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This article studies coding errors in occupational data, as the quality of this data is important\udbut often neglected. In particular, we recoded open-ended questions on occupation for last and\udcurrent job in the Dutch sample of the “Survey of Health, Ageing and Retirement in Europe”\ud(SHARE)  using  a  high-quality  software  program  for  ex-post  coding  (CASCOT  software).\udTaking CASCOT coding as our benchmark, our results suggest that the incidence of coding\uderrors  in  SHARE  is  high,  even  when  the  comparison  is  made  at  the  level  of  one-digit\udoccupational codes (28% for last job and 30% for current job). This finding highlights the\udcomplexity  of  occupational  coding  and  suggests  that  processing  errors  due  to  miscoding\udshould be taken into account when undertaking statistical analyses or writing econometric\udmodels. Our analysis suggests strategies to alleviate such coding errors, and we propose a set\udof equations that can predict error. These equations may complement coding software and\udimprove the quality of occupational coding

Measuring and Detecting Errors in Occupational Coding: an Analysis of SHARE Data