000018325 001__ 18325 000018325 005__ 20210803101032.0 000018325 0247_ $$2doi$$a10.22004/ag.econ.18325 000018325 037__ $$a1040-2016-85050 000018325 041__ $$aeng 000018325 245__ $$aOptimal Design of a Voluntary Green Payment Program Under Asymmetric Information 000018325 260__ $$c1995 000018325 269__ $$a1995 000018325 300__ $$a23 000018325 336__ $$aWorking or Discussion Paper 000018325 490__ $$aCARD Working Paper 95-WP 131 000018325 520__ $$aGreen payment programs, where the government pays farmers directly for environmental benefits, have been proposed as an alternative to the current method of achieving environmental benefits by restricting farming practices in exchange for deficiency payments. This paper presents a voluntary green payment program using the principles of mechanism design under asymmetric information. The information asymmetry arises because government knows only the distribution of individual farmers' production situations, rather than farm-specific information. The program is applied to irrigated corn production in the Oklahoma Panhandle, where nitrogen fertilizer is a nonpoint source of pollution. We demonstrate empirically that a green payment program can increase farm income, decrease pollution, and increase the net social value of corn production relative to current deficiency payment programs. 000018325 546__ $$aEnglish 000018325 650__ $$aEnvironmental Economics and Policy 000018325 700__ $$aWu, JunJie 000018325 700__ $$aBabcock, Bruce A. 000018325 8564_ $$9f2a0d427-1b03-456d-8833-066a8c2bf54e$$s834435$$uhttps://ageconsearch.umn.edu/record/18325/files/wp950131.pdf 000018325 887__ $$ahttp://purl.umn.edu/18325 000018325 909CO $$ooai:ageconsearch.umn.edu:18325$$pGLOBAL_SET 000018325 912__ $$nMade available in DSpace on 2007-03-07T23:37:30Z (GMT). No. of bitstreams: 1 wp950131.pdf: 834435 bytes, checksum: 1c37e734fa2de109e3730527aa472c3e (MD5) Previous issue date: 1995 000018325 980__ $$a1040 000018325 982__ $$gIowa State University>Center for Agricultural and Rural Development>CARD Working Paper Series