metadata: authors: 0: author: Fazekas,Mihaly
1: author: Abdou,Aly
2: author: Ibrahimi,Klea
3: author: Tóth,Bence
4: author: Veljanov,Zdravko
count: North Macedonia
docna: 0: docna: Measuring the Risk of Corruption and Its
            Price Impact in North Macedonia 2011–2022
docty: Policy Research Working Paper
abstracts: cdata!: Public procurement in North Macedonia
            amounted to 16 percent of total government expenditure in
            2018, or 5 percent of gross domestic product. The
            country&#x2019;s public procurement is also vulnerable
            to corruption risks, which typically push prices up, leading
            to overspending. To support better budget policies, this
            paper maps corruption and state capture risks using
            administrative data on public procurement and examines their
            impact on prices. The analysis finds that overall corruption
            risk decreased slightly in 2011&#x2013;22, with, for
            example, the single bidding rate remaining around 30
            percent. Estimated overpricing at the contracting stage due
            to corruption risks ranged between 5 and 6 percent of
            spending throughout the period. The highest savings
            potentials are presented by lowering single bidding,
            increasing the use of a simplified open procedure, and
            advertising tendering opportunities longer. Network analysis
            of contracting relationships reveals that the 2017 change in
            government weakened the centrality of high corruption risk
            groups of organizations, leading to an overall higher
            integrity core.
display_title: Measuring the Risk of Corruption and
            Its Price Impact in North Macedonia 2011–2022
listing_relative_url: /research/2024/12/34438633/measuring-risk-corruption-price-impact-north-macedonia-2011–2022
topicv3: Government Procurement m1326093 957,Corruption Prevention m1326530 2026,Corruption and Growth m1327172 1085,Public Procurement and Competition
            m1327470 2105
publishtoextweb_dt: 2024-12-19T00:00:00Z
dois: 10.1596/1813-9450-11007
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 67
author_keys: 0: author_key: 1318529
1: author_key: NA
2: author_key: 2182293
3: author_key: NA
4: author_key: NA
model_extractions: Policy Research Working Paper 11007 Measuring the Risk of Corruption and Its Price Impact in North Macedonia 2011–2022 _Mihaly Fazekas Aly Abdou Klea Ibrahimi Bence Tóth Zdravko Veljanov_ Economic Policy Global Department—Trade December 2024
Policy Research Working Paper 11007

# **Abstract**

Public procurement in North Macedonia amounted to 16 percent of total government expenditure in 2018, or 5 percent of gross domestic product. The country’s public procurement is also vulnerable to corruption risks, which typically push prices up, leading to overspending. To support better budget policies, this paper maps corruption and state capture risks using administrative data on public procurement and examines their impact on prices. The analysis finds that overall corruption risk decreased slightly in 2011–22, with, for example, the single bidding rate remaining around 30 percent. Estimated overpricing at the contracting stage due to corruption risks ranged between 5 and 6 percent of spending throughout the period. The highest savings potentials are presented by lowering single bidding, increasing the use of a simplified open procedure, and advertising tendering opportunities longer. Network analysis of contracting relationships reveals that the 2017 change in government weakened the centrality of high corruption risk groups of organizations, leading to an overall higher integrity core.

This paper is a product of the Economic Policy Global Department—Trade. 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 FazekasM@ceu.edu.

_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
# Measuring the Risk of Corruption and Its Price Impact in North Macedonia 20112022

Mihaly Fazekas<sup>1,3</sup> , Aly Abdou<sup>3</sup> , Klea Ibrahimi<sup>1</sup> , Bence Tóth<sup>2,3</sup> , Zdravko Veljanov<sup>1,3</sup>

- 1-Central European University

- 2-University College London

- 3-Government Transparency Institute

This paper is a product of the Macroeconomics, Trade and Investment Global Practice, conducted in the context of the World Bank North Macedonia Public Finance Review (P179795), led by Sanja Madzarevic-Sujster, Senior Economist. The authors thank the World Bank for funding this research project. 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 FazekasM@ceu.edu.

_Keywords_ : public procurement, competition, corruption, network analysis, North Macedonia _JEL classification_ : H57; D73; L14
# Contents

|Introduction ................................................................................................................................ 3|
|---|
|Regulatory Framework ............................................................................................................... 5|
|Data ........................................................................................................................................... 6|
|Measuring corruption risks in public procurement .....................................................................12|
|Mapping corruption risk distributions and trends .......................................................................16|
|Relationship between corruption risks and prices ......................................................................24|
|State capture risks in North Macedonia’s public procurement ...................................................35|
|How we estimate state capture ..............................................................................................35|
|Mapping state capture risks across North Macedonia ............................................................37|
|Conclusion ................................................................................................................................45|
|References ...............................................................................................................................47|
|Appendix I: Additional tables and figures...................................................................................49|
|Appendix II: Additional public procurement network analysis results .........................................59|

2
# Introduction

North Macedonia is coping with the aftermath of its deepest recession since 2001. The 3.9 percent growth of 2019 was reversed to -6.1 percent by the end of 2020 as the COVID-19 pandemic unfolded. The fiscal deficit increased to 8.8 percent of GDP in 2020 as a result of plummeting revenues and the unprecedented transfers to firms and households (of around 10 percent of GDP). This pushed up public debt by 11 percentage points in just one year to a new historical peak of 60.8 percent of the GDP.

Now that the recovery is starting to take hold, public finances need to be brought back onto a sustainable path. Given a gradual deficit-reduction trajectory, as announced in the government’s Fiscal Strategy 2021-26, public debt will remain above 60 percent of GDP by 2026. With current policies and new support packages discussed, the primary fiscal deficit (Public Enterprise for State Roads included) will not close even by 2026. The projected deficit reduction will come mainly from lower subsidies and higher revenues as the economy recovers. This is a lower adjustment effort than the average for the Western Balkans and is also based on optimistic growth expectations of above 5 percent on average by 2026.

Public procurement in North Macedonia amounted to 16% of the total government expenditures or 5% of GDP in 2018 (OECD 2020, chapter 7). According to the same report, countries from the Western Balkans face challenges in ensuring efficient spending. They maximize the value of their purchases. Combining this with the critical role played by public procurement in the provision of works, goods, and services, it is a category within the state budget that is closely scrutinized for spending reforms by organizations such as OECD (OECD 2020) as well as the EU (European Commission 2022).

Hence, one possible way to control state expenditures is to improve public procurement practices and enhance value for money, that is procuring the same goods and services but for better prices. North Macedonia continues to lag EU countries on governance indicators. The gap to the EU is most pronounced along institutions critical for economic growth such as the rule of law, control of corruption, and government effectiveness. One critical institutional area where governance weaknesses and state capture by private interests are evident is public procurement. Thanks to the digitalization of public procurement data and the introduction of national e-procurement systems, audits, and judicial institutions, analysts and civil society whistleblowers now have access to electronic public procurement transaction data, including the details of individual government contracts. Such administrative datasets allow us to analyze the cost of noncompetitive public procurement practices and corruption risks. Moreover, they also allow for a comprehensive analysis of economic relations, that is contracts, among public and private organizations revealing hidden partners of state capture risks.

This paper presents an in-depth analysis of public procurement in North Macedonia based on contract level data between 2011 and 2022. First, it gives a descriptive overview of the country’s public procurement market. Second, it calculates and validates a set of corruption risk indicators following the risk measurement approach introduced in Fazekas & Kocsis (2020) and applied in 3
the region to Bulgaria (Fazekas et al, 2023). Third, it maps the distribution of risks by various tendering dimensions. Fourth, it shows the relationship between corruption risk indicators and price savings. This allows us to see the estimated total savings achievable by reducing risky tendering practices and to identify the practices most closely linked to inflated contract values. Fifth, it analyzes tendering risks from a network perspective, that is looking into recurring high risk contracting patterns between public buyers and specific suppliers.

4
# Regulatory Framework

The overarching legislation on public procurement was adopted in 2007 (Official Gazette of the Republic of North Macedonia 136/2007) and subsequently amended to better reflect changing market realities. In 2019, for the purpose of better and closer harmonization with the EU guidelines and standards, the Public Procurement Law (PPL) underwent a major revision (Official Gazette No. 24/2019).

One of the most substantial changes in 2019 was the creation of a simplified open procedure that replaced the ‘competitive negotiated procedure’. This simplified open procedure can be applied following increased thresholds: 70,000 EUR (in Macedonian denars counter-value) for goods and services and 500,000 EUR (in Macedonian denars counter-value) for works, thus effectively replacing the ‘competitive negotiated procedure’ (whose thresholds stood at 20,000 EUR and 50,000 EUR). The introduction of the simplified procedure allows purchasing authorities to draw up a shortlist of potential suppliers and invite them to submit bids, much like the competitive negotiated procedure, and effectively cut short the period of advertisement. At least three bidders have to submit bids, hence this procedure type maintains some safeguards for competition. However, in practice the competition is limited as bidding is restricted to a select few, potentially leading to lower quality and higher prices. Furthermore, the PPL incorporates relevant provisions from the EU Directives, including the introduction of the Most Economically Advantageous Tender (MEAT, in Article 99) as the leading award criteria.

The legal framework is overseen by the Public Procurement Bureau (PPB), which is an independent body. The PPB is the central body responsible for coordinating and monitoring public procurement and also for providing training and guidance to public procurement officers and contractors. At the same time, it can conduct audits and investigations in cases of potential irregularities. However, certain limitations of the current legal framework remain as the country has repeatedly delayed the implementation of the Public-Private Partnership (PPP) Law and Law on Concessions (European Commission 2022).

5
# Data

- Our main data source is the official public procurement data portal of North Macedonia https://e <u>nabavki.gov.mk/. We follow the data collection, standardization and quality assurance procedures</u> outlined in Fazekas et al (2024) and further refined on https://opentender.eu/. We first annotate the source website and identify all the relevant fields required for the analysis as well as create enumerations for data fields which are published in Macedonian. For example, enumerating national procedure types to commonly used procurement procedure types such as open procedure or supply types such as goods, works or services. We then built a dedicated web crawler to scrape the HTML pages of all the available procurement publications such as call for tenders, contract awards, contract amendments and cancellations. We then use the publications’ identification numbers to correctly match the publications with each other to create a single record in our dataset that accurately represents the complete tendering process from publishing the call for tenders to the contract award (including any amendments). We also flag tenders that were canceled at any point during the tendering process to be excluded from the analysis. The collected raw data of the matched publications is then parsed into a standard data structure<sup>1</sup> allowing for consistent analysis over time within the same country, but also comparisons across countries.

We manually validate the collected data fields against the source publications to confirm that the data accurately matches the official information. The data goes through several rounds of data validation to ensure data collection accuracy. After a successful validation stage, we perform further cleaning to the dataset to reduce any errors, inconsistencies and missing values in the official data. For example, we geolocate the organization’s unstructured addresses using the HERE REST APIs<sup>2</sup> to standardize 1,429 unstructured address values to 764 unique values. This improves the dataset by removing spelling errors and having locations in different languages. Since the source lacks consistent organization IDs, we also generate a unique organization identification number. For procurement authorities, we use the lowest available geolocation (district when available) along with a cleaned name to generate the identification number. For suppliers, we use the structured supplier city, cleaned name and a supplier’s legal form to tag organizations.

In the following analysis, we use 169,830 tenders which resulted in 267,427 contracts awarded between 2011 and 2022. Figure 1 shows the seasonality of public procurement purchases both in terms of the number of tenders and contracts (Panel A) and total spending (Panel B). Public procurement data became steadily available in 2011 and the publication rate increased in the following years, whereas total spending covered by the dataset fluctuates around 2.5 billion to 5 MKD per month with a slight increase in spending during the second half of 2021.

> 1 For a full description of the data standard see: https://opentender.eu/mk/about/how-opentender-works

> 2 - <u>https://developer.here.com/develop/rest apis</u>

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# Measuring corruption risks in public procurement

This section outlines the Corruption Risk Index (CRI) methodology and the exact process for identifying the thresholds used to flag contracts as risky. We define corruption in public procurement as a set of practices that violate the principles of open competition by deliberately favoring one or a few bidders to the detriment of others. These practices would ensure that procurement authorities do not end up with the best value for money. Often both prices and the quality of the procured products suffer due to corrupt practices thus harming the public budget.

Based on the wide literature on public procurement practices, single bidding is one of the direct outcomes of corrupt practices and is frequently used as a signal of wrongdoing in the tendering process. Further evidence can be found in Fazekas & Kocsis (2020). Therefore, we use single bidding as our main corruption proxy in order to validate other practices of favoritism. In a regression framework, we identify those procurement practices which are associated with single bidding in otherwise competitive markets, as these indicate likely competition restriction supporting favoritism. For example, tenders with open procedures are usually the norm in many countries leading to multiple bidders submitting bids. We compare the relationship of restricted and open procedures regarding single bidding. A positive significant relationship would mean that the use of restricted procedures can be considered a risky practice as it has a positive (and significant) relationship with single bidding - i.e. single bidding is a more likely outcome (Coviello and Spagnolo 2018). We consider such statistical relationships as evidence for validity of our corruption risk indicators. It is important to note that using this methodology, we only identify corruption risk and not cases of actual corruption. Such risk indicators are useful as they point policymakers and other stakeholders at a wider set of contracts in the economy such as sectors, regions or periods, suffering from high corruption risks.

We can calculate and validity test seven public procurement corruption risk indicators<sup>3</sup> :

- **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 risks in the tendering process. Single bidding can be easily extracted from the data and has the key qualities necessary for an adequate indicator.

- **The length of the advertisement 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). An extremely short advertisement period could indicate corruption, and we find a strong connection between the length of the period and the likelihood of single bidding.

- **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

> 3 Another common indicator is whether or not the CFT document was published. We did not find cases where the contract notice was published but not the CFT document in the scraped dataset.

12
create more opportunities for issuers to repeatedly award contracts to the same company.

- **The length of the decision period** is a time difference between the submission deadline and the announcement of the contract award. An overly lengthy decision period gives the opportunity for multiple legal challenges against the tender, suggesting that the issuer attempted to limit competition. Additionally, it suggests that the issuer wants to award the contract to a specific company.

- **Supplier’ dependence** indicates the share of the contracts’ value received from the same procurement authority by the same supplier. A high share can indicate personal ties between the suppliers and the contracting authority, preventing the tenders to be contracted under fair competition.

- **Suppliers registered in a tax haven** flags all contracts by suppliers with their headquarters registered in a tax haven. Tax haven registration makes it harder to track the beneficial owners of a company and thus  opens the doors to many corruption issues.

- **The Benford’s law** indicator flags all contracts by buyers whose contract prices do not follow the Benford’s distribution. The Benford distribution has been used in many applications to detect fraud as naturally occurring numbers typically follow the Benford distribution. Violating this natural distribution may signal deliberate manipulation.

We use a regression framework to test the association of these corrupt practices with single bidding. In all of the regression models we control for contract prices using price deciles, contract types, buyer’s type and location, product markets identified by CPV divisions and year. By adding these control variables, we attempt to isolate the structural effects of each of the control variables from the tested relationship. Although we do not claim a causal relationship, a positive correlation indicates that the practice goes together with a higher likelihood of single bidding on average. In models 1-6 in Table 1, we show the association of each indicator with single bidding separately while we also demonstrate the combined effect of the indicators on single bidding as corrupt practices usually occur in parallel with each other (Model 7 in Table 1). All indicators are positively correlated with single bidding indicating that corruption risks increase with the risky behaviors as precisely defined in Table 2.

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**Table 1: Validation of corruption risk indicators using Single bidding as the main corruption risk proxy in an binary logistic regression framework- North Macedonia 2011-2022**

||||**Single b**|**idding is**|**the main**|**depende**|**nt variabl**|**e**|
|---|---|---|---|---|---|---|---|---|
|**Independent**<br>**variables**|**Corruption risk**<br>**level**|**(1)**|<br>**(2)**|<br>**(3)**|<br>**(4)**|<br>**(5)**|<br>**(6)**|<br>**(7)**|
|Procedure<br>type|Medium Risk<br>High Risk|0.309***<br>(0.012)<br>2.271***<br>(0.031)||||||0.130***<br>(0.017)<br>2.039***<br>(0.034)|
|Advertisement<br>period|<br>High Risk||0.460***<br>(0.011)|||||<br>0.165***<br>(0.015)|
|Decision<br>period|Medium Risk<br>High Risk|||0.427***<br>(0.012)<br>0.371***<br>(0.011)||||0.185***<br>(0.013)<br>0.337***<br>(0.012)|
|Tax Haven|Foreign Supplier<br>Supplier registered<br>in a tax haven||||0.828***<br>(0.038)<br>1.627***<br>(0.315)|||0.564***<br>(0.042)<br>0.953**<br>(0.344)|
|Supplier<br>Dependence||||||0.490***<br>(0.023)||<br>0.509***<br>(0.024)|
|<br>Benford’s Law|High Risk||||||0.073****<br>(0.010)|<br> <br>0.080***<br>(0.011)|
|N<br>pseudo R<sup>2</sup><br>All validity regr<br>identified by C<br>The stars repr|essions controls for c<br>PV divisions, and con<br>esent the significance|265,920<br>0.067<br>ontract v<br>tracting y<br>levels **|265,920<br>0.050<br>alues, bu<br>ear. Rob<br>* p<0.01,|265,920<br>0.050<br>yer types a<br>ust standa<br>** p<0.05,|267,920<br>0.047<br>nd locatio<br>rd errors s<br>* p<0.1|267,920<br>0.047<br>ns, contra<br>hown in b|<br>265,920<br>0.046<br>ct’s suppl<br>rackets be|<br>265,920<br>0.073<br>y type and markets<br>low the coefficients.|

Table 2 presents the exact definitions used to create the corruption risk indicators. All indicators are formulated to fall between 0 and 1. For some indicators, we also identify a medium risk category which takes the value of 0.5 (example procedure type risk). Each awarded contract receives a score for each indicator depending on the availability of the variable required to calculate the indicator. All risk indicators are simply averaged to calculate the composite risk score (CRI) for each contract (missing indicator values are not considered in the average score, hence the weight of individual risk factors might vary based on how many other factors are non-missing).

14
**Table 2: Corruption risk indicators thresholds used in North Macedonia’s composite risk score (CRI)**

|**Indicator name**|**Indicator definition**|
|---|---|
|Single bidder contract<br>(corr_singleb )|Low risk: more than one bid received<br>High risk: one bid received|
|Procedure type<br>(corr_proc)|Low risk: Negotiated with publication, Open, QualificationSystem<br>Medium Risk: Low estimated value procedure, Request For Proposal, Simplified Open<br>Procedure*.<br>High risk: Negotiated without publication, Other.|
|Length of advertisement<br>period<br>(corr_subm)|Number of days between publication of call for tenders and submission deadline<br>Low risk: from 15 to 365 days<br>High risk: from 1 to 14 days & missing values|
|Length of decision period<br>(corr_decp )|Number of calendar days between submission deadline and announcing of contract<br>award<br>Low risk: from 59 to 111 days<br>Medium risk: from 27 to 58 days & missing values<br>High risk: from 1 to 26 days & 112 to 730 days|
|Supplier’s dependence<br>(w_ycsh4)|Shows the supplier's contract share received from the same procurement authority in a<br>given year based on contract values.|
|Benford’s law<br>(corr_ben)|Flags contracts by buyers whose contract prices do not conform with the Benford’s<br>distribution<br>Low risk: Acceptable conformity, Close conformity<br>High risk: Marginally acceptable conformity, Nonconformity|
|Tax haven<br>(taxhav2)|Supplier registered in a country flagged as a tax haven based on the Financial Secrecy<br>Index by the Tax justice network|
|* Simplified open procedure<br>2019. THe amendment incr<br>heavy dependence on this n<br>We found that these newly i<br>to the previously used Open|s started to be used from 2019 after a law amendment that entered into force in February<br>eased the thresholds allowed for the use of this simplified procedure type which led to the<br>ew type compared to the previously used Open procedures (Palguta and Pertold 2017).<br>ntroduced Simplified procedures are correlated with higher single bidding risk compared<br>procedures and thus were included as risky procedure types.|

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<!-- Start of picture text --> 1<br>75<br>ia<br>Oo<br>.25<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br>—e*— Construction sector —e— Other sectors<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>75<br>iva<br>.25 eee see<br>0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Year<br>—e— Health and social work services —e— Other<br>—e*— Medical equipments, pharmaceuticals products<br><!-- End of picture text -->
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<!-- Start of picture text --> _ Iceland<br>_ Switzerland<br>United Kingdom<br>Finland<br>Sweden<br>Ireland<br>LuxembourgDenmar|<br>France<br>Romania<br>Latvia<br>BelgiumSpainMalta<br>Germany<br>Estonia<br>Norway<br>Netherlands<br>Italy<br>_Austria<br>Lithuania<br>Portugal<br>Slovenia<br>. Croatia<br>Republic of North Macedonia<br>Hungary<br>Slovakia<br>Bulgaria<br>CzechiaPolandyprus<br>Moldova<br>a a |<br>0 10 20 30 40 50<br>Single bidding (%)<br><!-- End of picture text -->
# Relationship between corruption risks and prices

This section analyzes the price impact of corruption risks in public procurement in North Macedonia. We attempt to quantify the potential savings that can be achieved if corruption risks were eliminated. To measure how pricey a contract is, we use the relative price which is a ratio between the final contracted price and the estimated contract value (i.e. auction reference price). The relative price captures the discount achieved by procurement authorities. We expect that tenders with high corruption risks suffer from higher relative prices. In other words, the ratio between the contracted price and the estimated contract value would be higher or, in more extreme cases, the final contracted prices would be higher than the estimated prices.

The method can be briefly summed up as follows: We run a linear regression model using CRI as the main predictor of the logarithm of relative prices (we use logarithm of prices to account for the uneven price distribution). The model controls for differences in contract value deciles, procuring authority type and location, supply type, product market (2-digit CPV divisions), year and month. We also test the corruption risk-price relationship with the individual corruption risk indicators and calculate the expected savings achieved if policy makers were to focus their efforts on reducing the risk scores of a specific corruption risk indicator. Based on the best fit estimation model (i.e. highest explanatory power), we predict the relative prices for each contract in the case of zero CRI (lowest possible corruption risk score). These no-risk contract value estimations enable us to estimate the savings to be made in each sector, time period, or geographical location. This method is further discussed in Abdou, <mark>Basdevant, David-Barrett & Fazekas (2022).</mark> We need accurate contract/lot level estimated contract values to calculate relative prices achieved by procuring authorities in the contracting process. However, the North Macedonia’s data portal only publishes estimated contract values at the tender level with no way to accurately break it down to the contract or lot level. Therefore, the analyzed sample had to be restricted to single-lot tenders where tender and lot values are necessarily the same. Furthermore, we also restrict relative prices to fall between 0.5 and 1.3 – as ratios outside this range might be due to data reporting errors. In Figure 15, panels A and B, we display the distribution of tenders in the dataset split over single-lot and multi-lot tenders.<sup>5</sup> Single-lot tenders represent 78.3% of all tenders (132,902 tenders) and 65.6% (335.5 billion MKD) of the total contract value available in the dataset.

> 5 We count the single-lot tenders based on the CFT notice url appearing only once in our lot level dataset.

24
<!-- Start of picture text --> 2<br>oO<br>no]<br>fc<br>2<br>Ke)<br>2<br>—<br>=}<br>Zz<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>[555 Multi-lot tenders [1 Single-lot tenders<br><!-- End of picture text --> Q4 = Fat fo) £ ToT = o jou ) 8 <!-- Start of picture text --> 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>[555 -Multi-lot tenders [1 Single-lot tenders<br><!-- End of picture text -->
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**Table 3: Regression results quantifying the correlation between CRI and the logarithm of relative price North Macedonia 2011-2022**

||**Log(Re**|**lative price) is the**|**main dependent**|**variable**|
|---|---|---|---|---|
|**Independent variables**|**(1)**|**(2)**|**(3)**|**(4)**|
|CRI|0.069***<br>(0.008)|0.173***<br>(0.009)|0.175***<br>(0.009)|0.179***<br>(0.013)|
|Contract value deciles||Yes|Yes|Yes|
|Contract supplytype||Yes|Yes|Yes|
|Contract market(CPV division)||Yes|Yes|Yes|
|Buyer Locations(NUTS codes)|||Yes|Yes|
|Buyer types|||Yes|Yes|
|Year||||Yes|
|Month||||Yes|
|N|113,747|113,747|113,747|113,747|
|R<sup>2</sup>|0.006|0.107|0.109|0.114|

Standard errors clustered on CPV divisions shown in brackets below the coefficients. The stars represent the significance levels *** p<0.01, ** p<0.05, * p<0.1 We also perform a similar analysis as above using each individual corruption risk indicator. For models 1 to 7 in Table 4, we use each corruption risk indicator as the main dependent variable and estimate its impact on relative prices. All individual risk indicators have a positive relationship with relative prices.<sup>6</sup> Eliminating single bidding from public tenders is associated with a 7.2% decrease in relative prices whereas relying on open procedures instead of high risk, non-open procedure types is associated with a 5.7% decrease in relative prices.<sup>7</sup> Similarly, avoiding higher risk, that is shorter, advertisement and decision periods is associated with a 6.2% and 1.3% decrease in relative prices, respectively. While full, detailed identification of causal impacts is beyond the scope of this paper, the results suggest that corruption risks are likely to have a considerable impact on prices and reducing those risks can help increase the procurement authorities' savings in procurement.

**Table 4: Regression results quantifying the correlation between corruption indicators and the logarithm of relative price  in North Macedonia 2011-2022**

|**Independent**<br>**variables**||**Log(Relative price)**<br>**Coefficient**<br>**(Cluster Std. errors in**<br>**bracket)**|**is the main dep**<br>**N**|**endent variable**<br>**R**<sup>**2**</sup>|
|---|---|---|---|---|
|**Model 1**||<br>0.072***|||
|Single bidding|High Risk|(0.006)|113,747|0.123|
|<br>**Model 2**||<br>0.057**|||
|Procedure type|High Risk|(0.020 )|113,747|0.094|
|<br>**Model 3**||<br>0.062***|||
|Advertisement<br>period|High Risk|(0.005)|113,747|0.097|
|**Model 4**||0.013***|||
|Decision period|High Risk|(0.002)|113,747|0.089|
|<br>**Model 5**||<br>0.038|||

6 The coefficient in Model 5 using Tax haven registration as the main indicator was not significant as there were only 12 instances within the analyzed sample.

7 Procedure type risk was switched to binary indicator for the price impact analysis because there was only one instance of the high risk type within the filtered analysis sub-sample.

27
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# State capture risks in North Macedonia’s public procurement

In this section, we outline our methodology for proxying state capture in public procurement using large-scale network analysis. We then apply this approach to analyze patterns of state capture in North Macedonia between 2011 and 2022.

To begin, we provide an overview of the North Macedonia’s public procurement contracting network and examine the level of cohesion across all sectors. We then leverage a government change in 2017 to observe the reconfiguration of the network before and after this political event. Next, we conduct an exploratory analysis of the construction sector to demonstrate the changes in the network structure and several network metrics before and after the government change of May 2017. Finally, we briefly review the configurations of ego networks of selected entities to determine the extent of state capture at the procurement organization level.

While this approach is complementary to analyzing political connections of suppliers, it offers a more comprehensive view of state capture across the entire procurement market. Estimating state capture through direct political connections is a robust approach, it may underestimate the true extent of state capture, as not all connections are easily discernible from publicly available information such as asset declarations. Therefore, the two approaches should complement each other for the most accurate estimation of state capture.

## How we estimate state capture

The concept of state capture is defined as a group-level phenomenon in which private sector companies and public procurement authorities collude to dominate government contracting and extract private benefits from public resources. This can occur in either direction, with the state dominating a network of suppliers or the other way around. State capture results in a failure to achieve public goals and obtain high value for public money.

Our method for assessing state capture involves differentiating the extent and distribution of corrupt practices in public procurement among suppliers and procurement authorities. This analysis is conducted through the lens of networks, with the aim of identifying patterns of corruption risk incidence and clustering of such risks. By utilizing this approach, it is possible to pinpoint state capture networks that are separate from both clean and randomly corrupt contracting networks. The framework is designed to not only identify high corruption risk connections, but also to take into account the consistency and interconnectedness of these patterns in order to point at captured contracting networks.

To demonstrate the essence of our network approach, four ideal types of contracting networks are described based on the degree and distribution of corruption risks: clean networks, corrupt but not captured networks, partially captured networks, and fully captured networks. A simplified version of these network structures is depicted in Figure 22.

35
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# Mapping state capture risks across North Macedonia

In this subsection, we examine the public procurement network in North Macedonia spanning from 2011 to 2022. Our primary objective is to conduct an empirical evaluation of state capture patterns present in the complete contracting network. By utilizing this approach, we aim to gain a comprehensive understanding of the extent and degree of state capture risks across various grouping variables, including geographical regions, time periods, and economic sectors.

The first step in the process is to condense the contract dataset into bi-modal graph data, which is achieved by calculating the average CRI score for each unique pair of buyers and suppliers. This aggregation process involves analyzing a total of 267,295 contracts which ultimately results in 92,195 distinct relationships represented as ties between nodes in the graph. The contracting network analyzed in this study has a total value of 512 billion MKD, which is distributed across 45 CPV sectors. The median procurement authority has 393 contracts, with a total value of 304 million MKD and a CRI score of 0.48. The median supplier in the network has 50 contracts, with a total value of 40 million MKD and a CRI score of 0.44. On average, a tie between a procuring authority and a supplier in the network represents 2.9 contracts, with a total value of 5.5 million MKD and a CRI score of 0.45. However, it is worth noting that the distribution is heavily skewed towards larger organizations, as the median value of a tie is only 500,000 MKD.

For the network representations that follow, our focus is solely on organizations with more than one tie. This allows us to capture contracting patterns across organizations, as isolated nodes offer limited insights in this regard. Additionally, we focus on the main component of the network to present a comprehensive analysis. As a result of this filtering process, we reduce the number of ties under investigation by 22%. In Figure 23 we present the complete contracting network in North Macedonia between 2011 and 2022.<sup>8</sup> Each link in the figure denotes a relationship between a buyer and supplier, with the color coding representing the average CRI between the two parties. It is clear that the low and high corruption risk ties are clustered together in the peripheral regions of the network. This clustering of high corruption risk ties suggests a higher likelihood of misconduct in those areas. In contrast, the center of the network features contracting activity with mixed corruption risk scores, depicted by the presence of white ties.

> 8 To improve the clarity of the figure, we have chosen to display only the ties between the nodes, omitting the nodes.

37
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authority's total contract value decreased slightly from 148 million MKD to 142 million MKD, while the median supplier's total contract value increased considerably from around 21 million MKD to 45 million MKD. This is also accompanied with an observed decrease in the average CRI score from 0.48 to 0.43. These changes suggest a shift in procurement patterns and possibly a change in the types of suppliers being awarded contracts due to redistribution of resources after the government change.

**Table 5: Summary of Procurement and Network Statistics before and after the government change in North Macedonia**

||**Jan 2011-May 2017**|**Jun 2017-Dec 2022**|
|---|---|---|
|**A) Procurement Statistics**|||
|**Total contract value**|~ 221 billion MKD|~ 254 billion MKD|
|**Number of procurement authorities**|1,215|1,356|
|**Number of suppliers**|9,527|6,940|
|**Median # contracts (PA)**|179|193|
|**Median # contracts (Supplier)**|34|68|
|**Median Total contract value (PA)**|~ 148 million MKD|~ 142 million MKD|
|**Median Total contract value (Supplier)**|~ 21 million MKD|~ 45 million MKD|
|**Average CRI**|0.48|0.43|
|**B) Network Statistics**|||
|**Number of ties**|39,939|53,173|
|**Average degree**|15.12|19.39|
|**Clusters**(Louvain algorithm)|225|62|
|**Modularity**(stronger clustering of buyer-supplier<br>ties)|0.405|0.350|
|**Closeness Centrality***(higher values given to<br>buyers/suppliers that are closer to other nodes in the<br>network)|0.256|0.272|
|**Betweenness Centrality***(higher values given|||
|to buyers/suppliers that create shorter paths between<br>two nodes)|185.186|9.401|

* 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 for nodes that are further apart to interact together through central agencies/firms

According to Panel B of Table 5, the number of distinct ties in the procurement network increased from 39,939 to 53,173 after the government change. The average degree in the network also rose from 15 to 19 connections, indicating an increase in network connectivity. However, there was no noticeable change in the closeness centrality of the network, although the average betweenness centrality score dropped sharply from 185.19 to 9.4. This suggests that after the government change, fewer nodes acted as brokers between different communities in the network. The Modularity score also decreased from 0.41 to 0.35, indicating a reduction in the number of 40
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sector. However, the number of procurement authorities increased by 46%, compared to only a 10% increase in the number of suppliers, suggesting a potentially more competitive procurement process. Additionally, there was an increase in the number of contracts for the median procurement authority and supplier, reflecting the growth in participating organizations.

Interestingly, we also found that the median procurement authority's total contract value increased by approximately 20 million MKD, and the median supplier's total contract value increased by 17.2 million MKD. We observe these changes along with drops in the average tie CRI from 0.49 to 0.42. These findings suggest that the construction sector may be experiencing growth and increased investment under the new government. Nevertheless, a further analysis is necessary to determine the underlying factors driving these changes and to assess their long-term impacts on the sector and the overall economy.

**Table 6: Summary of the Construction Sector Procurement and Network Statistics before and after the government change in North Macedonia.**

||**Jan 2011-May 2017**|**Jun 2017-Dec 2022**|
|---|---|---|
|**A) Procurement Statistics**|||
|**Total contract value**|~ 30 billion MKD|~ 45 billion MKD|
|**Number of procurement authorities**|571|835|
|**Number of suppliers**|1,235|1,360|
|**Median # contracts (PA)**|8|13|
|**Median # contracts (Supplier)**|3|10|
|**Median Total contract value (PA)**|~ 9 million MKD|~ 29 million MKD|
|**Median Total contract value (Supplier)**|~ 1.8 million MKD|~19 million MKD|
|**Average CRI**|0.49|0.42|
|**B) Network Statistics**|||
|**Number of ties**|2,128|3,993|
|**Average node degree**|2.357|3.683|
|**Average path length**|6.575|5.241|
|**Graph Density**|0.001|0.002|
|**Clusters**|158|93|
|**Modularity**(stronger clustering of buyer-<br>supplier ties)|0.852|0.666|
|**Closeness Centrality***(higher values<br>given to buyers/suppliers that are closer to<br>other nodes in the network)|0.1089|0.1723|
|**Betweenness Centrality***(higher values<br>given to buyers/suppliers that create|||
|shorter paths between two nodes)|0.0023|0.0016|

* 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 for nodes that are further apart to interact together through central agencies/firms

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# Conclusion

This paper uses comprehensive, economy-wide administrative data to assess corruption risks in North Macedonia’s public procurement and their implications for overpricing of contracts. It also looked at the networked nature of contracting risks and how some of these networks develop into state capture formations.

The analyzed data points at persistently high risks of corruption in North Macedonia, compared to EU countries. For example, the single bidding rate of North Macedonia is very similar to the Slovak Republic, Hungary and Bulgaria, but considerably higher than core Western European countries such as Germany or France. The overall corruption risk level slightly decreased throughout 2011-2022, with, for example, the single bidding rate remaining around 30%.

This paper finds a strong association between corruption risks and relative prices. The effect of the composite risk indicator, Corruption Risk Index (CRI), on relative prices is estimated to be strong, with the CRI score moving from its maximum (1) to its minimum (0) leading to 17.9% lower prices. Looking at individual risk factors points at actionable, directly influenceable levers for policy makers. Factors estimated to lead to higher prices are the type of procedure, length of advertisement period, and supplier dependence on a buyer. For instance, greater use of more competitive and open procedures or providing more time for suppliers to prepare bids are found to be associated with lower relative prices. Other factors, such as single bidding and supplier dependence on a buyer are only indirectly influenceable, but programs aimed at stimulating competition, reducing the documentation burdens on suppliers or eliminating transaction costs can create a more favorable environment for higher competition, and consequently lower the relative prices. These factors are also likely to influence prices considerably as for example single bidding is associated with a 7.2% increase in relative prices compared to multiple bidding (i.e. a tender receiving two or more bids).

Data analysis also reveals indications of state capture patterns. There are groups of suppliers and buyers with high corruption risk contracting relationships among them. This suggests that purchasing authorities with high CRI scores are more likely to attract suppliers with similarly high risks. The repeated contractual relationships between buyers and suppliers within these groups are likely to be more susceptible to state capture risks compared to other communities in the contracting network. However, the change in government in 2017 has led to the weakening of state capture patterns with the previously central capture groups moving outwards, to the periphery. Parallel to this, the center of the contracting network is now more populated by lowrisk communities, suggesting a lower degree of network capture.

Our analysis is not without limitations. Due to data limitations, we had to focus the price analysis on single lot tenders, which significantly reduces our sample. Notwithstanding the importance of relative price analysis in identifying actionable policies that can generate savings, there are further measures of value for money that can be used such as unit prices paid for standardized goods. In addition, the analysis could be further improved by more reliably identifying unique organizations by their legal identifiers. Further work could imply using advanced data science 45
techniques with available datasets for constant monitoring and analysis of prices and their determinants. Therefore, it is essential to ensure that public sector users have access to easily analyzable data, supporting their policy reforms and helping them communicate these reforms through continuous monitoring of risks and using policy insights driven by data in daily policymaking.

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