Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy

TLDR

Spatial econometrics is increasingly used in political science, yet most studies still rely on geographic distance. The authors argue that political economy notions of distance, such as relative trade or common dyad membership, are often more fruitful than geographic distance, and that the spatially autoregressive model should usually be preferred over the spatially lagged error model. They introduce a testable assumption that permits the straightforward incorporation of space—defined in any way—into time‑series cross‑section analyses. The paper provides examples of spatial analyses involving trade and democracy.

Abstract

Although spatial econometrics is being used more frequently in political science, most applications are still based on geographic notions of distance. Here we argue that it is often more fruitful to consider political economy notions of distance, such as relative trade or common dyad membership. We also argue that the spatially autoregressive model usually (but not always) should be preferred to the spatially lagged error model. Finally, we consider the role of spatial econometrics in analyzing time-series–cross-section data, and show that a plausible (and testable) assumption allows for the simple introduction of space (however defined) into such analyses. We present examples of spatial analyses involving trade and democracy.