Description
This project focuses on the problem of measurement error and investigates the feasibility of using hidden Markov models (HMMs) to correct for such error in categorical, longitudinal data. In doing so, we have first illustrate how measurement error poses a substantial threat to the validity and accuracy of estimates. We then demonstrate the need to use multiple-indicator HMM specifications, which can account for the nonignorable presence of systematic/dependent errors. Finally, we show that the use of such extended models is feasible. That is, even though such HMMs require record linkage, linkage error is largely not a problem. Furthermore, while their implementation process is complex and time-consuming, it can be simplified because error parameters can be re-used for a number of years.,In the LFS the weighting of the observations is twofold. First, inclusion weights are assigned to the observations. These weights correct for biased inclusion probabilities that are caused by the sampling method. Second, the final weights are constructed (by adjusting for sex, age, country of origin, official place of residence and some other regional classifications). These weights are used to reduce non-response bias.
While in our analyses the inclusion of weights did not significantly affect the results and therefore we decided to exclude them, this might not be the case in other applications, in particular when the weights vary substantially across respondents.,Response Rates: In general, according to colleagues from Statistics Netherlands, the response rate in the LFS was around 61% in 2009 and 53% in 2010. However, as in our analysis we used a sub-sample of the LFS data and selected (i) only individuals between 25 and 55 years of age and (ii) only those who could be linked to the ER data, we do not know the response rates for our sample. Statistics Netherlands has also indicated that the LFS is subject to relatively high panel attrition, which also leads to selectivity, but the exact rates are unknown
While the ER officially cannot be subject to drop-out as submission of reports is obligatory for all employers, 2,619 observations (out of a total of 133,290) are missing.
,Individuals on the Dutch labour market aged 25 to 55. .,The LFS is a sample survey.
The ER covers all individuals who are employed in the Netherlands.
,
While in our analyses the inclusion of weights did not significantly affect the results and therefore we decided to exclude them, this might not be the case in other applications, in particular when the weights vary substantially across respondents.,Response Rates: In general, according to colleagues from Statistics Netherlands, the response rate in the LFS was around 61% in 2009 and 53% in 2010. However, as in our analysis we used a sub-sample of the LFS data and selected (i) only individuals between 25 and 55 years of age and (ii) only those who could be linked to the ER data, we do not know the response rates for our sample. Statistics Netherlands has also indicated that the LFS is subject to relatively high panel attrition, which also leads to selectivity, but the exact rates are unknown
While the ER officially cannot be subject to drop-out as submission of reports is obligatory for all employers, 2,619 observations (out of a total of 133,290) are missing.
,Individuals on the Dutch labour market aged 25 to 55. .,The LFS is a sample survey.
The ER covers all individuals who are employed in the Netherlands.
,
| Date made available | 1 Jan 2020 |
|---|---|
| Publisher | Unknown Publisher |