Source: United Nations (n.d. ).
While these targets may appear abstract to many family businesses through careful analysis, specific actions of business can contribute to the targets above. The targets are designed to be aspirational targets for countries, but businesses can make a valuable contribution to these targets and specifically advance gender equity. Each of the targets will be examined and suggested contributions that family businesses can make are provided.
Target 5.1 – End all forms of discrimination against all women and girls everywhere. An important contribution is that family businesses can create inclusive and non-discriminatory workplaces where women have equal opportunities and are treated fairly. They can implement policies that prevent gender-based discrimination in hiring, promotion and compensation. In addition, they can provide equal training and development opportunities for all employees, regardless of gender.
Target 5.2 – Eliminate all forms of violence against all women and girls in the public and private spheres. Family businesses can promote a safe and respectful work environment that prevents harassment and violence. Family businesses can establish a zero-tolerance policy for harassment and violence. Provide training to employees on recognising and addressing gender-based violence. Support anti-violence campaigns and initiatives.
Target 5.3 – Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation. Family businesses may not have a direct influence on this target; they can consider supply chain impacts. Family businesses can engage with suppliers to ensure that their operations align with the principles of gender equality and human rights. By adopting responsible sourcing practices, family businesses can contribute to a broader movement that seeks to eliminate harmful practices from various industries.
Target 5.4 – Recognise and value unpaid care and domestic work. As a result of family businesses having employees that are ‘family’, family businesses are predisposed to support work–life balance for both women and men and encourage shared caregiving responsibilities. Family businesses that are aware of these issues offer flexible working arrangements, parental leave and childcare support. The businesses also promote the importance of valuing and sharing caregiving responsibilities within families.
Target 5.5 – Ensure women's full and effective participation and equal opportunities for leadership. Women's leadership and participation in decision-making is an important hallmark of many family businesses and is important in contributing to SDG#5. These businesses actively encourage and support women's advancement into leadership roles within the business. They also provide mentorship, training and networking opportunities for women employees. If these actions are taken on a national scale in family businesses, then the impact becomes significant for their contribution to national SDG targets.
Target 5.6 – Ensure universal access to sexual and reproductive health and reproductive rights. Depending on the country where the family business is located, the business can ensure that their employees have access to comprehensive sexual and reproductive health services. They can also provide health insurance coverage that includes health services and educational services for women and girls.
Target 5. a-b-c – Undertake reforms to give women equal rights to economic resources. Family businesses are often at the forefront of business advocating and promoting economic empowerment for women within their workforce and communities. They not only ensure equal pay for equal work but can also support women's entrepreneurship by providing training, access to financing and business development resources within their organisations. Advocating for sound policies and enforceable legislation for the promotion of gender equality is also something that many family businesses undertake.
Family businesses can contribute to these SDG#5 targets by integrating gender equality principles into their organisational culture, policies and practices. By fostering an environment of inclusivity, fairness and empowerment, family businesses can play a vital role in advancing gender equality and creating a positive impact on their employees, families and communities.
The above list provides a framework for how family businesses can focus and potentially repurpose strategies to achieve sustainability actions that align with the SDGs. Progressive family businesses that have a focus on sustainability and social responsibility are likely to already be focusing on such priorities but may not realise they contribute to a country's SDG targets and in particular SDG#5.
While having the potential to contribute to the achievement of SDG#5 related to gender equality and the empowerment of women and girls, family businesses may also encounter several challenges in their efforts. Family businesses may operate within cultural contexts where traditional gender roles and norms persist that hinder the adoption and commitment to the goal. In such situations, challenging these norms and promoting gender equality in a family business might face resistance from other family members, employees and the broader community. Succession is also an issue for SDG#5. Family businesses often face succession challenges, and traditional male-dominated values and attitudes might influence decisions about who takes over leadership roles. How to break the cycle of male-dominated leadership is a key question for equal opportunities for women in leadership. Family businesses often demand significant time and commitment, which can create challenges for women seeking a work–life balance. This is especially the case if caregiving responsibilities are disproportionately placed on them, and there are no equitable systems of caregiving to counter this impact.
There also might be limited access to education and training opportunities in some countries, and this can impact women's ability to contribute effectively to the family business and broader gender equality goals. Legal frameworks or cultural norms might limit women's rights, inheritance and property ownership, affecting their ability to engage fully in the family business and decision-making. Women might be under-represented in certain sectors or industries where family businesses operate. This can limit their access to networks, resources and mentorship opportunities. Like other businesses, family businesses may struggle with addressing gender pay gaps because of entrenched values and attitudes but also because of the imbalance of caregiving and its impact on the career advancement of women.
Lack of awareness and mindset changes are another challenge. Some family business owners might not fully recognise the importance of gender equality or may not be aware of the benefits it can bring to the business and society. Implementing gender equality initiatives may require changes in policies, practices and mindsets. Resistance to change, especially from traditional stakeholders, can hinder progress. The lack of resources in small to medium enterprises can limit investment in gender equality programs, training and resources. This also extends to the potential lack of resources for measuring the impact of gender equality initiatives and determining their direct influence on business outcomes. Disruptions such as the COVID-19 pandemic can also have an impact on the resources of a family business to innovate and transform culture and processes around SDG#5.
Despite these challenges, family businesses can overcome them by leveraging their strengths, values and commitment to long-term sustainability. Engaging in partnerships, seeking external expertise, gradually implementing changes and aligning gender equity initiatives with core business values can help family businesses make meaningful contributions to SDG#5 while addressing the challenges they may encounter.
The book series aims to contribute positively to providing evidence of the role of family businesses in effectively contributing to the SDGs. The family business case studies display support and provide some evidence of how they embody the principles of an SDG in their operations, culture and/or business philosophy. This book is one of a 17 vignette book series in which each book is comprised of a set of short, easy-to-read family business cases related to the unique SDG being discussed in the book. The format of the book series allows the works to be accessible to those working in the field beyond academia such as family business practitioners, family business owners, family business advisors, government and business policymakers, members of non-governmental organisations (NGOs), business associations and philanthropic centres, as well as to those who have a general interest in entrepreneurship and business.
The chapters in this book focus on businesses that have prioritised SDG#5 Gender Equality. The first chapter features Hacienda Las Flores, which is located in the city of Jutiapa in Honduras, Central America. Founded in 2011, it produces a unique collection of artisan and healthy chips made from natural ingredients without preservatives. It also uses 100% Honduran ingredients. Importantly for this chapter, the company was created by women and supports women in their personal and professional development. The authors are Karen Dubon who is the CEO and legal representative of Luna Inversiones (real estate) and Bay Island Harvest S de RL where the products of the Hacienda Las Flores and Paraiso brands are marketed and distributed. Silvia Paz is the CEO of Inversiones S y B S de RL (Hacienda Las Flores) since 2011. Dr Allan Discua Cruz is a member of the Pentland Center for Sustainability in Business and director of the Center for Family Business at Lancaster University Management School, United Kingdom.
The authors make the point in the chapter that the activities of women entrepreneurs and leaders in the creation and development of family businesses are often invisible and fraught with diverse paradoxes in Latin America. Despite this lack of visibility, women in Honduras have played a fundamental role in all social, economic and cultural aspects of the country and have become key actors in the creation and continuity of family enterprises. This case aims to show that family businesses in Latin America can be part of the inclusion and empowerment of women through guidance based on SDGs, specifically SDG#5. The company aligns with SDG#5 through the economic empowerment of women, providing equal pay for women and ensuring women also benefit from the supply chain of their products. The company encourages collaborators to participate in local initiatives that promote gender equality and the empowerment of women, such as motivational talks in schools and colleges.
The next chapter profiles a family business called Ballandean Estate Wines in Queensland, Australia. It is the oldest family-run winery in its local region and has passed the business on through five generations. Ballandean Estate Wines is home to two vineyards. Ballandean Estate, the original vineyard, is situated 800m above sea level and situated in Ballandean. In addition to selling wine as a product, Ballandean Estate Wines also offers an experience through the Cellar Door, as well as the barrel room. The family business proudly champions the women who lead the Ballandean Estate to where it is today. Within SDG#5 Gender Equality, target 5.5.2 is the proportion of women in managerial positions. This goal is evident within Ballandean Estate Wines which contributes positively to this target and has done so for generations. Over the generations, women in the family business have been leaders in the business. The women within the family note that they never felt like they couldn't do anything, be businesswomen or choose to work how they wanted to work. They commented that gender barriers never felt like a ‘thing’.
The next family business profiled is the Stanglwirt in Austria. This company is an award-winning 5-star green spa hotel and includes a wellness resort located on its farm. The company is family-led and owned by the Hauser Family in the 10th generation and was founded in 1609. The Stanglwirt successfully integrates SDG#5 on gender equality and empowerment. The history of the Stanglwirt displays a unique tradition in female leadership and role models. Many managerial positions at the Stanglwirt are taken by women, and this is due to a shared understanding built based on mutual trust, teamwork and focusing on individuals' abilities rather than any other attribute, such as kinship. The Stanglwirt constitutes a best practice example for a family business that prioritises gender equality. At the Stanglwirt, the share of women in managerial positions is around 80%. Studies show that in Europe and North America, female managers amount to less than 40% on average ( United Nations, 2023 ). The Stanglwirt is a leading family firm when it comes to the inclusion of female successors and managers. The authors of this chapter illustrate the values and position of the family business. They state that the company does not need to explicitly make gender equality a subject of discussion at the Stanglwirt as it is perceived as a natural component of the company's success. The Stanglwirt values confidence, trust and proactiveness, as put by one of the children. Hence today, the staff is naturally balanced according to their gender division.
The next chapter illustrates how gender equality and women's empowerment are exemplified in a small company called the Eather Group based in Australia. The company described is engaged in heavy haulage, earthmoving, material supply and disposal and crushing and screening processes. This chapter is pertinent in that there is a substantial gender gap in the transport industry, internationally, women represent 17.3% of the transport industry workforce, while just 9.5% of those working as machinery drivers and operators are females. The central message in the chapter is that gender equality is an objective attainable by companies in industries that traditionally have a large gender gap in employment. The principal actors, in this case, are Peter Eather, founder and managing director, his wife, Sally-ann, general manager and their daughter, Divinia, marketing manager. The latter is also the co-author of the chapter. This case study showcases women's involvement in a family business through the story-telling approach. Marketing and human resources (HR) strategies and goals explicitly discuss gender equality and are active parts of the business model. Financial strategies allow for training programs and related community initiatives to drive the gender goals of the business. The business model has strong gender values underpinning business operations to the point where supervisors are held accountable for achieving the company's vision for a more balanced workforce.
The last chapter profiles the family business Technica International. The company is a Lebanese family business founded by Mr Tony Haddad. As an engineering and automation solution company, Technica International is aware that engineers and technicians in the industry are mostly male. This is because females were particularly under-represented in science, technology, engineering and mathematics (STEM) education and, consequently, in STEM careers. Despite a general under-representation, the company is a showcase of positive motivation for, and engagement in, gender equality and women's empowerment. Technica International seeks to improve gender equality and women's empowerment within its business. The case first highlights the active involvement of the daughter of the founder, Cynthia Abou Khater, who joined the business in 2007 and who is currently Vice President overseeing the Strategy, Information Technology and HR departments. Cynthia was actively instrumental in attracting more female talent to the company and in developing an organisational environment that is conducive to women.
This final chapter focuses on the efforts of Technica International to promote gender equality in the organisation, and it examines the initiatives and practices adopted by the company to achieve this goal. Cynthia was influenced by her own experiences as a highly qualified woman and mother who was discriminated against in the labour market, and she wanted to do things differently at Technica International. When Cynthia started working at Technica International, there were no special policies in place for women. She understood that greater gender equality could create a positive effect on the company, leading to fresh perspectives, new ideas and a pleasant environment for both women and men working at the company. She recognised the need to provide support, especially for young women who just started a family to balance work and life.
The book used a case study method to gain insights into the practices of businesses incorporating the SDGs into their business strategies and management practices. The editors approached a range of potential authors to develop the case studies. They approached early career researchers, PhD students, family business academics, family business consultants, managers of family business centres and family business practitioners to consider submitting a case for the book. Interested authors were asked to choose the SDGs that best matched the family business of their choice and use a case study template provided by the editors of the book series to craft a case study on how the family business advanced the SDG (see Table 2 for template). A truly global response was received for the book series with participants from all over the globe.
Key Aspects of the Case Study Template Used by Authors in This Book.
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The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States.
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Ferreira et al., 2021 Ferreira , J. J. , Fernandes , C. I. , Schiavone , F. , & Mahto , R. V. ( 2021 ). Sustainability in family business–A bibliometric study and a research agenda . Technological Forecasting and Social Change , 173 , 121077 .
Muhmad and Muhamad, 2021 Muhmad , S. N. , & Muhamad , R. ( 2021 ). Sustainable business practices and financial performance during pre-and post-SDG adoption periods: A systematic review . Journal of Sustainable Finance & Investment , 11 ( 4 ), 291 – 309 .
UNEP, 2020 UNEP . ( 2020 ). A UN framework for the immediate socio-economic response to COVID-19 . https://unsdg.un.org/sites/default/files/2020-04/UNFramework-for-the-immediate-socio-economic-response-to-COVID-19.pdf . Accessed on July 26, 2023.
UNEP, 2021 UNEP . ( 2021 ). Leaving no one behind: Impact of COVID-19 on the sustainable development goals (SDGs) . https://www.undp.org/publications/ . Accessed on 26 July, 2023.
United Nations, 2021 United Nations . ( 2021 ). The 17 goals . https://sdgs.un.org/goals . Accessed on July 26, 2023.
United Nations, 2023 United Nations . ( 2023 ). Achieve gender equality and empower all woman and girls . https://unstats.un.org/sdgs/report/2022/Goal-05/ . Accessed on July 26, 2023.
United Nations (n and Indicators, United Nations . ( n.d. ). SDG Indicators, Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development . https://unstats.un.org/sdgs/indicators/indicators-list/
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1 Department of Management, TAMU 4221, Mays Business School, Texas A&M University, College Station, TX 77843-4221 USA
2 Department of Marketing and Management, The University of Texas at El Paso, El Paso, TX 79968 USA
Evidence-based policymaking (EBP) contends that policy decisions are successful when informed by evidence. However, where policy problems are “wicked” (systemic, ambiguous, complex, and conflictual), politics trumps evidence and solutions are never first best or permanent. Applying an EBP approach to solving wicked problems (WPs) therefore appears to be a daunting, impossible task. Despite the difficulties, we contend that blending insights from the EBP and WP literatures can provide actionable and practical policy advice to governments and MNEs for dealing with the WPs of the UN Sustainable Development Goals (SDGs). We support our thesis with a case study applying EBP to the WP of SDG 5 Gender Equality. We compare the statistical evidence from gender inequality indexes to SDG 5’s targets and indicators. We provide five insights from the EBP and WP literatures into why and how good evidence is necessary but not sufficient for progress on SDG 5. Building on these insights, we recommend that governments adopt an EBP approach employing public–private partnerships to address SDG 5. We also recommend that MNE executives use our new SDG Materiality Matrix, designed on EBP principles, to build SDG 5 into their global corporate social responsibility strategies.
On 25 September 2015, the United Nations (UN) General Assembly adopted the 2030 Agenda for Sustainable Development, a general pledge to “transform our world” and “leave no one behind” in terms of the economic, social, and environmental dimensions of sustainable development (UN, 2015b ). The 2030 Agenda established 17 Sustainable Development Goals (SDGs) with a long list of targets and indicators that were to be collected, shared, and monitored by the UN Member States.
The 2030 Agenda is a form of goal-based global governance, where the 17 global goals define the sustainable development aspirations of UN Member Countries and their major stakeholders (Fukuda-Parr, 2014 ). Global goals are instruments that “translate norms from the language of words to that of numbers, coupled with setting time bound targets” (Fukuda-Parr & McNeill, 2018 : 6). Since the SDGs are voluntary, lack sanctions, and have few mechanisms to ensure their achievement, the 2030 Agenda is a form of “soft” international law (van Zanten & van Tulder, 2018 : 212).
Progress on the 2030 Agenda has been slow, and criticisms have begun to appear. A January 2020 editorial in Nature ( 2020 ) argued that the SDGs were “not on track” and had a “bleak trend.” Nature noted that only two targets were close to being achieved, and predicted that most SDGs would miss their 2030 target date. Lack of funding (an estimated shortfall of 2.5 trillion USD) and lack of government commitment were suggested as possible causes. There are also other reasons why the SDGs may be off track. For example, scholars have argued that many SDG targets are so conceptually complex that they cannot be translated into measurable indicators (Breuer, Janetschek, & Malerba, 2019 ). The SDGs themselves may be a problem, both their lack of prioritization (Breuer et al., 2019 ) and their number; there may be “too many goals, too little focus” for meaningful policymaking (Selin, 2015 : 1).
The prospects for achieving the SDGs by 2030 have now become significantly worse since the COVID-19 pandemic spread across the globe in early 2020. In March, the estimated global cost of the pandemic for 2020 in terms of forgone world GDP was $1 trillion USD (Garten, 2020 ); by May, the estimate for 2020–2021 had risen to a 2-year loss of $8 trillion USD (UN, 2020 : 1). The UN Secretary-General has called the coronavirus the “most challenging crisis since World War II”, one that could cause a recession with “no parallel in the recent past” (Lederer, 2020 ). The effects are expected to be catastrophic for developing countries, and halt progress towards the SDGs (UNCTAD, 2020b ).
We believe that slow progress on the 2030 Agenda was inevitable, even before the coronavirus pandemic, because the issues the UN are addressing are wicked problems (WPs; Rittel, 1972 ; Rittel & Webber, 1973 ; Alford & Head, 2017 ; Head, 2019 ). WPs are “systemic in nature, complexly interrelated, and materialize at the interface between public–private and profit–nonprofit interests”; as a result, they cannot be handled with “old management or leadership mindsets, or with old organizational structures” (van Tulder, 2018 : 34). If WPs cannot be solved, policymakers may instead need to focus on managing or coping (Daviter, 2017 ; Head, 2019 : 183). Assuming WPs scholars are correct, and slow progress on the SDGs was inevitable, what can policymakers do to ensure that the SDGs get back on track, or – given the global crisis now unfolding – that the SDGs are not derailed permanently?
We contend that insights from the evidence-based policymaking (EBP) literature can be helpful in spotlighting difficulties and suggesting policy directions for managing the WPs of the 2030 Agenda. EBP puts “the best available evidence from research at the heart of policy development and implementation” (Davies, 2004 : 3). EBP scholars recognize that good evidence is necessary but not sufficient for good policymaking. Recognition that EBP is being applied to WPs can help dampen policymaker expectations, point out where difficulties and disputes are likely, and clarify achievable metrics for success.
We illustrate how insights from both the WP and EBP literatures can be useful for addressing the 2030 Agenda through a case study of SDG 5 Gender Equality, “achieve gender equality and empower all women and girls” (UN, 2015b : 14). Gender equality was originally a UN 2000–2015 Millennium Development Goal, which was carried over as SDG 5 in the 2030 Agenda (UN, 2015a , b ). The global goal for SDG 5 is to achieve gender equality, and to empower women and girls by eliminating gender disparities, discrimination, and violence against women (UN, 2015b ). A case study of SDG 5 is particularly appropriate because this year marks the 25th anniversary of the Beijing Declaration and Platform for Action that asserted that women’s rights were human rights (UN Women, 1995 ). This year is also the 20th anniversary of the UN Security Council’s Resolution 1325 on women, peace, and security, and the 10th anniversary of the establishment of UN Women (UN Women, 2020 ). Our study of SDG 5 is also especially salient, given that the harmful impacts of the COVID-19 pandemic are expected to fall disproportionately on women (Alon, Doepke, Olmstead-Rumsey, & Tertilt, 2020 ; UNCTAD, 2020a ).
We begin by reviewing the WP literature and its applicability to the 2020 Agenda, focusing on SDG 5. We next analyze the existing evidence on gender equality. We employ country-based comparisons of the best-available gender inequality indexes, and assess their ability to appropriately measure SDG 5’s targets and indicators. We then turn to the EBP literature and show how EBP can provide useful insights for policymaking when faced with WPs. We argue that the generation and dissemination of high-quality, reliable evidence is necessary but not sufficient for making progress on the 2030 Agenda. Policymakers must also prepare for the many “slips between the cup and the lip” that bedevil EBP in addressing WPs. Lastly, we build on these insights to develop policy recommendations for governments and multinational enterprises (MNEs) for dealing with the WP of SDG 5. Figure 1 provides an outline of our paper.
Applying evidence-based policymaking to the wicked problem of SDG 5 Gender Equality.
What are wicked problems.
The WP literature began as a critique of the systems approach to policymaking (Rittel, 1972 ; Rittel & Webber, 1973 ). In the systems approach, the planner implements a policy cycle with several steps: understand the problem; gather and analyze evidence; generate and assess solutions; and implement, test, and modify the solution. Rittel ( 1972 ) and Rittel and Webber ( 1973 ) criticized the systems approach, arguing that it could only handle “tame” problems. Since WPs were inherently unsolvable, their solutions could only be good or bad, not right or wrong; thus, a systems approach was unlikely to be successful.
Since the late 1970s, the WP literature has grown dramatically, in terms of both the number of scholarly disciplines and policy areas. 1 Ten key propositions underlie WP theory (see, e.g., McCall & Burge, 2016 ; Crowley & Head, 2017 ; Peters & Tarpey, 2019 : 236), which are summarized below:
WP scholars believe that most policy problems have some degree of wickedness, so they cannot be solved using rational-scientific methods (Newman & Head, 2017 ). 2 Complexity is viewed as a key driver of wickedness, both the complexity of the problem (factual uncertainty) and of the actors/institutions involved (Alford & Head, 2017 ; Daviter, 2019 ). As the number and diversity of stakeholders and institutional contexts rise, so does the heterogeneity of preferences and interests, increasing the likelihood of conflict (Bannink & Trommel, 2019 ). Uncertainty and ambiguity exacerbate both complexity and conflict, increasing the degree of wickedness (van Tulder, 2018 ).
Key issues in the WP literature are how to address a WP and how to define success. Since WPs “are never solved. At best they are only re solved – over and over again” (Rittel & Webber, 1973 : 160), policymakers cannot achieve first-best solutions. A frequent recommendation for addressing WPs, building on Rittel and Webber ( 1973 ), is that new and different ways of thinking are needed, and so also is collaboration or partnering among societal actors where they share joint responsibility for the solutions (Ney & Verweij, 2015 ; Crowley & Head, 2017 ; Daviter, 2017 ; Termeer & Dewulf, 2019 ).
An unexpected and paradoxical consequence of framing policy problems as wicked is that policymakers may choose to do nothing and just live with the problem (Bannink & Trommel, 2019 ). “Paralysis occurs when people experience or define the wickedness as so overwhelming that it discourages them and prevents them from doing anything about it” (Termeer, Dewulf, & Biesbroek, 2019 : 176). Paralysis can be particularly wicked when grand societal challenges are framed negatively (van Tulder, 2018 : 19). To avoid choice paralysis, policymakers are encouraged to explore “intelligent modes of imperfect governance” (Bannink & Trommel, 2019 : 198), and to look for solutions that are “clumsy” or “just viable”, which “everyone can more or less agree to live with” and are “responsive to different rationalities” (van Tulder, 2018 : 39). Policymakers are also encouraged to focus on identifying, valuing, and learning from “small wins” (Termeer & Dewulf, 2019 ). Small wins are preferable to either doing too little (i.e., settling for paralysis or “cherry-picking” the least wicked parts of a problem) or expecting too much (i.e., the solving of an inherently unsolvable problem).
Scholars clearly view the 2030 Agenda as an example of a WP (Head, 2019 ; van Tulder, 2018 ). A core thesis of van Tulder ( 2018 : 37) is that all 17 SDGs are WPs. The SDGs are “systemic in nature, complexly interrelated and materialize at the interface between public–private and profit–nonprofit interests. They are wicked both by nature and design” (van Tulder, 2018 : 36) because they suffer from uncertainty, complexity, erratic dynamics, and ambiguity – all symptoms of WPs. The SDGs are societal problems, not tame or technical problems; for example, the guiding principle of the 2030 Agenda is that no one must be left behind – a huge societal challenge. Specific vulnerable groups are also regularly mentioned (e.g., women, children, minorities, migrants, refugees) and no vulnerable groups can be left behind.
To explore the WP of the 2030 Agenda in more depth, we provide a case study of SDG 5 Gender Equality “achieve gender equality and empower all women and girls” (UN, 2015b : 14). SDG 5 is decomposed into 9 targets and 14 indicators (UN, 2019 ); see Table 1 . Ten of the other SDGs also include gender-specific indicators; as a result, 22% of the indicators for the 17 SDGs are gender specific (UN, 2019 : 21–23), implying that gender equality is an important, cross-cutting goal in the 2030 Agenda.
SDG 5 targets, indicators, and gender equality metrics
Sources : for SDG 5 targets and indicators: https://unstats.un.org/sdgs/indicators/indicators-list/ ; for GII metrics see http://hdr.undp.org/en/content/gender-inequality-index-gii ; for GGGI metrics see WEF ( 158 , Table 1); for SIGI see Branisa et al. ( 2014 and Supplemental Material); for EM2030 see EM2030 and Supplemental Material at https://data.em2030.org/2019-sdg-gender-index/methodology/ , for SDG Index see SDSN (2019, Tables 5 and 7). These metrics may cover partially or totally the target indicators.
The word “gender” refers to “the socially-constructed roles and responsibilities that societies consider appropriate for men and women” (Peace Corps, 2020 ). Gender equality means that “women and men enjoy the same rights and opportunities across all sectors of society, including economic participation and decision-making, [and that]…. the different behaviours, aspirations and needs of women and men are equally valued and favoured” (UNCTAD, 2016 : 31). Gender equality is a “fundamental human right” and “keystone of a prosperous, modern economy that provides sustainable inclusive growth” (OECD, 2017 : 3).
In the international business (IB) literature, gender equality has typically been defined as equal treatment of women and men in the workplace (Eden & Gupta, 2017 ; O’Brien, Fitzsimmons, Crane, & Head, 2017 ; UNCTAD, 2014 , 2018 ; UN Economic Commission for Europe, 2019b ). Workplace gender inequality is viewed as having many causes, including gender discrimination and stereotyping, undervaluation of women’s work, gender-based labor market segmentation, traditions and culture that treat men and women unequally, and work–life balance issues (UNCTAD, 2014 : 4). O’Brien et al. ( 2017 ) hypothesize that workplace gender inequality has three WP characteristics: divergence, complexity, and uncertainty. There are divergent views about the problem, no agreed definitions, and large differences in values, underlying beliefs and interpretations of findings. Workplace gender inequality also suffers from complexity due its multiple causes, lack of a dominant solution, and complex linkages with other societal issues. Lastly, uncertainty affects problem definition, prevents optimal solutions, and causes unintended consequences. The authors argue that policymaking requires methodological reflexivity, the ability to see multiple world views, and the need to pay attention to context. Rational approaches based on a single discipline cannot handle the WP of achieving workplace gender equality.
It is important to recognize, however, that SDG 5 is about more than workplace gender equality; it is also about the empowerment of women and girls . The goal of SDG 5 is equality between men and women in their rights and opportunities, their valuation and treatment, and their empowerment (i.e., the fostering of women’s voice and agency). Women’s empowerment can be defined as “a woman’s sense of self-worth, her decision-making power, her access to opportunities and resources, her power and control over her own life inside and outside the home, and her ability to effect change” (Peace Corps, 2020 ).
Women’s empowerment has been shown to be a fundamental and necessary input for economic and social development. 3 The most recent empirical evidence on the negative economic and social consequences of disempowering women (such as worse governance, more conflict, less stability, worse economic performance, and slower social progress) can be found in Hudson, Bowen and Nielsen ( 2020 ). The authors assert that women’s disempowerment is pervasive and embedded in societies around the world. Disempowerment has four dimensions: status (whether male and female groups engage as equals or as subordinate and superordinate), decision-making (whether decisions are made by one or both groups), conflict resolution (how disagreements are resolved, whether one group can be coerced against its will), and resource distribution (whether control over resources is by one or both groups). Hudson et al. ( 2020 ) argue that patrilineal/fraternal networks (e.g., tribes and clans) support and encourage practices that disempower women, such as violence towards women, personal status laws benefitting men, laws that prevent women from owning property, preferences for sons over daughters, and polygyny. The authors create a women’s disempowerment index to assess the presence or absence of these harmful practices in 176 countries over 2010–2015, finding that 56 countries score low on the index (mostly OECD countries), 40 countries score high (primarily in Sub-Saharan Africa, the Middle East, and West and South Asia), and the rest are in between (Hudson et al., 2020 : 54). The pervasiveness of these harmful practices against women lead the authors to conclude that “the true clash of civilizations is not about religion or ethnicity but about the subordination of women” (Hudson et al., 2020 : 377). Thus, whether measured as workplace gender equality or as women’s empowerment, SDG 5 is a wicked problem.
The gender inequality indexes.
One way to assess gender inequality is to examine government policies and laws promoting gender equality; however, policies on the books are not the same as policies in practice . A second way is to examine gender-based statistics, and, in fact, statistical indexes are the most common empirical method for assessing differences between men and women. The two best-known indexes are the UN Development Programme’s (UNDP’s) Global Inequality Index (GII), available since 2010, and the World Economic Forum’s (WEF’s) Global Gender Gap Index (GGGI), available since 2006. Given that the GII and GGGI were not developed for SDG 5, a few international and non-governmental organizations (NGOs) have started to build new indexes for SDG 5, responding to the UN ( 2015a , b ). These indexes (the OECD’s SIGI, the SDSN SDG Index, and the Equal Measures 2030 2019 SDG Gender Index) attempt to measure gender equality across countries and across time by collecting a broad set of socio-economic indicators on workplace gender inequality and women’s empowerment. Below, we briefly review the five indexes and compare them in terms of SDG 5’s targets and indicators.
The GII is designed to measure the human development costs of gender inequality; i.e., the higher the GII value, the greater the gender gap and the larger the loss in human development (UNDP, 2018 , 2019 ). The GII measures inequality between men and women in terms of economic opportunity, reproductive health, and empowerment. The GII combines women-specific indicators with indicators for both men and women; some scholars view this as an “odd mixture” of “women status” (level) and “gender inequality” (gap) (Permanyer 2013a , b : 940). Table 1 compares the components of GII to the SDG 5 targets and indicators; Table 2 summarizes the GII targets, indicators and data sources.
GII and GGGI targets and indicators
GII targets and indicators | GGGI targets and indicators |
---|---|
Economic opportunity • Labor force participation for women age 15 and older (ILO) • Labor force participation for men age 15 and older (ILO) | Economic participation and opportunity • Labor force participation: female/male (ILO, ILOSTATAT) • • • • |
Reproductive health • • | Health and survival • • |
Empowerment • Percentage of parliamentary seats held by women (IPU) • Percentage of women age 25 and older with at least some secondary education (UNESCO Institute for Statistics; Barro & Lee, ) • Percentage of men age 25 and older with at least some secondary education | Political empowerment • Seats in parliament: female/male (Inter-Parliamentary Union, Women in Politics) • • |
Educational Attainment (UNESCO Institute for Statistics Education Indicators) • • • Net secondary enrolment: female/male • |
Data sources in parentheses. Bold text shows that indicators are included in that index but missing from the other index.
ILO International Labor Organization; WEF World Economic Forum; UNESCO United Nations Educational, Scientific and Cultural Organization; UN United Nations; WHO World Health Organization; UNDESA United Nations, Department of Economic and Social Affairs; IPU Inter-Parliamentary Union.
The GGGI measures the gap between men and women in four target areas: economic participation and opportunity, health and survival, political empowerment, and educational attainment. The GGGI is reverse-coded from the GII so a higher GGGI score implies a country is closer to equality (GGGI = 1; GII = 0). Table 1 compares the components of GGGI to the SDG 5 targets and indicators, and Table 2 summarizes the GGGI targets, indicators and data sources. Table 2 also compares the GII and GGGI in terms of their targets and indicators; differences between the two indexes are shown in bold text.
The index is designed to disassociate the gender gap from a country’s level of economic development, i.e., the index uses the male–female gap, not the actual level, for each indicator. The GGGI also uses ratios, and caps each ratio at 1 (gender equality) for countries where women outperform or have reached parity with men on the indicator. 4 The index is also designed to capture outcomes rather than causal factors of gender inequality, such as culture or government policies. In sum, the GGGI measures gaps not levels, outcomes not inputs, and equality not empowerment (WEF, 2020 : 45).
The SIGI was created to track progress on gender equality for the 2015 Millennium Development Goals (Branisa, Klasen, Ziegler, Drechsler, & Jutting, 2014 : 31–32). Its five targets (discrimination in the family, restricted physical integrity, restricted access to productive and financial resources, and restricted civil liberties) are meant to capture the deprivation of women caused by gender gaps in social institutions (OECD, 2018a , b , 2019 ). Data from the OECD’s Gender, Institutions and Development Database are used to create the SIGI, which is currently available for 4 years (2009, 2012, 2014, 2018). The SIGI scores 120 countries and organizes them into quintiles; thus, the SIGI not only has a shorter time series but also includes fewer countries than either the GII or the GGGI (OECD, 2019 ). Table 1 compares the components of the SIGI to the SDG 5 targets and indicators. The index is reverse-coded like the GII where 0 represents perfect equality and 1 perfect inequality.
The EM 2030 index uses indicators developed by the Inter-Agency and Expert Group on SDG Indicators. The index was developed from several frameworks: the UN Women SDG Indicator Framework and Women Turning Promises into Action report, the UN Minimum Set of Gender Indicators agreed by the UN Statistical Commission in 2013, and the Ready to Measure study produced by DATA2x (EM 2030, 2019 ; Buvinic, Furst-Nichols, & Koolwal, 2014 ; Buvinic & Levine, 2015 ). There are two EM 2030 indexes; a broad index including 51 gender-related indicators from 14 SDGs and a narrower index for only SDG 5 indicators. Table 1 compares the components of the narrower index to the SDG 5 targets and indicators. Higher scores represent greater gender equality.
The Sustainable Development Solutions Network (SDSN) and Bertelsmann Stiftung developed the SDG Index and Dashboards to cover all 17 SDGs (Schmidt-Traub, Kroll, Teksoz, Durand-Delacre, & Sachs, 2017 ; Sachs, Schmidt-Traub, Kroll, LaFortune, & Fuller, 2019 ). The SDG targets are grouped into “five P’s: Prosperity, People, Planet, Peace, and Partnership.” Country scores range from 0 to 100, with higher scores representing greater gender equality (Sachs et al., 2019 : 1). The components of the SDSN gender index relative to the SDG 5 targets and indicators are reported in Table 1 .
To date, only the GII and GGGI have sufficient years of data to compare them over time; it will be some years before the other indexes are sufficiently developed to make robust historical comparisons. We focus below on the GII and GGGI, building on Eden and Gupta ( 2017 ).
Table 3 shows the GII scores for several years between 2000 and 2017. Note that lower GII values represent greater movement toward SDG 5. The world average GII score fell from 0.432 in 2000 to 0.350 in 2017; thus, the gender gap shrank by 18.9% between 2000 and 2017. Given that gender equality is defined as GII = 0, a 35% gap still exists between men and women as of 2017.
The UNDP gender inequality index, 2000–2017
Country average by Human Development Index (HDI) Group | 2000 | 2005 | 2010 | 2013 | 2014 | 2017 | Point gap 2000–2017 | Percent change 2000–2017 |
---|---|---|---|---|---|---|---|---|
World average | 0.4315 | 0.4199 | 0.3886 | 0.3754 | 0.3659 | 0.3500 | −0.0815 | −18.89 |
Very High HDI countries | 0.2112 | 0.1991 | 0.1825 | 0.1532 | 0.1500 | 0.1520 | −0.0592 | −28.04 |
High HDI countries | 0.4927 | 0.4119 | 0.3887 | 0.3560 | 0.3458 | 0.3457 | −0.1470 | −29.83 |
Medium HDI countries | 0.5419 | 0.5419 | 0.4939 | 0.4852 | 0.4716 | 0.4761 | −0.0658 | −12.14 |
Low HDI countries | 0.6746 | 0.6396 | 0.6035 | 0.5953 | 0.5929 | 0.6024 | −0.0722 | −10.70 |
Point gap between Low and Very High HDI countries | 0.4633 | 0.4404 | 0.4210 | 0.4421 | 0.4429 | 0.4504 | ||
Ratio of Low HDI to Very High HDI countries | 3.1936 | 3.2116 | 3.3064 | 3.8866 | 3.9520 | 3.9632 | ||
No. of countries with data points | 83 | 137 | 133 | 152 | 155 | 160 |
Source : Authors’ calculations based on GII data from the UNDP website: http://hdr.undp.org/en/content/gender-inequality-index-gii .
The UNDP also calculates and publishes the Human Development Index (HDI) in which countries are grouped into clusters based on their economic and social development levels. The HDI groupings can help us see how different country groups are progressing towards gender equality relative to one another and over time. We calculate and report the GII score in Table 3 for the four HDI groups in two different ways. First, for each row (HDI group), we calculate the difference in GII scores between the base year (2000) and the most recent year (2017), as a raw score and as a percent of the base year. Since lower GII scores reflect greater gender equality, negative numbers represent improved performance and closer movement toward SDG 5. Looking from left to right across each row (each HDI group), we find that gender equality improved (i.e., the GII score fell) for all four HDI groups. The improvement was largest in the High and Very High HDI countries. Second, looking vertically (by year), we calculate the gap in GII scores between the bottom (Low HDI) and top (Very High HDI) groups, as a raw score and ratio. The gap between the raw scores falls slightly between 2000 and 2017, implying a small narrowing of the gender gap. The ratio of the raw scores (Low HDI/Very High HDI) rises from 2000 to 2017, suggesting that Low HDI group lagged behind the other HDI groups in improvements in gender equality.
We also collected GGGI data for 2006–2017 and linked the GGGI data with the UNDP’s HDI country groups; our statistical results are reported in Table 4 . The world average GGGI rose between 2006 and 2017 from 0.663 to 0.698, implying a modest 3.5% improvement in gender equality. As of 2017, the gender gap recorded by the GGGI is 30.2%, a bit smaller than the 35% gap recorded by the GII. Looking across the rows in Table 4 , gender inequality fell in all four HDI groups, with the largest improvement in the Low HDI group. Looking down the columns, the gap between the raw scores of the low and very high HDI countries fell over the period, while the ratio of the raw scores (Low HDI/Very High HDI) rose slightly, indicating that the GGGI gap narrowed across the HDI groups over time. Thus, measured across HDI groups, the gender gap between Low and Very High HDI countries narrowed, suggesting that improvements in gender equality were larger in Low HDI countries.
The WEF global gender gap index, 2006–2017
Country average by Human Development Index (HDI) Group | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2016 | 2017 | Point gap 2006–2017 | Percent change 2006–2017 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
World average | 0.6626 | 0.6677 | 0.6751 | 0.6784 | 0.6786 | 0.6799 | 0.6844 | 0.6864 | 0.6947 | 0.6978 | 0.0352 | 5.3110 |
Very High HDI countries | 0.6903 | 0.6972 | 0.7016 | 0.7050 | 0.7121 | 0.7136 | 0.7154 | 0.7179 | 0.7223 | 0.7256 | 0.0353 | 5.1084 |
High HDI countries | 0.6637 | 0.6670 | 0.6763 | 0.6774 | 0.6734 | 0.6728 | 0.6756 | 0.6790 | 0.6856 | 0.6944 | 0.0307 | 4.6226 |
Medium HDI countries | 0.6543 | 0.6571 | 0.6621 | 0.6725 | 0.6709 | 0.6713 | 0.6758 | 0.6782 | 0.6936 | 0.6947 | 0.0404 | 6.1774 |
Low HDI countries | 0.6005 | 0.6102 | 0.6228 | 0.6246 | 0.6226 | 0.6314 | 0.6458 | 0.6463 | 0.6645 | 0.6643 | 0.0638 | 10.6215 |
Point gap between Low and Very High HDI countries | −0.0898 | −0.0871 | −0.0789 | −0.0804 | −0.0895 | −0.0822 | −0.0696 | −0.0716 | −0.0578 | −0.0613 | ||
Ratio of Low HDI to Very High HDI countries | 0.8699 | 0.8751 | 0.8876 | 0.8860 | 0.8743 | 0.8848 | 0.9027 | 0.9003 | 0.9200 | 0.9155 | ||
No. of countries with data points | 110 | 122 | 124 | 128 | 130 | 131 | 133 | 136 | 144 | 144 |
In calculating the overall GGGI average we used all 144 countries in the GGGI dataset. However, because North Korea is missing an HDI score and group, we used 143 countries in calculating the HDI country groupings.
Source : Authors’ calculations based on WEF data for the Global Gender Gap. Data for 2016 and 2017 are from WEF ( 2017 ). Data for 2006–2013 are from the Humanitarian Data Exchange at https://data.humdata.org/dataset/global-gender-gap-index-world-economic-forum .
Comparing the gii and gggi: why are their country rankings so different.
A comparison between the results for the GII in Table 3 and the GGGI in Table 4 reveals a perplexing picture. Table 3 shows a large gap in GII scores between Low and Very High HDI country groups that widens over time. Table 4 , on the other hand, shows that the gap in GGGI scores between Low and Very High HDI countries is much smaller and narrows over time. Both indexes are on a scale of 1 although they reverse code equality [gender equality = 0 (GII), 1 (GGGI)]. What explains the differences between the two indexes, when considered by HDI group?
We hypothesize that the puzzling findings noted above are due, first, to differences in the targets and indicators for the GII and GGGI. Table 2 puts in bold text the indicators that are included in one index but not the other. There are several differences; e.g., there are four economic indicators included in GGGI that are missing from GII (e.g., wage equality, female/male ratio of earned income).
A second reason is that the goals and how they are implemented differ between the two indexes. The GGGI is designed to remove country levels of economic development; i.e., the index reflects gaps in gender equality ignoring the development in each country. The GII, on the other hand, is designed to incorporate the loss in human development as a function of gender inequality (Permanyer, 2013a , b ; Piper, 2019 ; WEF, 2018 , 2020 ). The differential treatment of economic development is clear when we compare the GGGI and GII scores across the four HDI country groupings. Table 3 shows that the GII score for Low HDI countries is nearly four times the score for Very High HDI countries. Table 4 , on the other hand, shows very small differences in GGGI scores across the four HDI groups. Even when one inverts the ratio because GGGI is reverse-coded from GII, there is little variance by HDI level in the GGGI compared with the GII. This result is deliberate: the GGGI is designed to disassociate the gender gap from country levels of development, while GII is designed to include the impact of gender inequality on potential human development. As a result, some scholars worry that the GII may be proxying not only for differences in gender equality but also for differences in living standards across countries; this concern does not apply to the GGGI (Sotsky, Shibuya, Kolovich, & Kebhai, 2016 ).
As a second exploration of the underlying differences between the GII and the GGGI, we provide a brief two-country case study. We selected two developing countries: Mozambique and Nicaragua. Both countries are ranked, when compared with the world average, relatively low on the GII but relatively high on the GGGI. Table 5 provides data for the two countries in terms of all five gender indexes (where available). We highlight in bold text the score of the country (Mozambique or Nicaragua) that performs better on each indicator. We also provide average world scores for comparison.
Comparison of Nicaragua and Mozambique gender equality scores, 2017–2018
Mozambique | Nicaragua | World | |
---|---|---|---|
Overall SDG and SDG 5 scores and ranks | |||
UNDP GII Score (higher → less gender equality) | 0.552 | 0.441 | |
UNDP GII Country Rank (out of 160 countries) | 138 | ||
WEF GGGI Score (higher → more gender equality) | 0.741 | 0.680 | |
WEF GGGI Country Rank (out of 144 countries) | 29 | ||
OECD SIGI Score (higher → less gender equality) | .24 | .29 | |
OECD SIGI Country Rank (out of 120 countries) | 55 | ||
SDSN SDG 5 Score (higher → more gender equality) | 60.0 | 60.17 | |
SDSN SDG 5 Country Rank (out of 162 countries) | 81 | ||
EM 2030 SDG 5 Score (higher → more gender equality) | 61.1 | 62.0 | |
EM 2030 SDG 5 Country Rank (out of 129 countries) | 114 | ||
UNDP GII Indicators | |||
Female labor force participation rate (%, age 15+) | 50.3 | 48.7 | |
Male labor force participation rate (%, age 15+) | 74.6 | 84.0 | 75.3 |
Maternal mortality ratio (deaths per 100,000 live births) | 489 | 216 | |
Adolescent birth rate (births per 1000 women aged 15–19) | 135.2 | 44.0 | |
Share of seats in parliament (% held by women) | 39.6 | 23.5 | |
Female population, some secondary education (%, age 25+) | 16.1 | 62.5 | |
Male population, some secondary education (%, age 25+) | 27.3 | 46.6 | 70.9 |
WEF GGGI targets/indicators | |||
Economic participation and opportunity | 0.702 | 0.65 | |
Educational attainment | 0.857 | 0.96 | |
Health and survival | 0.977 | 0.98 | 0.97 |
Political empowerment | 0.34 | 0.20 | |
SDSN SDG 5 Indicators | |||
Unmet demand for contraception | 56.8 | NA | |
Female/male mean years of schooling | 54.3 | NA | |
Female/male labor force participation rate | 59.9 | NA | |
Seats held by women in national parliaments | 39.6 | NA |
Bold text highlights the score of the country (Mozambique or Nicaragua) that performs better on each indicator.
Sources : EM 2030 ( 2019 ), OECD ( 2019 ), Sachs et al. ( 2019 ), UNDP ( 2018 ), and WEF ( 2018 ).
As Table 5 shows, Nicaragua outperforms Mozambique on all five gender indexes and on most indicators. Both countries rank poorly on the GII (Mozambique = 138; Nicaragua = 106) but do well on the GGGI (Mozambique = 29; Nicaragua = 6). The first reason for why both countries do poorly on the GII but well on the GGGI is because their indicators also do so; i.e., selection of the indicators is a key reason for the anomaly. The second reason is also visible; i.e., the way the indicators are measured. By focusing on gaps not levels and outcomes not inputs, the GGGI takes economic development out of the equation, whereas the GII deliberately includes human development. 5 Thus, with GGGI – but not GII – women may be worse off (in absolute terms) even though the gender gap is small.
Our analysis of the GII and GGGI shows clearly that the two indexes are more narrowly focused than SDG 5. The targets and indicators for SDG 5 are designed to capture both gender inequality and empowerment, particularly for marginalized groups and others “left behind.” The SDG 5 targets also consider not only gender inequality outcomes but also their antecedents, such as government policies, laws, and customs. As Table 1 shows, with the exception of target 5.5 (women’s leadership) and target 5.c (policies and laws), neither GII nor GGGI cover the other targets and indicators in SDG 5.
We conjecture that the narrow foci of the GII and GGGI reflects an implicit focus on gender equality in the workplace rather than empowerment of women and girls. For example, neither the GII nor GGGI incorporate measures that Hudson et al. ( 2020 ) see as critical components of women’s disempowerment. 6 As a result, neither the GII nor the GGGI is as comprehensive as UN SDG 5, which treats gender equality from a holistic perspective, considering voice, agency, and the empowerment of women and girls.
Our assessment that the GII and GGGI do not capture women’s disempowerment suggests that the newer gender inequality indexes – SIGI, EM 230, and SDSN SDG 5 – should be better proxies for the targets on SDG 5. However, reliable and complete data are very hard to find for many SDG 5 indicators, particularly those for marginalized groups, and it will be years before longer time series datasets become available. 7 Time series indexes take time to build. We conclude that, at least for the present, although the three new gender indexes based on SDG 5 look very different on paper from the old gender inequality indexes (GII and GGGI), the new indexes in practice are much closer to the GII and GGGI, particularly for developing countries for which data for the SDG 5 targets and indicators are very scarce. Still, as these new indexes mature, better evidence on women’s empowerment should become available. 8
The five gender indexes that we have reviewed above vary enormously in focus, breadth and depth, and sophistication. Some indexes use indicators based on gaps between men and women; others look at women’s levels. Some indexes vary with a country’s level of economic development, while others attempt to remove the relationship between gender inequality and the standard of living. Some indexes are narrowly focused on women at work, while others include many socio-cultural and legal indicators designed to capture broader issues of women’s empowerment. Some indexes have long historical time-series datasets, while others are just beginning to finalize methodologies, collect data, and report statistics on a cross-section basis. Some indexes weight all their components equally, while others use unequal weights.
So how can policymakers decide which of the five gender inequality indexes is best for tracking their country’s performance on SDG 5? Similarly, how do MNE executives select an index to track their company’s gender equality performance, at home and in their foreign affiliates? We offer three insights into these questions. First, we argue that there is no single right answer. Policymakers and MNE executives need to match their selection of a gender inequality index with the specific gender equality goals and targets they are trying to achieve. The “best” gender inequality index for one government or one MNE may not be the same for another where their goals and targets differ. There may be no “best” index nor “off the shelf” solution; it may be that a single index is not sufficient and will need to be assembled from different indexes. Second, policymakers and MNE executives need to understand why and how the various indexes are constructed; that is, making a “deep dive” into the selection and measurement of indicators and construction of the indexes. A thorough analysis of the various indexes is a necessary first step to selecting or creating an inequality index that best fits the organization. Third, our overall assessment is that, given their strengths and weaknesses, the GII and GGGI are better able to usefully inform policymakers and MNE executives on how to move toward SDG 5 in terms of workplace gender equality. For more holistic measures of gender equality that take account of women’s empowerment, policymakers must look to the new SDG 5 gender indexes. Policymakers should also study Hudson et al.’s ( 2020 ) new women’s disempowerment measure.
We turn now to the second part of our analysis. Given the existing evidence on gender inequality, how can policymakers use this evidence to address the WP of SDG 5? We contend that EBP is the appropriate response.
The premise behind EBP is that policy decisions are more likely to result in better outcomes when informed by evidence (Scott, 2005 ). EBP uses “open, smart and trusted statistics [that are] relevant for the society” (Rademacher, 2019 : 524), and puts “the best available evidence from research at the heart of policy development and implementation” (Davies, 2004 : 3). 9 While EBP has been primarily used in OECD countries, EBP has also been applied in developing countries (Sutcliffe & Court, 2005 , 2006 ; Bartlett, 2013 ; Hewlett Foundation, 2018 ). 10
The original approach to EBP assumes a linear relationship between evidence and policy choice where the policymaker defines a problem, identifies what is known, collects the best available evidence, and makes a policy choice. The basic steps in EBP are outlined in Figure 1 . The current approach to EBP embeds evidence into the policy cycle process in a circular fashion, as shown in Figure 2 . The EBP policy cycle starts with agenda setting, moves through policy formulation, selection, and implementation, to monitoring, evaluation, and revision, in a circular fashion. 11
Evidence-based policymaking – a policy cycle approach.
Good evidence is a fundamental component of the EBP cycle. The key evidence steps in EBP are illustrated in the circled and bold text items in the second box in Figure 1 ; the sub-steps in the evidence collection phase are shown in the third box in Figure 1 . The evidence steps are also highlighted in Figure 2 as steps 5 (obtaining evidence), 7 (interpreting evidence), 13 (monitoring), and 14 (evaluation). Thus, collecting, analyzing, and using good evidence are key elements in successful EBP.
Can EBP be applied successfully to WPs? One might think that EBP should be applicable to WPs, given that both the WP and EBP literatures have their historical roots in the systems approach to planning. However, the WP literature arose as a critique of the systems approach, whereas the EBP literature adopted the systems approach and the policy cycle as part of EBP. As a result, neither literature has had much to do with the other for the past 40 years (Head, 2019 ).
In fact, the WP literature has been very critical of the EBP approach. WP scholars have long argued that the rational, step-based approach to policymaking illustrated in Figure 2 is incapable of addressing policy problems that are ill-defined, complex, and amorphous (Newman & Head, 2017 ; Daviter, 2019 ; Head, 2019 ; Termeer et al., 2019 ). Because poorly structured problems cannot be handled by “advanced and precise analytic methods”, EBP “will predictably fail to grapple with the challenge of wicked problems” (Daviter, 2019 : 67).
EBP is also criticized for its implicit assumption that “access to better knowledge….will lead to greater consensus on how to improve policy outcomes”. WP scholars argue instead that “political dynamics… cannot be neutralized by a dose of evidence, no matter how relevant or powerful” (Newman & Head, 2017 : 419). WP scholars assert that policymaking is based, not on scientific evidence, but on “stakeholder perceptions, values and interests in explaining how issues are scoped, priorities are set and possible solutions considered” (Head, 2019 : 188). Politics drives policy, not evidence, according to WP theorists. 12 As a result, the WP literature has been critical of and has ignored EBP for decades.
We contend that EBP can provide useful insights for addressing the WPs of the 2030 Agenda. Our thesis may appear overly optimistic, disingenuous, or even radical, given the criticisms of EBP by WP scholars noted above. We believe, however, that both literatures can inform each other and are particularly useful for analyzing the WP of the 2030 Agenda.
The timing may also be right for exploring connections between these two literatures. Recently, a few WP scholars have begun to take a second, more positive look at scientific approaches and EBP. For example, Head ( 2019 : 183) contends that, after ignoring insights from the policy studies and public management literatures for 40 years, WP theory would benefit from reconnecting with these fields. He argues that WP theorists need to “draw more deeply on cutting-edge developments in contemporary policy sciences”, and that policymakers need “to use best available evidence and to communicate the value of open processes for tackling complex and wicked problems” (Head, 2019 : 192). Given “populist distain for expertise” and “widespread lack of trust in the institutions of public governance”, Head ( 2019 : 192) concludes that WP theorists need to use “best available evidence.”
At the same time, EBP scholars have also moved away from the simple model in Figure 2 , recognizing that “it is a long away from getting the facts straight to analyzing complex policy problems” (Daviter, 2019 : 70). In pluralistic societies, political dynamics and embeddedness can matter as much or more than evidence in an EBP process, even when facing “tame” (technical or scientific) problems (Newman & Head, 2017 ). The current EBP literature is also sensitive to several issues that are inherent in WPs, such as complexity, uncertainty, ambiguity, and divergence (Head & Alford, 2015 ). Many ambiguities, for example, are likely involved in applying EBP to a WP: ambiguities in framing the issue (given interdependencies), determining the evidence (sorting opinions from facts, deciding what is relevant), selecting targets (that are typically moving), selecting and implementing a policy or policies (which may have unintended consequences), and monitoring outcomes (relative to an unknown counterfactual) (van Tulder, 2018 : 43–50).
Given these trends, we believe that the time is right for exploring how insights from the WP and EBP literatures could help devise appropriate public policies for managing the WPs of the SDGs.
Below, we explore five insights drawn from the EBP and WP literatures, which we believe could help policymakers address the WP of the 2030 Agenda.
The proper application of EBP requires the creation and use of good evidence, which raises methodological questions among stakeholders over “what is evidence” and how to make it accessible to policymakers. High-quality evidence is defined as the “best available” evidence that is appropriate for the problem and has been “ systematically searched, critically appraised, and rigorously analysed according to explicit and transparent criteria” (Davies, 2004 : 7). Most EBP scholars agree that all forms of evidence collected through a systematic process should be included, e.g., theory building, data collection, and analysis, and practice-based wisdom (Sutcliffe & Court, 2006 : 2). At the other end of the scale, EBP scholars also agree that using poor-quality research as evidence clearly reduces the efficacy of EBP.
However, once we move past these two zones of agreement (what is in/what is out), disputes emerge because the “devil is in the details.” A first problem is that definitions of what constitutes appropriate evidence can vary across stakeholders, ranging from narrow (e.g., peer-reviewed academic journal articles) to broad (e.g., professional experience, stakeholder accounts). Stakeholders also disagree on what constitutes evidence in different issue areas. Nutley, Davies and Walter ( 2003 : 31–32), for example, note that health care has an “established hierarchy of evidence for assessing what works”; whereas fields such as education and criminal justice are “riven with disputes” over what is appropriate evidence.
Moreover, evidence is generally assumed to be synonymous with empirical evidence, with the implicit assumption that “hard” evidence (official statistics, econometrics, indexes) is more rigorous than “soft” evidence (qualitative studies, expert evaluations). However, empirical findings are typically built on mathematical models that involve simplification and parsimony, which suggests that drawing policy recommendations from empirical results may not only be meaningless but potentially harmful. Saltelli and Giampietro ( 2017 : 64) note that it is “one thing…to use a model to simulate a policy, another story is the leap whereby the same model is used to justify one.”
Thus, the first step in EBP, the collection and provision of good evidence, is a necessary but onerous and contentious requirement for successful EBP. Moreover, while collecting, building, and analyzing “good evidence” are necessary steps, they are not sufficient. A successful EBP process needs to recognize and account for factors other than evidence that influence the policy process. Awareness of these potential problems is critically important for governments and MNE executives as they attempt to choose and implement the WP of the 2030 Agenda.
Even though the adage “better statistics, better decision-making” is widely accepted, there can be “slips between the cup and the lip” that derail an EBP process. Policymakers may be “flying blind: trying to make policy decisions in the dark”, even when good evidence exists, if the evidence cannot be found, or is misunderstood (Hewlett Foundation, 2018 : 3).
For example, high-quality evidence may not be sufficient to motivate policymakers if they do not have the policy capacity or level of expertise needed to understand or use the evidence (Newman, Cherney, & Head, 2017 ). Successful EBP requires the government sector to have skilled analytical staff that are trained in data analysis and policy evaluation (Head, 2010 ). The level of training and professional competence of civil servants and government legislators varies across countries, along with their access to resources and institutional infrastructure. Policy capacity is typically much lower in developing than in developed countries (Howes, Betteridge, Sause, & Ugyel, 2017 ).
Misuse and misunderstanding of research findings by policymakers can also be caused by the researchers themselves, who seldom consider “policymaker friendliness” when disseminating their research findings. Researchers need to communicate well to “make their research relevant and readable” (Oliver & Cairney, 2019 : 3). There are also multiple ways in which numbers can be misunderstood or misinterpreted, creating “mutant statistics” that lead to bad policymaking (Best, 2012 ). Examples include problems of generalizability, definition, inadequate measurement, bad samples, changing the meaning of statistics, garbling complex statistics, and compounding errors by creating chains of bad statistics. To prevent such technical biases, new institutions for good governance of evidence may be needed if EBP is to function effectively (Parkhurst, 2017 ).
It is also possible that the specialized knowledge of academics can be counterproductive, creating contentious debates over highly technical alternatives among experts on different sides of the problem. The uncertainty of social science and the different status of knowledge fields can cause policymakers to doubt or refuse to include particular studies as evidence, leading to disputes between policymaking groups. Where WPs are involved, there may be equally compelling evidence that supports competing perspectives, causing stakeholders to privilege the evidence that favors their own position (Daviter, 2019 : 67). A related concern is the politicization of science whereby policymakers bury, misuse, manipulate, or cherry-pick evidence to support and promote their own policy preferences. EBP can be turned into policy-based evidence, generated by mechanisms such as knowledge monopolization, blame avoidance, black-boxing, or oversimplification of the evidence (Strassheim & Kettunen, 2014 : 263). 13
While EBP scholars stress the role played by good evidence, they also recognize that exogenous and endogenous factors other than evidence are regularly – and legitimately – involved in policymaking. Examples include the experience, expertise, and judgment of policymakers; the constraints of finite resources; the importance of values such as ideology and political beliefs and the role of habit and tradition; the power exerted by lobbyists, pressure groups, and consultants; and, more generally, the pragmatics and contingencies of political life (Davies, 2004 ; Head, 2010 ; De Marchi, Lucertini, & Tsoukiàs, 2016 ; Richards, 2017 ; Saltelli & Giampietro, 2017 ).
In theory, the selection and measurement of targets and indicators for building good evidence should be decisions made on technical or scientific grounds. In practice, the evidence collection stage can become a highly contested, political activity because the measurement tools are essential to defining the goals (Fukuda-Parr & McNeill, 2018 ).
Getting appropriate “buy-in” from politicians and bureaucrats to using EBP can also be difficult. “[P]ractitioners need incentives to use evidence and to do things that have been shown to be effective. This also means not doing things that have been shown to be ineffective or even harmful” (Davies, 2004 : 20). Policymakers must have the capability, opportunity, and motivation to use evidence before they engage in behavioral change (Langer, Tripney, & Gough, 2016 : 4). Outside events, such as pressing but unqualified threats, can also cause policymakers to “throw caution to the wind”, ignore evidence, and make quick, precautionary decisions (Monaghan, Pawson, & Wicker, 2012 ). Arguing that evidence should prevail over pragmatism is difficult for politicians where politics and budget constraints are likely to be more important than evidence in driving policymaking (Richards, 2017 ).
These political realities at every stage in the EBP process accord well with the concerns of WP theorists. One of the core insights of the WP literature is that the political arena is the true battlefield in policymaking because public policy problems are inherently political problems (Newman & Head, 2017 ; Head, 2019 ). As the wickedness of a problem increases, conflicts among stakeholders with differing values become more important and the usefulness of scientific evidence diminishes.
Networks and partnerships among the various stakeholders in EBP – government, business, non-governmental organizations (NGOs), policy think tanks, academics, and the general public – are necessary for successful EBP, particularly in democratic systems and when dealing with WPs (Ney & Verweij, 2015 ; Crowley & Head, 2017 ; Daviter, 2017 ; Termeer & Dewulf, 2019 ). Partnerships are “voluntary and collaborative relationships between various parties, both State and non-State, in which all participants agree to work together to achieve a common purpose or undertake a specific task and to share risks and responsibilities, resources and benefits” (UN, 2005 , par. 8, p. 4).
Partnerships between government policymakers and their stakeholders can offer benefits, such as access to resources, capacity and coalition building, more policy relevant research, and linkages with stakeholders (Richards, 2017 ). EBP is more likely to be successful when the “actors in the evidence-to-policy ecosystem…..have regular opportunities to connect with each other, develop relationships of mutual trust and respect, and exchange ideas and learning” (Hewlett Foundation, 2018 : 8).
Academics and policymakers have historically been viewed as “two communities” with few contacts and bridges, either formal or informal, between them (Caplan, 1979 ). While evidence suggests the situation is better now than in the 1970s (Newman, Cherney, & Head, 2016 ), it is still the case that factors which could encourage networking, such as knowledge brokers, shared agendas, and common meeting places (e.g., joint conferences), are often missing or weak (Lugo-Gil, Jean-Baptiste, & Livia Frasso Jaramillo, 2019 ). For example, academic scholars are seldom motivated to work on policy issues nor encouraged to disseminate their research findings in ways that reach policymakers or to interact in policy circles. The “publish-or-perish” syndrome and the rewards systems in universities do not privilege policy-based research or reward linkages between government and academia, viewing them as consulting or secondary contributions to knowledge (Eden, Lund Dean, & Vaaler, 2018 , Chapter 28). 14 Academics need to learn the “dos and don’ts” of how to influence policy (Oliver & Cairney, 2019 ). For expert advice to be helpful, academic experts may also need to be more formally embedded in the policymaking process and able to think “outside the box” of their own discipline and specialty (Daviter, 2019 ).
A concern particularly salient for IB scholars arises from the difficulty of applying EBP in different country settings. Context matters, and “what works here may not work there.” A simple but telling example is the need for and difficulty of adapting policies that work in OECD countries to the very different and differing institutional contexts of developing economies. While EBP has been used mostly in OECD countries, 15 most EBP experts agree that the potential economic gains from successfully implementing EBP in developing countries are likely to be large, possibly much larger than those found in case studies of EBP in OECD countries (Sutcliffe & Court, 2005 , 2006 ; Bartlett, 2013 ; Hewlett Foundation, 2018 ).
Only a few developing countries have embraced and attempted to implement EBP; for example, Malawi (Government of Malawi, 2016 ) and Tanzania (Lubua & Maharaj, 2012 ). The reasons for why EBP initiatives have failed in developing countries include the application of orthodox approaches inappropriate to countries at different stages of development, weaker economic conditions and more difficult political environments (e.g., political volatility, corruption), and institutional voids (Data for African Development Working Group, 2014 ; Hewlett Foundation, 2018 ). Even where international organizations have developed EBP “toolboxes or kits” for developing countries, a common concern is that the toolkits may be ideologically-based, promoting orthodox policies inappropriate for countries at different stages of development (Sutcliffe & Court, 2005 , 2006 ; European Commission, 2017b ). The heterogeneity of developing economies also makes it difficult to translate the lessons from EBP case studies carried out in one developing country to another because the environmental contexts are so different.
Even for a single developing country, the hurdles can be interactive and reinforce one another. A particularly difficult “cocktail” is the mix of poor evidence, political realities, and weak academic–policymaker networks (Hantrais, Lenihan, & MacGregor, 2015 ). While there is some research on how to successfully apply EBP in developing country contexts (see, e.g., the Overseas Development Institute’s RAPID Framework; Sutcliffe & Court, 2005 , 2006 ; Court & Young, 2006 ), clearly the hurdles are higher and the success stories fewer in number. 16
Problems at the single country level are compounded in a multi-country framework where attempting to implement EBP generates huge coordination issues. Examples are Bartlett’s ( 2013 ) study of the hurdles faced by applying EBP to labor-skill policies in the EU enlargement countries 17 and Lofstedt & Schlag’s ( 2017 ) study of the debate over banning the chemical Bisphenol A in the European Union. 18
The problems of applying EBP to multiple countries are even more acute in developing countries. A useful (and timely, given the current COVID-19 pandemic) analysis of the challenges is the study by Andrus, Jauregui, De Oliveria and Ruiz Matus ( 2011 ) of the Pan American Health Organization’s ProVac Initiative in the Americas, which was designed to ensure that developing countries had equitable access to new vaccines. The authors found that more lives were saved more quickly when national governments had sufficient policy capacity, took responsibility for helping to pay for and distribute the vaccines, and were supported by strong partnerships with international organizations.
In sum, “what works there may not work here” reminds us that an EBP approach to WPs will be particularly difficult in multi-country cases with multiple stakeholders and wide differences in institutional contexts and levels of development. Top–down, “one size fits all” policies – even where the definition of success is tackling, managing or coping – are unlikely to be accepted or successful.
As we have shown above, determining what is and how to use high-quality evidence in an EBP framework is far from easy and especially difficult where WPs are involved. Despite these difficulties, we argue that policymakers can benefit from applying the EBP and WP literatures to the WPs of the SDGs. Drawing on these insights, we make some policy recommendations for governments and MNEs for addressing the WP of SDG 5 Gender Equality.
Recommendation #1: formally adopt ebp and prioritize completion of evidence collection.
The UN recognized early in the multi-stakeholder negotiations led to the 2030 Agenda that “good quality, verifiable evidence on progress toward achieving the 2030 Agenda” would be necessary, together with a formal monitoring and evaluation mechanism to ensure accountability and benchmark country progress (MacFeely, 2019b : 3). Implementation of the 2030 Agenda was to be based on “sound evidence and science, taking advantage of contemporary approaches from the sustainability sciences including systems thinking and analysis and quantitative modelling” (Allen, Metternicht, & Wiedmann, 2018 : 1454). Thus, the United Nations in 2015 implicitly, if not explicitly, recognized the importance of evidence and made a commitment to an EBP process for the 2030 Agenda.
UN Women has been actively leading the process for SDG 5, working with PARIS21, a worldwide network of statisticians and other stakeholders that are committed to EBP in the public sector. Their goal is to develop better gender statistics through assessing country-level data and statistical capacity gaps, with the ultimate goal of using these assessments to develop better strategies for national statistics (UN, 2013 , 2016 ; UN Women and PARIS21, 2019 ). UN Women ( 2018 , 2019a ) has set up a Women Count Data Hub with an SDGs Dashboard ( https://data.unwomen.org/countries ) where raw data are posted and analyzed, by country, for as many of the SDG indicators as possible. 19 The OECD is also heavily involved in the SDGs project, providing “snapshots” of OECD member countries and their performance on the SDGs (see, e.g., OECD, 2018 ). Once SDG 5’s targets and indicators are finalized, governments and intergovernmental agencies have committed to collecting and reporting statistics annually (UN Statistics Division, UNSD, 2020 ; UN, 2015b ; Allen et al., 2018 ). Socio-demographic statistics are to be collected on everyone, including marginalized populations (e.g., the homeless, migrants, minorities, and the underground economy) that are typically difficult or impossible to track. 20 Data collection so far has been difficult, and less than half the SDG indicators are high quality and many are completely missing. 21
The activities above involving SDG 5 are steps in the “evidence collection” stage of an EBP process. As Figure 1 shows, once a goal has been defined, the policymaker selects targets and indicators, identifies data sources, and assigns data collection roles and responsibilities. Where multiple entities are involved, a central entity typically coordinates and oversees the process by creating ex ante protocols, manuals, and training materials to guide collection, and by ex post cleaning, merging, and analyzing the submitted data. The last step is the generation and dissemination of the results.
Evidence collection occurs early in the EBP process. Why, in May 2020, 5 years after the SDGs were launched, are UN agencies and Member States still in the evidence-gathering stage for SDG 5 22 ? The slow progress appears to have multiple causes. UN Women ( 2018 : 54) lists three problems: the uneven coverage of gender indicators across goals and targets, the absence of internationally agreed standards for data collection, and the uneven availability of gender statistics across countries and over time. The existing gender inequality indexes, the GII and GGGI, were not closely tied to the broader targets and indicators of SDG 5 so new indexes had to be created; data collection for the new metrics has been hampered by lack of established methodologies for collecting and measuring the indicators.
A second reason why SDG 5 is still in the evidence collection phase is that national governments have failed to mainstream gender by not prioritizing gender statistics and/or by having weak and under-resourced statistical agencies (Thomas, Cordova Novion, de Haan, de León, Forest, & Iyer, 2018 ; UN, 2016 ; UN Women, 2018 ). Resource constraints on statistical agencies are likely to be even more important given the coronavirus pandemic and the global recession now underway.
A third causal factor is that selection and measurement of targets and indicators becomes a political activity when WPs are involved (Fukuda-Parr & McNeill, 2018 ). Politics can affect evidence collection in terms of, for example, determining priorities within complex targets, handling country composition changes over time, deciding which entities (national or international) are responsible for providing data, and allocating the financial costs of measurement (MacFeely, 2019b ). Governments may also be unwilling to collect and provide gender-related data to an international agency, especially governments with poor records on gender equality, which may prefer to hide or tamper with their statistics, fearing the reputational risks (UN Women, 2018 ).
Our assessment is that SDG 5 needs more momentum. The evidence collection phase needs completion and the process needs to move forward on other steps in the EBP process. We therefore recommend that the UN and UN Women first commit explicitly and publicly to an EBP approach to SDG 5 and, second, prioritize completion of the evidence collection phase. The symbolic effect of a formal restatement of commitment to EBP we believe would be useful for all stakeholders in the process. The commitment would also raise awareness among stakeholders of the need to set up formal milestones and to prioritize completion of the evidence collection phase in the EBP process.
Our second policy recommendation is that the UN and UN Women expand their partnering arrangements so they can move faster on the evidence collection phase for SDG 5. We recommend expansion both in terms of partners and data sources.
The UN has worked for many years to ensure independence and impartiality of official statistics through the UN Fundamental Principles of Official Statistics and UN Statistical Quality Assurance Frameworks (UN-SQAF, 2018 ). Only official or accredited statistical agencies that supply official statistics are currently acceptable sources of data for the SDG indicators (MacFeely & Nastav, 2019 ).
Imagine a 2 × 2 matrix with Agency (official/accredited vs. non-official/non-accredited) on one side and Data Source (official/accredited vs. non-official/non-accredited) on the other side. The UN’s current rules and procedures limit evidence collection for the SDGs to only one of the four cells in the 2 × 2 box: official/accredited agencies and official/accredited data sources. Given that the lack of data for many SDG indicators has slowed down completion of the data collection stage, we support proposals to – carefully – open up and include agencies and data sources in the other cells of the 2 × 2 matrix.
We recommend that UN statistical agencies should push forward rapidly on expanding their partnering to include non-official partners, as argued in MacFeely ( 2019b ) and MacFeely and Nastav ( 2019 ). UN Women could, for example, build on the unrealized potential in DATA2x, the collaborative technical and advocacy platform at https://data2x.org/ , which is estimating gaps in gender data, finding potential sources, and collecting data (Buvinic & Levine, 2015 ; Buvinic et al., 2014 ).
We also support recent proposals that unofficial data and statistics, both national and international, be certified and used as sources (MacFeely, 2019a ; MacFeely & Nastav, 2019 ). Expansion of acceptable data metrics is particularly important for developing countries where policy capacity and resources are limited. “Big data” could prove to be a more cost-effective, efficient, and fine-grained data source than official sources and of better quality than survey data (MacFeely, 2019a ). For example, DATA2x provides several fascinating case studies of gender inequalities, which were done using big data in developing countries (DATA2x, 2017 , 2019 ).
In order to expand partnering in terms of agencies and data sources, the UN and its statistical agencies will need to be more open to using unofficial routes for SDG indicators (MacFeely, 2019a ; MacFeely & Nastav, 74 ). We therefore also support a rapid certification process whereby unofficial agencies and data sources can be accepted by the UN specifically for the SDG indicators.
Generating high-quality good evidence for SDG 5 is only the first step in the EBP process. Governments must also mobilize and allocate resources to the achievement of gender equality. This means prioritizing gender-responsive investments, policies, and programs. Implementation, monitoring, and accountability are also needed. These next steps in the EBP process are likely to be very difficult for the UN and its Member States, for at least three reasons. First, short-run dislocation costs caused by the COVID-19 pandemic and global recession will make it difficult for governments to prioritize and fund gender equality initiatives. A second, longer-term reason is that the SDGs are a goal-based institution built on soft international law with little to no enforceability. “As a non-binding political commitment, the 2030 Agenda lacks enforceability. There are no defined consequences if countries fail to make serious efforts to meet the goals and targets” (UN Women, 2018 : 257). The third reason is simply that SDG 5 is a WP where politics may trump evidence.
How can national governments move forward on the policy implementation, monitoring, and evaluation stages of the EBP cycle to address the WP of SDG 5? We argue that a key requirement will be that governments build successful partnerships with other stakeholders, in particular with multinational enterprises. Public–private partnerships (PPPs) with MNEs will be critical, for example, in developing and implementing firm-level policies for gender equality and empowerment of women in the workplace. Potential benefits from partnering with other stakeholders include knowledge-sharing and capacity building, mobilization of resources, and achievement of joint goals through collaboration (Bull & McNeill, 2019 ).
Our commitment to PPPs as a solution to the “evidence using” stages of an EBP approach to the 2030 Agenda builds on the three evidence “lenses” in Head ( 2008 ): scientific knowledge (the research-based knowledge of specialists), political knowledge (the know-how, analysis and judgment of political actors), and practical implementation knowledge (the wisdom and practices of government bureaucrats). Head ( 2008 ) argues that viewing evidence more broadly as three lenses can help policymakers address the complexity and conflict inherent in WPs. Our modification contends that PPPs can also bring together “three lenses” on evidence: scientific (academics, scientists), political (government policymakers and bureaucrats), and practical implementation (MNEs) knowledge, to deal with the WP of SDG 5. We explore this idea below.
Recommendation #1: commit to a global corporate social responsibility strategy.
How MNE executives view the role of business in society has changed significantly over the years (Bull & Miklian, 2019 ; Eden, 2020 ). Definitions of social issues and corporate social responsibility (CSR) have broadened significantly as the social responsibility of business has evolved from its historical goal of “do no harm” to the more activist role of “doing good.” 23 More recently, CSR scholars have found that MNEs, in particular the largest ones are moving from “doing good” to “going above and beyond” mandated levels of government social policies (Eden, 2020 ; Schlegelmilch & Szöcs, 2020 ). Simply meeting government CSR regulations is no longer viewed as a differentiating factor; MNEs must exceed mandated levels of social and environmental activities to build a reputation and positively affect their financial performance (Miller, Eden, & Li, 2020 ).
The 2030 Agenda provides an opportunity for MNE executives to rethink their CSR strategies. We recommend that MNE executives shift from viewing CSR as a stand-alone activity located in their marketing departments to recognizing that CSR is an activity that can and should be linked strategically and dynamically to the MNE’s overall global strategy (Eden, 2020 ; Schlegelmilch & Szöcs, 2020 ). This new role should include building a commitment to global CSR into the MNE’s goals, scope, rules of engagement, capabilities, and management systems. Making a commitment to at least 1 of the 17 SDGs should be a core component of the global corporate social strategy for every MNE.
The 2030 Agenda is a new form of global governance by goal setting (van Zanten & van Tulder, 2018 ; Bull & McNeill, 2019 ). The agenda offers an opportunity for MNEs to use PPPs as a vehicle for solidifying a new role for business in society. MNEs, especially large MNEs with a global footprint, can be proactive agents of change that can serve society and address global problems (Kolk, Kourula, & Pisani, 2017 ; van Tulder, 2018 ). We therefore recommend that MNEs work pro-actively with UN agencies and national governments through PPPs to further the 2030 Agenda.
MNEs have been partnering with international organizations at least as far back as 1946 when the International Chamber of Commerce was given consultative status at the United Nations (Seitz, 2019 ). PPPs began to play a major role in MNE–state relations starting in the late 1990s (Bull & McNeill, 2019 ). For example, over the past 20 years, the primary forum for UN–business networking has been the UN Global Compact (UNGC), initiated in 1999 by UN Secretary-General Kofi Annan. 24 Other industry groups have also formed to support the SDGs; for example, the World Business Council for Sustainable Development (WDCSD, 2017 , 2018 ) whose members are primarily large MNEs.
The 2030 Agenda provides many opportunities for MNEs to work with national governments and international NGOs. Van Zanten and van Tulder ( 2018 : 226), in their study of MNE engagement with the SDGs, found that MNEs were using PPPs where the SDGs were complex and externally actionable, notably, SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 4 (quality education). While PPPs and strategic alliances are costly, they enable resource collaboration and knowledge-sharing benefits that often cannot be achieved by firms on their own.
We recommend that MNEs use PPPs especially for the evidence collection stage for the SDGs, in particular for SDG 5. The UN has asked businesses to partner with governments and NGOs to build more robust statistical indexes for the SDGs (Business for 2030, 2020 ). There will be some targets and indicators where the private sector has better and more direct access to data than governments (e.g., SDG 5 data on wages and salaries, access to childcare and maternity leave policies, and share of women in management and leadership roles). These are areas where MNEs can play a powerful role in improving gender statistics in the evidence collection stage, particularly where statistical agencies are weak.
Our third recommendation is that MNEs adopt an EBP approach to selecting and implementing their own CSR strategy for engagement with the 2030 Agenda. Given that the SDGs are voluntary goals without formal binding commitments or penalties, each MNE has the flexibility to select from among the “menu” or “smorgasbord” of the 17 SDGs, prioritizing/ignoring and spending/not spending on the SDGs’ multiple targets and indicators, as the firm’s executives so choose (van Zanten & van Tulder, 2018 ).
Our proposed framework builds on the EBP policy cycle in Figure 2 with the key difference that the EBP approach is applied to MNE decision-making. We call our framework the SDG Materiality Matrix because a key component of the matrix is the analysis of both ex ante and ex post materiality of SDG targets to the MNE. The concept of materiality reflects the impact of a decision on a firm or actor; materiality analysis is designed to determine “what really matters to company sustainability performance, commitment and strategies” (Calabrese, Costa, Ghiron, & Menichini, 2017 : 440; Bellantuono, Pontrandolfo, & Scozzi, 2018 ). Materiality analyses are typically carried out on an ex post basis, assessing the performance of the MNE’s CSR activities. Here, we expand the concept to include both ex ante and ex post estimates of materiality of SDG targets to the MNE. We argue that MNE executives can use our SDG Materiality Matrix to develop their internal evidence-based policies, first, for creating and disseminating good evidence for the SDGs, and second, for policy design and implementation of SDG policies inside their organizations. We illustrate our SDG Materiality Matrix in Figure 3 . Given the huge number of SDG targets and indicators, we recommend that SDG target selection by the MNE should be based on four factors: Quality of evidence for the target, Salience of the target to the MNE, Actionability of the target by the MNE, and Ethicality of the target for the MNE.
The SDG Materiality Matrix applied to SDG 5 Gender Equality.
An EBP approach starts with the requirement of good evidence. Thus, quality – the “best available evidence” – for each SDG in terms of its targets and its indicators is the first factor in our SDG Materiality Matrix. MNE executives should start by mapping and assessing the 17 SDGs and their targets and indicators, for example, by consulting the most recent edition of the E - Handbook on the SDGs (UNSD, 2020 ). Summaries of available datasets and questions are also provided in GRI and the UN Global Compact ( 2017 ) and UNSD ( 2020 ). 25
Our second factor is the salience of the evidence for the MNE. We argue that salience has two components: fit and materiality. In terms of fit , we argue that MNE executives should prioritize SDG targets based on their relevance to and fit with the MNE’s core business purpose (Szöcs & Schlegelmilch, 2020 ). The MNE’s corporate goals for performance (market, financial, and social) and organizational legitimacy must also be considered (Donoher, 2017 ; Terpstra-Tong, 2017 ). The MNE should also consider existing in-house programs (e.g., CSR activities) and how the SDG target would fit with the MNE’s capabilities (Szöcs & Schlegelmilch, 2020 ) and CSR programs (Schönherr, Findler, & Martinuzzi, 2017 ).
The second component of salience is materiality of the SDG target to the MNE. Here, we recommend that MNE executives estimate the expected benefits and costs (both private and social) from acting on the SDG target, the likely impact on firm performance, and in what ways the SDG target is relevant to the MNE’s internal and external stakeholders. How, for example, would adopting the SDG target likely affect the MNE’s exports and imports, employment, foreign direct investment (FDI), and global value chains? 26 Based on an assessment of fit and materiality, the MNE can determine which SDG targets have the greatest salience.
Our third factor is actionability by the MNE, which has three components: target scope, action type, and actors (we expect the three components to be interdependent). The first component is target scope ; that is, whether the target is aimed at the regional/global, country, industry, or firm level. The second component is the type of action required by the SDG target. Addressing the target could require the MNE to engage in actionability through market-based actions (e.g., new products), operational actions (e.g., processes and value chains), or regulatory actions (e.g., standard-setting) (Szöcs & Schlegelmilch, 2020 ). The third component of actionability – actors – addresses which entity or entities are responsible for the actions and what roles they play. Both target scope and action type should affect the choice of actors and roles. For example, if the target is actionable at the firm level, the MNE can, but does not have to, move on its own to address the target. Targets aimed at the industry are likely to require partnering with firms in the same industry. Targets at the national or international levels are more likely to require PPPs and alliances with NGOs. Similarly, standard-setting actions may need industry alliances or PPPs, whereas new product launches can be done inside the MNE.
Our last factor in the SDG Materiality Matrix is ethicality of the target, which is the level of effort that the MNE chooses for the SDG target. Here, we consider two components: the standard or norm embedded in the SDG target and the MNE’s choice of compliance level. First, SDG targets can be written as either proscriptive (“do not harm”) or prescriptive (“do good”) norms or standards, which can also be either voluntary or mandatory. Where the standard is mandatory, additional issues involve enforceability and sanctioning. Because the SDGs are “soft” law, only the first component (prescriptive or proscriptive behavior) is at issue here. The second component of ethicality is the MNE’s chosen level of compliance with the standard: below (symbolic), at (compliant), or above (substantive) the norm (Miller et al., 2020 ). We also consider the materiality of the MNE’s engagement with the SDG target in terms of monitoring, impact assessment, and reporting to governments and other stakeholders (Calabrese et al., 2017 ; Bellantuono et al., 2018 ). Note that, while the salience factor assesses materiality on an ex ante basis, the ethicality factor does the same on an ex post basis.
The four factors in our SDG Materiality Matrix for MNE engagement with the SDGs have their direct parallels in the EBP policy cycle recommended for government policymakers. As Figure 2 shows, given the best available evidence, policymakers should consult with stakeholders, interpret the evidence for the context, select their preferred option(s), determine the actors and their roles, implement the policies, and monitor and assess performance.
We hypothesize that MNEs will prioritize at least some SDG 5 targets and indicators as part of their global CSR strategy. Below, we apply the SDG Materiality Matrix to explore how this can be done.
In terms of selecting evidence based on its quality, we argue that businesses are best placed to “make a difference” if they select targets that focus on gender equality and empowering women in the workplace. In terms of indicators, we recommend that MNEs start with the GII and GGGI rather than the newer SDG gender indexes to assess and design policies for addressing the gender inequality gaps in their organizations. As we have argued above, the GII and GGGI are better measures (at least at present) of workplace gender inequalities than the newer indexes. MNEs should use metrics from the GII and GGGI to collect and monitor their own internal statistics on gender equality and empowerment.
In terms of salience, van Zanten and van Tulder ( 2018 : 220) report that MNE engagement with the SDGs has been “particularly high” for SDG 5. Their result accords with other literature on the importance and salience of gender equality to business both in terms of fit and materiality; see, for example, Accenture ( 2019 ), Ike, Donovan, Topple and Masli ( 2019 ), PwC ( 2016 ), and WBCSD ( 2018 ).
In terms of actionability, we recommend that MNEs should focus, first, on the collection and dissemination of internal evidence on SDG 5 and, second, on designing and implementing internal policies to foster gender equality. MNEs that adopt SDG 5 as one of their key social strategies need to mainstream gender in their organizations and set up EBP management and reporting systems to ensure implementation (Thomas et al., 2018 ).
We also recommend that the MNE’s workplace, for the purposes of SDG 5, be defined as including all domestic and foreign affiliates. This does not imply that the exact same gender equality policies must apply throughout the MNE group on a worldwide basis. Given the large differences across countries in both gender inequality antecedents and outcomes, a proactive and substantive strategy will be a difficult, expensive, and contentious undertaking (Terpstra-Tong, 2017 ). EBP predicts that top–down strategies based on global integration are likely to fail when “what works here does not work there.” We therefore recommend a bottom–up, locally responsive approach. MNEs should build partnerships with key stakeholders at the country level (e.g., employees, governments, suppliers and buyers, civil society) and use the partnerships to develop appropriate country-based gender equality policies. MNEs should also address “missing links”, such as second- and third-tier suppliers that often “fly below the radar” in (non)compliance with the lead firm’s and first-tier suppliers’ CSR initiatives (Serdijn, Kolk, & Fransen, 2020 ). The commitment to mainstreaming gender equality throughout the MNE group should be, initially, to “go above and beyond” country-level requirements for gender equality and, in the longer term, to “lift all boats” to the highest common denominator across the MNE’s local and foreign affiliates.
Lastly, in terms of ethicality, assuming MNEs do prioritize SDG 5, what norm should they adopt – do no harm or do good – and what level and materiality? Van Zanten and van Tulder ( 2018 ) hypothesized that good citizens (including MNEs) would be more likely to choose compliance with expected norms. Assuming that a “do good” standard is more costly than a “no harm” standard, the authors found that MNEs preferred SDG targets with “no harm” standards. 27 On the other hand, if MNE executives view CSR as a socially responsible contract with their stakeholders, the executives are more likely to choose pro-active levels of engagement with the SDG targets (Eden, 2020 ; Schlegelmilch & Szöcs, 2020 ). There is also empirical evidence that MNEs that “go above and beyond” mandated CSR levels earn a positive reputation, which positively affects firm performance (Miller et al., 2020 ). We therefore expect MNEs to benefit from positive reputation gains that come from exceeding government mandates in terms of their global CSR strategies. We therefore recommend that MNEs commit publicly to exceeding national standards as they mainstream gender equality throughout their organizations.
Finally, adopting SDG 5 as a corporate social strategy also involves dissemination to others and reporting standards. The most common of these are CSR reports (Eden, 2020 ; Schönherr et al., 2017 ); other examples for how to build SDG 5 activities into corporate reports can be found in GRI and UNGC (2018). In addition, MNE CEOs are now being asked to publicly sign and post the seven Women’s Empowerment Principles, designed to mainstream gender in business organizations (UN Women, 2019 ). 28 When CEOs of large MNEs commit to making gender equality a top strategic priority for their organizations, the positive signaling effect encourages others to follow suit. 29 The visibility of large MNEs encourages others to emulate their practices, creating a bandwagon effect (Van Zanten & van Tulder, 2018 : 225). We therefore recommend that, as part of mainstreaming gender equality in their organizations, MNE CEOs sign on to the seven Women’s Empowerment Principles.
In sum, our recommendations, drawn from the SDG Materiality Matrix, would mainstream gender equality throughout the MNE group and send a strong signal to stakeholders and other MNEs.
Gender equality is 1 of 17 “wicked problems” in the 2030 Agenda. The issue fulfills all the criteria: systemic, complexly interrelated, with material involvement by multiple actors at multiple levels across multiple countries (van Tulder, 2018 ). There is wide recognition that better evidence is needed to foster women’s voice and agency, and to meet the UN challenge to leave no one behind (Thomas et al., 2018 ; UN Women, 2018 ). Our paper explores how insights from EBP can be useful for tackling the WP of SDG 5 and the 2030 Agenda.
Our paper makes several contributions to the IB literature. First, to the best of our knowledge, there have been no articles in Journal of International Business Policy ( JIBP ) or Journal of International Business Studies ( JIBS ) on WPs or EBP. A keyword search of JIBS and JIBP failed to find a single article on either topic. 30 We believe both frameworks offer much value added for thinking about IB policies and problems. IB theory has a long history of adopting insights from other disciplines. We hope that our work will encourage other IB scholars to use these theoretical frameworks for their own research topics.
We contribute to the EBP literature by expanding it in three different contexts: first, from the single-country (domestic) level to the multi-country level of the 2030 Agenda; second, by applying EBP to the problem of gender inequality; and third, by developing a EBP for multinational enterprises in our new SDG Materiality Matrix. In addition, we contribute to the WP literature by applying it to SDG gender equality, building on O’Brien et al. ( 2017 ). We also bring together two literatures – WPs and EBP – that historically have had little to do with one another, and show that they can usefully inform each other. Our work here builds on Daviter ( 2019 ) and Head ( 2019 ).
We contribute to the literature on the SDGs, and SDG 5 in particular, by showing how high-quality evidence and EBP can assist in the attainment of the 2030 Agenda. Our paper also contributes to the literature on gender equality by exploring differences between five gender inequality indexes, building on Eden and Gupta ( 2017 ), and assessing their relative appropriateness as evidence in an EBP approach to SDG 5. Our paper also addresses the research agenda items outlined by Witte and Dilyard ( 2017 ), including how government policies on the SDGs affect MNE strategies and predict which firms will engage and how they will engage with the SDGs.
A key innovation in our paper is the introduction of a new EBP framework for MNEs, the SDG Materiality Matrix, based on four factors (Quality, Salience, Actionability, and Ethicality), which MNEs can use to select and implement policies for their preferred SDG targets. We also contribute to the CSR literature by expanding it to encompass the SDGs, applying CSR insights into our SDG Materiality Matrix, and developing the concepts of ex ante and ex post materiality.
Going forward, we argue that IB scholars need to better understand how, why, and where MNEs are involved in the SDG process. Our SDG Materiality Matrix could be used as the framework for case studies of MNE interactions with other SDGs. Lastly, our paper could be expanded to discuss the role of evidence and EBP for the SDGs from the perspective of other organizations, such as business schools and professional associations. To what extent are they involved in the 2030 Agenda and what policy processes are they adopting to ensure that no one is left behind and everyone’s voice is heard?
This paper was first presented at a JIBP Workshop at the Academy of International Business meetings in Copenhagen in June 2019. We thank the JIBP Special Issue Editors, especially Rob van Tulder, and the anonymous reviewers, for their thoughtful, detailed, and helpful comments on the paper. We thank Reilly Smith for her assistance with the datasets. Any remaining errors are the authors.
is Professor Emerita of Management and Research Professor of Law (joint appointment) at Texas A&M University. Her current research interests include transfer pricing and MNE strategies in the digital economy. She was 2008–2010 Editor-in-Chief of the Journal of International Business Studies and 2017–2018 President of the Academy of International Business. She is incoming Dean of the AIB Fellows for 2020–2023.
is an Associate Professor in the Department of Marketing and Management at the University of Texas at El Paso where she holds the Robert E. and Jacqeline Skov Professorship in Business Ethics. She received her PhD from Texas A&M University. Her current research interests include gender and diversity issues at work and international human resource management. She is the Associate Editor of Management Research : The Journal of the Iberoamerican Academy of Management .
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Accepted by Rob van Tulder, Area Editor, 21 May 2020. This article has been with the authors for three revisions.
Lorraine Eden, Email: ude.umat@nedel .
M. Fernanda Wagstaff, Email: ude.petu@ffatsgawf .
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npj Vaccines volume 9 , Article number: 166 ( 2024 ) Cite this article
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We compared the risks and benefits of COVID-19 vaccines using a causal pathway analysis to weigh up possible risk factors of thromboembolic events post-vaccination. The self-controlled case series (SCCS) method examined the association between thromboembolic events and vaccination while a case-control study assessed the association between thromboembolic events and COVID-19, addressing under-reported infection data issues. The net vaccine effect was estimated using results from SCCS and case-control studies. We used electronic health record data from Corewell Health (16,640 subjects in SCCS and 106,143 in case-control). We found increased risks of thromboembolic events post-vaccination (incidence rate ratio: 1.19, 95% CI: [1.08, 1.31] after the first dose; 1.22, 95% CI: [1.11, 1.34] after the second dose). Vaccination attenuated infection-associated thromboembolic risks (odds ratio: 4.65, 95% CI: [4.18, 5.17] in unvaccinated vs 2.77, 95% CI: [2.40, 3.24] in vaccinated). After accounting for vaccine efficacy and protection against infection-associated thromboembolic events, vaccination decreases thromboembolic event risk, especially during high infection rate periods.
The Coronavirus Disease 2019 (COVID-19) pandemic prompted a race to develop and distribute effective vaccines. Approximately 81.4% of the US population have been vaccinated with at least one dose, and 69.5% have completed the primary series of COVID-19 vaccination 1 . While the benefits of vaccination are widely acknowledged, concerns have emerged regarding the development of thromboembolic events after vaccination 2 . Phase 3 clinical trials were not statistically powered to identify rare adverse events 3 . The risks of new vaccines were not fully known during regulatory approval, particularly for mRNA-based vaccines (mRNA-1273 or BNT162b2), which were under authorized emergency use. Therefore, it is important to conduct post-marketing safety surveillance of the vaccines. More specifically, cases of venous thromboembolism following a mRNA-based vaccination were reported in 2022 after COVID-19 vaccines were administered in the US and some other countries 4 , 5 , 6 , 7 , drawing attention to the potential risk of thromboembolic events after the first vaccination dose. One study confirmed an increased risk of thromboembolism, ischemic stroke, and cerebral venous sinus thrombosis after the first dose of BNT162b2 8 , and another retrospective cohort study found an increased risk of cerebral venous thrombosis and portal vein thrombosis after any mRNA-based vaccination 9 . Moreover, a recent systematic review 10 has shown that thromboembolism is the most frequent cardiovascular complication following a mRNA-based vaccination. Despite those findings, vaccination is still recommended to reduce the likelihood of COVID-19, hospitalization, and mortality 8 , 11 . Furthermore, COVID-19 itself substantially increases the risk of thromboembolic events 12 , 13 , 14 , 15 , 16 , 17 , 18 , with a more prolonged and significant threat compared to vaccine-associated risks 8 . Therefore, studying the risk of thromboembolic events after COVID-19 vaccination should incorporate the protective effect of vaccines against COVID-19 severity and hence COVID-19-associated thromboembolic events.
Several studies have reported a positive correlation between thromboembolic events and mRNA-based vaccines, with reported incidence rate ratios (IRRs) between 1.04 and 1.22 8 , 19 , 20 , 21 , 22 . These studies used the self-controlled case series 23 (SCCS) design, which is a standard approach to studying adverse events of vaccines. The same design was used to evaluate the risk of thromboembolic events after COVID-19, with reported IRRs between 6.18 and 63.52 8 , 11 , 14 . However, since a thromboembolic event typically requires a hospital visit (emergency visit or hospital admission), subjects with a thromboembolic event are subject to a higher rate of COVID-19 testing, and so at a lower likelihood of misclassification as uninfected compared to subjects without an event. Hence, the SCCS design is subject to some risks of bias 24 , which we would expect to inflate the SCCS estimated relative risk (RR) of thromboembolic events after COVID-19.
The objective of this study is to evaluate whether the overall effect of the COVID-19 vaccination is to increase or decrease the risk of thromboembolic events. To do so, we first quantified the risk of thromboembolic events after mRNA-based vaccination using the SCCS method. Secondly, we evaluated the association between thromboembolic events and COVID-19 using a case-control study, avoiding the misclassification bias associated with the SCCS method. Finally, we conducted a risk-benefit analysis by comparing the magnitude of the increased risk through the direct effect of the COVID-19 vaccination with the reduced risk through the indirect pathway via protection against infection-associated thromboembolic events.
Our studies used electronic health record (EHR) data from the Corewell Health East (CHE, formerly known as Beaumont Health) and Corewell Health West (CHW, formerly known as Spectrum Health) healthcare systems, which includes demographics, mortality, hospital admissions, and COVID-19 testing. We obtained accurate COVID-19 vaccination records (vaccine types, dates, and doses) by linking EHR data at Corewell Health with the Michigan Care Improvement Registry (MCIR), giving more complete data for individuals who received the COVID-19 vaccines outside the healthcare system. We included all patients aged ≥ 18-years-old and were registered with a primary care physician within 18 months before Jan 1st, 2021.
We identified thromboembolic events based on ICD-10 (International Classification of Diseases version 10) codes from a hospital visit (emergency visit or hospital admission). These ICD-10 codes represent diagnoses for venous thromboembolism, arterial thrombosis, cerebral venous sinus thrombosis, ischemic stroke, and myocardial infarction (Supplementary Table 1 ). We also used patients with physical injury at a hospital visit (list of ICD-10 codes in Supplementary Table 2 ) to identify potential bias related to the misclassification and further leveraged them as a control group to estimate the effect of COVID-19 on thromboembolic events.
We used the SCCS design to examine the association of thromboembolic events and the first two doses of mRNA-based COVID-19 vaccines (mRNA-1273 or BNT162b2) from December 1st, 2020, to August 31st, 2022. The SCCS method compares the incidence rate of thromboembolic events before and after vaccination. In this method, subjects are under their own control, and comparisons are made within subjects, thus avoiding any time-invariant confounding. We included subjects who had a thromboembolic event and received at least one dose of the primary series of mRNA-based vaccines in the study period. The control period was defined from December 1st, 2020, to 28 days before the first dose of vaccination, excluding the period of 28 days prior to vaccination to avoid bias due to contra-indications 25 . Two separate risk periods for the first and second doses were defined until 28 days after vaccination, death, or August 31st, 2022, whichever occurred first (Supplementary Fig. 1 ). We also excluded subjects who had COVID-19 within 90 days before a thromboembolic event to remove the confounding effect of infection on that event. We used a conditional Poisson regression 22 with an offset for the length of each period to estimate the IRRs of dose one and dose two simultaneously. Specifically, the model has an independent variable of the period with three categories (control periods, and two risk periods after the first and second dose). Using the control period as the reference, we derived the IRRs for the two doses. As Poisson regression assumes the independence between recurrent events, therefore, we considered only events that occurred at least one year after the previous events.
In an initial analysis of the association between thromboembolic events and COVID-19, we used the SCCS design and included patients who had at least one positive COVID-19 test (PCR or antigen) and a thromboembolic event at a hospital visit during the same period as in the previous study of vaccination. However, due to the missing infection data in patients who did not have any hospital visits for thromboembolic events or other reasons, the SCCS design resulted in a biased estimate of the association between thromboembolic events and COVID-19. Patients visiting the hospital, almost always received a COVID-19 (PCR or antigen) test, especially early in the pandemic, while patients who did not visit the hospital were subject to underreporting infection data. This underreporting (or misclassification of infected as uninfected) led to an inflated IRR of thromboembolic events after COVID-19.
We proposed a simple and efficient method to quantify the association between thromboembolic events and COVID-19 while dealing with the misclassification issue. The main idea is to select a subset of control (i.e., subjects without thromboembolic events) who had a hospital visit for reasons independent of COVID-19 and therefore had complete infection data. To this end, we used patients who had a diagnosis code for physical injury (see Supplementary Table 2 ) at a hospital visit as the control group, since we would not expect any causal association between physical injury and COVID-19. We used a case-control design, in which patients with a thromboembolic event are considered as cases, and patients with a physical injury are considered as controls. If an individual had multiple hospital visits for thromboembolic events or physical injuries, we considered only the first visit. As physical injuries can be risk factors for thromboembolic events 26 , 27 , we therefore excluded patients who experienced both events at the same visit. We determined the COVID-19 status based on the COVID-19 test results during the 28 days prior to the date of the event (Supplementary Fig. 2 ). If an individual had a positive test result, this subject was classified as exposed to COVID-19, otherwise, unexposed. We compared the odds of infection (exposed) vs no infection (unexposed) in the cases (with thromboembolic events) vs controls (with physical injury) using a logistic regression model adjusted for age, race, gender, Charlson comorbidity index (CCI), number of visits, and prior vaccination status (yes/no). Patients who had any COVID-19 vaccine between the date of the positive COVID-19 test and the date of the event were removed. The number of visits was fit with a natural spline with three degrees of freedom. The CCI was obtained using the R package comorbidity and categorized into four categories, ‘0’, ‘1–2’, ‘3–4’, and ‘ ≥ 5’ 28 , 29 . Analyses were done after excluding patients with incomplete covariate data.
COVID-19 vaccines are protective against COVID-19 and COVID-19 severity 30 , 31 , 32 , and so can indirectly decrease the likelihood of experiencing a thromboembolic event. Hence, we conducted a risk-benefit analysis to estimate the net RR of thromboembolic events after vaccination by considering the role of vaccination in preventing infection-associated thromboembolic events. Figure 1 illustrates the direct and indirect effect of the COVID-19 vaccination on the occurrence of thromboembolic events while considering vaccine efficacy (VE). As presented in the diagram, the association between thromboembolic events and COVID-19 vaccination is described by two paths, the direct association between thromboembolic events and vaccination, and the indirect association between thromboembolic events and vaccination via potential reduction in the risk of thromboembolic events through decreasing the risk of COVID-19. We estimated the overall influence of vaccination on the occurrence of thromboembolic events by considering both direct and indirect paths.
COVID-19 (I), individuals with COVID-19. COVID-19 vaccination (V), individuals with COVID-19 vaccines. Thromboembolic events (Y), individuals with thromboembolic events. V → I indicates vaccine effect (VE) in preventing COVID-19, V → Y indicates the risk of thromboembolic events after COVID-19 vaccination, I → Y indicates the risk of thromboembolic events after COVID-19, V → Y (via I) indicates the risk of thromboembolic events after vaccination accounting for vaccine effect in reducing infection-associated thromboembolic events.
Let \({\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\) and \({\rm{P}}\left({\rm{I}}|\bar{{\rm{V}}}\right)\) be the probability of COVID-19 ( \({\rm{I}})\) in vaccinated ( \({\rm{V}}\) ) and unvaccinated ( \(\bar{{\rm{V}}}\) ) subjects, respectively. Let \({\rm{P}}\left({\rm{Y}}|\bar{{\rm{V}}},\bar{{\rm{I}}}\right),{\rm{P}}\left({\rm{Y}}|{\rm{V}},\bar{{\rm{I}}}\right),{\rm{P}}\left({\rm{Y}}|{\rm{I}},\bar{{\rm{V}}}\right),\) and \({\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) be the probability (or risk) of thromboembolic events ( \({\rm{Y}})\) in unvaccinated and uninfected, vaccinated and uninfected, unvaccinated and infected, and vaccinated and infected subjects, respectively.
With the above notations, for a vaccinated subject, the total risk of thromboembolic events is \({\rm{P}}\left({\rm{Y}}|{\rm{V}},\bar{{\rm{I}}}\right)+{\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) , where the product \({\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) is the indirect risk calculated by multiplying the risk of COVID-19 of a vaccinated subject and the risk of thromboembolic events given a COVID-19 in the vaccinated group. Similarly, the overall risk of thromboembolic events for an unvaccinated subject is given by \({\rm{P}}\left({\rm{Y}}|\bar{{\rm{V}}},\bar{{\rm{I}}}\right)+{\rm{P}}\left({\rm{I}}|\bar{{\rm{V}}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},\bar{{\rm{V}}}\right)\) . Hence the net RR ( \({{\rm{RR}}}_{{\rm{Net}}}\) ) of thromboembolic events for a vaccinated subject compared to an unvaccinated subject is
The terms \({{\rm{RR}}}_{{\rm{V}}}\) is the RR of thromboembolic events comparing vaccinated versus unvaccinated in subjects without COVID-19, and \({{\rm{RR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\) is the RR of thromboembolic events comparing subjects with and without COVID-19 in the unvaccinated group. The term \({{\rm{RR}}}_{{\rm{IV}}}\) is the RR of thromboembolic events in subjects who have both vaccination and infection, compared to the group of subjects who do not have any exposures.
We further defined VE as \({\rm{VE}}=1-{\rm{P}}({\rm{I|V}})/{\rm{P}}({\rm{I|}}\bar{{\rm{V}}})\) , then plugged VE into Eq. (1) to obtain
If \({{\rm{RR}}}_{{\rm{Net}}}\) is smaller than one, COVID-19 vaccination offers protection against thromboembolic events, with a lower \({{\rm{RR}}}_{{\rm{Net}}}\) implying a stronger protection.
Statistical analyses were performed in R 4.3.0. We reported odds ratio (OR) and IRR with 95% CIs and p -values from the two-sided test. We generated a figure for \({{\rm{RR}}}_{{\rm{Net}}}\) over a range of VE values based on the estimates of ORs and IRRs.
We used de-identified EHR data, the use of which was approved by the Institutional Review Board of Corewell Health.
During the study period from December 1st, 2020, to August 31st, 2022, there were 747,070 subjects at Corewell Health who received mRNA-based vaccines, among which 279,229 (37.38%) had the primary series of mRNA-1273 and 467,841 (62.62%) took BNT162b2. Overall, the number of fully vaccinated patients was 711,460 (95.23%), and 35,610 (4.77%) patients received only one dose. The median age was 57 (with interquartile range [IQR]: 40–69), and 59.81% of patients were female. There were 367,105 patients taking at least one COVID-19 test (antigen or PCR), among which 78,568 (21.4%) patients received positive results. The median age was 52 (with interquartile range [IQR]: 34–67), and 61.44% of patients were female.
In the study cohort of vaccination exposure, there were 16,640 patients who had at least one thromboembolic event and had the first dose of either mRNA-1273 or BNT162b2 vaccine. Patient demographics are presented in Table 1 . We identified 2724 events in the control period, 722 events within 28 days after the first dose, and 786 events within 28 days after the second dose.
In the study cohort of COVID-19 exposure, there were 18,004 patients who had a thromboembolic event (cases) and 88,139 patients who had a physical injury (controls) at a hospital visit. 16.96% of cases and 1.48% of controls had COVID-19 within 28 days before the event. Demographics of patients are presented in Table 2 .
Based on the SCCS analysis, we found an increased risk of thromboembolic events 28 days after the first dose (IRR = 1.19, 95% confidence interval (CI): [1.08, 1.31], p -value < 0.001), and after the second dose (IRR = 1.22, 95% CI: [1.11, 1.34], p -value < 0.001) of the mRNA-based vaccines.
We studied the risk of thromboembolic events in a 28-day window after vaccination based on prior research 8 . An event that occurs in a short period (such as 28 days) is more likely to be attributable to the vaccines. We also conducted a sensitivity analysis using a 60-day window after vaccination. The conclusions remained the same with slightly lower IRRs (IRR = 1.13, 95% CI: [1.03, 1.24] after the first dose, and IRR = 1.14, 95% CI: [1.05, 1.3] after the second dose).
Supplementary Figs. 3 and 4 show the IRRs for subgroup analyses by age (“18–31”, “31–50”, and “≥51”) and gender (female/male). We found that the effects of vaccination on thromboembolic events were similar between age groups and gender groups.
Naïve SCCS analysis showed a very large increased risk of thromboembolic events associated with COVID-19 (IRR = 19.36, 95% CI: [17.64, 21.26], p -value < 0.001). However, a similar analysis using the physical injury as an event also derived a large increased risk (IRR = 3.31, 95% CI: [3.10, 3.54], p -value < 0.001), indicating misclassification bias as COVID-19 should not substantially increase the risk of physical injury. In the case-control analysis with controls having a physical injury, we found that COVID-19 increased the risk of thromboembolic events but with a much smaller magnitude than the risk in the SCCS analysis (although it is still larger than the vaccination exposure). Moreover, the degree of the increased risks was modified by vaccination status (Fig. 2 ). The reported OR for the unvaccinated group was 4.65 (95% CI: [4.18, 5.17], p -value < 0.001) compared to 2.77 (95% CI: [2.40, 3.24], p -value < 0.001) for the vaccinated group. We observed the increased risks of thromboembolic events after COVID-19 in both groups, but vaccination appears to confer some protection against infection-associated thromboembolic events, given the lower OR. Alternatively, we divided the vaccinated group into four categories based on the time to the last vaccination (“≥365 days”, “180–365 days”, “90–180 days”, and “<90 days”). The effects of COVID-19 on thromboembolic events were similar across the four vaccinated groups. The results are in Supplementary Fig. 5 .
OR is denoted by a solid circle and a 95% CI is represented by a line. The x -axis is plotted on the natural log scale. CCI Charlson comorbidity index. Infection or non-infection refers to COVID-19.
We also conducted two sensitivity analyses. In the first analysis, rather than adjusting for the CCI, we adjusted individual risk factors that might be related to a thromboembolic event. These are congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, diabetes with complications, cancer, moderate or severe liver disease, and metastatic solid tumors. We included the above eight risk factors (present or absent) in the logistic regression model. The effect of COVID-19 on the outcome of thromboembolic events was similar to the analysis with CCI. Results can be found in Supplementary Fig. 6 .
We assumed that patients who visited hospitals were routinely tested for COVID-19, especially during the early pandemic. Based on Corewell Health’s policy, patients who visited the healthcare system before March 1st, 2022, were tested for COVID-19. In our study cohort, 74.05% of participants had a hospital visit before March 1st, 2022. We conducted a sensitivity analysis using only these patients and the conclusions remained the same. See results in Supplementary Fig. 7 .
Our analysis in the previous sections gave an IRR of 1.22 as the measure of the association between thromboembolic events and the second dose of COVID-19 vaccination, therefore, we set \({{\rm{RR}}}_{{\rm{V}}}\) = 1.22. We also obtained odd ratios \({{\rm{OR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\) = 4.65 and \({{\rm{OR}}}_{{\rm{IV}}}\) = 2.82 from the analysis using the case-control design. Since the RR is very close to the OR when the event is rare, we therefore set \({{\rm{RR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\) = 4.65 and \({{\rm{RR}}}_{{\rm{IV}}}\) = 2.82, as the thromboembolic events are rare 33 . Hence, plugging these estimators into Eq. (2), the \({{\rm{RR}}}_{{\rm{Net}}}\) becomes
Figure 3 illustrates the \({{\rm{RR}}}_{{\rm{Net}}}\) of thromboembolic events after COVID-19 vaccination as a function of VE. As VE increases from 0 to 1, \({{\rm{RR}}}_{{\rm{Net}}}\) decreases and reaches a point where vaccine benefits outweigh the harms. Specifically, vaccines with higher VE offer higher protection against thromboembolic events. For example, the effectiveness of mRNA-based COVID-19 vaccines against infection was 61% during the Delta period and 46% during the Omicron period 34 , 35 , 36 . Given an infection rate of 0.08 among unvaccinated subjects, the risk of thromboembolic events was decreased by 4.62% in the Delta period, which is higher than 2.07% in the Omicron period. Moreover, vaccines offer stronger protection during periods with higher infection rates. For example, with the infection rate of 0.1 in unvaccinated subjects, the reduction of the risk of thromboembolic events was higher (by 9.19% in Delta and 6.23% in the Omicron period), compared to the scenario when the infection rate was 0.08.
The x -axis is VE, and the y -axis is the net RR of thromboembolic events.
The list of ICD-10 codes for thromboembolic events is based on a previous publication 8 , including old myocardial infarction (I252). Old myocardial infarction (I252) reports for any myocardial infarction described as older than four weeks. However, our study cohort removed subjects with an I252 code who had any thromboembolic event with ICD-10 codes listed in Table S1 in the prior year. Therefore, we can consider observing I252 in the study period as a new incidence. There were 20,002 (18.84%) patients with a hospital visit associated with the I252 code. We conducted a sensitivity analysis by excluding these patients and the conclusions did not change. The estimated IRRs of thromboembolic events are 1.16 and 1.17 after vaccine dose 1 and dose 2, respectively, which are slightly smaller than the original results including the I252 code (IRRs were 1.19 and 1.22 after the first and second dose). The association between COVID-19 and thromboembolic events is higher in the unvaccinated group (OR = 5.77 without I252 and OR = 4.65 with I252) and similar in the vaccinated group (OR = 2.80 without I252 and OR = 2.77 with I252). Hence, given the same infection rate and VE, vaccination offered a stronger protection, compared to the analysis with the I252 codes. For example, given an infection rate in the unvaccinated population of 0.08 and a VE of 0.8, vaccination lowers the risk of thromboembolic events by 17.14% without I252, compared to 6.67% in the analysis with I252. Detailed results are in Supplementary Figs. 8 and 9 . We considered the analysis that includes the I252 code as the main analysis to represent more conservative results.
We found that both COVID-19 vaccination and COVID-19 increase the risk of thromboembolic events. However, evidence implies that the likelihood of experiencing a thromboembolic event after COVID-19 is much higher than after vaccination. Our analysis agrees with previous research, indicating that COVID-19 is a more dangerous risk factor for thromboembolic events than vaccination 8 , 11 , 12 , 13 , 14 .
Different from existing work, we evaluated the association between thromboembolic events and COVID-19 using a case-control study, avoiding the misclassification issue associated with the SCCS design. We also studied the effect of prior vaccination on reducing infection-associated thromboembolic events. Moreover, we included both COVID-19 vaccination and COVID-19 in the analysis of the risk of thromboembolic events and conducted a risk-benefit analysis by comparing the magnitude of the increased risk through the direct effect of COVID-19 vaccination with the reduced risk through the indirect pathway via protection against severe diseases. Our analysis provides evidence that COVID-19 vaccination directly increases the risk of thromboembolic events, but indirectly reduces the risk of infection-associated events. Results show that the indirect benefit of preventing infection-associated thromboembolic events outweighs the direct harm if the VE and infection rate reaches certain levels. Moreover, COVID-19 vaccination may have additional benefits in preventing thromboembolic events associated with COVID-19, as a higher rate of vaccination increases the overall level of immunity in the population, reducing the spread of the virus and conferring collective protection against infection-associated thromboembolic events and other health risks associated with COVID-19.
There are several limitations to this study. First, using ICD-10 codes to identify thromboembolic events may be subject to phenotype errors. Second, Corewell Health has 22 hospitals, and the catchment area for these hospitals is across many counties, hence patients may seek care at other facilities outside the Corewell Health system, leading to missing data such as infection data. To deal with the missing infection data, we used the case-control study. Moreover, the use of a prior number of hospital visits as covariates in the regression model mitigates the bias due to differing degrees of interaction with the Corewell Health system between infected and control subjects. However, patients with a hospital visit due to injuries may not be the perfect control group, but it is clearly better than a control group of patients without thromboembolic events. Therefore, we may not totally correct the bias, but we reduce it. Finally, the study population for vaccine doses 1 and 2 are different. If a subject had a thromboembolic event after the first vaccine dose, this subject is unlikely to receive the second dose, therefore, the population who received the second dose only includes subjects who did not have a thromboembolic event after the first dose.
Despite these limitations, our study makes a critical contribution to quantifying the net risk of thromboembolic events associated with COVID-19 vaccination. It accounts for both the direct effects of vaccination and the indirect effects of protection against COVID-19 and severe diseases. The dual consideration is vital for a comprehensive understanding of the risk-benefit profile. The mechanism of vaccination is to simulate the immune response the body has against infection using a dead/attenuated virus or mRNA, which can lead to side effects similar to those of the virus, albeit in a less severe form (e.g., thromboembolic events, myocarditis 37 , acute kidney injury 38 , 39 ). Our finding highlights the necessity of evaluating both the indirect benefits and direct harms of vaccination to provide a complete and accurate assessment of vaccine safety. This comprehensive approach ensures a balanced understanding of the risks and the benefits, reinforcing the overall safety and efficacy of vaccination programs.
Our risk-benefit analysis was conducted on the population level. This analysis can also be stratified by patient groups of interest. For example, the risk-benefit of vaccination might be different between older and younger populations. Moreover, our findings are for a broad range of thromboembolic conditions, so more research is needed on the specific biological mechanisms connecting COVID-19 and mRNA vaccination to these events, both to establish causality and help identify a more specific set of conditions or risk factors.
The datasets analyzed during the current study are not publicly available due to privacy or ethical restrictions.
Code for this study is available from the corresponding author on request.
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We thank Kevin Heinrich at Quire and Martin Witteveen-Lane for querying the data from the Corewell Health Epic system. This study was funded by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI158543. The funder played no role in the study design, data collection, analysis, and interpretation of data, or the writing of this manuscript.
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Division of Biostatistics & Health Informatics, Corewell Health Research Institute, Royal Oak, MI, USA
Huong N. Q. Tran
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
Malcolm Risk
William Beaumont University Hospital, Corewell Health East, Royal Oak, MI, USA
Girish B. Nair
Division of Biostatistics, Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
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H.N.Q.T.: manuscript writing, study design, statistical analysis, and data preparation. M.R.: manuscript writing and study design. G.B.: clinical advice and study design. L.Z.: manuscript writing, method development, study design, and statistical analysis.
Correspondence to Lili Zhao .
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Tran, H.N.Q., Risk, M., Nair, G.B. et al. Risk benefit analysis to evaluate risk of thromboembolic events after mRNA COVID-19 vaccination and COVID-19. npj Vaccines 9 , 166 (2024). https://doi.org/10.1038/s41541-024-00960-7
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As a crossroad of the ancient Silk Roads, the historic Lop Nur population of northwest China witnessed substantial cultural integration among the Han, Xiongnu, and Western Regions communities known as the ancient Loulan. However, the dietary practices of the Loulan population remains poorly understood due to the challenging, harsh environment for sampling. Here, we present human and faunal multi-isotope data from a simple-constructed cemetery dating to the Han-Jin Dynasties (130–320 AD). The results show that herbivores exhibited exceptionally high δ 15 N values, ranging from 10.2‰ to 15.5‰. Combined with regional climate conditions and direct evidence of extensive deposits of archeological animal manure found in Loulan sites, this phenomenon can be interpreted as indicative of extreme regional aridity and intensive fertilization. Humans had mixed C 3 /C 4 -based diets and relied on animal products. Our study indicates greater millet consumption among rural individuals compared to other more urban Loulan people. Additionally, our research reveals a much higher dietary diversity coupled with millet consumption of Loulan people in comparison to local Bronze Age communities.
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We are grateful to members who contributed to the Lop Nur project and to Renee D. Boucher from UCSC for editing the early draft.
This work was funded by the National Natural Science Foundation of China (Grant Nos. 42072210 & 42207508), the Chinese Academy of Sciences (Grant No. IGGCAS-201905), the Fundamental Research Funds for the Central Universities (Grant No. YJ202366), the Open Research Fund of Center for Archaeological Science, SCU, and the Ministry of Science and Technology, China (2014FY210500).
Authors and affiliations.
Center for Archaeological Science, Sichuan University, Chengdu, 610207, China
Xueye Wang & Guiying Zhang
School of Archaeology and Museology, Sichuan University, Chengdu, 610207, China
Archaeological Institute for Yangtze Civilization, School of History, Wuhan University, Wuhan, 430072, China
Kangkang Li
Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, China
Kangkang Li, Bing Xu, Xiaoguang Qin & Zihua Tang
School of Archaeology, Jilin University, Changchun, 130012, China
School of History, Xinjiang University, Urumqi, 830046, China
Xinjiang Institute of Cultural Relics and Archaeology, Urumqi, 830000, China
Loulan Museum, Ruoqiang, 841800, China
Jing Feng & Yingxin Jiao
Anthropology Department, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
Vicky M. Oelze
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X.W. and Z.T. designed the research and conducted the measurements. X.W., K.L., and Z.T. wrote the original manuscript and finalized the paper. X.Q., B.X., Z.T., K.L., D.W., X.H., J.F., and Y.J. performed the geological and archaeological surveys. D.W. identified the human skeletons. G.Z. & V.O. contributed to data interpretation.
Correspondence to Xueye Wang or Kangkang Li .
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The authors declare no competing interests.
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Wang, X., Li, K., Wei, D. et al. Diet along the eastern Silk Roads: an isotopic case study of ancient humans and livestock from the Han-Jin Dynasties in the Lop Nur region, northwest China. Archaeol Anthropol Sci 16 , 163 (2024). https://doi.org/10.1007/s12520-024-02068-4
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Published : 13 September 2024
DOI : https://doi.org/10.1007/s12520-024-02068-4
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