Women and corruption: evidence from multinational panel data

Yu Hao, Chun Ping Chang*, Zao Sun

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    In this paper we investigate whether the ratio of female population is related with lower corruption, utilizing a multinational panel data with 80 countries for the period 2000–2012 and employing the Generalized Method of Moments as well as the ordered probit regression methods. This is the first study on the impacts of different female groups on corruption. Overall speaking, the estimation results are pluralistic. Higher female ratios in the legislative branch and in the labor force are significantly associated with a lower level of corruption, while the female ratio in secondary enrollment is positively related with corruption; however, the female ratio of the whole population has insignificant impacts on corruption. The policy implications are that a simple enhanced female ratio and educational level are not the effective way to inhibit corruption in our sample countries, whereas improvements of the female ratio in the legislative branch and the labor force contribute to controlling corruption. These results are basically robust for the two estimation methods and for the two subsamples of developed and developing countries. As a result, the estimation results on the relationship between corruption and gender might vary remarkably when different indicators for the female groups are utilized, which should some light on future studies.

    Original languageEnglish
    Pages (from-to)1447-1468
    Number of pages22
    JournalQuality and Quantity
    Volume52
    Issue number4
    DOIs
    Publication statusPublished - 1 Jul 2018

    Keywords

    • Corruption
    • Female ratio
    • Generalized method of moments (GMM)
    • Multinational panel data
    • Ordered probit regression

    Fingerprint

    Dive into the research topics of 'Women and corruption: evidence from multinational panel data'. Together they form a unique fingerprint.

    Cite this