We present a growth model with spatial interdependencies in the heterogeneous technological progress and the stock of knowledge that, under certain conditions, yields agrowth-initial equation that can be taken to the data. We then use data on EU-NUTS2 regions and a correlated random e ects specication to estimate the resulting spatial Durbin dynamic panel model with spatially weighted individual e ects. QML estimatessupport our model against simpler alternatives that impose a homogeneous technology. Also, our results indicate that rich regions tend to have higher (unobserved) productivityand are likely to stay rich because of the strong time and spatial dependence of the GDP per capita. Poor regions, on the other hand, tend to enjoy productivity spillovers but arelikely to stay poor unless they increase their saving rates.This research was funded by grants ECO2014-55553-P and ECO2016-78652 (Ministerio de Econom a
y Competitividad), ECO2018-88888-P (AEI/FEDER, UE) and 2014FI B00301 (Agaur, Generalitat de
Catalunya). We thank participants at the 59th ERSA Congress (Lyon), 21st INFER Annual Conference
(Vrije Universiteit Brussel), UCLouvain Saint-Louis and the 6th Workshop in Industrial and Public Economics
(Universitat Rovira i Virgili) for useful comments. Usual caveats apply
