IDEAS home Printed from https://ideas.repec.org/p/col/000089/020642.html

Mujeres ayudan a mujeres: Representación política femenina y violencia de género

Author

Listed:
  • Sof√≠a Abondano Arbel√°ez

Abstract

La participación femenina en la política es importante para garantizar que los intereses de las mujeres sean representados a la hora de tomar decisiones. Esta investigación estudia si tener a mujeres como funcionarias públicas reduce la violencia contra la mujer en Colombia. Se provee evidencia de que el liderazgo femenino en el gobierno local reduce las llamadas a un hotline de la Policía que recolecta denuncias de mujeres por violencia intrafamiliar, y este impacto se va amplificando a lo largo de los anos. Además, la representación femenina a nivel municipal puede disminuir la media la tasa anual de estas solicitudes en un 22%. Al considerar mecanismos, las alcaldesas parecen ser más propensas a tomar medidas que den oportunidades a la población femenina en el campo laboral y educativo, disminuyendo de manera indirecta la incidencia de violencia contra la mujer en el hogar y el reporte de las víctimas a la línea de la Policía. El trabajo concluye resaltando la relevancia de incentivar la participación femenina en la política para mejorar las condiciones de vida de las mujeres en Colombia.

Suggested Citation

  • Sof√≠a Abondano Arbel√°ez, 2023. "Mujeres ayudan a mujeres: Representaci√≥n pol√≠tica femenina y violencia de g√©nero," Documentos CEDE 20642, Universidad de los Andes, Facultad de Economía, CEDE.
  • Handle: RePEc:col:000089:020642
    as

    Download full text from publisher

    File URL: https://repositorio.uniandes.edu.co/bitstream/handle/1992/65245/dcede2023-01.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    2. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    3. Shakeeb Khan & Denis Nekipelov, 2013. "On Uniform Inference in Nonlinear Models with Endogeneity," Working Papers 13-16, Duke University, Department of Economics.
    4. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2015.
    5. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    6. Plamen Nikolov & Hongjian Wang & Kevin Acker, 2020. "Wage premium of Communist Party membership: Evidence from China," Pacific Economic Review, Wiley Blackwell, vol. 25(3), pages 309-338, August.
    7. Yingying Dong & Arthur Lewbel, 2015. "A Simple Estimator for Binary Choice Models with Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 82-105, February.
    8. Christoph Rothe, 2017. "Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap," Econometrica, Econometric Society, vol. 85, pages 645-660, March.
    9. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.
    10. Lewbel, Arthur & Lin, Xirong, 2022. "Identification of semiparametric model coefficients, with an application to collective households," Journal of Econometrics, Elsevier, vol. 226(2), pages 205-223.
    11. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    12. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    13. Bontemps, Christian & Kumar, Rohit, 2020. "A geometric approach to inference in set-identified entry games," Journal of Econometrics, Elsevier, vol. 218(2), pages 373-389.
    14. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    15. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    16. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Working papers 2021/05, Faculty of Business and Economics - University of Basel.
    17. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2026.
    18. Jochmans, Koen & Henry, Marc & Salanié, Bernard, 2017. "Inference On Two-Component Mixtures Under Tail Restrictions," Econometric Theory, Cambridge University Press, vol. 33(3), pages 610-635, June.
    19. Mogens Fosgerau & Dennis Kristensen, 2021. "Identification of a class of index models: A topological approach," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 121-133.
    20. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • B54 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Feminist Economics
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

    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:col:000089:020642. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Universidad De Los Andes-Cede (email available below). General contact details of provider: https://edirc.repec.org/data/ceandco.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.