kingdom of saudi arabia provincial competitiveness...

KINGDOM OF SAUDI ARABIA

PROVINCIAL COMPETITIVENESS

STUDYAminur Rahman, Giacomo De Giorgi,

and Sara Abdullah Linjawi

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© 2018 The World Bank Group

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The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Directors or Executive Directors of the respective institutions of the World Bank Group or the governments they represent. The World Bank Group does not guarantee the accuracy of the data included in this work.

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TABLE OF

CONTENTS

IKINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

ACKNOWLEDGMENTS ..................................................................................................................................... V

ACRONYMS AND ABBREVIATIONS ............................................................................................................VII

EXECUTIVE SUMMARY ................................................................................................................................... IX

1. INTRODUCTION .............................................................................................................................................. 1

2. THE KINGDOM OF SAUDI ARABIA PCI: DESIGN AND METHODOLOGY .......................................... 3

2.1 The PCI: A Composite Index of 10 Subindices ................................................................................... 3

2.2 Selection of the PCI Subindices ........................................................................................................... 4

2.3 PCI Research Strategy .......................................................................................................................... 5

3. PROFILE OF THE PCI SAMPLE FIRMS .......................................................................................................7

3.1 Distribution by Size ..................................................................................................................................7

3.2 Distribution by Sector ............................................................................................................................7

3.3 Distribution by Age .............................................................................................................................. 10

3.4 Distribution by Legal Status .............................................................................................................. 10

3.5 Distribution by Nationality of Employees ....................................................................................... 10

3.6 Distribution by Gender of Employees ...............................................................................................12

IIIITABLE OF CONTENTS

3.7 Distribution by Market Orientation ...................................................................................................12

3.8 Distribution by Investment Plans ......................................................................................................12

3.9 Summary of Descriptive Statistics ...................................................................................................14

4. OVERALL PCI RANKINGS ...........................................................................................................................15

4.1 Overall PCI Rankings .............................................................................................................................15

4.2 Consistency of the PCI Rankings ...................................................................................................... 16

4.3 Robustness of the PCI Rankings ........................................................................................................17

5. PCI RANKINGS BY SUBINDEX ..................................................................................................................21

5.1 Entry Costs ..............................................................................................................................................21

5.2 Land Access and Security of Tenure ................................................................................................24

5.3 Transparency and Participation ....................................................................................................... 27

5.4 Time Costs of Regulatory Compliance ............................................................................................30

5.5 Informal Charges ..................................................................................................................................32

5.6 Legal Institutions and Dispute Resolution .....................................................................................33

5.7 Proactivity of Provincial Government and Attitude toward the Private Sector .................... 37

5.8 Labor Training and Skills ....................................................................................................................39

5.9 Infrastructure and Business Services ..............................................................................................41

5.10 Predictability and Risks ....................................................................................................................43

5.11 Summary ...............................................................................................................................................44

6. CONCLUDING REMARKS AND POLICY RECOMMENDATIONS .......................................................45

6.1 Local-Level Recommendations ..........................................................................................................45

6.2 National-Level Recommendations ...................................................................................................48

APPENDICES .....................................................................................................................................................49

Appendix 1.A: Detailed PCI Measurement Methodology ....................................................................49

Appendix 1.B: Sampling for the PCI Survey ...........................................................................................55

Appendix 1.C: Strata Breakdown .............................................................................................................58

Appendix 1.D: Definitions of Eligible Firm Size and Sectors ...............................................................60

Appendix 2: List of Subindices and Component Indicators ...............................................................60

Appendix 3: Consistency of PCI Rankings across Subindices ...........................................................65

Appendix 4: Unweighted PCI Scores .......................................................................................................66

Appendix 5: Weighted PCI Scores ........................................................................................................... 67

REFERENCES ....................................................................................................................................................69

IIIKINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

BOXES

Box 2.1: The 10 PCI Subindices ..................................................................................................................5

Box 4.1: Consistent High Performers .....................................................................................................16

Box 4.2: Consistent Low Performers ..................................................................................................... 17

Box 5.1: Experiences and Opinions on the Importance of Personal Connections ...................... 33

FIGURES

Figure 3.1: Distribution of Firms by Number of Employees ....................................................................7

Figure 3.2: Distribution of Firms by Sector Type ..............................................................................................8

Figure 3.3: Distribution of Firms by Sector ........................................................................................................8

Figure 3.4: Distribution of Firms by Age ..............................................................................................................9

Figure 3.5: Median Age by Province ......................................................................................................................9

Figure 3.6: Distribution of Firms by Legal Status ..........................................................................................10

Figure 3.7: Distribution of Firms by Nationality of Employees ...................................................................10

Figure 3.8: Percentage of Saudi Employees by Province ..............................................................................11

Figure 3.9: Distribution of Firms by Gender of Employees ...........................................................................11

Figure 3.10: Percentage of Female Employees by Province ..........................................................................11

Figure 3.11: Distribution of Firms by Market Orientation ............................................................................. 12

Figure 3.12: Distribution of Firms by Investment Plans ................................................................................ 12

Figure 3.13: Investment Plans by Province ....................................................................................................... 13

Figure 4.1: PCI Rankings ...........................................................................................................................16

Figure 4.2: Robustness of PCI Rankings .................................................................................................20

Figure 5.1: Entry Costs Subindex .............................................................................................................. 24

Figure 5.2: Land Access and Security of Tenure Subindex ................................................................. 25

Figure 5.3: Transparency and Participation Subindex ................................................................................. 28

Figure 5.4: Time Costs of Regulatory Compliance Subindex ..................................................................... 30

Figure 5.5: Informal Charges Subindex ............................................................................................................ 33

Figure 5.6: Legal Institutions and Dispute Resolution Subindex .............................................................. 35

Figure 5.7: Proactivity and Attitude towards Private Sector Subindex ..................................................37

Figure 5.8: Labor Training and Skills Subindex .......................................................................................41

Figure 5.9: Local Infrastructure and Business Services Subindex ................................................... 43

Figure 5.10: Predictability and Risks Subindex ..................................................................................... 43

Figure 1. A 1: Firms’ Evaluation of Electricity vs. Number of Power Outages ................................. 52

Figure 1. A 2: Firms’ Evaluation of Roads vs. Road Coverage ............................................................. 52

IVIVTABLE OF CONTENTS

TABLES

Table 3.1 Breakdown of Firms by Sector Type ...................................................................................................8

Table 3.2. Profile of PCI Sample Firms ............................................................................................................... 14

Table 4.2: Robustness Framework A ........................................................................................................ 17

Table 4.3: Robustness Framework B ..................................................................................................................18

Table 5.1: Indicators and Dimensions Comprising the Entry Costs Subindex ....................................... 23

Table 5.2: Benchmarking of Land Access and Security of Tenure Indicators .......................................24

Table 5.3: Indicators and Dimensions Compromising the Land Access and Security of Tenure Subindex................................................................................................................................................... 26

Table 5.4: Benchmarking of Transparency Indicators ................................................................................. 28

Table 5.5: Indicators and Dimensions Comprising the Transparency and Participation Subindex ................................................................................................................................................................ 29

Table 5.6: Indicators and Dimensions Comprising the Time Costs of Regulatory Compliance Subindex ..........................................................................................................................................31

Table 5.7: Benchmarking of Informal Payments Indicators ....................................................................... 32

Table 5.8: Indicators and Dimensions Comprising the Informal Charges Subindex ........................... 34

Table 5.9: Benchmarking of Legal Institutions and Dispute Resolution Indicators ............................ 35

Table 5.10: Indicators and Dimensions Comprising the Legal Institutions and Dispute Resolution Sub-Index............................................................................................................................................................... 36

Table 5.11: Benchmarking of Proactivity and Attitude toward the Private Sector Indicators ........ 36

Table 5.12: Indicators and Dimensions Comprising the Proactivity and Attitude toward the Private Sector Subindex .................................................................................................................................... 38

Table 5.13: Indicators and Dimensions Comprising the Labor Training and Skills Subindex ............ 40

Table 5.14: Indicators and Dimensions Comprising the Local Infrastructure and Business Services Subindex ................................................................................................................................................42

Table 6.1: Provinces’ Strengths and Weaknesses ......................................................................................... 46

Table 6.2: PCI Areas of Weakness and General Recommendations ........................................................ 48

Table 1.A.1: Selection of Firms for CATI Interviews Using a Sampling Interval ..................................... 50

Table 1.A.2: Component Loadings and Intermediate Composites ........................................................... 54

Table 1.A.2: Subindex Weights for the Final PCI Index.................................................................................. 55

Table 1.B.1: Disproportionate Sampling Strategy ...........................................................................................57

ACKNOWLEDGMENTS

VKINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

V

This report was produced for the Al-Madinah Al-Munawarah Governorate and in consultation with a number of public and private sector stakeholders in the Kingdom of Saudi Arabia.

The report was prepared by a World Bank Group team led by Aminur Rahman, Lead Economist, and comprised of Giacomo De Giorgi (Professor of Economics, University of Geneva and Consultant) and Ms. Sara Abdullah Linjawi (Consultant and Project Coordinator).The report was developed under the overall supervision of Nadir Mohammed, Country Director, Najy Benhassine, Director, and Andrei Mikhnev, Head. Joshua Seth Wimpey (Private Sector Development Specialist) provided valuable guidance for survey implementation. Paul Moreno-Lopez (Program Leader), Syed A. Mahmood (Lead Private Sector Specialist), and Joyce A. Ibrahim (Private Sector Development Specialist) provided valuable peer review comments. The team gratefully acknowledge the support they received from the Al Madinah Al-Munawarah Governorate, particularly from the Governorate’s point

person, Mohammed Alyousif, at various stages of this study. Any questions concerning the report should be directed to Aminur Rahman ([email protected]). Also, we thank Susan Boulanger for editing this publication and Aichin Lim Jones and Amy Quach for overall design and production. This report is based on firm survey data collected between January and April 2017 and on administrative data collected from publicly available sources. The fieldwork was completed by The Nielsen Company, Saudi Arabia.This report is a product of the staff of the World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Executive Directors, or the governments they represent.

VIIKINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

ACRONYMS AND

ABBREVIATIONS

CATI Computer-Assisted Telephonic Interview

CoC Chamber of Commerce

IDI In-Depth Interview

ISIC International Standard Industrial Classification

MSME Micro-, Small-, and Medium-sized Enterprise

OECD Organisation for Economic Co-operation and Development

OLS Ordinary Least Squares

PCA Principal Component Analysis

PCI Provincial Competitiveness Index

PPS Probability Proportional to Size

PSU Primary Sampling Unit

WBG World Bank Group

IXKINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

EXECUTIVE SUMMARY

Prepared under the patronage of the Al-Madinah Al-Munawarah Governorate, this report presents the findings of the Kingdom of Saudi Arabia’s first Provincial Competitiveness Study, which employed the Provincial Competitiveness Index (PCI), a newly developed policy tool for promoting competitiveness at the subnational level. Following a verifiable, perception-centered methodology used to construct similar indices in other countries, the PCI fulfills its aims through benchmarking and comparing competitiveness levels across the Kingdom’s thirteen provinces.

Combining hard and soft data, the PCI uses a composite index of 10 subindices to measure competitiveness across provinces, ranking each on a 100-point scale. The subindices, representing specific aspects of local business environments that can be influenced by province-level implementation of national policies, are measured on a scale from 1 to 10, with 1 being lowest and 10 highest. To convey the most policy-relevant information, subindex weights are statistically determined and applied in calculating final PCI scores, which are then tested for robustness using two alternative indexing methodologies. A sophisticated, multifaceted research strategy was employed to collect the hard and soft data used to construct the index. The steps were as follows: (i) design and pilot the PCI survey instrument; (ii) develop a sampling frame of the provincial population of firms using chamber of commerce listings; (iii) draw a statistically representative sample of firms in each

province using a multistage sampling procedure; and (iv) collect hard data from administrative sources. The PCI sample consisted of over 4,100 micro-, small-, and medium-sized enterprises (MSMEs). The sample was dominated by small and medium-sized enterprises mainly operating in wholesale and retail trade activities. The large majority of firms fit the profile of a relatively well-established, sole proprietorship catering to the local market within its province. These firms principally employed male expatriate workers rather than Saudi workers; a near negligible proportion of firms employed women. Of the sampled firms, only 15 percent affirmed plans to make new investments in their businesses over the next two years, indicating these firms’ overwhelming adoption of a “wait-and-see” attitude in response to the Kingdom’s current uncertain investment climate. PCI results for the 13 provinces indicate that Riyadh ranks highest in overall competitiveness, followed by

XXEXECUTIVE SUMMARY

Al Qassim, Makkah, and Hail. Northern Borders ranked lowest, preceded closely by Al Jouf and Assir. The PCI scores reflect large disparities in competitiveness between provinces. The capital province of Riyadh, for example, far outperforms the second-best-performing province of Al Qassim by more than 10 points. Similarly, the province of Al Jouf falls considerably behind the second-worst-performing province of Assir by more than 10 points. The findings suggest that local authorities in the more densely populated and commercial provinces are taking more pragmatic roles in promoting business environments conducive to investment than are authorities in the less densely and commercially populated provinces, a difference attributable to several possible circumstances, including lack of necessary skills, knowledge, or tools. The 10 subindices included in the aggregate PCI are Entry Costs, Land Access and Security of Tenure, Transparency and Participation, Time Costs of Regulatory Compliance, Informal Charges, Legal Institutions and Dispute Resolution, Proactivity of Provincial Government and Attitude toward the Private Sector, Labor Training and Skills, Local Infrastructure and Business Services, and Predictability and Risks. The first five subindices represent economic governance1 challenges faced by the private sector at the subnational level, as found in similar PCI exercises conducted in other countries. The remaining five subindices represent further areas of economic governance during stakeholder consultations with Saudi Arabia’s private sector revealed to be particularly significant. Across the 10 subindices, the rankings demonstrate a significant level of variation in province performance across the different competitiveness dimensions, even though all provinces are governed under the same national rules and regulations. Overall, the consistent best performers appear to be Riyadh, Al Madinah, and Al Qassim, while

the consistent worst performers are Northern Borders, Al Jouf, and Najran. Most provinces score well on the Entry Costs and Local Infrastructure and Business Services subindices, while most provinces score poorly on the Land Access and Security of Tenure, Transparency and Participation, and Labor Training and Skills subindices. This reveals key areas of weakness in the Saudi business environment requiring regulatory interventions at the national level and effective implementation at the subnational level to promote environments that enable private sector development at the local level. The PCI thus provides a powerful tool to aid Saudi Arabia’s policy makers in promoting competitiveness and diagnosing the key constraints to private sector development and growth at the subnational level. In particular, local officials, private sector representatives, and other stakeholders are encouraged to use the PCI results as a guide in identifying the relative strengths and weaknesses of their respective provinces. Moreover, policy makers are encouraged to view the PCI as a practical tool for mapping actionable policy agendas that, combined with consultation and partnership with the private sector, can improve performance and foster economic development. By conducting the PCI exercise at regular intervals, such as every two years, provincial planners can determine whether their provinces have been advancing in terms of absolute improvement (that is, over time regarding the various indicators) as well as vis-à-vis other provinces. At the national level, the government can use the PCI to recognize and reward top-performing provinces, helping to motivate other provinces to catch up. In this way, the PCI can act as a catalyst for instigating reform momentum at the local level and for fostering province-to-province learning and adoption of best practices.

1. The terms “economic governance” and “provincial competitiveness” are used interchangeably in this report. Both terms imply the interplay of rules and regulations and of the opportunities and risks created by local-level implementation of rules and regulations (which may have been formulated at the national level) that govern the creation and operation of businesses in a given location and that influence economic performance and outcomes.

1KINGDOM OF SAUDI ARABIA PROVINCIAL COMPETITIVENESS STUDY

1. INTRODUCTION

Economies around the world pursue private sector–led economic development agendas to promote growth, boost productivity, create jobs, and ensure shared prosperity. The recent experience of high-growth economies demonstrates that enhanced private sector competitiveness has the potential to spur unprecedented productivity improvements, efficiency gains, and high-value-added jobs. In recognition of this, in “Saudi Vision 2030” the Kingdom of Saudi Arabia specified private sector and MSME sector development as a vital strategic objective for transforming the Saudi economy, achieving long-term sustainable growth, and fostering shared prosperity.

The private sector is an engine for local economic development, but the environment in which it operates is shaped by government policies. Government actions can promote a thriving business environment by pursuing business-friendly policies, removing cumbersome and unnecessary regulations, creating a business-supportive infrastructure, and providing adequate, timely, quality public services. Conversely, government actions creating institutional barriers and constraints limit private sector growth and job generation. In Saudi Arabia, as in most countries, the decisions and actions taken at the subnational level are of particular importance in shaping the business climate and achieving high levels of local competitiveness. Essentially, businesses conduct their operations and regularly interact with various public authorities within their local environments. Although laws, rules, and regulations are established in a highly centralized

manner, they affect local business environments through their implementation at the local level, which often varies significantly between provinces. Understanding the interactions between private sector businesses and government authorities at the provincial level is thus crucial to understanding the potential for growth, as is understanding the extent to which local administration of laws, rules, and regulations varies in quality across the Kingdom’s provinces. In light of these considerations, under the patronage of the Al-Madinah Al-Munawarah Governorate, the World Bank Group (WBG) has developed the Kingdom’s First Provincial Competitiveness Index (PCI). The PCI provides a powerful tool for promoting competitiveness and identifying key constraints inhibiting private sector development and growth at the subnational level. Following a verifiable methodology, largely

221. INTRODUCTION

based on business owners’ perceptions regarding their local business environments, the PCI benchmarks and ranks all 13 provinces in the Kingdom according to the quality of their private sector enabling environments as measured by 10 key areas of competitiveness. This assessment can play a critical role in initiating reform momentum at the local level by fostering peer-to-peer learning across provinces, which in turn can facilitate adoption of best practices and subsequent improvements in lagging regions. Policy makers at the national and provincial levels can utilize the PCI findings to pursue reforms focused on specific provinces’ areas of underperformance, determining which processes require

reform at the national level and which require more effective action by the provincial government. This report presents the findings of this first PCI for the Kingdom of Saudi Arabia with the goal of identifying and facilitating reform initiatives. It begins by discussing the research design and measurement methodology used to construct the PCI. It then outlines the key characteristics of the more than 4,100 firms interviewed for the PCI. Next, it presents the province rankings by overall PCI score and by subindex scores. Finally, the report concludes with a discussion on how the findings of this study could be utilized for policy reforms.


2. THE KINGDOM OF SAUDI ARABIA PCI: DESIGN AND METHODOLOGY

2.1 The PCI: A Composite Index of 10 SubindicesThe Provincial Competitiveness Index, an initiative in line with previous WBG and Asia Foundation efforts, measures the level of competitiveness at the provincial level.2 “Level of competitiveness” refers to the quality of the regulatory and institutional environment and the nature and practice of economic governance affecting private sector formation and growth. A composite index with 10 subindices reflecting key aspects of local business environments subject to the influence of provincial authorities, the PCI assesses the competitiveness of all 13 provinces and ranks them on a 100-point scale.

The PCI scores derive from a combination of soft data, generated from a comprehensive survey of firm owners’ perceptions regarding their local business environment, and hard data, collected from government sources and published materials that assemble a collection of indicators. These indicators were compiled into 10 broad subindices using a 10-point scale. Added together, the 10 subindices provide a 100-point unweighted overall competitiveness score.

2. The Kingdom of Saudi Arabia PCI draws from similar indices conducted by the WBG and Asia Foundation in various countries, including Bangladesh, Cambodia, Sri Lanka, and Vietnam.

3. PCA is a statistical technique widely used to condense and summarize information contained in a large number of original variables into a smaller set of new composite dimensions with a minimum loss of information. For this report, this procedure enables construction of a few key dimensions of competitiveness on which to focus, even though each dimension is based on a wide range of competitiveness-related variables. In this way, the aggregate PCI measure and its submeasures remain relatively few and manageable for both policy making and tracking purposes, and due to the underlying construction process each measure is at the same time comprehensive.

To acknowledge the greater importance of some subindices relative to others and to ensure that the final scores convey the information most relevant for the provinces’ policy makers, weights were statistically determined for each subindex using a widely employed method based on principal component analysis (PCA).3 Using these weights, the overall score was recalculated to obtain the final weighted index. The weighted index and subindices were used to analyze and rank the provinces’ competitiveness.

442. THE KINGDOM OF SAUDI ARABIA PCI: DESIGN AND METHODOLOGY

To check the robustness of the overall rankings obtained using this indexing methodology, two further indexing approaches were adopted. The first involves regressing a binary variable, “Plans to make new investments,” from the PCI survey on the standardized values of the indicators to compute their provincial scores. The second method used PCA on standardized indicator values for each subindex to determine the provincial scores. Once the scores were calculated under each approach, they were summed together by subindex and normalized through exponentiation. The weighted sum of the normalized subindex scores leads to the final PCI rankings and is then compared to those built using the main indexing methodology. An important feature of the PCI is that province rankings are based on performance vis-à-vis other provinces, rather than on some external, idealized measure. As such, the PCI serves as a useful tool for local government officials, the private sector, and other stakeholders to compare a province’s performance with those of the other provinces and to identify the strengths and weaknesses of their business environments. It is worth noting, however, that this feature of the PCI means that even a province highly ranked in one subindex will almost certainly have room for improvement in another subindex. Another important feature of the PCI is its ability to compare levels of competitiveness between provinces, regardless of underlying conditions and factors such as natural and human capital endowments and proximity to markets. In other words, the PCI scores clearly reflect the differences in practice between provinces that determine the effectiveness of their enabling environments. More detailed explanations of the measurement methodology used to construct the PCI is provided in Appendices 1.A to 1.D of this report.

2.2 Selection of the PCI SubindicesMeasuring the different dimensions of competitiveness in a given location is at the core of the provincial competitiveness exercise. Selecting the most relevant subindices and indicators for measuring provincial competitiveness is thus vital. To achieve this, private and public sector stakeholders were consulted and asked to assess the relative importance of a list of proposed subindices and component indicators developed by examining the theoretical and contextual evidence on the key issues affecting the Saudi business environment.

The final list of 10 subindices forming the PCI appears in Box 2.1. The first five represent the subnational economic governance challenges faced by the private sector as found in similar PCI exercises conducted in other countries. The remaining five subindices represent further areas of economic governance revealed by stakeholder consultations to be particularly important to Saudi Arabia’s private sector. Appendix 2 provides a complete list of the subindices and the component indicators under each.

2.3 PCI Research StrategyThe PCI is primarily based on firm-level survey data, collected between January and April 2017, across all 13 provinces in the Kingdom. To ensure that the PCI survey accurately represents the population of MSMEs in each province, a multistage research strategy was followed, as described below.

2.3.1 PCI Survey InstrumentA survey instrument was designed to capture information on firm owners’ perceptions of their local business environment along the 10 key areas encompassed in the PCI subindices. Two key measures were taken to ensure the effectiveness of final survey questionnaire. First, qualitative in-depth interviews (IDIs) were administered prior to developing the questionnaire to better understand how business owners and managers evaluated the subindex topics. Second, a two-phase pilot survey tested the survey questionnaire to identify potential survey design flaws.

2.3.2 Sampling Frame Development A representative sampling frame of the provincial populations of micro-, small-, and medium-sized enterprises was developed using chamber of commerce (CoC) data for registered businesses in each province. Based on this data, computer-assisted telephonic interviews (CATI) were conducted to obtain information on a sample of 8,500 firms. This information was then used to estimate the population of eligible live firms in each province.

2.3.3 PCI Survey Sampling A multistage sampling procedure was used to draw a statistically representative sample of businesses for each of the 13 provinces using the probability proportional to size (PPS) method. Under this sampling procedure,


implicit stratification was used to select firms in three categories: province, sector, and size. The original CoC data overrepresented firms in some provinces and underrepresented firms in other provinces; therefore, a disproportionate sampling strategy was employed at the province level to ensure a minimum representation of firms based in smaller, less populated provinces. Once the survey data was collected, sampling weights were used to reweight the raw survey data back to its provincial sampling breakdowns.

Box 2.1: The 10 PCI Subindices1. Entry Costs: A measure of the time required to register and receive all relevant licenses and permits to start a

business, as well as the official and total costs to obtain business location licenses.

2. Land Access and Security of Tenure: A measure of the formal rights to business premises and the security of ten-ure once a business premise is properly acquired.

3. Transparency and Participation: A measure of the ease of obtaining the proper policy, planning, and legal docu-ments necessary to run a business, as well as the extent of private sector involvement in the decision making process relating to conduct of the business.

4. Time Costs of Regulatory Compliance: A measure of the amount of time firms spend on bureaucratic compliance and waiting periods and how often firms must undergo inspection by local agencies and the duration of those inspections.

5. Informal Charges: A measure of how much firms pay in informal charges for firm-level operations and to obtain public procurement contracts and whether paying those extra fees is predictable and leads to the expected re-sults or services.

6. Legal Institutions and Dispute Resolution: A measure of the confidence firms have in the fairness and equity of the legal system and whether they can appeal to a competent higher-level authority to resolve disputes over practices or misconduct of local government officials.

7. Proactivity of Provincial Government and Attitude toward the Private Sector: A measure of the province’s cre-ativity in implementing a central policy and designing initiatives for private sector development, in addition to a firm’s perceptions regarding the provincial government’s general attitude toward the private sector.

8. Labor Training and Skills: A measure of the availability of the skills required to conduct particular businesses.

9. Local Infrastructure and Business Services: A measure of the quantity and quality of local infrastructure and business services.

10. Predictability and Risks: A measure of the predictability of the local business environment and of the key risk factors experienced by businesses.

2.3.4 Hard Data Alongside the survey data collection, hard data was assembled from administrative archives and several published sources and materials. The data included information on business creation, registration, infrastructures, and labor training.


3. PROFILE OF THE PCI SAMPLE FIRMS

The PCI survey instrument provided the key information needed to identify the 4,160 MSMEs that ultimately formed the PCI sample. This section describes those MSMEs in terms of size, sector, age, legal status, employee nationality, employee gender, market orientation, and investment plans.

3.1 Distribution by SizeMicro and small businesses dominated the PCI sample. As Figure 3.1 illustrates, almost 43 percent of firms interviewed had five or fewer employees and 50 percent of the firms had fewer than 50. In contrast, less than 8 percent of firms are classified as medium-sized enterprises having 50 or more employees, the definition established by the recently formed Small and Medium Enterprise General Authority.

3.2 Distribution by Sector As shown in Table 3.1, approximately 46 percent of firms included in the PCI sample were concentrated in wholesale and retail trade activities. The remaining 54 percent were distributed between manufacturing and construction activities and service activities.

7.764%

42.5%49.74%

Medium SmallMicro

Figure 3.1: Distribution of Firms by Number of Employees


Table 3.1: Breakdown of Firms by Sector Type

Sector Type No. of Firms % of Firms

Manufacturing & Construction

1,022 24.6

Services 1,229 29.5Trade 1,909 45.5

Although firms in the trading sector were predominant in the total of interviewed firms, as Figure 3.2 illustrates, breakdown by sector varied considerably between provinces. In the province of Najran, for example, 32 percent of firms operated in trade, 28 percent in services, and 40 percent in manufacturing, whereas in the province of Assir, 53 percent of firms operated in trade, 27 percent in services, and only 20 percent in manufacturing. The degree of variation is further illustrated in Figure 3.3, which presents the more detailed sector breakdown used in the sample’s stratification design.

0 20 40 60 80 100

HailNorthern Borders

Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir

Eastern ProvinceRiyadh

Makkah

PercentManufacturing & Construction TradeServices

0 20 40 60 80 100Percent


Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir


Makkah

Manufacturing & ConstructionServicesTradeInformation & CommunicationManufacturing

Professional, Scientific & TechnologyTransportation & StorageWholesale & Retail Trade

Figure 3.2: Distribution of Firms by Sector Type

Figure 3.3: Distribution of Firms by Sector


3.3 Distribution by Age MSMEs in the sample are relatively well-established firms, with the 47 percent ranging between 6 and 10 years of age, as shown in Figure 3.4. Just over 10 percent of firms were under 3 years of age, and only 11 percent of firms were established more than 20 years ago. Figure 3.5 illustrates the median age of firms by province. In 7 of the 13 provinces, interviewed firms had a median age of 7 years.This finding has important implications for firm competitiveness. On the one hand, the low proportion of new firms may indicate a lack of business dynamism

Figure 3.4: Distribution of Firms by Age

Figure 3.5: Median Age by Province

and innovation and the need to improve the business environment. On the other hand, low enterprise turnover may reflect a more developed business environment in which firms have matured beyond the survival level and have a higher capacity for investment and productivity. Taking simple rank correlation between the final PCI score and the median age yields a value of 0.66. This suggests a strong positive association between median age and final PCI score. In other words, provinces where firms were of higher median age tended to exhibit more competitive business environments, a finding consistent with the second explanation.

0 20 40 60 80 100Percent


Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir


Makkah

Less than 3 3 to 5 6 to 10 11 to 20 More than 20

5 10 15Median Age

Al JoufAssir

NajranJizan

TabukAl Baha

Al MadinahNorthern Borders

MakkahAl Qassim

RiyadhEastern Province

Hail

0


3.4 Distribution by Legal Status Sole proprietorship dominated the legal status of surveyed firms, with less than 10 percent of firms operating under a different legal form. Limited liability company was the second most reported legal form, and partnership was the third most reported. Figure 3.6 illustrates this distribution.

90.6%

6.779%1.178% 1.32%

Limited Liability CompanyLimited Liability CompanyOther

Sole ProprietorshipJoint Stock CompanyAffiliate of a Foreign Company

Figure 3.6: Distribution of Firms by Legal Status

Figure 3.7: Distribution of Firms by Nationality of Employees

0 20 40 60 80 100


Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir


Makkah

Mean Percentage of Saudi Employees Mean Pecentage of Expatriate Employees

3.5 Distribution by Nationality of Employees Figure 3.7 shows the distribution of firms by employee nationality for each province as measured by the mean percentage of Saudi and foreign labor. In all 13 provinces, expatriate workers accounted for the bulk of the workforce, with Saudi workers constituting the minority. Figure 3.8 showcases the variation in mean percentage of Saudi workers between provinces. For example, sampled firms in Al Madinah, the province with the highest mean percentage, reported 33 percent Saudi employees, while sampled firms in Eastern Province, with the lowest mean percentage, reported only 19 percent Saudi workers.

3.6 Distribution by Gender of EmployeesAs Figure 3.9 strikingly highlights, female employees accounted for less 1 percent of total employees on average in the entire PCI sample. Figure 3.10 shows the proportion of female employees hired by province. Across all 13 provinces, the mean percentage of female employees was less than 2 percent; female employees were nonexistent in the surveyed firms in three provinces.


Figure 3.8: Percentage of Saudi Employees by Province

Figure 3.9: Distribution of Firms by Gender of Employees

99.18%

0.82%

Mean Percentage of Male EmployeesMean Percentage of Female Employees

0 10 20 30 40Mean Percentage of Saudi Employees

Eastern ProvinceTabukAssir

Al JoufNorthern Borders

Al BahaAl Qassim

RiyadhMakkah

HailNajran

JizanAl Madinah

Figure 3.10: Percentage of Female Employees by Province

0 .5 1 1.5 2

Mean Percentage of Female Employees

NajranJizan

Northern BordersAl Baha

Al QassimAl Madinah

Al JoufAssir

Eastern ProvinceTabuk

MakkahHail

Riyadh


12

15.1%

72.19%

12.72%

Don’t Know NoYes

Figure 3.11: Distribution of Firms by Market Orientation

0 20 40 60 80 100


Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir


Makkah

Mean % of Sales Sold within ProvinceMean % of Sales Sold in Other Provinces

Mean % of Sales Exported DirectlyMean % of Sales Exported Indirectly

3.7 Distribution by Market Orientation As expected for MSMEs, sales of surveyed firms were chiefly oriented toward the domestic Saudi market, as

Figure 3.12: Distribution of Firms by Investment Plans

3.8 Distribution by Investment Plans As Figure 3.12 shows, when asked about their investments plans, almost 72 percent of sampled firms stated that they did not intend to make any new investments in new land, buildings, machinery, or equipment for their business in the next two years; 15 percent stated that they did plan to make new investments; and 13 percent were undecided. Figure 3.13 illustrates the marked differences across provinces in terms of investment plans. In Jizan, for example, 31 percent of firms surveyed expressed an intention to invest, whereas less than 2 percent of firms in Northern Borders expressed this intention. These firms’ reluctance to make future investments is not surprising, given the current low level of business confidence following a period of low oil prices and reduced government expenditures. These circumstances, combined with the reform momentum recently developing in the Kingdom, may explain these firms’ wait-and-see attitude toward investment.

shown in Figure 3.11. More than 95 percent of sales occurred within the same province and direct or indirect export accounted for less than half a percentage point of sales.


0 20 40 60 80 100


Al JoufAl Qassim

Al MadinahAl Baha

TabukJizan

NajranAssir


Makkah

PercentYes Don’t KnowNo

Figure 3.13: Investment Plans by Province


Table 3.2. Profile of PCI Sample FirmsOriginal PCI Sample Reweighted Sample

Observations 4,160 4,160Size of Firm (No. of Employees)Micro (1–5) 42.5% 41.3%Small (6–49) 49.7% 51.1%Medium (50–99) 7.8% 7.6%Sector Type Manufacturing & Construction 24.6% 23.8%Services 29.5% 28.3%Trade 45.9% 47.9%Age Less than 3 10.1% 9%3 to 5 10.7% 11.9%6 to 10 46.7% 45.9%11 to 20 21.5% 21.8%Legal Status Sole Proprietorship 90.6% 90.6%Limited Liability Company 6.8% 7.1%Joint Stock Company 1.2% 1%Partnership 1.3% 1.1%Affiliate of a Foreign Company 0% 0%Other 0.1% 0.1%Nationality of Employees Saudi Employees (Mean %) 24.7% 24.4%Expatriate Employees (Mean %) 75.3% 75.6%Gender of Employees Male Employees (Mean %) 99.2% 99%Female Employees (Mean %) 0.8% 1%Market Orientation Sold Domestically within Same Province 95.6% 95.8%Sold Domestically in Other Provinces 4.2% 4%Exported Directly 0.2% 0.2%Exported Indirectly 0% 0%New Investment Plans Yes 15.1% 14%No 72.2% 74.8%Don’t Know 12.7% 11.2%

3.9 Summary of Descriptive Statistics Table 3.2 summarizes the statistics describing the surveyed firms. The second column provides the original

sample in which firms were oversampled in some provinces to ensure enough units were available to form valid inferences. The third column shows the reweighted, province-level sample used to construct the PCI.


4. OVERALL PCI RANKINGS

4.1 Overall PCI RankingsResults from Saudi Arabia’s first Provincial Competitiveness Study appear in Table 4.1, showing the ranking of each of the 13 provinces according to its final PCI score. This score is the sum of the 10 weighted subindices chosen to reflect the different dimensions of competitiveness. Because the subindex scores are normalized on a 10-point scale and the subindex weightings sum to 100, the PCI rankings are given on a 100-point scale. To achieve a perfect competitiveness score, a province must obtain the highest score in each subindex. Appendices 4 and 5 present the detailed weighted and unweighted PCI scores.

As indicated in Table 4.1, overall PCI scores vary significantly between the highest-ranking province, Riyadh, with a PCI score of approximately 76, to the lowest, Northern Borders, with a PCI score of approximately 41. Following Riyadh, the next highest-ranking provinces are Al Qassim, Makkah, and Hail, while the low-ranking provinces preceding Northern Borders are Al Jouf and Assir. These rankings are largely consistent with a priori expectations, as provincial authorities tend to be more progressive in the capital city and in larger, more densely populated provinces. Eastern Province presents a surprising case, however; despite being among the Kingdom’s three major population and commerce centers, it scores relatively poorly in business environment competitiveness.

An important feature of the PCI is that its rankings derive from comparison of the 13 provinces’ business environment performance relative to each other rather than to some external, idealized measure. It thus provides a means not only for interpreting rankings by final PCI scores, but also for constructing tiers of provinces with similar overall business environment conditions. Table 4.1 groups the provinces into four tiers on a 10-point scale: high (70+), medium-high (70–60), medium-low (60–50), and low (50–40). Four provinces fall into the medium-high tier, for example: Overall PCI scores for Al Qassim, Makkah, Hail, and Al-Baha differ by less than 10 points. Approximately 77 percent of firms in the PCI fall into the medium-high or medium-low tier.


Figure 4.1 illustrates the breakdown by subindex of the final PCI score for each province. Section 5 presents a detailed discussion of the rankings by subindex to distinguish each province’s relative strengths and weaknesses.

4.2 Consistency of the PCI Rankings Investigations to determine the consistency of the PCI rankings are detailed in Appendix 3, which provides province rankings across each of the 10 subindices, from highest to lowest, further divided into quartiles. Appendix 3 reveals the consistently high- and low-performing provinces. The consistently high-performing provinces—Riyadh, Al Qassim, and Madinah—jointly accounted for almost 50 percent of positions in the top quartile, but only 5 percent in the bottom quartile. Conversely, the consistently low-performing provinces—Northern Borders, Al Jouf, and Najran—together accounted for no positions in the top quartile, but 40 percent in the bottom quartile.

Table 4.1: PCI Rankings and TiersProvince PCI Score TierRiyadh 75.99 HighAI Qassim 65.68 Medium-High

Makkah 64.40 Medium-HighHail 64.32 Medium-HighAI Baha 60.73 Medium-HighTabuk 58.71 Medium-LowAI Madinah 56.83 Medium-LowJizan 56.36 Medium-LowEastern Province 53.74 Medium-LowNajran 52.32 Medium-LowAssir 51.83 Medium-Low

AI Jouf 41.53 LowNorthem Borders 41.26 Low

Box 4.1: Consistent High PerformersProvince Positions in Q1 Positions in Q2 Positions in Q3 Positions in Q4

Riyadh 8 1 1 0Al Madinah 3 3 2 2Al Qassim 3 4 3 0

0 20 40 60 80

Northern BordersAl Jouf

AssirNajran

Eastern ProvinceJizan

Al MadinahTabuk

Al BahaMakkah

HailAl Qassim

Riyadh

Entry Costs Land Access & Security of TenureTransparency & Participation Time Costs of Regulatory ComplianceInformal Payments Legal Institutions & Dispute ResolutionProactivity & Attitude toward Private Sector Labour Training & SkillsLocal Infrastructure & Business Services Predictability & Risks

Figure 4.1: PCI Rankings


Box 4.2: Consistent Low PerformersProvince Positions in Q1 Positions in Q2 Positions in Q3 Positions in Q4

Northern Borders 0 2 0 8Al Jouf 0 1 4 5Najran 0 3 4 3

4.3 Robustness of the PCI RankingsTo test for the sensitivity and robustness of the overall PCI rankings to changes in the indexing methodology and, in particular, to the weighting applied for hard data indicators, two statistically driven indexing frameworks were used, as described below.

4.3.1 Robustness Framework A The first framework involves regressing a binary variable “Plans to make new investments” from the PCI survey on standardized values of a subset of the indicators under each subindex with the least missing values, controlling for firm and province-level characteristics. The second column in Table 4.2 presents the positive beta coefficients obtained using ordinary least squares (OLS) regression.

To obtain the scores for each indicator, these positive beta coefficients were multiplied by the provincial mean for each respective standardized indicator. As an example, the third column in Table 4.2 lists the mean for Al Madinah Province for each of the standardized indicators, and the fourth column provides the computed scores. The indicator scores, once calculated, were summated by subindex to obtain subindex scores and normalized through exponentiation to scale between 0 and 1. That is, the exponential value of each subindex score was taken and divided by the maximum subindex score to obtain normalized values ranging between 0 and 1. The final PCI was then calculated as the weighted sum of the normalized subindex scores.

Table 4.2: Robustness Framework AStandardized Indicator Positive Beta

CoefficientMean of Standardized

Indicator (for AI Madinah)Indicator

ScoreTotal number of procedures, registrations, and licenses required to operate the business

0.050 -0.721 -0.036

Percentage of total senior management time spend dealing with government regulations per year

0.016 0.075 0.001

Number of inspections from municipality department 0.0306 -0.535 -0.016Number of inspections from Ministry of Labor 0.0246 -0.471 -0.012Average duration of inspections from municipality departments

0.053 -0.420 -0.022

Perceived risk of changes in rent conditions 0.000517 0.054 0.000Perceived ease of access to application for licenses and registrations

0.0129 -0.010 0.000

Frequency of government agencies soliciting comments on implementation of new laws and regulations

0.00104 -0.043 0.000

Confidence in the legal system 0.00371 0.144 0.001Perceived attitude of provincial government toward private sector

0.0148 -0.456 -0.007

Ease of hiring Saudi labor with skills required 0.000421 -0.413 0.000Ease of hiring foreign labor with skills required 0.016 -0.256 -0.004Assessment of electricity 0.00773 -0.316 -0.002Assessment of internet 0.060 -0.252 -0.015Assessment of water 0.032 -0.158 -0.005


Assessment of drainage 0.022 -0.129 -0.003Assessment of business services 0.0105 0.152 0.002Assessment of consultancy services 0.00619 -0.937 -0.006Perceived stability of regulatory environment 0.024 -0.405 -0.010Perceived difficulty of securing land 0.00319 -0.061 0.000Assessment of ease of working with government officials 0.00108 0.201 0.000Perceived degree of obstacle presented by labor regulations 0.000391 0.627 0.000Business density 0.041 0.479 0.020Number of industrial cities 0.334 0.920 0.307Number of committees per 10,000 registered firms 0.086 -0.613 -0.052Number of universities 0.299 0.315 0.094Paved roads per square kilometer 0.090 0.533 0.048Number of inspections from civil defense 0.0196 0.501 0.010Number of inspections from Ministry of Commerce 0.059 0.540 0.032Number of inspections from Customs 0.0130 0.407 0.005Duration of inspections from Civil Defense 0.00313 0.379 0.001Duration of inspections from Ministry of Labor 0.0322 0.419 0.013Duration of inspections from Ministry of Commerce 0.0164 0.412 0.007Duration of inspections from Customs 0.00240 0.432 0.001Days to obtain work visa 0.029 -0.431 -0.013Days experiencing a power outage 0.00287 0.048 0.000Perceived fairness of procedures for disputing changes in rent conditions

0.021 -0.339 -0.007

Perceived ease of access to information on required licenses and permits

0.00230 0.289 0.001

Perceived ease and clarity of information 0.014 -0.250 -0.004Confidence in knowledge of existing rules and regulations 0.00554 -0.193 -0.001Frequency of changes in local administration 0.017 -0.038 -0.001Frequency of experiencing misconduct during inspections 0.024 -0.069 -0.002Confidence in appealing for dispute resolution 0.023 -0.010 0.000Assessment of roads 0.0157 0.38 0.006Assessment of waste collection 0.036 0.11 0.004Perceived difficulty of completing all procedures 0.028 -0.22 -0.006Frequency of being informed in advance of changes in local administration

0.018 -0.01 0.000

Perceived degree of obstacle presented by visas 0.018 -0.48 -0.009

*Note: Standardized indicators with negative beta coefficients are multiplied by -1 to obtain positive beta coefficients.


4.3.2 Robustness Framework BThe second framework used to assess the robustness of the PCI rankings employed principal component analysis on standardized values of the indicators under each subindex separately. As a first step, this allowed division of the subindices into uncorrelated components or “baskets of variables,” as illustrated in Table 4.3 using the Land Access and Security of Tenure subindex as an example. Following standard procedure, the number of components was determined by those with associated Eigen values larger than 1. Under this condition, the first two components, which cumulatively explained 57 percent of the overall variance of the subindex, were selected. Next, the component loadings were obtained and rotated, and those with observed values larger than 0.5 (in absolute value) were multiplied by the provincial means of the standardized indicators to obtain the indicator scores. For Land Access and Security of Tenure, this includes perceived transparency and fairness of land zoning rules,

perceived fairness of the process for disputing changes in rent conditions from the PCI survey, and the number of industrial cities. The same procedure was applied to each subindex to obtain the indicator scores. Once these scores were calculated, they were aggregated by subindex and normalized using the same approach as for Framework A. Finally, the PCI was computed by taking the weighted sum of the normalized subindex scores.Figure 4.2 illustrates the final PCI rankings, using Framework A and B in the first two panels and the final rankings obtained using the original indexing framework (Framework C) in the third panel. As shown by the last two panels in the figure, province rankings overlap considerably between Robustness Framework B and the original indexing framework. The rank correlation coefficient between these two indices was found to be 0.63, further validating a strong positive association between the rank order of the highest- and lowest-performing provinces under each framework.

Table 4.3: Robustness Framework BEigen Values of Land Access and Security of Tenure Indicator

Component Eigen value Difference Proportion Cumulative

Comp. 1 1.84 0.81 0.37 0.37

Comp. 2 1.03 0.21 0.21 0.57Comp. 3 0.83 0.08 0.17 0.74Comp. 4 0.74 0.19 0.15 0.89

Comp. 5 0.56 0.11 1.00

Rotated Component Loadings of Land Access and Security of Tenure Indicator

Standard Indicators Comp. 1 Comp. 2

Perceived difficulty of accessing land -0.45 0.34

Perceived transparency and fairness of land zoning rules 0.53 0.19

Perceived risk of changes in rent conditions 0.43 -0.44Perceived fairness of process for disputing changes in rent conditions 0.54 0.15

Number of industrial cities 0.21 0.79


Figure 4.2: Robustness of PCI RankingsPCI Rankings: Indexing Framework A

PCI Rankings: Indexing Framework B

PCI Rankings: Indexing Framework C

0 .2 .4 .6 .8 1

RiyadhMakkah

Eastern ProvinceHail

Al QassimAl Baha

Al MadinahJizan

NajranNorthern Borders

Al JoufTabukAssir

0 .2 .4 .6

Al JoufNorthern Borders

TabukAl Madinah

MakkahNajran

AssirAl Baha


Al QassimRiyadh

Hail

0 20 40 60 80


AssirNajran


Al MadinahTabuk

Al BahaHail

MakkahAl Qassim

Riyadh


5. PCI RANKINGS BY

SUBINDEX

5.1 Entry CostsReducing the cost of business entry and streamlining time-consuming processes and regulations are crucial components of promoting competitiveness at the provincial level and of fostering environments conducive to investment. In recent years, Saudi Arabia has made significant improvements in the regulatory environment for business entry. A recent report by McKinsey and Company notes that the Kingdom’s formal process for starting a business has been simplified from 67 steps in 2006 to 21 steps in 2015 (McKinsey 2015). The commercial registration process has also been shortened, to 180 seconds, with the launch of a new online service by the Ministry of Commerce and Industry (Saudi Arabia, Ministry of Commerce and Industry 2014). At the same time, following elimination of minimum capital requirements in 2006, then the highest in the world at 1,057 percent of income per capita, official fees required to start business operations have declined from approximately 68 percent of income per capita in 2006 to 4 percent in 2017 (Al-Awwad 2007; World Bank 2006; World Bank 2017).

Notwithstanding these improvements, further reforms are needed as the Kingdom still lags behind other emerging countries in easing constraints on business formation. According to the 2017 Doing Business Report, Saudi Arabia currently ranks 147 of 190 economies on the ease of starting a business, which falls below the rankings of both comparable economies and the regional average. Its distance to frontier score is approximately 77, indicating that the Kingdom remains 23 percentage points from the frontier constructed from the best performance achieved across all economies in the Doing Business sample since 2005. This lagging performance can largely be attributed

to the many handovers involved in starting a business, through multiple ministries and municipal agencies; this number varies among provinces, but handover levels nevertheless make the process of starting a business both cumbersome and time-consuming.The Entry Costs subindex has thus been formulated and divided into two dimensions: time costs and monetary costs, which together reflect the true “opportunity cost” for firm entry in each province. The first dimension captures the time burden involved for a firm to register and obtain the key licenses and permits required to legally

22225. PCI RANKINGS BY SUBINDEX

operate a business. This dimension consists of nine indicators, summarized in Table 5.1. The first indicator, business density, is a hard data indicator that measures the number of new business location licenses per 1,000 of the working-age population. The next six indicators measure the waiting periods involved in completing a number of necessary procedures and licenses, along with the perceived level of difficulty of completing those procedures. For these indicators, to better capture the time involved in business entry procedures under the current regulatory environment, only new entrants (defined as firms less than three years of age) were invited to respond. For the final two indicators, all firms engaged in wholesale or retail trade and manufacturing, regardless of firm age, were eligible to respond regarding the number of licenses and permits required to legally operate their businesses. To obtain province scores for this dimension, indicators were first rescaled so that provinces with the shortest wait time received a score of 10, and provinces with the longest wait time received a score of 1.4 The weighted average of these indicators was then taken to obtain the final province scores, with the hard data indicator accounting for 40 percent of this dimension. The province scores for the time-costs dimension are shown in Table 5.1. Hail and Al Qassim, with the highest scores, are the best-performing provinces under this dimension. In both provinces, less than 2 percent of firms rated as difficult or very difficult the process for completing the required licenses and procedures, and the median waiting period for completing the listed procedures did not exceed 30 days. Overall, however, the reported waiting periods did not vary significantly across most provinces, and no more than 12 percent of firms in any province rated as difficult or very difficult the process for completing all procedures and licenses. This may be attributed to increased use of electronic portals to complete the necessary applications, which firms participating in qualitative interviews noted has made the process much easier and faster. Yet, although firms perceived that the process for obtaining the necessary licenses and permits was becoming easier, they also reported that the requirements for obtaining licenses and permits and legally starting a business were becoming more stringent, especially with regard to labor requirements.

Nevertheless, the results for this dimension must be interpreted with caution, as approximately 60 percent of new entrants reported hiring facilitators to assist with or handle business entry procedures. Respondents’ reported waiting periods may therefore not be fully accurate. The second dimension under the Entry Costs subindex measures the costs required for a firm to obtain a business location license, the key operating license granted by local municipalities. As indicated in Table 5.1, this dimension consists of two indicators: the official fees and the total costs of obtaining a business location license, which are divided to reflect any costs involved in hiring intermediaries and in fulfilling prerequisite procedures for obtaining the license. Taking the simple average of the scaled values of these indicators, the provincial scores for this dimension show that Najran and Hail offer the lowest-cost environments, with median official fees of SRl 900 and SRl 800, respectively, for a business location license, while Al Jouf and Al Madinah present the highest-cost environments, with median reported official fees at SR 10,000 and SR 30,000, respectively. In all provinces except the Northern Borders, firms reported incurring total costs exceeding the official fees required to obtain business location licenses from a low of 25 percent in the Eastern Province to a high of 90 percent in Al Qassim. On the one hand, this may reflect firms’ greater willingness to pay intermediaries to help obtain licenses due to lengthy or complicated processes involved. On the other hand, it may reflect the necessary set-up costs for obtaining the license, including bank guarantees, building and land rental costs, employee salaries, and equipment and materials, which firms described during the IDIs as representing sunk costs preceding the start of operations; such costs tend to be higher in more densely populated provinces. The final scores and rankings of the Entry Costs subindex are illustrated in Figure 5.1. The final subindex scores are simple averages of the two dimension scores. On the whole, Hail and Al Qassim have the lowest entry costs, with subindex scores of 9.06 and 8.19, respectively, whereas Al Baha and Al Madinah have the most burdensome procedures and costs, with subindex scores of 5.43 and 2.95, respectively. The experience across both dimensions suggests that, in relative terms, firms in Hail face the weakest barriers to entry, while firms in Al Madinah face the strongest.

4. The indicators were scaled around a 10-point scale through a simple normalization process, using the following formula: , where Province i is the individual province value, Minimum is the smallest provincial value, and Maximum is the largest provincial value among all of the provinces. For some indicators, a large number has a negative interpretation. In such cases, the index was reversed by subtracting the entire quantity from 11. An example of such a negative indicator would be the number of total inspections experienced by a firm. For such an indicator, the following formula was used: .

9*(Povincei – MinimumMaximum-Minimum

)+1

11-(9* (Provincei – MinimumMaximum-Minimum

)+1)


Tabl

e 5.

1: In

dica

tors

and

Dim

ensi

ons

Com

pris

ing

the

Entr

y Co

sts

Subi

ndex

Prov

ince

Makk

ahRi

yadh

Easte

rn

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al B

aha

Al

Madin

ahAl

Qa

ssim

Al Jo

ufNo

rthern

Bo

rders

Hail

Busin

ess d

ensit

y*4.3

87.7

4.17

5.44

4.73.4

74.8

94.0

34.6

9.32

6.28

10.03

14.19

Media

n day

s to o

btain

busin

ess

locati

on lic

ense

1014

1830

377

3060

3030

4430

30

Media

n day

s to r

egist

er wit

h the

Mi

nistry

of La

bor

77

207

41

1430

77

147

21

Media

n day

s to r

egist

er wit

h the

Ge

neral

Orga

nizati

on fo

r Soc

ial

Insura

nce

77

1221

31

730

77

77

7

Media

n day

s to o

btain

a Zak

at ce

rtifica

te7

710

103

17

907

47

77

Media

n day

s to o

btain

a Civil

De

fense

licen

se7

715

57

220

907

714

2114

Firms

ratin

g com

pletin

g all t

he

licens

es an

d proc

edure

s as

diffic

ult or

very

diffic

ult (%

)

6.1 2.

533.8

59.4

83.6

23.0

94.5

80.9

60

1.611

.8712

.090

Media

n num

ber o

f lice

nses

pe

rmits

requ

ired f

or a t

rading

firm

75

54

34

61

76

67

5

Media

n num

ber o

f lice

nses

an

d perm

its re

quire

d for

a ma

nufac

turing

firm

85

86

66

53

75

78

5

Dim

ensi

on 1

Sco

re4.

61 6

.51

4.29

4.8

5.43

5.48

4.87

3.16

4.91

6.81

4.53

5.82

8.2

4Me

dian o

fficial

fees

to ob

tain

busin

ess l

ocati

on lic

ense

(SRl)

1,800

1,500

6,000

630

900

2,000

1,000

1,500

25,00

080

06,0

004,0

0080

0

Media

n tota

l cos

t to ob

tain

busin

ess l

ocati

on lic

ense

(SRl)

3,000

3,500

8,000

2,800

1,500

8,000

3,500

15,00

030

,000

4,000

10,00

04,0

002,0

00

Dime

nsion

2 Sc

ore

9.55

9.52

7.98

9.79

9.95

8.72

9.62

7.71

19.5

77.6

78.9

8 9.

89Su

binde

x Sco

re7.0

88.0

26.1

47.3

07.6

97.1

07.2

45.4

32.9

58.1

96.1

07.4

0 9

.07Su

binde

x Sco

re7.0

88.0

26.1

47.3

07.6

97.1

07.2

45.4

32.9

58.1

96.1

07.4

0 9

.07No

te: T

he D

imen

sion

1sco

re is

the

weigh

ted a

vera

ge o

f the

first

nine

indic

ators

after

resc

aling

on

a 10

-poin

t sca

le. H

ard

data

indica

tors,

deno

ted b

y an

aste

risk

(*), a

ccou

nt for

40

perce

nt of

this

dimen

sion.

The D

imen

sion 2

scor

e is t

he av

erag

e of th

e las

t two i

ndica

tors a

fter r

esca

ling o

n a 10

-poin

t sca

le.

Dimension 1: Time Costs Dimension 2: Monetary


Table 5.2: Benchmarking of Land Access and Security of Tenure Indicators Comparable Indicator Kingdom of Saudi Arabia Cambodia Malaysia Bangladesh

Firms rating rental risks as high or very high (%)

31.23* (Al Qassim)

0 8 1.71

0 2 4 6 8 10

Al MadinahAl BahaAl Jouf

Eastern ProvinceMakkah

JizanTabuk

AssirNorthern Borders

NajranRiyadh

Al QassimHail

Time Costs Monetary Costs

Figure 5.1: Entry Costs Subindex

5.2 Land Access and Security of TenureEntrepreneurs in Saudi Arabia face several challenges when attempting to acquire land for business purposes and expansion. First, although Saudi Arabia has one of the lowest population densities in the world, the price of land in the Kingdom’s major cities has reached unprecedented levels in recent years (Dahlan 2015). While the 2017 introduction of a so-called white land tax instigated a shift in supply-and-demand dynamics that is likely to significantly reduce prices, for the large majority of MSMEs, the cost of purchasing land will remain extremely high. Second, little land-related information is available for entrepreneurs, with the country’s land administration system earning only1.5 of a maximum score of 6 on the 2017 Doing Business Transparency of Information Index (World Bank 2017). Moreover, until recently, no central registration system for land rights existed; property transfers occurred

through public notaries, who marked the title deeds held by landowners. Third, inconsistent application of land zoning rules is common, as shown by the Kingdom’s poor performance in the Doing Business overall Quality of Land Administration Index. Regarding the security of the formal rights to land and business premises once acquired, the 2017 Doing Business Land Dispute Resolution Index gives Saudi Arabia the low score of 4 from a maximum achievable score of 8. Thus, while the Kingdom has introduced reforms to address the land and housing crisis, along with a new computerized land registry system in 2015 (World Bank 2015a), further improving access to and security of land tenure is crucial to the health and development of the Saudi business climate; theoretical constructions show that these improvements would increase access to capital and encourage investment in long-term productivity (De Soto 2000).

Sources: The following indices, developed by the Asia Foundation, were used in the international benchmarking: the Cambodia Provincial Environment Scorecard (2009), the Malaysia Business Environment Index (2012), and the Bangladesh Economic Governance Index (2010).

Note: * Value for the best-performing province in Saudi Arabia, Al Qassim, was reversed for comparability.


The Land Access and Security of Tenure subindex was designed to capture these issues along two dimensions: land access and security of tenure. The first measures the ease of acquiring business premises, and the latter measures the security of tenure once land or business premises are properly acquired. The indicators comprising each dimension are summarized in Table 5.3. Given that the majority of interviewed MSMEs rent or lease property for business purposes and that very few, if any, reported owning land, these indicators mostly center on access and security of rent of premises for business purposes. The final scores and rankings of the Land Access and Security of Tenure subindex are presented in Figure 5.2, which displays a sharp contrast between the highest performing province, Riyadh, and the worst performing province, Najran. In terms of the first dimension, only 8 percent of Riyadh firms perceived difficulties securing ownership or leasing land as major or severe obstacles to business expansion; in comparison, almost 45 percent of firms in Najran did see difficulties securing ownership or leasing land as major obstacles. Similarly, nearly 63

percent of Riyadh firms perceived the application of land zoning rules as consistent and efficient, whereas only 33 percent of firms in Najran did so. Across all provinces, an average of 40 percent of firms reported difficulties securing ownership or leasing land.As for the second dimension, 45 percent of Riyadh firms rated risk of changes in rental contracts as low or very low, and 57 percent saw the process of disputing such changes as frequently or always fair. In Najran, only 21 percent and 20 percent of firms, respectively, held these views. Moreover, changes in rental conditions were a major concern for firms across all provinces. Examples cited during qualitative interviews included rent increases and evacuation before the termination of the contract period. It is worth noting that these concerns were raised in relation to private sector landlords rather than the public sector. Comparison to international standards, illustrated in Table 5.2, indicates that even the best-performing province, Al Qassim, lags behind the best-performing localities in other countries conducting similar indexing exercises.

NajranAl Madinah

Northern BordersAssirJizan

Al JoufMakkah

TabukEastern Province

Al BahaHail

Al QassimRiyadh

0 2 4 6 8 10Access to Land Security of Tenure

Figure 5.2: Land Access and Security of Tenure Subindex


Tabl

e 5.

3: In

dica

tors

and

Dim

ensi

ons

Com

prom

isin

g th

e La

nd A

cces

s an

d Se

curit

y of

Ten

ure

Subi

ndex

Prov

ince

Makk

ahRi

yadh

Easte

rn

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al B

aha

Al

Madin

ahAl

Qa

ssim

Al Jo

ufNo

rthern

Bo

rders

Hail

Numb

er of

indu

strial

citie

s*5

76

11

11

11

21

11

Firms

stati

ng th

at dif

ficult

ies

in se

curin

g own

ersh

ip or

lea

se of

land

is a

major

or

seve

re ob

stacle

to bu

sines

s ex

pans

ion (%

)

31.51

8.09

37.3

33.64

44.78

35.29

15.9

10.76

26.72

0.68

18.12

37.08

4.57

Firms

stati

ng th

at the

ap

plica

tion o

f land

zonin

g ru

les is

freq

uentl

y or a

lway

s effi

cient

and c

onsis

tent (

%)

36.42

62.91

46.22

36.13

32.78

46.33

53.35

45.08

29.44

37.62

32.21

22.14

43.72

Dime

nsion

1 Sc

ore

5.16

9.55

6.05

2.61

1.73.1

8 4

.834.6

2.59

5.33

3.31.4

7 4

.89Fir

ms ra

ting r

isk of

chan

ges

in re

ntal c

ontra

cts as

low

or

very

low (%

)

16.16

45.11

12.19

7.66

20.88

18.8

30.59

36.28

24.66

68.77

28.72

25.74

59.14

Firms

stati

ng th

at the

proc

ess

of dis

putin

g cha

nges

in le

ase

contr

acts

is fre

quen

tly or

alw

ays f

air (%

)

23.22

56.69

33.22

33.04

19.58

33.55

28.57

30.5

16.95

38.55

28.26

27.18

31.82

Dime

nsion

2 Sc

ore

2.34

8.26

3.18

2.82

2.27

3.7 4

.01 4

.64 2

.25 7

.953.8

33.4

9 6

.47Su

binde

x Sco

re 3

.75 8

.90 4

.61 2

.72 1

.99 3

.44 4

.42 4

.62 2

.42 6

.643.5

72.4

8 5

.68

Dimension 1: Land Access Dimension 2: Security of Tenure

Note:

Dim

ensio

n 1 sc

ore i

s the

weig

hted a

vera

ge of

the fi

rst th

ree i

ndica

tors a

fter r

esca

ling o

n a 10

-poin

t sca

le. H

ard d

ata in

dicato

rs ac

coun

t for 4

0 per

cent

of thi

s dim

ensio

n. Di

mens

ion 2

scor

e is t

he av

erag

e of

the la

st tw

o ind

icator

s afte

r res

calin

g on a

10-p

oint s

cale.


5.3 Transparency and Participation Transparency and Participation are complementary and instrumental pillars for creating a business environment conducive to private sector development. As several studies show, increasing transparency by improving the quality of legal, regulatory, and policy information available to business owners is essential in promoting competitiveness because asymmetric information can lead to market failures and distributive inefficiency (Kaufmann, Kraay, and Zoido-Lobatón 2002). Increasing dialogue between the business community and provincial authorities during the policy-making process represents an important channel for fostering a business-friendly environment. In terms of Transparency in Saudi Arabia, basic information on the laws and regulations governing business activities is provided by the relevant municipality, increasingly through electronic means. This information, however, can at times be unclear and difficult to access and understand. A study by the Riyadh Chamber of Commerce and Industry in 2010 found that more than half of small business owners surveyed rated ambiguity of governmental regulations and policies as their biggest obstacle in dealing with the government, and more than 65 percent indicated that they lacked sufficient information to run their businesses (Riyadh Chamber of Commerce and Industry 2010).As for Participation, during the formulation of development plans the views and interests of private sector businesses in Saudi Arabia are represented at the national level by the Saudi Council of Chambers, whereas at the subnational level, province-level chambers of commerce and industry and municipal subchambers have responsibility for promoting and developing the local business environment. Firms’ engagement with these chambers is known to be higher in provinces with a stronger business cultures, such as Riyadh and Makkah. The Transparency and Participation subindex measures both the ease of obtaining the proper policy, planning,

and legal documents necessary to run a business (under the first dimension, Transparency), and the extent of private sector involvement in the decision making process related to running a business (under the second dimension, Participation). The first dimension comprises six indicators, shown in Table 5.5. The first two indicators measure the extent to which firm owners can easily access information necessary to run their businesses, as well as the extent to which they can easily understand and use this information. The third indicator measures the perceived equity of access to this information, and the final three indicators measure the level of the predictability of changes to laws, regulations, and policies firms must consider in forecasting and changing their investment plans. Computing the simple average of the scaled values of these indicators, Table 5.5 shows that Al Qassim achieved the highest overall score of 7.88 under the dimension of Transparency, while Assir achieved the lowest score of 2.98. Regarding ease of access to information, Riyadh performed best, with more than 80 percent of firms rating as easy or very easy their access to policy, regulatory, and planning documents or information necessary to run their businesses; the worst-performing province was Assir, with less than 20 percent of firms rating access easy or very easy. Provinces’ performance in providing access to information was also correlated with performance in achieving clarity of information, a factor firms highlighted as important during the qualitative interviews. As for the predictability of changes in local administrations’ rules and policies, in the worst-performing province, Assir, 61 percent of firms reported that such changes materially affect their businesses frequently or always. The best-performing province was Hail, where only 26 percent of firms reported these difficulties. As indicated in Table 5.4, however, which benchmarks Hail’s performance against the best-performing localities in other countries, great room for improvement remains.


Table 5.4: Benchmarking of Transparency IndicatorsComparable Indicator Kingdom of

Saudi ArabiaCambodia Malaysia Bangladesh Vietnam

Firms believing that personal connections with local officials are frequently or always necessary to access documents (%)

14.34 (Eastern Province)

40 NA 1.77 47.42

Firms stating that they are confident or very confident of their knowledge of existing laws and policies (%)

92.22 (Al Qassim)

46.2* NA 52.88 NA

Firms stating that changes in local administration rules and regulations are always or frequently made (%)

25.79 (Hail)

NA NA 0 1.08

Firms stating that they are seldom or never informed of such changes (%)

21.18 (Al Jouf)

35 NA 63.26* NA

With regard to the second dimension, Participation, Table 5.5 summarizes the four indicators used to measure firm participation in local chambers of commerce and during the formulation of new policies. Calculating the simple average of the scaled values of these indicators shows that Hail achieved the highest score, 7.78, while Assir achieved the lowest score, 1.5. In Hail, 48.73 percent of firms stated that local government authorities frequently or always solicited comments from affected firms on the content and implementation of new laws or policies

concerning those businesses, while in Assir, less than half of firms responded this way. The final scores and rankings for the Transparency and Participation subindex appear in Figure 5.3. The province of Hail obtained the first clear overall ranking, with a score of 6.68, followed by Tabuk in second place and Riyadh in third place, with scores of 5.83 and 5.6, respectively. In contrast, the province of Assir obtained the marked lowest overall ranking, with a score of 2.24.

0 2 4 6 8

AssirJizan

Northern BordersNajran

Al JoufEastern Province

Al BahaMakkah

Al MadinahAl Qassim

RiyadhTabuk

Hail

Transparency Participation

Figure 5.3: Transparency and Participation Subindex

Sources: The following indices, developed by the Asia Foundation, were used in the international benchmarking: the Cambodia Provincial Environment Scorecard (2009), the Malaysia Business Environment Index (2012), the Bangladesh Economic Governance Index (2010), and the Vietnam Provincial Competitiveness Index (2016).

Note: * Value for the best-performing district in Bangladesh was reversed for comparability.


Tabl

e 5.

5: In

dica

tors

and

Dim

ensi

ons

Com

pris

ing

the

Tran

spar

ency

and

Par

tici

pati

on S

ubin

dex

Prov

ince

Makk

ahRi

yadh

East

ern

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al

Baha

Al

Madi

nah

Al

Qass

imAl

Jo

ufNo

rther

n Bo

rder

sHa

il

Firms

ratin

g acc

ess t

o a lis

t of p

olicy

, reg

ulator

y, an

d plan

ning d

ocum

ents

or in

forma

tion a

s eas

y or

very

easy

(%)

65.57

80.53

46.19

18.97

29.67

20.25

43.52

49.59

58.37

78.8

27.53

30.92

71.18

Firms

agre

eing o

r stro

ngly

agre

eing t

hat th

e co

ntent

of the

list o

f doc

umen

ts or

infor

matio

n is

clear

and e

asy t

o und

ersta

nd an

d use

(%)

66.51

90.4

7870

.0979

.9957

.4271

.6168

.5560

.4598

.8738

.5630

.5294

.01

Firms

belie

ving t

hat p

erso

nal c

onne

ction

s with

loc

al go

vern

ment

officia

ls ar

e fre

quen

tly or

alwa

ys

nece

ssar

y for

facil

itatin

g acc

ess t

o the

docu

ments

(%

)

31.66

41.05

14.34

30.36

24.35

37.92

24.98

32.28

25.99

33.81

36.36

29.13

35.25

Firms

belie

ving t

hat th

ey ar

e con

fiden

t or v

ery

confi

dent

of the

ir kno

wled

ge of

exist

ing ru

les,

laws,

and r

egula

tions

conc

ernin

g the

runn

ing of

the

ir bus

iness

es (%

)

66.42

84.19

82.67

56.69

71.55

57.65

72.92

60.07

59.49

92.22

42.77

38.26

79.51

Table

5.5:

Indica

tors a

nd D

imen

sions

Com

prisi

ng

the Tr

ansp

aren

cy an

d Par

ticipa

tion S

ubind

exFir

ms ag

reein

g tha

t cha

nges

to th

e loc

al ad

minis

tratio

n’s ru

les, la

ws, a

nd re

gulat

ions

mater

ially

affec

ting t

heir b

usine

sses

are f

requ

ently

or

alwa

ys m

ade (

%)

34.74

49.56

56.75

61.38

49.8

51.67

28.85

41.62

41.37

32.95

26.62

27.11

25.79

Firms

stati

ng th

at the

y are

seldo

m or

neve

r inf

orme

d of s

uch c

hang

es (%

)21

.8834

.1452

.5860

.0357

.6647

.5730

.7524

.335

.6938

.9221

.1834

.4440

.38

Dim

ensi

on 1

Sco

re6.

836.

595.

922.

984.

793.

236.

875.

895.

847.

884.

744.

397.

4Nu

mber

of ch

ambe

r com

mitte

es or

su

bcom

mitte

es pe

r 10,0

00 re

gister

ed fir

ms in

the

prov

ince*

6.12

1.96

5.43

5.34

5.82

7.66

12.91

7.15

20.46

23.53

13.44

010

6.1

Firms

that

are m

embe

rs of

at lea

st on

e cha

mber

of

comm

erce

comm

ittee o

r sub

comm

ittee (

%)

7.06

14.28

0.28

0.58

9.72

1.87

85.75

00

12.07

66.98

89.31

0

Firms

agre

eing t

hat m

embe

rships

in ch

ambe

rs of

comm

erce

are u

seful

or ve

ry us

eful (%

)43

.4359

.9867

.5934

.7253

.6744

.5251

.0256

.5166

.4430

.529

.8830

.0634

.64

Firms

stati

ng th

at loc

al go

vern

ment

autho

rities

fre

quen

tly or

alwa

ys so

licit c

omme

nts fr

om

affec

ted fir

ms on

new

laws o

r poli

cies (

%)

31.57

36.67

20.67

17.82

18.85

25.25

23.72

34.28

29.41

28.78

17.03

19.96

29.58

Tabl

e 5.5:

Indi

cato

rs an

d Di

men

sions

Com

prisi

ng th

e Tra

nspa

renc

y and

Par

ticip

atio

n Su

bind

exDi

men

sion

2 Sco

re3.3

34.5

93.3

21.5

2.72.7

54.7

94.0

94.5

73.1

52.8

13.0

85.9

8Su

binde

x Sco

re5.0

85.5

94.6

22.2

43.7

42.9

95.8

34.9

95.2

05.5

13.7

73.7

46.6

9

Dimension 1: Transparency Dimension 2: Participation

Note:

Dim

ensio

n 1 sc

ore i

s the

aver

age o

f the s

ix ind

icator

s afte

r res

calin

g on a

10-p

oint s

cale.

Dim

ensio

n 2 sc

ore i

s the

weig

hted a

vera

ge of

the f

our in

dicato

rs aft

er re

scali

ng on

a 10

-poin

t sca

le. H

ard

data

indica

tors,

deno

ted by

an as

terisk

(*),

acco

unt fo

r 40 p

erce

nt of

this d

imen

sion.


5.4 Time Costs of Regulatory ComplianceAccording to the World Economic Forum’s 2015 Global Competitiveness Report, top business executives in Saudi Arabia consider inefficient government bureaucracies to be one of the most problematic factors for doing business in the Kingdom (World Economic Forum 2015). A survey by the Riyadh Chamber of Commerce echoes this view among small business owners, with approximately 65 percent of surveyed firms ranking compliance with governmental procedures and regulations as the biggest obstacle facing SME creation and development. The length of time required to complete procedures, and the delays encountered, were singled out as the most problematic aspects of complying with governmental regulations. The 2017 Doing Business Report, for example, notes that it takes 228 hours in Saudi Arabia to comply with importing procedures for obtaining, preparing, and submitting documents during port or border handling, customs clearance, and inspections procedures, whereas these procedures require an average of 121 hours across the Middle East and North Africa and an average of only 9 hours across Organisation for Economic Co-operation and Development (OECD) countries.Time costs of regulatory compliance pose a major constraint for business development in the Kingdom. Although the efficiency of handling these bureaucratic proceedings likely varies with the effectiveness of provincial authorities, the high bureaucratic transaction costs Saudi business owners face in complying with complicated business regulations represent significant

opportunity costs of lost management time and human and financial resources that could be more productively employed. To capture the magnitude of this lost management time, the Time Costs of Regulatory Compliance subindex uses four indicators to measure time lost in pursuing bureaucratic compliance, the frequency with which firms must undergo inspections by local regulatory agencies, and the duration of those inspections. Table 5.6 summarizes these indicators and the final subindex scores for each province, and Figure 5.4 represents these finding graphically. According to participating firms’ experiences, Makkah and Riyadh have the lowest time burden for regulatory compliance, achieving scores of 8.54 and 7.90, respectively, and Al Baha and Northern Borders have the highest, with scores of 5.09 and 5, respectively. Although most firms interviewed in the PCI survey reported low frequency and short duration of inspections under this subindex, marked differences appear across provinces in the percentage of senior management time spent dealing with bureaucratic procedures and paperwork. In Makkah, for example, the median percentage of senior management time spent per year on regulatory compliance was 15 percent, compared to 85 percent in Al Baha. Drawing from the qualitative interviews, many firms attributed the time lost in completing bureaucratic procedures to lack of clarity in the rules and regulations and weak coordination between government entities working in related areas of concern to business operations.

0 2 4 6 8

Northern BordersAl Baha

Eastern ProvinceTabuk

HailAl Qassim

Al JoufAssirJizan

NajranAl Madinah

RiyadhMakkah

Figure 5.4: Time Costs of Regulatory Compliance Subindex


Prov

ince

Makk

ahRi

yadh

Easte

rn

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al B

aha

Al

Madin

ahAl

Qa

ssim

Al Jo

ufNo

rthern

Bo

rders

Hail

Media

n % of

senio

r ma

nage

ment

time s

pent

durin

g the

past

year

deali

ng

with

bure

aucra

tic pr

oced

ures

an

d pap

erwo

rk

1530

2050

2050

1085

105

55

5

Media

n num

ber o

f insp

ectio

ns

or m

anda

tory m

eetin

gs,

per fi

rm pe

r yea

r, with

loca

l go

vern

ment

agen

cies

10.8

1

.21

1.81

2.41.6

21.8

1.82.2

2.4

Media

n dur

ation

of

inspe

ction

s or m

anda

tory

meeti

ngs,

per fi

rm pe

r ye

ar, w

ith lo

cal g

over

nmen

t ag

encie

s

0.

60.8

0

.80.6

0.8

0.6

0.80.8

0.81.2

0.81

1

Firms

ratin

g as d

ifficu

lt or

very

diffic

ult w

orkin

g wi

th go

vern

ment

officia

ls to

comp

ly wi

th law

s and

re

gulat

ions (

%)

7.74

5.74

18.4

16.29

3.512

.46 6.

756.8

63.1

10.6

811

.7212

.751.7

2

Subin

dex S

core

8.54

7.9

6.0

26.4

77.0

66.9

6 6

.095.0

97.1

16.3

46.4

45

6.12

Note:

Sub

index

scor

e is t

he av

erag

e of th

e fou

r indic

ators

after

resc

aling

on a

10-p

oint s

cale.

Tabl

e 5.

6: In

dica

tors

and

Dim

ensi

ons

Com

pris

ing

the

Tim

e Co

sts

of R

egul

ator

y Co

mpl

ianc

e Su

bind

ex


5.5 Informal ChargesInformal charges or, more generally, corruption, represents a critical problem for businesses in many countries. Corruption not only restricts access to markets or services, it also hinders competitiveness by increasing the cost and uncertainty of doing business. In Saudi Arabia, corruption at the national level has declined over the last three years, as reflected by consecutive improvements in international rankings such as Transparency International’s 2015 Corruption Perceptions Index, The Heritage Foundation’s 2015 Freedom of Corruption Index, and the Worldwide Governance Indicator’s 2015 Control of Corruption Index (Transparency International 2015; Heritage Foundation 2015a; World Bank Group 2015b). Efforts to curb national-level corruption were enhanced by the establishment of the National Anti-Corruption Commission (Nazaha) in 2011 and by the increasing appearance in the Saudi press of articles and reports on prosecutions for corrupt dealings. In comparison, little information is disseminated about the prevalence and control of corruption at the subnational level. Measuring the extent of corruption across provinces by gauging the frequency, type, and amount of informal charges levied is therefore essential to designing adequate policies to counter it. The Informal Charges subindex measures firms’ informal payments to secure firm-level operations, as well as to obtain public procurement contracts; the subindex also provides information on the predictability of extra fees and the frequency with which they lead to the desired results or services. Indicators of informal

charges are grouped under two dimensions: firm-level corruption and systematic corruption. The first dimension, addressing firm-level corruption, uses the three indicators summarized in Table 5.8 to measure the scope and predictability of informal firm-level charges to obtain required services. Under this dimension, Hail is the worst-performing province, with a score of 1.43, and Al Madinah is the best-performing province, with a score of 9.69. As the values for the first indicator reflect, many firms were initially reluctant to answer questions pertaining to informal payments; across all provinces, less than 16 percent of firms on average reported these payments in their line of business. At the same time, however, on average 34 percent of firms across the provinces reported that additional payments frequently or always resulted in delivery of the desired service. This may indicate that firms became more open to answering these sensitive questions over the course of the interviews. Table 5.7 compares results on these indicators for the best-performing Saudi provinces with the best-performing localities in four other countries. The second dimension, systematic corruption, measures the extent of macro-level corruption over ten indicators, as captured in Table 5.8, by gauging the predictability, frequency, and type of corruption during a range of dealings with government entities, including specific measures relating to public procurement. Under this dimension, Al Baha is the worst-performing province, with a score of 4.16, and Tabuk is the best-performing province, with a score of 6.93, scores which signify much less variation than for the first dimension. In addition to capturing differences in firms’ perceptions on

Table 5.7: Benchmarking of Informal Payments IndicatorsComparable Indicator Kingdom of Saudi

ArabiaCambodia Malaysia Bangladesh Vietnam

Firms reporting that informal payments occur in their line of business

0.69 (Al Madinah)

2.5* 3 27.66 45.16

Firms reporting that with additional payment services are frequently or always delivered as expected (%)

9.87 (Tabuk)

10 NA 34 38.46

Firms reporting that procurement contracts are frequently or always transparent (%)

74.02 (Al Qassim)

30.59 NA 72.32 NA

Firms reporting that personal connections are important or very important for winning public procurement contracts (%)

31.13 (Tabuk)

39.62 NA 0 NA


Note: * Value for best-performing province in Cambodia was reversed for comparability.


the usefulness of informal payments when dealing with government officials, this dimension also encompasses firms’ perceptions of the relative importance of nonmonetary forms of corruption, particularly the role of personal connections in dealing with government entities. Table 5.8 shows that 60 percent of firms in Hail reported personal connections were essential or very useful for expediting government services, and 93 percent of firms perceived these connects as important or very important for winning public contracts. These views were also evident during the qualitative interviews with firms. The quotes in Box 5.1 illustrate some of the experiences and opinions firms expressed about this issue. The final scores and rankings of the Informal Charges subindex appear in Figure 5.5. Tabuk scored the highest, followed by Al Madinah and Eastern Province. The performance of these provinces is driven fairly equally by high scores in both dimensions. Under the first dimension, firm-level corruption, less than 10 percent of firms in all three provinces believed that services were frequently or always delivered as expected after payment of informal charges. Similarly, under the second dimension, systematic corruption, no more than 16 percent of firms in these three provinces believed that making an informal payment is essential or very useful in expediting government services.

Box 5.1: Experiences and Opinions on the Importance of Personal Connections

Medium-sized firm in Eastern Province: “Knowing someone in the ministry expedites the procedures.”Medium-sized firm in Riyadh: “Some examinations are not done in some entities or centers because of the business owner’s relations with individuals in higher positions .”

5.6 Legal Institutions and Dispute Resolution Upholding property rights through well-functioning legal institutions and dispute resolution mechanisms is, as affirmed in a vast literature, integral to a dynamic business environment. Saudi Arabia ranks 70 on the 2015 Index of Economic Freedom on Property Rights Index, a position that has remained largely unchanged since 2009 (Heritage Foundation 2015b). The Saudi Ministry of Justice has taken steps to increase the number of commercial courts across the provinces and to improve information technology to link courts and standardize court procedures. Despite these efforts, however, legal proceedings in Saudi Arabia remain generally slow paced. According to Doing Business Reports, the number of days required to enforce contracts increased from 360 days in 2006 to 575 days in 2017. Such lengthy court procedures negatively affect private sector incentives, discourage lending, and increase risk and uncertainty in commercial and financial relationships.

0 2 4 6 8

HailJizan

NajranAssir

RiyadhAl BahaMakkah

Al QassimAl Jouf

Northern BordersEastern Province

Al MadinahTabuk

Firm−level Corruption Systematic Corruption

Figure 5.5: Informal Charges Subindex


Tabl

e 5.

8: In

dica

tors

and

Dim

ensi

ons

Com

pris

ing

the

Info

rmal

Cha

rges

Sub

inde

xPr

ovin

ceMa

kkah

Riya

dhEa

ster

n Pr

ovin

ceAs

sirNa

jran

Jizan

Tabu

kAl

Ba

haAl

Ma

dina

hAl

Qa

ssim

Al

Jouf

North

ern

Bord

ers

Hail

Firms

repo

rting t

hat in

forma

l cha

rges

occu

r in

their l

ine of

busin

ess (

%)

19.25

21.56

6.23

12.69

11.95

22.55

9.118

.69 2

.17 4

.33 21

.716

.6338

.04

Firms

repo

rting t

hat fi

rms i

n the

ir line

of

busin

ess u

suall

y kno

w in

adva

nce t

he am

ount

of inf

orma

l pay

ment

requ

ired (

%)

17.11

23.21

6.63

20.78

19.99

39.62

10.47

5.52

0.69

11.65

12.25

9.49

48.67

Firms

belie

ving t

hat, w

ith ad

dition

al pa

ymen

t, se

rvice

s are

freq

uentl

y or a

lway

s deli

vere

d as

expe

cted (

%)

34.18

42.79

17.51

40.32

5351

.88 9

.8729

.2214

.9259

.4924

.6917

.5552

.4

Dim

ensio

n 1 S

core

6.07

4.98

8.83

6.02

5.37

3.32

8.81

7.15

9.69

6.13

6.75

7.77

1.43

Firms

stati

ng th

at ma

king a

n info

rmal

paym

ent

is es

senti

al or

very

usefu

l for e

xped

iting

gove

rnme

nt se

rvice

s (%

)

29.55

44.26

10.59

45.29

45.8

43.55

6.42

53.01

16.23

47.93

28.34

9.77

39.83

Firms

stati

ng th

at pe

rsona

l con

necti

ons (

i.e.,

friend

s/fam

ily re

lation

s) wi

th loc

al go

vern

ment

officia

ls ar

e ess

entia

l or v

ery u

seful

for

expe

diting

gove

rnme

nt se

rvice

s (%

)

33.35

46.34

16.51

41.76

26.86

15.84

9.02

49.04

22.54

39.87

29.45

16.83

60.87

Firms

stati

ng th

at firm

s in t

heir l

ine of

busin

ess

are f

requ

ently

or al

ways

expe

cted o

r req

ueste

d to

give i

nform

al pa

ymen

ts/gif

ts du

ring a

rang

e of

deali

ngs w

ith go

vern

ment

officia

ls (%

)

69.35

64.64

92.13

65.7

89.38

72.25

91.32

92.61

87.66

100

76.87

94.66

99.06

Firms

that

have

partic

ipated

in pu

blic

proc

urem

ent c

ontra

cts du

ring t

he pa

st thr

ee

year

s (%

)

9.74

8.83

2.815

.5516

.9317

.316.8

7.57

2.67

13.98

1.04

2.27

22.74

Firms

agre

eing t

hat in

forma

tion c

once

rning

pu

blic p

rocu

reme

nt co

ntrac

ts in

the pr

ovinc

e is

frequ

ently

or al

ways

tran

spar

ent (

%)

39.62

72.74

56.55

42.27

68.81

51.33

60.02

50.35

30.74

74.02

44.87

48.24

50.64

Firms

agre

eing t

hat p

roce

dure

s for

winn

ing

publi

c pro

cure

ment

contr

acts

in the

prov

ince

are f

requ

ently

or al

ways

fair (

%)

44.85

72.08

59.12

41.62

68.33

46.72

57.03

55.19

31.69

79.97

50.05

51.4

52.18

Firms

agre

eing t

hat p

erso

nal c

onne

ction

s are

im

porta

nt or

very

impo

rtant

for w

inning

publi

c pr

ocur

emen

t con

tracts

(%)

55.77

74.65

38.07

74.72

75.88

61.72

31.13

52.56

69.08

74.26

44.99

46.74

92.59

Firms

repo

rting t

hat fi

rms i

n the

ir line

of

busin

ess f

requ

ently

or al

ways

expe

rienc

e mi

scon

duct

by lo

cal g

over

nmen

t offic

ials d

uring

ins

pecti

ons (

%)

24.84

34.14

8.8

818

.1114

.223

.4 7

.1537

.5619

.5325

.6115

.94 6

.4121

.99

Firms

disa

gree

ing or

stro

ngly

disag

reein

g tha

t pre

feren

tial tr

eatm

ent o

r fav

oritis

m by

go

vern

ment

officia

ls do

es no

t exis

t

17.8

16.42

41.06

40.14

56.69

55.31

34.5

24.44

13.86

1.32

21.93

38.67

9.4

Firms

disa

gree

ing or

stro

ngly

disag

reein

g tha

t pre

feren

tial tr

eatm

ent o

r fav

oritis

m by

go

vern

ment

officia

ls is

not h

armf

ul

19.36

23.11

59.27

44.27

53.14

63.65

32.51

26.63

8.49

1.61

20.34

29.98

5.07

Dim

ensio

n 2 S

core

5.68

5.87

5.86

4.75

5.21

4.94

6.93

4.16

5.12

6.21

5.76

5.87

5.04

Subin

dex S

core

5.88

5.43

7.34

5.39

5.29

4.13

7.87

5.65

7.41

6.17

6.25

6.82

3.24

Dimension 1: Firm-level Corruption Dimension 2: Systematic Corruption

Note:

Dim

ensio

n 1 sc

ore i

s the

aver

age o

f the fi

rst th

ree i

ndica

tors a

fter r

esca

ling o

n a 10

-poin

t sca

le. D

imen

sion 2

scor

e is t

he av

erag

e of th

e las

t 10 i

ndica

tors a

fter r

esca

ling o

n a 10

-poin

t sca

le.


Table 5.9: Benchmarking of Legal Institutions and Dispute Resolution IndicatorsComparable Indicator Kingdom of Saudi Arabia Cambodia Malaysia Bangladesh Vietnam

Firms agreeing or strongly agreeing that the legal system will uphold their contract and property rights (%)

99 (Hail)

42 98 84 90

The Legal Institutions and Dispute Resolution subindex, characterized by the indicators in Table 5.10, measures firms’ confidence in the legal system’s fairness and equity and in their ability to appeal to competent persons at higher levels to resolve disputes over the conduct of local government officials. Figure 5.6 presents final scores and rankings of the Legal Institutions and Dispute Resolution subindex. As can be seen, the top three performers, Al Qassim, Hail, and Riyadh, achieved very

close scores of 9.75, 9.66, and 9.6, respectively. In all three provinces, no less than 96 percent of firms asserted their confidence that the legal system would uphold their contract and property rights. Table 5.9 shows that this level is comparable to the best-performing localities across a number of countries. In comparison, for the lowest-scoring provinces of Al Baha, Al Jouf, and Northern Borders, no more than 66 percent of firms expressed this confidence.

0 2 4 6 8 10


Al BahaMakkah

Eastern ProvinceAl Madinah

TabukJizanAssir

NajranRiyadh

HailAl Qassim

Figure 5.6: Legal Institutions and Dispute Resolution Subindex


3636

Tabl

e 5.

11: B

ench

mar

king

of P

roac

tivit

y an

d At

titu

de to

war

d th

e Pr

ivat

e Se

ctor

Indi

cato

rs

Com

para

ble I

ndica

tor

King

dom

of S

audi

Ara

bia

Ca

mbo

dia

Viet

nam

Firms

asse

ssing

as po

sitive

or ve

ry po

sitive

the a

ttitud

e of g

over

nmen

t offic

ials

towar

d the

priva

te se

ctor (

%)

92

(Al M

adina

h)58

67

Firms

agre

eing o

r stro

ngly

agre

eing t

hat p

rovin

cial o

fficial

s are

proa

ctive

in

explo

iting p

ossib

le fle

xibilit

y in t

he ad

minis

tratio

n of n

ation

al re

gulat

ions t

o mi

nimize

the r

egula

tory b

urde

n on b

usine

sses

(%)

88

(Al Q

assim

)77

89


Tabl

e 5.

10: I

ndic

ator

s an

d Di

men

sion

s Co

mpr

isin

g th

e Le

gal I

nsti

tuti

ons

and

Disp

ute

Reso

luti

on S

ub-In

dex

Prov

ince

Makk

ahRi

yadh

East

ern

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al

Baha

Al

Madi

nah

Al

Qass

imAl

Jo

ufNo

rther

n Bo

rder

sHa

il

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

the le

gal s

ystem

will

upho

ld the

ir con

tract

and p

rope

rty rig

hts (%

)

67.16

96.06

86.95

82.81

87.44

73.55

88.25

58.99

73.41

97.49

61.18

66.74

99.68

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

they c

an ap

peal

to co

mpete

nt pe

rsons

at

a high

er le

vel to

reso

lve di

spute

s ove

r co

nduc

t by l

ocal

gove

rnme

nt offi

cials

(%)

60.32

84.33

57.27

73.41

83.27

73.23

59.48

61.41

68.44

84.25

58.9

51

.281

.84

Subin

dex S

core

3.14

9.6 4

.92 6

.658.5

5.6 5.

362.3

9 4

.94 9

.75 2

.29 1

.86 9

.66

Sour

ces:

The f

ollow

ing in

dices

, dev

elope

d by t

he As

ia Fo

unda

tion,

were

used

in th

e inte

rnati

onal

benc

hmar

king:

the C

ambo

dia P

rovin

cial E

nviro

nmen

t Sco

reca

rd (2

009)

and t

he V

ietna

m Pr

ovinc

ial C

ompe

titive

ness

Ind

ex (2

016)

.

Note:

Sub

index

scor

e is t

he av

erag

e of th

e two

indic

ators

after

resc

aling

on a1

0-po

int sc

ale.


5.7 Proactivity of Provincial Government and Attitude toward the Private SectorThe development of the private sector generally and of SMEs in particular is a stated core objective of “Saudi Vision 2030,” and several programs have been initiated to help realize it (Saudi Arabia, Ministry of Economy and Planning 2016). Further assessment is needed, however, regarding the varying degree of these programs’ efficacy across provinces and of the extent to which private businesses see provincial authorities’ attitudes toward them as positive and supportive. Moreover, as Al Bakr (2015) suggests, increased cooperation and coordination among concerned governmental bodies in each province is necessary to unify efforts to create a thriving business environment.The Proactivity of Provincial Government and Attitude toward the Private Sector subindex is formulated using nine indicators, listed in Table 5.12, to measure aspects of the quality of local as compared to central governance, including the creativity and ability of province authorities to implement central policy, design initiatives for private sector development, and actively participate in assisting the private sector. The final subindex scores by province appear in Figure 5.7 and Table 5.12. The province of Riyadh achieved the highest score, 8.2, exceeding by less than one point the second-, third-, and fourth-ranked provinces of Tabuk,

Eastern Province, and Al Baha, which achieved equal scores of around 7.5. Riyadh received the highest score on most indicators under this subindex, with 86 percent of firms agreeing that provincial officials are proactive in exploiting flexibility in the administration of national regulations to minimize the regulatory burden on businesses; 83 percent of firms agreed that provincial officials were proactive in implementing initiatives to support private businesses. Overall, firms’ perceptions of government officials’ attitude toward the private sector differed markedly across provinces, with less than 40 percent in Al Jouf and Northern Borders perceiving it as positive, as compared to 92 percent of firms in Al Madinah, which, as Table 5.11 demonstrates, exceeds the best-performing localities in the benchmark countries. Similarly, firms’ assessment of the proactivity of local provincial governments in minimizing the regulatory burden within the confines of national laws and regulations also reflected significant differences between provinces, with the highest-scoring province of Al Qassim at par with the levels reported in the other countries in Table 5.11. During the qualitative interviews, several firms highlighted officials’ lack of sufficient training and skills along their lack of commitment to working hours as major constraints to completing governmental proceedings. The poor performance of some provinces, such as Jizan and Al Jouf, which also ranked lowest for this subindex, can be attributed to this problem.

0 2 4 6 8

Al JoufJizan

Northern BordersAssir

NajranHail

Al QassimAl Madinah

MakkahAl Baha

Eastern ProvinceTabukRiyadh

Figure 5.7: Proactivity and Attitude towards Private Sector Subindex


Tabl

e 5.

12: I

ndic

ator

s an

d Di

men

sion

s Co

mpr

isin

g th

e Pr

oact

ivit

y an

d At

titu

de to

war

d th

e Pr

ivat

e Se

ctor

Sub

inde

xPr

ovin

ceMa

kkah

Riya

dhEa

ster

n Pr

ovin

ceAs

sirNa

jran

Jizan

Tabu

kAl

Ba

haAl

Ma

dina

hAl

Qa

ssim

Al

Jouf

North

ern

Bord

ers

Hail

Firms

stati

ng th

at the

attitu

de of

prov

incial

go

vern

ment

officia

ls tow

ard t

he pr

ivate

secto

r is

posit

ive or

very

posit

ive (%

)

78.5

87.79

79.01

54.28

47.55

42.03

65.1

86.55

91.62

79.21

38.93

38.62

75.72

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

prov

incial

offic

ials a

re pr

oacti

ve in

explo

iting

poss

ible fl

exibi

lity in

the a

dmini

strati

on of

na

tiona

l regu

lation

s to m

inimi

ze th

e reg

ulator

y bu

rden

on bu

sines

ses (

%)

67.54

86.73

76.09

35.12

43.97

38.05

70.28

63.2

73.38

88.09

38.79

44.05

83.69

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

prov

incial

offic

ials a

re m

ore p

roac

tive i

n as

sistin

g the

priva

te se

ctor t

han c

are e

ntral

gove

rnme

nt offi

cials

(%)

62.04

87.39

63.23

46.09

38.9

41.71

64.03

63.35

45.48

71.2

35.73

35.77

61.04

Firms

agre

eing o

r stro

ngly

agre

eing t

hat lo

cal

publi

c offic

ials h

ave s

ufficie

nt kn

owled

ge an

d sk

ills to

fulfil

their

dutie

s (%

)

62.21

85.97

71.87

38.39

38.44

27.98

69.33

63.25

56.8

79.62

28.07

36.85

74

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

coor

dinati

on be

twee

n gov

ernm

ent a

genc

ies in

the

ir pro

vince

is go

od (%

)

67.45

83.32

73.06

39.72

43.5

36.64

64.16

65.09

52.32

79.36

40.36

44.11

74.38

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

prov

incial

offic

ials a

re pr

oacti

ve in

imple

menti

ng

initia

tives

that

supp

ort p

rivate

busin

esse

s (%

)

71.42

85.26

74.06

40.51

44.03

41.92

66.21

65.24

58.29

61.63

43.63

45.6

62.14

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

contr

acts,

land

, and

othe

r bus

iness

reso

urce

s mo

stly f

all in

to the

hand

s of e

nterp

rises

havin

g clo

se re

lation

ship

with

prov

incial

gove

rnme

nt offi

cials

(%)

52.01

71.47

51.38

44.16

53.93

42.48

38.35

4264

.9298

.4656

.650

.5394

.42

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

contr

acts,

land

, and

othe

r bus

iness

reso

urce

s mo

stly f

all in

to the

hand

s of la

rger

enter

prise

s or

state

-own

ed en

terpr

ises (

SOEs

) in th

eir

prov

ince (

%)

49.34

72.13

54.2

60.08

53.19

55.58

38.04

42.66

62.87

94.91

47.85

55.36

90.57

Firms

agre

eing o

r stro

ngly

agre

eing t

hat la

rger

en

terpr

ises o

r SOE

s rec

eive m

ore s

uppo

rt fro

m go

vern

ment

officia

ls tha

n do s

malle

r firm

s in

their p

rovin

ce (%

)

52.62

69.72

56.26

54.63

51.93

49.34

39.36

42.79

68.36

94.45

50.58

46.64

95.05

Subi

ndex

Sco

re7.1

58.2

17.5

53.9

84.0

53.7

17.5

97.5

45.7

45.7

43.5

53.9

65.3

4No

te: S

ubind

ex sc

ore i

s the

aver

age o

f the n

ine in

dicato

rs aft

er re

scali

ng on

a 10

-poin

t sca

le.


5.8 Labor Training and Skills Labor market conditions and regulations are often cited as key obstacles to doing business in Saudi Arabia. The 2015 Global Competiveness Report, for example, noted that local business executives find restrictive labor regulations and an inadequately educated workforce to be the most problematic factors when doing business in the Kingdom (World Economic Forum 2015). Efforts to increase the employment of nationals in the private sector have focused on education and vocational training programs to address the skills gap between the needs of private sector employers and potential workers in the Kingdom. Other aspects of this effort, as underscored in a report by the International Monetary Fund (IMF 2015), involve the implementation of a quota scheme (Nitaqat) and an ongoing policy of localization (Saudization). A study by Baqadir et al. (2011), however, finds that the perception among private sector employers is that the nation’s technical education fails to equip Saudi students with sufficient vocational training, skill levels, and work attitudes. As the study pinpoints, it is imperative to refine and tailor training programs to rectify mismatches between national workers’ skills and the specific needs of local businesses in each province by focusing on underlying factors, including work ethic, specialized knowledge, and general skills.The foreign labor supply, as reported by the Ministry of Labor, has been growing: The total number of foreign labor visas issued in 2014 was greater than 1.6 million, and the total number of work permits issued for the private sector increased to approximately 7.5 million, growing by around 57 percent between 2010 and 2014 (Saudi Arabia, Ministry of Labor 2014). More than 75 percent of the visas issued in 2014 were for businesses located in Riyadh, Makkah, and Eastern Province. As one study by the Jeddah Chamber of Commerce highlights, this indicates that the private sector continues to rely heavily on foreign labor to conduct operations (Jeddah Chamber of Commerce Research Centre 2015). Thus, as the IMF’s 2015 report indicates, until the composition and productivity of the national workforce can be improved, it is vital that provincial labor offices provide firms with access to foreign labor with the skills required to prevent declines in firm productivity and competitiveness. The labor training and skills subindex measures the availability of skills needed by businesses. It is split into two dimensions. The first covers the availability of skilled local labor and encompasses six indicators, as summarized in Table 5.13, that measure efforts by

provincial authorities to promote education, vocational training, and skills development for local industries and to assist in the placement of local labor. Scores under this dimension range from 9.49 for Riyadh to 2.1 for Najran. A similarly high level of variation is seen across the indicators, with 72 percent of Riyadh firms agreeing that they can easily hire Saudi labor with the skills required by their businesses, but less than 40 percent of firms in Najran agreeing. This, in turn, is mirrored by the 78 percent of Riyadh firms asserting that higher education provides local labor with the skills required by the market and by the 78 percent of firms believing that vocational training programs supply local labor with the skills required by the market; in Najran, in contrast, only 45 percent and 43 percent, respectively, find these institutions prepare the workforce adequately. Firms participating in qualitative interviews echoed these views, emphasizing the lack of necessary technical skills and training, along with reluctance to accept certain occupations and lack of commitment to jobs, as the leading difficulties with employing local labor. The second dimension under the Labor Training and Skills subindex is the availability of skilled foreign labor. The fourth indicator, reflected in Table 5.13, measures firms’ ability to obtain foreign labor with the skills required for their business operations. Under this dimension, provinces’ scores range from 9.79 for the Al Madinah to 4.03 for the Eastern Province. In comparison to indicators under the first dimension, the majority of firms across most provinces agree that they can easily hire foreign labor with the skills their businesses require. While to varying degrees a minority of firms across provinces perceived visa issues as posing a major obstacle to their business operations, opinions among firms varied widely regarding the extent to which labor regulation policies, particularly Saudization, constitute an obstacle. Only 3 percent of firms in Al Madinah, for example, perceived labor regulations to be a major or severe obstacle to running their businesses, compared to 67 percent of firms in Jizan. Taking the average of the scores across each dimension, Figure 5.8 illustrates the overall province scores for the Labor Training and Skills subindex. Riyadh, the top performer, has a score of 9, followed by Al Madinah and Al Baha. Northern Borders, the worst performer has a score of 3.14, followed closely by Najran and Assir. Across all provinces, the subindex scores are mainly driven by performance in the availability of foreign labor rather than local labor.


Tabl

e 5.

13: I

ndic

ator

s an

d Di

men

sion

s Co

mpr

isin

g th

e La

bor T

rain

ing

and

Skill

s Su

bind

exPr

ovin

ceMa

kkah

Riya

dhEa

ster

n Pr

ovin

ceAs

sirNa

jran

Jizan

Tabu

kAl

Ba

haAl

Ma

dina

hAl

Qa

ssim

Al

Jouf

North

ern

Bord

ers

Hail

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

highe

r edu

catio

n in t

heir p

rovin

ce su

pplie

s the

loc

al lab

or fo

rce w

ith th

e skil

ls re

quire

d by t

he

marke

t (%

)

73.14

75.54

51.44

30.96

44.49

34.95

56.07

70.49

78.31

72.96

41.42

31.52

56.40

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

voca

tiona

l train

ing pr

ogra

ms in

their

prov

ince

supp

ly the

loca

l labo

r for

ce w

ith th

e skil

ls re

quire

d by t

he m

arke

t (%

)

71.77

77.36

44.83

46.05

42.61

54.93

50.68

70.32

51.48

68.71

44.31

38.97

66.24

Firms

agre

eing o

r stro

ngly

agre

eing t

hat

educ

ation

al an

d voc

ation

al tra

ining

insti

tution

s eq

uip th

e loc

al Sa

udi la

bor f

orce

with

the s

oft

skills

and w

ork e

thics

requ

ired b

y bus

iness

es in

the

ir pro

vince

(%)

71.76

75.72

47.40

41.70

44.37

47.63

46.08

70.32

64.89

61.83

38.48

35.66

50.50

Numb

er of

unive

rsitie

s and

insti

tution

s of h

igher

ed

ucati

on

38

31

11

11

21

11

1

Numb

er of

voca

tiona

l and

tech

nical

traini

ng

institu

tes

614

510

22

52

66

32

2

Firms

agre

eing o

r stro

ngly

agre

eing t

hat th

ey

can e

asily

hire

Sau

di lab

or w

ith th

e skil

ls re

quire

d by t

heir b

usine

ss (%

)

70.77

72.16

43.57

38.72

49.78

57.76

55.32

87.65

82.14

52.13

53.75

51.06

47.99

Dim

ensio

n 1 S

core

6.57

9.49

3.28

2.65

2.11

2.60

3.39

5.75

5.83

5.10

2.15

1.36

3.44

Firms

agre

eing o

r stro

ngly

agre

eing t

hat th

ey

can e

asily

hire

fore

ign la

bor w

ith th

e skil

ls re

quire

d by t

heir b

usine

ss (%

)

84.51

89.83

73.92

85.49

82.08

76.88

66.31

96.03

91.53

96.26

50.19

44.63

93.92

Media

n num

ber o

f day

s nee

ded t

o obta

in a

foreig

n lab

or w

ork v

isa7

3035

6060

4540

205

3060

8030

Firms

stati

ng th

at vis

a-re

lated

issu

es po

se a

major

or se

vere

obsta

cle to

curre

nt bu

sines

s op

erati

ons (

%)

27.95

3.95

54.08

37.97

31.83

26.54

26.72

2.45

1.19

2.01

9.04

2.21

6.47

Firms

stati

ng th

at lab

or re

gulat

ions p

ose a

ma

jor or

seve

re ob

stacle

to cu

rrent

busin

ess

oper

ation

s (%

)

32.66

12.46

57.19

55.68

61.26

68.92

37.61

3.69

3.01

12.05

22.21

18.18

15.45

Dim

ensio

n 2 S

core

7.28

8.53

4.03

4.52

4.44

4.63

5.38

9.46

9.79

8.91

5.35

4.94

8.5

Subi

ndex

Sco

re 6

.92 9

.01 3

.663.5

93.2

8 3.

62 4.

38 7

.61 7

.81 7

.003.7

5 3

.15 5

.97

Dimension 2: Availability of Skilled Foreign Labor

Dimension 1: Availability of Skilled Local Labor

Note:

Dim

ensio

n 1 sc

ore i

s the

weig

hted a

vera

ge of

the s

ix ind

icator

s afte

r res

calin

g on a

10-p

oint s

cale.

Har

d data

indic

ators,

deno

ted by

an as

terisk

(*), t

ogeth

er ac

coun

t for 4

0 per

cent

of thi

s dim

ensio

n. Di

mens

ion

2 sco

re is

the a

vera

ge of

the f

our in

dicato

rs aft

er re

scali

ng on

a 10

-poin

t sca

le.


0 2 4 6 8 10

Northern BordersNajran

AssirJizan

Eastern ProvinceAl JoufTabuk

HailMakkah

Al QassimAl Baha

Al MadinahRiyadh

Availability of Skilled Local Labor Availability of Skilled Foreign Labor

5.9 Infrastructure and Business Services The quantity and quality of infrastructure and business services are vital elements in determining the success of all kinds of commercial activities. Essential infrastructure for doing business in Saudi Arabia includes highways, electricity, streetlights, and telephone lines, and necessary business services include garbage collection, water supply, technical advisory services, and information on raw materials and business partners. Saudi Arabia ranks as 30 globally in terms of overall infrastructure, according to the 2015 Global Competitiveness Report (World Economic Forum 2015). But although it ranks extremely high in some areas, such as mobile telephone subscriptions per 100 people and the quality of electricity supply, the Kingdom lags in areas such as fixed telephone lines per 100 people and the quality of infrastructure for air transport and railroads. The quality of physical infrastructure also differs considerably between and within provinces according to population density and distance to the provincial capital. As for business services, the Ernst and Young G20 Entrepreneurship Barometer (2013) reports that, although 43 percent of entrepreneurs agree that the

Figure 5.8: Labor Training and Skills Subindex

availability of business incubators and industry-specific training programs has increased since 2010, fostering the entrepreneurial business sector in the Kingdom will require a better support infrastructure (Ernst and Young 2013). At the provincial level, this may encompass technical advisory services and programs to educate prospective entrepreneurs on basic accounting, management, and financial planning skills. The local infrastructure and business services subindex measures the quantity and quality of local infrastructure using five indicators and the quantity and quality of business services using one; details appear in Table 5.14. Figure 5.9 compares the provincial subindex scores. The highest-ranking province, Jizan, is followed by Riyadh and Al Baha; the lowest ranking province is Northern Borders, preceded by Al Jouf and Tabuk. Provincial scores vary little in terms of local infrastructure—the highest province scores 4.81 and the lowest 1.7—indicating generally good performance in this dimension. Provincial scores vary considerably in terms of provision of business services; in this dimension, the highest province scores 4.72 and the lowest 0.5, underscoring some provinces’ poor performance in this dimension, particularly Tabuk, Al Jouf, and Northern Borders.


Tabl

e 5.

14: I

ndic

ator

s an

d Di

men

sion

s Co

mpr

isin

g th

e Lo

cal I

nfra

stru

ctur

e an

d Bu

sine

ss S

ervi

ces

Subi

ndex

Prov

ince

Makk

ahRi

yadh

East

ern

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al

Baha

Al

Madi

nah

Al

Qass

imAl

Jo

ufNo

rther

n Bo

rder

sHa

il

Firms

ratin

g a ra

nge o

f infra

struc

ture

servi

ces p

rovid

ed by

prov

incial

ag

encie

s as g

ood o

r ver

y goo

d (%

)

84.17

95.18

89.24

73.18

85.15

71.81

78.77

98.06

92.73

93.38

69.14

58.94

90.79

Media

n num

ber o

f day

s firm

s suff

er

from

electr

ical p

ower

outag

es pe

r yea

r0

00

20

00

00

00

00

Firms

ratin

g ser

vices

prov

ided a

t their

pr

ovinc

es’ a

irpor

ts as

adeq

uate

or ve

ry ad

equa

te (if

appli

cable

) (%

)

84

98.19

90.66

82.03

66.55

93.41

90.75

90.67

95.67

82.84

82.71

60.78

94.01

Firms

ratin

g ser

vices

prov

ided a

t their

pr

ovinc

es’ s

eapo

rts as

adeq

uate

or

very

adeq

uate

(if ap

plica

ble) (

%)

81.3

96

88.12

91.57

89.72

89.51

95.07

95.46

98.59

89.89

51.71

92.38

91.11

Kilom

eters

of pa

ved r

oads

per 1

00

squa

re ki

lomete

rs of

the pr

ovinc

e 9

.28 4.

48 1

.4418

.89 1

.2233

.81 2

.6548

.01 5

.0417

.66 3

.23 1

.56 6

.67

Dim

ensio

n 1 S

core

5.53

6.48

5.54

4.77

4.56

7.57

5.47

9.64

6.42

6.7 3

.65 3

.55 6.

2Fir

ms ra

ting t

heir p

rovin

ces’

busin

ess

supp

ort s

ervic

es as

very

good

or

exce

llent

(%)

20.46

28.08

16.63

29.38

19.14

28.36

10.47

20.45

25.61

18.28

11.88

7.84

20.91

Dim

ensio

n 2 S

core

6.28

9.46

4.67

10 5

.729.5

72.1

6.27

8.43

5.37

2.69

1 6

.46Su

b-In

dex S

core

5.90

7.97

5.11

7.38

5.14

8.57

3.79

7.95

7.42

6.03

3.17

2.27

6.33

Dimension 1: Local Infrastructure

Dimension 2: Local Business

Services

Tabl

e 5.

15: I

ndic

ator

s an

d Di

men

sion

s un

der P

redi

ctab

ility

and

Ris

ks S

ubin

dex

Prov

ince

Makk

ahRi

yadh

East

ern

Prov

ince

Assir

Najra

nJiz

anTa

buk

Al

Baha

Al

Madi

nah

Al

Qass

imAl

Jo

ufNo

rther

n Bo

rder

sHa

il

Firms

agre

eing o

r stro

ngly

agre

eing t

hat th

eir

prov

inces

’ bus

iness

and r

egula

tory e

nviro

nmen

t is

stable

and p

redic

table

(%)

75.12

81.41

56.11

46.12

47.34

54.73

62.76

83.46

85.99

90.51

41.3

34.24

81

Firms

ratin

g a lis

t of r

isk fa

ctors

as im

porta

nt or

very

impo

rtant

for do

ing bu

sines

s in t

heir

prov

ince (

%)

86.15

96.8

98.42

89.46

86.25

79.88

88.76

97.08

98.72

98.3

95.67

88.88

96.99

Subi

ndex

Sco

re 7

.27 5

.23 2.

824.1

6 5

.037.1

45.6

65.3

3 5

.145.6

2.29

3.35

5.15

Note:

Sub

index

scor

e is t

he av

erag

e of th

e two

indic

ators

after

resc

aling

on a

10-p

oint s

cale.

Note:

Dim

ensio

n 1 sc

ore i

s the

weig

hted a

vera

ge of

the fi

ve in

dicato

rs aft

er re

scali

ng on

a 10

-poin

t sca

le. H

ard d

ata in

dicato

rs, de

noted

by an

aster

isk (*

), tog

ether

acco

unt fo

r 40 p

erce

nt of

this d

imen

sion.

Dime

nsion

2 s

core

is th

e only

indic

ator u

nder

this

dimen

sion a

fter r

esca

ling o

n a 10

-poin

t sca

le.


5.10 Predictability and RisksThe Predictability and Risks subindex encompasses two indicators, summarized in Table 5.15, to measure the predictability of the local business environment

0 2 4 6 8

Northern BordersAl JoufTabuk

Eastern ProvinceNajran

MakkahAl Qassim

HailAssir

Al MadinahAl BahaRiyadh

Jizan

Local Infrastructure Business Services

Figure 5.9: Local Infrastructure and Business Services Subindex

Figure 5.10: Predictability and Risks Subindex

and the key risk factors businesses’ experience, such as strategic risk, financial risk, and economic risk. Figure 5.10 presents the results for this subindex. The highest-ranked province is Makkah, with a score of 7.27; the lowest ranked is Al Jouf, with a score of 2.29.

0 2 4 6 8

Al JoufEastern ProvinceNorthern Borders

AssirNajran

Al MadinahHail

RiyadhAl Baha

Al QassimTabukJizan

Makkah


5.11 Summary As the above discussion demonstrates, rankings by subindex show significant variation in province performance across competitiveness dimensions. Overall, the consistent best performers were Riyadh, Al Madinah, and Al Qassim, while the consistent worst performers were Northern Borders, Al Jouf, and Najran. Most provinces scored well on the Entry Costs and Local

Infrastructure and Business Services subindices, while most scored poorly on the Land Access and Security of Tenure, Transparency and Participation, and Labor Training and Skills subindices. These results reflect the key areas of weakness in the Saudi business environment, where national-level interventions are needed to improve enabling environments for private sector development in all provinces.


6. CONCLUDING REMARKS AND

POLICY RECOMMENDATIONS

The PCI provides Saudi Arabia with a powerful tool for promoting competitiveness and understanding key constraints on private sector development and growth at the subnational level. In particular, it offers local officials, the private sector, and other stakeholders a useful guide for identifying the provinces’ relative strengths and weaknesses and a practical aid to governments consulting with the private sector and working to develop actionable policy agendas for achieving a more enabling environment.

The PCI findings highlight several critical areas for policy makers at both the local and the national level seeking to initiate targeted reforms to address shortcomings in economic governance. This section summarizes recommendations suggested by these findings.

6.1 Local-Level RecommendationsThe following subsections outline recommendations for using the PCI as a key policy-analysis and planning tool that can aid policy makers in designing and implementing targeted reform interventions aimed at creating environments more conducive for doing business.

6.1.1 Benchmarking Overall Performance As an initial step, policy makers should use the overall PCI scores to determine how well their own provinces rank in relation to the others. In this regard, policy makers should consider both their provinces’ numerical

final PCI score and their position within the tiers; taken together, these rankings signify a province’s overall level of “business-friendliness.”

6.1.2 Diagnosis of Strengths and Weaknesses After a province’s ranking has been assessed at the overall PCI level, focus should shift to the subindex level. Section 5 presented the system of province rankings by subindex, dimension, and, in some cases, specific indicators of particular interest (within a subindex). Policy makers can evaluate and use these subindex findings to diagnose their provinces’ relative strengths and weaknesses. Table 6.1 lists each province’s three major strengths and weaknesses as indicated by their highest and lowest subindex scores.

46466. CONCLUDING REMARKS AND POLICY RECOMMENDATIONS

Table 6.1: Provinces’ Strengths and Weaknesses Province Strengths Weaknesses

Makkah Time Costs of Regulatory Compliance Land Access and Security of TenureEntry Costs Legal Institutions and Dispute ResolutionPredictability and Risks Transparency and Participation

Riyadh Legal Institutions and Dispute Resolution Transparency and ParticipationLand Access and Security of Tenure Informal ChargesLabor Training and Skills Predictability and Risks

Eastern Province

Proactivity of Provincial Government and Attitude toward the Private Sector

Predictability and Risks

Informal Charges Labor Training and SkillsEntry Costs Transparency and Participation

Assir Entry Costs Transparency and ParticipationLocal Infrastructure and Business Services Land Access and Security of TenureLegal Institutions and Dispute Resolution Labor Training and Skills

Najran Legal Institutions and Dispute Resolution Land Access and Security of TenureEntry Costs Transparency and ParticipationTime Costs of Regulatory Compliance Proactivity of Provincial Government and Attitude toward the

Private SectorJizan Local Infrastructure and Business Services Transparency and Participation

Entry Costs Land Access and Security of TenurePredictability and Risks Labor Training and Skills

Tabuk Informal Charges Local Infrastructure and Business ServicesProactivity of Provincial Government and Attitude toward the Private Sector

Land Access and Security of Tenure

Entry Costs Labor Training and SkillsAl Baha Local Infrastructure and Business Services Legal Institutions and Dispute Resolution

Labor Training and Skills Land Access and Security of TenureProactivity of Provincial Government and Attitude toward the Private Sector

Time Costs of Regulatory Compliance

Al Madinah Labor Training and Skills Land Access and Security of TenureInformal Charges Entry CostsLocal Infrastructure and Business Services Legal Institutions and Dispute Resolution

Al Qassim Legal Institutions and Dispute Resolution Transparency and ParticipationEntry Costs Proactivity of Provincial Government and Attitude toward the

Private SectorLabor Training and Skills Informal Charges

Al Jouf Time Costs of Regulatory Compliance Legal Institutions and Dispute ResolutionEntry Costs Predictability and RisksInformal Charges Land Access and Security of Tenure

Northern Borders

Legal Institutions and Dispute Resolution Entry Costs

Land Access and Security of Tenure Informal ChargesLocal Infrastructure and Business Services Time Costs of Regulatory Compliance

Hail Entry Costs Predictability and RisksTransparency and Participation Informal Charges


6.1.3 Action Planning, Implementation, and Monitoring With knowledge of their provinces’ unique strengths and weaknesses, policy makers can determine local priorities; set measurable, specific targets for improving areas of weak governance; and devise tailored action plans for realizing those targets. A particularly beneficial approach is for a province to set targets for scaling up its performance on the lowest-scored subindices to enter the first quarter of the best subindex value achieved. The specific component indicators under each subindex, as listed in Tables 5.1 to 5.12 above, can then be used to determine which measurable indicators to target. To take an example, Al Madinah province was among the lowest-performing provinces on the Entry Costs subindex. Policy makers for the province might set a target for improving its Entry Costs subindex score from 2.95 to the range of the first quarter of the subindex value, that is, between 8 and 9, as achieved by top performers. Once targets have been defined, action plans can be devised and implemented in coordination with the concerned government entities and private sector stakeholders. Progress toward achieving these targets should be regularly monitored. Reducing entry costs for Al Madinah, for example, as shown in Table 5.1, would require a significant reduction of monetary costs for obtaining business location licenses, which surveyed firms reported were very high compared to other provinces. This finding, in turn, opens up room for peer-to-peer exchanges between provinces, allowing Al Madinah to learn about high-performing provinces’ best practices for keeping monetary costs low. One appealing feature of the PCI is that it compares practices within the same country, which both strengthens the case for implementing effective practices in a lagging region (such as entry costs in Al Madinah) and makes implementation rapid and easy: If another province in the same country can lower its monetary cost of entry, why shouldn’t Al Madinah governorate be able to do the same? After all, its performance is not being compared against that of Singapore or New Zealand, but against another Saudi province, such as Assir. Similarly, Eastern Province, which scored poorly under the Labor Training and Skills subindex, can set a goal of achieving a higher score on the first dimension, availability of skilled labor, moving from 3 to 9 to reach

par with Riyadh, the top performer in this subindex; this improvement would significantly improve Eastern Province’s position in the rankings. Table 5.13 shows that only 43 percent of Eastern Province firms agreed or strongly agreed that they can easily hire Saudi labor with the skills their businesses require, a markedly lower percentage than in other provinces. Improving this dimension would require investigating ways of strengthening links between educational and vocational institutions and the private sector so that the skills taught in the institutions more closely track those needed in the market. Each province seeking to set policies to enhance competitiveness should therefore identify the highest- scoring province in the specific areas it plans to target for improvement. By ascertaining what policies and practices the high-scoring provinces used to achieve their ranking, the low-scoring province can learn the best practices to deploy. These may include small, low-cost interventions, such as use of online platforms or improved methods of disseminating information that may nonetheless have the potential to yield big improvements in the rankings. Finally, by conducting the PCI exercise at regular intervals (such as every two years), provinces can learn whether they have been advancing, both in absolute terms (that is, their own improvements over time along the indicators) and vis-à-vis other provinces. At the national level, the government can reward and recognize top-performers on the Provincial Competitiveness Index to motivate other provinces. In this way, the PCI can act as a catalyst, fostering reform momentum at the local level through province-to-province learning, which in turn can facilitate adoption of best practices and improvements in lagging regions.Numerous examples from international experience testify to the power of indexing exercises and rankings to trigger reform dynamics. In India, for example, a state-level regulatory reform program was driven by the central government, which prepared a list of reform actions in different business regulatory areas and asked state governments to implement them. States were then ranked, in 2015 and again in 2016, on their performance in implementing the reforms. This approach generated considerable enthusiasm and healthy competition among states. The resulting strong reform momentum helped elevate states that had lagged in 2015 to reach places among the leading reformers in 2016.

48486. CONCLUDING REMARKS AND POLICY RECOMMENDATIONS

6.2 National-Level RecommendationsBy looking at areas of consistent weakness across the provinces, the national government can develop and

Table 6.2: PCI Areas of Weakness and General RecommendationsAreas of Weakness General Recommendations

Private Sector Partnerships Enhance communication and partnership with the private sector through routine local activities and planning, for example, through private sector involvement in using the PCI

Access to Land Adopt more demand-based or private sector–based approaches to land development, along with ongoing land reform

Ensure the private sector transparent and efficient land allocation processes

Labor Regulations, Skills, and Employment of Women

Strengthen links between the supply of education and vocational training and market demand by consulting with the private sector to increase focus on subjects and skills needed to meet local needs

Provide vocational training and retraining according to the needs of labor demand

Legal Frameworks Further modernize the judicial environment and support the development of judicial and dispute resolution systems, especially commercial courts, to meet the specific needs of small enterprises

Local Coordination and Capacity Enhance coordination between local level agencies and strengthen linkages between local and national-level governance

Provide training programs for local government officials to build capacity to meet business owners’ needs

implement reforms to promote overall competitiveness. Examples of possible national-level approaches appear in Table 6.2.


APPENDICES

Appendix 1.A: Detailed PCI Measurement MethodologyThe PCI follows a verifiable and well-established indexing methodology used in previous World Bank Group and Asia Foundation economic growth initiatives. The methodology consisted of three main phases. First, survey and hard data were collected to assemble a collection of theoretically and contextually relevant indicators. Second, the indicators were standardized into a 10-point scale and compiled to construct the 10 subindices. Third, the subindices were used to calibrate the overall unweighted and weighted index.

1. Collection of Hard and Survey Data

1.1 Survey Data Collection The PCI is constructed principally using firm-level survey data collected across all 13 provinces of the Kingdom. A multistage research strategy, described below, was used to ensure the accuracy of its representation of the MSME population in each province.

Stage 1: PCI Survey Instrument

A survey instrument collected general information about firms and their performance and to capture firm owners’ perceptions of their local business environment along the 10 key areas represented by the PCI subindices. The questionnaire, first developed in English, was translated into Arabic and checked to ensure that the questions’ meanings remained intact.

Several measures were taken to ensure the survey instrument was effective in measuring provincial competitiveness in the Saudi context. First, qualitative in-depth interviews (IDIs) were conducted with a sample of 36 businesses in different provinces. These interviews helped further appraise the significance of the issues encompassed in the list of subindices previously developed in consultation with public and private sector representatives, but it also helped determine the effectiveness of their descriptions and how they were interpreted by business owners and managers. This information was used as a direct input in designing the survey instrument. Second, to test the survey questions and to identify potential survey design flaws, a two-phase pilot survey was conducted on a sample of 112 firms of different sizes

5050APPENDICES

and in different activity sectors across six provinces. Once the pilot was completed, the data collected and the qualitative feedback received from the field team were used to revise and finalize the survey instrument.

Stage 2: Sampling Frame Development

While the survey instrument was being finalized, a representative sampling frame of the provincial populations of micro-, small-, and medium-sized enterprises was being developed using chamber of commerce datasets of registered businesses in each province. In comparison to other sources considered, chamber of commerce datasets provide the most recent, comparable, and reliable listing of businesses in each province, since all firm owners must register for membership with the relevant chamber of commerce to complete proceedings with various government agencies. Using the chamber of commerce datasets, a computer-assisted telephonic interviews (CATI) listing exercise was conducted. The listing questionnaire solicited information on 8,500 firms, including sector, number of full-time employees, and age of firm; it also served to

confirm whether the businesses remained operational in the province. Firms were randomly selected for the telephone interviews using a sampling interval technique. As Table 1.A.1 illustrates, the sampling interval, s, was calculated by dividing the total number of firms listed under each province by the required sample size for each province. Every sth firm was contacted for interview. If the sth firm was unobtainable for interview, then every sth +1 firm was contacted. Thereafter, the next firm selected for interview followed an interval of s. A contact sheet was used throughout the telephonic interview process to record unobtainable firms (that is, those with no response, wrong numbers, interrupted interviews, and so on). The information obtained from the CATI listing exercise was then used to estimate the universe population of eligible live firms in each province by projecting the proportions of eligible and operating or “live” firms onto the original CoC population frame. This, in turn, was used as a basis for sampling for the PCI survey and was later reused to obtain post-sampling weights.

Table 1.A.1: Selection of Firms for CATI Interviews Using a Sampling IntervalProvince Total No. of Firms Sample Size Sampling Interval

Assir 35,563 500 71Hail 1,131 500 2Jizan 14,368 500 28Tabuk 11,615 500 23Riyadh 153,433 1,000 153Najran 20,617 500 41Al Madinah 15,640 1,000 15Al Baha 8,650 500 17Makkah 107,911 1,000 108Northern Borders 3,789 500 7Eastern Province 46,000 1,000 46Al Qassim 6,800 500 13Al Jouf 3,719 500 7Total 429,236 8,500 n.a.

Note: The abbreviation “n.a.” stands for “not applicable.”


Stage 3: Sampling for the PCI survey

A three-stage sampling approach was used to draw a statistically representative sample of businesses for each of the thirteen provinces using a method known as probability proportional to size (PPS). Under this sampling procedure, each element of the population was given a known, positive probability of being included in the sample. Primary sampling units (PSUs) were defined as the districts in the major city of each province, and secondary and tertiary units were defined by commercial areas and firms, respectively.The first stage of sampling used PPS, with size defined by the size of the population, to select a specific number of PSUs from a sampling frame of all PSUs in the major city in each province. While sampling was done using PPS, implicit stratification was employed in selecting firms. The stratification design had three levels: the provincial level, the sectorial level, and the firm size level. (Size was defined by number of full-time employees.) This helped ensure that the PSU sample was spread across all categories of eligible firms and thus accurately reflected each city’s firm composition. In the second stage, commercial areas in which firms existed in each PSU or district were selected, using when appropriate systematic equal probability sampling. Once the commercial areas were identified, the third stage involved selecting a specific number of firms within each of the selected PSUs, using a “random walk” approach with a sample interval of N, derived from the estimated total number of firms in the commercial area. Thus, this sampling procedure provided a strict sampling strategy that yielded the benefits of improved standard errors when estimating aggregates, while at the same time allowing fieldwork to proceed quickly and minimizing the discretion given to enumerators. A more comprehensive presentation of the sampling approach appears in Appendix 1.B.

Stage 4: Face-to-Face Interviews

Face-to-face interviews were conducted with the sampled businesses to capture their perceptions about and experiences doing business in their province. On the one hand, this survey distribution method yielded high response rates and thus inspires more confidence regarding the precision of the results. On the other hand, it may also have induced bias in the results by generating

high rates of nonresponse to sensitive questions or by introducing new “treatment effects” through the use of enumerators (Fowler 2013).Three steps were taken to minimize possible sources of bias. First, at the beginning of each interview, respondents were provided with a short introduction to the purpose of the study and the questionnaire and were reassured of the confidentiality of their responses. Second, enumerators underwent interview training and were instructed to read opinion-based or sensitive questions without changing the wording or providing explanations using commonly used terms so as not to influence respondents. Third, whenever possible, questions in the survey instrument were posed to elicit answers that would be directly comparable among respondents.

Stage 5: Data Validity

To ensure the reliability of firms’ responses, various internal and external validity checks were undertaken. To verify internal validity, quality checks were implemented to confirm the logical accuracy of the collected survey data. In addition to checks for out-of-range values, the quality checks included confirming that skip patterns were respected when relevant and that responses were tested for logical consistency. The total number of inspections from individual agencies had to be less than or equal to the total number of inspections, for example. Similarly, the total cost of obtaining a business location license had to be less than or equal to the total cost. The internal validity of responses was further ascertained by examining the coherence of responses to related questions. Figure 1.A.1 compares firms’ evaluation of the quality of electricity in their province with the number of power outages experienced in the previous year. These two measures correlated at -0.82, demonstrating that firms’ responses were consistent on this point. To check the external validity of firms’ responses, objective and easily verifiable hard data from published sources was used. Figure 1.A.2 presents an example. It shows the results of measuring the rank ordering of road coverage in each province, measured as kilometers of paved road per 100 square kilometers, against firms’ evaluation of road quality in their province. These two measures correlated at -0.15, perhaps reflecting that road quality is not the same as road coverage.

5252APPENDICES

3

3.5

4

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5

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Firm

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luatio

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0 .5 1 1.5 2

Mean Number of Power Outages Experienced

95% CI Fitted values

Najran

Eastern Province

Mean Firm Evaluation of Electricity (5−Point Scale)•

•• Riyadh••

•

AI Jouf•• AI Baha

• Hail• Al Qassim

Tabuk• •Makkah • Northern Borders

Jizan• Assir

Al Madinah

Figure 1. A 1: Firms’ Evaluation of Electricity vs. Number of Power Outages

Figure 1. A 2: Firms’ Evaluation of Roads vs. Road Coverage

3

3.5

4

4.5

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Mean

Firm

Eva

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0 10 20 30 40 50

Kilometers of paved road per 100 square kilometer95% CI Fitted values Mean Firm Evaluation of Roads (5−Point Scale)

Najran

AI Qassim

••Riyadh•

•

•

AI Jouf•

• AI BahaHail

• Tabuk

• Makkah

• Northern Borders

Jizan•Assir

Al Madinah

Eastern Province••

•


1.2 Hard Data CollectionHard data indicators and sources were assembled simultaneously with the collection of the survey data. These indicators serve several purposes. Most importantly, they help ameliorate perception bias and offset the “anchoring problem” in survey research.5 They also provide measures of structural endowments (such as infrastructure endowment, proximity to markets, and quality of human capital) used to obtain subindex weights.

2. Construction of the Subindices Once the survey and hard data was collected and subindices, dimensions, and indicators selected and finalized based on theoretical and contextual assessment, the PCI subindices were constructed as baskets of standardized indicators reflecting the ten most important issues faced by Saudi Arabia’s private sector.

2.1 Normalizing the Indicators Once their provincial means and median were obtained, the indicators were normalized around a 10-point scale through a simple normalization process, using the following formula:Where Province is the individual province value, Minimum is the smallest provincial value, and Maximum is the largest provincial value among all of the provinces.For some indicators, a large number has a negative interpretation. In such cases, the index was reversed by subtracting the entire quantity from 11. An example of such a negative indicator would be the number of total inspections experienced by a firm. For these, the following formula was used:

Indicators are normalized for three key reasons. First, normalized indicators can be transformed to a value based solely on each province’s score vis-à-vis other provinces. Second, it allows data from different indicators, which are often in different units, to be combined into one subindex. Third, by setting absolute values, normalization facilitates comparison of PCI scores over time.

2.2 Constructing the Indicators After normalizing the indicators, subindex scores were calculated as the simple average of the scaled indicators. If a subindex has multiple dimensions, the average of the dimensions was used instead, so that the dimensions receive equal weights.As in previous studied using hard data in a subindex, the general rule was followed that the indicator had to account for 40 percent of the total index. If hard data was used in a subindex containing multiple dimensions, however, the rule was adapted so that hard indicators accounted for only 40 percent of that particular dimension to keep from distorting the overall meaning of the index. One main motivation for adopting a methodology combining hard and soft data was to help ameliorate perception bias. The 40 percent weighting for hard data indicators is adequate to correct for perception bias without dominating the opinions of respondents. Nevertheless, to ensure that the overall PCI rankings were not sensitive to this weighting choice, two additional statistically driven indexing frameworks were used to check for robustness, as described in section 4 above.

3. Calibration of the Final PCI Index Once all the subindices were constructed, the final unweighted index was calibrated by simply adding the individual subindex scores. To ensure that the final scores offer the most policy relevant information to provincial officials, however, a weighting scheme based on principal component analysis (PCA) was employed to determine weights for each subindex statistically. Under this scheme, subindex weights signal local officials how to prioritize their reform interventions for the biggest impact. The weighting scheme followed a three-step approach based on a widely used method developed by Nicoletti, Scarpetta, and Boylaud (2000). First, PCA was applied on all the subindices to divide them into uncorrelated “components” or baskets of subindices and to obtain the component “loadings,” which measure the correlation between each individual subindex and the components. Following standard practice, the number of components was determined according to three criteria: (i) they have associated Eigen values larger than 1; (ii) they contribute

5. The anchoring problem encompasses biases that may result when respondents assess a situation based on personal experience without understanding how their experiences compare to those of other respondents in different provinces.

11 – 9* Provincei _ i-Minimum

Maximum _ Minimum +1( ( ( (

5454APPENDICES

individually to the explanation of the overall variance by more than 10 percent; (iii) they contribute cumulatively to the explanation of the overall variance by more than 60 percent. Under these conditions, the first four components were selected. Second, varimax rotation of the components was used to minimize the number of individual subindices with a high loading on the same component. Third, once the rotated component loadings were obtained, the subindex weights were constructed using the method outlined by Nicoletti, Scarpetta, and Boylaud (2000). This method exploits the fact that the square component loadings represent the proportion of the total unit variance of the subindex to group subindices with the highest loadings into “intermediate composite” indicators. As Table 1.A.2 shows, the first intermediate composite includes Land Access and Security of Tenure (with a weight of 0.18 given by the normalized square factor loadings), Transparency and Participation (weight of 0.26), Proactivity of Provincial Government and Attitude toward the Private Sector (weight of 0.29), and

Labor Training and Skills (weight of 0.19). The second intermediate composite includes Entry Costs (weight of 0.52) and Legal Institutions and Dispute Resolution (weight of 0.26). The third intermediate composite includes Informal Charges (weight of 0.20) and Local Infrastructure and Business Services (weight of 0.57). The final intermediate composite includes the Time Costs of Regulatory Compliance (weight of 0.35) and Predictability and Risks (weight of 0.47). Using these domain weights, the intermediate composites were then aggregated and weighted by the proportion of variance explained by the respective component to obtain the subindex weights. The results of this exercise appear in Table 1.A.2. As a last step, the final weights were rounded up and the subindices were grouped into three categories, with corresponding weights, as follows: high (15 percent), medium (10 percent), and low (5 percent). Using these calibrated weights, the final PCI was calculated as the weighted sum of all 10 subindices, with the final subindex scores reflecting the relative importance of each subindex.

Table 1.A.2: Component Loadings and Intermediate CompositesRotated Component Loadings Squared Loadings (Scaled to Sum to 1)

Comp. 1 Comp. 2 Comp. 3 Comp. 4 Comp. 1 Comp. 2 Comp. 3 Comp. 4

Entry Costs -0.05 0.72 -0.19 0.12 0.00 0.52 0.04 0.01

Land Access and Security of Tenure 0.42 0.28 0.18 -0.21 0.18 0.08 0.03 0.04Transparency and Participation 0.51 0.04 -0.18 0.07 0.26 0.00 0.03 0.01Time Costs of Regulatory Compliance 0.08 0.16 -0.06 0.59 0.01 0.03 0.00 0.35Informal Charges 0.26 -0.27 -0.45 -0.17 0.07 0.07 0.20 0.03Legal Institutions and Dispute Resolution 0.09 0.51 0.21 -0.26 0.01 0.26 0.04 0.07Proactivity of Provincial Government and Attitude toward the Private Sector 0.54 -0.09 -0.05 0.04 0.29 0.01 0.00 0.00

Labor Training and Skills 0.44 -0.10 0.27 0.14 0.19 0.01 0.07 0.02

Local Infrastructure and Business Services 0.03 -0.13 0.75 -0.01 0.00 0.02 0.57 0.00

Predictability and Risks -0.01 -0.04 0.08 0.68 0.00 0.00 0.01 0.47

Explained Variance 0.31 0.20 0.19 0.18 n.a.n.a. n.a. n.a.Proportion of Explained Variance 0.36 0.23 0.21 0.20 n.a. n.a. n.a. n.a.

Notes: The abbreviation “n.a.” stands for “not applicable.” “Explained variance” is the variance explained by each component. “Proportion of variance” is the variance explained by each component divided by the total explained variance by the three components.


Table 1.A.2: Subindex Weights for the Final PCI IndexDomain Weights

Weight of the RespectiveComponent

Weight Score (w_i)

Weights (∑w_i=1)

Final Weights

Importance

Entry Costs 0.52 0.23 0.12 14% 15% High

Land Access and Security of Tenure 0.18 0.36 0.06 8% 5% Low

Transparency and Participation 0.26 0.36 0.09 11% 10% Medium

Time Costs of Regulatory Compliance 0.35 0.20 0.07 9% 10% Medium

Informal Charges 0.20 0.21 0.04 5% 5% low

Legal Institutions and Dispute Resolution 0.26 0.23 0.06 7% 5% low

Proactivity of Provincial Government and Attitude toward the Private Sector 0.29 0.36 0.10 12% 15% High

Labor Training and Skills 0.19 0.36 0.07 8% 10% Medium

Local Infrastructure and Business Services 0.57 0.21 0.12 14% 15% Medium

Predictability and Risks 0.47 0.20 0.09 11% 10% Medium

Total n.a. n.a. 0.84 100% 100% n.a.

To check the robustness of the overall rankings obtained using this indexing methodology, two further indexing approaches were adopted. The first, a regression-based approach, involved regressing a binary variable “Plans to make new investments” from the PCI survey on standardized values of the indicators, controlling for firm and province-level characteristics. Using ordinary least squares regression, the indicators’ scores were obtained by multiplying the coefficients on the standardized indicators by their respective provincial means. The scores were then summated by subindex and normalized through exponentiation. The final PCI was calibrated as the weighted sum of the normalized subindex scores. The second approach employed PCA on standardized values of the indicators under each subindex separately. This allowed dividing the subindices into uncorrelated components or “baskets of variables” and obtaining component loadings for indicators with loadings of greater than 0.5 in absolute value. These loadings were then multiplied by the provincial means of the standardized indicators to obtain the indicators scores. The scores were then summated by subindex and normalized through exponentiation. The PCI was reached by taking the weighted sum of the normalized subindex scores.

The final PCI results obtained using both approaches were compared with results obtained using the indexing methodology described above to test the sensitivity of province rankings. The results of this exercise appear above in section 4.

Appendix 1.B: Sampling for the PCI Survey1. Multistage Sampling ProcedureA three-stage sampling approach using a method known as probability proportional to size (PPS) was used to draw the PCI survey’s province-level representative sample of firms. PPS is a cluster probability sampling method that accounts for the size of the population in a given area, with areas of denser populations more likely to be selected for the sample. For the PCI survey, it was observed that firms constituting the eligible population of interest were distributed among both residential and commercial or industrial areas and that the concentration of firms in residential areas was roughly proportional to the population of households. The expedient course was thus to conduct sampling using the size of the population of households as an indicator of the size of the population of firms.

Note: The abbreviation “n.a.” stands for “not applicable.”

5656APPENDICES

Once a sample of areas for primary sampling units (PSUs) was selected to reflect the composition of firms within each city, firms in residential areas were randomly selected for interview until 50 percent of the sample had been achieved. Thus, after the achieved number of interviews under each stratum were reviewed, purposive sampling was used in commercial and industrial areas until the required number of firms under each stratum was achieved. The steps taken to implement the PPS methodology under each stage of sampling is outlined below.

1.1 First Stage

As a first step, the PSUs were defined as the districts comprising the cities within each province. A sampling frame of the PSUs was established using a recent and complete list of the population in each district of the major city in each province. For the three largest provinces of Riyadh, Makkah, and the Eastern Province, a list of the districts in the second largest city was also used. The compiled list of PSUs was then sorted sequentially from smallest to largest according to population size. Using the PPS sampling technique, with the population as a measure of each PSU’s size, the number of interviews to be achieved within each PSU was determined according to city size and the total number of districts per city.

1.2 Second Stage

For each of the selected PSUs, field visits were conducted by enumerators to compile a list of all commercial areas where firms exist. The commercial areas were then selected using, when appropriate, systematic equal probability sampling.

1.3 Third Stage

Once the commercial areas were identified, firms were randomly selected for interviews using a sample interval of N, derived from the estimated total number of firms in the commercial area. This was achieved under a “random walk” approach, which involved the enumerators starting from a landmark and following a “left/right hand rule,” such that only one direction was used for the

walk. Thus, this sampling procedure provided a strict sampling strategy that yields the benefit of improved standard errors when estimating aggregates, while at the same time it allowed fieldwork to proceed quickly and minimized the discretion given to enumerators.

2. Stratification Design Under the above described sampling procedure, implicit stratification was maintained for the selection of firms, which helped ensure that the sample of PSUs was spread across the categories of eligible firms and presented a more accurate reflection of the composition of firms within each city. The stratification design was done at three levels: province level, sector level, and firm size level. Size class, defined in terms of number of full-time employees, was divided into three strata: micro, small, and medium. Any firms with more than 99 employees were ineligible for inclusion in the sample. Eligible sectors, defined as nonagricultural, nongovernment-owned sectors, were divided into eight strata, allowing a finer degree of stratification. Appendix 1.C provides a complete breakdown of the strata, and Appendix 1.D provides the detailed definitions of the eligible size and sector classifications included in the PCI.

3. Disproportionate Sampling Strategy The original CoC data yielded an overrepresentation of firms in some provinces and an underrepresentation of firms in other provinces. To ensure valid inferences, therefore, a disproportionate strategy was employed at the province level to ensure minimum representation of firms based in smaller, less populated provinces. Table 1.B.1 illustrates this process.

4. Postsampling Weights Once the interviews were completed and the data collected, it was necessary to reweigh the sample back to the estimated populations based on the CoC listing. The postsampling weights were obtained by simply dividing the estimated population in each stratum by the number of interviews achieved in each sample.


Table 1.B.1: Disproportionate Sampling StrategyProvince Proportionate

SampleProportionate

Sample (%)Disproportionate

SampleDisproportionate

Sample (%)Makkah 1,297 32% 860 22%Riyadh 970 24% 700 18%Eastern Province 430 11% 350 9%Assir 448 11% 350 9%Najran 198 5% 250 6%Jizan 171 4% 300 8%Tabuk 113 3% 240 6%Al Baha 101 3% 240 6%Al Madinah 99 2% 150 4%Al Qassim 88 2% 150 4%Al Jouf 39 1% 150 4%Northern Borders 37 1% 150 4%Hail 10 0% 110 3%Total 4,000 100% 4,000 100%

5858APPENDICES

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6060APPENDICES

Appendix 1.D: Definitions of Eligible Firm Size and SectorsSector Classification In line with the United Nations’ International Standard Industrial Classification (ISIC) system used by the Saudi Ministry of Commerce, eight sectors were considered eligible for the study. These can be grouped under three broad sector types, as follows:1. Industry, including manufacturing and construction 2. Wholesale and retail trade and repair of motor vehicles 3. Services, including accommodation and food service activities; transportation and storage; information and communication; professional, scientific, and technical activities; and administrative and support service activitiesSectors excluded from the study included agriculture, forestry, and fishing; mining and quarrying; electricity, gas, and water supply; financial intermediation; real estate; education; and health. Any state-owned firms or organizations were also ineligible.

Firm Size Definition Based on the definition of the Ministry of Commerce’s recently established Small and Medium Enterprise General Authority, the following classification of MSMEs was used to design the PCI:• Micro (1 to 5 employees) • Small (10 to 49 employees) • Medium (50 to 99 employees) Any firms over 100 employees were ineligible for the study.

Appendix 2: List of Subindices and Component Indicators6

Following is the complete list of subindices and component indicators used in the PCI study.

1. Entry Costs7

Dimension 1: Time Costs

• Business density in the province.*8 (+ve)9

• Median number of days it takes firms to obtain a business location license from the municipality.10 (-ve)

• Median numbers of days it takes firms to register with the Ministry of Labor. (-ve)

• Median number of days it takes firms to register with the General Organization for Social Insurance. (-ve)

• Median number of days it takes firms to obtain a certificate of Zakat. (-ve)

• Median number of days it takes firms to obtain a civil defense license. (-ve)

• Median number of licenses and permits required for a firm engaging in wholesale or retail trade to legally operate the business.11 (-ve)

• Median number of licenses and permits required for a firm engaging in manufacturing to legally operate the business. (-ve)

• Percentage of firms that rating as difficult or very difficult completion of all the licenses, permits, and procedures required to legally operate their businesses. (-ve)

6. Indicators denoted with an asterisk (*) are hard data indicators; all other indicators were collected from survey data.7. For most indicators under this subindex, the sample was limited to new entrants only (i.e., firms under three years of age) to better

capture the time and costs involved in the current state of regulatory processes governing business entry, which have undergone significant improvements in recent years.

8. Business density measures the number of new business registrations per 1,000 people aged 15 to 64. For the purpose of the PCS study, the number of new business registrations was measured by the total number of new business location licenses issued in each province in the past year (2015).

9. The interpretation of a high score for each indicator is given between brackets.10. For indicators involving duration, to ensure comparability, waiting periods were measured starting from the time the application or request is

submitted to the time the desired approval or service is received.11. Firms were classified according to the United Nations’ ISIC 4 (International Standard Industrial Classification of All Economic Activities),

adopted by the Saudi Ministry of Commerce for issuing certificates of commercial registrations.


Dimension 2: Monetary Costs

• Median amount of official fee(s) required to obtain a business location license from the municipality. (-ve)

• Median total costs to obtain a business location license from the municipality. (-ve)

2. Land Access and Security of Tenure

Dimension 1: Land Access

• Total number of industrial cities in the province.* (+ve)• Percentage of firms stating that difficulties in securing

ownership or lease of land are a major or severe obstacle to business expansion. (-ve)

• Percentage of firms agreeing that the application of land zoning rules (i.e., allocation decisions for residential, commercial, or other types of land) in their province is efficient and consistent. (+ve)

Dimension 2: Security of Tenure

• Percentage of firms rating as low or very low the risk of changes in rental contracts.12 (+ve)

• Percentage of firms believing that the process of disputing changes in lease contracts is frequently or always fair. (+ve)

3. Transparency and Participation

Dimension 1: Transparency

• Percentage of firms rating as easy or very easy access to a list of the policy, regulatory, and planning documents or information necessary to run their businesses.13 (+ve)

• Percentage of firms agreeing or strongly agreeing that the content of the above documents or information is clear and easy to understand and follow. (+ve)

• Percentage of firms believing that personal connections (i.e., friends and family relations) with local government officials are frequently or always necessary to facilitate access to the above documents. (-ve)

• Percentage of firms believing they are confident or very confident of their knowledge of existing rules, laws, and regulations concerning the running of their businesses. (+ve)

• Percentage of firms agreeing that changes are frequently or always made to the local administration’s rules, laws, and regulations materially affecting their businesses. (-ve)

• Percentage of firms stating that they are seldom or never informed of such changes. (-ve)

Dimension 2: Participation

• Percentage of firm owners that are members of at least one chamber of commerce committee or subcommittee.14 (+ve)

• Number of chamber committees or subcommittees per 10,000 registered firms in the province.* (+ve)

• Percentage of firms agreeing that memberships in chambers of commerce or other business associations are useful or very useful. (+ve)

• Percentage of firms stating that local government authorities frequently or always solicit comments from affected firms on the content and implementation of new laws or policies concerning businesses. (+ve)

12. Changes in agreed rental agreements include rent increases, sales to new parties, or new contractual terms that limit business activity.13 The list of documents and information includes information on acquiring the required licenses and permits; application and administrative

forms for the required licenses and permits; national laws and regulations concerning businesses; provincial land use allocation plans and maps; national/provincial investment incentive policies and transformation plans; plans for new provincial infrastructure projects; etc. The average score across the different documents or information were taken to form a single indicator of business owners’ perception of access to information necessary for running their businesses, where in each case the answer ranges from very easy to impossible.

14. When setting up their businesses, all firm owners must register for membership with the relevant chamber of commerce to complete proceedings with governmental departments. As of 2014, memberships can be completed online along with the commercial registration process. Therefore, this indicator aims to measure the participation of firms by asking whether firms have any additional memberships in committees or subcommittees under the chambers, which represent the views of specific sectors and/or activities and communicate them through the chamber to the relevant government bodies.

6262APPENDICES

4. Time Costs of Regulatory Compliance • Median percentage of senior management spent during

the past year on dealing with bureaucratic procedures and paperwork required by government regulations. (-ve)

• Median number of inspections by or mandatory meetings with a list of local government agencies firms are subjected to per year.15 (-ve)

• Median duration of inspections by or mandatory meetings with a list of local government agencies firms are subjected to per year. (-ve)

• Percentage of firms rating as difficult or very difficult working with government officials on complying with laws and regulations. (-ve)

5. Informal Charges16

Dimension 1: Firm-Level Corruption

• Percentage of firms reporting that informal payments occur in their line of business. (-ve)

• Percentage of firms reporting that firms in their line of business usually know in advance the amount of informal payment required. (-ve)

• Percentage of firms believing that, with additional payment, services are frequently or always delivered as expected. (-ve)

Dimension 2: Systematic Corruption

• Percentage of firms stating that making an informal payment is essential or very useful to expedite government services. (-ve)

• Percentage of firms stating that personal connections (i.e., friends or family relations) with local government officials are essential or very useful to expedite government services. (-ve)

• Percentage of firms stating that firms in their line of business are frequently or always expected or requested to give informal payments or gifts during a range of dealings with government officials (e.g., applying for public services, processing licenses and permits, and obtaining the desired type or number of visas). (-ve)

• Percentage of firms participating in public procurement contracts during the past three years. (+ve)

• Percentage of firms agreeing that information concerning public procurement contracts in the province is frequently or always transparent. (+ve)

• Percentage of firms agreeing that procedures for winning public procurement contracts in the province are frequently or always fair. (+ve)

• Percentage of firms agreeing that personal connections (i.e., friends or family relationships) are important or very important for winning public procurement contracts. (-ve)

• Percentage of firms disagreeing or strongly disagreeing that preferential treatment or favoritism (based on personal connections, nationality, etc.) by government officials does not exist. (-ve)

• Percentage of firms disagreeing or strongly disagreeing that preferential treatment or favoritism (based on personal connections, nationality, etc.) by government officials is not harmful. (-ve)

• Percentage of firms reporting that firms in their line of business frequently or always experience misconduct by local government officials during inspections (i.e., overzealous inspections, requests for informal charges, unfair treatment, discrimination, or harassment). (-ve)

15. The list of local government agencies includes municipality departments, Civil Defence, Ministry of Labor departments, Ministry of Commerce departments, Customs, etc.

16. One strand of the literature on corruption suggests that informal charges may be beneficial if they are predictable and expedite bureaucratic procedures (i.e., firms are able to factor bribe costs into their business plans and the bribes deliver expected results) (Kaufmann and Wei 1999; Campos, Lien, and Pradhan 1999; Méon and Weil 2010). While this “efficient grease” hypothesis lacks significant empirical support, it was echoed during the presurvey qualitative interviews in the context of the Saudi business environment. In particular, it was revealed that while most firm owners considered informal charges to be detrimental to doing business, they also acknowledged them as useful for expediting government procedures and ensuring their businesses’ smooth operation. At the same time, however, respondents frequently indicated that government officials, especially during inspections, would demand such payments even if businesses were in compliance with rules and regulations; that is, they would use compliance with local regulations to extract rent. Therefore, for the purpose of constructing the informal changes subindex, all indicators were negative scores, as corruption to grease the wheel of business cannot be justified.


17. As opposed to “hard” skills (i.e., specific, technical abilities such as writing and analytical skills), this indicator aims to capture business owners’ perceptions of the extent to which educational and vocational training in the province equips the local labor force with the “soft” skills and work ethics (i.e., reliability, commitment to working hours, etc.) required to meet the needs of businesses.

6. Legal Institutions and Dispute Resolution • Percentage of firms agreeing or strongly agreeing

that the legal system will uphold their contract and property rights. (+ve)

• Percentage of firms agreeing or strongly agreeing that they can appeal to competent persons at higher levels to resolve disputes over misconduct by local government officials (e.g., unfair treatment, harassment or discrimination, asking for informal charges). (+ve)

7. Proactivity of Provincial Government and Attitude toward the Private Sector • Percentage of firms stating that the attitude of

government officials in the province toward the private sector is positive or very positive. (+ve)

• Percentage of firms agreeing or strongly agreeing that provincial officials are proactive in implementing initiatives that support private businesses. (+ve)

• Percentage of firms agreeing or strongly agreeing that provincial officials are proactive in exploiting flexibility in the administration of national regulations to minimize the regulatory burden on businesses. (+ve)

• Percentage of firms agreeing or strongly agreeing that provincial officials are more proactive in assisting the private sector than are central government officials. (+ve)

• Percentage of firms agreeing or strongly agreeing that local public officials have sufficient knowledge and skills to fulfil their duties. (+ve)

• Percentage of firms agreeing or strongly agreeing that coordination is good between different government agencies in their province. (+ve)

• Percentage of firms agreeing or strongly agreeing that larger enterprises or state-owned corporations receive more support from government officials than do smaller firms in their province. (-ve)

• Percentage of firms agreeing or strongly agreeing that contracts, land, and other business resources in their province mostly fall into the hands of enterprises with close relationships with government officials. (-ve)

• Percentage of firms agreeing or strongly agreeing that contracts, land, and other business resources in their province mostly fall into the hands of larger enterprises or state-owned corporations. (-ve)

8. Labor Training and Skills

Dimension 1: Availability of Skilled Local Labor

• Percentage of firms agreeing or strongly agreeing that higher education in their province supplies the local labor force with the skills required by the market. (+ve)

• Number of universities and higher education institutions (both public and private) in each province.* (+ve)

• Percentage of firms agreeing or strongly agreeing that vocational training programs in their province supply the local labor force with the skills required by the market. (+ve)

• Number of vocational and technical training institutes (both public and private) in each province.* (+ve)

• Percentage of firms agreeing or strongly agreeing that they can easily hire Saudi labor with the skills required by their businesses. (+ve)

• Percentage of firms agreeing or strongly agreeing that educational and vocational training institutions equip the local Saudi labor force with the soft skills and work ethics required by businesses in their province.17 (+ve)

Dimension 2: Availability of Skilled Foreign Labor

• Percentage of firms agreeing or strongly agreeing that they can easily hire foreign labor with the skills required by their businesses. (+ve)

• Median number of days it takes firms to obtain a foreign labor work visa. (-ve)

• Percentage of firms stating that visa-related issues pose a major or severe obstacle to the current operation of their businesses. (-ve)

• Percentage of firms stating that labor regulations (i.e., Saudization policies) pose a major or severe obstacle to the current operation of their businesses. (-ve)

6464APPENDICES

9. Local Infrastructure and Business Services

Dimension 1: Local Infrastructure

• Percentage of firms rating as good or very good a range of infrastructure services provided by provincial agencies (e.g., road quality, water, electricity, etc.).18 (+ve)

• Median number of days firms suffer from electrical power outages per year. (-ve)

• Kilometers of paved roads per100 square kilometers in the province.* (+ve)

• Percentage of firms rating as adequate or very adequate services provided at airports in their province (if applicable). (+ve)

• Percentage of firms rating as adequate or very adequate services provided at seaports in their province (if applicable). (+ve)

Dimension 2: Business Services

• Percentage of firms rating as very good or excellent the business support services in their province (e.g., business information services, consultancy services, recruitment services, etc.). (+ve)

10. Predictability and Risks• Percentage of firms agreeing or strongly agreeing

that the business and regulatory environment in their province is stable and predictable. (+ve)

• Percentage of firms rating a list of risk factors as important or very important for doing business in their province.19 (-ve)

18. The average score across the different services was taken to form a single indicator of business owners’ rating of local infrastructure service, where in each case the answer ranges between “poor” and “excellent.” The same was applied to business owners’ rating of local business support services.

19. The risk factors include strategic risk, operational risk, regulatory risk, financial risk, labor risk, and economic risk. Definitions of these risk factors were provided to PCI respondents to ensure clarity and consistency.


Appe

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6666APPENDICES

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REFERENCES

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Dahlan, A. 2015. “Land Fees and Barriers to Real Estate Development.” Okaz Newspaper. October. http://www.okaz.com.sa/new/Issues/20151025/Con20151025804287.htm.De Soto, Hernando. 2000. The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books. Ernst and Young. 2013. “The EY G20 Entrepreneurship Barometer: Saudi Arabia Country Profile.” Ernst and Young Global. http://www.ey.com/GL/en/Services/Strategic-Growth-Markets/The-EY-G20-Entrepreneurship-Barometer-2013. Florini, A. 1999. “Does the Invisible Hand Need a Transparent Glove? The Politics of Transparency.” Paper prepared for the World Bank Conference on Development Economics, Washington, DC, April.Fowler Jr., F. J. 2013. Survey Research Methods. 5th ed. Thousand Oaks, CA: Sage Publications.Heritage Foundation. 2015a. Index of Economic Freedom: Freedom of Corruption Index. http://www.heritage.org/index/.

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———. 2015b. Index of Economic Freedom: Property Rights Index. http://www.heritage.org/index/.International Monetary Fund (IMF). 2015. “Saudi Arabia: Selected Issues.” Country Report No. 15/286. International Monetary Fund: Washington, D.C.Jeddah Chamber of Commerce Research Centre. 2015. “Index of Visas Distribution According to the Reality of the Private Sector and the Numbers of Work Permits During 2010–2014.” Jeddah, Saudi Arabia. December. http://www.jcci.org.sa/.Kaufmann, Daniel, Aart Kraay, and Pablo Zoido-Lobatón. 2002. “Governance Matters.” World Bank Policy Research Working Paper No. 2772, World Bank, Washington, DC. Kaufmann, Daniel, and Shang-Jin Wei. 1999. “Does ‘Grease Money’ Speed Up the Wheels of Commerce?” Policy Research Working Paper No. WPS 2254, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/189751468739475975/Does-grease-money-speed-up-the-wheels-of-commerce.Malesky, Edmund J. 2016. The Viet Nam Provincial Competitiveness Index 2016: Measuring Economic Governance for Business Development. Hanoi, Viet Nam: Viet Nam Chamber of Commerce and Industry. http://eng.pcivietnam.org/catalog-of-publications/pci-full-reports/.McKinsey Global Institute. 2015. “Saudi Arabia Beyond Oil: The Investment and Productivity Transformation.” McKinsey and Company. http://www.mckinsey.com/insights/mgi.Méon, Pierre-Guillaume, and Laurent Weill. 2010. “Is Corruption an Efficient Grease?” World Development 38 (3): 244–59. Nicoletti, G., S. Scarpetta and O. Boylaud. 2000. Summary Indicators of Product Market Regulation with an Extension to Employment Protection Legislation. OECD Publishing No. 226. Paris: OECD.

Riyadh Chamber of Commerce and Industry. 2010. “A Study of Small Enterprises as Key Engines for Desired Economic Growth.” Riyadh, Saudi Arabia. http://www.riyadhchamber.org.sa/. Saudi Arabia, Ministry of Commerce and Industry. 2014. “189 Seconds for Getting the Commercial Registration Electronically.” November. http://mci.gov.sa/en/MediaCenter/News/Pages/24-11-14-1.aspx. Saudi Arabia, Ministry of Economy and Planning. 2016. “Main Objectives of the Tenth Development Plan (2015–2019).” http://www.mep.gov.sa. Saudi Arabia, Ministry of Labor. 2014. “Ministry of Labor Statistical Yearbook 2014.” https://www.mol.gov.sa/. Transparency International. 2015. Corruption Perceptions Index. Transparency International, Berlin, Germany. http://www.transparency.org/cpi2015. World Bank. 2006. Doing Business 2006: Creating Jobs. World Bank Group: Washington, DC. ———. 2015a. “Business Reforms Pick Up in Middle East and North Africa, Despite Conflict, Says Doing Business Report.” October. World Bank, Washington, DC. http://www.worldbank.org/en/news. ———. 2015b. Worldwide Governance Indicators: Control of Corruption Index. World Bank Group: Washington, DC. www.govindicators.org/.———. 2017. Doing Business 2017: Equal Opportunity for All. World Bank Group: Washington, DC. World Economic Forum. 2015. “The Global Competitiveness Report 2015–2016.” World Economic Forum, Geneva, Switzerland.

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