Gender and age structure of the Czech entrepreneurial activity: any trend changes?

Entrepreneurship researchers and scholars are continuously underlining the importance of studying entrepreneurship over time and across countries (Wennekers et al., 2002; Santos et al., 2017) as the development of entrepreneurial activity is subject to changes in external and internal factors, currently described within the entrepreneurial ecosystem framework (Stam, 2015; Wurth et al., 2022).

Recalling the recent global pandemic crisis (Žítek and Klímová, 2020; Galindo-Martín et al., 2021; Zemtsov et al., 2021; Behr and Storr, 2022; Monllor et al., 2024), followed by the Ukraine conflict and subsequent energy and oil crises (Zahra, 2022; Kuckertz et al., 2023; Shatila et al., 2025) serving as disruptive events or the ongoing adaptation of the United Nations’ sustainable development goals (SDGs) (United Nations, 2023), it is likely to assume that the order and the structure of entrepreneurial activity have been significantly affected and possibly changed due to these events and ongoing changes.

In response, the scholars began using the theoretical concept of polycrises and investigating how these disruptive events influence the structure and development of entrepreneurial activity (Tunçalp, 2025). Therefore, this study aimed to address this uncertainty by providing novel insights into the gender and age aspects of entrepreneurship, extending the current understanding of demographic shifts and gender dynamics in entrepreneurial activity. This goes hand in hand with the current call for scholars to better understand the heterogeneity of entrepreneurship, such as female and senior entrepreneurship, and their representations at the country level (Haataja et al., 2025; Martins et al., 2026).

The research efforts are focused on the Czech context, providing an example of development in a small Central European open economy with above-average European levels of entrepreneurship. It is a country with a significant gender gap in the proportions of males and females pursuing entrepreneurial careers (Večerník, 2011; Křížková et al., 2014; Dvouletý, 2019; Novosák et al., 2023). Yet, the previous studies relied more on cross-sectional survey results than on longitudinal population data, which did not provide information about the age and gender of entrepreneurs until recently. Furthermore, age and gender were identified by entrepreneurship scholars (Simoes et al., 2016) as significant determinants of entrepreneurial engagement. Notably, with the ongoing adaptation of the United Nations’ SDGs (United Nations, 2023), emphasis on achieving gender equality and decent careers promoting well-being across all ages, including the elderly population (SDGs 3, 5 and 10), it is a vital question, how and to what extent has the structure of the Czech entrepreneurial activity changed. Therefore, this study seeks to answer the following two research questions (RQs) related to Czech entrepreneurship development:

The study answers these RQs by exploiting quarterly data from the Czech Ministry of Industry and Trade representing registered entrepreneurs and valid business licences for 2016–2023, i.e. the longest available period. The authors use statistical analysis (mainly paired t-tests) and demonstrate the development of the trend changes to explore whether women’s participation in entrepreneurship has significantly increased over the studied period and whether the age structure has shifted. Findings from this research provide value for the Czech policymakers and political representatives, as the promotion of achieving SDGs has received a lot of attention and political priority (Hájek et al., 2011; Květoň et al., 2014; Hojnik et al., 2022). Thus, it is relevant to examine what has changed over the last few years and to provide policy recommendations if the observed changes are insufficient.

Furthermore, the study provides insights into the development of Central European entrepreneurship, where the Czech Republic serves as an example, illustrating the post-pandemic and post-crisis entrepreneurship development within the population data, representing all physical entrepreneurs and business licences, serving thus as an example to follow by researchers from other Central European countries (Ghura et al., 2020), such as Poland (Fritsch et al., 2022), Slovakia (Pilková and Holienka, 2017) or Hungary (Tumiwa and Nagy, 2021).

A literature survey by Simoes et al. (2016) provides an extensive overview of the factors shaping entrepreneurial engagement, dividing those into several categories, including basic individual characteristics, family background, personality characteristics, human capital, health condition, nationality, ethnicity and access to financial resources. Each of these factors represents a significant source of heterogeneity determining the uniqueness of entrepreneurs and self-employed persons, extensively researched by prior literature, offering theoretical underpinnings for each category, mainly at the individual level (Cowling et al., 2019). Yet, much less is known about their role at the meso and macro levels of entrepreneurship analysis (Kim et al., 2016), which is crucial for entrepreneurship policymaking (Cieślik and van Stel, 2024). In this way, the decades of individual-level studies serve as a rich source of theoretical approaches suitable for understanding entrepreneurial activity at the meso and macro levels (Dvouletý, 2018), which differ over time and across regions, as Freytag and Thurik (2007) explain. The following subsections provide a literature review of the key established concepts related to the roles of age and gender as the two identified determinants of entrepreneurship that this study focuses on.

Simoes et al. (2016, p. 786) conclude that the relationship between age and entrepreneurship is the inverse U-shaped, with a threshold (peak) in the interval from 35 to 44 years, depending on the country, continent or time. Lévesque and Minniti (2011), together with Zhang and Acs (2018), explain that the low skewing of entrepreneurial activity toward young (left) and old (right) individuals can be associated with the labour market decisions described within the work–leisure decision model and by the resource-based view theory. Based these theoretical concepts, at a young age, the individuals willing to start a business have a substantial capacity for generating business ideas and starting a venture, yet they often lack a sufficient amount of human, financial and social capital while in the older cohorts, despite having sufficient amount of capital already, are becoming more risk averse and experience higher opportunity costs of leaving their secured jobs and stable incomes and pursuing more risky entrepreneurial journey (Lévesque and Minniti, 2011; Minola et al., 2016). In this matter, Zhang and Acs (2018) conclude that policymakers should be informed about the demographic structure of the entrepreneurial activity, monitor its skewness and, if the activity is skewed too much toward old or young cohorts, introduce adequate incentives. These can be used as remedies to reach the market equilibrium, i.e. the optimal number of entrepreneurs providing goods and services in the economy, for example, through incoming foreign entrepreneurs starting their ventures in the country (Malerba and Ferreira, 2020).

From the perspective of the gender gap, the proportion of females in entrepreneurship is significantly lower than that of their male counterparts (Dean et al., 2019). The theoretical underpinnings that aim to capture this gender gap focus on the role of women in the family (including caring responsibilities), their participation in the labour market and the overall cultural and societal factors that differ across countries (Ojong et al., 2021; Kyrgidou et al., 2021). The most prevailing concepts include the feminist, discrimination and career theories, yet according to the most recent literature review by Corrêa et al. (2022), the most crucial agenda for the policymakers now includes capacity building and enhancing the availability of training programmes for women, which could possibly mitigate the gender gap in entrepreneurship, as also highlighted recently by Dvouletý et al. (2022), who observed the situation in the Czech Republic. The most recent review of existing literature was elaborated by Corrêa et al. (2024). The authors conclude that the research agenda should start by analysing the context of country-level entrepreneurship and its development, understanding the family–work situation of females, i.e. the work–family conflicts, support and the role of broader family members (Xiong et al., 2020), and offering motivational and training programmes for those females who are willing to start a new business. The existing evidence in the literature is, according to these studies, not sufficient to bridge this knowledge gap, requiring more examples of good practices and successful training programmes to reach successful (measured through performance indicators) entrepreneurship. Such a lack of available literature still calls for a more structured theoretical framework describing female entrepreneurship (Corrêa et al., 2024).

The section starts by describing the availability of the data and the feasibility of answering the stated RQs. The study is based on the quarterly data obtained from the Czech Ministry of Industry and Trade (2023). The data cover the period of the years 2016–2023, starting with the fourth quarter of 2016 and ending with the third quarter of 2023. Unfortunately, no older data reflect the gender and age structure, so this is the longest available time range at the time this research was crafted.

The authors work with the indicators reflecting the number of entrepreneurs, i.e. natural/physical persons and the number of valid business/trade licences within the studied time period. Combining these two approaches is helpful, as the data on physical persons do not account for the legal entities included in the latter indicators. Yet, with these data, we cannot capture situations when physical persons and legal entities hold multiple valid business licences, which is a severe limitation of this approach. One also needs to mention that the data on age and gender structures were provided separately by the Czech Ministry of Industry and Trade (2023). They cannot be combined together, e.g. into cross-tabulations. There is no possible way how to connect these data or to extract all single records of registered entities from the Czech business register, i.e. Registr Ekonomických Subjektů (RES) (Czech Statistical Office, 2023), in a simple/automatic way.

Furthermore, we cannot fully ensure that all listed entities remain economically active despite their valid licences, which is an identified limitation of this approach towards measuring entrepreneurial activity (Dvouletý, 2021; Henrekson and Sanandaji, 2020). Some entrepreneurs and organisations might thus still be officially active, but their actual economic activity is already closed, so in this way, the provided official records overestimate the actual size of entrepreneurship. On the other hand, the available records do not include individuals starting their entrepreneurial careers, i.e. nascent entrepreneurs who have not registered their businesses officially yet. The official statistical records will be missing these individuals (Iversen et al., 2008); nevertheless, this is a limitation that we need to acknowledge.

In the first step, we present summary statistics (mean, standard deviation, minimum, maximum) for all collected data in Table 1, i.e. physical persons and licences across gender and age categories. On average, there were about 2,041,426 entrepreneurs and 2,964,426 licences registered. This is considerably higher than the findings of Dvouletý (2019), who studied Czech entrepreneurial activity over the years 2005–2017 and pointed out the significant differences between registered entities and those with verified economic activity.

Using data from the European Union Labour Force Survey, Dvouletý (2019) concluded that the male-to-female ratio is 2.5 and that the highest representation of Czech entrepreneurs is in the age cohort of 40–44 years, representing 15.8% of all self-employed individuals. The records from the Czech Ministry of Industry and Trade (2023) align with these earlier findings. Figure 1 shows the development of entrepreneurial activity over time. We can observe a slightly increasing trend, proceeding even through the global COVID-19 pandemic (Dvouletý, 2021). The only significant decline is visible at the beginning of 2023, which might be associated with the compulsory set-up of digital communication with public sector authorities and offices, so-called data boxes (in Czech Datové schránky, c.f. Peníze.cz, 2023), that the Czech Ministry of Interior set-up for all registered organisations. This policy action, enhancing the digitalisation of the Czech public sector, is expected to at least partially clear out the business register for inactive entities (Rozczynski, 2022); however, it might also push out from the official activity some individuals lacking computer and digital skills (Kruncl, 2023).

Figure 1 also clearly illustrates the gender differences, which we separately study in Figure 2, showing the gender gap, i.e. the ratio of male to female entrepreneurs (and licences) and its development over time. The gender gap was more or less stable over time, with a slight decreasing trend, which significantly changed its direction and rapidly grew into an increasing one at the beginning of 2023. Statistically speaking, the average ratio was lower compared with physical persons, i.e. 1.8 (1.8 in 2017, 1.7 in 2022), compared with the records of licences being 2.0 (2 in 2017, 2 in 2022), which means that the Czech entrepreneurial activity is still considerably male-driven (Procházka, 2016; Dvouletý, 2021; Hamplová et al., 2021).

In Figure 3, we offer insights into the age structure of the Czech entrepreneurial activity. The picture is almost identical for physical persons and licences. Interestingly, the shape is very much in line with the entrepreneurship and age nexus, documented in the individual-level studies, summarised, for instance, in the literature review article written by Simoes et al. (2016) or in an extensive analysis by Cowling et al. (2019). The age proportions beyond the age of 18 are meagre (13 persons on average based on a particular legislation process), increasing with age up to a peak in the cohort 41–45, constituting a share of 14.1% (14.5% if we look at licences) and then it declines with the elder age up to the retirement and post-retirement lifetime. Surprisingly, we calculate the percentage shares of individuals engaged in entrepreneurship after the official retirement age, i.e. 65 years; we end up with a pretty significant number, making 12% of the entrepreneurial population, i.e. 11.3% of all licences, which illustrates the economic activity of the elderly population.

In the following analysis, we study whether women’s participation in entrepreneurship has significantly increased over the studied period and whether the trend in the age structure has shifted after the COVID-19 pandemic. Our methodology relies on two main parts. The first part relies on conducting a series of paired t-tests that allowed us to split the studied period into two similarly long time windows (Sachs, 2012). The initial period starts with the fourth quarter of 2016 and ends by the fourth quarter of 2019, and the latter one covers the first quarter of 2020 and ends in the third quarter of 2023. We chose the second period to be slightly longer to be able to mitigate significant changes beginning after the beginning of the year 2023, yet our research team notes that we tried to run the above-mentioned paired t-tests even by excluding the year 2023 as a possible outlier, i.e. the second period ended up by the fourth quarter of 2022 in this robustness check, and the provided results very similar to those presented in this section. In the second part of the analysis, we conduct a time-series regression analysis controlling for the trend and seasonal developments to further support the results provided by the t-tests and enhance the robustness of the analysis.

Given the relatively limited number of statistical observations, we chose to set the decision-making threshold at the 5% level of statistical significance to interpret the results. As explained in the article’s previous section, we test the indicators reflecting both physical entrepreneurs and valid licences to obtain the most conclusive findings. The results obtained from the statistical testing are presented in  Appendices 1,  2 and  3. Table 2 summarises all statistically significant results in a more structured, reader-friendly manner. Table 3 reports the regression results.

As already explained, in Table 2, one may follow a summary of the statistical testing, including results concerning the gender gap ratio. Given the nature of our research question, expecting the higher inclusivity of females in Czech entrepreneurship, we tested the difference between the two periods. By interpreting the test results (for details, see  Appendix 1), we find, at the 5% level of statistical significance, that in the second period, the ratio of female entrepreneurs (persons) was slightly lower compared with the first period, but we were unable to prove this for the proportion of male to female licences.

We further comment on the changes in the age structure of the Czech entrepreneurs and the valid licences. Unfortunately, the research team could not work with the age category continuously and merge some of the age categories. Instead, we had to work with the provided five-year categories, which resulted in very complex testing across all age categories, from less than 18 years old to the 85 years and above. The logic of statistical testing assumed changes between the two periods, and the detailed reporting shows all statistically significant changes in the proportions of entrepreneurs (and licences) in the age groups, thus shifting the business demography structure. In statistical testing, we followed a similar approach and relied on the 5% level of statistical significance, and we again highlighted those cases where both licences and physical entrepreneurs experienced a change. For more convenient information, we have crafted the summary in Table 2 with transformed signs, providing a more intuitive overview of the results. The detailed results are reported in  Appendices 2 and  3.

The most remarkable changes in the age structure of Czech entrepreneurial activity, measured via the size of the estimated difference, reflect the increase in the age cohort 46–50 years (from 1.94 to 1.99 percentage points) at the expense of a decline in the age group 41–45 years (from −1.76 to −1.88 percentage points). Related was also a significant decline in the cohort of 31–35 years, constituting a decline of 1.3 percentage points, followed by a decline within the age category 26–30 years (from −0.52 to −0.55 percentage points). There was also a similarly high shift between the age categories 56–60 years (an increase from 0.76 to 0.84 percentage points) and 61–65 years (a decline from −0.68 to −0.73 percentage points). However, one of the most interesting observations is the continuously significant increase in all categories above 71 years, totalling 1.3 percentage points.

Once we reviewed those results, we further enhanced the robustness of the findings that had already been provided through regression analysis. Given the limited number of statistical observations, we followed Tsay (2005) and estimated regression models with quarterly dummy variables, adjusting for the seasonality and linear trend. The key variable was (as in the previous case), a dummy variable, representing post-pandemic development of the Czech entrepreneurial activity, i.e. tracking changes in the activity over the years 2020–2023. We estimated four regression models, accounting for the changes in the male-to-female entrepreneurs’ ratio, number of entrepreneurs aged above 46, percentage share of entrepreneurs aged above 46 and percentage share of entrepreneurs’ licences above 46. In all econometric models reported, the variables representing the post-pandemic period were found to be statistically significant at the 5% significance level. Overall, it can be concluded that these results further expanded the statistical testing. While we observe only a decent decline in the male-to-female entrepreneurs’ ratio, the results show a significant increase in both the absolute and relative size of the 46+ entrepreneurship in the post-pandemic period.

The results obtained from the statistical analyses allow us to answer the stated RQs. The first sought to investigate whether the gender gap in entrepreneurship had decreased over the study period. The answer is no. It stays at about 1.8–2 male entrepreneurs for every female one, and with the recent implementation of data boxes, it has even increased, although this might be a temporary situation. Then, we wanted to know how much the age structure has changed to determine whether any new policies supporting the ongoing sustainability of Czech entrepreneurship are needed (Lévesque and Minniti, 2011). Our results are positive and document trend shifts in several age cohorts. The obtained findings show that the proportions of entrepreneurs have significantly increased, which might be favourably reflected in the positive outcomes of entrepreneurship on economic development (Dvouletý, 2017), especially in the age cohorts above 46 years and even above the age of 60 and above 71 years. These observations are in line with the ongoing demographic trends of population ageing and the ability of individuals to stay active in the labour market even in post-retirement age (Maalaoui et al., 2023; del Olmo García et al., 2023; Chee, 2025).

The implications of these findings are twofold. Firstly, it is apparent that female entrepreneurship in the Czech Republic requires ongoing attention, as noted recently by Křížková et al. (2014), Prabhu (2020) or Dvouletý (2021), highlighting that women need a specific way of encouragement for starting a business, for example, through networking in female entrepreneurship clubs [Business and Professional Women CR (2023) organising Entrepreneurship Academy courses] or sharing success stories of the women entrepreneurs serving as inspiration to overcome lack of confidence and fear of failure. Therefore, if policymakers are up to the task of mitigating the gender gap among the Czech entrepreneurial population and achieving higher gender equality, then additional policy actions encouraging female participation in entrepreneurship would need to occur. These might include further support of entrepreneurial training, i.e. delivering necessary skills and knowledge specifically personalised to women to build and empower the community (Idrus et al., 2014; Bauer, 2011; Brixiová et al., 2020) within a specific public support programme within the European Union Structural Investment Funds, guaranteed by the Czech Ministry of Labour and Social Affairs. In addition, inspired by the previous research efforts (Love et al., 2024; Sawy et al., 2025), we believe that there is a space for improvement in the female entrepreneurial culture, which could be further promoted by the public campaigns and social media posts of successful role models of female entrepreneurs, serving as inspiration for women, considering joining an entrepreneurial career pathway and improving their self-esteem.

Secondly, the ongoing trends of population ageing and increased life expectancy provide opportunities for some individuals to remain economically active in the labour market as skilled professionals, working on a solo basis, based upon valid business licences. This entrepreneurship segment is named in the international literature as senior entrepreneurship, and it offers elderly individuals an opportunity to broaden their incomes and utilise their lifetime experience and knowledge even in the post-retirement age (Isele and Rogoff, 2014; Pilkova et al., 2014; Ratten, 2019; Fañanás-Biescas et al., 2026). Yet, according to the authors’ best knowledge, there is no publicly funded initiative helping the elderly population start and manage their businesses despite the identified benefits. From the Czech perspective, it, however, needs to be acknowledged that the enhanced digitalisation and lack of computer-related skills could be an obstacle for some individuals to officially set up a business (Kruncl, 2023), which opens another possibility for a specific publicly funded training programme, promoting digital skills with a focus on the elderly population and entrepreneurship-related tools.

Both of these recommendations could be further developed in future research activities, studying closely industries and types of business where female and senior entrepreneurs are more represented, their income and well-being, concerning the current strive for accomplishing SDGs 3, 5 and 10 within the Czech context. To better understand the roles of policymakers in promoting engagement of both of these groups systematically at the regional or country levels, one can utilise lessons from the entrepreneurial ecosystem (EE) framework (Wurth et al., 2022; Kakeesh, 2024) and national systems of entrepreneurship perspective (Friske et al., 2025), where specific support of both studied groups requires a deeper understanding of particular institutions and specific elements that lead to the increase of the female/elderly activity, i.e. to outputs (in terms of EE terminology), and the aggregated value created by these entrepreneurs as an outcome indicator, operationalised for, instance, as through sum of personal incomes, sales or employment created. In light of this recommendation, there is an ongoing challenge in measuring and aggregating well-being as an outcome indicator for the whole women and senior entrepreneurship segments and monitoring it over time (Kibler et al., 2024; Baldacchino and Sassetti, 2025).

This research advances the current body of knowledge on the determinants of entrepreneurship with a specific focus on age and gender by offering a country-level perspective on entrepreneurship development within a specific context (Simoes et al., 2016; Monllor and Murphy, 2017; Cowling et al., 2019; Cieślik and van Stel, 2024). The study also enriches the existing discussion on post-pandemic and post-crisis entrepreneurship development by offering insights from the Czech Republic, a country experiencing post-transitional institutional development. The article provides findings on the development of Czech entrepreneurial activity over the years 2016–2023, based on the official population data representing all physical entrepreneurs and business licences. Furthermore, it expands the current state of knowledge on gender and age characteristics of Czech entrepreneurs, based often on the data obtained from surveys with a limited number of respondents, such as the European Union Labour Force Survey (Dvouletý, 2019) or Global Entrepreneurship Monitor (Lukeš and Zouhar, 2016), despite their statistical representativity. Recommendation to continuously monitor the age and gender structure of entrepreneurial activity enriches the body of knowledge on the gender and age aspects of entrepreneurship and allows a more targeted (policy) approach to further understanding of demographic shifts and gender differences in the current polycrises times (Tunçalp, 2025; Dvouletý et al., 2025).

Nevertheless, despite the efforts made, this research study has limitations that could also be overcome in future studies. The primary burden lies in the data provided by the Czech Ministry of Industry and Trade (2023), which is unable to create and combine data into additional sub-categories, i.e. cross-tabulations across age and gender, nor does it work with the microdata. Here, we see a space for improvement in the data processing of the publicly available data, i.e. making the current database more relevant and useful for research and establishing the link between individual-level data, regional conditions and country-level institutions as outlined by EE scholars (Stam, 2015). At the same time, we could not entirely exclude officially registered entrepreneurs from the database, but, per se, inactive business entities, which might, to some extent, influence the presented findings. On the other hand, our data do not include early-stage entrepreneurs who have not yet registered their businesses officially, which is a group missing in the current analysis. From a methodological perspective, our analysis is based on a series of statistical tests and time-series analyses; however, more suitable and empirically robust approaches would be estimation of the panel-data-based econometric models, as in recent analyses of city-level entrepreneurship by Asencio et al. (2022).