World Giving Index – Results

World map showing countries by nominal GDP per...

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This is the final, long overdue blog post about the World Giving Index report. In this post I will be looking critically at the findings of the report.

How Nations Give

The first few ‘key findings’ of the report declare that nations give in many different ways, and that helping strangers is the main way most people give. These are fine, as they are only descriptive findings based on looking at the numbers. As such, their limitations will be the same as those of the research methodology overall that I pointed out in the last post.

Happy Nations and Wealthy Nations

The next key finding of the report is that “the link between the giving of money and happiness is stronger (a coefficient of 0.69) than the link between the giving of money and the GDP of a nation (0.58)” (World Giving Index, p. 6).

Measuring Happiness

Well, I have a few problems with this assertion. The first is that – as is systematic of so much we do nowadays – is that the report has quantified something which is very difficult to quantify (happiness) and stuck it in a report without so much as considering the fact that it might be problematic. The report measures happiness by asking:

Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. Suppose we say that the top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time, assuming that the higher the step the better you feel about your life, and the lower the step the worse you feel about it? Which step comes closest to the way you feel?

Which is an extremely convoluted way of asking: “How satisfied are you with your life at the moment, on a scale of 1 (worst) to 10 (best)?” The fact that the person’s measure of their happiness will not be comparable with others’ is not an issue, because presumably the hypothesis is that an individual’s self-reported happiness will affect whether they’re likely to donate money, and not how happy they are compared with other people. The problem, for me, is that happiness is so much more complex than slapping a number from one to ten on and reporting that number unproblematically, which this report does. All I want to see is some discussion of how and why happiness was operationalised in this way, just to reassure me that the researchers are aware of its limitations and don’t just accept things blindly.

Measuring Personal Wealth

Moving on, my second problem (you’ve guessed it) is with GDP. GDP is a national measure, and will apply unevenly to individuals of that nation. I think the authors of the report are really trying to examine if personal wealth affects monetary giving. I have no doubt that there is a positive correlation between GDP and personal wealth, but GDP does not directly measure this. Have a read of the wikipedia article on the limitations of GDP if you’re interested, but suffice to say that GDP has its limitations as a measure of wealth distribution in a county.

Nevertheless, we can still use GDP to give us an idea of what effect personal wealth has on monetary giving, but again I would have preferred to see some acknowledgement about its limitations from the report’s authors to reassure me they’ve thought about it.

The Correlations

The correlations are just a mathematical or statistical way of saying how much the independent variable (either GDP or happiness) affects the dependent variable (the amount of monetary giving), i.e. does personal wealth or personal happiness have a stronger effect on giving money. The 0.69 means that 69% of all the variance in giving money can be described by the happiness of the individual, and 58% is affected by the person’s wealth.

My problem here is that the report has quoted r values, where actually the r2 (r squared) is actually the coefficient. Therefore these figures should only be 0.48 and 0.34 for happiness and wealth, respectively. Don’t get me wrong, happiness is still apparently the more significant factor, but the fact that the report authors have quoted the wrong figures doesn’t fill me with hope. A further problem is that I don’t know how they applied the regression to get these figures. Were they all included in one regression analysis, were they done separately, how were they included, did they violate any of the assumptions of regression and, if so, what remedial steps did the authors take? All of these things aren’t addressed by the report, which suggests to me that the results of the report might not be reliable.


Finally, any good piece of research should be easily repeatable and the findings replicable. I carried out my own regression analysis using the data provided by the report authors and for GDP I took the IMF 2009 GDP per capita values for each county (the authors didn’t specify which source they used and there are a few). No matter what I tried I could not replicate the report’s findings.

In particular, the coefficients I calculated were 0.306 for happiness and 0.357 for GDP per capita (compared with 0.48 and 0.34 respectively). Therefore, my regression analysis suggests that GDP per capita (and therefore personal wealth) has more influence on monetary giving than happiness.

There could be a number of reasons for this. I’m not sure we’re using the same figures for GDP (exposing a weakness in using GDP anyway). I also don’t know if the authors ‘prepared’ the data in any way before they did their regression analysis (this is a perfectly legitimate thing to do, and makes sure the regression is robust) so I can’t replicate this. This is definitely a weakness in the report, and potentially a weakness in its analysis.


As you may have guessed by now, I’m highly sceptical of the finding that happiness plays more of a part in people’s decisions to donate money than their wealth. The report does not give enough information to be able to conduct an identical analysis, so I can’t check it by replicating their methods. However,  I have done a ‘best guess’ and get a significantly different answer to that of the report authors, which suggests there is a weakness somewhere in the report, its methods, its data, or any combination of the three. As a result I would not be hasty to say I know which is the more significant factor.

This, for me, undermines much of the rest of the report. I’m sure it’s probably fine, but my trust in it is gone so I am sceptical of the report’s findings overall. A shame, because there is a substantial amount of data in this report which might otherwise have been very useful.

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