Chrome Extension
WeChat Mini Program
Use on ChatGLM

Female House Ownership Drives the Positive Association Between Matriliny and Women’s Health in Meghalaya (India)

Loïa Lamarque,Michel Raymond, Banrida Langstieh,Alexandra Alvergne

Bulletins et Mémoires de la Société d’Anthropologie de Paris(2025)

Cited 0|Views0
Abstract
Matrilineal kinship, which traces descent through the mother's lineage, has been shown to have health benefits for both women and children, but the specific practices by which matriliny enhances female health outcomes remain unclear. In this study, we analysed the 2015 Demographic and Health Survey data from Meghalaya, India (4,109 women, 809 men, 3,197 children), a region where matrilineal and non-matrilineal communities coexist with a mixed combination of post-marital residence and inheritance transmission. We considered three practices usually associated with matrilineal systems (i) female house ownership, (ii) matrilocal post-marital residence and (iii) daughter-biased investment. We find no evidence for daughter-biased parental investment in medical care nor improved health with spatial proximity with kin. Instead, we demonstrate that female (vs. male) ownership correlates with substantial health benefits for women, including reduced risks of being anemic (OR = 0.72, SE = 0.093, p<0.001) and underweight (OR = 0.59, SE = 0.135, p<0.001), a benefit which extend to men, albeit to a lesser extent, and children. Boys and girls living in households owned by women are more likely to receive medical care when sick (+ 200%). The associations between female house ownership and better health outcomes remain after adjusting for age, wealth disparities, geographical area, fertility, or time since last birth. This results strongly suggest that female economic empowerment surpasses other matrilineal dimensions in improving health outcomes, providing novel insights to bridge the gender health gap.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest