{"id":1013,"date":"2018-11-12T18:16:16","date_gmt":"2018-11-12T18:16:16","guid":{"rendered":"http:\/\/re-design.dimiter.eu\/?p=1013"},"modified":"2018-11-12T18:16:16","modified_gmt":"2018-11-12T18:16:16","slug":"the-political-geography-of-human-development","status":"publish","type":"post","link":"http:\/\/re-design.dimiter.eu\/?p=1013","title":{"rendered":"The political geography of human development"},"content":{"rendered":"

The research I did for the previous post<\/a> on the inadequacy of the widely-used term ‘Global South’ led me to some surprising results about the political geography of development.<\/p>\n

Although the relationship between latitude and human development is not linear, distance from the equator turned out to have a rather strong, although far from deterministic and not necessarily causal, link with a country’s development level, as measured by its Human Development Index (HDI). Even more remarkably, once we include indicators (dummy variables) for islands and landlocked countries, and interactions between these and distance from the equator, we can account for more than 55% of the variance in HDI (2017). In other words, with three simple geographic variables and their interactions we can ‘explain’ more than half of the variation in the level of development of all countries in the world today. Wow! The plot below (pdf<\/a>) shows these relationships.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

In case you are wondering whether this results is driven by many small counties with tiny populations, it is not, When we run a weighted<\/em> linear regression with population size as the weight, the adjusted R-squared of the model remains still (just above) 0.50. On a sidenote, including dummies for (former) communist countries and current European Union (EU) member states pushed the R-squared above 0.60. Communist regime or legacy is associated with significantly lower HDI, net of the geographic variables, and EU membership is associated with significantly higher HDI.<\/p>\n

The next question to consider is whether the relationship between geography and development has grown weaker or stronger over time. There are many plausible ideas we might have about the influence of globalization, the spread of information and communication technologies, wars, and financial crises on the links between geography and development. When we look at the data, however, it turns out that the strength of the link has remained roughly the same since 1990. Wow! Despite of all global social and political transformations over the past 30 years, geography still play the same, rather larger role in constraining and enabling human development. The gif below shows the same plots for 1990, 2000, 2010, and 2017. While overal development grows over time, the relationship with distance from the equator remains roughly the same, as indicated by the slopes of the linear regression lines.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

Note that the way the HDI is constructed (HDI) makes changes in development over time not quite comparable (the index is capped at 1.0, so if you are an already highly developed country, there is not much scope to improve further your index). Also, the sample of countries for which there is available data is smaller in 1990 (N=144) than in 2017 (N=191).<\/p>\n

Since we mentioned population size, let’s consider the link between the population size of a country and its level of HDI. Are small countries more successful? Does it pay off to be a large state? Maybe countries with populations that are neither too big nor too small perform best?<\/p>\n

As the plot below (pdf<\/a>) shows, there is no clear relationship between population size and HDI. The linear regression line slopes slightly downwards but the ‘effect’ is not significant and it is not really linear. The loess fit meanders up and down without a clear pattern. It turns out there is no sweet spot for population size when it comes to human development. Small populations can be just as good, and just as bad, and bigger ones. There are tiny states that are successful, and ones that do pretty badly. The same for mid-sized, big, and enormous countries (not in terms of area, but population).<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

This lack of relationship is quite remarkable, but there is another surprise when we look at the change in development between 2000 and 2017. As the plot below (pdf<\/a>) shows, more populous countries have been more successful in improving their HDI over the past 18 years. It is not a huge difference, but given the overall small scale of the observed changes, it is significant and important.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

To sum up, while in general population size is not related to development, during the past two decades more populous countries have been more successful in improving their development index. This is of course good news, as it means that more people live longer, study longer, and enjoy higher standards of living.<\/p>\n

For now, this concludes my exploits in political geography, which turned out to harbor more insights that I expected, even when I have only explored a total of five variables. If you want to continue from here on your own, the R<\/code> script<\/a> for the figures is here and the datafile is here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

The research I did for the previous post on the inadequacy of the widely-used term ‘Global South’ led me to some surprising results about the political geography of development. Although the relationship between latitude and human development is not linear, distance from the equator turned out to have a rather strong, although far from deterministic and not necessarily causal, link with a country’s development level, as measured by its Human Development Index (HDI). Even more remarkably, once we include indicators (dummy variables) for islands and landlocked countries, and interactions between these and distance from the equator, we can account for more than 55% of the variance in HDI (2017). In other words, with three simple geographic variables and their interactions we can ‘explain’ more than half of the variation in the level of development of all countries in the world today. Wow! The plot below (pdf) shows these relationships.     In case you are wondering whether this results is driven by many small counties with tiny populations, it is not, When we run a weighted linear regression with population size as the weight, the adjusted R-squared of the model remains still (just above) 0.50. On a sidenote, including dummies for (former) communist countries and current European Union (EU) member states pushed the R-squared above 0.60. Communist regime or legacy is associated with significantly lower HDI, net of the geographic variables, and EU membership is associated with significantly higher HDI. The next question to consider is whether the relationship between…<\/p>\n

Continue readingThe political geography of human development<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"The political geography of human development","jetpack_is_tweetstorm":false},"categories":[11,13],"tags":[741,737,739,740,738,709],"jetpack_featured_media_url":"","jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7g3hj-gl","jetpack-related-posts":[{"id":969,"url":"http:\/\/re-design.dimiter.eu\/?p=969","url_meta":{"origin":1013,"position":0},"title":"The 'Global South' is a terrible term. 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