Employer-to-Employer Transitions and Time Aggregation Bias
研究使用丹麦日度就业数据，发现基于季度数据测算的雇主间转换率比日度数据高估约30%，且偏差具有顺周期性，衰退时减少超10%，并提出利用收入信息改进测算的算法。
The rate at which workers switch employers without experiencing a spell of unemployment is one of the most important labor market indicators. However, Employer-to-Employer (EE) transitions are hard to measure in widely used matched employer-employee datasets such as those available in the US. We investigate how the lack of the exact start and end dates for job spells affect the level and cyclicality of EE transitions using Danish data containing daily information on employment relationships. Defining EE transitions based on quarterly data overestimates the EE transition rate by approximately 30% compared to daily data. The bias is procyclical and is reduced by more than 10% in recessions. We propose an algorithm that uses earnings and not just start and end dates of jobs to redefine EE transitions. Our definition performs better than definitions used in the literature.