- 1 Introduction
- 2 Poverty Indices
- 2.1 At Risk of Poverty Threshold (svyarpt)
- 2.2 At Risk of Poverty Ratio (svyarpr)
- 2.3 Relative Median Income Ratio (svyrmir)
- 2.4 Relative Median Poverty Gap (svyrmpg)
- 2.5 Median Income Below the At Risk of Poverty Threshold (svypoormed)
- 2.6 Foster-Greer-Thorbecke class (svyfgt, svyfgtdec)
- 2.7 Watts poverty measure (svywatts, svywattsdec)
- 2.8 Clark-Hemming-Ulph class of poverty measures (svychu)

- 3 Inequality Measurement
- 3.1 The Gender Pay Gap (svygpg)
- 3.2 Quintile Share Ratio (svyqsr)
- 3.3 Lorenz Curve (svylorenz)
- 3.4 Gini index (svygini)
- 3.5 Amato index (svyamato)
- 3.6 Zenga Index and Curve (svyzenga, svyzengacurve)
- 3.7 Entropy-based Measures
- 3.8 Generalized Entropy and Decomposition (svygei, svygeidec)
- 3.9 Rényi Divergence (svyrenyi)
- 3.10 J-Divergence and Decomposition (svyjdiv, svyjdivdec)
- 3.11 Atkinson index (svyatk)

- 4 Multidimensional Indices

Some measures of poverty and income concentration are defined by non-differentiable functions so that it is not possible to use Taylor linearization to estimate their variances. An alternative is to use **Influence functions** as described in (Deville 1999Deville, Jean-Claude. 1999. “Variance Estimation for Complex Statistics and Estimators: Linearization and Residual Techniques.” *Survey Methodology* 25 (2): 193–203. http://www.statcan.gc.ca/pub/12-001-x/1999002/article/4882-eng.pdf.) and (Osier 2009Osier, Guillaume. 2009. “Variance Estimation for Complex Indicators of Poverty and Inequality.” *Journal of the European Survey Research Association* 3 (3): 167–95. http://ojs.ub.uni-konstanz.de/srm/article/view/369.). The convey library implements this methodology to work with `survey.design`

objects and also with `svyrep.design`

objects.

Some examples of these measures are:

At-risk-of-poverty threshold: \(arpt=.60q_{.50}\) where \(q_{.50}\) is the income median;

At-risk-of-poverty rate \(arpr=\frac{\sum_U 1(y_i \leq arpt)}{N}.100\)

Quintile share ratio

\(qsr=\frac{\sum_U 1(y_i>q_{.80})}{\sum_U 1(y_i\leq q_{.20})}\)

- Gini coefficient \(1+G=\frac{2\sum_U (r_i-1)y_i}{N\sum_Uy_i}\) where \(r_i\) is the rank of \(y_i\).

Note that it is not possible to use Taylor linearization for these measures because they depend on quantiles and the Gini is defined as a function of ranks. This could be done using the approach proposed by Deville (1999) based upon influence functions.

Let \(U\) be a population of size \(N\) and \(M\) be a measure that allocates mass one to the set composed by one unit, that is \(M(i)=M_i= 1\) if \(i\in U\) and \(M(i)=0\) if \(i\notin U\)

Now, a population parameter \(\theta\) can be expressed as a functional of \(M\) \(\theta=T(M)\)

Examples of such parameters are:

Total: \(Y=\sum_Uy_i=\sum_U y_iM_i=\int ydM=T(M)\)

Ratio of two totals: \(R=\frac{Y}{X}=\frac{\int y dM}{\int x dM}=T(M)\)

Cumulative distribution function: \(F(x)=\frac{\sum_U 1(y_i\leq x)}{N}=\frac{\int 1(y\leq x)dM}{\int{dM}}=T(M)\)

To estimate these parameters from the sample, we replace the measure \(M\) by the estimated measure \(\hat{M}\) defined by: \(\hat{M}(i)=\hat{M}_i= w_i\) if \(i\in s\) and \(\hat{M}(i)=0\) if \(i\notin s\).

The estimators of the population parameters can then be expressed as functional of the measure \(\hat{M}\).

Total: \(\hat{Y}=T(\hat{M})=\int yd\hat{M}=\sum_s w_iy_i\)

Ratio of totals: \(\hat{R}=T(\hat{M})=\frac{\int y d\hat{M}}{\int x d\hat{M}}=\frac{\sum_s w_iy_i}{\sum_s w_ix_i}\)

Cumulative distribution function: \(\hat{F}(x)=T(\hat{M})=\frac{\int 1(y\leq x)d\hat{M}}{\int{d\hat{M}}}=\frac{\sum_s w_i 1(y_i\leq x)}{\sum_s w_i}\)