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Social security administrative data are increasingly becoming available in many countries. This data has they have a long panel structure (large N, large T) and allows for the measurement of many different variables with high accuracy. It also captures short-term unemployment spells which are normally unavailable in survey data due to its design. However, the measurement of unemployment differs in both types of datasets. The resulting gap between total unemployment and registered unemployment is not constant across workers characteristics or time. In this paper, I present a simple, systematic method to expand the raw Spanish Social Security administrative data. I identify unemployed workers who are not receiving unemployment benefits, using information from the institutional framework and using the Labour Force Survey as a benchmark. The resulting unemployment rates and labour market flows are comparable across both datasets. Administrative data can also overcome some of the problems of the Labour Force survey, such as changes in the structure of the survey. This paper aims to provide a comprehensive guide on how to adapt administrative datasets to make them useful for studying unemployment.

Working Papers

In the last recession, the increase in long-term unemployment has been higher for younger workers than for older age groups. I propose a novel mechanism, search capital, to explain long-term unemployment patterns across different ages along the business cycle: ceteris paribus workers who have been successful in finding jobs in the recent past become more efficient at finding jobs in the present. Search capital increases with successful search experience and depreciates with tenure if workers do not search often enough. In labour markets where short-term jobs are a significant share of employment, this mechanism can explain cyclical bursts of long-term unemployment. Using Spanish administrative data, I provide empirical evidence that search capital, as proxied by the number of temporary jobs a worker has had, is negatively correlated with unemployment duration. The addition of search capital to a standard search model manages to replicate these empirical findings, while also generating increases of long-term unemployment by age and along the business cycle similar to those in the data. Although workers with stable jobs have higher welfare than workers with many employment spells when the economy is booming, they suffer higher loses during recessions because of their lower search capital.

In a recent paper, Alvarez, Borovičková and Shimer (ABS) revisit the analysis of the determinants of unemployment duration by proposing a new method (the ABS method thereafter) that directly estimates the importance of each component and implementing it using precise information on unemployment spells from social security administrative data for Austria. In this paper, we apply the ABS method to social security administrative data for Spain with the objective of comparing these two very different labor markets as well as Spain along the business cycle.  Administrative data have many advantages compared to Labor Force Survey data, but the incomplete nature of the data needs to be addressed in order to use the data for unemployment analysis (e.g., unemployed workers that run out of unemployment insurance have no labor market status in the data).  The degree and nature of such incompleteness are country-specific and are particularly important in Spain. Following Lafuente (2019), we approach the matter of data incompleteness in a systematic way by using information from the Spanish LFS data as well as institutional information. We hope that our approach will provide a useful way to apply the ABS method in other countries.  Our findings are as follows:  (i) The aggregate component is clearly the most important  one,  followed  by  heterogeneity  and  duration  dependence,  which  are  roughly comparable.  (ii) The relative importance of each component and, in particular, duration dependence is quite similar in Austria and Spain, especially when minimizing the effect of fixed-term contracts in Spain.  Similarly, we do not find big differences in the relative contribution  of  the  different  components  along  the  business  cycle  in  Spain.   (iii)  These comparisons suggest that statistical discrimination due to dynamic sample selection does not seem to be the main driver of duration dependence.