Country characteristics and public procurement variables : a statistical approach

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POLITECNICO DI MILANO

Facoltà di Ingegneria dei Sistemi

Corso di Laurea Magistrale di Ingegneria

Gestionale

Country characteristics and Public Procurement

variables: a statistical approach

Stefano Bruno

Matricola 782658 Thesis supervisor: Prof. Stefano Ronchi

A.Y. 2013/2014 Co-Tutor: Ing. Andrea Stefano Patrucco

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Politecnico di Milano 2

ABSTRACT ...

7

INTRODUZIONE ...

9

EXECUTIVE SUMMARY ...

11

A.Literature Review ...

11

B.Objective and Research Questions ...

13

C.Metodology ...

14

C.1 Database Building: Source Analysis

...

15

C.2 Statistical Models for Quantitatie Analysis

...

19

D.Dataset Analysis...

20

D.1 Descriptive Statistics

...

20

D.2 Linear Regression Models

...

21

D.3 Cluster Analysis

...

24

E.Conclusions ...

28

CHAPTER 1. LITERATURE REVIEW ...

31

1.1 Country Level Varables ...

31

1.1.1 Government Size

...

31

1.1.2 Macro Economic Indicators

...

33

1.1.3 Population Wellness

...

35

1.1.4 Political Government

...

35

1.1.5 Level of Transparency

...

36

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Politecnico di Milano

3

1.2 Public Procurement System ...

38

1.2.1 Pillars of the Public Procurement sysytem

...

40

1.2.2 Procurement Level Variables

...

47

1.2.2.1 Public Spending Amount ... 48

1.2.2.2 Procurement Department Structure and Organization ... 48

1.2.2.3 Procurement Costs ... 56

1.2.2.4 E-Procurement Maturity ... 57

1.3 Public Procurement and Country Economy ...

62

1.3.1 Size and Public Procurement Organizational Structure ...62

1.3.2 Economic Availability and Public Procurement Management ...65

1.3.3 Public Procurement and Population Wellness ...68

1.3.4 Public Procurement and Public Sector Employment ...69

1.3.5 Political Government and Public Procurement Strategy ...70

1.3.6 Tendency to Protect Local Economy & Discriminatory Procurement ..72

1.3.7 Open Government and Transparency ...75

1.3.8 E-Procurement and Public Sector Transparency ...80

CHAPTER 2. OBJECTIVES AND RESEARCH QUESTIONS ...

82

CHAPTER 3. METHODOLOGY ...

84

3.1 Database Building: Source Scouting ...

84

3.2 Database Building: Secondary Data...

84

3.3 Statistical Models for Quantitative Analysis ...

88

CHAPTER 4. DATA AND ANALYSIS ...

93

4.1 Dataset Characteristics...

93

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4

4.1.2 Variables Description ...95

4.1.2.1 Country Level Variables ... 95

4.1.2.2 Procurement Level Variables ...98

4.2 Descriptive Statistics ...

101

4.3 Linear Regression Models ...

106

4.4 Cluster Analysis ...

115

4.4.1 The Five Cluster Solution ... 117

4.5 Result Interpretation ...

121

4.5.1 Cluster 1 ... 121 4.5.2 Cluster 2 ... 123 4.5.3 Cluster 3 ... 124 4.5.4 Cluster 4 ... 125 4.5.5 Cluster 5 ... 127 4.5.6 Remarks on Data ... 129

CHAPTER 5: CONCLUSIONS, LIMITATIONS FUTURE

DEVELOPMENTS ...

131

ANNEXES ...

134

Annex A...

134

Annex B...

135

Annex C ...

136

Annex D ...

137

BIBLIOGRAPHY ...

138

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5

LIST OF FIGURES

Figure 1- Generic Institutional Model, Central Public Procurement Structure

Figure 2- Secondary Data Composition

Figure 3- Graphic Representation of the Sample

Figure 4- Graphic Representation of the Clusters

Figure 5- Life Expectancy Representation

LIST OF GRAPHS

Graph 1- Regression Curve (Public Spending and Gross Domestic Product)

Graph 2- Cluster 1 Public Spending distribution

Graph 4- Cluster 3 Public Spending distribution Graph 5- Cluster 4 Public Spending distribution

LIST OF TABLES

Table 1- Protectionism Rank

Table 2- Quantitative vs Qualitativ Methods Table 3- Research Sample

Table 4- Variables‘ Descriptive Statistics Table 5- Coefficients of Variation

Table 6- Pearson Correlations

Table 7- Main Pearson Correlations

Table 8- First Regression Model Summary Table 9- First Regression Model Anova Table

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6 Table 11- First Regression Model Residual Statistics

Table 12- Second Regression Model Summary

Table 13- Second Regression Model Anova Table

Table 14- Second Regression Model Coefficients

Table 15- Second Regression Model Residual Statistics

Table 16- Third Regression Model Summary Table 17- Third Regression Model Anova Table

Table 18- Third Regression Model Coefficients

Table 19- Fourth Regression Model Summary

Table 20- Fourth Regression Model Anova Table

Table 21- Fourth Regression Model Coefficients Table 22- Hierarchical Agglomeration Schedule

Table 23- Distances between groups

Table 24- K means Clustering SPSS Output

Table 25- Distances between Final Cluster Centres

Table 26- Number of components for each cluster

Table 27- Countries grouped into clusters

Table 28- Cluster 1 main variables Descriptive Statistics

Table 32- Cluster 5 main variables Descriptive Statistics Table 33- Average Values of Variables for each Cluster

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7

ABSTRACT

In the current economic context, Public Procurement plays a key role for governments, taking the most of spending activities in any country, and accounting for a percentage of GDP between 12% and 22%. It has often been used as an important tool to achieve economic and social objectives, as its size may have a great impact on national economies. By this, an efficient and effective management of procurement activities is essential, considering that Public Procurement management is strictly connected (and depends on) to the macro-economic country‘s variables and characteristics. As others authors have pointed out in the past such us Thai (2001) who studied how Public Procurement organizational structures within the executive branch vary with the size of the governmental units, from a very complex to a very simple structure. Glock & Broens (2013) underlined How the size of these municipality influences its purchasing organization, Mays (2011) wondered if there was any connection between spending and health outcomes exist, McCrudden (2004) sayd that Public Procurement has often been used to increase employment too, Lijphart (1999) or Kimani (2013) investigated the influence of political patronage on the operationalization of Public Procurement, Ralha ( 2012) stressed the importance of data management through e-procurement in public sectors in order to have greater transparency and less corruption, Croom & Jones (2007) discussed the efficiency benefits of e-procurement implementation in the pubic sector or Ssennoga (2006) who investigated the influence of protectionism on Public Spending.

Starting from this, the principal aim of this research is to understand more deeply how strong this correlation is, through: 1) identification of relevant countries macro-characteristics and procurement dimensions; 2) classifiction of world countries along these variables; 3) identification of country clusters and the most important relationship between macro-economic and procurement variables. With

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8 an in depth literature review and the use of secondary sources, the dataset has been designed including 26 variables (14 for macro-economic aspects and 12 for procurement) and 50 different world countries have been mapped, representing a uniqueness for the reaserch field. Then, some statistical analysis have been performed, as simple and multiple linear regression models for evaluating the existence of relationship between macro-country characteristics and procurement dimensions and Cluster Anlalysis in order to group the countries included basing on main variables. Results show the significative relationships (e.g between Public Spending and Size and GDP) as well as the presence of 5 five clusters, which would be useful in the future to assess possible best practices and therefore the most appropriate actions for public procurement management at government level.

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9

INTRODUZIONE

Il presente elaborato si colloca all‘interno del filone del ―Public Procurement‖ ossia degli acquisti del settore pubblico. Nell‘attuale contesto economico, il Public Procurement gioca un ruolo fondamentale per i governi, rappresenta una grossa fetta delle attività di spending in ogni paese nonchè una consistente percentuale del GDP, fra il 12 e il 22 per cento. È spesso stato utilizzato come importante strumento per raggiungere obiettivi economici e sociali, in quanto le sue rilevanti dimensioni hanno un impatto diretto sulle economie dei paesi. Per questo, un efficiente ed efficacie gestione delle attività di acquisto è essenziale, considerando che la gestione del Public Procurement è strettamente legata (e dipende da) le variabili macro economiche e le caratteristiche dei paesi. Molti autori in passato si sono interrogati su quali fossero le principali variabili discriminanti del Public Procurement ed evidenziato eventuali relazioni esistenti tra le macro caratteristiche del paese e micro dimensioni d‘acquisto. Ad esempio Thai (2001) studiò come la stuttura organizzativa del Public Procurement all‘interno del ramo esecuitvo cambi con le dimensioni delle unità governative, da una struttura molto complessa and una molto semplice. Glock & Broens (2013) sottolinearono come le dimensioni delle municipalità influenzassero la loro struttura di acquisto. Mays (2011) si interrogò su eventuali connessioni fra lo spending pubblico e la salute della popolazione. McCrudden (2004) studiò come il Public Procurement è stato spesso utilizzato per incrementare l‘occupazione del paese. Lijphart (1999) o Kimani (2013) valutarono l‘influenza politica sull‘operatività del Public Procurement. Ralha (2012) sottolineò l‘importanza della gestione dei dati attraverso i tools dell‘e-Procurement all‘interno del settore pubblico col fine di avere un maggior livello di trasparenza sul public spending e minore corruzione. Croom e Jones (2007) discussero i benefici in efficienza dell‘implementazione dell‘e-procurement nel settore pubblico. Ancora, Ssennoga (2006) si interrogò sull‘influenza del Protezionismo, o meglio delle tendenza a

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10 difendere l‘economia locale, sul Public Spending. Nella revisione della letteratura vengono riportati inoltre ulteriori studi ed interrogativi

Partendo da ciò, il fine principale della mia ricerca è innanzitutto capire più nel dettaglio quanto sia forte questa correlazione attraverso: 1) identificazione delle principali macro-caratteristiche e dimensioni d‘acquisto dei paesi; 2) classificazione dai paesi inseriti all‘interno del campione lungo tali variabili; 3) identificazione di cluster di paesi e delle principali relazioni tra caratteristiche macro e dimensioni d‘acquisto. Con un‘approfondita revisione della letteratura e l‘utilizzo di fonti secondiarie ho realizzato un database che include 26 fra variabili e indicatori (14 per le macro-caratteristiche e 12 per le dimensioni d‘acquisto) consentendo in tal modo di mappare 50 differenti paesi del mondo, ciò rappresenta una novità per tale campo di ricerca. Successivamente sono state eseguite alcune analisi statistiche come modelli di regression lineare semplice e multipla, per evidenziare l‘esistenza tra le macro-variabili e le dimensioni d‘acquisto micro, e la Cluster Analysis col fine di raggruppare I paesi sulla base delle principali variabili. I risultati hanno mostrato alcune relazioni significative (ad esempio tra il totale della spesa, le Dimensioni del paese e il PIL) come anche la presenza di cinque clusters che potrebbero risultare utili al fine di definire delle

best practice gestionali all‘interno di ciascun gruppo in grado di migliorare le

prestazioni e la gestione degli acquisti a livello paese.

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EXECUTIVE SUMMARY

A. LITERATURE REVIEW

Until now, the attention concerning purchases made by Public Bodies was a little bit poor; Public Procurement is lagging behind the private sector in terms of research and accumulated knowledge. Only recently, however, the Public procurement has begun to capture the attention of scholars and researchers mainly because it has a significative impact on government and country performance.

Country can be characterized by different macro-variables, as: 1) Size (Thai, 2011, Glock & Broens, 2013), 2) Number of inhabitants (Glock & Broens, 2013), 3) Number of public sector‘s employees as a share of population and as a share total labour force (McCrudden, 2004), 4) Employment Compensation (OECD Library, 2010), 5) Gross Domestic Product (also known as GDP) and GDP per capita (OECD Library), 6) Political Government (Lijphart, 1999, Covielloy & Gagliarducci, 2008, Kimani, 2013), 7) Population Wellness (Mays, 2011), 8) Tendency to protect local economy (Ssenoga, 2006), 9) Transparency (Frøystad, Heggstad and Fjeldstad, 2010). These variables seem to have an impact on Public Procurement scope and how its activities are organized and managed at different government level.

Some of the relevant procurement variables are 1) Public Spending Volume (OECD Library, 2010), 2) Public Spending as a share of GDP and as a share of Gross National Expenditure (European Commission, 2008, OECD Library), 3) Public Spending per Inhabitant, per km2 and per Public Employee, 4) Purchasing Structure (Bianchi & Guidi, 2010, OECD Library, 2007), 5) e-Procurement Maturity (Annual Public Procurement Implementation Review, 2012, Croom &

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12 Jones 2004 and 2010), 6) Organizational Status (Guidi & Bianchi, 2010, OECD Library, 2010), 8) Public Procurement Costs as a share of total Procurement (Strand, Ramada & Canton,2011), 7) Number of Public Tenders (OECD Library, 2010, TED, 2013). Possible relationship are clearly stated by some authors, for example:

Author and year of publication

Journal Type of relationship stated in the article

Thai (2001) Journal of Public Procurement

Size of government units and Procurement

Structure Glock & Broens (2013) Journal of Public

Procurement

Size of government units and Procurement

employees Ssennoga (2006) Journal of Public

Procurement

Protectionism and Public Spending Volume Croom & Jones (2007) Journal of Purchasing

and Supply Management

E-Procurement Maturity and Public Procurement

efficiency

Kimani (2013) OECD Journal Political Government and Public Procurement

efficiency

McCrudden (2004) Natural Resource Forum Public Procurement and Employment Mays (2011) Health Affairs Public Spending and

Population Health

Table 34 Reference Table

Starting from this picture on the main macro–economic variables, procurement dimension and relationships metioned by past authors, the present work aims to deep them and discover new ones in order to enrich research on public spending management.

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B. OBJECTIVES AND RESEARCH QUESTIONS

This research work has three main objectives:

• List the macro-economic and public procurement variables which characterize the most different countries;

• Higlight some interesting linkage between these two groups of variables; • Identify which of the previous variables can be used in order to group

world countries.

These objectives results in three equivalent research questions:

• Question 1: Which are the main macro and micro-variables that best characterize countries‘ Public Spending Management?

• Question 2: Does any relationship exist between these country level variables and procurement variables?

• Question 3: Is it possible to identify clusters of countries starting from these variables?

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C. METHODOLOGY

To answer the previous questions, different research approaches have been used:

• Secondary sources scouting and analysis for database building; • Statistical quantitative models for database analysis.

C.1 Database building: sources scouting

For secondary sources scouting, Internet was the main research tool; in particular ―Google Scholar‖ (a Google browser specialized in the research of scientific articles), ―Scopus database‖ (a database of abstracts and citations for articles in publications regarding research which is used to evaluate the H-index, published by Elsevier) and Universities‘ database have been used.

These research tools provide a great amount of report and articles form academic journals. Most of the articles appear in major magazines regarding Public Procurement topics such as: Journal of Purchasing and Supply Management; Public Administration; Journal of Public Procurement; Management Science Quarterly; Benchmarking: An International Journal; Industrial Marketing Management; Procedia Economic and Finance; Research Policy; Public Administration Law and Review.

Journals have been selected basing on their abstracts, keywords, content (quality of the content for research purpose), but also according to their relevance (number of citations).

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C.2 Database building: sources analysis

The sample used to achieve the objective proposed is the result of a process of data collection from a list of 50 nations chosen in order to have a representative sample of the all the world. These countries were chosen to preside over each continent and take into consideration the major nations of the entire world. The choise also depends on the available information, favoring those countries with a greater amount of available information about macro characteristics and procurement dimensions. For example, countries like Uganda or Tanzania, not emerging in the economic or political scenario, are very active as regards the Public Procurement Reforms.

Below, there is a list and a graphical representation of this sample.

Italy South Affrica France Portugal Lithuania

USA China Greece Slovakia Argentina

UK Singapore Finland Slovenia Indonesia

Germany Vietnam Czech

Republic

Spain Poland

Sweden Cambodia Netherlands Brazil Romania

Canada Japan Croatia India Israel

Switzerland Russia Austria Bulgaria Luxembourg Tanzania Australia Denmark Belgium Macedonia

Uganda Turkey Ireland Mexico Uruguay

Kenya Hungary Norway New

Zealand

Estonia

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Picture 3 Graphic Representation of the Sample

Countries have been classified along the variables through the help of Excel spreadsheets. The statistic evaluations on the database built in the earlier stages were carried out using the program IBM SPSS Statistics v21.0.

Below are shown the selected variables with a brief description.

Regarding the Country Variables:

The variable ―Size‖ (Thai, 2001, Glock & Broens, 2013) indicates the land area (Km2) of the countries. These data refer to ―CIA World Factbook‖. The ―Number of Inhabitants‖ (Thai, 2001, Glock & Broens, 2013) of each country were collected through the ―World Bank Database‖. The ―Population Wellness‖ (Mays, 2011) indicates the Life Expectancy (years). These data refer to ―World Health

Organization Data". The ―GDP‖ (Gross Domestic Product), which indicates the

total dollar value of all goods and services produced over a specific time period, and ―GDP per capita‖ of countries (Choi, 2009,Prier, McCue & Bevis, 2008) have been taken into account in order to evaluate their purchasing power. These data refers to the ―World Bank Database‖. The Public Sector Employment has been

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17 takend into account introducing the ―Number of Public Sector Employee‖, ―Number of Public Employee as a share of total population‖, ―Number of Public Employee as a share of total employment‖, ―Public Sector Employment Compensation‖, ―Public Sector Employment Compensation as a share of Total Spending‖ (OECD Library, 2007, Erridge & Henningan, 2005). These data refer to ―Laborsta Database‖ and ―World Bank Database‖. Another country‘s variable evaluated in the research is the ―Political Government‖ which indicates the Political System in charged (Thai, 2001, Gregorini &Longoni, 2010). These data refers to ―CIA World Factbooks‖.

The countries‘ ―Tendency to protect local economy‖ (Ssenoga, 2006) is evaluated on a rating scale (Low, Medium, High) counting the number of discriminatory laws or laws in favor of the local economy or through a consultation of specific papers. The ―Transparency of the Public Sector‖ (Frøystad, Heggstad & Fjeldstad, 2010) indicates the level of visibility or accessibility of information by people which is summed up by the ―Transparency Rank‖. This information refers to ―Corruption/Transparency index 2012‖ from Transparency International.

As regards Procurement Dimensions:

The ―Purchasing Structure‖ (Jones, 2007, OECD Library,2012, Comparative Survey across PPN 2010) indicates how Public Bodies structure their Procurement Process (which can be Centralized, Semi-Centralized or Hybrid-Centralized, Decentralized). It can be summed up by a rank (1 if the Purchasing Structure is Centralized; 2 if it‘s Semi-centralized; 3 if it‘s Decentralized but with a tendency to Centralization; 4 if it‘s Decentralized).

The ―Public Spending‖ (OECD Library) is defined as the sum of intermediate consumption (goods and services purchased by governments for their own use, such as accounting or IT services), Gross capital formation and acquisition less disposals of non-financial non-productive assets, Other current transfer consolidated and Social benefits and social transfers in kind for products supplied. These data refer mostly to the ―OECD Database‖ or to other sources

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18 such as the ―World Bank Data‖. Quantitative Indicators regarding countries‘ Public Spending have been calculated such as―Public Spending as a share of GDP (Gross Domestic Product)‖, ―Public Spending as a share of GNE (Gross National Expenditure)‖, ―Public Spending per Inhabitant‖, ―Public Spending per Km2‖ and ―Public Spending per Public Employee‖.

The ―e-Procurement Maturity‖ of the purchasing bodies represents the ability of the Public Bodies to purchase supplies, work and services through the Internet as well as other information and networking systems (Croom & Jones, 2007 & 2010, Ramanathan, 2004, EU-Japan Centre for Industrial Cooperation, 2010). It is evaluated on a rating scale (Low, Medium, High). The ―Public Procurement Cost as a share of total Procurement‖ (E. Commission, 2011) refers to Public

procurement in Europe Cost and effectiveness.

The ―Organizational Status‖ of Public Procurement bodies (OECD Library 2007) indicates how Public Bodies are organized within or subordinated to. Commonly these bodies are organized within Ministry of Finance or Treasury, Ministry of Works, Ministry of Regional Development, Office of the Prime Minister/ Chancellor/ President as a subordinate body, Council of Ministers, Parliament, Competition Authority or another public body, Office of Management and Budget and State Commissions. To facilitate the execution of statistical calculations, this variable can be summed up by a rank that varies from 1 to 3. The more the organization is near to the political leadership, the higher the rank. Finally, the ―Public Tenders‖ which indicates the number of Public Tenders per year of Governments. These data refers to the ―OJ/TED‖ (Tenders Electronic Daily, TED is the online version of the Supplement to the Official Journal of the European Union, dedicated to European public procurement) and to the ―Global Tenders Data‖ (globaltenders.com).

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C. 3 Statistical models for quantitative analysis

First some simple and multiple linear regressions have been performed in order to assess possible relationships between the variables. Linear regression models are one of the best-known learning and predictive methodologies in classical statistics. In its simplest form, linear regression is used to relate a dependent response variable Y to an independent predictor X through a linear regression in the form Y = aX + b (or multiple: Y = aX1 + bX2 +…+ c) where a and b are

parameters to be determined using the available observations (Vercellis, 2009). For each model are highlighted ―R Square‖ coefficient and the ―Coefficient of

Significance‖. The high valure of the ―R Square‖ coefficient is a good indicator of

the effectiveness of the model. This coefficient expresses the proportion of total variance explained by the predictive variables, and therefore by the regression model. The Coefficients of Significance (Sig<0.05) shows that the independent variables are particularly significant for the assessment of the dependent variable.

After a ―Cluster Analysis” has been performed. The aim of clustering models is to subdivide the records of a dataset into homogeneous groups of observations, called clusters, so that observations belonging to one group are similar to one another and dissimilar from observations included in other groups. Clustering methods must fulfill a few general requirements, as indicated below (Vercellis, 2009). The K-Means Cluster method has been adopted. This procedure attempts to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle a large number of cases. This algorithm, however, requires the indication of the number of clusters so, with the aim to identify the optimal number of clusters, I also took into account other clustering methods such as the Hierarchical Method. It was appropriate to apply other clustering algorithms and to compare the results obtained by different methods in order to evaluate the robustness of the model which shows cluster that effectively correspond to an actual regular pattern in the data.

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D. DATA ANALYSIS

The database has been explored first with static analysis (i.e. descriptive statistics), then with more dynamic approached (i.e. regression models, cluster analysis).

D.1 Descriptive statistics

Below, for some of the main variables‘, are given Range, Minimum, Maximum, Mean, Standard Deviation, Variance. The ―Coefficients of Variation‖ (CV) has also been calculated for each variable in order to compare them with each other. This coefficient allows us to evaluate the dispersion of the values around the average regardless of the measurement units. The CV is given by the following

formula:

𝐶𝑉 =

𝜎

|𝜇 |

.

(Where

𝜎

is the Standard Deviation and

𝜇

is the mean).

Descriptive Statistics

Range Min Max Mean Std. Dev. Var CV

Gross Domestic Product (Millions$) 14979560 10440 14990000 1227465.8 2461044.8 6056741555159.54 2.0 Public Spending as a % of GDP 23.4 6.60 30 18.25 5.35 28.635 0.29 Public Spending per Inhabitant 25132.68 47.51 25180.19 5911.2721 5480.98 0.092 0.92 Public Spending per Km2 34586421 2323.6 34588745 1480405.8 4941256.7 24416017914059 3.33 Public Spending per Public Employee ($) 277182.3 2002.7 279185.0 81237.684 70502.70 4970631278.175 0.86 Public Procurement Costs as a % of Total Procurement 4.0% 0.5% 4.5% 1.692% 1.1049% 1.221 0.65

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21 From these tables some observations on the sample can be derived. For example, countries are characterized by a mean value of the Gross Domestic Product of 1227465.8 millions $ but there is a big gap between the minimum and maximum value and the variable, GDP is aslo characterized by a high dispersion of the values around the average; the Public Spending accounts for a percentage of GDP of 18.25% (on average); the Public Spending per Inhabitant has a mean value of 5911.27 $; the Public Spending per Km2 has a mean value 1480405.8 $ and a high dispersion of the values around the average; the Public Spending per Public Employee has a mean value of 81237.68 $ and the Public Procurement Costs as a percentage of total public procurement are1.692% (on average).

Then the correletion between the variables has been evaluated in order to avoid including highly correlated variables as indipendent variables in the following linear regression models We can highlight the main correlations which will help us to speculate and build the linear regression models:

Variable 1 Variable 2 Perarson Correlation

Public Spending GDP 0.987

Inhabitants Public Sector Employees 0.843 eProcurement Rank Transparency Rank 0.760

Public Spending Size 0.738

Tenders GDP 0.685

Tenders Public Spending 0.681

Size Public Sector Employees 0.598

Table 7, Main Pearson Correlations

D.2 Linear Regression Models

Starting from the previous litterature the presence of relationships between macro-country characteristics and procurement dimensions has been evaluated.

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22 First of all, the connection between Public Spending and country‘s economic availability has been demostrated using the Gross Domestic Product as macro indicator. The GDP (the total dollar value of all goods and services produced over a specific time period) is one of the major economic indicators that generally reflect the state of the economy of the whole country. Many authors in the past treated the issue and underlined this relationship such us Choi (2009) or even Prier, McCue & Bevis (2008). Choi (2009) studied how an optimal public spending management could contribute to the improvement and stabilization of the local economy. Prier, McCue & Bevis, (2008) underlined the Public Procurement‘s role in integrating economic development.

Taking into account past authors and my own assumptions the following simple linear regression model have been calculated:

𝑃𝑢𝑏𝑙𝑖𝑐𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔 = 𝛼 ∗ 𝐺𝐷𝑃 + 𝑐

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23 The high valure of the ―R Square‖ coefficient (0.974) is a good indicator of a good model fitting. Thanks to the coefficient of significance (Sig=0.000<0.05), it has also been deduced that the variable GDP is positively correlated to country Public Spending (as could be even expected by the high value of the correlation between these variables, 0.987). Standardized GDP coefficient=0.987.

After that the variable Size has been taken into consideration, too. Glock & Broens (2013) investigated how the size of municipality influences its purchasing organization, structure and level of spending. This relationship (at country level) has been demostrated calculating the following multiple linear regression model:

𝑃𝑢𝑏𝑙𝑖𝑐𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔 = 𝛼 ∗ 𝑆𝑖𝑧𝑒 + 𝛽 ∗ 𝐺𝐷𝑃 + 𝑐

Also in this case the high valure of the ―R Square‖ coefficient (0.979) is a good indicator of a good model fitting. Thanks to the p-value test it has also been deduced that the variables are correlated to country Public Spending. (Standardized GDP coefficient=1.031 and Std Size Coeff.=-0.87).

Finally, through a multiple linear regression model, other relationships have been showed by setting as independent variables also the Tendency to Protect Local Economy and the level of Transparency of the Public Sector. Ssennoga (2006) investigated the influence of protectionism on Public Spending while Frøystad, Heggstad & Fjeldstad (2010) studied how corruption and low transparency in the public sector are important obstacles to the economic development and to an optimal Public Spending Management.

𝑃𝑢𝑏𝑙𝑖𝑐𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔 = 𝛼 ∗ 𝑆𝑖𝑧𝑒 + 𝛽 ∗ 𝐺𝐷𝑃 + 𝛾 ∗ 𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛𝑖𝑠𝑚𝑅𝑎𝑛𝑘 + 𝛿 ∗ 𝑇𝑟𝑎𝑛𝑠𝑝𝑎𝑟𝑒𝑛𝑐𝑦𝑅𝑎𝑛𝑘 + 𝑐

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24 The high valure of the ―R Square‖ coefficient (0.981) is a good indicator of a good model fitting. Thanks to the p-value test it has also been deduced that the variables are correlated to country Public Spending. (Standardize GDP coefficient=1.010, Std Size Coeff.=-0.77, Std Protectionism Coeff=0.022 and Std Transparency Coeff=-0.054).

After that the relationship between the e-Procurement Maturity and the level of Transparency of the Public Sector has been demostrated. Other authors have dealt with this issue such as Ralha (2012), who investigated how the implementation of e-procurement tools for the Public Procurement would improve the level of Transparency of the Public Sector.

𝑒𝑃𝑟𝑜𝑐𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑅𝑎𝑛𝑘 = 𝛼 ∗ 𝑇𝑟𝑎𝑛𝑠𝑝𝑎𝑟𝑒𝑛𝑐𝑦𝑅𝑎𝑛𝑘 + 𝑐

The high valure of the ―R Square‖ coefficient (0.739) is a good indicator of a good model fitting. Thanks to the coefficient of significance (Sig Transparency Rank<0.05), it has also been deduced that the variable Transparency Rank is correlated to e-Procurement Maturity. (Std Transparency coeff.=-0.760).

D.3 Cluster Analysis

The K-means method requires, as an input, the optimal number of clusters to be determined (five, for the case). The following variables have been included for classification: Size, Inhabitants, Public Sector Employment, Political Government Rank, GDP, Protectionism Rank, Transparency Rank, Public Spending, e-Procurement Rank, Purchasing Structure Rank, Organizational Status Rank, Tenders, Public Procurement Costs as % of Total Procurement. The Cluster Analysis results showed the presence of five cluster.

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25

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

Italy, Spain, Portugal, Greece,Turkey, Hungary, Czech Reupublic, Croatia, Slovakia, Slovenia, Lithuania, Poland, Romania, Macedonia, South Africa, Israel, New Zealand. China, Russia, India, Brazil. USA, UK, Germany, Japan, France. Sweden, Switzerland, Finland, Norway, Belgium, Netherlands, Austria, Denmark, Luxemburg, Estonia Ireland, Canada, Singapore, Australia. Tanzania, Uganda, Kenya, Vietnam, Cambodia, Indonesia, Argentina, Uruguay , Mexico.

Table 27 Countries grouped into clusters

Below is shown a graphical presentation of these clusters:

Picture 4 Graphic Representation of the clusters

Therefore performance indicators have been calculated such Cohesion Coefficients (indicator of homogeneity of the observations within each cluster, Vercellis, 2009). The K-means method (five cluster solutions) has a smaller overall cohesion (1.776-15) and represents the best algorithm considering that

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26 one clustering is preferable over another, in terms of homogeneity within each cluster, if it has a smaller overall cohesion (Vercellis 2009). After, it can be deduced that the cluster number 3 is composed of the major political-economic powers. The cluster number 2 is composed of highly populated countries which are experiencing (or which have experienced in the recent past) a strong economic development. Cluster 1 and cluster 4 are both constituted by a large majority of European countries but at the same time are clearly different; specifically countries characterized by greater economic and political stability belong to the cluster number 4. Finally, the cluster number 5 is composed of sub-equatorial countries, with not a brilliant economic or political situation, some of which belong to the so-called Third World. As can be seen in the summary table of the clustering model, clusters more distant, i.e. more different, are as expected the cluster 3 and cluster 5. Below ara shown the average values of the main variables of clusters:

Variables Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

Size (Km2) 242769.4 9617928.75 2230917.4 1367271.214 875784 Inhabitants (millions) 20.546 731.175 129.874 9.845 69.435

Life Expect (years). 76.944 71 81 80.929 68

P.S. Empl. (#) 1322405.556 30244950 7453340 942211.929 1936225 P.S. Salaries ($) 41932.2 n.p. 533084.8 43219.231 n.p. P.S.Empl. % of Total Empl. 17.031 15.433 19.16 21.96 7.884 GDP (millions $) 397766.7 3381500 5935200 550160.714 367693.3 P.Spending (millions $) 83526.5906 484071.75 1204414.2 109034.69 45353.48 Protectionism

(Low, Medium, High)

1.75 2.88 2.9 2.04 2.67 P.Structure (Centralized, Semicentralized, Decentralized) 2 3.5 2.4 1.86 3.5 eProcurement (Low, Medium, High)

2 1.75 2.9 2.5 1.17

Transparency

(Rank, from 1 which is a good practice to 10 which is a bad practice)

5 7.75 1.8 1.5 8.89

PP Cost% 1.093 n.p. 1.98 1.85 n.p.

Org. Status (Rank) 2.1 2.25 1.6 2.4 1.7

Tenders

(#public tenders per year)

33255 150750 215263.2 14664.786 2970

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27 From this last table some remarks can be stated, for example: the cluster number 2 is composed of the largest and most populated countries; the cluster number 3 is composed of the countries in which there is the highest life expectancy; the cluster number 3 is composed of the countries in which there is the highest cost of Public Sector Employees and the highest percentage of Public Sector Employees on the total labour force; in terms of Public Spending Volume, we can classify the clusters in descending order: Cluster 3 (1204414.2 millions $), Cluster 2 (484071.75 millions $), Cluster 1 (83526.5906 millions $) and finally Cluster 5 (45353.48 millions $); the cluster number 1 and the cluster number 4 are characterized by a low-medium tendency to protect local economy, the cluster number 5 is characterized by a medium-high tendency to protect local economy and finally the cluster number 2 and the cluster number 3 are characterized by a strong tendency to protect and promote the local economy; the cluster number 1 on averadge is composed of countries with a semicentralized purchasing structure, the cluster number 3 and the cluster number 5 by countries with a decentralized purchasing structure, the cluster number 4 by countries with a centralized or semi-centralized purchasing structure; the cluster number 1 is characterized by a medium level of procurement maturity, the cluster number 2 by a low-medium level of e-procurement maturity, the cluster number 3 by a high level of e-e-procurement maturity, the cluster number 4 by a medium-high level of e-procurement maturity and the cluster number 5 by a low level of e-procurement maturity; the cluster number 1 is characterized by a medium level of Transparency of the Public Sector, the cluster number 2 and the cluster number 5 are characterized by a low level of Transparency, the cluster number 4 and the cluster number 4 by a high level of Transparency; on average, the cluster number 4 is composed of countries where the public procurement function is closer to the political leadership; the cluster number 3 is composed of countries where there is the highest percentace of public procurement notices.

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E. CONCLUSIONS

The study performed enable the possibility to give answer to the research questions stated in the previous section.

Referring to RQ1 (“Which are the main macro and micro-variables that best characterize countries’ Public Spending Management?”)

Thanks to an in depth literature review and the use of secondary sources, the dataset has been designed including 26 variables (14 for macro-economic aspects and 12 for procurement) and 50 different world countries have been mapped, representing a uniqueness for the reaserch field. The variables taken into account for the research were chosen according to their importance recognized by authoritative past authors but also on the basis of personal assumptions. Below are shown some of the key macro-country characteristics and procurement dimensions (specifying the main reference).

Country can be characterized by different macro-variables, as: Size (Thai, 2011, Glock & Broens, 2013), Number of inhabitants (Glock & Broens, 2013), Gross Domestic Product (OECD Library), Political Government (Lijphart, 1999, Covielloy & Gagliarducci, 2008, Kimani, 2013), Tendency to protect local economy (Ssenoga, 2006), Transparency (Frøystad, Heggstad and Fjeldstad, 2010). These variables seem to have an impact on Public Procurement scope and how its activities are organized and managed at different government level. Some of the relevant procurement dimensions are: Public Spending Volume (OECD Library, 2010), Purchasing Structure (Bianchi & Guidi, 2010, OECD Library, 2007), e-Procurement Maturity (Annual Public Procurement Implementation Review, 2012, Croom & Jones 2004 and 2010) or Organizational Status (Guidi & Bianchi, 2010, OECD Library, 2010).

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For RQ2 (“Does any relationship exist between these country level variables and procurement variables?”)

Thanks to this dataset some relationships between these macro-country characteristics and procurement dimensions have been demostrated through statistical quantitative models for database analysis such as simple and multiple linear regressions. Specifically it can be shown possible relationships between:

• Public Spending and Gross Domesti Product which is used as an economic indicator aimed to reflect the state of the economy of the whole country, (main starting point references: Choi, 2009, Prier, McCue & Bevis, 2008);

• Public Spending, Size and GDP in order to demostrate how the size of country influences its purchasing organization, structure and level of spending (main starting point reference: Glock & Broens, 2013);

• Public Spending, Size, GDP, Tendency To protect Local Economy and Level of Transparency of the Public Sector in order to see the influence of protectionism on Public Spending and how corruption and low transparency in the public sector are important obstacles to the economic development and to an optimal Public Spending Management, (main starting point references: Ssennoga, 2006, Frøystad, Heggstad & Fjeldstad, 2010);

• E-procurement Rank and Transparency of the Public Sector in order to investigate how the implementation of e-procurement tools for the Public Procurement would improve the level of Transparency of the Public Sector, (main starting point reference: Ralha, 2012) .

However, it was not possible to demonstrate all the relationships expected from the literature review. This does not mean that such relationships do not exist but simply that they could be demonstrated with other more complex statistical models (different from linear regression models). This could be a starting point for future research.

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For RQ3 (“Is it possible to identify clusters of countries starting from these variables?”)

The Cluster Analysis was a good tool for identifying countries linked by similar public purchasing beahaviors and macro characteristics. This analysis makes it possible to isolate and bring together groups with specific trends that otherwise would not have been able to predict or imagine due to the limited initial knowledge of the phenomenon. Obviously, the results are influenced by several success factors such as the choice of data by the researcher and, above all, the ability to identify or create the discriminating variable. Consequently, any wrong choices could be a limitation of this research. Anyway, through this analysis, it has been shown the presence of five clusters of countries within the sample that effectively correspond to an actual regular pattern in the data.

A starting point for future researches could be the assessment of possible best practices or even identify for each cluster which are the most appropriate actions for public procurement management by country.

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CHAPTER 1: LITERATURE REVIEW

The following literature review can be divided into three sections: Macro-country characteristics, Public Procurement Scenario and Possible Relationships between Macro-country Characteristics and Procurement Dimensions.

1.1 COUNTRY LEVEL VARIABLES

Basing on macroeconomic literature country can be characterized by different macro-variables, as: 1) Size (Thai, 2011, Glock & Broens, 2013), 2) Number of inhabitants (Glock & Broens, 2013), 3) Number of public sector‘s employees as a share of population and as a share total labour force (McCrudden, 2004), 4) Employment Compensation (OECD Library, 2010), 5) Gross Domestic Product (also known as GDP) and GDP per capita (OECD Library), 6) Political Government (Lijphart, 1999, Covielloy & Gagliarducci, 2008, Kimani, 2013), 7) Population Wellness (Mays, 2011), 8) Tendency to protect local economy (Ssenoga, 2006), 9) Transparency (Frøystad, Heggstad and Fjeldstad, 2010). Each of these will be briefly described in the following sections.

1.1.1 Government Size

The government may be defined as a group of entities or units that deliver public services for individual or collective consumption, and redistribute income and wealth.

―General Government‖ is a term used to described all government entities at whatever level, central, regional or local. The system of national accounts

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32 (SNA93 and ESA95) classify the government into four categories which may be defined as follows (Allen & Tommasi, 2001)

:

 Central Government; The national government in federal and unitary countries. In general, central government Is responsible for those functions that affect the country as a whole: for example, national defence, conduct of relations with other countries and international organizations, establishment of legislative, executive and judicial functions that cover the entire country, and delivery of public services such as healthcare and education. Non-market, non-profit institutions controlled and mainly financed by central government are included in the central government.

 Local Government; Local Government is a collection of public bodies with authority over subdivision of a significant area of county‘s territory. It is either the third tier in federal countries or the second and third tiers in unitary countries (region, countries, municipalities, etc). To exist as separate entity, a local government body must have the authority to exercise powers independently from other levels of general government.

 State Government; State Government has independent authority for certain functions in a significant part of a country‘s territory. This intermediate level of government exists in all countries with a federal constitution (for example, the Lander in Germany). Regional government authorities have similar characteristics in terms of territorial jurisdiction but are generally found in countries that do not have federal constitutions.

 Social Security Funds; Funds that provide social benefits to the community through a social insurance scheme that generally involves compulsory contributions by participants. In most countries, such funds are separately

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33 organized from the other government activities, have their own budget, and hold their assets and liabilities separately. Social security system that does not hold their assets and liabilities separately are not called Social Security Funds. In the GFS, the preferred treatment of Social Security Funds is to classify them as a part of the level of government at which they operate. An alternative treatment is to group all Social Security Funds into separate subsectors. Funded government employee pension plans are not Social Security Funds. They are financial corporations and are excluded from the general government sector.

Glock & Broens (2013) indentify the size as the number of inhabitants, dimensions or the number of employees. In this research these aspects have been taken into account through variables as ―Size‖ which indicates the land area of the country, ―Number of Inhabitants‖, ―Public Sector Employment‖ (with the corresponding quantitative indicators) which indicates the number of employees of the public bodies. The total public sector employment covers all employment of general government sector as defined in System of National Accounts (1993) plus employment of publicly owned enterprises and companies, resident and operating at central, state (or regional) and local levels of government. It covers all persons employed directly by those institutions, without regard for the particular type of employment contract. The general government sector employment is the total employment of all government units, social security funds and non-market Non Profit Institutions that are controlled and mainly financed by public authority. The employment of publicly owned enterprises and companies is the employment of all units producing goods or services for the market and which are mainly owned and/or controlled by government units.

1.1.2 Macro Economic Indicators

The ―Gross Domestic Product‖ has often been used as macro indicator by many researchers. The GDP represents the total dollar value of all the finished goods and services produced within a country's borders in a specific time period, usually

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34 calculated on an annual basis. It includes all of private and public consumption, government outlays, investments and exports less imports that occur within a defined territory. It is one of the major economic indicators that generally reflect the state of the economy of the whole country. Theoretically, GDP can be viewed in three different ways (Callen, 2012):

The production approach sums the ―value-added‖ at each stage of production, where value-added is defined as total sales less the value of intermediate inputs into the production process. For example, flour would be an intermediate input and bread the final product; or an architect‘s services would be an intermediate input and the building the final product.

The expenditure approach adds up the value of purchases made by final users— for example, the consumption of food, televisions, and medical services by households; the investments in machinery by companies; and the purchases of goods and services by the government and foreigners.

The income approach sums the incomes generated by production-for example, the compensation employees receive and the operating surplus of companies (roughly sales less costs).

GDP in a country is usually calculated by the national statistical agency, which compiles the information from a large number of sources. In making the calculations, however, most countries follow established international standards. The international standard for measuring GDP is contained in the System of

National Accounts, 1993, compiled by the International Monetary Fund, the

European Commission, the Organization for Economic Cooperation and Development, the United Nations, and the World Bank (Anderson, 1992). A measure of the total output of the country is also the ―per capita GDP‖ that takes the GDP and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. A rise in per capita GDP signals growth in the economy and tends to translate as an increase in productivity.

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1.1.3 Population Wellness

Population Wellness indicates the overall health status of the population of a country which has been often evaluated through the Population Life Expectancy (years). Life expectancy represents the probable number of years a person will live after a given age, as determined by mortality in a specific geographic area. It may be individually qualified by the person's condition or race, sex, age, or other demographic factors (Mosby's Medical Dictionary, 2009). There are great variations in life expectancy between different parts of the world, mostly caused by differences in public health, medical care, and diet. Below is shown a graphical representation of this index.

Image 5 Life Expectancy Representation 1.1.4 Political Government

The country Political Governments can be roughly classified into the following types (CBBC Types of Government, 2012):

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36  Dictatorship; Rule by a single leader who has not been elected and may use force to keep control. In a military dictatorship, the army is in control. Usually, there is little or no attention to public opinion or individual rights.  Totalitarian; Rule by a single political party. People are forced to do what

the government tells them and may also be prevented from leaving the country.

 Theocracy; A form of government where the rulers claim to be ruling on behalf of a set of religious ideas, or as direct agents of a deity.

 Monarchy; A monarchy has a king or queen, who sometimes has absolute power. Power is passed along through the family.

 Parliamentary; A parliamentary system is led by representatives of the people. Each is chosen as a member of a political party and remains in power as long as his/her party does.

 Republic; A republic is led by representatives of the voters. Each is individually chosen for a set period of time.

 Anarchy; Anarchy is a situation where there is no government. This can happen after a civil war in a country, when a government has been destroyed and rival groups are fighting to take its place.

1.1.5 Level of Transparency

Transparency promotes accountability and provides information for citizens about what their Government is doing so it‘s promoted as one of the most important medicines against corruption (Catharina Lindstedt & Daniel Naurin, 2004). Transparency is not a new concept. Its modern roots can be traced back to efforts by democratic societies to bring openness to government dealings. Many jurisdictions have sunshine laws, open meeting requirements or open information acts. In the USA, the Freedom of Information Act dates back to the mid 1960‘s or other legislative initiatives, including the 1946 Administrative Procedure Act and the 1989 Whistleblower Protection Act, intended to achieve greater governmental transparency. Today, most national governments, states, provinces,

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37 municipalities and other government jurisdictions have committed to increased transparency. Some have been compelled to do so by overriding legislation from a court or higher authority. But most recognize that a transparent government is an essential element of a free and democratic society. Governmental transparency can be also defined as the ability to find out what is going on inside a public sector organization through avenues such as open meetings, access to records, the proactive posting of information on Web sites, whistle-blower protections, and even illegally leaked information. Without governmental transparency and freedom of information, it is much more difficult to hold elected and appointed officials accountable for their actions. The release of information promotes democratic accountability (Piotrowski & Van Ryzin, 2007).

1.1.6 National Tendency to Protect Local Economy

Government tendency to protect local economies refers to actions and policies that restrict or restrain international trade, often done with the intent of protecting local businesses and jobs from foreign competition. Typical methods of protectionism are import tariffs, quotas, subsidies or tax cuts to local businesses and direct state intervention (Investment dictionary. Academic. 2012). Hidden protectionism and industrial policy may boost specific industries or exports, but could cause underinvestment in human and physical capital. Brazil and India have been held back because their governments funnelled state resources to preferred sectors and constituencies instead of boosting their economies‘ underlying potential, slowing down their growth. In China, covert protectionism helped domestic manufacturers achieve formidable market share at home and abroad, but excessive lending by state-owned banks to state-owned enterprises and local government caused investment and property bubbles. The country‘s leaders say they want to reinvigorate the role of private enterprise, but whether that extends to foreign companies remains to be seen (Hodgson, 2003).

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1.2 PUBLIC PROCUREMENT SYSTEM

Until now, the attention concerning purchases made by Public Bodies was a little bit poor; Public Procurement is lagging behind the private sector in terms of research and accumulated knowledge. Only recently, however, the it has begun to capture the attention of scholars and researchers mainly because it has a significative impact on government and country performance. Public Procurement represents the purchase by governments and state-owned enterprises of goods, services and works. It is one of the largest government spending activities in any country, it accounts for a significant percentage of GDP and has a direct impact on the economy. A representative average for the industrialized countries is around 20 per cent of GDP (Government Finance Statistics, IMF). Public Procurement is alternatively defined as the purchase of commodities and contracting of construction works and services if such acquisition is effected with resources from state budgets, local authority budgets, state foundation funds, domestic loans or foreign loans guaranteed by the state, foreign aid as well as revenue received from the economic activity of state. Public procurement thus means procurement by a procuring entity using public funds (World Bank, 1995). Today governments all over the world have received a great deal of attention as providers of essential services, such as health, education, defense and infrastructure (Baily, 2000). In detail, countries‘ Public Procurement can be divided basing on the COFOG classification (2012):

 General Public Services  Defence

 Public Order and Safety  Economic Affairs

 Environment Protection

 Housing and Communities Amenities  Health

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39  Education

 Social Protection

Since 1990 all public administrations of both rich and poor countries have faced increasingly stringent budget constraints, a greater focus on efficiency, legality and transparency. The experts have had to deal with changes of technology and the introduction of new government and environmental restrictions. Successfull purchases and contracts are often an indicator of good management of the government. We can easily understand why Public Procurement is considered a critical task, in fact, considering that the majority of people buy items and services in their private lives, they can easily understand that the successes or failures of the government depends on the manner in which it purchases (Bartle & Korosec, 2003).

Currently, the Public Procurement is at the center of the financial debate for two reasons (Thai, 2010):

1. It is funded with public money and must answer to taxpayers on how the money has been spent to demonstrate transparency and efficiency;

2. the strategic role that the purchases could play in improving the entire public expenditure.

Over the years, public procurement has sometimes been used to accomplish a variety of policy objectives: to increase overall demand, stimulate economic activity and create employment; to protect domestic firms from foreign competition; to improve competitiveness among domestic firms by enticing ‗national champions‘ to perform R&D activities; to remedy regional disparities; and to create jobs for marginal sections of the labour force (Martin, 1996). Public procurement internationally is moving, in many countries, towards a policy role, and focusing less on transactional procurement. This is enabling an alignment of procurement policy with government policy, effectively engaging procurement as a lever of economic, technological or social reform.

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1.2.1 Pillars of the Public Procurement sysytem

From an academic perspective, the most diffused and accepted model for the describing the Public Procurement system is the one developed by Thai (2010), who revised his previous version (2001), describing the functioning of public procurement at two level: 1) public procurement system and 2) the government framework and environment within which the procurement system is operated. While the second part of the model tries to link procurement activities with the contingent factors that may affect them, it is worthwhile to explore more in detail the first level where, according to the author, the public procurement system can be managed effectively or ineffectively depending on four pillars:

1) procurement organization,

2) procurement laws and regulations,

3) procurement workforce, and

4) procurement process and procedures.

Procurement organization

When designing the Procurement organization, there are two potential choices of configuration: Unitary vs federal systems; centralized vs decentralized organization.

In some countries there is a central procurement office for the whole nation, such as Uganda, Kenya, etc. In other countries, public procurement organizational structure is very complicated. In the United States, at the federal level, although procurement regulations are applied to all federal agencies, the General Services Administration is a central procurement office for civilian agencies, except the Department of Defense that does have its dependent procurement office (Robinson, 2010).

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41 The placement of procurement authority, which has not to be confused with the location of procurement personnel (Kamann, 2004), represents a critical organizational choice. Centralization occurs when all of the rights, powers, duties, and authority relating to public procurement are vested in a central procurement officer; that central authority often delegates some of these powers to others, but the point remains that they stay with that central figure (Scott, 2002). Such delegations are normally carried out within a regulatory or policy framework by means of specific letters or memoranda to those receiving the delegated powers; they very precisely delineate the delegated contract approval authority in terms of capital amounts and commodities as well as whether or not the assigned authority may be further delegated. Decentralization occurs when procurement personnel from other functional areas can decide unilaterally on sources of supply or negotiate with suppliers directly (Dobler and Burt, 1996). It should be noted that procurements of low value goods and services by clients, procurement cards, blanket orders, or standing offers do not represent decentralization because the procurement system establishes those mechanisms and monitors their use (Thai, 2001). Indeed, the automation of these processes through tools such as electronic catalog ordering or applications using electronic data interchange really represents a kind of ―virtual centralization.‖ (Croom and Brandon-Jones, 2010). Procurement is able to achieve the benefits of enhanced control and better data for monitoring and planning through the provision of these kinds of end-user tools.

When functioning properly, procurement centralization yields the following benefits (Johnson et al, 2009):

 Minimizing duplication of procurements by central coordination;

 Avoiding haphazard procurement practices and maximizing efficiency because procurement officials with professional training and expertise are more efficient than less skilled user departments‘ managers or operational managers whose procurement responsibility is secondary;

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42  Saving operational managers‘ time so that they can focus on their core

responsibilities;

 Lowering overall transaction costs due to consolidation of orders;

 Achieving volume discounts through the consolidation of procurements;  Reducing shipping and handling charges through the consolidation of

shipments;

 Receiving better prices and better services offered by suppliers because their sales, shipping, and invoicing expenses are reduced;

 Resulting in more efficient inventory control because of agencywide knowledge of stock levels, material usage, lead times, and prices;

 Facilitating procurement control and accountability

On the other hand, some potential disadvantages of centralized purchasing stem from any suboptimal relationships that may develop between the central procurement office and the clients it serves. Disadvantages might include (Johnson et al, 2009):

 Lack of sensitivity to the unique priorities and operational realities of different user departments;

 Insufficient engagement of the central procurement office in the operational planning process;

 User departments‘ possibility of bypassing blanket agreements negotiated by purchasing because specific commodities are not included, thereby foregoing any advantage of consolidated procurements;