| Country | Income Group | USD Purchasing Power |
|---|---|---|
| Russia | High income | $3.19 |
| Belarus | Upper middle income | $3.93 |
| Egypt | Lower middle income | $6.40 |
| Sierra Leone | Low income | $4.44 |
Countries with the highest USD purchasing power by income group (2023).
This morning I was reading about a Ukrainian drone strike on several Russian oil refineries, which supposedly disrupted 15% of Russia's oil refining capacity . Given the events taking place in recent years, I got curious about the value of the Russian ruble relative to the US dollar. I decided to look up the exchange rate.
This isn't the first time I've done this, but it's the first time I've ever thought about the result. I know that as of today
1.00 USD = 98.23 RUB
, however, one ruble does not buy you a drink
(okay, one and some change)
. So this isn't really the metric I'm looking for.
After some research, I found another metric called PPP
(Purchasing Power Parity)
. As far as I understand, PPP is the amount of a foreign currency needed to match the same purchasing power as one US dollar. As of the time of writing the ruble's PPP conversion rate is
29.18 RUB
. Dividing the exchange rate by the PPP, it seems
1.00 USD = 3.36 USD
in Russia today. This is called the RER
(Real Exchange Rate)
.
This is really cool! It gives me the context I've been looking for, and failing to find, every time I've compared the US dollar with a foreign currency. Naturally, the next thing I want to know is where the dollar is worth the most. I searched for PPP data via Google's
dataset search
and found the World Bank's
data page
. From here I downloaded the
PA.NUS.FCRF
and
PA.NUS.PPP
datasets. Please note that
the latest year in these datasets is 2023
.
import pandas as pd
# Load dataset data.
fx = pd.read_csv("fx.csv", usecols=[0, 67], names=["name", "exchange_rate"], header=0)
ppp = pd.read_csv("ppp.csv", usecols=[0, 67], names=["name", "ppp_rate"], header=0)
meta = pd.read_csv("metadata.csv", usecols=[2, 4], names=["income_group", "name"], header=0)
# Merge on country name.
df = fx.merge(ppp, on="name").merge(meta, on="name").dropna()
# Calculate real exchange rate.
df["real_exchange_rate"] = df["exchange_rate"] / df["ppp_rate"]
# Sort by income group and real exchange rate.
income_group_order = {
"High income": 1,
"Upper middle income": 2,
"Lower middle income": 3,
"Low income": 4,
}
df["income_order"] = df["income_group"].map(income_group_order)
df = df.sort_values(["income_order", "real_exchange_rate"], ascending=[True, False]).drop(columns=["income_order"])
# Export to CSV.
df.to_csv("rer.csv", index=False)
You can download the CSV here .
| name | exchange_rate | ppp_rate | income_group | real_exchange_rate |
|---|---|---|---|---|
| Russian Federation | 85.1620083333333 | 26.6700705903736 | High income | 3.193167713776956 |
| Guyana | 208.5 | 79.104804384633 | High income | 2.6357438289867448 |
| Brunei Darussalam | 1.34307960114934 | 0.519210636342662 | High income | 2.5867721251052176 |
| Romania | 4.57429166666667 | 1.84382686054144 | High income | 2.4808683312724 |
| Bulgaria | 1.80896666666667 | 0.76805086383036 | High income | 2.3552693602154684 |
| Bahrain | 0.376 | 0.173018958851266 | High income | 2.1731722494251313 |
| Panama | 1.0 | 0.469468516383161 | High income | 2.130068290210628 |
| Poland | 4.20366666666667 | 1.996096 | High income | 2.105944136287368 |
| Croatia | 0.924839558470698 | 0.44457389377739 | High income | 2.08028310122454 |
| Hungary | 353.088333333333 | 174.101839 | High income | 2.028056310957939 |
| Oman | 0.3845 | 0.194872289898959 | High income | 1.9730870930872866 |
| Chile | 840.066526651499 | 435.895551 | High income | 1.9272197771330293 |
| Saudi Arabia | 3.75 | 1.96966638802559 | High income | 1.9038757135714903 |
| Latvia | 0.924839558470698 | 0.502885 | High income | 1.8390676963335513 |
| Seychelles | 14.0181106992979 | 7.66322976969878 | High income | 1.82926926642954 |
| Lithuania | 0.924839558470698 | 0.506025 | High income | 1.8276558637828133 |
| Slovak Republic | 0.924839558470698 | 0.520553 | High income | 1.7766482153991965 |
| Trinidad and Tobago | 6.75026237934502 | 3.83972390680919 | High income | 1.7580072273879936 |
| Greece | 0.924839558470698 | 0.526881 | High income | 1.7553101335419155 |
| Portugal | 0.924839558470698 | 0.535429 | High income | 1.7272870137230107 |
| Macao SAR, China | 8.06339008333333 | 4.67065475274328 | High income | 1.7263939447884362 |
| Czechia | 22.1980833333333 | 13.211473 | High income | 1.6802125950174747 |
| Singapore | 1.34276666666667 | 0.8037836176702 | High income | 1.6705573952337254 |
| Slovenia | 0.924839558470698 | 0.560446 | High income | 1.6501849571068363 |
| Qatar | 3.64 | 2.26432104089352 | High income | 1.6075458975391697 |
| Spain | 0.924839558470698 | 0.583709 | High income | 1.584418877335621 |
| Malta | 0.924839558470698 | 0.590611605815723 | High income | 1.5659014305913548 |
| Korea, Rep. | 1305.6625 | 836.252596 | High income | 1.5613254969195933 |
| Cyprus | 0.924839558470698 | 0.592371856544119 | High income | 1.5612483075515884 |
| United Arab Emirates | 3.6725 | 2.36632559202145 | High income | 1.5519842292128285 |
| Estonia | 0.924839558470698 | 0.597148 | High income | 1.548761041602246 |
| Andorra | 0.924709622987356 | 0.603563370465285 | High income | 1.5320837350923078 |
| Kuwait | 0.30718535125088 | 0.200950622738202 | High income | 1.5286608574041611 |
| Uruguay | 38.8239166666667 | 25.708087339119 | High income | 1.5101830079591276 |
| St. Kitts and Nevis | 2.7 | 1.80590982834959 | High income | 1.4950912596048684 |
| Italy | 0.924839558470698 | 0.625682 | High income | 1.4781303577067872 |
| Japan | 140.49110006234 | 95.096275 | High income | 1.4773565006866987 |
| Antigua and Barbuda | 2.7 | 1.82975886781575 | High income | 1.475604270863892 |
| Hong Kong SAR, China | 7.82958333333333 | 5.52967391559928 | High income | 1.4159213459668891 |
| Aruba | 1.79 | 1.35282116097067 | High income | 1.323160852034313 |
| Sint Maarten (Dutch part) | 1.79 | 1.36793336469739 | High income | 1.3085432713281164 |
| France | 0.924839558470698 | 0.710572 | High income | 1.3015423609017778 |
| Puerto Rico | 1.0 | 0.770766479679474 | High income | 1.297409820437253 |
| Germany | 0.924839558470698 | 0.728102 | High income | 1.2702060404595756 |
| Belgium | 0.924839558470698 | 0.734306 | High income | 1.2594743315058 |
| Austria | 0.924839558470698 | 0.737191 | High income | 1.254545373547287 |
| Netherlands | 0.924839558470698 | 0.764616 | High income | 1.2095477448427683 |
| Sweden | 10.6101612965536 | 8.788485 | High income | 1.207279900523651 |
| Ireland | 0.924839558470698 | 0.771239 | High income | 1.199160776971468 |
| Canada | 1.3499086407939 | 1.137311 | High income | 1.1869300840261812 |
| United Kingdom | 0.804538906734353 | 0.682911 | High income | 1.178102134442633 |
| Finland | 0.924839558470698 | 0.794931 | High income | 1.1634211755117085 |
| Norway | 10.5633333333333 | 9.226233 | High income | 1.1449237552675398 |
| New Zealand | 1.62843333333333 | 1.4701 | High income | 1.1077024238713897 |
| Palau | 1.0 | 0.906895733161873 | High income | 1.1026625922183149 |
| Australia | 1.50519106560509 | 1.369197 | High income | 1.0993239582069563 |
| Faroe Islands | 6.88970258583334 | 6.32639711711029 | High income | 1.0890404851759212 |
| Luxembourg | 0.924839558470698 | 0.855511 | High income | 1.0810376003005198 |
| Nauru | 1.50519106560508 | 1.39457700706714 | High income | 1.0793172825719868 |
| Denmark | 6.88970258583334 | 6.412601 | High income | 1.0744006349113784 |
| Israel | 3.6673747160226 | 3.566703 | High income | 1.0282254272426383 |
| Bahamas, The | 1.0 | 0.976986677146743 | High income | 1.0235554111346397 |
| United States | 1.0 | 1.0 | High income | 1.0 |
| Iceland | 137.942952429967 | 144.241483 | High income | 0.9563334316936204 |
| Switzerland | 0.89849 | 1.001601 | High income | 0.8970538168392405 |
| Barbados | 2.0 | 2.2313319471701 | High income | 0.8963256240455445 |
| Cayman Islands | 0.83333 | 0.939650949246276 | High income | 0.8868505913482454 |
| Bermuda | 1.0 | 1.15651546560852 | High income | 0.8646663444953011 |
| Turks and Caicos Islands | 1.0 | 1.22592700357511 | High income | 0.8157092527399672 |
| Belarus | 3.00735 | 0.76535733770338 | Upper middle income | 3.929341043523805 |
| Suriname | 36.7758691666667 | 9.55969401696286 | Upper middle income | 3.8469713676411676 |
| Ukraine | 36.5738081166667 | 10.5171315081732 | Upper middle income | 3.4775459533090385 |
| Azerbaijan | 1.7 | 0.51335400569404 | Upper middle income | 3.31155495261335 |
| Thailand | 34.8021885808364 | 10.6520148270449 | Upper middle income | 3.2671930283532364 |
| Malaysia | 4.56062343240897 | 1.42505898097785 | Upper middle income | 3.2003050352902247 |
| Indonesia | 15236.8846620506 | 4819.78068019626 | Upper middle income | 3.1613232371042574 |
| Algeria | 135.842933333333 | 43.309804948654 | Upper middle income | 3.1365399473486844 |
| Mongolia | 3465.73653626198 | 1123.87249557112 | Upper middle income | 3.083745309116041 |
| Georgia | 2.62795833333333 | 0.8682649562153 | Upper middle income | 3.026677875827671 |
| Kazakhstan | 456.165 | 153.008087182993 | Upper middle income | 2.981313003765877 |
| Colombia | 4325.955 | 1453.585153 | Upper middle income | 2.9760588783339066 |
| North Macedonia | 56.9471916666667 | 20.1883064969905 | Upper middle income | 2.82080082720935 |
| Paraguay | 7288.87205374128 | 2611.63708069054 | Upper middle income | 2.7909207246414334 |
| Namibia | 18.4463083333333 | 6.85561514941412 | Upper middle income | 2.6906860918104067 |
| Botswana | 13.5963833333333 | 5.08342112692872 | Upper middle income | 2.674652167082585 |
| Gabon | 606.569750165917 | 228.016964877431 | Upper middle income | 2.6601957029468157 |
| Armenia | 392.476302158971 | 148.09152970721 | Upper middle income | 2.650227889028706 |
| Moldova | 18.1639377592166 | 6.94626629149713 | Upper middle income | 2.6149210233202456 |
| Equatorial Guinea | 606.569750165917 | 232.636328870431 | Upper middle income | 2.607373289937668 |
| Bosnia and Herzegovina | 1.80920280030082 | 0.6980051142244 | Upper middle income | 2.5919620980301064 |
| Fiji | 2.250125 | 0.87449005196417 | Upper middle income | 2.573071008579287 |
| Mauritius | 45.267225 | 17.8132300485792 | Upper middle income | 2.5412137426255583 |
| Kosovo | 0.924709622987356 | 0.36400029367827 | Upper middle income | 2.540409002539657 |
| Iraq | 1312.5 | 519.04448037598 | Upper middle income | 2.5286850156835596 |
| Montenegro | 0.924709622987356 | 0.365897321309507 | Upper middle income | 2.5272380231643137 |
| South Africa | 18.4502441785 | 7.31314484629252 | Upper middle income | 2.5228878363941547 |
| Albania | 100.645 | 40.69405658432 | Upper middle income | 2.473211285570875 |
| Ecuador | 1.0 | 0.411517703096167 | Upper middle income | 2.4300291153362883 |
| Dominican Republic | 56.1576 | 23.4887612459898 | Upper middle income | 2.3908285078076523 |
| Guatemala | 7.83197416666667 | 3.28243453685422 | Upper middle income | 2.386026005616119 |
| El Salvador | 1.0 | 0.425934641179098 | Upper middle income | 2.347778046959833 |
| Serbia | 108.402691666667 | 46.430288281713 | Upper middle income | 2.334740870203953 |
| Libya | 4.81288333333334 | 2.14524254209143 | Upper middle income | 2.24351477229292 |
| Peru | 3.74382620959376 | 1.74387042414011 | Upper middle income | 2.146848847121089 |
| Argentina | 296.258041666667 | 139.721870365667 | Upper middle income | 2.1203412242573636 |
| Brazil | 4.99437976287199 | 2.43595911597318 | Upper middle income | 2.050272408154398 |
| Maldives | 15.3867943548387 | 7.79468299354335 | Upper middle income | 1.97401156244381 |
| Dominica | 2.7 | 1.37319817963894 | Upper middle income | 1.9662129181600874 |
| China | 7.08399842343631 | 3.636979368794 | Upper middle income | 1.9477697575681658 |
| St. Vincent and the Grenadines | 2.7 | 1.39588580059389 | Upper middle income | 1.9342556524690382 |
| St. Lucia | 2.7 | 1.40899822145366 | Upper middle income | 1.9162550803040879 |
| Belize | 2.0 | 1.07932602259569 | Upper middle income | 1.8530082274771482 |
| Mexico | 17.7587166666667 | 9.888037 | Upper middle income | 1.7959799975128228 |
| Grenada | 2.7 | 1.61448431734975 | Upper middle income | 1.6723606237514737 |
| Jamaica | 154.158779031722 | 92.3153999131695 | Upper middle income | 1.6699140032618767 |
| Costa Rica | 544.050775505632 | 327.883375 | Upper middle income | 1.6592813694980175 |
| Marshall Islands | 1.0 | 0.886641984792284 | Upper middle income | 1.1278509445210547 |
| Iran, Islamic Rep. | 42000.0 | 89391.8126957291 | Upper middle income | 0.46984168609444305 |
| Egypt, Arab Rep. | 30.6264136980311 | 4.78731351848859 | Lower middle income | 6.39741132051452 |
| Pakistan | 280.356111655149 | 56.1913681393929 | Lower middle income | 4.9893092291270555 |
| Myanmar | 2100.0 | 435.000427057019 | Lower middle income | 4.827581467465401 |
| Lao PDR | 17688.8735909054 | 3934.87360775655 | Lower middle income | 4.495410870640551 |
| Tajikistan | 10.844525 | 2.53616258002838 | Lower middle income | 4.275958128787883 |
| India | 82.5992764460784 | 20.2025584710366 | Lower middle income | 4.088555247321613 |
| Nepal | 132.115460088756 | 33.3945919991419 | Lower middle income | 3.9561932690224455 |
| Uzbekistan | 11734.8334198669 | 3010.59375215095 | Lower middle income | 3.8978468654174367 |
| Sri Lanka | 327.506533333333 | 86.7026933502495 | Lower middle income | 3.7773513218363073 |
| Bangladesh | 106.309484297483 | 28.6306777887607 | Lower middle income | 3.7131319447566833 |
| Kyrgyz Republic | 87.8561255862776 | 24.3566662198431 | Lower middle income | 3.6070669439441683 |
| Tunisia | 3.10616666666667 | 0.8870532514667 | Lower middle income | 3.50166876851054 |
| Viet Nam | 23787.3191666667 | 6802.47065679253 | Lower middle income | 3.496864649157162 |
| Ghana | 11.0204083333333 | 3.30229367094792 | Lower middle income | 3.337198151177725 |
| Mauritania | 36.4891666666666 | 11.1409315789782 | Lower middle income | 3.2752347869650262 |
| Angola | 685.020237761449 | 209.794208294387 | Lower middle income | 3.2652009001135833 |
| Kenya | 139.846383759617 | 43.2872477957024 | Lower middle income | 3.230660087692179 |
| Timor-Leste | 1.0 | 0.312766289840095 | Lower middle income | 3.19727551364714 |
| Nicaragua | 36.4411942796586 | 11.4589278696861 | Lower middle income | 3.180157401641525 |
| Lesotho | 18.4502441785 | 5.86804659349166 | Lower middle income | 3.1441884253208636 |
| Eswatini | 18.4535924917449 | 5.92510608224558 | Lower middle income | 3.1144746162504315 |
| Cameroon | 606.569750165917 | 194.896445831458 | Lower middle income | 3.112266863452526 |
| Zambia | 20.212017866195 | 6.59718586610757 | Lower middle income | 3.06373327603704 |
| Cambodia | 4110.65274641577 | 1345.07260471262 | Lower middle income | 3.0560824241112448 |
| Bolivia | 6.91 | 2.33149271757576 | Lower middle income | 2.9637664951340192 |
| Benin | 606.654982255763 | 204.79149665514 | Lower middle income | 2.9623055261778943 |
| Philippines | 55.630363223193 | 19.2623140452142 | Lower middle income | 2.8880415454037616 |
| Senegal | 606.569750165917 | 216.025734320491 | Lower middle income | 2.807858758466357 |
| Congo, Rep. | 606.569750165917 | 219.416910771968 | Lower middle income | 2.7644621740040027 |
| Morocco | 10.1314260970897 | 3.88255216894554 | Lower middle income | 2.6094758437827474 |
| Comoros | 454.991236691823 | 186.33907498391 | Lower middle income | 2.441738195443498 |
| Jordan | 0.71 | 0.304436983272024 | Lower middle income | 2.3321739440756204 |
| Honduras | 24.6016468236818 | 11.074832836622 | Lower middle income | 2.2214011883167792 |
| Cabo Verde | 101.804812544702 | 47.9065770590459 | Lower middle income | 2.125069641674156 |
| Djibouti | 177.721 | 88.9353953404672 | Lower middle income | 1.9983157360423154 |
| Haiti | 141.035912702189 | 73.283773979726 | Lower middle income | 1.924517598414168 |
| Kiribati | 1.50519106560508 | 0.894037028455559 | Lower middle income | 1.6835891777382914 |
| Zimbabwe | 3509.17222037558 | 2141.72616559256 | Lower middle income | 1.6384784743966945 |
| Samoa | 2.73843913026323 | 1.68984001763853 | Lower middle income | 1.620531589783314 |
| Papua New Guinea | 3.58962274932016 | 2.29810324831541 | Lower middle income | 1.5619936797667726 |
| Solomon Islands | 8.37559677613046 | 6.08024172616716 | Lower middle income | 1.3775104927300705 |
| Micronesia, Fed. Sts. | 1.0 | 0.946806210921461 | Lower middle income | 1.0561823406574078 |
| Vanuatu | 119.1125 | 120.981394829571 | Lower middle income | 0.9845522129067552 |
| Sierra Leone | 21.3048751103818 | 4.79466308027478 | Low income | 4.443456141481547 |
| Gambia, The | 61.0963330077956 | 16.7090284171668 | Low income | 3.6564862709207815 |
| Madagascar | 4429.57921419022 | 1229.32981409516 | Low income | 3.6032472029896896 |
| Burundi | 2574.05174879329 | 723.894519014926 | Low income | 3.5558381520778104 |
| Rwanda | 1160.09869425 | 345.099547235674 | Low income | 3.3616349356081603 |
| Burkina Faso | 606.569750165917 | 194.362478765867 | Low income | 3.1208171145862122 |
| Togo | 606.569750165917 | 194.657960329262 | Low income | 3.1160798620303543 |
| Uganda | 3726.14045997085 | 1214.19713968138 | Low income | 3.068810111798348 |
| Mali | 606.569750165917 | 198.393517980443 | Low income | 3.057407098480459 |
| Malawi | 1161.09436666667 | 382.276491678728 | Low income | 3.037315639180017 |
| Guinea-Bissau | 606.569750165917 | 201.600530046641 | Low income | 3.0087706119898834 |
| Niger | 606.569750165917 | 206.287025878039 | Low income | 2.9404163814186406 |
| Chad | 606.569750165917 | 221.496090534004 | Low income | 2.738512217996898 |
| Mozambique | 63.8858333333333 | 23.7231957049634 | Low income | 2.69296911461077 |
| Central African Republic | 606.569750165917 | 238.799784946826 | Low income | 2.5400766181635506 |
| Congo, Dem. Rep. | 2340.03570436145 | 949.243486308259 | Low income | 2.4651585584876403 |
| Ethiopia | 54.6009480676569 | 22.1628313691994 | Low income | 2.4636269237484743 |