A large number of theoretical models assume that the more profitable firms will grow while less profitable firms will decline. In this view, selection pressures operate to redistribute market share to the more profitable firms. In fact, one would expect that profitable firms have not only the means to finance expansion, but also the motivation to grow, since they can obtain a larger amount of profits from a larger sales base (Holzl & Werner, 2010). In addition, it has been observed that, while profit rates are heterogeneous across firms, they display a high degree of persistence (Dosi, 2007; Mueller, 1977), while firm growth rates do not display much persistence. This leads to question the expected relationship between profits and growth (Geroski & Mazzucato, 2002). Further investigation, based on regression analysis, has generally shown that firm growth rates cannot be explained in terms of financial performance, whether the latter is measured in terms of profit rates (Bottazzi, Secchi, & Tamagni, 2008; Coad, 2007) or growth rates of the amount of profits (Coad & Rao, 2010). While there may be a statistically significant relationship between the two, the magnitude of the effect is so low that it would be a valid approximation to view the two variables as independent. Furthermore, advanced econometric techniques also show that profits have a negligible causal effect on firm growth rates (Coad, 2007). Instead, it appears that growth has more of a positive effect on profits, than does profits on growth (Coad, 2007, 2010).

Geert Hofstede conducted one of the most comprehensive studies of how values in the workplace are influenced by culture. He defines culture as “the collective programming of the mind distinguishing the members of one group or category of people from others” (Hofstede, 1991). During 1978–83, he conducted detailed interviews with hundreds of IBM employees in 53 countries. Through standard statistical analysis of large data sets, he determined patterns of similarities and differences among the replies. From this data analysis, he developed five dimensions of culture (Hofstede, 1991). In the 1990s, Hofstede published results of his research, initially he developed four dimensions in culture, but added a fifth dimension in 1991.

In the 2000s, research by Minkov (2007) using data from the World Values Survey allowed a new calculation of the fifth, and the addition of a sixth dimension (Hofstede, Hofstede, & Minkov, 2010). The six dimensions are: Power Distance, related to the different solutions to the basic problem of human inequality; Uncertainty Avoidance, related to the level of stress in a society in the face of an unknown future; Individualism versus Collectivism, related to the integration of individuals into primary groups; Masculinity versus Femininity, related to the division of emotional roles between women and men; Long Term versus Short Term Orientation, related to the choice of focus for people's efforts: the future or the present and past; Indulgence versus Restraint, related to the gratification versus control of basic human desires related to enjoying life (Hofstede, 2011).

As found in the Hofstede study, each country has been positioned relative to other countries through a score on each dimension. The dimensions are statistically distinct and do occur in all possible combinations, although some combinations are more frequent than others. After the initial confirmation of the country differences in IBM in data from management trainees elsewhere, the Hofstede dimensions and country scores were validated through replications, using the same or similar questions with other cross-national populations. Between 1990 and 2002 six major replications (14 or more countries) used populations of country elites, employees and managers of other corporations and organizations, airline pilots, consumers and civil servants (Hofstede, 2011).

Power Distance has been defined as the extent to which the less powerful members of organizations and institutions (like the family) accept and expect that power is distributed unequally. It suggests that a society's level of inequality is endorsed by the followers as much as by the leaders. Power and inequality, of course, are extremely fundamental facts of any society. All societies are unequal, but some are more unequal than others (Hofstede, 2011).

Uncertainty Avoidance is not the same as risk avoidance; it deals with a society's tolerance for ambiguity. It indicates to what extent a culture programs its members to feel either uncomfortable or comfortable in unstructured situations. Unstructured situations are novel, unknown, surprising, and different from usual. Uncertainty avoiding cultures try to minimize the possibility of such situations by strict behavioral codes, laws and rules, disapproval of deviant opinions, and a belief in absolute truth (Hofstede, 2011).

Individualism on the one side versus its opposite, Collectivism, as a societal, not an individual characteristic, is the degree to which people in a society are integrated into groups. On the individualist side we find cultures in which the ties between individuals are loose: everyone is expected to look after him/herself and his/her immediate family. On the collectivist side we find cultures in which people from birth onwards are integrated into strong, cohesive in-groups, often extended families (with uncles, aunts and grandparents) that continue protecting them in exchange for unquestioning loyalty, and oppose other ingroups.

Masculinity versus its opposite, Femininity, again as a societal, not as an individual characteristic, refers to the distribution of values between the genders which is another fundamental issue for any society, to which a range of solutions can be found. The IBM studies revealed that women's values differ less among societies than men's values; men's values from one country to another contain a dimension from very assertive and competitive and maximally different from women's values on the one side, to modest and caring and similar to women's values on the other. The assertive pole has been called ‘masculine’ and the modest, caring pole ‘feminine’ (Hofstede et al., 2010).

The dimension Long-Term versus Short-Term Orientation turned out to be strongly correlated with recent economic growth. As none of the four IBM dimensions was linked to economic growth, obtain Bond's permission to add his dimension as a fifth to four (Hofstede & Bond, 1998).

Lastly, the sixth and new dimension, it was also based on recent World Values Survey items and is more or less complementary to Long-versus Short-Term Orientation; in fact, it is weakly negatively correlated with it. It focuses on aspects not covered by the other five dimensions, but known from literature on “happiness research”. Indulgence stands for a society that allows relatively free gratification of basic and natural human desires related to enjoying life and having fun. Restraint stands for a society that controls gratification of needs and regulates it by means of strict social norms (Hofstede, 2011).

Some societies are unequal, but the degree of inequality varies considerably. Power can also be distributed unevenly and thus influence the high growth of business. In line with this argument, our third hypothesis has tried to explore the impact of Power Distance on the firm growth rates:H3

Power Distance influences businesses’ growth rates.

In order to reach the proposed objective, a set of research hypotheses was formulated that relate the firm growth rates, with several variables (independent). Our goal is to study if these variables (size and age; innovation and growth; profits and growth; and national culture) increase or refrain the creation of new high-growth businesses.

Table 1 presents a summary of the literature as well as the measurement used by the various authors in their studies. Table 2 provides an overview of the databases used to perform the statistical analysis displayed in the subsequent chapter.

This research strategy has allowed to construct a composite database, where the variables collected from the three databases were combined.

In this study, we applied the multivariate statistical analysis tools to study the Organisation for Economic Co-operation and Development that OECD Countries covered include Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States. Non-OECD countries include Brazil, China, India, Indonesia, Russia, and South Africa.

The OECD database includes information on a range of issues to assist governments to foster prosperity and fight poverty through economic growth and financial stability. It aims to help ensure the environmental implications of economic and social development are taken into account (OECD.Stat, 2017).

The OECD Factbook provides indicators selected from more than 40 OECD statistical databases for all OECD member countries and, when available and considered internationally comparable, for Brazil, India, Indonesia, the People's Republic of China, the Russian Federation and South Africa. The country statistical profiles provide a broad selection of indicators, illustrating the demographic, economic, environmental and social developments, for each of the 35 OECD members, and the Russian Federation. The dataset also covers the five key partner economies with which the OECD has developed an enhanced engagement program (Brazil, China, India, Indonesia and South Africa) (OECD.Stat, 2017). Topics covered include population and migration; production; household income, wealth and debt; globalization, trade and foreign direct investment (FDI); prices, interest rates and exchange rates; energy and transportation; labor, employment and unemployment; science and technology including research and development (R&D); environment including natural resources, water, air and climate; education resources and outcomes; government expenditures, debt, revenues, taxes, foreign aid; and, health status, risk and resources (OECD.Stat, 2017). In total, it presents 447 subject that complement the aforementioned topics covered and it includes population and migration; production; household income, wealth and debt; globalisation, trade and foreign direct investment (FDI); prices, interest rates and exchange rates; energy and transportation; labor, employment and unemployment; science and technology including research and development (R&D); environment including natural resources, water, air and climate; education resources and outcomes; government expenditures, debt, revenues, taxes, foreign aid; and, health status, risk and resources (OECD.Stat, 2017).

The data provided by Geert Hofstede was also used. It was developed between 1967 and 1973, and has performed a large survey of the national subsidiaries of a multinational corporation: compared to the answers of 117,000 IBM matched employees on the same attitude survey in different countries. Initially, it focused his on the 40 largest countries, and then extended it to 50 countries and 3 regions.

Finally, the Eurostat database was used, with over 4600 datasets containing more than 1.2 billion statistical data values, and covers all areas of European society (Eurostat, 2017). This database covers about 32 countries including the 28 EU member states as well as 4 EFTA Countries (Switzerland, Iceland, Liechtenstein and Norway).

With the multivariate linear regression, it is aimed to study which variables influence the high growth rates of firms. To meet this goal, one can use a linear regression model considering growth rate as the dependent variable. Given the limited number of countries in each database, the combination of two databases allowed using a larger number of observations, but it required using two linear multivariate models (given differences in the measurement of the two databases) for which the dependent variables were defined as follows:

Dependent variable: OCDEHighGrowth – which will be named as “variable y1” refers to rate of high growth enterprises (20% or higher growth based on employment): number of high growth enterprises as a percentage of the population of active enterprises with at least 10 employees of firms in countries, available in the OCDE site (OECD.Stat, 2017).

Dependent variable: EUROSTATHighGrowth – which will be named as “variable y2” refers to the share of high-growth enterprises measured in employment: number of high-growth enterprises divided by the number of active enterprises with at least 10 employees, i.e. high growth enterprises (growth by 10% or more) and related employment of firms in countries, available in the EUROSTAT site.

The choice of dependent variables to be considered in the analysis was based on the hypothesis formulated. Thus, all variables are related to the dimensions under analysis: size and age, Innovation and growth and National Culture, as summarized in Table 2.

The main objective of this paper was to explore the contribution of certain dimensions studied in previous research. In particular, we have analyzed the literature the size and age of the companies, as well as the contribution of innovation and growth and the well-known study of Geert Hofstede to explain the high growth businesses.

One important contribution of this paper is to relate power distance in the society with high growth in firms, showing a statistically significant relationship. On the other hand, Uncertainty Avoidance influences negatively the growth rates of firms in the countries, as well, there is a negative relation with Masculinity because it negatively influences the high growth of the business.

This paper sheds light on an important aspect of economic growth, and it is expected that it raises interests in the academy, in the governmental policy and amongst entrepreneurs, because it exploits real data of three databases with great impact in the measures adopted by the different countries associated, includes information on the range of issues to help governments foster prosperity and fight poverty through economic growth and financial stability.

One of the main limitations of this study is the heterogeneity in the measurement of the variables found in the databases and used in the statistical analyses. Such limitation did not allow basing the empirical results on one single database with a larger number of observations, but rather it had to be based into two smaller databases. This limitation may have impacted on the significance of the different variables, and, as a consequence, other variables could have shown statistical significance for a higher number of observations.

In addition to our contribution, further research may explore the same relationships on an individual country basis, in order to test if different relationships may emerge from the data (in this case a higher number of observations may be found). This is based on the fact that the national economic environment may be conducive to further growth, while in others limitations may be found. It is important to note that the institutional and legal framework may help explaining such differences, but mostly the expectations of the economic agents may contribute with significant insights.