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Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques
Author(s) -
Lacombe Donald J.
Publication year - 2004
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2004.tb01128.x
Subject(s) - estimation , econometric model , sample (material) , autoregressive model , econometrics , spatial econometrics , economics , latent variable , set (abstract data type) , public policy , econometric analysis , statistics , mathematics , economic growth , computer science , chemistry , management , chromatography , programming language
A popular approach to examining the effects of public policy has been to rely on a spatial data sample of border counties as in Holmes (1998)—border counties from a sample of states that are used in conjunction with least‐squares estimation techniques in an attempt to isolate the policy impact while controlling for spatial dependence that often arises from latent or unobserved variables. This technique is in the spirit of control‐group methodologies from the laboratory sciences. This paper contrasts border‐county estimation results from Holmes' (1998) approach and those from a related methodology set forth in Holcombe and Lacombe (2003), with estimates from a spatial autoregressive model explicitly accounting for within‐state and between‐state public policy effects. As an illustration, the paper examines the effects of Aid to Families with Dependent Children (AFDC) and Food Stamp payments on female‐headed households and female labor force participation using the three different methods.