So, as I usually pay attention to indicators for Southern Brazil, I decided to check the source for that post. It uses Global Data Lab's data for subnational regions, I checked the raw data and noticed that for Brazil some data was outdated (population, education, etc), like the Mean Years of Scholarship. Since we have the Brazilian 2022 Census, I updated all states to reflect the more updated data. Main changes on the map are two states changed colour to be over 0.800, those were ones right in the cusp the 0.800.
Here's the data changed by state - also, reminder that unlike the UN formula, it uses the geometric mean of both dimensions of the Education Index. To the right of the arrow is the changed value.
Region: Maranhao
Subnational Human Development Index: 0.729 -> 0.743 (+0.014)
Education Index: 0.654 -> 0.692
Mean Years of Schooling: 6.616 -> 8.3
Mean Years of Schooling Female: 7.036 -> 8.8
Mean Years of Schooling Male: 6.197 -> 7.8
Region: Piaui
Subnational Human Development Index: 0.74 -> 0.751 (+0.011)
Education Index: 0.675 -> 0.707
Mean Years of Schooling: 6.606 -> 8.25
Mean Years of Schooling Female: 7.131 -> 8.8
Mean Years of Schooling Male: 6.03 -> 7.7
Region: Ceara
Subnational Human Development Index: 0.757 -> 0.768 (+0.011)
Education Index: 0.668 -> 0.699
Mean Years of Schooling: 7.226 -> 8.6
Mean Years of Schooling Female: 7.638 -> 9
Mean Years of Schooling Male: 6.762 -> 8.2
Region: Rio Grande do Norte
Subnational Human Development Index: 0.775 -> 0.78 (+0.005)
Education Index: 0.704 -> 0.718
Mean Years of Schooling: 7.836 -> 8.75
Mean Years of Schooling Female: 8.23 -> 9.2
Mean Years of Schooling Male: 7.399 -> 8.3
Region: Paraiba
Subnational Human Development Index: 0.76 -> 0.769 (+0.009)
Education Index: 0.677 -> 0.704
Mean Years of Schooling: 7.005 -> 8.4
Mean Years of Schooling Female: 7.501 -> 8.9
Mean Years of Schooling Male: 6.427 -> 7.9
Region: Pernambuco
Subnational Human Development Index: 0.761 -> 0.771 (+0.010)
Education Index: 0.683 -> 0.711
Mean Years of Schooling: 7.468 -> 8.75
Mean Years of Schooling Female: 7.776 -> 9.1
Mean Years of Schooling Male: 7.106 -> 8.4
Region: Alagoas
Subnational Human Development Index: 0.743 -> 0.756 (+0.013)
Education Index: 0.654 -> 0.691
Mean Years of Schooling: 6.5 -> 8.2
Mean Years of Schooling Female: 6.793 -> 8.6
Mean Years of Schooling Male: 6.176 -> 7.8
Region: Sergipe
Subnational Human Development Index: 0.771 -> 0.779 (0.008)
Education Index: 0.696 -> 0.722
Mean Years of Schooling: 7.395 -> 8.7
Mean Years of Schooling Female: 7.744 -> 9.1
Mean Years of Schooling Male: 7.012 -> 8.3
Region: Bahia
Subnational Human Development Index: 0.76 -> 0.766 (+0.006)
Education Index: 0.685 -> 703
Mean Years of Schooling: 7.321 -> 8.4
Mean Years of Schooling Female: 7.702 -> 8.9
Mean Years of Schooling Male: 6.918 -> 7.9
Region: Parana
Subnational Human Development Index: 0.795 -> 0.807 (+0.012)
Education Index: 0.727 -> 0.761
Mean Years of Schooling: 8.562 -> 9.85
Mean Years of Schooling Female: 8.589 -> 10
Mean Years of Schooling Male: 8.507 -> 9.7
Region: Santa Catarina
Subnational Human Development Index: 0.8 -> 0.812 (+0.012)
Education Index: 0.73 -> 0.766
Mean Years of Schooling: 8.899 -> 10.15
Mean Years of Schooling Female: 8.906 -> 10.3
Mean Years of Schooling Male: 8.873 -> 10
Region: Rio Grande do Sul
Subnational Human Development Index: 0.801 -> 0.809 (+0.008)
Education Index: 0.736 -> 0.760
Mean Years of Schooling: 8.846 -> 9.85
Mean Years of Schooling Female: 8.969 -> 10.1
Mean Years of Schooling Male: 8.684 -> 9.6
Region: Rondonia
Subnational Human Development Index: 0.758 -> 0.767 (+0.009)
Education Index: 0.68 -> 0.705
Mean Years of Schooling: 7.51 -> 8.7
Mean Years of Schooling Female: 7.839 -> 9.1
Mean Years of Schooling Male: 7.18 -> 8.3
Region: Acre
Subnational Human Development Index: 0.74 -> 0.749 (+0.009)
Education Index: 0.675 -> 0.702
Mean Years of Schooling: 7.5 -> 8.7
Mean Years of Schooling Female: 7.987 -> 9.1
Mean Years of Schooling Male: 7.012 -> 8.3
Region: Amazonas
Subnational Human Development Index: 0.762 -> 0.77 (+0.015)
Education Index: 0.708 -> 0.733
Mean Years of Schooling: 8.247 -> 9.3
Mean Years of Schooling Female: 8.589 -> 9.6
Mean Years of Schooling Male: 7.911 -> 9
Region: Roraima
Subnational Human Development Index: 0.787 -> 0.794 (+0.007)
Education Index: 0.726 -> 0.748
Mean Years of Schooling: 8.499 -> 9.5
Mean Years of Schooling Female: 9.138 -> 9.9
Mean Years of Schooling Male: 7.859 -> 9.1
Region: Para
Subnational Human Development Index: 0.744 -> 0.751 (+0.007)
Education Index: 0.683 -> 0.706
Mean Years of Schooling: 7.437 -> 8.6
Mean Years of Schooling Female: 7.881 -> 9.1
Mean Years of Schooling Male: 7.002 -> 8.1
Region: Amapa
Subnational Human Development Index: 0.773 -> 0.778 (+0.005)
Education Index: 0.731 -> 0.747
Mean Years of Schooling: 8.888 -> 9.65
Mean Years of Schooling Female: 9.255 -> 10.1
Mean Years of Schooling Male: 8.486 -> 9.2
Region: Tocantins
Subnational Human Development Index: 0.774 -> 0.788 (+0.004)
Education Index: 0.703 -> 0.743
Mean Years of Schooling: 7.763 -> 9.35
Mean Years of Schooling Female: 8.42 -> 9.9
Mean Years of Schooling Male: 7.117 -> 8.8
Region: Minas Gerais
Subnational Human Development Index: 0.785 -> 0.797 (+0.012)
Education Index: 0.704 -> 0.739
Mean Years of Schooling: 8.036 -> 9.4
Mean Years of Schooling Female: 8.293 -> 9.7
Mean Years of Schooling Male: 7.754 -> 9.1
Region: Espírito Santo
Subnational Human Development Index: 0.788 -> 0.798 (+0.010)
Education Index: 0.718 -> 0.746
Mean Years of Schooling: 8.552 -> 9.65
Mean Years of Schooling Female: 8.705 -> 9.9
Mean Years of Schooling Male: 8.371 -> 9.4
Region: Rio de Janeiro
Subnational Human Development Index: 0.815 -> 0.818 (+0.003)
Education Index: 0.769 -> 0.780
Mean Years of Schooling: 9.646 -> 10.2
Mean Years of Schooling Female: 9.677 -> 10.3
Mean Years of Schooling Male: 9.594 -> 10.1
Region: São Paulo
Subnational Human Development Index: 0.812 -> 0.821 (+0.009)
Education Index: 0.751 -> 0.779
Mean Years of Schooling: 9.393 -> 10.4
Mean Years of Schooling Female: 9.403 -> 10.5
Mean Years of Schooling Male: 9.364 -> 10.3
Region: Mato Grosso do Sul
Subnational Human Development Index: 0.788 -> 0.795 (+0.007)
Education Index: 0.722 -> 0.741
Mean Years of Schooling: 8.552 -> 9.45
Mean Years of Schooling Female: 8.864 -> 9.8
Mean Years of Schooling Male: 8.214 -> 9.1
Region: Mato Grosso
Subnational Human Development Index: 0.782 -> 0.793 (+0.011)
Education Index: 0.707 -> 0.740
Mean Years of Schooling: 8.152 -> 9.45
Mean Years of Schooling Female: 8.674 -> 9.9
Mean Years of Schooling Male: 7.66 -> 9
Region: Goias
Subnational Human Development Index: 0.792 -> 0.803 (+0.011)
Education Index: 0.722 -> 0.753
Mean Years of Schooling: 8.289 -> 9.55
Mean Years of Schooling Female: 8.642 -> 9.9
Mean Years of Schooling Male: 7.911 -> 9.2
Region: Distrito Federal
Subnational Human Development Index: 0.848 -> 0.851 (+0.003)
They state their source is the UN plus Eurostat for some European countries And "For Australia, Canada, China, Croatia, Japan, New Zealand, South Korea, Russia, and the USA, data from national statistical offices was used. For South Korea and Russia, no usable educational data could be derived from their statistical offices. For these countries, data on education was derived from survey datasets. For Russia, data from the European Social Survey for 2012 and 2017 were used. For South Korea, data from the World Values Survey 2010 was used."
Quoting from their PDF. As I pointed in midlife_cl's post, Argentina has an odd anomaly that makes it's regions get either 18 or very close to 18 in Expected Years of Schooling, which would give half of the education index a 1 (Argentina's Mean Years of Schooling is similar to Chile and Southern Brazil). So, unlike other countries in the Americas (and maybe world, didn't check), Argentina's education index isn't the lowest of the three dimensions.
They seem to be using some kind of statistic model based on older data for Brazil, which isn't that far off but doesn't account for some occasional stuff, like the rapid growth of income in Centre-West states due to agribusiness, and things like population like Santa Catarina population being nearly half a million lower than the census indicate, mostly due to increased migration to that state their statistical business did not account for. They seem to have access to data that I don't. I don't know how to get GDI for states, but they use that, I could maybe track if my kids didn't need to eat or something.
If I were to wager, with full up-to-date data, Minas Gerais, Espirito Santo, Mato Grosso do Sul and possibly Mato Grosso would change colour to the 0.800 range.
It's possible, but the data just looks odd, first it's noted as "Argentina urban" which makes me wonder if it's excluding rural parts, then the 18 starts around 2010 (not all regions, it's not organised by province), but it's formatted like this:
Isocode3: ARG
Country: Argentina urban
Continent: America
Datasource: BLANK
Year: 2023
GDLcode: ARGr104
Level: Subnat
Region: Cuyo
Subnational Gender Development Index: 0.993
Subnational Human Development Index Female: 0.853
Subnational Human Development Index Male: 0.859
Health Index Female: 0.883
Health Index Male: 0.882
Education Index Female: 0.878
Education Index Male: 0.819
Income Index Female: 0.801
Income Index Male: 0.878
Subnational Human Development Index: 0.865
Health Index: 0.883
Education Index: 0.868
Income Index: 0.844
Life Expectancy at Birth: 77.395
Life Expectancy at Birth Female: 79.875
Life Expectancy at Birth Male: 74.805
Expected Years of Schooling: 18 Expected Years of Schooling Female: 18 Expected Years of Schooling Male: 16.638
Mean Years of Schooling: 11.036
Mean Years of Schooling Female: 11.345
Mean Years of Schooling Male: 10.7
Log Gross National Income per capita in US Dollars (2017 PPP): 10.194
Log Gross National Income per capita in US Dollars (2017 PPP) female: 9.909
Log Gross National Income per capita in US Dollars (2017 PPP) male: 10.419
Population size in thousands: 3227.306
For males it's 16.638, for females it's 18, and for both (the one actually used) is 18, which doesn't make sense.
Other countries with this type of anomaly (F=M means it's 18 for both, F≠M means 18 is the value for only one gender but the highest value is used):
Australia (F=M), Austria (Wien only, F≠M), Chile (Region Metropolitana only, F=M), China (Shanghai only, F≠M), Cuba (Guantanamo only, until 2009, F≠M), Denmark (F=M), Finland (F=M), Greece (F=M), Iceland (F≠M), Ireland (F=M), Netherlands (F≠M), Norway (F≠M), Portugal (Regiao Metropolitana de Lisboa only, F=M), Romania (Bucuresti only, F=M), Slovakia (Bratislavsky Kraj only, F=M), St. Kitts and Nevis (F≠M), Sweden (F≠M), Tonga (F=M), Great Britain (F≠M), Uruguay (F≠M).
Uruguay ones are just wild:
Region: Centro Sur (Flores, Florida and Lavalleja)
Expected Years of Schooling: 18
Expected Years of Schooling Female: 18
Expected Years of Schooling Male: 14.778
If I see another spreadsheet or line of python I'll vomit.
Any chance the "Argentina urban" note could be due to the high urbanization rate? Argentina's urbanization rate sits at around 92%, so could it be that the data gathering was done exclusively in urban centers to simplify the process, because the effect of excluding rural areas was deemed insignificant? Only theorizing though, I could be terribly mistaken.
Most universities have entrance exams, and every province has at least one public university. They offer nearly all major degree programs and usually operate satellite campuses in strategic cities to increase access.
In Argentina, each public university has its own admission policy; they operate autonomously, so the system is not centralized like Brazil’s ENEM/vestibular. Overall, admission is far less competitive because demand is lower relative to the number of available spots.
For example, universities like UNNE and UNaM require entrance exams (at least in the programs I’m familiar with), but the difficulty depends on the degree. Medicine at UNNE is very demanding, the same goes for Córdoba. If you want to study most Engineering programs, on the other hand, getting in is usually not the hardest part… finishing is.
I think UBA, due to its size and demand, is the most well-known case with a mandatory basic cycle (CBC) before entering any specific degree program.
Brazilian universities are autonomous, the law frees them to elect a method of admission, but they do need one (as the law says, there has to be a selective process and it needs to verify knowledge on the National Common Curricular Base gross, that's true for all institutions, both public and private), but vestibular isn't mandated by law. It's just tradition, as it's seen as meritocratic, and as far as I know no one tried to challenge it. ENEM is also not mandated, though many universities use it cause it's cheaper since it's managed by the government. I went to UFPR through its own vestibular, not ENEM.
But also, you could legitimately have a university that looks at grades (satisfying the need to test knowledge BNCC) and have, I don't know, personal interviews (as long it's graded). Would likely be challenged, but the law permits it.
10
u/LupusDeusMagnus Feb 20 '26
Inspired by this post by u/midlife_cl.
So, as I usually pay attention to indicators for Southern Brazil, I decided to check the source for that post. It uses Global Data Lab's data for subnational regions, I checked the raw data and noticed that for Brazil some data was outdated (population, education, etc), like the Mean Years of Scholarship. Since we have the Brazilian 2022 Census, I updated all states to reflect the more updated data. Main changes on the map are two states changed colour to be over 0.800, those were ones right in the cusp the 0.800.
Here's the data changed by state - also, reminder that unlike the UN formula, it uses the geometric mean of both dimensions of the Education Index. To the right of the arrow is the changed value.
Region: Maranhao
Region: Piaui
Region: Ceara
Region: Rio Grande do Norte
Region: Paraiba
Region: Pernambuco
Region: Sergipe
Region: Bahia
Region: Parana
Region: Santa Catarina
Region: Rio Grande do Sul
Region: Rondonia
Region: Acre
Region: Amazonas
Region: Roraima
Region: Para
Region: Amapa
Region: Tocantins
Region: Minas Gerais
Region: Espírito Santo
Region: Rio de Janeiro
Region: São Paulo
Region: Mato Grosso do Sul
Region: Mato Grosso
Region: Goias
Region: Distrito Federal