Environmental Engineering Reference
In-Depth Information
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The poorest households consumed and sold wild foods to a much lesser
degree than wealthier households. They seemed to have much lower access to
wild foods and to the market than wealthier households.
The
discussion
moves away from statistical analysis and towards a more anthropo-
logical understanding of the factors behind the results. The differences in access to
wild resources were explained using entitlements theory (Sen 1981). It is only by
spending time in the community, and using participatory methods, that this
understanding can be built up. Hence this paper is exemplary in showing how the
different research techniques can be married to produce a fuller understanding
than could be obtained from any one methodological approach on its own. It also
presents and then answers extremely clear and focused research questions.
3.3.2.3 Income and price elasticities of bushmeat and fish demand
Wilkie and Godoy (2001) aimed to estimate the elasticities of demand for bush-
meat and fish in four indigenous communities in Bolivia. Elasticities of demand
measure the extent to which consumption of a good changes as prices (of the good
itself or of substitute goods) or incomes change. The basic principles of this are cov-
ered in Chapter 1. For example, if a new logging company starts operating near a
hunting village, incomes in that village are likely to rise. This might increase their
demand for bushmeat, and so increase hunting rates to supply that demand, with
potential consequences for sustainability.
Elasticities of demand can be estimated in two ways:
●
by using a time-series of prices and quantities (for example, from a market);
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by surveying households with different incomes who are paying different
prices for a good (for example, because they are in different villages). This is
the method that Wilkie and Godoy use.
There needs to be variation in price, income and quantity consumed in the dataset,
otherwise relationships cannot be distinguished. The time-series method uses
regression analysis to see which factors (e.g. prices, incomes) best explain trends in
quantities purchased over time. The problem with this is that it can be difficult to
assign causation to these correlations (i.e. to be sure that variables are actually influ-
encing each other rather than just co-varying). The cross-sectional household sur-
vey method correlates differences in consumption between households with price
and incomes. This approach's problem is that it assumes that current differences
between households are caused by the same processes that would cause changes in
consumption over time. For example, that if a poor household in the sample
became richer, its consumption would change to be like that of the rich households
in the sample. Neither problem is insurmountable, they just require careful data
collection and analysis. Particular attention needs to be paid to missing variables,
which can bias the elasticity estimates. For example, variation in wildlife densities
between villages can affect both price and consumption of bushmeat, distorting
the underlying relationship between the two parameters.
