metadata: authors: 0: author: Fazekas,Mihaly
1: author: Poltoratskaia,Viktoriia
2: author: Tóth,Bence
count: Bulgaria
docna: 0: docna: Corruption Risks and State Capture in
            Bulgarian Public Procurement
docty: Policy Research Working Paper
abstracts: cdata!: This paper sets out to measure and
            analyze corruption risks, patterns of favoritism, and state
            capture in public procurement in Bulgaria. It draws on two
            main types of data: large-scale administrative data on
            public procurement and the list of politically exposed
            persons. The analysis rests on calculating individual
            corruption risk indicators (or red flags), such as single
            bidding in competitive markets, and creating a composite
            Corruption Risk Index based on these indicators. It maps the
            distribution of these red flags over time, across different
            regions and markets. The analysis finds that Bulgaria shows
            high corruption risk among other examined countries in the
            European Union, with weak institutions contributing to slow
            gross domestic product per capita convergence to Western
            European countries. The results point out that corruption
            risks have deteriorated over time. Combining
            suppliers&#x2019; political connections information with
            public procurement corruption risk data shows that
            connections are associated with higher risks, in particular
            connections to local government members and state-owned
            enterprises. The large-scale analysis of buyer-supplier
            contracting networks points at state capture patterns where
            groups of buyers and suppliers repeatedly connect in high
            corruption risk procurement contracts. Such groups have
            gained more power and control over a larger share of
            contracts since 2016. Finally, policy recommendations are
            provided in three areas: enhancing data scope and quality,
            introducing a data-driven approach to corruption risk
            assessment, and improving public procurement policy and
            practices to reduce noncompetitive tenders.
display_title: Corruption Risks and State Capture
            in Bulgarian Public Procurement
listing_relative_url: /research/2023/05/34063440/corruption-risks-state-capture-bulgarian-public-procurement
topicv3: Government Procurement m1326093 957,Anti-Corruption m1326754 832,Integrity and Anti-Corruption m1327483 313
publishtoextweb_dt: 2023-05-16T00:00:00Z
dois: 10.1596/1813-9450-10444
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1318529
1: author_key: 2122117
2: author_key: NA
model_extractions: Policy Research Working Paper 10444 Corruption Risks and State Capture in Bulgarian Public Procurement _Mihaly Fazekas Viktoriia Poltoratskaia Bence Tóth_ Macroeconomics, Trade and Investment Global Practice May 2023
Policy Research Working Paper 10444

# **Abstract**

This paper sets out to measure and analyze corruption risks, patterns of favoritism, and state capture in public procurement in Bulgaria. It draws on two main types of data: large-scale administrative data on public procurement and the list of politically exposed persons. The analysis rests on calculating individual corruption risk indicators (or red flags), such as single bidding in competitive markets, and creating a composite Corruption Risk Index based on these indicators. It maps the distribution of these red flags over time, across different regions and markets. The analysis finds that Bulgaria shows high corruption risk among other examined countries in the European Union, with weak institutions contributing to slow gross domestic product per capita convergence to Western European countries. The results point out that corruption risks have deteriorated over time. Combining suppliers’ political connections information with public procurement corruption risk data shows that connections are associated with higher risks, in particular connections to local government members and state-owned enterprises. The large-scale analysis of buyer-supplier contracting networks points at state capture patterns where groups of buyers and suppliers repeatedly connect in high corruption risk procurement contracts. Such groups have gained more power and control over a larger share of contracts since 2016. Finally, policy recommendations are provided in three areas: enhancing data scope and quality, introducing a data-driven approach to corruption risk assessment, and improving public procurement policy and practices to reduce noncompetitive tenders.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 misi.fazekas@gmail.com.

_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
# Corruption Risks and State Capture in Bulgarian Public Procurement

Mihaly Fazekas*, Viktoriia Poltoratskaia*, Bence Tóth** May 3, 2023

*Central European University and Government Transparency Institute

**University College London and Government Transparency Institute

JEL classification D73

## Keywords: _corruption, favoritism, state capture, public procurement_

_The research has been prepared as part of the Bulgaria Country Economic Memorandum “A Path to High_ _<u>Income” led by Desislava Nikolova (EECM2) and Marc Schiffbauer (EECM2), project code AA-P176338-ASA-BB.</u>_

**<mark>Acknowledgments:</mark>** <mark>The team is thankful to Peter Horn and Aly Abdou for their research assistantship for the project. The report greatly benefited from useful inputs and feedback from the World Bank team, including Maciej Drozd, Damian Iwanowski and Adela Ivanova Delcheva Nachkova, as well as peer reviewers Ashley Taylor, Alexandre Borges de Oliveira, Tom Bundervoet and Dessislava Valerieva Kuznetsova. The team is grateful to Olga Šipka for help with data collection, as well as Public Procurement Agency and Deputy Minister of Finance of Bulgaria for cooperation regarding data interpretation.</mark>
# 1. Introduction

Institutional and governance challenges are key constraints that reduce Bulgaria’s economic potential and private sector productivity. Bulgaria continues to lag most European Union (EU) countries on governance indicators.<sup>1</sup> The gap with the rest of the EU is most pronounced along institutions critical for economic growth such as the rule of law, control of corruption, and government effectiveness.<sup>2</sup> One critical institutional area where governance weaknesses and state capture by private interests are evident is public procurement (European Commission-DG REGIO, 2022, chapter 7). Owing to the digitalization of public procurement data in the EU and the introduction of a comprehensive national e-procurement system, auditors and judicial institutions, government analysts, businesses, and civil society now have access to electronic public procurement data, including the details of individual contracts and tenders (World Bank, 2020). For the first time, these provide tools to work with big data by discovering the cost and beneficiaries of non-competitive public procurement tenders and linking corruption risks in public procurement to state capture and identifying their economic impacts. Among others, the <u>opentender.eu database on public procurement created by the EU-funded DIGIWHIST</u> project allows analysts to identify and validate ‘red flags’ for corruption that may signal competition flaws, misuse of public funds, or outright corruption.

Reducing corruption risk in public procurement has the potential to boost private sector productivity growth (World Bank, 2022). Public procurement amounts to more than 12 percent of gross domestic product (GDP) in Bulgaria. The access to contracts thus has a major impact on the allocation of resources in the economy. It can either achieve value for money out of scarce fiscal resources and support the growth of more productive firms or result in an inefficient use of public money through helping unproductive firms to accumulate scarce physical and human capital and stay in the market. Reducing corruption in public procurement also reflects on the enforcement of competitive market structures in the economy and thus supports a policy environment that encourages competition based on innovation and efficiency instead of lobbying for policy privileges. Competitive public procurement practices can thus boost economic growth in two fundamental ways:

- Increasing the efficiency of resource allocations across firms and raising aggregate productivity growth

- Contributing to a level playing field among all firms in the economy, which encourages innovation and ensures survival and growth of the most competitive enterprises.

Against this background, the main goal of this paper is to provide a comprehensive analysis of corruption risks and state capture in public procurement in Bulgaria during 2011-2019. To measure these risks, this paper develops and validates a range of corruption risk indicators (CRIs) and builds a network-based state capture assessment framework. For measuring corruption risks, the paper calculates a composite score of individual ‘red flags’, called the corruption risk indicator (CRI), as an objective proxy measure of institutionalized corruption in public procurement (Fazekas and Kocsis 2020). For measuring state capture, the clustering of high corruption risk contracting relationships among buyers and suppliers is applied (Fazekas and Tóth 2016). These measurement innovations adapted to the specific Bulgarian context enable us to track the development, distribution, and structure of corruption risks and state capture across years, regions, and sectors, to name a few key analytical dimensions.

> 1 European Commission. Public administration and governance: Bulgaria. 2020. URL: https://commission.europa.eu/system/files/2021-10/bulgaria-ht0321342enn.en_.pdf. 2 The World Bank Worldwide Governance Indicators.

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The application of big data analytics to Bulgarian public procurement can support anti-corruption policy in at least two fundamental ways:

- It facilitates decisions about monitoring, audit, and investigations concerning individual transactions and organizations.

- It informs country or sector-wide policy decisions on resource allocation (for example, capacity development efforts) and regulations (for example, on reporting thresholds or publicity requirements).

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# 2. Data used for the analysis

We used two procurement data sources for the analysis. First, all tenders and contracts were collected from the old national e-procurement portal, http://www.aop.bg. Second, we collected all publications from the new national e-procurement portal, https://app.eop.bg. We collected the data by using automated web scrapers that are adapted to the specificities of the source websites and data repositories. The combined national public procurement dataset includes 148,637 contracts for 2011– 2019, characterized by 129 variables.

We also collected the Bulgarian tenders from the EU-wide Tenders Electronic Daily (TED) portal that was used for cross-checking data availability on the national portal and for the European comparative analysis as TED data are more comparable across European countries than national data. The details of data collection and processing are outlined in Annex 1.

We also collected data on politically exposed persons (PEPs) and linked it to public procurement data to carry out additional analysis. The list of PEPs was compiled by the Center for the Study of Democracy, including information from politicians’ asset declarations, the Panama and Pandora Papers, and the Magnitsky Act (for details see Annex 6). The identified individuals were then linked to relevant companies based on whether a PEP is an owner or official of the company. As a result, 4,566 companies were flagged as politically connected by the Center for the Study of Democracy. We matched these companies to the public procurement data, leading to 197 unique PEP-connected firms, either as a supplier or buyer (for example, a state-owned enterprise [SOE] awarding a procurement contract) in the procurement dataset. The PEP-connected companies were parties to around 36,500 contracts (we considered PEP connection throughout the whole period for matching, that is, we adopted a time-invariant PEP definition).

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# 3. The risk of corruption in Bulgaria

## 3.1 How we measure corruption risks

This section outlines the framework for measuring corruption risks in Bulgaria. It specifies the definition of corruption and the individual risk indicators (red flags), as well as the operationalization of the CRI. It reviews evidence for the validity of these indicators and CRIs.

The CRI methodology rests on identifying a range of risk factors, conducting validity tests on them, and pulling them together into a composite score. Any measurement of corruption has to start with a suitable definition of corruption in public procurement. Following academic literature as well as World Bank publications, we define corruption in public procurement as the allocation of public contracts by violating the principles of open and fair procurement to benefit some connected actors to the detriment of all others. This definition leads to a range of potentially valid individual CRIs based on vast literature offering conceptual and qualitative support for their validity. For example, single bidding is used as a risk indicator because the submission of only one bid in a tender, in a competitive market, is the simplest indication of restricted competition, allowing for overpricing or extracting rents.

Next, we validate and tailor elementary risk indicators by estimating how well they fit with the corruption definition (that is, whether the indicators behave as theory predicts). For example, it can be verified that a suspiciously short advertisement period predicts single bidding on competitive markets because short advertisement can be used to limit competition to favor one firm. The length of the advertisement period, number of days, can also be a predictor of single bidding. This allows identifying thresholds beyond which corruption risks are likely to increase substantially. In general, regression analysis is used to identify valid ‘red flags’ that are likely to signal corruption and to assign the values of variables, such as advertisement period length or procedure types, to groups of high, medium, and low corruption risk. The number of risk categories for a risk factor in a country is determined by their regression fit (that is, the analysis aimed to coarsen risk information while retaining explanatory power).

Ultimately, those variables are selected for inclusion in the composite CRI whose coefficients are large and statistically significant in regressions predicting single bidder contracts (or alternative outcome variable indicating institutionalized, stable corruption such as buyer spending concentration), which is the simplest sign of lack of competition. The regression setup controls for a number of likely confounders of bidder numbers, for example, (a) institutional endowments measured by type of buyer (for example, municipal, national); (b) product market; (c) contract size (log contract value); (d) type of delivered items/services (goods, services, or works); or (e) regulatory changes as proxied by year of contract award. Regressions were run for Bulgaria to best capture national specificities of effective measures to limit competition and institutional endowments.

Table 1 presents internal validity regressions on the relationship between corruption risk outcomes (single bidding) and corruption inputs (other red flags such as length of submission period and decision period, procedure type, and call for tender publication). All tests confirm the selected risk indicators.

5
||||Dependent variable:|||
|---|---|---|---|---|---|
||||Single bidding|||
||(1)|(2)|(3)|(4)|(5)|
|Procedure type (negotiated)|0.611"**||||0.534"**|
||(0.015)||||(0.017)|
|Procedure type (non-open)|2.047°**||||1.059°**|
||(0.022)||||(0.028)|
|Decision period (5 to 8 days)||0.447°**|||0.422***|
|||(0.023)|||(0.023)|
|Decision period (1 to 4days)||1.544°**|||1.263"**|
|||(0.018)|||(0.018)|
|No call for tenders|||1.488"**||0.758"""|
||||(0.017)||(0.029)|
|Submission period (from 5 to 8 days)||||0.258""*|-0.014|
|||||(0.016)|(0.018)|
|Submission period (missing)||||1.105"**|-0.095"**|
|||||(0.014)|(0.022)|
|Observations<br>|193,980<br>|193,980<br>|193,980<br>|193,980<br>|193,980<br>|
|LogLikelihood|-102,755.100|-102,283.300|-103,566.100|-104,590.800|-98962.630|
|Akaike Inf. Crit.<br>|206,210.200|205266.600|207,830.200|209,881.700|198,635.300<br>|
|Note:|||||*p<0.1; “*p<0.05; ***p<0.01|
||Includedcontrolsnotshow|nare:Buyerlocation,Buye|rtype,Supl.location,Contr|acttype,YearFE, Mar|ketFE,Contractvaluedeciles|
**Table 2. Validated CRIs (‘red flags’) and their definitions for Bulgarian public procurement data**

|**Indicator name**|**Indicator definition**|
|---|---|
|Single bidder contract|0 = more than one bid received<br>1 = one bid received|
|Call for tenders publication|0 = call for tenders advertised<br>1 = call for tenders not advertised|
|Procedure type|0 = open procedure<br>0.5 = negotiated/accelerated procedures<br>1 = non-open procedure type (for example, direct contracting)<br>(for detailed definition see Annex 3)|
|Length of submission period|Number of days between publication of call for tenders and submission<br>deadline<br>0 = from 12 to 183 days<br>0.5 = from 7 to 11 days<br>1 = from 1 to 6 days|
|Length of decision period|Number of calendar days between submission deadline and announcing of<br>contract award<br>0 = from 9 to 365 days<br>0.5 = from 5 to 8 days<br>1 = from 1 to 4 days|
|Buyer’s dependence|Shows the contracting authorities’ contract share rewarded to the same<br>supplier in a given year|

**Single bidding** indicates that a given tender only had one bidder during the procurement process, hence there was no competition for the contract. The lack of competition is one of the main signs of corruption in the public procurement system. Single bidding can be easily extracted from the data and has the key qualities necessary for an adequate indicator (Abdou, Czibik, Tóth, and Fazekas 2021).

**The length of the submission period** is the time difference between the first publication date of the contract notice and the deadline until which suppliers can submit their bids (bidding deadline). A sufficiently short submission period could indicate corruption, and we find a strong connection between the length of the period and the likelihood of single bidding.

Not publishing a **call for tenders** in the official journal makes it less likely that eligible bidders notice the bidding opportunity and bid.<sup>4</sup> In addition, not publishing a call for tenders in the official journal weakens competition, allowing the issuer to more easily award contracts repeatedly to the same company (Fazekas, Tóth, and King 2016).

**Procedure type** indicates the type of procedure by which the tender will be contracted. Non-open procedures, which are less transparent and competitive, create more opportunities to limit the range of bids received and to exclude bids. Additionally, they create more opportunities for issuers to repeatedly award contracts to the same company.

> 4 Some tenders do not have a call for tenders' publication despite a legal requirement, hence this indicator captures the actual and not the legally prescribed publishing practices of public buyers.

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# 3.2.2 Bulgarian corruption risks from the broader European perspective

The observed trend toward lower competitiveness and higher risks in Bulgarian public procurement follows broader European trends, albeit the magnitude of deterioration is greater.

To better understand and interpret the above risk profiles and trends in Bulgaria, we compare selected risk factors to other European countries. These comparisons are based on a narrower dataset than the one used so far to make the composition of compared procurement markets more similar. We use the TED dataset which is the online version of the 'Supplement to the Official Journal’ of the EU, dedicated to European public procurement. All tenders and contracts announced on TED follow the EU procurement directives, which implies that the regulatory frameworks are largely comparable across countries. TED publishes about 700,000 procurement award notices a year, including 250,000 calls for tenders which are worth approximately €670 billion. Tenders published on TED are different from national sources in many aspects, most importantly they tend to be of higher value (publication on TED is mandatory for high-value contracts albeit thresholds differ by supply type, among others). In spite of these differences between TED and national Bulgarian public procurement data, the subsequent comparisons can help contextualize and interpret corruption risks in Bulgaria.

To further increase comparability of corruption risk trends between European countries (European Economic Area [EEA] hitherto) and Bulgaria, we narrow our focus to a standard risk indicator: single bidding. Single bidding reflects the competitiveness of tenders and therefore can be a proxy for measuring potential corruption risks due to deliberately excluding non-connected firms from tenders. Single bidding is one of the most universally applicable CRIs, as it does not require identifying risk thresholds or classifying categories into risk groups. When there is only one bid submitted in a tender, the single bidding variable takes the value of 1, otherwise it is 0. For the sake of comparability, average annual values for each country are used.

The share of single bidding contracts has increased steadily in Bulgaria since 2011 and it is higher than corresponding shares in the other EU countries where data are available. First, we compare Bulgaria to selected EU member states, both among low and high corruption risk countries. (Figure 11). In this comparison, Bulgaria has featured the highest single bidding rate since 2018 relative to Germany, France, Italy, Romania, and the Slovak Republic. While there has been a consistent upward trend for most of the other countries since 2011 (the Slovak Republic is the only country which managed to lower its single bidding rate over the period), Bulgaria experienced a steady and steep increase in the share of single bidding.

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|Regression Res|ults: Time invaria|ntPEP|
|---|---|---|
||Depe|ndent variable:|
|||cri|
||q)|(2)|
|PC supplier,time invariant|0.031°"*<br>(0.003)||
|PC buyer and supplier, time invariant||0.035"<br>(0.007)|
|Constant|0.632"**|0.632***|
||(0.055)|(0.055)|
|Observations|132,521|132,521|
|R2|0.451|0.451|
|Adjusted R2|0.450|0.450|
|Residual Std. Error (df= 132291)|0.195|0.195|
|FStatistic(df=229;132291)|475.408""*|474.689°**|

Note: “p<0.1; ““p<0.05; **“p<0.01 Included controls not shown are: Supl. location, Contract type, Year FE, Market FE, Contract value deciles
|Regression Re|sults: Time invariantPEP|
|---|---|
||Dependent variable:|
||cri|
||()<br>(2)|
|PC supplier with legislative PEP, time invar.|0.001<br>(0.006)|
|PC supplier with local gov.memberPEP,time invar.|0.051°**<br>(0.004)|
|PC supplier withSOEmemberPEP,time invar.|0.062***<br>(0.007)|
|PC buyerand supplierwith legislativePEP,time invar.|0.254***<br>(0.073)|
|PC buyerand supplierwith local gov. member PEP,time invar.|0.093***<br>(0.035)|
|PC buyerand supplierwithSOEmemberPEP,time invar.|0.119***<br>(0.017)|
|Constant|0.976"<br>0.975"**|
||(0.202)<br>(0.202)|
|Observations|132,521<br>132,521|
|R2|0.459<br>0.458|
|AdjustedR2|0.458<br>0.457|
|Residual Std. Error<br>|0.194 (df= 132237)<br>0.194 (df= 132239)<br> <br>|
|F Statistic|396.373" (df=283; 132237)<br>398.100""* (df=281; 132239)|
|Note:|*p<0.1; **p<0.05; ***p<0.01<br>Included controls not shown are: Buyer location, Buyertype, Supl. location,|
||Contracttype,YearFE,MarketFE,Contractvaluedeciles|
Therefore, we can conclude that the presence of political connections in public procurement indeed is systematically used for private gain. Based on the coefficients presented in the regression models, this effect is not a major driver of a high CRI in Bulgarian procurement but is contributing to it. Yet, the presence of political connections also increases the contract value, which is clear from both annual and overall distributions. Markets with a high share of politically connected companies can also generate additional risks for tendering procedures and may deserve additional attention.

25
# 4. The risk of state capture in Bulgaria

This section describes our approach to estimate state capture in public procurement using large-scale network analysis. It then analyzes patterns of state capture found in Bulgaria in 2011–2019. The analysis starts off with an overview of the Bulgarian public procurement contracting network, then it moves on to identifying cohesive groups of contracting organizations where corruption is the norm. It concludes by analyzing state capture patterns in different subsamples.

This approach is complementary to the previous section analyzing political connections of suppliers as it aims to gauge the same set of phenomena. However, estimating state capture using proven and direct political connections serves as a robust, but arguably lower-bound estimate to state capture. There are plenty of connections which cannot easily be discerned from publicly available information such as asset declarations. Whereas applying large-scale network analysis to the contracting network of suppliers and buyers allows for analyzing state capture across the whole procurement market. This approach does not suffer from the risk of underestimation like the political connections-based approach, instead it may overestimate state capture. Hence, the two approaches are best utilized in tandem.

## 4.1. How we estimate state capture

Building on our definition and measurement of corruption, we conceptualize state capture as a group phenomenon where private sector companies along with public organizations work together to assert dominance over government contracting with the objective of extracting private rents from public resources. This definition allows for state capture to occur in both directions, whether it is the state dominating a network of suppliers or the other way around. Captured organizations then fail to foster public goals to ensure high value for public money, that is, the lowest possible price for the highest achievable quality.

We develop a measure of state capture by distinguishing between different degrees and distributions of corruption risks in public procurement across suppliers and buyers. Viewing public procurement contracting relationships as networks allows for delineating the incidence and clustering of corruption risks. In this framework, state capture is systemic corruption that results from clusters of individuals forming tightly knit cliques. On the one hand, corruption can arise from isolated attempts to manipulate procedures and extract private rents that are not systemic―that is, not clustered among the same actors over time. In this case, we cannot consider that as state capture. On the other hand, if corruption manifests itself in closed networks where all transactions have a high risk of corruption, we can consider that as state capture. The analysis of the network structure of corruption thus allows us to go beyond merely quantifying corruption risks; in particular, it permits identification of state capture networks that operate differently from both clean and randomly corrupt contracting networks.

Looking at the degree and distribution of high corruption risk, considering contractual relationships between suppliers and buyers as two distinct dimensions allows us to describe four ideal types of contracting networks: (a) clean network, (b) corrupt but not captured network, (c) partially captured network, and (d) fully captured network. We depict a simplified version of each of these network structures in Figure 17. Public organizations (issuers/buyers) and private firms (suppliers) are represented as _I_ and _S_ , respectively. The dashed lines represent low corruption risk contracts and solid lines indicate high corruption risk contracts. Panel (i) illustrates a clean network without high corruption risk ties in either of the two clusters (C1 and C2). Panel (ii) visualizes corruption without state capture in that high corruption risk ties are randomly distributed among different buyer-supplier 26
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**Table 6: Network statistics for 2014–2016 and 2017–2019, Bulgaria**

|Statistic|(1)<br>(2014–2016)|(2)<br>(2017–2019)|
|---|---|---|
|Total contract value|BGN 17.02 billion|BGN 20.74 billion|
|Average CRI|0.36|0.40|
|Nodes<br>Buyers<br>Suppliers|13,885<br>2,815 (20%)<br>11,070(80%)|9,661<br>1,907 (20%)<br>7,754(80%)|
|Edges|28,764|19,263|
|Graph density|0.0003|0.0004|
|Clusters|284|150|
|**Modularity**(stronger clustering of buyer-supplier ties)|0.617|0.550|
|**Betweenness centrality**<sup>**a**</sup> (higher values given to<br>buyers/suppliers that create shorter paths between<br>two nodes)|0.111|0.082|
|**Eigenvector centrality**<sup>**a**</sup>(higher values given to<br>buyers/suppliers that are connected to<br>buyers/suppliers with more ties)|17.582|12.462|

_Note:_ a. These aggregated centrality measures are network measures based on the centrality scores of each node in the network. The higher the centrality scores, the more likely the nodes that are further apart will interact through central agencies/firms.

To probe potential areas of state capture, we investigate the behavior of high- and low-risk portions of the buyer-supplier network separately and contrast them with each other. In line with the above theoretical model, we expect high-risk contracting relationships to be clustered if state capture is a widespread phenomenon in Bulgaria. We thus split the network into two subnetworks, representing the bottom 30 percent and top 30 percent of the average CRI of the contracting relationships. In addition, we use algorithms to detect buyer-supplier clusters that are characterized by a dense web of connections among them but by sparse connections with other buyer-supplier clusters.<sup>17</sup> We display the low and high corruption risk groups of buyers and suppliers separately in Figure 20. In these figures, the nodes are colored according to the identified clusters.<sup>18</sup> Clusters across both low- and high-risk portions of the contracting network are highlighted with the same color, that is, it is possible to see if the same network is present in the high- and/or low-risk parts of the network. In an imagined world, clusters would appear in only one of the subsamples (either bottom 30 percent or top 30 percent) which would indicate either complete state capture (if it only appears in the top 30 percent) or no state capture (if it appears in the bottom 30 percent sample).

The coloring of clusters reveals that most clusters contain low- as well as high-risk contracts, which suggests that there are few completely captured groups. Instead, the public procurement network in Bulgaria reveals different degrees of state capture among public agencies and the awarded firms. On the one end of the spectrum, we can identify, for example, the orange cluster on the top left of the

> 17 Specifically, we run the community detection algorithm in Gephi (Bastian, Heymann, and Jacomy 2009) which implements the modularity algorithm proposed by (Blondel et al. 2008) on the complete network. The algorithm identifies clusters based on organizations’ proximity to each other where the higher tie weights act as an attraction force to get nodes with similar edge weights and more interactions closer.

> 18 The identified communities are automatically assigned different colors to each node belonging to that community/cluster.

30
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network. Buyers and suppliers in the high corruption risk network have more interactions among themselves compared to other agencies and firms that are only in the low corruption risk network.<sup>21</sup>

**Table 7: Network statistics for the complete network and low/high corruption risk, Bulgaria, 2017–2019**

|Statistic|(1)<br>Complete network|(2)<br>Bottom<br>30th percentile|(3)<br>Top<br>30th percentile|
|---|---|---|---|
|CRI percentile value|—|0.20|0.52|
|Total contract value|BGN 20.74 billion|BGN 7.10 billion|BGN 4.86 billion|
|Nodes<br>Buyers<br>Suppliers|9,664<br>1,907 (20%)<br>7,754(80%)|3,684<br>848 (23%)<br>2,836(77%)|4,678<br>1,572 (34%)<br>3,106(66%)|
|Edges|19,263|5,267|5,964|
|Graph density|0.0004|0.0008|0.0005|
|Clusters|150|135|245|
|**Modularity**(stronger clustering of<br>buyer-supplier ties)|0.550|0.707|0.654|
|**Betweenness centrality**<sup>**a**</sup> (higher<br>values given to buyers/suppliers that<br>create shorter paths between two<br>nodes)|0.082|0.094|0.158|
|**Eigenvector centrality**<sup>**a**</sup>(higher values<br>given to buyers/suppliers that are<br>connected to buyers/suppliers with<br>more ties)|12.462|1.167|11.313|

_Note:_ a. These aggregated centrality measures are network measures based on the centrality scores of each node in the network. The higher the centrality scores, the more likely the nodes that are further apart interact through central agencies/firms.

To illustrate the different types of state capture patterns found in Bulgaria, we show in Figure 21 the three largest clusters found in 2017–2019. Cluster A is the largest cluster and although it is not fully captured, it demonstrates a high risk of state capture pattern where the high corruption risk ties dominate the center of the network with the lower corruption risk ties remaining on the periphery. Cluster B is the second largest and shows a partial state capture pattern as well. High corruption risk ties are found in the center, mixed with other low corruption risk ties.<sup>22</sup> Cluster C shows a network with several centers that are not captured and one captured center. This shows a pattern where high corruption risk procedures are not conducted in a systemic pattern throughout the network.

> 21 Each of the betweenness and eigenvector centralization metrics are commonly used network-level metrics stemming from the node-level centrality scores. The betweenness centralization attributes higher centrality scores to nodes that are connected to more edges. A node’s eigenvector centrality, on the other hand, considers the importance of neighboring nodes as well. In our case, the top 30 percent sample has a higher betweenness centralization of 0.158 compared to 0.094 for the bottom 30 percent subnetwork. Similarly, the top 30 percent sample also has higher eigenvector centralization of 11.313 compared to 1.167 for the bottom 30 percent subnetwork. This indicates that activity in the high corruption risk subnetwork is conducted in a more centralized pattern compared to the low corruption risk subnetwork.

> 22 It is also observed that the high corruption risk ties in cluster B have lower CRI scores than the corruption risk ties in cluster A.

32
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# 5. Conclusions and policy lessons

Based on the analysis, a set of data-driven policy recommendations can be put forward. Some of the recommendations provided in this section aim at improving the data infrastructure underpinning any future analysis, while others point at ways to improve public procurement policy and practice.

## 5.1. Improve the data publication framework

Data publication is generally of high quality, and a wide range of variables is available on the procurement notices. The share of missing data, including tender and organization identifiers, is low compared to many European countries. Yet, further steps are needed in the following directions:

1. Improve the access to and usability of the detailed public procurement data by introducing a bulk download option for structured data covering tenders published on both the old and the new national portals with clear distinction between tenders published according to the national threshold regulation versus those above the EU threshold regulation.

2. Limit the number of different publication templates. The use of 20+ different publication templates increases the chance of data errors.<sup>23</sup> Despite the many notice types, unawarded/cancelled contracts are only revealed indirectly in award notices.

3. Identifiers can be missing and not well structured, which makes it hard to connect tenders and contracts related to the same organizations. While the IDs for contracting authorities are mostly complete, unique identifiers are often missing for contractors.<sup>24</sup> 4. Missing values: certain fields are missing from the publications, such as information on subcontracting, consortia, or estimated prices. These should be stated explicitly in the source of each tender/lot.<sup>25</sup> 5. Additional data improvements: introduce clear correspondence between lots and resulting contracts. Multi-lot tenders often result in fewer contracts and it is not always possible to track such cases.<sup>26</sup>

## 5.2. Introduce continuous monitoring of corruption risks and use data-driven insights in day-to-day policy making

The analysis in this paper demonstrates the viability and potential of data-driven corruption risk analytics in Bulgaria and beyond. Communicating and making good use of such complex and rich datasets is nevertheless a challenge. Currently, a trove of datasets relevant to anti-corruption monitoring and policy making exists in Bulgaria. However, most users in the public sector and beyond lack advanced data science techniques to work with the data directly. Instead, users have varying levels and types of expertise, requiring a flexible and easily accessible way of communicating datadriven insights and supporting policy reform. This will be best achieved by using an interactive dashboard that contains not only the source data but also performance and risk indicators and warns the user about data quality shortcomings and analytical challenges. The dashboard could start by

> 23 For example, there are only three publications that are ‘Социални и други специфични услуги – комунални услуги’.

> 24 For example, https://app.eop.bg/today/121395, https://app.eop.bg/today/124511.

> 25 For example, each tender should contain explicit information on whether it is a public tender/framework/dynamic purchasing system.

> 26 Note that useful improvements have been already made to address this issue. Awarded contracts refer to lot numbers (or to a list of lot numbers) on the new procurement portal, which allows connecting lots with contracts in principle. Furthermore, the 2019/1780 regulation prescribes publishing item level submitted prices - albeit they are sometimes missing and the number of bids submitted to individual lots are not yet traceable.

34
displaying public procurement data and gradually evolve into a comprehensive anti-corruption data system through linking further datasets. The implementation of a comprehensive data analytics framework needs to rely on supportive regulations, organizational setup, and sufficient resources, as well as advances and investments in technological solutions.

Recommendations:

1. A flexible and easy-to-use dashboard for data-driven corruption risk assessment should be developed and gradually the underlying data scope should be extended by linking it to additional datasets.

2. The data scope could further be expanded by linking it to high-value datasets, allowing for a more comprehensive and detailed risk assessment. Such datasets could include, among others, beneficiary ownership data or data on sanctions and convictions.

3. High-quality data on investment projects and performance as well as its easy access and disclosure can facilitate oversight by relevant public institutions, enable monitoring by civil society, and promote confidence and consultation of both domestic and foreign investors.

4. Depending on the desired scope of the dashboard, Bulgaria will need to prioritize different kinds of data and conduct an in-depth assessment of the institutional, organizational, technological, and resource changes that may be required to move toward a more comprehensive corruption risk dashboard.

5. To ensure the continuous improvement and fine-tuning of the corruption risk framework developed in this paper, it will be important to establish a feedback loop in which results of the analysis are incorporated into the design of future analytics.

# 5.3. Improve public procurement and budget policies

Based on the analysis of individual red flags in Bulgaria and the available evidence around the world, a number of specific improvements can be made to the current public procurement regulatory framework. We review each of the key risk factors and propose specific reform pathways below.

**1. Fostering competition** is essential for the reversal of the decline in corruption control in Bulgaria in the last few years. Single bidding is one of the strongest determinants of high prices and belowoptimum quality in public procurement. While a high number of bidders should not be pursued at all costs, lowering the incidence of tenders with no or little competition is a safeguard for value for money in public procurement (Fazekas and Blum 2021).

**Recommendation** : Foster competition to further reduce the number of single bidder contracts and increase the average number of bidders. Competition can be promoted by breaking down market entry barriers and by reducing the transaction costs of competing.

**Implementation** : Borges (2019) shows that more open (less restrictive) technical and financial conditions in tenders can lead to stronger competition, hence greater savings. Furthermore, matching tender sizes to the market capacity and median bidder size can also successfully increase the number of bidders. For example, breaking up large tenders into smaller lots can help smaller enterprises compete. It is also crucial to make sure that open tender procedures apply and this practice is not used to engage in noncompetitive procedures such as direct negotiations. Moreover, Fazekas and Kocsis (2020) show that bid evaluation criteria, which are more concrete and objective, encourage a higher number of bidders and lower corruption risks in the EU. The implementation of such interventions could be closely monitored using real-time public procurement data and the dashboard advocated earlier.

35
**2. Improving tender design by allowing more time for bidding** . Short advertisement periods can lead to a lower level of competition and suboptimal tender performance, while they are also often corruptly abused to exclude non-connected bidders.

**Recommendation** : Limit the use of short advertisement periods so that potential bidders have adequate time to submit their proposals.

**Implementation** : Policy makers should directly influence advertisement periods by setting minimum required lengths and closing loopholes for short periods. Moreover, advertisement periods can be indirectly influenced by training contracting authorities on the importance of high-quality tender designs. Furthermore, guidance for suppliers on efficient preparation of tender documentation can help smaller companies compete. Related to these interventions, making full use of e-attestations (that is, the electronic storage and verification of certificates and other official documents) also allows to cut down the administrative burden of bidding and hence enables bidders to submit quality bids in a relatively short time frame. The implementation of such improvements in tender design could be closely monitored using real-time public procurement data and a dashboard of the type advocated earlier.

**3. Reducing the use of non-competitive types of procedures** . After 2016, there has been a significant increase in corruption risks due to the more frequent use of non-open procedure types in Bulgaria. Closed procedures, such as direct negotiations and awarding, significantly increase prices and often lead to contract implementation delays. One of the widely used corruption techniques is applying nonopen procedure types in an unjustified way, for example, splitting contracts so that the value of each part is below the threshold of the required open procedure. Although this practice does not necessarily aim at corruption rent extraction—institutions might use it to decrease the administrative burden by avoiding a difficult procedure—it certainly weakens competition and transparency and hence increases the risk of corruption. The implementation of such requirements could be closely monitored using real-time public procurement data and a dashboard of the type advocated earlier.

**Recommendation** : Increase the share of open procedures among all tenders to guarantee the broadest possible participation of bidders.

**Implementation** : The use of non-open procedure types can be decreased both through regulatory and capacity-building tools. National legislation can be amended to make it harder to use direct awards, negotiated procedures, and other non-open procedure types. For example, it is possible to lower the thresholds for the mandatory use of fully open tendering or amending the procedural requirements for invitations to tender, making them more open (for example, requiring the submission of four bids rather than merely requesting three bids). The use of central frameworks for widely used standardized goods such as office supplies could be further encouraged or made mandatory (Fazekas and Blum 2021). Training procurement officials on the use of pro-competition tendering practices, including the efficient use of open procedure types could further complement the above regulatory changes.

**4. Increasing the publication of calls for tenders.** We documented a sizable increase in the absence of call for tenders after 2016 which not only decreases transparency but affects corruption risks and market competition. If a contracting authority does not make a call for tenders publicly available, only connected bidders will know about the tendering opportunity. This corruption technique is strongly associated with using non-open procedure types, but it can also take place independently of them.

**Recommendation** : Require the publication of a call for tenders as well as the most essential procurement documents, in all or nearly all tenders.

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**Implementation** : It is relatively straightforward to introduce legislative changes that require the publication of a call for tenders, including all relevant conditions for bidding, for all or nearly all tenders. The implementation of such requirements could be closely monitored using real-time public procurement data and the dashboard advocated earlier.

**5. Improve budgeting practices to limit end-of-year inefficiencies.** We documented a sizable increase in corruption risks at the end of each year (quarterly CRI jumps in the fourth quarter of most years since 2011). This is most likely due to pressures to spend allocated budgets at the end of the financial year, otherwise next year’s allocation will be lower (this is the so-called ‘December fever’). The need for spending the remaining budget quickly increases the room for corruption and inefficient spending.

**Recommendation** : Introduce more flexible budgeting rules limiting end-of-year rushed spending.

**Implementation** : There is a range of good budgetary practices for limiting end-of-year spending inefficiencies. Allowing carry-over of funds from the end of one fiscal year to the next represents a simple adjustment that can cushion at least some of the detrimental effects. Moreover, allowing organizational units to retain some of the savings made during the financial year could greatly dampen motivations for rushed spending at the end of the year. More fundamentally, abandoning incremental budgeting practices for alternatives such as performance or program budgeting could largely eliminate the documented inefficiencies and heightened corruption risks.

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