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author's proof - DARP

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AUTHOR'S PROOF<br />

Reference distributions and inequality measurement<br />

JrnlID 10888_ArtID 9238_Proof# 1 - 08/01/13<br />

Q2<br />

sensitive inequality where, in terms of “distance from maximum inequality,” the 253<br />

change in the distribution is inconclusive. 254<br />

Theoretical distribution Finally let us consider how inequality changed using a con- 255 Q4<br />

tinuous reference distribution F ∗ . The last panel of Table 2 tabulates the results for 256<br />

three F ∗ from the Beta distribution family. Did UK income inequality, interpreted 257<br />

as a distance from a Beta-family reference distribution increase We can see that 258<br />

the values of J α are not statistically different between 1979 and 1988 when the 259<br />

Beta(1,1) (uniform) or Beta(2,5) (unimodal, right skewed) is used as the reference 260<br />

distribution distribution, while they are statistically different when the Beta(2, 2) 261<br />

(unimodal symmetric) is used as the reference distribution. 262<br />

The estimates of the standard GE inequality measures J α,1,1 and of those of J α,k,p 263<br />

and J α in Table 2 provide us with different information about divergence of the 264<br />

empirical distribution from the chosen reference distribution. By varying the values 265<br />

of k and p, one can specify the exact skewness of the reference distribution one would 266<br />

like to measure distance of the empirical distribution from. Likewise, by varying the 267<br />

values of α one can focus on different parts of the income distribution. A large value 268<br />

of α implies a greater weight on parts of the distribution where the observed incomes 269<br />

are vastly different from the corresponding values in the reference distribution. 270<br />

Finally, one can choose specific parametric distributions which correspond to the 271<br />

relevant reference distribution that the researcher is interested in. 272<br />

5 Conclusion 273<br />

The problem of comparing pairs of distributions is a widespread one in distributional 274<br />

analysis. It is often treated on an ad-hoc basis by invoking the concept of norm 275<br />

incomes and an arbitrary inequality index. 276<br />

Our approach to the issue is a natural generalisation of the concept of inequal- 277<br />

ity indices where the implicit reference distribution is the trivial perfect-equality 278<br />

distribution. Its intuitive appeal is supported by the type of axiomatisation that 279<br />

is common in modern approaches to inequality measurement and other welfare 280<br />

criteria. The axiomatisation yields indices that can be interpreted as measures of 281<br />

divergence and are related to the concept of divergence entropy in information 282<br />

theory [10]. Furthermore, they offer a degree of control to the researcher in that 283<br />

the J α indices form a class of measures that can be calibrated to suit the nature of the 284<br />

economic problem under consideration. Members of the class have a distributional 285<br />

interpretation that is close to members of the well-known generalised-entropy class 286<br />

of inequality indices. 287<br />

In effect the user of the J α -index is presented with two key questions: 288<br />

UNCORRECTED PROOF<br />

1. the income discrepancies underlying inequality are with reference to what 289<br />

2. to what kind of discrepancies do you want the measure to be particularly 290<br />

sensitive 291<br />

As our empirical illustration has shown, different responses to these two key ques- 292<br />

tions provide different interpretations from the same set of facts. 293

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