Journal number 3 ∘ Beka Patsatsia ∘ Nino Kharebava ∘ David Jalagonia ∘
Examining Public Procurement Success: The Impact of Competition on Efficiency in Georgian Public Procurement

Annotation.This article investigates the relationship between competition and the efficiency of public procurement contracts in Georgia across four key sectors: information technology, healthcare, agriculture, and oil. The study aims to analyze the influence of competition among bidders on the efficiency of public procurement contracts in Georgia. Drawing on a comprehensive dataset of procurement tenders, the analysis examines various quantitative factors that influence the efficiency of public procurement. These factors include the number of bidders, received bids, contract size, distance from the project site, announcement period. Additionally, the study examines the industry and procurement method and their influence on the number of competitors.

This study employs a diverse array of statistical methods to investigate public procurement processes based on a dataset of 240 observations from the State Procurement Agency's e-system spanning 2021-2022.The research involves data classification into two procurement types, hypothesis testing, assessment of normality and variance, non-parametric testing, and descriptive statistics. Welch's ANOVA is applied to gauge the impact of industry type on the number of bidders. Additionally, multiple linear regression models explore the influence of various factors on public cost savings. The findings provide insights into the complex dynamics of public procurement and its dependencies on procurement methods, industry sectors, and other critical variables.

The article argues that the number of participants in public procurement plays a critical role in shaping competition, efficiency, and decision-making processes in Georgia. It emphasizes the need to strike a balance between attracting a sufficient number of bidders while ensuring fair and transparent competition.

Keywords: Public procurement, Bidding, Bidder Competition, Public Procurement Efficiency 

1. Introduction

At the heart of every nation's economy lies the significant role of government procurement. Government procurement is not only a means of satisfying the needs of end-users with goods and services, but also a strategic instrument that governments use to achieve their socio-economic goals. This deliberate allocation of the Gross Domestic Product (GDP) has a dual purpose: it ensures a fair distribution of financial resources and fosters a business environment that is rich in competition, innovation, and sustainability.

Public procurement's effectiveness depends on various factors. One of them is the smooth operation of the financial security system, which supports successful procurement practices. Another factor is the strategic alignment of government funding priorities, which determines where and how resources are allocated. However, the most important factor is the wise and responsible use of the available financial resources, which reflects the need for sound resource management.

Public procurement represents a distinct form of utilizing public funds and serves as a pivotal instrument within the realm of state budgetary policies. While science delineates fundamental roles like distribution, regulation, and oversight in relation to public finances, it becomes evident that the system for ensuring financial security in public procurement operates through the functions of fund allocation and control. The concept of competition, a cornerstone of procurement practices, seeks to strike a delicate balance between state objectives and individual aspirations. It acts as a mechanism than channels the diverse interests of participants toward a common goal while maintaining a level playing field that nurtures innovation, quality, and efficiency.

The purpose of this article is to analyze the influence of competition among bidders on the efficiency of public procurement contracts in Georgia. We argue that the number of participants in public procurement plays a crucial role in shaping competition, efficiency, and decision-making processes. Having a greater number of participants tends to be beneficial, provided certain constraints are met.

In the theoretical section of the article, we examine the principles of public procurement, including competition, and its impact on achieving fairness, quality, and price balance. The concept of buyer power and its influence on the procurement process is also examined. In practical section hypothesis testing, assessment of normality and variance, non-parametric testing, and descriptive statistics conducted. Welch's ANOVA is applied to determine the impact of industry type on the number of bidders. Additionally, multiple linear regression models explore the influence of various factors on public cost savings.

2. Theoretical foundation

The principles of public procurement include maximum economy, efficiency, proportionality, integration, transparency, competitiveness, accountability, integrity, sustainability (Nedoshovenko A., 2022; Bou-Ghanem D., 2022, Lucacka, 2018). These principles aim to ensure the best use of resources, fair competition, value for money, and the achievement of government objectives. Competitive public procurement is considered as a process of stimulating competition between suppliers of goods and services using various methods of public procurement in order to achieve the best balance of fairness, quality and price. This includes creating an environment where many suppliers will be able to compete with each other for contracts, which can lead to higher quality products or services at more competitive prices (Fourie & Malan, 2021).

The principle of competition is clearly reflected in the directives of the European Union on the conclusion of state contracts. EU directives include the forms for conducting procurement procedures and the acceptable procedures for concluding contracts with suppliers. The directives define clear conditions under which a certain stage of the procurement procedure must be completed, for example, submission of applications, consideration of proposals, announcement of results, etc. Also, the directive prohibits revision of the terms of the contract after the contract has been concluded with the chosen supplier.

When we talk about competition, it is important to consider the concept of "power" of market participants in terms of private and public relations. One of the leading aspects is the power of the public buyer, the influence it has on the supplier. In the context of public procurement, buyer power is the ability of the public sector to negotiate favorable terms, terms and prices in the process of purchasing goods, services and works, which affects the level and intensity of competition between potential bidders, as well as the results of the procurement process (Dotecon, 2004).

The concept of buyer power is important because of the large-scale nature of public procurement and the potential impact it can have on the market and suppliers. Consequently, the state has considerable power, which it uses to increase the best quality and, in general, the efficiency of the process for the costs incurred. Government purchasing power can be exercised through various means: competitive tender/auction, competitive negotiations, specifications and requirements, framework agreements, etc. One of the important tasks of public procurement is to ensure the number of participants in the tender. Thus, according to studies, in order to achieve savings in the procurement process, the buyer needs to receive at least 4 proposals (Thai et al., 2009), which is impossible with a minimum number of bidders. For example, in the field of agricultural procurement in 2022, in 85 % of the announced reverse auctions (SPA) the number of participants did not exceed 1, and the price reduction was within 2%.

However, the power of the state buyer is not absolute and it may cause certain deviations in the process of competitive relations, and the impact of competition may become negative. There are many obstacles to ensuring the principles of competition in public procurement. One difficulty is the emphasis on the quantity of competitors in the competitive process, which frequently encourages resellers of the same product to participate and inhibits the promotion of innovation (Dobretsov, 2022). Another issue is that measuring procurement efficiency lacks economic significance because regulatory laws favor cost-cutting and procedural compliance over effectiveness (Melnikov, 2022). Competitive advantages in public procurement practices can vary significantly due to different procurement practices and the diversity of goods, services and works.

The structure of the industry, such as the number of competitors, their size, and market concentration, can influence public procurement. There may be less competition in concentrated industries with a small number of dominant suppliers, which can affect the procurement process and perhaps lead to higher pricing or lower innovation. The procurement process can be influenced by industry dynamics. For instance, in industries with high demand and limited supply, such as during periods of rapid infrastructure development, the competition for procurement contracts may be intense, potentially affecting bidding strategies and contract terms.

Industries that demand specialized knowledge, expertise, or innovative solutions can also shape procurement processes. Procurement entities often prioritize suppliers with industry-specific expertise or innovative capabilities to meet their unique requirements. Moreover, the regulatory environment plays a crucial role as industry-specific regulations and policies can impact public procurement practices. Government regulations may impose specific requirements or preferences for procuring goods and services from particular industries, thereby influencing the competitive landscape and the selection of suppliers. Thus, industry analysis enables timely consideration and response to negative consequences such as low procurement competition, bid rigging, undue influence over procurement officials, potentially through bribery, lobbying, or other unethical practices, and a lack of capacity or expertise in the sector.

Various scientific sources highlight different quantitative criteria for assessing the competitiveness of public procurement. Firstly, this is a savings criterion. Price has long been used as an evaluation criterion in public procurement to determine the best value for money (Smith C., 2013). However, there are differing perspectives on using the lowest bid price as the only evaluation criterion. Some believe that it may not always result in the best quality offer (Ssenoga F., 2010). On the other hand, other variables such as sustainability and innovation in public procurement must be considered. For example, the Dutch government has made sustainability and innovation important criterion in their procurement activities (Ochrana F., Hrncirova K., 2015).

The number of participants in public procurement is a crucial criterion that affects competition, efficiency, and decision-making processes. Research shows that having more participants in the provision of a public good is generally beneficial, as long as certain constraints are met (Hellwig M., 2001). Another indicator that is correlated with the number of bidders is the number of received bids made by bidders. The research on this indicator is focused on the question of how many offers are needed to achieve a price reduction of 15-20% (Branneman et al., 1987, Gupta, S., 2002) or a reduction in expected costs associated with the bidding process and material costs (Lie, Edward A., 1987).

Factors such as project value, distance from the project site to the supply source, and state region also influence the number of bidders in competitive bidding (Azman M., 2014). The size of mark-ups of public procurement suppliers is another factor influencing the results of public tenders. Mark-ups are affected not only by transaction costs associated with factors such as contract size, type, or company experience, but also by collusive tendering and bid rigging (Jones A., Kovacic W. 2019, Pavel J. 2018).

In conclusion, various quantitative criteria have been highlighted in scientific literature for assessing the competitiveness of public procurement. While price savings have traditionally been a significant criterion, there are differing perspectives on using the lowest bid price as the sole evaluation factor. It is acknowledged that prioritizing the lowest price may not always result in the best quality offer. Various criteria and factors contribute to a comprehensive understanding of competitiveness in public procurement. The literature emphasizes the need to consider multiple dimensions beyond price, such as sustainability, innovation, and the dynamics of bidder participation, to ensure optimal outcomes in public procurement processes.

Therefore, considerations such as sustainability and innovation have gained importance in public procurement. The number of participants in public procurement plays a crucial role in shaping competition, efficiency, and decision-making processes. Research suggests that having a greater number of participants in the provision of public goods tends to be beneficial, provided certain constraints are met. The number of bids is another indicator closely related to the number of participants, and research has explored the optimal number of offers needed to achieve specific price reductions or cost savings.

Several factors influence the number of bidders in competitive bidding, including project value, distance from the project site to the supply source, and state region Factors such as transaction costs, contract size, type, company experience, as well as collusive tendering and bid rigging, can influence the mark-ups set by suppliers.

3. Research methodology. The analysis uses 240 data for the period 2021-2022 collected from State Procurement Agency’s e-system. First of all, we classified the announcement by 2 types of procurement (120 announcement in each group): NAT - electronic tender without auction and SPA – reverse type auction, in order to identify the dependence of the number of bidders on the type of procurement. Accordingly, hypothesis 1 is the following: There is no difference between the NAT and SPA groups with respect to the dependent variable Number of Bidders. Although, test for normal distribution of NAT and SPA indicated the data within the NAT and SPA groups do not deviate significantly from a normal distribution,  the Levene test of equality of variance yields a p-value of 0.01, which is below the 5% significance level. Thus, if there is no variance equality in the samples, alternative method that do not rely on equal variances assumptions, such as non-parametric tests, might be more appropriate for making valid statistical inferences.

The result of non-parametric test (pic. 1) shows that there is a significant difference in the distribution of the "Number of Bidders" between the "NAT" and "SPA" categories of the "Procurement Method." The difference in ranks (mean rank values of 128.7 in the "NAT" category and 112.33 in the "SPA" category) suggests that the "NAT" category tends to have higher values of the "Number of Bidders" variable compared to the "SPA" category.

The second factor that we investigated if there are significant differences in the average number of bidders among different industry types. By conducting an ANOVA analysis, we can assess whether industry type has a statistically significant influence on the number of bidders. If the ANOVA shows a significant difference, it indicates that industry type is associated with varying levels of bidders. This information can be valuable for understanding how different industries attract bidders and potentially identifying factors that contribute to these variations.

Pic. 1 Hypothesis Test Summary

 

Source: Authors calculations

Hypothesis 2. There is no difference between the 4 categories (Agriculture, Oil and Oil products, IT, Healthcare) of the independent variable Industry with respect to the dependent variable Number of Bidders. The choice of categories was predetermined by their economic importance, as they play a crucial role in the economy, contributing to employment, GDP, and overall development. Public procurement within these sectors often involves significant financial transactions and investments. The Agriculture, Oil, Healthcare, and IT sectors are directly linked to the delivery of essential public services. Public procurement within these sectors influences the quality, accessibility, and affordability of services provided to citizens.

Descriptive statistics results presented in table 1. Across all industries (Total), there were 240 observations, and the average Number of Bidders was 1.54. The standard deviation of 0.91 represents the overall variability in the Number of Bidders across the entire dataset, considering all industry categories. 

Table 1. Descriptive statistics

	n	Mean	Std. Deviation
Agriculture	60	1.57	0.89
Oil and Oil products	60	1.92	1.24
IT	60	1.35	0.71
Healthcare	60	1.33	0.57
Total	240	1.54	0.91

Source: Authors calculations 

As both Levene's Test and the Brown-Forsythe Test indicate a violation of the assumption of equal variances across the groups being compared (see Table 2), Welch’s ANOVA was conducted. 

Table 2. Levene test of variance equality

Test	F	df1	df2	p
Levene's Test (Mean)	5.64	3	236	.001
Brown-Forsythe-Test (Median)	6.65	3	236	<.001

Source: Authors calculations 

The present study employed Welch's test, a modified version of the independent samples t-test that accommodates unequal variances, to examine the significance of mean differences among groups. The analysis aimed to address the violation of the assumption of equal variances. The Welch's test yielded a test statistic (F-value) of 4.33, with degrees of freedom (df1 = 3, df2 ≈ 127.37) estimated based on the sample sizes and variances. The calculated p-value of 0.006 indicated a statistically significant result.

This finding provides robust evidence supporting the presence of significant mean differences between the groups under investigation. The obtained F-value of 4.33 suggests a considerable disparity between the means compared to the within-group variability. The estimated degrees of freedom (df1 = 3) reflect the number of groups being compared, while the approximate df2 value of 127.37 accounts for the unequal variances observed in the data.

In summary, the application of Welch's test revealed a statistically significant difference in means among the examined groups. These findings substantiate the alternative to null hypothesis that the means of the dependent variable exhibit notable variations across the groups represented by the independent variable. The industry has an influence on the number of participants, further research is needed to determine the industry specific factors influencing the dependent variable.

Then we conducted a multiple linear regression analysis to examine the influence of the variables Number of Bidders, the Number of Offers, Distance, Contract Size, Announcement Period on the variable Savings (Decrease in Price) received by the buyer on depending on the procurement method. We separately analyzed 120 cases in the NAT category and the same number in the SPA category. Also, we exclude variable Number of offers form NAT category, since this method does not involve active bidding, and although participants change the offer before the expiration of the announcement, nevertheless, there is a high dependence between number of offers and number of bidders.

So, in NAT category the regression model showed that the variables Announcement period, Distance, Contract Size and Number of Bidders explained 46,14% of the variance from the variable Decrease in Price. An ANOVA was used to test whether this value was significantly different from zero. Using the present sample, it was found that the effect was significantly different from zero, F=24,63, p = <.001, R2 = 0,46. The standardized coefficients beta are independent of the measured variable and are always between -1 and 1. The larger the amount of beta, the greater the contribution of the respective independent variable to explain the dependent variable Decrease in Price. In this model, the variable Number of Bidders has the greatest influence on the variable Decrease in Price. 

Table 3. Coefficients (Nat Category) 

 

Source: Authors calculations 

The p-value for the coefficient of Announcement period (see Table 3) is 0.186 and greater than the significance level of 0.05. Therefore, we fail to reject the null hypothesis that the coefficient of the "Announcement period" variable is zero in the population. This means that there is not enough evidence to suggest that the "Announcement period" has a significant effect on the dependent variable.

The p-value for the coefficient Distance also greater than the significance level of 0.05 (0.143). Similar to the previous case, we fail to reject the null hypothesis that the coefficient of the "Distance" variable is zero in the population. This implies that there is not enough evidence to support a significant relationship between the "Distance" variable and the dependent variable.

The p-value for the coefficient Contract Size of 0.018 is smaller than the significance level of 0.05. This suggests that there is significant evidence to support the claim that the "Contract Size" variable has a non-zero effect on the dependent variable.

The p-value for Number of Bidders reported as <0.001, indicating that it is smaller than the significance level of 0.05. Consequently, we reject the null hypothesis that the coefficient of the "Number of Bidders" variable is zero in the population. This indicates strong evidence in favor of the claim that the "Number of Bidders" variable has a non-zero effect on the dependent variable.

In summary, the analysis suggests that the "Contract Size" and "Number of Bidders" variables have coefficients that are significantly different from zero in the population, indicating a meaningful impact on the dependent variable. However, there is no significant evidence to support a non-zero coefficient for the "Announcement period" and "Distance" variables.

In SPA category regression analysis was performed to examine the influence of the variables Contract Size, Number of offers and Number of Bidders on the variable Decrease in Price. The regression model showed that the variables Contract Size, Number of offers and Number of Bidders explained 41,73% of the variance from the variable Decrease in Price. An ANOVA was used to test whether this value was significantly different from zero. Using the present sample, it was found that the effect was significantly different from zero, F=27,69, p = <.001, R2 = 0,42.

 In this model, the variable Number of Bidders has the greatest influence on the variable Decrease in Price.

The p-value for the coefficient of Contract Size is 0.764. Since this p-value is greater than the significance level of 0.05, the null hypothesis that the coefficient of Contract Size is zero in the population is maintained. Therefore, the Contract Size variable does not have a statistically significant impact on the dependent variable.

The p-value for the coefficient of Number of offers is 0.066. This p-value is greater than the significance level of 0.05, indicating that the null hypothesis that the coefficient of Number of offers is zero in the population is maintained. Thus, it is assumed that the coefficient for the variable Number of offers in the population is not significantly different from zero. This suggests that the Number of offers variable does not have a statistically significant effect on the dependent variable.

The p-value for the coefficient of Number of Bidders is 0.001. In contrast to the previous variables, this p-value is smaller than the significance level of 0.05. Therefore, the null hypothesis that the coefficient of Number of Bidders is zero in the population is rejected. This indicates that the Number of Bidders variable has a statistically significant impact on the dependent variable, implying that it is an important predictor in the regression model.

4. Implication and Further Research.

The study's analysis revealed a significant relationship between industry type and the number of bidders in procurement processes. This finding implies that different sectors, such as Agriculture, Oil and Oil Products, IT, and Healthcare, exhibit distinct patterns when it comes to bidder participation. Industries often have unique characteristics, regulations, and market dynamics that can influence the level of competition in procurement. For example, industries with higher economic importance and more significant financial transactions may attract more bidders due to the potential for lucrative contracts. This result reinforces the importance of tailoring procurement strategies and policies to the specific needs and characteristics of each industry. Policymakers and procurement professionals should consider the industry context when designing procurement processes to maximize competition and achieve optimal outcomes.

The regression analyses demonstrated the pivotal role of the Number of Bidders variable in explaining variations in Savings (Decrease in Price) in both NAT and SPA procurement categories. The positive association between the Number of Bidders and savings underscores the significance of competition in driving cost reductions for buyers. Procurement processes with a higher number of bidders tend to result in more competitive pricing, which benefits the procuring entity and, by extension, taxpayers or stakeholders. This finding emphasizes the need for strategies that promote bidder diversity and attract a sufficient number of participants to foster competition. Such strategies can include transparent procurement procedures, outreach to potential bidders, and efforts to minimize entry barriers.

The research did not reveal positive relationship between the number of offers and savings in Georgia's public procurement that can be attributed to such factors as Market Structure, Bidding Strategies, and Bidder Behavior. The structure of the procurement market in Georgia characterized by a limited number of dominating suppliers, who reduce the potential for competition and limiting the impact of the number of offers on savings. Bidders' strategies can vary widely. If bidders adopt conservative strategies, submitting fewer but higher-quality offers, the relationship between the number of offers and savings may be less pronounced. Bidder behavior can be influenced by factors such as risk aversion, information availability, and expectations of their competitors. If bidders in Georgia perceive that submitting multiple offers is not cost-effective or does not significantly increase their chances of winning, they may choose to submit fewer offers.

To gain a deeper understanding of why the number of offers may not significantly impact savings in Georgia's public procurement, further research could explore these factors in more detail. This might involve conducting surveys or interviews with bidders and procurement officials, analyzing specific procurement procedures and regulations, and examining changes in procurement practices over time. Additionally, a larger and more representative dataset could provide more robust insights into the relationship between the number of offers and savings in the Georgian context.

While the study provides valuable insights, there are areas where further research is warranted:

-     Additional research can delve deeper into the industry-specific factors that influence bidder participation. Understanding why certain industries attract more bidders than others can lead to more targeted procurement strategies.

-     Investigating the specific factors affecting savings in procurement processes is essential. The study hints at Contract Size and Announcement Period in the NAT category, but further exploration can provide a more comprehensive understanding of the drivers of cost savings.

-     Comparative studies across different regions or countries could shed light on how procurement strategies and industry characteristics vary and impact outcomes. This can provide a broader perspective on optimizing procurement policies.

-     Long-Term Impact: Research could explore the long-term effects of bidder participation and competition on cost savings, quality of services, and overall efficiency in public procurement. Longitudinal studies can reveal trends and allow for continuous policy adjustments.

In conclusion, this study provides critical insights into the relationships between industry type, bidder participation, and cost savings in procurement processes. It not only supports the need for industry-specific approaches but also highlights the central role of competition in achieving cost reductions. Further research in this area can deepen our understanding and lead to more effective procurement policies and strategies, ultimately benefiting governments, organizations, and the public. 

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