Volume 26 Issue 2 Article 2 June 2024 Firm-Level, Macroeconomic, and Institutional Determinants of Firm-Level, Macroeconomic, and Institutional Determinants of Firm Growth: Evidence From Europe Firm Growth: Evidence From Europe Anž e Burger University of Ljubljana, Faculty of Social Sciences, Ljubljana, Slovenia Andreja Jaklič University of Ljubljana, Faculty of Social Sciences, Ljubljana, Slovenia Klemen Knez University of Ljubljana, Faculty of Social Sciences, Ljubljana, Slovenia Patricia Kotnik University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia, patricia.kotnik@ef.uni-lj.si Matija Rojec University of Ljubljana, Faculty of Social Sciences, Ljubljana, Slovenia; Institute for Economic Research, Ljubljana, Slovenia and Institute of Macroeconomic Analysis and Development, Ljubljana, Slovenia Follow this and additional works at: https://www.ebrjournal.net/home Part of the Growth and Development Commons, and the Organizational Behavior and Theory Commons Recommended Citation Recommended Citation Burger, A., Jaklič , A., Knez, K., Kotnik, P ., & Rojec, M. (2024). Firm-Level, Macroeconomic, and Institutional Determinants of Firm Growth: Evidence From Europe. Economic and Business Review, 26(2), 81-103. https://doi.org/10.15458/2335-4216.1336 This Original Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. ORIGINAL ARTICLE Firm-Level, Macroeconomic, and Institutional Determinants of Firm Growth: Evidence From Europe Anže Burger a , Andreja Jakliˇ c a , Klemen Knez a , Patricia Kotnik b, * , Matija Rojec a,c,d a University of Ljubljana, Faculty of Social Sciences, Ljubljana, Slovenia b University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia c Institute for Economic Research, Ljubljana, Slovenia d Institute of Macroeconomic Analysis and Development, Ljubljana, Slovenia Abstract To examine the main drivers of rm growth, we estimated a model integrating rm-level, industry-specic as well as country-level determinants, aiming at a comprehensive explanation of rm growth. We used a large dataset of European rms for the 2005–2017 period and combined Amadeus rm-level data with macroeconomic variables and multidimensional measures of institutional framework, based on a range of sources. Using different panel regression model specications, we found the most consistent relationships for rm-level determinants. Among country-level determinants, infrastructure quality, inward FDI, natural resources, and inequality show a consistently positive and signicant relation with rm growth. Keywords: Firm growth, Firm-level determinants, Internal determinants, Country-level determinants, Institutional deter- minants JEL classication: D21, D22, D24 Introduction A fter the Great Recession of 2008, EU countries experienced large differences in rm growth. For example, the average employment growth of EU en- terprises over the 2008–2014 period ranged from a negative 3.9 percent in Spain to 2.2 percent in Lithua- nia (Hallak & Harasztosi, 2019). What are the factors behind these differences? Finding an answer to this question is crucial for policy makers that aim at creat- ing favourable conditions for rm performance. According to the resource-based theory of a rm, rm growth depends primarily on factors internal to the rm, such as technology, skilled personnel, ef- cient procedures, brand names, trade contacts, and so forth, and their efcient combination (Coad, 2007). However, the optimum rm size theory posits that rm growth also depends on a number of exoge- nous variables, such as the country’s macroeconomic environment, institutional setting, and business envi- ronment (Geroski, 2000). The extensive empirical lit- erature on rm growth suggests a long list of growth determinants (see Coad, 2009 for an overview). Sig- nicant rm-specic factors include rm size, age, export propensity, intangible capital (as an indicator of the rm’s innovation capacity), ownership of the rm, the rm’s nancial sources (indicating nancial constraints), and rm productivity. Apart from these, empirical literature also points to the importance of the industry in which the rm operates as well as the macroeconomic factors and institutional environ- ment in the country, suggesting that rm growth is to a certain extent determined by factors external to the rm. However, as Ipinnaiye et al. (2017) argue Received 19 October 2023; accepted 26 February 2024. Available online 5 June 2024 * Corresponding author. E-mail address: patricia.kotnik@ef.uni-lj.si (P . Kotnik). https://doi.org/10.15458/2335-4216.1336 2335-4216/© 2024 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 82 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 in their study of drivers of SME growth, not enough is known about the role of external growth deter- minants, leaving a gap in the extant rm growth literature. In addition, instead of testing the effects predicted by a particular theory, integrative models are needed to determine to what extent rms’ growth depends on industry and country specics as well as their own rm-level characteristics. Our study seeks to contribute to this debate. The objective of the paper is to identify the factors on the rm, industry, and macro levels that stimulate or impede the growth of EU enterprises. Based on the applied theoretical framework and empirical literature, we estimated a comprehensive model of rm growth that integrates rm-level, industry-specic, as well as macroeconomic and in- stitutional determinants, aiming at an explanation, as complete as possible, of the phenomenon of rm growth. The study utilized a large dataset of Euro- pean rms for the 2005–2017 period. To analyse the main factors that drive rm growth (measured as revenue and employment growth), we used different panel regression model specications. All the rele- vant rm-level determinants of growth identied in the literature and available in the Amadeus database were taken into account. Apart from these, two other sets of factors were included in the model, the indus- try in which the rm operates and country-specic factors. To include the latter, we followed the ap- proach of D’Olio et al. (2013); in modelling the factors of productivity growth in Europe, they combined Amadeus rm-level data on productivity and rm characteristics with various country-level data (busi- ness environment, FDI, infrastructure quality, credit availability). By way of applying a multidimensional measure of the institutional framework, we tested to what extent the differences in rm growth were due to the home country of the rm. A number of macroe- conomic variables were tested, together with a set of composite indicators constructed to measure more specic differences in the institutional environment such as bureaucracy and regulation, including labour market regulation, tax systems, healthcare and educa- tion, political environment, rule of law, and security, as well as measuring the overall development of the infrastructure and nancial system. Our paper contributes to the literature on rm growth by integrating three sets of determinants in explaining growth: 1) rm-level, 2) industry-specic, and 3) macroeconomic and institutional factors. To the best of our knowledge, this is the rst attempt at investigating such a comprehensive set of determi- nants of rm growth that also includes institutional drivers. Whereas the study by Ipinnaiye et al. (2017) uses a similar approach of integrating macroeconomic determinants with internal drivers and provides ev- idence that the macroeconomic environment affects SME growth (both directly and indirectly), it does not include the institutional environment in the analy- sis. We provide evidence on the extent to which the growth of EU enterprises depends on this wide range of determinants, identifying those that are more im- portant than others and thus more deserving of the attention of policy makers and managers. Our results reveal a signicant role of rm-, industry-, and country-level determinants of rm growth. The most consistent relationships were found for rm-level determinants, while macroeconomic and institutional determinants show lower consis- tency and require more detailed examination. Positive and statistically signicant relation with rm growth among rm-level determinants was identied for labour productivity and the share of intangible cap- ital, while age and level of debt have a negative relation with rm growth. Among macroeconomic determinants, we found that inward FDI, presence of natural resources, and inequality correlate positively with rm growth. The signicance, size, and direction of the relation varies the most among institutional determinants, with the exception of infrastructure, which shows the highest and consistently positive and signicant relation with rm growth. The rest of the paper is organized as follows. We review the theory and the empirical evidence on de- terminants of rm growth in Section 1. Section 2 describes the data and the methodological approach. Section 3 presents the results and discusses the main ndings of the study. We conclude with Section 4. 1 Theoretical considerations and empirical evidence The extensive literature on rm growth attributes rm heterogeneity to a number of sources, depend- ing on an underlying theory. In his review of the main theories of rm growth, Geroski (2000) classies them into models of optimum rm size, predicting that rms will tend to grow to their optimum size (see, e.g., Viner, 1952), stage theories where rms evolve through several phases of growth (see, e.g., Greiner, 1998), and models based on Penrose’s (1959) theory of the growth of the rm. Penrose’s theory contains two types of arguments. The rst is the “managerial limits to growth” hypothesis, stating that “rm growth is led by an internal momen- tum generated by learning-by-doing” (Coad, 2009, p. 32). Managerial limits refer to one of the two types of rm-specic capabilities that Penrose identies, where managerial capabilities are associated with ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 83 execution of ideas and entrepreneurial capabilities with subjective evaluations of market conditions, per- ceiving the opportunities, and being willing to act on them (Penrose, 1959, p. 36). The second type of argu- ment links to ‘resource-based view’ of the rm where “rms are composed of idiosyncratic congurations of resources” (Coad, 2007, p. 33), the use of which gen- erates rm growth (for more see Coad, 2007; Geroski, 2000). The purpose of our research can best be sum- marized by a combination of optimum size and resource-based theories of rm growth. The model of optimum rm size suggests that optimum size de- pends on a number of exogenous variables (Geroski, 2000). On the other hand, resource-based theory at- tributes rm growth to inherent factors within the rm, such as technology, skilled personnel, efcient procedures, brand names, trade contacts, and so forth (Coad, 2007; Wernerfelt, 1984) and their efcient combination. Whereas extensive literature exists on rm-specic characteristics as a source of heterogene- ity in rm growth, little is known about the role of external determinants, such as macroeconomic con- ditions, and of the combined effects of internal and external drivers in rm growth (Ipinnaiye et al., 2017). Available empirical studies show low explanatory power of individual theories of rm growth and a strong stochastic element in explaining it. Coad (2009) claims that the main result of empirical work on rm growth is that it is the stochastic element that is pre- dominant, in other words, that “rm growth appears to be an idiosyncratic and fundamentally random process” (p. 58). In such circumstances, “it is mean- ingful to follow Penrose and suppose that growth is not just a means to obtain a certain size, but rather it is an end in itself, a constructive application of spare re- sources. Indeed, in the presence of learning-by-doing and dynamic increasing returns, a lack of growth would be akin to stagnation” (p. 59). Consequently, he proposes that the way forward is through empirical analysis and quotes Starbuck (1971, p. 126), saying that the subject needs “solid, systematic empirical research directed toward explicit hypotheses and uti- lizing sophisticated statistical methods” (Coad, 2007, pp. 59–60). Our approach is motivated by a need to contribute to this debate and to aim at an explanation of the phenomenon of rm growth that is as com- plete as possible, instead of testing effects predicted by a particular theory. Based on the theoretical per- spectives and empirical literature, we have built an integrative model of rm growth that includes inter- nal as well as external determinants. In selecting rm-level determinants of rm growth, we drew on empirical studies that focus on variables such as rm size, age, R&D and innovation activity and human capital, export-related variables, national- ity of ownership, as well as the rm’s nancial sources to capture the impact of nancial constraints, produc- tivity, and the dynamics of the rm’s growth in the previous period. As external drivers of rm growth, we included industry-specic, macroeconomic, and institutional factors. What follows is a brief look at the main ndings of the literature on the scope and direction of these determinants of rm growth. Firm size is one of the basic variables included in em- pirical analyses of rm growth. Conventional wisdom has claimed that expected rm growth rates are inde- pendent of size (Gibrat’s Law); however, more recent analyses tend to demonstrate a negative relationship between a rm’s size and growth (Almus, 2000; Bot- tazzi & Secchi, 2003; Cabral & Mata, 2003; Calvo, 2006; Dunne & Hughes, 1994; Goddard et al., 2002; McPher- son, 1996; Reichstein & Jensen, 2005; Yasuda, 2005; Zhou & De Wit, 2009). Smaller rms grow faster; if for no other reason, this is because they have to reach the size of minimal efciency (Audretsch et al., 2004). The predominant nding on rm age is that there is a negative relationship between rm age and growth (Dunne et al., 1989; Evans, 1987; Geroski & Gugler, 2004; Glancey, 1998), although some analyses do not conrm this (Barron et al., 1994; Das, 1995). Fort et al. (2013, p. 27), who specically analyse the role of a rm’s age and size in business cycles, nd that young/small businesses are more cyclically sensitive, so that the relative decline in employment during re- cession is greater for young and small businesses than for large and mature businesses. Two other determinants with a positive impact on rms’ growth that are regularly put forward by the literature are R&D and innovation activity (Coad, 2009; Dugal & Morbey, 1995; Freel, 2000; Geroski & Machin, 1992; Geroski & Toker, 1996; Hall & Mairesse, 2006; Manseld, 1962; Rauch et al., 2005; Roper, 1997), as well as the level of human capital (Hamilton et al., 2003; Iranzo et al., 2008; Navon, 2010; Parrotta et al., 2014; Unger et al., 2011). An alternative aspect of this research relates to intangible capital. The role of the accumulation of intangible capital as a source of SME growth has attracted increased attention. It has been shown (Corrado et al., 2009; Haskel et al., 2018; Piekkola, 2011; Van Ark et al., 2009) that intangible capital contributes up to one third of overall pro- ductivity growth in the US, EU, and Japan. Research linking intangible capital to growth and productivity of SMEs is rare and fragmented, focusing primarily on human capital, competencies, or R&D. As far as export propensity is concerned, the dom- inant conclusion of the literature is that export- oriented rms are more productive and generally more successful than local-market-oriented rms 84 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 (Aw-Roberts et al., 1997, 1998; Bernard et al., 2005; Bernard & Wagner, 1997; Burger et al., 2008; Clerides et al., 1998; Criscuolo et al., 2005; Hahn, 2004; Hallward-Driemeier et al., 2002; Head & Ries, 2003; Van Biesebroeck, 2005); therefore, one expects that they will be, in principle, more successful in terms of growth. The literature suggests that rms with lower levels of indebtedness and those that are less dependent on ex- ternal sources of nancing have better capacity to grow. This is especially important in periods of economic re- cession, when nancial limitations are one of the main factors that restrain rm growth (Braun & Larrain, 2005; Bricongne et al., 2012; Desai et al., 2004; Fagiolo & Luzzi, 2006; Kroszner et al., 2007; Manova et al., 2015). Any model of rm growth must also contain productivity as a control variable (see Alvarez & Görg, 2009). According to Coad (2009, p. 25), it is logical to expect that more productive rms grow while less productive ones stagnate or reduce in size. Still, em- pirical analyses do not always conrm this (Bottazzi et al., 2006). One possible explanation is that rms may increase their productivity with increasing (or decreasing) extent of their operations (Haltiwanger et al., 1998). The industrial sector in which a rm operates im- portantly codetermines its growth dynamics (see Audretsch, 1995; Audretsch & Mahmood, 1994; Coad, 2009; Gabe & Kraybill, 2002; Geroski & Toker, 1996). This is all the more relevant in times of economic recession (see Bricongne et al., 2012; Chor & Manova, 2012; Eaton et al., 2011; Jiang et al., 2009; Levchenko et al., 2010; Roubinchtein & Ayala, 2009). Coad (2009) also puts forward the importance of macroeconomic factors for rm growth. Income inequality is a country- specic macroeconomic variable that deserves spe- cial attention. Some of the recent empirical work addresses the debate on whether inequality has a pos- itive or negative effect on growth, and the conclusion is still open (Ferreira et al., 2022). Theoretical work has identied a number of channels through which inequality can affect economic growth, and most of them predict a negative effect. These transmission channels include (Neves & Silva, 2014): the credit market imperfection channel (with the core idea that inequality is detrimental to growth as it prevents the poor from carrying out investments in human and physical capital, in the presence of borrowing con- straints); the scal policy channel (where taxation and redistributive government expenditure increase when inequality increases, leading to negative effects on investment incentives and thus growth); socio- political instability channel (where inequality leads to political instability and social unrest, negatively inuencing investments and growth); and savings channel (which predicts a positive effect as inequality directs resources towards the rich, who have a higher marginal propensity to save than the poor, leading to greater aggregate savings and higher investment and growth). However, these theoretical transmission channels are likely to operate differently over differ- ent time horizons, as shown by Halter et al. (2014). More specically, the positive effects of inequality on growth associated with higher savings and invest- ment tend to be based on economic mechanisms and are therefore likely to operate in the shorter run, while the negative effects tend to operate in the long run as they often involve political economy channels. In ad- dition, the results will be different in developed and developing countries, as the transmission channels are not the same in both types of economies (Topuz, 2022). In developed countries, income inequality can have a benecial effect, through an increase in avail- able savings and investment, and indeed the impact of inequality on growth has been shown to be posi- tive in high-income OECD and European economies (Castelló-Climent, 2010). The conclusion that inequal- ity can be growth-enhancing in some circumstances and growth-inhibiting in others is corroborated by a meta-analysis of the empirical literature of recent decades, which shows that the effect of inequality on growth is negative and more pronounced in less de- veloped countries than in rich countries and that the relationship works differently in the short and long run (Neves et al., 2016). Empirical evidence is building up on the impor- tance of institutional factors. Institutions are the rules of the game, composed of formal and informal con- straints, in which organizations and entrepreneurs are the players (North, 1994). The institutional environ- ment creates a socio-economic ecosystem in which rms operate and which inuences the allocation and use of resources as well as the returns and risks of investing rms (Sobel, 2008; Xu, 2010). The busi- ness and institutional environment encompasses a wide range of factors relevant for rm-level growth, from the availability of infrastructure, the supply of human capital, access to nance, and the basic func- tions of government (such as containing corruption) to barriers to entry and exit, tax environment for rms, as well as labour regulations (Reyes et al., 2021). The quality of such an environment has been shown to explain cross-country differences in pro- ductivity (Hall & Jones, 1999). For a long time, only country-level data on the business environment was available, but the multicollinearity of its various as- pects is severe at this level, and some of the important measures can only be obtained through rm-level data (Xu, 2010). This has been resolved by the abun- dance of rm-level data since 2000, which has led to ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 85 an extensive literature on the impact of the business environment on rm performance, largely, but not exclusively, focused on developing countries (Ganau & Rodríguez-Pose, 2019; Nichter & Goldmark, 2009; Pereira & Temouri, 2018; Xu, 2010). This literature is quite fragmented and usually deals with the effects of specic elements of the busi- ness environment. One of the exceptions is a study by Reyes et al. (2021), which examines a comprehen- sive list of business and institutional environment variables to explain growth at the rm level. They conclude that modern infrastructure, access to - nance, and basic government protection together with the presence of a strong agglomeration environment are important determinants of rm growth, while labour regulations, taxes, and access to land are not. They also show that the effects of the environment depend on rm size (small rms need a stronger busi- ness environment than larger rms) and age (younger rms show faster growth due to infrastructure, labour exibility and ease of entry) as well as the country’s level of development (Reyes et al., 2021). Turning to the results for specic elements of the business environment, our rst consideration is the infrastructure. Physical infrastructure is an important factor in explaining rm performance in developing countries, especially in countries with a low stock of infrastructure (Xu, 2010). However, modern in- frastructure has also been shown to be important for the productivity of rms in developed countries. Evidence of the positive productivity effects of in- formation and communication technology (ICT) is accumulating (Cardona et al., 2013; Stanley et al., 2018; Vu et al., 2020), bringing the importance of ICT infrastructure to the fore. The same applies to trans- port infrastructure such as roads, railways, airports, and ports, which enable connectivity and ensure better connections between companies, customers, and suppliers with the help of a logistics system (Bergantino et al., 2023). Recent empirical studies con- rm that such infrastructure determines rm-level productivity (Bergantino et al., 2023; Branco et al., 2023; Khanna & Sharma, 2021; Wan et al., 2024). The importance of the educational system as an element of the business environment that has a pos- itive impact on rm productivity is supported by numerous studies (see for example Backman, 2014; Gennaioli et al., 2012). Firm-level studies on the productivity impact of bureaucratic burden are rare, however. Some conclusions can be drawn from a study that analysed the impact of regional institu- tional quality on rms in Western Europe and found government effectiveness to be the most important institutional dimension beneting rm productivity (Ganau & Rodríguez-Pose, 2019). Government effec- tiveness was measured as a variable that captures the perception of the quality of public services and could be interpreted as a proxy for bureaucratic qual- ity. The same study found no evidence for control of corruption and rule of law as two of the elements of institutional quality, although control of corruption shows a positive effect when considered individu- ally in the model (Ganau & Rodríguez-Pose, 2019). However, Reyes et al. (2021) conrm that containing corruption, together with basic safety provided by the government, is an important determinant of rm growth. Gemmell et al. (2018) claim that in countries with higher statutory tax rates, productivity catch-up of small rms is slower. According to Fernández- Villaverde and Ohanian (2018), the lagging of Euro- pean productivity growth behind the US since the mid-1970s is due to higher tax rates and increased reg- ulatory barriers that have reduced competition and new business formation. Lack of or slow structural reforms are another factor with a negative impact on rm growth (de Almeida & Balasundharam, 2018; Kouamé & Tapsoba, 2019; Masuch et al., 2018). A number of authors point to the importance of a ex- ible enough setting that allows the entry and exit of rms (Acemoglu et al., 2019; Foster et al., 2018; Lewrick et al., 2018; Storz et al., 2017), where the exit of less productive rms frees up skilled labour for newly entering rms (Acemoglu et al., 2018). Lastly, the nancial system should be considered as an element of the institutional environment. Finan- cial intermediaries and markets play an important role in mobilizing savings and ensuring that resources are channelled into productive sectors. However, the empirical literature on the role of nancial develop- ment in economic growth has only developed since the 1990s, mostly focusing on cross-country evidence (Ang, 2008). This literature has produced consistent results showing that there is a positive relation- ship between nancial development indicators and economic growth (Ang, 2008; Levine, 2003; Valick- ova et al., 2015). For example, a seminal study by Demirgüç-Kunt and Maksimovic (1998) used rm- level data to test whether nancial development affects the degree to which rms’ investment in protable growth opportunities is constrained and showed that well-developed nancial systems are im- portant in facilitating rm growth. However, since the global economic crisis of 2007–2008, these con- clusions have been reconsidered. Some studies argue that there is such a thing as too much nance (Law & Singh, 2014). Over the last decades, nancial sec- tors have grown rapidly, and empirical evidence is accumulating showing that the level of nancial de- velopment is only benecial up to a certain point, after 86 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 which the relationship between nance and growth becomes negative (Arcand et al., 2015; Law & Singh, 2014; Samargandi et al., 2015). Thus, the traditional view that nance and growth present a linear rela- tionship is challenged by evidence suggesting that it is non-linear (with an inverted U-shape) and that there is a nance threshold in the nance–growth nexus. Arcand et al. (2015) nd that this threshold is reached when credit to the private sector reaches 80–100 percent of GDP . Above this threshold, more nance is associated with less growth (Arcand et al., 2015). The question of the nance–growth nexus still appears to be unresolved. This is not only due to the issues of endogeneity and the weaknesses of cross- country studies (Berger et al., 2020), but also due to the question of appropriate measurement of - nancial development (Levine, 2003; Valickova et al., 2015). In summary, our main hypothesis is that rm growth depends not only on rm-level factors, but also on the industry in which the rm operates and on macroeconomic and institutional character- istics of the country concerned. In modelling rm growth, we thus took into account all those deter- minants which had been identied as important by the empirical literature and which we were able to test with the available data. We measured rm growth by two indicators: growth of employment and growth of revenue. The following rm-level de- terminants of growth were tested: a rm’s initial size, age, intangible capital, structure of the rm’s - nancial sources, productivity, skill intensity, and the industry in which the rm operated. To test the ex- tent to which differences in rm growth are due to country specic factors, we followed the approach of D’Olio et al. (2013). In modelling the factors of pro- ductivity growth in Europe, they combined Amadeus rm-level data on productivity and other rm char- acteristics with various country-level data (business environment, FDI, infrastructure quality, credit avail- ability). The macroeconomic variables tested are the size of the domestic market, tariff barriers, income inequality, inward FDI, as well as dependence on natural resources. In addition, a set of composite in- dicators were constructed, to measure more specic differences in the institutional framework including tax systems, educational and health sectors, regula- tory framework (overall and labour-market-specic), political environment, rule of law, and security, as well as measuring overall development of infrastruc- ture and nancial system. Year-specic effects were added. 2 Data and methodological approach 2.1 Data 2.1.1 Firm-level data The data on rm growth and rm-specic factors, including the data on the industry, was taken from the Bureau van Dijk’s Amadeus database. Amadeus is a comprehensive rm-level database on European companies containing annual account items on ap- proximately 21 million companies across Europe. Different historical waves of Amadeus were used so that non-surviving rms were included. 1 A database of nancial and other relevant data was thus built for rms from all available European countries. Con- solidated and unconsolidated accounting data are available in Amadeus, and we used unconsolidated accounts. We restricted the analysis to the period 2005–2017. 2.1.2 Country-specic data Sources of data for country-specic macroeconomic and institutional variables include the World Bank’s ease of doing business indicators, world develop- ment indicators, education indicators, health and population statistics, and worldwide governance in- dicators, data from the World Justice Project, Global Competitiveness Index indicators by the World Eco- nomic Forum, and Centre for Business Research’s Labour Regulation Index. The following set of vari- ables is included in the model: size of the domestic market, tariff rate, rate of unemployment, share of inward FDI in GDP , natural resources abundance, and income inequality. In addition, we used a multidi- mensional measure of the business/entrepreneurial environment to identify how differences in institu- tional arrangements across countries inuence rm growth in a country. We constructed a series of 12 synthetic indicators that are country–year-specic, each being calculated from a series of subindicators that are listed in the Sup- plemental material (Appendix, Table A1). All subindi- cators were rst normalized to the interval [0, 1]. When choices of synthetic indicators were made, some of the indicators were inversed, so that values between 0 and 1 offered the same meaningful inter- pretation in all sets of subindicators within each syn- thetic index (e.g., murder rates were used inversely within the safety synthetic indicator). An aggregate synthetic index, normalized to the interval [0, 1], was then calculated as a simple average of all selected subindicators. 1 We used the following Amadeus data vintages: 2017, 2015, 2012, 2009, and 2006. ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 87 The rst institutional index is Bureaucracy, which measures the ease of enforcing contracts, obtaining building permits, paying taxes, starting a business, trading across borders, and so forth. The higher the value, the more efcient the bureaucracy is. The second is Financial system, which measures the devel- opment of the nancial sector, protection of minority investments, ease of getting credit, quality of insur- ance and nancial services, and so forth. A higher value of this indicator corresponds to a more devel- oped and more stable nancial sector. Next is Regula- tion, which rates the quality of regulation, efciency of regulatory enforcement, and burden of government regulation. A higher index implies higher overall regulatory quality and lower regulatory burdens for rms. The fourth index is Labour market regulation, which quanties the degree of labour rights, such as the right to unionization, right to strike, severance pay, length of notice period, procedural constraints on dismissal, and the like. Ahigher value of this indicator corresponds to more labour rights. The fth index is Infrastructure and measures the quality of infrastructure such as roads, railroads, ports, air transport, telecommunication, and elec- tricity. A higher infrastructure index represents a more developed infrastructure. The sixth indicator is Healthcare, which rates the quality and accessibility of healthcare services, health expenditures, immuniza- tion, mortality rates, and so forth. A higher index corresponds to a more developed, successful, and ac- cessible healthcare. Next is Taxes, which measures the level of different types of taxation, from value-added taxes to corporate and prot taxes, as well as compul- sory social contributions. A higher index represents higher overall taxation. Macroeconomic stability, the eighth indicator, measures the strength of the macroe- conomic aggregates and lack of excessive imbalances such as trade decit, income inequality, old age de- pendency, and so forth. A higher value corresponds to more stable overall macroeconomic and broad so- cial conditions required for economic development. The ninth index is Political environment, capturing the political stability, accountability, government power limits, trust in political institutions, and absence of corruption. A higher index corresponds to a more stable political environment and larger share of demo- cratic control. Rule of law is the tenth synthetic index and measures the freedoms enjoyed by individuals and businesses, absence of discrimination and violence, effectiveness and timeliness of the judiciary, and the protection of property rights. A higher index corresponds to a more effective and indiscriminatory justice system. The in- dicator that follows is Security, measuring the absence of crime, civil conict, terrorism, and organized crime and the reliability of police services. A higher value corresponds to a safer environment. The twelfth syn- thetic indicator is Education, quantifying the quality of the education system, abundance of human cap- ital, enrolment rates to different levels of education, internationally comparable test scores, and aggregate expenditures on education and R&D. A higher edu- cation index implies higher quality, accessibility, and success of the educational system. 2.2 Methodological approach To analyse the rm-, industry-, and country-level factors that drive rm growth, we used the dynamic panel regression model which is traditionally used in empirical verication of the growth theory of the rm. The expanded dynamic specication of such an autoregressive distributed lag model can be written as follows: y it Day it 1 CbX it CgC it C! i Cd c Cl j Ct t Cn it iD 1; 2;:::; NI tD 2; 3;:::; T (1) where y it represents the selected performance indi- cator, that is, revenue and employment of rm i in year t, y it 1 is a lagged value of the dependent vari- able, X it is a vector of rm-level control variables, C it denotes a vector of country-specic determinants, ! i is the unobserved rm-specic xed effect, d c is a vector of country dummies that capture the time- invariant country-specic effect, l j denotes a set of industry dummies to control for industry-specic growth trends, t t are time dummies to control for region-wide common year shocks, andn it is an error term. Revenue/employment at time t thus depends on revenue/employment in the previous period and is correlated with other control variables. Control variables X it include rm age, size, productivity, av- erage wage, indebtedness, share of intangibles in total assets, and other rm-level characteristics that the theory and past empirical studies suggest as factors of rm growth. Where appropriate, these variables en- tered specication with a lag of one year to avoid the problem of simultaneity. The time period studied was 2005–2017, encompassing the entire business cycle. Apart from FE estimation of the above model, we also report results of the between estimator (BE). The reasoning behind using BE is our expectation that the long-term average values of revenue and employ- ment (conditional on their lagged values) as well as long-term average growth of revenue and employ- ment might be correlated to the long-term average differences in values of X and C between countries and rms. This is especially relevant for interpreting institutional and other country-specic parameters 88 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 since their effect on the dependent variable is mostly observed in cross-section. If we expect (i) the time average of revenue and employment (conditional on their lagged values) to be reected by the long term average differences in X and C between countries and rms as well as (ii) current values of revenue and employment (conditional on their lagged values) to react differently to temporary departures from the individual rm average values of X and C, we can rewrite our model as follows: y it Day it 1 Cb 1 ¯ X i Cb 2 (X it ¯ X i )Cg 1 ¯ C i Cg 2 (C it ¯ C i ) C! i Cd c Cl j Ct t Cn it where ¯ X i P t X it =T i and analogously for ¯ C i . In this model,b 1 andg 1 reveal how cross-country and cross- rm differences in the average values of X andC affect a rm’s size (conditioned on its past size). Parameters b 2 and g 2 , on the other hand, show how temporary departures from the average values of X and C af- fect rm size. The BE estimates b 1 and g 1 , while the within estimator FE estimatesb 2 andg 2 , and neither estimates the other. Thus, even when estimating equa- tions such as (1), it is worth comparing the within and between estimators. We complement the above AR(1) specication in Equation (1) with a more direct modelling of employ- ment and revenue growth rate: ˙ y it Day it 1 CbX it CgC it C! i Cd c Cl j Ct t Cn it iD 1; 2;:::; NI tD 2; 3;:::; T (2) where ˙ y it 2(Y it Y it 1 ) Y it CY it 1 and Y it (y it ) denotes (log of) rev- enue or employment of rm i in year t. Growth rate ˙ y it is dened as a relative change with respect to the two- year average, and is by construction bound between 2 and 2 to limit the effect of potential outliers, that is, rms that increase employment or revenue from a very low base or those that decrease them to close to zero. Despite such a denition of growth rate, most of the growth rates are very close to the values dened either by (y it y it 1 ) or Y it Y it 1 Y it 1 . 3 Results Empirical analysis highlighted the interplay of a number of rm-, industry-, and country-level deter- minants. For most of the institutional determinants and country-specic factors, we have not found any consistent statistical evidence of their effect on rm growth, while internal factors have a signicant cor- respondence with rm performance. Table 1 shows the results for autoregressive distributed (AR 1) lag model separately for revenue (columns 1, 2, and 3 for OLS, FE, and BE estimates) and employment growth (columns 4, 5, and 6 for OLS, FE, and BE estimates). Table 2 further shows the results for a more direct modelling of employment and revenue growth rate. In the growth specication model, the selected de- terminants explain much less variation of revenue and employment growth than in the previous case. R 2 in these regressions (Table 2) are much lower and range between 3% and 13% across different specica- tions, while the autoregressive distributed lag model explains between 19% and 95% of variation. The large number of variables in all specications used is normally related also to their statistical signicance; therefore, we also considered adjusted p-values with the simple Bonferroni correction. The most consistent relations were found for rm- level determinants, especially age, skills, and produc- tivity. A positive and highly statistically signicant (with one of the highest t-values) relation to rm growth was conrmed for labour productivity, while age and rm growth are related negatively (older rms will grow less likely). Skills correlate positively to revenue growth, but negatively to employment growth, and the correspondence is consistent re- gardless of whether we consider the autoregressive distributed lag model or growth specication model. However, any interpretation of this result is limited by the fact that skills are measured by the rm average wages, which have a high correlation with the depen- dent variable, that is, rms with higher employment have lower average wages and rms with higher rev- enue have potential for higher wages, which might explain the observed correspondence. The share of intangible capital exhibits a positive relation to both, revenue and employment growth, across all spec- ications. As predicted in theory, level of debt is consistently negatively related to both, revenue and employment growth, across all specications. Among country-level determinants, unemploy- ment turned out as the most signicant. Measuring the effects on the growth of each rm separately with the FE regression exhibits a negative correlation be- tween unemployment and revenue and employment growth. This means that higher aggregate unemploy- ment negatively inuences rm growth. While this represents the effects of an endogenous economic cycle, long-term cross-country differences in unem- ployment have the opposite effect (as obtained by the BE regression). The long-term relationship between growth and unemployment is also related to the long- standing macroeconomic question of whether there is a trade-off between high growth rates and low unemployment (Aghion & Howitt, 1994; Bean & Pis- sarides, 1993; Caballero & Hammour, 1996; Eriksson, 1997). Technological innovations may not only im- prove growth but also increase unemployment in the short term because automation makes some workers ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 89 Table 1. Revenue and employment growth determinants (AR(1) specication), 2005–2017. Revenue growth equations Employment growth equations (1) (2) (3) (4) (5) (6) OLS FE BE OLS FE BE ln(revenue) t 1 0.671 0.187 0.727 0.0597 0.0684 0.0266 (0.000202) (0.000301) (0.000405) (9.07e 05) (0.000138) (0.000151) ln(emp) t 1 0.276 0.305 0.237 0.899 0.485 0.948 (0.000265) (0.000522) (0.000548) (0.000119) (0.000240) (0.000205) age t 0.00198 0.0100 0.00268 0.000686 0.0129 0.00124 (1.86e 05) (0.000495) (4.09e 05) (8.33e 06) (0.000227) (1.53e 05) ln(avg.wage) t 1 0.190 0.208 0.165 0.0964 0.264 0.0275 (0.000332) (0.000486) (0.000622) (0.000149) (0.000223) (0.000232) debtleverage t 0.000663 0.00169 0.000334 0.000279 0.000334 9.38e 05 (0.000102) (0.000131) (0.000139) (4.57e 05) (6.01e 05) (5.20e 05) ln(lab.prod.) t 1 0.0528 0.0574 0.0528 0.00596 0.00763 0.00564 (5.98e 05) (6.59e 05) (0.000132) (2.68e 05) (3.02e 05) (4.95e 05) intang.share t 0.0344 0.0642 0.0279 0.0334 0.0530 0.0156 (0.00105) (0.00177) (0.00211) (0.000472) (0.000811) (0.000788) Country-level determinants: GDP PPP t 0.00713 0.551 1.273 0.709 0.468 0.284 (0.0437) (0.0452) (0.0250) (0.0196) (0.0207) (0.00932) inwardFDI t 0.0723* 0.283 4.533 0.209 0.181 0.739 (0.0420) (0.0378) (0.0910) (0.0188) (0.0173) (0.0340) natur.resources t 0.327 0.234 0.722 0.0996 0.0412 0.105 (0.0231) (0.0240) (0.0194) (0.0103) (0.0110) (0.00726) unemployment t 0.768 0.725 0.287 0.351 0.412 0.107 (0.0126) (0.0116) (0.0126) (0.00566) (0.00533) (0.00470) Equality - GINI coef t a 0.213 0.169 0.280 0.107 0.0946 0.0386 (0.00748) (0.00724) (0.00813) (0.00335) (0.00332) (0.00304) avg.tariff t 0.469 1.021 0.590 0.105 0.231 0.00513 (0.0265) (0.0245) (0.0158) (0.0118) (0.0112) (0.00591) Synthetic institutional indices: Education t 0.122 0.190 0.0212 0.0445 0.191 0.0284 (0.0105) (0.0105) (0.00782) (0.00469) (0.00481) (0.00292) Taxes t 0.171 0.184 0.155 0.108 0.144 0.0210 (0.00567) (0.00498) (0.0119) (0.00254) (0.00228) (0.00444) Healthcare t 0.113 0.281 0.478 0.241 0.0111 0.0122 (0.0202) (0.0199) (0.0245) (0.00904) (0.00915) (0.00916) Bureaucracy t 0.103 0.491 0.335 0.0528 0.306 0.0855 (0.0109) (0.0110) (0.0139) (0.00490) (0.00504) (0.00520) Infrastructure t 2.150 2.933 0.320 0.896 0.787 0.219 (0.0481) (0.0495) (0.0191) (0.0215) (0.0227) (0.00713) Financial system t 0.194 0.160 0.0171 0.314 0.107 0.0287 (0.0128) (0.0127) (0.0128) (0.00575) (0.00582) (0.00478) Political envir t 0.969 0.839 0.257 0.724 0.0480* 0.134 (0.0662) (0.0617) (0.0235) (0.0297) (0.0283) (0.00877) Rule of law t 1.490 1.547 0.550 1.522 5.362 0.171 (0.280) (0.256) (0.0262) (0.125) (0.117) (0.00978) Regulation t 0.408 0.602 0.237 0.201 0.0608 0.115 (0.0404) (0.0390) (0.0154) (0.0181) (0.0179) (0.00577) Security t 1.535 6.128 0.236 2.582 4.742 0.00778 (0.257) (0.245) (0.00886) (0.115) (0.113) (0.00331) Labour market t 0.273 0.225 0.107 0.0616 0.375 0.00956 (0.0161) (0.0156) (0.00611) (0.00720) (0.00714) (0.00228) Macro stability t 1.750 2.375 1.449 0.912 0.982 0.290 (0.0267) (0.0249) (0.0293) (0.0119) (0.0114) (0.0109) Constant 2.028 12.04 0.160 2.630 3.420 0.244 (0.268) (0.462) (0.0238) (0.120) (0.212) (0.00890) Country effects yes yes no yes yes no Year effects yes yes yes yes yes yes Industry effects yes yes yes yes yes yes Observations 15,487,532 15,487,532 15,487,532 15,487,532 15,487,532 15,487,532 R 2 .823 .189 .837 .929 .383 .951 Number of id 3,328,871 3,328,871 3,328,871 3,328,871 Notes: Dependent variable is log of revenue for (1)–(3) and log of employment for (4)–(5). Standard errors are in parentheses. p < :01, p < :05, p < :1. a The interpretation for GINI coef. is reversed: a higher value of the indicator reects higher equality. 90 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 Table 2. Revenue and employment growth determinants (growth rate specication), 2005–2017. Revenue growth equations Employment growth equations (1) (2) (3) (4) (5) (6) (7) OLS FE BE OLS OLS FE BE ln(emp) t 1 0.00919 0.117 0.000517 0.0327 0.349 0.0164 (7.52e 05) (0.000225) (0.000135) (6.77e 05) (0.000182) (0.000112) ln(revenue) t 1 0.00689 (5.20e 05) age t 0.00259 0.00341 0.00300 0.000615 0.00152 0.00564 0.00117 (7.31e 06) (0.000228) (1.36e 05) (6.57e 06) (6.57e 06) (0.000184) (1.13e 05) ln(avg.wage) t 1 0.0163 0.0336 0.0199 0.0522 0.0660 0.192 0.0113 (0.000126) (0.000223) (0.000200) (0.000113) (0.000118) (0.000180) (0.000166) debtleverage t 0.000363 0.000427 0.000214 0.000177 0.000237 0.000269 7.05e 05 (4.02e 05) (6.03e 05) (4.63e 05) (3.61e 05) (3.64e 05) (4.87e 05) (3.84e 05) ln(lab.prod.) t 1 0.0180 0.0205 0.0149 0.00648 0.00555 0.00645 0.00555 (2.33e 05) (3.02e 05) (4.34e 05) (2.10e 05) (2.13e 05) (2.45e 05) (3.60e 05) intang.share t 0.0334 0.0679 0.0306 0.0203 0.0126 0.0370 0.00572 (0.000415) (0.000814) (0.000701) (0.000373) (0.000376) (0.000658) (0.000581) Country-level determinants: GDP PPP t 0.363 0.142 0.676 0.738 0.557 0.523 0.220 (0.0172) (0.0208) (0.00830) (0.0155) (0.0156) (0.0168) (0.00689) inwardFDI t 0.420 0.476 1.653 0.140 0.150 0.148 0.640 (0.0165) (0.0174) (0.0303) (0.0149) (0.0150) (0.0141) (0.0251) natur.resources t 0.142 0.136 0.222 0.131 0.132 0.0551 0.0708 (0.00908) (0.0110) (0.00646) (0.00817) (0.00823) (0.00892) (0.00536) unemployment t 0.564 0.550 0.112 0.308 0.257 0.349 0.0911 (0.00497) (0.00535) (0.00418) (0.00447) (0.00450) (0.00433) (0.00347) Equality - GINI coef t a 0.311 0.348 0.0534 0.0783 0.103 0.0697 0.0339 (0.00294) (0.00333) (0.00270) (0.00265) (0.00267) (0.00269) (0.00224) avg.tariff t 0.231 0.348 0.171 0.113 0.104 0.229 0.0210 (0.0104) (0.0113) (0.00527) (0.00936) (0.00943) (0.00913) (0.00437) Synthetic institutional indices: Education t 0.104 0.156 0.0419 0.0482 0.00310 0.173 0.0188 (0.00412) (0.00482) (0.00260) (0.00371) (0.00373) (0.00390) (0.00216) Taxes t 0.225 0.211 0.00844** 0.0906 0.103 0.121 0.0186 (0.00223) (0.00229) (0.00395) (0.00201) (0.00202) (0.00185) (0.00328) Healthcare t 0.385 0.380 0.00337 0.217 0.255 0.0358 0.0145 (0.00794) (0.00918) (0.00816) (0.00715) (0.00720) (0.00742) (0.00677) Bureaucracy t 0.0841 0.333 0.292 0.0358 0.0148 0.234 0.101 (0.00431) (0.00505) (0.00463) (0.00388) (0.00390) (0.00409) (0.00384) Infrastructure t 1.021 1.444 0.0653 0.958 0.839 0.825 0.182 (0.0189) (0.0228) (0.00635) (0.0170) (0.0171) (0.0184) (0.00526) Financial system t 0.491 0.452 0.230 0.237 0.295 0.0796 0.0581 (0.00505) (0.00584) (0.00426) (0.00454) (0.00457) (0.00472) (0.00353) Political envir t 1.425 0.176 0.103 0.599 0.879 0.138 0.102 (0.0260) (0.0284) (0.00781) (0.0234) (0.0236) (0.0230) (0.00648) Rule of law t 0.643 0.683 0.407 1.531 1.883 3.942 0.0985 (0.110) (0.118) (0.00871) (0.0989) (0.0996) (0.0952) (0.00722) Regulation t 0.147 0.361 0.0210 0.185 0.194 0.0184 0.109 (0.0159) (0.0179) (0.00514) (0.0143) (0.0144) (0.0145) (0.00426) Security t 0.404 2.315 0.0572 2.481 2.604 4.342 0.0131 (0.101) (0.113) (0.00295) (0.0909) (0.0915) (0.0913) (0.00244) Labour market t 0.00576 0.0163 0.00874 0.0633 0.0357 0.318 0.0147 (0.00633) (0.00717) (0.00203) (0.00569) (0.00573) (0.00580) (0.00169) Macro stability t 1.545 1.757 0.235 0.817 0.807 0.891 0.239 (0.0105) (0.0114) (0.00974) (0.00943) (0.00950) (0.00925) (0.00808) Constant 0.587 1.674 0.508 2.713 2.885 2.069 0.108 (0.105) (0.213) (0.00790) (0.0949) (0.0955) (0.172) (0.00655) Country effects yes yes yes yes yes yes yes Year effects yes yes yes yes yes yes yes Industry effects yes yes yes yes yes yes yes Observations 15,487,532 15,487,532 15,487,532 15,487,532 15,487,532 15,487,532 15,487,532 R 2 .080 .096 .138 .049 .036 .298 .054 Number of id 3,328,871 3,328,871 3,328,871 3,328,871 Notes: The dependent variable is growth rate of revenue for (1)–(3) and growth rate of employment for (4)–(7). Standard errors are in parentheses. p < :01, p < :05, p < :1. a The interpretation for GINI coef. is reversed: a higher value of the indicator reects higher equality. ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 91 redundant or unemployable due to inadequate skills and education. In addition, innovations may force some less successful rms to downsize or even go bankrupt (Eriksson, 1997). In a more recent approach, Schubert and Turnovsky (2018) augment the standard endogenous growth model by introducing search unemployment and wage bargaining, where unem- ployment arises due to the time-consuming and costly process of matching job vacancies with job-seeking agents. In their model, as in our long-run results, there is a weak positive correlation between growth and unemployment in the long run. This is also consistent with the endogenous dynamic growth cycle model (Goodwin, 1967), where employment determines the wage pressures (high employment upward pressure, high unemployment downward pressure). Following a similar business cycle mechanism, tem- poral changes in GDP per capita are positively cor- related with rm revenue and employment growth. On the other hand, BE estimations suggest that the differences in the cross-country dimension in the overall development have a negative relation with rm growth rates (more developed countries’ rms grow slower on average). For most of the institutional and other country-specic factors, the results of the FE regression bear no interpretational value. Since FE regression measures the effect within each rm separately, only variables that have a potentially en- dogenous effect on the cyclical nature of the economic growth can have a meaningful interpretation (unem- ployment rate and GDP per capita). The time variance of most institutional indicators is very small, and even where it is not small, it is impossible to conceptually link it with the growth pattern of each individual rm. Due to this fact, we only interpreted the results of the BE regression, which measures the effect of the long-term differences in the institutional framework. The inward FDI and presence of natural resources both have a long-term positive relation with rm growth, whereas the results for the effects of tariffs are inconclusive. The positive association between FDI and growth is consistent with review studies, most of which conclude that FDI has a positive im- pact on economic growth (Almfraji & Almsar, 2014; Iamsiraroj & Uluba¸ so˘ glu, 2015; Lasbrey et al., 2018). However, the evidence is far from clear. Iamsiraroj and Uluba¸ so˘ glu (2015) report that less than half of the studies analysed found a positive and statisti- cally signicant effect, while almost a third reported a negative effect of FDI on growth. The reasons pro- posed in the literature for a signicant proportion of negative FDI-growth associations emphasize the importance of absorptive capacity, especially trade openness (Iamsiraroj & Uluba¸ so˘ glu, 2015), nancial development (Alfaro et al., 2004; Durham, 2004), skilled labour (Borensztein et al., 1998; Li & Liu, 2005), domestic endowments, trade restrictions and friendly investment climate (Iamsiraroj, 2016), as well as a bridgeable technology gap (Li & Liu, 2005). Other studies suggest that FDI can reduce economic growth due to the mediating mechanisms of dependence (Amin, 1974; Frank, 1979) and decapitalization, that is, the crowding out of host country savings or diver- sion of domestic capital from other more productive sectors (Bornschier, 1980). Some of the strongest pieces of microeconomic evi- dence for the positive impact of FDI on growth come from studies of rm-level productivity spillovers, a process by which FDI catalyses productivity improve- ments in other domestic rms. It has been shown that vertical spillovers are more likely than horizon- tal, intra-industry spillovers (Rojec & Knell, 2018). As at the aggregate level, the effectiveness of FDI also depends on preexisting conditions (Batten & Vo, 2009). Determinants such as the country of origin of the FDI (Gorodnichenko et al., 2014), the owner- ship structure of domestic rms (Branstetter, 2006; Iršová & Havránek, 2013; Monastiriotis & Alegria, 2011; Smarzynska Javorcik, 2004), a moderate tech- nology gap (Iršová & Havránek, 2013; Todo, 2006), and previous experience with foreign rms (Iršová & Havránek, 2013) have been shown to have a signi- cant impact on the strength of productivity spillovers. Görg and Greenaway (2001, 2004) suggest ve main reasons for the possible absence of positive effects in empirical studies. First, MNCs often ensure that their technological advantages and other rm-specic assets and advantages are not leaked to domestic competitors (Baltagi et al., 2015; Perri & Anders- son, 2014); second, MNCs may draw demand away from domestic rms through increased competition (Aitken & Harrison, 1999; Gorg & Strobl, 2001; Kon- ings, 2001); thirdly, positive spillovers may only affect a small number of domestic rms due to geographical distance, absorptive capacity, rm size, industry char- acteristics, and technology gap (Aitken & Harrison, 1999; Keller & Yeaple, 2009; Kokko et al., 1996); fourth, spillovers may only occur through FDI-induced ver- tical integration and not horizontally; and fth, the strength of FDI spillovers depends on a number of characteristics of the host country, such as the rule of law, well-functioning markets, and an undistorted trade and foreign investment regime. Higher inequality was found in our study to be linked positively with growth rates. This result is consistent with some recent empirical work showing that inequality has a different effect on growth in the shorter run, when positive effects are expected to pre- vail, compared to the long run, when the effects of higher inequality tend to be negative (Halter et al., 92 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 2014). The result is also consistent with previous ev- idence that the effects are different in developed and developing countries (Neves et al., 2016) and become positive in high-income economies (Castelló-Climent, 2010). When interpreting our results on inequality and growth, we therefore have to bear in mind that we were looking at developed countries and measuring short- to medium-term effects. Tests for institutional determinants also require deeper inquiry. The size and direction of the impact varies the most among institutional determinants. Only the development of infrastructure shows a con- sistently positive and signicant relation with rm growth in all specications, which is consistent with recent ndings on the importance of ICT and trans- port infrastructure (Bergantino et al., 2023; Cardona et al., 2013). A lower bureaucratic burden and bet- ter educational system exhibit positive correlation to rm growth in cross-country BE specications, which is also in line with existing empirical evidence. The nancial system, on the other hand, shows a statis- tically signicant negative relation to rm growth in most of the specications. This nding corroborates empirical evidence that began to accumulate after the global economic crisis of 2007–2008, showing that the relationship between nance and growth is non- linear and turns negative for high-income countries (Arcand et al., 2015; Law & Singh, 2014). Arcand et al. (2015) nd that nancial depth starts having a nega- tive effect on growth when credit to the private sector reaches 80–100 per cent of GDP . For the European Union countries, the value of domestic credit to the private sector did not fall below 89 per cent in the period 2005–2017 (World Bank, n.d.), suggesting that the countries in our sample have reached a threshold above which nancial deepening can become a drag on economic growth. Another possible explanation for our result on the nancial system is the issue of measurement. How to adequately measure nancial development remains an important challenge (Val- ickova et al., 2015). Our synthetic indicator includes some elements of nancial activity but may be inade- quate for capturing effective nancial intermediation. Further research on this topic is needed. Results of BE regressions also indicate that countries with bet- ter rule of law garner rms with higher revenue and employment growth, while in countries with better macroeconomic stability, rms exhibit lower growth rates on average. Most other institutional variables, including health- care, regulation, security, taxes, political environment, and labour market regulation have lower t-values, and their direction of effect varied based on whether we predicted employment or revenue growth or used AR revenue and employment regression models. For these institutional indicators, we have not found enough statistical evidence to consistently prove that cross-country differences in these institutional elds have any effect on rm growth. The study has some limitations. Some of the data we used in our analysis, such as the World Bank’s Doing Business data, are subject to criticism. The World Bank’s Doing Business indicators have been reviewed several times as their methodology and re- liability have been questioned (Arslan, 2020; Berg & Cazes, 2007). The data limitations associated with these indicators are due to several factors. Firstly, the reliance on interviews, which are often conducted with a limited number of respondents, leads to a bias in the results, as demonstrated by the signicant uc- tuations in indicators such as innovativeness, which cannot change drastically on an annual basis. Sec- ondly, the methodology for selecting representative cases lacks transparency, leading to a potential se- lection bias and ignoring the diversity of solutions offered by different national jurisdictions. Thirdly, en- forcement procedures are not taken into account, so crucial aspects of labour market dynamics are ne- glected. In addition, the aggregation and weighting system used in the creation of the indicators may overlook important variables and adjustment chan- nels, while subjective interpretations and biases in the formulation of the questionnaire further under- mine the reliability of the data. However, given the limitations and increasing use of secondary data in economics and business studies over the past fty years (Nielsen et al., 2020) and the declining share of primary research in rm growth (Cerar et al., 2021), the integration of multi-level secondary sources (in- cluding survey-based data) can partially compensate for the shortcomings of data sources. 4 Conclusions Our results implicate that explaining rm-level growth outcomes requires the inclusion of explana- tory variables from multiple levels since rm, in- dustry, and country determinants interplay in the process of rm growth. We have produced new em- pirical evidence on growth determinants of European rms in the period from 2005 to 2017. The empir- ical evidence is in line with theoretical predictions; rm-level factors have been identied as the most important. Productivity and skills, reecting manage- rial and resource limits, have been identied as the most relevant and signicant determinants of rm growth. The role of unemployment as a determinant is found to be dual in character. On the one hand, the between effects support the theory of the endogenous ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 93 economic growth cycle (Goodwin, 1967), where un- employment levels are inversely related with upward wage pressures, linking higher unemployment with the lowest point in terms of output in the economic cycle, thus positively correlating unemployment with individual rm growth. On the other hand, the short- term changes in unemployment have a procyclical effect on rm growth rates, where a fall of aggre- gate unemployment is associated with higher rm growth. Inward FDI, natural resources, the development of infrastructure, educational system, rule of law, and a better-functioning bureaucracy are shown to have a positive long-term relation with rm growth. Our results suggest that at a higher level of a country’s nancial development, more nance is related to less growth. We have also found that higher inequality has a positive relation with rm growth. On the other hand, we have not found statistical evidence of the relevance of long-term differences in labour market regulation, overall taxation, security, the political en- vironment, healthcare system, and regulation for rm growth. The results of our study have managerial and pol- icy implications. In a complex interplay of many determinants at different levels, we have been able to identify those that unambiguously stimulate rm growth. These should be supported through manage- rial and policy incentives. The ndings suggest that rm-level growth strategies should include striving for productivity, high skill levels, and the creation of intangible resources. Managers and entrepreneurs should also consider the quality of the institutional environment when deciding where to locate their op- erations, focusing on the factors that our study has identied as relevant for rm growth. In the case of the environment in which they already operate, they might consider these ndings when assessing how to overcome the environment’s limitations or capitalize on its strengths. While managers can focus on the factors that have an impact on rm capabilities, policy makers can help create an environment that provides business opportunities and lowers the risks associated with investment. 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Bureaucracy Source 1 Cost of building permits (inverse) DB 2 Number of building permit procedures (inverse) DB 3 Duration of building permit procedures (inverse) DB 4 Cost of enforcing contracts (inverse) DB 5 Number of procedures for enforcing contracts (inverse) DB 6 Duration of enforcing contracts (inverse) DB 7 Paying taxes: payments (number per year) (inverse) DB 8 Paying taxes: time (inverse) DB 9 Registering property: cost (% of property value)—score DB 10 Registering property: procedures (number)—score DB 11 Registering property: Time (days)—score DB 12 Starting a business: cost—men (% of income per capita)—score DB 13 Starting a business: procedures required—men (number)—score DB 14 Starting a business: time—men (days)—score DB 15 Bureaucracy to trade across borders—score DB 16 Cost of business start-up procedures (% of GNI per capita) (inverse) WDI II. Financial system 1 Getting credit total score DB 2 Protecting minority investment DB 3 Account ownership at a nancial institution or with a mobile-money-service provider (% of population ages 15 C) WDI 4 Automated teller machines (ATMs) (per 100,000 adults) WDI 5 Commercial bank branches (per 100,000 adults) WDI 6 Bank capital to assets ratio (%) WDI 7 Bank nonperforming loans to total gross loans (%) (inverse) WDI 8 Domestic credit provided by nancial sector (% of GDP) WDI 9 Insurance and nancial services (% of commercial service exports) WDI 10 Insurance and nancial services (% of commercial service imports as a share of commercial service exports) (inverse) WDI 11 Net foreign assets (current LCU) per capita WDI 12 Strength of legal rights index (0D weak to 12D strong) WDI 13 Protection of minority shareholders’ interests WEFGCI III. Regulation 1 Regulatory quality WGI 2 Resolving insolvency score DB 3 Starting a business: paid-in minimum capital (% of income per capita)—score DB 4 Factor 6: Regulatory enforcement DB 5 Government regulations are effectively enforced DB 6 Government regulations are applied and enforced without improper inuence DB 7 Administrative proceedings are conducted without unreasonable delay DB 8 Due process is respected in administrative proceedings DB 9 The government does not expropriate without lawful process and adequate compensation DB 10 Burden of government regulation GCIWEF 11 Efciency of legal framework in settling disputes GCIWEF 12 Efciency of legal framework in challenging regulations GCIWEF 13 Strength of auditing and reporting standards GCIWEF IV . Labour market 1 Vulnerable employment, total (% of total employment) (modelled ILO estimate) (inverse) WDI 2 Wage and salaried workers, total (% of total employment) (modelled ILO estimate) WDI 3 Subindicator “Valid grounds for dismissals” ILO 4 Subindicator “Prohibited grounds for dismissals” ILO 5 Subindicator “Maximum probationary (trial) period” ILO 6 Subindicator “Procedural requirements for dismissals” ILO 7 Subindicator “Notice periods” ILO 8 Subindicator “Severance pay” ILO 9 Subindicator “Redundancy pay” ILO 10 Subindicator “Redress” ILO (continued on next page) ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 99 Table A1. (continued) 11 Maximum probationary (trial) period, in months ILO 12 Average notice period ILO 13 Average redundancy pay ILO 14 Average severance pay ILO 15 The law, as opposed to the contracting parties, determines the legal status of the worker CCBR 16 Part-time workers have the right to equal treatment with full-time workers CCBR 17 Part-time workers have equal or proportionate dismissal rights to full-time workers CCBR 18 Fixed-term contracts are allowed only for work of limited duration CCBR 19 Fixed-term workers have the right to equal treatment with permanent workers CCBR 20 Maximum duration of xed-term contracts CCBR 21 Agency work is prohibited or strictly controlled CCBR 22 Agency workers have the right to equal treatment with permanent workers of the user undertaking CCBR 23 Annual leave entitlements CCBR 24 Public holiday entitlements CCBR 25 Overtime premia CCBR 26 Weekend working CCBR 27 Limits to overtime working CCBR 28 Duration of the normal working week CCBR 29 Maximum daily working time CCBR 30 Legally mandated notice period CCBR 31 Legally mandated redundancy compensation CCBR 32 Minimum qualifying period of service for normal case of unjust dismissal CCBR 33 Law imposes procedural constraints on dismissal CCBR 34 Law imposes substantive constraints on dismissal CCBR 35 Reinstatement normal remedy for unfair dismissal CCBR 36 Notication of dismissal CCBR 37 Redundancy selection CCBR 38 Priority in reemployment CCBR 39 Right to unionization CCBR 40 Right to collective bargaining CCBR 41 Duty to bargain CCBR 42 Extension of collective agreements CCBR 43 Closed shops CCBR 44 Codetermination: board membership CCBR 45 Codetermination and information/consultation of workers CCBR 46 Unofcial industrial action (the legality of industrial action does not depend on trade union involvement or authorization) CCBR 47 Political industrial action (political strikes are regarded as contra bonos mores under the general criminal and civil law, and hence prohibited. Strikes must be directed against the primary employer) CCBR 48 Secondary industrial action (secondary and solidarity strikes are viewed as unlawful for the same reason as political strikes) CCBR 49 Lockouts (prohibition) CCBR 50 Right to industrial action CCBR 51 Waiting period prior to industrial action CCBR 52 Peace obligation (strikes may not be called while a collective agreement, which generally implies a contractual peace obligation, is in force) CCBR 53 Compulsory conciliation or arbitration (there is no requirement of compulsory conciliation or arbitration although a strike is unlawful if its object is subject to compulsory arbitration under codetermination law) CCBR 54 Replacement of striking workers CCBR V . Infrastructure 1 Getting electricity score DB 2 Air transport, freight (million ton-km) per capita WDI 3 Air transport, passengers carried per capita WDI 4 Air transport, registered carrier departures worldwide per capita WDI 5 Fixed telephone subscriptions (per 100 people) WID 6 Mobile cellular subscriptions (per 100 people) WID 7 Internet users (per 100 people) EI 8 Personal computers (per 100 people) EI 9 Mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene (per 100,000 population) (inverse) HPS 10 People practicing open defecation (% of population) (inverse) HPS 11 People practicing open defecation, rural (% of rural population) (inverse) HPS (continued on next page) 100 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 Table A1. (continued) 12 People practicing open defecation, urban (% of urban population) (inverse) HPS 13 People using at least basic drinking water services (% of population) HPS 14 People using at least basic drinking water services, rural (% of rural population) HPS 15 People using at least basic drinking water services, urban (% of urban population) HPS 16 People using at least basic sanitation services (% of population) HPS 17 People using at least basic sanitation services, rural (% of rural population) HPS 18 People using at least basic sanitation services, urban (% of urban population) HPS 19 People using safely managed drinking water services (% of population) HPS 20 People using safely managed sanitation services (% of population) HPS 21 Electric power consumption (kWh per capita) WDI 22 Electric power transmission and distribution losses (% of output) (inverse) WDI 23 Electricity production from coal sources (% of total) WDI 24 Electricity production from hydroelectric sources (% of total) WDI 25 Electricity production from natural gas sources (% of total) WDI 26 Electricity production from nuclear sources (% of total) WDI 27 Electricity production from oil sources (% of total) WDI 28 Electricity production from oil, gas and coal sources (% of total) WDI 29 Fixed broadband subscriptions (per 100 people) WDI 30 Logistics performance index: Ability to track and trace consignments (1D low to 5D high) WDI 31 Logistics performance index: Competence and quality of logistics services (1D low to 5D high) WDI 32 Logistics performance index: Ease of arranging competitively priced shipments (1D low to 5D high) WDI 33 Logistics performance index: Efciency of customs clearance process (1 D low to 5D high) WDI 34 Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1D low to 5D high) WDI 35 Logistics performance index: Overall (1D low to 5D high) WDI 36 Logistics performance index: Quality of trade and transport-related infrastructure (1D low to 5D high) WDI 37 Quality of port infrastructure, WEF (1D extremely underdeveloped to 7D well developed and efcient by international standards) WDI 38 Rail lines (total route-km) per capita WDI 39 Railways, goods transported (million ton-km) per capita WDI 40 Railways, passengers carried (million passenger-km) per capita WDI 41 Water productivity, total (constant 2010 US$ GDP per cubic meter of total freshwater withdrawal) WDI 42 Quality of overall infrastructure GCIWEF 43 Quality of roads GCIWEF 44 Quality of railroad infrastructure GCIWEF 45 Quality of port infrastructure GCIWEF 46 Quality of air transport infrastructure GCIWEF VI. Healthcare 1 Specialist surgical workforce (per 100,000 population) WDI 2 Adolescent fertility rate (births per 1,000 women ages 15-19) (inverse) HPS 3 Completeness of birth registration (%) HPS 4 Completeness of death registration with cause-of-death information (%) HPS 5 Current health expenditure per capita, PPP (current international $) HPS 6 Current health expenditure (% of GDP) HPS 7 Domestic general government health expenditure (% of current health expenditure) HPS 8 Domestic general government health expenditure (% of GDP) HPS 9 Domestic general government health expenditure (% of general government expenditure) HPS 10 Domestic general government health expenditure per capita (current US$) HPS 11 Domestic general government health expenditure per capita, PPP (current international $) HPS 12 Hospital beds (per 1,000 people) HPS 13 Immunization, DPT (% of children ages 12-23 months) HPS 14 Immunization, Hib3 (% of children ages 12-23 months) HPS 15 Immunization, measles (% of children ages 12-23 months) 16 Immunization, Pol3 (% of one-year-old children) HPS 17 Incidence of tuberculosis (per 100,000 people) (inverse) HPS 18 Lifetime risk of maternal death (%) (inverse) 19 Maternal mortality ratio (modelled estimate, per 100,000 live births) (inverse) HPS 20 Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) (inverse) HPS 21 Number of deaths ages 5-14 years per capita (inverse) HPS 22 Number of infant deaths per capita (inverse) HPS 23 Nurses and midwives (per 1,000 people) HPS (continued on next page) ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 101 Table A1. (continued) 24 Out-of-pocket expenditure (% of current health expenditure) (inverse) HPS 25 Out-of-pocket expenditure per capita (current US$) (inverse) HPS 26 Out-of-pocket expenditure per capita, PPP (current international $) (inverse) HPS 27 Physicians (per 1,000 people) HPS 28 Maternal leave benets (% of wages paid in covered period) HPS 29 Number of weeks of maternity leave HPS 30 Risk of catastrophic expenditure for surgical care (% of people at risk) (inverse) HPS 31 Risk of impoverishing expenditure for surgical care (% of people at risk) (inverse) HPS VII. Taxes 1 Other taxes (% of prot) DB 2 Paying taxes: Labour tax and contributions (% of commercial prots) DB 3 Prot tax DB 4 Social contributions (% of revenue) WDI 5 Tax revenue (% of GDP) WDI 6 Taxes on goods and services (% of revenue) WDI 7 Taxes on goods and services (% value added of industry and services) WDI 8 Taxes on income, prots and capital gains (% of revenue) WDI 9 Taxes on income, prots and capital gains (% of total taxes) WDI VIII. Macro stability 1 External balance on goods and services (% of GDP) WDI 2 Final consumption expenditure (% of GDP) WDI 3 Birth rate (inverse) WDI 4 Employment WDI 5 GDP per capita, PPP (constant 2011 international $) WDI 6 Foreign direct investment, net inows (% of GDP) WDI 7 Labour force participation rate WDI 8 Life expectancy WDI 9 Ratio of female to male labour force participation rate (%) (national estimate) WDI 10 Total natural resources rents (% of GDP) WDI 11 Unemployment, total (% of total labour force) (national estimate) (inverse) WDI 12 Age dependency ratio (% of working-age population) HPS 13 General government nal consumption expenditure (% of GDP) WDI 14 GINI index (World Bank estimate) (inverse) WDI 15 Gross xed capital formation (% of GDP) WDI 16 Refugee population by country or territory of asylum per capita (inverse) WDI 17 Share of youth not in education, employment or training, total (% of youth population) (inverse) WDI 18 Tariff rate, applied, simple mean, all products (%) (inverse) WDI IX. Political environment 1 Control of corruption: estimate WGI 2 Government effectiveness: estimate WGI 3 Political stability and absence of violence/terrorism: estimate WGI 4 Voice and accountability: estimate WGI 5 Factor 1: constraints on government powers WJP 6 Government powers are effectively limited by the legislature WJP 7 Government powers are effectively limited by the judiciary WJP 8 Government powers are effectively limited by independent auditing and review WJP 9 Government ofcials are sanctioned for misconduct WJP 10 Government powers are subject to non-governmental checks WJP 11 Transition of power is subject to the law WJP 12 Factor 2: absence of corruption WJP 13 Government ofcials in the executive branch do not use public ofce for private gain WJP 14 Government ofcials in the judicial branch do not use public ofce for private gain WJP 15 Government ofcials in the police and the military do not use public ofce for private gain WJP 16 Government ofcials in the legislative branch do not use public ofce for private gain WJP 17 Factor 3: open government WJP 18 Publicized laws and government data WJP 19 Right to information WJP 20 Civic participation WJP 21 Complaint mechanisms WJP (continued on next page) 102 ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 Table A1. (continued) 22 Diversion of public funds GCIWEF 23 Public trust in politicians GCIWEF 24 Irregular payments and bribes GCIWEF 25 Favouritism in decisions of government ofcials GCIWEF 26 Efciency of government spending GCIWEF 27 Transparency of government policymaking GCIWEF X. Rule of law 1 Rule of law: estimate WGI 2 Factor 4: fundamental rights WJP 3 Equal treatment and absence of discrimination WJP 4 The right to life and security of the person is effectively guaranteed WJP 5 Due process of law and rights of the accused WJP 6 Freedom of opinion and expression is effectively guaranteed WJP 7 Freedom of belief and religion is effectively guaranteed WJP 8 Freedom from arbitrary interference with privacy is effectively guaranteed WJP 9 Freedom of assembly and association is effectively guaranteed WJP 10 Fundamental labour rights are effectively guaranteed WJP 11 Factor 7: civil justice WJP 12 People can access and afford civil justice WJP 13 Civil justice is free of discrimination WJP 14 Civil justice is free of corruption WJP 15 Civil justice is free of improper government inuence WJP 16 Civil justice is not subject to unreasonable delay WJP 17 Civil justice is effectively enforced WJP 18 Alternative dispute resolution mechanisms are accessible, impartial, and effective WJP 19 Factor 8: criminal justice WJP 20 Criminal investigation system is effective WJP 21 Criminal adjudication system is timely and effective WJP 22 Correctional system is effective in reducing criminal behaviour WJP 23 Criminal system is impartial WJP 24 Criminal system is free of corruption WJP 25 Criminal system is free of improper government inuence WJP 26 Due process of law and the rights of the accused WJP 27 Property rights GCIWEF 28 Intellectual property protection GCIWEF 29 Judicial independence GCIWEF XI. Security 1 Intentional homicides (per 100,000 people) (inverse) WDI 2 Losses due to theft and vandalism (% of annual sales of affected rms) (inverse) WDI 3 Factor 5: order and security WJP 4 Crime is effectively controlled WJP 5 Civil conict is effectively limited WJP 6 People do not resort to violence to redress personal grievances WJP 7 Business costs of terrorism GCIWEF 8 Business costs of crime and violence GCIWEF 9 Organized crime GCIWEF 10 Reliability of police services GCIWEF XII. Education 1 Scientic and technical journal articles per capita WDI 2 Adjusted net enrolment rate, lower secondary, both sexes (%) EI 3 Adjusted net enrolment rate, primary, both sexes (%) EI 4 Barro-Lee: average years of primary schooling, age 15C, total EI 5 Barro-Lee: average years of secondary schooling, age 15C, total EI 6 Barro-Lee: average years of tertiary schooling, age 15C, total EI 7 Barro-Lee: average years of total schooling, age 15C, total EI 8 Barro-Lee: percentage of female population age 15C with no education EI 9 Barro-Lee: percentage of female population age 15C with primary schooling. Completed primary EI 10 Barro-Lee: percentage of female population age 15C with primary schooling. Total (incomplete and completed primary) EI 11 Barro-Lee: percentage of female population age 15C with secondary schooling. Completed secondary EI (continued on next page) ECONOMIC AND BUSINESS REVIEW 2024;26:81–103 103 Table A1. (continued) 12 Barro-Lee: percentage of female population age 15C with secondary schooling. Total (incomplete and completed secondary) EI 13 Barro-Lee: percentage of female population age 15C with tertiary schooling. Completed tertiary EI 14 Barro-Lee: percentage of female population age 15C with tertiary schooling. Total (incomplete and completed tertiary) EI 15 Barro-Lee: percentage of population age 15C with no education EI 16 Barro-Lee: percentage of population age 15C with primary schooling. Completed primary EI 17 Barro-Lee: percentage of population age 15C with primary schooling. Total (incomplete and completed primary) EI 18 Barro-Lee: percentage of population age 15C with secondary schooling. Completed secondary EI 19 Barro-Lee: percentage of population age 15C with secondary schooling. Total (incomplete and completed secondary) EI 20 Barro-Lee: percentage of population age 15C with tertiary schooling. Completed tertiary EI 21 Barro-Lee: percentage of population age 15C with tertiary schooling. Total (incomplete and completed tertiary) EI 22 Cumulative drop-out rate to the last grade of lower secondary general education, both sexes (%) (inverse) EI 23 Cumulative drop-out rate to the last grade of primary education, both sexes (%) (inverse) EI 24 Duration of compulsory education (years) EI 25 Early school leavers from primary education, both sexes (number) EI 26 Effective transition rate from primary to lower secondary general education, both sexes (%) EI 27 Enrolment in early childhood education, both sexes per capita EI 28 Enrolment in early childhood education, public institutions, both sexes share EI 29 Enrolment in lower secondary education, both sexes (number) per capita EI 30 Enrolment in lower secondary education, public institutions, both sexes share EI 31 Enrolment in post-secondary non-tertiary education, both sexes (number) per capita EI 32 Enrolment in post-secondary non-tertiary education, public institutions, both sexes (number) share EI 33 Enrolment in pre-primary education, both sexes (number) per capita EI 34 Enrolment in pre-primary education, public institutions, both sexes (number) share 35 Enrolment in primary education, both sexes (number) per capita EI 36 Enrolment in primary education, public institutions, both sexes (number) share EI 37 Enrolment in secondary education, both sexes (number) per capita EI 38 Enrolment in secondary education, public institutions, both sexes (number) share EI 39 Enrolment in secondary vocational, both sexes (number) share EI 40 Enrolment in tertiary education, all programmes, both sexes (number) per capita EI 41 Enrolment in upper secondary education, both sexes (number) per capita EI 42 Enrolment in upper secondary education, public institutions, both sexes (number) share EI 43 Enrolment in upper secondary vocational, both sexes (number) share EI 44 Expenditure on education as % of total government expenditure (%) EI 45 Government expenditure on education as % of GDP (%) EI 46 Graduates from tertiary education, both sexes (number) per capita EI 47 Harmonized test scores, total EI 48 Labour force with advanced education (% of total labour force) EI 49 Labour force with basic education (% of total labour force) EI 50 Labour force with intermediate education (% of total labour force) EI 51 Lower secondary completion rate, both sexes (%) EI 52 Ofcial entrance age to compulsory education (years) (inverse) EI 53 Out-of-school adolescents of lower secondary school age, both sexes (number) per capita (inverse) EI 54 Out-of-school children of primary school age, both sexes (number) per capita (inverse) EI 55–77 PISA: 15-year-olds RESULTS below Level 1 EI 78 Primary completion rate, both sexes (%) EI 79 Pupil-teacher ratio in lower secondary education (headcount basis) EI 80 Pupil-teacher ratio in pre-primary education (headcount basis) EI 81 Pupil-teacher ratio in primary education (headcount basis) EI 82 Pupil-teacher ratio in secondary education (headcount basis) EI 83 Pupil-teacher ratio in tertiary education (headcount basis) EI 84 Pupil-teacher ratio in upper secondary education (headcount basis) EI 85 Rate of out-of-school children of primary school age, both sexes (%) (inverse) EI 86 Rate of out-of-school youth of upper secondary school age, both sexes (%) (inverse) EI 87 Expenditure on secondary education (% of government expenditure on education) WDI 88 Expenditure on tertiary education (% of government expenditure on education) WDI 89 Research and development expenditure (% of GDP) WDI 90 Researchers in R&D (per million people) WDI Notes: DB—Doing Business; WDI—World Development Indicators by the World Bank (WB); EI—WB Education Indicators; HPS—WB Health and Population Statistics; WGI—Worldwide Governance Indicators; WJP—World Justice Project; GCIWEF—Global Competitiveness Index by World Economic Forum; CCBR - Labour Regulation Index (Cambridge: Centre for Business Research); ILO – International Labour Organization.