Add wos sample results library

02-data/intermediate/wos_sample/0f3f7044962d1d1ee205317aef4590dc-fu-chao-and-wolpin/info.yaml

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+abstract: 'We develop a model of crime in which the number of police, the crime
+
+  rate, the arrest rate, the employment rate, and the wage rate are joint
+
+  outcomes of a subgame perfect Nash equilibrium. The local government
+
+  chooses the size of its police force and citizens choose among work,
+
+  home, and crime alternatives. We estimate the model using metropolitan
+
+  statistical area (MSA)-level data. We use the estimated model to examine
+
+  the effects on crime of targeted federal transfers to local governments
+
+  to increase police. We find that knowledge about unobserved MSA-specific
+
+  attributes is critical for the optimal allocation of police across
+
+  MSA''s.'
+affiliation: 'Fu, C (Corresponding Author), Univ Wisconsin, Madison, WI 53706 USA.
+
+  Fu, Chao, Univ Wisconsin, Madison, WI 53706 USA.
+
+  Wolpin, Kenneth, I, Rice Univ, Houston, TX 77251 USA.
+
+  Wolpin, Kenneth, I, Univ Penn, Philadelphia, PA 19104 USA.'
+author: Fu, Chao and Wolpin I, Kenneth
+author_list:
+- family: Fu
+  given: Chao
+- family: Wolpin I
+  given: Kenneth
+da: '2023-09-28'
+doi: 10.1093/restud/rdx068
+eissn: 1467-937X
+files: []
+issn: 0034-6527
+journal: REVIEW OF ECONOMIC STUDIES
+keywords: Crime; Multiple equilibria; Estimation; Efficient police allocation
+keywords-plus: 'SEARCH MODEL; EDUCATION; MARKET; IDENTIFICATION; UNEMPLOYMENT;
+
+  DETERRENCE; PUNISHMENT; INEQUALITY; DROPOUT; SCHOOL'
+language: English
+month: OCT
+number: '4'
+number-of-cited-references: '50'
+pages: 2097-2138
+papis_id: 30c4d73aa144e35eaee28c37f60b5cbd
+ref: Fu2018structuralestimation
+times-cited: '13'
+title: 'Structural Estimation of a Becker-Ehrlich Equilibrium Model of Crime: Allocating
+  Police Across Cities to Reduce Crime'
+type: Article
+unique-id: WOS:000446103800005
+usage-count-last-180-days: '0'
+usage-count-since-2013: '26'
+volume: '85'
+web-of-science-categories: Economics
+year: '2018'