000			02042nab a2200241 c 4500
999			_c150351 _d150351
003			ES-MaIEF
005			20250227110339.0
007			ta
008			250227t2024 xxk||||| |||| 00| 0 eng d
040			_aES-MaIEF _bspa _cES-MaIEF
100	1		_972350 _aJelveh, Zubin
245	1	0	_aPolitical language in economics _c Zubin Jelveh, Bruce Kogut and Suresh Naidu
504			_aBibliografía
520			_aDoes academic writing in economics reflect the political orientation of economists? We use machine learning to measure partisanship in academic economics articles. We predict the observed political behaviour of a subset of economists using phrases from their academic articles, show good out-of-sample predictive accuracy and then predict partisanship for all economists. We then use these predictions to examine patterns of political language in economics. We estimate journal-specific effects on predicted ideology, controlling for author and year fixed effects, that accord with existing survey-based measures. We show considerable sorting of economists into fields of research by predicted partisanship. We also show that partisanship is detectable even within fields, even across those estimating the same theoretical parameter. Using policy-relevant parameters collected from previous meta-analyses, we then show that imputed partisanship is correlated with estimated parameters, such that the implied policy prescription is consistent with partisan leaning. For example, we find that going from the most left-wing authored estimate of the taxable top income elasticity to the most right-wing authored estimate decreases the optimal tax rate from 77% to 60%.
650		4	_943408 _aELECCIONES
650		4	_947993 _aPENSAMIENTO POLITICO
650		4	_948489 _aECONOMISTAS
650		4	_948669 _aVALORES SOCIALES
700	1		_972351 _aKogut, Bruce
700	1		_972353 _aNaidu, Suresh
773	0		_9172583 _oOP 282/2024/662 _tThe Economic Journal _w(IEF)330 _x 0013-0133 [papel] _g v. 134, n. 662, August 2024, p. 2439-2469.
942			_cART