help pshare

Title

pshare -- Compute and graph percentile shares

Syntax

Percentile share estimation:

pshare [estimate] varlist [if] [in] [weight]
[,
estimate_options
]

Computing contrasts between outcome variables or subgroups:

pshare contrast [base]
[,
contrast_options
]

where base is the name of the outcome variable or the value of the
subpopulation to be used as base. base may also be #1, #2,
or #3, etc. to refer to the 1st, 2nd, or 3rd, etc. outcome variable
or subpopulation. See the contrast() option of
pshare estimate for more details

Drawing a stacked bar chart of the results:

pshare stack
[,
stack_options
]

Drawing a histogram of the results:

pshare histogram
[,
histogram_options
]

estimate_options Description

Main
proportion report shares as proportions; the default

percent report shares as percentages

density report shares as densities

sum report shares as sums (outcome totals)

average report shares as averages

generalized report generalized shares

normalize(spec) normalize
results with respect to the specified total

gini also report Gini coefficient(s)

Percentiles
nquantiles(#) use # percentile groups of equal size; default
is nquantiles(5) (quintiles)

percentiles(numlist) use percentile groups corresponding to the
specified cumulative percentages

pvar(pvar) base percentile groups on
pvar instead of the outcome variable

step determine lorenz ordinates from step function; the default
is to employ linear interpolation

Over
over(varname) compute results for subpopulations defined by
the values of varname

total include overall results across all subpopulations; only
allowed with over()

Contrast/Graph
contrast[(spec)] compute
differences in percentile shares between outcome variables or subpopulations

stack[(options)] draw
a stacked bar chart of the results; options are
stack_options as described below

histogram[(options)] draw
a histogram of the results; options are
histogram_options as described below

SE/SVY
vce(vcetype) vcetype may
be analytic (the default), cluster clustvar,
bootstrap or jackknife

cluster(clustvar) synonym for
vce(cluster clustvar)

svy[(subpop)] take account
of survey design as set by svyset, optionally restricting
computations to subpop

nose supress computation of standard errors and confidence
intervals

Reporting
level(#) set confidence level; default is level(95)

noheader suppress output header

notable suppress output table

nogtable suppress table of Gini coefficients

display_options standard
reporting options as described in
[R] estimation options

pweights, iweights, and fweights are allowed; see weight.

contrast_options Description

ratio compute ratios instead of differences

lnratio compute logarithms of ratios instead of differences

stack[(options)] draw
a stacked bar chart of the results; options are
stack_options as described below

histogram[(options)] draw
a histogram of the results; options are
histogram_options as described below

display_options standard
reporting options as described in
[R] estimation options

stack_options Description

Main
vertical vertical bar plot

horizontal horizontal bar plot; the default

proportion population axis displays proportion, not percent

reverse order percentile groups from top to bottom, not from
bottom to top

keep(list) select and order
outcome variables or subpopulations to be included in the graph

sort[(options)] order
outcome variables or subpopulation by size of top share or as specified by
options

gini(%fmt) specify format for Gini
coefficients; default format is %9.3g

nogini omit Gini coefficients

Labels/Rendering
labels(labels) specify
custom axis labels for outcome variables/subpopulations

plabels(labels) specify
custom legend labels for percentile groups

barwidth(#) set width of bars; default is barwidth(0.75)

barlook_options affect rendition of the plotted bars

p#(barlook_options) affect rendition of #th segment of the stacked bars

values[(%fmt)] include
values of percentile shares as marker labels

marker_label_options affect rendition of the values included as marker labels

Add plots
"addplot(addplot_option:plot)" add other plots to the graph

Y-Axis, X-Axis, Title, Caption, Legend, Overall
twoway_options any options other than by()
documented in [G] twoway_options

histogram_options Description

Main
vertical vertical bar plot; the default

horizontal horizontal bar plot

proportion population axis in proportion, not percent

keep(list) select and order
results to be included as subgraphs

max(#[, options]) truncate
bars from above

min(#[, options]) truncate
bars from below

prange(min max) restrict range of percentile
groups to be included in the graph

gini(%fmt) specify format for Gini
coefficients; default format is %9.3g

nogini omit Gini coefficients from subgraph labels

Labels/Rendering
barlook_options affect rendition of the plotted bars

step draw results as step function instead of bars

addstep[(line_opts)] draw results as step function in addition to bars

spikes[(#)] draw a series of spikes instead
of bars; implies noci

labels(labels) specify
custom labels for subgraphs

byopts(byopts) specify how
subgraphs are combined

overlay combine results in single plot instead of using
subgraphs; implies noci

o#(options) affect rendition
of #th plot; for use with overlay

psep[(labels)] use different
styling for each percentile group

p#(options) affect rendition
of #th plot; for use with psep()

Confidence intervals
level(#) set confidence level; not allowed if ci() is
bc, bca, or percentile

ci(citype) choose type of
bootstrap CI; citype may be normal (the default), bc,
bca, or percentile

ciopts(options) affect
rendition of the plotted confidence spikes; see help
graph twoway rcap

cibelow place the confidence interval spikes behind the
plotted bars

noci omit confidence intervals

Add plots
"addplot(addplot_option:plot)" add other plots to the graph

Y-Axis, X-Axis, Title, Caption, Legend, Overall
twoway_options any options other than by()
documented in [G] twoway_options

Description

pshare estimate computes percentile shares or, more generally,
quantile shares for one or several outcome variables or subpopulations from
individual level data (grouped data is not supported). Percentile shares
are often used in inequality research to study the distribution of income
or wealth. They are defined as differences between Lorenz ordinates of the
outcome variable. Technically, the observations are sorted in increasing
order of the outcome variable and the specified percentiles (quintiles by
default) are computed from the running sum of the outcomes. Percentile
shares are then computed as differences between percentiles, divided by
total outcome (for methodological details see Jann 2016).

Given the results form pshare estimate for several outcome variables
or subpopulations, pshare contrast computes differences in
percentile shares between outcome variables or subpopulations.

pshare stack draws a stacked bar chart of the results from
pshare estimate or pshare contrast. One stacked bar is drawn
for each outcome variable or subpopulation.

pshare histogram plots the results from pshare estimate or
pshare contrast as a histogram. In case of multiple outcome
variables or multiple subpopulations, several subgraphs are
drawn. Confidence intervals are included as capped spikes.

pshare without arguments replays the previous results. Reporting
options may be applied.

Options for pshare estimate

Main

proportion, percent, density, sum, average,
and generalized select the type of results to be computed. The
default is proportion, that is, to report percentile shares as
proportions. Use option percent to report percentile shares as
percentages. Furthermore, use option density to report densities,
defined as outcome shares divided by population shares (so that in a bar
chart the areas of the bars are proportional to the outcome shares).
Outcome sums (totals) and average outcomes can be requested by options
sum and average, respectively. Finally, use option
generalized to report generalized percentile shares, defined as
differences between generalized Lorenz ordinates. Only one of
proportion, percent, density, sum, average,
or generalized is allowed.

normalize(spec) causes results to be normalized with
respect to the specified total (not allowed in combination with sum,
average, or generalized). spec is

[over:][total]

where over may be

. the subpopulation at hand (the default)
# the subpopulation identified by value #
## the #th subpopulation
total the total across all subpopulations

and total may be

. the total of the variable at hand (the default)
* the total of the sum across all analyzed outcome variables
varlist the total of the sum across the variables in varlist
# a total equal to #

total specifies the variable(s) from which the total is to be
computed, or sets the total to a fixed value. If multiple variables are
specified, the total across all specified variables is used (varlist may
contain external variables that are not among the list of analyzed outcome
variables). over selects the reference population from which the total
is to be computed; over is only allowed if the over() option has
been specified (see below). Subpopulation sizes (sum of weights) are taken
into account for the computation of densities (option density) if
over is provided, so that the densities reflect
multiples of the average outcome in the reference population.

gini causes Gini coefficients (a.k.a. concentration indices if
pvar() is specified) to be computed and reported in a separate table.
Variance estimation for Gini coefficients is not supported.

Percentiles

nquantiles(#) specifies the number of (equally sized) percentile
groups to be used. The default is to use quintiles, that is,
nquantiles(5). This is equivalent to typing
percentiles(20 40 60 80).

percentiles(numlist) specifies, as percentages, the percentiles to
be used as threshold for the percentile groups. For example, for deciles
type percentiles(10 20 30 40 50 60 70 80 90), or, as a shorthand,
percentiles(10(10)90). To compute shares of the bottom 50%, 50-90%,
90-95%, 95-99%, 99-99.9%, and the top 0.1%, for example, you could type
percentiles(50 90 95 99 99.9).

pvar(pvar) causes the percentile groups to
be based on variable pvar instead of the outcome variable. That is,
observations will be sorted in increasing order of pvar and
percentiles will be determined from the running sum of the outcome variable
across this sort order (using averaged values within ties of pvar).
Use this option to analyze relations between different variables (e.g. how
wealth is distributed across different income groups). If pvar() is
specified, the computed percentile shares correspond to differences between
ordinates of the "concentration curve" of the outcome variable with respect
to pvar.

step causes the Lorenz ordinates to be determined from the step
function of cumulative outcomes. The default is to employ linear
interpolation in regions where the step function is flat.

Over

over(varname) reports results for each subpopulation defined by the
values of varname. Only one outcome variable is allowed if
over() is specified.

total causes additional overall results across all subpopulations to
be reported. total is only allowed if over() is specified.

Contrast/Graph

contrast[(spec)] causes differences in percentile
shares to be computed between outcome variables or between subpopulations,
where spec is

[base] [, ratio lnratio ]

To report contrasts as ratios instead of differences, specify the ratio
suboption; to report contrast as logarithms of ratios, specify the
lnratio suboption.

If over() is specified together with total, the default is to
use the overall total across subpopulations as base for the contrasts. In all
other cases, the default is to compute adjacent contrasts (i.e. using the
preceding outcome variable or subpopulation as base). Alternatively,
specify base to select the base for the contrasts.

In case of multiple outcome variables, base is the name of the outcome
variable to be used as base. For example,

. pshare estimate y1990 y2000 y2010, contrast(y1990)

computes differences in percentile shares with respect to y1990.
Likewise, if over() is specified, base is the value of the
subpopulation to be used as base. For example,

. pshare estimate wage, over(race) contrast(1)

computes differences with respect to race==1. Alternatively, base
may also be #1, #2, #3, etc. to use the 1st, 2nd, 3rd,
etc. outcome variable or subpopulation as the base for the contrasts. For
example,

. pshare estimate wage, over(race) contrast(#2)

uses the second subpopulation as base for the contrasts.

stack[(options)] draws
a stacked bar chart of the results. options are as described for
pshare stack below.

histogram[(options)] draws
a histogram of the results. options are as described for
pshare histogram below.

SE/SVY

vce(vcetype) determines how standard errors and confidence intervals
are computed. vcetype may be:

analytic
cluster clustvar
bootstrap [, bootstrap_options]
jackknife [, jackknife_options]

The default is vce(analytic), using approximate formulas for variance
estimation assuming independent data. For clustered data, specify
vce(cluster clustvar), where clustvar is the variable
identifying the clusters. Methods and formulas are based on Binder and
Kovacevic (1995; also see Kovacevic and Binder 1997). For bootstrap and
jackknife estimation, see help vce_option. Variance estimation
is not supported if iweights or fweights are specified.

cluster(clustvar) is a synonym for vce(cluster clustvar).

svy[(subpop)] causes the survey design to be taken
into account for variance estimation. Methods and formulas are based on
Binder and Kovacevic (1995). The data need to be set up for survey
estimation; see help svyset. Specify subpop to restrict survey
estimation to a subpopulation, where subpop is

[varname] [if]

The subpopulation is defined by observations for which varname!=0 and
for which the if condition is met. See help svy and
[SVY] subpopulation estimation for more information on subpopulation
estimation.

The svy option of pshare only works if the variance
estimation method is set to Taylor linearization by svyset (the
default). For other variance estimation methods you may use the usual svy
prefix command. For example, you could type svy brr: pshare ... to
use BRR variance estimation. pshare does not allow the svy
prefix for Taylor linearization due to technical reasons. This is why the
svy option is provided.

nose suppresses the computation of standard errors and confidence
intervals. Use the nose option to speed-up computations when analyzing
population data. The nose option may also be useful to speed-up computations with
prefix commands that use replication techniques for variance estimation,
such as, e.g., svy jackknife. Options vce(bootstrap) and
vce(jackknife) imply nose.

Reporting

level(#) specifies the confidence level, as a percentage, for
confidence intervals. The default is level(95) or as set by
set level.

noheader suppresses the output header; only the coefficient table is
displayed.

notable suppresses the coefficient table.

nogtable suppresses the table containing Gini coefficients.

display_options are standard reporting options such as cformat(),
pformat(), sformat(), or coeflegend. See
[R] estimation options.

Options for pshare contrast

ratio causes contrasts to be reported as ratios. The default is to
report contrasts as differences.

lnratio causes contrasts to be reported as logarithms of ratios. The
default is to report contrasts as differences.

stack[(options)] draws
a stacked bar chart of the results. options are as described for
pshare stack below.

histogram[(options)] draws
a histogram of the results. options are as described for
pshare histogram below.

display_options are standard reporting options such as cformat(),
pformat(), sformat(), or coeflegend. See
[R] estimation options.

Options for pshare stack

Main

vertical and horizontal specify whether a vertical or a
horizontal bar plot is drawn. The default is to draw a horizontal bar plot.

proportion scales the population axis as proportion
(0 to 1). The default is to scale the axis as percentage (0 to
100).

reverse orders percentile groups from top to bottom (the richest
are leftmost, the poorest are rightmost). The default is to
order percentile groups from bottom to top (the poorest
are leftmost, the richest are rightmost).

keep(list) selects and orders the results to be included as
separate bars. Use keep() with multiple outcome variables or
subpopulations. In case of multiple outcome variables, spec is a list
of the names of the outcome variables to be included. In case of
over(), list is a list of the values of the subpopulations to be
included. list may also contain total for the overall results
(if overall results were requested). Furthermore, list may also
contain elements such as #1, #2, #3, etc. to refer to the
1st, 2nd, 3rd, etc. outcome variable or subpopulation. See the
keep() option of pshare histogram for examples.

sort[(options)] orders the bars for the
different outcome variables or subpopulation by the level of inequality.
If sort is specified without argument, the bars are sorted in
ascending order of the outcome shares of the top percentile group. The
options for alternative sorting are:

gini sort by Gini coefficients
descending sort in descending order
tlast place total last
tfirst place total first

gini(%fmt) sets the format for the Gini coefficients
included in the graph as secondary axis labels; see help format. The
default format is %9.3g. Gini coefficients will only
be included if information on Gini coefficients is available in the
provided results (i.e. if the gini option has been applied to
pshare estimate).

nogini suppresses the Gini coefficients. This is only relevant if the
gini option has been specified when calling pshare estimate.

Labels/Rendering

labels(labels) specifies custom axis labels for the included outcome
variables or subpopulations. The default is to use the variable labels of
the outcome variables or the value labels of the subpopulations,
respectively. spec is a list of labels that are applied one-by-one to
the displayed bars (from top to bottom, or left to right). Use quotes if a
label contains spaces, e.g. labels("label one" "label two" ...). Type
empty string to use the default label for a specific bar. For example,
labels("label 1" "" "label 3") specifies custom labels for the first
and third bars, and uses default labels for the other bars.

plabels(labels) specifies custom labels for percentile groups in the
legend. The default is to use labels composed of the values of
the lower bound and the upper bound of the group. spec is a list of
labels that are applied one-by-one to the groups (from left to right, or
bottom to top). Use quotes if a label contains spaces,
e.g. labels("label one" "label two" ...). Type empty
string to use the default label for a specific group. For example,
labels("label 1" "" "label 3") specifies custom labels for the first
and third groups, and uses default labels for the other groups.

barwidth(#) sets the width of the bars as proportion of the spacing
between bar positions. The default is barwidth(0.75), leaving white
space of 1/3 barwidth between the bars.

barlook_options are options that affect the rendition of the plotted
bars. See [G] barlook_options.

p#(barlook_options) affects the rendition of #th segment of the
stacked bars (corresponding to the #th percentile group). barlook_options
are as described in [G] barlook_options. For
example, to use khaki colored bars for the 3rd percentile group, type
p3(color(khaki)), or, to print a thick red border around the bars of the 5th
percentile group, type p5(lcolor(red) lwidth(thick)). If the values()
option has been specified, marker_label_options are allowed
within p#().

values[(%fmt)] prints the values of the percentile
shares as marker labels at the center of the bar segments and, optionally,
set the display format for the values; see help format. The
default format is %9.3g. Use marker_label_options to
affect the rendition of the labels. To use differential rendition by bar
segment, specify marker_label_options within the p#() options
(see above).

marker_label_options are options that affect the rendition of the values
included as marker labels using the values() option. See
[G] marker_label_options. Do not use
mlabel() or mlabvposition().

Add plots

addplot(plot) provides a way to add other plots to the generated
graph. See [G] addplot_option.

Y-Axis, X-Axis, Title, Caption, Legend, Overall

twoway_options are general twoway options, other than by(), as
documented in [G] twoway_options.

Options for pshare histogram

Main

vertical and horizontal specify whether a vertical or a
horizontal bar plot is drawn. The default is to draw a vertical bar plot.

proportion scales the population axis in terms of proportions
(0 to 1). The default is to scale the axis in terms of percentages (0 to
100).

keep(list) selects and orders the results to be included as
subgraphs. Use keep() if pshare estimate has been applied to
multiple outcome variables or subpopulations. In case of multiple outcome
variables, list is a list of the names of the outcome variables to be
included. Example:

. pshare estimate y1990 y2000 y2010
. pshare graph, keep(y2010 y1990)

In case of over(), list is a list of the values of the
subpopulations to be included. list may also contain total for
the overall results (if overall results were requested). Example:

. pshare estimate wage, over(race) total
. pshare graph, keep(total 1 2)

Furthermore, list may also contain elements such as #1,
#2, #3, etc. to refer to the 1st, 2nd, 3rd, etc. outcome
variable or subpopulation. Example:

. pshare estimate wage, over(race)
. pshare graph, keep(#1 #3)

max(#[, options]) top-codes results at #
(i.e. truncates the bars from above);
min(#[, options]) bottom-codes results at #
(i.e. truncates the bars from below). This is useful if there are large
differences in the plotted values and you want to restrict the axis
range. The truncated values will be included in the graph as marker
labels. options are:

format(%fmt) set the format (default is %9.3g)
marker_label_options affect rendition of the labels
nolabels omit the marker labels

prange(min max) restricts the range of the percentile
groups to be included in the graph. Only results for percentile groups
whose lower and upper cumulative population bounds (in percent) are within
min and max will be plotted. min and max must
be within [0,100]. For example, to include only the lower half of the
distribution, type prange(0 50).

gini(%fmt) sets the format for the Gini coefficients included in the
subgraph labels; see help format. The default format is
%9.3g. Gini coefficients will only be included if information on Gini
coefficients is available in the provided results (i.e. if the gini
option has been applied to pshare estimate).

nogini suppresses the Gini coefficients. This is only relevant if the
gini option has been specified when calling pshare estimate.

Labels/Rendering

barlook_options are options that affect the rendition of the plotted
bars. See [G] barlook_options.

step causes a step function to be drawn instead of histogram bars.

addstep[(line_options)]
causes a step function to be drawn on top of the histogram bars. Specify
line_options to affect the rendering of the step function line; see
[G] line_options.

spikes[(#)] causes (equally spaced) spikes to be
drawn instead of histogram bars. # specifies the number of spikes. The
default is to draw 100 spikes, one for each percentile. Specifying
spikes implies noci (see below).

labels(labels) specifies custom labels for the included subgraphs.
The default is to use the variable labels of the outcome variables or the
value labels of the subpopulations, respectively. labels is a list of
labels that are applied one-by-one to the subgraphs. Use quotes if a label
contains spaces, e.g. labels("label one" "label two" ...). Type
empty string to use the default label for a specific subgraph. For example,
labels("label 1" "" "label 3") specifies custom labels for the
first and third subgraphs, and uses default labels for the other subgraphs.

byopts(byopts) determines how subgraphs are combined. byopts
are as described in [G] by_option.

overlay causes results from the different outcome variables or
subpopulations to be included in the same graph instead of using separate
subgraphs. overlay and psep() are not both allowed. Specifying
overlay implies noci (see below).

o#(options) affects the rendition of the bars of the #th outcome
variable or subpopulation if overlay has been specified. options are:

step draw step function instead of bars
addstep[(line_opts)] draw step function in addition to bars
barlook_options affect rendition of the plotted bars

psep[(labels)] causes different rendering to be used
for each percentile group and includes a corresponding legend in the
graph. The default is to draw all bars in the same style. psep() and
overlay are not both allowed.

p#(options) affects the rendition of the bars of the #th
percentile group if psep() has been specified. options are:

barlook_options affect rendition of the plotted bars
ciopts(options) affect rendition of the confidence spikes

Confidence intervals

level(#) specifies the confidence level, as a percentage, for
confidence intervals. The default is the level that has been used for
computing the pshare results. level() cannot be used together
with ci(bc), ci(bca), or ci(percentile). To change the
level for these confidence intervals, you need to specify level()
when computing the results.

ci(citype) chooses the type of CI to be plotted for results that have
been computed using the bootstrap technique. citype may be:

normal normal-based CIs; the default
bc bias-corrected CIs
bca bias-corrected and accelerated CIs
percentile percentile CIs

bca is only available if BCa confidence intervals have been requested
when running pshare estimate.

ciopts(options) specifies options that affect the rendition of the
plotted confidence spikes, e.g. line_options. The available set
of options depends on plot type. The default plot type is capped spikes; see
help graph twoway rcap. Use the recast() option to
change the plot type. For example, type ciopts(recast(rspike)) for
(uncapped) spikes; see graph twoway rspike. Available plot types are
range plots as listed in twoway.

cibelow causes the confidence interval spikes to be placed behind the
plotted bars. The default is to draw the spikes in front of the bars.

noci omits confidence interval spikes from the plot.

Add plots

addplot(plot) provides a way to add other plots to the generated
graph. See [G] addplot_option.

Y-Axis, X-Axis, Title, Caption, Legend, Overall

twoway_options are general twoway options, other than by(), as
documented in [G] twoway_options.

Examples

. sysuse nlsw88
. pshare estimate wage
. pshare histogram

. pshare estimate wage, percentiles(20 40 60 70 80 90 95 97 99) density
. pshare histogram, yline(1)

. pshare estimate wage, percentiles(20 40 60 70 80 90 95 97 99) density vce(bootstrap)
. pshare histogram, yline(1)

. pshare estimate wage, over(union)
. pshare histogram, yline(1)

. pshare estimate wage, over(union)
. pshare contrast 0
. pshare histogram, yline(0)

. pshare estimate wage, over(industry) total gini
. pshare stack, sort(gini tlast descending)

For further examples see Jann (2016).

Stored results

pshare estimate stores the following in e():

Scalars

e(N) number of observations

e(N_over) number of subpopulations

e(N_clust) number of clusters

e(k_eq) number of equations in e(b)

e(bins) number of bins (percentile groups) per equation

e(df_r) sample degrees of freedom

e(rank) rank of e(V)

e(level) confidence level for CIs

Macros

e(cmd) pshare

e(cmdline) command as typed

e(depvar) name(s) of outcome variable(s)

e(pvar) name of variable specified in pvar()

e(type) proportion, percent, density, sum, average, or generalized

e(norm) # or names of reference variables or empty

e(normpop) total or overvar = # or empty

e(percentiles) percentile thresholds

e(step) step or empty

e(gini) gini or empty

e(over) name of over() variable

e(over_namelist) values from over() variable

e(over_labels) labels from over() variable

e(total) total or empty

e(contrast) contrast or empty

e(baseval) + or value/name of base for contrasts

e(ratio) ratio or empty

e(lnratio) lnratio or empty

e(wtype) weight type

e(wexp) weight expression

e(clustvar) name of cluster variable

e(vce) vcetype specified in vce()

e(vcetype) title used to label Std. Err.

e(title) title in estimation output

e(properties) b V or b

Matrices

e(b) estimates (proportions, percent, densities, sums, or averages)

e(V) variance-covariance matrix of estimates

e(_N) numbers of observations in subpopulations

e(G) Gini coefficients (if gini is specified)

e(L_ul) upper bounds of Lorenz ordinates

e(L_ll) lower bounds of Lorenz ordinates

e(prop) population proportions

e(ul) upper bounds of cumulative population percentages

e(ll) lower bounds of cumulative population percentages

e(mid) midpoints of cumulative population percentages

Functions

e(sample) marks estimation sample

If the svy option is specified, various additional results as described
in help svy are stored in e().

References

Binder, D. A., M. S. Kovacevic (1995). Estimating Some Measures of Income
Inequality from Survey Data: An Application of the Estimating Equations
Approach. Survey Methodology 21(2): 137-145.

Jann, B. (2016). Assessing inequality using percentile shares. The
Stata Journal 16(2): 264–300. (working paper)

Kovacevic, M. S., D. A. Binder (1997). Variance Estimation for
Measures of Income Inequality and Polarization - The Estimating Equations
Approach. Journal of Offcial Statistics 13(1): 41-58.

Author

Ben Jann, University of Bern, jann@soz.unibe.ch

Thanks for citing this software as follows:

Jann, B. (2015). pshare: Stata module to compute and graph percentile shares. Available from
http://ideas.repec.org/c/boc/bocode/s458036.html.

Also see

Online: help for
pctile,
graph twoway bar

From the SSC Archive:
lorenz,
sumdist,
svylorenz