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> But this is obviously not true. Just look at the list of top 10 by GDP and check out how many of them are relatively small. There absolutely is a correlation
by erwald 3y ago
> But this is obviously not true. Just look at the list of top 10 by GDP and check out how many of them are relatively small.
There absolutely is a correlation between land mass and nominal GDP.
- zimpenfish 3y agoAssuming I've converted [1] and [2] correctly into this SQLite3 database and queried it correctly, not really, no. | name | gdp_pos | gdp | land_pos | land | |----------------|---------|----------|----------|---------| | United States | 1 | 26854599 | 4 | 9147593 | | China | 2 | 19373586 | 3 | 9596961 | | Japan | 3 | 4409738 | 62 | 377976 | | Germany | 4 | 4308854 | 63 | 357114 | | India | 5 | 3736882 | 7 | 3287263 | | United Kingdom | 6 | 3158938 | 79 | 242495 | | France | 7 | 2923489 | 49 | 543940 | | Italy | 8 | 2169745 | 72 | 301339 | | Canada | 9 | 2089672 | 2 | 9984670 | | Brazil | 10 | 2081235 | 5 | 8515767 | [1] https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal) https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nomi... [2] https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area https://en.wikipedia.org/wiki/List_of_countries_and_dependen...
- dwaltrip 3y agoThe countries with the 2 highest GDPs, China and the US, are in the top 4 largest countries. India, #5 in GDP, is #7 in area. I guarantee that if you plot the countries of the world by GDP and area, you will see a trend line. It also makes sense. More area = higher chance of larger population and more natural resources. And more space to carry out economic activities with said people and resources. Edit: I just queried Wolfram Alpha about this. It generated a plot for me, which shows what I expected. Check it out: https://www.wolframalpha.com/input?i=list+of+countries+with+area+and+gdp https://www.wolframalpha.com/input?i=list+of+countries+with+... Edit2: bonus feature, GPT-4 wrote me a script to plot this also, check it out: https://chat.openai.com/share/ffa45c61-8b7a-44e1-b757-041f31df5099 https://chat.openai.com/share/ffa45c61-8b7a-44e1-b757-041f31...
- deleted 3y ago[deleted]
- jgeada 3y agoSeriously, a loglog plot? Even in that, there is a seriously wide dispersion to your correlation. And then look at the same data on a linear plot.
- dwaltrip 3y agoThe linear plot in that Wolfram link is messed up. It doesn't show all the data (caps out at 800 billion GDP). Here's a corrected linear plot, from the script that I linked (commenting out the log-log scaling): https://ibb.co/9bBgwH8 https://ibb.co/9bBgwH8 There is clearly a correlation, even on linear. It's a little messy, but it's undeniably there. The starting point for this discussion was about the relationship between a country's size and population and it's power and influence. The correlation between area and GDP demonstrates that there is a meaningful relationship. Btw, what is your specific complaint about a log-log plot? Country data points for area and GDP span many orders of magnitude, which makes it harder to visualize any patterns on a linear plot. I also don't understand your point about the dispersion. The correlation and trend is pretty clear. No one said the correlation was 99%. Edit: I've calculated Pearson's correlation coefficient for this data [1]. The result is 0.82, which indicates a strong positive correlation. [1] https://en.wikipedia.org/wiki/Pearson_correlation_coefficient https://en.wikipedia.org/wiki/Pearson_correlation_coefficien...
- zimpenfish 3y ago> The result is 0.82, which indicates a strong positive correlation. datamash gave me 0.52 for Pearson. Which is "eh, maybe".
- deleted 3y ago[deleted]
- erwald 3y agoThat's weird, are you looking only at the top 10 countries? I've reproduced dwaltrib's results using World Bank data on 251 countries, and I get a Pearson's r of 0.82 and a p value of 5.6e-61 (!). I.e. a strong correlation, with high confidence. It makes sense too -- larger countries generally have more people, and more people generally generate more economic activity. Code if you want to try yourself: import pandas as pd gdp = pd.read_csv("~/Downloads/API_NY.GDP.MKTP.CD_DS2_en_csv_v2_5551501.csv").set_index("Country Name") land_area = pd.read_csv("~/Downloads/API_AG.LND.TOTL.K2_DS2_en_csv_v2_5552158.csv").set_index("Country Name") gdp["GDP"] = gdp["2020"] gdp["Land"] = land_area["2020"] gdp = gdp.dropna(subset=["GDP", "Land"]) from scipy import stats print(stats.pearsonr(gdp.Land, gdp.GDP)) #+RESULTS: : PearsonRResult(statistic=0.8151313879150333, pvalue=5.621180589722219e-61)