_on procurement market integration?_ Answering this question is challenging because we face an identification problem. As mentioned above, low levels of integration in government procurement could also be driven by bilateral natural frictions unrelated to the government’s home bias. In particular, factors like geography, information frictions, or lack of historical ties between regions and countries could prevent non-local firms from being competitive relative to local firms. Therefore, unless we account for the effects of these bilateral frictions, comparing procurement flows across regions and countries would be inconclusive about the government’s role in impeding procurement market integration.

**Novel strategy.** To overcome this challenge, we propose a new identification strategy that exploits variation in the geographical scope of the home bias. In particular, we hypothesize that the government’s home bias will coincide with the geographical scope at which a particular government operates. Under this hypothesis, regional governments will care about the regional border and will discriminate against all firms from other regions. In contrast, national governments will care about the national border and will discriminate against firms from other countries but will be indifferent relative to all national

To account for the above-mentioned natural frictions (as well as for firm-specific factors), our strategy relies on observing how subnational and national governments in a particular region buy a specific good from local and non-local firms. Intuitively, our approach exploits a double difference. First, we compare how much a specific firm sells a given product to national vs. subnational governments. Second, we look at how these relative purchases change for local vs. non-local<sup>5</sup> To implement this strategy we need very rich data on procurement contracts with information about the winning firm’s location as well as about the buying government agency. We leverage a detailed dataset of 1.2 million procurement contracts awarded in France and Spain between 2009 and 2019. In our dataset, we observe the winning establishment and the buying government agency, as well as their geographic location and contract characteristics.

**Classifying agencies into government-types.** A crucial part of our data work consists of correctly identifying and classifying more than 30,000 government agencies into “national” or “subnational” categories. National governments refer to agencies that belong to the national government (i.e., central or federal administration) but are located anywhere within the country. In contrast, subnational agencies belong to a government that exclusively operates locally (i.e., an individual region or territory within the country). Let’s consider again the case of Catalonia. In our data, we observe offices of the Spanish Ministry of Finance ( _Delegaci ́on Especial de la Agencia Estatal de Adminisistraci ́on Tributaria en Catalu ̃na_ ) and the Catalan Tax Authority ( _Departament d’Economia i Hisenda_ ). Both of these agencies have offices in Barcelona and we will observe them buying the same good from the same firm at a given point in time. Our classification procedure

> 5 Alternatively, one can exploit a slightly different type of variation by comparing how much a given firm sells same product locally vs. non-locally across government types. In the paper, we show that the two types of variation lead to similar results.

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