I am not clear whether you regard
measure choice as a predictor or a response.
And in any case I can imagine
situations in which both views are reasonable.
Soil erosion loss could depend on
conservation measures, which choice of conservation
measures might depend on many things.
If it were the latter, I think it is up to you how you define
a composite response. Nor is it clear to me that
you need do that. If this were my problem
I would look at
(a) logit models for the individual responses
and
(b) a multinomial logit for the 8 possible choices of
fert[iliser?]
man[ure?]
and fanyaju[???].
If conservation measures are predictors, you would
need to look not just at dummies but also at interactions.
Nick
[email protected]
Ronnie Babigumira
> Something has been slowly eating away at me and at this point I have
> decided to seek help from the list. I have plot level data on use of
> soil conservation measures and would like to construct a "single"
> dependent variable for each household for use in a multinomial logit.
> The data look something like this
>
> hhd_id plnum fert man fanyaju
> 1001 1 0 1 0
> 1001 2 0 1 1
> 1001 3 1 0 1
> 1002 1 0 0 0
> 1002 2 1 1 1
> 1003 1 1 0 0
>
> Where
> hhd_id: Household id
> plnum: Plot number (a household may have more than one plot)
> And fert, man and fanyaju are 3 possible soil conservation measures a
> household may undertake (it is possible that more than one measure can
> be applied to a plot)
>
> My question is how do I go about with constructing a single dependent
> variable for use in a multinomial logit in this case (and
> would this be
> correct). I have considered the simplest case where I
> construct a simple
> dummy for each plot indicating whether or not a household
> used at least
> one of the measures however, I feel that it would be more
> interesting if
> I could say something on the determinants for the use of the different
> measures
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