Heterogeneous Income Profiles Model with Fixed Effects: Incorporating Labour Income Shocks*

Nayoung Lee, Hyungsik Roger Moon

Research output: Contribution to journalArticlepeer-review


We provide an alternative econometrics methodology to estimate a standard heterogeneous income profiles (HIP) model. Our alternative setup allows for the HIP coefficients to be fixed in the sense that they can be arbitrarily correlated with the explanatory variables of the HIP equation and can be treated as parameters to be estimated. As an empirical application, we analyse the extent to which different sources of labour income shocks account for the persistence and variation of composite income shocks based on the HIP model. Our estimation results from the Panel Study of Income Dynamics (PSID) suggest that the random effect assumption of no correlation can lead to biases. We also find that job displacements account for the persistence of income shocks the most, especially for high school educated individuals.

Original languageEnglish
Pages (from-to)1377-1407
Number of pages31
JournalOxford Bulletin of Economics and Statistics
Issue number6
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
We thank the participants at various conferences including the Econometric Society World Congress and the Workshop on the Estimation of Economic Models of Earnings Dynamics and at the seminars of Chung‐Ang University, Seoul National University, Singapore Management University, Sogang University and Tsinghua University for their constructive comments. Special thanks to Wallace Mok for sharing his disability data. Moon acknowledges that this work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF‐2014S1A5A8012177). All errors are ours.

Publisher Copyright:
© 2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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