Abstract
This article introduces a pioneering dataset from a survey of civil society organizations (cso s) in the metropolitan region of Vienna, Austria. The survey was conducted between October 2019 and December 2020 and provides a comprehensive overview of the current state of the civil society sector in Vienna. It comprises a representative sample of 358 cso s and an additional targeted sample of 235 large cso s. The anonymized dataset is stored at the Austrian Social Science Data Archive (aussda). It can be freely accessed after the end of the embargo period in May 2025. The survey includes more than 60 questions covering a wide range of topics, including organizational goals and activities, beneficiary and staff demographics, different forms of organizing and related practices, performance metrics, budgeting, funding sources, and collaborative efforts. The dataset is a valuable resource for scholars interested in studying the inner workings, relationships, and societal contributions of civil society organizations, and it appeals to a variety of scholarly debates.
- –Related data set “Civic Life of Cities: Survey of Civil Society Organizations in Vienna, Austria (suf edition)” with doi www.doi.org/10.11587/UZ3B4D in repository “Austrian Social Science Data Archive (aussda)”
1. Introduction
This article introduces a novel dataset that originates from a survey of civil society organizations (cso s) in the metropolitan region of Vienna, Austria. Carried out between October 2019 and December 2020, the survey provides unique insights into the civil society landscape of a region that covers a cosmopolitan urban core as well as suburban and rural parts (see Figure 1).1 The dataset encompasses a representative sample of 358 cso s and an additional targeted sample of 235 large cso s. The anonymized dataset is stored at the Austrian Social Science Data Archive (aussda) and can be freely accessed after the end of the embargo period in May 2025.
The Vienna metropolitan region
Citation: Research Data Journal for the Humanities and Social Sciences 9, 1 (2024) ; 10.1163/24523666-bja10052
Note: Data stem from kdz Center for Administrative Research, 2019. The orange area represents the urban core, while the turquoise areas depict the suburban and rural regions, all included in the sample.Vienna’s civil society sector is notable for its turbulent history, with complex power dynamics resulting in a blend of social democratic and corporatist elements. The sector is characterized by substantial government funding, influential service and advocacy cso s, extensive volunteer involvement, and efforts to promote integration among diverse ethnic and religious groups, including those tied to the Catholic Church, political parties, global organizations, and institutional entrepreneurs challenging the corporatist status quo (Maier et al., 2022). Moreover, as a global city, Vienna represents a European model of welfare and urban governance similar to countries like Germany and Switzerland also known for cooperative relations between cso s and government agencies, and Scandinavian countries with strong social-democratic elements.
The dataset presented here stems from a comprehensive cross-sectional survey designed to capture a broad spectrum of organizational objectives and practices, thus, offering insights into the complex tapestry of civil society in the Vienna metropolitan region. The questionnaire, consisting of over 60 questions, was structured around key areas of interest, including organizational goals, funding sources, workforce and beneficiary demographics, decision-making, performance metrics, and collaborative activities, all of which illuminate the operations and societal roles of cso s.2 Beyond its local context, this dataset is a resource for scholars in social and political sciences, organization studies, and urban studies interested in investigating civil society organizations, their interactions with the social environment, and their societal contributions.
2. Problem
The dataset presented here originated from our desire to generate more empirical evidence on the contentious issue of cso s using practices usually associated with the corporate world (Maier et al., 2016; Suykens et al., 2019), specifically, contributing to the discussion on how a business-like form of organizing, (i.e., managerialism—in contrast to professionalist, democratic, and other forms of organizing), and business-like funding (i.e., commercial funding—in contrast to philanthropic funding, membership fees, and government funding) affect the societal contributions of cso s.
The belief that organizations should (Hvenmark, 2013) rely on the knowledge and practices of business management and employ professional managers to survive in a competitive and demanding environment (Salamon, 2012) gained momentum in the 1990s and 2000s. It has since been widely recognized by civil society scholars, resulting in a variety of conceptualizations such as marketization (Eikenberry, 2009), professionalization (Salamon, 1999), organizational rationalization (Hwang & Powell, 2009), becoming more ‘business-like’ (Maier et al., 2016), and managerialism (Hvenmark, 2013; Maier & Meyer, 2011). Scholarly debate on the social implications of this trend has only recently shifted from a somewhat Manichean—good or bad—perspective, often based on small-scale qualitative case studies (Backman & Smith, 2000; Eikenberry, 2009; Eikenberry & Kluver, 2004; Hustinx & De Waele, 2015; Hvenmark, 2013; Sanders, 2012; Skocpol, 2004), to a more nuanced approach that incorporates both qualitative and quantitative research designs (Beaton et al., 2021; Corple, 2023; Hersberger-Langloh et al., 2021; Sandberg et al., 2020; Suykens et al., 2019, 2023). This rich tradition of theoretical discussion and growing empirical evidence provides a foundation for situating the current dataset.
The dataset holds the potential to empirically investigate key questions such as: What drives the adoption of managerial practices and commercial funding in cso s? How do managerial practices, along with reliance on both mission-related and mission-unrelated commercial funding, influence the societal roles or performance of cso s? Furthermore, the data allow for the exploration of tensions and synergies between commercial income, managerial practices, and forms of organizing that are more traditional for cso s, such as democratic governance and the involvement of members and volunteers.
Beyond the immediate organizational context, this dataset offers an opportunity to examine the broader interactions between cso s and their urban and wider social environment (e.g., Brandtner, 2022; Karner et al., 2023). For instance, the dataset enables researchers to study how cso s contribute to building social capital (e.g., Bradshaw & Fredette, 2012; Foster & Meinhard, 2015); what factors drive various forms of collaboration with other organizations, government, and the private sector; and the determinants and consequences of cso demographics and constituent relationships, including their representational capacity (e.g., Rolf et al., 2022).
3. Sampling and Data Collection
The dataset stems from a comprehensive cross-sectional survey conducted between October 2019 and December 2020, which covers civil society organizations in the metropolitan region of Vienna, Austria. This region is home to approximately 2.6 million people living in three federal states (Vienna, Lower Austria, and Burgenland) and 211 municipalities. The region consists of an urban core zone (with a high density of residents and working population and a high number of inhabitants) and outer zones (adjacent administrative units with close socioeconomic ties to the urban core, as evidenced by commuting patterns; for exact criteria, see Statistik Austria, 2016). The geographic area was defined following the definition by Wonka and Laburda (2010) of ‘Stadtregionen’ (German for city regions) and using the data from 2019 according to kdz Center for Administrative Research (2019).
As the eligible population for our sampling, we targeted self-governed private organizations with a full formal restriction on the distribution of profits and with non-compulsory participation, thus following the guidelines for the definition of civil society organizations outlined by Salamon and Sokolowski (2016). We identified this population by using the Austrian Register of Associations (Vereinsregister) to find all associations in the region, and by screening the Austrian Register of Companies (Firmenbuch), applying the criteria outlined by Salamon and Sokolowski (2016) to identify all civil society corporations and cooperatives. Both registers were accessed through Compass Verlag llc. In 2017, when we drew the first sample, the region housed approximately 22,000 associations, 282 nonprofit corporations, 29 nonprofit cooperatives, and 121 nonprofit foundations, equating to one cso per 116 residents. Foundations were not included, since charitable foundations in Austria are mostly purely grant-making (Millner, 2024), and the survey focused on operative civil society organizations.3
Sampling proceeded in two steps: first, we drew a random sample from the entire population. According to a prior estimation based on data from Neumayr et al. (2017, p. 289), we expected that the majority (~90%) of cso s in the region would be small organizations with an annual budget of less than €25,000 and an all-volunteer staff. Survey data from the first sample confirmed this expectation—small cso s predominated in the region (see Table 5). The first sample yielded 358 completed questionnaires, with an average response rate of 50.3% from the effective sample of 712 organizations. To assess the representativeness of the sample, we compared the distribution of fields of activity (we manually assigned cso s to one field of activity as defined in the International Classification of Nonprofit Organizations, icnpo; Salamon & Anheier, 1996) of the sample with a recent semi-automatic classification (developed by Litofcenko et al., 2020). The Pearson chi-square test showed no significant sample deviation from the population structure (at the 95% significance level). Furthermore, a Pearson chi-squared test showed no significant deviation between the geographical distribution of cso s in the sample and the population. To assess this, we used Austrian zip code regions (Postleitgebiet), indicated by the first two digits of the Austrian zip code system. Thus, the sample can be considered representative in terms of activity areas and geographical distribution.
Due to the predominance of small cso s in the region, we drew a second random sample of cso s, specifically targeting organizations with an annual budget of €25,000 or more. The primary purpose of this additional sample is to facilitate analyses of relationships at the organizational level (e.g., how the use of managerial practices relates to the societal roles cso s prioritize, see Terzieva et al., 2024). At the organizational level, size can be an important additional influence and, thus, a necessary control variable. Moreover, the skewness of the first sample towards small organizations would considerably limit statistical power. Since information about the budget size or other size indicators for the total population of cso s is not publicly available in Austria, we had to draw on a non-representative sample. Specifically, we used the business database provided by Herold Business Data llc in collaboration with ksv1870 as a sampling frame. This database, unlike the Register of Associations, the Register of Companies, or any other publicly available database, provides information on the annual budget of organizations. However, since the original purpose of this database is to provide addresses for targeted marketing, cso s with a below-average financial credit rating—compared not only to nonprofits but also to for-profit companies—were excluded. As the information on budget size is collected by ksv1870 using telephone self-disclosure, we could only include organizations that had provided such information. Therefore, the additional sample should not be used for descriptions at the sectoral level. If used for analyses at the organizational level, the bias towards financially healthy organizations must be considered. Nevertheless, these additional data facilitate statistical analysis at the organizational level, investigating mechanisms where no systematic difference between more or less financially strong organizations is to be expected. In total, 235 of the 405 large civil society organizations contacted responded to the survey (58% response rate). The representativeness of the data collected in this second step could not be assessed in the same way as in the first step due to a lack of data on the fields of activity and geographical distribution in the population of large cso s. All large cso s included in the first sample were excluded from the second sample.
In total, 593 cso s completed the survey, with an average response rate of 53% from the effective sample of 1,117 organizations (see Table 1). The survey specifically targeted the organizations’ top leaders, such as executive directors or presidents. To initiate the survey process, we first mailed hand-signed letters of invitation. After two weeks, we began contacting and reminding potential respondents via telephone and email. Most respondents completed the survey online and approximately one-fifth requested to complete the survey over the phone or in person with a researcher (see Table 1).4 The survey was available in German and English, with almost all respondents (98.7%) opting for German. Extensive descriptive analyses of the survey data have been published by Maier, Meyer, and Terzieva (2022).
To ensure the quality of the survey, pretests were conducted with a convenience sample of cso leaders known to the research team. The purpose of these pretests was to reduce the number of items, evaluate the clarity of the questions, and assess how each question/item was interpreted by respondents. Once data were collected, the data cleaning process (using ibm spss) focused on improving the quality and accuracy of the dataset by identifying and addressing any incomplete or erroneous data points. First, dropout cases, cases with missing crucial data such as the organization’s name or funding sources, or duplicate entries were omitted from the dataset. Missing data for other variables was minimal; no values were imputed for those missing data. Next, each thematic block of the questionnaire was cleaned separately. During data cleaning, plausibility checks were performed to check for inconsistent within-survey responses. For instance, questions with an open-ended option (e.g., ‘other, namely’) were reviewed to check if the information provided was covered by the given options. If this was the case, we recoded the response into the respective variable. We also conducted an external plausibility check for variables measuring the demographic composition of beneficiaries and the workforce. In these questions, respondents were asked to imagine 10 typical workforce members or beneficiaries and provide information on their age, gender, and first language. Several respondents appeared to have misunderstood the scale, particularly mixing up the percentages of German-speaking and non-German-speaking workforce members. To correct this, we reviewed the organization’s website for relevant information and, where appropriate, reversed the mixed responses. Throughout the data cleaning and transformation process, we preserved the original variables as they appeared in the raw data file. All modifications and corrections were carried out exclusively on copies of the original variables.
To further expand the analytical scope of the dataset, we added some indicators: Using desk research, we manually assigned each organization to a main field of activity, following the International Classification of Nonprofit Organizations (icnpo; Salamon & Anheier, 1996; see Table 2). Using the Austrian Register of Associations, we determined the registered address of each participating organization and assigned it to a corresponding district code. This categorization allows us to distinguish between urban, suburban, and rural organizations. Exact geographic locations are not included in the dataset to ensure the anonymity of participants. In addition, we used registry data to add the age of each organization. If this information was not available in the registry, we used the organizations’ websites to obtain this information. The websites were also used to determine the religious and political affiliation of the organization.
Finally, we acknowledge several limitations of the dataset. The dataset is confined to the 2019/20 survey period and the particular geographical location. Furthermore, it mostly relies on a single data source, which could introduce key informant bias. To address this potential bias, the survey focused on objectively verifiable questions rather than subjective judgments and spread topics across different parts of the questionnaire. The availability of both a German and an English version of the questionnaire aimed to reduce the language barrier for Vienna’s sizeable immigrant community and, thus, increase the response rate and validity of answers from this group. However, it may have also introduced a language bias. The subsample of large cso s excludes organizations with a below-average credit rating and organizations without self-reported budget information.
4. Data
- –Civic Life of Cities deposited at aussda—doi: www.doi.org/10.11587/UZ3B4D
- –Temporal coverage: October 2019-December 2020
This section zooms in on important descriptive characteristics of the surveyed organizations, distinguishing between the two samples and the full dataset in each table. Table 4 shows the distribution of cso s according to their main field of activity. Most cso s in the region are membership organizations, as shown in Table 3, and rely heavily on volunteer labor (see Table 4). Table 5 and Table 6 provide some financial information, while Table 7 shows the prevalence of managerial practices.
5. Concluding Remarks
A major challenge in researching organizations is the need for and difficulty of collecting quantitative data on a large scale at the organizational level, as most empirical studies rely on small samples and case studies that examine one or a few organizations. While conceptual and qualitative studies remain vital for theory development, larger datasets enable us to test and refine these theoretical frameworks. With its detailed, organization-level data, the presented dataset provides a unique resource for understanding the multifaceted nature of cso s in a contemporary urban context and an increasingly market-oriented environment. It allows for an in-depth exploration of organizational structures, practices, funding, and societal roles shaping the civil society landscape in the metropolitan region of Vienna. Given the rich context of Vienna’s unique blend of social democratic and corporatist elements, this dataset also invites comparative studies across regions and countries. By combining this dataset with other data, researchers can explore how cso s differ across political, social, and economic environments. For civil society, nonprofit management, and urban studies scholars, the dataset offers opportunities for a wide range of research projects and facilitates a more nuanced and data-driven debate about the workings, challenges, and contributions of civil society organizations.
Acknowledgements
Data collection was made possible by grant P 33229 from the Austrian Science Fund (fwf).
Conflict of Interest
The authors have no competing interests to declare.
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The survey data were collected as part of a global research collaboration called the Civic Life of Cities (clc) Lab, involving seven urban centers: San Francisco, Seattle, Shenzhen, Singapore, Sydney, Taipei, and Vienna (for an overview of the purpose and history of the global project, see Brandtner & Powell, 2022). These cities employed a common core questionnaire, allowing researchers to explore the commonalities and disparities among civil society organizations in different social, political, and cultural contexts.
The full questionnaire is available at the aussda repository (upon registration), along with a detailed codebook that breaks down all variables included in the dataset, ensuring transparency and facilitating reproducibility in future research.
This sampling decision was made in accordance with sampling in other regions covered by the Civic Life of Cities Lab.
We employed a tiered approach to maximize the response rate, starting with the least resource–intensive option of an online survey. For respondents who were reluctant to participate online, we progressively offered a phone or video interview, or even a face–to–face interview as more personalized and resource-intensive options. Hence, no meaningful response rates for separate survey modes can be calculated.
