st: Right-skewed dependent variable and spatial autocorrelation

[Francisco Rowe <frowe@ucn.cl>]

Hi,

I am trying to estimate a model in which the dependent variable is the share of people with bachelor degree or above. Its distribution has some particular properties. It is right skewed, non-negative and fluctuates between 0 and 0.5 (theoretically up to 1). 

To estimate a model with a dependent variable with these characteristics, the strategy proposed by Papke and Wooldridge (1996) could be used (Baum 2008). However, it does not account for spatial autocorrelation, which based on theoretical grounds and previous research seems to play a role. How can spatial autocorrelation be taken into account in the strategy proposed by Papke and Wooldridge (1996)?

Regarding this, two options appear as alternatives:
 
1) Normalise the dependent variable and run either a spatial autorregresive or error model. However, normalisation is problematic given the zero values in the dependent variable. Someone suggested that I could use this transformation: y*=log(y+1), so map zeros to zeros. Does it seem appropriate?

2) Instead of using the share as dependent variable, use the count of people and run a poisson or zero-inflated poisson model with spatial autocorrelation. However, for this alternative, I don’t know any command in Stata that can do this. Is there any? I also know a package in R that do this (spatcounts), but it is poorly documented so it is hard to know that input data structure and learn what it does.

Do you suggest any other alternatives?

Baum, C 2008, 'Stata tip 63: Modeling proportions', Stata Journal, vol. 8, no. 2, pp. 299-303.
Papke, L and Wooldridge, J 1996, 'Econometric methods for fractional response variables with an application to 401 (k) plan participation rates', Journal of Applied Econometrics, vol. 11, no. 6, pp. 619-32.

I will appreciate your comments and suggestions.

Regards,

Francisco.
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