000			01330nab a2200217 c 4500
999			_c148386 _d148386
003			ES-MaIEF
005			20231025130225.0
007			ta
008			231025t2023 us ||||| |||| 00| 0|eng d
040			_aES-MaIEF _bspa _cES-MaIEF
100	1		_966838 _aKönig, Johannes
245		0	_aBias in tax progressivity estimates _c Johannes König
500			_aResumen.
504			_aBibliografía.
520			_aTax progressivity is central in public and political debates when questions of vertical equity are raised. Applied, structural research demands a simple way to capture it. A power function approximation delivers one parameter that captures the residual income elasticity — a summary measure of progressivity. This approximation is accurate, tractable, and interpretable, and hence immensely popular. The most common procedure to estimate this parameter, a log ordinary least squares specification, produces biased and inconsistent estimates. A nonlinear estimator solves this issue and, using different data sets, I find differences in estimates between 6 and 14 percent
650			_aIMPUESTOS _947460
650		4	_948160 _aPROGRESIVIDAD
650		4	_943299 _aELASTICIDAD IMPOSITIVA
773	0		_9170356 _oOP 233/2023/2 _tNational Tax Journal _w(IEF)86491 _x 0028-0283 _g v. 76, n. 2, June 2023, p. 267-289
942			_cART