Pearson's Correlation for percentage data (observations within variable sum up to 100%) Suppose the following dataset:
        | Region 1 | Region 2
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Party 1 |   20%    |   10%
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Party 2 |   20%    |   30%
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Party 3 |   60%    |   60%
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Let's suppose there's been an election in two regions (i.e. different people voting) for three political parties 1, 2 and 3 (same parties in both regions) and the fraction of the votes is as above.
Question:
Is it reasonable and correct to compute a Pearson's Correlation Coefficient between two regions to asses whether the people in the regions voted similarly? Would Spearman's rho or Kendall's tau make any difference?
I have pretty strong intuition and, so to speak, soft arguments that it might be inappropriate and something like Cramér's V should be used instead, but I'm looking for some theory/statistics based explanation.
 A: There is no unique answer to this. It depends on what you mean by "similarity", which one could say is implicitly defined by the measure you choose. Note that the data alone or statistical theory cannot tell you what "similarity" you should be interested in!
Personally, for the given situation, I would not use Spearman or Kendall correlation, because this loses the precise quantitative information in your values, which I think is important (particularly if you don't have that many parties). 
Neither would I use Cramer's $V$, because this is equivalent to the $\chi^2$-test, which relies heavily on the underlying number of observations $n$, and I don't think that you'd rate the difference between your percentages differently if that was based on 100,000 rather than 100 people (the other answer made me think though that if you want a significance test of equality, this would actually be fine; I just don't think that in your example this makes much sense, because there is no reason why perfect equality should hold, and with enough voters you will always find significance).
Pearson correlation doesn't look obviously bad to me at first sight, although I wonder whether you could just compute the $L_1$-distance between the proportion vectors, i.e., summing up the componentwise differences. That gives you a distance measure (or 100% minus that a similarity measure) that is very straight and simple to interpret. The thing with Pearson correlation is that this is in the first place useful for detecting linear dependence, but here the only possibility of having linear dependence is equality, so the situation is quite restricted compared to the one Pearson is made for.
Another approach to deal with such "compositional" data is so-called Aitchison geometry. For computing a dissimilarity this would amount to computing a distance between log-ratio transforms, see here. John Aitchison has motivated this through some axioms, although I believe that these are not very relevant to the situation that you outline here. Personally I believe that this approach would give you something that is too dominated by the differences between small proportions, a well known problem with Aitchison's approach.    
A: The most general answer: It depends.
The crucial question is, what exactly do you mean by "voted similar". In statistics, often people tend to just use some given measure and then assume that it fits what they are testing. But it is important to first clarify what the terms mean.
As a somewhat sophisticated example. Assume Party 1 and Party 2 are two types of left-wing parties, and Party 3 is a conservative party. If you want to test if the political climate in the two states is similar, then surely differences between voter outcomes between Party 1 and Party 2 don't matter as much as differences in the outcomes between 3 and 1 or 2. 
If you are just trying to find something descriptive, however, and don't care about deeper meaning of your concept, then the Pearson correlation can be a great tool, even when dealing with percentages (but beware the points below).
Most often, however, when people ask if such-and-such is correct then they are asking about inferences from these measures. So you might want to know if the p-value for a Pearson correlation on this data makes any sense.
Here are some key points, that might help you to decide this yourself (these apply to p-values from Pearson correlations):


*

*How many degrees of freedom does your data have (the data, not the correlation coefficient)? You might be tempted to say three, because there are three numbers. But are there really three independent measurements?

*Also, Pearson Correlations assumes normality. Can percentages ever be normal distributed?


Addendum:
Since there seems to be some confusion on why the Pearson Correlation might still be useful in this case, here a simple example.
Assume you clarify your question in the following way: "Given two random voters ($V_1$ and $V_2$), one from each state, what is the probability that both voted for the same party". This would give a simple measure of similarity, that also has a straight forward interpretation.
Let's do the calculation: 
Assume the ratio of people who voted for each party in state 1 is given as $p_{1,1},\ldots,p_{N,1}$ (there are $N$ parties) and in state 2 it is given as $p_{1,2},\ldots,p_{N,2}$. The probability that $V_1$ voted for the first party is $p_{1,1}$ and likewise for all other parties. If $V_1$ voted first party, the probability that $V_2$ also did it (conditional probability) is $p_{1,2}$. Because those two voters can be assumed to be independent, the joint probability is $p_{1,1}\cdot p_{1,2}$. We have to sum up over all possible votes for $V_1$, so we get
$\rho = \sum_{i=1}^N p_{i,1}\cdot p_{i,2}$. 
This is looking quite similar to a correlation coefficient, isn't it?
