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{* 26 May 2008}{...}
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help for {hi:ineqdeco, ineqdec0}{right:Stephen P. Jenkins (May 2008)}
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{title:Inequality indices, with optional decomposition by subgroup}
{p 8 17 2} {cmd:ineqdeco} {it:varname} [{it:weights}]
[{cmd:if} {it:exp}] [{cmd:in} {it:range}]
[, {cmdab:by:group}{cmd:(}{it:groupvar}{cmd:)}
{cmdab:w:elfare} {cmdab:s:ummarize} ]
{p 8 17 2} {cmd:ineqdec0} {it:varname} [{it:weights}]
[{cmd:if} {it:exp}] [{cmd:in} {it:range}]
[, {cmdab:by:group}{cmd:(}{it:groupvar}{cmd:)}
{cmdab:w:elfare} {cmdab:s:ummarize} ]
{p 4 4 2} {cmd:fweight}s and {cmd:aweights} are allowed; see help {help weights}.
{title:Description}
{p 4 4 2}
{cmd:ineqdeco} and {cmd:ineqdec0} estimate a range of inequality and related
indices commonly used by economists, plus decompositions of a subset of these
indices by population subgroup. Inequality decompositions by subgroup are useful
for providing inequality profiles at a point in time, and also for analyzing
secular trends using shift-share analysis. Unit record (`micro' level) data
are required. (If you have grouped (banded) data, you should not use this program
unless you know what you're doing. On estimation of inequality indices from grouped
data, see e.g. Cowell and Mehta, 1983.) Observations with values of {it:varname}
less than or equal to zero are excluded from calculations using {cmd:ineqdeco}.
By contrast, calculations using {cmd:ineqdec0} do not exclude these observations:
values of {it:varname} less than or equal to zero are valid (unless otherwise
excluded using the {cmd:if} or {cmd:in} options). As a consequence, the
portfolio of indices that is estimated by {cmd:ineqdec0} is restricted.
See below for details.
{p 4 4 2}
Inequality indices estimated by {cmd:ineqdeco} are: members of the single parameter
Generalized Entropy class GE(a) for a = -1, 0, 1, 2; the Atkinson class
A(e) for e = 0.5, 1, 2; the Gini coefficient, and the percentile ratios
{bind:p90 / p10} and {bind:p75 / p25}.
Also presented are related summary statistics such as
subgroup means and population shares. Optionally presented are indices
related to the Atkinson inequality indices, namely
equally-distributed-equivalent income Yede(e), social welfare indices W(e), and the Sen
welfare index: see below for details.
The indices estimated by {cmd:ineqdec0} are the percentile ratios
p90/p10 and p75/p25, GE(2) = half the squared coefficient of variation,
the Gini coefficient, and Sen's welfare index.
{p 4 4 2}
The inequality indices differ in their sensitivities to income differences
in different parts of the distribution. The more positive a is, the more
sensitive GE(a) is to income differences at the top of the distribution;
the more negative a is, the more sensitive it is to differences at the
bottom of the distribution. GE(0) is the mean logarithmic deviation,
GE(1) is the Theil index, and GE(2) is half the square of the coefficient
of variation. The more positive e > 0 (the 'inequality aversion parameter')
is, the more sensitive A(e) is to income differences at the bottom of
the distribution. The Gini coefficient is most sensitive to income
differences about the middle (more precisely, the mode).
{p 4 4 2}
For textbook reviews of inequality measurement from the perspective of economists,
see Cowell (1995) or Jenkins (1991). See also Cowell (2000). On the characterization of
Generalized Entropy indices, and their subgroup decomposability properties, see e.g.
Shorrocks (1984) and references therein. On the Atkinson indices, see Atkinson (1970).
The decomposition of Atkinson indices is discussed by Blackorby et al. (1981).
For extensive empirical illustrations of inequality index decomposition, see
inter alia Jenkins (1995) who also applies the decomposition of
inequality trends proposed by Mookherjee and Shorrocks (1982). Cowell and Jenkins (1995)
compare decompositions based on Generalized Entropy and Atkinson indices.
The welfare indices estimated here are discussed by Sen (1976),
and Jenkins (1997) who also provides empirical illustrations.
{p 4 4 2}
{it:groupvar} must take non-negative integer values only. To create such a
variable from an existing variable, use the {help egen} function {cmd:group}.
By default, observations with missing values on {it:groupvar} are excluded
from calculations when the {cmd:bygroup} option is specified. If you wish to
include them, create a new variable with the {help egen} function {cmd:group}
and use its {cmd:missing} option. The {help egen} function {cmd:group} is
also useful for multi-way decompositions. E.g. for a decomposition by sex
and region, create a new {it:groupvar} defining sex-region combinations by
specifying sex and region in {cmd:group(}{it:varlist}{cmd:)}.
{p 4 4 2}
Bootstrapped standard errors for the estimates of the indices can be
derived using {help bootstrap}. Standard errors derived using
linearization methods can be calculated for GE(a) using {cmd:svygei},
for A(e) using {cmd:svyatk}, and for the Gini using {cmd:svylorenz}.
{title:Technical details}
{p 4 4 2}
Consider a population of persons (or households ...), i = 1,...,n,
with income y_i, and weight w_i. Let f_i = w_i / N, where N = SUM w_i.
(In what follows all sums are over all values of whatever is subscripted.)
When the data are unweighted, w_i = 1 and N = n.
{p 4 4 2}
Arithmetic mean income is m. Suppose there is an exhaustive partition of
the population into mutually-exclusive subgroups k = 1,...,K.
{p 4 4 2}
The Generalized Entropy class of inequality indices is given by
{p 8 8 2}GE(a) = [1 / (a (a - 1)] { [SUM f_i (y_i / m)^a] - 1 },
{bind:a != 0 and a != 1,}
{p 8 8 2}GE(1) = SUM f_i (y_i / m) log(y_i / m),
{p 8 8 2}GE(0) = SUM f_i log(m / y_i).
{p 4 4 2}Each GE(a) index can be additively decomposed as
{p 8 8 2}GE(a) = GE_W(a) + GE_B(a)
{p 4 4 2}where GE_W(a) is Within-group Inequality and GE_B(a) is Between-Group
Inequality and
{p 8 8 2}GE_W(a) = SUM [v_k^(1-a)] . [s_k^a] . GE_k(a)
{p 4 4 2}
where v_k = N_k / N is the number of persons in subgroup k divided by the
total number of persons (subgroup population share), and s_k is the
share of total income held by k's members (subgroup income share).
(Strictly speaking, v_k is the sum of the weights in subgroup k divided by the
sum of the weights for the full estimation sample.)
{p 4 4 2}
GE_k(a), inequality for subgroup k, is calculated as if the subgroup were a
separate population, and GE_B(a) is derived assuming every person within
a given subgroup k received k's mean income, m_k.
{p 4 4 2}
Define the equally-distributed-equivalent income
{p 8 8 2}Yede(e) = [SUM f_i (y_i)^(1-e)]^(1 / (1 - e)), {bind:e > 0 and e != 1,}
{p 16 16 2}= exp( SUM f_i . log y_i ), e = 1.
{p 4 4 2}
The Atkinson indices are defined by
{p 8 8 2}A(e) = 1 - Yede(e) / m.
{p 4 4 2}
These indices are decomposable (but not additively decomposable):
{p 8 8 2}A(e) = A_W(a) + A_B(a) - A_W(a) . A_B(a)
{p 4 4 2}where
{p 8 8 2}A_W(a) = 1 - [SUM (v_k) . Yede_k / m] and
{p 8 8 2}A_B(a) = 1 - Yede / [SUM v_k. Yede_k ].
{p 4 4 2}Social welfare indices are defined by
{p 8 8 2}W(e) = [Yede(e)^(1-e)] / (1 - e), e > 0, e != 1;
{p 8 8 2}W(1) = log Yede(1).
{p 4 4 2}Each of these welfare indices is an increasing function of a
`generalized mean of order (1 - e)', Yede(e). All the welfare indices
are additively decomposable:
{p 8 8 2}W(e) = SUM v_k W_k(e).
{p 4 4 2}The Gini coefficient is given by
{p 8 8 2}G = 1 + (1 / N) - [2/(m . N^2)] [SUM (N - i + 1) y_i]
{p 4 4 2}
where persons are ranked in ascending order of y_i. The Gini coefficient
(and the percentile ratios) cannot be written as the sum of a term summarizing
within-group inequality and a term summarizing between-group inequality.
{p 4 4 2}
Sen's (1976) welfare index is given by
{p 8 8 2}S = m(1 - G).
{title:Options}
{p 4 8 2}
{cmd:bygroup(}{it:groupvar}{cmd:)} requests inequality decompositions by population
subgroup, with subgroup membership summarized by {it:groupvar}.
{p 4 8 2}
{cmd:welfare} requests calculation of equally-distributed-equivalent incomes and
welfare indices in addition to the inequality index calculations.
{p 4 8 2}
{cmd:summarize} requests presentation of {cmd:summary, detail} output for {it:varname}.
{title:Saved results}
r(p5), r(p10), r(p25) Percentiles p5, p10, p25,
r(p50), r(p75), r(p90) p50, p75, p90,
r(p95) p95
r(p90p10), r(p75p25) Percentile ratios p90/p10, p75/p25,
r(p25p50), r(p10p50) p25/p50, p10/p50,
r(p90p50), r(p75p50) p90/p50, p75/p50
r(gem1), r(ge0), GE(a), for a = -1, 0, 1, 2
r(ge1), r(ge2)
r(gini) Gini coefficient
r(ahalf), r(a1), r(a2) A(e), for e = 0.5, 1, 2
r(mean), r(sd), r(Var) mean, standard deviation, variance
r(min), r(max) minimum, maximum
r(N), r(sumw) Number of observations, sum of weights
{p 4 4 2}If the {cmd:welfare} option is specified, also saved are:
r(edehalf), r(ede1) Yede(e) for e = 0.5, 1, 2
r(ede2)
r(whalf), r(w1) W(e) for e = 0.5, 1, 2, and
r(w2), r(wgini) Sen's welfare measure
{p 4 4 2}If the {cmd:bygroup} option is specified, also saved are:
r(within_gem1) GE_W(a), for a = -1, 0, 1, 2
r(within_ge0)
r(within_ge1)
r(within_ge2)
r(between_gem1) GE_B(a), for a = -1, 0, 1, 2
r(between_ge0)
r(between_ge1)
r(between_ge2)
r(within_ahalf) A_W(a), for e = 0.5, 1, 2
r(within_a1)
r(within_a2)
r(between_ahalf) A_B(a), for e = 0.5, 1, 2
r(between_a1)
r(between_a2)
r(gem1_k), r(ge0_k) GE_k(a), for a = -1, 0, 1, 2, and
r(ge1_k), r(ge2_k) each subgroup k, where the values of k
correspond to the values of {it:groupvar}
in the estimation sample. See r(levels) below.
r(ahalf_k), r(a1_k) A_k(a), for a = 0.5, 1, 2, and
r(a2_k) each subgroup k
r(gini_k) Gini for each subgroup k
r(mean_k), r(lambda_k) subgroup mean (m_k), and relative mean (m_k/m)
r(lgmean_k) subgroup log mean, log(m_k)
r(theta_k) subgroup income share, s_k
r(sumw_k) subgroup sum of weights
r(v_k) subgroup population share, v_k
r(levels) macro containing the set of values of {it:groupvar}
(the number of unique values = K)
{p 4 4 2}If the {cmd:welfare} option is specified, also saved are:
r(whalf_k), r(w1_k) W_k(a), for a = 0.5, 1, 2, and
r(w2_k) each subgroup k
r(edehalf_k), r(ede1_k) Yede_k(a), for a = 0.5, 1, 2, and
r(ede2_k), r(wgini_k) Sen's welfare measure, for each subgroup k
{p 4 4 2}For the convenience of users of earlier versions of these programs,
a selected set of estimates is also saved in global macros, as follows.
S_9010, S_7525 Percentile ratios p90/p10, p75/p25
S_im1, S_i0, S_i1, S_i2 GE(a), for a = -1, 0, 1, 2
S_gini Gini coefficient
S_ahalf, S_a1, S_a2 A(e), for e = 0.5, 1, 2
{title:Examples}
{p 4 8 2}{cmd:. ineqdeco x [aw = wgtvar]}
{p 4 8 2}{cmd:. ineqdec0 x [aw = wgtvar]}
{p 4 8 2}{cmd:. ineqdeco x, by(famtype) w}
{p 4 8 2}{cmd:. ineqdeco x if sex==1, w s}
{p 4 8 2}{cmd:. // bootstrapped standard errors for Gini in Stata version 8}
{p 4 8 2}{cmd:. preserve}
{p 4 8 2}{cmd:. keep if x > 0 & x < .}
{p 4 8 2}{cmd:. version 8: bootstrap "ineqdeco x" gini = r(gini), reps(100)}
{p 4 8 2}{cmd:. restore}
{p 4 8 2}{cmd:. // bootstrapped standard errors for Gini in Stata version 9}
{p 4 8 2}{cmd:. preserve}
{p 4 8 2}{cmd:. keep if x > 0 & x < .}
{p 4 8 2}{cmd:. bootstrap gini = r(gini), reps(100): ineqdeco x }
{p 4 8 2}{cmd:. restore}
{p 4 8 2}{cmd:. // multi-way decomposition}
{p 4 8 2}{cmd:. egen sexXregion = group(sex region) }
{p 4 8 2}{cmd:. ineqdeco x, by(sexXregion) }
{p 4 4 2}
Further examples are provided in the downloadable materials accompanying the
presentation by {browse "http://econpapers.repec.org/paper/bocasug06/16.htm":Jenkins (2006)}.
{title:Author}
{p 4 4 2}Stephen P. Jenkins {break}
London School of Economics
{title:Acknowledgements}
{p 4 4 2}For comments and suggestions, I am grateful to Philippe Van Kerm and Nick Cox.
Thanks also to Johannes Giesecke and Austin Nichols who drew attention to a bug in the subgroup
decomposition calculations that arose when if/in qualifiers led to no observations in one of more
of the by groups. Austin provided code to fix the bug.
{title:References}
{p 4 8 2}
Atkinson, A.B. 1970.
On the measurement of inequality.
{it:Journal of Economic Theory} 2: 244{c -}63.
{p 4 8 2}
Blackorby, C., Donaldson, D., and Auersperg, M. 1981.
A new procedure for the measurement of inequality
within and between population subgroups.
{it:Canadian Journal of Economics}
14: 665{c -}85.
{p 4 8 2}
Cowell, F.A. 1995.
{it:Measuring Inequality.}
Hemel Hempstead:
Prentice-Hall/Harvester-Wheatsheaf.
{p 4 8 2}
Cowell, F.A. 2000.
Measurement of inequality.
In {it:Handbook of Income Distribution Volume 1},
eds A.B. Atkinson and F. Bourguignon.
Amsterdam: Elsevier Science, 59{c -}85.
{p 4 8 2}
Cowell, F.A. and Jenkins, S.P. 1995.
How much inequality can we explain? A methodology and an application to the USA.
{it:Economic Journal} 105: 421{c -}430.
{p 4 8 2}
Cowell, F.A. and Mehta, F. 1983.
The estimation and interpolation of inequality measures.
{it:Review of Economic Studies} 49: 273{c -}290.
{p 4 8 2}
Jenkins. S.P. 1991.
The measurement of income inequality.
In L. Osberg (ed.)
{it:Economic Inequality and Poverty: International Perspectives.}
Armonk, NY: M.E. Sharpe.
{p 4 8 2}
Jenkins, S.P. 1995.
Accounting for inequality trends: decomposition analyses for the UK, 1971-86.
{it:Economica} 62: 29{c -}63.
{p 4 8 2}
Jenkins, S.P. 1997.
Trends in real income in Britain: a microeconomic analysis.
{it:Empirical Economics} 22: 483{c -}500.
{p 4 8 2}
Jenkins, S.P. 2006. Estimation and interpretation of measures of inequality,
poverty, and social welfare using Stata. Presentation at North American
Stata Users' Group Meetings 2006, Boston MA.
{browse "http://econpapers.repec.org/paper/bocasug06/16.htm"}.
{p 4 8 2}
Mookherjee, D. and Shorrocks, A. 1982.
A decomposition analysis of the trend in UK inequality.
{it:Economic Journal} 92: 886{c -}992.
{p 4 8 2}
Sen, A.K. 1976.
Real national income.
{it:Review of Economic Studies} 43: 19{c -}39.
{p 4 8 2}
Shorrocks, A.F. 1984.
Inequality decomposition by population subgroups.
{it:Econometrica} 52: 1369{c -}88.
{title:Also see}
{p 4 13 2}
{help inequal7} if installed;
{help sumdist} if installed;
{help svylorenz} if installed;
{help svygei} if installed;
{help svyatk} if installed;
{help povdeco} if installed.