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Medieval European traditions in representation and state capacity today

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Abstract

Rich economies are characterized by the coincidence of, on the one hand, high state capacity and, on the other, well-functioning markets and the rule of law. They have states that are powerful and centralized and yet also limited. Furthermore, relatively low rates of shadow economic activity and tax evasion suggest that citizens perceive their states’ limitations to be credible. This suggests that a state’s ability to be credibly limited may facilitate its investments in state capacity. Consistent with this, we explore the potential link between historical traditions of representative governance institutions and state capacity today. We report that medieval and early modern representative assembly experiences positively correlate with higher tax revenues, smaller shadow economies, greater state control of violence and yet fewer state resources dedicated to violence. Relative to tax revenues, the evidence regarding shadow economies and violence is more robust to various controls and samples.

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Notes

  1. See Stasavage (2016) and the references cited therein.

  2. North et al. (2009) describe the ability of a government to centralize and consolidate the control of violence as one of the “doorstep conditions” leading towards what they call an “open access society”, where all citizens are respected equally and governed under the rule of law. If the powerful and centralized-yet-limited conundrum cannot be solved, then it is hard to imagine the centralization and consolidation of violence as one the relevant doorsteps.

  3. Friedman et al. (2000) find no evidence that higher tax rates are associated with greater shadow economy size. Rich economies are the ones that generally have higher tax rates.

  4. Murphy (2019) provides a “state economic modernity index” that aims to measure “a state’s willingness and ability to perform its core public goods functions, and the state’s size relative to the economy” (p. 73). He demonstrates that this index displays statistically significant correlations with both income levels and growth rates across countries; however, once country fixed effects are included in the regressions, those correlations are no longer significant.

  5. Nunn (2009) provides an excellent overview of the early studies in this literature. La Porta et al. (2008) review the legal origins literature specifically.

  6. By attempting to transfer their norms and values, members of an older generation impose costs and/or benefits on members of a younger one; likewise, members of a younger generation generate costs and/or benefits for members of an older one when they make efforts to adopt the latter’s norms and values.

  7. These assemblies had antecedents in the tribal customs of Germanic barbarian groups that succeeded the Western Roman Empire (Downing 1989; Barnwell and Mostert 2003; van Zanden et al. 2012). See Young (2015) for a discussion of these early Germanic institutions circa 50 BC-50 AD and how they were influenced by Julius Caesar’s conquest of Gaul.

  8. The weakest of European monarchs faced difficulties in convoking assemblies in the first place. Boucoyannis (2015) argues that only relatively strong European monarchs (e.g., those of England in the High Middle Ages) were able to compel members of the estates to attend their assemblies. As Stasavage (2016, p. 158) puts it: “[A] ruler’s authority must be sufficiently weak that he cannot simply extract what he wants without consent [but where] some degree of authority is necessary in order to get the people to show up at all.” This is also consistent with the fact that weaker (and particularly early medieval) monarchs resorted traveling incessantly to gain consent for their policies from the leading men of their realms (Marongiu 1968, p. 23; Bernhardt 1993; de Jouvenel 1993 [1947], p. 7; Ertman 1997, pp. 44, 236; Heather 2009, p. 529).

  9. This is consistent with Barzel’s (2000) theory of rulers who guarantee property rights (and, in doing so, relinquish some power) to their subjects in exchange for the increased wealth (and thus extraction) that the property rights provide the subjects with incentives to create.

  10. See Marongiu (1968, pp. 33–37) and Stasavage (2016, pp. 150–152) for a concise discussion of the development and application of different variants of the original maxim.

  11. See Hanson and Sigman (2013) and Lindvall and Teorell (2017) for a recent reviews of this literature and expositions on the subject.

  12. Note that the actual goods and services being produced need not be illegal per se, which would constitute black market activity. Rather it is the production that is illegal. For example, practicing medicine without a license and out of the government purview would be shadow economic activity, despite the fact that medical services are widely produced legally by licensed medical practitioners.

  13. As with some of the other capacity indicators, we exclude the year 2016 despite its availability due to a lack of 2016 data in most of our controls.

  14. Due to data limitations on other control variables Stasavage’s (2010) empirical analysis includes only 24 of these polities. For a full list of the secondary sources see the online appendix to Stasavage (2010): http://politics.as.nyu.edu/docs/IO/5395/onlineappendix.pdf. An alternative data set on 32 European assemblies for a comparable time period is assembled by van Zanden et al. (2012).

  15. Since the averages (ASSEMBLY_AVG; FREQUENCY_AVG) are taken over intervals (out of a possible 11) during which the polity is observed (existed), the final term in the product (PERIODS_IN_SAMPLE) adjusts the measure upward for polities that are observed during a larger portion of the overall 550 year period.

  16. Note that we here speak of an initial assignment of assembly experiences to 15 present-day European countries. This is because those initial assignments are not ultimately the Assembly Experience values corresponding to these countries in the estimations. See below and, in particular, footnote 19.

  17. Taking an unweighted average is arbitrary; in particular cases (e.g., historical France and Burgundy each taken equally in relation to present-day France) it may seem questionable. However, what would be a weighted average alternative that can be applied generally is unclear. We have therefore decided to take the most straightforward approach.

  18. While these are the best estimates available and have been used in the economic development literature (e.g., Comin et al. 2010) there are clearly conceptual and practical issues that go along with them. The main appendix to Putterman and Weil (2010) does a good job of discussing these issues: http://www.brown.edu/Departments/Economics/Faculty/Louis_Putterman/Appendix%20to%201500%20Origins%20Matrix%201.1.doc. A recent paper by Easterly and Levine (2016) assembles data on European population shares during the time of colonization, which is distinct from the Putterman and Weil descendancy shares (where, e.g., someone who moves from the UK to the US today can still potentially count towards the UK descendancy share in the US). Easterly and Levine show that colonization shares are important in accounting for income levels today even when controlling for descendancy shares. However, our use of the descendancy shares is appropriate because we hypothesize that long-institutional memory of assembly activity is transmitted through the generations, such that descendants from circa 1500 populations bring it with them to their destinations regardless of whether they reach those destinations in 1500 or 1900.

  19. This is the case for all of the countries in our sample including the 15 present-day European countries that matched to the Stasavage polities. (For example, then the Assembly Experience of France that enters our estimations is not the assembly experience of medieval/early modern France. To the extent that there are descendants of individuals from the other 14 European countries circa 1500 in present-day France, the historical assembly of those 14 countries also factor in.) Therefore, we construct Assembly Experience consistently across all countries in our sample, European or otherwise. Doing so is appropriate given our hypothesis of long institutional memory that is transmitted across through both time and space.

  20. In “Appendix 1” we provide detailed examples of how assembly experiences are calculated for a present-day country that matches to historical Stasvage polities (France) and a present-day country that does not (Canada).

  21. The seven categories include East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups for details.

  22. See Gwartney et al. (2016, pp. 275–277) for details on the methodology employed in combining data from these sources into a country’s score.

  23. This is the measure that Dincecco et al. (2019) primarily focus their comparative study of whether “war makes states” holds historically in Africa vis-à-vis Asia and Europe.

  24. http://www.ggdc.net/maddison/Historical_Statistics/vertical-file_02-2010.xls (last accessed July 21, 2017).

  25. Recent studies have empirically linked aspects of culture to economic development outcomes (e.g., Guiso et al. 2009; Williamson 2009; Tabellini 2010; Gorodnichenko and Roland 2011, 2012, 2017; Davis 2016) and measures of institutional quality (e.g., Licht et al. 2007; Tabellini 2008; Williamson and Kerekes 2011; Klasing 2013; Alesina and Giuliano 2015; Davis and Abdurazokzoda 2016; Tarabar 2017).

  26. Our region classification comes from World Bank Classifications (https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups). Regions include: (1) East Asia and Pacific, (2) Europe and Central Asia, (3) Latin America and the Caribbean, (4) Middle East and North Africa, (5) North America, (6) South Asia, and (7) Sub-Saharan Africa.

  27. When considering the effects on military resources and the consolidation of violence, one concern is the North American and European countries that have a common and credible defense policy via NATO. We re-ran the Table 7 estimations excluding NATO countries; the results are not meaningfully different. (Those results are available upon request.)

  28. The only statistically significant result for legal system and property rights reported in Table 13 is for the monopoly on force measure (5% level). The point estimate is somewhat smaller than the benchmark (0.630 vs 0.903).

  29. See Kerekes and Williamson (2012) for a discussion of Icelandic institutions during the 930–1262 period.

  30. Also recall that about a third of the countries in our analysis have zero assembly experience values. For these countries, going to the average assembly experience of the Stasavage countries (3.463) would be associated with changes in the shadow economy, monopoly on force, and military personnel measures of − 0.042, 1.277, and − 2.493 respectively (35%, 62%, and 32% of the relevant standard deviations).

  31. Brambor et al. (2006) emphasize that coefficients in models with an interaction term cannot be interpreted separately and straightforwardly. In particular, the conditional standard error for Assembly Experience is here [Var(β1) + Var(β3) × (historical conflict 1400–1799)2 + 2 × Cov(β1,β3) × (historical conflict 1400–1799)]1/2, where β1 is the coefficient on Assembly Experience and β3 is the coefficient on its interaction with Historical Conflict 1400–1799.

  32. E.g., referencing Table 3, an English descendancy share is given positive weight; while a German descendancy share receives 0 weight.

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Correspondence to Andrew T. Young.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank David Stasavage for generously providing us with his data on medieval and early modern European assembly experiences. We also thank two anonymous referees for valuable comments that have resulted in an improved paper. Remaining errors are our own.

Appendices

Appendix 1: Examples of constructing Assembly Experience for present-day France and present-day Canada

1.1 Appendix 1.1: France

There are two historical polities that match to present-day France: Burgundy (Flanders) and France (Table 3). In the Stasavage data, each of these historical polities is observed for up to 11 intervals of 50 years each (1250–1800).

For each interval and each polity, ASSEMBLY takes the value of 1 if an assembly existed; 0 otherwise. Also for each interval and each polity, FREQUENCY takes a value equal to the number of times the assembly met divided by 50.

Interval

Burgundy (flanders)

France

ASSEMBLY

FREQUENCY

ASSEMBLY

FREQUENCY

1

1

1

0

0

2

1

1

1

0.10

3

1

1

1

0.14

4

1

1

1

0.12

5

1

1

1

0.04

6

1

1

1

0.02

7

NA

NA

1

0.16

8

NA

NA

1

0.02

9

NA

NA

0

0

10

NA

NA

0

0

11

NA

NA

0

0

Note that not all historical polities are observed for every 50-year interval. (Here, “NA” denotes “not available” or, in other words, there is no observation.)

For each historical polity, we calculate the average ASSEMBLY and FREQUENCY values over observed intervals (ASSEMBLY_AVG and FREQUENCY_AVG). In this case:

$$ \begin{aligned} & ASSEMBLY\_AVG = \, \left( {1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1} \right)/6 \, = \, 1\;{\text{for}}\;{\text{Burgundy }}\left( {\text{Flanders}} \right); \\ & FREQUENCY\_AVG = \, \left( {1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1} \right)/6 \, = \, 1\;{\text{ for}}\;{\text{Burgundy }}\left( {\text{Flanders}} \right); \\ & ASSEMBLY\_AVG = \, \left( {0 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 1 \, + \, 0 \, + \, 0 \, + \, 0} \right)/11 \, \\ & \quad = \, 0.636\;{\text{for}}\;{\text{France}}; \\ & FREQUENCY\_AVG = \, \left( {0 \, + \, 0.10 \, + \, 0.14 \, + \, 0.12 \, + \, 0.04 \, + \, 0.02 \, + \, 0.16 \, + \, 0.02 \, + \, 0 \, + \, 0 \, + \, 0} \right)/11 \\ & \quad = 0.055\;{\text{for}}\;{\text{France}}. \\ \end{aligned} $$

We also record the PERIODS_IN_SAMPLE out of the possible 11 intervals for each polity: = 6 for Burgundy (Flanders); = 11 for France. Then our measure of assembly experience for each historical polity is

$$ \begin{aligned} Assembly\,Experience &= (ASSEMBLY\_AVG \times FREQUENCY\_AVG \times PERIODS\_IN\_SAMPLE) \\ &= \, (1 \times 1 \times 6) = 6\;{\text{for}}\;{\text{Burgundy }}\left( {\text{Flanders}} \right) \\ &= \, (0.636 \times 0.055 \times 11) = 0.384\;{\text{for}}\;{\text{France}} \\ \end{aligned} $$

Since present-day France is matched to both of these historical polities, we take the average of the two values: (6 + 0.384)/2 = 3.192.

However, the above value is not the assembly experience value that enters estimations for present-day France. For every present-day country in our sample (matched to historical Stasavage polities or not), we employ the Putterman and Weil (PW; 2010) descendancy shares to reckon the historical assembly experience of its population. The PW descendancy shares indicate the share of a present-day country’s population that descended from (whose ancestry can be traced back to) a particular country circa 1500.

Consider present-day France. There are 15 present-day countries that are matched to the historical Stasavage polities (Table 3). That matching is, again, not to directly determine present-day countries’ historical assembly experiences; rather, it gives us an approximate geographic correspondence between historical assembly experiences and the units of observation in the PW data. (E.g., PW is going to provide us with descendancy shares from Italy; not from Florence, Genoa, Milan, etc.)

A large portion of present-day France’s population traces back to the historical polities of Burgundy (Flanders) and France—and therefore their assembly experiences—but not its entire population. Some of its population traces back to, e.g., England and its assembly experiences. Therefore, using the PW descendancy shares as the weights, present-day France’s assembly experience (to be used in the estimations) is:

$$ \begin{aligned} & Assembly\,Experience_{\text{Present-Day France}} \\ & \quad = (0^{{\prime }} Assembly\;Experience_{\text{Austria Matched}} ) \, + \, (0^{{\prime }} Assembly\;Experience_{\text{Denmark Matched}} ) \, \\ & \quad \quad + (0.017^{{\prime }} Assembly\;Experience_{\text{Spain Matched}} ) + (0^{{\prime }} Assembly\;Experience_{\text{United Kingdom Matched}} ) \\ & \quad \quad + (0^{{\prime }} Assembly\;Experience_{\text{Hungary Matched}} ) + (0^{{\prime }} Assembly\;Experience_{\text{Netherlands Matched}} ) \, \\ & \quad \quad + (0^{{\prime }} Assembly\;Experience_{\text{Poland Matched}} ) + (0.015^{{\prime }} Assembly\;Experience_{\text{Portugal Matched}} ) \, \\ & \quad \quad + (0^{{\prime }} Assembly\;Experience_{{{\text{Russian Fed}} . {\text{ Matched}}}} ) + (0^{{\prime }} Assembly\;Experience_{\text{Sweden Matched}} ) \, \\ & \quad \quad + (0^{{\prime }} Assembly\;Experience_{\text{Belgium Matched}} ) + (0.873^{{\prime }} Assembly\;Experience_{\text{France Matched}} ) \, \\ & \quad \quad + (0^{{\prime }} Assembly\;Experience_{\text{Switzerland Matched}} ) + (0.010^{{\prime }} Assembly\;Experience_{\text{Germany Matched}} ) \, \\ & \quad \quad + (0.019^{{\prime }} Assembly\;Experience_{\text{Italy Matched}} ) = 3.388. \\ \end{aligned} $$

Note that the “Matched” part of the subscripts refers to assembly experience values that arise from directly matching historical polities to their present day equivalents; hence, Assembly ExperienceFrance Matched is equal to the 3.192 that was computed above. Also note that, unsurprisingly, present-day France’s assembly experience (used in the estimations) is very close to that 3.192 value. This is because, also unsurprisingly, the large majority (over 87%) of France’s population is descendant from individuals living in that same geography circa 1500.

1.2 Appendix 1.2: Canada

Now let us consider present-day Canada. This is, of course, a country that does not match (in the sense that France does) to any historical Stasavage polities. Yet its population carries with them historical assembly experiences from some of those polities. Just as in the case of present-day France, we calculate Canada’s assembly experience as:

$$ \begin{aligned} & Assembly\;Experience_{\text{Present-Day Canada}} \\ & \quad = (0^{{\prime }} Assembly\;Experience_{\text{Austria Matched}} ) + (0.003^{{\prime }} Assembly\;Experience_{\text{Denmark Matched}} ) \, \\ & \quad \quad + (0.006^{{\prime }} Assembly\;Experience_{\text{Spain Matched}} ) + (0.351^{{\prime }} Assembly\;Experience_{\text{United Kingdom Matched}} ) \, \\ & \quad \quad + (0.008^{{\prime }} Assembly\;Experience_{\text{Hungary Matched}} ) + (0.025^{{\prime }} Assembly\;Experience_{\text{Netherlands Matched}} ) \, \\ & \quad \quad + (0.021^{{\prime }} Assembly\;Experience_{\text{Poland Matched}} ) + (0.011^{{\prime }} Assembly\;Experience_{\text{Portugal Matched}} ) \, \\ & \quad \quad + (0.006^{{\prime }} Assembly\;Experience_{{{\text{Russian Fed}} . {\text{ Matched}}}} ) + (0.006^{{\prime }} Assembly\;Experience_{\text{Sweden Matched}} ) \, \\ & \quad \quad + (0.002^{{\prime }} Assembly\;Experience_{\text{Belgium Matched}} ) + (0.182^{{\prime }} Assembly\;Experience_{\text{France Matched}} ) \, \\ & \quad \quad + (0.002^{{\prime }} Assembly\;Experience_{\text{Switzerland Matched}} ) + (0.076^{{\prime }} Assembly\;Experience_{\text{Germany Matched}} ) \, \\ & \quad \quad + (0.045^{{\prime }} Assembly\;Experience_{\text{Italy Matched}} ) = 4.067. \\ \end{aligned} $$

Again not surprisingly, Canada’s assembly experience is largely based on the English (Great Britain) and French (Burgundy (Flanders) and France) historical experiences; because over 53% of its population is descendant from individuals living in those geographies circa 1500.

Appendix 2: Constructing an assembly experience measure with discounting

The Stasavage (2010) data are for 50-year intervals and we will consider an annual interest rate of r = 0.01 or r = 0.05.

To demonstrate how we construct an assembly measure with discounting, consider a simple example with only 50-year intervals 1 through 5 with 5 being the most recent. Using the variable names introduced in Sect. 3, the example data are as follows.

 

ASSEMBLY

FREQUENCY

Was the PERIOD recorded?

1

0

2

0

3

0

0/50

1

4

1

25/50

1

5 (most recent)

1

50/50

1

The discount factor is (1 + r)−X

$$ \begin{aligned} & ASSEMBLY\_AVG\_DISC = \, 1*\left( {1 \, + r} \right)^{ - 0} + \, 1*\left( {1 \, + r} \right)^{ - 50} + \, 0*\left( {1 \, + r} \right)^{ - 100} \\ & FREQUENCY\_AVG\_DISC = \, \left( {50/50} \right)*\left( {1 \, + r} \right)^{ - 0} + \, \left( {25/50} \right)*\left( {1 \, + r} \right)^{ - 50} + \left( {0/50} \right)*\left( {1 \, + r} \right)^{ - 100} \\ & PERIODS\_IN\_SAMPLE\_DISC = \, 1*\left( {1 \, + r} \right)^{ - 0} + \, 1*\left( {1 \, + r} \right)^{ - 50} + \, 1*\left( {1 \, + r} \right)^{ - 100} + 0*\left( {1 \, + r} \right)^{ - 150} + \, 0*\left( {1 \, + r} \right)^{ - 200} \\ \end{aligned} $$

Based on the above components, and analogous to the construction of (1) in Sect. 3:

$$ \begin{aligned} & Discounted\;Assembly\;Experience \\ & \quad = \left( {ASSEMBLY\_AVG\_DISC*FREQUENCY\_AVG\_DISC*PERIODS\_IN\_SAMPLE\_DISC} \right) \\ \end{aligned} $$

Appendix 3: Marginal effects of assembly experience conditional on historical conflict level (with 95% confidence bands)

figure a

Notes: These graphs correspond to the estimates provided in Table 17.

Appendix 4: State capacity values for each individual country

Country

Tax share of GDP

Shadow economy

Monopoly on force

Military (per-capita)

Total taxes

Income taxes

Expenditures

Personnel

Afghanistan

0.073

0.022

N/A

2

388

7.473

Albania

0.175

0.035

0.327

8.4

5023

6.78

Algeria

0.347

0.182

0.309

6.8

11,730

9.187

Angola

0.202

0.154

0.44

6.8

9940

5.657

Argentina

0.122

0.027

0.241

8

337

2.595

Armenia

0.17

0.048

0.426

9

37,056

16.313

Australia

0.23

0.164

0.141

N/A

981

2.564

Austria

0.261

0.123

0.099

N/A

276

3.763

Azerbaijan

0.139

0.059

0.522

6.4

133

9.498

Bahrain

0.022

0.005

0.193

8.6

275

20.511

Bangladesh

0.077

0.018

0.336

6.4

615

1.455

Belarus

0.174

0.019

0.445

10

35

17.713

Belgium

0.254

0.149

0.236

N/A

347

3.512

Belize

0.213

0.064

0.468

N/A

87

3.586

Benin

0.148

0.032

0.537

8.4

3169

0.934

Bhutan

0.106

0.059

0.269

7.5

N/A

10.464

Bolivia

0.144

0.018

0.623

6.6

231

8.028

Bosnia and Herzegovina

0.202

0.019

0.342

8

85

4.604

Botswana

0.258

0.105

0.303

9.8

1039

5.396

Brazil

0.146

0.061

0.376

7

241

3.684

Bulgaria

0.194

0.049

0.308

9.8

154

9.62

Burkina Faso

0.132

0.033

0.384

8.2

3493

0.836

Burundi

0.136

0.042

0.367

5.8

7393

6.861

Cambodia

0.101

0.017

0.46

7.8

40,213

14.88

Cameroon

N/A

N/A

0.324

6.8

7648

1.349

Canada

0.128

0.097

0.175

N/A

526

1.996

Cape Verde

0.203

0.061

0.358

N/A

1439

2.404

Central African Republic

0.094

0.014

0.419

2

2878

1.087

Chad

N/A

N/A

0.401

4.8

13,233

3.276

Chile

0.174

0.063

0.182

10

125,885

6.648

China

0.098

0.028

0.112

9

482

2.508

Colombia

0.132

0.045

0.333

4.8

344,175

8.534

Comoros

N/A

N/A

0.391

N/A

N/A

N/A

Congo, Dem. Rep.

0.057

0.015

0.464

2.6

2509

1.931

Congo, Rep.

0.078

0.021

0.451

6.5

24,458

3.06

Cook Islands

N/A

N/A

N/A

N/A

N/A

N/A

Costa Rica

0.137

0.036

0.267

10

N/A

2.966

Cote d’Ivoire

0.141

0.033

0.434

3

9115

1.018

Croatia

0.2

0.028

0.303

9.8

1227

7.503

Cuba

N/A

N/A

N/A

10

185

6.684

Cyprus

0.325

0.125

0.313

N/A

287

10.743

Czech Republic

0.137

0.05

0.171

10

4661

3.377

Denmark

0.327

0.159

0.186

N/A

4127

3.91

Dominican Republic

0.13

0.033

0.323

9

1101

5.129

East Timor

0.964

0.896

N/A

N/A

23

1.078

Ecuador

N/A

N/A

0.336

8

103

3.871

Egypt, Arab Rep.

0.14

0.063

0.342

8.2

257

10.145

El Salvador

0.184

0.059

0.456

6.6

29

4.948

Equatorial Guinea

0.094

0.087

0.318

N/A

132,700

1.629

Eritrea

N/A

N/A

0.393

7

612

49.724

Estonia

0.013

0.005

0.288

10

193

4.945

Ethiopia

0.085

0.019

0.343

6.4

54

2.231

Fiji

0.226

0.07

0.325

N/A

106

4.26

Finland

0.211

0.071

0.191

N/A

438

5.525

France

0.224

0.107

0.16

N/A

687

5.426

Gabon

N/A

N/A

0.524

N/A

58,722

4.428

Gambia

0.091

0.014

0.469

N/A

124

0.533

Georgia

0.17

0.051

0.649

4.8

163

7.494

Germany

0.112

0.045

0.156

N/A

401

2.926

Ghana

0.137

0.048

0.429

8

9

0.505

Greece

0.214

0.077

0.303

N/A

489

14.458

Guatemala

0.112

0.032

0.547

5

100

3.15

Guinea

N/A

N/A

0.399

7.6

62,576

1.515

Guinea-Bissau

N/A

N/A

0.364

N/A

5096

5.761

Guyana

N/A

N/A

0.318

N/A

7098

3.462

Haiti

N/A

N/A

0.533

3

N/A

0.308

Honduras

0.152

0.046

0.463

6

349

2.403

Hong Kong, China

N/A

N/A

0.147

N/A

N/A

N/A

Hungary

0.218

0.073

0.252

10

29,083

4.213

Iceland

0.238

0.09

0.158

N/A

7567

0.47

India

0.102

0.049

0.203

7.6

1352

2.21

Indonesia

0.112

0.054

0.198

7

164,092

2.503

Iran, Islamic Rep.

0.059

0.033

0.179

8.2

1545,438

7.908

Iraq

0.011

0.006

N/A

2.8

146,605

15.895

Ireland

0.235

0.119

0.169

N/A

213

2.328

Israel/Palestine

0.271

0.124

0.22

N/A

7128

24.947

Italy

0.224

0.121

0.296

N/A

445

7.054

Jamaica

0.238

0.104

0.341

8

2690

1.059

Japan

0.098

0.054

0.108

N/A

38,125

2

Jordan

0.162

0.036

0.174

7.6

110

17.801

Kazakhstan

0.133

0.06

0.389

9

11,286

5.37

Kenya

0.166

0.082

0.331

5.4

1004

0.755

North Korea

N/A

N/A

N/A

10

2716

54.711

South Korea

0.143

0.062

0.264

10

561,290

13.783

Kuwait

0.01

0.001

0.193

9.75

437

8.41

Kyrgyzstan

0.172

0.035

0.379

6.8

1148

3.51

Laos

0.133

0.031

0.303

8.6

22,430

21.623

Latvia

0.205

0.044

0.26

10

100

3.461

Lebanon

0.15

0.033

0.316

4.2

467,069

19.318

Lesotho

0.441

0.103

0.313

6.333

163

1

Liberia

0.177

0.061

0.432

7.2

2

1.979

Libya

N/A

N/A

0.336

8

285

10.395

Lithuania

0.053

0.02

0.277

10

84

7.693

Luxembourg

0.249

0.117

0.107

N/A

382

3.087

Macedonia

0.177

0.059

N/A

8.4

3298

7.479

Madagascar

0.103

0.02

0.426

8

6297

1.138

Malawi

0.149

0.066

0.385

9

527

0.47

Malaysia

0.151

0.099

0.315

9.8

457

4.857

Mali

0.126

0.031

0.387

5

4473

0.928

Malta

0.405

0.175

0.298

N/A

90

5.085

Mauritania

N/A

N/A

0.323

6.5

7386

6.232

Mauritius

0.165

0.033

0.226

9.8

346

1.728

Mexico

0.104

0.054

0.317

6

543

2.418

Moldova

0.175

0.01

0.434

5.8

65

2.497

Mongolia

0.183

0.057

0.173

8.4

29,665

6.308

Morocco

0.219

0.082

0.34

8

710

7.821

Mozambique

0.196

0.077

0.372

7.8

110

0.471

Myanmar

N/A

N/A

0.514

4.2

19,682

10.287

Namibia

0.286

0.111

0.281

9

1109

6.946

Nepal

0.113

0.024

0.375

5

575

5.026

Netherlands

0.21

0.097

0.142

N/A

475

2.939

New Zealand

0.284

0.182

0.134

N/A

546

2.108

Nicaragua

0.132

0.041

0.426

7.4

178

2.406

Niger

N/A

N/A

0.397

5.8

1857

0.709

Nigeria

0.019

0.018

0.563

4.8

1288

1.08

Norway

0.265

0.143

0.205

N/A

7542

5.384

Oman

0.023

0.01

0.199

10

691

16.637

Pakistan

0.095

0.031

0.331

4

2616

5.728

Panama

N/A

N/A

N/A

7.8

N/A

3.599

Papua New Guinea

0.15

0.089

0.34

5.8

20

0.451

Paraguay

0.117

0.023

0.345

6.8

145,078

4.646

Peru

0.149

0.056

0.524

6.2

173

6.398

Philippines

0.128

0.058

0.393

6.4

1059

1.737

Poland

0.165

0.044

0.265

10

611

4.334

Portugal

0.211

0.083

0.238

N/A

299

8.435

Puerto Rico

N/A

N/A

N/A

N/A

N/A

N/A

Qatar

0.196

0.185

0.159

10

4318

11.179

Romania

0.174

0.057

0.301

9.8

301

8.034

Russia

0.141

0.013

0.426

7.2

10,663

9.807

Rwanda

0.135

0.054

0.363

8.6

3845

5.155

St. Vincent & the Grenadines

0.23

0.064

N/A

N/A

N/A

N/A

Samoa

0.214

0.052

N/A

N/A

N/A

N/A

Sao Tome and Principe

0.142

0.044

N/A

N/A

N/A

N/A

Saudi Arabia

N/A

N/A

0.167

7.8

5661

9.184

Senegal

0.183

0.048

0.433

7.2

6825

1.497

Serbia and Montenegro

N/A

N/A

N/A

9.2

85

21.503

Serbia and Montenegro

0.213

0.034

N/A

9

7795

8.913

Sierra Leone

0.087

0.03

0.415

7.4

15,939

1.733

Singapore

0.132

0.062

0.119

10

2105

33.826

Slovakia

0.167

0.059

0.166

10

148

3.879

Slovenia

0.194

0.049

0.26

10

207

6.086

Somalia

N/A

N/A

N/A

1

3

1.404

South Africa

0.253

0.143

0.259

8

550

1.423

Spain

0.143

0.069

0.252

N/A

296

4.97

Sri Lanka

0.116

0.021

0.455

6.8

6800

11.335

Sudan

N/A

N/A

N/A

3.4

128

5.076

Swaziland

0.223

0.06

0.4

N/A

435

N/A

Sweden

0.273

0.049

0.199

N/A

4569

3.868

Switzerland

0.095

0.035

0.09

N/A

577

3.713

Syrian Arab Republic

0.163

0.094

0.196

7

4133

18.91

Taiwan, China

N/A

N/A

0.269

10

N/A

N/A

Tajikistan

N/A

N/A

0.43

6.2

30

1.851

Tanzania

0.119

N/A

0.522

7.6

7670

0.681

Thailand

0.15

0.063

0.506

6.2

1921

6.518

Togo

0.165

0.031

0.373

8.4

4273

1.56

Tonga

N/A

N/A

N/A

N/A

N/A

N/A

Trinidad and Tobago

0.255

0.153

0.344

N/A

582

2.854

Tunisia

0.196

0.071

0.353

8.6

79

4.575

Turkey

0.184

0.054

0.313

8

320

9.038

Turkmenistan

N/A

N/A

N/A

9

N/A

4.756

Uganda

0.129

0.044

0.387

6.8

21,078

1.641

Ukraine

0.162

0.042

0.448

8.6

530

5.699

United Arab Emirates

0.003

0

0.287

9.8

6768

10.017

United Kingdom

0.253

0.124

0.133

N/A

525

2.975

United States

0.104

0.095

0.094

N/A

1780

4.92

Uruguay

0.179

0.04

0.457

10

3908

7.514

Uzbekistan

0.171

0.055

N/A

7

1751

2.829

Venezuela

N/A

N/A

0.314

7

387

4.449

Vietnam

0.2

0.079

0.151

9.6

533,723

6.066

Yemen

N/A

N/A

0.283

5.2

9874

5.694

Zambia

0.146

0.068

0.453

8.8

102

1.401

Zimbabwe

0.183

0.062

0.606

6.4

21

3.882

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Pavlik, J.B., Young, A.T. Medieval European traditions in representation and state capacity today. Econ Gov 21, 133–186 (2020). https://doi.org/10.1007/s10101-020-00236-5

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  • DOI: https://doi.org/10.1007/s10101-020-00236-5

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