Risk-sharing networks and insurance against illness

TLDR: The authors used detailed data on all insurance networks within a village in Tanzania and found evidence consistent with at least partial insurance of non-food consumption via networks, while full risk-sharing occurs within these networks.

About: This article is published in Journal of Development Economics.The article was published on 2006-12-01 and is currently open access. It has received 431 citations till now. The article focuses on the topics: Consumption (economics) & Developing country.

Figures

Table 3: Responses to the two major shocks of the past 10 years (percentage of shocks for which the response was considering to be very important ).

Table 4: Mean and standard deviation of non-deflated food, non-food (excluding medical expenditures) and total consumption across the 5 survey rounds (in TSh per week per adult equivalent).

Table 5: Impact of health shocks on consumption: average consumption change between rounds (in percent).

Table 6: Testing risk-sharing and network effects. LHS=changes in log consumption per adult (i.e. fixed effects within estimator). IV regressions with robust standard errors. Z-scores in brackets.

Table 2: Coping strategies used in the past 10 years (in response to the 2 worst shocks that have affected household in the past 10 years).

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Risk-sharing networks and insurance against illness*" ?

This paper uses detailed data on all insurance networks within a village in Tanzania ; networks are not clustered but largely overlapping. The authors thank Philemon Charles, Augustina John, Obadiah Kyakajumba, Respichius Mitti, George Musikula, Isaya Mukama, Adelina Rwechungula, and Taddeo Rweyemamu for their excellent work on the field, Julius Majula for taking charge of the data entry and all the people of the community of Nyakatoke for their continuous hospitality and willingness to provide us with the data. 

Q2. What is the meaning of the term "Aggregated economic index"?

Since this aggregate economic index can be viewed as an independent way of measuring current conditions, with measurement error that is likely to be uncorrelated with measurement error in consumption, the authors used the average network index as an additional instrument in their regression. 

Q3. What is the important strategy to cope with health shocks?

Mutual insurance is cited as the most important strategy to cope with health shocks, while households identify specific network partners. 

Q4. What is the main argument for the test for risk-sharing?

The main argument is that different types of consumption may have a differential sensitivity to shocks and also, they may suffer from different types of measurement error, affecting the ability of their tests to identify any failure in risk-sharing or network effects. 

Q5. What are the main staples that are not easily or commonly traded?

the main staples, such as bananas or cassava are not easily or commonly traded, being bulky relative to value, so stocks will often not be monetised when shocks occur. 

Q6. What is the main reason why casual labour is used?

Its use may be limited by seasonalities in the labour market and by the very nature of the shock (e.g. death, illness or imprisonment of an important labour force in the household). 

Q7. What is the predictable part of Hit?

The predictable part of Hit is measured through a fixed effects regression of Hit on household characteristics, consumption in period t-1 and time dummies. 

Q8. How does the author test whether extended families are altruistically linked?

Linking the data of parents and children allows these authors to specify a test of whether extended families are altruistically linked, by testing whether consumption decisions are based on a common budget constraint, i.e. individual consumption within the family is independent of the distribution of income between the households. 

Q9. How can the authors address the measurement error problems in the consumption of network members?

By using IV-estimation, the authors may also be able to address measurement error problems in the consumption of network members (on this, see also Ravallion and Chaudhuri, 1997). 

Q10. How do the authors know that all networks in the village overlap with each other?

The authors know that all regression specifications control for aggregate village resources through time dummies and the authors know that all networks in the village overlap with each other. 

Q11. Why did respondents have a more vivid recollection of the shocks?

This is probably due to the fact that respondents have a more vivid recollection of these shocks, and that the authors inquired about shocks that occurred since the formation of their household (which is less than 10 years ago for younger respondents). 

Q12. Where does Fafchamps and Lund find that mutual insurance takes place?

In an empirical study of the rural Philippines, Fafchamps and Lund (2002) find that mutual insurance takes place through networks of relatives and friends and not at village level. 

Q13. What is the main argument for the use of an alternative endogenous variable?

Even if health shocks were to shift preferences for non-food items, it is unlikely that the preferences of one individual in the household could have such a huge effect on the non-food consumption of the whole household, given that the average household size is 4.7.16As a further test of the possibility that non-separability is driving their results, the authors use an alternative endogenous variable.