IDEAS home Printed from https://ideas.repec.org/a/taf/mpopst/v33y2026i1p42-63.html

Effects of schooling, unemployment, and income on the birth rate in Mexico: evidence from an augmented ARDL model

Author

Listed:
  • Ramón Valencia-Romero
  • Vanessa Rivas-Ayala

Abstract

This research was carried out to identify the variables that influenced, in the long and short term, the birth rate in Mexico for the period 1991–2023. The tested variables were the years of education, the unemployment rate, and the Gross Domestic Product (GDP) per capita, as proxies of schooling, unemployment, and income, respectively. An econometric methodology called Augmented Autoregressive Distributed Lag (A-ARDL) model was utilized, owing to its robustness over the standard ARDL model. The model was estimated and tested with and without the presence of structural breaks, due to three events of external origins to the Mexican economy: the financial crisis at the end of 2008, the fall in oil prices in 2015, and the onset of the COVID-19 pandemic in Mexico. The results suggest that women’s schooling is the main factor influencing the birth rate, and it has short- and long-term effects on this rate. The income variable does not affect the birth rate; it is not statistically significant. Also, the unemployment rate was discarded because its incorporation did not allow cointegration among the variables under study. Moreover, the econometric analysis confirms that the three events of external origins had effects on the dynamics of births in Mexico. In this sense, it is concluded that an A-ARDL model, with structural breaks, allows us to model the behavior of the birth rate in Mexico for the period 1991–2023.

Suggested Citation

  • Ramón Valencia-Romero & Vanessa Rivas-Ayala, 2026. "Effects of schooling, unemployment, and income on the birth rate in Mexico: evidence from an augmented ARDL model," Mathematical Population Studies, Taylor & Francis Journals, vol. 33(1), pages 42-63, January.
  • Handle: RePEc:taf:mpopst:v:33:y:2026:i:1:p:42-63
    DOI: 10.1080/08898480.2025.2612640
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08898480.2025.2612640
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/08898480.2025.2612640?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    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:taf:mpopst:v:33:y:2026:i:1:p:42-63. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GMPS20 .

    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.