{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 3: GDP and life expectancy\n", "\n", "by Michel Wermelinger, 27 August 2015, updated 5 April 2016 and 18 October 2017, minor edits 20 December 2017\n", "\n", "This is the project notebook for Part 3 of The Open University's _Learn to code for Data Analysis_ course.\n", "\n", "Richer countries can afford to invest more on healthcare, on work and road safety, and other measures that reduce mortality. On the other hand, richer countries may have less healthy lifestyles. Is there any relation between the wealth of a country and the life expectancy of its inhabitants?\n", "\n", "The following analysis checks whether there is any correlation between the total gross domestic product (GDP) of a country in 2013 and the life expectancy of people born in that country in 2013." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting the data\n", "\n", "Two datasets of the World Bank are considered. One dataset, available at , lists the GDP of the world's countries in current US dollars, for various years. The use of a common currency allows us to compare GDP values across countries. The other dataset, available at , lists the life expectancy of the world's countries. The datasets were downloaded as CSV files in March 2016." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearSP.DYN.LE00.IN
0Arab World201370.631305
1Caribbean small states201371.901964
2Central Europe and the Baltics201376.127583
3East Asia & Pacific (all income levels)201374.604619
4East Asia & Pacific (developing only)201373.657617
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" ], "text/plain": [ " country year SP.DYN.LE00.IN\n", "0 Arab World 2013 70.631305\n", "1 Caribbean small states 2013 71.901964\n", "2 Central Europe and the Baltics 2013 76.127583\n", "3 East Asia & Pacific (all income levels) 2013 74.604619\n", "4 East Asia & Pacific (developing only) 2013 73.657617" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings\n", "warnings.simplefilter('ignore', FutureWarning)\n", "\n", "from pandas import *\n", "\n", "YEAR = 2013\n", "GDP_INDICATOR = 'NY.GDP.MKTP.CD'\n", "gdpReset = read_csv('WB GDP 2013.csv')\n", "\n", "LIFE_INDICATOR = 'SP.DYN.LE00.IN'\n", "lifeReset = read_csv('WB LE 2013.csv')\n", "lifeReset.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cleaning the data\n", "\n", "Inspecting the data with `head()` and `tail()` shows that:\n", "\n", "1. the first 34 rows are aggregated data, for the Arab World, the Caribbean small states, and other country groups used by the World Bank;\n", "- GDP and life expectancy values are missing for some countries.\n", "\n", "The data is therefore cleaned by:\n", "1. removing the first 34 rows;\n", "- removing rows with unavailable values." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gdpCountries = gdpReset[34:].dropna()\n", "lifeCountries = lifeReset[34:].dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transforming the data\n", "\n", "The World Bank reports GDP in US dollars and cents. To make the data easier to read, the GDP is converted to millions of British pounds (the author's local currency) with the following auxiliary functions, using the average 2013 dollar-to-pound conversion rate provided by . " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearNY.GDP.MKTP.CDGDP (£m)
34Afghanistan20132.045894e+1013075
35Albania20131.278103e+108168
36Algeria20132.097035e+11134016
38Andorra20133.249101e+092076
39Angola20131.383568e+1188420
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" ], "text/plain": [ " country year NY.GDP.MKTP.CD GDP (£m)\n", "34 Afghanistan 2013 2.045894e+10 13075\n", "35 Albania 2013 1.278103e+10 8168\n", "36 Algeria 2013 2.097035e+11 134016\n", "38 Andorra 2013 3.249101e+09 2076\n", "39 Angola 2013 1.383568e+11 88420" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def roundToMillions (value):\n", " return round(value / 1000000)\n", "\n", "def usdToGBP (usd):\n", " return usd / 1.564768\n", "\n", "GDP = 'GDP (£m)'\n", "gdpCountries[GDP] = gdpCountries[GDP_INDICATOR].apply(usdToGBP).apply(roundToMillions)\n", "gdpCountries.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The unnecessary columns can be dropped." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryGDP (£m)
34Afghanistan13075
35Albania8168
36Algeria134016
38Andorra2076
39Angola88420
\n", "
" ], "text/plain": [ " country GDP (£m)\n", "34 Afghanistan 13075\n", "35 Albania 8168\n", "36 Algeria 134016\n", "38 Andorra 2076\n", "39 Angola 88420" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "COUNTRY = 'country'\n", "headings = [COUNTRY, GDP]\n", "gdpClean = gdpCountries[headings]\n", "gdpClean.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The World Bank reports the life expectancy with several decimal places. After rounding, the original column is discarded." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryLife expectancy (years)
34Afghanistan60
35Albania78
36Algeria75
39Angola52
40Antigua and Barbuda76
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" ], "text/plain": [ " country Life expectancy (years)\n", "34 Afghanistan 60\n", "35 Albania 78\n", "36 Algeria 75\n", "39 Angola 52\n", "40 Antigua and Barbuda 76" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LIFE = 'Life expectancy (years)'\n", "lifeCountries[LIFE] = lifeCountries[LIFE_INDICATOR].apply(round)\n", "headings = [COUNTRY, LIFE]\n", "lifeClean = lifeCountries[headings]\n", "lifeClean.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining the data\n", "\n", "The tables are combined through an inner join on the common 'country' column. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryGDP (£m)Life expectancy (years)
0Afghanistan1307560
1Albania816878
2Algeria13401675
3Angola8842052
4Antigua and Barbuda76776
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" ], "text/plain": [ " country GDP (£m) Life expectancy (years)\n", "0 Afghanistan 13075 60\n", "1 Albania 8168 78\n", "2 Algeria 134016 75\n", "3 Angola 88420 52\n", "4 Antigua and Barbuda 767 76" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdpVsLife = merge(gdpClean, lifeClean, on=COUNTRY, how='inner')\n", "gdpVsLife.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the correlation\n", "\n", "To measure if the life expectancy and the GDP grow together, the Spearman rank correlation coefficient is used. It is a number from -1 (perfect inverse rank correlation: if one indicator increases, the other decreases) to 1 (perfect direct rank correlation: if one indicator increases, so does the other), with 0 meaning there is no rank correlation. A perfect correlation doesn't imply any cause-effect relation between the two indicators. A p-value below 0.05 means the correlation is statistically significant." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The correlation is 0.501023238967\n", "It is statistically significant.\n" ] } ], "source": [ "from scipy.stats import spearmanr\n", "\n", "gdpColumn = gdpVsLife[GDP]\n", "lifeColumn = gdpVsLife[LIFE]\n", "(correlation, pValue) = spearmanr(gdpColumn, lifeColumn)\n", "print('The correlation is', correlation)\n", "if pValue < 0.05:\n", " print('It is statistically significant.')\n", "else:\n", " print('It is not statistically significant.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value shows a direct correlation, i.e. richer countries tend to have longer life expectancy, but it is not very strong." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Showing the data\n", "\n", "Measures of correlation can be misleading, so it is best to see the overall picture with a scatterplot. The GDP axis uses a logarithmic scale to better display the vast range of GDP values, from a few million to several billion (million of million) pounds." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "gdpVsLife.plot(x=GDP, y=LIFE, kind='scatter', grid=True, logx=True, figsize=(10, 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot shows there is no clear correlation: there are rich countries with low life expectancy, poor countries with high expectancy, and countries with around 10 thousand (104) million pounds GDP have almost the full range of values, from below 50 to over 80 years. Towards the lower and higher end of GDP, the variation diminishes. Above 40 thousand million pounds of GDP (3rd tick mark to the right of 104), most countries have an expectancy of 70 years or more, whilst below that threshold most countries' life expectancy is below 70 years. \n", "\n", "Comparing the 10 poorest countries and the 10 countries with the lowest life expectancy shows that total GDP is a rather crude measure. The population size should be taken into account for a more precise definiton of what 'poor' and 'rich' means. Furthermore, looking at the countries below, droughts and internal conflicts may also play a role in life expectancy. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryGDP (£m)Life expectancy (years)
87Kiribati10866
141Sao Tome and Principe19566
111Micronesia, Fed. Sts.20269
168Tonga27773
37Comoros38363
157St. Vincent and the Grenadines46173
140Samoa50973
180Vanuatu51272
65Grenada53873
60Gambia, The57860
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" ], "text/plain": [ " country GDP (£m) Life expectancy (years)\n", "87 Kiribati 108 66\n", "141 Sao Tome and Principe 195 66\n", "111 Micronesia, Fed. Sts. 202 69\n", "168 Tonga 277 73\n", "37 Comoros 383 63\n", "157 St. Vincent and the Grenadines 461 73\n", "140 Samoa 509 73\n", "180 Vanuatu 512 72\n", "65 Grenada 538 73\n", "60 Gambia, The 578 60" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the 10 countries with lowest GDP\n", "gdpVsLife.sort_values(GDP).head(10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryGDP (£m)Life expectancy (years)
95Lesotho141849
160Swaziland291649
32Central African Republic98350
146Sierra Leone309250
33Chad827651
41Cote d'Ivoire1999851
3Angola8842052
124Nigeria32910052
30Cameroon1889655
153South Sudan847355
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" ], "text/plain": [ " country GDP (£m) Life expectancy (years)\n", "95 Lesotho 1418 49\n", "160 Swaziland 2916 49\n", "32 Central African Republic 983 50\n", "146 Sierra Leone 3092 50\n", "33 Chad 8276 51\n", "41 Cote d'Ivoire 19998 51\n", "3 Angola 88420 52\n", "124 Nigeria 329100 52\n", "30 Cameroon 18896 55\n", "153 South Sudan 8473 55" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the 10 countries with lowest life expectancy\n", "gdpVsLife.sort_values(LIFE).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "To sum up, there is no strong correlation between a country's wealth and the life expectancy of its inhabitants: there is often a wide variation of life expectancy for countries with similar GDP, countries with the lowest life expectancy are not the poorest countries, and countries with the highest expectancy are not the richest countries. Nevertheless there is some relationship, because the vast majority of countries with a life expectancy below 70 years is on the left half of the scatterplot.\n", "\n", "Using the [NY.GDP.PCAP.PP.CD](http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD) indicator, GDP per capita in current 'international dollars', would make for a better like-for-like comparison between countries, because it would take population and purchasing power into account. Using more specific data, like expediture on health, could also lead to a better analysis." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }