Explaining spatially dependent transitions at different moments in time

What is it about?

This paper adopts a spatial probit approach to explain interaction effects among cross-sectional units when the dependent variable takes the form of a binary response variable and transitions from state 0 to 1 occur at different moments in time. The model has two spatially lagged variables, one for units that are still in state 0 and one for units that already transferred to state 1. The parameters are estimated on observations for those units that are still in state 0 at the start of the different time periods, whereas observations on units after they transferred to state 1 are discarded, just as in the literature on duration modeling. Furthermore, neighboring units that did not yet transfer may have a different impact than units that already transferred. We illustrate our approach with an empirical study of the adoption of inflation targeting for a sample of 58 countries over the period 1985--2008.

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Photo by Andrew Ridley on Unsplash

Photo by Andrew Ridley on Unsplash

Why is it important?

We illustrate our approach with a study of the adoption of IT for a sample of 58 countries over the period 1985--2008. We investigate whether countries that did not adopt IT yet interact with other countries, thereby, making a distinction between countries that also did not adopt IT yet and countries that did.

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