Abstract
AbstractThis reference paper describes the sampling and contents of the IZA Evaluation Dataset Survey and outlines its vast potential for research in labor economics. The data have been part of a unique IZA project to connect administrative data from the German Federal Employment Agency with innovative survey data to study the out-mobility of individuals to work. This study makes the survey available to the research community as a Scientific Use File by explaining the development, structure, and access to the data. Furthermore, it also summarizes previous findings with the survey data.JEL codesC81; H43; J68
Highlights
In modern welfare states, active labor market policies (ALMP) such as job search assistance, training programs, public employment programs and wage subsidies are intended to reintegrate the unemployed back into the labor market
Summary and outlook This paper introduces the IZA ED Survey, which has been created to overcome data limitations in empirical labor research, to provide more evidence about how successful job search and ALMP interventions operate
The new Scientific Use Files provided by the International Data Service Center of IZA cover a large and representative population of around 18,000 unemployed individuals who entered unemployment insurance in Germany between May 2007 and June 2008
Summary
Active labor market policies (ALMP) such as job search assistance, training programs, public employment programs and wage subsidies are intended to reintegrate the unemployed back into the labor market. In contrast to population-representative surveys, this survey has the advantage that it captures a large entry sample of unemployed individuals and includes large shares of participants in ALMP programs. As a first step to overcome such limitations and obtain empirical evidence on the effectiveness of labor market policies, many European countries have recently opened their administrative databases for scientific research. The advantages of administrative data are straightforward: they are consistently and accurately collected, resulting in highly reliable data covering a large number of observations (in some cases even 100% of the population). They are regularly updated such that long time periods are observable usually and the specific use of ALMP programs is directly visible. Important variables for scientific research such as social networks, personality traits, cognitive skills, attitudes or ethnic identity are usually not important for administrators and are not included in administrative databases
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