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Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 2 new columns ({'Lower Confidence Interval', 'Upper Confidence Interval'}) and 1 missing columns ({'Standard Error'}). This happened while the csv dataset builder was generating data using hf://datasets/reach-vb/world-happiness-clone/2016.csv (at revision 80ee9c616208bf6ad969bf65751be61e54b6f0d0) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast Country: string Region: string Happiness Rank: int64 Happiness Score: double Lower Confidence Interval: double Upper Confidence Interval: double Economy (GDP per Capita): double Family: double Health (Life Expectancy): double Freedom: double Trust (Government Corruption): double Generosity: double Dystopia Residual: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1986 to {'Country': Value(dtype='string', id=None), 'Region': Value(dtype='string', id=None), 'Happiness Rank': Value(dtype='int64', id=None), 'Happiness Score': Value(dtype='float64', id=None), 'Standard Error': Value(dtype='float64', id=None), 'Economy (GDP per Capita)': Value(dtype='float64', id=None), 'Family': Value(dtype='float64', id=None), 'Health (Life Expectancy)': Value(dtype='float64', id=None), 'Freedom': Value(dtype='float64', id=None), 'Trust (Government Corruption)': Value(dtype='float64', id=None), 'Generosity': Value(dtype='float64', id=None), 'Dystopia Residual': Value(dtype='float64', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 2 new columns ({'Lower Confidence Interval', 'Upper Confidence Interval'}) and 1 missing columns ({'Standard Error'}). This happened while the csv dataset builder was generating data using hf://datasets/reach-vb/world-happiness-clone/2016.csv (at revision 80ee9c616208bf6ad969bf65751be61e54b6f0d0) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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Country
string | Region
string | Happiness Rank
int64 | Happiness Score
float64 | Standard Error
float64 | Economy (GDP per Capita)
float64 | Family
float64 | Health (Life Expectancy)
float64 | Freedom
float64 | Trust (Government Corruption)
float64 | Generosity
float64 | Dystopia Residual
float64 |
---|---|---|---|---|---|---|---|---|---|---|---|
Switzerland | Western Europe | 1 | 7.587 | 0.03411 | 1.39651 | 1.34951 | 0.94143 | 0.66557 | 0.41978 | 0.29678 | 2.51738 |
Iceland | Western Europe | 2 | 7.561 | 0.04884 | 1.30232 | 1.40223 | 0.94784 | 0.62877 | 0.14145 | 0.4363 | 2.70201 |
Denmark | Western Europe | 3 | 7.527 | 0.03328 | 1.32548 | 1.36058 | 0.87464 | 0.64938 | 0.48357 | 0.34139 | 2.49204 |
Norway | Western Europe | 4 | 7.522 | 0.0388 | 1.459 | 1.33095 | 0.88521 | 0.66973 | 0.36503 | 0.34699 | 2.46531 |
Canada | North America | 5 | 7.427 | 0.03553 | 1.32629 | 1.32261 | 0.90563 | 0.63297 | 0.32957 | 0.45811 | 2.45176 |
Finland | Western Europe | 6 | 7.406 | 0.0314 | 1.29025 | 1.31826 | 0.88911 | 0.64169 | 0.41372 | 0.23351 | 2.61955 |
Netherlands | Western Europe | 7 | 7.378 | 0.02799 | 1.32944 | 1.28017 | 0.89284 | 0.61576 | 0.31814 | 0.4761 | 2.4657 |
Sweden | Western Europe | 8 | 7.364 | 0.03157 | 1.33171 | 1.28907 | 0.91087 | 0.6598 | 0.43844 | 0.36262 | 2.37119 |
New Zealand | Australia and New Zealand | 9 | 7.286 | 0.03371 | 1.25018 | 1.31967 | 0.90837 | 0.63938 | 0.42922 | 0.47501 | 2.26425 |
Australia | Australia and New Zealand | 10 | 7.284 | 0.04083 | 1.33358 | 1.30923 | 0.93156 | 0.65124 | 0.35637 | 0.43562 | 2.26646 |
Israel | Middle East and Northern Africa | 11 | 7.278 | 0.0347 | 1.22857 | 1.22393 | 0.91387 | 0.41319 | 0.07785 | 0.33172 | 3.08854 |
Costa Rica | Latin America and Caribbean | 12 | 7.226 | 0.04454 | 0.95578 | 1.23788 | 0.86027 | 0.63376 | 0.10583 | 0.25497 | 3.17728 |
Austria | Western Europe | 13 | 7.2 | 0.03751 | 1.33723 | 1.29704 | 0.89042 | 0.62433 | 0.18676 | 0.33088 | 2.5332 |
Mexico | Latin America and Caribbean | 14 | 7.187 | 0.04176 | 1.02054 | 0.91451 | 0.81444 | 0.48181 | 0.21312 | 0.14074 | 3.60214 |
United States | North America | 15 | 7.119 | 0.03839 | 1.39451 | 1.24711 | 0.86179 | 0.54604 | 0.1589 | 0.40105 | 2.51011 |
Brazil | Latin America and Caribbean | 16 | 6.983 | 0.04076 | 0.98124 | 1.23287 | 0.69702 | 0.49049 | 0.17521 | 0.14574 | 3.26001 |
Luxembourg | Western Europe | 17 | 6.946 | 0.03499 | 1.56391 | 1.21963 | 0.91894 | 0.61583 | 0.37798 | 0.28034 | 1.96961 |
Ireland | Western Europe | 18 | 6.94 | 0.03676 | 1.33596 | 1.36948 | 0.89533 | 0.61777 | 0.28703 | 0.45901 | 1.9757 |
Belgium | Western Europe | 19 | 6.937 | 0.03595 | 1.30782 | 1.28566 | 0.89667 | 0.5845 | 0.2254 | 0.2225 | 2.41484 |
United Arab Emirates | Middle East and Northern Africa | 20 | 6.901 | 0.03729 | 1.42727 | 1.12575 | 0.80925 | 0.64157 | 0.38583 | 0.26428 | 2.24743 |
United Kingdom | Western Europe | 21 | 6.867 | 0.01866 | 1.26637 | 1.28548 | 0.90943 | 0.59625 | 0.32067 | 0.51912 | 1.96994 |
Oman | Middle East and Northern Africa | 22 | 6.853 | 0.05335 | 1.36011 | 1.08182 | 0.76276 | 0.63274 | 0.32524 | 0.21542 | 2.47489 |
Venezuela | Latin America and Caribbean | 23 | 6.81 | 0.06476 | 1.04424 | 1.25596 | 0.72052 | 0.42908 | 0.11069 | 0.05841 | 3.19131 |
Singapore | Southeastern Asia | 24 | 6.798 | 0.0378 | 1.52186 | 1.02 | 1.02525 | 0.54252 | 0.4921 | 0.31105 | 1.88501 |
Panama | Latin America and Caribbean | 25 | 6.786 | 0.0491 | 1.06353 | 1.1985 | 0.79661 | 0.5421 | 0.0927 | 0.24434 | 2.84848 |
Germany | Western Europe | 26 | 6.75 | 0.01848 | 1.32792 | 1.29937 | 0.89186 | 0.61477 | 0.21843 | 0.28214 | 2.11569 |
Chile | Latin America and Caribbean | 27 | 6.67 | 0.058 | 1.10715 | 1.12447 | 0.85857 | 0.44132 | 0.12869 | 0.33363 | 2.67585 |
Qatar | Middle East and Northern Africa | 28 | 6.611 | 0.06257 | 1.69042 | 1.0786 | 0.79733 | 0.6404 | 0.52208 | 0.32573 | 1.55674 |
France | Western Europe | 29 | 6.575 | 0.03512 | 1.27778 | 1.26038 | 0.94579 | 0.55011 | 0.20646 | 0.12332 | 2.21126 |
Argentina | Latin America and Caribbean | 30 | 6.574 | 0.04612 | 1.05351 | 1.24823 | 0.78723 | 0.44974 | 0.08484 | 0.11451 | 2.836 |
Czech Republic | Central and Eastern Europe | 31 | 6.505 | 0.04168 | 1.17898 | 1.20643 | 0.84483 | 0.46364 | 0.02652 | 0.10686 | 2.67782 |
Uruguay | Latin America and Caribbean | 32 | 6.485 | 0.04539 | 1.06166 | 1.2089 | 0.8116 | 0.60362 | 0.24558 | 0.2324 | 2.32142 |
Colombia | Latin America and Caribbean | 33 | 6.477 | 0.05051 | 0.91861 | 1.24018 | 0.69077 | 0.53466 | 0.0512 | 0.18401 | 2.85737 |
Thailand | Southeastern Asia | 34 | 6.455 | 0.03557 | 0.9669 | 1.26504 | 0.7385 | 0.55664 | 0.03187 | 0.5763 | 2.31945 |
Saudi Arabia | Middle East and Northern Africa | 35 | 6.411 | 0.04633 | 1.39541 | 1.08393 | 0.72025 | 0.31048 | 0.32524 | 0.13706 | 2.43872 |
Spain | Western Europe | 36 | 6.329 | 0.03468 | 1.23011 | 1.31379 | 0.95562 | 0.45951 | 0.06398 | 0.18227 | 2.12367 |
Malta | Western Europe | 37 | 6.302 | 0.04206 | 1.2074 | 1.30203 | 0.88721 | 0.60365 | 0.13586 | 0.51752 | 1.6488 |
Taiwan | Eastern Asia | 38 | 6.298 | 0.03868 | 1.29098 | 1.07617 | 0.8753 | 0.3974 | 0.08129 | 0.25376 | 2.32323 |
Kuwait | Middle East and Northern Africa | 39 | 6.295 | 0.04456 | 1.55422 | 1.16594 | 0.72492 | 0.55499 | 0.25609 | 0.16228 | 1.87634 |
Suriname | Latin America and Caribbean | 40 | 6.269 | 0.09811 | 0.99534 | 0.972 | 0.6082 | 0.59657 | 0.13633 | 0.16991 | 2.79094 |
Trinidad and Tobago | Latin America and Caribbean | 41 | 6.168 | 0.10895 | 1.21183 | 1.18354 | 0.61483 | 0.55884 | 0.0114 | 0.31844 | 2.26882 |
El Salvador | Latin America and Caribbean | 42 | 6.13 | 0.05618 | 0.76454 | 1.02507 | 0.67737 | 0.4035 | 0.11776 | 0.10692 | 3.035 |
Guatemala | Latin America and Caribbean | 43 | 6.123 | 0.05224 | 0.74553 | 1.04356 | 0.64425 | 0.57733 | 0.09472 | 0.27489 | 2.74255 |
Uzbekistan | Central and Eastern Europe | 44 | 6.003 | 0.04361 | 0.63244 | 1.34043 | 0.59772 | 0.65821 | 0.30826 | 0.22837 | 2.23741 |
Slovakia | Central and Eastern Europe | 45 | 5.995 | 0.04267 | 1.16891 | 1.26999 | 0.78902 | 0.31751 | 0.03431 | 0.16893 | 2.24639 |
Japan | Eastern Asia | 46 | 5.987 | 0.03581 | 1.27074 | 1.25712 | 0.99111 | 0.49615 | 0.1806 | 0.10705 | 1.68435 |
South Korea | Eastern Asia | 47 | 5.984 | 0.04098 | 1.24461 | 0.95774 | 0.96538 | 0.33208 | 0.07857 | 0.18557 | 2.21978 |
Ecuador | Latin America and Caribbean | 48 | 5.975 | 0.04528 | 0.86402 | 0.99903 | 0.79075 | 0.48574 | 0.1809 | 0.11541 | 2.53942 |
Bahrain | Middle East and Northern Africa | 49 | 5.96 | 0.05412 | 1.32376 | 1.21624 | 0.74716 | 0.45492 | 0.306 | 0.17362 | 1.73797 |
Italy | Western Europe | 50 | 5.948 | 0.03914 | 1.25114 | 1.19777 | 0.95446 | 0.26236 | 0.02901 | 0.22823 | 2.02518 |
Bolivia | Latin America and Caribbean | 51 | 5.89 | 0.05642 | 0.68133 | 0.97841 | 0.5392 | 0.57414 | 0.088 | 0.20536 | 2.82334 |
Moldova | Central and Eastern Europe | 52 | 5.889 | 0.03799 | 0.59448 | 1.01528 | 0.61826 | 0.32818 | 0.01615 | 0.20951 | 3.10712 |
Paraguay | Latin America and Caribbean | 53 | 5.878 | 0.04563 | 0.75985 | 1.30477 | 0.66098 | 0.53899 | 0.08242 | 0.3424 | 2.18896 |
Kazakhstan | Central and Eastern Europe | 54 | 5.855 | 0.04114 | 1.12254 | 1.12241 | 0.64368 | 0.51649 | 0.08454 | 0.11827 | 2.24729 |
Slovenia | Central and Eastern Europe | 55 | 5.848 | 0.04251 | 1.18498 | 1.27385 | 0.87337 | 0.60855 | 0.03787 | 0.25328 | 1.61583 |
Lithuania | Central and Eastern Europe | 56 | 5.833 | 0.03843 | 1.14723 | 1.25745 | 0.73128 | 0.21342 | 0.01031 | 0.02641 | 2.44649 |
Nicaragua | Latin America and Caribbean | 57 | 5.828 | 0.05371 | 0.59325 | 1.14184 | 0.74314 | 0.55475 | 0.19317 | 0.27815 | 2.32407 |
Peru | Latin America and Caribbean | 58 | 5.824 | 0.04615 | 0.90019 | 0.97459 | 0.73017 | 0.41496 | 0.05989 | 0.14982 | 2.5945 |
Belarus | Central and Eastern Europe | 59 | 5.813 | 0.03938 | 1.03192 | 1.23289 | 0.73608 | 0.37938 | 0.1909 | 0.11046 | 2.1309 |
Poland | Central and Eastern Europe | 60 | 5.791 | 0.04263 | 1.12555 | 1.27948 | 0.77903 | 0.53122 | 0.04212 | 0.16759 | 1.86565 |
Malaysia | Southeastern Asia | 61 | 5.77 | 0.0433 | 1.12486 | 1.07023 | 0.72394 | 0.53024 | 0.10501 | 0.33075 | 1.88541 |
Croatia | Central and Eastern Europe | 62 | 5.759 | 0.04394 | 1.08254 | 0.79624 | 0.78805 | 0.25883 | 0.0243 | 0.05444 | 2.75414 |
Libya | Middle East and Northern Africa | 63 | 5.754 | 0.07832 | 1.13145 | 1.11862 | 0.7038 | 0.41668 | 0.11023 | 0.18295 | 2.09066 |
Russia | Central and Eastern Europe | 64 | 5.716 | 0.03135 | 1.13764 | 1.23617 | 0.66926 | 0.36679 | 0.03005 | 0.00199 | 2.27394 |
Jamaica | Latin America and Caribbean | 65 | 5.709 | 0.13693 | 0.81038 | 1.15102 | 0.68741 | 0.50442 | 0.02299 | 0.2123 | 2.32038 |
North Cyprus | Western Europe | 66 | 5.695 | 0.05635 | 1.20806 | 1.07008 | 0.92356 | 0.49027 | 0.1428 | 0.26169 | 1.59888 |
Cyprus | Western Europe | 67 | 5.689 | 0.0558 | 1.20813 | 0.89318 | 0.92356 | 0.40672 | 0.06146 | 0.30638 | 1.88931 |
Algeria | Middle East and Northern Africa | 68 | 5.605 | 0.05099 | 0.93929 | 1.07772 | 0.61766 | 0.28579 | 0.17383 | 0.07822 | 2.43209 |
Kosovo | Central and Eastern Europe | 69 | 5.589 | 0.05018 | 0.80148 | 0.81198 | 0.63132 | 0.24749 | 0.04741 | 0.2831 | 2.76579 |
Turkmenistan | Central and Eastern Europe | 70 | 5.548 | 0.04175 | 0.95847 | 1.22668 | 0.53886 | 0.4761 | 0.30844 | 0.16979 | 1.86984 |
Mauritius | Sub-Saharan Africa | 71 | 5.477 | 0.07197 | 1.00761 | 0.98521 | 0.7095 | 0.56066 | 0.07521 | 0.37744 | 1.76145 |
Hong Kong | Eastern Asia | 72 | 5.474 | 0.05051 | 1.38604 | 1.05818 | 1.01328 | 0.59608 | 0.37124 | 0.39478 | 0.65429 |
Estonia | Central and Eastern Europe | 73 | 5.429 | 0.04013 | 1.15174 | 1.22791 | 0.77361 | 0.44888 | 0.15184 | 0.0868 | 1.58782 |
Indonesia | Southeastern Asia | 74 | 5.399 | 0.02596 | 0.82827 | 1.08708 | 0.63793 | 0.46611 | 0 | 0.51535 | 1.86399 |
Vietnam | Southeastern Asia | 75 | 5.36 | 0.03107 | 0.63216 | 0.91226 | 0.74676 | 0.59444 | 0.10441 | 0.1686 | 2.20173 |
Turkey | Middle East and Northern Africa | 76 | 5.332 | 0.03864 | 1.06098 | 0.94632 | 0.73172 | 0.22815 | 0.15746 | 0.12253 | 2.08528 |
Kyrgyzstan | Central and Eastern Europe | 77 | 5.286 | 0.03823 | 0.47428 | 1.15115 | 0.65088 | 0.43477 | 0.04232 | 0.3003 | 2.2327 |
Nigeria | Sub-Saharan Africa | 78 | 5.268 | 0.04192 | 0.65435 | 0.90432 | 0.16007 | 0.34334 | 0.0403 | 0.27233 | 2.89319 |
Bhutan | Southern Asia | 79 | 5.253 | 0.03225 | 0.77042 | 1.10395 | 0.57407 | 0.53206 | 0.15445 | 0.47998 | 1.63794 |
Azerbaijan | Central and Eastern Europe | 80 | 5.212 | 0.03363 | 1.02389 | 0.93793 | 0.64045 | 0.3703 | 0.16065 | 0.07799 | 2.00073 |
Pakistan | Southern Asia | 81 | 5.194 | 0.03726 | 0.59543 | 0.41411 | 0.51466 | 0.12102 | 0.10464 | 0.33671 | 3.10709 |
Jordan | Middle East and Northern Africa | 82 | 5.192 | 0.04524 | 0.90198 | 1.05392 | 0.69639 | 0.40661 | 0.14293 | 0.11053 | 1.87996 |
Montenegro | Central and Eastern Europe | 82 | 5.192 | 0.05235 | 0.97438 | 0.90557 | 0.72521 | 0.1826 | 0.14296 | 0.1614 | 2.10017 |
China | Eastern Asia | 84 | 5.14 | 0.02424 | 0.89012 | 0.94675 | 0.81658 | 0.51697 | 0.02781 | 0.08185 | 1.8604 |
Zambia | Sub-Saharan Africa | 85 | 5.129 | 0.06988 | 0.47038 | 0.91612 | 0.29924 | 0.48827 | 0.12468 | 0.19591 | 2.6343 |
Romania | Central and Eastern Europe | 86 | 5.124 | 0.06607 | 1.04345 | 0.88588 | 0.7689 | 0.35068 | 0.00649 | 0.13748 | 1.93129 |
Serbia | Central and Eastern Europe | 87 | 5.123 | 0.04864 | 0.92053 | 1.00964 | 0.74836 | 0.20107 | 0.02617 | 0.19231 | 2.025 |
Portugal | Western Europe | 88 | 5.102 | 0.04802 | 1.15991 | 1.13935 | 0.87519 | 0.51469 | 0.01078 | 0.13719 | 1.26462 |
Latvia | Central and Eastern Europe | 89 | 5.098 | 0.0464 | 1.11312 | 1.09562 | 0.72437 | 0.29671 | 0.06332 | 0.18226 | 1.62215 |
Philippines | Southeastern Asia | 90 | 5.073 | 0.04934 | 0.70532 | 1.03516 | 0.58114 | 0.62545 | 0.12279 | 0.24991 | 1.7536 |
Somaliland region | Sub-Saharan Africa | 91 | 5.057 | 0.06161 | 0.18847 | 0.95152 | 0.43873 | 0.46582 | 0.39928 | 0.50318 | 2.11032 |
Morocco | Middle East and Northern Africa | 92 | 5.013 | 0.0342 | 0.73479 | 0.64095 | 0.60954 | 0.41691 | 0.08546 | 0.07172 | 2.45373 |
Macedonia | Central and Eastern Europe | 93 | 5.007 | 0.05376 | 0.91851 | 1.00232 | 0.73545 | 0.33457 | 0.05327 | 0.22359 | 1.73933 |
Mozambique | Sub-Saharan Africa | 94 | 4.971 | 0.07896 | 0.08308 | 1.02626 | 0.09131 | 0.34037 | 0.15603 | 0.22269 | 3.05137 |
Albania | Central and Eastern Europe | 95 | 4.959 | 0.05013 | 0.87867 | 0.80434 | 0.81325 | 0.35733 | 0.06413 | 0.14272 | 1.89894 |
Bosnia and Herzegovina | Central and Eastern Europe | 96 | 4.949 | 0.06913 | 0.83223 | 0.91916 | 0.79081 | 0.09245 | 0.00227 | 0.24808 | 2.06367 |
Lesotho | Sub-Saharan Africa | 97 | 4.898 | 0.09438 | 0.37545 | 1.04103 | 0.07612 | 0.31767 | 0.12504 | 0.16388 | 2.79832 |
Dominican Republic | Latin America and Caribbean | 98 | 4.885 | 0.07446 | 0.89537 | 1.17202 | 0.66825 | 0.57672 | 0.14234 | 0.21684 | 1.21305 |
Laos | Southeastern Asia | 99 | 4.876 | 0.06698 | 0.59066 | 0.73803 | 0.54909 | 0.59591 | 0.24249 | 0.42192 | 1.73799 |
Mongolia | Eastern Asia | 100 | 4.874 | 0.03313 | 0.82819 | 1.3006 | 0.60268 | 0.43626 | 0.02666 | 0.3323 | 1.34759 |
Dataset Card for World Happiness Report
Dataset Summary
Context
The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.
Content
The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.
Inspiration
What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?
What is Dystopia?
Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.
What are the residuals?
The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.
What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?
The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.
If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.
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This dataset was shared by @unsdsn
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The license for this dataset is cc0-1.0
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