Per-country rates of Esperanto speakers

Posted on by svendvnielsen

Dansk  Esperanto

Update: I added confidence intervals on all estimates.

Esperanto

In August 2009 a danish popular science book made a big impression on me. It was “Det Virkelige Menneske” (The Real Human) by Dennis Nørmark and Lars Andreassen, and it explained many sides of human culture using evolutionary theory. About language they write (my translation):

The fact that languages are the best indicators of who is in and who is out, might explain why most languages contain very complicated, and often completely unnecessary rules. […] If you mess up the grammar, it is immediately revealed that you do not belong to the group, and the more complicated language, the easier it is to discover misfits.

The quotation addresses one of the secondary purposes of languages, namely to maintain identity by exposing foreigners. Esperanto is a language without that purpose. With easy pronunciation, logical grammar, and a modular word building mechanism, it is a lot easier than conventional languages. Esperanto was constructed in 1887 by LL Zamenhof to unite and bring peace to the world.  Peace is not the best description of the following 100 years, but Esperanto is still a very good language. A visionary dream of me and most Esperanto speakers is to make Esperanto a global second language, which would make the world more optimal and more equal. Therefore, Esperanto is also a missionary language which size and demographics are of particular interest.

Esperanto demographics

Thanks to the available data from UEA, esperantujo.directory, edukado, Pasporta Servo, and Lernu! (and Wikipedia), I have made a dataset containing membership numbers in most countries of the world.

Population UEA Lernu! esp.dir pasporto edukado nat. org
Afghanistan 27.70 1 48 0 0 0 0
Albania 2.80 14 64 0 1 5 33
Algeria 39.20 6 503 1 1 8 0
Andorra 0.08 0 167 0 8 0
Angola 21.50 2 42 1 0 0
Argentina 41.50 29 1965 27 16 61 140
Armenia 3.00 12 92 0 0 1 27
Australia 23.10 40 2568 23 16 52 130
Austria 8.60 44 780 4 8 23 79
… … … … … … … …
Zimbabwe 14.20 1 30 0 1 1 0

For explanation of the columns see the method section. I fitted a model to this data and used it to estimate the relative frequency of Esperanto speakers in each country. Using population census data from Lithuania, Estonia, Russia, New Zealand (but not Hungary), I have scaled all relative frequencies to get the total number of Esperanto Speakers in each country. The total number of Esperanto speakers is estimated to 62983.9 with confidence interval [59077,68176]/[31460,183420](See Confidence intervals for explanation).

datamapsco-5
Every country is colored according to its density of Esperanto speakers. The square root transformation is only visual. The pink countries are countries without data.
europeesperanto
Densities of Esperanto speakers in the European countries.

The numbers behind the maps are in a table in the end of the post. Even though the maps do not show, Andorra has the highest density of Esperanto speakers. The countries with the highest number of Esperanto speakers are (in order) Brazil, France, USA, Germany, Russia, Poland and Spain.

The model itself assumes that numbers of members are proportional to Esperanto speakers. This is not always true because the organizations are not equally popular in all countries. The model tries to make up for it by allowing some deviation in a few categories without changing the relative estimated frequency. However, if all categories are underrepresented in a country, the relative estimated frequency will be too low. Hungary is such a country because the model estimates the number of Esperanto speakers to be 1997.5 while a recent population census found that number to be 8397. One explanation could be that Esperanto is being taught in hungarian schools which produces esperanto speakers who are less reliant on international organisations.

The definition of an Esperanto speaker is someone who would answer Esperanto when asked about spoken languages by the authorities.

Methods

Model

I will give an intuitive explanation of the model using Lithuania as an example. It has the observations

pop UEA Lernu! esp.dir paspo. edukado nat. org.
Lithuania 3 million 43 5127 8 13 32 960

According to the UEA column, 43 members of the 5501 UEA members are Lithuanian. That indicates that 0.78% of all Esperanto speakers come from Lithuania. However, according to the Lernu! column, 2.88% of all Esperanto speakers come from Lithuania. We would like a single number that takes all columns into account. One could take an average, (0.78+2.88+\cdots + 5.8)/6, but then Lernu! is as important as esperantujo.directory despite the fact that it has more than 100 times more users. Another option is to take the average of all the numbers, that is (43+5127+\cdots + 960)/206886, but this number will always be very close to the Lernu! average. I would like something in between the two. Something that puts extra weight on the many Lernu! users, but does not let Lernu! determine everything.

  • The number of members of organization j in country i is N_i \cdot p_i \cdot \alpha_{j} \cdot w_{ij} where
    • N_i is the number of inhabitants in country i.
    • p_i is the relative frequency of Esperanto speakers in country in i.
    • \alpha_j is the total number of members of organization j.
    • w_{ij} is the number to make the equation be true. Hopefully it is close to 1.
  • For each country p_i is chosen such that the w_{ij}‘s are not ‘far away’ from 1. Defining ‘far away’ is also model choice.
    • ‘far away’ is defined such that it both satisfies some nice mathematical properties and reduces the distance between the w_{ij}‘s and 1.
  • Let m_i be the actual number of Esperanto speakers in country i. We know that number for Estonia, Lithuania, Russia and New Zealand. Using those numbers we calculate the number of Esperanto speakers per unit of p_i per inhabitant.
    • Calculate b_i=m_i/(p_i \cdot N_i) for the four mentioned countries.
    • Caluclate the average \bar b=(b_{\textup{Estonia}}+\cdots+b_{\textup{New Zealand}})/4
    • The number of Esperanto Speakers in country i is \bar b \cdot N_i \cdot p_i

Strict mathematical explanation of the model

Let x_{ij} be the number of members of organization j in country i. Let N_i be the population size of country i. Let \alpha_j=\sum_i x_{ij}/\sum_{i,j} x_{ij}. Then the model is

\displaystyle x_{ij} \sim b^-(\kappa=\kappa_j, \mu= N_i\cdot \alpha_{j}\cdot p_i),\quad p_i\sim U(0,1), \kappa_j \sim \Gamma(2,2)

with independence within the x_{ij}‘s (conditioned on the $p_i$’s and $\kappa_j$’s), within the p_i‘s, and within the \kappa_j‘s. The rates are ideally estimated with

\displaystyle \textup{argmax}_{p_1, \dots, p_n}\Bigl\{ \max_{\kappa_1, \dots, \kappa_6} P\bigl(p_1, \dots, p_n, \kappa_1,\dots, \kappa_6  \mid (x_{ij})_{i,j=1,1}^{n,6} \bigr) \Bigr\}.

but in practice I make the constraint \kappa_1=\kappa_3=\kappa_4=\kappa_5 because of convergence problems.

The confidence intervals were calculated using Bayesian bootstrap. In the first type of confidence interval, we assume that \bar{b} is known. In the second type, we assume that the census numbers are random and bootstraps those also.

Confidence intervals

We would not be surprised if the true number of esperanto speakers in the world was not precisely 62983.9. We rather believe that the true number could be somewhere in an area around 62983.9. A confidence interval specifies such an area using statistics. One can believe any value in the confidence interval without disagreeing with the assumptions of the model.

I calculated two types of confidence intervals. The first assumes that the scaling factor is known. The scaling factor is the number of census esperanto speakers per ‘internet’ esperanto user (see \bar b for more precise definition). The second confidence interval does not assume that the scaling factor is known. Instead it includes the randomness of the scaling factor estimate. I have boldfaced the intervals I recommend looking at. When the two intervals were identical, I only write one.

 

Data

Data is collected from the following websites

  • UEA is an international Esperanto interest group whose goal is to spread Esperanto and promote language equality. They have put their member numbers on their website, but some clicking is necessary to retrieve the numbers.
  • lernu.net is the largest international learning portal for Esperanto. Many profiles are inactive and belong to people who were only briefly interested in Esperanto. Therefore, the number of users from a country could be higher than the actual number of Esperanto speakers in that country.
  • Esperantujo.directory is an online ‘adress book’ of Esperanto speakers. I am not in it (yet).
  • Pasporta Servo is a service for hosting Esperanto speakers for free. The numbers in the pasporta servo column are the numbers of registered hosts in each country.
  • Edukado is another learning portal, which is more serious than lernu.
  • National organization sizes are the sizes of the national esperanto organizations associated with the UEA.

There are missing data in the dataset. E.g. Andorra has a missing value on esperantujo.directory because the country is too small to appear on their map. Angola does not have a value on national organization because they do have a national esperanto organization but its size is not disclosed on the UEA website. The missing values do not cause inference problems in the model.

Resources

The data and my R-scripts are available on github. I have used datamaps.co to make the maps.

Results

Below are all the countries listed according to their frequency of Esperanto speakers. Frequency is the number of Esperanto speakers per 1 million inhabitants. Total is the total number of Esperanto speakers. Proportion is how big a share each country has of the total number of Esperanto speakers.

Rank Frequency Total Proportion
Andorra 1 [1,2] 620.03 [277.3,1039.9]/ [180.4,2246.5] 48.4 [22,81]/ [14,175] 0.0008 [0.0003,0.0013]
Lithuania 2 [1,6] 249.32 [156.7,482.6]/ [119.2,936.9] 748 [470,1448]/ [357,2811] 0.0119 [0.0072,0.0225]
Iceland 3 [2,8] 210.07 [109.7,375]/ [71.5,729.9] 67.9 [35,121]/ [23,236] 0.0011 [0.0006,0.0019]
Hungary 4 [2,6] 203.82 [151.1,283.8]/ [98.3,695.9] 1997.5 [1481,2781]/ [963,6820] 0.0317 [0.0242,0.0449]
Luxembourg 5 [2,10] 196.82 [93.9,285.1]/ [60.4,681.1] 112.2 [53,163]/ [34,388] 0.0018 [0.0008,0.0026]
New Caledonia 6 [3,16] 122.85 [64.1,234.2]/ [47.2,450.3] 32.9 [17,63]/ [13,121] 0.0005 [0.0003,0.001]
Belgium 7 [6,12] 109.48 [77.4,135.8]/ [38.1,320.4] 1237.1 [875,1535]/ [430,3621] 0.0196 [0.0132,0.0239]
Finland 8 [5,15] 105.89 [69.9,155.2]/ [48.7,331.7] 582.4 [384,854]/ [268,1824] 0.0092 [0.006,0.0137]
France 9 [5,17] 104.01 [61.4,132.8]/ [44.2,327.8] 6906.6 [4078,8816]/ [2933,21763] 0.1097 [0.067,0.1381]
Denmark 10 [5,16] 97.34 [65.7,145]/ [44.6,321.7] 554.8 [375,827]/ [254,1834] 0.0088 [0.0061,0.013]
Sweden 11 [8,16] 91.99 [67.8,117.3]/ [40.2,265.1] 910.7 [671,1162]/ [398,2625] 0.0145 [0.0109,0.0183]
Switzerland 12 [8,20] 89.44 [55.6,115.4]/ [36.2,270.9] 724.4 [451,935]/ [293,2195] 0.0115 [0.0071,0.0152]
Netherlands 13 [7,19] 84.87 [60.9,115.1]/ [37.2,267.3] 1442.8 [1036,1956]/ [632,4544] 0.0229 [0.0159,0.0304]
Czech Republic 14 [10,19] 76.23 [59,90.3]/ [34.9,245.3] 800.4 [619,948]/ [366,2575] 0.0127 [0.0095,0.0154]
Estonia 15 [7,26] 74.02 [41.9,117.6]/ [34.3,165.2] 96.2 [55,153]/ [45,215] 0.0015 [0.0009,0.0024]
Norway 16 [10,23] 68.99 [50,96.3]/ [30.9,226.5] 358.7 [260,501]/ [160,1178] 0.0057 [0.0041,0.0079]
Slovenia 17 [10,32] 60.79 [33.2,95.7]/ [22.3,230.3] 127.7 [70,201]/ [47,484] 0.002 [0.0011,0.0032]
Liechtenstein 18 [14,62] 59.67 [11.1,74.2]/ [8.3,199] 2.2 [0,3]/ [0,7] 0 [0,0]
Slovakia 19 [10,28] 59.4 [39.2,81.3]/ [23.7,182.7] 320.8 [212,439]/ [128,986] 0.0051 [0.0034,0.007]
Poland 20 [16,24] 57.55 [48.9,65.5]/ [28.6,154.9] 2215.9 [1882,2524]/ [1100,5965] 0.0352 [0.0294,0.0404]
Croatia 21 [14,28] 55.09 [39,71.3]/ [24.4,175.7] 231.4 [164,300]/ [102,738] 0.0037 [0.0026,0.0048]
Israel 22 [15,34] 50.56 [31.2,70.6]/ [20.8,174.8] 409.5 [253,572]/ [169,1416] 0.0065 [0.004,0.0091]
Latvia 23 [13,35] 49.01 [30.8,80.3]/ [21.1,159.9] 98 [62,161]/ [42,320] 0.0016 [0.0009,0.0025]
Germany 24 [19,30] 47.51 [36.1,58.5]/ [18.6,138.1] 3871.8 [2942,4769]/ [1519,11256] 0.0615 [0.0475,0.0754]
Spain 25 [20,28] 47.38 [39.5,54.3]/ [21.2,133.4] 2198.5 [1835,2519]/ [985,6192] 0.0349 [0.0292,0.0399]
New Zealand 26 [18,33] 46.42 [31.4,58.1]/ [21.8,141.4] 208.9 [141,261]/ [98,636] 0.0033 [0.0022,0.0043]
Bulgaria 27 [18,38] 46.12 [28.1,63.3]/ [19,124] 332 [202,455]/ [136,893] 0.0053 [0.0032,0.0072]
Austria 28 [22,36] 38.77 [29.4,48.5]/ [18.1,121] 333.4 [252,417]/ [155,1041] 0.0053 [0.004,0.0066]
Brazil 29 [24,39] 36.5 [27.5,47.6]/ [16.3,104.5] 7314 [5509,9545]/ [3276,20939] 0.1161 [0.0887,0.148]
Malta 30 [16,45] 35.25 [22.3,66.3]/ [17.5,144.4] 15 [9,28]/ [7,61] 0.0002 [0.0002,0.0005]
Greenland 31 [26,93] 34.24 [3.9,42]/ [4.4,98.8] 1.9 [0,2]/ [0,6] 0 [0,0]
Mongolia 32 [19,63] 34.05 [11.1,61.6]/ [10.1,118.6] 95.3 [31,172]/ [28,332] 0.0015 [0.0005,0.0026]
Australia 33 [28,41] 31.92 [26.2,36.6]/ [15.5,98.7] 737.4 [605,845]/ [358,2281] 0.0117 [0.0092,0.0136]
Bosnia and Herzegovina 34 [27,47] 30.7 [18.7,44]/ [13.9,81.5] 116.7 [71,167]/ [53,310] 0.0019 [0.0011,0.0027]
Republic of Serbia 35 [27,49] 29.7 [18,40.8]/ [11.8,98.6] 210.9 [128,290]/ [84,700] 0.0033 [0.002,0.0046]
Canada 36 [30,41] 29.36 [25,34.7]/ [14.7,88.8] 1033.4 [880,1220]/ [518,3128] 0.0164 [0.0141,0.0199]
Costa Rica 37 [28,45] 28.99 [21.7,40.9]/ [12.4,80] 142.1 [106,200]/ [61,392] 0.0023 [0.0017,0.0032]
Uruguay 38 [29,46] 28.9 [22,37.9]/ [13.3,94] 98.3 [75,129]/ [45,320] 0.0016 [0.0012,0.0021]
Ireland 39 [27,48] 28.88 [19.3,41.8]/ [13.5,91.6] 132.8 [89,192]/ [62,421] 0.0021 [0.0014,0.0031]
Italy 40 [33,46] 26.13 [21.1,31.5]/ [12.2,71.1] 1562.5 [1259,1882]/ [730,4250] 0.0248 [0.0198,0.0295]
Togo 41 [27,63] 25.9 [10.8,39.3]/ [8.9,79] 183.9 [77,279]/ [63,561] 0.0029 [0.0012,0.0045]
Cyprus 42 [35,56] 25.58 [14,30.5]/ [8.8,79.7] 20.5 [11,24]/ [7,64] 0.0003 [0.0002,0.0004]
United Kingdom 43 [35,47] 24.5 [21,30.1]/ [13.5,72.3] 1595.3 [1367,1961]/ [879,4709] 0.0253 [0.0214,0.0318]
Albania 44 [23,62] 24.26 [11.7,47.6]/ [7.5,80.5] 67.9 [33,133]/ [21,225] 0.0011 [0.0005,0.0021]
Cuba 45 [34,66] 22.08 [10.9,31.7]/ [6.8,72.9] 249.5 [124,358]/ [76,823] 0.004 [0.002,0.0058]
Portugal 46 [34,54] 20.64 [15.6,30.5]/ [10,67.3] 212.6 [161,315]/ [103,693] 0.0034 [0.0025,0.0047]
Nicaragua 47 [39,61] 19.16 [12.5,27.6]/ [8.5,57.6] 116.9 [76,168]/ [52,352] 0.0019 [0.0012,0.0027]
Macedonia 48 [38,63] 18.54 [10.9,27.1]/ [8.3,61.5] 38.9 [23,57]/ [17,129] 0.0006 [0.0004,0.0009]
United States of America 49 [39,60] 18.34 [12.8,26.2]/ [7.4,63.6] 5847.1 [4079,8367]/ [2360,20290] 0.0928 [0.0674,0.1335]
Hong Kong 50 [28,84] 17.81 [5.6,39.5]/ [5,92.4] 131.8 [42,292]/ [37,684] 0.0021 [0.0007,0.0047]
Argentina 51 [45,62] 17.58 [12.5,22.4]/ [8,54.5] 729.6 [520,929]/ [332,2260] 0.0116 [0.0081,0.0144]
The Bahamas 52 [27,73] 17.39 [7.7,41.1]/ [6.4,78.1] 6.6 [3,16]/ [2,30] 0.0001 [0,0.0003]
Ukraine 53 [46,61] 16.19 [13.1,22.6]/ [7.8,51.5] 694.7 [562,970]/ [336,2209] 0.011 [0.0087,0.015]
Armenia 54 [32,71] 16.06 [8.1,33.9]/ [5.4,67.8] 48.2 [24,102]/ [16,203] 0.0008 [0.0004,0.0016]
Russia 55 [48,63] 15.96 [11.6,19.8]/ [7.8,50.5] 2298.1 [1667,2852]/ [1130,7276] 0.0365 [0.0272,0.0468]
Belarus 56 [48,63] 15.71 [11.7,18.4]/ [7.8,48.5] 149.3 [111,175]/ [74,460] 0.0024 [0.0018,0.0028]
Puerto Rico 57 [41,81] 15.53 [5.8,26.4]/ [5.2,57.5] 54.3 [20,92]/ [18,201] 0.0009 [0.0003,0.0014]
Chile 58 [44,69] 14.45 [8.5,23.3]/ [6.5,53.8] 254.4 [150,410]/ [114,948] 0.004 [0.0024,0.0063]
Colombia 59 [50,70] 13.09 [9.2,19.4]/ [6.3,48.3] 616.7 [433,916]/ [298,2275] 0.0098 [0.0069,0.0147]
Benin 60 [44,90] 12.5 [4.7,23.6]/ [3.8,46.3] 132.5 [50,250]/ [40,490] 0.0021 [0.0008,0.0041]
Iran 61 [47,74] 12.5 [7.7,20.3]/ [4.9,44.4] 968.7 [598,1571]/ [383,3437] 0.0154 [0.0095,0.0246]
Belize 62 [46,90] 12.24 [4.2,20.4]/ [2.6,38.8] 4.7 [2,8]/ [1,15] 0.0001 [0,0.0001]
Romania 63 [53,73] 11.87 [8.5,16.9]/ [4.7,35.4] 235.1 [168,335]/ [93,700] 0.0037 [0.0024,0.0055]
South Korea 64 [55,75] 11.67 [7.6,16]/ [5,38.1] 585.8 [383,803]/ [251,1911] 0.0093 [0.0061,0.0129]
Japan 65 [43,87] 11.31 [5,25.5]/ [4,42.6] 1439.9 [637,3243]/ [513,5420] 0.0229 [0.0102,0.0489]
Taiwan 66 [49,82] 10.53 [6,18.9]/ [4.2,38.4] 247.5 [140,444]/ [99,902] 0.0039 [0.0022,0.0072]
Cape Verde 67 [55,115] 10.45 [1.6,14.2]/ [2.3,34.4] 5.5 [1,8]/ [1,18] 0.0001 [0,0.0001]
East Timor 68 [43,100] 10.44 [3.1,23.1]/ [2.7,40.9] 12.5 [4,28]/ [3,49] 0.0002 [0.0001,0.0004]
Venezuela 69 [57,83] 9.66 [5.5,14.8]/ [3.4,30.9] 293.5 [168,449]/ [103,938] 0.0047 [0.0026,0.0069]
Burundi 70 [56,112] 9.6 [2.2,14.7]/ [2.4,32.7] 96.9 [22,148]/ [25,331] 0.0015 [0.0004,0.0024]
Greece 71 [61,76] 9.46 [7.2,11.9]/ [4.5,31.1] 102.2 [77,129]/ [48,336] 0.0016 [0.0012,0.0021]
Mexico 72 [59,82] 8.74 [5.7,12.6]/ [3.6,28.1] 1069 [696,1539]/ [435,3438] 0.017 [0.0114,0.0249]
Peru 73 [54,90] 8.44 [4.4,14.8]/ [3.2,29.3] 256.6 [133,451]/ [98,891] 0.0041 [0.002,0.0072]
El Salvador 74 [60,91] 8.2 [4,12.8]/ [2.6,29.7] 51.6 [25,81]/ [16,187] 0.0008 [0.0004,0.0013]
Georgia 75 [60,82] 8.19 [5.5,13.3]/ [3.7,25.6] 30.3 [20,49]/ [14,95] 0.0005 [0.0003,0.0008]
Panama 76 [55,111] 8.07 [1.8,16.2]/ [1.5,39.8] 31.5 [7,63]/ [6,155] 0.0005 [0.0001,0.001]
Brunei 77 [67,125] 7.97 [1.1,10.1]/ [1.2,24.6] 3.3 [0,4]/ [0,10] 0.0001 [0,0.0001]
Moldova 78 [66,90] 7.92 [3.9,10.5]/ [3.2,24.2] 28.5 [14,38]/ [11,87] 0.0005 [0.0002,0.0006]
Dominican Republic 79 [57,100] 7.49 [2.7,14.7]/ [2,23.3] 77.9 [28,153]/ [21,242] 0.0012 [0.0005,0.0024]
Montenegro 80 [63,93] 6.69 [3.6,11.3]/ [2,24.1] 4.2 [2,7]/ [1,15] 0.0001 [0,0.0001]
Ecuador 81 [63,92] 6.66 [3.8,11.4]/ [2.9,26.2] 104.6 [60,178]/ [46,412] 0.0017 [0.001,0.0029]
Senegal 82 [65,96] 6.55 [3.2,11.3]/ [2.5,21.5] 96.9 [48,167]/ [37,318] 0.0015 [0.0008,0.0027]
Kazakhstan 83 [77,93] 5.34 [3.8,6.9]/ [2.7,16.6] 90.8 [65,118]/ [45,282] 0.0014 [0.001,0.0018]
Guatemala 84 [72,107] 5.18 [2.5,8.4]/ [2.6,18.5] 80.2 [39,131]/ [41,287] 0.0013 [0.0006,0.0021]
Nepal 85 [72,106] 5.09 [2.4,8.9]/ [1.4,18.2] 157.9 [74,277]/ [43,563] 0.0025 [0.0011,0.0045]
Honduras 86 [77,110] 5.04 [2.2,7.3]/ [1.9,14.5] 40.8 [18,59]/ [15,117] 0.0006 [0.0003,0.0009]
Bolivia 87 [74,107] 5.03 [2.4,8.1]/ [1.6,18.3] 55.3 [26,89]/ [18,202] 0.0009 [0.0004,0.0014]
United Arab Emirates 88 [79,106] 4.69 [2.5,6.8]/ [2.5,16.2] 46.4 [25,67]/ [25,161] 0.0007 [0.0004,0.0011]
Swaziland 89 [56,117] 4.54 [1.8,13.7]/ [1.3,24.5] 5.9 [2,18]/ [2,32] 0.0001 [0,0.0003]
Trinidad and Tobago 90 [79,140] 4.51 [0.7,6.4]/ [0.7,15.8] 6.3 [1,9]/ [1,22] 0.0001 [0,0.0002]
Vanuatu 91 [34,113] 4.39 [2,35.6]/ [1.5,53.6] 1.2 [1,10]/ [0,15] 0 [0,0.0002]
Vietnam 92 [81,105] 4.34 [2.4,6.1]/ [1.8,12.9] 389.7 [218,551]/ [162,1158] 0.0062 [0.0034,0.0087]
Maurtitius 93 [79,141] 4.27 [0.7,6]/ [0.6,14.8] 5.5 [1,8]/ [1,19] 0.0001 [0,0.0001]
Morocco 94 [75,118] 3.81 [1.5,8.8]/ [0.9,17.3] 125.7 [51,291]/ [31,569] 0.002 [0.0008,0.0045]
Tunisia 95 [81,118] 3.78 [1.5,6]/ [1.5,11.5] 42.3 [16,67]/ [17,129] 0.0007 [0.0003,0.0011]
Qatar 96 [85,111] 3.6 [2,4.9]/ [1.2,13.3] 9.4 [5,13]/ [3,35] 0.0001 [0.0001,0.0002]
Paraguay 97 [81,134] 3.57 [1,6.3]/ [0.8,15.6] 24.3 [7,43]/ [5,106] 0.0004 [0.0001,0.0007]
Suriname 98 [89,132] 3.54 [0.9,4.4]/ [0.5,10.8] 1.9 [1,2]/ [0,6] 0 [0,0]
Turkey 99 [86,115] 3.42 [1.7,5.1]/ [1.1,12.1] 256.5 [128,385]/ [82,904] 0.0041 [0.002,0.0062]
Maldives 100 [89,141] 3.37 [0.7,4]/ [0.7,10] 1.2 [0,1]/ [0,3] 0 [0,0]
Lebanon 101 [89,122] 3.23 [1.3,4.7]/ [0.9,11.1] 19.4 [8,28]/ [6,67] 0.0003 [0.0001,0.0005]
Tajikistan 102 [82,130] 3.18 [1,5.5]/ [0.8,11.7] 27.4 [9,47]/ [7,100] 0.0004 [0.0001,0.0007]
Malaysia 103 [82,123] 2.94 [1.2,5.8]/ [0.8,9.9] 93.6 [39,184]/ [24,314] 0.0015 [0.0006,0.0029]
Azerbaijan 104 [94,117] 2.78 [1.6,3.6]/ [1.1,9.5] 27.2 [16,35]/ [11,93] 0.0004 [0.0002,0.0006]
South Africa 105 [86,123] 2.77 [1.3,5.2]/ [0.9,10.2] 146.9 [69,273]/ [47,543] 0.0023 [0.0011,0.0043]
Bhutan 106 [96,144] 2.7 [0.6,3.3]/ [0.5,9.2] 2.2 [0,3]/ [0,7] 0 [0,0]
Madagascar 107 [93,117] 2.66 [1.6,4.1]/ [1.1,8.1] 60.8 [37,95]/ [25,186] 0.001 [0.0006,0.0015]
Kyrgyzstan 108 [95,116] 2.6 [1.7,3.7]/ [1,8.2] 15.9 [10,22]/ [6,50] 0.0003 [0.0002,0.0004]
Gabon 109 [97,147] 2.55 [0.5,3.5]/ [0.4,9.3] 4.6 [1,6]/ [1,17] 0.0001 [0,0.0001]
Algeria 110 [92,115] 2.52 [1.8,3.9]/ [1.2,9.4] 98.8 [70,151]/ [46,368] 0.0016 [0.0011,0.0023]
Democratic Republic of the Congo 111 [94,127] 2.51 [1.1,3.4]/ [0.8,7.5] 169.3 [76,232]/ [57,504] 0.0027 [0.0013,0.0038]
Fiji 112 [98,136] 2.45 [0.8,3]/ [0.5,8.1] 2.2 [1,3]/ [0,7] 0 [0,0]
Djibouti 113 [99,153] 2.4 [0.4,3]/ [0.4,7.3] 2.2 [0,3]/ [0,7] 0 [0,0]
Jamaica 114 [98,155] 2.27 [0.3,3.4]/ [0.3,7.8] 6.1 [1,9]/ [1,21] 0.0001 [0,0.0001]
Pakistan 115 [90,136] 2.24 [0.8,4]/ [0.6,8.4] 408.3 [137,734]/ [105,1537] 0.0065 [0.0022,0.0119]
Kuwait 116 [96,144] 2.22 [0.6,3.4]/ [0.5,7.3] 9.3 [3,14]/ [2,31] 0.0001 [0,0.0002]
Namibia 117 [100,157] 2.17 [0.3,3.1]/ [0.3,7.6] 5 [1,7]/ [1,17] 0.0001 [0,0.0001]
Central African Republic 118 [89,154] 1.89 [0.4,4.9]/ [0.2,7.9] 9.5 [2,24]/ [1,39] 0.0002 [0,0.0004]
Republic of the Congo 119 [88,138] 1.64 [0.7,4.6]/ [0.5,10.2] 7.7 [4,21]/ [2,48] 0.0001 [0.0001,0.0003]
Philippines 120 [102,138] 1.57 [0.7,2.8]/ [0.6,5.2] 154.1 [69,271]/ [60,515] 0.0024 [0.0011,0.0043]
Jordan 121 [108,152] 1.49 [0.4,2.3]/ [0.4,5.3] 14.6 [4,22]/ [4,52] 0.0002 [0.0001,0.0004]
Ghana 122 [105,139] 1.42 [0.8,2.6]/ [0.5,5.1] 40.1 [22,73]/ [14,144] 0.0006 [0.0003,0.0012]
Solomon Islands 123 [116,159] 1.35 [0.3,1.6]/ [0.3,3.7] 0.9 [0,1]/ [0,2] 0 [0,0]
Guinea Bissau 124 [117,158] 1.28 [0.3,1.6]/ [0.4,3.7] 1.9 [0,2]/ [1,6] 0 [0,0]
Thailand 125 [118,135] 1.22 [0.8,1.6]/ [0.6,3.6] 81.9 [56,107]/ [38,242] 0.0013 [0.0009,0.0017]
China 126 [115,135] 1.2 [0.9,1.7]/ [0.6,3.8] 1626.6 [1236,2365]/ [778,5185] 0.0258 [0.02,0.0362]
Equatorial Guinea 127 [119,166] 1.17 [0.2,1.4]/ [0.2,3.1] 1.4 [0,2]/ [0,4] 0 [0,0]
Guyana 128 [119,161] 1.16 [0.2,1.4]/ [0.3,3.2] 0.9 [0,1]/ [0,2] 0 [0,0]
Mauritania 129 [118,169] 1.07 [0.1,1.4]/ [0.2,3.7] 4 [0,5]/ [1,14] 0.0001 [0,0.0001]
Haiti 130 [115,163] 1.03 [0.2,1.8]/ [0.2,3.6] 11.5 [2,20]/ [3,39] 0.0002 [0,0.0003]
Rwanda 131 [105,152] 1.02 [0.4,2.8]/ [0.4,4] 11.9 [5,33]/ [4,46] 0.0002 [0.0001,0.0005]
Gambia 132 [124,158] 1.01 [0.3,1.3]/ [0.2,2.9] 1.9 [1,2]/ [0,6] 0 [0,0]
Syria 133 [118,153] 1 [0.4,1.5]/ [0.4,3.1] 18.7 [7,27]/ [8,57] 0.0003 [0.0001,0.0004]
Botswana 134 [124,161] 0.98 [0.2,1.2]/ [0.2,2.9] 2.2 [0,3]/ [0,6] 0 [0,0]
Ivory Coast 135 [112,150] 0.98 [0.4,2]/ [0.3,4.2] 22.2 [10,47]/ [8,95] 0.0004 [0.0002,0.0008]
Cambodia 136 [121,145] 0.91 [0.6,1.4]/ [0.4,3.1] 14.3 [10,22]/ [6,49] 0.0002 [0.0002,0.0004]
Zimbabwe 137 [120,147] 0.85 [0.6,1.3]/ [0.4,2.8] 12.1 [8,19]/ [6,40] 0.0002 [0.0001,0.0003]
Indonesia 138 [123,150] 0.84 [0.5,1.2]/ [0.3,2.9] 209.9 [119,304]/ [77,718] 0.0033 [0.0018,0.005]
Saudi Arabia 139 [118,170] 0.84 [0.1,1.6]/ [0.1,3.5] 24.1 [3,46]/ [2,102] 0.0004 [0.0001,0.0007]
Niger 140 [119,151] 0.81 [0.5,1.5]/ [0.3,3.3] 16.7 [9,31]/ [7,69] 0.0003 [0.0001,0.0005]
Cameroon 141 [119,151] 0.79 [0.4,1.4]/ [0.3,3] 17.9 [9,32]/ [8,69] 0.0003 [0.0001,0.0005]
Libya 142 [127,168] 0.78 [0.1,1]/ [0.2,2.4] 5 [1,7]/ [1,15] 0.0001 [0,0.0001]
United Republic of Tanzania 143 [120,165] 0.7 [0.2,1.4]/ [0.2,2.4] 38.8 [11,79]/ [11,133] 0.0006 [0.0002,0.0013]
Angola 144 [130,162] 0.69 [0.2,1.1]/ [0.2,2] 14.9 [5,23]/ [4,43] 0.0002 [0.0001,0.0004]
Uzbekistan 145 [135,150] 0.68 [0.5,0.9]/ [0.3,2.2] 21.6 [16,28]/ [9,68] 0.0003 [0.0003,0.0004]
Malawi 146 [116,166] 0.67 [0.2,1.6]/ [0.2,3] 11.3 [3,27]/ [4,51] 0.0002 [0,0.0004]
Laos 147 [134,164] 0.64 [0.2,0.9]/ [0.1,2.1] 4.2 [1,6]/ [1,14] 0.0001 [0,0.0001]
Egypt 148 [125,164] 0.64 [0.2,1.3]/ [0.2,2.8] 52.5 [14,104]/ [15,229] 0.0008 [0.0002,0.0017]
Sri Lanka 149 [126,159] 0.58 [0.3,1.2]/ [0.2,2.2] 12.3 [6,25]/ [5,46] 0.0002 [0.0001,0.0004]
Zambia 150 [122,161] 0.55 [0.2,1.4]/ [0.1,2.3] 8.7 [4,22]/ [2,36] 0.0001 [0.0001,0.0003]
Uganda 151 [121,165] 0.54 [0.2,1.4]/ [0.1,3] 20 [7,53]/ [5,113] 0.0003 [0.0001,0.0008]
Liberia 152 [141,170] 0.53 [0.1,0.7]/ [0.1,1.8] 2.2 [0,3]/ [0,7] 0 [0,0]
Kenya 153 [139,164] 0.48 [0.2,0.7]/ [0.2,1.5] 21.1 [9,32]/ [7,65] 0.0003 [0.0001,0.0005]
Mali 154 [145,158] 0.45 [0.4,0.6]/ [0.2,1.4] 8.3 [7,10]/ [4,25] 0.0001 [0.0001,0.0002]
Oman 155 [147,171] 0.42 [0.1,0.5]/ [0.1,1.3] 1.9 [0,2]/ [0,6] 0 [0,0]
Afghanistan 156 [144,167] 0.4 [0.1,0.6]/ [0.1,1.4] 11.2 [4,16]/ [3,38] 0.0002 [0.0001,0.0002]
Iraq 157 [134,168] 0.4 [0.1,0.9]/ [0.1,1.5] 14.6 [4,34]/ [3,57] 0.0002 [0.0001,0.0005]
Nigeria 158 [142,163] 0.39 [0.2,0.6]/ [0.2,1.6] 67.7 [38,112]/ [27,272] 0.0011 [0.0006,0.0018]
Sierra Leone 159 [148,172] 0.37 [0.1,0.5]/ [0,1.2] 2.6 [0,3]/ [0,8] 0 [0,0.0001]
Eritrea 160 [150,172] 0.36 [0.1,0.4]/ [0.1,1.2] 1.9 [0,2]/ [0,6] 0 [0,0]
Turkmenistan 161 [154,173] 0.29 [0.1,0.4]/ [0.1,0.9] 1.4 [0,2]/ [0,4] 0 [0,0]
Chad 162 [122,173] 0.28 [0,1.2]/ [0,2.6] 4.1 [1,18]/ [1,38] 0.0001 [0,0.0003]
Burkina Faso 163 [149,171] 0.26 [0.1,0.5]/ [0.1,0.8] 5 [2,10]/ [2,15] 0.0001 [0,0.0002]
Yemen 164 [155,173] 0.23 [0,0.3]/ [0,0.8] 6.3 [1,9]/ [1,22] 0.0001 [0,0.0001]
Sudan 165 [157,171] 0.22 [0.1,0.3]/ [0.1,0.7] 9 [2,12]/ [2,31] 0.0001 [0,0.0002]
Guinea 166 [159,173] 0.2 [0.1,0.3]/ [0,0.7] 2.6 [1,3]/ [0,9] 0 [0,0.0001]
Myanmar 167 [158,168] 0.2 [0.1,0.3]/ [0.1,0.6] 11 [8,16]/ [5,31] 0.0002 [0.0001,0.0002]
India 168 [159,170] 0.17 [0.1,0.3]/ [0.1,0.6] 219 [134,348]/ [79,755] 0.0035 [0.002,0.0056]
Lesotho 169 [162,173] 0.16 [0,0.2]/ [0,0.4] 0.3 [0,0]/ [0,1] 0 [0,0]
Mozambique 170 [165,173] 0.13 [0,0.2]/ [0,0.5] 3.5 [1,5]/ [1,12] 0.0001 [0,0.0001]
Ethiopia 171 [167,173] 0.1 [0.1,0.1]/ [0,0.3] 10.3 [5,14]/ [4,35] 0.0002 [0.0001,0.0002]
Bangladesh 172 [168,173] 0.1 [0.1,0.1]/ [0,0.3] 15.1 [10,19]/ [6,48] 0.0002 [0.0002,0.0003]
Somalia 173 [169,173] 0.08 [0,0.1]/ [0,0.2] 0.9 [0,1]/ [0,3] 0 [0,0]
South Sudan 174 [173,174] 0.02 [0,0]/ [0,0.1] 0.3 [0,0]/ [0,1] 0 [0,0]
Papua New Guinea 175 [175,175] 0 [0,0]/ [0,0] 0 [0,0]/ [0,0] 0 [0,0]

8 thoughts on “Per-country rates of Esperanto speakers”

  2. About Hungary: no, Esperanto is not taught in schools. The answer is even more simple: the Hungarian language education is terrible, so the Hungarians don’t really speak languages. Because of this the Hungarian governments try to raise the number of language speakers at least among the most educated. So in Hungary you need to have a language exam to receive a college/university degree (BA, BSC, MA, MSC). For several years Esperanto and Lovari were extremely popular options as they were accepted. As these languages are easier to learn, it takes less time and money to achieve results. If I remember well Esperanto and Lovari were removed from the accepted languages a few years ago but the ones already taken a language exam are still here.
    Thanks for this very professional article!

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    1. There can’t be a decimal point in the actual number of esperanto speakers but there can be a decimal point in an *estimate* of the number of esperanto speakers and the decimal numbers in this post are estimates. I could have signalled that 62983.9 is an estimate by writing “approximately 60000” instead of “62983.9”. I didn’t, because I also wanted to emphasize that the the estimate was the result of a computation and not a guess.

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