Medieval European traditions in representation and state capacity today

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. 1.

    See Stasavage (2016) and the references cited therein.

  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 11.

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

  12. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 24.

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

  25. 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. 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. 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. 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. 29.

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

  30. 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. 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. 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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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

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.

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)

figurea

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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Keywords

  • Representative assemblies
  • State capacity
  • Political economy
  • Medieval Europe
  • Institutions
  • Property rights
  • Rule of law
  • Growth and development

JEL Classification

  • D72
  • O10
  • O43
  • P14
  • P16
  • P48
  • P50