Biggest gains from migration in Germany
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The 50 districts in Germany with the highest net migration per 10,000 residents, and the 50 with the lowest, out of 400 with a published figure.
Biggest gains from migration
| District | State | Postal code | Net migration per 10,000 residents | |
|---|---|---|---|---|
| 1 | Suhl | Thuringia | 98527 | 418.6 |
| 2 | Hof | Bavaria | 95028 | 406.6 |
| 3 | Frankfurt (Oder) | Brandenburg | 15230 | 386.9 |
| 4 | Schwerin | Mecklenburg-Vorpommern | 19055 | 383.3 |
| 5 | Landshut | Bavaria | 84026 | 362.7 |
| 6 | Baden-Baden | Baden-Württemberg | 76524 | 353.2 |
| 7 | Gera | Thuringia | 07546 | 345.7 |
| 8 | Bamberg | Bavaria | 96050 | 336.7 |
| 9 | Mönchengladbach | North Rhine-Westphalia | 41169 | 329.2 |
| 10 | Straubing | Bavaria | 94312 | 323.7 |
| 11 | Eifelkreis Bitburg-Prüm | Rhineland-Palatinate | 54646 | 320.9 |
| 12 | Erlangen | Bavaria | 91052 | 308.6 |
| 13 | Chemnitz | Saxony | 09112 | 304.7 |
| 14 | Teltow-Fläming | Brandenburg | 14980 | 298.0 |
| 15 | Kusel | Rhineland-Palatinate | 67742 | 285.2 |
| 16 | Memmingen | Bavaria | 87700 | 277.2 |
| 17 | Coburg | Bavaria | 96444 | 277.0 |
| 18 | Regensburg | Bavaria | 93045 | 269.5 |
| 19 | Havelland | Brandenburg | 14662 | 268.7 |
| 20 | Oder-Spree | Brandenburg | 15566 | 268.4 |
| 21 | Nürnberg | Bavaria | 90353 | 268.3 |
| 22 | Leipzig | Saxony | 04207 | 267.2 |
| 23 | Holzminden | Lower Saxony | 37635 | 264.0 |
| 24 | Emden | Lower Saxony | 26723 | 263.7 |
| 25 | Dessau-Roßlau | Saxony-Anhalt | 06846 | 263.4 |
| 26 | Pirmasens | Rhineland-Palatinate | 66953 | 262.3 |
| 27 | Lüchow-Dannenberg | Lower Saxony | 29479 | 260.2 |
| 28 | Schweinfurt | Bavaria | 97420 | 258.3 |
| 29 | Brandenburg an der Havel | Brandenburg | 14772 | 256.0 |
| 30 | Dingolfing-Landau | Bavaria | 84183 | 250.2 |
| 31 | Wittmund | Lower Saxony | 26465 | 250.0 |
| 32 | Uelzen | Lower Saxony | 29578 | 246.4 |
| 33 | Halle (Saale) | Saxony-Anhalt | 06106 | 245.9 |
| 34 | Heidekreis | Lower Saxony | 29646 | 242.8 |
| 35 | Waldeck-Frankenberg | Hesse | 34549 | 239.5 |
| 36 | Gießen | Hesse | 35410 | 239.2 |
| 37 | Jerichower Land | Saxony-Anhalt | 39291 | 238.0 |
| 38 | Berlin | Berlin | 12039 | 235.5 |
| 39 | Dahme-Spreewald | Brandenburg | 15749 | 235.4 |
| 40 | Kaiserslautern | Rhineland-Palatinate | 67657 | 234.1 |
| 41 | Cochem-Zell | Rhineland-Palatinate | 56825 | 233.9 |
| 42 | Diepholz | Lower Saxony | 27327 | 232.3 |
| 43 | Bremerhaven | Bremen | 27574 | 231.8 |
| 44 | Wilhelmshaven | Lower Saxony | 26386 | 229.9 |
| 45 | Ilm-Kreis | Thuringia | 98716 | 229.8 |
| 46 | Bernkastel-Wittlich | Rhineland-Palatinate | 54516 | 228.5 |
| 47 | Barnim | Brandenburg | 16321 | 227.6 |
| 48 | Rhein-Hunsrück-Kreis | Rhineland-Palatinate | 55496 | 227.3 |
| 49 | Kyffhäuserkreis | Thuringia | 37355 | 226.7 |
| 50 | Altenkirchen (Westerwald) | Rhineland-Palatinate | 57580 | 226.0 |
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Biggest losses to migration
| District | State | Postal code | Net migration per 10,000 residents | |
|---|---|---|---|---|
| 1 | Schwabach | Bavaria | 91125 | 47.4 |
| 2 | Ahrweiler | Rhineland-Palatinate | 53506 | 50.1 |
| 3 | Neumünster | Schleswig-Holstein | 24536 | 73.0 |
| 4 | Hildburghausen | Thuringia | 98666 | 84.3 |
| 5 | Südwestpfalz | Rhineland-Palatinate | 66978 | 89.7 |
| 6 | Fürth, Landkreis | Bavaria | 90574 | 95.4 |
| 7 | Rhein-Pfalz-Kreis | Rhineland-Palatinate | 67165 | 101.5 |
| 8 | Hagen | North Rhine-Westphalia | 58091 | 101.9 |
| 9 | Kiel | Schleswig-Holstein | 24113 | 102.0 |
| 10 | Herne | North Rhine-Westphalia | 44629 | 102.3 |
| 11 | Jena | Thuringia | 07743 | 102.4 |
| 12 | Würzburg | Bavaria | 97074 | 107.8 |
| 13 | Karlsruhe | Baden-Württemberg | 76131 | 109.5 |
| 14 | Frankenthal (Pfalz) | Rhineland-Palatinate | 67227 | 110.2 |
| 15 | Erzgebirgskreis | Saxony | 09399 | 112.0 |
| 16 | Mansfeld-Südharz | Saxony-Anhalt | 06347 | 113.5 |
| 17 | Dachau | Bavaria | 85247 | 113.6 |
| 18 | Remscheid | North Rhine-Westphalia | 42857 | 113.8 |
| 19 | Stuttgart | Baden-Württemberg | 70327 | 113.8 |
| 20 | Alzey-Worms | Rhineland-Palatinate | 67577 | 114.1 |
| 21 | Münster | North Rhine-Westphalia | 48135 | 114.1 |
| 22 | Krefeld | North Rhine-Westphalia | 47802 | 115.1 |
| 23 | Schweinfurt, Landkreis | Bavaria | 97508 | 116.2 |
| 24 | Köln | North Rhine-Westphalia | 50767 | 117.6 |
| 25 | Bochum | North Rhine-Westphalia | 44793 | 119.1 |
| 26 | Haßberge | Bavaria | 97478 | 119.7 |
| 27 | Bottrop | North Rhine-Westphalia | 46240 | 121.2 |
| 28 | Worms | Rhineland-Palatinate | 67549 | 122.3 |
| 29 | Kronach | Bavaria | 96349 | 123.0 |
| 30 | Saale-Orla-Kreis | Thuringia | 07806 | 123.7 |
| 31 | Kulmbach | Bavaria | 95361 | 124.5 |
| 32 | Wartburgkreis | Thuringia | 99815 | 125.9 |
| 33 | Miltenberg | Bavaria | 63924 | 127.1 |
| 34 | Ludwigsburg | Baden-Württemberg | 71696 | 127.2 |
| 35 | Bamberg, Landkreis | Bavaria | 96158 | 127.3 |
| 36 | Nürnberger Land | Bavaria | 91227 | 127.4 |
| 37 | Essen | North Rhine-Westphalia | 45131 | 128.0 |
| 38 | Odenwaldkreis | Hesse | 64739 | 128.0 |
| 39 | Tübingen | Baden-Württemberg | 72135 | 129.9 |
| 40 | Mannheim | Baden-Württemberg | 68147 | 130.2 |
| 41 | Salzgitter | Lower Saxony | 38236 | 130.4 |
| 42 | Ludwigshafen am Rhein | Rhineland-Palatinate | 67067 | 130.9 |
| 43 | Roth | Bavaria | 91166 | 132.0 |
| 44 | Main-Spessart | Bavaria | 97828 | 132.2 |
| 45 | Gelsenkirchen | North Rhine-Westphalia | 45886 | 132.4 |
| 46 | Erfurt | Thuringia | 99091 | 132.8 |
| 47 | Hamm | North Rhine-Westphalia | 59065 | 133.5 |
| 48 | Dillingen a.d.Donau | Bavaria | 89429 | 133.6 |
| 49 | Viersen | North Rhine-Westphalia | 41751 | 134.0 |
| 50 | Würzburg, Landkreis | Bavaria | 97256 | 134.7 |
One row per district. Germany publishes these figures only by Kreis — the rates need a labour force or population estimate that does not exist below district level — so one row covers every postal code inside that district. The postal code shown is one of them. Places whose figure the statistical offices withhold are absent rather than shown as zero.
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