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[Excerpt] In this chapter, instead of using the similarity in the cleaned datasets to investigate economic fundamentals, we focus on the differences in the underlying ‘dirty’ data. We describe two data elements that remain fundamentally different across countries, and the extent to which they differ. We then proceed to document some of the problems that affect longitudinally linked administrative data in general, and we describe some of the solutions analysts and statistical agencies have implemented, and some that they did not implement. In each case, we explain the reasons for and against implementing a particular adjustment, and explore, through a select set of case studies, how each adjustment or absence thereof might affect the data. By giving the reader a look behind the scenes, we intend to strengthen the reader’s understanding of the data. Thus equipped, the reader can form his or her own opinion as to the degree of comparability of the findings across the different countries.


Suggested Citation
Vilhuber, L. (2005). Adjusting imperfect data: Overview and case studies. Retrieved [insert date] from Cornell University, ILR School site:

Required Publisher Statement
Copyright by University of Chicago Press. Final paper published as Vilhuber, L. (in press). Adjusting imperfect data: Overview and case studies. In E. P. Lazear and K. L. Shaw (Eds.) Wage structure, raises and mobility: International comparisons of the structure of wages within and across firms. Chicago: University of Chicago Press.