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LABOUR SUPPLY ESTIMATION AND PUBLIC POLICY

 

作者: Derek Hum,   Wayne Simpson,  

 

期刊: Journal of Economic Surveys  (WILEY Available online 1994)
卷期: Volume 8, issue 1  

页码: 57-81

 

ISSN:0950-0804

 

年代: 1994

 

DOI:10.1111/j.1467-6419.1994.tb00175.x

 

出版商: Blackwell Publishing Ltd

 

关键词: labour supply estimation;income maintenance experiments;econometrics;experimental panel data;public policy

 

数据来源: WILEY

 

摘要:

Abstract.Economists devote considerable energies towards refining their econometric techniques to overcome difficulties connected with conducting empirical research. Despite advances in technique. it is not clear whether further refinement in this direction is worthwhile for policy purposes. It may be that no further amount of statistical adjustment of inadequate data will increase understanding, and that better data is simply necessary to add to our knowledge. But rarely is sufficient credit paid to new forms of data. In short, econometric technique is emphasized to the neglect of data innovation, as if new data were merely lying about waiting for an ingenious suggestion for use. This paper surveys advances of the last twenty five years in estimating labour supply for policy purposes with a view towards appreciating the relative contribution of both improvements in econometric technique as well as developments of new data.After briefly detailing the key parameters which economists have sought to estimate, we describe the early ‘first generation’ research (circa 1970), which is plagued by problems of unobservable variables, measurement errors, truncation and selectivity bias, and non linear budget constraints. ‘Second generation’ research constitute attempts to resolve one or more of these difficulties, and the respective contribution of econometric technique and new data is acknowledged and assessed, including the contribution of data generated by large scale social experiments in which participants are randomly assigned to different guaranteed income plans and their labour supply behaviour m

 

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