Factors influencing price reduction in public procurement of goods in vietnam from 2015 to 2020

VIETNAM NATIONAL UNIVERSITY, HANOI VIET NAM JAPAN UNIVERSITY HOANG THI BINH FACTORS INFLUENCING PRICE REDUCTION IN PUBLIC PROCUREMENT OF GOODS IN VIETNAM FROM 2015 TO 2020 MASTER’S THESIS VIETNAM NATIONAL UNIVERSITY, HANOI VIET NAM JAPAN UNIVERSITY HOANG THI BINH FACTORS INFLUENCING PRICE REDUCTION IN PUBLIC PROCUREMENT OF GOODS IN VIETNAM FROM 2015 TO 2020 MAJOR CODE : PUBLIC POLICY : 8340402.01 SUPERVISOR: DR DANG QUANG VINH Hanoi, 2021 COMMITMENT I hereby declare that the thesis "Factors influencing price reduction in public procurement of goods in Vietnam from 2015 to 2020” is my own research under the supervision of Dr Dang Quang Vinh The data used in the thesis is truthful, the quantitative analysis and conclusions of the thesis were not public in any other research The source of citation for this thesis is fully stated I am able and willing to take responsibility for my thesis Hanoi,21 July, 2021 Author Hoang Thi Binh ACKNOWLEDGEMENTS During my completion of master thesis, I get many valuable encouragements, guidance and support that help me to complete and attain knowledge and wonderful experiences With all my respect and gratitude, I would like to express my sincere appreciation to: My supervisor, Dr Dang Quang Vinh – Lecturer of Master’s Program in Public Policy for his enthusiastic instruction throughout my research’s process His insightful advices, scientific knowledge and timely support has inspired me and helped me a lot in improving the research My Vietnamese and Japanese professors at Master’s Program in Public Policy, including Dr Nguyen Thuy Anh, Dr Vu Hoang Linh, Dr Phung Duc Tuan, Prof Kawashima Hiroichi, Prof Fujimoto Koji, Prof Okamoto Naohisa, for their valuable comments, interesting courses and their attention to my master thesis progress I would also want to send my gratitude to Ms Pham Thi Lan Huong, Program Assistant of Master’s Program in Public Policy for her kind support in both academic issues and training procedures needed Finally, I would like to thank my family for being a wonderful moral support that gave me so much motivation and enthusiasm to overcome difficulties in the research process as well as the whole master course TABLE OF CONTENTS LIST OF TABLES i LIST OF DIAGRAMS ii CHAPTER I INTRODUCTION 1.1 Necessity of research 1.2 Research objective and research question 1.3 Research’s scope and methodology 1.4 Contributions 1.5 Research’s structure CHAPTER II: THEORETICAL BASIS Overview of public procurement .5 2.1.1 Concept of public procurement 2.1.2 The role of Public Procurement 2.1.3 Bidder selection in public procurement 2.2 Public procurement process 13 2.3 Factors influencing price reduction in public procurement 16 2.3.1 Concept of price reduction in public procurement 16 2.3.2 Price reduction and efficiency issue in public procurement 16 2.3.3 Factors influencing price reduction and effectiveness of public procurement 17 2.4 Overview of implementation of public procurement in Vietnam from 2015-2020 .18 2.4.1 Governing policies on public procurement 18 2.4.2 Application of online bidding and information disclosure on national eprocurement system 19 2.4.3 Overview implementation of public procurement in Vietnam from 2015 to 2020 22 CHAPTER III: QUANTITATIVE ANALYSIS ON FACTORS INFLUENCING PRICE REDUCTION IN PUBLIC PROCUREMENT OF GOODS IN VIETNAM FROM 2015-2020 24 3.1 Introduction 24 3.2 Sampling 24 3.3 Software 24 3.4 Analytical method 24 3.5 Descriptive statistic of the data set 27 3.6 Regression and findings 29 3.6.1.Regression with all independent variables and with all observations 29 3.6.2 Correlation between independent variables 32 3.6.3 Regression with either ebid or compt 34 3.6.4 Overall findings about the influence of factors on price reduction 37 CHAPTER 4: CONCLUSIONS AND PUBLIC POLICY RECOMMENDATIONS 44 4.1.Conclusions .44 4.2 Public policy recommendations .44 REFERENCES 47 LIST OF TABLES Table 2.1: Application of bidder selection form…………………………………… 11 Table 2.2: Bidder selection form: Vietnam’s regulation and the World Bank’s guidelines…………………………………………………………………………… 13 Table 2.3: Procurement legal documents…………………………………………… 18 Table 2.4: Application of online bidding…………………………………………… 20 Table 2.5: The scale of public procurement market ………………………………….22 Table 2.6: Price reduction by year…………………………………………………… 22 Table 3.1: Variables in regression model…………………………………………… 25 Table 3.2: Descriptive statistic on variables………………………………………… 27 Table 3.3: Descriptive statistic of 759 observations………………………………… 28 Table 3.4: Regression with all independent variables……………………………… 29 Table 3.5: Regression with all observations………………………………………… 31 Table 3.6 Correlation between independent variables……………………………….33 Table 3.7: Regression without ebid………………………………………………… 34 Table 3.8: Regression without compt……………………………………………… 36 Table 3.9: Findings of regressions…………………………………………………….38 Table 3.10 Regression with nobid from to 4……………………………………….40 Table 3.11: Regression with nobid from 4……………………………………………42 i LIST OF DIAGRAMS Diagram 2.1: Types of public procurement package………………………………… Diagram 2.2: Types of bidding selection………………………………………… …10 Diagram 2.3: Critical bidding process………………………………………… ……15 ii CHAPTER I INTRODUCTION 1.1 Necessity of research Government spending is indispensable for every country The government, like every other entity, needs expenses to maintain its existence and perform its role in society in society In macroeconomics, we know that: AD = C + Ig + Ip + G + NX In which: AD is aggregate demand; C is consumption Ig, Ip are government investment and private investment G is government expenditure NX is net export Following this idea, when it comes to the economy from the perspective of aggregate demand, we talk about the presence of government expenditure and government investment In addition, many studies mention importance of public procurement According to Nigel Caldwell, Helen Walker and associates (2005), public procurement promotes competitive markets Max Rolfs-tam (2009), considers public procurement as a tool for innovation Carla Roberta Pereira, Martin Christopher, Andrea La-go Da Silva (2014), point out the role of public procurement in supply chain recovery Louise Knight, Christine Harland and associates (2007) state “The public sector represents about 40 to 45 per cent of many economies in the developed world in terms of spend on providing services and procuring from the private sector However, it is astonishing for such a vital, significant part of all nations’ economies that so little research has been conducted on public procurement across nations and even within nations to improve procurement to deliver these benefits” When reviewing the developments of public procurement in the last two decades of the twentieth century, Khi V Thai (2001) wrote that: “Although public procurement is considered is a major function of government [….] Public procurement has been a neglected area of academic education and research” In Vietnam, according to the ministerial-level research in 2012 of the Public Procurement Agency - Ministry of Planning and Investment, public procurement plays an important role in promoting the development of the economy Public procurement performs its role in the following aspects: - Firstly, establishment of public procurement market with the connection between buyers who is government and sellers who are bidders, thereby promoting the development of industries and fields; and - Secondly, promotion of technology transfer, experience and opportunity sharing among public procurement market participants; and - Thirdly, encouragement of economic renovation from the centralizing and “asking & giving” mechanism to market mechanisms; and - The fourth, promotion of international economic integration, by opening the public procurement market According to 2020 data of the Ministry of Planning and Investment, with a scale of 48% of total budget revenue and 15.34% of GDP, the public procurement market promotes and creates business opportunities for 650,000 domestic enterprises This figure is consistent with the description of UNICITRAL, 2011, where procurement spending can account for 10-20% of GDP and up to 50% or even more of total government spending In Vietnam, when it comes to the term “efficiency” of public procurement, the price reduction is an indispensable indicator A public procurement package that fascinates many bidders and ends up with a high price reduction considered an effective public procurement package According to inspection reports and annual reports on public procurement of the Ministry of Planning and Investment, the price reduction varies among ministries, sectors, and localities Table 12 shows a high correlation between ebid and compt Therefore, the two variables should be not included in regression together 3.6.3 Regression with either ebid or compt In this section, for the reason of correlation between ebid and compt, either ebid or compt shall be included in regression For del.NA data set, with the command, reg reduct packval compt oneEnv entunit Region Nobid Year, robust and command reg reduct packval oneEnv entunit Region Nobid Ebid Year, robust, we get two results as follows: Table 3.7: Regression without ebid Linear regression Number of obs =759 F (15, 743) = 19.69 Prob > F = R-squared = 0.295 Root MSE = 10.611 Reduct Coef Robust Std Err t P>t [95% Conf Interval] packval -0.00019 6.21E-05 -3.04 0.002 -0.00031 -6.7E-05 Compt 2.323321 0.831091 2.8 0.005 0.691756 3.954887 2.691964 1.69028 1.59 0.112 -0.62633 6.010256 -0.91605 1.457149 -0.63 0.53 -3.77667 1.944568 2.185281 1.319233 1.66 0.098 -0.40459 4.775149 -0.97892 2.817836 -0.35 0.728 -6.51079 4.552948 Sector oneEnv 34 entUnit 2.002787 1.022919 1.96 0.051 -0.00537 4.010943 0.968341 1.072867 0.9 0.367 -1.13787 3.074552 -0.08827 0.887436 -0.1 0.921 -1.83045 1.653906 3.156776 0.305823 10.32 2.556396 3.757157 2016 -1.83309 1.255238 -1.46 0.145 -4.29732 0.631148 2017 1.307235 1.337415 0.98 0.329 -1.31833 3.932796 2018 -0.69451 1.301649 -0.53 0.594 -3.24986 1.860841 2019 0.075364 2.244072 0.03 0.973 -4.33011 4.480841 2020 -1.53723 2.358449 -0.65 0.515 -6.16724 3.092789 -2.12294 3.137984 -0.68 0.499 -8.28331 4.037434 Region Nobid Year _cons In regression without e-bid: - Influence of packval, compt, nobid are significant at 0.05 level; - In contrast, the influence of oneEnv, region, and year are not significant, even at 0.1 level; - For the variable of entUnit, its influence is nearly significant at 0.05 level; - For the variable of the sector, similar to the regression with full independent variables and full observation, p-values of sector and sector suggest that procurement of sector and sector have higher price reduction in comparison with procurement in sector 35 Table 3.8: Regression without compt Linear regression Number of obs = 759 F( 15, 743) = 20.53 Prob > F = R-squared = 0.3011 Root MSE = 10.565 Reduct Coef packval Robust Std Err t [95% Conf P>t Interval] -0.00017 6.15E-05 -2.84 0.005 -0.0003 -5.4E-05 2.765542 1.69238 1.63 0.103 -0.55687 6.087957 -0.96488 1.457498 -0.66 0.508 -3.82619 1.896424 2.445513 1.321072 1.85 0.065 -0.14797 5.038992 oneEnv -0.83564 2.810616 -0.3 0.766 -6.35333 4.682054 entUnit 1.708108 1.018898 1.68 0.094 -0.29216 3.70837 0.862564 1.069639 0.81 0.42 -1.23731 2.962439 -0.31061 0.888563 -0.35 0.727 -2.055 1.433787 Nobid 3.084334 0.299003 10.32 2.497343 3.671325 Ebid 3.680309 0.839585 4.38 2.032069 5.32855 Sector Region 36 Year 2016 -1.9672 1.248738 -1.58 0.116 -4.41868 0.484273 2017 1.429339 1.326498 1.08 0.282 -1.17479 4.03347 2018 -0.96776 1.297951 -0.75 0.456 -3.51584 1.580331 2019 -0.2825 2.234025 -0.13 0.899 -4.66825 4.10325 2020 -2.04924 2.350174 -0.87 0.384 -6.66302 2.564529 -2.8077 3.136629 -0.9 0.371 -8.96541 3.350012 _cons In the regression without compt: - The influence of packval, ebid, nobid are significant at 0.05 level; - In contrast, the influence of oneEnv, region, year are not significant, even at 0.1 level; - For the variable of entUnit, its influence is not significant at 0.05 but significant at 0.1 level; - For the variable of the sector, similar to the regression without ebid, pvalues of sector and sector suggest that procurement in sector and sector have higher price reduction in parallel with procurement in sector In addition, the R-square of the two models are nearly the same and equal to R-square in regression with both nobid and ebid 3.6.4 Overall findings about the influence of factors on price reduction A different way of regression leads to different regression result, especially with coefficient and p-value of independent variables However, with some variables, there are similarities in different regression, for example: in all regression, the influence of ebid, nobid, compt is significant at 0.05-level and influence of oneEnv, region and year is not significant at 0,1-level Findings for the influence of factors on price reduction in detail are: 37 Table 3.9: Findings of regressions Variable/factor Packval Findings within study p-value and coefficient considerably change different model With 591 observation regression, p-value is lower than 0.05 and coefficient is minus With 1191 observation regression, p-value exceeds 0.1 and coefficient is plus Influence of pacval may be possible but the direction is still vague Sector p –values of sector and sector suggest that procurement of sector and sector have higher price reduction in comparison with procurement in sector Compt The influence of compt is significant at 0.05 at all regressions and from Table 13, coefficient is positive This indicates that the application of competitive bidding leads to higher price reduction Coefficient value of this variable in Table 13 shows that application of competitive bidding contributes 2.323321 % in price reduction OneEnv p-value of oneEnv overtake 0.1 many times at all regressions so that the number of envelopes has no significant influence on price reduction Entunit p-value of entunit ranges from 0.007 to 0.1 and coefficient is positive in all regressions so that we conclude that procuring entity of enterprises get a higher price reduction in comparison with public entities Coefficient value of entunit in Table 11 shows that the price reduction of enterprises is 2,080302 % - higher than price reduction of public entities Region p-value highly is higher than 0.1, so that region where bidding process takes place has no significant influence on price reduction at this level 38 Variable/factor Findings within study Nobid p-value = 0.000 at all regressions and its coefficient are almost plus so that we conclude that higher nobid significantly lead to higher price reduction Coefficient value of nobid in Table 11 shows that increasing of one bidder leads to increasing of 3.140141% in price reduction Ebid p-value = 0.000 at all regressions and its coefficient are always positive, so that we conclude that application of e-bidding significantly leads to higher price reduction Coefficient value of ebid in Table 11 shows that application of ebidding leads to increasing of 4.321321% in price reduction Year p-value of year clearly exceeds 0.1 so that this variable has no significant influence on price reduction Further discussion with nobid As mentioned in chapter 2, according to the view of M Keisler and William A Buehring (2005), an increase in the number of bidders has a significant impact until reaching bidders In this study, in addition to regression with the full observation and the full independent variables on data set full.obs and del.NA, packages in which the number of participating bidders ranges from to and to higher will be reviewed In dataset nobid to 4, and data set nobid from 4, which is picked up from full.obs or del.NA, by command reg reduct packval compt oneEnv entunit Region Nobid Year, robust, we get results in table 16 and 17 39 Table 3.10 Regression with nobid from to Linear regression Number of obs = F (15, 619) = 10.01 Prob > F = R-squared = 0.1596 Root MSE = 9.0385 Reduct Coef 635 Robust Std Err t [95% Conf P>t Interval] packval -0.00015 5.99E-05 -2.49 0.013 -0.00027 -3.2E-05 Compt 1.944343 0.734262 2.65 0.008 0.502397 3.386289 2.171875 1.620937 1.34 0.181 -1.01133 5.355076 -0.25699 1.374336 -0.19 0.852 -2.95592 2.441934 1.505994 1.161994 1.3 0.195 -0.77593 3.787922 oneEnv -0.79634 3.460457 -0.23 0.818 -7.59199 5.999321 entUnit 2.177518 0.949362 2.29 0.022 0.313158 4.041878 0.337315 0.730888 0.46 0.645 -1.09801 1.772635 2.278524 0.78954 2.89 0.004 0.728024 3.829025 2.013277 0.413625 4.87 1.200999 2.825556 -1.195 1.211934 -0.99 0.325 -3.575 1.185001 Sector Region Nobid Year 2016 40 2017 1.005861 1.244997 0.81 0.419 -1.43907 3.450791 2018 -1.7743 1.110711 -1.6 0.111 -3.95552 0.406914 2019 0.360404 2.548388 0.14 0.888 -4.64413 5.364938 2020 -1.05718 2.79615 -0.38 0.705 -6.54827 4.433912 -0.73797 3.631936 -0.2 0.839 -7.87038 6.39444 _cons 41 Table 3.11: Regression with nobid from Linear regression Number of obs = F (13, 161) = Prob > F = R-squared = 0.2391 Root MSE = 13.967 Reduct packval Robust Std Err Coef -0.00034 Compt 176 0.000172 t P>t [95% Conf Interval] -1.98 0.049 -0.00068 -1.02E-06 (omitted) Sector 20.93474 4.913792 4.26 11.23095 30.63854 14.58346 6.874831 2.12 0.035 1.006985 28.15993 22.49332 4.119243 5.46 14.3586 30.62803 oneEnv -5.43878 5.581649 -0.97 0.331 -16.4615 5.583903 entUnit 3.933176 3.874616 1.02 0.312 -3.71845 11.5848 1.643431 4.122612 0.4 0.691 -6.49794 9.784798 -9.09448 2.916485 -3.12 0.002 -14.854 -3.33498 2.506596 0.753846 3.33 0.001 1.017895 3.995296 Region Nobid 42 Year 2016 0.559496 5.53649 0.1 0.92 -10.374 11.493 2017 7.832505 4.994781 1.57 0.119 -2.03123 17.69624 2018 9.449392 5.820684 1.62 0.106 -2.04534 20.94413 2019 2.753514 5.40314 0.51 0.611 -7.91665 13.42368 2020 -0.45053 5.784012 -0.08 0.938 -11.8729 10.97178 -10.6405 9.637517 -1.1 0.271 -29.6727 8.39179 _cons Despite lower R-squared and less observations, p-value and coefficient of independent variables in regressions with nobid to and nobid from data set are quite consistent with previous regression P-value of nobid is 0.000 in case nobid ranges from to and is 0.001 in case nobid higher than Therefore, although there is slight change in p-value, nobid in both of the cases are significant at 0.05 and even higher 0.01 A similar result is found if we replace compt by ebid in the regression model Possible bias of findings: Although regression results with the above data set help to reach some considerable findings, bias is unavoidable In detail: - Firstly, due to the limitations regarding time, data extraction technology from the national e-Procurement system, the data in this study is not the big data of all bidding packages but of 1191 packages Therefore, findings within this research may change when conducting a study on big data of all public procurement packages Therefore, bias may come from the size and randomness of sampling - Secondly, although there are regulations, guidelines about cost norms, the requirement of reference to similar successful package or price information of different suppliers for building up package value, there is a certain possibility of raising package value from the procuring entities to get a higher reduction price and display an effective public procurement process 43 CHAPTER 4: CONCLUSIONS AND PUBLIC POLICY RECOMMENDATIONS 4.1 Conclusions From quantitive analysis from this study, the following conclusions are provided: - The application of competitive bidding, e-bidding and increasing of bidder are important factors to get a higher price reduction; - In the condition that package value is built up properly, enterprises are more possible to get a higher price reduction than public enities; - Price reduction varies by sector, for example, procurement of goods in the electrical sector is significantly leads to a higher price reduction in parallel with the health sector; - Variable of year, region, number envelops in bidding has no significant relationship with price reduction This result with the variable of year is different from initial expectation according to the annual reports of MPI However, further clarification for this difference requires more research later and with bigger data; - There are an existance of the relationship between package value and the price reduction but the dimension is still vague 4.2 Public policy recommendations From quantitive analysis of this study and upon conclusions, public policy recommendation within this research are: From finding in which application of competitive bidding leads to higher price reduction, the first recommendation is competitive bidding must continue to be the priority Accordingly, the proposal of project owners, procuring entities on increasing of the threshold for noncompetitive bidding and expanding the cases of sole source should not be accepted From finding in which application of e-bidding significantly leads to higher price reduction, pushing up online bidding is recommended The application of online bidding is the right solution to improve transparency as well as increase price reduction in public procurement However, the national e-procurement system needs 44 to be upgraded for more user-friendly similar to online shopping sites such as Lazada, Tiki, SpeedL Complaints of users regarding difficulty in using this system need to be timely remedied to encourage the use of e-bidding Regarding function and capacity of national e-procurement, due to the importance of quantitative research, collection and extraction of big data of whole public procurement package for researching and finding evidence in policy-making need more attention For finding regarding number of bidder, an increasing in the number of participated bidders is an important factor to get a higher price reduction To fascinate more bidders, it is necessary to supplement regulations, together with requirement to enhance transparency in bidding information The circumstance of restricting bidders to buy bidding docurements on site (eg: No available bidding documents today, no one responsible for issuing bidding documents this time…) need to be considered and imported into legal documents Provisions in the bidding documents have to provide the opportunity for a large number of bidders and not orient to one or a few bidders This requires more detailed guidelines of MPI in building up bidding docurements, as well as suitable punishment for the barrier in bidding document For example, procuring entities who establish bidding document that restricts the participation of bidders will not be responsible for establishing another bidding document According to finding about package value, there is an existance of the relationship between package value and price reduction but the dimension of the relation is vague In this regard, it may be appropriate to continuously allow project owners to decide on the size of the bidding package reasonably, as long as it is suitable for the nature of the project and the potential bidder market The fifth, one of findings is that procuring of enterprises get a higher price reduction in comparison with public entities The difference in price reduction between state entities and enterprises in this study suggests an argument in which state agencies are carrying out procedures, including procurement procedures, in an inefficient manner in comparison with enterprises Therefore, it is necessary to simplify, speed up and be 45 transparent in the bidding procedures of state agencies Besides, hiring enterprises to bidding procedure is also a solution for these state agencies For example, a public hospital does not mobilize their doctors - who specialize in medical treatment to carry out bidding procedures Instead, an enterprise operating in the field of procurement can be employed for the procurement of drugs./ 46 REFERENCES AndréS GóMez-Lobo & 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