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Global economies: Analysing measures of economic growth, prosperity, inflation and unemployment
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  • This project analyses the patterns of key macroeconomic measures presented in the world database for global economies. Three analysis were performed:

    1. Examining the relationships between gdp, gross_savings, total_investment, and net export of economies in the EU using a correlation matrix
    2. Comparing average GDP per capita, average gross savings, average total investment, and average net export across different income groups
    3. Analysing the relationship between inflation rate and unemployment rate between different income groups in 2015. Conclusions will be drawn after benchmarking against the Phillips Curve Model (which suggests an inverse relationship between inflation rate and unemployment rate)
    Unknown integration
    DataFrameavailable as
    df3
    variable
    -- Building a correlation matrix to examine relationships between GDP, gross_savings, total_investment, and net export of economies in the EU
    
    DROP TABLE IF EXISTS correlations;  
    
    CREATE TEMP TABLE gdp_components AS
      SELECT 
    	e.code, 
        e.year,
        e.gdp_percapita,
    	e.gross_savings,
    	p.size, 
        e.total_investment, 
        e.exports,
        e.imports
      FROM world.economies AS e
      INNER JOIN world.populations AS p
      ON e.code = p.country_code AND e.year = p.year
      INNER JOIN world.eu_countries AS eu
      ON e.code = eu.code 
      WHERE e.year IN (2015);
      
    CREATE TEMP TABLE gdp_final AS 
      SELECT
    	code,
    	year,
        ROUND(((gdp_percapita * size)/1000000000)::numeric, 2) AS gdp,
    	gross_savings,
    	total_investment, 
    	exports - imports AS net_export 
      FROM gdp_components
      WHERE total_investment IS NOT NULL
        AND (exports - imports) IS NOT NULL
        AND (gdp_percapita * size/1000000000) IS NOT NULL;
    
    CREATE TEMP TABLE correlations AS
    SELECT 
      'gdp'::varchar AS measure,
      CORR(gdp, gdp) AS gdp, 
      CORR(gdp, gross_savings) AS gross_savings, 
      CORR(gdp, total_investment) AS total_investment, 
      CORR(gdp, net_export) AS net_export
    FROM gdp_final;
    
    INSERT INTO correlations 
    SELECT 
      'gross_savings'::varchar AS measure,
      CORR(gross_savings, gdp) AS gdp,
      CORR(gross_savings, gross_savings) AS gross_savings,
      CORR(gross_savings, total_investment) AS total_investment,
      CORR(gross_savings, net_export) AS net_export
    FROM gdp_final; 
    
    INSERT INTO correlations
    SELECT
      'total_investment'::varchar AS measure,
      CORR(total_investment, gdp) AS gdp,
      CORR(total_investment, gross_savings) AS gross_savings,
      CORR(total_investment, total_investment) AS total_investment,
      CORR(total_investment, net_export) AS net_export
    FROM gdp_final;
    
    INSERT INTO correlations
    SELECT
      'net_export'::varchar AS measure,
      CORR(net_export, gdp) AS gdp,
      CORR(net_export, gross_savings) AS gross_savings,
      CORR(net_export, total_investment) AS total_investment,
      CORR(net_export,net_export) AS net_export
    FROM gdp_final;
    
    SELECT 
      measure,
      ROUND(gdp::numeric,2) AS gdp,
      ROUND(gross_savings::numeric,2) AS gross_savings,
      ROUND(total_investment::numeric,2) AS total_investment, 
      ROUND(net_export::numeric,2) AS net_export
    FROM correlations;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df
    variable
    -- Comparing average GDP per capita, average gross savings, average total investment, and average net export across different income groups across global countries
    
    SELECT
      income_group, 
      ROUND(AVG(gdp_percapita::numeric),2) AS avg_gdp_percapita,
      ROUND(AVG(gross_savings::numeric),2) AS avg_gross_savings_million,
      ROUND(AVG(total_investment::numeric),2) AS avg_total_investment_million,
      ROUND(AVG((exports-imports)::numeric),2) AS avg_net_export_million 
    FROM world.economies
    GROUP BY income_group
    ORDER BY AVG(gdp_percapita::numeric) DESC;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df4
    variable
    -- Comparing average GDP per capita, average gross savings, average total investment, and average net export across different income groups across EU countires 
    
    SELECT
      e.income_group, 
      ROUND(AVG(e.gdp_percapita::numeric),2) AS avg_gdp_percapita,
      ROUND(AVG(e.gross_savings::numeric),2) AS avg_gross_savings_million,
      ROUND(AVG(e.total_investment::numeric),2) AS avg_total_investment_million,
      ROUND(AVG((e.exports-e.imports)::numeric),2) AS avg_net_export_million 
    FROM world.economies AS e
    INNER JOIN world.eu_countries AS eu
    ON e.code = eu.code
    GROUP BY e.income_group
    ORDER BY AVG(e.gdp_percapita::numeric) DESC;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df5
    variable
    -- Analysing the relationship between inflation rate and unemployment rate between different income groups in 2015 
    -- Conclusions will be drawn after benchmarking against the Phillips Curve Model (which suggests an inverse relationship between inflation rate and unemployment rate) 
    
    DROP TABLE IF EXISTS correlations; 
    
    CREATE TEMP TABLE high_inflation_unemployment AS 
    SELECT
      code,
      income_group,
      inflation_rate,
      unemployment_rate
    FROM world.economies
    WHERE year = 2015
      AND inflation_rate IS NOT NULL
      AND unemployment_rate IS NOT NULL
      AND income_group IN ('High income'); 
      
    CREATE TEMP TABLE upper_middle_inflation_unemployment AS 
    SELECT
      code,
      income_group,
      inflation_rate,
      unemployment_rate
    FROM world.economies
    WHERE year = 2015
      AND inflation_rate IS NOT NULL
      AND unemployment_rate IS NOT NULL
      AND income_group LIKE ('Upper%'); 
      
    CREATE TEMP TABLE lower_middle_inflation_unemployment AS 
    SELECT
      code,
      income_group,
      inflation_rate,
      unemployment_rate
    FROM world.economies
    WHERE year = 2015
      AND inflation_rate IS NOT NULL
      AND unemployment_rate IS NOT NULL
      AND income_group LIKE ('Lower%'); 
    
    CREATE TEMP TABLE correlations AS
    SELECT 
      'High income'::varchar AS income_group, 
      CORR(inflation_rate, unemployment_rate) AS correlation
    FROM high_inflation_unemployment; 
    
    INSERT INTO correlations
    SELECT
      'Upper middle income'::varchar AS income_group, 
      CORR(inflation_rate, unemployment_rate) AS correlation 
    FROM upper_middle_inflation_unemployment;
    
    INSERT INTO correlations 
    SELECT 
      'Lower middle income'::varchar AS income_group,
      CORR(inflation_rate, unemployment_rate) AS correlation
    FROM lower_middle_inflation_unemployment;
    
    SELECT 
      income_group,
      ROUND(correlation::numeric, 2) AS correlation,
      (CASE WHEN correlation < 0 THEN 'Negative'
        WHEN correlation = 0 THEN 'No correlation'
        WHEN correlation > 0 THEN 'Positive' END) AS relationship,
      (CASE WHEN ABS(correlation::numeric)<=0.09 THEN 'Trivial'
        WHEN ABS(correlation::numeric)<=0.29 THEN 'Low to medium'
        WHEN ABS(correlation::numeric)<=0.49 THEN 'Medium to essential'
        WHEN ABS(correlation::numeric)<=0.69 THEN 'Essential to very strong'
        WHEN ABS(correlation::numeric)<=0.89 THEN 'Very strong'
        WHEN ABS(correlation::numeric)<=0.99 THEN 'Almost perfect'
        WHEN ABS(correlation::numeric)=1 THEN 'Perfect' END) AS strength,
      (CASE WHEN correlation < 0 THEN 'Movement along'
        WHEN correlation = 0 THEN 'Counteracts'
        WHEN correlation > 0 THEN 'Shift' END) AS phillips_curve_model,
      (CASE WHEN correlation < 0 THEN 'AD'
        WHEN correlation = 0 THEN 'Uncertain'
        WHEN correlation > 0 THEN 'SRAS' END)	AS growth_driven_by
    FROM correlations; 
    
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.