming the obstacles caused by a lack of knowledge that characterize the current usage of SROI. On the other hand, the dissemination of knowledge and skills should be more substantially endorsed by government. First, by making more financial resources available, and second, by issuing policies and guidelines that should guide organizations interested in assessing the social value of their activities, without owning the necessary skills and resources, to productively implement the SROI method, therefore overcoming the “practical and ideological barriers” ( Millar and Hall, 2013 , p. 923) that have led so far to a low uptake of SROI as a performance measurement tool ( Arvidson and Lyon, 2014 ).

Discussion and conclusions

The evolution of standardized bureaucracies into a more convoluted system involving the public, private and third sectors ( Mintzberg, 2015 ; Osborne, 2006 ) led to the creation and interest of multiple dimensions of value: the economic, social and environmental ( Emerson, 2003 ). These new dimensions were integrated into what was defined as a blended value ( Manetti, 2014 ; Nicholls, 2009 ). Therefore, the interest in tracking, assessing and maximizing the blended value produced either by an organization or a program implicated the development of several types of measurements. Among these methodological approaches, the SROI, first used in the nonprofit sector, has progressively overcome its initial nonprofit confinement, and consequently was promoted in academia as a social impact assessment tool. Based on a systematic review of the literature highlighting the potential and limitations related to the academic and professional development of the SROI model, this study aims to systematize the academic debate and contribute to the future research agenda of blended value accounting.

A systematic literature review produced a final data set of 284 studies. The results show that despite the procedural accuracy characterizing the description of the model, bias-driven methodological implications, availability of resources and sector specificities can influence the type of approach taken by scholars and practitioners (optimistic vs cautionary).

Despite its limitations being grouped into two main sets – lack of standardization and lack of resources – this study has endeavored to highlight the benefits of the SROI model. It has been identified as a tool to legitimize ( Klemelä, 2016 ; Luke et al. , 2013 ; Manetti, 2014 ) and strengthen the collaboration and active involvement of organizations’ stakeholders, as they are a crucial part of the SROI process ( Nicholls et al. , 2012 ), and, finally, manage the consciousness regarding the social impact potentially produced by the activities carried out, which may offset the insistence on financial returns ( Hervieux and Voltan, 2019 ).

Specifically, the study has highlighted three key points to problematize the academic debate and design of the future research agenda of accounting regarding a blended valu e proposition. The first point regards the need to overcome dichotomous perspectives between scholars supporting the monetization methods versus those who see it as reductive measurement and, simultaneously, preferring the usage of participatory and narrative methods able to manage complexity. Consequently, the future of SROI seems to be directed toward the integration of methods that simultaneously include monetization and attention paid to the complexity of social and environmental value through qualitative insights. The trade-off between monetization and complexity reduces the potential of both perspectives. If monetization methods might suffer from the problem of reductionism and economism ( Farr and Cressey, 2019 ; Maier et al. , 2015 ; Moody et al. , 2015 ; Mook et al. , 2015 ), the participatory and narrative methods fail to express an instantaneous message to favor an effective representation of the blended value generated. These qualitative methods are usually characterized by less effective narratives compared to those expressed by the SROI ratio. Therefore, the two perspectives, rather than being seen as alternatives, should be interpreted as complementary.

However, this poses a key challenge for the future research agenda related to finding integrable solutions capable of balancing the two functions. On the one hand, managing to preserve the ability of the SROI to produce an instantaneous signal and, simultaneously, enriching this message through qualitative elements without diminishing the complexity characterizing social and environmental issues.

In addition, the results showed two important facts that should be considered in future research. First, applied studies outnumbered the theoretical counterparts which is considering that SROI was first developed and promoted in the nonprofit sector by the REDF as a tool to apply the blended value proposition ( Emerson, 2003 ). Therefore, the SROI soon became the applicative principle of the blended value theory, which, to be validated, needs to be pragmatically implemented and tested in different contexts, justifying the higher number of applied studies and reports in comparison with theoretical studies. Second, the results show the prevalence of studies coming from Anglo-American contexts and a clear minority of studies stemming from developing countries.

As Qian and colleagues stated, “extant social and environmental accounting (SEA) research in the developing countries context is limited” (2021 , p. 22). Therefore, more research from previously underexplored contexts including that of emerging countries should be conducted to offer valuable insights from a perspective that is different from the one offered by wealthy economies where the SROI was originally developed.

The second point focuses on the extension of blended value studies by scaling the units of analysis, typically projects and/or organizations, toward value chains and ecosystems. Regarding the use of SROI as a signal, it is crucial not only to focus on the technical issue of calculation but also rather to direct the attention toward the relationships that this signal is able to represent. The key issue is related to the role that the SROI might play within value chains and multistakeholder ecosystems as a common signal that works as “mediating technology” ( Puyou and Quattrone, 2019 ) among the different actors. SROI has increasingly been used within impact investing and impact finance contexts where the presence of different types of stakeholders (public administration, profit companies, investment funds, nonprofit organizations) is more frequent ( Addy et al. , 2019 ). In these contexts, therefore, SROI plays a role of mediating technology, focusing on diverse issues, and provides a blended value evaluation of the ecosystem that ensures the sharing of information pertaining to the interests and perspectives of the different actors involved. In the future research agenda, the mediating role of SROI should gain more attention, especially in complex ecosystems characterized by a wide range of stakeholders with conflicting views and interests ( Dentoni et al. , 2016 ).

The third point concerns the evolution of the accounting systems. Although the current split between financial and nonfinancial disclosure is widely criticized in the literature ( Erkens et al. , 2015 ; Maas and Sampers, 2020 ), practitioners continue to encounter severe obstacles to the adoption of integrated social, environmental and economic accounting reporting systems. In this regard, the lack of standardization has been identified as one of the most hindering elements for tools such as SROI to capture and assess the blended value ( Banke-Thomas et al. , 2015 ; Goudet et al. , 2018 ; Jones et al. , 2020a , 2020b ; Vaileanu, 2017 ; Walk et al. , 2015 ). More specifically, different scholars claim that even though economic, social and environmental values currently deserve equal attention at both academic and practical levels, a fully reliable blended accounting and reporting system is currently unavailable ( Nicholls, 2018 ). Therefore, the main challenge is to accelerate the sharing process of metrics and databases to feed a blended accounting system with the same type of information regarding the economic, social and environmental values generated by organizations, activities and programs. A few steps have been taken in this direction at the international level, such as the Data Stewards Network [ 2 ] initiative promoted by the GovLab and the Rockefeller Foundation that aims at opening the public and private data sets; the International Network for Data on Impact and Government Outcomes [ 3 ] based at the Go Lab of the Oxford University that promotes the sharing of measurements especially related to impact finance advocating for a disclosure of data on outcome, indicators and financial proxies; and finally the Impact Weighted Accounts [ 4 ] research project promoted by the Harvard Business School that tackles the challenge of creating a new accounting system. However, sporadic examples and initiatives do not fully count to have reliable integrated accounting and reporting systems and, therefore, much of the efforts have to be made in this direction to allow both academics and practitioners to gain trust in social, environmental and economic values that define the new era of accounting.

In conclusion, taking into consideration the analysis of the 284 studies, there is a clear sign of a willingness to improve this methodology and extend it as much as possible, contributing to the research agenda designed above and advancing knowledge in those three directions. Indeed, the overall results showed that academia has definitely been moving forward in the development of SROI, confirming what Arvidson and Lyon stated in their study, claiming that SROI “aims to both prove and improve” (2014, p. 5).

This study has some limitations. From a methodological point of view, this review relies only on peer-reviewed articles; therefore, future research may involve additional data sources, especially considering the sheer volume of grey literature now available. Second, generalization of the results should be done cautiously, especially with regard to the solutions found in the literature because, regardless of the type of limitations found in this literature analysis, the results of both the benefits and limitations should be contextualized.

Therefore, future research is required to elucidate the dynamics that drive these obstacles toward a more holistic application of the SROI methodology.

Preferred reporting items for systematic review and meta-analyses flowchart

Temporal academic evolution of SROI

SROI executive summary

Main sections	Items (described through quali-quantitative information)
1 Scope and stakeholders	1.1 A description of the organization: its activities and values (if relevant), the activity under analysis, including location, main customers or beneficiaries
	1.2 An explanation of SROI, whether it is forecasted or evaluative, the purpose and scope of the analysis
	1.3 A discussion of stakeholders, i.e. types and numbers
	1.4 A description of how stakeholders were involved and the numbers that were consulted
2 Outcomes and evidence	2.1 A description of the theory of change for each stakeholder, i.e. how inputs lead to outputs and outcome (presented in a table as well as in narrative form)
	2.2 Description of the indicators and data sources used for each outcome
	2.3 Quantity of inputs, outputs and outcomes achieved for each stakeholder group
	2.4 Analysis of the investment required for the activity
	2.5 The length of time over which the outcome is expected to last or against which the outcome will be attributed to the activity
	2.6 Description of the financial proxy to be used for each outcome, together with the source of the information for each proxy and a discussion of the proxies chosen
3 Impact	3.1 Description of the other areas or groups against which deadweight is estimated. Deadweight is defined as the measure of the amount of outcome that would have occurred even if the activity had not taken place
	3.2 Description of the other organisations or people to which outcomes have been attributed. The attribution is the assessment of how much of the outcome comes from the contribution of other organisations or people
	3.3 Description of the basis for any estimates of attribution and deadweight, flagging up any data gaps and areas for improvement
	3.4 Description of displacement, if included. It indicates the displacement given by new negative elements that overlap with preexisting positive elements. It is also called the “substitution effect,” because it occurs when the externalities determined by an intervention have negative effects not foreseen by the activity
	3.5 Description of the total impact, including the drop off. The drop off indicates the reduction of the impact across time. The calculation of the impact computes the net present value of each outcome. It is important to consider in the formula (the discount rate, usually set at 3.5%). Having calculated the present value of the benefits, the value of the inputs (the investment) has to be deducted to arrive at the net present value (NPV). NPV = [present value of benefits] [value of investments]
4 Social return calculation	4.1 Calculation of the social return, showing sources of information, including a description of the type or types of social return calculation used. =
	4.2 Description of the sensitivity analysis carried out and why. The sensitivity analysis is a process through which the calculation is tested by analyzing which assumptions have the greatest effect on the model
	4.3 Description of the changes to quantities as a result of the sensitivity analysis
	4.4 Comparison of the social return in the sensitivity analysis
5 Audit trail	5.1 Stakeholders identified but not included and rationale for this
	5.2 Outcomes identified but not included, for each stakeholder, and the rationale
	5.3 Any financial proxies not included and the rationale

Own elaboration from The Guide to Social Return on Investment (2015)

	Type of paper
Author/s’ Approach	Applied
Author/s’ Approach	Legitimation function	Managerial and communicative function	Strengthening function	Theoretical	Total
Optimistic	79*	39*	63*	45*	226*
Cautionary	12**	7**	5**	34**	58**
Total	91	46	68	79	284

Country	Applied
Country	Legitimation function	Managerial and communicative function	Strengthening function	Theoretical	Total
	6*		3* 1**	1*	11
	9*	4*	8*		21
	12* 3**	13*	13*	7* 5**	53
	51* 9**	16* 6**	34* 4**	22* 8**	150
	1*	6* 1**	5*	15* 21**	49
Total	91	46	68	79	284

*Optimistic and **Cautionary

Source: Authors’ own elaboration

From Go Lab Glossary, University of Oxford: Soft outcomes depend on measurement which is more subjective and less quantifiable available at: https://golab.bsg.ox.ac.uk/knowledge-bank/glossary/#s

https://medium.com/data-stewards-network

https://golab.bsg.ox.ac.uk/knowledge-bank/indigo/

https://www.hbs.edu/impact-weighted-accounts/Pages/default.aspx

Studies included in the data set

Addy , C. , Chorengel , M. , Collins , M. and Etzel , M. ( 2019 ), “ Calculating the value of impact investing, an evidence-based way to estimate social and environmental returns, business and society ”, Harvard Business Review , Vol. 97 No. 1 , pp. 102 - 109 .

Aguilar-Agudo , A. , Herruzo-Cabrera , J. , Ochoa-Sepulveda , J. and Pino-Osuna , M.J. ( 2019 ), “Social return of investment (SROI) in evidence-based treatments”, Clínica y Salud [online] , Vol. 30 No. 1 , pp. 13 - 20 , doi: 10.5093/clysa2019a4 , Epub 02-Nov-2020. ISSN 21740550 .

Akingbola , K. , Phaetthayanan , S. and Brown , J. ( 2015 ), “A-way express courier”, Nonprofit Management and Leadership , Vol. 26 , pp. 173 - 188 , doi: 10.1002/nml.21188 .

Arduini , D. and Zanfei , A. ( 2014 ), “ An overview of scholarly research on public e-services? A meta-analysis of the literature ”, Telecommunications Policy , Vol. 38 Nos 5/6 , pp. 476 - 495 .

Arvidson , M. and Lyon , F. ( 2014 ), “ Social impact measurement and non-profit organisations: compliance, resistance, and promotion ”, Voluntas: International Journal of Voluntary and Nonprofit Organizations , Vol. 25 No. 4 , pp. 869 - 886 .

Arvidson , M. , Lyon , F. and McKay , S. ( 2010 ), The Ambitions and Challenges of SROI , TSRC , Birmingham .

Arvidson , M. , Battye , F. and Salisbury , D. ( 2014 ), “The social return on investment in community befriending”, International Journal of Public Sector Management.

Ashton , K. , Schröder-Bäck , P. , Clemens , T. , Dyakova , M. , Stielke , A. and Bellis , M.A. ( 2020 ), “The social value of investing in public health across the life course: a systematic scoping review”, BMC Public Health , Vol. 20 , pp. 1 - 18 , doi: 10.1186/s12889-020-08685-7 .

Banke-Thomas , A.O. , Madaj , B. , Charles , A. and van den Broek , N. ( 2015 ), “ Social return on investment (SROI) methodology to account for value for money of public health interventions: a systematic review ”, BMC Public Health , Vol. 15 No. 1 , p. 582 .

Barber , M. and Jackson , S. ( 2017 ), “ Identifying and categorizing cobenefits in state-supported Australian indigenous environmental management programs: international research implications ”, Ecology and Society , Vol. 22 No. 2 , p. 11 .

Bhaumik , U. , Norris , K. , Charron , G. , Walker , S.P. , Sommer , S.J. , Chan , E. , Dickerson , D.U. , Nethersole , S. and Woods , E.R. ( 2013 ), “A cost analysis for a community-based case management intervention program for pediatric Asthma”, Journal of Asthma , Vol. 50 No. 3 , pp. 310 - 317 .

Bosco , A. , Schneider , J. and Broome , E. ( 2019 ), “The social value of the arts for care home residents in England: a social return on investment (SROI) analysis of the imagine arts programme”, Maturitas , Vol. 124 , pp. 15 - 24 , doi: 10.1016/j.maturitas.2019.02.005 . Epub 2019 Mar 13. PMID: 31097173 .

Braadbaart , O. and Yusnandarshah , B. ( 2008 ), “ Public sector benchmarking: a survey of scientific articles, 1990-2005 ”, International Review of Administrative Sciences , Vol. 74 No. 3 , pp. 421 - 433 .

Chun , T. , Zellman , G. , Stecher , B. and Giddens , E. ( 2001 ), “ An evaluation strategy developed by RAND for the broad foundation ”, RAND CORP SANTA MONICA CA .

Cooney , K. and Lynch-Cerullo , K. ( 2014 ), “ Measuring the social returns of nonprofits and social enterprises: the promise and perils of the SROI ”, Nonprofit Policy Forum , Vol. 5 No. 2 , pp. 367 - 393 , doi: 10.1515/npf-2014-0017 .

Corvo , L. , Pastore , L. and Ghibelli , M. ( 2021b ), “ Who likes SIBs? A bibliometric analysis of academic literature (time span 1990-2018) ”, Contemporary Issues in Sustainable Finance , Palgrave Macmillan , Cham , pp. 5 - 36 , doi: 10.1007/978-3-030-65133-6_2 .

Corvo , L. , Pastore , L. , Manti , A. and Iannaci , D. ( 2021a ), “ Mapping social impact assessment models: a literature overview for a future research agenda ”, Sustainability , Vol. 13 No. 9 , p. 4750 , doi: 10.3390/su13094750 .

Cuozzo , B. , Dumay , J. , Palmaccio , M. and Lombardi , R. ( 2017 ), “ Intellectual capital disclosure: a structured literature review ”, Journal of Intellectual Capital , Vol. 18 No. 1 , pp. 9 - 28 , doi: 10.1108/JIC-10-2016-0104 .

Daems , R. , Maes , E. , Mehra , M. , Carroll , B. and Thomas , A. ( 2014 ), “Pharmaceutical portfolio management: global disease burden and corporate performance metrics”, Value Health , Vol. 17 No. 6 , pp. 732 - 738 , doi: 10.1016/j.jval.2014.07.005 , PMID: 25236997 .

Davies , L.E. , Taylor , P. , Ramchandani , G. and Christy , E. ( 2019 ), “Social return on investment (SROI) in sport: a model for measuring the value of participation in England”, International Journal of Sport Policy and Politics , Vol. 11 No. 4 , pp. 585 - 605 .

Dentoni , D. , Bitzer , V. and Pascucci , S. ( 2016 ), “ Cross-sector partnerships and the co-creation of dynamic capabilities for stakeholder orientation ”, Journal of Business Ethics , Vol. 135 No. 1 , pp. 35 - 53 .

Emerson , J. ( 2003 ), “ The blended value proposition: integrating social and financial returns ”, California Management Review , Vol. 45 No. 4 , pp. 35 - 51 , doi: 10.2307/41166187 .

Emerson , J. and Twersky , F. ( 1996 ), New Social Entrepreneurs: the Success, Challenge, and Lessons of Non-Profit Enterprise , Creation. The Roberts Foundation , San Francisco .

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Erkens , M. , Paugam , L. and Stolowy , H. ( 2015 ), “ Non-financial information: state of the art and research perspectives based on a bibliometric study ”, Comptabilité Contrôle Audit , Vol. 21 No. 3 , pp. 15 - 92 , doi: 10.3917/cca.213.0015 , Tome .

Everard , M. , Longhurst , J. , Pontin , J. , Stephenson , W. and Brooks , J. ( 2017 ), “Developed-developing world partnerships for sustainable development (1): an ecosystem services perspective”, Ecosystem Services , Vol. 24 , pp. 241 - 252 , doi: 10.1016/j.ecoser.2016.09.020 .

Faivel , S. Ghosh , S. Hilton , O. James , D. and Peppercorn , D. ( 2012 ), “ Social return on investment: lessons learned in Australia ”, available at: www.socialventures.com.au/assets/SROI-Lessons-learned-in-Australia.pdf

Farr , M. and Cressey , P. ( 2019 ), “ The social impact of advice during disability welfare reform: from social return on investment to evidencing public value through realism and complexity ”, Public Management Review , Vol. 21 No. 2 , pp. 238 - 263 .

Fraser , A. , Tan , S. , Lagarde , M. and Mays , N. ( 2018 ), “ Narratives of promise, narratives of caution: a review of the literature on social impact bonds ”, Social Policy and Administration , Vol. 52 No. 1 , pp. 4 - 28 , doi: 10.1111/spol.12260 .

Fujiwara , D. ( 2015 ), The Seven Principle Problems of SROI , Simetrica , London .

Gair , C. ( 2002 ), “ A report from the good ship SROI. The Roberts Foundation 2002 ”, available at: www.redf.org/download/sroi/goodshipsroi2.doc

Gibbon , J. and Dey , C. ( 2011 ), “ Developments in social impact measurement in the third sector: scaling up or dumbing down? ”, Social and Environmental Accountability Journal , Vol. 31 No. 1 , pp. 63 - 72 .

Gosselin , V. , Boccanfuso , D. and Laberge , S. ( 2020 ), “ Social return on investment (SROI) method to evaluate physical activity and sport interventions: a systematic review ”, International Journal of Behavioral Nutrition and Physical Activity , Vol. 17 No. 1 , p. 26 .

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Hazenberg , R. , Seddon , F. and Denny , S. ( 2015 ), “ Intermediary perceptions of investment readiness in the UK social investment market ”, VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , Vol. 26 No. 3 , pp. 846 - 871 .

Hervieux , C. and Voltan , A. ( 2019 ), “ Toward a systems approach to social impact assessment ”, Social Enterprise Journal , Vol. 15 No. 2 , pp. 264 - 286 , doi: 10.1108/SEJ-09-2018-0060 .

Higham , A. , Barlow , C. , Bichard , E. and Richards , A. ( 2018 ), “ Valuing sustainable change in the built environment: using SuROI to appraise built environment projects ”, Journal of Facilities Management , Vol. 16 No. 3 , pp. 315 - 353 .

Hirunsalee , S. , Denpaiboon , C. and Kanegae , H. ( 2013 ), “Public attitudes toward the additional roles of university in disaster management: case study of Thammasat University in 2011 Thailand floods”, Procedia Environmental Sciences , Vol. 17 , pp. 899 - 908 .

Hoffmann , H. , Jäckel , D. , Glauser , S. , Mueser , K.T. and Kupper , Z. ( 2014 ), “ Long-term effectiveness of supported employment: 5-year follow-up of a randomized controlled trial ”, The American Journal of Psychiatry , Vol. 171 No. 11 , pp. 1183 - 1190 , doi: 10.1176/appi.ajp.2014.13070857 , PMID: 25124692 .

Hutchinson , C.L. , Berndt , A. , Forsythe , D. , Gilbert-Hunt , S. , George , S. and Ratcliffe , J. ( 2019 ), “ Valuing the impact of health and social care programs using social return on investment analysis: how have academics advanced the methodology? A systematic review ”, BMJ Open , pp. 9 - e029789 , doi: 10.1136/bmjopen-2019-029789 . PMID: 25124692 .

Hummels , H. ( 2012 ), “ Coming out of the investors’ cave? Making sense of responsible investing in Europe in the new millennium ”, Business and Professional Ethics Journal , Vol. 31 No. 2 , pp. 331 - 348 .

Iafrati , S. ( 2015 ), “The investment and regenerative value of addiction treatment”, Drugs and Alcohol Today , Vol. 15 No. 1 , pp. 12 - 20 , doi: 10.1108/DAT-10-2014-0037 .

Jackson , A. and McManus , R. ( 2019 ), “ SROI in the art gallery; valuing social impact ”, Cultural Trends , Vol. 28 Nos 2/3 , pp. 132 - 145 .

Jones , C. , Hartfiel , N. , Brocklehurst , P. , Lynch , M. and Edwards , R.T. ( 2020a ), “ Social return on investment analysis of the health precinct community hub for chronic conditions ”, International Journal of Environmental Research and Public Health , Vol. 17 No. 14 , p. 5249 .

Jones , C. , Windle , G. and Edwards , R.T. ( 2020b ), “ Dementia and imagination: a social return on investment analysis framework for art activities for people living with dementia ”, The Gerontologist , Vol. 60 No. 1 , pp. 112 - 123 .

Jones , J. and Tuan , M. ( 2000 ), “ Guide to reading the SROI reports ”, REDF .

Keane , L. , Hoare , E. , Richards , J. , Bauman , A. and Bellew , W. ( 2019 ), “ Methods for quantifying the social and economic value of sport and active recreation: a critical review ”, Sport in Society , Vol. 22 No. 2 , pp. 2203 - 2223 .

King , N. ( 2014 ), “ Making the case for sport and recreation services: the utility of social return on investment (SROI) analysis ”, International Journal of Public Sector Management , Vol. 27 No. 2 , pp. 152 - 164 .

Klemelä , J. ( 2016 ), “ Licence to operate: social return on investment as a multidimensional discursive means of legitimating organisational action ”, Social Enterprise Journal , Vol. 12 No. 3 , pp. 387 - 408 , doi: 10.1108/SEJ-02-2015-0004 .

Kousky , C. , Ritchie , L. , Tierney , K. and Lingle , B. ( 2019 ), “Return on investment analysis and its applicability to community disaster preparedness activities: calculating costs and returns”, International Journal of Disaster Risk Reduction , available at: www.sciencedirect.com/science/article/pii/S2212420919300457

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87 Lombardo , G. , Mazzocchetti , A. , Rapallo , I. , Tayser , N. and Cincotti , S. ( 2019 ), “ Assessment of the economic and social impact using SROI: an application to sport companies ”, Sustainability , Vol. 11 No. 13 , p. 3612 .

88 Grandia , J. and Voncken , D. ( 2019 ), “ Sustainable public procurement: the impact of ability, motivation, and opportunity on the implementation of different types of sustainable public procurement ”, Sustainability , Vol. 11 No. 19 , p. 5215 .

89 Keane , L. , Hoare , E. , Richards , J. , Bauman , A. and Bellew , W. ( 2019 ), “ Methods for quantifying the social and economic value of sport and active recreation: a critical review ”, Sport in Society , Vol. 22 No. 12 , pp. 2203 - 2223 .

90 Hutchinson , C.L. , Berndt , A. , Forsythe , D. , Gilbert-Hunt , S. , George , S. and Ratcliffe , J. ( 2019 ), “ Valuing the impact of health and social care programs using social return on investment analysis: how have academics advanced the methodology? A systematic review ”, BMJ Open , Vol. 9 No. 8 , p. e029789 .

91 Bellucci , M. , Nitti , C. , Franchi , S. , Testi , E. and Bagnoli , L. ( 2019 ), “ Accounting for social return on investment (SROI): the costs and benefits of family-centred care by the Ronald McDonald House Charities ”, Social Enterprise Journal , Vol. 15 No. 1 , pp. 46 - 75 .

92 Farr , M. and Cressey , P. ( 2019 ), “ The social impact of advice during disability welfare reform: from social return on investment to evidencing public value through realism and complexity ”, Public Management Review , Vol. 21 No. 2 , pp. 238 - 263 .

93 Solórzano-García , M. , Navío-Marco , J. and Ruiz-Gómez , L.M. ( 2019 ), “ Ambiguity in the attribution of social impact: a study of the difficulties of calculating filter coefficients in the SROI method ”, Sustainability , Vol. 11 No. 2 , p. 386 .

94 Leung , Z.C. , Ho , A.P. , Tjia , L.Y. , Tam , R.K. , Chan , K.T. and Lai , M.K. ( 2019 ), “ Social impacts of work integration social enterprise in Hong Kong–workfare and beyond ”, Journal of Social Entrepreneurship , Vol. 10 No. 2 , pp. 159 - 176 .

95 Bosco , A. , Schneider , J. and Broome , E. ( 2019 ), “ The social value of the arts for care home residents in England: a social return on investment (SROI) analysis of the imagine arts programme ”, Maturitas , Vol. 124 , pp. 15 - 24 .

96 Tulla , A.F. and Vera , A. ( 2019 ), “ Could social farming be a strategy to support food sovereignty in Europe? ”, Land , Vol. 8 No. 5 , p. 78 .

97 Purwohedi , U. and Gurd , B. ( 2019 ), “ Using social return on investment (SROI) to measure project impact in local government ”, Public Money and Management , Vol. 39 No. 1 , pp. 56 - 63 .

98 Tanaree , A. , Assanangkornchai , S. , Isaranuwatchai , W. , Thavorn , K. and Coyte , P.C. ( 2019 ), “ Integrated treatment program for alcohol related problems in community hospitals, Songkhla province of Thailand: a social return on investment analysis ”, Plos One , Vol. 14 No. 1 , p. e0209210 .

99 Kousky , C. , Ritchie , L. , Tierney , K. and Lingle , B. ( 2019 ), “ Return on investment analysis and its applicability to community disaster preparedness activities: calculating costs and returns ”, International Journal of Disaster Risk Reduction , Vol. 41 , p. 101296 .

100 Ravulo , J. , Said , S. , Micsko , J. and Purchase , G. ( 2019 ), “ Utilising the social return on investment (SROI) framework to gauge social value in the fast forward program ”, Education Sciences , Vol. 9 No. 4 , pp. 290 .

101 Shi , Y.P. , Joyce , C. , Wall , R. , Orpana , H. and Bancej , C. ( 2019 ), “ A life satisfaction approach to valuing the impact of health behaviours on subjective well-being ”, BMC Public Health , Vol. 19 No. 1 , pp. 1 - 11 .

102 Davies , L.E. , Taylor , P. , Ramchandani , G. and Christy , E. ( 2019 ), “ Social return on investment (SROI) in sport: a model for measuring the value of participation in England ”, International Journal of Sport Policy and Politics , Vol. 11 No. 4 , pp. 585 - 605 .

103 Banke-Thomas , A. , Madaj , B. and Van Den Broek , N. ( 2019 ), “ Social return on investment of emergency obstetric care training in Kenya ”, BMJ Global Health , Vol. 4 No. 1 , p. e001167 .

104 Broad , G. , Ortiz , J. and Meades , S. ( 2019 ), “ Public libraries: measuring their value ”, Public Library Quarterly , Vol. 38 No. 3 , pp. 309 - 319 .

105 Jackson , A. and McManus , R. ( 2019 ), “ SROI in the art gallery; valuing social impact ”, Cultural Trends , Vol. 28 Nos 2/3 , pp. 132 - 145 .

106 Lophongpanit , P. , Tongsiri , S. and Thongprasert , N. ( 2019 ), “ Social return on investment for patient treated by continuous ambulatory peritoneal dialysis: a case study in Ubon Ratchathani province, Thailand ”, ClinicoEconomics and Outcomes Research , Vol. 11 , p. 569 .

107 Ballamingie , P. , Poitevin-DesRivières , C. and Knezevic , I. ( 2019 ), “ Hidden harvest's transformative potential: an example of community economy ”, Journal of Agriculture, Food Systems, and Community Development , Vol. 9 No. A , pp. 125 - 139 .

108 Green , K.R. ( 2019 ), “ Social return on investment: a women’s cooperative critique ”, Social Enterprise Journal , Vol. 15 No. 3 , pp. 320 - 338 .

109 Ricciuti , E. and Bufali , M.V. ( 2019 ), “ The health and social impact of blood donors associations: a social return on investment (SROI) analysis ”, Evaluation and Program Planning , Vol. 73 , pp. 204 - 213 .

110 Aguilar-Agudo , A. , Herruzo-Cabrera , J. , Ochoa-Sepúlveda , J.J. and Pino-Osuna , M.J. ( 2019 ), “ Retorno social de la inversión (SROI) en tratamientos psicológicos basados en la evidencia ”, Clínica y Salud , Vol. 30 No. 1 , pp. 13 - 20 .

111 Baker , C. , Courtney , P. and Knepil , G. ( 2019 ), “ Evaluating societal outcomes of orthognathic surgery: an innovative application of the social return on investment methodology to patients after orthognathic treatment ”, British Journal of Oral and Maxillofacial Surgery , Vol. 57 No. 2 , pp. 145 - 150 .

112 Feuerherdt , L. , Peevor , S. , Clinch , M. and Moore , T. ( 2019 ), “ Social return on investment: application for an indigenous rangelands context ”, The Rangeland Journal , Vol. 41 No. 3 , pp. 177 - 183 .

113 Vargas , R. , Miller , B. , Anhalzer , G. , Mickelson , A. and Kulkarni , K. ( 2019 ), “ Evaluating progress of a social venture in Wakiso district Uganda ”, 2019 IEEE Global Humanitarian Technology Conference (GHTC) , IEEE , pp. 1 - 8 .

114 Mihalopoulos , C. , Le , L.K.D. , Chatterton , M.L. , Bucholc , J. , Holt-Lunstad , J. , Lim , M.H. and Engel , L. ( 2020 ), “ The economic costs of loneliness: a review of cost-of-illness and economic evaluation studies ”, Social Psychiatry and Psychiatric Epidemiology , Vol. 55 No. 7 , pp. 823 - 836 .

115 Jones , C. , Windle , G. and Edwards , R.T. ( 2020 ), “ Dementia and imagination: a social return on investment analysis framework for art activities for people living with dementia ”, The Gerontologist , Vol. 60 No. 1 , pp. 112 - 123 .

116 Moral Torres , E. ( 2020 ), “ Social value of a set of proposals for the ideal approach of multiple sclerosis within the Spanish National Health System: a social return on investment study ”, BMC Health Services Research , Vol. 20 No. 1 , p. 84 .

117 Courtney , P. and Powell , J. ( 2020 ), “ Evaluating innovation in European rural development programmes: application of the social return on investment (SROI) method ”, Sustainability , Vol. 12 No. 7 , p. 2657 .

118 Ruiz-Lozano , M. , Tirado-Valencia , P. , Sianes , A. , Ariza-Montes , A. , Fernández-Rodríguez , V. and López-Martín , M.C. ( 2020 ), “ SROI methodology for public administration decisions about financing with social criteria. A case study ”, Sustainability , Vol. 12 No. 3 , p. 1070 .

119 Merino , M. ( 2020 ), “ The social return on investment of a new approach to heart failure in the Spanish National Health System ”, ESC Heart Failure , Vol. 7 No. 1 , pp. 131 - 138 .

120 Oosterhoff , M. , Van Schayck , O.C. , Bartelink , N.H. , Bosma , H. , Willeboordse , M. , Winkens , B. and Joore , M.A. ( 2020 ), “ The short-term value of the ‘healthy primary school of the future’ initiative: a social return on investment analysis ”, Frontiers in Public Health , Vol. 8 , p. 401 .

121 Vongchan , P. , Chompunth , C. and Phoochinda , W. ( 2020 ), “ Green business model of biomass very small power producers in Thailand ”, International Journal , Vol. 19 No. 72 , pp. 102 - 108 .

122 Jones , C. , Hartfiel , N. , Brocklehurst , P. , Lynch , M. and Edwards , R.T. ( 2020 ), “ Social return on investment analysis of the health precinct community hub for chronic conditions ”, International Journal of Environmental Research and Public Health , Vol. 17 No. 14 , p. 5249 .

123 Samuel , F. and Hatleskog , E. ( 2020 ), “ Why social value? ”, Architectural Design , Vol. 90 No. 4 , pp. 6 - 13 .

124 Schoen , V. , Caputo , S. and Blythe , C. ( 2020 ), “ Valuing physical and social output: a rapid assessment of a London community garden ”, Sustainability , Vol. 12 No. 13 , p. 5452 .

125 Tulla , A.F. , Vera , A. , Guirado , C. and Valldeperas , N. ( 2020 ), “ The return on investment in social farming: a strategy for sustainable rural development in rural Catalonia ”, Sustainability , Vol. 12 No. 11 , p. 4632 .

126 Ashton , K. , Schröder-Bäck , P. , Clemens , T. , Dyakova , M. , Stielke , A. and Bellis , M.A. ( 2020 ), “ The social value of investing in public health across the life course: a systematic scoping review ”, BMC Public Health , Vol. 20 No. 1 , pp. 1 - 18 .

127 Venezia , E. and Pizzutilo , F. ( 2020 ), “ Evaluation tools for transport infrastructures: social return on investments ”, European Transport\Trasporti Europei (2020) .

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129 Refki , D. , Mishkin , K. , Avci , B. and Abdelkarim , S. ( 2020 ), “ Using social return on investment to evaluate the public art exhibit breathing lights ”, Poetics , Vol. 79 , p. 101401 .

130 Grzeszczyk , T.A. and Pełszyński , J. ( 2020 ), “ Towards a conceptualization of a social efficiency notion in management sciences ”, Ekonomia i Prawo , Vol. 19 No. 1 , pp. 33 - 46 .

131 Phoochinda , W. ( 2020 ), “ Assessment of social return on investment from the utilisation of oil palm’s residues ”, Journal of Oil Palm Research , Vol. 32 No. 1 , pp. 145 - 151 .

132 Ashton , K. , Parry-Williams , L. , Dyakova , M. and Green , L. ( 2020 ), “ Health impact and social value of interventions, services, and policies: a methodological discussion of health impact assessment and social return on investment methodologies ”, Frontiers in Public Health , Vol. 8 , p. 49 .

133 Gosselin , V. , Boccanfuso , D. and Laberge , S. ( 2020 ), “ Social return on investment (SROI) method to evaluate physical activity and sport interventions: a systematic review ”, International Journal of Behavioral Nutrition and Physical Activity , Vol. 17 No. 1 , pp. 1 - 11 .

134 Hutchinson , C. , Berndt , A. , Cleland , J. , Gilbert-Hunt , S. , George , S. and Ratcliffe , J. ( 2020 ), “ Using social return on investment analysis to calculate the social impact of modified vehicles for people with disability ”, Australian Occupational Therapy Journal , Vol. 67 No. 3 , pp. 250 - 259 .

135 Baker , C. , Courtney , P. , Kubinakova , K. , Crone , D. and Billingham , D. ( 2020 ), “ Assessing the broader social outcomes of a community health programme through a social-ecological framework ”, International Journal of Health Promotion and Education , Vol. 58 No. 3 , pp. 137 - 151 .

136 Carretero , G. ( 2020 ), “ Multidisciplinary approach to psoriasis in the Spanish National Health System: a social return on investment study ”, Global and Regional Health Technology Assessment , Vol. 7 No. 1 , pp. 50 - 56 .

137 Ravulo , J. , Said , S. , Micsko , J. and Purchase , G. ( 2020 ), “ Social value and its impact through widening participation: a review of four programs working with primary, secondary and higher education students ”, Cogent Social Sciences , Vol. 6 No. 1 , p. 1722307 .

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140 Davies , L.E. , Taylor , P. , Ramchandani , G. and Christy , E. ( 2021 ), “ Measuring the social return on investment of community sport and leisure facilities ”, Managing Sport and Leisure , Vol. 26 Nos 1/2 , pp. 93 - 115 .

141 Barman , E. , Hall , M. and Millo , Y. ( 2021 ), “ Demonstrating value: how entrepreneurs design new accounting methods to justify innovations ”, European Accounting Review , pp. 1 - 30 .

142 Wood Daly , M. ( 2020 ), “ Dollars and $ense: uncovering the socio-economic benefit of religious congregations in Canada ”, Studies in Religion/Sciences Religieuses , Vol. 49 No. 4 , pp. 587 - 610 .

143 Saenz , C.S. ( 2020 ), “ A new mapping outcome method to measure social return on investment: a case study in Peru ”, Social Responsibility Journal , Vol. 17 No. 4 , pp. 562 - 577 .

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Open Access

Peer-reviewed

Research Article

Analysis of the impact of RCEP on the industrial and innovation chains of China’s textile and clothing industry

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliation Director of Cross Border E-Commerce Teaching and Research Office, School of Economics and Social Welfare, Zhejiang Shuren University, Hangzhou, China

Roles Data curation, Formal analysis, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Social Welfare, Zhejiang Shuren University, Hangzhou, China

Li Yang,
Pivithuru Kumarasinghe

Published: August 30, 2024
https://doi.org/10.1371/journal.pone.0309708
Peer Review
Reader Comments

This research examines the impact of the Regional Comprehensive Economic Partnership (RCEP) on the textile and apparel industry within its member nations. The study seeks to understand the implications of RCEP on trade dynamics, innovation chains, and industrial integration in the textile sector. The study uses both quantitative analysis of trade data and qualitative assessment of policy frameworks to analyze changes in textile trade and patterns among RCEP members through UN Comtrade data. Qualitative analysis is conducted to examine RCEP policies related to intellectual property protection, investment regulations, and innovation cooperation. The findings reveal a significant increase in textile trade volume among RCEP member countries following the agreement’s implementation. China emerges as a key player, experiencing substantial growth in textile exports to RCEP nations, particularly driven by tariff reduction initiatives. RCEP provisions stimulate demand for innovation within the textile industry, fostering collaborative efforts in scientific research and development.

Citation: Yang L, Kumarasinghe P (2024) Analysis of the impact of RCEP on the industrial and innovation chains of China’s textile and clothing industry. PLoS ONE 19(8): e0309708. https://doi.org/10.1371/journal.pone.0309708

Editor: Muhammad Hashim, National Textile University, PAKISTAN

Received: April 1, 2024; Accepted: August 16, 2024; Published: August 30, 2024

Copyright: © 2024 Yang, Kumarasinghe. 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 for this study are publicly available with the title “RCEP on the Industrial and Innovation Chains of China's Textile and Clothing Industry” from the Mendeley Data repository ( https://doi.org/10.17632/sgvmg6c9zz.1 ).

Funding: This study received support from the Research project of the Zhejiang Provincial Department of Education (Y202351899) and Zhejiang Federation of Humanities and Social Sciences Circles Base research projects (2014JDZ01).

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

After eight years of negotiations, the Regional Comprehensive Economic Partnership (RCEP) was signed in November 2020. It officially took effect on January 1, 2022, and became fully effective for all 15 signatory countries on June 2, 2023. This marked a new full implementation stage for the free trade area with the world’s largest population, economy, and trade scale. The RCEP has played a significant role in improving the trade and investment levels in the entire region. It has effectively promoted the establishment of a more efficient, closer, mutually beneficial cooperation system among its members for industrial and supply chains [ 1 , 2 ]. This has added continuous momentum to the region’s sustained economic recovery and development. Chinese President Xi Jinping has also advocated the "deployment of innovation chains around industrial chains and the layout of industrial chains around innovation chains" to promote high-quality economic development in China. The study aims to analyze the RCEP’s impact on regional trade dynamics, economic integration, and industrial development. Through empirical analysis and strategic insights, we aim to better understand the RCEP’s role in facilitating sustainable economic growth and enhancing regional cooperation in the post-pandemic era.

2. Literature review

The concept of industrial and innovation chains has long been around in economic theories. Adam Smith’s theory of specialization was the starting point for an industrial chain. It is a series of interconnected industries with input-output relationships. An industrial chain spans multiple industries upstream and downstream of the production process, forming a chain-like industrial organization [ 3 ]. Innovation chains refer to activities involving multiple stakeholders at different stages, aiming to bring about innovation [ 4 ]. Innovation is mutual feedback between internal technological drivers and external market demand pulls in innovation chains, which leads to introducing new products or processes [ 5 ]. Michael Porter’s theory of competitive advantage highlights the integration of production and innovation factors along the chain according to market demands. This integration enhances production efficiency and innovation capabilities [ 6 ].

Since the signing and implementation of the RCEP, scholars have been interested in its economic effects. Reduced tariffs have led to significant trade effects and favorable conditions for multinationals to redesign production based on member countries’ comparative advantages [ 7 ]. RCEP has encouraged the East Asian region to rely more on internal economic circulation and reduce its dependence on the US market. Participation in international economic activities influences innovative activities [ 8 ]. Regional trade agreements can enhance government credibility, strengthen mutual credibility between member countries, and incentivize the formation of innovative cooperation relationships within the international innovation cooperation network [ 9 ]. These agreements provide channels for introducing cutting-edge technologies and knowledge spillovers, improving technological innovation in developing economies [ 10 ].

The textile industry’s industrial and innovation chains have been undergoing rapid evolution. They are integrating new technologies, embracing workplace innovations, adopting sustainable efficiencies, and inventing products and processes to meet the changing demands of global consumers and markets [ 11 ]. This evolution is driven by the need for increased efficiency and productivity and the desire to create more sustainable and environmentally friendly practices. Innovation in the textile industry involves creating new products and improving processes and business models. For instance, investments in logistics, telecommunications, predictive technologies, and manufacturing processes have helped countries integrate into more lucrative value chains and improve the lives of millions of people [ 12 , 13 ]. China is a significant player in the global textile and apparel industry and is a member of the RCEP [ 14 ]. As a result, the country has undergone significant changes in its industrial and innovation chains.

The RCEP agreement has played a pivotal role in driving the transformation and upgrading of China’s textile industry, leading to the integration of its industrial and innovation chains. The impact of the RCEP on China’s textile industry can be examined from two perspectives: Firstly, from an industrial chain perspective, the RCEP agreement has facilitated the free flow of goods among member states, which has helped reduce trade barriers and improved the efficiency of China’s textile industrial chain [ 14 ]. This has resulted in China’s better integration into the regional industrial chain and enhanced the competitiveness of its textile industry. Secondly, from an innovation chain perspective, the RCEP agreement has encouraged technological exchanges and cooperation among member states. This has allowed China’s textile industry to access advanced technologies and innovative ideas, promoting the development of its innovation chain.

Sustainability has become a critical aspect of the textile industry. With the increasing demand for eco-friendly products and more stringent regulations, business players and policymakers need to develop sustainability innovation in the textile industry. This includes practices like eco-design, eco-label, life cycle assessment, cleaner production, ecoefficiency, waste handling, supply chain management, and enzymatic textile processing. This article analyzes the impact of RCEP on China’s textile industrial chain and innovation chain based on authoritative data sources and the RCEP agreement text.

3. Methodology

The analysis of the impact of the RCEP on the industrial and innovation chains of China’s textile and clothing industry employs a descriptive research methodology. This methodology was chosen because it is suitable for describing the industry’s characteristics, situation, and trends without manipulating variables, which aligns with the objectives of this study.

The descriptive research design was deemed appropriate as it allows for the systematic collection and analysis of data to describe the industry’s current state and objectively assess RCEP’s impact. Given that the researcher has no control over the variables in the study, a descriptive research design provides a structured framework for examining the complex interactions and dynamics within the textile and clothing industry post-RCEP implementation.

Data for this study were collected from various sources, including the UN Comtrade database, data from the Ministry of Commerce of China, and the text of the RCEP agreement. All data underlying the findings of this study are freely available to other researchers. The data for this research, titled "RCEP on the Industrial and Innovation Chains of China’s Textile and Clothing Industry," is accessible through Mendeley Data [ 15 ]. These sources offer a diverse range of information, encompassing trade patterns, investment flows, and regulatory frameworks pertinent to the textile and clothing industry within the context of RCEP. The collected data were analyzed using tables and ratios to identify patterns, trends, and relationships within the textile and clothing industry post-RCEP. Tables presented quantitative data in a structured format, facilitating comparisons and interpretations. At the same time, ratios were calculated to assess key performance indicators and measure the impact of RCEP on industrial and innovation chains.

In order to ensure that the research findings are reliable and valid, we implemented several measures. Firstly, we collected data from authoritative sources known for accuracy and credibility, such as the UN Comtrade database and official government sources. Secondly, we designed the research methodology to be systematic and replicable, enhancing the study outcomes’ reliability. Additionally, we employed triangulation of data from multiple sources to corroborate findings and minimize bias, thereby enhancing the validity of the research. Finally, we conducted the research process transparently, with clear documentation of data collection and analysis procedures, enabling scrutiny and verification by peers and stakeholders. These measures collectively contribute to the robustness and trustworthiness of the research findings.

4. Results and findings

The subsequent section delves into the results and findings obtained from the analysis of RCEP’s impact on the textile industry. Through comprehensive examination and evaluation of various factors such as trade dynamics, innovation integration, and industrial transformation, this section aims to shed light on the specific outcomes and implications of RCEP implementation for the textile sector within the region.

4.1 Trade dynamics of textile and apparel products within RCEP member nations

China has always been a major player in the global textile industry. A report by CCID Consulting titled "2022 China’s Top 100 County Economies Research" reveals that 8 out of the top 10 counties in the ranking have the textile industry as their main industry. In fact, in 43 countries with a GDP exceeding one trillion yuan, the textile and clothing industry is considered a crucial pillar for economic development. This has led to a clustering and scaling effect in the industry. Since 2000, the textile and clothing trade scale in RCEP countries has expanded. From 2000 to 2021, the world’s exports of textile and clothing trade increased by 132%, while the exports of RCEP countries increased by 290.7%, more than twice the global growth rate. Among them, China’s exports have grown by 517.2%, nearly four times the global growth rate. From 2000 to 2021, the share of RCEP textile and clothing exports worldwide increased from 28.73% to 48.38%, while China’s share has increased from 13.54% to 36.03%. As a result, the RCEP region has become the world’s most important textile and clothing manufacturing center. Fig 1 shows the relationship between the RCEP and China’s worldwide textile and clothing industry export share.

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https://doi.org/10.1371/journal.pone.0309708.g001

In 2021, out of the top 20 countries and regions that exported textiles and clothing globally, three were three members of the RCEP: China, Vietnam, and Japan. These three countries together accounted for 54% of the total exports of the top 20 countries and regions, with China alone contributing 44.9% of the total share. In 2022, China continued to maintain its leading position in the exports of textiles and clothing, with a year-on-year growth rate of 4.9%. Table 1 lists the top 20 countries that export textiles and garments.

https://doi.org/10.1371/journal.pone.0309708.t001

On the demand side, countries such as Japan, South Korea, and Australia are significant import markets for textile and clothing products worldwide. In 2022, their imports of clothing products (HS61 and 62) reached $25.1 billion, $12.2 billion, $9.6 billion, and $8.7 billion, respectively. China’s vast consumer market is experiencing an increasing demand for imports, while the Association of Southeast Asian Nations (ASEAN), as a rapidly developing garment manufacturing base, also has a massive import scale for textile intermediate products. Therefore, from the global textile and clothing trade landscape perspective, the RCEP region is not only the most important textile and clothing manufacturing center in the world but also a rapidly growing and significant consumer market with the most extraordinary global growth potential.

4.2 Textile trade dynamics between China and RCEP member nations

Table 2 below provides detailed information on the trade volume and proportion of China’s textile and clothing exports to the other 14 member countries of RCEP for 2022. China’s exports of textile and clothing products to RCEP member countries have shown growth, and in 2022, the total export value reached US$95.02 billion, representing a 9.3% year-on-year increase. Laos was the export market with the highest growth rate for China’s textile exports.

https://doi.org/10.1371/journal.pone.0309708.t002

From the perspective of textile and clothing product classification, in 2022, the export growth rates of yarn, textile fabrics, textile products, and clothing between China and RCEP countries were superior to those of similar products traded with the rest of the world. China’s imports of textile products decreased during this year, but the decline with RCEP countries was also smaller than that of similar products traded with the rest of the world. Fig 2 compares the growth rates of China’s textile product exports to global and RCEP countries in 2022.

https://doi.org/10.1371/journal.pone.0309708.g002

For textile machinery products closely associated with textile and clothing production, only HS8447 (knitting machines and stitch-bonding machines) exhibited a higher export growth rate from China to RCEP countries compared to similar products exported globally. Conversely, for HS8445 (machinery for pre-treatment of textile fibers and textile production), China experienced a higher import growth rate from RCEP countries than from the rest of the world for similar products. Fig 3 compares China’s export growth rate of textile machinery products to global and RCEP countries in 2022.

https://doi.org/10.1371/journal.pone.0309708.g003

4. 3. Impact of RCEP implementation on the reconstruction and structure of the textile industry supply chain

The textile industry chain involves various components, including raw materials like cotton, silk, wool, polyester, and chemical fibers, the loom industry, weaving and dyeing, apparel, home textiles, and light textile markets [ 16 ]. The textile and clothing industry is essential for technological, design, and business model innovation, integrating manufacturing and service economies. With the rise of economic globalization, the international division of labor has shifted towards intra-product trade, and industrial chains have transcended national boundaries, giving rise to global industrial chains.

Within the RCEP countries, there are differences in the competitive industries of each country within the textile and clothing industrial chain. China has advantages in areas such as synthetic filament yarn, synthetic staple fibers, nonwovens, unique woven fabrics, industrial textiles, knitted fabrics, knitted clothing, and made-up textile articles. Due to its historical and technological accumulation, Japan has solid technological advantages in high-end fibers such as carbon fiber. Korea has technological solid advantages in fibers such as spandex, and both Japan and Korea have certain advantages in nonwoven, knitted clothing and made-up textile articles. Australia and New Zealand have vast pastures and high-quality wool, thus having advantages in cotton and wool. ASEAN countries have shown advantages in the clothing sector and some textile areas.

Several ASEAN countries have identified the textile and clothing industry as critical in promoting economic development. For example, Vietnam, Cambodia, and Indonesia have developed significant textile and clothing sectors. These governments have introduced many supportive policies in areas such as investment, finance, approval, and tariff concessions to facilitate this. These policies have attracted textile and clothing companies worldwide to invest and build factories in these countries [ 2 ]. These countries have been known to introduce various supportive policies to stimulate economic development and attract investment. Vietnam’s main advantages are finished textile products (spinning) and carpets. Indonesia is highly competitive in the carpet sector, Thailand is highly competitive in synthetic staple fibers, and Singapore is highly competitive in synthetic filament yarn.

China strategically aligns with RCEP member countries in the textile industry chain. ASEAN and Japan are key partners, ranked as the third and fourth largest export markets for China’s textile industry. Korea and Australia, as globally significant textile and clothing consumption markets, are crucial export destinations for China’s textile industry end products. In terms of imports, ASEAN has emerged as China’s primary source of imported textile and clothing products. Japan and Korea are vital sources of imported functional fabrics, chemical fiber textile clothing, textile dyes, and other products for China. Australia and New Zealand also significantly provide China with a wealth of high-quality wool and other raw textile materials.

4.4. The impact of RCEP on the textile industrial chain within the region

The impact of the RCEP on the textile industrial chain within the region is multifaceted. One significant aspect is the tariff reduction under the RCEP, which has led to notable changes in trade dynamics and industrial competitiveness.

4.4.1. Tariff reduction under the RCEP.

The RCEP aims to remove tariff and non-tariff barriers to trade within the region. This is expected to reduce trade costs and product prices significantly. Over 90% of intra-regional trade in goods will eventually be tariff-free due to the RCEP. China, Japan, and South Korea have established their first free trade agreements, which marks a significant step in their economic and trade relations. This will reduce trade costs between China and Japan, with the proportion of Chinese products with zero tariffs on Japan eventually reaching 86% and the proportion of Japanese products with zero tariffs on China reaching 88%. Upon implementing the RCEP, Japan immediately reduced tariffs to zero for 33.7% of Chinese textile and clothing products, with zero-tariff rates reaching 71.3%, 99.3%, and 99.3% within 11 years, 16 years, and 21 years, respectively. Meanwhile, China immediately reduced tariffs to zero for 10% of Japanese textile and clothing products, with zero-tariff rates reaching 83.3%, 90.7%, and 91.8% within 11 years, 16 years, and 21 years, respectively.

However, Japan’s average annual tariff reduction on Chinese textile and clothing products is only 0.5% to 0.8%, which limits the promotional effect on Chinese exports to Japan in the short term. The more significant the proportion of Chinese products with tariffs gradually reduced to zero, the less significant or even damaging the growth of China’s exports of such products to Japan after the implementation of the RCEP. Table 3 shows the tariff reduction and export growth of textiles exported by China to Japan.

https://doi.org/10.1371/journal.pone.0309708.t003

4.4.2. Rules of Origin under the RCEP.

The rules of origin for textile and clothing products within the RCEP are relatively lenient. For most products under chapters 50–56 and all products under chapters 57–63, the criterion for determination is "chapter change" without additional conditions. This means that if all non-originating materials used in producing goods have changed at the first two digits of the Harmonized System (HS), they are considered originating.

Previously, the ASEAN-Japan Free Trade Agreement imposed specific conditions on clothing product exports to Japan. Knitted fabrics must be manufactured within the free trade area to qualify for duty-free treatment in Japan. If Chinese enterprises invested in clothing manufacturing in ASEAN countries and wanted to export to Japan for duty-free treatment, they had to invest in fabric production within the region. Only then would they be considered as originating from ASEAN and qualify for duty-free treatment in Japan (except for least developed countries such as Cambodia and Myanmar).

However, the implementation of the RCEP changed the game. Fabrics imported from China by ASEAN countries are processed into clothing within the ASEAN change HS code chapter. When these products are exported to Japan, they are considered as originating within the RCEP region according to the rules of origin under the RCEP, thus qualifying for duty-free treatment. This means that products that were previously subject to the rules of origin under the ASEAN-Japan and Vietnam-Japan Free Trade Agreements that necessitated local production in ASEAN or were ineligible for duty-free treatment due to the inability to produce in ASEAN can now enjoy duty-free treatment in Japan.

Vietnam and Indonesia are China’s largest export markets for upstream textile products, such as yarn and intermediate textile fabrics. In 2022, China exported yarn worth US1.396 billion to Vietnam, an increase of 9.73%. Yarn exports to Indonesia increased by 26.66%, while textile fabric exports increased by 22.37%. During the same period, China’s yarn exports to Cambodia, the Philippines, and Myanmar increased by 34%, 46%, and 40%, respectively, while textile fabric exports to Cambodia and Myanmar increased by 5.67% and 43.05%, respectively.

Table 4 presents the growth rate of yarn and textile fabrics exported by China to ASEAN countries in 2022.

https://doi.org/10.1371/journal.pone.0309708.t004

https://doi.org/10.1371/journal.pone.0309708.g004

Additionally, the RCEP facilitates ASEAN countries’ making the most of China’s production advantages in intermediate products, including yarn and fabrics, to enhance their exports to Japan. This will lead to an optimal allocation of resources within the textile industry production chain.

4.4.3. Investment provisions of the RCEP.

The investment regulations of the RCEP consist of two parts: textual rules and negative lists. The textual rules are primarily described in Chapter 10 (Investment) and two annexes (Customary International Law and Expropriation). Furthermore, there are provisions related to investment in other chapters of the agreement, such as Chapter 1 (Initial Provisions and General Definitions), Chapter 17 (General Provisions and Exceptions), and Chapter 19 (Dispute Settlement). Along with the textual rules, Annex 3 of the RCEP agreement (Schedules of Reservations and Non-Conforming Measures on Services and Investment) lists each member country’s negative lists in the investment field [ 17 ].

The investment regulations of the RCEP cover four aspects: investment protection, investment liberalization, investment promotion, and investment facilitation. These rules maintain the key content of traditional investment agreements and reflect new advancements in international investment contracting practices. All 15 member countries have made high-level open commitments using negative lists for investments in five non-service sectors: manufacturing, agriculture, forestry, fisheries, and mining. The RCEP’s investment rules positively impact attracting foreign investment, creating a favorable business environment, and expanding international cooperation in the textile and apparel industry. Based on data from the Ministry of Commerce of China, China’s textile industry directly invested US$3.15 billion in RCEP countries from 2015 to 2020, accounting for 38% of China’s overall direct investment during the same period.

Historically, the proportion of mutual investment between China and Japan in their respective total foreign direct investment (FDI) has been relatively low. In 2020 and 2021, Japan’s FDI in China accounted for only 2.34% and 2.26% of China’s total FDI, respectively. Similarly, China’s direct investment in Japan was even less, comprising only 0.32% and 0.43% of China’s outbound FDI. This indicates significant potential for growth in bilateral direct investment between China and Japan. Table 5 shows that in 2022, China attracted a 17.68% increase in FDI from Japan, much higher than the overall growth rate of 9.02% for China’s FDI and the 6.46% growth rate for FDI from Asia. However, due to factors such as the COVID-19 pandemic, China’s outbound investment showed negative growth in 2022. Combined with Japan’s economic reasons, China’s investment in Japan experienced a steep decline with a growth rate of -47.98%, much lower than the overall growth rate of -8.78%. Table 5 shows Chinese investment in Japan and Japanese FDI in China.

https://doi.org/10.1371/journal.pone.0309708.t005

After implementing the RCEP, China achieved a significant breakthrough by using a negative list format to commit to non-service sector investments in a free trade agreement for the first time. Japan has opened up investments in agriculture, forestry, fisheries, and mining sectors, excluding only a few sensitive areas. The RCEP has significantly improved market access between China and Japan and enhanced transparency of their investment policies. This not only benefits Chinese investors in entering the Japanese market and addresses the imbalance in investment between the two countries but also encourages Japanese enterprises to expand their investments in China and strengthen existing investments.

Therefore, in the short term, the implementation of the RCEP has improved the business environment, and its promotional effect on attracting Japanese investment in China is more pronounced.

4.5. The impact of RCEP on the layout of the innovation chain in the regional textile industry

The innovation chain in the textile industry refers to a systematic collection of innovative activities that involve various entities and links in the industry. These entities include upstream, midstream, and downstream enterprises [ 18 ]. The innovation chain is guided by market demand, and the enterprises integrate innovative resources through various activities. The outcome of the chain is the commercialization of textile and apparel products [ 19 ].

The textile innovation chain has a chronological sequence that follows the general innovation chain. It starts with innovative demand, basic research, applied research, design and development, production, and sales, and ends with industrialization and diffusion. Horizontally, the textile innovation chain corresponds to different segments of the industrial chain, such as loom innovation, material innovation, design innovation, manufacturing innovation, supply chain innovation, and marketing innovation [ 18 – 20 ]. This can be more specifically illustrated in the following Fig 5 .

https://doi.org/10.1371/journal.pone.0309708.g005

RCEP has significantly impacted the regional textile and apparel innovation chain and accelerated advancements in it, enhancing the industry’s competitiveness globally.

4.5.1. Stimulate demand for innovation.

After analyzing the data, it has been found that many countries within the region are significant importers of traditional textile and clothing products globally. Implementing the RCEP leads to an increase in the market size within the region, providing companies with better market opportunities. At the same time, implementing the RCEP has impacted the reconstruction and expansion of the textile industry chain within the region. Therefore, based on the vertical composition of the textile innovation chain, the textile industry chain’s expansion and the consumer market’s growth due to the RCEP will create a higher practical demand for innovative outcomes and stimulate innovation within the region.

4.5.2. Promote international cooperation in scientific and technological innovation.

International cooperation in technological innovation involves collaboration among countries to acquire knowledge in scientific, technological, and innovative fields. This cooperation can occur in various stages of the innovation chain, including primary and applied research, design, and development [ 19 , 20 ]. The RCEP, a trade agreement between member states, has established a new framework for cooperation in technological innovation within the textile industry.

4.5.3. Intellectual property protection.

The RCEP, or Regional Comprehensive Economic Partnership, has 83 articles covering various aspects of intellectual property, such as general provisions, basic principles, copyrights, trademarks, geographical indications, patents, industrial designs, and the enforcement of intellectual property rights. With the RCEP provisions, any contracting party can regulate and manage any infringement committed by other contracting parties, thus breaking geographical restrictions. This enables better protection and development of intellectual property within the region.

Moreover, the RCEP agreement’s Chapter 14 discusses the importance of small and medium-sized enterprises (SMEs) in enhancing their awareness, understanding, and application of the intellectual property system. This chapter also encourages innovation, improves market access thresholds, and ensures enterprises can engage in technological research and development and transform research outcomes without worrying about technology infringement or plagiarism. Thus, the RCEP framework promotes knowledge sharing and technology transfer within the region, encouraging cooperation among member countries in technological innovation.

4.5.4. Optimizing the allocation of innovation resources.

The RCEP has established commitment standards that facilitate the movement of natural persons, intellectual property, service trade, and investment. This helps innovation resources flow easily within the region, making the entire region more attractive to external innovation resources. Enterprises can easily access innovation resources from other countries in the region, including technology, talent, and capital, thanks to the RCEP framework. This also contributes to the development of the regional innovation network, enhancing the region’s overall innovation capability. The free movement of talents among RCEP member countries facilitates innovative exchanges and collaborative research, enabling member countries to share scientific and technological resources and research outcomes, thereby promoting the rapid development of technological innovation in the textile and clothing industry.

4.5.5. Provision of collaborative innovation platforms.

The RCEP member countries can use the platform to strengthen international cooperation in technological innovation. This can be achieved by establishing fixed mechanisms for scientific and technological cooperation, promoting enterprise participation, and encouraging collaboration in new technologies [ 21 , 22 ]. By doing so, member countries can create more opportunities and platforms for cooperation. This will help them jointly develop new technologies, share resources and experiences, and tackle global challenges.

4.5.6. Promote innovation transformation and dissemination.

The RCEP has helped to reduce trade barriers between its member countries, making it easier for goods and services to move freely within the region. This has allowed innovative technologies and products to enter other member countries’ markets more quickly, facilitating their commercialization and industrialization. Additionally, the RCEP’s rules of origin have further reduced resource allocation costs and improved efficiency, creating a favorable business environment for foreign-invested enterprises. Both of these factors contribute to the application and transformation of inventions from innovative countries within the industrial chains of member countries.

The RCEP also provides more cooperation opportunities and resource support for innovative countries, promoting the further development and optimization of their inventions. Some Chinese enterprises are even implementing a "Made in China, Manufactured Globally" strategy by transferring part of their production capacity and technology to other RCEP member countries to utilize local resources better and save costs. Meanwhile, these Chinese enterprises focus on research and development of core technologies and production. The RCEP has incorporated data flow and information storage provisions into its institutional framework, creating a relatively lenient institutional environment for data transmission within the region while ensuring national security and personal privacy. Article 11 of Chapter 12 stipulates that "the Parties shall maintain their current practice of not imposing customs duties on electronic transmissions between the Parties," significantly reducing the transaction costs of data transmission among RCEP member countries and promoting cross-border data flow and aggregation, which is conducive to the dissemination of innovation achievements.

4.6. The RCEP integrates the textile and apparel industrial and innovation chains

The industrial and innovation chains are interconnected yet distinct systems, mutually supporting, depending on, integrating, and advancing each other. Their integration is evident in the amalgamation of production and innovation entities, processes, and technological advancements [ 23 ]. In summary, RCEP facilitates the integration of textile and apparel industrial and innovation chains by merging production entities, processes, and technologies, thereby promoting industrialization and innovation.

4.6.1. RCEP’s role in integrating production and innovation entities in the textile industry.

The production entities in the textile industry are the enterprises along the industrial chain. In contrast, the innovation entities include enterprises, scientific research institutions, and universities engaged in innovative activities in the textile industry [ 24 ]. Firstly, the cooperation mechanisms under the framework of the RCEP agreement provide countries with more platforms for exchange and cooperation, including technical seminars, industry matching meetings, and other activities. These events allow production and innovation entities to understand each other’s needs, share experiences and resources, and facilitate deeper cooperation in technological research and development, product innovation, and market development [ 24 ]. Secondly, the RCEP provides a more stable and transparent institutional environment, offering better institutional guarantees for cooperation among enterprises, reducing market uncertainty, and encouraging enterprises to integrate production and innovation actively. Furthermore, it also facilitates the exchange and cooperation among the innovation entities in the textile industry of various member countries in the region, contributing to establishing a cross-regional innovation network in the textile industry.

4.6.2. RCEP’s role in integrating the textile industry’s production and innovation processes.

Firstly, the RCEP has facilitated technological innovation in the textile production process. Under the framework of the RCEP, textile enterprises can more easily introduce advanced production equipment and technologies from other countries, which can improve production efficiency and provide strong support for their innovation activities. Secondly, technological innovation in the textile industry has better promoted the upgrading of regional industrial chains. Under the RCEP framework, research and development achievements in the textile field by one country’s innovation entities can achieve commercial application of the technology in other member countries. Thirdly, the RCEP has strengthened the market orientation in the textile industry’s innovation process. Opening national markets under the RCEP framework is conducive to enterprises more accurately grasping market demands and trends, thereby targeting innovation activities.

4.6.3. RCEP’s role in integrating technological advancement and industrialization in the textile industry.

The RCEP has positively impacted technological advancement in the textile industry. Its implementation has resulted in increased competition within the domestic textile market, forcing companies to improve the quality of their products and add more value to them. Consequently, enterprises have been prompted to invest more in technological research and development, accelerate their transformation and upgrading process, and raise their technological capabilities. The intensity of research and development investment in China’s textile industry has increased from 0.46% in 2012 to 1.02% in 2022. As of 2022, textile enterprises above a specific size have spent 53.51 billion yuan on R&D, marking a 3.82% increase.

Furthermore, the RCEP has accelerated the progress of technological industrialization. With technological innovation being the driving force behind new industrial boundaries, the development of material technology and industrial textiles is leading to faster integration of the textile industry with various fields such as the health industry, digital economy, bio-economy, green economy, and aerospace, among others. The RCEP has provided a conducive business and legal environment for technological innovation, which has expedited the process of technological industrialization.

4.7. Environmental, social, and governance (ESG) goals and impact of RCEP

The RCEP framework encourages the adoption of advanced technologies and practices that reduce the environmental impact of textile production. Member countries are likely to implement cleaner and more efficient production processes, significantly lowering greenhouse gas emissions, reducing waste, and conserving water resources [ 25 ]. For instance, introducing eco-friendly materials and energy-efficient machinery can play a crucial role in minimizing the environmental footprint of the textile industry. RCEP promotes harmonizing environmental regulations and standards across member countries [ 26 ]. This can lead to more stringent environmental protections and encourage companies to adhere to higher sustainability standards, fostering a more environmentally responsible industry. The RCEP agreement includes provisions that can positively impact labor standards and working conditions in the textile industry. By promoting fair trade practices and encouraging adherence to international labor standards, RCEP can help improve the well-being of workers in the textile sector. This includes better wages, safer working conditions, and eliminating exploitative labor practices. Enhanced economic activity and investments in the textile sector due to RCEP can lead to greater community development [ 14 ]. Increased employment opportunities and improved infrastructure can uplift local communities, contributing to social stability and growth. The RCEP’s emphasis on stable and transparent institutional environments supports better corporate governance in the textile industry. Companies are encouraged to adopt more transparent business practices, improve reporting standards, and enhance accountability, which can build investor confidence and drive sustainable growth. The RCEP framework can foster a culture of corporate social responsibility among textile enterprises. By adhering to international best practices and engaging in ethical business conduct, companies can contribute positively to society and the environment, aligning their operations with broader governance goals.

5. Conclusion

The Regional Comprehensive Economic Partnership (RCEP) is a pivotal agreement reshaping the dynamics of the member countries’ textile and apparel industries. This research elucidates the agreement’s transformative effects on trade, innovation, and industrial integration within the region through an in-depth analysis of its impact on various facets of the industry. The Regional Comprehensive Economic Partnership (RCEP) has transformed the textile industry in member countries. RCEP has increased textile trade among members, with China emerging as a dominant player. Tariff reduction initiatives have increased competitiveness and market access for member states. Eliminating tariff barriers and lenient rules of origin have stimulated demand for innovation, driving technological advancements in the industry. Intellectual property protection under the RCEP framework has fostered a conducive environment for knowledge sharing and technology transfer. RCEP has promoted international cooperation and accelerated technological industrialization within the textile industry. It has positioned member countries to thrive in an increasingly interconnected and competitive global market. The RCEP is a pivotal agreement reshaping the dynamics of the member countries’ textile and apparel industries. Furthermore, including ESG considerations highlights the RCEP’s comprehensive benefits beyond mere economic gains, emphasizing its role in promoting sustainable, socially responsible, and well-governed practices within the textile sector.

6. Limitations

The focus is primarily on quantitative trade dynamics and qualitative policy assessments, with less emphasis on technological advancements and geopolitical dynamics. Time constraints limit the inclusion of recent developments. While the findings offer insights into the textile industry within RCEP nations, generalizing them to other industries or regions may be limited. Time constraints restrict capturing recent developments, and the complexity of interactions within the industry poses challenges in fully interpreting relationships. Despite these limitations, the study provides valuable insights into the impact of RCEP on the textile industry and lays the groundwork for future research.

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Competitive advantages of sustainable startups: systematic literature review and future research directions.

1. Introduction

2. materials and methods, 2.1. stage 1—identification: searching the databases, 2.2. stage 2—screening: elimination of duplicates and linguistic adjustment, 2.3. stage 3—eligibility: analysis of titles, keywords, and abstracts, 2.4. stage 4—inclusion: detailed analysis and selection of articles.

Identification of the study: title of the article, authors, and year of publication;
Background: a brief description of the research problem;
Objectives of the study: main objective of the article;
Methodology: description of the research methods used;
Sample and data: sample size, source, and types of data;
Results: main findings of the study;
Limitations of the study: limitations or biases identified by the authors;
Contribution to the literature review: relation of the article to the present research;
Summary and conclusions: general conclusions of the study;
Additional comments: study quality, research relevance, and possible gaps.

3. Results and Discussion

3.1. descriptive analysis, 3.1.1. annual frequency of publications, 3.1.2. distribution by journal, 3.1.3. main authors, 3.1.4. co-authorship analysis, 3.1.5. countries analysis, 3.1.6. keyword analysis, 3.1.7. research methodologies, 3.2. content analysis, 3.2.1. competitive advantages of sustainable startups, 3.2.2. impact of competitive advantages on the esg perspective of sustainable startups, environmental perspective, social perspective, governance perspective, 3.2.3. future research directions on the competitive advantages of sustainable startups, economic–environmental balance in sustainable startups, impact of public policies and regional contexts, innovation, sustainability, and business models, the role of investors and market dynamics, startup performance and sustainability, 4. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Authors	Title	Journal	Year
Ariztia and Araneda [ ]	A “win-win formula:” environment and profit in circular economy narratives of value	Consumption Markets and Culture	2022
Bergmann and Utikal [ ]	How to Support Start-Ups in Developing a Sustainable Business Model: The Case of an European Social Impact Accelerator	Sustainability	2021
Beyhan and Fındık [ ]	Selection of Sustainability Startups for Acceleration: How Prior Access to Financing and Team Features Influence Accelerators’ Selection Decisions	Sustainability	2022
Boada et al. [ ]	Including Sustainability Criteria in the Front End of Innovation in Technology Ventures	Sustainability	2023
Bolis et al. [ ]	Sustainability Is All about Values: The Challenges of Considering Moral and Benefit Values in Business Model Decisions	Sustainability	2021
Costa et al. [ ]	Transformative Business Models for Decarbonization: Insights from Prize-Winning Start-Ups at the Web Summit	Sustainability	2023
De Angelis [ ]	Circular economy business models as progressive business models: Evidence from circular start-ups	Business Strategy and the Environment	2024
De Lange [ ]	Start-up sustainability: An insurmountable cost or a life-giving investment?	Journal of Cleaner Production	2017
Du et al. [ ]	Sustainable competitive advantage under digital transformation: an eco-strategy perspective	Chinese Management Studies	2024
Frare and Beuren [ ]	The role of green process innovation translating green entrepreneurial orientation and proactive sustainability strategy into environmental performance	Journal of Small Business and Enterprise Development	2022
Gidron et al. [ ]	The Impact Tech Startup: Initial Findings on a New, SDG-Focused Organizational Category	Sustainability	2023
Giones et al. [ ]	Balancing financial, social and environmental values: Can new ventures make an impact without sacrificing profits?	International Journal of Entrepreneurial Venturing	2020
Hegeman and Sørheim [ ]	Why do they do it? Corporate venture capital investments in cleantech startups	Journal of Cleaner Production	2021
Henry et al. [ ]	A typology of circular start-ups: An Analysis of 128 circular business models	Journal of Cleaner Production	2020
Hoogendoorn et al. [ ]	Goal heterogeneity at start-up: are greener start-ups more innovative?	Research Policy	2020
Horne and Fichter [ ]	Growing for sustainability: Enablers for the growth of impact startups—A conceptual framework, taxonomy, and systematic literature review	Journal of Cleaner Production	2022
Huang et al. [ ]	Influence of Ambidextrous Learning on Eco-Innovation Performance of Startups: Moderating Effect of Top Management’s Environmental Awareness	Frontiers in Psychology	2020
Jacob and Arcot [ ]	Patents and sustainable innovation in Indian Startups	Journal of World Intellectual Property	2023
Keskin et al. [ ]	Innovation process of new ventures driven by sustainability	Journal of Cleaner Production	2013
Keskin et al. [ ]	Product innovation processes in sustainability-oriented ventures: A study of effectuation and causation	Journal of Cleaner Production	2020
Klofsten et al. [ ]	Start-ups within entrepreneurial ecosystems: Transition towards circular economy	International Small Business Journal	2024
Kuckertz et al. [ ]	Responding to the greatest challenges? Value creation in ecological startups	Journal of Cleaner Production	2019
Lange and Banadaki [ ]	ESG consideration in venture capital: drivers, strategies and barriers	Studies in Economics and Finance	2023
Leendertse et al. [ ]	The sustainable start-up paradox: Predicting the business and climate performance of start-ups	Business Strategy and the Environment	2021
Li et al. [ ]	Relationship between green entrepreneurship orientation, integration of opportunity and resource capacities and sustainable competitive advantage	Frontiers in Psychology	2022
Liu and Zhang [ ]	Driving Sustainable Innovation in New Ventures: A Study Based on the fsQCA Approach	Sustainability	2022
Mansouri and Momtaz [ ]	Financing sustainable entrepreneurship: ESG measurement, valuation, and performance	Journal of Business Venturing	2022
Nunes et al. [ ]	Challenges of business models for sustainability in startups	RAUSP Management Journal	2022
Oliveira-Dias et al. [ ]	Fostering business model innovation for sustainability: a dynamic capabilities perspective	Management Decision	2022
Palmié et al. [ ]	Startups versus incumbents in ‘green’ industry transformations: A comparative study of business model archetypes in the electrical power sector	Industrial Marketing Management	2021
Piccarozzi [ ]	Does Social Innovation Contribute to Sustainability? The Case of Italian Innovative Start-Ups	Sustainability	2017
Rok and Kulik [ ]	Circular start-up development: the case of positive impact entrepreneurship in Poland	Corporate Governance	2021
Serio et al. [ ]	Green Production as a Factor of Survival for Innovative Startups: Evidence from Italy	Sustainability	2020
Sharma et al. [ ]	Machine Learning Strategies Fueling Economic Progress for Start-ups	Int. Journal of Intelligent Systems and Applications in Engineering	2024
Song and Xiang [ ]	Driving New Venture Sustainability: A Study Based on Configuration Theory and Resource Orchestration Theory	Sustainability	2023
Speckemeier and Tsivrikos [ ]	Green Entrepreneurship: Should Legislators Invest in the Formation of Sustainable Hubs?	Sustainability	2022
Sreenivasan and Suresh [ ]	Factors influencing sustainability in start-ups operations 4.0	Sustainable Operations and Computers	2023
Susteras and Zamith Brito [ ]	Value proposition development under uncertainty: A theoretical framework grounded on the trajectory of cleantech start-up founders	Journal of Cleaner Production	2023
Tiba et al. [ ]	Sustainability startups and where to find them: Investigating the share of sustainability startups across entrepreneurial ecosystems and the causal drivers of differences	Journal of Cleaner Production	2021
Tiba et al. [ ]	The lighthouse effect: How successful entrepreneurs influence the sustainability-orientation of entrepreneurial ecosystems	Journal of Cleaner Production	2020
Van Opstal and Borms [ ]	Startups and circular economy strategies: Profile differences, barriers and enablers	Journal of Cleaner Production	2023
Voinea et al. [ ]	Drivers for sustainable business models in start-ups: Multiple case studies	Sustainability	2019
Wagner and Kabalska [ ]	Between Involvement and Profit: Value (Un-)Captured by a Born-Social Start-Up	Journal of Social Entrepreneurship	2023
Zhang et al. [ ]	How Do New Ventures Implementing Green Innovation Strategy Achieve Performance Growth?	Sustainability	2022

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Click here to enlarge figure

Database Searches	Scopus	Web of Science	Total
Title, abstract, keywords	846	444	1290
Title, abstract, keywords (journal articles)	510	345	855

Journal	Number of Publications
Sustainability	13
Journal of Cleaner Production	11
Business Strategy and the Environment	2
Frontiers in Psychology	2
Chinese Management Studies	1
Consumption Markets and Culture	1
Corporate Governance	1
Industrial Marketing Management	1
International Journal of Entrepreneurial Venturing	1
Int. Journal of Intelligent Systems and Applications in Engineering	1
International Small Business Journal	1
Journal of Business Venturing	1
Journal of Small Business and Enterprise Development	1
Journal of Social Entrepreneurship	1
Journal of World Intellectual Property	1
Management Decision	1
RAUSP Management Journal	1
Research Policy	1
Studies in Economics and Finance	1
Sustainable Operations and Computers	1

Authors	Title	Citations
Henry et al. [ ]	A typology of circular start-ups: An Analysis of 128 circular business models	367
Keskin et al. [ ]	Innovation process of new ventures driven by sustainability	238
De Lange [ ]	Start-up sustainability: An insurmountable cost or a life-giving investment?	112
Hegeman and Sørheim [ ]	Why do they do it? Corporate venture capital investments in cleantech startups	90
Kuckertz et al. [ ]	Responding to the greatest challenges? Value creation in ecological startups	90
Mansouri and Momtaz [ ]	Financing sustainable entrepreneurship: ESG measurement, valuation, and performance	79
Horne and Fichter [ ]	Growing for sustainability: Enablers for the growth of impact startups—A conceptual framework, taxonomy, and systematic literature review	73
Tiba et al. [ ]	Sustainability startups and where to find them: Investigating the share of sustainability startups across entrepreneurial ecosystems and the causal drivers of differences	69
Tiba et al. [ ]	The lighthouse effect: How successful entrepreneurs influence the sustainability-orientation of entrepreneurial ecosystems	69
Piccarozzi [ ]	Does social innovation contribute to sustainability? The case of Italian innovative startups	64

Category	Competitive Advantage	Description	References
Aligning economic and environmental benefits	Avoiding the trade-off between profit and sustainability	Startups manage to generate profits while protecting the environment	[ , ]
Aligning economic and environmental benefits	Financial viability combined with environmental impact	The combination of environmental impact and financial viability positions startups favorably	[ , , ]
Innovation and product development	Integrating sustainability criteria into product development	Facilitates the creation of innovative and efficient solutions	[ , , ]
	Adaptive approach to product innovation	Enables startups to navigate efficiently in uncertain environments	[ , , ]
	Proactive market orientation	Facilitates continuous adjustment to market demands	[ , , ]
Financial and Institutional resources	Affiliation with accelerator programs and incubators	It gives legitimacy, makes it easier to obtain financing, and provides support in the early stages of development	[ , , ]
	Prior access to financing	It signals credibility to investors and accelerators	[ ]
	Ownership of patents related to ESG factors	Makes startups more attractive to investors	[ , , ]
Use of advanced technologies	Integration of advanced technologies	Optimizes processes, increases operational efficiency, and attracts investment	[ , ]
Use of advanced technologies	Focus on continuous innovation	Essential to ensure long-term sustainability and growth	[ , , ]
Creating sustainable and social value	Creating sustainable value	Aligns moral values with the creation of economic and social value, attracting like-minded stakeholders	[ , , ]
Creating sustainable and social value	Inclusion of socially vulnerable groups in the production process	Broadens the employee base and improves human capital, increasing the company’s legitimacy and acceptance	[ , , ]
Circularity and Sustainability strategies	Adoption of circularity strategies	It allows for greater retention of the value of resources, strengthening the startup’s resilience and adaptability	[ , , ]
Circularity and Sustainability strategies	Advanced circularity strategies	They allow greater retention of the value of resources	[ , , ]
Positioning and reputation	Positioning as leaders in sustainability	Improves reputation and legitimacy	[ , , ]
Positioning and reputation	Strong collaborative networks and a robust knowledge base	They drive continuous innovation	[ , ]
Human resources and management	Ability to attract and retain qualified talent	Improves customer satisfaction and reduces capital costs	[ , , ]
Human resources and management	Ambidextrous learning and environmental awareness among top management	Promotes the effective integration of sustainable practices, raising eco-innovation performance	[ , , ]
Engagement with the SDGs	Direct approach to the SDGs	Attracts more funding and generates positive impact in areas such as health, education, well-being, and gender equality	[ ]
Flexibility and adaptability	Organizational flexibility and rapid adaptation to market changes	Essential for the sustainability and competitiveness of startups	[ , , , ]

Perspective	Impact	References
Environmental	Implementation of innovations that minimize environmental impact and promote regenerative practices	[ , ]
	Financial viability and positive environmental impact	[ , ]
	Advanced technologies for operational efficiency	[ , ]
	Circularity strategies and green process innovations	[ , , ]
	Development of sustainable products that meet environmental regulations	[ , , ]
Social	Moral values are integrated into the generation of economic and social value, attracting stakeholders	[ , ]
	Inclusion of socially vulnerable groups, promoting diversity	[ ]
	Alignment with the Sustainable Development Goals (SDGs), improving quality of life	[ ]
	Strengthening relations with investors, clients, and the community	[ ]
	Improving human capital and increasing legitimacy	[ , ]
Governance	Efficient management practices and regulatory compliance	[ , ]
	Affiliation with accelerator programs and access to funding	[ , ]
	Ownership of patents and integration of advanced technologies	[ , , , ]
	Organizational flexibility and ability to respond quickly to the market	[ , ]
	Proximity to research centers and universities for access to advanced knowledge	[ , , , ]

Future Research	Research Questions	Sources of Support
Economic–environmental balance in sustainable startups	RQ 1. How do sustainable startups balance economic and environmental value creation in different sectors?	[ , , , ]
	RQ 2. How does integrating ESG criteria impact startups’ financial performance and innovative capacity?	[ , , ]
	RQ 3. What is the effect of a broader definition of sustainable startups in creating value and communicating their environmental impact?	[ , ]
Impact of public policies and regional contexts	RQ 4. How do public policies related to sustainability influence the promotion of different types of startups, and what are their effects in the short, medium, and long term?	[ , , , ]
	RQ 5. What regional particularities influence the effectiveness of social impact acceleration programs in different economic and political environments?	[ , ]
	RQ 6. How do variations in regional contexts influence the attractiveness of sustainable startups for investors?	[ , , ]
Innovation, sustainability, and business models	RQ 7. How can the integration of ESG dimensions in a Sustainable Business Model (SBM) be optimized to increase the effectiveness of sustainable startups?	[ , , ]
	RQ 8. How do different types of green innovation affect the environmental performance of sustainable startups?	[ , , , ]
	RQ 9. What are the strategies for engaging communities and stakeholders in co-creating sustainable innovations?	[ , ]
	RQ 10. How can sector diversification influence understanding the factors that drive sustainable business models in different industries?	[ , , ]
The role of investors and market dynamics	RQ 11. How do investors influence the definition and validation of value propositions in sustainable startups?	[ , , ]
	RQ 12. What factors motivate founders and investors to engage with sustainable startups?	[ , ]
	RQ 13. How do small and medium-sized companies contribute to the success of sustainable startups through corporate venture capital investments?	[ , ]
	RQ 14. How does the integration of ESG criteria vary between the different stages of investment in startups?	[ , ]
Startup performance and sustainability	RQ 15. How do the objectives of sustainable startups evolve, and what factors influence these changes?	[ , , ]
	RQ 16. Which variables related to the entrepreneurial context affect the sustainable performance of startups?	[ , , ]
	RQ 17. How does the quality and extent of networking influence the success of sustainable startups?	[ , , ]
	RQ 18. How can adopting ESG practices increase the resilience and longevity of sustainable startups?	[ , ]

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Martins de Souza, A.; Puglieri, F.N.; de Francisco, A.C. Competitive Advantages of Sustainable Startups: Systematic Literature Review and Future Research Directions. Sustainability 2024 , 16 , 7665. https://doi.org/10.3390/su16177665

Martins de Souza A, Puglieri FN, de Francisco AC. Competitive Advantages of Sustainable Startups: Systematic Literature Review and Future Research Directions. Sustainability . 2024; 16(17):7665. https://doi.org/10.3390/su16177665

Martins de Souza, Adriano, Fabio Neves Puglieri, and Antonio Carlos de Francisco. 2024. "Competitive Advantages of Sustainable Startups: Systematic Literature Review and Future Research Directions" Sustainability 16, no. 17: 7665. https://doi.org/10.3390/su16177665

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Published: 29 August 2024

Trade openness, economic growth and economic development nexus in South Africa: a pre- and post-BRICS analysis

Micaela Naledi Monyela 1 &
Charles Shaaba Saba ORCID: orcid.org/0000-0001-6230-7292 1

Humanities and Social Sciences Communications volume 11 , Article number: 1108 ( 2024 ) Cite this article

Metrics details

Business and management

This research analyses the interplay between trade liberalisation, economic growth, and economic development in South Africa. The research focuses on two distinct periods, pre-BRICS (1991 to 2010) and post-BRICS (2011 to 2021) and aims to assess economic growth and development trajectories which are intertwined with liberalisation. A Vector Error Correction Model (VECM) is used to account for potential cointegration among the variables. The study reports a long-run equilibrium relationship among the variables. The study finds that trade openness substantially influences GDP growth in the post-BRICS period and highlights a unidirectional causal relationship between trade liberalisation and economic growth. The research also reveals a positive association between trade openness and economic development, implying that openness fosters growth and facilitates broader development outcomes in South Africa. The research underscores the importance of trade openness as a driving force for economic growth and economic development in emerging economies like South Africa.

The dynamic relationship between trade openness, foreign direct investment, capital formation, and industrial economic growth in China: new evidence from ARDL bounds testing approach

Political stability and foreign direct investment inflows in 25 Asia-Pacific countries: the moderating role of trade openness

Revisited the role of foreign aid in capital formation: experience of South Asian countries

Introduction.

Brazil, Russia, India, China, and South Africa, also known as BRICS, is an identified potentially powerful economic bloc with a framework that enables these countries to jointly contribute to fighting hunger and penury among their populations (Mazenda 2016 ). The concept of BRIC originated from a study conducted at Goldman Sachs in 2001, identifying Brazil, Russia, India, and China as rapidly emerging and expanding economies on route to becoming global powerhouses and dominating the global economy. Furthermore, BRICS was formed with the expectation that its member nations would collectively dominate global economic growth by the year 2050. The main aim of BRICS is to encourage commercial, political, and cultural cooperation amongst BRICS nations and to pursue growth with inclusive, sustainable solutions, with the critical foundation being poverty reduction (Mazenda 2016 ).

South Africa joined BRICS in 2010 after former President Jacob Zuma emphasized the importance of participating in this group, as emerging economies are essential for reshaping global economic and political institutions to be more equitable and balanced (Besada et al. 2013 ). South Africa joined BRICS to enhance South-South relations and align with its foreign policy objectives, aiming to gain economic benefits like increased trade and investment prospects and political benefits such as a stronger international voice. Additionally, South Africa’s connection with BRICS highlights its regional power and its capacity to advocate for the interests of Africa. The BRICS economies collectively represent a significant portion of the global GDP, population, and global growth, as well as a substantial share of lobal exports (Mason et al. 2017 ; Staff 2013 ; Clements et al. 2023; New Development Bank 2022 ). Economic integration has become a key feature of globalisation, influencing the economic landscapes of nations and regions around the world. The formation of economic blocs, such as BRICS, aims to enhance cooperation, boost economic growth, and foster development among member countries. Understanding the implications of economic integration, particularly for a member country like South Africa, is crucial for policymakers and stakeholders.

Economic integration involves unifying economic policies between different states through the partial or complete abolition of tariff and non-tariff trade restrictions. It aims to reduce costs for consumers and producers and increase trade among the involved countries. Established in 2009 and joined by South Africa in 2010, BRICS exemplifies such integration, representing a significant portion of the world’s population, GDP, and trade.

The aim of this study is to determine whether trade openness has had an influence on the economic growth and development of South Africa since joining BRICS. Furthermore, the study will compare the South African economy pre- and post-BRICS to establish whether there are variations in the economic growth and development of the South African economy since joining BRICS and how the growth and development differs from that of pre-BRICS. Finally, the study will analyse the nexus between trade openness, economic growth, and development in South Africa and investigate if joining BRICS has stimulated economic growth and development in South Africa.

While reviewing the current literature on openness and economic growth, researchers conducted theoretical and empirical studies to evaluate how foreign trade affects trade openness in BRICS countries. Rani and Kumar ( 2018 ) analysed the effect of openness on growth in BRICS nations by employing a panel data analysis. They used annual data from a sample period from 1991 through to 2016. They concluded that BRICS nations achieved high economic growth in the post-liberalisation era, which helped them grow. However, the most remarkable aspect of the current research founded on the growth of the BRICS nations pre- and post-BRICS is that these studies have primarily focused on the openness-growth nexus, and none incorporate economic development. Nevertheless, the causal link between trade openness and growth is crucial because whatsoever the causality direction between the variables, it will ultimately influence the development level one way or another. Thus, the primary intent of this paper is to tackle this gap in the existing research.

Burange et al. ( 2019 ) utilised a time series analysis, which allowed for a breakdown of significant instabilities within trade openness during a time interval and distinguished specific characteristics of each country to study the causal relationship between openness and growth for each BRICS nation. In another study, Rani et al. ( 2018 ) utilised panel data from 1993 to 2015 to examine probable cointegration and causality direction between foreign direct investments, openness, and growth within BRICS nations. The research outcomes proved that trade liberalisation positively impacts growth and suggested BRICS liberalise trade as this could strengthen the BRICS member countries’ position in the global economy.

Shayanewako ( 2018 ) applied various analytical methods such as the cointegration approach, the causality test by Granger, and panel data evaluation to examine the link between growth and openness within the context of BRICS. The results revealed that the variables have a long-run relationship, with bi-directional causation stemming from trade openness towards economic growth being evident in almost every BRICS nation. The study also reported unidirectional causality between trade openness and production growth. However, the research confined its analysis to the openness-growth nexus for the BRICS nations without specific emphasis on a particular nation and how the nexus affects the development of the countries under study. It suggested the need for more comprehensive empirical research that includes more variables, data, and practical techniques.

The literature on the openness-growth link is referenced in most studies, and many practical outcomes have suggested a long-run positive relationship in the openness-growth link. The key objective of this research paper is to evaluate the openness-growth-development interplay in South Africa pre-and post-BRICS. The study further analyses the economic performance of South Africa to establish how the nation has developed post-BRICS and compares this development to that of pre-BRICS. Therefore, this investigation sets itself apart from prior research in the following ways:

The focus is on the period 1980 to 2021 using the most recent total trade (% GDP) as a proxy for trade openness (the UNCTAD reliable catalogue on trade) and HDI (economic development proxy) data for South Africa for the pre-and post-BRICS eras. South Africa, as a member of the BRICS group, has undergone significant economic transformations. Trade openness, marked by the reduction of trade barriers and increased integration into the global economy, has been a crucial component of South Africa’s economic policy. While numerous studies have examined the impact of trade openness on economic growth, the broader implications for economic development remain underexplored.

This paper aims to fill this gap by examining the nexus between trade openness, economic growth, and economic development in South Africa. Specifically, it analyses the periods before and after South Africa’s inclusion in BRICS (pre- and post-2010) to understand how trade policies have influenced the country’s overall development. By incorporating economic development into the analysis, this study offers a more holistic view of South Africa’s progress. Understanding the broader effects of trade policies can help policymakers design strategies that promote sustainable and inclusive growth and development. Understanding the specific impacts of BRICS membership on South Africa’s economy can help in fine-tuning policies to leverage international partnerships more effectively and address any adverse effects or disparities that may arise. This research also contributes to the literature by providing empirical evidence on the interplay between trade openness, economic growth, and economic development in a BRICS context.

Furthermore, studying the trade openness-economic growth-development nexus for the case of South Africa enables assessment of the impact of this membership on South Africa’s trade patterns, economic growth, and development indicators, since the BRICS partnership emphasizes economic cooperation, trade enhancement, and investment flows among member countries (Zhongxiu and Qingxin 2020 ). Economic growth does not automatically translate into development, therefore, investigating the trade openness-development indicator nexus for South Africa in this study provides a comprehensive understanding of how trade policies affect broader socio-economic outcomes. This is particularly relevant in assessing whether the economic benefits of BRICS membership have been inclusive and equitable (McKay et al. 2000 ). South Africa’s role within BRICS presents a unique opportunity to study South-South trade relations. Unlike traditional North-South trade dynamics, BRICS promotes cooperation among emerging economies, which can offer different insights into trade-induced growth and development (Chuang 2002 ). Analyzing these dynamics provides a deeper understanding of how emerging economies can collectively influence each other’s economic trends, especially in the case of South Africa (Besley and Cord 2007 ). From an academic perspective, this study enriches the literature on the trade openness-economic growth-development nexus by providing empirical evidence from a significant emerging economy. Practically, it offers data-driven recommendations for South African policymakers, businesses, and trade negotiators to optimize their strategies in the international economic arena.

The study uses the Johansen cointegration analysis to evaluate the long-run liberalisation effects on growth and development in South Africa pre- and post-BRICS by employing the Johansen and Juselius ( 1990 ) procedure, which demonstrates superior performance in comparison with traditional residual-based assessments when conducting robust tests in the context of time-series reliance. Previous studies have not utilized the VECM framework to investigate the topic at hand. Therefore, subsequent to determining the long-run cointegrating relationships, the VECM is used to estimate the short-run disequilibrium correlation between variables (Saba 2023 ). To address the gap in existing research, this paper aims to evaluate the relationship between trade liberalisation, economic growth, and development in South Africa before and after BRICS, focusing on the short and long-term equilibrium presence as well as the causal direction between these variables. Thus, the VECM framework is applied to analyse the connection between the variables from 1980 to 2021.

Many studies analyse the trade openness effect on economic growth, and the empirical outcomes showcase a positive link. Results from Rani and Kaur ( 2018 ), on a study of openness on growth from 1991 to 2016, indicated that the post-liberalisation era has helped BRICS economies achieve high growth in their economies. However, only a few studies have analysed how liberalisation and growth have impacted the development of the economy of South Africa; hence, this study will explore this scenario. Thus, questions worth asking are: What is the trade-growth-development nexus in South Africa since joining BRICS, and how has the openness-growth link affected the development of South Africa? What has been the impact of trade openness on economic growth and economic development indicators, such as Human Development Index, in the pre- and post-BRICS periods? How do the effects of trade openness on economic growth and development compare between the two periods?

The remainder of the study highlights the review of existing literature for theoretical and empirical studies related to the topic at hand, followed by the methodology, the results and interpretation, and the discussion of the results. The last section provides an overview of the entire paper, while also providing policy recommendations based on the results.

Literature review

The literature review comprises of two sections. The initial section includes the traditional theoretical information about the interplay between openness, growth, and development that led to the study. The second section incorporates present empirical research related to the topic. The relationship between openness and growth has been explored extensively by various academics. However, there is no unanimity among the existing studies on this topic. Therefore, this literature review focuses on relevant theories and empirical literature to bridge the knowledge gap on this subject.

Theoretical review

During the last few decades, there have been extensive studies done in both theoretical and empirical literature on the connection between openness and growth. However, there still needs to be agreement on whether greater openness to trade leads to economic growth. Different schools of thought, such as the comparative advantage theory of international trade and the endogenous growth theory, have been used to incorporate trade openness, economic growth, and development. The comparative advantage theory emphasises the gains from trade when countries specialise in production goods for which they possess a comparative advantage. In the context of South Africa, the approach highlights the potential advantages of trade openness. Kruger (1978) and Bhagwati (1978) argued that the liberalisation of trade promotes specialisation in sectors with economies of scale, which ultimately leads to improved long-term productivity and efficiency. Multiple studies have corroborated the positive impact of trade openness on the potential for economic growth (Ajayi and Araoye 2019 ; Shayanewako 2018 ; Rani and Kaur 2018 ).

As per the endogenous growth theory, a direct correlation exists between trade openness and economic growth, attributed to the widespread dissemination of advanced technologies globally (Frankel and Romer 1999 ). The endogenous growth theory postulates that trade openness can facilitate innovation and knowledge transfer. This theory is particularly relevant in the post-BRICS era, where increased international collaboration can enhance South Africa’s technological capabilities. A country’s degree of openness directly impacts its capability to utilise technologies produced in developed economies, leading to more rapid growth. Additionally, Pettinger ( 2019 ) suggests imitation cost plays a crucial role in the trade-growth relationship. While many arguments suggest that developing economies can benefit from international trade with technologically developed countries, some opposing views highlight the potential drawbacks of openness on growth. In cases where a nation specialises in sectors where research and development activities are not core, openness may be detrimental to growth (Abdul et al. 2012 ).

Trade openness either positively or negatively affects international trade. According to economic theory, opening trade can stimulate economic development and growth. The idea of a relationship between trade and growth goes back to the days of Karl Marx and Adam Smith (Burange et al. 2019 ). Many investigations have been done to explore the connection between the openness of trade and growth in BRICS countries, with a particular emphasis on South Africa. However, only some studies have researched the relation between trade liberalisation and development in the BRICS nations.

Openness-growth nexus

Literature on the trade-growth nexus is broadly documented on theoretical and empirical platforms, even though it has mixed empirical results. The comparative advantage theory and the endogenous growth theory explain the positive correlation between the adoption of trade strategies and growth through human capital and knowledge, critiquing neoclassical growth theories such as the Solow ( 1957 ) technological progress growth model which is entirely exogenic; highlighting the trade-growth relationship. The comparative advantage theory states that a nation specialising in producing goods or services at a lower opportunity cost than its trading partners will be more economically competitive (Shayanewako 2018 ). As a result, trading those goods or services with other nations results in the economy’s growth.

Openness-development nexus

Few empirical or theoretical studies were conducted on the trade openness-development nexus. The few studies on the development-trade openness nexus have primarily utilised financial development as a proxy for development, while others have incorporated human capital accumulation as a proxy. However, in this study, the Human Development Index will be utilised as a development proxy to analyse whether the results of the previous studies with alternative proxies for development may have similar results to that of this study or yield different results.

Empirical literature

Rani and Kaur ( 2018 ) used an absolute time-series analysis. They applied a descriptive method and econometric and statistical techniques to study the trade liberalisation effect on growth for BRICS nations at aggregate and disaggregate levels from 1991 to 2016. They discovered that the post-liberalisation era helped BRICS nations achieve high growth and that increased integration with the world economy has the potential to reduce poverty through employment opportunities in the export industry. A study which utilised diverse, dynamic panel data models with a combined cross-section and period fixed effects model for 95 nations from 1980 to 2017 studied the influence of trade liberalisation on the relation between resource abundance and growth (Majumder et al. 2020 ). The study concluded that even though resource abundance can be a curse to the growth of economies, trade openness causes a significant decrease in the resource curse, lowering the chances of the resource curse affecting growth. Fatima et al. ( 2020 ) assessed the link between GDP growth and trade liberalisation with an analysis of the human capital accumulation (HCA) role using the System Generalised Methods of Moments (SGMM) dynamic panel data models estimator from 1980 to 2014. They concluded that when considering HCA, the results suggested that a non-linear pattern existed amongst trade liberalisation and growth in which trade might negatively impact actual output when nations display low HCA levels.

Many empirical studies on the openness-growth nexus acknowledge a positive relationship between variables, regardless of the trade openness proxies and methodologies used. A study by Tahir and Azid ( 2015 ) found that in developing countries, the relationship between trade openness and economic growth is positive and statistically significant. They further found that frequent price fluctuations are detrimental to long-run economic growth. Furthermore, a study examining the impact of trade openness on economic growth by Cheung and Ljungqvist ( 2021 ) used a panel data analysis while also utilising a linear regression model with fixed effects and found that trade openness has a positive and significant effect on economic growth. Ajayi and Araoye ( 2019 ) used the cointegration test and the Engle and Granger test to evaluate the trade liberalisation effect on the growth of Nigeria with secondary statistics from 1970 to 2016. The findings showcased the existence of equilibrium and a long-run positive economic relationship.

Trade openness and development nexus

Thi Thuy and Trong ( 2021 ) conducted an empirical study of the financial and trade liberalisation influence on economic advancement from 2003 to 2017 for 64 developing nations. They employed the Bayesian model averaging procedure to account for model uncertainty. They captured the relation between openness and financial development, and the outcomes suggested that trade openness is a fundamental financial development determinant. They further revealed an insignificant relationship between financial growth and simultaneously opening both capital and trade accounts. Another paper by Kim et al. ( 2010 ) explored the dynamic effects of trade liberalisation on financial development by employing the Pooled Mean Group approach of Pesaran et al. (1999). The study’s outcomes determined the coexistence of the long-term positive and the short-term negative relationships between trade openness and financial development. Levchenko et al. ( 2004 ) used an OLS technique with a fixed effects model and, therefore, argued that the financial development of a country is endogenic and thus trade influenced.

The results of the connection between trade liberalisation and development have shown a link between the variables of the tests used. Panel data analysis used by Fatah et al. ( 2012 ) found a positive link between trade liberalisation and development, while a theoretical model utilised by Beck ( 2002 ) also concluded that a possible causal link exists between financial advancement and trade.

Openness-growth-development nexus

Throughout the review of analyses on trade liberalisation, growth, and development nexus, it was discovered that only a few, if any, studies have been conducted on the nexus between the three variables. There have been studies done on the link between trade liberalisation and growth. A landmark study done by Frankel and Romer ( 1999 ) discovered that there was a positive relationship between the two variables, which is both significant and weak. However, a study done by Fatima et al. ( 2020 ) used the system generalised method of moments (SGMM) estimator and discovered that subsequent studies have not found the trade liberalisation impact on growth due to potential endogeneity concerns and misspecifications in estimation.

There have been few studies done on trade openness’s impact on development. Separate analyses were done (Bandura 2020 ; Bandura 2022 ) on 26 sub-Saharan African nations from 1982 to 2016, with both studies having different outcomes. The first study discovered that trade liberalisation negatively affects financial development. In contrast, the second study revealed no objective evidence of trade openness impacting financial development. Both studies utilised similar models, which had minor differences. Both studies used the SGMM methodological approach, however, the second approach was estimated with five-year (non-overlapping) averaged data.

The primary intent of this research is to assess the connection between trade liberalisation and growth and development, with HDI as a proxy for development. The primary aim is to determine if a causal link exists between the variables and if the relationship is interrelated. The Human Development Index (HDI) is a composite statistic that measures a country’s average achievements in three basic aspects of human development: health (life expectancy at birth), education (mean years of schooling and expected years of schooling), and standard of living (Gross National Income per capita). By incorporating these dimensions, HDI provides a more holistic and comprehensive measure of development compared to using economic growth indicators alone, such as GDP per capita. This allows for a better understanding of how trade liberalisation impacts various facets of human well-being, not just economic performance.

Most existing studies focus either on the trade openness-growth nexus or the trade openness-development nexus. Few, if any, have comprehensively and simultaneously examined the interconnectedness between trade liberalization, economic growth, and development. By our study integrating these three dimensions, we provided a holistic understanding of how trade openness concurrently influences both economic growth and development. Previous studies have predominantly used financial development or human capital accumulation as proxies for development. There is a lack of research employing the Human Development Index (HDI) as a comprehensive measure of development. By using HDI, our research provides a broader perspective on development, capturing health, education, and standard of living, and thus offering a more nuanced analysis of the impact of trade openness, as mentioned earlier. There is limited research specifically comparing the effects of trade openness on economic growth and development before and after South Africa’s BRICS membership. Our study fills this gap by analysing two distinct periods (1991 to 2010 and 2011 to 2021), providing insights into how BRICS membership has influenced South Africa’s economic and development trajectories.

While many studies have used various econometric methods, the application of Granger causality, Vector Error Correction Model (VECM), Impulse Response, and Variance Decomposition techniques in a single study is rare. Our study employs these advanced techniques to robustly assess the causal relationships and dynamic interactions between trade openness, economic growth, and development, ensuring rigorous and comprehensive analysis. Many studies offer general findings applicable to multiple countries but lack context-specific insights, particularly for South Africa within the BRICS framework. By focusing on South Africa, our research provides detailed, context-specific insights that are crucial for policymakers, highlighting how trade policies can be optimized for inclusive growth and development within the unique South African context.

Methodology and data

This section outlines the design of the research and the procedures used to inspect the interrelationship between trade openness, growth, and development in South Africa pre- and post-BRICS. The employment of this methodology in this empirical analysis is crucial as it aims to achieve the research objectives and gain insight into the economic performance and development dynamics of South Africa over time.

The main priority of this paper is to explore how trade openness, measured by total trade, influences the economic growth and development indicators in South Africa, while taking into consideration the impact of its participation in the BRICS association. The study seeks to assess whether any changes have occurred in the openness-growth-development nexus by examining data before and after South Africa made an entry into the BRICS group.

Analytical framework

Growth is a function of development and trade and as a country improves and develops in various dimensions, it tends to experience higher levels of economic growth. Additionally, the level of trade activity and liberalization in an economy has a substantial impact on the overall growth of the economy (Krueger and Myint 2023 ; Gwaindepi et al. 2014 ). This emphasizes that changes in trade volumes, patterns, and policies can directly influence the pace and trajectory of economic expansion in a nation. The study is centred on an augmented endogenous growth model that assumes this implication. The comparative advantage theory is given importance and trade liberalization principles are highlighted for their significant impact on economic growth and development. The importance of the comparative advantage theory is well defined, and it stresses that if a nation enjoys an absolute advantage in production, then trade will be beneficial for the involved partners (Ricardo 2005 ).

The Cobb-Douglas production model with constant returns to scale is used to denote the aggregate output function at time t. This model is a mathematical representation of the relationship between inputs and outputs in the production of a good or service.

Y — Output; A — Technology;K — Capital; L — Labour; TRD-Calculated ration of total trade; β 1 , β 2 and β 3 — Labour and capital elasticities, respectively

The Cobb-Douglas function includes independent variables to control for their effects on the production process and to account for their potential impact on the relationship between the main factors and output. The explanatory variables are not primary factors, but they do have an impact on the output. Additionally, an error term is included in the function to account for various factors that could affect production, however, are not explicitly considered in the model.

In this study, several variables were used to measure different aspects of economic growth and development. Y represents per capita GDP, economic growth proxy. Per capita GDP is selected as a proxy for economic growth because it measures the average economic output per person and accounts for population changes, providing a standardized and comparable measure of economic well-being and growth across different periods. HDI represents the Human Development Index and serve as a proxy for development. TRD was used to measure trade openness through total trade, defined as the sum of exports and imports as a percentage of GDP. This measure captures the overall trade activity and economic integration of South Africa with the global market. It reflects the extent to which the country is open to international trade, which is crucial for understanding the impact of trade policies on economic performance.

To ensure a comprehensive analysis, the study includes several control variables that capture other critical factors influencing economic growth. The paper includes exchange rates (EXR), foreign direct investment (FDI) and government expenditure (GEXP) as control variables. The exchange rate is included to control for its impact on the trade balance, competitiveness, and investment decisions. Fluctuations in the exchange rate can significantly affect the relative prices of exports and imports, thereby influencing the overall economic performance. FDI is a major source of capital inflows, technology transfer, and job creation. By including FDI as a control variable, the study can account for its direct impact on economic growth and ensure that the observed effects of trade openness are not conflated with the contributions of foreign investments. Government spending is a crucial determinant of economic activity, influencing demand, public investment, and economic stability. Including government spending as a control variable allows the study to isolate the effects of trade openness from fiscal policy measures. ε is the idiosyncratic term presumed to be distributed normally whereas ${\beta }_{1}$ ,…, ${\beta }_{6}$ are the elasticities of output for the variables on the right-side of the equation. The government assumes that technological parameter A is determined by their spending. A is a control variable that can impact economic growth and development, especially when investments are made towards infrastructure, education, and healthcare. It is therefore integrated into the below production function:

Where GEXP is government spending (%GDP), and g is the Harrod-neutral technological advancement exogenous rate, according to Pettinger ( 2019 ), changes in expenditure affects both the supply- and demand-side of the economy. Higher state spending might potentially reduce inequality, improve education, and labour productivity, and potentially lead to the inefficiency of government spending on the supply side. The research analyses the relationship between trade openness, economic growth, and development indicators over time using a time series approach. The log-linear form of the model is used for the empirical estimation, and it consists of two main models which stem from other models, and these two main models are written as follows:

Details on the variables in both Eqs. 4 and 5 can be found in 3.3.1 and 3.3.2. The above models show an analytical approach for evaluating the connections between trade openness, economic growth and development which are the focus of the study.

Statistical tools and techniques applied

To help smooth out the fluctuations in the data series and make it fit and linear for econometric assessment (Verma and Rani ( 2016 )), the variables are first logged and converted into a log series.

Selection criteria for the lag length

Determining the optimal lag length of models is crucial before running more complex econometric tools. This ensures that the models are effective and well-defined. The process involves selecting the number of past observations to include in the models, which affects model complexity, computational efficiency, overfitting prevention, and forecasting accuracy. There are various lag selection criteria available, but the most frequently used are AIC, SBC, and HQ. Past studies, including Verma and Rani ( 2016 ), Visalakshmi and Lakshmi ( 2016 ), and Singh et al. ( 2022 ), have primarily utilized AIC as their lag selection criterion.

Unit root test

The study analyses the time-series features to establish the relationship between the variables using a 3-stage approach. The first stage involves using a unit root test to determine the integration order. This is important because using stationary data to estimate the causal link can lead to spurious results (Granger and Newbold 1974 ).

To conduct the unit root test, the ADF t-test is applied. This test is crucial for selecting appropriate models, performing cointegration analysis, ensuring model validity, avoiding spurious regressions, and improving forecast accuracy in econometrics. The ADF test handles more complicated models compared to the Dickey-Fuller test, and it is more robust (Saba 2021 ; David et al. 2024 ). The equation used for conducting the ADF test is given below.

The order of integration signifies the number of differencing operations that are necessary to make a non-stationary time series stationary. The ADF test determines if a series is stationary, and rejecting the null hypothesis indicates that the series is stationary. The equation for the test involves a set of variables, including Y, and a random error term represented by ε.

Cointegration test

The second approach is the cointegration test of Johansen used when exploring the long-run dynamic relationship between different variables under study (Johansen and Juselius 1990 ). Using this test, the cointegrating vectors for the variables in this study are estimated for the establishment of long-run relationships. Therefore, the equation below is used:

Y is an $n\times 1$ vector (in this case a column vector for South Africa) for the variables integrated of order d.

$\mu$ is a vector intercept.

${\Pi }_{1}$ to ${\Pi }_{p}$ are an $n\times n$ coefficient matrices of coefficients.

${\varepsilon }_{t}$ is a normal independently distributed error term with zero covariance and mean matrix ∧ .

The trace statistics ( ${J}_{{trace}}$ ) and the maximum eigenvalue statistics ( ${\lambda }_{\max }$ ) are tests for examining the cointegration equation and were provided by Johansen ( 1988 ). They have the following equations, respectively:

With T as the sample number and the predicted ${i}^{{th}}$ ordered eigenvalue from $\varPi$ matrix as $\hat{\lambda }$ . The Johansen maximum likelihood approach follows the standard approach of calculating the maximum eigenvalue first value and that of the trace statistics, and then both are compared with the fitting critical values. The test focuses on the log-likelihood ratio and is sequentially conducted. The following is the maximum eigenvalue test equation:

Both tests have the same null hypothesis stating the existence of r cointegrating relationships in the model with an alternative hypothesis stating that there is $r+1$ cointegrating relationships in the model.

Granger causality

The cointegration test suggests a long-run relationship between the variables. However, the causality direction between the variables is not determined. This test measures the causal relationship concerning two distinct time series, which are short-term (Granger 1988 ). The procedure portrays that if the previous values of a variable, for example, GDP denoted X in the equation, contribute significantly to forecasting the importance of another variable, for instance, LHDI denoted Y in the equation, then the assumption would be that the GDP series Granger causes LHDI and the LHDI series Granger causes GDP (Granger 1988 ). Furthermore, it is possible to find unidirectional and bi-directional causality amongst two data series. The following equations are performed based on the causality test:

X and Y are the variables under study.

${\varepsilon }_{1t}$ and ${\varepsilon }_{2t}$ are the error terms.

$t$ is the time interval.

$k$ are the variable lag numbers.

$m$ is the maximum lag number for the model observations.

Y Granger causes X if the ${\gamma }_{1}$ coefficients are significant while those for ${\gamma }_{2}$ are not, and vice versa. If the causality runs in both directions, the ${\gamma }_{1}$ and ${\gamma }_{2}$ are significant. According to Tripathi and Seth ( 2014 ), the variables (Y and X) will be independent if both ${\gamma }_{1}$ and ${\gamma }_{2}$ are statistically indifferent from zero.

Vector error correction model

To obtain the long- and short-run relationship between variables, the VECM is used (Engle and Granger 1987 ). VECM analyses the dynamics and relationships between non-stationary time-series, particularly when there is cointegration. It provides insights into the long-run equilibrium, short-run dynamics, and causality of variables. Prakash et al. ( 2017 ) stated that the VECM is further used for estimating the short-run disequilibrium after the establishment of the cointegrating relationship in long-run. They further stated that the VECM is a simple error correction model that is multivariate. The cointegrated term of the VECM is known as the error correction term and the VECM illustrates the adjustment speed from short-run to long-run equilibrium (Tripathy, 2015 ). The model will be employed in this study and denoted as follows:

Where ${{LGDP}}_{t}$ and ${{LHDI}}_{t}$ represent logarithms of GDP per capita (economic growth proxy) and HDI (proxy for development) respectively; whereas ${\triangle {LHDI}}_{t-i}$ , ${\triangle {LGDP}}_{t-i}$ , ${\triangle {LTRD}}_{t-j}$ , ${\triangle {LEMP}}_{t-j}$ , ${\triangle {LEXR}}_{t-j}$ , ${\triangle {LGEXP}}_{t-j}$ , and ${\triangle {LFDI}}_{t-j}$ are the logged differences in the dependent variables along with the control variables’ differences in the study capturing short-run disturbances. ${\varepsilon }_{1t}$ and ${\varepsilon }_{2t}$ are the error terms which are uncorrelated serially, and ${{ECT}}_{t-1}$ is the error correction term that measuring the depth of the previous disequilibrium and is obtained from the long-run cointegration relationship.

Null hypothesis: It has been found that there is no significant relationship between trade liberalization and growth and development in South Africa, both before and after the BRICS era.

Alternative hypothesis: There is significant relationship between trade liberalization and economic growth and development in South Africa, both before and after the BRICS era. This research is being conducted to prove the presence of a relationship between trade openness, economic growth, and development, and it can be observed that the association has evolved for the South African economy since South Africa’s entry into BRICS. It furthermore aims to prove that openness and growth have impacted the development of South Africa through the economic performance.

To perform a time analysis of pre- and post- BRICS with respect to South Africa, a balanced annual dataset is considered for the period 1991 to 2021, informed by data availability. The analysis is based on two dependent variables with the same explanatory variables. The study involves trade openness, with total trade (sum of imports and exports) as a proxy and was obtained from the UNCTAD database. The study also includes economic growth and development with GDP per capita growth (annual %) and Human Development Index (HDI) as proxies of the two variables, respectively; obtained from the WDI database. It furthermore includes control variables, labour, and employment, which were also obtained from the WDI.

Dependent variables

The dependent variables evaluated in this model are economic growth (measured as real GDP) and development with Human Development Index (HDI) is a proxy. Both HDI and GDP will be measured as logarithms and denoted as LHDI and LGDP, respectively.

Independent variables

EXR and FDI are some control variables in this study whereby EXR influences trade performance and economic growth, especially for open economies with significant trade exposure, whereas FDI plays a critical part in economic growth and development by injecting capital, technology, and knowledge to a nation. EMP denotes the control variable employment which is a crucial economic indicator that can capture the trade and development impact in the labour market and overall economic conditions. GEXP is government expenditure which is a control variable whose level can affect economic growth and development, particularly if investments are directed towards infrastructure, education, and healthcare. All control variables are sourced from WDI and have influences on either economic growth, trade, or development.

Trade openness can positively impact economic growth by providing access to production that is not available domestically (Keho 2017 ). However, a study by Malefane and Odhiambo ( 2018 ) found that openness can have a negative impact on growth in the short- and long-run. According to Zeng, Zhou ( 2021 ), FDI has a direct impact on economic growth which is both significant and positive. They further stated that FDIs help increase employment rates as they help businesses expand. Hence, FDI has a positive effect on both growth and development. Increased government expenditure may boost short-term aggregate demand and growth, however, it may also cause inflation and crowding out of private investment (Pettinger 2019 ).

The implemented methodology therefore aims to examine the complex interplay between trade liberalization, growth, and development in South Africa with emphasis on its pre- and post-BRICS eras. The chosen methodology will allow for exploration of the relationships between the variables, analysis of time series data, and meaningful conclusions with regards to the trade openness impact on economic and developmental trajectories of South Africa will be made.

With support of time series data ranging from time distinct periods (1991 to 2010 and 2011 to 2021), the trade openness influence on the growth development dynamics of South Africa will be assessed using appropriate econometric approaches, thus, facilitating a thorough examination on the openness-growth-development nexus in South Africa.

Empirical results and discussion

Descriptive statistics analysis.

The study begins this section with the preliminary findings analysis, including descriptive statistics and stationarity results. Table 1 presents the descriptive statistics for both pre- and post-BRICS periods. Pre-BRICS, LTRD and LHDI have skewness values close to zero, indicating a normal distribution, while post-BRICS, only LRGDP follows a normal distribution. The skewness values of the other variables in both periods are far from zero, indicating they do not follow a normal distribution. Additionally, kurtosis measures the thickness or flatness of a distribution. Variables with kurtosis values below or equal to three are normally distributed and platykurtic. In the pre-BRICS period, all variables except LEMP are platykurtic, whereas in the post-BRICS period, only LRGDP and LTRD are platykurtic. Platykurtic distributions have lower values below the sample mean.

Lag length selection

The lags selected for different econometric procedures conducted to achieve the objectives of this study are presented in Table 2 . The annual data used in this study did not provide the required number of observations to run the lag selection test post-BRICS, thus, the annual data was converted to quarterly data to reach the minimum required observations. The optimal lag values in accordance with the three criterion, AIC, SBC, and HQ, for both eras came out to be 1.

The Augmented Dickey-Fuller (ADF) t-test was used to investigate for the stationarity of the variables for the entire era because it does not take into consideration structural breaks in the data (Singh et al. 2022 ). The results of this test, indicated in Table 3 , show that the variables LEMP, LGEXP, LHDI, and LTRD are integrated of order one, I(1), meaning they are non-stationary at their levels but become stationary after differencing once. Although LEXR, LFDI, and LGDP show signs of stationarity at levels, their clear stationarity at first differences confirms that they are also integrated of order one. Thus, all variables in the study are I(1), justifying the use of techniques that require variables to be integrated of the same order, such as the Vector Error Correction Model (VECM) and other cointegration approaches.

Johansen cointegration test

After estimating the unit root, the cointegration check is done among the variables using the same order of integrated variables. The Johansen and Juselius ( 1990 ) procedure is used for this purpose, which involves two test statistics - the trace and maximum eigenvalue tests. The economic growth model’s cointegration test results for both eras are shown in Tables 4 and 5 , respectively. There are six/five vectors of targeted variables for both eras, with two out of six integrated at 5% and 10% levels of critical value. The null hypothesis of no cointegrated vector(s) is rejected against the alternative hypothesis of cointegrated vector(s) at 5% and 10% significance levels. There exist two distinct long-run equilibrium relationships among the variables for pre-BRICS, while post-BRICS suggests that more variables are moving together.

The causality test results performed at one lag for both pre- and post-BRICS are reported in Table 6 . In the pre-BRICS era, the results suggest that LTRD granger causes LRGDP, implying that the previous LTRD values offer predictive information about the current values of LRGDP, but LRGDP does not granger cause LTRD, implying unidirectional causality between the variables and an asymmetric predictive relationship between the variables and no bidirectional causality. Post-BRICS, the results suggest that LRGDP granger causes LHDI, which is not reciprocated, while LTRD continues to have a unidirectional relationship with LRGDP. These results indicate that the variables granger causing can predict the changes of the variables they granger cause in the short-run. From the results, the unexpected finding that EMP (Employment Rate) and FDI (Foreign Direct Investment) do not granger cause GDP, despite their established importance in economic growth models like the Cobb-Douglas growth model, can be attributed to the intricate nature of economic relationships within our study context. EMP and FDI are undoubtedly significant drivers of economic activity; however, their direct causal impact on GDP may be influenced by various complex factors. These factors might include structural changes such as shifts in economic policies or technological advancements that alter the transmission channels between EMP, FDI, and GDP over time. Additionally, endogeneity issues or omitted variables not accounted for in the model may contribute to bias in the granger causality test results. Factors such as productivity changes, government policies, or sectoral shifts could also play a role in shaping these dynamics.

Table 7 shows the VECM outcomes of which the pre-BRICS estimations present that model 1 and model 2 illustrate that disequilibrium will adjust into long-run equilibrium at the speeds of 5% and 0.48% for model 1 and model 2, respectively. The dependent variables are both significant at t-statistics greater than 2.3%, which is the minimum average. For Model 1, the past value of LHDI is significant, with t-statistic greater than the minimum average, implying that HDI has an impact on itself and is thus desirable to sustain development. For model 2, the past value of LRGDP is significant with a t-statistic greater than the minimum average, implying that LRGDP has an impact on itself and is desirable to sustain growth. Furthermore, LTRD is significant and has a positive impact on LRGDP. A unit rise in LTRD will lead to a 0.206 need rise in LRGDP.

The post-BRICS results also show that model 1 and model 2 are significant and will therefore adjust their disequilibrium into long-run equilibrium at the speeds of 0.33% and 1.6%, respectively. For model, LRGDP is significant and has a positive impact on LHDI. Whereas LTRD has a positive and significant impact on LRGDP in model 2. These results indicate that the system has the tendency to correct deviations from the long-term equilibrium by moving the variables in a direction that brings them back toward that equilibrium.

The causality test findings, conducted separately for the pre- and post-BRICS, shed light on the intricate connection between trade openness, economic growth, and development in South Africa. The results report a unidirectional relationship between the variables. The shift in predictive relationships from non-significant to significant between some of the variable pairs in the post-BRICS era underscores the nature of these interactions. The comparative analysis results suggest that the trade openness, growth, and development nexus in South Africa has evolved post-BRICS. Furthermore, the post-BRICS evolution offer a wealth of opportunities for South Africa, and it might ultimately foster sustainable development and growth for the country.

The results of this test coincide with the results found by Gries, Redlin ( 2020 ) that no causality exists between trade openness and economic development, and with the results found by Menyah et al. ( 2014 ) that financial development granger causes trade openness, however, trade openness does not granger cause economic growth nor development. However, these results are a contradiction to the results found by Zahonogo ( 2017 ), where Zahonogo found that trade openness granger causes economic growth.

The VECM results suggest that there have been changes in the impact of the economic indicators on the economic growth and human development index since South Africa joined BRICS. The aim of this study was to analyse the influence of liberalization growth and development in South Africa post-BRICS and given that the study confirms that most of the economic indicators included in the study have had positive influences on the growth and development of South Africa, it can be confirmed that liberalization has had a positive impact on growth and development. Further, few studies similar to this one have been conducted, however, the few that were conducted either focused on financial development or development was not included in the study. Furthermore, a study Bandura ( 2022 ) found that financial development exerts a large causal impact on trade, while Rani and Kumar ( 2018 ) found that a long-run causal relationship exists between financial development, trade openness and economic growth.

Impulse response analysis

The impulse response evaluates the impact of a unit shock applied to each series and its effect on the VAR system, highlighting the response intensity of the endogenous variables to shocks or innovations (Saba 2019). This analysis aims to trace the time path of various shocks and understand how the VECM system responds. Figures 1 , 2 , Tables 8 and 9 illustrate the VECM system’s reactions to standard deviation shocks and innovations in this study.

The line graphs illustrate how the variables human development index (HDI), economic growth (GDP), and trade openness (TRD) respond to a one-standard-deviation shock to each of the seven variables in the system. This first set of line graphs shows the IRFs for each variable in response to shocks during the period before South Africa joined BRICS. In each graph, the solid lines represent the point estimates of the impulse responses. Confidence bands around these lines indicate the 95% confidence intervals, showing the range of uncertainty around the estimates. These bands are drawn above and below the solid lines, illustrating where the true impulse response is likely to fall. The y-axis measures the magnitude of the response, while the x-axis represents the time periods following the initial shock.

The line graphs illustrate how the variables human development index (HDI), economic growth (GDP), and trade openness (TRD) respond to a one-standard-deviation shock to each of the seven variables in the system. This second set of line graphs illustrates the IRFs for the same variables in the period following South Africa’s accession to BRICS. In each graph, the solid lines represent the point estimates of the impulse responses. Confidence bands around these lines indicate the 95% confidence intervals, showing the range of uncertainty around the estimates. These bands are drawn above and below the solid lines, illustrating where the true impulse response is likely to fall. The y-axis measures the magnitude of the response, while the x-axis represents the time periods following the initial shock.

In the pre-BRICS era, the response of economic growth to a positive shock in HDI is negative both in the short- and long-run, indicating that improvements in human development might initially lead to a reduction in growth. This could be attributed to the reallocation of resources towards social sectors such as education and healthcare, which might not immediately boost economic output. Similarly, the response of trade openness to a positive shock in HDI is negative, suggesting that increased focus on human development may temporarily reduce trade activities, potentially due to shifts in policy priorities. The response of HDI to its own shock is positive but gradually decreasing over time. This diminishing effect could imply that initial improvements in human development are followed by a plateau, possibly due to diminishing returns on social investments. The response of trade openness and HDI variables to a positive shock in growth is positive. Notably, HDI exhibits positive values that decrease at a slow pace, indicating sustained but gradually diminishing improvements in human development following economic growth. Growth itself has positive values that decrease slowly, reflecting the typical economic cycle where growth momentum slows over time. Openness shows a rapid increase from the first to the second period, followed by a sharp drop in the third period, and then a gradual increase, suggesting an initial boost in trade due to economic growth, followed by adjustments and stabilization.

The impulse response analysis post-BRICS reveals complex dynamics among the variables HDI, economic growth, and total trade. When HDI experiences a shock, the response of growth shows no significant change in the first period but then increases markedly in the second and third periods before experiencing a sharp decline in the fourth period. After this decline, growth shows a slight decrease between the fifth and sixth periods and then begins to gradually increase. This pattern suggests that improvements in human development initially boost economic activity, but this boost is followed by a period of adjustment before the positive effects stabilize. Openness’ response to a shock in HDI is initially neutral but turns negative from the second period onwards. This negative response indicates that improvements in human development may initially lead to a reduction in trade activity, possibly due to shifts in domestic consumption or production patterns. When growth is the impulse variable, the response of HDI is negative, indicating that economic growth does not immediately translate into improvements in human development, possibly due to inequality or lagging social investments. The response of growth to its own shocks shows positive values that fluctuate over the first six periods, indicating volatility in economic growth, which then starts to decrease slowly, suggesting a gradual stabilization of economic activity. Trade openness, in response to growth shocks, shows no significant change in the first period but turns negative afterward, implying that growth may initially harm trade, possibly due to increased domestic focus or changes in trade policies.

The impulse response analysis from the VECM estimation provides intricate insights into the trade openness-human development- economic growth nexus in the South African context, both pre- and post-BRICS. These results can be related to existing empirical studies, and economic theories. Focusing on the variables of interest from the above results, this study’s findings align with theories suggesting that the negative short-term impact of HDI on economic growth and trade openness is due to the reallocation of resources towards non-immediate growth sectors like health and education, which can initially slow economic output and reduce trade activity (Ranis et al. 2000 ; Streeten ( 1994 )). The economic intuition behind this result is that investments in human capital often yield long-term benefits but may not directly contribute to immediate economic productivity, thus explaining the initial negative impact on economic growth and trade openness.

For the positive long-term economic growth-HDI nexus result, this aligns with the endogenous growth theory, which posits that human capital is a critical driver of sustained economic growth (Lucas 1988 ). The economic rationale is that, over time, as the quality of human capital improves, productivity increases, leading to sustained economic growth, which in turn may further enhance human development outcomes. For the trade-economic growth nexus, the finding that economic growth initially boosts but then potentially harms trade in the short term can be related to Balassa’s stages of economic development theory. According to this theory, initial growth boosts trade, but as the economy matures, shifts in comparative advantages affect trade patterns (Balassa 1965 ). The economic rationale is that initial economic growth often leads to increased trade activities. However, as the economy grows, structural changes may shift focus towards more domestically oriented industries or advanced technological sectors, which might not rely as heavily on traditional forms of trade.

Variance decomposition analysis

The strength of causation is further confirmed by the variance decomposition and impulse response analysis performed using a VECM estimation process with the orthogonalized Cholesky ordering technique (Saba 2023 ). Tables 8 and 9 presents the variance decomposition results for trade openness, economic growth, and economic development over 10 periods, with the second period representing the short run and the tenth period representing the long run. In pre-BRICS (see Table 8 ), HDI’s forecast error variance is entirely explained by its own shocks (100%). The proportion of variance explained by HDI’s own shocks gradually decreases to around 93.58% by the tenth period. Small contributions from other variables such as employment, exchange rates, economic growth, and total trade start to appear. For example, employment contributes around 3.74% by the tenth period, indicating that employment shocks have a small but growing influence on HDI. Economic growth’s variance is mainly explained by exchange rates (68.15%) and its own shocks (26.58%). The contribution from exchange rate remains dominant (around 74.02% by the tenth period), while the influence of HDI is minor (around 3.56%), suggesting that exchange rate shocks are the primary driver of GDP fluctuations, with minimal impact from HDI. Trade’s variance is largely explained by its own shocks (64.58%) and exchange rate (27.58%). The contribution from exchange rate decreases slightly to 26.88% by the tenth period, while its own shocks continue to dominate (around 65.58%). This indicates that trade dynamics are mainly influenced by trade-related shocks and exchange rate variations.

Post-BRICS (see Table 9 ), HDI’s variance is entirely explained by its own shocks (100%) and the proportion of variance explained by HDI’s own shocks decreases more rapidly compared to the pre-BRICS period, down to around 80.63% by the tenth period. Contributions from variables such as employment (7.57%) and growth (5.26%) increase, indicating a stronger influence from employment and growth shocks on HDI post-BRICS. The variance for growth is predominantly explained by exchange rate (68.85%) and HDI (10.29%). Exchange rate continues to have a strong influence (76.45% by the 10th period), with HDI’s influence remaining significant (around 9.05%). This suggests that exchange rate shocks continue to be the primary driver of economic growth fluctuations, but HDI has a slightly increased impact compared to the pre-BRICS period. Trade’s variance is primarily explained by FDI (63.83%) and exchange rate (12.97%). The contribution from FDI decreases to 48.68% by the tenth period, while the influence of exchange rate increases to 24.38%, indicating that trade dynamics are increasingly influenced by exchange rate shocks post-BRICS, with a reduced but still significant impact from foreign direct investment.

The variance decomposition results provide a unique perspective on economic interactions in the South African context, particularly focusing on the periods before and after BRICS membership. Pre-BRICS, the economic intuition behind the trade openness-growth nexus results indicates that trade’s variance was mainly influenced by its own shocks, suggesting that trade policies and external factors predominantly drove trade activities. This autonomy in trade dynamics implies that trade openness was perhaps less integrated with other domestic economic variables. Post-BRICS, the increased influence of the exchange rate on trade dynamics, coupled with a reduction in the influence of FDI, suggests that exchange rate volatility became a more significant determinant of trade flows. This could be due to the enhanced integration of the South African economy with global markets, where exchange rate movements directly impact the competitiveness of exports and imports. Additionally, post-BRICS, the increase in HDI’s influence on economic growth suggests that as the economy transitioned and possibly diversified, the quality of human capital began playing a more significant role in driving economic performance. This shift likely reflects a growing acknowledgment of human capital as a critical factor for sustaining economic growth in a more competitive and integrated market environment.

For the HDI-growth nexus results, the findings align with human capital theory, as described by Becker ( 1964 ) and Schultz ( 1961 ), which posits that investments in human capital are crucial for economic growth, though the effects may manifest over a prolonged period. Regarding the trade openness-growth nexus findings, studies such as those by Frankel and Romer ( 1999 ) indicated that trade openness positively correlates with economic growth by exposing countries to more extensive markets and enabling technology transfer. However, the finding that the variance in trade openness is predominantly explained by external shocks like FDI and exchange rates differs from studies that argue domestic factors like infrastructure, policy environment, and industrial capacity also significantly impact trade openness (Rodrik 1998 ).

Conclusion and policy recommendations

This research is one of the few which empirically examined the trade liberalisation effect on growth and development. The analysis focused on South Africa before and after joining the BRICS bloc and is centred on the econometric time series for the period 1980 to 2021. The Johansen cointegration and vector error correction models are applied to investigate the relationship between the variables. The unit root test properties of the data were analysed using the ADF test followed by the cointegration test and the granger causality test, with the results suggesting a unidirectional relationship between trade openness and economic growth. The empirical results indicate that all the variables were stationary at first difference and that the application of the cointegration test shows that there exist cointegration relations, which then leads to employing the VECM technique. The VECM technique found that a positive connection exists between openness and growth and between growth and economic development.

The results of the cointegration test shows that both Trace and Max-Eigenvalue tests indicate exactly one cointegrating vector among the variables in the pre-BRICS period. This suggests a long-term equilibrium relationship among the studied variables before South Africa joined BRICS. However, both Trace and Max-Eigenvalue tests indicate that there are two cointegrating vectors among the variables in the post-BRICS period, thus suggesting more complex long-term equilibrium relationships among the studied variables after South Africa joined BRICS.

The shift from one to two cointegrating vectors implies that the economic structure and interactions within South Africa have evolved significantly post-BRICS. This could be due to various factors such as increased trade, investment flows, and economic policies influenced by BRICS membership. The pre-BRICS period’s single cointegrating relationship suggests a relatively straightforward economic environment. In contrast, the post-BRICS period’s multiple cointegrating vectors highlight the increased complexity and interdependence resulting from deeper integration into a larger economic bloc. This transition can be further analysed by investigating specific policies, trade agreements, and economic activities that have changed due to BRICS membership, providing a comprehensive understanding of how South Africa’s economic landscape has been reshaped.

The VECM results suggest that in the pre-BRICS era, a depreciating exchange rate adversely affects HDI, likely due to increased import costs and reduced purchasing power. Employment levels and foreign direct investment, although theoretically expected to bolster development, did not exhibit significant long-term impacts on HDI. Similarly, higher GDP levels did not guarantee higher HDI, suggesting that economic growth during this period may have been unevenly distributed or insufficiently inclusive. Government expenditure and total trade also showed minimal influence on HDI, both in the short- and long-term, implying inefficiencies or limited effectiveness in policy implementation or market integration. In the post-BRICS era, while the negative impact of exchange rate depreciation on HDI persists, its magnitude slightly diminishes, indicating a moderated effect. However, employment exhibits a surprising negative relationship with HDI, suggesting structural issues in the labour market or the quality of jobs created. Foreign direct investment continues to show a lack of significant impact on HDI, with a potentially slight adverse effect, possibly due to profit repatriation or sectoral concentration. GDP’s association with HDI remains negative but insignificant, reinforcing concerns about the inclusiveness of growth. Government expenditure, notably, displays a significant negative impact on HDI in the long term, hinting at inefficiencies or misallocation of resources. Trade openness continues to have a negligible impact on HDI in both the short and long term, indicating limited immediate benefits from trade expansion.

The error correction term analysis reveals contrasting adjustment speeds between the pre-and post-BRICS periods. In the pre-BRICS phase, the slow adjustment rate (2.18% per period) suggests a gradual return to equilibrium, possibly indicating economic policies with longer implementation horizons. Conversely, the post-BRICS era exhibits a significantly faster adjustment rate (28.97% per period), indicating more responsive economic mechanisms or policy frameworks that facilitate quicker corrections towards long-term equilibrium. These findings underscore the complex interplay between economic variables and human development, highlighting the nuanced impacts of policy interventions and external economic shocks across different periods of economic integration and development.

In the pre-BRICS era, a positive shock to HDI initially shows a negative response in economic growth both in the short and long term. This suggests that resources diverted towards improving human development, such as education and healthcare, may not immediately translate into higher economic output, potentially due to the time lag in realizing the benefits of social investments. Similarly, the negative response of trade openness to HDI shock indicates that an initial emphasis on human development might lead to a temporary reduction in trade activities, possibly reflecting shifts in policy priorities away from trade-related sectors. Over time, HDI itself responds positively to its own shock, but the positive values diminish gradually, suggesting that initial gains in human development reach a plateau as the benefits of social investments stabilize. In contrast, the post-BRICS period reveals a more nuanced picture. When HDI experiences a positive shock, economic growth initially shows no significant change in the first period but then exhibits marked increases in subsequent periods, followed by a sharp decline and subsequent gradual recovery. This pattern suggests that while improvements in human development initially stimulate economic activity, there are subsequent adjustments and stabilizations, possibly indicating the need for sustained policies to maintain growth momentum. Trade openness, however, responds negatively to HDI shocks from the second period onwards, suggesting that enhancements in human development may initially detract from trade activities, possibly due to shifts in domestic consumption or production patterns. When economic growth acts as the impulse variable, HDI responds negatively, implying that growth does not immediately translate into improved human development outcomes, which could be attributed to persistent inequalities or delayed social investments. Growth itself shows fluctuating positive responses to its own shocks over the first six periods, reflecting the inherent volatility in economic cycles that gradually stabilizes. Trade openness, in response to growth shocks, shows no significant immediate change but turns negative thereafter, indicating that economic growth might initially divert attention away from trade-related policies or international economic engagements.

The outcomes suggest that liberalisation impacts both economic growth and development in the long-run, implying that a reduction in trade barriers leads to more trade, and increased trade stimulates the economy, thus leading to a nation developing. The empirical results revealed that there is a long-run relationship between trade openness and economic growth and that the connection was stimulated for South Africa after joining BRICS, while the results further suggest the same for the development of the country, indicating that the venture into BRICS stimulated growth and boosted development in South Africa. To ascertain long-term growth and development, these subsequent policy discussions should be taken into consideration. If policymakers desire to stimulate the long-run growth and development, additional attention must be paid to trade policies and on how to take advantage of the liberalisation to specialise in certain production, and thus export more.

The South African government, policymakers, and relevant stakeholders within the BRICS community should implement policies to reduce trade barriers, non-tariff barriers, and customs-related delays to promote increased trade within the BRICS bloc. They can further pursue bilateral and multilateral trade agreements to expand market access for South African goods and services. Additionally, FDI can be encouraged by creating attractive investment climates through legal and regulatory reforms, incentives, and infrastructure development.

The government should formulate, implement, evaluate, and review policies that cushion the economy from abrupt shifts caused by global trade dynamics, such as enhancing trade diversification and negotiating more stable trade agreements. Additionally, the government should consider implementing fiscal policies that support sectors likely to be negatively impacted in the short term by shifts in trade patterns. Efforts to enhance employment quality should focus on boosting HDI and economic growth by supporting job creation not just in quantity but also in quality, particularly in sectors expected to drive sustainable growth. Investing in social safety nets and job transition programs is essential to support workers as the economy adapts to new industries and technologies. The government should use exchange rate management as a tool to maintain competitiveness in international markets while preventing excessive volatility that could disrupt economic growth. Economic policies should be designed to directly leverage improvements in HDI, such as promoting industries that rely heavily on skilled labour, to ensure that human capital improvements translate into economic gains more rapidly. Accelerating investments in education and health will enhance the quality of human capital, ensuring that the workforce can effectively contribute to and benefit from economic growth.

The findings on the report are an initial assessment, with the use of the total trade data sourced from UNCTAD, which allows for extending the empirical analysis into the complex trade openness, economic growth, and development nexus. Thus, the study is open for further research. The analysis of the trade openness-economic growth-development nexus in South Africa, focusing on both pre- and post-BRICS periods, has yielded significant insights. However, there are several areas for further research that could enhance our understanding and address potential limitations of the current study: (i) Future studies should explore the role of technological advancements and innovation in mediating the relationship between trade openness and economic development. Assessing how technology transfer through trade and foreign direct investment (FDI) influences productivity and growth in South Africa will add value to future studies. (ii) Future studies should conduct regional analyses to understand how trade openness affects different provinces or regions within South Africa. This could help identify whether certain areas benefit more from trade liberalization and BRICS membership, thereby providing targeted policy recommendations. (iii) Future studies should analyse the impact of the COVID-19 pandemic on the relationship between trade openness, economic growth, and development in South Africa. Assessing how the pandemic has influenced trade policies, economic performance, and development indicators, and proposing measures to mitigate negative impacts, is crucial. And (iv) future studies should consider issues of structural breaks in their analysis.

Data availability

All data generated or analysed during this study are not included in this submission but are publicly available in United Nations Conference on Trade and Development (UNCTAD: https://unctadstat.unctad.org/datacentre/ ) and World Development Indicators (WDI: https://databank.worldbank.org/source/world-development-indicators ) databases.

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Monyela, M.N., Saba, C.S. Trade openness, economic growth and economic development nexus in South Africa: a pre- and post-BRICS analysis. Humanit Soc Sci Commun 11 , 1108 (2024). https://doi.org/10.1057/s41599-024-03604-2

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Democracy and Foreign Direct Investment in BRICS-TM Countries for Sustainable Development

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Published: 05 September 2024

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Ibrahim Cutcu ORCID: orcid.org/0000-0002-8655-1553 1 &
Ahmet Keser ORCID: orcid.org/0000-0002-1064-7807 2

The study aims to examine the long-term cointegration between the democracy index and foreign direct investment (FDI). The sample group chosen for this investigation comprises BRICS-TM (Brazil, Russia, India, China, South Africa, Turkey [Türkiye], and Mexico) countries due to their increasing strategic importance and potential growth in the global economy. Data from 1994 to 2018 were analyzed, with panel data analysis techniques employed to accommodate potential structural breaks. The level of democracy serves as the independent variable in the model, while FDI is the dependent variable. Inflation and income per capita are considered control variables due to their impact on FDI. The analysis revealed a long-term relationship with structural breaks among the model’s variables. Democratic progress and FDI demonstrate a correlated, balanced relationship over time in these countries. Therefore, governments and policymakers in emerging economies aiming to attract FDI should account for structural breaks and the correlation between democracy and FDI. Furthermore, the Kónya causality tests revealed a causality from democracy to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. From FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia. Thus, the findings suggest that supporting democratic development with macroeconomic indicators in BRICS-TM countries will positively impact foreign direct capital inflows.

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Introduction

Economies and governments require capital infusion to augment their production and employment levels. Underdeveloped and developing nations, despite having an abundance of land and labor, grapple with capital deficiencies. Consequently, these countries often seek foreign direct investment (FDI) to address this capital shortfall. Even emerging market economies are not immune to this phenomenon, with challenges intensifying globally post-COVID-19 pandemic. Khan et al. ( 2023 ) highlighted the pivotal role of institutional quality and good governance in attracting FDI. The need for FDI has grown exponentially in an increasingly globalized world characterized by interdependence among states. Democracy and the democratic status of states emerge as critical indicators of institutional quality. Kilci and Yilanci ( 2022 ) posit that the prolonged pandemic triggered the third most significant recession since the Great Depression of 1929 and the Global Financial Crisis of 2008–2009. Consequently, the demand for FDI has surged, positioning foreign investment as the foremost resource for fostering sustainable economic development. In light of the provided frame, this study addresses the following research questions:

What factors attract foreign direct investment to a country?

Which factors positively impact FDI?

Reviewing the existing literature reveals that scholars from diverse disciplines address similar questions using political variables like political stability and democracy levels or economic variables such as economic stability and natural resources . However, the impact of democracy on FDI is often overlooked . For example, studies by Baghestani et al. ( 2019 ) and Gür ( 2020 ) investigated variables like oil prices, exchange rates, exports, imports, and the global innovation index but seldom considered democracy’s role in attracting FDI . Similarly, studies examining the relationship between democracy and FDI, like those by Yusuf et al. ( 2020 ) and Ahmed et al. ( 2021 ), generally excluded data from BRICS-TM countries.

Li and Resnick ( 2003 ) assert that the two paramount features of modern international political economy are the proliferation of democracy and increased economic globalization . It has become apparent that FDI inflow is a manifestation of high-level globalization and the diffusion of democracy. According to the United Nations Conferences on Trade and Development (UNCTAD), 2002 data between 1990 and 2000, three-quarters of the total international foreign direct capital was directed toward democratic and developed countries (Busse, 2003 ).

The conceptualization of democracy, within both theoretical and historical frameworks, has been marked by inherent challenges (Suny, 2017 ). Aliefendioğlu ( 2005 ) defines democracy as the amalgamation of the ancient Greek terms “Demos” and “Kratos,” centered on the principle of self-governance by the people. In essence, democracy encompasses the utilization of popular sovereignty by and for the citizenry (Keser et al., 2023 ). Haydaroğlu and Gülşah ( 2016 ) contend that the contemporary manifestation of democracies is rooted in representative democracy, wherein individuals exercise their sovereignty by selecting representatives to act on their behalf. The spread of liberal or representative democracy is believed to be a driving force behind this shift in economic structures. The relational intersection between FDI flow and democratic mechanisms needs to be investigated. At this point, Voicu and Peral ( 2014 ) argue that economic development and modernization operate as background factors that affect the development of support for democracy. Therefore, an opinion emerges that there is an inevitable intersection between FDI flow and democratic mechanism.

Despite the sustained attention from academia and the public, the detailed understanding of democracy’s effect on FDI remains limited (Li & Resnick, 2003 ). There is a noticeable gap in the literature concerning studies investigating the impact of democracy on FDI, specifically in BRICS-TM countries , which are emerging markets that attract significant FDI. Moreover, the absence of structural break panel cointegration tests in previous analyses accentuates these gaps, forming the primary motivation for this research . The study aims to fill these voids by empirically examining the relationship between democracy and FDI using data from the emerging markets of BRICS-TM countries. These countries require substantial foreign capital and are crucial for the stable development of the global economy since they are expected to become pivotal centers in the multipolar world system. The study differs from other publications, employing unique methods, such as structural break panel cointegration tests, to address these objectives.

Reducing costs, increasing employment-oriented production, and enhancing export capacity are paramount in global competition. If a country cannot achieve these advancements with its existing potential and dynamics, attracting foreign capital becomes imperative, necessitating the creation of multiple attraction points to entice foreign direct investments. Consequently, attracting foreign capital is significant in today’s globalized world. This study provides insights into this pressing issue in the contemporary global competitive landscape by analyzing the long-term relationship between democracy and foreign direct investment. Considering their prominence in the world economy due to recent economic growth and competitive structures, the selection of BRICS-TM countries as a sample group underscores the study’s importance. The study acknowledges the strategic importance and increasing power of BRICS-TM countries, especially China and India, which have consistently attracted significant foreign capital in recent years. Using panel data analysis techniques that incorporate structural breaks addresses a crucial gap in the literature, offering a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. However, data constraints related to model variables alongside the limitations of evaluating results within the framework of the chosen sample group are acknowledged later in the “ Discussion ” section.

Lastly, there appears to be a gap in the existing literature concerning studies that investigate the impact of democracy on FDI flow in BRICS-TM countries . The countries that attract more FDI than others raise the question of whether their democracy level empirically influences the amount of FDI. Moreover, upon examining the limited studies exploring the relationship between democracy and FDI, it is evident that none applied the structural break panel cointegration test in their analyses. These gaps collectively serve as the primary motivation for this research. Thus, the study aims to address these gaps in the existing literature and scrutinizes whether there is cointegration between the level of democracy and FDI in a country by utilizing sample group data from emerging markets of BRICS-TM countries. This selection is significant as these countries are among emerging economies with considerable developmental potential. In essence, this study aims to empirically unveil the relationship between democracy and FDI , a crucial requirement for developing economies striving to attract more foreign capital for sustainable development . Additionally, this study employs distinctive methods, such as the structural break panel cointegration test, to investigate the subject, further elaborated in the “ Research Method and Econometric Analysis ” section.

In global competition, the imperative to reduce costs, increase employment-oriented production, and enhance export capacity is paramount. Given a country’s potential and dynamics, if these enhancements prove elusive, the necessity arises to attract foreign capital and establish various attraction points to incentivize foreign direct investments. Therefore, attracting foreign direct investment (FDI) to a country holds tremendous significance in today’s globalized world. Before investing, foreign capital rigorously assesses the potential profit opportunities and scrutinizes various socio-economic indicators, especially democracy. For these reasons, by analyzing the long-term relationship between democracy and foreign direct investment in the BRICS-TM sample, this study incorporates analyses and inferences regarding this crucial challenge in today’s globally competitive environment.

Furthermore, it is anticipated that the strategic importance and influence of BRICS-TM countries will continue to escalate in the upcoming years. Notably, countries in the sample group, particularly China and India, have consistently attracted substantial foreign capital, and their economies exhibit ongoing growth. As evident from the graphical analysis in the study, China stands out as the world leader in attracting foreign direct investment. Considering the economic size of Russia and Brazil, the geo-strategic location of Türkiye, and the natural resource wealth of China, India, and Mexico, it is apparent that these countries are central attractions for foreign direct capital. Events with significant consequences on the global stage, such as economic crises, wars, earthquakes, and elections, can induce substantial fluctuations and structural breaks in national economies. Hence, using panel data analysis techniques that allow for structural breaks in the study fills a critical gap in the literature. This approach provides a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. The primary limitation in the study’s analysis is the constraint arising from the variables included in the model. Additionally, selecting the BRICS-TM sample group as the focus on developing countries can be considered another limitation, restricting the evaluation of results within this specific sample framework. The study anticipates that the policy recommendations derived from the analysis findings will guide policymakers, market players, and new researchers.

The article is organized into the following sections: (1) “ Introduction ” section: This section initially furnishes broad information concerning the subject matter, elucidating the lacunae in the existing literature and delineating the limitations of the study. (2) “ Theoretical Frame and Literature Review ” section: Subsequently, the second section delves into the examination of the theoretical framework, scrutinizing the prevailing status of the literature. (3) “ Research Method and Econometric Analysis ” section: The third segment comprehensively addresses the research methodology employed and expounds upon the econometric analysis conducted. (4) “ Results ” section: The ensuing fourth chapter presents the study’s findings and results. (5) “ Discussion ” section: These results and findings are then systematically expounded upon in the fifth chapter within the context of the current literature. (6) “ Conclusion ” section: Culminating the study is a concluding section encapsulating the critical insights derived, followed by policy recommendations.

Theoretical Frame and Literature Review

As previously indicated, scarce studies have delved into the correlation between democracy and foreign direct investment (FDI). A comprehensive examination of the existing literature reveals a notable dearth of research focused on BRICS-TM countries, with most of them overlooking “democracy” as a variable and/or the connection between “democracy and FDI.” Conversely, researchers investigating FDI predominantly explore its associations with other variables, such as “exports and imports.”

The Status of the Literature on BRICS-TM Countries and Democracy and Foreign Direct Investment

The following two tables summarize the status of the current literature on the issue and its findings. In Table 1 , the literature on BRICS and/or BRIC + S + T + M countries, as well as its variables, methods, and findings, is given. Then, in Table 2 , the studies researching the relationship between democracy and FDI, their methodology, sample groups, and findings are summarized.

As can be seen in Table 1 , BRICS-TM countries were very rarely studied, and almost all of these studies neglected “democracy” as a variable and/or the relation between “democracy and FDI.” Alternatively, the studies that did examine FDI researched its relation with other variables such as export and import. Unique methods, such as structural break panel cointegration tests, were applied to investigate the issue, and this method comprises the novel part of the study. The details can be seen under the “ Research Method and Econometric Analysis ” section.

In summary, the literature review provided in Table 1 covers the relationship between democracy, foreign direct investment (FDI), and various other economic variables, focusing on BRICS-TM countries. Below is an analysis of the essential findings and gaps identified in the literature:

By applying AI (ChatGPT) to the information provided in Table 1 (studies on BRIC + S + T + M countries), key findings are double-checked and summarized below:

Limited focus on BRICS-TM countries: The literature review notes a scarcity of studies on BRICS-TM countries, with a lack of attention to the “democracy” variable in the context of FDI.

Variable relationships explored: Various studies investigate the relationships between different economic variables and FDI, such as oil prices, exchange rates, gross domestic product (GDP), international tourism, economic output, carbon emissions, exports, imports, and innovation.

Diverse methodologies: Researchers employ diverse methodologies, including directional analysis, panel ARDL cointegration, survey research, and panel cointegration, to analyze the relationships among variables.

Within this frame, a summary of the studies investigating the relationship between democracy and FDI or using similar variables is given in Table 2 .

As presented in Table 2 , none of the above studies analyzed the relationship among democracy, FDI, inflation , and GDP variables for BRICS-TM countries. In addition, none of the studies applied a structural break panel cointegration test in their analysis. All these gaps motivate the authors of this study to conduct such research.

Additionally, applying AI (ChatGPT) to the information provided in Table 2 , key findings from Table 2 are double-checked and summarized below (studies on the relationship between democracy and economics):

Limited studies on democracy and FDI in BRICS-TM: The literature highlights a gap in research, as none of the studies in Table 2 specifically analyze the relationship between democracy, FDI, inflation, and GDP variables in BRICS-TM countries.

Contradictory findings on democracy and economic growth: The studies in Table 2 present contradictory findings on the impact of democracy on economic growth. Some find a positive and significant effect, while others do not establish a significant relationship.

Methodological variety: Various methods, such as dynamic fixed effects, panel data regression analysis, panel cointegration, and causality analysis, are employed to explore the relationships between democracy, FDI, and economic growth.

Upon inspection of the limited studies, contradictory results emerge, even when employing data from diverse sample groups. An illustrative example is found in the work of Busse ( 2003 ), whose research can be summarized as follows:

Results from regression analysis between FDI and democracy reveal that analogous to studies by Rodrik ( 1996 ) and Harms and Ursprung ( 2002 ), multinational corporations (MNCs) exhibit a preference for countries where political rights and freedoms are legally and practically safeguarded.

Countries that enhance their democratic rights and freedoms tend to attract more FDI per capita than predicted (Busse, 2003 ).

Li and Resnick ( 2003 ) posited that investors typically favor regimes with advanced democracy and robust legal systems over states where their properties are at risk in dictatorial regimes. From this standpoint, one can infer that a significantly high level of democracy correlates with a markedly high level of FDI. In other words, property rights violations are diminished in developing countries with robust democracies, leading to increased FDI levels (Li & Resnick, 2003 ).

However, Haggard ( 1990 ) presents a contrary perspective, arguing that authoritarian regimes may appeal more to investors seeking to safeguard their economic assets and properties. An amalgamation of opposing views arises: investors from countries with underdeveloped democracies prefer collaboration with authoritarian regimes, whereas investors from developed nations lean toward familiar democratic regimes.

Despite the contradictory and complex findings from the limited number of studies on the potential relationship between democracy and FDI, it is contended that two influential factors contribute to investment flow toward countries with legally guaranteed and well-developed democratic rights. Firstly , as proposed by Spar ( 1999 ), a transition occurs from critical sectors like agriculture and raw materials to production and tertiary sectors in the flow and stock structure of FDI in developing countries. Secondly , there is a transformation in the interest and motivation of multinational enterprises toward developing countries based on sectoral development (Busse, 2003 ). This underscores the impact of democratic organizations established to secure democratic rights on FDI. In instances where poor democratic governance renders a country less appealing to foreign investors, the country faces a dilemma: choosing between the limited options of “loss of foreign capital” or “democratization” (Li & Resnick, 2003 ). Spar ( 1999 ) emphasizes that as the reliance on governments and their policies decreases, the need for a more democratic environment, a reliable and stable legal system, and appropriate market conditions becomes increasingly crucial for the overall well-being of the country’s economy.

Upon scrutinizing the most recent studies on the subject, a trend of contradictory findings becomes apparent. For instance, Yusuf et al. ( 2020 ) found that the democracy coefficient, as a variable signifying its impact on economic growth, lacks significance for West African countries in the short and long run. In contrast, Putra and Putri ( 2021 ) asserted that “democracy has a positive and significant effect on economic growth in 7 Asia Pacific countries.” Similar to Yusuf et al., in a panel data analysis encompassing the period from 1970 to 2014 and involving 115 developing countries, Lacroix et al. ( 2021 ) concluded that “democratic transitions do not affect foreign direct investment (FDI) inflows.”

A comprehensive review of existing empirical studies reveals a notable scarcity in the number of inquiries into the relationship between democracy and foreign direct investment (FDI) (Li & Resnick, 2003 ). Moreover, the available studies yield contradictory results on this matter. Addressing this issue, it is noteworthy that Oneal ( 1994 ) conducted one of the initial qualitative examinations on the impact of regime characteristics on FDI. Despite not identifying a statistically valid relationship between regime type and FDI flow, Oneal’s research is an early exploration of this intricate relationship.

Explorations into the connection between investor behavior and political regime characteristics, particularly in determining whether democratic or authoritarian features foster more foreign direct investment (FDI), have yielded divergent outcomes. Derbali et al. ( 2015 ) found a statistically significant relationship between FDI and democratic transformation. Through an econometric analysis encompassing a sample of 173 countries, with 44 undergoing democratic transformation between 1980 and 2010, the authors observed a substantial increase in FDI flow associated with democratic transitions.

Castro ( 2014 ) conducted a test examining the relationship between foreign direct investment (FDI) flow (the ratio of FDI flow to GDP) and indicators of “democracy” and “dictatorship” using a dynamic panel data model. Despite the analysis results failing to furnish evidence supporting a direct connection between FDI and democracy, the author emphasizes that this outcome does not negate the impact of political institutions on the flow of FDI. According to Mathur and Singh ( 2013 ), their study stands out as the inaugural examination focusing on the “importance given to economic freedom rather than political freedom” in the decision-making process of foreign investors. The authors concluded that contrary to conventional expectations, even democratic countries may attract less foreign direct investment (FDI) if they do not ensure guaranteed economic freedom. Malikane and Chitambara ( 2017 ) conducted a study exploring the relationship between democracy and foreign direct investment (FDI), employing data from eight South African countries from 1980–2014. The research findings indicate a direct and positive impact of FDI on economic growth due to the robust democratic institutions emerging as crucial catalysts in the respective sample countries.

Consequently, Malikane and Chitambara’s ( 2017 :92) study suggests that the influence of FDI on economic growth is contingent upon the level of democracy in the host country. Upon scrutinizing the studies above, a pattern of conflicting findings emerges concerning the relationship between the level of democracy and the influx of foreign direct investment (FDI) to a country . Studies commonly emphasize that the impact of democracy on FDI depends upon each country’s developmental stage. The prevalence of confusion, varying findings, and conflicting results underscores the significance of empirical analyses on this matter. A comprehensive examination of the overview identified gaps, and the need for new research is detailed under the subsequent subheading.

Overview of the Literature, Identified Gaps, and Requirements for New Research

After a detailed overview of the existing literature, the main features and gaps can be identified as follows:

Limited studies on democracy and FDI: The literature notes a scarcity of studies examining the relationship between democracy and FDI, and existing studies present conflicting results.

Context-dependent impact of democracy: Contradictory findings suggest that democracy’s impact on FDI may vary depending on a country’s development level.

Gap in BRICS-TM studies: The identified gap in the literature is the lack of research specifically addressing the relationship between democracy and FDI in BRICS-TM countries. The need for a structural break panel cointegration test is also emphasized.

Influence of political institutions: Some studies argue that solid democratic institutions positively influence FDI, while others suggest that economic freedom, rather than political freedom, may be more crucial for attracting FDI.

Requirements for new research: To fill the gap in the literature, new research should be conducted specifically targeting BRICS-TM countries.

Thus, when c onsidering the contradictory findings, future studies should explore the contextual factors influencing the relationship between democracy and FDI in different country settings. Conducting longitudinal analyses could provide insights into the dynamic relationship between democracy and FDI over time. Comparative studies between countries with different levels of democratic development can help in understanding the nuanced impact of democracy on FDI. Last but not least, given the emphasis on structural break panel cointegration tests, future research could incorporate these analytical tools for a more comprehensive understanding of the relationships under consideration.

Last but not least, Olorogun ( 2023 ) conducted research using data from sub-Saharan countries from 1978 to 2019 and found a “long-run covariance between sustainable economic development and foreign direct investment (FDI)” and a “significant level of causality between economic growth and financial development in the private sector, FDI, and export.” So, if a significant relationship can be found between democracy and foreign direct investment, the results may also provide a useful assessment for sustainable development.

In summary, while the literature review reveals valuable insights into the complex relationship between democracy, FDI, and economic variables, there is a clear need for more targeted research in the context of BRICS-TM countries by further exploration of the contextual factors influencing these relationships.

Research Method and Econometric Analysis

This section of the study delves into the analysis methods and interpretations of the relationship between democracy and foreign direct investment (FDI). The presentation encompasses the dataset and model specifications concerning the variables under scrutiny. Specifically, analyses were conducted utilizing econometric analysis programs, namely, EViews 12 , Gauss 23 , and StataMP 64 . The study culminated with interpreting findings and formulating policy recommendations based on the results obtained.

Data Set and Model

The study scrutinized the hypothesis to address the initial research inquiry, asserting a correlation between democracy and foreign direct investment (FDI). The research targeted BRICS-TM countries (Brazil, Russia, India, China, South Africa, Türkiye, Mexico) recognized for their increasing prominence in the global economy and anticipated growth in strategic significance. These seven emerging markets were chosen due to their demonstrated potential to attract FDI. The research covered annual data spanning 1994–2018 by employing panel data analysis techniques capable of accommodating structural breaks. Both democracy and foreign direct investments are susceptible to the influence of local and global dynamics, which can induce significant disruptions in the variables.

Consequently, the study utilized tests allowing for structural breaks to enhance the robustness of the analyses. The investigation aimed to uncover the long-term relationship between foreign direct investment and democracy , a critical indicator of economic development for emerging markets in recent years. The model developed for examining the relationship between democracy and foreign direct investment within the specified sample and data range is represented by Eq. 1 :

In the model, cross-section data is represented by i = 1, 2, 3,…. N , while the time dimension is represented by t = 1, 2, 3,….. T , and the error term is by ɛ.

The study’s model setup and variables were adapted from Yusuf et al. ( 2020 ), Putra and Putri ( 2021 ), and Lacroix et al. ( 2021 ) in the literature. Figure 1 shows the research design.

Research design

Table 3 shows the variables and data sources used in the model.

The study designated foreign direct investment (FDI), denoted as LNFDI, as the dependent variable. The independent variable was conceptualized as the democracy variable (DEMOC). To account for potential influencing factors, inflation (INF) and per capita income (PGDP) variables, known to impact FDI, were introduced into the model as control variables to draw upon insights from the existing literature. In the context of panel data analyses, selecting control variables involves consulting the literature to identify factors with substantial influence on the dependent variable. When examining factors impacting foreign direct investment (FDI), a frequently encountered category comprises various macroeconomic variables, among which inflation and per capita income are recurrently employed. Given the study’s sample composition—comprising the BRICS-TM countries—these two variables were incorporated into the model as control variables. This decision was motivated by their recurrent utilization in the literature and their direct relevance to foreign direct investments and production costs. Furthermore, the inclusion of these variables addressed a shared data constraint.

During the data collection phase, the study utilized indices reflecting “political rights” and “civil liberties,” which were acknowledged indicators of “democracy” in the literature. These indices, sourced from the Freedom House Database ( 2020 ), were incorporated into the analysis by calculating their means, which were then used as values for the democracy variable. This approach aligns with the practices of several researchers in the existing literature, such as Kebede and Takyi ( 2017 ), Doucoligaos and Ulubasoglu ( 2008 ), and Tavares and Wacziarg ( 2001 ), who have employed this index. The index operates on a scale from 1 to 7, where 1 represents the highest state of democracy and 7 corresponds to the lowest state. To facilitate analyses, calculations, and interpretation, the index values were scaled to ensure a range between 0 and 100.

Freedom House assesses the degree of democratic governance in 29 countries from Central Europe to Central Asia through its annual “Nations in Transit” report. The democracy score encompasses distinct ratings on various facets, including national and local governance, electoral processes, independent media, civil society, judicial framework and independence, and corruption. Most researchers (Dolunay et al., 2017 ; Martin et al., 2016 ; Osiewicz & Skrzypek, 2020 ; Steiner, 2016 ) frequently utilize the data provided by Freedom House in their studies. In addition to the independent variable of democracy (DEMOC), the model integrates control variables influencing FDI. Capitation (LNPGDP) and inflation (INF) variables were incorporated within this framework. A review of the existing literature reveals that factors affecting FDI, including inflation and per capita income, have been employed in models by researchers (Botric & Skuflic, 2005 ; Chakrabarti, 2001 ; Jadhav, 2012 ; Ranjan & Agraval, 2011 ; Vijayakumar et al., 2010 ).

In the literature, various variables such as “trade openness, level of human capital, unemployment rates, government supports, tax costs,” which are believed to influence foreign capital, are employed as control variables in models. On the other hand, in some research, the impact of institutional quality, such as democracy and governance, on environmental quality is studied. Within this frame, Shahbaz et al. ( 2023 ) found that “institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy.” However, in this study, inflation and per capita income variables were chosen due to their prominence as the most frequently used variables in the literature (detailed in the “ Theoretical Frame and Literature Review ” section) and their comprehensive impact on foreign direct capital in terms of macroeconomics.

Furthermore, a shared data problem is evident in all variables from 1994 to 2018 for the BRICS-TM country sample group, particularly in variables other than the control variables in the model. Nevertheless, these issues have yet to be encountered as inflation and per capita income variables are comprehensive and fall within general macroeconomic data. Additionally, including many control variables in the model might obscure the significance of the effect on the dependent variable in hypothesis tests examining the relationship between democracy and foreign direct investment. Consequently, real GDP data, rather than nominal, were utilized in the analysis, and the logarithm of the data was represented as LNGDP.

As explored earlier, foreign investors prioritize economic freedom over political freedom when making investment decisions (Mathur & Singh, 2013 ). In this context, the assurance of economic liberty and the legal protection of property rights may be linked to the level of democracy, particularly in developed countries. This condition explains why the relevant variables should be incorporated into the model and tested. The logarithm of FDI (LNFDI) and per capita income (LNPGDP) variables were employed in the analyses. The rationale behind the logarithmic transformation lies in its capacity to facilitate the interpretation of analysis results and standardize variables on a specific scale. Additionally, taking logarithms of series does not result in information loss in data; it also aids in mitigating autocorrelation issues and allows the series to exhibit a normal distribution.

Econometric Method

The primary motivation behind the conducted study is to investigate the impact of the variable “democracy” on foreign direct investments through newly developed panel data analysis tests that allow for structural breaks, which are not commonly used in political science. In this regard, the study aims to be one of the pioneering works testing the relationship between variables related to political science and economics with an interdisciplinary perspective through innovative empirical studies. The methodological framework of this study, which analyzes the relationship between democracy and FDI through annual data from the 1994–2018 periods using panel data analysis and causality test, is outlined below:

Graphical representation of variables and analysis of descriptive statistics,

CD lm1 (Breusch & Pagan, 1980 ), CD lm1 , and LM adj tests (Pesaran et al., 2008 ) were used in the analysis to find the presence of cross-section dependence of variables.

Panel LM test (Im, Lee, & Tieslau, 2010 ) determined whether variables in the model have a unit root.

Delta test (Pesaran & Yamagata, 2008 ) was used to determine the homogeneity or heterogeneity of variables.

Cointegration test with multiple structural breaks (Westerlund & Edgerton, 2008 ) was conducted to determine the presence of cointegration between variables.

Kónya’s causality test (Kónya, 2006 ) was conducted to investigate the existence of causal relationships between variables.

In terms of methodology, the study aims to address a significant gap in the literature on democracy. Given the chosen sample group and the specified period, it becomes evident that structural changes must be considered in the analysis because the variables of democracy and foreign direct investment are particularly susceptible to global developments, leading to substantial shifts in the markets. A literature review indicates a preference for general country-based time series analyses over new-generation tests, with classical panel data analyses commonly employed for the selected country group. In summary, an examination of the literature reveals that studies on this issue predominantly rely on first- and second-generation linear panel data analysis techniques. Therefore, incorporating unit root and cointegration tests is crucial in significantly contributing to the literature, particularly by acknowledging and addressing structural breaks in the study. Additionally, it aligns with the theoretical framework that variables such as democracy and foreign direct capital investments, susceptible to the influence of global developments, are prone to structural changes. Consequently, employing panel data analysis techniques with structural breaks gains significance and enhances the motivation and scientific robustness of the study, mainly when a substantial data range is available.

The study focuses on the BRICS-TM countries: Brazil, Russia, India, China, South Africa, Türkiye Footnote 1 (Turkey), and Mexico . These nations have gained prominence in the global economy, and their strategic significance is anticipated to grow. The selection of this sample group is based on their demonstrated high performance and potential to attract substantial foreign direct investment globally. The study’s unique contribution lies in its examination of the impact of the democracy variable on foreign direct investments within this specific country group, employing innovative techniques not commonly found in the existing literature. Furthermore, the potential increase in foreign direct investment within these countries is expected to influence national and per capita incomes positively. The continuous enhancement of economic well-being and the rising accumulation of foreign direct investments could position these countries as new focal points of attraction in the medium and long term, fortifying their appealing characteristics.

Descriptive Statistics and Graphical Analysis of Variables

Graphical analyses provide valuable insights into the changes and fluctuations of variables over the years in econometric studies. The visual representation and interpretations of the study variables are presented in Fig. 2 .

Graphical representation of variables

The graphical analysis reveals the trend and volatility of FDI over the study period (1994–2018). Peaks and troughs may indicate significant events or economic shifts influencing FDI.

Democracy index: The graphical representation illustrates the changes in the democracy index across the selected countries. Distinct patterns or shifts may be observed, indicating periods of democratic development or regression.

Inflation (INF): The inflation variable is depicted graphically, highlighting its trajectory over the analyzed years. Fluctuations in inflation rates may correlate with economic events impacting FDI.

Per capita income (PGDP): The per capita income variable is visually presented, demonstrating its variations and trends. Per capita income changes can influence countries’ attractiveness for foreign investments.

These graphical analyses serve as a foundation for understanding the dynamics of the variables under investigation and provide a visual context for further econometric interpretations.

So Fig. 2 provides a comprehensive overview of the variables examined in the study. The following key observations can be made:

Foreign direct investment (FDI): China stands out as the leader in attracting the highest FDI among the BRICS-TM countries. South Africa exhibits the lowest FDI levels in the sample group.

Democracy index: China also holds the highest score in the democracy index, indicating its position as the most democratic among the selected countries. South Africa, on the other hand, has the lowest democracy index score.

Per capita income (PGDP): Russia demonstrates the highest per capita income among the countries, suggesting a relatively higher economic well-being. India, conversely, has the lowest per capita income in the sample group.

Inflation (INF): Russia and Türkiye experience the highest inflation rates, while other countries exhibit fluctuating patterns at lower and similar levels.

Table 4 provides a detailed overview of the descriptive statistics for the variables under consideration. The following key statistics offer insights into the central tendencies and variations within the sample group.

The analysis of the basic descriptive statistics in Table 4 yields several noteworthy findings:

Kurtosis values: The INF variable stands out with a kurtosis value exceeding 3, indicating a sharp peak and heavy tails in its distribution. All other variables exhibit kurtosis values below 3, suggesting relatively normal distributions without excessively heavy tails.

Skewness values: LNFDI and LNPGDP variables display negative skewness values, suggesting a longer left tail in their distributions. DEMOC and INF variables exhibit positive skewness values, indicating longer right tails in their distributions.

Jarque–Bera test: The Jarque–Bera test results indicate that the variables are statistically significant and deviate from a normal distribution. This departure from normality suggests that certain factors or events influence the distributions of the variables.

These findings provide insights into the shapes and characteristics of the variable distributions. As indicated by skewness and kurtosis values, the deviations from normality suggest that the variables may be subject to specific influences or events, contributing to their non-normal distributions. Researchers should consider these distributional characteristics when interpreting the results and drawing conclusions from the dataset.

Cross-section Dependence Test

The escalating interdependence among countries in global economies has rendered them susceptible to the impact of positive or negative developments in one nation affecting others. This phenomenon directly results from the deepening global integration associated with globalization. Consequently, econometric studies must incorporate cross-section dependence tests to gauge the extent of interaction between nations. Such tests aim to quantify how a shock in one country reverberates across borders, influencing other countries of the global economic landscape.

Studies addressing cross-section dependency (Andrews, 2005 ; Pesaran, 2006 ; Phillips & Sul, 2003 ) emphasize that failing to account for cross-section analysis may lead to biased and inconsistent results. Thus, all analyses should consider cross-sectional dependence in relevant studies (Breusch & Pagan, 1980 ; Pesaran, 2004 ).

The tests used to determine cross-section dependence were as follows:

When the time dimension is greater than the cross-section dimension ( T > N ), analyses were conducted using Breusch and Pagan’s ( 1980 ) CD lm1 test.

In cases when the time dimension is equal to the cross-section dimension ( T = N ), the CD lm2 test (Pesaran, 2004 ) was used to conduct analyses.

In cases when the time dimension was smaller than the cross-section dimension ( T < N ), analyses were conducted by CD lm test (Pesaran, 2004 ).

In cases when the time dimension is both smaller and greater than the cross-section dimension, analyses were conducted (LM adj ) test (Pesaran et al., 2008 ).

This study’s analysis focuses on the relationship between democracy and FDI across BRICS-TM countries, involving seven countries. With annual data spanning 1994–2018, the cross-section dimension is denoted by N = 7 and the time dimension by T = 25. Given that T > N , the study utilized the CD lm1 test (Breusch & Pagan, 1980 ) and CD lm1 and LM adj tests (Pesaran et al., 2008 ).

Given that T > N for the countries and time dimension, the decision-making is informed by the results of the CD lm1 and LM adj tests. Notably, LM adj test results were prioritized, considering the potential bias in cross-section dependency tests associated with the CD lm1 test. The findings of the cross-section dependence tests are presented in Table 5 .

Upon reviewing Table 5 , it is evident that the probability values for all variables are less than 0.01. Consequently, based on the LM adj test results, the null hypothesis stating “there is no dependence between sections” is rejected, while the alternative hypothesis suggesting “cross-section dependence between sections” is accepted.

The outcomes of the tests align with the characteristics of the contemporary global landscape, where any impactful event or development in one of the BRICS-TM countries has reverberations across others. Whether positive or negative, changes in one BRICS-TM nation can influence others, particularly in areas related to foreign direct investment (FDI) and democracy. As a result, policymakers in these countries should craft their future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy. Indeed, the obtained result is consistent with theoretical expectations. The observed interdependence and influential power of the BRICS-TM country group align with the current dynamics of the globalized world. Their growing significance in the world economy and their strategic importance reinforces the decision that developments within these countries have substantial implications beyond their borders. This outcome urges the need for a nuanced approach to respond to the interconnected nature of these nations in the contemporary global landscape.

Panel Unit Root Test

In the initial phase of the econometric analysis, the stationarity of the variables in the models was determined through unit root analyses to address the spurious regression problem. Accurate results cannot be obtained when a unit root is present in a series of variables (Granger & Newbold, 1974 ). In panel data analysis, the primary consideration in stationarity tests is whether the countries are independent of each other or not. Unit root tests in panel data analysis comprise first- and second-generation tests, each with distinct characteristics. The first generation of unit root tests is further divided based on the homogeneity and heterogeneity assumptions of the countries. Some authors conducted tests under the homogeneity assumption (Breitung, 2005 ; Hadri, 2000 ; Levin et al., 2002 ), while some others pursued their analysis under the heterogeneity assumption (Choi, 2001 ; Im et al., 2003 ; Maddala & Wu, 1999 ).

Additionally, second-generation tests incorporate cross-section dependency into their analyses, whereas first-generation tests do not account for it. Given the dynamics of the global world, the use of second-generation tests in the literature is deemed more beneficial, as it is more realistic to assume that other countries will be affected by a shock experienced by one of the countries in the panel. Panel unit root tests have gained broader acceptance in time series analysis due to their ability to provide more meaningful results than standard stationarity tests. In recent years, there has been a preference for tests that allow for structural breaks, especially in series sensitive to economic variations such as foreign trade, exchange rates, and foreign capital. Hence, this study utilized panel unit root tests that consider structural breaks to assess the stationarity of variables susceptible to cyclical fluctuations, including democracy, inflation, per capita income, and FDI. Conducting stationarity tests without accounting for structural breaks can yield misleading results, making panel LM unit root tests with structural breaks the method of choice for this study.

The panel LM test (Im, Lee, & Tieslau, 2010 ) examines series in models with a level and trend, considering single and two breaks. In this study, analyses with a single break were preferred due to the shortness of the specified time interval and the events expected to cause breaks in the given period. The LM test statistics were employed to assess the hypothesis of “there is a unit root” (ϕ i = 0). Compared to others, a distinctive feature of this test is its allowance for different breaking times for different countries. Moreover, it permits a structural break under both zero and alternative hypotheses, providing an additional advantage. The asymptotic distribution of the test follows the standard normal distribution, and it remains unaffected by the presence of a structural break. Table 6 presents the stationarity analysis results of the series for seven countries based on the model allowing breaks in level.

The analysis of Table 6 yields the following observations:

In unit root models allowing for a constant break, it is evident that all variables in the panel become stationary when their differences are calculated. In other words, since the series are stationary for the entire panel at the I(1) level, the necessary conditions for cointegration tests are met. The cointegration test indicates that global and local developments in countries cause structural breaks when considering these break dates.

On a country basis, the following conclusions can be drawn from Table 6 :

For the series whose differences are calculated, the FDI variable is stationary at the level value in Russia and India, while the same variable is stationary in India and Türkiye.

The per capita income variable is stationary at a level value only in Türkiye. However, the same variable is stationary in Brazil, India, and Türkiye for the series whose differences are computed.

The inflation variable is stationary at the level value in South Africa and Mexico. However, the same variable is stationary for the series whose differences are computed in Brazil, Russia, and China.

The democracy variable is stationary at the level value in Brazil, South Africa, and Türkiye. However, the variable is stationary in Brazil, Türkiye, and Mexico for the series whose differences are computed.

Table 7 shows the stationarity analysis results of seven countries based on the model that allows breaks in level and trend.

The results in Table 7 can be analyzed based on the following points:

General panel evaluation: Foreign direct investment (FDI) and per capita income variables are stationary at the level values when the panel is considered whole. Taking the difference of these variables increases the degree of stationarity. Inflation and democracy variables, among the other variables in the model, are stationary in the series when the difference is taken. However, they exhibit unit root characteristics at the level values. Overall, all series are stationary at the I(1) level with structural breaks for the entire panel. This suggests that the necessary conditions for the cointegration test are met. The dates of structural breaks indicate that social, political, and economic developments may have caused these breaks in the BRICS-TM countries included in the sample . These findings imply that significant events and changes in the socio-political and economic landscape of the BRICS-TM countries likely influence the structural breaks in the series.

Results from Table 7 can be interpreted on a country-specific basis as follows:

Brazil: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

Russia: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

India: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

China: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

South Africa: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

Türkiye: FDI is stationary at the level value, per capita income is stationary at the level, and inflation and democracy are stationary at the difference.

Mexico: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

These country-specific findings indicate variations in the stationarity characteristics of the variables, highlighting the importance of considering individual country dynamics in the analysis. The results of the panel unit root tests, both with and without structural breaks, provide insights into the stationarity of the variables. The interpretation suggests that a shock to one of the countries included in the model can lead to permanent effects that do not dissipate immediately. As confirmed by the tests, the non-stationarity of the series establishes the necessary condition for cointegration tests.

Moreover, when the same tests are conducted by taking the first-order differences of all series to achieve stationarity, it is observed that the variables become stationary at the I(1) level. This indicates that the variables are integrated in the first order, aligning with theoretical expectations. The I(1) characteristic implies that the variables exhibit a tendency to return to equilibrium after a shock, supporting the notion of long-run relationships among the variables.

Homogeneity Test of Cointegration Coefficients

The homogeneity of coefficients plays a crucial role in determining the relationship between variables in panel data studies. It helps organize subsequent tests used in the analysis. The homogeneity test examines whether the change in one country is affected at the same level by other countries. Coefficients are expected to be homogeneous in models for countries with similar economic structures, while they may be heterogeneous for countries with different economic structures. Pesaran and Yamagata ( 2008 ) developed the delta test based on Swamy ( 1970 ) to determine whether the slope parameters of cross-sections are homogeneous. The null hypothesis for this test is “slope coefficients are homogeneous.” Homogeneity, in the context of panel data analysis, implies that the coefficients of the slopes are the same for all units or entities within the panel. On the other hand, heterogeneity indicates that, at least in one of the entities, the slope coefficients differ from those in the rest of the panel. Testing for homogeneity helps assess whether the relationship between variables is consistent across all units or if there are significant variations.

As seen in Table 8 , the delta homogeneity test was performed to determine whether the slope coefficients of the model differ between units.

The delta test results indicate that the slope coefficients vary between units in the long term, given that the probability values for both test statistics are smaller than 0.05, as presented in Table 8 . This result suggests that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. The obtained result aligns with expectations and is consistent with the theory, indicating that the countries within the BRICS-TM sample exhibit different structures, and the coefficients are heterogeneous. This result suggests that the relationship between variables varies across these countries, emphasizing the sample group’s diverse economic characteristics and behaviors.

Panel Cointegration Test with Structural Break

Different methods are employed to determine the existence of long-term cointegration among the model’s variables. One set of methods is first-generation tests, which do not require cross-section dependence. The second set includes second-generation tests that consider cross-section dependence but do not incorporate structural breaks (Koç & Sarica, 2016 ). To obtain realistic and unbiased results, it is crucial to conduct tests that take structural breaks into account in cointegration analyses. Therefore, the panel cointegration test-PCWE (Westerlund & Edgerton, 2008 ) was employed, given that the series is stationary at the I(1) level.

PCWE was developed based on unit root tests that utilize Lagrange multiplier (LM) statistics, obtained from multiple repetitions (bootstrap). The merits of this test can be succinctly summarized as follows (Koç & Sarica, 2016 ; Göçer, 2013 ):

It takes into account cross-section dependency and structural breaks.

It accommodates heteroscedasticity and autocorrelation.

It identifies breaks at different dates for each country in terms of both constants and slopes.

Potential inherent problems in the model can be addressed with fully adjusted least squares estimators.

This test is effective in yielding reliable results even with small sample sizes.

This study opted for PCWE tests, given their robust characteristics. Additionally, considering the limited number of countries in the sample and the anticipation of few structural breaks in the specified period, the PCWE test was the preferred choice. As depicted in Table 9 , the determination of statistically significant cointegration between variables is made based on the significance levels of the probability values.

As indicated in Table 9 , cointegration is observed at a 5% significance level in the regime change model and a 1% significance level in the model without a break. The presence of cointegration suggests a long-term relationship between the variables of democracy and FDI in BRICS-TM. In simpler terms, democratic developments and FDI are correlated over the long run, indicating a balanced relationship between them. Future researchers may explore the direction of these variables across different samples. This study specifically tested the existence of a long-term relationship between FDI and democracy, and the inclusion of structural breaks was found to be significant. Governments and decision-makers, particularly in developing countries like BRICS-TM, should consider the relationship between democracy and FDI by taking structural breaks into account to attract foreign investment effectively. Therefore, it is emphasized that “any development related to democracy has the potential to influence FDI, and considering this factor is beneficial in the formulation and implementation of socio-economic policies.” No cointegration is observed in the “change at level” model. Indeed, the obtained results align with the study’s hypothesis. Considering the periods of structural breaks in the countries within the sample, it becomes evident that a long-term relationship exists between the variables incorporated into the model. This issue underscores the importance of considering not only the overall relationship between democracy and FDI but also the specific historical contexts and transitions in individual countries that might contribute to this relationship.

Regarding structural breaks in countries in the sample within the scope of cointegration in the regime change model, local and global developments, in general, cause breaks. The reasons for structural break dates in the sample countries are given in Table 10 .

The following items can be aligned with the breaking dates provided in Table 10 :

A recovery in macroeconomics and positive expectations toward agreements with the IMF became prominent after Russia’s transition economies in 1996.

2000 in Brazil is known as the period when the rapid growth trend started after passing the targeted inflation after the 1999 Russian Crisis.

Membership of China in the International Trade Union was evaluated as an essential development in the global economy in 2001.

Experiencing the biggest crisis in history in Türkiye in 2002 and starting a dominant single-party regime were remarkable developments.

The 2005 Election results in Mexico and the hurricane disasters, including an 8.7-magnitude earthquake, created significant socio-economic problems that year.

The ANC party’s coming to power alone in South Africa in 2009 was commented on as a consistent process for the national and regional economy; this situation also removed a series of uncertainties.

The devaluation experienced in India in 2016 has created a significant break.

Of course, the impact of such structural breaks should be considered. Toguç et al. ( 2023 ) argued that “differentiating these short-term and long-term effects has implications for risk management and policymaking.” Since structural break increases risks and uncertainty, foreign capital prefers to invest in other destinations.

Kónya’s Causality Test

This test (Kónya, 2006 ) investigates the existence of causality between variables using the seemingly unrelated regression (SUR) estimator (Zellner, 1962 ). One advantage of this test is that the causality test can be applied separately to the countries that make up the heterogeneous panel. Another important advantage is that it is unnecessary to apply unit root and cointegration tests, as country-specific critical values are produced. According to the test results, if the Wald statistics calculated for each country are greater than the critical values at the chosen significance level, the null hypothesis of “no causality between the variables” is rejected. In other words, a Wald statistic greater than the critical value indicates that there is causality between the variables.

The Kónya causality test results provided in Table 11 revealed a causality from democracy (DEMOC) to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. In addition, from FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia.

According to the results in Table 12 for the causality between foreign direct investment (FDI) and PGDP, the Kónya causality tests revealed a one-way causality from PGDP to FDI at a 10% significance level in Mexico.

According to the results provided in Table 13 for the causality between foreign direct investment (FDI) and inflation (INF), the results of the Kónya causality tests revealed a one-way causality from inflation to FDI at a 10% significance level in Türkiye and, conversely, a one-way causality from FDI to inflation at a 10% significance level in South Africa.

The study investigated the nexus between democracy and foreign direct investment (FDI) using annual data from a sample of seven countries within emerging markets from 1994–2019. According to cross-section dependence test results, all variables’ probability values were less than 0.01, indicating significant cross-section dependence. The rejection of the null hypothesis, stating “there is no dependence between sections” in favor of the alternative hypothesis suggesting “there is cross-section dependence between sections,” aligns with the contemporary global landscape. In today’s interconnected world, any impactful event or development in one of the BRICS-TM countries has reverberations across others, particularly in areas related to FDI and democracy. These findings underscore the imperative for governments and policymakers in these countries to craft future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy.

Furthermore, the outcomes of the panel unit root test indicate that all variables in the panel become stationary at the I(1) level when their differences are calculated, meeting the necessary conditions for cointegration tests. This result suggests that global and local developments in countries cause structural breaks when considering these break dates. Variations in stationarity characteristics of variables were observed on a country basis, highlighting the importance of considering individual country dynamics in the analysis.

The delta homogeneity test results suggest that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. This aligns with expectations and emphasizes the diverse economic characteristics and behaviors within the sample group of BRICS-TM countries.

The Westerlund-Edgerton cointegration test results reveal significant cointegration between variables, observed at a 1% significance level in the model without a break and a 5% level in the regime change model. This result signifies a sustained relationship between FDI and democracy in BRICS-TM countries over the long term. Future researchers may explore the direction of these variables across different samples, while governments and decision-makers should consider this relationship, particularly in developing countries, to attract foreign investment effectively.

Kónya’s causality test results also provided significant causality between some of the variables in some countries within the sample group. Firstly, there is a causality from democracy (DEMOC) to FDI in Mexico (1% significance level), in China (5% significance level), and in Russia (10% significance level). Secondly, there is also a significant causality from FDI to democracy (DEMOC) in Mexico (5% significance level) and in Russia (10% significance level). Thirdly, a one-way causality could only be found from PGDP to FDI in Mexico (10% significance level). Fourthly, there is also a one-way causality from inflation to FDI in Türkiye (10% significance level) and a one-way causality from FDI to inflation in South Africa (10% significance level). Thus, Kónya’s causality test results supported the hypothesis of the research with significant results.

In conclusion, the empirical findings establish a statistically significant and robust relationship between the level of democracy and the flow of FDI in BRICS-TM countries. These findings underscore the intertwined nature of political and economic dynamics within these nations and highlight the importance of considering both aspects in policy formulation and decision-making processes.

The relationship between the democracy level and foreign direct investment (FDI) of BRICS-TM countries is an area that requires further exploration. Subsequently, comparing the findings of this study with those of previous research reveals its significance. While earlier studies predominantly concentrated on the preferences of host countries in attracting foreign investment, few delved into the factors influencing foreign investors’ choices. A notable exception is by Li and Resnick ( 2003 ), who highlighted the pivotal question of “Why do companies invest in foreign countries?” and proposed a theory positing that “democratic institutions impact FDI flow in both positive and negative ways” (Li & Resnick, 2003 :176). Their conclusions from data analysis of 53 developing countries spanning 1982–1995 align with the current study’s outcomes. Specifically, they found that (1) advancements in democracy lead to heightened property rights protection, fostering increased FDI inflows, and, (2) conversely, democratic improvements in underdeveloped nations result in diminished FDI flows. These findings correspond with our study, given that the sampled countries are a mix of developing and developed nations, mirroring the first scenario described by Li and Resnick.

Derbali et al. ( 2015 ) concluded in a similar vein in their study, examining a massive dataset spanning from 1980 to 2010 with 173 countries, 44 of which underwent democratic transformation. Their observation that “variables related to human development and individual freedom initiate the democratic transformation process, contrary to the social heterogeneity variable” aligns with the results of the present study when interpreted in reverse. This scenario prompts a chicken-and-egg question: Does the level of democracy positively influence the flow of FDI, or does FDI flow positively impact the level of democracy? The authors tackled this issue in the second stage of their analysis and determined that democratic transformation leads to a substantial increase in FDI inflows. Our findings corroborate this perspective with evidence from a different sample group of countries.

Malikane and Chitambara ( 2017 ) concluded in their study analyzing the relationship between FDI, democracy, and economic growth in eight South African countries from 1980 to 2014 that the FDI variable exhibits a direct and positive impact on economic development, explicitly implicating that strong democratic institutions serve as notable drivers of economic growth. Their findings suggest that the effect of FDI on economic growth is contingent on the level of democracy in the host country. In another study on developing countries, Khan et al. ( 2023 ) found that specific determinants of good governance, such as control of corruption, political stability, and voice and accountability, significantly attract FDI inflows. However, other determinants, including government effectiveness, regulatory quality, political system, and institutional quality, significantly reduce FDI inflows. On the contrary, they found that in Asian countries, all institutional quality indicators except control of corruption have a significant and positive effect on FDI inflows (Khan et al., 2023 ). The significant relationships identified between these phenomena across various indicators for developing and Asian countries align with the findings of our study.

Developed and developing nations actively engage in concerted efforts to attract foreign capital investments in the contemporary global economic landscape. Foreign direct investments (FDIs) stand out as a pivotal form of investment that significantly influences a country’s growth and development trajectory. The inflow of direct foreign capital brings multifaceted contributions to a nation’s economy, encompassing vital aspects such as capital infusion, technological advancement, elevated management standards, expanded foreign trade opportunities, employment generation, sectoral discipline, access to skilled labor, and risk mitigation.

In addition to all these, foreign direct investment (FDI) holds significant importance not only in the general context of sustainability but also specifically in sustainable development. To better understand this close relationship between sustainable development and FDI, first briefly examine the concept of sustainability. Simply put, sustainability entails maintaining a favorable condition through methods that cause no harm yet are supportable, legally and scientifically verifiable, defendable, and implementable (Ratiu, 2013 ). From a developmental perspective, it signifies maintaining continuity without losing control. According to Menger ( 2010 ), sustainability can be defined as the ability to grow and survive independently. The author emphasizes that the concept of sustainability is closely related to “creativity” and “cultural vitality,” as well as being an “internally growing” and “self-sustaining” trend with innovative effects that also attract different social strata.

Within the context of all these existing barriers and dilemmas, managing the process of reducing the negative aspects while increasing and offering the positives to people must be handled with care. This intricate process, termed sustainable development, is like the search for the cosmos in chaos as it aims to balance the economic, environmental, and social dimensions of both local urban areas and regional and national areas, and even the global sphere, especially with climate change becoming one of the main negative impacts on the environmental dimension. Gazibey et al. ( 2014 ) also noted that, while some problem areas, such as “poverty reduction” are mainly related to the economic and somewhat to the social dimensions of sustainability, other issues like “climate change” and “reduction of carbon footprint” are more related to the environmental dimension. An in-depth examination reveals that many problems, which may initially seem related to a single dimension, are intertwined with multiple dimensions. Thus, while attracting foreign direct investment to a country may seem primarily related to the economic dimension at first glance, it is closely linked to environmental and social dimensions.

In its most straightforward approach, meeting and satisfying the basic needs of individuals will subsequently prioritize higher-level needs. This, in turn, will support sustainable development in all three dimensions. Thus, while foreign capital invested in a country may initially support economic sustainability, its contribution to the socio-economic levels of individuals will lay the groundwork primarily for social and educational improvement in the medium and long term, secondarily for environmental enhancement to result in a more livable environment. For example, Xu et al. ( 2024 ) argued that “China is currently exploring a sustainable development mode of collaborative governance.” In a good level of governance, all social partners expected to be affected by the possible policies are included in the decision-making process. This process is related to and supports the participation dimension of democracy. So, as the pieces of a chain, a good level of democracy supports the level of governance, and governance supports the accumulation of FDI and economic performance. Consequently, these favorable conditions might pave the way for sustainable development. Another study (Olorogun, 2023 ) found a long-run relationship between financial development in the private sector and economic growth in sub-Saharan Africa, with the data spanning from 1978 to 2019. According to the results of the author’s research, there is a long-run covariance between sustainable economic development and foreign direct investment (FDI) and a significant level of causality between economic growth and financial development in the private sector, FDI, and export.

Indeed, sustainability resembles a ball resting on a three-legged stool: Any absence or imbalance in one of this tripod’s economic, social, or environmental legs will cause the ball to fall. In other words, sustainable development requires addressing all three dimensions in a balanced manner.

This idea brings us to the focus of this research: The level of democracy and the FDI variable and the relationship between these variables essentially concerns all three dimensions. In countries with a higher level of democracy, the possibility of developing policies that consider citizens’ demands and preferences is higher than in countries with lower levels of democracy. Conversely, in countries with lower levels of democracy , the likelihood of prioritizing the preferences and gains of specific individuals or groups over issues such as sustainability, environmental protection, and social welfare is higher. Consequently, this situation will negatively affect both the potential level of FDI attracted to the less developed country and, ultimately, the sustainable development momentum.

To sum up, numerous factors play a crucial role in shaping decisions related to foreign direct investments. Particularly in underdeveloped and developing countries, where domestic capital accumulation might be insufficient, the preference for attracting direct foreign capital investments emerges as a strategic choice over external borrowing. This strategic approach is driven by fostering economic development and sustainable growth while leveraging the benefits associated with foreign capital inflows.

The empirical evidence on the relationship between democracy and the level of foreign direct investment (FDI) often presents conflicting results, influenced by variations in study periods and sample compositions. Notably, these disparities can be traced back to the differing development levels of countries under scrutiny.

Reviewing previous studies reveals a recurring pattern wherein developed countries exhibit a positive and significant correlation between democracy and FDI. Conversely, in underdeveloped or developing nations, a negative relationship tends to prevail between these two variables. This disparity hinges on the distinct behavior of capital owners seeking to invest in already developed countries, where business transactions are grounded in established legal frameworks, property rights, and the rule of law. In contrast, underdeveloped and developing countries often witness capital owners engaging in potentially illicit and unethical business dealings with high risks and potential returns.

These arrangements are frequently based on different interests and assurances with individuals and groups in positions of power. In essence, the ease of resource acquisition, processing, and exportation in underdeveloped countries becomes contingent upon the presence of authoritarian regimes. Such relationships of interest with authoritarian regimes provide investment security for global investors. However, these regimes—keen on preserving these relationships—are disinclined to have their dealings exposed, which in turn leads to increased pressure on their citizens. The resulting mutualistic relationship transforms into a lucrative exploitation process.

When the outcomes of the panel data analysis incorporating structural breaks were examined, it was found that all variables demonstrated significance at the 1% level. The cross-sectional dependency analysis results indicated a significant cross-sectional relationship between the variables. In the panel unit root test, it was observed that the variables in the model exhibited unit roots at the level, but their differences rendered all variables stationary. The delta homogeneity test findings suggested that the variables lacked homogeneity. Furthermore, the results of the panel cointegration test with structural breaks affirmed a long-term relationship, with significance levels of 1% in the model without breaks and 5% in the regime change model. Lastly, the reached bidirectional and one-directional causality between FDI and democracy and other economic variables like inflation and PGDP in the sample group countries require policymakers to focus on each variable carefully especially on the level of democracy if they aim to reach a high level of FDI.

In conclusion, the findings of this study suggest the presence of a long-term relationship between democracy and FDI also supported by causality in some countries within the sample, as revealed through the analysis of data from BRICS-TM countries within emerging markets spanning the period 1994–2018. The significance of this relationship is particularly evident when considering the impact of structural breaks. It is emphasized that governments and policymakers in emerging markets (including those in BRICS-TM), which aim to bolster their economy’s resilience against various shocks, should not only consider structural breaks but also recognize the intricate connection between democracy and FDI. The study underscores that developments in democracy have the potential to influence FDI, emphasizing the importance of factoring this relationship into the formulation and execution of socio-economic policies. Lastly, using panel tests with a structural break, a method uncommonly employed in the empirical analysis of the democracy variable, may contribute as an additional dimension to the existing literature in this field.

In analyzing the relationship between democracy and foreign direct investment, the findings suggest a long-term relationship in all models except for the level change model. These results highlight the significance of democratic developments in the BRICS-TM countries influencing the inflow of foreign direct capital. Therefore, policymakers in emerging markets, particularly within BRICS-TM countries, are encouraged to prioritize democracy and foster democratic developments to attract foreign direct investments. Additionally, given the impact of global and local developments leading to structural breaks, it becomes crucial for these policymakers to closely monitor and interpret international and global events that may affect the resilience of their national economies, both negatively and positively. By doing so, emerging markets can enhance their resilience against various shocks, enabling policymakers to adeptly prepare their economies, private sectors, and stock markets for potential global risks.

Opting for direct foreign capital investments over external debt or short-term investments is a more rational approach for developing countries to accumulate capital for their overall development. As many countries seek to address the scarcity of capital, the understanding of the contributions of foreign capital to development improves, while global competition intensifies to attract foreign capital. Therefore, policymakers should focus on enhancing macroeconomic indicators such as inflation and national income and fostering democratic development, a fundamental trust factor for foreign capital. Demographic and institutional factors also affect the global or social fiscal pressure (Nuță & Nuță, 2020 ). Thus, as an institutional factor, positive developments at the level of democracy are fundamental in attracting foreign capital.

It is crucial for developing countries to prioritize and keep pace with indicators that foreign capital considers significant. Global companies prioritize countries they can trust, where investments can swiftly yield profits due to potential risks. The foundation of democracy in developing nations starts in the family and education realms. Proper education on the importance and necessity of democracy in the curriculum contributes to long-term awareness of democracy. Developing effective education policies within families can address intra-family democracy, fostering a culture of democracy throughout the country.

The reasons listed up to this point reiterate that attracting foreign direct investments to a country is of utmost critical importance for supporting sustainable development in all aspects of the nation. As discussed in the discussion section, while sustainability may appear to be solely related to the economic dimension at first glance, an increase in foreign direct investment toward a country has the potential to indirectly and positively impact the social and environmental dimensions of sustainability as well. When considering that the level of democracy also has a similar effect on the level of FDI, it should be expected that the level of democracy in a country is strongly correlated with the issue of sustainable development.

In conclusion, new researchers interested in this subject are recommended to conduct analyses on different country groups. Updating established models and testing hypotheses using various socio-economic indicators and analysis methods can further contribute to the literature.

Data Availability

The data set is uploaded to the system as a supplementary file and also uploaded to Figshare with the https://doi.org/10.6084/m9.figshare.21701966 .

Turkey’s name changed to Türkiye: According to the United Nations (UN)-Türkiye, the country’s name has been officially changed to Türkiye at the UN upon a letter received on June 1 from the Turkish Foreign Ministry (UN-Türkiye. (2022)). Turkey’s name changed to Türkiye, URL: https://turkiye.un.org/en/184798-turkeys-name-changed-turkiye , Accessed on: 02.07.2022.

Abbreviations

Brazil, Russia, India, China, South Africa, Türkiye, Mexico

The Democracy Index variable

Ecological footprint

Foreign direct investment

Gross domestic product

Logarithm of foreign direct investment

Logarithm of per capita income

Multinational corporations

Per capita income

Political institutions

Regression coefficient value

World Development Indicators

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Acknowledgements

We appreciate all the efforts and time spent by the editorial office members and anonymous reviewers for all their comments, which contribute to the quality of the article.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). No funds were received from any institution.

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Ibrahim Cutcu

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Practice Points/Highlights

1. From 1994 to 2018, there was significant cointegration between democracy and foreign direct investment (FDI) in BRICS-TM countries among the emerging markets.

2. Democratic developments and FDI move together in the long run and have a balanced relationship between them in Emerging Market Economies.

3. Policymakers in BRICS-TM countries need to develop democracy awareness and ensure democratic developments to attract foreign direct investment to secure a resilient economy in these emerging economies

4. Governments and decision-makers in emerging economies, such as BRICS-TM, who want to attract FDI need to consider the structural breaks and the relationship between democracy and FDI .

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Cutcu, I., Keser, A. Democracy and Foreign Direct Investment in BRICS-TM Countries for Sustainable Development. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02205-3

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Analysis of the impact of RCEP on the industrial and innovation chains of China’s textile and clothing industry

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4.2 Textile trade dynamics between China and RCEP member nations

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