PovertyAndInequalityMeasures

Make a wee dict with :theilbetween and :theilwithin See WB eqns 6.7/6.8. TODO

1. there are some papers on decomposing Atkinson, but I

don't understand them ..

1. the over time 3-part version

popindc : Inequal for the population as a whole subindices : an array of dics, one for each subgroup of interest

Chop a dataset with populations and incomes into numbins groups in a form suitable for e.g. a Gini curve.

• col1 is cumulative population,
• 2 cumulative income/whatever,
• 3 threshold income level.

As above, but using any DataFrame like thing that supports the TableTraits.isiterabletable interface

generate a subset of one of our datasets with just the elements whose incomes are below the line. Probably possible in 1 line, once I get the hang of this a bit more.

internal function that makes a sorted array with cumulative income and population added

internal function that makes a sorted array with cumulative income and population added

calculate a Gini coefficient on one of our sorted arrays

Make a struct of inequality measures. This is mainly taken from chs 5 and 6 of the World Bank book.

1. rawdata a matrix with cols with weights and incomes
2. atkinson_es inequality aversion values for the Atkinson indexes
3. generalised_entropy_alphas
4. weightpos - column with weights
5. incomepos - column with incomes

Returned is a Dict of inequality measures with:

• Gini;
• Atkinson, for each value in atkinson_es;
• Theil;
• generalised_entropy;
• Hoover;
• Theil;
• Palma.

See WB chs 5 an 6, and Cobham and Sumner on the Palma.

Also in the struct are:

• total_income
• total_population
• average_income
• deciles.

As above but using the iterable table interface; see: https://github.com/queryverse/IterableTables.jl weightcol and incomecol are names of columns

Make a dictionary of inequality measures. This is mainly taken from chs 5 and 6 of the World Bank book.

1. rawdata a matrix with cols with weights and incomes
2. atkinson_es inequality aversion values for the Atkinson indexes
3. generalised_entropy_alphas
4. weightpos - column with weights
5. incomepos - column with incomes

Create a dictionary of poverty measures.

This is based on the World Bank's Poverty Handbook by Haughton and Khandker.

Arguments:

• rawdata - an nxm array of real nunbers; each row is an observation; one col should be a weight, another is income; positions assumed to be 1 and 2 unless weight and incomepos are supplied
• line - a poverty line, assumed same for all obs (so income is assumed to be already equivalised)
• foster_greer_thorndyke_alphas - coefficients for FGT poverty measures; note that FGT(0) corresponds to headcount and FGT(1) to gap; count and gap are computed directly anyway but it's worth checking one against the other.
• growth is (e.g.) 0.01 for 1% per period, and is used for 'time to exit' measure.

Output is dictionary with an entry for each measure.

The measures are:

• headcount;
• gap;
• Foster Greer Thorndyke, for each of the values in foster_greer_thorndyke_alphas;
• Watts;
• time to exit, for the supplied growth rate;
• Shorrocks;
• Sen.

See World Bank, ch. 4.

As above, but using the QueryVerse IterableTables interface rawdata - basically anything resembling a dataframe; see: [https://github.com/queryverse/IterableTables.jl] throws an exception if rawdata doesn't support iterabletable interface

Internal version, once we have our datatset