Civil society active in corporate accountability dataset

Data in Brief 63 (2025) 112172 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Invited Data Manuscript Civil society active in corporate accountability dataset Henry P. Rammelt Department of Political Science and Administration, National School of Political Studies and Public Administration (SNSPA), Bucharest Romania, Postal address: SNSPA, Bd. Expozi¸t iei, Nr. 30 A, 010324 Bucharest, Romania a r t i c l e i n f o Article history: Received 8 February 2024 Revised 11 September 2025 Accepted 7 October 2025 Available online 15 October 2025 Dataset link: Civil Society Active in Corporate Accountability Dataset (Original data) Keywords: Global civil society Corporate accountability Civil society organizations Forms of action Values Blame attribution Funding a b s t r a c t Corporate accountability constitutes one of the most relevant current discussions regarding features of the contemporary economic order. Global civil society is thought to contribute to putting pressure on states and corporations alike to provoke structural change and to provide (temporary) relief for corporate wrongdoings. However, scholarship pertaining to the endeavours of (global) civil society in holding corporations accountable is hampered by a lack of data-driven information. This dataset represents a maiden effort to offer global and comprehensive data on civil society organizations engaged in the field of corporate accountability. It stands as the primary resource delivering quantitative data on the diverse array of actions and organizations active in the field. Comprising 42 variables categorized into seven groups, the dataset encompasses structural and organizational information (such as organizational characteristics and staff), information on normative orientations and framing (such as goals, values, and blame attribution), and comprehensive information on the activities undertaken and forms of action employed by these organizations. It also incorporates a broad range of network variables (e.g., partner organizations, participation in coalitions, etc.) and details on funding sources (such as annual budget, funders, type of funding, etc.). The classifications are grounded in prevalent practices in the application of Action Organization Analysis (AOA), Social Network Analysis (SNA), and Protest Event Analysis (PEA). It facilitates systematic and cross-national analyses. Consequently, it lends itself to various analyses, including, E-mail address: Henry.r[email protected] https://doi.org/10.1016/j.dib.2025.112172 2352-3409/© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/ ) 2 H.P. Rammelt / Data in Brief 63 (2025) 112172 but not limited to, exploring the interplay between funding and activities, the relationship between location and political orientation, and the correlation between forms of action and values, descriptive statistics, and so forth. The coding process relied on information retrieved from organizations’ websites and their annual reports. Both current and past activities, when mentioned, were systematically coded. Data collection took place between October 2022 and April 2023. Thus far, the dataset has been used to investigate the impact of donor dependency on civil society. ©2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/ ) Specifications Table Subject Political Science Specific subject area Global civil society active in or related to the field of corporate accountability Data format Raw data: .csv file (data on 42 variables for 290 civil society organizations) .pdf file (codebook containing description of variables) Type of data Table Data collection The data was collected using a methodological approach situated in-between Social Network Analysis (SNA) and Action Organization Analysis (AOA), informing both the sampling strategy and the coding. Four hub websites of transnational networks active in the field of corporate accountability were identified through the Global Civil Society Database (1), their extensive networks were mapped (2), and a random sample of 290 civil society organizations was coded (3). The coding of 42 variable for each of the selected organization was achieved using a survey instrument with questions derived from Protest Event Analysis (PEA), SNA, and AOA. Trials, adaptation of the sampling mechanism, and reliability tests resulted in an easy to use codebook and an innovative dataset [ 1 ]. Data source location National School of Political and Administrative Studies, Bucharest (Romania) For the identification of four initial hub websites: Global Civil Society Database ( https://uia.org/ybio/ ). Data accessibility Repository name: Mendeley Data Data identification number: 10.17632/pfp7zmxcyx.1@@Direct URL to data: https://data.mendeley.com/datasets/pfp7zmxcyx/1 1. Value of the Data • These data provide unique insights into the functioning and structure of global civil society. • They are useful in understanding the activities, organizational configurations, and values of a global network of civil society organizations active in or related to the field of corporate accountability. • Researchers, civil society practitioners, and policymakers may benefit from this dataset through its relational (funding and partners) and ideational data (values and political orientation), as well as from information on the activities of organizations. • These data can be analysed with univariate, bivariate, and multivariate methods as well as with social network analysis methods. H.P. Rammelt / Data in Brief 63 (2025) 112172 3 2. Background This publicly available dataset [ 1 ] responds to a dire need of quantitative data on organizations struggling for corporate accountability. It is timely as there has been a growing interest in the role corporations in contemporary society. They have become the main social and economic force and their economic power allows them to shape economic policies and influence political decision-making [ 2 , 3 ]. Scholars of political science and international relations turned to civil society to mitigate the impact corporations exert over our daily lives [ 4 , 5 ]. And indeed, civil society can exert pressure on corporations [ 6 , 7 ]. However, it has also been demonstrated that activism and civil society are becoming more corporatized and co-opted by corporate interests [ 8–11 ]. The discrepancies between different studies, can be understood, amongst others as an indicator of a lack of sources for studying the field. What are the main drivers mobilizing anti-corporate campaigns? How do organizations mobilize? Whom do they hold responsible for wrongdoings, and what solutions do they advance? Where is their funding coming from? Scholarship could thus far not systematically answer such questions. The dataset is relevant, as this is at least partially to be explained by the absence of available data. 3. Data Description The dataset [ 1 ] is the first source providing quantitative data on 290 organizations active in the field of corporate accountability (CA). Included organizations are either directly involved in the struggle for or in the immediate network (direct partner) of such an organization. It includes 42 main variables grouped into seven categories: identification, funding, profile/network, activities/beneficiaries, values/orientation, blame attribution and routes, and staff/professionalization. Table 1 provides an overview of these categories with examples of variables. The complete list of variables is provided in the openly accessible codebook together with the dataset itself ( doi:10.17632/pfp7zmxcyx.1 ). Table 2 summarizes the distribution of cases (coded civil society organizations) across regions and Table 3 provides information on the types of organizations covered in the dataset. The geographic composition of the sample aligns with existing datasets on civil society, such as the Humanitarian Organizations Database (HOD) compiled by Egger and Schopper [12] [ 12 ], where eight countries (six in North America and Europe, one in Africa, and one in Asia) account for > 40 % of all organizations. This distribution reflects broader structural patterns: Africa has become a focal point for human rights issues linked to corporate activities [ 13 ], while most transnational corporations are headquartered in Europe and North America. The predominance of organizations from Africa (23 %), Europe (29 %), and North America (28 %) in our dataset therefore lends credibility to its geographical representativeness. 4. Experimental Design, Materials and Methods Our methodological approach is inspired by protest event analysis (PEA), a well-established technique in social movement studies [ 14 , 15 ]. PEA consists of systematically coding protest actions as reported in newspapers or other media, enabling researchers to trace mobilization patterns and action repertoires across space and time. Building on this tradition, data collection employed an action organization analysis approach (AOA) which applies similar principles to organizational websites rather than media reports [ 16 ]. AOA is used to map networks of organizations, capture their activities, and provide unmediated information on organizational structures, frames, and repertoires through multiple steps of sampling and coding. While AOA usually relies on the identification of hub-websites through systematic Google searches and relevant literature compilations [ 16 ] to draw the sample, for this dataset a slightly adapted approach was used for the sampling. Inspired by methods that apply social network analysis (SNA) to identify central 4 H.P. Rammelt / Data in Brief 63 (2025) 112172 Table 1 List of Variables. Category Variables Type Explanation Identification Organization name, website link, region, ZIP code, active in corporate accountability, active in private security String (organization name, website link, ZIP code); Nominal (region, coded 1–6); Binary (active in CA, private security) Basic identifiers and scope; indicate whether an organization is active in CA or private security. Funding and Funders Annual budget, biggest/2nd/3rd funder, biggest/2nd/3rd funder amount, funding type, list of funders Numeric (budget, amounts); Categorical (funder names); Categorical, multi-coded (funding type, list of funders) Captures financial base and funding sources, showing organizational resource structures. Funders are coded from a pre-defined list. Profile and Network Organization type, umbrella/platform membership, level of activity, number of orgs in umbrella/platform, partners, partner organizations Categorical (org type, partners); Binary (membership, partners yes/no); Ordinal (level, umbrella size) Captures organizational form and network embeddedness through memberships and partnerships. Partner organizations are coded from a pre-defined list. Activities and Beneficiaries Field of activity/repertoire, beneficiaries, beneficiary location Categorical, multi-coded (activities, beneficiaries); Categorical (beneficiary location) Captures repertoires of action and identifies target beneficiaries across different geographic levels. Values and Orientation Stances toward companies (5 items: profit, regulation, environment, labor, markets); copy-claim fields for stances; declared values (22 categories: solidarity, dignity, human rights, social justice, ecology, tradition, religion, etc.); overall assessment left–right Binary (stances); String (copy-claims); Categorical, multi-coded (values); Ordinal (left–right) Captures ideological orientation; copy-claims ensure reliability; declared values capture humanitarian, rights-based, ecological, economic, community, democratic, and religious principles; left–right orientation derived from stance and value variables. Blame Attribution and Routes to Goals Attribution of responsibility, proposed routes to achieve goals Categorical, multi-coded (attribution, routes) Captures how organizations assign responsibility for harms and what solutions they advocate. Staff and Professionalization Number of staff, professionalization index Ordinal (staff); Scale/Count (professionalization index, 0–8) Captures organizational capacity and degree of formalization via staff size and institutional features. Table 2 Distribution across regions. Region North America Europe Africa South America Asia Australia Count 84 82 67 29 20 3 Percentage 29.5 28.8 23.5 10.2 7 1.1 nodes in relational structures [ 17–19 ], we identified hub-websites through the free version of the Global Civil Society Database (2022), which provides “profiles of non-profit organizations working worldwide in all fields of activity.” [ 20 ]. The database is maintained by the Union of International Associations, best known for its flagship publication ‘Yearbook of International Organizations’, and is considered to be one of the most comprehensive databases on civil society organizations [ 20 ]. H.P. Rammelt / Data in Brief 63 (2025) 112172 5 Table 3 Types of Organizations. Type of Organization PRIMARY Type of Organization SECONDARY Count Percentage Count Percentage Political - Civil/ human rights group 102 35.5 62 28.4 Political - Environmental group 31 10.8 27 12.4 Political –Protest Group 5 1.7 10 4.6 Political - Labor Rights 4 1.4 8 3.7 Professional Organizations - Unions/ Labor organizations 12 4.2 2 0.9 Professional Organizations – Researchers, Academics, Intellectuals 4 1.4 2 0.9 Professional Organizations –Lawyer Associations 6 2.1 6 2.8 Law Firm 4 1.4 1 0.5 (International) Platform/ Federation 40 13.8 8 3.7 Humanitarian Organization 15 5.2 11 5.0 Charity/ Foundation 12 4.7 19 8.7 Think Tank 27 9.4 23 10.6 Informal Citizen Group (like NIMBY) 8 2.8 2 0.9 Information Platform 11 3.8 23 10.6 First, by filtering for “corporate accountability’ and “corporate justice”, we identified four hub-organizations (African Coalition for Corporate Accountability; Corporate Accountability; European Coalition for Corporate Justice; International Corporate Accountability Roundtable). From the websites of these hub-organizations, constituting the initial nodes of the network, we extracted links to the websites of 195 organizations listed as partners or network, in the second step. Subsequently, we scanned the “about” sections of these partner websites for any mentions of corporate accountability and corporate justice, in the third step. Then, in the fourth step, we extracted links to partner organizations from the websites that contained references to these terms, resulting in the identification of 677 additional partners. The link extraction process yielded a sample of 876 websites in total. Hence, the four hub-organizations listed in total 195 partners, and these 195 organizations contained links to 677 of their partners that included in the ‘about’ sections references to CA and corporate justice. As a result, all organizations in the sample are either directly active in the field of corporate accountability or directly linked to such organizations. This full-network approach made it possible to clearly define the boundaries of the field and to identify a global sample of both formal and informal organizations engaged in corporate accountability. The unit of analysis is organizations. The link-extraction process, yielding a global sample 876 websites, provides a basis for mapping and systematically comparing cross-national data across diverse organizational types. This data collection methodology aimed to mitigate methodological biases inherent to methods analysing existing media content, such as PEA, by targeting primary sources rather than their representations. Consequently, the dataset is composed of an unmediated population of organizations including those that are influential in the public discourse, and informal, marginal, or grassroots groups. Multiple pre-tests were performed, leading to refinements in the codebook and adjustments to the sampling method. In the final step, we drew a random sample of 290 organizational websites by assigning each case a number through a (pseudo-)random generator, after which their characteristics were systematically coded. A power analysis confirmed that this sample size was sufficient to detect a moderate effect (OR = 2.3) with 0.8 power at α= 0.05. Coding followed the principle of “counting what you see”: organizational features were recorded based exclusively on explicit, observable information in their public materials (e.g., website text, annual reports, logos, acknowledgments). This principle avoids subjective interpretation and ensures reproducibility, while also reflecting how organizations themselves choose to present their missions, activities, and partnerships to external audiences. Most variables are binary (0/1) indicators, coded as present ( = 1) or absent ( = 0). A smaller number of variables 6 H.P. Rammelt / Data in Brief 63 (2025) 112172 are ordinal (e.g., staff size), continuous (e.g., annual budget), or text (e.g., verbatim copy claims supporting ideological stances). For instance, when an organization explicitly mentioned funding from membership fees and philanthropic foundations, these categories were coded accordingly, and named funders listed in financial reports were recorded in variable M (“Funders”). This principle is applied to all variables but OF (‘Overall Assessment Left-Right’). OF was assessed based on the variables directly prior to it (MY-OE). These variables provide the basis for the left-right assessment, by capturing organizational stances toward corporations and markets. This allows for assessing the political leaning/ orientations of organizations engaged in the field of CA. Variables MY–NG are adapted from the Pew Research Center’s Ideological Consistency Scale. To avoid arbitrary coding, we introduced corresponding “copy claim” fields (MZ, NB, ND, NF, NH). Coders were required to paste a verbatim statement from the organization’s website that justified each stance coding. These probing questions serve as reliability checks and ensure transparency. Data collection took place between October 2022 and March 2023 and, as such, provides a snapshot of the publicly available data during that timeframe. Limitations One limitation is related to the sampling strategy and external validity. While the data grasps a network of organizations (although a very important one), there are also other active networks of organizations. The sample is the extended network of the four organizations that were identified through the Global Civil Society Database [ 20 ]. The network and the extended network also only include partners that were directly mentioned or linked. It cannot be excluded that organizations collaborate with other groups, networks, and organizations that are not publicly acknowledged or not discoverable online. The sample, therefore, might overemphasize the weight of organizations that are very visible to the detriment of less professional and visible organizations. Second, quantifying data derived from qualitative analysis always implies a certain level of simplification. Subtle differences can remain hidden. A third limitation relates to measurement: organizations often present their mission and activities strategically on their websites, for instance highlighting certain goals or activities to comply with funding requirements. This means that the coding reflects the public self-presentation of organizations rather than their complete range of activities or underlying motivations. Most of these limitations are to be encountered with other methods that are less comprehensive or that are affected by additional biases. Ethics Statement The authors have read and follow the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms. No permissions were required. Credit Author Statement Henry P. Rammelt: Conceptualization, Methodology, Data Collection, Original Draft Preparation. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the author used ChatGPT in order to check language and grammar. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article. H.P. Rammelt / Data in Brief 63 (2025) 112172 7 Data Availability Civil Society Active in Corporate Accountability Dataset (Original data) (Mendeley Data) Acknowledgements This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101002993 — CORPACCOUNT). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] H. Rammelt, Civil society active in corporate accountability dataset, mendeley data, V1, 2024, https://doi.org/10. 17632/pfp7zmxcyx.1 . [2] W. Streeck, La transformation de l’organisation de l’entreprise en Europe: une vue d’ensemble, in: R. M. Solow (Ed.), Institutions Et croissance: Les chances D’un Modèle économique européen. Bibliothèque Albin Michel Économie, Paris, 2001, pp. 175–230. [3] C. Crouch, D. della Porta, W. Streeck, Democracy in neoliberalism? Anthropolog. Theory 16 (2016) 497–512, doi: 10. 1177/1463499616677904 . [4] M. Keck, K. Sikkink, Activists Beyond Borders: Advocacy Networks in International Politics, Cornell University Press, Ithaca, 1998, doi: 10.7591/9780801471292 . [5] T. Hale, Transnational actors and transnational governance in global environmental politics, Annual Rev. Politic. Sci. 23 (2020) 203–220, doi: 10.1146/annurevpolisci050718032644 . [6] J.G. Dale, Free Burma: Transnational legal Action and Corporate Accountability, University of Minnesota Press, Minneapolis, 2011, doi: 10.5749/minnesota/9780816646463.0 01.0 0 01 . [7] J. Reinecke, J. Donaghey, Stitching Governance For Labor rights. Towards transnational Industrial Democracy?, Cambridge University Press, Cambridge, 2023, doi: 10.1017/9781108764421 . [8] M. Edwards, D. Hulme, Too close for comfort? The impact of official aid on nongovernmental organizations, World Dev 24 (1996) 961–973, doi: 10.1016/0305-750X(96)0 0 019-8 . [9] M. Yaziji, J. Doh, NGOs and corporations: Conflict and Collaboration, Cambridge University Press, Cambridge, 2009, doi: 10.1017/CBO9780511626708 . [10] P. Dauvergne , G. LeBaron , Protest Inc.: The corporatization of Activism, Polity Press, Cambridge, 2014 . [11] N. Banks, D. Hulme, M. Edwards, NGOs, states, and donors revisited: still too close for comfort? World Dev 66 (2015) 707–718, doi: 10.1016/j.worlddev.2014.09.028 . [12] C. Egger, D. Schopper, Organizations involved in humanitarian Action: introducing a new dataset, Int. Stud. Quart. 66 (2022) sqac009, doi: 10.1093/isq/sqac009 . [13] D. S Olawuyi , The emergence of right-based approaches to resource governance in Africa: false start or new dawn, Sustain. Develop. Law Policy 15 (2015) 13–27 . [14] R. Koopmans , D. Rucht , Protest event analysis, in: B. Klandermans, S. Staggenborg (Eds.), Methods of Social Movement Research, University of Minnesota Press, Minneapolis, 2002, pp. 231–259 . [15] S. Hutter, Protest event analysis and its offspring, in: D. della Porta (Ed.), Methodological Practices in Social Movement Research, Oxford University Press, Oxford, 2014, pp. 335–367, doi: 10.1093/acprof:oso/9780198719571.0 03.0 014 . [16] M. Kousis , M. Giugni , C. Lahusen , Action organization analysis: extending protest event analysis using hubs-retrieved websites, Am. Behav. Scient. 62 (2018) 739–757 . [17] S. Jones , Memory relations. Cross-border collaboration between mnemonic actors in Germany, Central Eastern Europe, and the MENA region, Revue d’études Comparatives Est-Ouest 2 (2020) 225–259 . [18] M.M. Hughes, P. Paxton, S. Quinsaat, N. Reith, Does the global north still dominate women’s international organizing? A network analysis from 1978 to 2008, Mobilization 23 (2018) 1–21, doi: 10.17813/1086671X2311 . [19] S. Zajak , A.-M. Nikolas , G. Gortanutti , J. Lauber , Codebook for a website-based network analysis of the cooperation between trade unions and social movement organisations in Europe, Ruhr-Univ. Bochum (2018) . [20] Global Civil Society Database, Union of International Associations, 2022. https://uia.org/ybio/