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The graph is claiming that countries should appear on the line - above the line are doing better than average; below the line are doing worse than average. Ver
by ronaldx 13y ago
The graph is claiming that countries should appear on the line - above the line are doing better than average; below the line are doing worse than average.
Vertical distance from the line is what you're looking for, so NZL is a crazy distance below. NOR, GRC and IRL are the furthest above the line.
However, another way to view this graph is to say that structural policies have no predictive power.
If you take out the outliers: the countries away from the central blob (e.g. Greece, Portugal and USA: all countries with exceptional circumstances), then the correlation is gone or perhaps even negative - meaning this is a poor model and a pointless exercise.
- pcrh 13y agoI went to the real source [1], which provides this data: http://i.imgur.com/d46JG8Q.png http://i.imgur.com/d46JG8Q.png, and plotted it. The R(square) value for the fit it 0.2. The Pearson r is 0.44 with a p-value of 0.02 i.e. potentially useful. Taking out Greece, Portugal and USA changes the R(square) to 0.002, the Pearson r to 0.05 with a p-value of 0.8, i.e without the outliers there is no correlation between the values. So, indeed, the outliers are driving the association... However, one is not permitted to cherry-pick data, so the "outliers" have to be left in. [1]Barnes, S., Bouis, R., Briard, P., Dougherty, S., and Eris, M. (2011).The GDP impact of reform: a simple simulation framework,OECD Economics Department Working Papers, no. 834, Paris, France: OECD. PDF file: http://www.oecd-ilibrary.org/docserver/download/5kgk9qjnhkmt.pdf?expires=1397838927&id=id&accname=guest&checksum=583ADE3E363128945532B8219957347D http://www.oecd-ilibrary.org/docserver/download/5kgk9qjnhkmt...
- ronaldx 13y ago>However, one is not permitted to cherry-pick data, so the "outliers" have to be left in. I respect why you think this, but I don't agree. Identifying points on the graph which don't fit with the pattern of the rest of the data is a reasonable way to identify outliers. Even if you choose not to exclude those, there's a greater problem: Correlation is not robust if it depends on 2 or 3 points being in just the right place. And, robustness is one of the things you really ought to check for if you calculate Pearson's r. If you get different results with outliers in and out, that's a problem for your results. In my opinion, any significant result here is just statistical noise. See Wikipedia for further discussion: https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Robustness https://en.wikipedia.org/wiki/Pearson_product-moment_correla...
- pcrh 13y agoI agree that the statistics on this association are pretty lousy, even with the outliers in. However, one would preferably find an specific reason to exclude outliers, rather than rely on intuition.