{smcl}
{* 29 March 2009}{...}
{hline}
help for {hi:ineqfac}{right:Stephen P. Jenkins (March 2009)}
{hline}
{title:Inequality decomposition by factor components, following Shorrocks (1982a, b)}
{p 8 17 2} {cmd:ineqfac} {it:facvars} [{it:weights}]
[ {cmd:if} {it:exp}] [{cmd:in} {it:range} ]
[, {cmd:i2} {cmdab:s:tats} {cmdab:tot:al(}{it:newvarname}{cmd:)} ]
{p 4 4 2} {cmd:fweight}s and {cmd:aweights} are allowed; see help {help weights}.
{title:Description}
{p 4 4 2}
{cmd:ineqfac} provides an exact decomposition of the inequality of total
income into inequality contributions from each of the factor components
of total income. More specifically, given the set of factors
facvars = {factor_1 factor_2 ... factor_F},
{p 4 4 2}
define the variable totvar such that for each observation in the data set,
F
totvar = SUM (factor_f).
f=1
{p 4 4 2}
Shorrocks (1982a) proved that there was a unique 'decomposition rule'
for which inequality in totvar across observations could be expressed
as the sum of inequality contributions from each of the factor components,
and which also satisfied some other basic axioms. The decomposition rule
is the "proportionate contribution of factor f to total inequality", s_f:
s_f = rho_f*sd(factor_f)/sd(totvar)
{p 4 4 2}
where rho_f is the correlation between factor_f and totvar, and sd(.) is the
standard deviation. Equivalently, s_f is the slope coefficient from the
regression of factor_f on totvar. Observe that for each observation,
F
SUM (s_f) = 1.
f=1
{p 4 4 2}
Factor components with a positive value for s_f make a disqualizing contribution
to inequality in total income; factor components with negative s_f values make
an equalizing contribution.
{p 4 4 2}
Shorrocks shows that choice of the decomposition rule is an issue independent
of that concerning which index is used to summarise inequality. However there
happens to be a nice link with the case in which inequality is measured using
the coefficient of variation, for one can also re-write s_f as:
s_f = rho_f*[m(factor_f)/m(totvar)]*[CV(factor_f)/CV(totvar)]
{p 4 4 2}
where m(.) is the mean, and CV(.) is the coefficient of variation =
sd(.)/m(.). If m(factor_f) > 0 for every f, then also
s_f = rho_f*[m(factor_f)/m(totvar)]*[I2(factor_f)/I2(totvar)]^(.5)
{p 4 4 2}
where I2(.) = half the squared coefficient of variation (a member of the
Generalised Entropy class of inequality measures).
{p 4 4 2}
Thus total inequality can be written in terms of the factor correlations with
total income, the factor shares in total income = m(factor_f)/m(totvar), and
the factor inequalities (summarized using CV or I2).
{p 4 4 2}
{cmd:ineqfac} reports the estimates for each factor component of:
s_f, S_f = s_f*CV(totvar), m(factor_f)/m(totvar), CV(factor_f), and
CV(factor_f)/CV(totvar), plus, optionally, the correlations, means
and standard deviations of the factor components and totvar. Optionally,
inequality may be summarised using I2 rather than CV as long as m(factor_f) > 0, for every f.
{p 4 4 2}
{cmd:ineqfac} was designed as a tool for income distribution analysis in the case
where the current sample contains observations on income components for each
of a set of income receiving units (e.g. families, households, persons).
In this case, facvars might include labour income, income from investments
and pensions, cash transfers, etc. See Shorrocks (1982b) and Jenkins (1995)
for examples. {cmd:ineqfac} may also be used to summarise and compare the
riskiness of portfolios of wealth holdings: s_f has the same form as
the "beta coefficient" used in finance analysis.
{title:Options}
{p 4 8 2}
{cmd:stats} provides the means, standard deviations, and correlations
of the factor components and totvar.
{p 4 8 2}
{cmd:total(}{it:newvarname}{cmd:)} creates a new variable equal to the sum of
the factor components for each observation.
{p 4 8 2}
{cmd:i2} summarises inequality using I2 rather than CV. The option is available
only if every factor mean is positive.
{title:Saved results}
r(mean_total), r(sd_total), mean, standard deviation, variance and CV for Total
r(var_total), r(cv_total)
r(sf_f), r(mean_f), proportionate inequality contribution, mean,
r(sd_f), r(var_f), r(cv_f) standard deviation, variance, CV, and
r(share_f) share of total (mean_f/mean_total), for each factor, f.
"f" is replaced by the factor's varname.
r(N) Number of non-missing observations used
r(nfactor) Number of factor variables
r(varlist) The names of the factor variables
{title:Example}
{p 4 8 2}{cmd:. ineqfac labour invest transfer, stats}
{p 4 8 2}{cmd:. ineqfac labour invest transfer, stats total(grossi) }
{p 4 8 2}{cmd:. generate negtax = -tax }
{p 4 8 2}{cmd:. ineqfac labour invest transfer negtax, stats total(disposablei) }
{title:Author}
{p 4 4 2}Stephen P. Jenkins {break}
Institute for Social and Economic Research{break}
University of Essex, Colchester CO4 3SQ, U.K.
{title:Acknowledgement}
{p 4 8 2}This is an update of {cmd:ineqfac}, first written for Stata version 5.
Andreas Peichl beta-tested the revised code.
{title:References}
{p 4 8 2}
Jenkins, S.P. 1995. Accounting for Inequality Trends: Decomposition Analyses for the U.K., 1971-1986.
{it:Economica} 62: 29{c -}63.
{p 4 8 2}
Shorrocks, A.F. 1982a. Inequality Decomposition by Factor Components.
{it:Econometrica} 50: 193{c -}212.
{p 4 8 2}
Shorrocks, A.F. 1982b. The Impact of Income Components on the Distribution of Family Incomes.
{it:Quarterly Journal of Economics} 98: 311{c -}326.
{title:Also see}
{p 4 13 2}
{help ineqdeco} {help ineqdec0} {help ineqrbd} if installed