have a clear geographical scope: hospitals, universities, social security agencies, and independent institutions. To achieve our goal, we apply the following three-steps procedure.

1. First, we use official sources that contain lists with public entities’ names and the type of government to which they belong in each country. For the case of Spain, we use two different lists: one provided by the _Plataforma de Contrataci ́on del Sector P ́ublico_ (PCSP) and one provided by the Ministry of Finance. These two lists together contain 10,788 different entities. For the case of France, we use three different lists. One provided by the official website of the French administration (service-public.fr), one provided by the open platform for French public data (data.gouv.fr), and one provided by the National Statistics Bureau of France (INSEE). The combination of these three lists contain 5,726 different entities in France. Using these list of entities, we are able to classify into the above-mentioned categories the buyers of 58% of our observations for Spain and 14% of our observations for France.

2. Second, we construct a list of 270 keywords for Spain and 235 for France. For example, if the buyer’s name contains the word _alcalde_ , i.e., Mayor, we classify it as a local government type. Importantly, the construction of these keywords is based on the official lists used in the previous step. As a robustness check, we apply these keywords procedure to the institutions that were already classified in the first step and find a successful rate of 99%. After applying the first and second step, we are able to classify 97% of the buyers in Spain and 76% in France.

3. And third, we use official lists of territorial organizations that contain all Spanish and French regions, provinces, and municipalities. If the name of the buyer contains the name of one of these territories, we classify the buyer as belonging to that particular geographical level. For example, _Galicia_ is a Spanish region. Therefore, the buyer _Xunta de Galicia_ is classified as a regional government.<sup>11</sup> In this last step, we manually check every assignment to make sure that we do not do obvious mistakes.

After applying our procedure, we end up having the buying agencies of 84% of our observations classified into the following government types: national, regional, provincial, local, hospitals, universities, social security agencies, and independent institutions.<sup>12</sup> Throughout the paper, we will exclude hospitals, universities, social security agencies, and independent institutions. Additionally, for simplicity, we re-classify regional, provincial, and local entities into _subnational governments_ and national entities into _national governments_ .

> 11Some territorial organizations of different levels share the same name. For instance, _Barcelona_ might refer to a municipality or a province. We leave those cases unclassified.

> 12Most of the unclassified buyers can not be classified either because they are too generic (for instance, “president” or “director”) and do not appear in the official lists, or because they are private companies, which sometimes are subject to EU publicity regulation.

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