The spatial weight matrix that connects cross-sectional units is a key element in the empirical implementation of the model. We experiment with three alternative specifications. First, we measure the economic distance between each pair of countries by the magnitude of their bilateral trade, following the view that bilateral trade intensities capture economic interactions and shock spillovers across countries, so that countries that trade more are economically more connected, see e.g. Frankel and Rose (1998).

To construct the bilateral trade weight matrix, we use information on bilateral trade taken from the IMF Direction of Trade Statistics (DOT). For the Czech Republic, Hungary, Poland, Slovakia and Slovenia, the trade data is collected from the Centre d’Etudes Prospectives et d’Informations Internationales (CEPII) online database. Specifically, for a pair of countries _i_ and _j_ , _i_ = _j_ , entry _i_ , _j_ of the trade spatial weight matrix _W_ is defined as where _Exportsij_ denotes the exports from country _i_ to country _j_ , and _Importsji_ are the imports of country _i_ from country _j_ . Once _W_ has been computed, it is re-scaled dividing each of its elements by the sum of its corresponding row, so that the rows of the rescaled matrix sum to unity.<sup>11</sup> The second specification of the spatial weight matrix is based on bilateral foreign direct investment (FDI) positions.<sup>12</sup> The underlying logic is that the higher is the share of outward and/or inward investments of country _i_ from country _j_ , the more economically interdependent countries _i_ and _j_ are, resulting in larger spillovers from one country to the other; see, for instance, Asgharian, Hess and Liu (2013) and Chinn and Forbes (2004).

We construct the bilateral FDI weight matrix in a similar fashion to the bilateral trade weight matrix, using data on bilateral FDI positions taken from the OECD International Direct Investment Statistics, and replacing imports and exports in the above definition of _Wij_ with the stock of outward foreign direct investment from country _i_ to country _j_ and the stock of inward foreign direct investment from country _j_ to country _i_ , respectively.

Finally, the third specification of the weight matrix is based on pure geographical distance. In particular, following Ertur and Koch (2007), we use a weight matrix _WD_ based on inverse squared distance.<sup>13</sup> The elements of _WD_ are defined (before row normalization) as where _dij_ is the great-circle distance between the capital cities of countries _i_ and _j_ .<sup>14</sup>

> 11Such row standardization of the weight matrix facilitates the interpretation of the model coefficients, see Anselin (1988).

> 12 Ideally, we would want to use the bilateral positions of countries’ international portfolios, rather than just their FDI positions. However, such information is not available for our sample coverage.

> 13We also experimented with a matrix based on the negative exponential of squared distance. The results were very similar to those obtained with inverse squared distance, and to save space they are not reported.

> 14The great-circle distance, the shortest distance between any two country capitals, is computed as: _dij_ = _radius×_

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