Characterising risk sharing in extended family networks: Socially close and distant connections in risk sharing

Malde, Bansi (2017). Characterising risk sharing in extended family networks: Socially close and distant connections in risk sharing. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-852288

Risk is extremely prevalent in rural areas of developing countries, but markets for credit and insurance are undeveloped and government-provided social insurance is very rare. Households in these contexts resort to informal tools, such as gifts and inter-personal transfers, to share idiosyncratic risk. Such informal risk sharing occurs within social networks, with family networks, particularly, forming a “natural” risk sharing institution. This research aims to deepen our understanding of risk sharing in family networks, focusing explicitly on the role of network structure (representation of who is related to who). This is likely important because many informal tools employed for risk sharing rely on bilateral relationships. Therefore, who one is linked with and who their links are further linked with will shape both how risk is shared and the amount shared. Specifically, this research will use unique data from Mexico with information on family ties and socio-economic variables to: (1) How do socially close connections (i.e. parents, siblings and children of the head and spouse residing outside the household) and socially distant connections (i.e. the family of one’s siblings’ spouses; as well as uncles, aunts, cousins) affect how well a household’s consumption is protected against idiosyncratic fluctuations in its income? (2) To what extent does the quality of a connection matter in its effectiveness in providing risk sharing?

Data description (abstract)

This depository was used to analyse how socially close (e.g. parents, children, siblings) and distant (e.g. cousins, and siblings' spouse's families) connections within households' within-village extended family networks influence their informal insurance in rural Mexico. Socially close connections are considered to be more effective in enforcing informal risk sharing arrangements, but may be more economically similar and less numerous than socially distant connections, and thereby provide fewer risk sharing opportunities. Insurance is measured using information on household consumption and income; while extended family connections were identified using an algorithm that exploits the Mexican naming system. Based on the identified connections, the full within-village extended family map was constructed and a graph theory based measure of social distance was used to identify socially close and distant connections. A second related collection titled "Characterising risk sharing in extended family networks: Group Size and Informal Risk Sharing" includes files associated with another study conducted under the same grant using data on extended family in rural Malawi (see Related Resources).

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