The IAB/ZEW Start-Up Panel

1.1 Motivation and Objectives

The formation of new companies plays an essential role in driving innovation, regional competitiveness, and economic growth (Acs and Audretsch 1988; Audretsch and Peña-Legazkue 2012; Haltiwanger, Jarmin, and Miranda 2013; Haltiwanger 2022). New businesses contribute to the development of innovative products and services and foster the diffusion of novel technologies (Audretsch et al. 2016). Importantly, radical innovations are more likely to be implemented by newcomers rather than established firms as the latter tend to be more path-dependent in existing technologies (Acs and Audretsch 1988; Yin and Zuscovitch 1998; Schneider and Veugelers 2010; Chapman and Hottenrott 2022). Besides their own innovation activities, new firms thus contribute to innovation ecosystems in multiple ways: They produce knowledge spillovers through engaging in trial and error, they exercise pressure to innovate on incumbents and thereby increase other firms’ incentives to invest in Research and Development (R&D), and they create jobs that diversify local labor markets (Henderson 1993; Schneider and Veugelers 2010; Haltiwanger, Jarmin, and Miranda 2013). In addition to driving innovation and economic growth, young firms provide a valuable context for studying labor dynamics, human resource practices, and strategic decision-making. Their flexible structures and resource constraints offer insights into how organizations adapt, manage talent, and formulate strategy under uncertainty (Berman, Schallmo, and Kraus 2024; Brixy and Murmann 2025; Grimpe, Murmann, and Sofka 2019).

However, researchers and policymakers often know little about the activities, successes, and failures of newly founded companies. Especially the first couple of years of a firm’s life cycle are decisive for the trajectory that the company follows. Founders and their characteristics are extremely relevant to understanding a company’s development in these early phases. Young firms develop and change fast, thus, understanding activities and impacts requires longitudinal information that allows tracking these developments as they happen. Yet, most young companies are not subject to public financial reporting and are underrepresented in classical (socio)economic surveys. Information about founders, teams, financing structures, and innovation-related activities is not easily observable. Hence, researchers face the challenge of finding reliable information on newly founded businesses.

The objective of the IAB/ZEW Start-Up Panel is to address this gap by offering representative, longitudinal, and reliable data on young companies in Germany. Its primary goal is to provide this information for both research purposes and for deriving policy advice. The panel focuses on capturing companies early in their life cycle to gather detailed insights on the founding team, founders’ motivations, the founding process, financial resources, and factors influencing the company’s development from its first year onward. With its focus on newly founded companies, the IAB/ZEW Start-Up Panel adds to population survey initiatives such as the Global Entrepreneurship Monitor (GEM) or the KfW Entrepreneurship Monitor that aim at measuring entrepreneurial intentions in the general population and additionally have a focus on self-employment.^[1]

This is a unique initiative in Germany to not only capture start-up activities on a cross-sectional basis using one-time surveys but to establish a longitudinal data resource that studies start-up trajectories continuously over time. Internationally, a few similar initiatives had been started but have been discontinued after some years. The value of such a data resource, however, increases with time as it allows to study new businesses over the business cycle, in different political contexts, and in changing institutional environments. The Kauffman Firm Survey (KFS) is a well-known panel study of U.S. businesses founded in 2004 and tracked over their early years of operation, through 2011.^[2] The KFS focuses on the nature of new businesses, characteristics of the firms’ strategies, products and services, financial and organizational arrangements, and employment patterns. This data set is related to several further data sources, such as the Annual Survey of Entrepreneurs (ASE), which provides snapshots of selected economic and demographic characteristics of firms and business owners in 2014, 2015, and 2016.^[3] This survey, conducted by the United States Census Bureau, is the largest survey of entrepreneurs in the United States so far and supplements the Survey of Business Owners (SBO), which had been discontinued in 2012.^[4] Another comparable initiative has been started in Italy with the Research on Entrepreneurship in Advanced Technologies (RITA) database, developed at Politecnico di Milano. The RITA database was created in 1999 and was updated and extended in 2002, 2004, and 2008. It contains detailed information on Italian new technology-based firms collected via firm-level surveys.^[5]

The IAB/ZEW Start-Up Panel augments survey-based data sources for the business sector that focus on established legally independent companies such as the KfW SME Panel or the Mannheim Innovation Panel (MIP).^[6] In contrast to the IAB/ZEW Start-Up Panel, these panels do not cover start-ups since they focus on established firms and typically do not collect information on individual founders or owners. It also adds to the establishment panel of the German Federal Employment Agency (IAB Establishment Panel)^[7] that only observes establishments (not legally independent companies) if they have at least one employee who is subject to social insurance contributions. During the start-up period, a large share of firms do not meet this requirement.

1.2 History of the Panel

The panel was established in 2008 by the Leibniz Centre for European Economic Research (ZEW) in collaboration with the Kreditanstalt für Wiederaufbau (KfW), and Creditreform, Germany’s largest credit rating agency.^[8] It is designed as an annual survey that addresses approximately 6,000 young companies per year. In 2014 and 2015, the survey continued without the KfW as the Mannheim Start-up Panel with some adjustments, i.e. a changed industry composition (the trade sector and personal consumer-oriented services were no longer part of the sample, see Section 2.1), thus reducing the number of surveyed ventures to around 5,000 per year. Since 2016, the panel has been conducted in collaboration with the German Institute for Employment Research (IAB) as the IAB/ZEW Start-up Panel, reinstating broader sector inclusion (including the trade sector and personal consumer-oriented services again). During the COVID-19 pandemic, the survey experienced a structural break. Due to the severe lockdowns and restrictions, as well as the turbulences experienced by many companies in Germany during 2020, the survey was conducted in a shortened fashion, and a smaller number of firms were included in the survey. This implies a break in the time series for some indicators, such as annual R&D expenditures or information on external financing sources. In total, until 2024, the panel has surveyed 40,000 firms and their founders, yielding more than 140,000 firm-year observations. Figure 1 shows the annual number of companies surveyed between 2008 and 2024.

Figure 1:

Number of firms surveyed over time.

2.1 Stratification Design

The population of companies relevant to the IAB/ZEW Start-up Panel is drawn from the Mannheim Enterprise Panel (ZEW-MUP). The ZEW-MUP is a panel database on economically active organizations in Germany, which is set up and maintained by the ZEW. The basic information in the ZEW-MUP comes from Creditreform. It contains information on the population of companies in Germany (roughly nine million companies). This includes about 3.1 million currently active companies, as well as companies that have been deregistered or closed down. It also contains information on educational and research institutions, provided they have a tax number. The information in the MUP includes selected structural key figures (contact and ownership information, date of foundation, legal form, location, sector of economic activity, i.e. industry), which can be used to facilitate stratified sampling.^[9]

The ZEW-MUP maintains panel information on ‘economically active’ companies, defined as those either registered in the commercial register, using external capital, trade credit, or other financing sources, or demonstrating significant economic activity, such as maintaining extensive customer relationships. Consequently, the IAB/ZEW Start-up Panel initially adopts a broad definition of a start-up, encompassing any independently formed new business. The panel intends to track the development of newly founded firms for the first critical stage of the life cycle (Van Praag 2003). Thus, companies enter the panel at a maximum age of three years and are contacted in subsequent years until they reach the age of seven years.^[10] Empirical evidence on firm survival indicates that hazard rates, i.e. the probability of a firm’s market exit, peak when firms are between two and four years old. However, for some industries, that peak is delayed for up to seven years. By covering these first seven years of a firm’s life, the panel aims to capture the critical early years, which are characterized by a particularly high risk of failure before the hazard rates stabilize to levels similar to those of incumbent firms (Van Praag 2003; Brüderl, Ziegler, and Preisendörfer 2019; Agarwal and Audretsch 2001).

To ensure generalizability, the sample is stratified by founding year and industry.^[11] The companies in the IAB/ZEW Start-up Panel are classified into 11 industry classes based on the WZ-2008 (NACE Revision 2) definition of economic activities.^[12] See Table 1 for an overview of the industry classification and aggregation of WZ 2008 codes to industry classes. Start-ups from high-technology industries are expected to play a particularly significant role in driving innovation, structural change, and job creation (Schneider and Veugelers 2010; Audretsch et al. 2016; Fudickar and Hottenrott 2019). To reflect this importance, around 40 % of the firms included in both the gross and net samples operate in high-technology industries. Within the total population, only about 7 % of the firms (and start-ups) belong to the high-tech sector. This oversampling of high-tech start-ups allows for more detailed analyses of these firms and enables more meaningful comparisons between start-ups from different sectors as compared to a fully random sampling approach that could result in too few observations from such industries. Consequently, the IAB/ZEW Start-up Panel includes almost all high-technology manufacturing firms recorded by Creditreform in its gross sample that fit the age constraint. Coverage rates for other sectors are necessarily smaller, although the panel still collects data on a substantial number of start-ups from these industries.

The 11 industries are composed as follows: The high-tech sector is divided into cutting-edge technology and high-technology in manufacturing, as well as technology-intensive services and software. Cutting-edge technology includes those manufacturing sectors that exhibit an average research and development intensity of over 7 %. High-technology encompasses manufacturing sectors with an average R&D intensity of between 2.5 % and 7 %. Low- and medium-tech industries include non-technology-intensive sectors in manufacturing, knowledge-intensive service providers, business-related service providers, creative consumer-oriented service providers, other consumer-oriented service providers, the construction industry, as well as retail. Figure 2 shows the composition of industries and the oversampling of high-tech industries. The chosen sampling design ensures a representative data source for all young firms in Germany – controlling for industry and founding years – and offers the possibility to address research questions focusing on firms on the technological frontier driving innovation, structural change, and job creation.

Figure 2:

Industry composition of the IAB/ZEW start-up panel.

It is important to note that the IAB/ZEW Start-up Panel started with 10 industry groups, where creative and other personal consumer-oriented services were aggregated into consumer-oriented services. In 2014 and 2015, the industries were adjusted for the first time.^[13] During this period, the trade sector was not surveyed, and the consumer-oriented services were divided into creative consumer-oriented services on the one hand and other personal consumer-oriented services on the other. Only creative consumer-oriented services were integrated into the survey sample in these years. However, starting in 2016, trade and other personal consumer-oriented services were added to the survey sample again, which resulted in the 11 industry groups presented above.

Since 2015, the sample of the IAB/ZEW Startup Panel has been supplemented by an additional sample for the state of Baden-Württemberg. The same sampling design as described above is used. Approximately 570 companies from Baden-Württemberg are added to the initial survey each year. In 2019, an additional sample for the state of North Rhine-Westphalia was introduced analogously, with approximately 830 companies surveyed each year. Both additional samples were paused during the COVID-19 pandemic in 2020. The subsamples expand the panel, offering the possibility for meaningful regional analyses.

To make meaningful statements about the overall population of young companies in Germany, the described stratification design must be taken into account. The IAB/ZEW Start-up Sample includes weighting factors that allow for extrapolations both to the founding period and to the entire panel.

2.2 Interviews

Survey data is collected through computer-assisted telephone interviews (CATI). While the survey questions are developed by the partner institutions IAB and ZEW, the interviews are conducted by the survey company uzbonn. On average, each interview lasts approximately 25 min. Interviewees must be part of the founding team or involved in the management of the firm. The field phase typically spans from spring to autumn, lasting up to six months. Companies are contacted multiple times during this period.

Since 2023, respondents have also been offered the option to complete the survey in a written format online. Information regarding the scheduling of telephone interviews and login credentials for the web-based survey is provided through both postal mail and email. In 2023, 21 % of respondents chose the online option, and in 2024, it was 18 %. The share only slightly differs among respondents who have participated in the survey before and those contacted for the first time. In 2024, 15 % of the initial survey was conducted online versus 19 % of the follow-up survey, in 2023, 28 % and 21 %, respectively. Interestingly, the firms’ response rate in the follow-up survey in 2024 was higher among firms that conducted their initial survey in 2023 online (around 4 percentage points). On average, response rates range from approximately 25 %–44 % for companies contacted for the first time, while companies that have previously participated in the panel exhibit higher response rates, averaging at 64 %. Section 2.4 discusses non-response bias and panel attrition in more detail.

2.3 Structure of the Survey

The survey is structured as a panel survey, with distinct questionnaires for initial interviews and follow-up interviews in subsequent years. Table 2 gives an overview of the topics addressed in these interviews.^[14] The first-time questionnaire begins with a series of screening questions designed to identify independent start-ups and confirm the appropriate respondents. This includes checking whether the company is not older than three years, is an independent new venture, and belongs to one of the industries listed above. The questionnaire then includes detailed questions covering (mostly time-invariant) characteristics of the founders, their backgrounds, and details on the founding process. The panel questionnaire also starts with a screening process, again confirming an appropriate respondent and ensuring that the firm has not been acquired by another company. It then primarily focuses on business activities during the reference year, along with any changes in strategies, management decisions, and firm outcomes. It is shorter than the initial questionnaire, as questions related to the founding process are not repeated. However, the additional space is often used to incorporate questions into the survey to address specific topics of interest. These questions allow for a deeper exploration of emerging trends, industry-specific developments, or other areas of strategic importance. Table 3 lists these topics and the respective years they were included in the survey. The list is not exhaustive and only includes topics that have been addressed up to this point. The list of special interest topics will continue to evolve as the panel progresses.

As mentioned above, the regular survey routine of the IAB/ZEW Start-up Panel was disrupted in 2020 due to the COVID-19 pandemic. Beyond firms that were already included in the panel, the sample of firms entering the panel for the first time was restricted to those founded in 2019 (rather than firms founded in the previous three years, i.e. 2017, 2018, or 2019, as is usually the case), and special subsamples were suspended for that year. Instead of the usual initial and panel survey, two special surveys were conducted focusing on the effects of the COVID-19 pandemic on young firms in Germany. The first survey was carried out in spring 2020, followed by a second one in autumn 2020. In both surveys 4,000 companies were interviewed, 900 of those participated in the initial survey – all of them of the founding cohort 2019 – and 3,100 in the panel survey. Approximately 60 % of the 4,000 companies participated in both the spring and autumn surveys. The goal of this approach was to document and assess the economic impact of the crisis. Consequently, the variable set of the IAB/ZEW Start-up Panel was adjusted, and only some of the usual questions from the initial and panel surveys were included. This resulted in a disruption of the standard panel structure, caused by both the specific focus of these surveys and the challenges of contacting young firms and conducting surveys under pandemic conditions. As of 2021, the survey procedure returned to its pre-pandemic format.

2.4 Panel Attrition

The development of a panel dataset, such as the IAB/ZEW Start-up Panel, relies on firms participating in the annual surveys on a recurring basis. As of January 2025, the panel comprises a total of around 40,000 firms, with an average of 3.5 observations per firm. The average response rate for the initial survey is 34 %, while follow-up surveys achieve an average response rate of 64 %. Table 4 shows the average panel response rates for each of the 11 industry classifications.^[15] Response rates, and thus attrition rates, do not differ largely between industries.^[16]

Response rates are often considered an important quality indicator in survey research. Low response rates reduce sample sizes, decrease statistical power, restrict the applicability of advanced statistical methods, and raise concerns about the external validity of the sample (Rogelberg and Stanton 2007). For panel surveys response rates directly influence the survey’s ability to maintain its longitudinal structure. The annual response rates of firms in economic panel surveys vary and are influenced by multiple factors, such as survey design, follow-up efforts, the relationship with respondents, and the perceived relevance of the survey (Hiebl and Richter 2018). Surveys targeting owner-managers generally achieve lower response rates than those directed at individuals at lower levels of the corporate hierarchy (Pielsticker and Hiebl 2020; Hiebl and Richter 2018). In their meta-study Hiebl and Richter (2018) find that response rates for surveys – not panels – targeting family businesses lie at around 21 %. Comparable survey panels on established firms in Germany include the Mannheim Innovation Panel (MIP) with average response rates of around 26 % (ZEW 2025) or the Establishment Panel of the German Federal Employment Agency (IAB-BP) with an average response rate of 44 %, composed of 17 % for the initial survey and 73 % for follow-up surveys (Bächmann et al. 2023).^[17] As mentioned earlier, panel surveys focusing on entrepreneurs and young and small firms are rare, making it increasingly challenging to compare response rates. However, compared to studies on family businesses and panels on larger established companies in Germany, the IAB/ZEW Start-up Panel demonstrates a solid level of engagement, and the panel benefits from significant follow-up efforts.

Attrition in the IAB/ZEW Start-Up Panel is influenced by multiple factors. The first is typical panel attrition due to non-response – firms denying participation in follow-up surveys. Secondly, some firms may also exit the panel due to changes in their name, address, or phone number, making it impossible to contact them again. Thirdly, attrition at least partly results from firms exiting the market. Compared to more established businesses, young firms exhibit significantly lower survival rates. Market exits, in this case, include firms that formally file for bankruptcy, as well as companies that simply cease their business operations. The latter case might also include business acquisitions. Distinguishing between non-response and firm closure is typically a challenging task. However, the collaboration between the IAB/ZEW Start-up Panel and Creditreform enables access to closure information recorded in the Creditreform database. Creditreform’s decentralized organizational structure, with staff across more than 100 local offices possessing in-depth knowledge of their respective markets, significantly enhances the accuracy of determining a firm’s actual survival status. Comparing total attrition to information on firm survival shows that approximately 21 % of attrition can be attributed to firms exiting the market. Figure 3 shows the share of attrition due to firm exit over the lifetime of a firm in the survey. While in the second year, only about 7 % of the firms that participated in the first year but not in the second had exited the market, this share increases over time. In the last year, the share of attrition accounted for by exits reached 30 %.^[18]

Figure 3:

Share of panel attrition attributed to firm exits.

3.1 Firm Growth and Performance

Assessing the performance of newly-founded firms is a key challenge in entrepreneurship research. The IAB/ZEW Start-up Panel provides several indicators that capture ‘performance’ from various angles, such as financial performance, innovation, employee and sales growth, and more qualitative indicators, such as environmental outcomes.

Gottschalk, Muller, and Niefert (2010), for example, analyse the determinants of young firms’ initial size. They show that in addition to differences simply based on the sector of activity, start-up size is driven by firms’ entry strategies. Firms with entry strategies based on the exploitation of new market opportunities are initially larger while start-ups established for reasons of necessity start smaller. In addition, they show that founders’ human capital is an important predictor of start-up size.

Going deeper into founder profiles, Gottschalk and Niefert (2013) study the performance of start-ups over up to four years after foundation and find that female-founded firms perform worse for various performance indicators. However, they also document significant gender differences in many of the firms’ fundamental characteristics, which could explain this difference. Compared to male entrepreneurs, female entrepreneurs have a lower level of formal education, fewer years of professional experience, found in smaller teams, found more often out of necessity, and are more likely to be active in the retail, services, and low-tech industries – differences that explain parts of female entrepreneurial underperformance.

Looking at public start-up support as a driver of new venture success, Hottenrott and Richstein (2020) investigate the effects of participation in start-up support programs on the performance of start-ups in high-tech and knowledge-intensive sectors. By distinguishing between grants and subsidized loans they find that both grants and subsidized loans facilitate tangible investment, employment, and revenue growth. The results show that grants are better suited for R&D investments but that when loans are combined with grants, they contribute to higher innovation performance.

Linking external economic conditions and start-up performance, Brixy and Murmann (2025) study the question of whether firms founded during or outside economic crises have greater growth potential. They use the IAB/ZEW Start-Up Panel and the link to employer-employee data at IAB to show that young firms respond to cyclical conditions in highly heterogeneous ways. Their results reveal that the average new firm found it easier to hire its first employees when it was founded during the crisis and that these firms achieved counter-cyclical growth by hiring career entrants.

Moreover, Camarero Garcia and Murmann (2025) analyze how the potential duration of unemployment benefits affects whether new firms are founded out of opportunity or necessity and how this affects their growth potential. They document that longer potential benefit duration implies longer actual unemployment and is related to more necessity entrepreneurship and worse startup outcomes in terms of sales and employment growth. They explain this result by showing that a mix of compositional and individual-level duration effects plays a role in this outcome.

Utilizing information on digitalization in more recent years of the survey, Rodepeter, Gschnaidtner, and Hottenrott (2024) study the adoption of big data analytics (BDA) in young firms and analyze its impact on firm performance measures. The findings show that new ventures using BDA do not have an immediate advantage. Although BDA-adopting start-ups have higher sales, they also have a higher variability in sales, higher personnel costs, and a higher risk of failure. Conditional on survival, however, BDA-adopting firms show higher employee growth and are more likely to secure venture capital (VC) financing.

3.2 Innovation

Other studies – rather than considering firm performance more generally – have focused explicitly on the innovation success of new firms.

Brixy, Brunow, and D’Ambrosio (2020), for example, investigate how ethnic backgrounds that are relatively unlikely to occur, i.e. “unusualness” relates to innovation in newly founded firms. Their results show that unusualness has a positive association with start-up innovation. Likewise, looking at location factors as drivers of start-up performance, Ebert, Brenner, and Brixy (2019) study the effects of regional externalities on the survival of young firms contingent on the start-up’s innovation activities. They show that introducing market novelties is not necessarily beneficial for newly founded firms and might even endanger their survival. Moreover, they find that being located in spatial proximity to similar firms is important for start-ups in non-high-tech environments and has a positive influence on survival for less innovative companies. For high-tech start-ups, a diverse economic structure is more conducive to performance.

Making use of the special questions during the COVID-19 Pandemic, Hottenrott and Schoonjans (2024) explore the implications of economic crises on young companies. They analyse the strategic crisis management responses of start-ups while focusing on the role of decision-makers’ non-cognitive and cognitive traits and find that founders’ personality impacts the choice of crisis management strategy in the COVID-19 context. Moreover, they show that migration experience and education are positively associated with innovative crisis responses.

3.3 Financing and Investments

Several studies have analysed financing structures, decisions, and success in raising external capital. Achleitner, Braun, and Kohn (2011), for example, provide a comprehensive overview of financing structures and their firm- and founder-related drivers. They find that the pecking-order theory holds for new ventures in Germany but also show that in some cases individual motives lead to deviant financing patterns. Their findings also indicate that the extent of financing and the choice of capital sources are driven by a multitude of firm as well as owner characteristics.

A more recent study by Taglialatela and Mina (2024) also investigates new firms’ capital structure and explores how the pursuit of innovation influences firms’ reliance on different types of financing. The results show that innovation activities are a relevant predictor of start-ups’ revealed preferences for finance. However, they also show that the effects on the type and order of financing sources depend on the degree of information asymmetries related to R&D, the firms’ human capital endowments, and the market introduction of new products and processes. These results confirm that the traditional pecking order theory does not hold for all new firms.

Looking at gender differences in access to entrepreneurial finance, Lins and Lutz (2016) investigate whether access to venture capital for women entrepreneurs is more constrained than for men. The results indeed confirm the hypothesis of a gender gap with respect to external equity funding. They find that female entrepreneurs receive less venture capital than their male counterparts and that this effect is particularly strong in the case of entrepreneurs with a university degree and high R&D activity.

The data also allows for an investigation of sectoral differences with regard to financing. For example, Hottenrott, Lins, and Lutz (2018) examine the relationship between new ventures’ subsidy receipt and long-term bank loans. They find that subsidized young firms are more likely to use bank loans and to have larger shares in their financing mix from banks than non-subsidized firms. Their results further indicate that this effect is stronger in highly information-opaque sectors.

Berger and Hottenrott (2021) investigate how the receipt of VC by young companies relates to public start-up subsidies and whether the link depends on the type of VC. For a large sample of knowledge-intensive start-ups, they show that there is a strong correlation between subsidies and all sources of VC (Government VC, Independent VC, Corporate VC, and Business Angels) but that when they account for firm characteristics that drive both selection into public subsidies as well as into VC financing, subsidies are mainly linked to Government VC and Business Angel financing. Subsequent studies extend these insights. Combining information from the IAB/ZEW Start-up Panel data with applicant data from the investor subsidy program and adding ownership information from the ZEW-MUP database, Berger and Gottschalk (2025) show that angel investor grants encourage new investors to enter the risk finance market and that these investments positively affect the performance of firms.

Ayoub, Gottschalk, and Müller (2017), on the other hand, study the performance of academic spin-offs that received public funding from the German EXIST Business Start-Up Grant. Their results suggest that these start-ups are smaller and make higher losses than science-based entrepreneurial firms with comparable characteristics. The authors interpret these results to be driven by the financial contracting structure of the program compared to private venture capital funding and by the resulting adverse selection and incentive effects on the entrepreneurs.

Weuschek (2025) also looks at the effects of public start-up support. She analyses the financing situation of firms after receiving public funding and shows that public funding is associated with a significant reduction in the future probability of experiencing financial constraints. She differentiates between grants and subsidized loans and concludes that the former tend to improve relationships with equity investors and that subsidized loans or loan guarantees tend to improve relationships with external providers of both equity and debt capital.

Exploring whether and how founder personality links to entrepreneurs’ use of start-up subsidies and other sources of financing, Chapman and Hottenrott (2024) show that entrepreneurial orientation plays a mediating role in the relationship between the Big Five personality traits and start-up subsidies. They find, however, little evidence for a direct association between founders’ personality and subsidies. In addition, they show that personality is not associated with bank financing and borrowing from family and friends. In contrast to this, the patterns for venture capital financing are similar to those for subsidies.

Only few studies – so far – have focused on scaling activities and strategies in young firms. One such study can be found in Becker, Hottenrott, and Anwesha (2025) who investigate the impact of CEO personality on R&D and investment activities. In particular, they focus on R&D versus tangible investments and show that scaling decisions in entrepreneurial firms are strongly imprinted by the CEO’s personality.

3.4 Entrepreneurial Strategies

The panel data combination of panel questions and special interest topics also allows to investigate entrepreneurial strategies. Looking at spin-off companies, Fryges, Müller, and Niefert (2014) examine whether knowledge transfers from the mother company affect employment growth and post-entry innovation activities of spin-offs. They find that corporate spin-offs outperform other start-ups founded by former employees of incumbent private firms that are not based on an essential idea in terms of post-entry innovation activities.

The detailed information on founders facilitates the analysis of heterogeneity in strategy choice among start-ups. Lins and Lutz (2017), for example, examine differences in opportunity recognition between men and women and how family involvement influences this relationship. They find that women are indeed less likely to recognize entrepreneurial opportunities, that family involvement increases opportunity recognition for both sexes, and that this effect is significantly stronger for women.

Moreover, Gottschalk, Greene, and Müller (2017) investigate whether it is more difficult for habitual entrepreneurs to use their experiential knowledge than novice entrepreneurs. They find that new firms run by habitual entrepreneurs close just as quickly as those run by novice entrepreneurs. They also document that habitual entrepreneurs are just as likely as novices to experience bankruptcy.

Finally, Grimpe, Murmann, and Sofka (2019) investigate how establishing a middle management level relates to attention to innovation in start-ups. They find that middle management is positively related to introducing product innovations with a stronger effect when founders possess greater pre-existing knowledge and when the startup’s industry offers more innovation opportunities.