IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v182y2019i4p1607-1632.html
   My bibliography  Save this article

A latent class growth model for migrants’ remittances: an application to the German Socio‐Economic Panel

Author

Listed:
  • Silvia Bacci
  • Francesco Bartolucci
  • Giulia Bettin
  • Claudia Pigini

Abstract

We propose a latent class mixture growth model with concomitant variables to study the time profiles of international remittances sent by first‐generation migrants in Germany from 1996 to 2012. The latent class approach enables us to identify homogeneous subgroups of migrants associated with different trajectories for their remitting behaviour, which can be interpreted in the light of the theoretical economic background. In addition, the inclusion of concomitant covariates allows us to uncover whether the assignment of migrants to a specific subgroup can be ascribed to their observable characteristics (e.g. their intention to return home), as conjectured by the theoretical models. The model proposed is easily estimated through an expectation–maximization algorithm. Results show that migrants can be clustered in three groups, two of which reflect the evolution of remittances predicted by economic theory.

Suggested Citation

  • Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2019. "A latent class growth model for migrants’ remittances: an application to the German Socio‐Economic Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1607-1632, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1607-1632
    DOI: 10.1111/rssa.12475
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12475
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12475?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Etilé, Fabrice & Frijters, Paul & Johnston, David W. & Shields, Michael A., 2021. "Measuring resilience to major life events," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 598-619.
    2. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1607-1632. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.