Unformatted text preview: Burgess & Zhuang (2002) Intra-household Allocation has been largely conﬁned to census data on mortality and enrollment rates
differenced by sex and doesn’t allow us to understand what drives gender
biases. The behavioural mechanisms that underlie differential outcomes between the sexes remain incompletely understood. 9.1 Burgess & Zhuang (2002) Modernisation and Son Preference
One approach: Use demand analysis
(i) how does allocation vary according to the gender and age of the recipient
(ii) even if welfare of household members same, per capita consumption
will not provide correct ranking of living standards within household (e.g.
because children/elderly need to consume less). Equivalence scales – improve measures of welfare and inequality.
Mainly focussing on (i) but (ii) is also important because it provides insights which allows us to test for presence of gender bias
Problem: don’t have individual data. Methods rely on detecting gender
effects in the aggregate spending patterns of households. Unpack demand
equations to examine whether the presence of individuals of similar ages
but of opposite sexes affect key areas of household spending.
To look at these effects: Run demand equations (Engel curves) where different age classes (n j ) have been broken down by gender so that separate
γi j coefﬁcients for males and females can be calculated:
J −1 wi = αi + βi ln x + ηi ln n + ∑ γi j
+ δi z + ui
n where wi is the budget share of the ith commodity, x the total household
expenditure, n the household size, n j is number of household members
in sex-age class j. Also include vector of variables (z) which control for
location and relevant socio-economic characteristics of the household. Development Economics, LSE Summer School 2007 186 ...
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- Spring '10
- Zhuang, household members