The Role of a Nation's Culture in the Country's Governance: Stochastic Frontier Analysis
TThe Role of a Nation’s Culture in the Country’s Governance:Stochastic Frontier Analysis
Vladimír Holý
Prague University of Economics and BusinessWinston Churchill Square 1938/4, 130 67 Prague 3, Czech [email protected]
Tomáš Evan
Czech Technical University in PragueThákurova 2077/7, 160 00 Prague 6, Czech Republictomas.evan@fit.cvut.cz
February 11, 2021
Abstract:
What role does culture play in determining institutions in a country? This paper arguesthat the establishment of institutions is a process originating predominantly in a nation’s cultureand tries to discern the role of a cultural background in the governance of countries. We use the sixHofstede’s Cultural Dimensions and the six Worldwide Governance Indicators to test the strengthof the relationship on 94 countries between 1996 and 2019. We find that the strongest culturalcharacteristics are Power Distance with negative effect on governance and Long-Term Orientationwith positive effect. We also determine how well countries transform their cultural characteristicsinto institutions using stochastic frontier analysis.
Keywords:
Hofstede’s Cultural Dimensions, Worldwide Governance Indicators, Technical Efficiency,Stochastic Frontier Analysis.
JEL Codes:
C23, C44, O15, O43.
In recent years, the growing body of literature claims a strong relationship between culture as definedand measured by Hofstede et al. (2010) and institutions as systematically described i.a. by North(1990), Olson (1996, 1998) and a large variety of socio-economic phenomena. Culture and economicinstitutions are both independently important for economic development but their impact is strongerwhen combined (Williamson and Mathers, 2011). This combined effect is at least of the same mag-nitude as variables of the standard production function (Evan and Bolotov, 2021). The relationshipbetween culture and institutions remains, however, notoriously complex and thus difficult to measure.In this paper we have decided to discern the role of a cultural background in governance of a country.Specifically, we have two goals:1. To examine the strength of the relationship between cultural characteristics of countries andthe quality of their institutions.2. To determine how well individual countries transform their cultural characteristics into institu-tions.In line with Williamson and Mathers (2011), we consider that the establishment of institutionsis a process originating in a nation’s culture. Holders of a culture conducive to development maychoose to formalize informal institutions into formal ones thus promoting this development. To thisend, we employ stochastic frontier analysis of Aigner et al. (1977) and Meeusen and van Den Broeck(1977), which studies how efficiently a producer can transform inputs into outputs. As inputs, we use
Hofstede’s Cultural Dimensions (Hofstede et al., 2010) measuring the characteristics of a culture. As1 a r X i v : . [ ec on . GN ] F e b utputs, we use Worldwide Governance Indicators (Kaufmann et al., 2011) measuring the quality ofinstitutions. Furthermore, to capture the economic environment, we use the gross domestic product(GDP) per capita. This framework allows us to achieve both of our research goals.The rest of the paper is structured as follows. In Section 2, we lay out the theoretical foundationsand review the related economic literature. In Section 3, we describe the cultural dimensions and thegovernance indicators. In Section 4, we specify the stochastic frontier model we use. In Section 5, wediscuss the results of the efficiency analysis. We conclude the paper in Section 6.
The idea of a significant cultural influence over institutions and governance is over a hundred yearsold (Weber, 1905) but was strongly criticised by both Marxists (Grossman, 2006) and by mainstreameconomists and remains a hotly debated issue to this day (Blum and Dudley, 2001; Becker andWoessmann, 2009; Cantoni, 2015; Kersting et al., 2020). Mainstream economics has not readilyeven accepted institutions as a source of economic growth and development, innovation or quality ofgovernment, as the criticisms of the separate school of New Institutional Economics attests (Coase,1960, 1998; North, 1968, 1990; Olson, 1998; Hall and Jones, 1999, i.a.). The idea has thus onlyvery slowly made its appearance in economics before growing exponentially in recent years. Thereare papers using statistical analysis linking culture to innovation (Zien and Buckler, 1997; Telliset al., 2009; Williams and McGuire, 2010), population growth (Shennan, 2001; Yacout and Hefny,2015; Kumar et al., 2019), environmental issues (Peng and Lin, 2009; Nagy and Konyha Molnárné,2018; Dangelico et al., 2020), tax systems and collection (Alm and Torgler, 2006; Koenig et al., 2012;Čábelková and Strielkowski, 2013), corruption (Huber, 2001; Yeganeh, 2014), software piracy (Husted,2000; Simmons and Tan, 2002), and terrorism (Meierrieks and Gries, 2013; Kluch and Vaux, 2017)as well as a vast array of other systems or institutions. The first meta-analyses have appeared (Taraset al., 2010; Büschgens et al., 2013) as there is enough data to attempt more complex studies. It seemsthe pendulum has swung from the cautious Boettke: “we cannot assume away cultural influences aseconomists have often done” (Boettke, 2009, p. 436) to the more radical Landes: “Max Weber had itright. If we learn anything from the history of economic development, it is that culture makes almostall the difference” (Landes, 2000, p. 2).The idea that culture has a significant impact on the reality surrounding us either directly orindirectly through institutions in a variety of situations can be accepted. There is agreement on theprimacy of human marketable capital, or culture as defined by Olson (1996) for both the improvementof institutions (e.g. democratization) and economic growth. There is also overwhelming evidence fora causal link between particular economic institutions, most notably property rights and economicfreedom, and economic development (for a recent literature review and summary of evidence, pleaseconsult i.e. Feld and Kirchgässner, 2008; Acemoglu and Robinson, 2010; Czeglédi, 2014; Wanjuu andle Roux, 2017). To establish a definite causal link between institutions and economic growth hasproven difficult. It means among other things understanding the technology of the transmission ofinstitutional quality to economic growth and development. This includes three challenges as describedby Docquier (2014), i.e. (i) disentangling the causal effects and reversing the causal effects, (ii)accounting for unobserved shocks affecting both institutions and growth, and (iii) capturing the lagstructure of the relationship. Acemoglu et al. (2005) claims political institutions as a fundamentalcause of long-run economic growth. According to Acemoglu et al. (2005), the knowledge of politicalinstitutions and the distribution of resources, or de facto political power, are sufficient to determineall the other variables in the system.Some authors suggested that current measurement strategies have conceptual flaws and Humancapital rather than political institutions have a causal effect on economic growth (Djankov et al.,2003; Glaeser et al., 2004) also hinting at potentially significant reverse causal effects: “institutionaloutcomes also get better as the society grows richer, because institutional opportunities improve”(Glaeser et al., 2004, p. 298). The standard institutionalists’ approach that civil rights and democraticinstitutions cause development has been rather weakened in recent years as several studies usingGranger regressions have found no evidence of this causality (Paldam and Gundlach, 2012; Murtin2nd Wacziarg, 2014). Some authors returned to Lipset’s hypothesis (Lipset, 1959; Barro, 1997)reversing that causation and suggesting that development leads to democracy and civil rights andthe issue remains unresolved (Acemoglu et al., 2014; Jung and Sunde, 2014; Czeglédi, 2015). In ourpaper we decided to control for the economic environment as our prime aim is to examine the role ofa cultural background in governance of a country.While we do not fully understand the relationship between institutions and economic growth ordevelopment it is more difficult still to determine the relationship between culture and institutions, orwhich one is more powerful in explaining the development in societies. A point in case is “Long-TermPersistence”, a paper by Guiso et al. (2016) in which they argue that Italian cities experiencing self-governance in the Middle Ages had a higher level of civic capital than other Italian cities. They offerthree hypotheses with different causalities between culture and institutions (participation in publiclife in communes teaches people to cooperate and it is not forgotten; past democratic institutionschange levels of trust and fairness in societies; historical events lead to changes in socialisation).Apart from complexity and long time periods for change, the issue of complementarities arises be-tween culture and institutions hindering definitions and identification of channels of causality (Alesinaand Giuliano, 2015) . The complementarity hypothesis suggests the strength of the impact of cultureand institutions lies in their combination. The impact of a high trust culture complemented by ahigh level of rule of law enforcement on the business environment would be a good example. Bothculture and institutions would be significant in regression analysis. Williamson and Mathers (2011)shed some light on the issue by testing it. They claim it is the substitution effect between culture andinstitutions which is more important. They describe this important mechanism: “a culture conduciveto economic growth may choose to formalize the informal institutions into institutions associated witheconomic freedom” (p. 316). Once the formal rules (institutions) are credible “the informal normsand mechanisms once relied upon for economic interaction and exchange, such as trust networks,may be rendered much less important” (p. 316). Culture is important when institutions promotingeconomic freedom and thus growth are absent but diminishing in significance when those institutionsare established. This implies the different relative importance of culture and institutions dependingon the level of economic development of particular countries. Less developed countries should ceterisparibus have a stronger influence of culture on institutions than more developed ones. The term culture in its broad sense includes both civic culture (formal, institutions) and personalculture (informal) (North, 1990; Olson, 1996, i.a.). In this text, we use the narrow definition referringonly to personal culture as we want to discern the difference between the two. To measure culture, weuse
Hofstede’s Cultural Dimensions . In our efficiency analysis, these dimensions act as input variables.Hofstede et al. (2010) define the six cultural dimensions in the following way:1.
Power Distance (PDI) is the extent to which the less powerful members of institutions andorganizations within a country expect and accept that power is distributed unequally.2.
Individualism (IDV) pertains to societies in which the ties between individuals are loose: ev-eryone is expected to look after him- or herself and his or her immediate family. Collectivism asits opposite pertains to societies in which people from birth onward are integrated into strong,cohesive in-groups, which throughout people’s lifetime continue to protect them in exchange forunquestioning loyalty.3.
Masculinity (MAS) refers to society where emotional gender roles are clearly distinct: men aresupposed to be assertive, tough, and focused on material success, whereas women are supposedto be more modest, tender, and concerned with the quality of life. A society is called femininewhen emotional gender roles overlap: both men and women are supposed to be modest, tender,and concerned with the quality of life. Please also refer to Alesina and Giuliano (2015) for a literature review of culture and institutions. Uncertainty Avoidance (UAI) is the extent to which the members of a culture feel threatenedby ambiguous or unknown situations.5.
Long-Term Orientation (LTO) stands for the fostering of virtues oriented toward future rewards– in particular, perseverance and thrift. Its opposite pole, short-term orientation, stands forthe fostering of virtues related to the past and present – in particular, respect for tradition,preservation of “face”, and fulfilling social obligations.6.
Indulgence (IVR) stands for a tendency to allow relatively free gratification of basic and naturalhuman desires related to enjoying life and having fun. Its opposite pole, restraint, reflects aconviction that such gratification needs to be curbed and regulated by strict social norms.We standardize the variables to lie between 0 and 1. Note that as culture is viewed as unchangeable,the variables are static. The source of the data is Hofstede Insights (2020) with the methodology de-scribed in Hofstede et al. (2010). Alternatively, the personal culture could be measured by Schwartz’sTheory of Basic Human Values and Tabellini’s Indicators of Individual Values and Beliefs. The the-ory of Hofstede’s cultural dimensions, however, remains the most popular tool. For a survey of theliterature based on Hofstede’s Cultural Dimensions, we refer to Kirkman et al. (2006) and Beugelsdijket al. (2017).To define civic culture or institutions we use the
Worldwide Governance Indicators consistingof six indicators for over 200 countries and territories over the period 1996–2019. In our efficiencyanalysis, these indicators play the role of output variables. Kaufmann et al. (2011) define the sixgovernance indicators in the following way:1.
Voice and Accountability (VA) captures perceptions of the extent to which a country’s citizensare able to participate in selecting their government, as well as freedom of expression, freedomof association, and a free media.2.
Political Stability and Absence of Violence/Terrorism (PV) captures perceptions of the like-lihood that the government will be destabilized or overthrown by unconstitutional or violentmeans, including politically-motivated violence and terrorism.3.
Government Effectiveness (GE) captures perceptions of the quality of public services, the qualityof the civil service and the degree of its independence from political pressures, the quality ofpolicy formulation and implementation, and the credibility of the government’s commitment tosuch policies.4.
Regulatory Quality (RQ) captures perceptions of the ability of the government to formulate andimplement sound policies and regulations that permit and promote private sector development.5.
Rule of Law (RL) captures perceptions of the extent to which agents have confidence in andabide by the rules of society, and in particular the quality of contract enforcement, propertyrights, the police, and the courts, as well as the likelihood of crime and violence.6.
Control of Corruption (CC) captures perceptions of the extent to which public power is exercisedfor private gain, including both petty and grand forms of corruption, as well as "capture" ofthe state by elites and private interests.The variables are standardized to have zero mean and unit standard deviation. Higher values cor-respond to better governance. The source of the data is World Bank (2020) with the methodologydescribed in Kaufmann et al. (2011).Finally, we consider the gross domestic product (GDP) per capita to be a control variable repre-senting the economic environment. For the importance of inclusion of the operating environment inan efficiency analysis, see e.g. Holý (2020). To remove the trend in time but to keep differences inlevels between countries, we define our
GDP Level variable as the difference between the logarithmof GDP per capita in USD current prices and the logarithm of the mean GDP per capita in eachyear. Note that suitable specification of the GDP variable is of great importance as it can influenceresults of an efficiency analysis (see e.g. Holý and Šafr, 2018). The source of the data is InternationalMonetary Fund (2020). 4
Stochastic Frontier Model
To capture dependence between the inputs (Hofstede’s Cultural Dimensions) and the outputs (theWorldwide Governance Indicators) and assess efficiency of the individual countries, we utilize theframework of stochastic frontier analysis of Aigner et al. (1977) and Meeusen and van Den Broeck(1977). We also include GDP Level as a control variable. We consider a separate stochastic frontiermodel for each output variable but employ the same structure for all six models. For a textbooktreatment of stochastic frontier analysis, we refer to Kumbhakar and Lovell (2000), Coelli et al.(2005), Fried et al. (2008). For a recent survey of the efficiency literature, we refer to Daraio et al.(2020).First, we build a standard linear regression model, estimate it by the ordinary least squaresmethod and verify whether there is inefficiency present in the data. Technical inefficiency manifestsas negatively skewed residuals. All output variables exhibit negative skewness ranging from -0.993(RQ) to -0.027 (CC). It is therefore suitable to include the inefficiency variable into the model andutilize stochastic frontier analysis. Evenmore, the inefficient component in stochastic frontier modelsis dominant for all six output variables as its share of the total variance ranges from 0.804 (PV) to0.922 (VA).Second, we specify whether efficiencies are static or time-varying. As our main explanatoryvariables are static, we consider the static model to be more meaningful in our application. Toquantify this, we start with the time-varying model of Battese and Coelli (1992) and find that thetrend in efficiencies is quite negligible as its associated coefficient ranges from -0.006 (PV) to 0.003(GE). We therefore resort to the time-invariant specification in the fashion of Pitt and Lee (1981).Third, we specify the distribution of the technical inefficiency variable. As proposed by Stevenson(1980), we start with the two-parameter truncated normal distribution and find that the mode µ ranges from 0.896 (GE) to 1.089 (CC). Clearly, the parameter µ is significantly different from 0 andthe distribution does not reduce to the half-normal distribution with zero mode. We therefore stickwith the truncated normal distribution.Our final model is described as follows. Let N be the number of countries, T the number of years, M the number of output variables, K the number of input variables and L the number of controlvariables. Dependent variable Y itj ; i = 1 , . . . , N ; t = 1 , . . . , T ; j = 1 , . . . , M then follows Y itj = α j + K (cid:88) k =1 β jk X itk + L (cid:88) l =1 γ jk Z itl − U ij + V itj , where X itk are input variables, Z itl are control variables, U ij are non-negative random variablescapturing technical inefficiency and V itj are random variables representing the error term. We assumethat U ij are i.i.d. with the normal distribution N( µ j , θ j σ j ) truncated at zero, V itj are i.i.d. withthe normal distribution N(0 , (1 − θ j ) σ j ) and U ij are independent of V itj . Note that we utilize theparametrization of Battese and Corra (1977) for U ij and V itj . The model includes the constantparameter α j , the parameters for the input variables β jk , the parameters for the control variables γ jl ,the variance of the random component σ j , the ratio between the variance of the inefficiency variableand the error term θ j and the mode of the inefficiency variable µ j . We estimate the model by themaximum likelihood method. Our data sample consists of N = 94 countries observed over T = 21 time periods from 1996 to2019 with years 1997, 1999 and 2001 missing. Furthermore, there are additional an 13 observationsmissing. We therefore have 1961 observations in total.We estimate both the linear regression model and the stochastic frontier model. The estimatedcoefficients are reported in Figure 1 and Table 1. The models are able to capture a large portion ofvariability in the output variables. Specifically, the R statistic in the regression model ranges from0.545 (PV) to 0.813 (GE). Both models have very similar values of the coefficients suggesting the5obustness of our approach. The significance of the coefficients, however, differs. In the stochasticfrontier models, there are much fewer significant variables. As the stochastic frontier model is basedon a more general distribution, it is more reliable and we focus solely on it from now on.We find that the direction of the effect of Hofstede’s Cultural Dimensions is consistent across allsix Worldwide Governance Indicators. Individualism, Long-Term Orientation and Indulgence have apositive effect on governance while Power Distance, Masculinity and Uncertainty Avoidance have anegative effect. The only exception is the effect of Uncertainty Avoidance on Voice and Accountibility,which is positive. Power Distance and Long-Term Orientation play a significant role in five out of sixindicators suggesting their universal impact. Masculinity, on the other hand, is found significant onlyfor Political Stability and Control of Corruption; Individualism only for Voice and Accountability.The GDP control variable has a significant positive effect for all indicators. When omitted from themodel, however, the results do not distinctly change.Finally, we examine efficiency of the individual countries. In general, the efficiency score lies in theinterval from 0 to 1 with lower values indicating inefficiency. The efficiency averaged over countriesranges from 0.357 (CC) to 0.423 (GE). The efficiency averaged over the output variables is shown inFigure 2 for the individual countries.The countries that did well in our efficiency analysis are Chile and Uruguay in Latin America,Mozambique, Burkina Faso and Ivory Coast in Africa, Slovakia and Poland and Portugal in Europeas well as Malaysia in Asia. All of these countries have high values in Hofstede’s Cultural Dimensionswhich we identified as a roadblock to good governance and development. All of the above mentionedcountries have at least the score of 60 in Power Distance, they are Short-Term Oriented (below 40,with the exception of Slovakia) and generally have a high Uncertainty Avoidance (above 50, with theexception of Malaysia and Mozambique). These countries seem to have good governance relative totheir culture.The other end is represented by countries whose culture would allow for relatively good governance,yet, it is not in place. If we omit countries in civil war, these are Venezuela and to a lesser extentArgentina in Latin America, Angola, Nigeria and Algeria in Africa, Iran in Asia and Russia andBelarus in Europe. The rest of the countries fall in the middle of the sample as they seem to have asgood governance as their culture would predict.When sorted by Worldwide Governance Indicators, countries occupied an entire range of possibleoutcomes in efficiency. A group of three countries (Portugal, Slovakia and the former Portuguesecolony of Cape Verde) ranked highest in the Voice and Accountability indicator, all above 0.9, whilefour countries namely Iran, Saudi Arabia, Libya and China ranked below 0.1. In the Political Stabilityand Absence of Violence indicator, São Tomé & Príncipe, Mozambique and Slovakia, respectively werethe only countries reaching the 0.9 mark, while Iraq, Turkey and Nigeria, followed closely by Russiawere the worst. Again, with the Government Effectiveness indicator only three countries achieved thevalue of 0.9. These are Malaysia, Portugal and Chile. Libya, Venezuela and Angola were the worstand the only countries of the sample below 0.2 level. Chile and Hong Kong both above 0.9, followedby Slovakia (0.777) were the three best countries in the Regulatory Quality indicator and Libya, Iranand Venezuela the three worst. Chile, Portugal and Hong Kong also were the best in Rule of Lawwhile Venezuela, Libya and Angola were the three worst. Finally, the Control of Corruption indicatorsaw Singapore, Uruguay and Chile at the top and Angola, Latvia and Lithuania at the bottom. Theunexpected inclusion of the latter two countries suggests we might see an anti-corruption movementin those countries as the culture of those countries (Power Distance just 44) seems to be hostile tothe current levels of corruption.The overall results in mean efficiency score are as follows: Portugal (0.822), Chile (0.821), Slovakia(0.758), Hong Kong (0.743) and Ghana (0.686) are the top five countries and Libya (0.126), Venezuela(0.143), Iran (0.144), Angola (0.156) and Russia (0.186) are the five bottom ones. It is of some interest that Portugal and former Portuguese colonies did very well in our results as they have cultureswith high Power Distance but decent results in governance indicators. Angola is the only exeption where the WorldwideGovernance Indicators are at alarming levels. IndulgenceLong−Term OrientationUncertainty AvoidanceMasculinityIndividualismPower Distance V o i c e and A cc oun t ab ili t y P o li t i c a l S t ab ili t y G o v e r n m en t E ff e c t i v ene ss R egu l a t o r y Q ua li t y R u l e o f La wC on t r o l o f C o rr up t i on −2−1012 Coefficient
Ordinary Least Squares −1.559***1.261**−0.1300.484*0.686*0.895*** −0.1730.207−0.912***−0.585**0.5660.215 −1.168***0.557−0.240−0.701***0.879***0.482*** −1.511***0.017−0.179−0.3720.780**0.326 −1.717***0.621−0.587−0.4360.770***0.415 −1.967***0.544−0.784**−0.753***0.815***0.568**
IndulgenceLong−Term OrientationUncertainty AvoidanceMasculinityIndividualismPower Distance V o i c e and A cc oun t ab ili t y P o li t i c a l S t ab ili t y G o v e r n m en t E ff e c t i v ene ss R egu l a t o r y Q ua li t y R u l e o f La wC on t r o l o f C o rr up t i on −2−1012 Coefficient
Stochastic Frontier Analysis
Figure 1: Estimated coefficients of the linear regression model and the stochastic frontier model.Table 1: Estimated coefficients with standard errors of the stochastic frontier model.
VA PV GE RQ RL CCConstant 0.882 ∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.485) (0.582) (0.277) (0.263) (0.314) (0.355)Power Distance Index − ∗∗∗ − − ∗∗∗ − ∗∗∗ − ∗∗∗ − ∗∗∗ (0.393) (0.487) (0.191) (0.400) (0.505) (0.382)Individualism 1.261 ∗∗∗ ∗ − − ∗∗∗ − − − ∗ − ∗∗∗ (0.251) (0.223) (0.229) (0.338) (0.324) (0.241)Uncertainty Avoidance 0.484 ∗∗ − ∗∗∗ − ∗∗∗ − ∗ − ∗ − ∗∗∗ (0.229) (0.219) (0.108) (0.200) (0.245) (0.124)Long-Term Orientation 0.686 ∗∗ ∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.290) (0.298) (0.176) (0.251) (0.222) (0.219)Indulgence 0.895 ∗∗∗ ∗∗∗ ∗∗∗ (0.262) (0.251) (0.144) (0.211) (0.270) (0.180)GDP Level 0.076 ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.016) (0.026) (0.017) (0.018) (0.016) (0.016)Parameter σ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.063) (0.111) (0.032) (0.050) (0.066) (0.035)Parameter θ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.013) (0.044) (0.017) (0.023) (0.027) (0.009)Parameter µ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.115) (0.213) (0.101) (0.090) (0.319) (0.074) Note: ∗∗∗ p < . ; ∗∗ p < . ; ∗ p < . .000.250.500.751.00 Eff. Score
Mean Efficiency Scores
Figure 2: Mean efficiency scores obtained from the stochastic frontier model.
We have found an albeit rudimentary measurement of the strength of the relationship between culturalcharacteristics of countries and the quality of their institutions in 94 countries over 1996–2019. Wehave also determined efficiency scores for the transformation of cultural characteristics into institutionsin each of those individual countries. The results of our study pose several policy implications andavenues for further research. After more detailed analysis, policies on foreign aid levels can beimproved as it might be possible to discern which countries’ governance can be relatively successfullyimproved and/or which countries’ governments are more effective in reforming national institutionsfor example in situations of adverse cultural patterns. The migration policies of countries decidingon levels and source countries of economic migration can also be improved as the difference betweenthe influence of culture and governance in each country becomes clearer. There are also significantimplications for international security studies and related fields.
Funding
The work of Vladimír Holý was supported by the Czech Science Foundation under project 19-08985S.
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