metadata: authors: 0: author: Garcia Santana,Manuel Jose
1: author: Santamaría,Marta
count: France,Spain
docna: 0: docna: Understanding Home Bias in Procurement :
            Evidence from National and Subnational Governments
docty: Policy Research Working Paper
abstracts: cdata!: Are governments locally biased when
            buying goods and services? Can this home bias explain the
            low integration of procurement markets?  Using one million
            procurement contracts awarded in France and Spain, this
            paper explores whether the home bias follows the
            government's geographical scope: national governments
            have a national bias, while subnational governments have a
            local bias. The relative home bias across governments is
            estimated by comparing how local and non-local
            establishments sell the same product to national and
            subnational governments in the same destination. This paper
            finds that the governments&#x2019; home bias explains a
            big part of the high local concentration in procurement.
display_title: Understanding Home Bias in
            Procurement : Evidence from National and Subnational Governments
listing_relative_url: /research/2023/02/34006441/understanding-home-bias-procurement-evidence-national-subnational-governments
topicv3: Public Sector Management m1326314 696,Trade m1326924 698,Macroeconomic and Structural Policies
            m1327380 439
publishtoextweb_dt: 2023-02-21T00:00:00Z
dois: 10.1596/1813-9450-10311
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2174911
1: author_key: NA
model_extractions: Policy Research Working Paper 10311 Understanding Home Bias in Procurement Evidence from National and Subnational Governments _Manuel García-Santana Marta Santamaría_ Development Economics Development Research Group February 2023
Policy Research Working Paper 10311

# **Abstract**

Are governments locally biased when buying goods and services? Can this home bias explain the low integration of procurement markets? Using one million procurement contracts awarded in France and Spain, this paper explores whether the home bias follows the government’s geographical scope: national governments have a national bias, while subnational governments have a local bias. The relative home bias across governments is estimated by comparing how local and non-local establishments sell the same product to national and subnational governments in the same destination. This paper finds that the governments’ home bias explains a big part of the high local concentration in procurement.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mgarciasantana@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Understanding Home Bias in Procurement: Evidence from National and Subnational Governments**<sup>∗</sup>

Manuel Garc ́ıa-Santana<sup>∗∗</sup> Marta Santamar ́ıa<sup>§</sup> World Bank, CEPR University of Warwick

**JEL codes:** F10, F13, F15.

**Keywords:** Public procurement, border effects, economic integration.

∗We thank Paula Bustos, Gene Grossman, Doireann Fitzgerald, Keith Head, David Atkin, Thierry Mayer, Cailin Slattery (discussant), Xiangyu Shi (discussant) and Jaume Ventura, and participants at FED board, Harvard-MIT, Toronto, Princeton, ERWIT, Barcelona SF, Minneapolis FED, Notre Dame, WB, Groningen, STEG Plenary Theme Conference, and SED for providing comments and suggestions. We thank also thank Miquel Lorente and Santiago Iglesias for their invaluable research assistance and the financial support from _Fundaci ́on Ram ́on Areces._ Manuel thanks the IES Section at Princeton University, where he carried out a big part of this work while visiting as a Peter B. Kenen Fellow.

> ∗∗mgarciasantana@worldbank.org https://garciasantana.weebly.com/

> §marta.santamaria@warwick.ac.uk https://sites.google.com/site/msantamariaupf/
# **1 Introduction**

Modern economies are characterized by large governments that play a unique role as final producers of public goods and services. Throughout this production process, governments of all levels (local, provincial, regional and national) actively buy goods and services from private firms operating in most industries. Overall, the value of government purchases accounts for around 12% of the global GDP.

Government purchases are extremely locally concentrated, both across countries and regions. In 2010, governments awarded less than 2% of the value of procurement contracts to foreign firms in countries like Italy, France, or Spain. Within Spain, only 40% of public procurement expenditure in the region of Catalonia was awarded to firms located outside of Catalonia (of which more than 39% was awarded to firms in Spain and the remaining 1% to foreign firms).<sup>1</sup> One possible explanation for this phenomenon is the presence of natural frictions such as transportation costs, language and cultural barriers, or information constraints, which perhaps are particularly severe in the context of government purchases.

**Home bias in government procurement.** An alternative explanation for this phenomenon is the presence of exceptionally high levels of home bias in government procurement. For example, in the case of the US, the federal government openly and actively discriminates against foreign firms when making its sourcing decisions. In fact, the “Buy American” scheme represented one of the main blocks in Joe Bidden’s trade policy agenda during the 2020 presidential campaign.<sup>2</sup> In the case of Canada, this preferential treatment is not only implemented towards national firms but also firms within the same region. In March 2022, the government of Ontario passed a bill whose main block consists of changing the procurement rules so that purchases must give preferences to local (Ontario-based) firms.<sup>3</sup>

This phenomenon is particularly intriguing in the case of Europe. Even though the EU regulation based on “the single market spirit” should make the market for public procurement perfectly integrated, “Improving access to procurement markets” is one of the six strategic priorities to improve the public procurement system of the European Commission.<sup>4</sup>

**Research question.** In this paper, we ask: _What is the effect of governments’ home bias_

> 1All these numbers come from our own calculations using TED data (see also Kutlina-Dimitrova and Lakatos (2014)). For comparison, in Appendix C, we use our data together with input-output tables and data on trade flows across regions to show that import penetration rates in procurement are significantly lower than those in overall trade also when looking at narrowly defined sectors.

> 2The Buy American Act (1933) requires the US government to “prefer” US-made products and services in its purchases. “Joe Biden will mobilize the talent, grit, and innovation of the American people and the full power of the federal government to bolster American industrial and technological strength and ensure the future is made in all of America by all of America’s workers. Biden believes that American workers can out-compete anyone, but their government needs to fight for them.” (Link)

3 “By harnessing our immense buying power, BOBI (building Ontario businesses initiative) will allow our government to build our businesses in every corner of our province and support new jobs for our workers.”(Link) 4See for instance “Making Public Procurement work in and for Europe”. (Link) 1
_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.

2
classifies the former as a national and the latter as a subnational government agency.<sup>6</sup>

**Preliminary evidence.** To build preliminary evidence on how different government levels contribute to the high local concentration rates observed in public procurement, we start by providing a simple decomposition. The share of procurement value awarded to local establishments in a given region – around 53% on average – can be expressed as a weighted average of this same share computed separately for the two government levels, using the procurement expenditure shares of each government as weights. First, we show that the share of procurement value awarded to local establishments is systematically higher for subnational governments than for national governments, 56% relative to 29% on average. Importantly, we show that compositional effects do not drive these differences. In particular, subnational governments exhibit significantly higher local shares even when looking at narrowly defined products. Second, we document that subnational governments systematically account for most of the procurement value awarded in a given region, 87% relative to 13% on average. A naive back-of-the-envelope calculation implies that if subnational governments behaved as national governments (by awarding only 29% of the procurement value to local firms) the average local share of procurement value would be reduced by almost half (from 53% to 29%).

**A standard model of trade applied to procurement flows.** Our principal empirical analysis will be guided by a 2-country multi-region version of the canonical model of international trade with heterogeneous firms (Melitz, 2003) applied to trade in procurement. The main building blocks of our model are the same as in Chaney (2008) or Breinlich and Cu ̃nat (2016). Regions are asymmetric, firms’ productivity is Pareto distributed, and there exists an homogeneous good sector that pins-down a region’s equilibrium wage. Our model version includes national and subnational governments operating in each region. These governments produce final goods by combining tradable intermediate inputs produced by heterogeneous firms from all regions.

The use of this model is motivated by some novel empirical facts we document about procurement integration. First, we show that firms’ participation in procurement markets increases with the size of the destination market (constructed as total public procurement expenditure). Second, firms participate disproportionally more in their own country. Third, firms participate disproportionally more in their own region within their country, particularly when selling to subnational governments. And fourth, the firms’ sales distribution within an origin has a remarkably similar shape across destinations, and a Pareto well approximates the shape of this distribution. The first, second, and fourth facts are remarkably similar to the facts documented by Eaton, Kortum, and Kramarz (2011) in the context of participation of French firms in export markets. Given this evidence, we build a model of trade in procurement following this literature.<sup>7</sup>

> 6In the case of the US, for example, an equivalent example would be any federal agency located in California (national government) vs. any agency that belongs to the Government of California (subnational government).

> 7The gravity structure that explains procurement sales in our model strongly resembles the one implied by a model that better captures how government contracts are allocated in practice. In particular, we show that our estimated gravity equation is the same as the one predicted by a discrete choice model where individual government agencies first choose the contract size and then the winning firm from a set of potential candidates. The logic of

3
Although stylized, the model will help us quantify the effects of the government home bias on procurement integration across regions and countries while accounting for firm heterogeneity. In the model, we allow the two levels of government to be potentially biased against domestic non-local firms, i.e., firms located in a different region within the country, and against foreign firms, i.e., firms located in a different country. This home bias manifests in two ways: through an intensive margin – which we model as governments having a preference for goods produced by local or domestic firms, and through an extensive margin – which we model as governments imposing a higher entry cost on non-local or foreign firms. Crucially, we allow all these home biases to vary across government levels.

**Reduced-form estimation of the relative home bias.** We follow a two-step strategy to estimate the relative home bias across government types. In the first step, we estimate the relative home bias in the intensive margin, which we identify by exploiting variation in procurement flows within the same _product-destination-government type_ across local and non-local firms. In this step, we find that subnational governments systematically buy relatively less from non-local firms. In particular, we show that the subnational to national purchases gap, i.e.,<sup>subnational</sup> national purchases<sup><u>purchases</u>,is23%</sup> lower for non-local domestic firms (firms located outside the region). Additionally, we find mixed evidence on the relative home bias against foreign firms, with our estimates being not (or weakly) significant and unstable across difference specifications.

In the second step, we estimate the relative home bias in the extensive margin, which we identify by exploiting variation in _firms’ local vs. non-local participation rates_ across government types. We find that the subnational to national participation rate gap is 37% lower for non-local domestic firms. We also find that the subnational to national participation rate gap is 68% lower for foreign firms, suggesting that entry costs for foreign firms are relatively higher when selling to subnational governments.

**Model parametrization.** We parametrize the model to Spain and France, where 76 regiongovernment types (38 regions and two government types) are active in public procurement. The variation we exploit in our reduced-form estimation relies on a double difference ( local vs. non-local and subnational vs. national governments). Therefore, like in a difference-in-differences setting, the levels are not identified. To be able to parametrize the whole model and run counterfactuals, we proceed as follows. First we assume that preferences for local firms are the same for the national and subnational governments. To pin down the levels of home bias towards non-local domestic firms, we assume that national government agencies do not discriminate against non-local domestic firms (neither in the intensive nor in the extensive margin). That is, national agencies do not care about the origin of the firm as long as the firm is “national”. Although arbitrary, this assumption is conservative because it minimizes the overall size of the government home bias. Given this assumption and our estimate of the relative biases, we can recover the levels of home bias towards domestic firms for the subnational government.

this result is similar to that in Head and Ries (2008), where heterogeneous investors bid to obtain ownership of foreign subsidiaries.

4
To pin down the level of home bias towards foreign firms, we calibrate the national government’s biases so that the model jointly matches two relevant moments in the data. For the extensive margin home bias, we calibrate the entry cost imposed by national governments on foreign firms to the observed average fraction of firms from a given region that sells to national governments in a _foreign_ region. For the intensive margin of home bias, we calibrate the preference parameter towards foreign firms of national governments to the average share of procurement value awarded to _foreign_ firms by national governments. Again, given these levels for the national government, we can recover their counterparts for the subnational governments using our estimates of the relative home biases.

Lastly, we parametrize the rest of parameters in the model as follows. First, we set the elasticity of substitution across varieties and the shape of the Pareto productivity parameter to standard values used in the literature. Second, we calibrate the amount of equipped labor in each region and the relative size of each government level so that the model matches the relative size of each regiongovernment level in the data. Finally, we parametrize _origin-destination_ frictions as a function of distance and calibrate the distance elasticity to match the level of the aggregate local share of procurement observed in the data. Conceptually, this last step consists of attributing all the residual variation in procurement flows, i.e., the one not explained by governments’ home bias, to the existence of origin-destination bilateral frictions.

**Quantification.** We use the model to quantify the effect of the governments’ home bias on procurement market integration within and across countries. We start by asking: _What is the effect of the relative home bias across governments on within country procurement integration?_ To answer this question, we eliminate the bias of the subnational government against non-local domestic firms (essentially equating it to the national government). We find that removing the subnational government home bias against foreign firms would substantially increase procurement flows across regions within France and Spain. In particular, the average share of procurement value awarded to non-local domestic firms would increase by 20%.

We next ask: _What is the effect of the government home bias on international procurement integration?_ Our first finding is that removing the relative home bias across governments would significantly impact procurement integration across countries. In particular, by making the entry costs paid by foreign firms when selling to subnational governments equal to those paid when selling to national governments, the share of procurement awarded to foreign firms would increase by 50%. However, it would remain low, i.e., average participation rate of 0.43%.

Our second finding is that removing national or subnational governments’ home bias would generate very different quantitative effects. We show that the share of procurement awarded to foreign firms would be around two times larger in a counterfactual in which national government agencies had no preference for domestic firms. Again, despite this large increase, the share of procurement awarded to foreign firms would remain relatively low, i.e., 1.24%. In contrast, we show that removing completely subnational governments’ preference for domestic firms would enormously affect procurement market integration between France and Spain. In particular, the 5
average share of procurement awarded to foreign firms would increase by a factor of 50, reaching a value of around 15.34%.

There are two reasons why the impact of subnational governments on procurement trade flows across countries is so much larger. First, subnational governments account for more than 80% of total procurement expenditure. Second, our estimates suggest that subnational governments impose a fixed entry cost to foreign firms around 80% higher than the one set by national governments.

**Concerns and limitations.** Our methodology is subject to several potential concerns and limitations. For our strategy to be valid, we need to assume that the above-mentioned natural frictions that reduce trade in procurement are constant within an origin-destination across government types. However, it is possible that agencies belonging to national and subnational governments have different levels of information, which could explain why local shares are lower in national governments. For example, public officials working in a national government agency in Barcelona may share information with their colleagues in Madrid and have more information about potential suppliers outside Catalonia than public officials from a subnational agency in Barcelona. This type of information asymmetry across government types would violate our identification assumption that natural frictions (information frictions in this case) vary at the origin-destination level but not across government types. In particular, our estimates of the subnational governments’ home bias would be upward-biased.

We believe that our framework, combined with several robustness checks, minimizes the extent of this concern. First, our data consists of contracts published in the Tenders Electronic Daily (TED) journal of the European Commission, which ensures that procurement projects are publicly advertised, and firms from anywhere in Europe should easily be able to learn about the projects and compete for them. This publicity obligation applies to contracts above a given monetary threshold that should be attractive to both domestic and foreign firms. Second, our identification of the intensive margin parameter exploits variation within firms that already sell to both government levels in their home region and outside. Therefore, it implies that at least one agency within each government level out of the home region already has a sourcing relationship with the firm. And third, for a subsample of projects for which we have information on all the firms bidding, we show that subnational governments systematically award the contracts to local firms even when firms from other regions with similar productivity compete for the same project.

Another potential concern is that the granularity of our data is not narrow enough to make sure that we are comparing similar contracts awarded by the two government levels, both in terms of size and the good or service procured. To deal with this issue, we show that our results are robust to cutting the data in many alternative ways. For example, we show that our baseline results hold when controlling for 4-digit sectors or when carrying out the analysis within extremely homogenous goods, e.g., printing paper.

Lastly, one could be concerned that our results are driven by national and subnational governments using different purchasing strategies when procuring goods, works, or services. For example, given the centralized structure of national governments in Spain and France, it could be possible 6
that the national government buys goods or services using its agencies located in the country’s capital and then distributes the procured goods or services to offices and agencies of the national government in other regions. To address this concern, we show that our results are similar when dropping all the purchases from the capital regions, Madrid and Ille-de-France.

**Final suggestive evidence.** Finally, we show some evidence that reconcile our findings with the fact that discriminatory measures against non-local firms are not allowed in the EU. We show that higher transparency in the procurement process is associated with a convergence in the estimated relative home bias between subnational and national governments. In particular, we show that the estimated differences in home bias across government levels significantly decrease when using only procurement contracts awarded through an electronic auction or directly founded by the EU.<sup>8</sup>

# **2 Contribution and related literature**

**Border effects.** The segmentation of international goods markets, usually referred to as border effects, was postulated as one of the “Six Major Puzzles” in international macroeconomics by Obstfeld and Rogoff (2000). In his pioneering paper, McCallum (1995) found that trade among Canadian provinces was twenty times greater than trade between Canadian provinces and U.S states. Although extensive subsequent work convincingly showed that the border effects are not as extreme as initially argued (Anderson and Van Wincoop, 2003), a fair interpretation of the current literature is that border effects exist and are quantitatively relevant (Head and Mayer, 2014). In fact, and perhaps more surprisingly, border effects also seem to be present across (supposedly) perfectly integrated countries (Santamar ́ıa, Ventura, and Ye ̧silbayraktar, 2021) and even across regions within the same country (Wolf, 2000). In our paper, we aim to contribute to the understanding of this puzzle by investigating the extent to which differences in home bias across government types, i.e., national vs. subnational, can account for the strong border effects observed in international and inter-regional public procurement flows.

**EU market integration.** Our paper contributes to the literature that aims to understand the causes of market fragmentation in the EU (Head and Mayer (2021), Chen (2004), Head and Mayer (2000)). In that respect, our paper is closely related to Herz and Varela-Irimia (2020) and Mulabdic and Rotunno (2022), which to the best of our knowledge, were the first two papers documenting the presence of border effects in European government procurement. Our results show that the observed high fragmentation of government procurement can be explained by the presence of hidden protectionism across and within countries, which governments implement by concentrating high shares of their purchases on local firms.

**Determinants of home bias.** Our baseline findings are silent about the causes of this protectionism. One possibility is that governments internalize consumers’ preferences, which may

> 8Consistent with our evidence, Lewis-Faupel, Neggers, Benjamin, Olken, and Pande (2016) find that Indonesian regions with e-procurement are more likely to have winners from outside regions and better procurement outcomes.

7
be biased towards locally produced goods (Morey (2016)) or consumers’ identities, which may be expressed by consuming local varieties (Atkin, Colson-Sihra, and Shayo (2021)). A second possibility is that regional and national governments maximize their respective citizens’ welfare and use public procurement to manipulate the terms of trade. In that respect, our paper is related to the vast literature that analyzes optimal trade policy (Limao, 2008). A third possibility is that governments’ home bias is simply driven by “politics”. Applying the Grossman and Helpman (1994)’s theory to the context of public procurement, it could be that the presence of locally based lobbies biases governments’ choices towards local firms, even if that is not in the interest of local consumers. Along these lines, recent work shows that firms with political connections are more likely to win grants and procurement contracts (Choi, Penciakova, and Saffie (2022) in the case US and Baltrunaite (2020) in the case of Lithuania).

**Trade in services.** The richness of our data enables us to observe trade in procurement services, allowing us to contribute to an active literature on the tradability of services (Mu ̃noz (2021), Dingel, Gottlieb, Lozinski, and Mourot (2022)). Procurement services account for one third of procurement contracts in our data (and up to two thirds if we count construction as a service). Using our granular establishment-level data, we find that the gravity equation derived from a standard theoretical framework provides as good a fit for services as for goods, confirming its suitability to study trade in services.

**Government decentralization and economic outcomes.** Finally, and more tangentially, our paper contributes to the debate about the optimal degree of government decentralization (e.g., Oates (1985), Besley and Coate (2003), Treisman (2007), Tricaud (2021)). Our results present a new channel through which descentralization affects economic outcomes. In particular, our results suggest that a higher share of government expenditure accounted for by subnational governments will be associated to a higher segmentation of economic activity both within and across countries within the EU.

The rest of the paper is organized as follows. In section 3, we explain how we built our dataset and present some summary statistics. In section 4, we present some novel stylized facts that motivate using a canonical model of trade with heterogenous agents for our analysis. In section 5, we present our model. In section 6, we explain our strategy to estimate the governments’ home bias. In section 7, we analyse potential concerns and limitations of our empirical findings and explore different mechanisms behind them. In section 8, we explain how we calibrate the rest of the parameters of the model. In section 9, we use the calibrated model to quantify the role of the governments’ home bias in the integration of procurement markets. Finally, in section 10, we conclude.

# **3 Micro Data on Procurement Flows**

We use publicly available data from Opentender, a procurement platform funded by a European Union Horizon 2020 project devoted to increasing the transparency of the procurement sector in Europe. The Opentender dataset consists of merging data from different national sources and 8
Tenders Electronic Daily (TED), which is the online version of the supplement to the Official Journal of the EU dedicated to European public procurement. All procurement contracts above a certain threshold awarded by European public institutions must be published in TED.<sup>9</sup> Therefore, although many contracts below the threshold are published in TED, relatively big projects will be over-represented in our data.

Because we want to focus on procurement contracts that are comparable across countries, we focus our analysis on those contracts published by TED only, with the intention to avoid sample selection issues that might arise when combining data from national platforms for different countries. National regulations concerning public procurement not subject to the EU Directives do not need to converge in many essential aspects. Among others: the choice of procedure, its publication, the set of thresholds, the choice of participants, and the awarding criteria. On the contrary, EU Directives assure that the procurement legal framework is the same for all Member States. We provide details about the procurement system in the European Union in Appendix A.

We currently restrict our sample to contracts awarded in France and Spain. Overall, our final dataset contains 1,194,482 lot-level contracts between 2009 and 2019. Each lot observation contains information of a contract between an establishment (supplier or bidder) and a contracting authority (buyer). For each awarded lot we have data on: the procedure used to allocate the contract, the product code of the tender (9-digit products classified according to the Common Procurement Vocabulary, CPV), the year, the estimated price, the final price, the name and location of the government agency buying, and the name and location of the firm to which the establishment selling belongs. Our data has two limitations related to the level of detail at which contract information is reported. First, for some firms we do not know the municipality, so we always use the region of the firm as the firm’s location to keep as many firms in our analysis as possible. Second, for most of the observations, the product code information is not provided at the 9 digit level, but at a more aggregated category (between 2 and 9 digits)<sup>10</sup> . We next explain our procedure to classify buying agencies into national vs. subnational governments. We provide more detailed descriptive statistics of our final data set in Appendix B.

# **3.1 Classifying agencies into government types: national vs. subnational**

At the core of our data work is to classify buying agencies into different government types. Our main goal is to divide all contracting authorities into four geographical levels: national, regional, provincial, and local. Additionally, we want to isolate some government agencies that do not 9Currently, these thresholds are determined by the EU Directives (Directive 2014/24/EU on public procurement, and Directive 2014/25/EU on procurement by entities operating in the water, energy, transport and postal services sectors). The thresholds for publication depend on the type of government and type of contract. For example, the threshold for supplies and services contracts by central government authorities is e139,000. We provide more details about the procurement system in Europe in Appendix A.

> 10In our clean sample, 10.6% of observations only have information at the 2-digit product code level, 62.2% of observations have information of the product code at the 3 to 5 digits level and 27.2% of contracts have information at 6 digits or higher.

9
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.

10
**The size of expenditure: national vs. subnational governments.** According to our micro data, subnational governments account for a significantly higher fraction of awarded procurement value than national governments in most regions. When computing a simple average across regions, we find that around 87% of procurement value is awarded by subnational governments. In 33/38 regions, national governments account for less than 20% of the awarded procurement value. The only two regions in which the share of procurement value awarded by national governments is significantly higher are the two regions where the capitals are: Madrid (73%) and and Ile de France (64%). To have a sense on how well our micro data reproduces the procurement value awarded by national vs. subnational governments at the national level in Spain and France, we compare the share of expenditure done by the two governments with the one reported by the OECD.<sup>13</sup>

According to the OECD, the “Sub-central government procurement as a percentage of general government procurement, excluding social security funds” was around 56% for France and 81% for Spain in the year 2010. We find slightly larger numbers in our micro-data, especially for France. We believe the differences come from the following reasons. First, the OECD subcentral government share of procurement is computed at the country level rather than as an average of the subnational share in each region. This means that larger regions account for a higher weight in the average. In the case of France, the number is dominated by the share of subcentral procurement expenditure in Ille de France, that is lower than average. In addition, our data only contains a subset of projects whose value is above a certain threshold whereas, in principle, the OECD should report numbers based on the universe of procurement contracts. Third, our classification of agencies into types of governments may not be the same as the one used by the OECD. And fourth, because we are not sure about how to classify them, we excluded hospital, universities and independent institutions, whereas the OECD is most likely considering them by classifying them into one of the two categories.

# **3.2 National vs. subnational governments: preliminary evidence**

Before introducing our stylized facts at the firm level, we provide some preliminary evidence that points towards a different purchasing behavior of subnational and national governments. As mentioned before, procurement market integration seems to be low, given the high level of geographical concentration of procurement expenditure.<sup>14</sup> In this subsection, we show that subnational governments tend to buy significantly higher fractions of their purchases from firms located within the same region than national governments.

We denote total sales by firms located in region _o_ to region _r_ by _Xor_ . We denote region _r_ ’s total expenditure, i.e., expenditure by the national government plus the subnational government,

## 13See OECD Link.

> 14Although the focus of this paper is on differences across government types, in section C of the Appendix we also show that import penetration rates are significantly lower in procurement (as a whole) than in the private sector, both when looking at across countries and within-country procurement flows.

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by _Xr_ . We define the expenditure share of region _r_ on goods and services produced by firms in region _o_ as: Given this notation, we define the local share of procurement as the share of procurement value in region _r_ awarded to firms from that same region _r_ : which is simply the sum of the expenditure by both national and subnational governments on firms located within the same region ( _Xrr_<sup>_n_+</sup><sup>_X_</sup> _rr_<sup>_s_)dividedbythesetwogovernments’totalexpenditure</sup> ( _Xr_<sup>_n_+</sup><sup>_X_</sup> _r_<sup>_s_).Aftersomesimplemanipulationswegetthefollowingexpression:</sup> Equation (2) shows that the share of procurement value awarded to local firms in a given region ( _πrr_ ) can be decomposed into a weighted average of the respective shares for the two types of governments ( _πrr_<sup>_n_and</sup><sup>_π_</sup> _rr_<sup>_s_), using each government’s share of expenditure in the region as weights.The</sup> numbers on top of the variables represent the simple averages across regions. A couple of things are worth noticing. First, subnational governments account for a much higher fraction of total procurement expenditure than national governments (0.87 vs. 0.13 on average). Second, subnational governments allocate significantly more expenditure to local firms than national governments. On average, national governments purchase 29% of their procurement value from local firms while subnational governments (panel B) purchase 56% of value from local firms. Figure I shows the local share of expenditure by national governments (panel A) and subnational governments (panel B) in each region.

**A naive counterfactual.** We next perform a naive counterfactual to assess how much of the observed aggregate local share of procurement can be potentially explained by the different purchasing behavior of subnational vs. national governments. To that end, we set _πrr_<sup>_s_equalto</sup><sup>_π_</sup> _rr_<sup>_n_</sup> for all regions and recompute the implied local share, _πrr_ . Table I shows the results from this counterfactual. Overall, we find that forcing subnational governments to behave like national governments would decrease the aggregate local share of procurement by 42% on average. There is high variation across regions, though. In the regions of Andalucia (ESP) or Normandie (FR) the difference between subnational and national governments respectively explains 46% and 51% of the aggregate local procurement share. This is a combination of the large gap in the local procurement share across governments (48% vs. 25% for Andalucia and 69% vs. 32% in Normandie) and the

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<!-- Start of picture text --> (-) x ((——)<br>NEC,<br>(-) x((—)<br>NAC<br><!-- End of picture text --> »
Table I A naive counterfactual exercise

||||Data|||Count|erfactual|
|---|---|---|---|---|---|---|---|
||(1)<br>_X_<sup>_n_</sup><br>_r_<br>_X_<sup>_n_</sup><br>_r_ <sup>+</sup><sup>_Xs_</sup>_r_|(2)<br>_π_<sup>_n_</sup><br>_rr_|(3)<br>_X_<sup>_s_</sup><br>_r_<br>_X_<sup>_s_</sup>_r_+_X_<sup>_s_</sup>_r_|(4)<br>_π_<sup>_s_</sup><br>_rr_|(5)<br>_πrr_|(<br>|6)<br>_πrr_|
|Andalusia (ESP)|0.04|0.25|0.96|0.48|0.46|0.25|_↓_46%|
|Catalonia (ESP)|0.02|0.40|0.98|0.60|0.59|0.40|_↓_33%|
|Madrid (ESP)|0.73|0.68|0.27|0.80|0.71|0.68|_↓_<br>5%|
|Ile de France (FR)|0.64|0.68|0.36|0.88|0.75|0.68|_↓_<br>9%|
|Haute Normandie (FR)|0.10|0.32|0.90|0.69|0.65|0.32|_↓_51%|
|Bretagne (FR)|0.20|0.31|0.80|0.68|0.61|0.31|_↓_48%|
|mean|0.13|0.29|0.87|0.56|0.53|0.29|_↓_45%|

**Notes:** This table shows the results from applying a naive counterfactual to the decomposition shown in equation 2. This counterfactual consists of setting _πrr_<sup>_s_equalto</sup><sup>_π_</sup> _rr_<sup>_n_forallregionsandrecomputingtheimplied</sup><sup>_πrr_.Columns1-5showthedifferentcomponentsof</sup> equation 2 as measured in the data for each region. Column 6 shows _πrr_ computed in the naive counterfactual and the percentage change with respect to the observed _πrr_ .

local expenditure, and _Xrr_<sup>_j,n/X_</sup> _r_<sup>_j,n_</sup> and _Xr_<sup>_j,n/_�</sup><sup>_J_</sup> _j_<sup>_X_</sup> _r_<sup>_j,n_</sup> are their respective counterparts for a national government. Therefore, the local expenditure share of a given government type can be expressed as a weighted average of the local expenditure shares across different products, where the weights are the expenditure shares of each product.

In figure II, we plot the _within_ component for 47 different product categories for subnational vs. national governments. In particular, for each product category, we first compute its local expenditure in each region and then take the average across regions. First, we find a strong positive correlation in local expenditure shares across government types, which points towards the fact that some product categories are intrinsically more tradable than others. For example, the product categories “Laboratory, optical and precision equipments (38)” and “Software package and information systems (48)” exhibit a low local expenditure share for the two governments. In contrast, product categories (15) “Food, Beverage, Tobacco and related products” and (80)“Education and training services” exhibit a high local expenditure share for the two governments. Second, and more importantly, we find that subnational governments have systematically higher local expenditure shares across product categories. In particular, we find that being the case in 43 out of the 47 product categories.<sup>15</sup> To further explore the possibility that differences in local expenditure shares across governments are driven by composition, we compute counterfactual local shares for subnational governments

> 15The only categories for which this does not hold are (09) “Petroleum, fuel, electricity and other sources of energy”, (91) “Membership organisation services”, (19) “Leather and textile fabrics, plastic and rubber materials” and (33) “Medical equipments, pharmaceuticals and personal care products”.

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# Figure II

Local expenditure share within products <!-- Start of picture text --> 80<br>77<br>09 15 60<br>90<br>19 91 35 6331 7332 7139144355 4465 925003 98 51 45 857016<br>33 249330 3472 42 7918 75 41<br>37<br>66 48 38 64 22<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>subnational governments<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>national governments .3<br>.2<br>.1<br>0<br><!-- End of picture text -->

**Notes:** Figure II shows the “within” component in equation 3 of the main text. In particular, it shows the term _Xrr_<sup>_j,n_</sup> � _Xr_<sup>_j,n_</sup> � for different _j_ ’s averaged across regions. Each observation corresponds to a two-digit product (division) of the CPV

in which we keep, for each product category, their actual _within_ component but set the _across_ component to their counterparts of national governments. The main result from running this counterfactual exercise is that subnational governments would still have systematically higher local expenditure shares than national governments. In the data, national governments spend around 29% on local firms, whereas this number is 56% for subnational governments. In the counterfactual, we equate the expenditure composition of subnational governments to that of national governments and we find that the local share of subnational governments would slightly decrease to 55%.<sup>16</sup>

# **4 Firm-level stylized facts on procurement market integration**

We next present some empirical regularities about firms’ participation in procurement markets, within and across countries, that will motivate our model. We first show that firms’ participation in procurement markets (defined by the destination region) follows the same overall patterns documented in the context of exporting markets in the private sector (e.g., Eaton, Kortum, and Kramarz (2011)). However, we show that the patterns related to participation in local regions significantly differ depending on whether the buyer is a subnational or national government.

Our dataset contains 192,155 establishments in total (defined as a firm at a specific origin region). Out of them, 66% (126,935) only sell to governments at home, i.e., the same region where they are located. This means that 34% of the firms in our sample “export” to governments out of their home region, either within the same country or across country borders.

> 16See Appendix D for details on how we perform this counterfactual.

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# Figure III

Procurement flows destination (entry) <!-- Start of picture text --> IDF<br>CAT<br>MAD CHA BANBOU PICPOCLORAUVCVLHANALSMIPLAR AQUBREPALNPC PAC RHA<br>VAL PVA AND LIM FRC<br>RIO CANNAVEXT ASTIBAMURCYM CYL GAL ARA COR<br>IDF<br>MAD<br>MIPLAR CAT<br>PAC<br>AQU PVA<br>CHA BOU HAN NPC VAL<br>POC CVL BREPAL RHA MUR CYL ARA AND<br>LIM PIC LORAUVALS EXT ASTIBACYM GAL<br>1 10 100 1 10 100<br>Market size (in awarded value, in billions of eur) Market size (in awarded value, in billions of eur)<br>Firms located in Catalonia Firms located in Ile de France<br>(a) (b)<br>10000<br>10000<br>1000<br>1000<br>100<br>100<br>Number of firms Number of firms<br>10<br>10<br>1 1<br><!-- End of picture text -->

**Notes:** Figure III shows the number of firms from Ile de France (panel A) and from Catalonia (panel B) that sell to different regions as a function of total procurement expenditure at destination measured in ebillions.

We choose the Spanish region of Catalonia and the French region Ile de France to illustrate the empirical regularities we find in the overall sample. Although Catalonia and Ile de France are quite different in their geographical location within the country, their size and their institutional role in Spain and France, we observe the same pattern of firm participation in public procurement. Similar patterns emerge for the other regions in Spain and France.

**Fact 1.** _Firms’ participation in procurement increases in market size as measured by governments’ total expenditure on procurement but i) firms participate disproportionately more in their own country and ii) within their country, firms participate disproportionately more in their own region._

Figure III shows the number of firms from Catalonia (panel A) and from Ile de France (panel B) that sell to different destination markets as a function of the (procurement) market size of the destination region. Destination regions in the home country are plotted in blue, while foreign destination regions are plotted in red. The first clear pattern is that firms’ participation (number of firms in each market) increases with the market size of the destination measured as governments’ total expenditure in procurement. The second pattern is that, conditional on market size at destination, firms’ participation is higher within the home country. Comparing home destinations (blue) and foreign destinations (red) we see a clear participation gap. The third pattern that emerges is that, within the home country, the home region (Catalonia and Ile de France respectively) is an outlier with an exceptionally high entry of firms, well above the linear trend.

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# Figure IV

Procurement flows destination (entry) by government type <!-- Start of picture text --> CAT<br>MAD<br>CAT<br>VAL ANDPVA MAD<br>RIO NAVEXT AST MUR IBA CYL GAL ARA<br>CAN<br>CYM VAL<br>ARA<br>IBA PVACYLMURAND<br>LAR AQU EXT GAL<br>IDF CAN<br>HAN NPC CYM AST MIP<br>CHA PIC ALS AUVBRE NAV LIM CVL<br>1 10 100 1 10 100<br>Market size (in awarded value, in billions of eur) Market size (in awarded value, in billions of eur)<br>(a) subnational (Catalonia) (b) National (Catalonia)<br>IDF IDF<br>NPC PAC RHA<br>LIM CHAFRC BOUPICBANALS CVL LORPOCHANAUVMIPLARBRE AQU PAL PAL AQUCVLLORNPCBREMIP RHAPAC<br>COR LIMHAN AUV<br>POC ALSBOU LAR<br>PIC<br>CHA<br>BAN COR FRC<br>MAD<br>PVA CAT<br>VAL<br>EXT MUR ARA<br>1 10 100 1 10 100<br>Market size (in awarded value, in billions of eur) Market size (in awarded value, in billions of eur)<br>(c) subnational (Ile de France) (d) National (Ile de France)<br>10000 10000<br>1000 1000<br>100 100<br>Number of firms Number of firms<br>10 10<br>1 1<br>10000<br>10000<br>1000<br>1000<br>100 100<br>Number of firms Number of firms<br>10 10<br>1 1<br><!-- End of picture text -->

**Notes:** IV shows the number of firms from Catalonia (panels A and B) and Ile de France (panels C and D) selling to different regions-government types as a function of total procurement expenditure at destination measured in ebillions.

**Fact 2.** _The difference in participation of firms in their home region is particularly strong for subnational governments._

Figure IV shows firm participation in Catalonia and Ille de France respectively disaggregated by subnational and national governments. After breaking down the number of firms partcipating by government level we see the second stylised fact: while participation in the home region is on trend for sales to national governments, participation of firms in their home region is a clear outlier when selling to the subnational government.

**Fact 3.** _The firms’ sales distribution within an origin o has a remarkably similar shape across destinations r. The shape of this distribution is well approximated by a Pareto except for the very_

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# Figure V

Sales distribution in procurement <!-- Start of picture text --> Rhône-Alpes Provence-Alpes-Côte d'Azur COMUNIDAD DE MADRID ANDALUCÍA<br>Nord-Pas-de-Calais Bretagne PAÍS VASCO CASTILLA Y LEÓN<br>.001 .01 .1 1 .001 .01 .1 1 .001 .01 .1 1 .001 .01 .1 1<br>Fraction of firms selling at least that much Fraction of firms selling at least that much<br>Firms located in Ile de France Firms located in Catalonia<br>(a) (b)<br>1000 1000<br>100 100<br>10 10<br>1 1<br>.1 .1<br>.01 .01<br>.001 .001<br>.0001<br>.0001<br>1000 1000<br>100 100<br>10 10<br>1 1<br>.1 .1<br>Sales in market relative to mean .01 Sales in market relative to mean .01<br>.001 .001<br>.0001 .0001<br><!-- End of picture text -->

**Notes:** V plots the distribution of sales by firms in Ille de France and Catalonia, respectively, in their four largest destination markets.

_lower end of the distribution._ Figure V plots the distribution of sales by firms in Ille de France and Catalonia, respectively, in their four largest destination markets. As we can see in both figures, the sales distribution has a very similar shape across all destinations that seems to follow very closely a Pareto distribution.

We have seen that public procurement markets are not integrated: they present a high degree of geographical concentration in purchases and low cross-regional and cross-country participation rates of firms, with subnational governments driving these patterns. In the next section, we use these empirical facts to build a model of trade in procurement to quantify the role of the governments in deterring procurement market integration.

# **5 Model**

In this section, we present a multi-region static model of trade in procurement which is heavily built on Chaney (2008) and Breinlich and Cu ̃nat (2016). There are R regions indexed by _o, r_ . Each region is populated by _Lr_ consumers who inelastically supply one unit of labor. The utility of a representative consumer from region _r_ is given by: where _Cr_<sup>_q_is the consumption of a private perfectly tradable homogeneous good;</sup><sup>_C_</sup> _r_<sup>_n_is the consump-</sup> tion of non-tradable final good produced by the national government located in region _r_ ; and _Cr_<sup>_s_</sup> is the consumption of a non-tradable final good produced by the subnational government located in region _r_ . Given this utility function, _μ_ governs the share of expenditure on goods produced 18
by governments, and _μγr_<sup>_n_and</sup><sup>_μγ_</sup> _r_<sup>_s_thesharesofexpenditureongoodsproducedbynationaland</sup> subnational governments respectively.

# **5.1 The private good sector**

The private good _q_ is an homogeneous freely tradable good produced by a perfectly competitive representative firm that has access to the following CRS production function: The perfectly competitive firm’s problem yields to the following first order condition: Since the good is freely tradable, there will be a unique price in all region regions, _P_<sup>_q_</sup> . By setting this private good sector as the numeraire we have that: which means that wages are perfectly pinned down by the different regions’ productivity levels.

# **5.2 Governments**

Governments produce non-tradable final goods by combining intermediate inputs produced by private sector firms. The representative government of type _g_ in region _r_ combines intermediate inputs from private sector firms as follows: where _yor_<sup>_g_(</sup><sup>_j_)istheintermediategoodusedbygovernment-type</sup><sup>_g_inregion</sup><sup>_r_providedbyfirm</sup> _j_ producing in region _o_ , and _σ >_ 1 is the elasticity of substitution across varieties. Ω<sup>_g_</sup> _or_<sup>isthe</sup> endogenous set of firms from origin _o_ that sell in destination _d_ to government type _g_ .<sup>17</sup> Hence we can write the demand by government-type _g_ from region _r_ for the good produced by firm _j_ located in region _o_ as:

> 17In our baseline version of the model, we assume that national and subnational governments use the same production function and differ only in the home bias parameter _αor_<sup>_g_.Thisismotivatedbythefactthatsectoral</sup> composition did not play any role in explaining the share of procurement awarded to local firms in section 3.2. We extend our one-sector model to a multi-sector setting where governments may have different sectoral spending shares in section E of the Appendix, and include sector*government fixed effects accordingly in our estimations in Section 6.

19
where _Pr_<sup>_g_istheaggregatepriceindexpaidbygovernment-type</sup><sup>_g_inregion</sup><sup>_r_;</sup><sup>_Y_</sup> _r_<sup>_g_istotaldemand</sup> by that government; and _p_<sup>_g_</sup> _or_<sup>(</sup><sup>_j_) is the price charged by firm</sup><sup>_j_from region</sup><sup>_o_.The parameter</sup><sup>_α_</sup> _or_<sup>_g_(for</sup> which we use blue color throughout the model section) represents an exogenous demand shock that is _origin-destination-government_ type specific. This parameter will allow us to capture the extent of the governments’ home-bias in the intensive margin, i.e., the fact that governments award less procurement value to non-local or foreign firms. For convenience, we write the demand function in terms of sales: where _Xr_<sup>_g_=</sup><sup>_Y_</sup> _r_<sup>_gP g_</sup> _r_<sup>istotalexpenditurebygovernment-type</sup><sup>_g_inregion</sup><sup>_r_.</sup>

**Alternative model: Discrete choice model of individual government agencies.** An alternative way to model the governments’ behavior, that gives identical empirical predictions on the intensive margin, is to consider individual government agencies that award contracts within each government level in a given destination. These individual government agencies can be modelled as a collection of statistically identical agencies that independently allocate contracts by choosing one firm among the available candidates in two stages: in the first stage, the agency chooses the quantity of procurement goods desired given the utility function; in the second stage, the government agency chooses the winning firm among the candidates. Under reasonable assumptions, this choice follows a multinomial logit model. This alternative model is a closer representation of the award process in public procurement. In particular, the individual government agencies can choose the candidate they prefer but cannot modify the value of the contract since this value is posted before selecting the winning firm.

As shown in Anderson, De Palma, and Thisse (1992), when the idiosyncratic term in the utility that each bidding firm gives a government agency is drawn from an extreme value distribution, the aggregate demand that represents this collection of government agencies is the same as the aggregate demand from the CES production function of the representative government. This equivalence of aggregate demands between the multinomial logit model of discrete choice and the CES model has been used in previous papers such as Head, Mayer, and Ries (2009), on the purchase of offshoring services, and Head and Ries (2008), on the choice of FDI stocks by multinational corporations. We specify an alternative discrete choice model of individual government agencies and show the above-mentioned equivalency in Section F of the Appendix.

# **5.3 Firms: production of intermediate goods**

There is a continuum of firms in each region who compete under monopolistic competition with firms from its own region and with firms from other regions. Firms have access to a constant marginal cost technology where labor is the only factor of production. A firm _j_ in region _o_ has efficiency _z_ ( _j_ ) and its cost of labor is _wo_ . Therefore, the unit cost of producing is: 20
We assume that firms’ productivity is distributed over [1 _,_ + _∞_ ] and according to a Pareto distribution with a shape parameter _θ_ (with _σ −_ 1 _< θ_ ). Therefore, the measure of firms in region _o_ that can produce their variety with efficiency at least _z_ is _z_<sup>_−θ_</sup> .

# **5.3.1 Firms’ problem**

Since there are constant marginal costs and the demand functions in each market are independent of each other, we can split a firm’s problem into independent problems in each market. Let’s focus on a firm from region _o_ producing good _j_ selling to destination _r_ and government type _g_ . The firm’s profits in this market are given by: where _τor_ measures origin-destination bilateral natural frictions such as transportation costs, information, etc. and _Eor_<sup>_g_(for which we use red color throughout the model section) captures the cost of</sup> entry into different markets. This parameter, which we assume to be _origin-destination-government type_ specific, will allow us to capture the government’s home bias in the extensive margin, i.e., the fact that barriers to entry for non-local and foreign firms may vary at government-type level. The firm maximizes the following problem: which yields to the optimal price charged by the firm:

**Entry.** A firm _j_ producing in region _o_ will sell to government-type _g_ in region _r_ if and only if Π<sup>_g_This implies that there will be a productivity cutoff,</sup><sup>_z_ ̄</sup><sup>_g_</sup> _or_<sup>(</sup><sup>_j_)</sup><sup>_>_0.</sup> _or_<sup>, that measures how productive</sup> a firm from region _o_ must be in order to sell to government type _g_ in region _r_ :

Notice that this productivity cutoff is not only origin-destination but also government type specific. Intuitively, within a given origin-destination, a higher number of firms will sell to government-types 21
with lower barriers to entry _Eor_<sup>_g_orhigherdemandshocks</sup><sup>_α_</sup> _or_<sup>_g_.Also,asitisstandardinthistype</sup> of models, the productivity cutoff is increasing in firms’ origin wages _wo_ and transportation costs _τor_ , and decreasing in market size _Xr_<sup>_g_andthepriceindex</sup><sup>_P g_</sup> _r_<sup>.</sup>

**Sales.** Sales to government-type _g_ in region _r_ by firm _j_ producing in region _o_ are given by:

This equation will be crucial to implement our identification strategy. As it is commonly the case in these models, firms’ sales are increasing in market size _Xr_<sup>_g_and how competitive firm</sup><sup>_j_is relative</sup> to other firms selling into that market ( _Pr_<sup>_gz_(</sup><sup>_j_)</sup><sup>_/τorwo_).Thenovelfeatureofthisequationisthat</sup> firms’ sales within a particular destination to a given government type depend on the governments’ preference parameter _αor_<sup>_g_.That is, conditional on origin-destination factors, variation in how much</sup> a firm _j_ sells across government types identifies their relative _αor_<sup>_g_.</sup>

## **5.4 Price index and equilibrium**

The aggregate price index paid by government-type _g_ in region _r_ is given by: Notice that, as in Chaney (2008), we are assuming that the mass of potential entrants in a given region _o_ is equal to its size measured by _woLo_ . This assumption, together with wages being exogenously pinned down in the private sector, will allow us to simultaneously solve for all the relevant equilibrium variables. We provide a more formal explanation on how we compute the equilibrium of the model in Appendix G.

# **6 Estimating the government home bias**

In this section, we present our strategy to estimate the home-bias parameters across government types, both in terms of the intensive and the extensive margin. The intensive margin of home bias is captured by the origin-destination-government type parameter _αo_<sup>_g_</sup> ( _c_ ) _r_ ( _c_<sup>_′_</sup> )<sup>.Noticethat,because</sup> we will allow the _α_ ’s and _E_ ’s to depend on whether the firm is from a different region within the same country or from a foreign region, we have introduced some extra notation that reflects the country where the region is, i.e., _c_ , _c_<sup>_′_</sup> .

## **6.1 Intensive Margin**

To estimate the intensive margin of the government home-bias, we impose the following structure: 22
We denote with _α_<sup>_g_</sup> the preference for domestic non-local firms of government type _g_ and with _α_ ̃<sup>_g_</sup> the preference for foreign firms of government type _g_ . Notice that we normalize to one the preference ̃ for local firms ( _o_ = _r_ ) of the two governments. In addition, we assume that the parameters _α_<sup>_g_</sup> and _α_<sup>_g_</sup> vary across governments but are constant across regions. That is, subnational/national governments have the same preferences in every region.

Our hypothesis is that the size of the home bias overlaps with the geographical scope of the government. In the case of the subnational government, we expect it to discriminate against non ̃ local firms ( _o_ = _r_ ), no matter whether they are domestic or not, i.e., _α_<sup>_s_</sup> _<_ 1 and _α_<sup>_s_</sup> _<_ 1. In the case of the national government, our hypothesis is that it is indifferent between buying local or non-local as long as the firm is domestic, i.e., _α_<sup>_n_</sup> = 1, but it will discriminate against firms from other countries, i.e., _α_ ̃<sup>_n_</sup> _<_ 1.

We use the sales equation in the model (equation 18) to estimate the parameters _{α_<sup>_g_</sup> _,_ ̃ _α_<sup>_g_</sup> _}_ . By taking logs on both sides and using the above structure for _αo_<sup>_g_</sup> ( _c_ ) _r_ ( _c_<sup>_′_</sup> )<sup>,wecanwritetheempirical</sup> counterpart of equation (18) as: where _β_ 1 will identify the relative bias against non-local domestic firms, _β_ 2 will identify the relative bias against foreign firms, FE _j_ are firm fixed effects, FE _r_ ( _c′_ ) _g_ are destination-government type fixed effects, FE _kg_ are government type-sector fixed effects, FE _o_ ( _c_ ) _r_ ( _c′_ ) are origin-destination fixed effects, and _εjko_ ( _c_ ) _r_ ( _c′_ ) _g_ is an error term. Firm fixed effects control for firm’s productivity and wages paid at origin. Destination-government type fixed effects control for the size of the government in a given region and the price index it faces, while government type-sector fixed effects control for differences in sectoral compositions across government types.<sup>18</sup> Finally, origin-destination fixed effects control for any kind of bilateral friction like distance, geography, information frictions, etc.

The interaction term _(domestic non-local) × (subnational gov)_ allows us to estimate the relative home bias against domestic non-local firms. In particular, we can show that exp( _β_ 1) = _α_<sup>_s_</sup> _/α_<sup>_n_</sup> , under the normalisation imposed on _α_ above. The identification of the relative home bias ( _α_<sup>_s_</sup> _/α_<sup>_n_</sup> ) exploits two sources of variation. The first variation we use is the difference in sales of firm _j_ to

> 18In our baseline regressions, we use 3-digits to define a sector (412 different categories). In the robustness section, we show that our main results are robust to more stringent definition of sectors.

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the subnational and the national governments in region _r_ , (<sup><u>Subnational</u></sup> National sales<sup><u>sales</u>),thatwecallthe“sub-</sup> national sales gap”. This within-firm difference in sales to the same destination across government types controls for firm-specific factors as well as for the effect of origin-destination factors. The second source of variation we use is comparing this “subnational sales gap” between local and domestic non-local firms. This second variation tells us whether the relative demand by subnational and national governments is the same for local vs. non-local firms. This double difference resembles a Difference-in-Differences setting, and allows us to estimate the relative home bias across governments. A similar intuition follows for the identification of the relative bias against foreign ̃ ̃ firms, and we can show that exp( _β_ 2) = _α_<sup>_s_</sup> _/α_<sup>_n_</sup> . Further details on the identification of the relative biases are provided in appendix I.

Our dependent variable, log _Xo_<sup>_g_</sup> ( _c_ ) _r_ ( _c_<sup>_′_</sup> ) _k_<sup>(</sup><sup>_j_), is the (log) aggregate sales of plant</sup><sup>_j_located in region</sup> _o_ selling _k_ to government type _g_ in region _r_ , across the years 2009-2019. We estimate our regression in two different samples. In our preferred sample, we drop governments and firms located in the capital regions of Madrid and Ille de France. There are two reasons for this. First, dropping the capitals minimize the possibility of observing “centralized” purchases. Second, as we showed in section 3.2, the capital regions are important outliers in the share of expenditure accounted for by national governments and in the local share of national governments. In the alternative sample, we include all governments and plants, including those from the capital regions. Results are reported in Table II, columns 1 and 2.

**Results.** Our preferred estimation is reported in column 1 of Table II, showing the results from estimating equation (21) in the sample without capital regions. First, we find a negative and significant estimate of the domestic non-local home bias, i.e., _β_ 1 = _−_ 0 _._ 262. This estimate implies the subnational governments buy less from non-local domestic firms, relative to local firms, than national governments do. In terms of magnitude, our estimate implies that the “subnational sales gap” is around 23% lower for non-local firms (relative to local firms). Through the lens of our model, this implies that exp( _β_ 1) = exp( _−_ 0 _._ 262) = _α_<sup>_s_</sup> _/α_<sup>_n_</sup> = 0 _._ 77. The result including capital regions, reported in column 2 is almost identical.

Second, we find a small and insignificant estimate of _β_ 2 = _−_ 0 _._ 021, that we cannot distinguish from zero. This means that the “subnational sales gap” is not different between local and foreign firms. This estimate does not mean that foreign firms face no bias, since there could be a common discrimination across governments in levels. But we do not find that subnational governments discriminate more against foreign firms. Through the lens of our model, that implies that exp( _β_ 2) = ̃ ̃ exp(0 _._ 00) = _α_<sup>_s_</sup> _/α_<sup>_n_</sup> = 1 _._ 00. The results for foreign firms are different when we include capital regions: we find a positive and weakly significant coefficient, indicating at differences in government preferences for foreign firms in capital and non-capital regions. We discuss this result and our interpretation of it, together with other robustness checks, in Section 7.

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# **6.2 Extensive margin**

We now turn to the estimation of the home bias in entry costs that affects the extensive margin behaviour of firms. Since the productivity distribution is Pareto, we can use equation (17) and express the share of firms in region _o_ selling to destination _r_ and government type _g_ as: To estimate the extensive margin of governments’ home-bias, i.e., _Eor_<sup>_g_,weimposethefollowing</sup> structure: Notice that, as in the case of _αor_<sup>_g_, we normalize to one the entry costs for local firms (</sup><sup>_o_=</sup><sup>_r_) for the</sup> two government types. We also assume that the parameters _E_<sup> ̃</sup><sup>_g_</sup> and _E_<sup>_g_</sup> vary across governments but are constant across regions. Similarly to the intensive margin home bias, our hypothesis is that the size of entry costs depends on the geographical scope of the government. In the case of the subnational governments, we expect them to impose higher entry costs to non-local firms than to local firms, i.e., _E_<sup> ̃</sup><sup>_s_</sup> _>_ 1 and _E_<sup>_s_</sup> _>_ 1. In the case of national governments, our hypothesis is that they are indifferent between buying local or non-local as long as the firm is domestic, i.e., _E_<sup>_n_</sup> = 1, but impose higher entry barriers to non-domestic firms, i.e., _E_<sup> ̃</sup><sup>_n_</sup> _>_ 1.

The participation share described in equation (E.1) can be re-written as: where S<sup>_g_</sup> _o_ ( _c_ ) _r_ ( _c_<sup>_′_</sup> ) _k_<sup>istheshareoffirmslocatedin</sup><sup>_o_(</sup><sup>_c_)thatsellprocurementin</sup><sup>_r_(</sup><sup>_c′_)togovern-</sup> ment _g_ in sector _k_ , FE _r_ ( _c′_ ) _g_ are destination-government type fixed effects, FE _o_ ( _c_ ) _r_ ( _c′_ ) are origindestination fixed effects, FE _gk_ are government type-sector fixed effects and _uo_ ( _c_ ) _r_ ( _c′_ ) _gk_ is an error term. Destination-government type fixed effects control for the size of the government in a given region and the price index it faces, while government type-sector fixed effects control for differences in sectoral compositions across governments. Origin-destination fixed effects control for any kind of natural friction like distance, geography, information frictions, etc. We estimate interaction coefficients, _γ_ 1 and _γ_ 2, using the Poisson-Pseudo Maximum Likelihood estimator (Silva and Tenreyro, 2006) to allow for the inclusion of zeros in participation.

As in the intensive margin regression, we identify the relative bias in participation rates for nonlocal domestic and foreign firms using two interaction terms: A domestic non-local _×_ subnational 25
government interaction and a foreign _×_ subnational government interaction, following the same intuition.

This regression, together with values of _σ_ and _θ_ and the estimates from regression (21), will allow us to identify the relative entry costs imposed by the two government types (see Appendix J for details). In particular, we can show that exp 1 _<u>γ−θ</u>_ <u>1</u> _σ_ = _E_<sup>_Ens/_</sup> _/α_<sup>_αsn_andexp</sup> 1 _<u>γ−θ</u>_ <u>2</u> _σ_ = _EE_ ̃ ̃ _n_<sup>_s_</sup> _//αα_ ̃ ̃ _n_<sup>_s_.The</sup> � � � � relative entry cost faced by domestic non-local firms is identified by comparing the “subnational participation gap”, ( Participation<sup>Participation</sup> rate<sup>rate</sup> in<sup>in</sup> national<sup>subnat</sup><sup><u>g</u></sup> gov<sup>ov),fornon-localfirmsrelativetolocalfirms.Inthe</sup> same way, the relative entry cost to non-domestic firms is identified by comparing the difference in the “subnational participation gap” between local and foreign firms.

# Table II

Estimating Governments’ Home bias

||**Intensive **|**margin**|**Extensi**|**ve margin**|
|---|---|---|---|---|
|Dependent variable:|log(Value|<sup>_g_</sup>(_f_)_o,r_)|Share of|plants<sup>_g_</sup><br>_o,r,g_|
||(1)|(2)|(3)|(4)|
|domestic non-local=1 _×_ sub.gov=1|-0.262|-0.239|-0.597|-0.468|
||(0.056)|(0.042)|(0.069)|(0.082)|
|foreign=1 _×_ sub.gov=1|-0.021|1.263|-0.804|-1.157|
||(0.558)|(0.624)|(0.304)|(0.247)|
|Plant FE|Yes|Yes|N/A|N/A|
|Origin x Dest. FE|Yes|Yes|Yes|Yes|
|Gov type x Dest. FE|Yes|Yes|Yes|Yes|
|Gov type x Sector FE|Yes|Yes|Yes|Yes|
|Including Capital Regions|No|Yes|No|Yes|
|Observations|97859|116017|142235|173828|
|R-squared|0.564|0.561|0.425|0.440|
|Estimator|OLS|OLS|PPML|PPML|

**Notes:** Columns 1 and 2 show the results of estimating equation 21. Column 3 and 4 show the results of estimating equation 24. The variable “domestic non-local x sub.gov” refers to the interaction variable 1 ( _o_ = _r . c_ = _c_<sup>_′_</sup> ) 1 ( _g_ = _s_ ). The variable “foreign x sub.gov” refers to the interaction variable 1 ( _c_ = _c_<sup>_′_</sup> ) 1 ( _g_ = _s_ ) _._

**Results.** Columns 3 and 4 of Table II report the results from estimating equation (24) in the subsample without capital regions as well as in the complete sample. First, we find a negative and significant coefficient on the domestic non-local bias, i.e., _γ_ 1 = _−_ 0 _._ 597, which implies that the “subnational participation gap” of non-local domestic firms is around 44% lower than for local firms. The result is slightly smaller but very similar in magnitude when we use the sample

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including capital regions (Column 4).

Second, we find a negative and significant coefficient on the foreign bias, _γ_ 2 = _−_ 0 _._ 804, in the sample with no capital regions (column 3). This coefficient tells us that, in the extensive margin, subnational governments also discriminate more against foreign firms, since the participation gap (contract participation with the subnational government relative to the national government) is 55% lower than for local firms. The results are similar, just slightly more negative, when adding capital regions as well.

We now use these estimates to recover the model-implied relative entry cost that governments impose to domestic non-local and foreign firms. To recover the relative entry cost as implied by the model, we need values for the CES elasticity _σ_ , the Pareto shape parameter _θ_ , and the relative home bias in the intensive margin (see equation (17)). In particular, for the entry cost that domestic-non local firms pay, we know that where the last step comes from using standard values of _σ_ = 5, _θ_ = 8, and our results for the intensive margin home bias, _β_ 1 = _−_ 0 _._ 262. This number implies that subnational governments impose just a weakly higher fixed cost to domestic non-local firms than the national governments. The intuition for why we find such a small gap in entry costs across governments despite the large differences in participation is the following. Through the lens of the model, firm entry, and hence participation rates, are also driven by the intensive margin home bias _α_ . Given the large differences in _α_ across the two governments estimated in the previous section, only a tiny difference in entry costs is needed to rationalize the observed differences in entry rates through the lens of the model. To recover the implied relative entry cost for foreign firms, we apply the same formula: This calculation implies that subnational governments impose a fixed cost to foreign firms that is around 1.5 times larger than its counterpart imposed by national governments. The reason for this higher gap is two-fold. First, as mentioned above, there is a larger difference in foreign participation rates across governments than in domestic participation. Second, we estimated no ̃ ̃ significant difference in the intensive margin home bias for foreign firms, i.e., _α_<sup>_s_</sup> _/α_<sup>_n_</sup> = 1. The combination of these two factors is interpreted by the model as a large difference in relative entry costs for foreign firms.

# **7 Robustness, threats to identification and mechanisms**

In this section, we discuss potential issues with our identification strategy and explain how we address them. We also present some results that shed light on the potential mechanisms that could explain the estimated governments’ home bias.

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# **7.1 Robustness of results**

**Alternative empirical specifications:** Our first robustness test is to repeat the baseline estimation with different sets of fixed-effects, that control better for sectoral heterogeneity. First, we include _government type-destination-sector_ fixed effects to control for varying sectoral spending shares at the government type-region level. Then we use our data at a lower level of sectoral aggregation (4 digit sectors) and we repeat the baseline estimation and the estimation with _government level-destination-sector_ fixed effects. In addition, we extend the baseline estimation by adding _plant-sector_ fixed effects to control for the fact that a firm may be selling a different good to each government level in the same region, while we would like to keep the good/service constant. Finally, we extend our model by adding _firm-destination region_ demand shocks. These demand shocks have been found to be relevant to explain export behaviour, in both the intensive and the extensive margin (Eaton, Kortum, and Kramarz (2011), Crozet, Head, and Mayer (2012)). We show how to extend our model to allow for the existence of firm-destination specific demand shocks in part K of the Appendix. In our setting, we observe the same firm selling to different governments within a region, so we can control for this typically unobserved firm-destination region shocks by including _firm-destination region_ fixed effects.

Figures VI and VII plot the regression coefficients of each of these alternative models. We denote baseline the results for the full sample, at 3 digit of disaggregation (3d) and including origindestination, plant, government-region and government-sector fixed effects (where government refers to the government type: national or subnational). Figure VI reports the intensive margin results. Reassuringly, the bias against domestic non-local firms is precisely estimated and very stable across the different specifications. In contrast, the bias against foreign firms is much less precisely estimated and not always statistically different from zero. Figure VII reports the results from the extensive margin estimation. Again, the bias in participation rates of domestic-non local firms is more stable and precisely estimated. The bias against foreign firms is much less stable and the precision is much lower, due to the reduced sample of foreign participation. However, both coefficients are negative and we can reject that they are equal to zero.

**Further empirical evidence.** We provide now further empirical evidence on the home bias across governments. First, we consider an alternative outcome variable: the total number of contracts awarded. Table IX in the Appendix shows that subnational governments award a lower number of contracts to non-local firms. This finding indicates that the home bias partly operates through the mechanism of awarding a larger number of contracts to local firms. Our second additional exercise is to estimate the magnitude of the relative home bias (in both margins) in three subsamples with different tradeability: goods purchases, construction purchases and service purchases. We find that the magnitude of the relative home bias is higher for goods and construction than for services in the intensive margin ( _β_<sup>_goods_</sup> = _−_ 0 _._ 405, _β_<sup>_works_</sup> = _−_ 0 _._ 331, _β_<sup>_serv_</sup> = _−_ 0 _._ 136). In the extensive margin, the coefficients are more similar across sectors. Results are presented in table X in the Appendix.

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<!-- Start of picture text --> I<br>]<br>I<br>I Model<br>Se<br>Foreign x Subnat ee Baseline<br>a a fo) Baseline: 4d<br>Baseline+Plant-dest FE<br>| fo) Baseline+Plant-sec FE<br>DV:# Contracts<br>© Gov-reg-sec FE 3d<br>o°o | Gov-reg-sec FE 4d<br>Domestic x Subnat ° I| fe) No capital. buyers<br>a<br>No capitals (B or S)<br>I<br>I<br>l<br>-1 0 1 2 3<br>Coefficient<br><!-- End of picture text -->
<!-- Start of picture text --> I<br>I<br>I<br>I<br>l<br>ee © l<br>Foreign x Subnat 5 Model<br>a ce Baseline<br>fe) Baseline 4d<br>Gov-reg-sec FE 3d<br>fo) Gov-reg-sec FE 4d<br>No capital buyers<br>Domestic x Subnat —o- II fe) No capitals: (B or S)<br>l<br>—_O= !<br>l<br>I<br>l<br>I<br>-2.5 -2.0 -1.5 -1.0 -0.5 0.0<br>Coefficient<br><!-- End of picture text -->
of the auctions. To see whether “being local” systematically increases the probability of wining an auction, we regress a winner dummy against a local dummy, auction fixed effects, and other controls as firms’ size or firms’ TFP. The main result from running these regressions is that being local systematically increases the probability of winning the auction. We show the results in table XI in the Appendix. Notice that because we are adding auction fixed effects in all the specifications, at least one non-local firm is competing in all of them. Without any control at the firm level, we find that being local increases the probability of winning an auction by 14.8%. One possibility is that local firms are systematically more productive than non-local firms competing for the same projects. To control for this, we also run specifications in which we include firm size or productivity as controls. Controlling for TFP does not change the results, we find that being local increases the probability of winning by 17.3%.

**Quality heterogeneity within our narrowly defined products.** A second concern that we investigate is the fact that our set of fixed effects may not be able to control correctly for potential heterogeneity in the good, work or service provided by the firm.

A first issue that could arise is the existence of multi-product firms that sell different goods or services to different government agencies. Since we are trying to estimate the intensive margin local bias, we want to make sure we are comparing how the national and the subnational governments value, or prefer, the same good, service or work. The presence of multi-product plants that may sell different goods or services to different governments in the same region does not seem to be an issue. Figure VI shows that specification “Baseline+Plant-sec FE” includes plant _×_ sector fixed effects (at 3 digit level of sectoral disaggregation) and delivers an almost identical effect for the domestic firms and a slightly less significant effect for foreign firms, with very similar point estimates.

A second concern comes from the incomplete information we have about the product-code of the contract in our data, as already mentioned in section 3. Our dataset includes the CPV (Common Procurement Vocabulary) product code of the contract, but for some contracts we only know the aggregated CPV code (the codes range from 2 to 9 digits). Due to this limitation, controlling for the CPV code sometimes means controlling only for the aggregate product or service code, which may contain heterogeneous products or services. For instance, only 27% of our data has details at the 6 digit level or higher. To dig deeper into this we use a second strategy: estimating the local bias within very finely defined CPV codes. This within-product estimation requires us to focus on CPV codes at the 6 digits or higher, and repeat our estimation within-CPV code.

For this, we start by selecting only CPV codes with 6 digits or more, and, among them, the ones in which we observe the average firm a sufficient amount of times to perform our intensive margin estimation.<sup>19</sup> We focus on goods contracts since services and works have been found to be less differentiated, and therefore may be more heterogeneous even within code. We find 5 products

> 19To be consistent with the rest of the analysis, we first keep the contracts from governments by dropping contracts where the buying agency is a hospital or university. Among these we keep contracts in which the product is defined at level of sectoral disaggregation of 6 or more digits. Among those finely defined products we keep those

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for which we have enough observations to perform the estimation of the intensive and extensive margin within product code, and that define a unique, narrow product.<sup>20</sup> The coefficients on the interaction (non-local) _×_ (sub-nat gov.) are reported in figure VIII.

We find that subnational governments have a stronger preference for local firms in 4 out of the 5 codes in the intensive margin and 5 out of 5 in the extensive margin. Some examples are printing paper, road signs and disposable catering supplies. We believe that the results of these estimations within unique product codes confirms that our results are not driven by plants selling different products to different government agencies.

# Figure VIII

## Estimation within individual product code - 6 digits

<!-- Start of picture text --> Products in 6-digit defined sectors Products in 6-digit defined sectors<br>Printing paper Printing paper<br>Road Signs Road Signs<br>Disposable catering supplies Disposable catering supplies<br>Modular and portable buildings Modular and portable buildings<br>Hardware Hardware<br>-8 -6 -4 -2 0 2 -8 -6 -4 -2 0 2<br>Intensive margin coefficient Extensive margin coefficient<br>(a) (b)<br><!-- End of picture text -->

**Centralized purchases.** The final potential threat to identification is the possibility that national and subnational governments use different purchasing strategies when procuring goods, works or services. In particular, given the centralised structure of national governments in Spain and France, it is possible that the national government buys goods or services using its agencies located in the country capital and then distributes the procured goods or services to offices and agencies of the national government in other regions. This system of purchases could be the reason behind the lower local bias of the national governments.

The first piece of evidence against this concern is that the larger local bias of subnational governments is present in both goods, construction and services. And while this centralized purchasing system would be worrying regarding good purchases, it could not be applied to services (some of which are non-tradable) or to construction. The second piece of evidence we provide is that we estimate our baseline regression after dropping all buyers and sellers located in Madrid and Ille de France (we also report results by dropping only buyers -governments- in Madrid and that are commonly traded.

> 20Since the number of observations is quite reduced, we use the estimation with one border level.

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Ille de France in figures VI and VII. By dropping purchases that happen in the capitals we use only variation coming from all the other regions in France and Spain.

Let us now compare the results of the subsample without capital regions with the results from the full sample (columns 1 vs 2 and 3 vs 4 in table II). The estimated relative bias against domestic non-local firms is very stable across subsamples, for both the intensive and extensive margins.

However, the estimated relative bias against foreign firms varies substantially across subsamples (columns 1 and 2). Including capital regions uncovers a positive and weakly significant coefficient on the interaction between _(foreign) × (subnational gov)_ . This suggests that subnational governments discriminate less against foreign firms in the capital regions. We interpret this positive sign as evidence of the discrimination of national governments against foreign firms, in capital regions. Consistent with our initial hypothesis about the scope of the home bias, national governments located in the capital region have a stronger bias against foreign firms than subnational governments. This means that national governments express their nationalistic preferences against foreign firms but only in the capital region of the country.

# **7.3 Mechanisms**

We have provided robust evidence for a stronger home bias of subnational governments. We have identified this preference without characterising the possible micro-foundations behind it. On the one hand, subnational governments may have a stronger local bias in order to boost local economic activity and to maximise local citizens’ welfare. On the other, subnational governments may have a stronger local bias as a result of lobbying by local firms or following electoral interests of reelection. We cannot disentangle these two motives using our data. In this last subsection, we provide some suggestive evidence of potential mechanisms that may be driving our results.

**Transparency of the process.** We first provide some evidence on the correlation between higher transparency in the procurement process and the home bias of subnational relative to the national governments. We leverage information from our dataset on the following variables related to the procurement process. First, we know whether part of the contract will receive funds from the EU. Second, we know whether the process involved an electronic auction. Finally, we know whether the procurement process involved a centralized purchase of different agencies jointly. These three variables can be interpreted as increasing the transparency degree of the award process. We use them to see whether the relative local bias is robust to these process characteristics. The estimation of the intensive margin bias parameter is reported in table XIII. Our estimation shows that these variables are correlated with the local home bias. If a contract is partially funded by the EU, the relative bias of the subnational government is lower (column 2 relative to column 1). If the contract is awarded with an electronic auction, the relative bias of the subnational government disappears (column 4). Finally, when the procedure follows a centralised joint purchase, we also estimate a smaller local bias by subnational governments (column 6 relative to 5).

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**Political structure.** Another potential driver of the relative home bias may be the structure of the political system. In countries where subnational governments have higher degrees of political autonomy, they may have more discretion or stronger incentives to award contracts to local firms. The higher degree of autonomy may also be a stronger driver for firms lobbying to seek protection or favours from the political class (Grossman and Helpman, 1994). Our data allows us to test this hypothesis by comparing the strength of the relative bias in France and Spain, exploiting the differences in political structure between the two countries. In particular, France is a presidential system where regions have restricted political autonomy, while Spain is a federal parliamentary system, where regions have many devolved competencies and a high degree of autonomy. If the degree of political autonomy of the subnational government is correlated with the home bias, we expect to find a stronger relative bias in Spain than in France. To investigate this question we run our baseline specification at the 3-digit sector-level separately for France and Spain. Our results, reported in table XIV, reject this hypothesis. The relative home bias is very similar but slightly stronger in France than in Spain: The intensive margin coefficient is _β_<sup>_FR_</sup> = _−_ 0 _._ 261<sup>_∗∗∗_</sup> in France relative to _β_<sup>_ES_</sup> = _−_ 0 _._ 153<sup>_∗∗_</sup> in Spain, while the extensive margin coefficient is _β_<sup>_FR_</sup> = _−_ 0 _._ 869<sup>_∗∗∗_</sup> in France relative to _β_<sup>_ES_</sup> = _−_ 0 _._ 715<sup>_∗∗∗_</sup> in Spain. Therefore, the political structure does not seem to be driving the differences in home bias across government levels in our data.

# **7.4 Summary of empirical results**

Our results and robustness tests uncover important differences in the preferences for domestic non-local and foreign firms across government types.

**Intensive margin** Regarding the aggregate sales in procurement markets, we can summarise our intensive margin results as follows: the subnational to national sales gap is lower for domestic non-local firms than for local firms, highlighting that subnational governments have a weaker preference for domestic non-local firms. On the contrary, the subnational to national sales gap is equal for foreign and local firms, meaning that subnational and national governments have similar preferences for foreign firms relative to local firms.<sup>21</sup> This does not mean that there is no bias against foreign firms. Remember that we are including origin-destination fixed effects in all specifications and this fixed effects will absorb any differences in the dependent variable that vary at the origin-destination level and are constant across governments. Our finding that the coefficient on the “foreign _×_ sub.gov” interaction is zero for foreign firms tells us that the bias is constant across governments.

> 21In some specifications and subsamples we have found a positive but weakly significant coefficient for the preferences for foreign firms by the subnational government, but this seems to be driven by the behaviour of the national government in the capital regions. Dropping capital regions brings the coefficient to zero, as explained in the previous subsection

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**Extensive margin** Regarding the participation of firms in procurement, our extensive margin results confirm that the subnational to national participation gap is lower for both domestic nonlocal and foreign firms. These results were robust to different specifications and to excluding or including capital regions. We used the structure of our model to compute the entry costs that rationalise these findings. Using our estimated coefficients, together with our intensive margin results, we find that subnational governments impose slightly higher entry costs on domestic non local firms than national governments ( _E_<sup>_s_</sup> _/E_<sup>_n_</sup> = 1 _._ 036) and impose substantially higher entry costs on foreign firms than national governments ( _E_<sup> ̃</sup><sup>_s_</sup> _/E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 54).

# **8 Parametrization**

In this section, we explain how we choose the parameter values of the model. We calibrate our model to 38 Spanish and French regions.<sup>22</sup> We define a market as a region-government type (national or subnational). We next explain our parametrization strategy, which combines our estimated parameters from Section 6 with some predetermined choices and a joint calibration of the remaining parameters. We summarize the parameter values in table III.

**Governments’ home bias:** _α_ **and** _E_ **.** The identification strategy presented in sections 6.1 and 6.2, which exploits variation across government types, allows us to estimate the relative intensive margin home biases, _α_ ’s, and extensive margin home biases, _E_ ’s, across government types but not their levels. In order to pin down the levels, we proceed as follows.

1. <u>Bias towards local frms:</u> We assume that national and subnational governments behave equally towards local firms. We normalize both their preference parameter _α_ and entry cost _E_ for local firms to 1. This assumption is implicit in the way we parametrize _α_ and _E_ in sections 6.1 and 6.2 (equations 21 and 24).

2. Bias against domestic non-local firms: We assume that national governments do not discriminate against non-local domestic firms. That is, they treat non-local domestic firms as they treat local firms. This means that we set _α_<sup>_n_</sup> =1 and _E_<sup>_n_</sup> = 1. Therefore, the national government has the same bias towards domestic firms as towards local firms. Given this assumption, we can use our estimate of _β_ 1 = _α_<sup>_s_</sup> _/α_<sup>_n_</sup> and recover _α_<sup>_s_</sup> = 0 _._ 769. Similarly, we can use our estimate of _γ_ 1 (together with _θ_ , _σ_ , and _β_ 1) and recover _E_<sup>_s_</sup> = 1 _._ 036. The reason for this relatively small difference in fixed costs across governments is that, through the lens of the model, the estimated gap in the intensive margin _α_ across governments is almost enough to generate the difference in participation rates observed in the data.

- Although these two assumptions are admittedly arbitrary, we emphasize that relaxing them (in a reasonable way) would not fundamentally affect our main results. Assumption 1 is

> 22We exclude the Canary Islands, the two Spanish autonomous cities (Ceuta and Melilla), and the five overseas departments and regions of France (French Guiana, Guadeloupe, Martinique, Mayotte, and Reunion).

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just a normalization and should not affect any of the counterfactuals. Assumption 2 is conservative in the sense that it minimizes the overall effect of the governments’ home bias on procurement flows.

3. Bias against foreign firms: To pin down the biases against foreign firms, we first calibrate the national government’s biases so that the model matches two relevant moments in the data. In particular, we calibrate _E_<sup> ̃</sup><sup>_n_</sup> to match the average participation rate in foreign regions for national governments, which is defined as the fraction of firms in a given origin-region that sell to a national government in any foreign destination-region. In the data, the value of this moment is quite low, 0.01%. To match that number, the model requires _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 408. We calibrate _α_ ̃<sup>_n_</sup> to match the average share of procurement value awarded to foreign firms by national governments in a given region. We measure a value of 0.37% in the data, and we ̃ ̃ recover _α_<sup>_n_</sup> = 0 _._ 165 from the model. Given these values for _α_<sup>_n_</sup> and _E_<sup> ̃</sup><sup>_n_</sup> , we use our estimates ̃ of _β_ 2 and _γ_ 2 (together with _θ_ , _σ_ ) to recover _α_<sup>_s_</sup> = 0 _._ 165 and _E_<sup> ̃</sup><sup>_s_</sup> = 2 _._ 169.<sup>23</sup> We can then summarize our parametrization of governments’ home bias as follows. Subnational governments exhibit a strong home bias against non-local and foreign firms, both in the intensive and extensive margin. In terms of the intensive margin, they have preference parameters for non-local domestic and foreign firms that are respectively around 23% ( _α_<sup>_s_</sup> = 0 _._ 769) and 84% ( ̃ _α_<sup>_s_</sup> = 0 _._ 165) lower than their counterpart for local firms. In terms of the extensive margin, they impose a slightly higher fixed costs on non-local domestic than on local firms ( _E_<sup>_s_</sup> = 1 _._ 036) and impose fixed costs on foreign firms that are more than two times higher than their counterparts for domestic firms ( _E_<sup> ̃</sup><sup>_s_</sup> = 2 _._ 169).

National governments exhibit a strong home bias towards domestic firms. They have a preference parameter for foreign firms that is around 84% ( ̃ _α_<sup>_n_</sup> = 0 _._ 165) lower than its counterpart for local and non-local domestic firms, and they impose a fixed cost to foreign firms that is 40% higher than its counterpart for local and non-local domestic firms ( _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 408).

**Transportation costs** _τor_ **.** In order to parametrize transportation costs, we assume that they are a function of distance given by some elasticity _φ_ , i.e., _τod_ = distance<sup>_φ_</sup> _od_<sup>.Toidentify</sup><sup>_φ_,wemake</sup> the model match the observed average share of procurement value awarded to local firms (53%). We find a value of _φ_ equal to 0.173.

**Productivity distribution and the Armington elasticity.** As mentioned above, and following the previous literature (for instance Broda and Weinstein (2006) and Eaton and Kortum (2002)), we choose _σ_ = 5 for the elasticity of substitution across varieties and _θ_ = 8 for the shape parameter of the Pareto distribution of firm productivities.

> 23Remember that we use the coefficients estimated after dropping the capital cities in Spain and France, since they exhibit several irregularities. Thus, we use a calibration in which subnational and national governments do not have a different bias against foreign firms in the intensive margin

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# Table III

Model parameters

|Parameter|Description|Source/Target|Value|
|---|---|---|---|
|**Home bias**||||
|_α_<sup>_n_</sup>|Pref. for non-local domestic frms (nat. gov.)|Assumption|1.000|
|_α_<sup>_s_</sup>_/α_<sup>_n_</sup>|Pref. for non-local domestic frms (relative)|Estimated|0.769|
| ̃_α_<sup>_n_</sup>|Pref. for foreign frms (nat. gov.)|Calibrated|0.165|
| ̃_α_<sup>_s_</sup>_/_ ̃_α_<sup>_n_</sup>|Pref. for foreign frms (relative)|Estimated|1.000|
|_E_<sup>_n_</sup>|Entry cost for non-local domestic frms (nat. gov.)|Assumption|1.000|
|_E_<sup>_s_</sup>_/E_<sup>_n_</sup>|Entry cost for non-local domestic frms (relative)|Estimated|1.036|
| ̃_E_<sup>_n_</sup>|Entry cost for foreign frms (nat. gov.)|Calibrated|1.408|
| ̃_E_<sup>_s_</sup>_/_ ̃_E_<sup>_n_</sup>|Entry cost for foreign frms (relative)|Estimated|1.540|
|**Other parameters**||||
|_φ_|Elasticity of trade cost w.r.t distance|Calibrated|0_._18|
|_σ_|Elasticity of substitution|Broda and Weinstein (2006)|5.00|
|_θ_|Pareto shape parameter|Eaton and Kortum (2002)|8.00|
|_Lr_|Labor endowment|Calibrated|region-specifc|
|_γr_|Relative size of sub-national gov.|Calibrated|region-specifc|
|_μ_|Size of the government sector|Calibrated|0.30|

**Notes:** This table shows the parameter values in our baseline parametrization. The top panel shows the parameters related to governments’ home bias. The bottom panel shows the rest of parameters.

**Size of governments.** There are two moments related to the size of governments in the model. The first moment is the size of subnational governments relative to national governments ( _Xr_<sup>_s_</sup> vs. _Xr_<sup>_n_).GiventheCobbDouglasstructureintheutilityfunction(equation4),wechoosethe</sup> parameter _γr_<sup>_s_so that the model generates the observed share of total procurement that is awarded</sup> by subnational governments in each region. The second moment is the size of the government sector relative to the private sector. We impose this moment to be the same across all regions (0.30) and choose _μ_ accordingly. Notice that the value of this parameter will be inconsequential for our main results. The reason is that all our counterfactuals will consist on changing governments’ home bias parameters and we will only look at changes in trade flows within the government sector.

**Remaining parameters.** In order to measure differences in economic size across regions, we compute total procurement expenditure in each state _Xr_ = _Xr_<sup>_s_+</sup><sup>_X_</sup> _r_<sup>_n_andcalibrate</sup><sup>_Lr_sothat</sup> the model replicates the observed distribution. We find large differences in economic size across regions. For example, Ile-de-France (the largest region) accounts for 29.1% of the total observed procurement expenditure in France and Spain, while La Rioja (the smallest region) only accounts for 0.1%. Finally, in our baseline calibration, we do not allow for heterogeneity in productivity of the private sector across regions and set _Ar_ = 1 for all regions.

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## **8.1 Outcome of the model**

Table IV shows the value of some relevant moments related to the intensive and extensive margin of procurement flows. The first column shows these moments as measured in the data (with a bullet symbol next to the ones used as targets in the calibration). The second column shows the same moments as measured in the model under our baseline parametrization. We next discuss the performance of the model along several dimensions of the data.

The model generates _local shares_ that are relatively well aligned with the data. Local shares refer to the share of procurement value in a particular region awarded to firms located in that same region.<sup>24</sup> This is not surprising for the aggregate ( _∼_ 53% data vs. _∼_ 53% model) because that moment is a calibration target. In terms of the difference across governments, the model is able to reproduce around one third of the observed difference: 56% vs. 29% in the data and 53% vs. 43% in the model.

In terms of _non-local domestic share_ , which refers to the share of procurement value in a particular region awarded to firms from different regions but from the same country, the model does a good job in the aggregate (47% vs. 47%) and for the subnational government (43% vs 45%) but underestimates it for national governments (70% vs 56%).

With respect to _foreign shares_ , the share of procurement value in a particular region awarded to foreign firms, the model matches relatively well the foreign share that we use as a target (0.37% vs. 0.32%), i.e., the one for national governments, but significantly overestimates the one for subnational governments (0.15 vs. 0.28%) and hence the aggregate (0.16 vs. 0.28%) Finally, the model’s performance in terms of participation rates is mixed. The model generates a fraction of firms that sell in their own region (participation rate in the local region) that is too high as compared to the data. In the model, around 97% and 13% of the firms respectively sell to the subnational and national government in their own region. These two numbers compare to the 71% and 7% observed in the data. The model, however, can explain around 73% of the difference between the two governments. The model does relatively well in terms of the participation rate in non-local domestic regions, which is defined as the fraction of firms from a particular region that sell in another domestic region (excluding its own): 0.86% vs. 0.76% for national governments and 2.17% vs. 2.81% for subnational governments. Finally, the model also does well predicting the extremely low participation rates in foreign regions, which is the fraction of firms from a particular region that sell in a given foreign region.

# **9 Counterfactuals**

In this section, we use the model to run counterfactuals that are informative about the effects of governments’ home bias on procurement flows across regions and countries. In particular, we are interested in understanding the role that the governments play in explaining the lack of integration

> 24Notice that this is the same definition as the one used in Section 3.2. See Appendix L for the exact formulas used to compute the numbers in IV.

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in procurement markets both at the region (Block # 1) and at the country level (Block # 2).

# **9.1 Procurement integration within countries (Block # 1)**

**Relative home bias towards local firms.** We start by quantifying the effects of subnational governments’ home bias on procurement trade flows across French and Spanish regions. We do that by running a counterfactual in which we set subnational governments’ biases against domestic nonlocal firms to their counterpart values for national governments ( _α_<sup>_s_</sup> = _α_<sup>_n_</sup> = 1 and _E_<sup>_s_</sup> = _E_<sup>_n_</sup> = 1). We keep the estimated bias against foreign firms unchanged.

Column C1 of Table IV shows the results from this counterfactual. The main finding from this counterfactual is that procurement market integration would increase substantially. The average share of procurement value awarded to non-local domestic firms would increase by around 10 pp (47.0% in the baseline and 56.7% in C1), which represents an increase of around 20%. Obviously, the effect comes entirely from a reduction in the local share for subnational governments (53.75% in the baseline vs. 43.10% in C1). In terms of intensive vs. extensive margins, we find that a big part of the effect comes from the latter: the fraction of firms that sell to subnational governments in non-local domestic regions increases significantly, from 2.81 to 5.43, which represents an increase of around 90%.

# **9.2 Procurement flows across countries (Block # 2)**

In the second set of counterfactuals, we use our model to investigate the role that the estimated government’s home bias plays in explaining the extremely low observed shares of procurement value awarded to foreign firms, both in France and Spain. We run three counterfactuals. In counterfactual C2, we eliminate the gap between the two governments’ home bias towards domestic firms, this is, we eliminate the relative bias. In counterfactual C3, we completely eliminate the level of home bias of subnational governments. In counterfactual C4, we completely eliminate the level home bias of national governments.

**Relative home bias towards domestic firms.** Column C2 of table IV shows the results of making the home bias towards domestic firms equal across government types. In particular, we  ̃  ̃ reduce _E_<sup> ̃</sup><sup>_s_</sup> so that _E_<sup> ̃</sup><sup>_s_</sup> = _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 408 (notice that _α_<sup>_s_</sup> and _α_<sup>_n_</sup> are already the same in the baseline). We find that the average aggregate share of procurement awarded to foreign firms increases by 0.14 pp (0.28% in the baseline and 0.42% in C2), which represents an increase of 50%. This effect entirely comes from an increase in the foreign share for subnational governments (0.28% in the baseline and 0.43% in C2). We also find that a significant part of this effect is coming from the participation margin: foreign firms’ participation in subnational governments more than doubles in C2.

**Absolute home bias of subnational governments.** In counterfactual C3, we remove the subnational government’s home bias towards domestic firms completely. In particular, we set

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Table IV Model outcome / counterfactuals

|||Data|Baseline|C1|C2|C3|C4|
|---|---|---|---|---|---|---|---|
|Local Share (%)|Nat. (_g_ =_n_)|29.29|43.05|43.05|43.05|43.05|37.23|
||Sub. (_g_ =)|56.47|53.75|43.10|53.67|44.79|53.75|
||Aggregate|52.79<sup>_•_</sup>|52.69|43.09|52.62|44.38|52.29|
|Non-local domestic|Nat. (_g_ =_n_)|70.33|56.63|56.63|56.63|56.63|49.47|
|Share (%)|Sub. (_g_ =_s_)|43.39|45.97|56.69|45.90|38.63|45.97|
||Aggregate|47.05|47.03|56.69|46.96|40.28|46.47|
|Foreign Share (%)|Nat. (_g_ =_n_)|0.37<sup>_•_</sup>|0.32|0.32|0.32|0.32|13.31|
||Sub. (_g_ =_s_)|0.15|0.28|0.21|0.43|16.58|0.28|
||Aggregate|0.16|0.28|0.22|0.42|15.34|1.24|
|Participation rate|Nat. (_g_ =_n_)|7.07|13.44|16.48|13.45|14.82|12.58|
|in local region (%)|Sub. (_g_ =_s_)|71.01|97.27|96.24|97.27|97.06|97.28|
|Participation rate|Nat. (_g_ =_n_)|0.86|0.76|1.06|0.76|0.84|0.72|
|in non-local|Sub. (_g_ =_s_)|2.17|2.81|5.43|2.81|2.74|2.81|
|domestic regions (%)||||||||
|Participation rate|Nat. (_g_ =_n_)|0.01<sup>_•_</sup>|0.01|0.01|0.01|0.01|0.11|
|in foreign regions (%)|Sub. (_g_ =_s_)|0.01|0.01|0.01|0.02|1.37|0.01|

**Notes:** This table shows some market integration moments, measured in the data and in different versions of the model. “Baseline” refers to our calibrated version of the model. “C1” is informative about the role of governments in procurement market integration across regions. In particular, we decrease _α_<sup>_s_</sup> so that _α_<sup>_s_</sup> = _α_<sup>_n_</sup> = 1 and _E_<sup>_s_</sup> so that _E_<sup>_s_</sup> = _α_<sup>_n_</sup> = 1 . “C2”, “C3”, and “C4” refer to counterfactuals that are informative about the role of governments in procurement market integration across countries. In “C2”, we decrease _E_<sup> ̃</sup><sup>_s_</sup> so that _E_<sup> ̃</sup><sup>_s_</sup> = _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 40. In “C3”, we set _E_  ̃<sup>_s_</sup> = 1 _._ 00 and _α_  ̃<sup>_s_</sup> = 1 _._ 00. In “C4”, we set _E_  ̃<sup>_n_</sup> = 1 _._ 00 and _α_  ̃<sup>_n_</sup> = 1 _._ 00. The numbers reported for the “Local Share”, “Non-local domestic share”, and “Foreign Share” have been computed taking the average across all regions. The “Participation rate in the local region” is the average across all origin-regions. The “Participation rate in non-local domestic regions” refers to the average across all origin-non local domestic destination pairs. The “Participation rate in foreign regions” refers to the average across all origin-foreign destination pairs. Those numbers with a bullet next to them are calibration targets (see section 8).

̃ _E_<sup>_s_</sup> = 1 _._ 00 and _α_ ̃<sup>_s_</sup> = 1 _._ 00 while keeping the rest of parameters unchanged. In column C3 of IV, we show that the average share of procurement value awarded to foreign firms in the counterfactual would be around 50 times its counterpart in the baseline (15.34% in C3 vs. 0.28% in the baseline). In terms of participation, we find that firms’ foreign participation in subnational governments would increase by 1.36 pp (from 0.01% to 1.37%).

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**Absolute home bias of national governments.** In counterfactual C4, we remove national government’s home bias towards domestic firms completely. In particular, we set _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 00 and _α_  ̃<sup>_n_</sup> = 1 _._ 00 while keeping the rest of parameters unchanged. In column C4 of IV, we show that the foreign share would be around 4.4 times its counterpart in the baseline (1.24% vs. 0.28%).

## **9.3 Summary of the results from counterfactuals**

The main results from our counterfactuals can be summarized as follows. First, we find that the subnational government’s home bias towards local firms has strong effects on procurement flows across regions both within Spain and France. In particular, removing that bias would increase the share of procurement value awarded to non-local domestic firms by around 20% (from around 47.0% to 56.7%).

Second, we also find that the subnational government’s home bias reduces procurement market integration across countries. We first show that by setting the bias of the subnational government towards domestic firms to the level of the national government, the average share of procurement awarded to foreign firms would increase by 50% but still remain quite low (0.42%). However, we show that eliminating the level of the subnational government’s home bias towards domestic firms completely would dramatically increase the foreign share by a factor of 50 (from 0.28% to 15.34%).

And third, we find that national governments explain less of the low procurement integration across countries: removing the home bias of these governments towards domestic firms completely would increase the foreign shares by a factor of 4 (from 0.28% to 1.24%). The fact that subnational governments play a bigger role in explaining the lack of market integration across countries is a combination of two factors: (i) national governments exhibit a home bias towards domestic firms in the extensive margin that is significantly lower than its counterpart for subnational governments ̃ ( _E_<sup> ̃</sup><sup>_n_</sup> = 1 _._ 408 vs. _E_<sup>_s_</sup> = 2 _._ 169); and more importantly (ii) they account for a low share of the regions’ total procurement expenditure (0.13 vs. 0.87). That is: even though removing their biases substantially increases their procurement expenditure on foreign firms, that translates into a small increase in the aggregate.

# **10 Conclusions**

Governments of all kinds are sizeable active buyers in modern economies. Public procurement, defined as the value of governments’ purchasing goods and services from private firms, accounts for around 12% of global GDP. In fact, governments are the main buyers in major industries like construction, medical equipment, transport, waste management, or energy.

In the case of the EU, for example, over 250,000 public authorities spend 2 trillion euros of public money on purchasing goods and services every year. While improving the current public procurement systems is at the core of many countries’ agendas for industrial policy, we still know very little about the specific factors driving governments’ purchases and their implications for the distribution of economic activity across firms, regions, and countries.

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Our paper shows that the government’s home bias, especially that of subnational agencies, explains a big part of the high local concentration levels in government procurement across regions and countries. Our results offer a new perspective on the debate on how integrated the EU is, providing novel evidence consistent with the presence of hidden protectionism that governments of all kinds implement by systematically biasing their purchases towards local firms.

Our paper raises extremely relevant questions from a policy point of view: _To what extent do these biases cause resource inefficiencies within the EU? What are the associated productivity losses?_ Given our results on the different degrees of home bias across government levels: _Would it be possible to design policies to align regional governments, national governments, and the EU incentives? How different would the distribution of economic activity in the EU be under a less/more centralized configuration of the EU states?_ We believe these are exciting new avenues to understand better the integration of regional and international markets.

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