Spatial Diversity in Local Government Revenue Effort under Decentralization: A Neural-Network Approach

Author:

Warner Mildred E1,Pratt James E2

Affiliation:

1. Department of City and Regional Planning, 215 W Sibley Hall, Cornell University, Ithaca, NY 14853-6701, USA

2. Department of Applied Economics and Management, Warren Hall, Cornell University, Ithaca, NY 14853, USA

Abstract

Decentralization reflects a global trend to increase the responsiveness of state and local governments to economic forces, but it raises the challenge of how to secure redistributive goals. Theoretically, as the equalizing impact of federal aid declines under devolution, we expect subnational state-level government policy to become more important, and geographic diversity in local governments' efforts to raise revenue to increase. In this paper we explore the impact of state fiscal centralization and intergovernmental aid on local revenue effort with the aid of Census of Governments data for county areas from 1987 for the Mid-Atlantic and East North Central region of the United States, with particular attention paid to rural counties. The 1987 period was chosen because it is the first year in which state policy trends diverged from federal decentralization trends and both state aid and state centralization increased while federal aid to localities continued to decline. Using a neural-network approach, we explore the spatially differentiated impact of state policy and find complementary responses in effort among some localities and substitution responses among others. Classification-tree analysis of this diversity suggests that decentralization and the competitive government it promotes are likely to exacerbate inequality among local governments.

Publisher

SAGE Publications

Subject

Management, Monitoring, Policy and Law,Public Administration,Environmental Science (miscellaneous),Geography, Planning and Development

Cited by 38 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3