000			01594nab a2200241 c 4500
999			_c150258 _d150258
003			ES-MaIEF
005			20250211090110.0
007			ta
008			250211t2024 xxu||||| |||| 00| 0eng d
040			_aES-MaIEF _bspa _cES-MaIEF
100	1		_972299 _aBryan, Gharad
245	1	0	_aBig loans to small businesses _bpredicting winners and losers in an entrepreneurial lending experiment _c Gharad Bryan, Dean Karlan and Adam Osman
504			_aBibliografía.
520			_aWe experimentally study the impact of relatively large enterprise loans in Egypt. Larger loans generate small average impacts, but machine learning using psychometric data reveals "top performers" (those with the highest predicted treatment effects) substantially increase profits, while profits drop for poor performers. The large differences imply that lender credit allocation decisions matter for aggregate income, yet we find existing practice leads to substantial misallocation. We argue that some entrepreneurs are overoptimistic and squander the opportunities presented by larger loans by taking on too much risk, and show the promise of allocations based on entrepreneurial type relative to firm characteristics.
650		4	_948108 _aPRESTAMOS
650		4	_943277 _aEGIPTO
650		4	_954507 _aEMPRENDEDORES
650		4	_948254 _aRENDIMIENTOS DE ACTIVIDADES EMPRESARIALES Y PROFESIONALES
700			_9946 _aKarlan, Dean
700	1		_972300 _aOsman, Adam
773	0		_9172579 _oOP 234/2024/9 _tThe American Economic Review _w(IEF)103372 _x 0002-8282 _g v. 114, n. 9, September 2024, p. 2825-2860.
942			_cART