- 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 Entropy-based Measures
- 3.6 Generalized Entropy and Decomposition (svygei, svygeidec)
- 3.7 J-Divergence and Decomposition (svyjdiv, svyjdivdec)
- 3.8 Atkinson index (svyatk)
- 3.9 Which inequality measure should be used?

In this book, we demonstrate how to measure poverty and income concentration in a population based on microdata collected from a complex survey sample. Most surveys administered by government agencies or larger research organizations utilize a sampling design that violates the assumption of simple random sampling (SRS), including:

- Different units selection probabilities;
- Clustering of units;
- Stratification of clusters;
- Reweighting to compensate for missing values and other adjustments.

Therefore, basic unweighted R commands such as `mean()`

or `glm()`

will not properly account for the weighting nor the measures of uncertainty (such as the confidence intervals) present in the dataset. For some examples of publicly-available complex survey data sets, see http://asdfree.com.

Unlike other software, the R `convey`

package does not require that the user specify these parameters throughout the analysis. So long as the svydesign object or svrepdesign object has been constructed properly at the outset of the analysis, the `convey`

package will incorporate the survey design automatically and produce statistics and variances that take the complex sample into account.