Working Paper: CEPR ID: DP7379
Authors: Kurt Schmidheiny; Marius Brülhart
Abstract: It is well understood that the two most popular empirical models of location choice - conditional logit and Poisson - return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region's gain is the other regions' loss. In contrast, the Poisson model implies a positive-sum economy, in which one region's gain is no other region's loss. We also show that all intermediate cases can be represented as a nested logit model with a single outside option. The nested logit turns out to be a linear combination of the conditional logit and Poisson models. Conditional logit and Poisson elasticities mark the polar cases and can therefore serve as boundary values in applied research.
Keywords: Conditional Logit; Firm Location; Nested Logit; Poisson; Count Model; Residential Choice
JEL Codes: C25; H73; R3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Conditional logit model (C25) | Zero-sum allocation (C72) |
| Poisson model (C59) | Positive-sum allocation (D51) |
| Conditional logit model (C25) | Fixed total agent numbers (L85) |
| Increase in one region's attractiveness (R11) | Decrease in another's (conditional logit model) (C25) |
| Increase in one region's attractiveness (R11) | Increase in total number of agents (Poisson model) (C69) |
| Nested logit model (C35) | Combination of reallocations within and outside regions (R23) |
| Conditional logit model (C25) | Unchanged total firm counts across regions (R30) |
| Poisson model (C59) | Total counts change based on locational attractiveness (R23) |
| Nested logit model (C35) | Total number of firms can vary based on changes in attractiveness (L25) |