{smcl} {* 06Aug2017}{...} {hline} help for {hi:svyatk}{right:Biewen and Jenkins (revised August 2017)} {hline} {title:Estimation of GE inequality indices from complex survey data} {p 4 12}{cmd:svyatk} {it:varname} [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,} {cmdab:e:psilon(}{it:#}{cmd:)} {cmdab:sub:pop(}{it:varname}{cmd:)} {cmdab:l:evel(}{it:#}{cmd:)} {p}{cmd:svyatk} typed without arguments redisplays the last estimates. The level option may be used. {p}The survey design variables must be set beforehand by {cmd:svyset}, see help {help svyset}. {p}Warning: Use of {cmd:if} or {cmd:in} restrictions will not produce correct variance estimates for subpopulations in many cases. To compare estimates for subpopulations, use the {cmd:subpop()} option. {title:Description} {p}{cmd:svyatk} provides estimates of finite-population Atkinson inequality indices, together with their associated variance estimates. The Atkinson class of inequality indices, A(e), is characterized by a sensitivity parameter, e (`epsilon'), where e > 0. The program calculates A(e) for e = 0.5, 1, 1.5, and 2, and for one additional value (which defaults to e = 2.5, unless set otherwise using the {cmd:epsilon} option). The larger that e is, the more sensitive that A(e) becomes to differences at the bottom of the distribution of {it:varname}. {p}Sampling variances are calculated using a method proposed by Woodruff (1971). The derivations assume that the sample under consideration is sufficiently large that a Taylor series approximation to the index holds. For full details of the derivation of the sampling variances, see Biewen and Jenkins (2006). {p}The program may also be used to calculate sampling variances in the case where there are i.i.d. observations: see Biewen and Jenkins (2006). {p}A companion program, {cmd:svygei}, provides estimates of Generalized Entropy inequality indices, using the same methods. For estimates of the Gini Coefficient and Lorenz ordinates, see {cmd:svylorenz}. {title:Options} {p 0 4}{cmd:epsilon} allows the user to choose a value of e (default = 2.5). {p 0 4}{cmd:subpop({it:varname})} specifies that estimates be computed for the single subpopulation defined by the observations for which {it:varname}!=0. Typically, {it:varname}=1 defines the subpopulation and {it:varname}=0 indicates observations not belonging to the subpopulation. For observations whose subpopulation status is uncertain, varname should be set to missing. {title:Examples} {p 8 12}{inp:. * (1) Income inequality among individuals using household survey data with obs = individual} {p 8 12}{inp:. * Weight = individual sample weight} {p 8 12}{inp:. svyset psu_id [pweight = xewght], strata(strata_id) } {p 8 12}{inp:. svyatk income} {p 8 12}{inp:. * (2) Income inequality among individuals using household survey data with obs = individual} {p 8 12}{inp:. * weight = individual sample weight; survey PSU and strata not provided; household ID known} {p 8 12}{inp:. use income_ind, clear} {p 8 12}{inp:. svyset hh_id [pweight = xewght]} {p 8 12}{inp:. svygei income} {p 8 12}{inp:. * (3) Income inequality among individuals using survey data with obs = household; } {p 8 12}{inp:. * all persons in same household have same income; survey PSU and strata not provided } {p 8 12}{inp:. * weight = household weight x household size} {p 8 12}{inp:. use income_hh, clear} {p 8 12}{inp:. svyset [pweight = xhh_wt] } {p 8 12}{inp:. svyatk income} {title:Authors} {p 4 4}Martin Biewen, University of Tuebingen, Germany{break} {p 4 4}Stephen P. Jenkins, London School of Economics, U.K.{break} {title:Acknowledgement} {p 4 4}After we released our program in 2005, the syntax for {cmd:svyset} changed. Users could continue to use our program under version control. This update makes this redundant. We thank Philipp Poppitz for updating the program from version 8.2 to version 10. {title:References} {p 4 4} Biewen, M. and S.P. Jenkins 2006. Variance estimation for Generalized Entropy and Atkinson inequality indices: the complex survey data case. {it: Oxford Bulletin of Economics and Statistics} 68: 371{c -}383 . {p 4 4}Woodruff, R.S. 1971. A simple method for approximating the variance of a complicated estimate. {it:Journal of the American Statistical Association} 66: 411{c -}4. {title:Also see} {p 1 14}Manual: {hi:[U] 30 Overview of survey estimation}, {hi:[SVY]}{p_end} {p 0 19}On-line: help for {help svy} and, if installed, {help svygei}, {help geivars}, {help ineqdeco}, {help svylorenz}.{p_end}