World Bank Document...Co-Task Team Leader, Co-Author : Mehdi Benyagoub Contributing Authors : Gael Raballand, Randa Akeel, Laurent Gonnet, Lina Badawy, Phlippe Alby, George Clarke,

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Finance and Private Sector DevelopmentMiddle East and North Africa RegionThe World Bank

FEBRUARY 2014

TunisiaInvestment Climate Assessment

Enterprises’ Perception in Post Revolution

© 2014 The International Bank for Reconstruction and Development/ The World Bank1818 H Street, NW Washington, D.C. 20433Telephone: 202-473-1000Internet: www.worldbank.orgE-mail: [email protected]

All rights reserved

A copublication of the World Bank and the International Finance Corporation

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

Rights and Permissions

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Design and Printed by: Macro Graphics Pvt. Ltd. www.macrographics.com

Currency Equivalents(Exchange rate as of September 1, 2013)

Currency Unit US$ 1

:=

Tunisian Dinar (TND)1.64 TND

Fiscal YearJanuary 1 – December 31

Vice President : Inger Andersen

Country Director : Neil Simon Gray

Sector Director : Loïc Chiquier

Sector Manager : Simon C. Bell

Task Team Leader, Lead Author : Djibrilla Issa

Co-Task Team Leader, Co-Author : Mehdi Benyagoub

Contributing Authors : Gael Raballand, Randa Akeel, Laurent Gonnet, Lina Badawy, Phlippe Alby, George Clarke, Peter McConaghy, Annoula Rysova

Editing Assistance : Marjorie Espiritu, David Phan, Stephanie Hubbard, Stefanie Ridenour

Abbreviations and Acronyms v

AbbreviAtions And Acronyms

ADTPAssociation de Développement Technologique Innovation et Production

CEM Country Economic Memorandum or Monitoring

CPS Country Partnership Strategy

DPR Development Policy Review

ES Enterprise Survey

EU European Union

FAMEX Fonds d’Acces aux Marches d’Exportation (Export Market Acess Fund)

FDI Foreign Direct Investment

FIDEXFonds d’Innovation et de Developpement des Exportations (Innovation and Export Development Fund)

FSAP Financial Sector Assessment Program

GATT General Agreement on Tariffs and Trade

GDP Gross Domestic Product

IACE Institut Arabe des Chefs d’Entreprise

ICA Investment Climate Assessment

ICS Investment Climate Survey

ICT Information and Communications Technology

IME Industrial Mechanical Engineering

IP Intellectual Property

IPR Intellectual Property Rights

ISN Interim Strategy Note

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014vi

IT Information Technology

LLC Limited Liability Company

LMIC Lower Middle Income Countries

MCCG Materials for Construction, Ceramics and Glass

MENA Middle East and North Africa region

MFI Microfinance Institutions

MNC Multinational Corporations

MSME Micro Small and Medium Enterprises

NPL Non-performing Loans

OECD Organization for Economic Co-operation and Development

PCR Public Credit Registry

PSD Private Sector Development

R&D Research and Development

SME Small and Medium Enterprises

SOE State-Owned Enterprise

SSA Sub-Saharan Africa

STI Science Technology and Innovation Strategy

TCLF Textiles and Clothing, Leather and Footwear

TFP Total Factor Productivity

TND Tunisian Dinar

UMIC Upper Middle Income Countries

UN United Nations

UNCTAD United Nations Conference on Trade and Development

USD United States Dollar

VAT Value Added Tax

WTO World Trade Organization

table of contents vii

tAble oF contents

Acknowledgements xv

Introduction xvii

Executive Summary xxi

Economic and Private Sector Performances; Profile and Trends xxi

Leading Constraints Perceived by Tunisian Firms xxiii

Governance and Regulatory Constraint xxvi

Innovation Amongst Tunisian Firms xxviii

Labor Markets and Workers’ Skills xxviii

Infrastructure Challenges xxiv

Access to Finance xxx

Chapter 1: Economic and Private Sector Performances, Profile and Trends 1

Growth and Performance: Is the Tunisian Economy Generating Long Term Growth? 2

Employment Responses to Economic Development Policy 4

Labor Market Indicators 5

Firm Profile, Performance and Productivity in Tunisia 6

Firm Profile in Tunisia’s Private Sector 6

Labor Productivity 7

Total Factor Productivity 8

Labour Costs 8

Firm Performance by Firm Type 10

Has Tunisia Undergone Transformational Economic Changes Over the Last 15 Years? 11

Structural trends of Tunisia’s Economy 11

Export Policy in Tunisia 12

Export Orientation and Performance 14

Export Diversification 14

Export Markets 15

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014viii

Technological Sophistication in Exports 16

Creative Destruction 17

What Drives Firm Dynamism in Tunisia: A Look at Recent Trends 18

Firm Dynamics Entry and Exit: Sectoral Analysis 18

Effects on Productivity and Employment: Has there been a creative destruction process? 19

Conclusions and Recommendations 20

Chapter 2: Governance and Regulatory Framework 23

Direct and Indirect Regulatory Burden on Firms 24

Importance of Discretion and Arbitrary Application of Rules 27

Uneven Enforcement of Regulations for Private Sector 28

Recommendations 32

Chapter 3: Innovation Technology & Exports for Private Business in Tunisia 35

Definition of innovation and scope of the analysis 35

Firm Innovation in Tunisia: The General Picture 36

Innovation and Its Added Value to the Economy 39

Firm Innovation and Contribution to Exports 39

Job Creation 40

What Differentiates an Innovative Firm in Tunisia? 41

Technology Use and R&D 41

Employee Skills and Training 41

Barriers and Challenges Faced by Innovative Firms 42

Macroeconomic Framework 43

Labor Force Qualifications 43

Access to Finance 44

Customs/foreign trade regulations and competition from the informal sector 44

The Tunisian Innovation Ecosystem 45

Recommendations 45

Science Technology and Innovation (STI) Strategy 46Short-term 46Medium- term 48Long -Term 48

Chapter 4: Labor Markets and Workers Skills 51

Characteristics of the Labor Market 51

Main Characteristics of Employees: Education and Training 52

table of contents ix

Hiring Employees 56

Employee Compensation 58

Level of Remuneration 58

Wage Structure 59

Labor Taxation 62

Conclusions and Recommendations 63

Chapter 5: Infrastructure Challenges Facing Firms in Tunisia 65

Electricity 65

Telecommunications 67

Water 69

Conclusion and Recommendations 69

Chapter 6: Access to Finance 71

The Tunisian Financial Sector 71

Major Impediments to Firms Access to Finance 72

Inefficiencies in the Bankruptcy Regime 73

Outdated Collateral Regime 73

Insufficient Competition in the Banking Sector 73

Low Quality of the Credit Demand and Risk Assessment 74

Firms’ Access to Finance 74

Limited Credit Information Sharing 75

Chapter 7: Policy Recommendations 77

Employment 77

Regulatory Environment 78

Innovation and Entrepreneurship 79

Access to Financial Services 80

Annexes 83

Annex I: Standard Tables 83

Annex II: Measures of Firm Performance and Total Factor Productivity Analysis 95

Total Factor Productivity Estimation 97

Methodology 97

Methodological Issues 100

Annex III: ICA Enterprise Survey Sample Composition & Population Frame 103

Annex IV: Regression Tables & Additional Relevant Data Used in the Innovation Chapter 105

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014x

List of Figures

Figure 1: GDP Growth Per Capita (average 2000-2012) xxi

Figure 2: Unemployment Trends in Tunisia xxii

Figure 3: Survey Results Indicating the Top Constraints Perceived by Businesses xxiv

Figure 4: Prevalence of Corruption and Delays for Services xxv

Figure 5: Innovation Reported by Firms (% Each Sector) xxvi

Figure 6: Innovative - Non-Innovative Firms by Major/Very Severe Obstacle to Current Operations xxvii

Figure 7: Percentage of Companies Providing Formal Training xxviii

Figure 8: Percentage of Firms Rating “Electricity” as a Major or Very Severe Constraint xxix

Figure 9: Wait Days and Informal Payments for Obtaining an Electricity Connection in MENA xxx

Figure 10: Percentage of Firms Using Bank Finance A) by Regional Comparison B) by Size xxxi

Figure 11: GDP Annual Growth (%) 3

Figure 12: GDP Growth Per Capita (average 2000-2012) 2

Figure 13a and 13b: Evolution of FDI as a Share of GDP a) in Tunisia b) Comparison with other Countries 4

Figure 14a and 14b: Growth and Unemployment of University Graduates; a) Population with Tertiary Education; b) Evolution of Unemployment 5

Figure 15: Labor Force Participation Rates amongst Youth, Tunisia and Comparators 6

Figure 16: Employment to Population Ratio, Comparators 6

Figure 17: Labor Productivity in the Manufacturing Sector 7

Figure 18: Total Factor Productivity is Low in Tunisia, Relative to Countries at Similar Levels of Per Capita Income 8

Figure 19: Labor Costs as a Function of Per Capita GDP 9

Figure 20: Unit Labor Costs in Tunisia and other Low and Middle Income Countries 10

Figure 21: Differences in Total Factor Productivity by firm type 10

Figure 22: Tunisia’s Evolution of Sector Shares in GDP 12

Figure 23: Manufacturing Sector Value-added Growth (10-year average) 12

Figure 24: Manufacturing Sector Value-added Growth (10-year average) 12

Figure 25: Exports of Goods and Services 13

Figure 26: Number of Products Exported 15

table of contents xi

Figure 27: Tunisia’s High-Technology Exports (% of manufactured exports, annual growth rate) 16

Figure 28: High-Technology Exports (% of manufactured exports avg. 15 year) 17

Figure 29: Firm Density and Development 18

Figure 30: Entry and Exit rates in Manufacturing (2000-2011) 19

Figure 31: Entry and Exit Rates in Services (2000-2011) 19

Figure 32: Perception of the Corruption Constraint among MENA Firms 24

Figure 33: Perception of Corruption Index (WGI) 24

Figure 34: Prevalence of Corruption and Delays for Services 25

Figure 35: Percentage of Informal Payment Request to Speed Things Up 26

Figure 36: Percentage of Senior Management’s Time Spent Dealing with Regulations 26

Figure 37: Losses Due to Investment Climate Weaknesses (in % of sales) 27

Figure 38: Corruption Payment by Firm Size 27

Figure 39: VAT Reimbursement Frequency by Number of Days 31

Figure 40: The Vicious Circle Between Tax and Customs Agents’ Behaviors and Some Private Sector Operators 31

Figure 41: Benchmarking of Cargo Dwell Time 32

Figure 42: Ratio between Longest Dwell Times/Average 32

Figure 43: Innovative vs. Non-Innovative by Capital Share 35

Figure 44: Innovation Reported by Firms 36

Figure 45: Distribution of Innovative & Non-innovative Firms by Size 37

Figure 46: Innovative/Non-innovative Firms by Industry in Firms 0-5 Years Old 37

Figure 47: Innovative/Non-innovative Firms by Industry 38

Figure 48: Innovative/Non-innovative Firms by Industry 38

Figure 49: Innovation Firms by Region 39

Figure 50: Distribution of Innovative and Non-innovative Firms by Exports 40

Figure 51: Innovative vs. Non-innovative Firms by Number of Jobs Created 40

Figure 52: Innovative vs. Non-innovative Firms and Number of Jobs Created by Firm Age 40

Figure 53: Innovative vs. Non-innovative Firms by Various Input Variables 41

Figure 54: Firms Indicating that They Do R&D by Industry 42

Figure 55: Innovative vs. Non-innovative Firms by Labor Skill Level and Training 42

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xii

Figure 56: Contribution of Innovative vs. Non-innovative Firms to High Value Added Jobs 42

Figure 57: R&D Expenditure and GDP Growth in Tunisia 43

Figure 58: Innovative/Non-innovative Firms by Major/Very Severe Obstacle to Current Operations 43

Figure 59: Innovative/Non-Innovative Firms and Access to Finance as a Major Obstacle by Firm’s Size and Age 44

Figure 60: Innovative/Non-Innovative Firms by Practices of Competitors that Impose Major/Severe Obstacles 45

Figure 61: Percentage of Companies Providing Formal Training 54

Figure 62: Constraints Related to the Employees’ Qualifications and Type of Industry 62

Figure 63: Labor Taxation 63

Figure 64: Percentage of Firms Rating “Electricity” as a Major or Very Severe Constraint 65

Figure 65: Wait Days and Informal Payments for Obtaining an Electricity Connection in MENA 67

Figure 66: Telecom Indicators in Tunisia, by Firm Size 68

Figure 67: Structure of the Banking System; Share in Total Outstanding Credit 71

Figure 68: Evolution of Credit to GDP 72

Figure 69: Credit to Private Sector 72

Figure 70a and b: Percentage of Firms Using Bank Finance a) by Regional Comparison b) by Size 74

Figure 71: Percentage of Firms Rating “Access to Finance” as a Major or Severe Constrain 75

Figure 72a and b: Percentage of Firms Audited, and Percentage of Firms Having a Loan (Audited/non-Audited) 75

Figure 73: Collateral Requirements 76

Figure 74: Better Access to Finance in Tunisia: Key Policy Recommendations 81

Figure 75: Is the Establishment a Part of a Larger Company, by Non-innovative vs. Innovative Firms 108

Figure 76: Innovative Firms by Age Group 108

Figure 77: Has the Establishment Benefited from a Facility Upgrade Scheme? 109

Figure 78: Innovative vs. Non-innovative Firms, by Inability to Benefit from Facility Upgrade Scheme 109

Figure 79: Availability of Scientist and Engineers 109

table of contents xiii

List of Tables

Table 1: ICA Survey Results 29

Table 2: Types of Competitors’ Practices which Harm your Company 30

Table 3: VAT Reimbursement: Delays and Constraints 30

Table 4: Average Number of Jobs Created by Firms according to Firm Age 41

Table 5: Job Category 51

Table 6: Constraints Related to Labor Regulations 52

Table 7: Constraints Related to Employees’ Qualifications 53

Table 8: Educational Level of Production Workers 53

Table 9: Formal Vocational Training 54

Table 10: The Determinants of Training 55

Table 11: Delays in Hiring 56

Table 12: Availability of Labor Force 57

Table 13: Lack of Experience and Training 57

Table 14: Monthly Earnings by Sector in 2011 58

Table 15: Monthly Earnings in 2011 59

Table 16: Wage Determination within a Company 60

Table 17: Wage Determination 62

Table 18: Electricity Loss by Region 66

Table 19: Power Supply Indicators 67

Table 20: Telecom Indicators, Tunisia and Comparators 69

Table 21: Delays for Water Connections across Regions 69

Table 22: Survey Sample Structure 83

Table 23: Globalization of Markets and Inputs 84

Table 24: Competitors and Suppliers 85

Table 25: General Constraints to Operation 86

Table 26: Infrastructure Indicators 87

Table 27: Sources of Finance 88

Table 28: Credits, Loans, and Liabilities 89

Table 29: Financial Sector: Auditing, Transaction Costs, and Property Rights 90

Table 30: Regulatory Burden and Administrative Delays 91

Table 31: Governance: Uncertainty and Corruption 92

Table 32: Technology Indicators 93

Table 33: Workers 94

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xiv

Table 34: Manufacturing and Service Companies by Size and Region 107

Table 35: Manufacturing and Service Companies by Size and Sector 107

Table 36: Manufacturing and Service Companies by Region and Sector 107

Table 37: Indirect Exports and Input Variables 108

Table 38: Direct Exports and Input Variables 108

AcKnoWledGements xv

AcKnoWledGements

This Investment Climate Assessment (ICA) is based on a survey of 600 Tunisian enterprises to better understand their perception of the business environment in post revolution Tunisia, as well as the dynamics and diversity of the Tunisian private sector. The intention of the ICA is to inform policy-makers on the current private sector performance and business constraints to guide the reform agenda.

The ICA was undertaken in cooperation with the Institut Arabe des Chefs d’Entreprises (IACE). The activity was led by Djibrilla Issa, (Lead Author), and Mehdi Benyagoub (Co-Author). The team included Lina Tarek Badawy (Data Analysis), Gael Raballant (Governance Chapter), Laurent Gonnet (Access to Finance Chapter), Randa Akeel (Innovation Chapter), George R. Clarke (Productivity Chapter), Philip Alby (Labor Market Chapter), Annoula Rysova (Policy Research Analysis on Innovation) and Marjorie Espiritu (overall report editing). David Phan, Peter McConaghy, Jade Salhab, Stephanie Ridenour and Stephanie Hubbard provided valuable contributions and editorial assistance.

Special thanks go to the peer-reviewers Gabi Afram, Thomas Haven, Thomas Kenyon and Jean-Michel Marchat. Antonio Nicifora and Hebba Elgazzar provided useful comments.

The activity was developed under the overall supervision of Simon Bell, Sector Manager, and the general direction of Loic Chiquier, Sector Director. Simon Gray Country, Director, and Eileen Murray, Resident Representative, have provided continuous strategic guidance. The team wishes to thank Maourane El Abassi who provided useful guidance.

The authors wish to extend their gratitude to Mejdi Hassen, Director General of the Institut Arabe des Chefs d’Entreprises (IACE), Farouk Kriaa, Chief Project Manager and Professor of Statistics and all members of the IACE. The authors acknowledge the support from the survey team of IACE, in particular, Mourad Kriaa. These partners at IACE were instrumental in the successful completion of the survey. The report was designed and printed by Macro Graphics Pvt. Ltd., New Delhi, India.

introduction xvii

introduction

Located in the heart of the North African coastline, adjacent to vital shipping channels connecting Europe and Asia, Tunisia has long been a regional economic influence in the Middle East and North Africa. Although socialist policies dominated the Tunisian economy throughout the latter half of the 20th century, Tunisia has re-focused its economic strategy on key indicators such as foreign investment, domestic job creation, exports and tourism. These have become the backbone of the Tunisian economy and have helped strengthen relationships with primary trading partners in the European Union. Exports such as textiles, computer arts and petrochemicals now account for a significant portion of the Tunisian economy. This liberal economic development strategy created years of stable annual growth and improved living standards well into the 2000s. However, this growth was unequal due to a culture of corruption and cronyism that reached its height during the era of former President Zine el Abidine Ben Ali from 1987 through 2011. Years of progressive economic growth were stymied by rising unemployment and government waste. Discontent for the Ben Ali government boiled over in January 2011 with a revolution to overthrow the president, ruling party and Parliament. In the ensuing months of uncertainty, declines in tourism and investment contributed to an overall economic decline that lasted throughout much of the year. The political climate also remained uncertain as Tunisians worked to form a new coalition government and transition into a democratic system.

Despite these uncertainties and external factors which hamper growth, Tunisia’s economy began the process of recovery in 2012. Rebounding from the contraction of 1.9% of GDP in 2011, the economy grew by 0.6% in 2012. Unemployment receded from 18.9% in 2011 to 16.7% in 2012, albeit still well above the pre-revolution level of 13%. Much of the growth was the result of a rebound in tourism and mining, and increased consumption resulting from large public expenditures on wages and social programs.

Private Sector Development (PSD) in Tunisia remains a key challenge going forward. Before the revolution, a system of privileged access to public resources and efforts to shield favored companies from competitive forces limited the benefits of economic liberalization and growth. A persistent inability

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xviii

to address youth unemployment, combined with growing unhappiness about the lack of political and economic fairness and openness, fed revolutionary fervor. There is an important regional dimension to unemployment and the lack of opportunity in Tunisia. Tunisian researchers estimate that in some governorates unemployment is double the national average while firm population density is less than half the figure found in urban centers. Political uncertainty and persistent social domestic tensions, all combined with the weak Eurozone performance and the instability in Libya, are key factors impeding faster recovery.1

The transition period will continue to have a significant impact on the country’s political and socio-economic landscape. The government’s challenge will be to put an end to the past legacy of cronyism and jumpstart more inclusive economic growth to provide employment for Tunisian men and women, while maintaining social cohesion.

By surveying 600 firms across the country, the Investment Climate Assessment (ICA) aims to identify current priority challenges confronted by firms operating in Tunisia, including their constraints and costs, and document their performance under prevailing conditions. Second, it draws on the regional and global experience of the World Bank to provide best practice principles and examples for Tunisia’s reform priorities. Third, by grounding discussion of these issues in hard facts and rigorous analysis, the ICA provides the basis for consultations and consensus-building around a reform strategy and action-plan to promote inclusive, private sector-led growth.

The ICA complements on-going efforts to enhance equitable growth and competitiveness in Tunisia. With its focus on the real economy, the ICA is a natural complement to the recent Financial Sector Assessment

1 The World Bank, Tunisia Overview: http://www.worldbank.org/en/country/tunisia/overview.

Investment Climate Assessment are critical instruments to: (i) quantify features of the investment climate that matter most for productivity and income growth, especially for the poor; (ii) track changes in the investment climate within a country, and (iii) compare performance within regions or countries. ICA’s focus on microeconomic and structural dimensions of a nation’s business environment, viewed in an international perspective. To this end, ICAs look in detail at factors constraining the effective functioning of product markets, financial and non-financial factor markets, and infrastructure services which include weaknesses in the legal, regulatory and institutional framework. ICAs also provide the tools and analytical framework to identify reform priorities in a country’s investment climate, by linking constraints to firm-level costs and productivity.

ICAs at the World Bank

introduction xix

Program (FSAP). It also acts as a vehicle to bring the lessons of regional work on investment climate, financial inclusion and integration into the Tunisian context, grounded in the experience of Tunisian firms. The ICA will also be an input to the future Country Partnership Strategy (CPS). It has informed the design of the Competitiveness and Export Development Project under preparation. The ICA also coordinated with and can provide empirical data to the Development Policy Review (DPR) for two of its three modules i.e.: (i) the policy environment for attracting investment, and jobs creation; and (ii) developing regionally sensitive growth strategies.

As the economy continues to recover and the political landscape solidifies, Tunisia’s government faces real challenges reassuring businesses and investors. Through insight and best practices from new democracies around the world, coupled with empirical data collected through the ICA, Tunisia can embark upon the meaningful task of creating stability, reducing unemployment, streamlining disparities and strengthening the country’s financial system.

executive summary xxi

executive summAry

ECONOMIC AND PRIVATE SECTOR PERFORMANCES; PROFILE AND TRENDS

Tunisia enjoyed a 4.8% average annual growth in GDP over most of the 2000s, placing the country among the leading growth countries in the MENA region and well above the world average of 2.68% (Figure 1). While Tunisia’s growth has been on par with that of Turkey and Lower Middle Income Countries (LMIC), it is significantly lower than the growth experienced by Upper Middle Income Countries (UMIC) including China, Malaysia, and Thailand. The January 2011 revolution posed both challenges and opportunities for the country. On one hand, the revolution increased economic uncertainty and joblessness; growth fell to -1.1% in fiscal year 2011 while unemployment increased from 13% in 2010 to 18.9% in 2011 and then to 16.7% in 2012. At the same time, the revolution has provided Tunisia an opportunity to rethink its private sector growth model. The country stands to reflect on the lessons learned over decades of economic liberalization and utilize empirical data to develop policies that will lead to long-term growth.

Figure 1: GDP GROWTH PER CAPITA (AVERAGE 2000-2012)

Source: WDI/INS, 2012.

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investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxii

Unemployment is one of the most pressing socio-economic issues facing the country. The unemployment rate in Tunisia has traditionally hovered around 12 to 13%, however, this figure has increased substantially as a result of the Eurozone crisis and the 2011 revolution (Figure 2). The national unemployment rate was estimated at 16.7% for the last quarter of 2012, with close to 653,000 unemployed. Unemployment disproportionally impacts youth and educated individuals. Among graduates of higher education, the unemployment rate was 33.2%, with 45% of female graduates unemployed, compared to 22.6% for their male counterparts. This indicates a growing structural imbalance between the demand for unskilled labor, and an increasing supply of skilled labor.

Through lowering trade barriers, integrating into international supply chains, and incentivizing investment, Tunisia’s gradual integration into the world economy has facilitated a shift away from agriculture and raw materials (phosphates, oil, and gas) towards manufacturing and services. Tunisia has achieved some structural transformation seen in other high-growth economies. However, productivity and output gaps remain low, particularly among micro firms, and relative to countries with similar levels of development. Firms comprised over 100 workers, account for over a third of all jobs, more than all one-person firms combined. Eighty six percent of all Tunisian firms, however, are one-person enterprises. Labor force participation in Tunisia hovers around 50% for the overall population and remains comparable to countries in the region. Labor productivity is lower in Tunisia than would be expected given Tunisia’s per capita income. The move toward services has been driven mainly by low value-added activity such as individual unit in transports, real estate and some hotels and restaurants.

Figure 2: UNEMPLOYMENT TRENDS IN TUNISIA

Source: INS Data; and Barro-Lee, 2011.

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executive summary xxiii

Tunisia’s growth and development over the past decades have been fuelled by the export of goods, though exports remain hampered by slow EU growth and low diversity. The export sector grew at an average yearly rate of about 5.3% between 1997 and 2010. However, Tunisia’s exports are less dynamic than they used to be and are evolving at a slower pace than global trade trends. Over the past decade, Tunisia’s exports have grown 3.41% per year, significantly lower than fast growing exporters such as Romania or Vietnam, and even slower than Morocco and Jordan. Though Tunisian exports have diversified, they remain concentrated within a small number of products and sectors. Tunisian firms are also highly vulnerable to negative shifts in EU growth due to their reliance on European markets. The EU accounted for over 70% of Tunisia’s exports in 2010, while France, Italy and Germany accounted for 56% of all exports. Finally, though technological content in Tunisia’s exports has increased steadily over the past fifteen years, the average percentage of high-tech exports was only 5% in 2011.

LEADING CONSTRAINTS PERCEIVED BY TUNISIAN FIRMS

The Investment Climate Survey (ICS) seeks to identify the leading factors that prevent enterprise growth and development by asking business managers how they perceive constraints to the operation and growth of their business. The survey uses a set of indicators at the regional level that provide comparisons of conditions among regions and with other countries and regions.

Instability and uncertainty are the top constraints noted by firms. Fifty-five percent of firms rate political instability as a severe constraint. Likely a result of the recent political transition, the overall investment climate is viewed by operators as unpredictable, unfair and costly. Corruption, informality, and market factors such as finance, infrastructure, and labor are of significant concern to private firms (Figure 3). A second tier of concerns is comprised of regulatory factors including customs and trade regulations. Crime and disorder also figure strongly among this second tier group.

The level of workers’ skills and education are the second leading perceived constraint to firm operations. Thirty nine percent of sampled firms perceive the available skills of the workforce as a weakness. Many firms have responded that candidates do not meet their required qualification expectations. For instance, 70% of respondents stated that the types of engineers and/or professionals available on the job market do not possess adequate skills required for the position.

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxiv

Firm location is an important factor. Firms located along the coastal areas of the greater Tunis region had fewer complaints about the business environment than did their peers located in the hinterland. For instance, operators in the Jandouba region pay almost six times more for security and have suffered significant losses related to disorder (12%). Firms in Sfax, Gabes, and Mednine are overall more constrained than firms located in any other region.

GOVERNANCE AND REGULATORY CONSTRAINT

Twenty nine percent of firm managers rated corruption as a severe or very important constraint, ranking this challenge as the overall sixth leading constraint identified. Bureaucratic and regulatory requirements continue to impose a significant burden on businesses. For instance, it can take up to 60 days for an industrial electrical connection to be set up and almost six months for a construction permit to be approved. Likewise many firms are subject to informal payment requests ranging from 5% for import licenses to 23% for construction permits. More than a quarter of all firms surveyed declared that they have to provide some form of informal payment in order to accelerate a transaction with the administration.

There is significant space for discretionary power by bureaucrats and politicians in the various applications of the law. Only 26.5% of managers disagree with the statement that the rules and regulation governing their main activity are unpredictable, while 43% of managers do not believe these

Figure 3: SURVEY RESULTS INDICATING THE TOP CONSTRAINTS PERCEIVED BY BUSINESSES

Source: Author’s calculations, The World Bank Enterprise Survey, 2012.

Political instability

Access to Financing (Ex: Collateral)Electricity

Corruption

Customs and Trade RegulationsTax Rates

TelecommunicationsTransportation

Labor Regulations (Like Social Insurance)Courts

Theft, disorder and crimesTax Administration

Licensing and PermitsAccess to Land

Water

0.0 10.0

9.911.212.012.2

20.0 30.0 40.0 50.0 60.0

Regulatory UncertaintyFormalities for starting a business

Practices of competitors in the informal sector

Macroeconomic uncertaintyInadequately educated workforce

13.214.5

20.021.422.222.522.7

24.525.825.9

28.933.533.9

34.839.0

55.6

executive summary xxv

laws and regulations are applied evenly. In addition, many firms complain that their competitors are not subject to the same types of cost and regulations that they themselves face.

Import cargo dwell time and VAT reimbursement variations represent examples of uneven and discretionary enforcement of regulations. When compared to other MENA countries, including in the sub-region, cargo dwell time in Tunisia is, on average, the worst after Algeria (close to 10 days), much worse than Morocco (below 5 days) and not better than Lebanon and Egypt. The large magnitude in variances suggest that even firms with similar profiles are subject to different treatment, VAT reimbursement occurs almost 200 days after the request has been lodged (accounting for 15% of total sales). Small firms often do not find the delay associated with long and heavy VAT procedures worth the amount they could receive.

Key Messages and Recommendations to Address Regulatory and Governance Constraints

The uneven and discretionary enforcement of regulations adds to the time and money needed for doing business in Tunisia. Authorities must directly address regulatory burdens, procedural delays, and uneven enforcement. Steps to do so include:

Reducing the delay and ensuring predictability of key service delivery, �including operating licenses, import licenses, construction permits, and electricity connections.

Cutting discretionary measures by increasing accountability such as �imposing deadlines and explicit recourse alternatives to help govern the interaction between the administration and enterprises.

Continuing customs reform to help reduce uneven enforceability �of regulations. Upgrading the customs information system and introducing a comprehensive computerized risk management system designed to reduce human intervention in customs activities will increase efficiency, transparency and improve governance.

Figure 4: PREVALENCE OF CORRUPTION AND DELAYS FOR SERVICES


180.00

160.00

140.00

120.00

100.00

80.00

60.00

40.00

20.00

0.00Telephone

4.7616.09

4.86

19.06 16.9619.28

16.88

59.16

23.00

157.55

OperatingLicense

ImportLicense

Electricity ConstructionPermit

% �rm that were requested a gift Delays for the service (Days)

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxvi

INNOVATION AMONGST TUNISIAN FIRMS

Innovation, defined as the absorption and adaptation of technology to develop new unproven products and processes for commercialization, is important for Tunisian firms to produce higher-value -added products and expand into new products and markets. Of Tunisian firms, 47.5% have introduced a new product or improved on an existing one. The highest level of innovation is reported by firms in the ICT sector, followed closely by textiles and agribusiness (Figure 5).

Firm size and age are particularly important factors for innovation. Large firms report the highest level of innovation, at 55.8%, followed by medium and small firms with 45.2% and 44%, respectively. Large firms tend to invest in Research and Development (R&D) and have more capital to invest in new technology and production processes needed to maintain market share and competitiveness. Amongst young firms (up to five years), higher concentrations of innovative firms exist in tourism, trade, and ICT.

Innovative firms are found to create more jobs than non-innovative firms, except for older firms (15+ years) where innovation and job creation are negatively correlated. High-risk innovative firms in Tunisia that succeed beyond three years will go on to contribute more to job creation than all other SMEs. Innovative firms are significantly more likely to engage in R&D (44% versus 22%), use technology and the Internet, and invest in vocational training for their employees, than non-innovative firms.

Innovative and non-innovative firms indicate similar issues to be major/very severe obstacles, however, with certain variations. Macroeconomic framework and labor force qualifications are severe/major obstacles to both types of firms. Overall, innovative firms consider issues such as political instability, electricity and access to finance, less of a major/very severe obstacle (Figure 6). Compared to non-innovative firms, they consider macroeconomic framework and lack of labor qualifications, to be more of a major/very severe obstacle.

Ser

vice

sAgrbus.53.4%

Clothes30.9%

Textiles64.3%

IME46.6%

Other51.3%MCCG*

51.9%

Trade48.4%

Tourism26.1%

ICT73.3%

Constr50%

Health42.9%

Manufacturing

Figure 5: INNOVATION REPORTED BY FIRMS (% EACH SECTOR)


Notes: IME – Industry mechanical engineering; MCCG – Materials for construction, ceramics and glass.

executive summary xxvii

Key Messages and Recommendations to Foster Innovation among Tunisian Firms

Empirical analysis demonstrates that innovation is critical to job creation in Tunisia. Tunisia must focus on developing and fostering an ecosystem that allows all firms to integrate technology into core operations. This will allow firms to develop new products and processes, move into value-added sectors, and enter new markets. Developing and growing this innovation ecosystem entails:

Establishing a source of revenue for R&D support that is not exposed �to government fiscal volatility. The Tunisian government may consider setting up an endowment fund through public-private partnerships or implementing tax breaks to support R&D.

Promoting training of skilled labor in technology-driven fields. The �government should support programs such as the training program developed by the Association of Firms in Technology which provides training by ICT companies for in-demand skills.

Developing mechanisms that would encourage equity investments �and angel investors to invest in early start-ups and growing innovative SMEs. This includes developing public-private investment funds, linking start-up firms to existing early stage financing funds, promoting diaspora finance in tech-oriented startups, and increasing the role of FAMEX/FIDEX to cultivate access to external linkages for foreign firms.

Figure 6: INNOVATIVE – NON-INNOVATIVE FIRMS BY MAJOR/VERY SEVERE OBSTACLE TO CURRENT OPERATIONS


Non-innovative

% o

f �rm

s sa

ying

“ye

s”

Ele

ctric

ity

Tele

com

mun

icat

ions

Tran

spor

t

Wat

er s

uppl

y

Acc

ess

to la

nd

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tom

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n tr

ade

regu

latio

ns

Com

petit

ion

from

info

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Tax

rate

s

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ith ta

x au

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ities

Form

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es to

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Unc

erta

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usin

ess

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ns

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Pol

itica

l ins

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Cor

rupt

ion

Cou

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Acc

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to �

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Legi

slat

ion

Lack

of t

rain

ing

and

qual

i�ca

tions

Mac

roec

onom

ic fr

amew

ork

70%

60%

50%

40%

30%

20%

10%Innovative

0%

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxviii

Strengthening intellectual property frameworks and providing support �to firms to manage Intellectual Property (IP) issues through government institutions including FAMEX, INNORPI, and Ghazala ICT.

LABOR MARKETS AND WORKERS’ SKILLS

The lack of workers with relevant skills is a severe problem for Tunisian companies. While there are an alarmingly high number of educated young people unemployed, private sector firms complain about the many difficulties they face when hiring employees. Over 39% of companies surveyed believe this constraint as an obstacle for the company. Similarly, 66% of firms believe there is a shortage of experience and training among skilled workers, engineers, and technicians. At the industry level, more than 70% of companies in the clothing and textile industry report issues with labor availability.

Education levels are relatively high among workers. The vast majority of companies have, on average, production workers who have completed more than seven years of study. Large companies appear to have a slight advantage regarding the education level of their production labor force. Forty six percent of workers show an average education level greater than 10 years of education. Similarly, foreign companies seem to attract more skilled labor than domestic companies. However, exporting companies show a significantly lower level of education than companies targeting the domestic market.

A majority of firms provide formal training to their employees, which exceeds what is observed in the region (Figure 7). Company size appears

Source: Investment Climate Surveys in Tunisia, World Bank.

Note: Exclusively for companies in the manufacturing sector. Source: Investment Climate Surveys in Tunisia, World Bank.

60.0

50.0

40.0

30.0

20.0

Algéríe(2007)

Jordanie(2006)

Maroc(2007)

Roumanie(2009)

Maurice(2009)

Turquie(2008)

Coree(2005)

Liban(2009)

Bresil(2009)

Tunisie(2011)

10.0

17.3

23.9 24.7 24.9 25.628.8

39.5

52.4 52.9 54.3

0.0

Figure 7: PERCENTAGE OF COMPANIES PROVIDING FORMAL TRAINING

executive summary xxix

to be the main factor determining the decision to offer formal training, with larger firms being significantly more likely to train than smaller firms.

Key Messages and Recommendations to Address Labor and Skill Mismatches in Tunisia

Companies must forge closer links with universities, schools, and �training centers to ensure graduates possess the skills required to meet the demands of the private sector.

The government can promote matching labor supply and demand �through tax incentives for hiring young graduates without experience. Alternatively, the State could reduce taxation on the amount incurred on the companies’ in-house training programs.

While maintaining quality and academic rigor, the education system �could be adapted to provide shorter and more practical training schemes for skills demanded by the private sector.

Training courses provided by public universities and private schools �must be reformed to increase quality, as they are considered disparate and opaque to both employers and students.

Strengthen the role of the National Agency for Employment and �Autonomous Work (Agence Nationale de l’Emploi et du Travail Indépendant) to address labor mismatches by focusing on recent graduates and other vulnerable groups.

Revise the labor code to focus more on income protection through �social safety nets of the unemployed rather than labor protection. This would help improve labor mobility and incentivize firms to hire more regularly.

INFRASTRUCTURE CHALLENGES

Electricity is perceived as a leading constraint to firm operations in Tunisia, although perceptions vary by location (Figure 8). Gaining access to electricity takes an average of 59 days, one of the highest regional averages. Seventeen percent of firms are expected to make informal payments in order

Figure 8: PERCENTAGE OF FIRMS RATING “ELECTRICITY” AS A MAJOR OR VERY SEVERE CONSTRAINT

Source: The World Bank, Enterprise Survey, 2012.

50.00

33.5

Tuni

sia

all

Tuni

s

Sou

sse-

Mah

dia

-M

onas

tir

Sfa

x-G

abes

-M

edni

ne

Jend

oub

a-S

idi-

Bou

zid

29.333.9

31.4

43.945.0040.0035.0030.0025.0020.0015.0010.005.000.00

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxx

to obtain an electricity connection, also a relatively high figure compared to other MENA countries (Figure 9). Businesses located outside the greater Tunis region are more constrained across most indicators measuring the reliable delivery of power.

Key Messages and Recommendations to Address Infrastructure Challenges in Tunisia

Address lengthy delays for the connection of infrastructure services, �particularly in the Jendouba/Sidi Bouzid/Kaasserine/Kairouan regions.

Bring more transparency to the provision of infrastructure services. �This can be done with stronger local oversight of procedures and introducing the possibility of recourse if delays exceed an agreed upon period for the provision of infrastructure-related services.

ACCESS TO FINANCE

The use of bank finance for firm financing is high by regional comparison. Of surveyed firms, 54.7% have a loan from a financial institution. Tunisian firms finance 63.8% of their working capital and 55.3% of their investments using internal funds. Compared to other MENA countries, the reliance on bank finance in Tunisia is particularly high (Figure 10). Large firms tend to rely more on bank credit (64.1% report having a loan), than medium (53.9%) and small firms (44.7%).

Figure 9: WAIT DAYS AND INFORMAL PAYMENTS FOR OBTAINING AN ELECTRICITY CONNECTION IN MENA

Source: The World Bank, Various Enterprise Survey, 2012.

Days to obtain Was a gift expected?

140

120

100

80

60

40

20

0

num

ber

of d

ays

per

cent

Egypt Jordan Iraq Turkey Morocco Romania Algeria Tunisia Lebanon

90

100

80

70

60

50

40

30

20

10

0

14

32.032.4

7.74

17

5.1

18 19

5.0 5.38.9

40

11.516.9

49

59

118

executive summary xxxi

Access to finance, however, remains perceived as a leading constraint by firms, especially for SMEs. Over 34% of firms rate access to finance as major or very severe constraint. As in most countries, this perception is related to the size of firms; small firms are generally more likely to identify access to finance as a serious constraint over large firms. However, in Tunisia, more medium-sized firms rate access to finance as major or very severe constraint (36.7%) than small firms (33.5%), but less than large firms (22.1%). It is also worth noting that the perception of access to finance as a major constraint has been dwarfed by the other current concerns, chief among these the political instability and the economic downturn. Access to finance is thus perceived as less constraining.

Access to finance constraints can be explained by factors related to inefficiencies in the Tunisian financial sector. Historically high levels of Non-Performing Loans (NPL), compounded by civil unrest and high bank exposure, imply that Tunisian businesses are generally experiencing financial distress, an overall de-leveraging, and are unable to access additional credit to finance operations. First, there are significant deficiencies in the bankruptcy regime. Second, Tunisia suffers from an outdated collateral regime that is slow and costly. Lenders thus have little incentive to ease collateral requirements or find more efficient collateral procedures, such as relying on movable assets. Third, there is little competition in the banking sector, with many banks failing to meet risk management standards and key regulatory requirements. The lack of competition causes a narrow range of available products with high collateral requirements. Fourth, only 32% of firms surveyed had their financial statements certified by an external auditor, which significantly increases

Figure 10: PERCENTAGE OF FIRMS USING BANK FINANCE A) BY REGIONAL COMPARISON B) BY SIZE


100%

90%

80%

70%

60%

50%

40%

30%

20%

47.958.2

12.3 13.3 14.6

61.2

24.3 19.3

47.3

33.434.3

56.8

8.917.1 17.3

28.130.0

52.9 54.7

39.828.5

10%

0%

Yem

en

Egy

pt

Syr

ia

Mor

occo

Jord

an

Leb

anon

Tuni

sia

Have loans Voluntary exclusion Do not have loans

100%

25.2

30.1

44.753.9

30.0

16.2 11.7

24.1

64.1

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Sm

all

Med

ium

Larg

e

Has loans Voluntary exclusion Does not have loans

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 2014xxxii

the probability of gaining access to bank financing. Finally, the public credit registry in Tunisia does not provide sufficiently reliable data to allow lenders to undertake a quality credit assessment, particularly for consumers and MSMEs.

Key Messages and Recommendations to Address Access to Finance Challenges in Tunisia

Critical deficiencies in the financial sector contribute to making access to bank financing difficult and cumbersome. Key reforms include:

Developing a standardized system for collateral to reduce barriers �for SMEs and create a more equitable borrowing platform. This system would establish a benchmark through a comprehensive legal framework, allow for collateral enforcement, and facilitate access to information through a collateral registry.

Strengthening prudential regulation and enforcement to ensure banks �are not overly risk-adverse due to their institutional health.

Modernizing the credit information sharing system to ensure reliable �data is being provided to financial institutions to provide a full and efficient credit assessment. Promote the participation of non-regulated entities such as microfinance institutions, telecoms, retailers, and utilities to provide additional data sources to assess credit risk.

Encouraging banks to enter partnerships with innovative �entrepreneurs, providing them with their expertise on financial management, accounting and human resources management. This will mitigate the risk perceived by banks in lending to entrepreneurs and provide Tunisian banks with valuable insights and understanding of challenges facing SMEs.

chapter: 1: economic and private sector performances, profile and trends 1

ChAPTER: 1

economic And privAte sector perFormAnces, proFile And trends

Through steady structural reforms and good macroeconomic management, Tunisia has experienced relative success in growing its economy over the past decades. The country enjoyed a 4.8% average annual growth in GDP over most of the 2000s, placing Tunisia among the leading growth countries in the MENA region. This was largely attributed to significant growth in manufacturing, services, and exports backed by a series of structural reforms in competition, labor, investment, and trade policy. Tunisia’s accession to the GATT (1989), WTO (1994), and the completion of a free trade agreement with the EU (1996) allowed for a gradual integration with the global economy through lowering of trade barriers. In addition, Tunisia prioritized export development and completed a series of reforms that focused on decreasing the cost and time of trade through strategic investments in customs and trade infrastructure, as well as facilitating export market linkages for Small and Medium Enterprises (SMEs). SME development was further promoted through domestic programs consisting of a combination of financing support and business development services. These policies contributed to a progressive opening to international trade, a stabilization of the country’s macroeconomic position, and a progressive adoption of market-oriented reforms. Trade openness, measured as imports plus exports as a share of GDP, nearly doubled between 1986 and 2008 when it reached almost 126%. Partially as a result of these measures, Tunisia experienced strong growth in its export sector, which grew at an average yearly rate of about 4.38% between 1997 and 2010. However as this chapter and the report will show, Tunisia’s current development model shows signs of limitations.

While Tunisia has been a regional leader in growth and competitiveness amongst countries within the Middle East North Africa (MENA), it ranks below average when compared to emerging markets including India, China, and Malaysia. Export growth has not been matched by job creation and widespread productivity gains. The January 2011 revolution, fueled by widespread anger and frustration over the lack of social and political inclusion, has increased economic uncertainty and joblessness. Growth fell to -1.1% in fiscal year 2011, while unemployment increased from 13% in 2010 to 18.9% in 2011 and then to 16.7% in 2012. Unemployment is highest

investment climAte Assessment: enterprises’ perception in post revolution tunisiA | FebruAry 20142

among educated young individuals, particularly college graduates. In 2011 the national unemployment rate reached 35% among 15-29 year olds. Persistently high unemployment is in partially structural, due to very low employment to population ratios, rapid growth in the labor force (particularly among graduates), and a widening skill gap. Demographic trends suggest that unless the pace of economic growth accelerates substantially, unemployment will continue to worsen in the coming decade.

The goal of this chapter is to evaluate trends, opportunities and constraints in Tunisia’s productive sector and outline areas of policy reform to address key bottlenecks. The chapter analyzes different performance metrics of the Tunisian economy, including the overall performance, exports performance, job creation, firm performance and productivity.

GROWTH AND PERFORMANCE: IS THE TUNISIAN ECONOMY GENERATING LONG TERM GROWTH?

Tunisia’s economy has grown at a respectable pace during the past two decades, particularly when compared to other MENA countries. Growth averaged over 5% during the nineties, while the country enjoyed a 4.8% average annual growth in GDP over most of the 2000s (Figure 11). Over this period, some growth increase in productivity and private investment occurred in response to the policy measures liberalizing the trade regime, attaining macroeconomic stability, and adopting a set of market-oriented reforms to enhance the country’s competitiveness. This growth lifted the per-capita GDP to just over 6,000 Tunisian dinars in 2010 (nearly $4,200 at market exchange rates) and while at the same time a significant reduction in poverty was achieved.2

While Tunisian growth has been significant in the MENA region, Tunisia’s achievements in attaining broad-based growth have been relatively

2 Since 2000, poverty declined by more than half, from 32.4% to 15%.

Figure 11: GDP ANNUAL GROWTH (%)


10.00

8.00

6.00

4.00

2.00

0.00

-2.00

-4.00

1992 1993 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011


modest when compared to a large sample of 22 developing nations and emerging economies. For many of Tunisia’s regional and global competitors, per capita growth for the least 10 years has remained below average (Figure 12). While Tunisia’s growth has been on par with that of Turkey and the lower Middle Income Countries (LMIC), it is significantly lower than the growth experienced by more dynamic Upper Middle Income Economies (UMIC) including China, Malaysia, and Thailand.

Tunisia’s acceleration into the world economy, coupled with domestic reforms aimed at increasing firm competitiveness, have contributed to increasing investment in the country over the past two decades. Tunisia’s private sector gross fixed capital formation, which measures the acquisition of new or existing fixed assets in the economy, increased from an average of 13% during 1985-1995 to 19% during 1996-2009. Although Tunisia’s ratio of private investment to GDP at 19.9% from 2000-2008, was less than neighboring Morocco (23.4%) and Romania (21%), it exceeded the average for a country of its income level. Investment was particularly important in exporting sectors such as electrical and mechanical manufacturing, which became integrated with EU production networks and saw significant growth as a result of trade and competitiveness reforms.

The inflow of Foreign Direct Investment (FDI) in Tunisia over the past decade was the result of significant economic integration and targeted reforms to improve competitiveness. Admission into the GATT and WTO in 1994 and the signing of the association agreement with the European Union in 1995 paved the way for gradual tariff elimination throughout the 2000s.3 The government also implemented a series of fiscal and financial incentives

3 Under the term of the Association Agreement, the EU and Tunisia would commit themselves to cooperate in a wide range of areas including: strengthened political dialogue, trade, economic, social and cultural issues. The Agreement also foresees financial co-operation to accompany reform measures in Tunisia. An important component of the Association Agreement is the clause providing for the establishment of an EU-Tunisia free trade area by the year 2010.

Figure 12: GDP GROWTH PER CAPITA (AVERAGE 2000-2012)


Yem

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Mex

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Alg

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Sou

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Bul

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7.00

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4.00

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2.913.42


to create an “off-shore” sector with generous fiscal and financial incentives to attract FDI and enhance export competitiveness. Although hovering around 1 to 4% of GDP between 1986 and 2005, Tunisia FDI inflows average nearly 5% of GDP from 2005 to 2010. These FDI flows were some of the highest ratios in the region and high even compared to other country groups (Figure 13a and 13b). From 2006 to 2010, the majority of FDI inflows went into the energy sector (60%), followed by manufacturing (25%) and services (8%).

Ongoing economic uncertainty surrounding the political and economic transition since the 2011 revolution has led to significant contraction in FDI. FDI decreased from 5.8% of GDP in 2008 to 0.9% in 2012. The Tunisian investment authority estimates that in 2011 alone, at least 41 foreign companies shut down operations, leading to 2,800 lost jobs.

Employment Responses to Economic Development Policy

Despite the country’s relatively solid growth rate, high unemployment is one of the most urgent socio-economic issues facing Tunisia. The unemployment rate in Tunisia has traditionally hovered around 12 to 13%, however, this figure has increased substantially as a result of the Eurozone crisis and the 2011 revolution. The national unemployment rate was estimated at 16.7% for the last quarter of 2012, with close to 653,000 unemployed. The current pace and trends of the Tunisian economy remain insufficient to create adequate levels of employment. Since 2006, 327,000 jobs have been created, representing an average of 55,000 jobs annually.

The efforts to expand tertiary education have not been matched by corresponding job opportunities. As a result, in recent years the increase in unemployment has mostly fallen on young and educated individuals. This trend reflects a growing structural imbalance between the demands for unskilled labor, and an increasing supply of young graduate job seekers. The share of population

Figure 13a and 13b: EVOLUTION OF FDI AS A SHARE OF GDP A) IN TUNISIA B) IN COMPARISON WITH OTHER COUNTRIES

10.0%

9.0%

8.0%

7.0%

6.0%

5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

1980 1984 1988 1992 1996 2000 2004 2008

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

Tuni

sia

Alg

eria

Egy

pt

Jord

an

Mor

occo

Syr

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Up

per

MIC

s

EU

10

2005-09

2000-04

1990-99


aged 15 or more with a tertiary education nearly quadrupled between 1990 and 2010, increasing from 3.7% to 12.3% (Figure 14a and b). While unemployment among university graduates was virtually negligible until the 1990s, nearly every fourth university graduate was unemployed by the end of 2010, (Figure 14b).

Labor Market Indicators

An analysis of broader labor market indicators provides insight into the structural challenges facing the Tunisian economy. Labor force participation in Tunisia hovers around 50% for the overall population and remains comparable to countries in the region, for example Morocco (51%) and Jordan (45%). However, labor force participation is lower than emerging markets including Turkey (52%) or Malaysia (62%) and significantly less than upper middle income countries (74%). Labor force participation amongst youth (ages 15-24) stood at approximately 33% in 2011 and is significantly lower than comparator countries in the Mediterranean (Figure 15). Only 28% of women participate in the labor force, a figure near average for the MENA region though well below other middle income regions.

Similarly, the employment to population ratio is also very low at 40% and has not evolved in the past ten years (Figure 16). This stagnation is particularly troublesome given that Tunisia is nearing the end of its youth bulge, implying that many youth have entered the labor force over the past 10 years. While this rate is typical of the MENA region, it is well below the value of other emerging and advanced economies.

Figure 14a and 14b: GROWTH AND UNEMPLOYMENT OF UNIVERSITY GRADUATES; A) POPULATION WITH TERTIARY EDUCATION; B) EVOLUTION OF UNEMPLOYMENT

Source: Barro-Lee, 2011 and INS;Doemel and Duclos, 2013.

Note: EU11 refers to the new EU member states, excluding Cyprus and Malta, and including Croatia.

20%

18%

16%

14%

12%

10%

Pop

ulat

ion

with

ter

tiary

ed

ucat

ion

(% o

f pop

ulat

ion

age

15 a

nd a

bov

e)

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

8%

6%

4%

2%

0%

Tunisia Egypt Jordan

EU11 Morocco

20%

15%

10%

5%Une

mp

loym

ent

rate

In c

onst

ant

TND

mill

ions

0%

1984

1989

1995

1997

1999

2001

2003

2006

2007

2009

2011

15

10

5

0

Unemployed with University Degree (left axis)

Unemployed with Secondary Degree (left axis)

Unemployed with Primary Degree or Less (left axis)

GDP per Capita (right axis)

Output per Worker (right axis)


FIRM PROFILE, PERFORMANCE AND PRODUCTIVITY IN TUNISIA

Firm Profile in Tunisia’s Private Sector

Tunisia’s private sector is characterized by a high number of very small firms engaged in small-scale activity. Eighty-six percent of all Tunisian firms are one-person enterprises and only 0.4% of all firms employ more than 100 workers. Many of these firms are found in small-scale retail industry (including textiles, leather, and woodworking production) and manufacturing for exports, including automobile components, electrical and mechanical equipment. They also play an active role in the real estate and tourism sectors.

Firms employing more than 100 workers play an important role in employment creation in the country, despite their small share of the formal private sector firms. Large firms account for more than a third of all jobs, more than all one-person firms combined.

Figure 15: LABOR FORCE PARTICIPATION RATES AMONGST YOUTH, TUNISIA AND COMPARATORS

Source: World Development Indicators/International Labor Organization.

70

60

50

40

30

20

10

0

1996

1997

1998

1999

2000

2001

2002

2003

2004

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2006

2007

2008

2009

2010

2011

% o

f Lab

or F

orce

Tunisia

Jordan

Malaysia

Morocco

Romania

Turkey

Upper Middle Income

Figure 16: EMPLOYMENT TO POPULATION RATIO, COMPARATORS

Source: World Development Indicators/International Labor Organization.

9080706050403020100

Wes

t B

ank

and

Gaz

a

Iraq

Jord

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Alg

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Iran

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In order to assess the overall performances of firms sampled, this section evaluates several measures of firm performance including labor productivity, labor cost, and total factor productivity. For each measure, the estimates are paired against a large sample of countries to place Tunisia’s firm performance in an international setting.

Labor Productivity

Labor productivity is lower in Tunisia than would be expected given Tunisia’s per capita income4. In Tunisia, value-added per worker is close to $8,000 according to estimates based on the IC survey (Figure 17). This is lower than in other countries at similar levels of development (i.e., below a linear projection based on per capita income). Compared to other countries at similar income levels, one would expect labor productivity to be closer to about $12,000 per worker. Results suggest that in part, the low labor productivity observed in Tunisia is the result of a low level of capital intensiveness relative to other countries with similar levels of per capita income.

4 Enterprise Surveys are firm-level surveys of a representative sample of an economy’s private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. Value-added per worker measures the value of the goods and services that a firm produces less the cost of the raw materials and intermediate inputs (such as engine parts or textiles) used to produce the output divided by the number of workers in the firm shows per capita income plotted against labor productivity (value-added per worker) for countries where World Bank Enterprise Surveys have been completed since 2006. Value-added per worker measures the value of the goods and services that a firm produces less the cost of the raw materials and intermediate inputs (such as engine parts or textiles) used to produce the output divided by the number of workers in the firm shows per capita income plotted against labor productivity (value-added per worker).

Figure 17: LABOR PRODUCTIVITY IN THE MANUFACTURING SECTOR

Source: Author calculations based on data from World Bank Enterprise Surveys.

Note: All data points are for the median firm on each measure of performance. For presentational purposes the chart is shown only for countries with per capita GDP between $0 and $16,000. Countries with GDP per capita over this amount are included when we calculate the linear projection.

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Total Factor Productivity

Total Factor Productivity (TFP) in Tunisia remains lower than in other countries with similar levels of per capita income. Total factor productivity tends to be higher for firms in countries where per capita income is higher (Figure 18). The correlation between per capita income and total factor productivity is about 0.74. Although Tunisia’s TFP is higher than in most low-income countries, it remains lower than in other countries with similar levels of per capita income.

Labor Costs

Although productivity measures provide some information about firm competitiveness in Tunisia relative to firms in other middle-income countries, they can be misleading when considered in isolation. One issue is that firms can be competitive even when labor and total factor productivity are low if wages are comparatively lower. For this reason, it is useful to look at labor costs in addition to labor and total factor productivity when assessing competitiveness.

Labor costs are relatively low in Tunisia compared to countries at similar levels of per capita income.5 The median firm reports that labor costs per worker are about $3,520 (Figure 19). Based on per capita income alone, one would expect the median firm to have labor costs that were equal to about $4,500 per worker. Labor costs can reflect differences in things such as worker

5 The cost of labor that is used in this study is per worker labor costs, (the cost of wages, salaries, bonuses, other benefits, and social payments for workers at the firm) divided by the number of workers. The data are taken from the firms’ accounts. It includes wages and salaries paid to all workers and managers, not just production workers.

Figure 18: TOTAL FACTOR PRODUCTIVITY IS LOW IN TUNISIA, RELATIVE TO COUNTRIES AT SIMILAR LEVELS OF PER CAPITA INCOME

Source: Author’s calculations based on data from World Bank Enterprise Surveys.

Note: All data points are for the median firm on each measure of performance. For presentational purposes the chart is shown only for countries with per capita GDP between $0 and $16,000. Countries with GDP per capita over this amount are included when we calculate the linear projection. The coefficients are from the LAD regressions (see appendix) for description. In practice, however, the results are virtually identical in the OLS and frontier regressions.

LAD Tunisia Linear (LAD)

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education and worker skills. If labor costs are high because workers are highly educated and the firm experiences higher productivity as a result, firms can remain competitive even when they pay high wages. In contrast, if labor costs are high because of scarcity or because unions manage to extract rents, high labor costs might hurt competitiveness. Given that both labor productivity and labor costs are low in Tunisia, it is not immediately clear whether high labor productivity makes up for high labor costs.

Unit labor costs in Tunisia are very close to the average for other countries at similar levels of GDP at about 37%. Unit labor costs can help shed more light on the issue of labor cost relative to productivity. This is done by taking differences in productivity into account when assessing differences in labor costs.7 Unlike the partial productivity measures discussed above, unit labor costs do not consistently increase as per capita income increases (Figure 20). This suggests that in most countries, productivity and labor costs increase in tandem. Low labor costs thus appear to mostly cancel out low labor productivity. Given that firms do not appear to be highly capital intensive, at least when measured using the sales value, suggesting that Tunisian manufacturing firms could potentially be competitive on international markets.

It is, however, important to note that although unit labor costs are not high relative to the average middle income economy, they are higher than in many of the successful exporters from East Asia. For example, unit labor costs are only about 26% in Thailand and about 30% in Vietnam. In 2005, the time of the last Enterprise Survey in China, unit labor costs were only about 13%, although labor costs appear to have been increasing rapidly over the past 8 years.

6 All data points are for the median firm on each measure of performance. For presentational purposes the chart is shown only for countries with per capita GDP between $0 and $16,000. Countries with GDP per capita over this amount are included when we calculate the linear projection.

7 Because of limitations related to the data collected in the Enterprise Surveys, the measure that we use, labor costs as a percentage of value-added, is an approximation to true unit labor costs (i.e., more accurate unit labor costs do not measure output in dollars but instead use physical measures of production).

Figure 19: LABOR COSTS AS A FUNCTION OF PER CAPITA GDP6


Labor Costs per worker Tunisia Linear (Labor Costs per worker)


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Firm Performance by Firm Type

Tunisian exporters are about 25% more efficient than non-exporters. Across the whole cross-country sample, exporters are about 14% more efficient than non-exporters. The difference appears to be larger in Tunisia. It is important to be cautious about interpreting this result causally. That is, although it is possible that the discipline of competing in export markets causes firms to become more productive (the learning-by-exporting hypothesis), it is also possible that only firms that are already efficient are able to enter export markets (the self-selectivity hypothesis).

Foreign owners: On average, across all countries, foreign-owned firms are about 18% more productive than similar domestic firms. The difference between foreign-owned firms and domestic firms in Tunisia is smaller, about eight percent.

Access to Credit: On average, firms that have access to credit including loans, lines of credit or overdraft facilities, are about 14% more productive than other firms. Although this could be because access to credit improves performance, it is also possible

Figure 20: UNIT LABOR COSTS IN TUNISIA AND OTHER LOW AND MIDDLE INCOME COUNTRIES

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Note: All data points are for the median firm on each measure of performance. For presentational purposes the chart is shown only for countries with per capita GDP between $0 and $16,000. Countries with GDP per capita over this amount are included when we calculate the linear projection.


Figure 21: DIFFERENCES IN TOTAL FACTOR PRODUCTIVITY BY FIRM TYPE

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that banks are more likely to lend to more productive enterprises. In contrast, Tunisian firms with access to credit are less, not more, productive than other firms, about 18% less productive. This may suggest that Tunisian banks do not effectively allocate credit to the most productive firms.

Technology and Training: In general, technologically advanced firms appear to be more productive than other firms. On average, firms that are ISO certified are about 14% more productive than firms that are not. Similarly, firms that use the Internet to communicate with customers and suppliers are about 27% more productive. Firms with training programs are about 9% more productive than firms that do not. Finally, firms that license technologies from foreign-owned firms are about 5% more productive than firms that do not. The differences are generally large for manufacturing firms in Tunisia.

HAS TUNISIA UNDERGONE TRANSFORMATIONAL ECONOMIC CHANGES OVER THE LAST 15 YEARS?

Recent research and policy analysis suggests that a country can attain long-term accelerated growth through steady transformation from agricultural production towards high-value-added goods and services, where capital and labor are most productive. East Asian countries such as Korea, Malaysia, Singapore, and China have sustained high levels of economic growth over the past two decades through a combination of dynamic agricultural sectors, high levels of savings and investment, investment in human capital, and particularly high growth in exports. Value-added sectors also draw on technology to innovate and create additional value.

Structural Trends of Tunisia’s Economy

Since independence, output of the Tunisian economy has moved away from agriculture and raw materials (phosphates, oil, and gas) towards services and manufacturing. The share of services in GDP rose from 48.7% in 1990 to 59.7% in 2010. Throughout the 1970s and 1980s manufacturing remained concentrated in a few areas of production, such as clothes, textiles, and leather goods. Manufacturing as a share of GDP increased from 10% in 1971 to 18% in 2009. This increase is significant given the fact that the manufacturing sector was non-existent at independence.

Over the past 10 years, growth in the manufacturing sector progressed at a pace similar to the economy as a whole, growing at 3.45% (Figure 22). The sector has experienced increased volatility in the last five years due to the global financial crisis, ongoing economic instability in the Eurozone, and the 2011 revolution. Manufacturing, particularly electrical and mechanical


goods for export to EU countries, remains an increasingly important component of Tunisia’s broader economic development strategy. The manufacturing sector’s value-added growth, however, remains below the average of 22 global comparators (Figure 23).

EXPORT POLICY IN TUNISIA

Export development is an important factor in a country’s ability to undergo a process of structural transformation and long-term growth. Research (Maloney, 2003; Rodrick, 2004) has demonstrated a strong linkage between export dynamism and growth, particularly through diversification and scaling-up. Countries like Tunisia increase their productivity through more sustained diffusion of knowledge and the acquisition of new technologies and processes.

Figure 22: TUNISIA’S EVOLUTION OF SECTOR SHARES IN GDP

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Figure 23: MANUFACTURING SECTOR VALUE-ADDED GROWTH (10-YEAR AVERAGE)



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A more diversified export basket of goods and services tends to play a positive role in expanding the structure of the industrial base by increasing competition. Finally, more diversified exports reduce weaknesses and exposures due to external shocks. Exporting countries are subjected to more intense international competitive pressures.

Tunisia’s growth and development over the past decades have been fuelled by the export of goods. The export sector grew at an average yearly rate of about 5.3% between 1997 and 2010. Tunisia initiated two waves of economic structural changes in its export sector. The first wave involved diversifying away from oil towards light manufacturing and tourism. This was done through government policy which created an off-shore sector, with generous fiscal and financial incentives to attract FDI and attract investors. Fiscal incentives provided by the government are among the main drivers of the development of the off-shore industries in Tunisia (Hammouda, 2010). These incentives began in 1972 and include duty-free treatment of imported raw inputs (e.g. equipment and materials), exemption from export taxes, reductions in corporate income tax, and tax holidays. They, in large part, enabled the development of a textile export sector destined largely for the European market. Investors were further attracted to Tunisia because of its geographical proximity to Europe, the relatively cheap labor and higher productivity compared to regional neighbors, and political stability under the Ben Ali regime.

During the second wave of economic structural change in Tunisia, major shifts occurred within the manufacturing sector. Electronic and mechanical engineering emerged as important exporting sectors and became highly integrated into global value chains. The off-shore sectors benefited from increasing integration with the EU production networks following the Tunisia-EU Association Agreement. They have been able to attract investment, raise factor productivity and boost job creation. More than 90% of workers in these industries have been sub-contractors working in vertical integration with production networks in Europe. However, the value-added of the exporting industries remained low and they are subject to stringent competition in the European markets.

Figure 25: EXPORTS OF GOODS AND SERVICES (AVG. 10 YRS. ANNUAL GROWTH %)

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Export Orientation and Performance

The export sector accounts for close to 46% of the GDP on average over the last 15 years, and near 48% for 2012. The sector employs close to 20% of the total workforce (Duclos and Doemeland, 2013). However Tunisia’s exports are less dynamic than they used to be and are evolving at a slower pace than global trends. Tunisia has gradually liberalized its foreign trade and investment since the mid-1980s, as evidenced by the joining of key global trade regimes (GATT, 1990; WTO, 1995) and the signing of the Association Agreement with the EU. The latter particularly led to the progressive adoption of free trade in industrial products between 1996 and 2008. Tunisian exports increased by 5.1% per year in the 1990s.

Over the past decade, however, exports of goods and services on average have progressed at a lower rate when compared with lower and upper middle income emerging economies. Tunisia’s exports have grown 3.41% over the past decade, significantly lower than fast growing exporters such as Romania or Vietnam, and even slower than Tunisia’s neighbors Morocco and Jordan. A small number of select sectors, such as mining and electrical engineering, saw significant growth over the last decade. In 2008, exports slipped below 50% per capita in response to the recessionary conditions in Europe.

Export Diversification

Analyzing export composition and diversification (i.e., number and types of products exported, and their destinations) provides additional insight into the relative depth and resilience of the export sector for a given country. A wide body of empirical literature demonstrates that diversifying exports leads to significant knowledge spillovers (Feenstra and Kee, 2004), expands and stimulates industries, and reduces the volatility of export revenues (Gourdon, 2010). Tunisia exports approximately 1,200 products, based on Comtrade’s six digit definitions of export greater than 100,000 US dollars in volume. This is more diversified than other countries in the MENA region, although significantly below the level of diversification of other developing nations as well as fast growing economies (Figure 26).

While over the last decade the structure of Tunisian exports has made some progress in terms of diversification, they are still concentrated on a small number of products and sectors. Four products of the top 20 exports belong to the garment, textile, and shoes sector that account for nearly a quarter of total exports. Textiles and Clothing, Leather and Footwear (TCLF) and the mechanical and electrical sectors combined, account for approximately 65% of exports. Services continue to have a relatively low share of export composition. Around 80% of Tunisia’s service export revenues come from tourism, travel, and transport services.


The lack of diversification and limited production of higher-value-added goods for exports can, in part, be explained by the way in which Tunisian exports are integrated into the global supply chains. Tunisian exporters still largely remain subcontractors to foreign companies; only small minorities have become direct exporters. As a result, Tunisian exporters continue to produce relatively simple goods that can be easily replaced with similar products from lower cost locations like China, India, and Bangladesh.

Export Markets

Tunisia’s main export markets have remained almost unchanged despite the slight diversification and have moderate growth prospects. The European Union accounted for over 70% of Tunisia’s exports in 2010 (France, Italy and Germany alone accounted for 56% of all exports). As a result, Tunisian firms are highly vulnerable to negative shifts in EU growth. Tunisian firms are also highly dependent on vertical integration from European supply chains. Growth prospects for Tunisia’s export sectors have decreased recently as a result of ongoing economic instability and fiscal contraction in the EU.

Tunisian exports to the rest of the MENA region are low. Only 8% of Tunisia’s exports are shipped to other MENA partners, of which 4.2% now goes to Libya and 2.8% to Algeria. Because of the lack of intra-regional trade, Tunisia is not only losing direct market opportunity but also opportunities for future investments from Multinational Corporations (MNC). The location decisions of multinationals are influenced significantly by the size of the market, the scope for effective sourcing of inputs, and the ability to move inputs quickly and cheaply across national boundaries. While both Tunisia and Morocco are well-connected to EU production networks, there is very little cross-border trade in components for products subsequently exported to the EU.

Exports to Sub Saharan Africa (SSA) are marginal at approximately four percent. GDP in SSA expanded by 4.6% in 2012 and is expected to grow at

Figure 26: NUMBER OF PRODUCTS EXPORTED

Source: Comtrade 6 digits 2006-2011 (Greater than 100,000 US Dollars).

AlgeriaSaudi Arabia

SyriaEgypt

JordanMorocco

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least 6% between 2013 and 2015 (World Bank Global Economic Prospects). SSA is already a market for Tunisia’s engineering, health, and education services, although this could expand significantly. Similarly, trade potential in manufacturing with African nations is so far untapped and represents significant market opportunity as well as an opportunity to decrease dependence on European supply chains.

Technological Sophistication in Exports

Current research (Krishna and Maloney, 2011; Haussman et al, 2007) shows that a country’s capacity to move up toward a higher-value-added basket of goods is associated with elevated and sustained levels of growth. Recent trends show that higher-value-added goods endowed with a high degree of technological content are among the fastest growing global exports (Diop, 2012). Connected, developing countries are increasingly becoming exporters of high-tech products thanks to greater trade openness, greater ability to master and use technologies, and a rise in Foreign Direct Investments (WTO, 2009). For countries such as Tunisia, there is pressure to move up the technological value chain because competing in labor-intensive, cheaper manufacturing is very difficult due to cost and scale advantages held by the likes of China. Thus, examining the technological sophistication of Tunisia’s exports can provide important insights into broader, long-term growth and competitiveness challenges.

Over the last 15 years the technological content in Tunisia’s exports has increased steadily but slowly. This reflects a constant move up the technological production ladder, albeit from a very low base (Diop and Khalil 2012). The share of medium and high tech exports has increased steadily, while low tech exports have declined significantly as a percentage of total exports, from 56.7% in 1996 to 38.3% in 2009. The percentage of growth in high-technology manufacturing exports increased from 2% in 1995 to approximately 6% in 2011 (Figure 27). However this is in large part due to the changes in production patterns of a small number of products, mainly in textiles. In Tunisia, the share of exports of textiles and textile products in total exports dropped by almost half, from 44% to 24% between 2003 and 2010.

Figure 27: TUNISIA’S HIGH-TECHNOLOGY EXPORTS (% OF MANUFACTURED EXPORTS, ANNUAL GROWTH RATE)

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As of 2010, automobile parts and components had overtaken textiles and clothing as Tunisia’s largest export sector, accounting for 30% of total exports (against 9% in 1995).

Creative Destruction

To a certain extent the firm performance results observed earlier can also be contextualized by looking at the broader firm population trends in dynamism. Recent empirical research (Klapper and Love, 2010) points to the existence of a positive association between levels of development and firm entry density, as measured by the number of new firms registered per 1000 capita compared to relative levels of development as well as by income. Freund et al (2012) find that relationships between firm density and income levels exist but are less direct, since exporting firms in more developed economies show lower churning rates (lower entries and lower exits over existing firms ratios, as an indicators of firm dynamism). This is reflected by the non-linear relationship in Figure 28.

The figure following (Figure 29) illustrates the relationship between development levels and firm entry density for Tunisia and a sample of 37 developing countries.8 Tunisia boasts nearly 602,222 registered firms of all types, which is a relatively high number for its population size (INS 2012). Given that most of these firms are individually-owned micro enterprises, looking at the number of Limited Liability Companies (LLCs) to assess the magnitude of firm creation in Tunisia is appropriate. Firm entry density for Tunisian LLCs is estimated at around 0.6 (number of newly registered LLC per 1000 capita). Taking into account the development level of Tunisia, firm entry density is higher than most MENA comparators (as represented by orange dots). Firm density in Tunisia is relatively close to countries with similar levels of development but remains well below

8 The developing nation sample is a mix of regional comparators, newer and established exporters and development levels below, close and above Tunisia’s.

Figure 28: HIGH-TECHNOLOGY EXPORTS (% OF MANUFACTURED EXPORTS AVG. 15 YR.)

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countries with higher levels of development (and the trend line) such as Turkey, Chile or Malaysia.

What Drives Firm Dynamism in Tunisia: A Look at Recent Trends

Firm dynamism can be measured by analyzing entry and exit patterns in a particular economy over a given period of time. The creation of new businesses can be measured by entry rates, the ratio of new firms over existing firms. During the last decade the rate of new business entry has declined slightly, from 12% in 2000 to 8.8% in 2011 (Figure 29). Exit rates have been also slowly declining during the same period, however at a slower pace than entry rates. Exit rates averaged 6.2% over the period, compared to an average of 10.1% for entry rates. Churning rate9, which measures the total entry and exit patterns of firms over a given time period, provides an aggregate indicator for firm dynamism in a given economy. The combined decline in both entry and exit rate, however slight, results in a downward churning rate trend. The fact that entry rates have been declining faster than exit rates implies the churning rate in Tunisia is being driven mainly by entry. This suggests that overall firm dynamism has been decreasing for the last decade with an average churning rate of 15.8% for the period down from 22.2% in 2000.

Firm Dynamics Entry and Exit: Sectoral Analysis

Desegregated data of firms’ entry and exit patterns between 2000 and 2011 (Figure 30) provide insights on sectoral trends. The firm renewal process (or churning rate) is declining slower in the services sector when compared to the manufacturing sector. Average entry and exit rates between 2000 and 2011 years were 8.4% and 9.9% in the services sector. The average churning rate in the services sector for the last decade averaged 13.8%, down

9 Churning rates, a measure of firm dynamism, is calculated by adding lower entries and lower exits and dividing by existing firms ratios.

Figure 29: FIRM DENSITY AND DEVELOPMENT

Source: INS, RNE 2011, Author’s calculations.

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Figure 30: ENTRY AND EXIT RATES IN MANUFACTURING (2000-2011)

Figure 31: ENTRY AND EXIT RATES IN SERVICES (2000-2011)

Source: The World Bank, Diop and Khalil, 2012.

Source: INS, RNE 2011, Author’s calculations. Note: Construction and Trading Firms were not included.

from 21% in 2000. Manufacturing entry rates have been declining along with exit rates, thereby generating relatively low churning rates. Churning rates in the manufacturing sector have declined from 20% in 2000 to approximately 12% in 2010.

Within the larger services sector, three subsectors drive the firm dynamics observed in services: transport (driven mainly by the allocation of individual licenses with 52,560 firms in 2000 to 94,836 in 2011); real estate services (with 23,897 new licenses in 2000 and 56,000 in 2011) and the education/health sector (with 8,000 licenses in 2000 and 20,000 in 2011). Overall, these trends suggest a relative decline in the productive tissue of the manufacturing sector, as evidenced by the decreasing overall churning rates. The services sector, on the other hand, has been more dynamic, a reflection of higher entry rates, a higher churning rate, and a more dynamic overall renewal process in the sector.

Effects on Productivity and Employment: has there been a creative destruction process?

Transition economies are often characterized by high firm turnover, whereby the process of creative destruction plays an important role in

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productivity growth (Haltiwneger and Scarpetta, 2003). With high turnover, new firms replace less productive ones, increasing competitive pressure and contributing to the exit of firms performing below average.

With regard to Tunisia, recent research (Rijkers et al, 2013) finds that allocative efficiency, the process whereby the renewal and destruction of firms result in productive gains, is actually low. This is due to high productivity dispersion within size categories, which is indicative of frictions and distortions (Rijkers et al, 2013). This small reallocation from the creative destruction process is not inconsistent when there is a lack of higher-value-added sector firms in an economy. This trend has been observed in Tunisia over the past decade, being driven primarily by smaller firms. As noted previously, 86% of all Tunisian firms are one-person enterprises and only 0.4% of all firms employ more than 100 workers. Self-employment, especially in services, comprises 74% of all net job creation (Rijkers et al, 2013) and accounts for more than all other groups of firms combined. Because smaller firms tend to grow less, the result has been an inadequate job creation response due to productivity gains caused through allocative efficiency.

CONCLUSIONS AND RECOMMENDATIONS

This chapter has examined overall performance trends and characteristics of the Tunisian economy, analyzing growth, employment, and firm performance, productivity, and dynamism trends. It also provided a bird’s-eye overview of the results of Tunisia’s investments in an export-driven growth economy facilitated through the creation of off-shore manufacturing and services sectors. Through this analysis, this chapter has attempted to gauge whether a process of transformation into higher productivity and value-added activities has taken place over the past decade or so.

While growth has been commendable over the past two decades, its performance lags when compared to emerging markets that have witnessed a faster and more pronounced process of structural transformation across most metrics that the report examined, including productivity, technological sophistication of exports, and employment responses. Unemployment, which stood at 16.7% in the last quarter of 2012, is perhaps the most pressing public policy challenge Tunisia is facing. Unless the pace of economic growth accelerates substantially, demographic trends suggest that unemployment is set to worsen in the coming years. Exports, while a major source of growth over the past two decades in Tunisia, are declining as a relative share of GDP. They are not the engine of technological spillover witnessed on many export-driven countries that have sustained long


bouts of growth. Although firms are more likely to export than similar firms in other middle income countries, exports are still highly concentrated in a small number of products (textiles, electrical sector). Exports are also limited mainly to the sluggish EU market, and often lack high levels of technological sophistication that has been associated with sustained growth in other emerging markets.

Through lowering trade barriers, integrating into international supply chains, and liberalizing and incentivizing investment, Tunisia’s gradual integration into the world economy has facilitated a structural shift from an economy based on agriculture and primary products to one based on manufacturing for exports, and more recently in services. As such, Tunisia has achieved certain elements of structural transformation seen in other high-growth economies. This has occurred, however, both with limitations and without achieving the type of transformational influence observed in fast growth performers. The move towards services has been driven mainly by low value-added activities such as individual units in transport, some real estate, and some hotels and restaurants. Meanwhile the recent growth of electrical and light mechanical manufacturing, although considered high to medium technological in content, has not been significant enough to make a transformational impact.

Unlocking Tunisia’s private sector potential by alleviating the most present constraint can ultimately facilitate a process of transformation which would result in moving up the value chain in traditional export sectors and product sophistication. Similarly, the country could be in a better position to promote investment in newer, knowledge-intensive products and services. It will also be paramount for Tunisia to find a path to an elevated and sustained growth rate, necessary to begin reducing high unemployment. As such the authorities must address some of the more persistent binding structural constraints to growth and private sector development. The incentive structure which favored disproportionately an off-shore sector with its share of induced distortions and imbalances has shown its limits. The remainder of this report looks at several dimensions of the investment climate and related constraints that prevent Tunisia’s private sector from exploiting its full potential, as well as participating in full structural transformation in order to accelerate growth and reduce unemployment.

chapter: 2: Governance and regulatory Framework 23

GovernAnce And reGulAtory FrAmeWorK

ChAPTER: 2

In From Privilege to Competition (Benhassine et al, 2009), published prior to the Arab spring, the author found that one of the most important limitations to private sector growth and development in the MENA region was policy uncertainty. Also prominently noted was discretion in implementing the rules in a region where incumbents have always had a noticeable role.10

In order to make productive investments, private enterprises require a predictable regulatory environment with clear rules that are applied evenly, irrespective of firm characteristics. Historically, the state in Tunisia has played an ambiguous/dual role in promoting private sector growth. On the one hand, it has signaled support for private enterprises by providing various incentives (e.g., fiscal). On the other hand, it has utilized the same instruments to favor some companies, thereby undermining the long-term credibility of public policies as well as the enabling governance environment. It became well-known that the administration has sometimes served the interests of the connected and privileged belonging to the inner circle of the Ben Ali regime. Regulations were used for decades for rent extraction and even predation during the Ben Ali regime as documented in World Bank (2013)11.

In a post-revolution environment, it is even more crucial to examine whether some of the practices inherited from the past regime have continued; and to what degree they are still present. Of paramount importance would be to assess whether post-revolutionary conditions have led to and added erosion in the application of the rule of laws and/or a widespread use of a prevailing cronyism system. The first part of this chapter looks at the direct and indirect cost of the regulatory burden on firms; that is the rules and regulations affecting firms and the cost they represent to firms in

10 Benhassine, Najy (2009), From Privilege to Competition: Unlocking Private-led Growth in the Middle East and North Africa. MENA development report. Washington D.C.-The World Bank. Available at: http://documents.worldbank.org/curated/en/2009/01/11409150/privilege-competition-unlocking-private-led-growth-middle-east-north-africa. Other research (Kiefer 2004) suggest that de jure regulatory reform does not always impact investment or employment. “The results contrast with recent research that uses the cross-sectional dimension of the Doing Business data and finds strong cross-country correlations between regulatory burdens and economic outcomes.”

11 World Bank (2013, forthcoming), Tunisia- Development Policy Review.


terms of compliance to regulations. The second part examines the importance of discretion and arbitrary application of rules. The last part provides some possible recommendations to alleviate the most pressing issues related to this topic.

DIRECT AND INDIRECT REGULATORY BURDEN ON FIRMS

Close to a third of the firms surveyed in Tunisia complained about corruption. Twenty nine percent of firm managers rated corruption as a severe or very important constraint, placing it as the overall sixth leading constraint identified from a total of 20. On a regional basis, it does appear that Tunisian firms tend to complain less than their peers (Figure 32).

However, in terms of ranking, most of the countries included rate corruption as one of their top five constraints. This result suggests that the perception of corruption varies according to the evolution of the environment in which firms operate. There are no firm-level survey panels to confirm this, but a look at the World Governance Indicators reveals a deterioration of perceived corruption after post-revolutionary Tunisia. Interestingly, this result is combined with an improvement in “voice and accountability” (Figure 33).

Tunisia is not unique in this paradox, whereby two governance-related sub-components move in opposite directions. Keefer (2004) shows

Figure 32: PERCEPTION OF THE CORRUPTION CONSTRAINT AMONG MENA FIRMS (% OF FIRM MANAGERS WHO RATE CORRUPTION AS A MAJOR OR SEVERE CONSTRAINT TO THEIR BUSINESS)

Source: Various Enterprise Surveys.

Yemen, Rep. 74.0

67.1

66.5

66.0

64.3

43.8

38.4

28.9

27.3

11.9

Syrian Arab Rep.

West Bank and Gaza Economies

Lebanon

Algeria

Egypt, Arab Rep.

Jordan

Tunisia

Morocco

Oman

0 10 20 30 40 50 60 70 80

Figure 33: PERCEPTION OF CORRUPTION INDEX (WGI)

Source: The World Bank, Perception of Corruption Index, 2012.

60

40

20

0

2007 2008 2009 2010 2011

Control of CorruptionVoice and Accountability


that in other post-revolutionary environments, such as Indonesia after the fall of the Suharto regime, similar trends were observed; which can be attributed to a period of change characterized by an erosion of the former system of enforcement which maintained or even created more space for discretion.

Firms seem to transition from a more predictable corrupt/predatory environment to an open democratic system that does not yet present the attributes of a stable and predictable policy framework. This trend enables more uncertainty and a higher perception of corruption than before. In addition, when freedom of expression is institutionalized, open discussions on corruption may flourish and increase the perception that corruption is a widespread phenomenon.

Looking further into quantitative data from the survey aimed at capturing governance and regulatory/bureaucratic issues shows that Tunisian firms operate in an environment where petty corruption seems prevalent. Bureaucratic and regulatory requirements continue to impose a significant burden on businesses. For instance, it can take up to 60 days for industrial electrical connections (Figure 34) and almost six months for a construction permit. Likewise many firms are subject to informal payment requests ranging from 5% for import licenses to 23% for construction permits. The firm survey results show that occurrences of informal payment request are dependent on the nature of the services demanded by the firm. Where long delays are frequent, like in the case of building permits, and electricity connections, cases of informal payment are more prevalent.

A more concerning and revealing result regarding corruption is best illustrated in (Figure 35). The prevalence of corruption “to speed things up” is among the highest by international standard. More than a quarter of all firms surveyed declared that they have to provide some form of informal payment in order to accelerate any form of interaction with the administration.

Figure 34: PREVALENCE OF CORRUPTION AND DELAYS FOR SERVICES


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Figure 35: PERCENTAGE OF INFORMAL PAYMENT REQUEST TO SPEED THINGS UP

Figure 36: PERCENTAGE OF SENIOR MANAGEMENT’S TIME SPENT DEALING WITH REGULATIONS



35

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Furthermore, the time allocated by managers to fulfill the firm’s regulatory requirement seems to confirm the multiplicity of interaction with agencies (Figure 36). According to the survey, close to 25% of the managers’ time are spent on meeting regulatory and bureaucratic burdens. This figure appears relatively high by international standards. In some instances, field interviews reveal that firms have dedicated personnel whose sole responsibility is to ensure that the firm fulfills all its administrative and bureaucratic requirements. This is especially the case for medium to large firms that can afford it.

While some firm characteristics such as size or market orientation do not appear to be influencing factors, location shows significant variances in the time spent dealing with meeting the firm’s bureaucratic requirements. In the Greater Tunis areas, firm managers spent close to 35% of their time meeting bureaucratic requirements while this figure was as low as 7% in other regions. The quality of public services to firms is impacted by the intensity of the demand, but the way certain regulations are applied appear to be an influencing factor for this type of variance.

It is estimated that close to 13% of the firms’ annual sales were spent to deal with regulations and administrative interaction (direct and indirect costs including compliance time. Figure 36 shows that Tunisia is among the most costly amongst MENA comparators12.

The high losses of IC weaknesses combined with the management compliance time are indicative of the important frequencies of interaction

12 It should be noted that over half of the costs are triggered by losses associated with theft and spoilage (a widespread phenomenon after the revolution). In the absence of this, Tunisia would be slightly lower than regional peers.


Figure 37: LOSSES DUE TO INVESTMENT CLIMATE WEAKNESSES (IN % OF SALES)

Figure 38: CORRUPTION PAYMENT BY FIRM SIZE



0

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en

Iraq

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45.0040.0035.0030.0025.0020.0015.0010.00

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Petty corruption (By �rm size) SmallPetty corruption (By �rm size) MediumPetty corruption (By �rm size) Large

necessary to meet bureaucratic requirements. These losses point to a problem not unique to Tunisia: implementation of regulations can be uneven, time consuming, and costly to firms. Therefore, it is very likely that issues are solved through negotiations that are characterized by highly discretionary decisions, therefore rendering some benchmarking challenges.

Difference of informal payment by firm size can be interpreted as a proxy for uneven enforcement of regulations. It is especially true when large and small firms pay the same amount of bribes for operation and import licenses. It is rather difficult to explain common payment between small and large firms since they have various capacities.

Overall in terms of regulatory burdens, the survey results point to issues of perceived corruption, long delays associated with requirements for compliance and an environment where the uneven application of regulations could be important. In the next section, the report will show that there are significant margins in the way regulations are applied and that some areas of the investment climate are more prone to these types of behaviors than others. Research on this topic points to the use of the recurring instrument used to solidify rent/monopolies and predation. In the case of Tunisia, as will be shown, the survey results seem to consolidate these findings. A large part of the problem seems to occur around trade and fiscal regulations.

IMPORTANCE OF DISCRETION AND ARBITRARY APPLICATION OF RULES

Overall, the Tunisian investment climate, as measured by this survey, has not fared particularly worse than other MENA countries or international


competitors. What makes a difference for firms is the application of rules, not as intended. That is not to say that Tunisia does not suffer from certain policies and regulations that impede private sector growth. In fact, the application of rules in some areas of the investment climate allow for discretionary power among bureaucrats and politicians. For instance during the 1970s, Tunisian authorities established a policy framework designed to attract foreign direct investment and to further develop export-oriented industries. This economic model has been in place for more than three decades and has had some notable results. It is still today considered appropriate among policy-makers. However, as was the case in many countries, the set of incentives which included various tax incentives favoring export-oriented firms enabled the emergence of a dual economy and created some incentives for domestic firms to become off-shore. This model had a rationale in the 70s and 80s, and was followed successfully by many countries over the years, most notably Mauritius. However, the accumulation of fiscal incentives to exporters became increasingly costly, and its impact on employment began to fade. According to COPA (2012), exporters now benefit for up to two thirds of fiscal incentives while 90% of fiscal incentives benefit only a mere 2,500 companies (out of several million of companies in Tunisia). This becomes even more problematic because employment created by exporters is the most costly per job created.13

The unintended consequences of the proliferation of various fiscal regimes have been strengthening possible discretion by public officials in selected administrations (where these policies had more impact), creating space for the uneven enforcement of regulations, and facilitating instances of corruption. This has been particularly acute for fiscal and customs administrations, as the survey results do point to these types of practices as the report will show.

UNEVEN ENFORCEMENT OF REGULATIONS FOR PRIVATE SECTOR

The ICA survey can provide proxies for measuring the diversity of firm treatment across agencies by providing associated costs and delays by firm characteristics.

How firms perceive the rules and regulations that govern their activities is an indication of the overall conditions in which they deal with the administration. Survey results show that 26.5% disagree with the statement that the rules and regulation governing their main activity are unpredictable. This number does not appear very high. In addition, this result does not vary

13 COPA (2012), Coût/bénéfice des incitations fiscales et financières à l’investissement, mimeo.


Tunisia (All)

Small Medium Large DomesticForeign-owned

Non-Exporter

ExporterRegion

A B C D

Laws and regulations that govern your activity are unpredictable (% disagreeing) 26.52 22.64 28.04 27.41 27.22 23.93 27.46 26.17 21.37 23.14 31.4 35.34

Do you believe that the laws and regulations governing your activities are generally applied evenly (in a consistent manner from all type of firms) 42.28 42.5 43.84 38.52 45.03 30.43 46.64 37.84 32.33 41.67 47.11 57.76

Table 1: ICA SURVEY RESULTS

Source: The World Bank Enterprise Survey, 2012.

across firm characteristics. What seems to be of more concern to the business community is the way in which the same rules and regulations affecting their activities are applied. When asked, 42% of firms said they disagreed with this statement, a significantly larger share. We also find major differences across firm types with regard to this assessment. Foreign-owned firms and exporters are much less concerned with the uneven application of regulations (only 30% and 32% respectively disagree with the statement). Foreign-owned firms, and to a certain extent exporters, can face significantly different operating conditions in their investment climate than domestic ones. This further signals differences in regimes and treatment.

Perceptions of informal behavior by competitors show that many firms complain that their competitors are not subject to the types of cost and regulations as they themselves face. Regulations which are not perceived as evenly applied across the spectrum of firms resonate closely with the manager’s perception of anti-competitive practices. When asked, firms mentioned that the leading cause of anti-competitive behavior, mainly through tax and tariff evasion, represented a severe constraint. Managers’ judgment of informality is closely related to the way firms are treated by the administration and should be considered a symptom of weaknesses of the overall IC rather than a cause. This contributes to an environment where firms always try to get “a better deal” than their competitors, knowing that the agencies in charge do not apply the same rules for everyone.

Approximately half of the surveyed firms disagree that regulations are applied in a fair and even manner across the spectrum of agencies described. Perception of uneven application of the rules at customs and trade regulations stands out with only 37% of firms agreeing that the statement is true; while 49% disagree with regard to taxes (Table 2).


According to Table 2, customs duties evasion is a marginal problem for foreign-owned companies (17%). This could be explained by the fact that many foreign-owned companies are import/export or assembly and therefore benefit from duties exemption. This seems to be corroborated by the figure of non-exporters, who perceive this problem much more strongly.

On the other hand, tax-related problems seem to affect all companies across the board (the difference between foreign-owned and non-exporters is much lower). This is also illustrated below using questions about their experience with VAT reimbursement.

In Table 3, approximately one out of six companies filed a request for VAT reimbursement. However, when interviewed regarding the leading reasons for not requesting a VAT reimbursement, one third noted the fear of future audits, and one half noted the long and cumbersome procedures.

Problems of long and cumbersome procedures seem to be confirmed by data; on average, VAT reimbursement occurs almost 200 days after the request has been filed (accounting for 15% of total sales). It is likely that larger firms’ applications for VAT reimbursement are associated with much larger sums, hence the longer delays for large firms (over 270 days) compared to small firms (66 days on average). This indicates that large firms are more prone to waiting for large VAT paybacks, whereas small firms do

Table 2: TYPES OF COMPETITORS’ PRACTICES WHICH HARM YOUR COMPANYAll Firms Foreign-owned Non-Exporter

Fiscal evasion 49 34 50

Customs duties evasion and trade-related regulations 37 17 35


Note: Percentage of companies stating that this is a major or very severe constraint.


Table 3: VAT REIMBURSEMENT: DELAYS AND CONSTRAINTS

Tuni

sia

(All)

Sm

all

Med

ium

Larg

e

Dom

estic

Fore

ign-

owne

d

Non

-E

xpor

ter

Exp

orte

r

During the year 2012, have you requested a VAT reimbursement? (in percentage of surveyed companies)

16 10 17 21 16 12 16 15

What are the main reasons why you did not request a VAT reimbursement?

Fear of future tax audits 30 32 35 9 31 17 33 23

Excessively long procedures 25 28 22 29 26 17 28 21

Excessively cumbersome procedures

19 16 20 27 19 20 18 22

Other 26 24 23 35 24 46 21 34

What has been the time to get VAT reimbursed and how much did it represent in percentage of total sales?

(in days) 187 66 159 274 189 175 179 201

(in percentage of total sales) 15 17 14 15 13 23 16 13


not find the delay associated with long and heavy procedures worth the amount they could receive. In addition, the fear of an audit is of more concern for small firms than larger ones. Small companies, on average, wait for 66 days, whereas large companies wait for 274 days. This is counterintuitive since for capacity reasons, large companies should be reimbursed earlier.

Certain discretion is plausible when reviewing the frequency of VAT reimbursement by the number of days. It presents some peaks at 20/40 and 70 days, which are difficult to explain but point nonetheless at different treatment of VAT processing time across firms.

As indicated earlier, instruments such as fiscal incentives or fiscal systems create discretionary space in their application. Hibou (2011) describes what she calls “a vicious circle” that can occur between tax and customs agents’ behavior and private sector operators, which gives an advantage to the best-connected firms. This approach legitimizes a bargaining culture of tax and tariff negotiation, in turn, leading to tax and tariff evasion (Figure 39). The bargaining process will affect most negatively impact the least-connected firms and give a stronger competitive advantage for the best-connected firms14.

Another illustration of discretion and uneven enforcement of regulations is illustrated with cargo dwell time, i.e. time for cargo to exit the main port of the country (Figure 41). When compared to countries in the sub-region,

14 Chekir, Hamouda, Ménard, Claude, Nucifora, Antonio, Raballand, Gaël and Rijkers, Bob (2013), “Governance, predation and firms’ growth in Tunisia”, mimeo.

Figure 39: VAT REIMBURSEMENT FREQUENCY BY NUMBER OF DAYS

.002

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kernel = epanechnikov, bandwidth = 12.9674

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Figure 40: THE VICIOUS CIRCLE BETWEEN TAX AND CUSTOMS AGENTS’ BEHAVIORS AND SOME PRIVATE SECTOR OPERATORS

Bargainingprocess betweensome taxpayers/

importers andtax/customs

adminsitration

Bargainingculture,

dual accounts andpervasivecorruption

Tax/tariffevasion

Legitimacy fromtax/customs to

inspect

Source: Adapted from Hibou (2011), p.144.


cargo dwell time in Tunisia is on average the worst after Algeria (close to 10 days). The cargo dwell time is significantly higher than Morocco, which is the lowest among these countries with five days of dwell time.

Discretion and unpredictability seem to also play a role. Figure 42 measures the ratio between the longest dwell times to the average for all the companies surveyed. Ideally, the ratio should be rather close to one since importers have rather similar cargo to import. Again, this ratio is the worst for Tunisia, indicating the possibility that an importer will face much longer dwell times than the average. Dwell time can capture many factors but, in general, it is a proxy for some bargaining processes to reduce fees, bribes and duties.

Uneven enforcement of regulations seems also prevalent among regions in Tunisia. For instance, in Tunis, more than two thirds of the surveyed companies feel that regulations are enforced fairly, compared to just above 40% in Region D (Jendouba, Beja, Kef, Siliana, Sidi Bouzid, Kasserine, and Kairouen). The administrative burden and unpredictability of the enforcement of regulations seems to be higher in remote regions compared to Tunis. This also could explain why a gap has been growing between Tunis and some hinterland regions. There appears to be a double sanction for companies residing in the hinterland regions, who usually endure poorer infrastructure and services as well as more unpredictable regulations.

RECOMMENDATIONS

The findings indicate that beyond the measurable direct and indirect costs that the regulatory and bureaucratic burdens impose on firms, there is a significant discrepancy issue related to how policies and regulations

Figure 41: BENCHMARKING OF CARGO DWELL TIME

Figure 42: RATIO BETWEEN LONGEST DWELL TIMES/AVERAGE

Source: The World Bank, Various Enterprise Surveys Source: The World Bank, Various Enterprise Surveys

40.00

35.00

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Average days to clear customs(IMPORTS)Longest days to clear customs (IMPORTS)

2.4

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rocco

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ia

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are applied today. Widespread before the revolution, the transition has not yet yielded a significant improvement in regularly uncertainties.

In order to provide a more predictable environment to firms and to reduce capture and predation, Tunisian authorities need to send a strong message by addressing the issues where it is currently the most prevalent. Areas of intervention may include continuation of the modernization of customs initiated in 2006.

Another area of intervention would be to rethink the current incentive system which continues to exacerbate distortions that may no longer be needed. Many countries which suffered from similar types of distortions as Tunisia, such as Mauritius, have successfully transitioned to a simplified and more equitable environment.

chapter: 3: innovation technology & exports for private business in tunisia 35

innovAtion technoloGy & exports For privAte business in tunisiA

ChAPTER: 3

Innovation is an important driver of competitiveness and economic growth. The innovation cycle, starting from the initial idea or identification of a problem or need, to the point where the output is sold in the market, requires a dynamic and holistic ecosystem supported by the appropriate set of policies and resources. To create high wage jobs through a growth-oriented, dynamic private sector, Tunisian industries need to move up the value chain and export higher-value-added goods. Innovation through the assimilation and diffusion of global technology will be a key factor in shifting the Tunisian industry towards this goal.

This chapter looks at the data of the sample firms surveyed and identifies where and how much innovation is taking place in the Tunisian private sector. Also examined are the characteristics of innovative firms in comparison to other firms and the value-add provided to the Tunisian economy, specifically in terms of job creation and competitiveness. The last part of the chapter highlights the perception to challenges and barriers that are viewed by innovative firms compared to non-innovative firms. It concludes with suggested areas of reforms and interventions to address these challenges and promote innovation. The recommendations draw, in addition to the data findings, on research and assessments of the Tunisian innovation ecosystem and best practice models around the world.

DEFINITION OF INNOVATION AND SCOPE OF THE ANALYSIS

The enterprise survey tool used

in this ICA is not an innovation

survey and, as such, does not

80%

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% o

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ital h

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..

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100%

17%

82%

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85%

Locals or local businesses

Foreigners or foreign businessesPublic sector

Other

Figure 43: INNOVATIVE VS. NON-INNOVATIVE BY CAPITAL SHARE

Source: The World Bank, Various Enterprise Surveys.


capture the full spectrum of indicators on innovation. However, it does

include innovation-related questions that shed light on the status of Tunisian

firms in this area. Hence, it is important to first clarify the definition of innovation

applied in this report and the limitations on the scope of analysis that can be

done. Based on the questions asked in the enterprise survey, the definition of

innovation used in this report is “firms that produced or introduced a new or

improved product or process to the market”. Firms referred to as ‘innovative’

in this report indicates that firms answered ‘yes’ to the question “have you

introduced a new or improved product to compete in the market?” This report

does not distinguish between process and production innovation, and thus

refers to innovation as a whole.

Firm Innovation in Tunisia: The General Picture

Foreign investment

Based on the stated definition, the sample indicates that 47.5% of Tunisian firms are innovative in that they have introduced a new product or improved on an existing one to compete in the markets. The level of innovation brought in by foreign firms is very small. While foreign ownership in non-innovative firms is 17%, foreign ownership in innovative firms is 14%, confirming the prevailing literature that the majority of foreign-owned businesses are there to capitalize on lower production costs in Tunisia. Promoting foreign investment at the higher end of the value chain is more likely to bring innovation into the country through technology transfer and access to knowledge in external markets.

Industry

A breakdown of innovative firms as a percentage of each sector shows that the highest level of innovation is reported by firms in the ICT sector, followed closely by those in textiles and agribusiness. Innovation reported by firms in construction and materials for construction is also significant enough to warrant consideration.

Figure 44: INNOVATION REPORTED BY FIRMS (% EACH SECTOR)

Notes: IME – Industry mechanical engineering; MCCG – Materials for construction, ceramics and glass.

Ser

vice

s

Agrbus.53.4%

Clothes30.9%

Textiles64.3%

IME46.6%

Other51.3%MCCG*

51.9%

Trade48.4%

Tourism26.1%

ICT73.3%

Constr50%

Health42.9%

Manufacturing


Firm Size

Large firms in Tunisia report the highest level of innovation. According to the data, 55.8% of large firms are innovative, followed by medium and small firms with 45.2% and 44%, respectively. The distribution of innovative and non-innovative firms in the sample according to size indicates that medium-sized firms are better performing.(Figure 45).

Firm Age

Viewing the distribution of innovative and non-innovative firms from the perspective of firm age provides further insight. Firms in operation for over fifteen years showed the highest levels of innovation across most sectors whereas very young firms (less than 5 years) and firms between five and fifteen years old had lower innovation distributed across various industries. The figures below show the comparative advantage in innovation by sector for start-ups, growth stage, and older established firms. Amongst young firms (up to five years), the highest levels of innovation are found in the textiles, clothing and agribusiness sectors. However, trade, tourism, and ICT industries also show a significantly higher proportion of young firms that are innovative compared to those in the same age group and sector that are non-innovative (Figure 46).

Among firms who have been in operation between five and fifteen years, the highest levels of innovation are found in trade, textiles and Industrial Manufacturing and Electricity (IME). The proportion of innovative firms in

Figure 45: DISTRIBUTION OF INNOVATIVE & NON-INNOVATIVE FIRMS BY SIZE

Non-innovative Innovative

60%

31%25%

20%

27%

50% 48%

40%

% o

f �rm

s

Small Medium Large

20%

0%


Figure 46: INNOVATIVE/NON-INNOVATIVE FIRMS BY INDUSTRY IN FIRMS 0-5 YEARS OLD

1

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Innovative Non-innovative Gap


ICT is also significantly higher than other sectors (Figure 47). Older firms show the highest levels of innovation compared to all the other age groups concentrated markedly in agribusiness, IME, and trade. The proportion of innovative older firms in those sectors also surpasses their counterpart non-innovative firms in the same industries (Figure 48).

In summary, the findings show that overall a significant level of innovation by SMEs is taking place in services, textiles, and ICT. Tourism and agribusiness in particular have seen an increased entry of innovative start-ups. Given that SMEs constitute the majority of the private sector (more than 95%, Tunisia DPR 2010), it is important to improve the business environment and innovation ecosystem for these sectors in order to promote more opportunities for competitive start-ups and SMEs. A significant share of large and established firms, who have been in business for more than 15 years, are innovating in agribusiness, IMG, MCCG and trade. Innovation in large firms is also important for competitiveness and, more importantly in this case, for moving production processes up the value chain. The increased importance of Tunisian exports in electronic and mechanical engineering product parts may have played a part in the level of innovation found in manufacturing. However, Tunisia needs to further promote these sectors in order to experience positive economic impact.

Region A (Ariana, Tunis, Manouba, Monastir and Ben Arous) has the highest concentration of the innovative sectors mentioned above (ICT, textiles, agribusiness, MCCG, construction, trade, and IME). Although the highest concentrations of ICT firms are found in Region A, Region C (Sfax, Gabes, Menine) has a higher aggregate level of innovation. A more in-depth look at the types of ICT firms that operate in the region can help explain

Figure 47: INNOVATIVE/NON-INNOVATIVE FIRMS BY INDUSTRY

Figure 48: INNOVATIVE/NON-INNOVATIVE FIRMS BY INDUSTRY

Agr

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In. M

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and

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ICT

Tour

ism

Hea

lth

Con

stru

ctio

n


-2

-5

12

02

-4 -5

-11

11

1 1

-15

-10

-5

0

5

10

15

20

25

%

Innovative �rms aged 5-15 years

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10

15

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Innovative �rms 15+ years


this paradox. For example, agents or representatives of foreign technology companies, trading in wholesale or retail, are largely not innovative. The high level of SMEs found in those areas are mostly non-dynamic firms, especially in Region D. Firms in Region D complained more than firms in other regions about constraints in the business environment particularly security and power issues.

INNOVATION & ITS ADDED VALUE TO THE ECONOMY

This section focuses on key value-added outputs/results15 that innovative firms provide in the Tunisian economy, such as the number of jobs created or level of exports. The following section examines the specific value-added inputs16 that innovative firms apply specifically focusing on employee skill level and/or amount of training offered by the firm.

Firm Innovation and Contribution to Exports

Although Tunisia enjoys relatively high levels of exports as a result of government reforms, its export products continue to have low technological content. Today the country’s exports are even less dynamic and are evolving at a slower pace.

Export sales, a key indicator for competitiveness, tends to be positively linked to innovation as firms strive to grow by capturing other markets. According to the data, about half of both exporting and non-exporting firms indicate that they innovate, indicating a relatively high level of firms who innovate for the national market. A distribution analysis of innovation (Figure 50) shows that innovative firms contribute to exports equally as non-innovative firms.

15 Value-added outputs/results variables in this case are defined as those factors that are produced or happen directly as a result of the firm’s activity such as exports, number and types of jobs created, revenues, growth etc.

16 Value-added inputs are defined as inputs the firm uses and invests in its production and business operations such as human capital skills and training offered, technology use, and research and development.

Figure 49: INNOVATION FIRMS BY REGION


Notes: Region A: Ariana, Tunis, Manouba, Ben Arous; Region B: Sousse, Mahdia, Montasir, Kariaoun; Region C: Sfax, Gabes, Mednine; Region D: Not specified.

70

60

50

40

53.14

41.46

62.3

26.72

%

30

20

10

Region A Region B Region C Region D

0


Job Creation

The positive linkage between job creation and innovation is an important value-added outcome. Figure 51 shows the distribution of the number of jobs created over a firm’s lifetime, for innovative versus non-innovative firms. Innovative firms show a higher net positive number of jobs created at 52%, compared to 39% for non-innovative firms.

To further understand where job creation is taking place, the analysis looked at the average number of jobs created by all firms in the sample, based on their age. Start-ups, which typically have a high failure rate, create a net job loss after 3 years, while older firms which tend to be larger have the highest average contribution to jobs.

A distribution analysis by innovation and number of jobs created by firm age groups (Figure 52) shows that innovative firms contribute more to job creation in every age group, except for very old firms over fifteen years of age, where job creation is lower by innovative firms. Like all small firms, innovative start-ups (less than 3 years) which tend to be particularly risky with a high chance of failure, exhibit net job loss (Table 4).

However, the high risk innovative start-ups that succeed beyond three years will grow and go on to contribute more to job creation than all other SMEs. In terms of job loss, innovative older firms are likely to be

Figure 50: DISTRIBUTION OF INNOVATIVE AND NON-INNOVATIVE FIRMS BY EXPORTS


30%

25%

20%

15%

10%11%

9%

28% 27%

5%

0%

% o

f tot

al a

nnua

l sal

es

Non-Innovative Innovative

Indirect exports Direct exports

Figure 51: INNOVATIVE VS. NON-INNOVATIVE FIRMS BY NUMBER OF JOBS CREATED

Figure 52: INNOVATIVE VS. NON-INNOVATIVE FIRMS AND NUMBER OF JOBS CREATED BY FIRM AGE

0

-50

50

100

150

No.

of j

obs

crea

ted


39

Innovative vs. Non-Innovative �rms by number of jobs created since strat-up

Excludes outside values*Number of jobs created = (# full-time employees in 2012 # of full-time employees upon start-up)

52

Innovative Non-innovative

-19.4

13.7

33.6

83.8

6.0 9.6 11.4

94

-40.0

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ber

of j

obs

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ted



large manufacturing firms who have invested heavily in capital to lower production costs and have reduced their need for labor, particularly at the low end of the value chain. The result is a net job loss, but new jobs created are most likely higher-value-added jobs due to innovation and upgrading of technology.

WHAT DIFFERENTIATES AN INNOVATIVE FIRM IN TUNISIA?

Technology Use and R&D

Higher use of technology, including the Internet, is a distinctive characteristic of innovative firms. Engaging in R&D activities is also a distinctive characteristic of innovation. For example, in Tunisia 44% of innovative firms engage in R&D development, compared to only 22% of non-innovative firms (Figure 53).

Figure 54 shows the breakdown of R&D done by firm type, according to industry. The data highlights that the highest level of R&D is done by firms in construction, ICT, agribusiness, IME, and textiles, respectively.

Employee Skills and Training

A higher proportion of innovative firms, compared to non-innovative firms, have employees with university degrees and offer vocational training to their permanent employees. Innovative firms tend to hire

Table 4: AVERAGE NUMBER OF JOBS CREATED BY FIRMS ACCORDING TO FIRM AGE

Age group Average number of jobs created

Std. Dev. Number of firms

Less than 3 years old -4.48 88.71 40

3-5 years old 10.96 43.21 85

6-15 years old 22.95 65.83 210

15+ 83.90 402.75 237

Total 572

Figure 53: INNOVATIVE VS. NON-INNOVATIVE FIRMS BY VARIOUS INPUT VARIABLES


% o

f firm

s re

por

ting

“yes

”

100%

90%

80%

70%

60%

50%

40%

30% 26%34%

93%

ISO Certification

Email Own Website

BroadbandInternet

R&D Patent Using Internet forR&D

98%

59%

79%85%

92%

22%

44%

6%

15%

69%

85%

20%

10%

0%



university graduates due to the prevalence of technology and engineering in innovation. The data shows that innovative firms have more high skilled jobs compared to non-innovative firms (Figure 55). Although there is a link between innovative firms employee training, it is challenging to establish causality.

BARRIERS AND CHALLENGES FACED BY INNOVATIVE FIRMS

In order to develop the appropriate recommendations to support innovation, this section examines the unique challenges indicated by innovative compared to non-innovative firms.

Figure 54: FIRMS INDICATING THAT THEY DO R&D BY INDUSTRY

Ser

vice

s

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bus.

45.8

8

Clothing

20.43

Textiles

37.5Mech. & elec.

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.59

Build

Mat

eria

l

23.5

3

Trade

17.98

Tourism

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ICT48.28

Construction

50

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4.29

Manufacturing

Figure 55: INNOVATIVE VS. NON-INNOVATIVE FIRMS BY LABOR SKILL LEVEL AND TRAINING

% o

f firm

s re

por

ting

“yes

”

80%70%60%50%40%30%20%10%

0%

60%65%

15%20%

70%

45%55%

39%

52%57%

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kers

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Figure 56: CONTRIBUTION OF INNOVATIVE VS. NON-INNOVATIVE FIRMS TO HIGH-VALUE-ADDED JOBS

% o

f firm

s re

por

ting

“yes

”

80%

60%

40%

20%

0%Managerial staff Engineers/technicians Skilled workers Unskilled workers Traders

50%59%

53%62%

67%64%55%57%

52%46%




Macroeconomic Framework

Compared to non-innovative firms, innovative firms consider the macroeconomic framework to be a major/very severe obstacle. On the other hand, innovative firms as a whole consider issues such as political instability, electricity, and access to finance to be less of a major/very severe obstacle. In terms of the macroeconomic environment, innovative firms are particularly vulnerable to shocks and volatility given their exposure to exports, inflation and the financial sector. In addition, as the government focuses on stabilizing growth and managing the fiscal balance, public investments in innovation activities such as R&D take a back seat. Data from the World Bank Development Indicators shows a correlation between public expenditure on R&D and GDP. During Tunisia’s high growth period, between 2002 and 2004, R&D expenditure shot up and continued throughout the next three years when the economy sustained a GDP growth between 5–6%.

Labor Force Qualifications

Lack of qualifications in the labor force is a significant barrier, but more so in soft skills rather than hard technical skills. This is a problem faced by all firms in Tunisia but it is critically important for innovative firms who

Figure 57: R&D EXPENDITURE AND GDP GROWTH IN TUNISIA

GD

P g

row

th

R &

D e

xpen

ditu

re (%

of G

DP

)

1996

1998

2000

2002

2004

2006

2008

2010

8%

6%

4%

2%

0%

-2%

-4%

1.2%

1.0%

0.8%

0.6%

0.4%

0.2%

0.0%GDP growth R & D expenditure

Source: World Bank WDI, 2012.

Figure 58: INNOVATIVE/NON-INNOVATIVE FIRMS BY MAJOR/VERY SEVERE OBSTACLE TO CURRENT OPERATIONS

Non-innovative

% o

f �rm

s sa

ying

“ye

s”

Ele

ctric

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Tele

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ions

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Com

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rate

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ities

Form

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ess

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ion

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and

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ploy

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Mac

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onom

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amew

ork

(in�a

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ange

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)

70%

60%

50%

40%

30%

20%

10%Innovative

0%


compete in global markets. In the Global Competitiveness Index, Tunisia ranks the highest among comparable countries in the MENA region in terms of availability of scientists and engineers, ranking 8th out of 142 countries globally. It is followed by Jordan at number 20 then Lebanon at 30. However, jobs at the higher end of the value chain also require good communications and marketing skills. Individual one-on-one interviews with business leaders and entrepreneurs indicated that the labor force does not suffer from lack of technicians, ICT, engineering and science specialists. The problem lies in the soft skills such as languages, marketing and communications skills. Figure 58 looks at factors that are indicated by a firm as major to very severe obstacles to operations, including lack of training and qualifications.

Access to Finance

Access to finance is a major/severe obstacle for very young innovative firms (start-ups) and firms on the verge of high expansion. Overall, access to finance appears to be less of a major/very severe obstacle to innovative firms. Breaking down the results by firm age sheds more light on this issue. Innovative firms up to three years old indicated that access to finance is a major/severe obstacle, at a significantly higher proportion than their non-innovative counterparts. Innovative firms that are ten to fifteen years old have a similar perception of access to finance as a major/severe obstacle (Figure 59).

Customs/foreign trade regulations and competition from the informal sector

Uncompetitive practices have a strong negative effect on innovation. Figure 60 looks deeper into the uncompetitive practices by other firms that affect innovative firms. These practices mostly relate to violation of property rights/counterfeiting followed by evasion of labor regulations, evasion of customs/tariffs/foreign trade regulations, and tax evasion.

Figure 59: INNOVATIVE/NON-INNOVATIVE FIRMS AND ACCESS TO FINANCE AS A MAJOR OBSTACLE BY FIRM’S SIZE AND AGE

Firm age


% �

rms

ind

icat

ing

acce

ss t

o �n

ance

as

maj

or/

seve

re o

bst

acle

35

3.59

3025201510

50

-5

3-5

yrs.

-0.94-3.93

-0.22

-10

< 3

yrs

.

11.08

5-10

yrs

.

10-1

5 yr

s.

15+

yrs

.


Figure 60: INNOVATIVE/NON-INNOVATIVE FIRMS BY PRACTICES OF COMPETITORS THAT IMPOSE MAJOR/SEVERE OBSTACLES

% o

f firm

s sa

ying

“ye

s”


Tax

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ion

Cus

tom

s ta

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/fo

reig

n tr

ade

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asio

n

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orre

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n of

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g

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ent

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it

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rent

ial

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tmen

t

60%

50%

40%

30%

20%

10%

0%

48%50%

32%39%

31%

38%

12%

29% 23% 20% 19%14%

THE TUNISIAN INNOVATION ECOSYSTEM

During the past decade, Tunisia made various investments in innovation infrastructure. The building of a knowledge-based economy constituted a key component in the National Development Strategy (2007-2011). The Association de Developpement Technologique Innovation et Production (ADTP) was established to coordinate and lead the national efforts in innovation. Several policy reforms were undertaken in education and the business environment, eight specialized technology parks were built across the country dedicated to the main industrial sectors ranging from biomedical to ICT, and the government also supported the establishment of incubators for every region. Access to the internet was significantly improved, and the country’s Intellectual Property (IP) system was strengthened.

Despite all these initiatives, Tunisia still lags behind most comparable countries in the region in terms of innovation and technological advancement. The Bank’s 2010 DPR calls for “critical reforms to strengthen Tunisia’s innovation system that include: (i) adopting a balanced innovation strategy, (ii) enhancing the efficiency and effectiveness of research and development (R&D) spending, (iii) adapting the supply of adequate skills and competencies, and (iv) strengthening the financing of innovation”.

RECOMMENDATIONS

The data on innovative SMEs in Tunisia, as compared to all other SMEs, point to their positive contribution to job creation, especially at the higher end of the value chain and to exports. As such, there is high merit for the Tunisian government to work on promoting the growth of this group and work on the key issues that they face. The challenges faced by innovative firms are the same as all other firms, but addressing the particular areas that are found to be more challenging for innovative firms can help promote growth and employment generation in the private sector. Tunisia also needs to address


inefficiencies in its innovation ecosystem. It is beyond the scope of this report to provide a full innovation policy strategy and implementation framework, thus the recommendations below are intended to provide a base for the launching process to transform Tunisia into an innovation-led economy.

Science Technology and Innovation (STI) Strategy

Tunisia needs to build a strong cohesive STI strategy focusing on the needs of industrial upgrading, productivity growth and technology progress, specifically capacity in technological absorption and assimilation. Key industries like agribusiness, construction and construction material, textiles and ICT should be focus sectors. Putting in place a strong ecosystem for technology, the ICT industry and investment in advanced technology purchases will support innovation and growth in the services, trade and tourism sectors (areas where Tunisian firms show potential) and accelerate technological development of the country’s traditional industries like textiles and clothing. Capacity to absorb and assimilate these technologies must accompany their introduction via mechanisms that ensure knowledge transfer.

Policy and mechanisms should also be put in place to increase R&D and the commercialization of the products. Doing all this will not be easy and policy makers should keep in mind that all the successfully innovative economies that exist today took decades to build. However, discouragement is not an option and a country does not have to wait until its innovation system is completed to enjoy prosperity. Tunisians should take comfort in the fact that by achieving this goal the country can begin to reap immediate systemic benefits in employment and development. By using a strategic programmatic approach, where specific investment climate challenges are addressed in the short-term, and broader policy, capacity, and infrastructure bottlenecks are tackled over the medium- and long-term, sustainable development can be achieved.

Short-term

Access to Finance

Innovative Tunisian firms face challenges in accessing financing when they are at the very early stage and at the point when they are experiencing high growth. The banking sector, as well as Microfinance Institutions (MFIs), experienced severe setbacks after the revolution. Since then, specific mechanisms and programs have been put into place to put liquidity into the financial sector and help encourage lending. However, innovative start-ups and SMEs operating mostly in the services sector, where collateral is not available, face a financing gap at the very early stage (0-3 years) and at


their expansion stage (10-15 years). Tunisia needs to develop mechanisms that would encourage equity investments and angel investors to invest in early start-ups and growing innovative SMEs. Most investors in the region typically support existing firms that have a track record due to a lower level of risk as opposed to early stage firms.

Tunisia’s few public-private investment funds have had limited investments in innovative start-ups. These fund managers point out that the private sector is driven by market return and the ability to exit investments in the stock market. In Tunisia, most innovation investments by these funds are in ICT companies that are seeking to expand in external markets. Thus, one of the elements that inhibit private equity investment in early stage firms is the high transaction cost required to develop them. This factor impacts the potential return and ability of fund managers to focus on other investments. The other discouraging factor is the lack of market exit. Innovative ICT companies operate at a very fast pace by nature, where they develop a market-ready concept in six months to a year. The Tunisian SME stock market (Marche Alternative) is not very flexible in that companies must have three years of audited financial statements. Tunisia can learn from the public-private equity funding models that have been developed in the region and in other countries. These funds have addressed the issue of balancing the private sector’s need for profit and the government’s objective for development. Funds and SMEs can also be provided with help to access external markets where they can exit. This requires resources and effort by government agencies like FAMEX/FIDEX to cultivate the diaspora and facilitate their linkage to their host market.

Trade Regulations

Both trade and property protections are essential for the survival and growth of innovative firms. The enterprise data indicate that the main issues related to both areas are the lack of regulatory enforcement and tax evasion. Innovative firms, unlike established, old family enterprises, rely on equity capital to support their operations more than other firms. They are also more likely to exit in the capital markets. These financial mechanisms require firms to follow transparent practices which put them at a particular disadvantage to uncompetitive practices from other firms. Hence, in addition to being an important agenda for the private sector, proper enforcement of trade regulations and taxes will be very beneficial for innovative SMEs.

Fostering the Diaspora

Noting that many immigrants maintain linkages with family back home, the country’s large diaspora is a potential source of investment and support during hard times. In the case of Tunisia, efforts must be made to provide an


institutionalized outreach and coordination mechanism to connect diaspora with entrepreneurs and businesses in their home country. An example of such a program is the Chile Global, which works to connect local entrepreneurs with Chile’s diaspora all over the world.

Medium-term

Intellectual Property Protection

Protection of Intellectual Property Rights (IPR) is very weak in Tunisia and it is imperative for the country to implement an effective IPR system. Until such a mechanism is operational, innovative companies need the support, networking capacity, and knowledge to establish IPR protection externally. This capacity needs to be placed in FAMEX, INNORPI and the Ghazala ICT technopole, since this sector suffers the most from piracy and duplication. Coordination between all these entities will ensure an effective level of protection and enforcement which is particularly important for foreign investment and bringing of technological know-how into the country.

Weaknesses in Labor Force Qualifications

Taxing firms to provide government programs for skills and vocational training has proven ineffective in many countries. Rather, government can put into place incentive mechanisms and national policy to assist enterprises in attracting and developing high-level talent skills, such as linking talent/skills training to performance, as well as reward and tax incentives for firms to provide training in technology and engineering. One of the most successful post-graduate job preparation training programs in Tunisia is that of the Association of Firms in Technology. The training provided by this Association is led and managed by a leading Tunisian ICT company and an executive member. It provides training in areas that are in demand by the rest of the Association members and the transition to employment for many of those graduates has been made possible through this program. The government should support such programs and integrate incentive mechanisms to encourage other sector associations and groups to do the same. Private sector-led employment training has proven to be more effective than government vocational centers that are often disconnected from the demands of the private sector.

Long-Term

Research & Development

Empirical evidence from developed countries attests to the private returns from investment in R&D, which average 28%17. Thus, there is merit for

17 Wieser, 2005.


Tunisia to develop a framework for priority areas in R&D that meet its national development plan. This should be accompanied by a carefully formulated visionary strategy for a sustainable source of revenue for R&D support, which is not completely exposed to government fiscal volatility. Some governments set up endowment funds through public/private partnerships, allowing both sides to leverage R&D funding. Recent trends have focused on tax incentives to encourage and allow the private sector to invest in commercially viable R&D. Also used in several countries are: customs exemptions, import VAT deductions for equipment used for R&D and testing purposes, and accelerated depreciation of machinery used for R&D. A third component in a sound R&D strategy is how to facilitate the commercialization of new and adapted technology. This will need a strong and streamlined IPR system and a technology transfer office that is clearly defined in scope and objectives. Government-sponsored R&D must have clear criteria and monitoring systems, Performance assessment should incorporate all these elements.

Government Investment in Innovation

In most countries, the government has been the primary catalyst in promoting and developing innovation systems. For example, governments have direct grant funding as well as direct investment programs for strategic R&D or achievement of economies of scale in a key industry. The government is also an important user of innovative technology. Public procurement policy can be leveraged to promote and encourage innovation and use of innovative products and processes in production. Policies can be put in place where the government would be the first to buy new products developed in the market, ensuring a market for such products. The government can also include incentives for the use of innovative products in large public construction projects and provide criteria in its monitoring and reward mechanism with respect to where knowledge transfer is taking place. Government procurement reforms must ensure a level playing field. They must also open opportunities for SMEs to benefit through rewards to FDI and large corporations that use local SMEs and transfer knowledge and manufacturing of innovative technology to them.

chapter: 4: labor markets and Workers skills 51

lAbor mArKets And WorKers sKills

ChAPTER: 4

CHARACTERISTICS OF THE LABOR MARKET

In the manufacturing sector, a major part of the formal labor force, about 80%, is active with a permanent, full-time status (Table 5). This does not allow companies to be flexible with their labor force and cope with possible changes in trends. Large companies use less temporary employees than other companies. Similarly, exporting companies may be penalized in international markets by excessive use of a permanent labor force (80%). In contrast, the services and tourism sectors make extensive use of temporary workers – nearly 50% of their labor force. Contrary to what is observed in the manufacturing sector, large companies rely on this type of more flexible labor. Foreign and export companies in these sectors resort far less to these temporary contracts than their Tunisian non-exporters counterparts.

The regulatory framework related to the Labor Code can have a major influence on the labor market in Tunisia. Even if more than one in four companies in the manufacturing sector declares that labor legislation is

Table 5: JOB CATEGORY

Manufacturing

Sector Services Tourism

Permanent, full-time labor force 79.5% 52.4% 51.3%

Temporary labor force 20.5% 47.6% 48.7%

By Size

Small Permanent, full-time labor force 71.8% 57.9% 100.0%


Average Permanent, full-time labor force 70.5% 69.7% 45.2%


Big Permanent, full-time labor force 83.6% 48.5% 55.0%


By Capital Structure

Foreign Private Enterprise Permanent, full-time labor force 79.9% 83.8% 84.9%


Tunisian Private Enterprise Permanent, full-time labor force 79.3% 42.2% 45.9%


By Market Orientation

Exporter Permanent, full-time labor force 80.2% 73.1% 40.1%


Non-Exporter Permanent, full-time labor force 77.2% 52.4% 53.2%


Source: Survey of Investment Climate in Tunisia, World Bank, 2012.


a major or severe constraint, this particular constraint is ranked in twelfth position among all constraints (out of 20). In the services sector, only 15% of companies consider the current labor legislation as a major or severe problem for business growth (Table 6). The tourism sector does not seem affected by the labor laws at all, and in the three sectors covered by the survey, virtually none of the respondents mentioned labor laws as the biggest obstacle to the proper management of the company.

Small businesses seem to be less affected than others by the constraints that may be related to labor legislation. Companies’ market orientation and capital structure do not seem to make a major difference in the evaluation of this constraint.

MAIN CHARACTERISTICS OF EMPLOYEES: EDUCATION AND TRAINING

The issues of worker education and training and the ability for the economy to absorb this labor have often been placed at the heart of the Tunisian revolution. The fact that a large mass of educated workers could not find a job in the labor market is often cited as a trigger of the 2011 revolution. Overall, the Tunisian educational system seems relatively strong. The literacy rate among adults was 74.3% in 2012. In 2009, the gross enrollment rate was 109% at the elementary level and 90% at the secondary level. However, as shown in Table 7, employee training is a severe problem for Tunisian companies. In fact, over 42% of companies surveyed in the manufacturing sector believe this constraint is an obstacle for the company. Similarly, about 12% of manufacturing companies mention employee training availability as the main obstacle to their economic development. This phenomenon is less apparent in other areas, but it remains problematic.

This discontent of entrepreneurs vis-à-vis the quality of the labor force seems to affect businesses of all sizes. However, it seems that foreign

% of companies indicating that the Labor Legislation constitutes a major or severe obstacle

Manufacturing Sector Services Tourism

Sample 25.61% 15.03% 5.26%

By Size

Small 15.94% 14.46% 0.00%

Average 27.75% 14.55% 11.11%

Big 27.19% 23.08% 0.00%


Foreign Private Enterprise 20.41% 18.18% 0.00%

Tunisian Private Enterprise 27.24% 14.79% 6.25%


Exporter 25.57% 11.54% 33.33%

Non-Exporter 25.68% 14.63% 0.00%

Table 6: CONSTRAINTS RELATED TO LABOR REGULATIONS


companies are less subject to this constraint. As will be seen later, pay attractiveness offered by this type of companies may explain this difference.

As indicated in Table 8, the majority of companies have production workers who have on average completed more than seven years of study. Large companies appear to have a slight advantage regarding the education level of their production labor force. In fact, 46% of the production labor force at large companies have completed an average education level of 10 years. Similarly, foreign companies seem to attract more skilled labor than domestic companies. However, exporting companies show a significantly lower level of education than companies targeting the domestic market.

Since a significant percentage of businesses suffer from the lack of labor force education, their responses have been analyzed to determine whether formal vocational training has developed in Tunisia. In practice, a little over half of the companies in Tunisia provide formal training to their employees. This ranks Tunisia higher than other countries with recent data available. The proportion of companies providing training to their employees exceeds what is observed in Tunisia, Lebanon, Morocco, and Turkey

Table 7: CONSTRAINTS RELATED TO EMPLOYEES’ QUALIFICATIONSPercentage of companies indicating that the availability of employee education constitutes a major or severe obstacle


Sample 42.41% 31.58% 25.00%

By Size

Small 31.88% 26.83% 33.33%

Average 47.39% 36.36% 11.11%

Big 38.79% 38.46% 40.00%





Exporter 43.45% 34.62% 66.67%

Non-Exporter 40.54% 30.33% 23.08%


Table 8: EDUCATIONAL LEVEL OF PRODUCTION WORKERS

Percentage of companies with production workers having averaged the following number of study years

0-3 years of study

4-6 years of study

7-9 years of study

10-12 years of study

13 years or more

Manufacturing Sector 2.17% 15.22% 37.92% 38.65% 6.04%

By Size

Small 0.00% 19.72% 36.62% 38.03% 5.63%

Average 3.91% 14.35% 37.39% 38.26% 6.09%

Big 0.00% 14.16% 39.82% 39.82% 6.19%


Foreign Private Enterprise 2.00% 14.00% 32.00% 46.00% 6.00%

Tunisian Private Enterprise 2.23% 15.61% 39.81% 36.31% 6.05%


Exporter 2.67% 14.50% 38.55% 38.17% 6.11%

Non-Exporter 1.32% 16.45% 36.84% 39.47% 5.92%


(Figure 61). It therefore appears that Tunisian companies are tracking ahead on this issue.

Not all companies practice the same policy on vocational training (Table 9). Foreign and exporting companies seem to have a more developed training system, and the vast majority of large corporations try developing training programs to address the shortage of adequate labor force skills. The size of companies seems to be a determining factor in the development of vocational training programs.

In the manufacturing sector, production workers benefit the most from on the job and vocational training. However, statistical analysis

Figure 61: PERCENTAGE OF COMPANIES PROVIDING FORMAL TRAINING

60.0

50.0

40.0

30.0

20.0

Algerie(2007)

Jordanie(2006)

Maroc(2007)

Roumanie(2009)

Maurice(2009)

Turquie(2008)

Coree(2005)

Liban(2009)

Bresil(2009)

Tunisie(2011)

10.0

17.3

23.9 24.7 24.9 25.628.8

39.5

52.4 52.9 54.3

0.0


Note: Exclusively for companies in the manufacturing sector.

Table 9: FORMAL VOCATIONAL TRAINING

Percentage of companies having provided formal training to their employees


Sample 54.3% 56.3% 52.6%

By Size

Small 34.3% 42.7% 33.3%

Average 50.0% 68.5% 50.0%

Big 75.0% 92.3% 80.0%





Exporter 58.3% 55.6% 100.0%

Non-Exporter 47.4% 55.8% 46.2%

% of Employees Trained

Workers excluding the production sector 41.3%

Workers in the production sector 62.54%

Source: Survey of Investment Climate in Tunisia, 2012.


conducted so far does not allow simultaneous control of the confounding factors affecting the decision of companies that provide training. We present the results of a regression (decrementation) to fill this gap in Table 10. The study estimates a Probit model on manufacturing data in which the dependent variable is the formal training provided by companies. A set of control variables that capture company characteristics such as nationality, size and market orientation were included. In addition, the economic activity of the company was controlled by including variables such as the total amount of sales made by the company. We also take into account the composition of the labor force. Lastly, a dummy variable is included to control whether the company captures the level of training and competence of employees as a major obstacle to its operation and growth. Dummy variables are also included to control for the type of business of the company.

The results show that company size appears to be the main factor determining the decision to offer formal training. The composition of

Dependent Variable Formal Training

(1) (2) (3)

Constant -0.178 -1.243 -1.346

(0.48) (1.77)* (1.89)*

Size of Companies

Average 0.377 0.261 0.261

(1.98)** (1.22) (1.20)

Big 1.097 0.874 0.868

(4.78)*** (3.20)*** (3.13)***

Labor force

% of Non-production employees 0.003 0.007 0.007

(0.76) (1.65)* (1.65)*

% of production employees -0.004 -0.001 -0.001

(1.11) (0.26) (0.30)

Average level of education of a production employee 0.046 0.050 0.084

(1 if the level is greater than 9 years of studies, 0 otherwise) (0.33) (0.33) (0.54)

Availability of employees' training and competence

Constitutes a major obstacle (dummy variable) 0.031

(0.21)

Other characteristics

Logarithm of total sales in 2011 0.051 0.055

(1.13) (1.22)

Foreign company 0.007 0.117 0.112

(0.04) (0.64) (0.59)

Age of the company -0.000 0.003 0.003

(0.04) (0.56) (0.57)

Export company 0.183 0.137 0.157

(1.13) (0.80) (0.90)

In manufacturing sector yes yes yes

Number of observations 397 342 336

Wald Chi2(10) 35,69 36,090 38,010

Prob>Chi2 0,000 0,000 0,000

Pseudo R2 0,065 0,076 0,082

Table 10: THE DETERMINANTS OF TRAINING

Note: Robust Standard errors in parentheses, *** significant at 1%, ** Significant at 5%, * Significant at 10%.


the labor force, in turn, slightly influences the likelihood to provide training to employees. In fact, most companies have a large proportion of white-collar workers (executives, managers and directors) which results in a high probability of offering formal training. Surprisingly, the fact that managers perceive the level of training and competence of workers as obstacles to smooth operation of the business does not significantly influence the decision to offer training. The size of the business seems to be the only crucial factor and other variables do not appear to be critical in the decision to offer a training system for employees.

HIRING EMPLOYEES

This section reviews the impact of the availability of adequately trained labor force on the hiring strategy of companies. Table 11 gives insight on the time required to find and hire a particular type of worker. As can be expected, the higher the level of education and training sought, the longer the hiring process takes. However, some differences exist depending on the type of business. For example, companies operating in the textile sector consistently report much longer recruiting times than the average, regardless of the category of the workers. Conversely, foreign companies seem to have much less difficulty in finding people to hire.

Beyond the delays incurred to find a particular category of workers, companies also reported difficulties in the recruitment process. Table 12 shows the possible issues associated with the limited availability

Table 11: DELAYS IN HIRINGAverage time (weeks) to hire an employee in 2011/2012

Executives Senior Engineers/Technicians

Skilled Workers

Unqualified Workers

Commercial Representatives

Manufacturing Sector Sample 10.3 8.9 7.6 6.0 8.8

By Size

Small 5.4 2.8 2.3 3.4 2.5

Average 10.4 10.2 8.9 6.3 7.1

Big 11.6 8.6 6.9 6.0 13.4


Foreign Private Enterprise 5.4 5.2 4.1 3.6 3.5

Tunisian Private Enterprise 11.5 10.0 8.5 6.7 10.3


Exporter 11.9 10.7 9.0 6.8 11.0

Non-Exporter 6.9 5.4 3.9 3.9 4.8

By Industry

Food 5.5 5.0 4.3 3.1 5.9

Clothing 11.4 11.5 8.5 8.9 3.0

Textile 19.4 22.2 15.1 15.1 35.8

Mechanical and Electrical 11.5 9.3 8.1 4.2 7.0

Other Manufacturers 3.5 5.0 5.4 1.4 6.3

Building Materials 14.9 7.2 2.4 2.4 4.6

Construction 1.0 1.0 1.0 - -



of labor force while Table 13 indicates whether a particular category lacks of experience or training.

The least available category of workers in the manufacturing sector is by far that of skilled workers, followed by engineers and technicians. Note that for managers and unskilled workers the percentage of businesses

Table 12: AVAILABILITY OF LABOR FORCE

Percentage of Companies Stating that the Availability of Skilled Labor Force is Limited

ExecutivesSenior Engineers/

TechniciansSkilled

workersUnqualified

workersCommercial

Representatives

Manufacturing Sector Sample 55.8% 58.2% 66.3% 56.0% 50.2%

By Size

Small 50.0% 45.0% 61.4% 52.1% 52.5%

Average 57.3% 58.2% 71.3% 56.9% 46.7%

Big 56.0% 64.4% 59.3% 56.3% 55.2%





Exporter 53.2% 58.0% 70.2% 60.3% 50.3%

Non-Exporter 61.0% 58.8% 58.8% 47.6% 50.0%

By Industry

Food 52.5% 51.7% 63.6% 60.9% 51.9%

Clothing 62.3% 67.8% 77.8% 68.0% 53.8%

Textile 61.5% 58.8% 72.5% 51.3% 62.1%

Mechanical and Electrical 51.5% 54.7% 47.8% 42.9% 38.3%

Other Manufacturers 59.3% 56.7% 67.7% 42.9% 51.9%

Building Materials 51.4% 56.8% 70.7% 63.2% 50.0%

Construction 0.0% 100.0% 66.7% 0.0% 0.0%


Table 13: LACK OF EXPERIENCE AND TRAININGPercentage of Businesses Stating that experience/training of potential labor is limited

Executives Senior Engineers/Technicians

Skilled Workers

Unqualified Workers

Commercial Representatives

Manufacturing Sector Sample 68.8% 71.1% 72.8% 64.8% 63.2%

By Size

Small 63.0% 56.4% 67.3% 72.3% 53.8%

Average 67.5% 72.2% 70.6% 60.8% 61.5%

Big 73.9% 75.9% 80.4% 68.5% 71.6%





Exporter 68.2% 72.2% 75.3% 68.1% 62.7%

Non-Exporter 70.0% 69.1% 67.9% 58.0% 64.2%

By Industry

Food 63.5% 64.9% 64.6% 59.4% 57.7%

Clothing 68.7% 77.2% 81.3% 74.3% 61.8%

Textile 70.3% 69.7% 80.5% 63.4% 64.3%

Mechanical and Electrical 71.2% 68.2% 67.2% 67.7% 59.2%

Other Manufacturers 82.1% 83.3% 81.8% 66.7% 89.3%

Building Materials 60.5% 65.8% 63.2% 52.6% 54.8%

Construction 100.0% 100.0% 66.7% 50.0% 100.0%



reporting limited availability is essentially the same. Regarding the skilled worker category, foreign and exporting companies seem to be those most affected by this lack of availability. At the industry level, more than 70% of companies in the clothing and textile industry report issues with this type of labor availability.

Skilled workers are those who are most lacking in experience and/or training, according to companies. Again, the most affected companies are foreign and exporting businesses operating in the textile and clothing industry.

EMPLOYEE COMPENSATION

Compensation data presented below has been provided by the companies and not from individual interviews with employees. These data have been aggregated to each company.

Level of Remuneration

Table 14 and Table 15 show the average level of monthly earnings for production employees, expressed in dollars using the average exchange rate for 2011. Compensation levels vary greatly across sectors. Manufacturing companies provide on average the lowest monthly salary ($313), which is pulled down by the compensation levels observed in the textiles and clothing industries. Companies operating in the services sector offer much more attractive salaries ($432 on average). This is the direct consequence of the high salaries offered to production workers employed in the ICT sector ($592).

The following table (Table 15) shows the increase in salary based on education level, in all three sectors. The relatively high level of compensation

Table 14: MONTHLY EARNINGS BY SECTOR IN 2011Average monthly gross wage of a production worker (U.S. $)*


Agribusiness 330

Clothing 261

Textile 278

Mechanics 341

Building Materials 334

Other manufacturing sectors 329

Construction 585

Commerce 419

ICTs 592

Tourism 312 370

Health 407 212

Total 313 432 362

Source: Survey of Investment Climate in Tunisia, World Bank, 2012. Note: (*) Except social security and other taxes.


previously observed in the services sectors stems from an elevated compensation offered to employees with a high level of education (13 years and more). In the manufacturing sector, there are two levels of educational “return on investment”: the first level at three years of study and second level at 13 years. In fact, companies with a relatively low educated labor force offer a monthly salary equivalent to $248. Beyond three years of study, the average monthly wage rises to $290 for companies where workers have a proven record of up to 9 years of study. Beyond 13 years of education, companies will offer up to $ 434 per month on average.

In the manufacturing sector, the average monthly wage increases with the size of the company. Large companies offer $337 to their production workers against $291 in small businesses. Insignificant wage differential is observed between foreign and domestic companies. Surprisingly and contrary to what can be observed in other countries with available data, exporting companies compensate their employees less than companies focused on the domestic market.

WAGE STRUCTURE

The above statistics suggest the persistence of substantial wage differentials. It is, therefore, necessary to analyze the factors responsible for this situation. In such a framework, the determinants of wages rely both on the employees’ individual skills and on the company’s intrinsic characteristics. A simple way to separate the respective influences is to estimate wage functions using various specifications (Table 16). Three econometric models were estimated: The first is a variant of the basic Mincer model (1974) that assumes employers are able to discover the differences in productivity between employees on the basis of their human capital, and adjust the

Table 15: MONTHLY EARNINGS IN 2011

Average monthly gross wage of a production worker (U.S. $)

0-3 years of study

4-6 years of study

7-9 years of study

10-12 years of study

13 years or more

Total

Services Sector 343,0 301,9 360,0 543,8 432,4

Tourism Sector 382,2 322,3 468,9 361,5

Manufacturing Sector 247,7 292,4 290,2 331,3 434,1 312,5

By Size

Small 277,3 287,2 295,7 336,2 291,2

Average 247,7 290,1 285,0 321,5 495,4 307,7

Big 314,3 301,5 374,4 386,9 336,9


Foreign Private Enterprise 247,7 265,2 293,1 328,4 377,5 309,3

Tunisian Private Enterprise 247,7 299,6 289,4 332,5 456,7 313,5


Exporter 240,6 275,9 289,0 329,3 393,0 306,6

Non-Exporter 272,5 315,8 292,3 334,9 536,7 322,9



compensation accordingly. The second model is more comprehensive and includes, in addition to labor force characteristics, variables representing various company-specific characteristics. Lastly, the third model takes the previous specifications, while also taking into account the employees’ economic performance and the position of the company faced with the issue of the labor force’s lack of training.

The results of the econometric estimates (Table 16) clearly demonstrate that when the analysis focuses only on the employee characteristics (column (1)), the level of compensation depends positively on the average educational level of production workers. Note that this effect remains robust to the various different specifications outlined above. When it is assumed that the level of remuneration is dependent on some companies’ intrinsic characteristics (column [2]), it is found that the human capital dimension remains an important determinant in terms of wages. Regarding company characteristics, it is noted that what matters most is the size of the business, its location, and to a lesser extent, its market orientation. Thus, the major

Table 16: WAGE DETERMINATION WITHIN A COMPANY

Dependent VariableLogarithm of the average gross monthly salary of a

production employee

(1) (2) (3)

Constant 5.668 5.537 5.202

(135.19)*** (104.62)*** (46.07)***

Labor Force Characteristics

Average level of education of a production employee 0.138 0.127 0.125

(1 if the level is greater than 9 years of studies, 0 otherwise) (4.18)*** (3.87)*** (3.75)***

Company Characteristics

Average 0.036 0.036

(0.92) (0.92)

Big 0.096 0.038

(1.98)** (0.75)

Foreign company 0.018 0.047

(0.43) (1.13)

Company located in the Greater Tunis area 0.087 0.119

(2.66)*** (3.60)***

Age of the company 0.001 0.002

(1.37) (1.64)

Export company 0.067 0.041

(1.80)* (1.11)

Companies and their Training Program

The company offers formal training (dummy variable) 0.091

(2.82)***

Available training and competence of staff is a major or severe issue 0.021

(= 1 if a major obstacle, otherwise: 0) (0.68)

Business Performance

Logarithm of total sales in 2011, per employee 0.030

(2.95)***

Sectors yes yes yes

Number of observations 531 523 442

R2 0.19 0.24 0.32



exporting companies located in the Greater Tunis area offer a higher average monthly wage for their production labor force.

The last specification (column [3]) also takes into account the performance of employees by introducing the total sales per employee. Not surprisingly, this last variable is both positive and significant. Companies with more efficient employees are more able to reward them. Given the lack of adequately trained labor force, a company offering formal training to their employees also offers them higher pay. Trained workers increase their productivity while at the same time causing a revaluation of their salary. The scarcity of a well-trained employee seems to have given rise to something comparable to a training bonus. The location of the company in the greater Tunis area plays a positive influence on the level of remuneration. However, the report notes that the variable controlling for the level of constraint represented by a worker’s lack of training or skills has no significant influence on the average wage level. Thus, companies facing this problem do not seem to be trying to attract better-trained workers by offering them a more attractive remuneration. As mentioned above, companies focus more on their internal training scheme to tackle this issue, thus inducing salary increases.

The three specifications above are all performed by controlling for the business sector in which the company operates. The following table presents the coefficients and their significance in the most complete specification (Table 17). Only five of these sectors significantly influence the average salary offered to production employees. We have on the one hand construction companies, commercial companies and ICTs providing significantly greater remuneration. On the other hand, the clothing and textiles industries offer smaller salaries. This finding is to be linked with Figure 62 to describe the degree of stress related to lack of training and competence of the labor force by sector of activity.

The companies operating in the commerce and ICT industries seem to have fewer issues with workers’ skills. These companies seem to pursue a high pay policy to attract good quality workers. In contrast, the study finds that companies operating in the clothing and textile industries have not opted for a high pay policy. Therefore, they fail to attract enough workers with an adequate level of qualification, and a large part of them are facing severe problems regarding the quality of the available labor force. Companies operating in the construction industry are the ones who claim to be most affected by the lack of properly trained workers (50% of those surveyed). In contrast to the garment and textiles enterprises, it seems that construction companies opt for an ambitious compensation policy to attract workers with adequate training.


Table 17: WAGE DETERMINATION

Dependent Variable Logarithm of the average gross monthly salary of a production employee

(3)

Sectors

Food Reference

Clothing -0.130

(2.24)**

Textile -0.162

(2.47)**

Mechanical and Electrical -0.028

(0.51)

Other Manufacturers -0.023

(0.33)

Building Materials 0.067

(1.07)

Construction 0.378

(2.38)**

Commerce 0.162

(2.82)***

ICTs 0.352

(4.32)***

Health 0.062

(0.34)

Tourism 0.052

(0.74)


Figure 62: CONSTRAINTS RELATED TO THE EMPLOYEES’ QUALIFICATIONS AND TYPE OF INDUSTRY


LABOR TAXATION

Workers’ compensation only partially reflects the true cost of labor for businesses. The following chart shows the different tax rates applied to wages. To a large extent, income tax is collected through withholding. This de facto transforms it into the cost to the employer. There are, however, many exemptions. These exemptions preferentially apply to sectors considered “booming” and to companies that recruit Tunisian workers. Thus, exporting companies are exempted from the government’s Special Funds. Companies that recruit Tunisian employees with two years of post-high-school education (“Bac +2” level) are totally exempt from payroll taxes for 5 years. Companies

Con

stru

ctio

n

Gar

men

t

Oth

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actu

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Text

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Mec

hani

c an

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Bui

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ater

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Agr

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s

Com

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ICT

Tour

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Hea

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60%

50%

40%

30%

20%

10%

0%

50.0% 46.9% 43.6% 42.6% 41.9% 41.2%37.6% 33.7% 33.3%

26.1%

14.3%


creating new work teams and companies recruiting Tunisian personnel holding at least a “Bac +4” level diploma are exempt at 50% from payroll taxes for 5 years. These policies indicate a clear ambition to fiscally help companies hire graduate workers.


The major issue of the labor market in Tunisia appears to be a typical problem between supply and demand. The country has a population of graduates that are not engaged in a particularly important activity. On the other hand, the survey noted a very large number of companies complaining about the many difficulties (e.g., availability, delays, training, and/or experience) they face when hiring employees. Education and type of training undertaken by the youth seem more suited to positions in the public service. However, the public does not appear able to absorb such a large amount of labor force.

The report therefore recommends a strategy aimed at matching labor supply and demand. From this point of view, the example of skilled workers is very striking, especially in the clothing and textiles sectors. Both sectors must first improve their very negative brand image and convince graduates of the attractiveness of these industries. A communication campaign at the company level should be implemented in the short-term, to increase awareness of job profiles sought by companies. Symmetrically, the state should also better communicate its needs for labor, thus better influencing the career choices of prospective students.

In the longer term, companies need to forge much closer links with universities, schools, and training centers. These “training providers” are

Figure 63: LABOR TAXATION

Income Taxe

Employer Contribution to Social Security

Vocational Training Taxe paid by employer

Others Others

1.0%1.0%

16.3%

15.5%

65.7%

0.5%

Income Taxe

Employer Contribution to Social Security

Vocational Training Taxe paid by employer

1.0%1.0%

19.2%

15.5%

62.8%

0.5%


required to show a fine knowledge and reactive demand from businesses. Companies and the state must also coordinate to establish a genuine block-release training program. In this sense the study recommends further tax incentives for hiring young graduates without experience. Symmetrically, the State could reduce taxation on the amounts incurred on the companies’ in-house training programs.

In terms of remuneration, the lack of available labor in some sectors (ICT) pushes the proposed salaries upwards. This makes employment opportunities even less attractive, and thus the training choice of future workers in sectors such as textiles and clothing.

Regarding the education system, Tunisia should continue to offer high level education to as many people as possible. However, the system should adapt its teaching system into shorter and more practical training schemes ensuring far better “employability” in line with the needs of private companies. In addition, a major effort remains to be done regarding the quality of training courses, as these are still being considered very disparate and opaque to both employers and students.

The National Agency for Employment and Autonomous Work (Agence Nationale de l’Emploi et du Travail Indépendant) should not only fulfill its role of providing unemployment benefits, but also fully embrace its role of an organization capable of matching supply and demand for labor. The Agency should focus particularly on certain categories of job seekers, such as graduates, rather than treating all applicants on an equal footing. Personalized support of the job seeker must be implemented to facilitate the hire and/or proper training.

The government should also facilitate labor mobility by allowing companies to adapt their labor force to economic shocks and by implementing social safety nets to improve the consumption level of the unemployed. This is why a revision of the Labor Code focusing more on income protection rather than labor protection would help improve labor mobility.

chapter: 5: infrastructure challenges Facing Firms in tunisia 65

inFrAstructure chAllenGes FAcinG Firms in tunisiA

ChAPTER: 5

ELECTRICITY

Reliable and predictable provision of infrastructure services is essential to help firms maintain a competitive position. The authorities have an essential role to play in regulating infrastructure services and assuring that public goods are provided adequately. Although the Tunisian Government has been developing electrification in Tunisia, particularly in rural areas, reliable access to electricity remain a challenge to many firms in Tunisia.

Lack of access to electricity is perceived as a leading constraint to firm operations in Tunisia. As noted previously, access to electricity is ranked as the fifth leading constraint to businesses in Tunisia, with one third of firms citing it is a major or very severe constraint. Firm perception of such constraints varies by location within Tunisia (Figure 64). For instance, close to 44% of firms in Region D (which includes the cities Jendouba, Beja, Kef, Siliana, Sidi Bouzid, Kasserine, and Kairouen) perceive electricity as a major or very severe constraint, as opposed to 29% of firms in Region A (Ariana, Tunis, Manouba, Ben Arous, Bizerte, Nabeul), 34% in Region B (Sousse, Mahdia, Montasir), and 31% in Region C (Sfax, Gabes, Mednine).

Looking at power indicator across regions covered by the surveys confirms the perceptions of firms with regard to regional differences (Table 18). Businesses located outside the main region of Greater Tunis are more handicapped across most indicators measuring the reliable delivery of power. The region covering Jendouba/Sidi Bouzid/Kaasserin/Kairouan appears particularly affected; firms in this region are subject to

Figure 64: PERCENTAGE OF FIRMS RATING “ELECTRICITY” AS A MAJOR OR VERY SEVERE CONSTRAINT


50.00

33.5

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twice as many interruptions as in the Greater Tunis area, resulting in three times more losses than the national average Furthermore the delays for obtaining an electrical connection varies significantly by location, where the longest are found in the Grand Tunis area (73 days), followed by 61 days in Sfax, Gabes, and Mednine.

Besides regional differences, another important factor seems to be long delays. Access to electricity is reportedly slow, taking an average of 59 days, which is one of the highest regionally. Waiting for two months means that despite the relatively low/easy business registration process in Tunisia, electrical connection delays and further hinders new firms wanting to begin their operations.

Long delays in the delivery of services create more space for discretion. The ICS interviews with private firms indicated that “petty” corruption when dealing with public enterprises in position of monopoly, such as electricity, is a major source of barriers to business development.18 According to the Investment Climate survey, 17% of the firms are expected to make informal payments in order to obtain an electricity connection, a relatively high figure when compared to other MENA countries (Figure 65). The level of petty corruption in Tunisia is highest in small (24%) and medium (21%) vs. large firms (only 5%), as well as firms in Jendouba, Beja, Kef, Siliana, Sidi Bouzid, Kasserine, and Kairouan (44%).

The quality of power supply appears overall reliable. International comparison of the power supply indicator show moderate weaknesses when compared on an international and regional basis. For instance, in terms of power outages, Tunisia and Brazil share similar figures. Tunisia has one of the least interrupted power supplies in MENA, with an average of only six power outages per year. The losses resulting from these outages are also one of the lowest in MENA at three percent of sales. Due to the high quality and efficiency of government supplied electricity, only 19% of Tunisian firms own a generator (Table 19). This puts Tunisia ahead of countries like Morocco, Algeria, Egypt, Syria, Lebanon, and Yemen.

18 World Bank, “Barriers to Private Firm Dynamism in Tunisia: A Qualitative Approach”, Hamouda Chekir and Claude Menard, September 2012.

Table 18: ELECTRICITY LOSS BY REGION

Tunis Region Region B Region C Region D

Power interruptions (Number of times last year) 4.5 5.1 8.2 8.6

Losses due to power interruptions (% of annual sales) 1.1 2.9 2.4 3.9

% firms with a generator 16.8 25.8 18.2 14.3

Delays for electrical connection (Days) 73.0 26.2 60.8 54.8

Length of power interruption (hours) 7.1 3.3 9.2 7.2


Regional differences and delays are contributing factors in explaining the reason behind Tunisia’s rating of electricity as a high constraint, but these factors alone do not account for the level of complaints associated with electricity. In particular, the prices of industrial and commercial electricity in Tunisia are among the highest in the MENA region. High electricity prices are a serious concern for firms, particularly among the energy intensive manufacturing sectors.

TELECOMMUNICATIONS

The development of telecommunications in Tunisia has an important aspect of the country’s development strategy. Policies including regulations aimed at opening other sectors and the development of ICT infrastructure have been in place since the early 2000s. Today, despite the element of capture that this industry has been subjected to (it is known and documented that the license to exploit mobile and internet provision were attributed to elements close to the Ben Ali regime),19 telecommunication infrastructure and

19 World Bank, “Barriers to Private Firm Dynamism in Tunisia: A Qualitative Approach”, Hamouda Chekir and Claude Menard, September 2012.

Figure 65: WAIT DAYS AND INFORMAL PAYMENTS FOR OBTAINING AN ELECTRICITY CONNECTION IN MENA

Days to obtain Was a gift expected?

140

120

100

80

60

40

20

0

Num

ber

of d

ays

Per

cent

Egypt Jordan Iraq Turkey Morocco Romania Algeria Tunisia Lebanon

90

100

80

70

60

50

40

30

20

10

0

14

32.032.4

7.74

17

5.1

18 19

5.0 5.38.9

40

11.516.9

49

59

118


Table 19: POWER SUPPLY INDICATORSBrazil Chile China Egypt South Arica Morocco Turkey Jordan Tunisia

Power Interruptions (Days) 7.2 2.7 NA 21.1 5.9 2.7 10.2 3.2 6.63

Losses due to power interruptions (% of annual sales)

3.1 3.5 4.2 7.4 3.7 3.3 2.3 3.24 2.87

% of firms with a generator 17 25.6 18.3 19.3 9.5 16.3 NA 14.77 18.9

Delays for electrical connection (Days)

22.6 35.3 NA 107.9 7.3 18.8 6.1 45.12 63.51

Length of power interruption (hr) 5.8 2.57 NA 2.9 4.1 1.8 5.6 2.17 6.76


delivery appears adequate to the needs of firms. In fact, Tunisia has some of the best ICT indicators in the MENA region.

The ICS shows that usage among firms in Tunisia is widespread, with one of the highest number of cellular mobile and Internet subscribers in the region. According to the ICA survey, telecommunications were not cited by firms as a significant constraint. However, Tunisia has been subjected to Internet outages during and in the aftermath of the political turmoil, resulting in a much lower intensity.

Telecommunications seems to be a moderate concern for firms, as 22% have rated it as a major or severe constraint. The impact of telecom interruptions on small firms was the highest. Even though small firms witnessed a relatively lower number of telecom outages (seven times/year), the average duration of the outage was the highest (48 hours), resulting in a loss of 4% of total annual revenues. The frequency and duration of such outages on medium and large firms are considerable as well.

Internet usage for business operations is high among Tunisian firms. A vast majority of firms in Tunisia use email (96%) and have their own website (70%). Additionally, 89% of firms have an Internet broadband subscription. Firms mainly use the Internet to communicate with customers and suppliers (98%), provide services to customers (87%), and make purchases related to the needs of the institution (86%). Some firms also use the Internet for research in innovation of products and services (77%). According to the World Development Indicators (WDI), Tunisia is among the highest Internet users in the region (lagging only behind Morocco and Lebanon). Tunisia also has one of the highest numbers of mobile cellular subscriptions in MENA (Table 20).

Figure 66: TELECOM INDICATORS IN TUNISIA, BY FIRM SIZE


Freq of telecom outages last year Average duration of telecomoutages (hours)

Tunisia Small Medium Large

60

117

1311

23

48

17 18

24

1 1

50

40

30

20

10

0

Loss as percent of total annual sales due to telecom outages


WATER

Water remains a long term development issue for the country, even though a relatively modest number of firms report owning a well (25%). About 65% of all water need is sourced from a public water system, usually an indication of sound water management. As a result, few firms invest in self-provision of water and consequently water is not rated as an obstacle by Tunisian enterprises. However, obtaining a connection can be a lengthy process. On average, obtaining a water connection in 2012[?] took almost 2 months, with considerable variation depending on region. It can take up to 50 days for firms located in the greater Tunis region and 52 days for Region C, compared to 25 and 18 days for Regions B and D, respectively. Table 21 shows the delays for water connections by region.

Table 21: DELAYS FOR WATER CONNECTIONS ACROSS REGIONS

Tunis region Region B Region C Region D

Delay for a water connection (days) 50 25 52 18


Overall the quality of infrastructure, as measured by ICS indicators, appears adequate, noting issues of regional disparities for the provision of electricity and to a lesser degree telecommunications. There are, however, challenges associated with lengthy delays for the connection to infrastructure services which create more space for discretion. This was particularly evident in the instances of informal payment requests in the provision of electricity and water.

In order to consolidate its relative comparative advantages, Tunisia will have to strengthen its transport/infrastructure network and bring more transparency in the process of provision of infrastructure services. This can be done with stronger local oversight of procedures and introducing the possibility of recourse if delays exceed an agreed upon period for the provision of infrastructure related services.

In addition, the authorities may wish to address the issue of regional disparities with regard to the quality and reliability of electricity, telecommunication and water.

Table 20: TELECOM INDICATORS, TUNISIA AND COMPARATORSInternet users (per 100 people) Mobile cellular subscriptions (per 100 people)

Algeria 14 99

Yemen 14.9 47

Syria 22.4 63

Egypt 35.6 101

Jordan 35.7 118

Tunisia 38.8 117

Morocco 51 113

Lebanon 52 79

Source: World Bank, World Development Indicators, 2011.

chapter: 6: Access to Finance 71

Access to FinAnce

ChAPTER: 6

THE TUNISIAN FINANCIAL SECTOR

The Tunisian financial sector is small and dominated by banks, with assets equal to about 115% of GDP. This figure is somewhat lower than those of its regional peers such as Egypt, Jordan, Lebanon, and Morocco. There are 21 on-shore banks, including three large state-owned banks with 37% of banking sector assets; three large private domestic banks with 28% of total assets; and six foreign-owned private banks with a 28% share. More than half of total bank credit is provided to firms in the industrial, trade, and tourism sectors. A large part of the remaining credit appears to be provided to the public sector. The non-bank financial sector is relatively small. The equity and fixed-income markets are still relatively modest, with a market capitalization equal to 24% of GDP, lower than in regional peer countries such as Jordan (112%) and Morocco (76%). Private equity remains small. The leasing sector, with nine institutions, accounted for 15.5% of private gross fixed capital formation in 2010.

Although State-owned banks provide credit universally, they maintain a development role. The ICA results show that State-owned banks compete with the private banks on all the different segments of the firms market. However, they are more active in some specific segments such as the financing of new investments for small firms (50% market share), the financing of the tourism sector (50% market share) and the financing of firms located in underserved regions (64% market share).

Despite the economic downturn that started in 2011, financial intermediation remained relatively high by international and regional standards. It increased slowly but steadily from 2006 to 2009, and then

Figure 67: STRUCTURE OF THE BANKING SYSTEM; SHARE IN TOTAL OUTSTANDING CREDIT

Source: Central Bank of Tunisia.

34%

25%

41%

Public banks

Domestic private banks

Foreign private banks


more rapidly since 2010. This progress was made possible thanks to a sharp decline of non-performing loans, which dropped from 21% to 12% between 2005 and 2012.

The Central Bank of Tunisia adopted contra-cyclical measures to support access to finance. It is worth noting that banks continued to extend credit to firms in the first two years that followed the Revolution, although at a slower pace. This was largely due to the contra-cyclical measures implemented by Central Bank, illustrated by a significant increase of its collateralized short-term loans to banks, a sharp reduction of mandatory reserves and successive cuts of Central Bank interest rates. These measures offset the liquidity squeeze faced by the banks in the aftermath of the revolution. The Central Bank of Tunisia also adopted a circular strategy allowing the Tunisian banks to avoid NPL classification for the restructured loans resulting from the economic difficulties. As a result, NPLs ratios and provision needs remained artificially stable, as well as the solvency ratios. However, this prudential measure masks the growing vulnerability within the banking system and, as a consequence, may jeopardize the confidence of the stakeholders, namely the depositors, lenders, and shareholders.

MAJOR IMPEDIMENTS TO FIRMS ACCESS TO FINANCE

This paradox of a relatively high percentage of firms using bank finance and a relatively high rate of finance as a perceived constraint)

Figure 68: EVOLUTION OF CREDIT TO GDP

Source: World Development Indicators.

100

80

60

Tunisia

40

20

0

EgyptRomaniaMalaysia Turkey JordanLebanonMauritius Morocco

Figure 69: CREDIT TO PRIVATE SECTOR

Source: World Development Indicators.

90

80

70

60

50

40

30

20

10

0

2006 2007 2008 2009 2010 2011 2012


can be explained by a combination of several factors: i) inefficiencies in the bankruptcy regime, ii) an outdated collateral regime, iii) an insufficient competition amongst banks, iv) a low quality in the credit demand and v) a limited credit information sharing system.

Inefficiencies in the Bankruptcy Regime

Historically high levels of NPLs, compounded by civil unrest and high bank exposure, imply that Tunisian businesses are generally experiencing financial distress and an overall de-leveraging, and are unable to access additional credit to finance operations. Tunisia’s bankruptcy framework is thus of critical importance in improving stakeholder recovery, reducing creditor risk and facilitating asset disposal. Tunisia currently has two laws dealing with restructuring and bankruptcy, setting out the provisions on business rescue. These regulations have resulted in a fragmented bankruptcy regime with duplicate and overly lengthy processes for business rescue and business exit. Mindful of this major impediment, the Government is preparing a new legal framework aimed at achieving a better balance between lender and borrower.

Outdated Collateral Regime

A sound collateral regime is essential to facilitate firms’ access to credit as lenders want to recover, in part or in full, their loans if a borrower defaults. In Tunisia, lenders use a very narrow range of securities, limited to mortgage and personal guarantees. There are multiple reasons for this, instead of reliance on particular instruments, chief among them difficult collateral enforcement, a complex legal framework, and lack of a [credit?] registry. As collateral enforcement is known to be very slow and costly, lenders have no incentive to take security that would be more sensitive to time and costs (which is the case for movable, tangible and intangible assets, which can fluctuate very rapidly). Also, the legal framework regarding security is complex and based on the principle that parties could not agree contractually beyond what is expressly allowed by law. If there were a centralized collateral registry, some of these uncertainties could be avoided.

Insufficient Competition in the Banking Sector

Until recently, most banks were allowed to carry out their activities without meeting risk management standards and key regulatory requirements. This situation is changing as the Central Bank of Tunisia has started tightening prudential requirements and strengthening their enforcement. Nonetheless, the regulatory and prudential framework permitted the survival of relatively weak credit institutions with limited skills and resources to manage risks and innovate in new products as well as in new processes. As a result, Tunisian


banks are competing on a narrow range of products while limiting their risk by over-collateralizing their loans. In this context, State involvement in the banking sector has been an additional undermining factor. Although State participation in banks has decreased over the last decade, it still plays a critical role directly through State-owned banks and indirectly through State-owned enterprises (which are important clients of all commercial banks, both as debtors and as depositors). Recapitalizations of public banks initiated by the State, without changing their governance structure, have created additional distortions vis-à-vis other commercial banks.

Low Quality of the Credit Demand and Risk Assessment

According to the ICA survey, only 31.9% of firms had their financial statements certified by an external auditor. In addition, non-audited firms’ financial statements are generally of poor quality and not reliable. For private firms, having certified financial statements would increase the probability of using a loan by seven percentage points. For small firms, having audited financial statements would increase this probability from 12.8% to 29.8%. In addition, banks complain about the poor quality of loan demand, pointing out the lack of managerial skills of borrowers. Finally, although the Central Bank made it mandatory for banks to establish a dedicated SME unit, few of them have actually invested in capacity- building (e.g., training of loan officers, sectoral and economic studies, credit risk assessment technologies) to really improve their ability to lend to SMEs.

Firms’ Access to Finance

The use of bank finance is high by regional comparison (Figure 70). The ICA results show that 54.7% of surveyed firms have a loan from a financial

Figure 70a and b: PERCENTAGE OF FIRMS USING BANK FINANCE A) BY REGIONAL COMPARISON B) BY SIZE


100%

90%

80%

70%

60%

50%

40%

30%

20%

47.958.2

12.3 13.3 14.6

61.2

24.3 19.3

47.3

33.4 34.3

56.8

8.917.1 17.3

28.130.0

52.9 54.7

39.828.5

10%

0%

Yem

en

Egy

pt

Syr

ia

Mor

occo

Jord

an

Leb

anon

Tuni

sia

Have loans Voluntary exclusion Do not have loans

100%

25.2

30.1

44.7 53.9

30.0

16.2 11.7

24.1

64.1

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Sm

all

Med

ium

Larg

e

Has a loan Voluntary exclusion Doesn’t have loans


institution. Tunisian firms finance 63.8% of their working capital and 55.3% of their investments using internal funds. The use of bank loans depends on the characteristics of the firm. Large firms tend to rely more on bank credit (64.1% report having a loan), than medium (53.9%) and small firms (44.7%).

Despite the contra-cyclical measures implemented by the CBT, access to finance

continues to be perceived as a leading constraint by firms, especially for SMEs. According to the ICA survey, over 34% of firms rate access to finance as a major or very severe constraint. As in most countries, this perception is related to the size of firms; small firms are generally more likely to identify access to finance as a serious constraint than are large firms. However, in Tunisia, it is worth noting that more medium sized firm’s rate access to finance as a major or very severe constraint (36.7%) than small firms (33.5%).

LIMITED CREDIT INFORMATION SHARING

Tunisia has long established a Public Credit Registry (PCR) that is accessible to all the banks and leasing companies. However, this financial

Figure 71: PERCENTAGE OF FIRMS RATING “ACCESS TO FINANCE” AS A MAJOR OR SEVERE CONSTRAIN


70

60

50

40

30

20

10

0

33.90

Yem

en

Alg

eria

Iraq

Lib

ya

Egy

pt

Tuni

sia

Jord

an

Leb

anon

Figure 72a & b: PERCENTAGE OF FIRMS AUDITED, AND PERCENTAGE OF FIRMS HAVING A LOAN (AUDITED/NON-AUDITED)

Source: The World Bank, various Enterprise Surveys.

70

80

90

100

60

50

40

30

20

10

0

31.9 32.2

54.6

79.686.3

Yem

en

Tuni

sia

Syr

ia

Jord

an

Leb

anon

40

10

15

20

25

30

35

5

0

35.31

28.06

Has a loan Doesn’t have a loan


infrastructure does not provide sufficiently reliable data to allow the lenders to undertake a solid credit assessment (especially for consumers and MSMEs). Data returned to lenders is in an aggregated form which lacks credit history details. Not all data on credit facilities is included (e.g. credit cards). There are data quality issues due to weak validation mechanisms and checks, among others. This has made it difficult for financial institutions to utilize the PCR to the fullest, to better assess loan applications. Moreover, non-regulated entities do not contribute data to the PCR (e.g. microfinance institutions, telecoms, retailers, utilities, etc.) which further deprives banks (and the other lenders) from conducting sound credit risks assessments.

Over-collateralization excludes numerous firms and businessmen from access to finance. Given all the constraints listed above, Tunisian credit institutions try to mitigate their lending activities by demanding a high level of coverage by collateral (mainly mortgage). Tunisia has the highest collateral requirements amongst MENA countries. This strategy, which was proven insufficient to protect the banks against defaulting borrowers20, tends to exclude firms and businessmen which do not own the required collateral. The establishment eight years ago of the SOTUGAR, a partial credit guarantee scheme, and the BFPME, a public credit institution focused on startups, only marginally addressed this issue.

20 With a NPL ratio of 13%, Tunisia is among the worst performers in the MENA region.

Figure 73: COLLATERAL REQUIREMENTS

Source: The World Bank, various Enterprise Surveys.

200

180

160

140

120

100

80

60

40

20

0

Average Value of Collateral Required as a Percent of Loan Value

Percent of Firms that Require Collateral

Egypt

Jordan

Syria

Morocco

Lebanon

Algeria

Tunisia

chapter: 7: policy recommendations 77

policy recommendAtions

ChAPTER: 7

Evidence shows that the Tunisian model of growth is limited. Its incentives have proved increasingly costly and in most cases have stifled innovation, weakened linkages and created space for weak governance and discretionary behavior.

A transition is needed towards an even playing field in terms of incentives and firm treatment that promote stronger linkages, more efficient transfers of technology and moving up the value chain. A gradual approach to remove some of the incentives associated with the off-shore sector must be undertaken in parallel to measures designed to alleviate some of the most binding business environment constraints. The report has identified four main areas of intervention with steps laid out in the section below: (i) Employment; (ii) Regulatory Environment; (iii) Innovation and Entrepreneurship; and (iv) Access to Financial Services.

EMPLOYMENT

Tunisia’s past economic growth and successes in trade have not been translated into sustainable, high skill job growth. An implication of the growth model Tunisia pursued is that many jobs tend to be low skilled jobs in industries feeding upper level manufacturers in Europe. Actions must be taken to increase the number and the quality of jobs in order to address the increasing unemployment in this politically volatile country.

The report recommends the Tunisian Government undertake the following:

Encourage businesses and SMEs to hire local labor by adopting �tax policies, continuing the practice of targeted incentives for new graduates and reducing or eliminating training taxes.

Provide incentives and business development services to college �graduates, young entrepreneurs, and skilled technicians to set up start-up companies, even with limited scope, in order to encourage innovation and enterprising projects.


Offer incentives to foreign companies and manufacturers who choose �Tunisia as the location of their R&D efforts.

Reduce the information gap between supply and demand of �employment. Suppliers, such as academic and vocational training centers, need to acquire a more acute understanding of employer requirements. This can be achieved if public institutions and the private sector coordinate their actions to put in place relevant training programs.

Encourage the National Agency for Employment and Independent �Work to better target job seekers such as new graduates in order to provide the services according to different types of profiles.

Adapt the Labor Code to allow more flexibility, while protecting �workers. This could be achieved by adopting a mechanism based more on income than employment numbers.

REGULATORY ENVIRONMENT

Tunisia’s institutional dysfunctions facilitate corruption which is transmitted throughout the multiple interactions between the bureaucracy and firms. There is significant space for discretion in the application of the law by bureaucrats and politicians. Even though the political elites associated with the previous system of cronyism have been removed, a culture of discretion still persists throughout the country, providing fertile ground for profiteering and corruption.

In order to fight corruption, it is essential to remove institutional incentives that facilitate misconduct. Tunisia must make rules, procedures and interactions as transparent as possible. Clarity of regulations, standardized and transparent procedures and service standards for administrative approvals are among the measures proposed below and can help to address the uncertainty faced by businesses regarding the application of regulations that apply to them.

The report recommends the Tunisian government undertake the following:

Continue customs reforms. The process, which began in 2006, �has the potential of increasing economic activity and inspiring the business community by reducing trade barriers and opportunities for corruption. Specifically, reforms for upgrading the customs information system and introducing a comprehensive computerized risk management system designed to reduce human intervention in


customs activities will increase efficiency, transparency and improve governance. These reforms could be the catalyst for a meaningful and broader regulatory reform process.

Reduce discretionary margins by increasing accountability through �imposing deadlines and explicit recourse alternatives to help govern the interaction between the administration and enterprises.

Simplify procedures by eliminating overlap and the multiplicity of �agents, particularly those that relate to trade and fiscal incentives. For instance, standardization and automation of forms and procedures can contribute significantly to remove the human “error” margin. This would shift the centralization focus from the authority to standards and mechanism. A uniform mechanism must be applied for all applicants, which will reduce discrepancy and eliminate the power of individuals in the bureaucratic process to practice discrimination.

Reduce uncertainty in the market by sending clear signals and �avoiding the multiplicity of strategies and programs typical of Tunisia in recent years. Such reform will serve to signal a more even playing field and remove incentives associated with re-exports.

Ensure predictability of key service delivery and reduce their delay, �including: operating licenses, import licenses, construction permits, and electricity connections.

INNOVATION AND ENTREPRENEURSHIP

The evidence and analysis confirm the positive contribution of innovative SMEs to the job creation process. This report has identified the major challenges innovative SMEs face because of instable macroeconomic conditions, lack of a skilled labor force, weak financial markets, trade regulations and property protections.

The report recommends [the Tunisian Government undertake?] the following:

Set up non-government funds to provide grants and financing to �innovative SMEs. The process can be facilitated by:

Allocating a budget to fund these centers by the government. �

Offering tax incentives to these organizations. �

Forming partnerships between stakeholders of economic growth, �governmental agencies, private sectors and local communities to encourage the forming of innovative SMEs.


Define training standards for skills-based programs, which can be �adopted by existing institutions and meet the labor needs of the business population at large. The accreditation process should be independent from the government and granted through an association of the stakeholders and institutions.

Form a partnership between internationally recognized institutions/ �centers and Tunisian institutions to serve as the main source of providing technical and expert advice on training programs and training standards.

Develop a source of revenue for R&D support which is not completely �exposed to government fiscal volatility. The Tunisian government may consider setting up an endowment fund through public/private partnerships or implementing tax breaks to support R&D.

Develop mechanisms that would encourage equity investments and �angel investors to invest in early start-ups and growing innovative SMEs. This includes developing public-private investment funds, linking start-up firms to existing early stage financing funds, promoting diaspora finance in tech-oriented startups, and increasing the role of FAMEX/FIDEX to cultivate access to external linkages for foreign firms.

Strengthen Intellectual Property (IP) protection frameworks and �providing support to firms to manage IP issues through government institutions including FAMEX, INNORPI, and Ghazala ICT.

ACCESS TO FINANCIAL SERVICES

The ICA reveals that adequate infrastructure exists in Tunisia to promote innovation and business growth. However, the existing infrastructure has not been utilized to its full potential, partly because of the flaws in the procedures to finance innovative SMEs and business expansion. It seems that the banking industry still treats innovative SMEs like any other business projects, thus discouraging potential entrepreneurs.

The report recommends the following:

The banking sector adopt a risk assessment process similar to the �ones used by venture capital businesses to assess the true risks associated with new SMEs.

A new horizon for returns on investment must be formulated, which �takes into account the risks associated with the innovative SMEs.


The banks must be encouraged to enter into partnerships with �innovative entrepreneurs, providing them with their expertise on financial management, accounting and human resources management. This will reduce the risk faced by entrepreneurs and provide Tunisian banks with valuable insights and understanding of challenges facing SMEs.

A standardized system for collateral should be implemented, to �reduce barriers for SMEs and create a more equitable borrowing platform. This system would create a benchmark through a comprehensive legal framework, allow for collateral enforcement, and facilitate access to information through a collateral registry. See Figure 74 for graphical representation of recommendations.

Figure 74: BETTER ACCESS TO FINANCE IN TUNISIA: KEY POLICY RECOMMENDATIONS

Reform of the Bankruptcy Regime

Reform of the Colateral Regime(including a Centralized Collateral Regitry)

Modernized Credit Information Sharing System

Banking Sector

Competition

ImprovedFinancial

Infrastructure

Better Access to

Strengthening of PrudentialRegulation and Enforcement

Restructuration of the State Owned Banks

Annex: i: standard tables 83

stAndArd tAbles

ANNEx: I

Table 22: SURVEY SAMPLE STRUCTURE

Tunisia (Manufacturing and Services) - 2012Total of 600 firms

Firm Size (%) Sector

Percent Percent

Small (>=5 and >=19 emps) 26.9 Manufacturing 70.7

Medium (20<= × <99 emps) 50.3 Services 26.0

Large (100 + emps) 22.7 Tourism 3.3

Ownership structure % Firm Location (%)

Percent Governorate Percent

Private domestic individuals, companies or organizations

83.8 Region A (Grand Tunis: Ariana, Tunis, Manouba, Ben Arous, Bizerte, nabeul)

39.8

Private foreign individuals, companies or organizations

15.3 Region B (Sousse, Mahdia, Montasir) 20.5

Government or State 0.2 Region C (Sfax, Gabes, Mednine) 20.3

Other 0.7 Region D (Jendouba, Beja, Kef, Siliana, Sidi bouzid, Kasserine, Kairouen,....)

19.3

Ownership structure % Industry

Percent Percent

Government Owned > = 10%

0.7 Food 14.7

Private 99.3 Garments 16.2

Ownership structure % Textiles 9.3

Percent Electrical and mechanical industries 14.7

Foreign Owned >= 10% 19.7 Other industries 6.5

Domestic 80.3 Building materials, ceramics, and glass 8.7

Market Orientation % Commerce 15.5

Percent Tourism 7.7

Exporters >= 10% 51.2 ICT 5.0

Non-Exporters 48.8 Construction and public works 0.7

Principle owner’s gender Health 1.2

Percent Corporate form

Female 38.9 Percent

Male 61.1 Shareholding company with shares trade in the stock market 27.0

Top manager’s gender Shareholding company with non-traded shares or shares traded privately

66.0

Percent Sole proprietorship 0.7

Female 31.6 Partnership 0.0

Male 68.5 Limited partnership 1.3

Other 5.0


Tab

le

23

: G

LOB

ALI

ZAT

ION

OF

MA

RK

ETS

AN

D IN

PU

TS

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Per

cent

of

Sal

es

Nat

iona

l sal

es62

.685

.357

.446

.472

.422

.599

.626

.962

.962

.751

.068

.055

.649

.780

.870

.851

.790

.786

.9

Ind

irect

exp

orts

10.8

6.6

13.7

9.4

10.2

13.3

0.1

21.0

10.5

11.1

15.2

8.8

9.4

14.8

6.5

14.0

14.4

2.0

0.0

Dire

ct e

xpor

ts26

.98.

129

.444

.217

.864

.20.

352

.426

.726

.633

.923

.635

.635

.612

.815

.234

.28.

013

.1

Mai

n m

arke

t in

whi

ch e

stab

lishm

ent

sold

its

mai

n p

rod

uct

Loca

l23

.544

.317

.611

.927

.76.

838

.49.

621

.125

.415

.127

.416

.218

.032

.035

.416

.640

.843

.8

Nat

iona

l38

.439

.938

.537

.043

.617

.156

.321

.639

.537

.932

.441

.238

.027

.952

.535

.437

.642

.125

.0

Inte

rnat

iona

l38

.115

.843

.951

.128

.876

.15.

368

.839

.536

.852

.431

.445

.754

.115

.629

.245

.917

.131

.3


Tab

le

24

: C

OM

PE

TITO

RS

AN

D S

UP

PLI

ER

S

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

% o

f firm

s co

mp

etin

g ag

ains

t in

form

al fi

rms

41.4

47.8

42.3

33.1

46.2

22.6

49.4

34.7

41.6

41.9

41.2

41.6

35.1

36.7

55.4

43.4

39.8

47.0

33.3

Ave

rag

e N

umb

er o

f C

om

pet

ito

rs (%

of

firm

s w

ithi

n ea

ch c

ateg

ory

)

Non

e8.

03.

99.

49.

95.

419

.42.

113

.88.

77.

79.

77.

38.

49.

75.

87.

810

.22.

65.

0

One

4.2

2.6

4.5

5.3

3.4

7.4

3.2

5.0

3.7

4.6

2.8

4.8

5.3

2.7

5.0

2.6

4.7

2.0

10.0

Two-

Five

26.6

31.2

22.9

29.0

27.7

22.2

32.5

21.6

21.0

29.6

19.9

29.7

28.4

15.9

28.1

31.9

27.2

27.3

10.0

Mor

e th

an 5

61.2

62.3

63.2

55.7

63.5

50.9

62.2

59.7

66.7

58.2

67.6

58.3

57.8

71.7

61.2

57.8

57.9

68.2

75.0

Ple

ase

rate

the

deg

ree

of

seve

rity

tha

t th

e fo

llow

ing

pra

ctic

es o

f yo

ur c

om

pet

ito

rs r

epre

sent

to

yo

ur in

stit

utio

n (%

say

ing

maj

or

or

very

sev

ere

cons

trai

nt)

Tax

evas

ion

49.5

44.9

52.6

50.9

51.4

34.4

49.7

49.6

53.1

47.2

39.5

53.7

39.2

45.8

60.9

53.7

52.4

44.6

41.7

Eva

sion

of t

ariff

s an

d/o

r tr

ade

regu

latio

ns36

.634

.335

.741

.739

.816

.735

.238

.338

.235

.633

.338

.036

.438

.647

.219

.236

.940

.00.

0

Eva

sion

of l

abor

re

gula

tions

34.5

26.6

41.7

30.0

35.7

25.0

28.0

44.5

35.9

33.5

30.7

36.0

32.1

45.8

36.5

25.9

39.0

28.3

8.3

Vio

latio

n of

pro

per

ty

right

s/lic

ense

s21

.617

.423

.324

.121

.920

.015

.928

.625

.918

.724

.420

.421

.826

.123

.514

.025

.315

.70.

0

Pre

fere

ntia

l tre

atm

ent

in

acce

ss t

o cr

edit

21.7

19.8

23.2

21.7

23.0

12.1

21.4

22.2

21.1

22.1

21.4

21.8

26.7

22.5

12.4

26.9

22.8

21.4

7.7

Any

oth

er p

refe

rent

ial

trea

tmen

ts17

.714

.014

.830

.818

.413

.317

.118

.914

.019

.812

.119

.822

.011

.112

.822

.221

.114

.60.

0


Tab

le

25

: G

EN

ER

AL

CO

NS

TRA

INTS

TO

OP

ER

ATIO

N

% fi

rms

sayi

ng

“maj

or”

or

“ver

y se

vere

” co

nstr

aint

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Tele

com

mun

icat

ions

22.7

20.4

24.8

20.9

21.8

25.7

18.2

26.9

21.5

23.7

25.0

21.6

27.1

20.7

15.6

23.5

22.0

24.8

20.0

Ele

ctri

city

33.5

32.3

36.7

28.4

34.0

30.7

34.2

34.0

36.4

32.0

36.4

32.3

29.3

33.9

31.4

43.9

36.0

27.7

25.0

Tran

spo

rtat

ion

22.5

15.1

24.8

26.2

20.9

28.6

19.9

25.0

20.7

23.6

23.1

22.3

23.9

23.7

21.2

19.8

24.5

17.9

15.0

Wat

er12

.08.

614

.210

.512

.87.

911

.912

.59.

713

.411

.412

.39.

711

.616

.412

.511

.413

.415

.0

Acc

ess

to L

and

12.2

9.5

13.7

12.2

13.3

6.7

12.8

11.2

12.5

12.1

9.3

13.6

11.8

6.8

14.3

16.7

12.0

13.5

5.6

Reg

ulat

ory

Unc

erta

inty

9.9

8.2

11.4

8.5

9.8

9.4

9.9

9.9

9.3

10.4

9.4

10.1

9.6

9.5

12.6

7.7

9.8

10.1

10.0

Tax

Rat

es24

.525

.027

.416

.427

.78.

429

.619

.922

.725

.923

.724

.923

.027

.233

.315

.022

.929

.715

.0

Tax

Ad

min

istr

atio

n14

.59.

617

.015

.316

.16.

114

.914

.717

.312

.614

.914

.417

.515

.810

.811

.414

.714

.015

.0

Cus

tom

s an

d T

rad

e R

egul

atio

ns25

.816

.528

.029

.924

.928

.620

.629

.725

.625

.725

.026

.328

.620

.436

.316

.527

.223

.76.

7

Lab

or

Reg

ulat

ions

(Lik

e S

oci

al In

sura

nce)

22.2

14.6

24.7

26.0

22.8

19.8

19.9

24.4

22.7

22.1

23.6

21.6

23.4

22.9

26.2

14.8

25.6

15.0

5.3

Inad

equa

tely

ed

ucat

ed

wo

rkfo

rce

39.0

29.3

44.2

38.8

41.2

29.6

35.2

42.9

40.6

37.9

37.6

39.5

33.3

42.2

48.8

36.8

42.4

31.6

25.0

Lice

nsin

g a

nd P

erm

its

13.2

9.3

16.7

10.2

14.0

9.8

14.7

12.1

11.8

14.3

15.6

12.1

12.0

13.9

10.6

17.8

13.2

14.2

5.9

Acc

ess

to F

inan

cing

(E

x: C

olla

tera

l33

.933

.539

.722

.135

.427

.534

.934

.033

.934

.131

.934

.928

.032

.829

.851

.434

.435

.85.

6

Co

rrup

tio

n28

.925

.531

.227

.329

.825

.930

.527

.528

.129

.528

.429

.327

.923

.141

.124

.528

.732

.410

.0

Pra

ctic

es o

f co

mp

etit

ors

in t

he

info

rmal

sec

tor

25.9

27.5

27.6

20.8

28.0

17.5

31.6

20.5

25.5

26.4

26.2

25.8

22.3

22.9

38.1

23.9

26.2

27.4

6.3

Co

urts

21.4

16.1

23.4

22.7

22.9

15.3

22.6

20.3

20.2

22.1

19.0

22.5

22.8

17.5

29.9

12.9

20.8

23.2

20.0

The

ft, d

iso

rder

and

cr

imes

20.0

21.3

21.3

15.9

20.9

15.6

23.2

16.6

24.1

17.3

21.0

19.6

14.9

16.0

14.2

40.0

19.3

19.6

38.9

Po

litic

al in

stab

ility

55.6

50.6

58.3

55.6

54.8

58.4

56.3

53.4

55.8

55.8

56.0

55.6

57.8

52.1

50.0

60.9

55.0

56.0

65.0

Form

alit

ies

for

star

ting

a

bus

ines

s11

.212

.113

.06.

312

.56.

312

.910

.113

.59.

914

.19.

97.

26.

19.

626

.613

.07.

80.

0

Mac

roec

ono

mic

un

cert

aint

y 34

.829

.632

.643

.937

.225

.534

.734

.929

.838

.231

.036

.343

.029

.136

.121

.133

.639

.725

.0


Tab

le

26

: IN

FRA

STR

UC

TUR

E IN

DIC

ATO

RS

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Freq

of p

ower

out

ages

(Tim

es la

st y

ear)

6.5

6.7

6.7

5.5

6.8

4.7

5.8

7.2

5.8

7.0

6.4

6.5

4.5

5.1

8.2

8.6

7.1

4.2

6.3

Ave

rage

dur

atio

n of

pow

er o

utag

es (h

ours

)6.

75.

97.

06.

66.

67.

06.

66.

84.

38.

24.

47.

87.

13.

39.

27.

27.

25.

51.

6%

of s

ales

lost

due

to

pow

er o

utag

es2.

62.

23.

41.

02.

62.

51.

83.

42.

42.

82.

62.

61.

12.

92.

43.

93.

01.

60.

4Fr

eq o

f wat

er o

utag

es (T

imes

last

yea

r)6.

24.

66.

76.

45.

97.

15.

57.

14.

27.

86.

56.

03.

85.

88.

56.

57.

23.

22.

8A

vera

ge d

urat

ion

of w

ater

out

ages

(hou

rs)

29.2

18.3

32.0

34.5

33.3

11.6

7.8

52.1

5.6

48.4

8.0

39.5

9.2

11.7

129.

76.

434

.117

.31.

7%

of s

ales

lost

due

to

wat

er o

utag

es2.

31.

63.

00.

42.

80.

31.

73.

01.

62.

92.

82.

03.

50.

71.

12.

83.

00.

20.

1Fr

eq o

f tel

ecom

out

ages

(Tim

es la

st y

ear)

11.0

6.9

12.7

10.6

8.9

17.9

9.1

13.0

11.8

10.5

14.4

9.3

8.2

20.4

5.1

12.5

11.8

8.2

15.6

Ave

rage

dur

atio

n of

tel

ecom

out

ages

(hou

rs)

23.4

47.7

17.3

18.4

24.7

19.5

19.4

27.2

20.4

25.5

35.6

16.8

25.0

15.1

41.4

16.9

19.8

35.0

17.5

% o

f sal

es lo

st d

ue t

o te

leco

m o

utag

es1.

64.

31.

10.

71.

71.

41.

81.

51.

51.

72.

21.

21.

23.

21.

51.

21.

32.

70.

3%

of s

ales

lost

bec

ause

of l

ead

tim

es b

y su

pp

lier

last

yea

r6.

06.

85.

95.

46.

34.

85.

36.

65.

16.

65.

26

55.

24.

87.

27.

76.

15.

311

.8E

xpec

ted

gift

s to

ob

tain

A t

elep

hone

con

nect

ion

4.8

12.9

1.5

3.5

4.0

8.0

6.6

3.2

5.4

4.4

5.9

4.4

6.0

0.0

2.8

7.4

3.6

2.8

33.3

An

elec

tric

ity c

onne

ctio

n16

.923

.520

54.

818

.29.

517

.215

.915

.218

.615

.417

.720

.00.

010

.544

.416

.116

.733

3A

con

stru

ctio

n p

erm

it23

.038

.525

.512

.525

.015

.025

.021

.729

.616

.417

.224

.317

.78.

730

.440

.018

.834

.620

0A

n im

por

t lic

ense

17.0

11.1

20.6

11.5

10.1

43.5

12.2

21.7

22.9

12.5

22.2

14.5

11.1

4.8

6.7

48.0

16.5

16.7

33 3

An

oper

atin

g lic

ense

(bus

ines

s lic

ense

)4.

92.

87.

02.

74.

56.

16.

73.

78.

32.

47.

13.

90.

00.

05.

126

.34.

63.

050

.0N

umb

er o

f day

s to

ob

tain

:A

tel

epho

ne c

onne

ctio

n16

.117

.312

.422

.913

.824

.418

.214

.512

.519

.212

817

.518

.810

.58.

024

.215

.912

.838

.5A

n el

ectr

icity

con

nect

ion

59.2

51.5

58.7

65.8

60.5

56.0

57.8

59.4

53.2

53.0

45.6

66.7

73.0

26.2

60.8

54.8

60.1

60 7

22 5

A c

onst

ruct

ion

per

mit

157.

635

7.4

104.

914

7.6

106.

337

4.7

198.

511

5.5

130.

218

3.1

148.

116

3.7

315.

974

.869

.374

.810

5.4

309.

952

.2A

n im

por

t lic

ense

19.3

33.2

17.4

16.4

21.0

10.6

22.0

17.0

12.4

24.7

11.1

23.2

19.8

26.7

25 5

7.0

17.2

22.5

51.3

An

oper

atin

g lic

ense

(bus

ines

s lic

ense

)19

.126

.420

.68.

517

.624

.423

.615

.925

.614

.430

.514

.516

.820

.910

.845

.917

.721

.752

.5In

tern

et%

of fi

rms

usin

g em

ail

95.8

90.6

97 3

98.5

95.2

98.

393

.398

.095

.296

.496

.895

.399

.2 9

2.6

91 8

96.6

96.7

94.2

90.0

% o

f firm

s ha

ving

the

ir ow

n w

ebsi

te70

.459

.068

.886

.868

.976

.165

.374

.366

.472

.968

.171

.684

.068

.070

.544

.869

.771

.675

.0%

of fi

rms

havi

ng a

sub

scrip

tion

to a

bro

adb

and

inte

rnet

co

nnec

tion

88.8

76.9

91.7

96.3

87.

3 94

.982

.294

.787

.089

.891

.087

.897

.189

.387

.772

.490

.185

.985

.0

Will

you

use

you

r in

tern

et c

onne

ctio

n fo

r:To

com

mun

icat

e w

ith c

usto

mer

s an

d s

upp

liers

97.9

95.9

98.5

98.5

98.1

97.3

97.4

98.2

98.5

97.5

98.2

97.8

98.3

99.1

96.3

97.6

97.9

97.7

100.

0P

urch

ases

rel

ated

to

the

need

s of

the

inst

itutio

n86

.182

.686

.488

.486

.783

.582

.889

.492

.482

.092

.882

.988

.487

.286

.078

.687

.382

.288

.2To

pro

vid

e se

rvic

es t

o cu

stom

ers

87.3

84.3

87.9

88.6

87.2

87.3

81.6

91.9

86.4

87.7

86.8

87.4

88.5

97.3

86.0

72.6

87.6

87.0

82.4

Res

earc

h in

inno

vatio

n of

pro

duc

ts a

nd s

ervi

ces

77.2

75.4

75.1

83.5

78.0

73.8

73.0

80.0

76.5

77.6

77.4

77.0

86.9

71.6

83.8

51.2

76.8

78.7

73.3


Tab

le

27

: S

OU

RC

ES

OF

FIN

AN

CE

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Sha

re o

f w

ork

ing

cap

ital

fro

m

Inte

rnal

fund

s or

ret

aine

d e

arni

ngs

63.8

61.3

65.8

62.7

60.4

77.9

55.3

73.2

63.8

63.6

68.9

61.5

67.9

74.2

52.1

57.4

65.1

59.7

71.8

Bor

row

ed fr

om T

unis

ian

priv

ate

com

mer

cial

ban

ks17

.215

.516

.222

.019

.29.

020

.913

.319

.815

.615

.717

.918

.212

.921

.015

.917

.916

.57.

1

Bor

row

ed fr

om fo

reig

n p

rivat

e co

mm

erci

al b

anks

0.7

0.3

0.9

0.5

0.6

1.1

0.4

0.9

0.2

1.0

0.2

0.9

0.7

0.4

0.2

1.5

0.7

0.7

0.0

Bor

row

ed fr

om s

tate

ow

ned

ban

ks o

r ot

her

pub

lic

inst

itutio

n5.

99.

94.

63.

87.

11.

28.

72.

74.

27.

13.

77.

02.

54.

24.

116

.34.

011

.43.

5

Bor

row

ed fr

om a

non

-ban

k fin

anci

al in

stitu

tion

0.6

0.3

0.7

1.0

0.7

0.5

0.8

0.4

0.4

0.8

0.9

0.5

0.5

1.6

0.1

0.4

0.8

0.2

0.0

Cus

tom

er o

r su

pp

lier

cred

its6.

68.

06.

55.

07.

05.

08.

64.

75.

37.

44.

47.

54.

44.

316

.43.

16.

76.

72.

4

Leas

ing

3.3

3.5

3.7

2.3

3.8

1.3

4.0

2.7

3.9

3.0

3.8

3.1

3.5

2.1

4.5

2.9

3.2

3.5

4.1

Oth

er1.

81.

11.

62.

81.

24.

01.

12.

12.

31.

42.

41.

52.

30.

31.

52.

51.

51.

311

.2

Fina

ncin

g o

f ne

w in

vest

men

ts f

rom

Inte

rnal

fund

s or

ret

aine

d e

arni

ngs

55.3

53.6

53.9

58.8

51.0

73.4

47.7

63.1

53.6

56.2

58.7

53.9

59.4

56.4

43.7

59.0

56.3

49.5

73.2

Ow

ner'

s co

ntrib

utio

n or

issu

ed n

ew e

qui

ty s

hare

s1.

52.

21.

50.

81.

60.

82.

01.

01.

91.

20.

61.

82.

00.

61.

80.

81.

71.

00.

0

Issu

ance

of d

ebt

0.5

0.9

0.5

0.2

0.6

0.0

080.

20.

60.

41.

40.

10.

70.

00.

30.

70.

21.

40.

0

Bor

row

ed fr

om a

priv

ate

com

mer

cial

ban

k13

.79.

111

.921

.214

.79.

713

.713

.515

.712

.310

.814

.913

.611

.916

.812

.314

.512

.09.

7

Bor

row

ed fr

om s

tate

ow

ned

ban

ks o

r ot

her

pub

lic

inst

itutio

n6.

39.

25.

84.

87.

03.

48.

54.

16.

46.

35.

86.

54.

310

.33.

110

.65.

69.

32.

0

Bor

row

ed fr

om a

non

-ban

k fin

anci

al in

stitu

tion

6.1

8.0

6.9

3.2

7.3

1.5

9.5

2.7

4.5

7.3

4.4

6.8

2.4

8.5

11.4

5.7

5.1

10.2

0.0

Cus

tom

er o

r su

pp

lier

cred

its5.

47.

26.

22.

36.

12.

65.

15.

85.

95.

05.

85.

24.

54.

67.

45.

66.

23.

80.

0

Oth

er11

.39.

813

.58.

711

.98.

612

.99.

611

.511

.312

.510

.813

.37.

815

.65.

410

.513

.115

.0


Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Sha

re w

ith o

verd

raft

faci

litie

s O

R

line

of c

red

it ex

clud

ing

loan

s73

.562

.977

.477

.179

.250

.979

.666

.874

.272

.772

.773

.874

.064

.782

.472

.272

.277

.370

.0

Sha

re w

ith a

LO

AN

53.5

44.7

53.9

64.1

59.0

30.4

57.0

49

.557

.051

.146

.556

.555

.740

.762

.852

.253

.752

.952

.6

For

the

mo

st r

ecen

t lo

an

Sha

re t

hat

req

uire

col

late

ral

86.8

92.9

89.2

77.1

88.7

70.6

89.4

83.3

89.1

85.0

88.0

86.3

85.5

75.0

90.7

93.3

87.7

85.2

80.0

Ave

rage

val

ue o

f col

late

ral

req

uire

d (a

s %

of t

he lo

an)

178.

514

2.3

199.

717

1.5

176.

919

2.6

165.

219

1.2

186.

017

3.1

207.

016

7.9

219.

920

7.5

131.

212

9.4

179.

217

6.0

182.

9

Wha

t w

as t

he m

ain

reas

on

for

not

app

lyin

g f

or

a lo

an?

No

need

for

a lo

an

-est

ablis

hmen

t ha

d s

uffic

ient

ca

pita

l

61.9

54.5

64.9

67.3

59.4

70.5

59.1

65.1

64.9

60.5

71.9

57.3

64.8

71.8

60.3

50.0

61.0

65.2

57.1

Ap

plic

atio

n p

roce

dur

es w

ere

com

ple

x11

.816

.811

.03.

613

.36.

414

.69.

17.

614

.46.

114

.54.

811

.512

.121

.611

.413

.57.

1

Inte

rest

rat

es w

ere

too

high

3.7

4.0

4.7

0.0

4.8

0.0

4.1

3.4

3.1

4.2

2.6

4.3

5.6

2.6

1.7

3.4

4.1

3.4

0.0

Col

late

ral r

equi

rem

ents

wer

e to

o hi

gh2.

93.

01.

65.

53.

31.

32.

92.

90.

04.

71.

83.

43.

21.

35.

22.

32.

93.

40.

0

Siz

e of

loan

and

mat

urity

wer

e in

suffi

cien

t0.

30.

00.

50.

00.

40.

00.

00.

60.

00.

50.

00.

40.

80.

00.

00.

00.

00.

07.

1

Did

not

thi

nk it

wou

ld b

e ap

pro

ved

7.5

8.9

7.3

5.5

8.1

5.1

9.9

4.6

9.9

6.1

4.4

9.0

4.0

3.9

6.9

15.9

7.7

6.7

7.1

Do

not

wan

t to

dea

l with

inte

rest

ra

tes

4.3

7.9

3.1

1.8

5.2

1.3

4.7

4.0

5.3

3.7

4.4

4.3

5.6

1.3

10.3

1.1

4.5

4.5

0.0

Oth

er7.

75.

06.

816

.45.

515

.44.

710

.39.

26.

18.

86.

811

.27.

73.

55.

78.

53.

421

.4

Tab

le

28

: C

RE

DIT

S, L

OA

NS

, AN

D L

IAB

ILIT

IES


Tab

le

29

: FI

NA

NC

IAL

SE

CTO

R: A

UD

ITIN

G, T

RA

NS

AC

TIO

N C

OS

TS, A

ND

PR

OP

ER

TY R

IGH

TS

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Sha

re o

f firm

s w

hose

fina

ncia

l sta

tem

ents

ar

e au

dite

d b

y ou

tsid

e au

dito

rs31

.920

.037

.035

.831

.036

.126

.636

.436

.728

.636

.130

.232

.537

.016

.442

.233

.528

.423

.5

Sha

re o

f la

nd t

hat

is

Ow

ned

56.5

47.4

55.9

68.6

62.2

34.2

66 8

46.0

56.0

56.6

46.7

60.9

53.2

49.0

65.7

61.3

55.9

54.9

84.2

leas

ed o

r re

nted

43.2

51.7

44.3

31.4

37.5

65.8

32 6

54.0

43.9

43.0

53.1

38.9

46.1

51.0

34.6

38.7

44.2

44 0

15.8

% fi

rms

tryi

ng t

o ge

t se

ttle

d in

an

area

ot

her

than

Gre

ater

Tun

is a

nd C

oast

al a

reas

26.9

25.5

27.3

28.3

27.4

24.0

28.9

25.4

32.1

23.7

29.6

25.7

15.7

13.6

17.5

66.1

28.5

19.3

55.6

Co

nstr

aint

s p

reve

ntin

g f

rom

set

tlin

g in

an

area

oth

er t

han

Gre

ater

Tun

is a

nd C

oas

tal a

reas

Inad

equa

te in

fras

truc

ture

17.4

21.3

13.9

21.3

17.4

17.4

18.0

16.7

21.0

15.6

13.0

19.4

7.0

32.6

21.2

18.4

17.7

17.6

0.0

Roa

d n

etw

ork

10.8

10.2

11.1

11.5

11.7

7.3

13.4

8.6

8.9

12.0

5.6

13.2

7.0

11.2

19.7

10.5

11.4

10.2

0.0

Lack

of q

ualifi

ed m

anp

ower

16.0

16.7

16.1

14.8

15.3

18.8

15.1

17.2

17.7

15.1

16.7

15.7

7.0

29.2

22.7

10.5

17.7

13.0

0.0

No

par

ticul

ar c

onst

rain

t57

.857

.458

.357

.459

.252

.261

.154

.659

.756

.464

.854

.664

.649

.456

.152

.657

.058

.383

.3

Oth

er23

.120

.423

.924

.622

.326

.121

.524

.716

.127

.116

.726

.025

.318

.021

.229

.022

.824

.116

.7


Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

% s

enio

r m

anag

emen

t's

time

spen

t d

ealin

g w

ith r

egul

atio

ns24

.827

.224

.822

.124

.825

.124

.624

.323

.725

.429

.022

.936

.022

.423

.37.

623

.229

.226

.5

% r

even

ues

typ

ical

ly p

aid

to

offic

ials

to

"get

th

ings

don

e"4.

16.

04.

01.

74.

42.

85.

92.

65.

33.

55.

03.

82.

34.

16.

35.

63.

74.

87.

4

% o

f firm

s re

qui

red

to

mak

e gi

fts

or in

form

al

pay

men

ts t

o p

ublic

offi

cial

s to

"ge

t th

ings

d

one"

58.5

65.3

59.9

45.3

60.0

52.0

64.5

54.0

57.5

59.1

53.3

60.6

39.6

69.4

50.0

83.8

58.2

59.5

57.1

% fi

rms

obta

inin

g a

gove

rnm

ent

cont

ract

or

trie

d t

o ge

t on

e21

.622

.6

18.5

26.7

24.8

8.8

28.6

14.9

22.1

21.3

18.2

23.2

22.9

17.0

31.1

14.0

20.2

26.7

11.1

% o

f con

trac

t va

lue

pai

d a

s in

form

al p

aym

ent

to s

ecur

e a

gove

rnm

ent

cont

ract

3.0

2.8

4.7

1.1

3.2

1.8

3.9

1.7

5.2

2.0

3.0

3.1

2.6

1.6

4.3

2.2

3.6

2.2

0.0

"Law

s an

d r

egul

atio

ns t

hat

affe

ct y

our

bus

ines

s op

erat

ions

are

pre

dic

tab

le"

(% d

isag

reei

ng)

26.5

22.6

28.0

27.4

27.2

23.9

27.5

26.2

33.2

22.3

26.6

26.6

21.4

23.1

31.4

35.3

26.8

27.3

15.0

"Law

s th

at a

ffect

you

r b

usin

ess

are

gene

rally

fa

irly

app

lied

con

sist

ently

for

each

firm

" (%

d

isag

reei

ng)

42.3

42.5

43.8

38.5

45.0

30.4

46.6

37.8

41.1

43.3

38.2

44.3

32.3

41.7

47.1

57.8

41.7

42.9

50.0

"Law

s th

at a

ffec

t yo

ur b

usin

ess

are

gen

eral

ly f

airl

y ap

plie

d c

ons

iste

ntly

fo

r ea

ch fi

rm"

(% s

ayin

g y

es)

Just

ice

52.5

54

.049

.858

.849

.5

64.9

48

.257

.054

.051

.656

.8

50.6

54.3

74.7

45.7

35.2

53.6

51.4

40.0

Tax

syst

em50

.557

.944

.157

.450

.550

.751

.350

.742

.854

.447

.052

.042

.963

.755

.745

.749

.754

.140

.0

Cus

tom

s an

d fo

reig

n tr

ade

regu

latio

ns37

.228

.637

.054

.435

.345

.528

.244

.935

.537

.646

.232

.649

.346

.222

.922

.940

.130

.333

.3

Lab

or r

egul

atio

ns55

.253

.255

.557

.453

.562

.358

.052

.755

.354

.865

.250

.257

.162

.644

.353

.353

.260

.653

.3

Pro

per

ty r

ight

s46

.645

.246

.948

.544

.754

.643

.149

.351

.343

.655

.342

.556

.446

.230

.044

.848

.642

.240

.0

Num

ber

of

insp

ecti

ons

and

req

uire

d m

eeti

ngs

wit

h o

ffici

als

Tax

Insp

ecto

rate

1.9

1.6

2.0

2.1

2.0

1.3

1.9

1.9

1.6

2.2

1.5

2.1

1.9

1.4

2.1

2.1

2.0

1.4

3.1

Lab

or In

spec

tion

2.3

1.8

2.0

3.2

2.2

2.9

2.0

2.5

2.3

2.3

2.2

2.3

2.7

2.1

2.0

2.2

2.3

2.2

3.0

Was

a g

ift o

r in

form

al p

aym

ent

ever

exp

ecte

d/r

eque

sted

?

Tax

Insp

ecto

rate

9.0

12.8

8.5

6.5

8.3

8.8

9.0

9.3

10.6

7.4

8.8

9.1

14.7

0.0

2.9

12.1

7.4

12.8

16.7

Lab

or In

spec

tion

6.3

8.3

7.2

3.7

7.2

3.3

4.0

8.6

6.3

6.5

4.9

7.0

10.0

1.6

6.8

5.2

7.8

0.0

0.0

Imp

ort

s

Avg

. day

s to

cle

ar c

usto

ms

(day

s)

9.0

10.1

9.0

8.5

10.3

5.

5 12

.86.

89.

28.

78.

89.

18.

47.

114

.06.

48.

8 8.

837

.5

Long

est

day

s to

cle

ar c

usto

ms

(day

s)

21.2

23.3

20.1

21.7

23.5

15

.028

.016

.919

.422

.119

.022

.320

.113

.936

.912

.519

.9

26.9

45.0

Tab

le

30

: R

EG

ULA

TOR

Y B

UR

DE

N A

ND

AD

MIN

ISTR

ATIV

E D

ELA

YS


Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Exp

ort

s

Ayg

. day

s to

cle

ar c

usto

ms

(day

s)4.

72.

64.

75.

15.

14.

03.

54.

83.

85.

44.

34.

94.

46.

14.

53.

64.

93.

40.

0

Long

est

day

s to

cle

ar c

usto

ms

(day

s)9.

65.

29.

211

.010

.48.

16.

49.

96.

711

.78.

210

.310

.59.

09.

57.

89.

87.

40.

0

% fi

rms

mak

ing

info

rmal

pay

men

ts t

o ex

ped

ite

cust

oms

clea

ranc

e p

roce

dur

es21

.817

.920

.526

.022

.420

.020

.822

.226

.618

.419

.822

.427

.77.

319

.735

.122

.221

.20.

0

Wha

t ar

e yo

ur s

ales

pro

ject

ions

fo

r th

e ne

xt fi

nanc

ial y

ear?

Sal

es w

ill in

crea

se65

.8

65.3

68.6

60.5

65.9

65.4

65

.765

.9

65.1

66.3

66

.165

.662

.772

.371

.660

.267

.662

.950

.0

Sal

es w

ill r

emai

n th

e sa

me

18.5

21.3

15.5

21.0

18.6

17

.819

.617

.620

.417

.220

.6

17.6

21.7

13.9

10.3

24.8

18.1

18.9

22.2

Sal

es w

ill d

ecre

ase

15.7

13.3

15.9

18.6

15.5

16.8

14.8

16.5

14.6

16.6

13.3

16

.815

.713

.918

.115

.014

.318

.227

.8

Per

cent

incr

ease

in s

ales

22.6

30

.921

.314

.623

.6

18.3

21

.323

.623

.322

.217

.6

24.7

20.5

23.6

31.2

15.3

23.9

17

.9

29.2

Per

cent

dec

reas

e in

sal

es20

.3

20.6

19

.920

.820

.4

19.9

19

.520

.723

.2

18.6

23.4

19.3

20

.812

.318

.025

.9

18.8

23.4

18.0

Tab

le

31

: G

OV

ER

NA

NC

E: U

NC

ER

TAIN

TY A

ND

CO

RR

UP

TIO

N

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Sec

urit

y (C

rim

e)

% fi

rms

pay

ing

for

secu

rity,

for

exam

ple

eq

uip

men

t, p

erso

nnel

, or

pro

fess

iona

l sec

urity

se

rvic

es

63.9

55.9

64.0

73.3

64.4

61.2

63.2

64.7

67.0

61.9

58.7

66.2

58.7

66.9

63.1

72.2

65.1

59.4

73.7

% o

f tot

al a

nnua

l sal

es fo

r se

curit

y 2.

73.

82.

62.

12.

72.

53.

02.

43.

12.

42.

72.

71.

52.

42.

04.

92.

52.

94.

4

% o

f firm

s ex

per

ienc

ing

loss

es d

ue t

o th

eft,

ro

bb

ery,

van

dal

ism

, or

arso

n25

.926

.424

.029

.625

.328

.527

.823

.530

.722

.626

.325

.426

.417

.521

.437

.925

.526

.330

.0

vand

alis

m, o

r ar

son

as a

% o

f tot

al a

nnua

l sal

es7.

510

.68.

82.

47.

76.

68.

85.

56.

08.

79.

16.

72.

48.

19.

312

.17.

07.

225

.0

Tab

le

30

: (C

ON

TD...

)


Tab

le

32

: TE

CH

NO

LOG

Y IN

DIC

ATO

RS

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

% fi

rms

with

ISO

Cer

tifica

tion

29.8

13.3

26.4

57.0

24.7

50.9

19.2

38.9

31.0

28.9

33.2

28.4

37.8

27.6

27.9

18.3

36.2

12.3

31.3

% fi

rms

with

pen

din

g ap

plic

atio

n fo

r an

ISO

Cer

tifica

tion

8.0

5.1

10.9

5.2

8.3

6.9

7.3

8.7

9.6

7.0

5.4

9.1

5.2

7.3

7.4

14.8

8.3

7.1

6.3

% fi

rms

forc

ed t

o re

duc

e p

rices

in r

esp

onse

to

low

er p

rices

by

dom

estic

com

pet

ition

32.8

34.0

30.0

36.1

34.2

26.6

35.4

29.4

34.0

32.4

30.7

33.8

33.7

30.8

38.3

27.6

27.4

44.5

45.0

% fi

rms

forc

ed t

o re

duc

e p

rices

in r

esp

onse

to

low

er p

rices

by

fore

ign

com

pet

ition

28.8

18.3

29.6

37.6

26.8

36.3

17.3

37.2

27.8

29.5

29.3

28.6

33.7

30.4

30.9

15.6

29.9

24.1

37.5

% fi

rms

that

intr

oduc

es a

new

pro

duc

t or

ser

vice

or

upgr

aded

an

exis

ting

one

in r

esp

onse

to

dom

estic

com

pet

ition

44.6

45.6

41.7

48.7

46.7

35.1

46.9

41.6

42.0

46.4

40.0

46.9

49.0

34.5

66.7

26.7

43.0

52.9

15.8

% fi

rms

that

intr

oduc

es a

new

pro

duc

t or

ser

vice

or

upgr

aded

an

exis

ting

one

in r

esp

onse

to

fore

ign

com

pet

ition

38.8

27.2

40.0

47.1

36.3

47.1

27.5

46.5

34.3

41.9

39.6

38.6

45.7

34.6

53.9

19.6

41.6

33.0

18.8

% fi

rms

usin

g a

pat

ent

10.2

9.3

10.4

10.9

10.0

11.0

10.5

10.1

7.9

11.8

8.6

11.0

17.1

6.9

6.8

4.6

11.1

8.6

0.0

% fi

rms

utili

zing

res

earc

h an

d d

evel

opm

ent

(R&

D)

32.1

28.2

26.2

49.6

30.7

37.1

23.7

39.9

31.1

32.8

31.3

32.2

37.2

33.3

35.8

16.7

35.8

22.2

29.4

Whe

re is

yo

ur R

&D

dep

artm

ent

loca

ted

?

With

in t

he b

usin

ess

unit,

but

no

ded

icat

ed d

epar

tmen

t to

R&

D57

.165

.958

.350

.058

.751

.259

.454

.856

.557

.156

.157

.357

.860

.043

.977

.855

.558

.110

0.0

With

in t

he b

usin

ess

unit,

with

a d

edic

ated

dep

artm

ent

to R

&D

34.6

20.5

29.2

50.0

33.3

39.5

29.7

38.3

31.9

36.6

36.8

33.9

36.1

35.0

39.0

16.7

37.0

29.0

0.0

Out

sid

e th

e b

usin

ess

unit

8.2

13.6

12.5

0.0

8.0

9.3

10.9

7.0

11.6

6.3

7.0

8.9

6.0

5.0

17.1

5.6

7.5

12.9

0.0


Tab

le

33

: W

OR

KE

RS

Tunisia

Small

Medium

Large

Domestic

Foreign-owned

Non-Exporter

Exporter

Female owner

Male owner

Female manager

Male manager

Region A

Region B

Region C

Region D

Manufacturing

Services

Tourism

Ave

rage

num

ber

of p

erm

anen

t w

orke

rs in

201

295

1150

299

7816

961

124

101

9112

980

146

7459

5410

862

75

Ave

rage

num

ber

of p

erm

anen

t w

orke

rs in

201

191

1147

291

7316

760

120

9985

124

7613

972

5652

105

5474

Ave

rage

num

ber

of p

erm

anen

t w

orke

rs in

201

087

1146

283

6916

456

117

9879

123

7113

468

5650

102

4880

% fi

rms

offe

ring

voca

tiona

l tra

inin

g to

per

man

ent

emp

loye

es b

esid

e tr

aini

ng o

n th

e jo

b54

.838

.653

.476

.953

.360

.350

.558

.553

.555

.154

.654

.758

.147

.155

.055

.854

.356

.352

.6

Leve

l of

educ

atio

n o

f p

rod

ucti

on

emp

loye

es

0-3

year

s1.

60.

03.

10.

01.

51.

80.

72.

40.

92.

02.

21.

30.

95.

00.

00.

92.

20.

00.

0

4-6

year

s11

.810

.211

.713

.911

.612

.410

.813

.111

.112

.38.

713

.38.

320

.215

.36.

215

.23.

50.

0

7-9

year

s31

.323

.632

837

.732

.128

.327

.335

.434

.229

.233

.530

.129

.031

.937

.329

.237

.915

.210

.5

10-1

2 ye

ars

38.4

34.4

39.3

40.8

37.3

43.4

38.9

37.5

40.4

37.0

34.1

40.6

36.8

33.6

33.1

52.2

38.7

33.8

68.4

13 y

ears

and

ab

ove

{uni

vers

ity d

egre

e or

ab

ove)

17.0

31.9

13.1

7.7

17.5

14.2

22.3

11.7

13.3

19.5

21.6

14.8

25.0

9.2

14.4

11.5

6.0

47.6

21.1

Annex: ii: measures of Firm performance and total Factor productivity Analysis 95

meAsures oF Firm perFormAnce And totAl FActor productivity AnAlysis

ANNEx: II

The study focuses on several measures of firm productivity. These are calculated in a uniform way in all countries with available Enterprise Survey data from between 2006 and 2011.

The Enterprise Surveys collect financial data in the local currency in the country being surveyed. To make cross-country comparisons, the financial data needed to be converted into a common currency in a single year (i.e., to control for inflation and exchange rate differences). To do this, all values are converted into 2009 US dollars (US$). For firm surveys conducted between 2006 and 2009 (that is, surveys with accounting data from between 2005 and 2008), data are inflated to 2009 values in local currency using the GDP deflator. For surveys converted after 2010, the values are deflated in a similar way. The values in 2009 local currency are then converted into US$ using 2009 exchange rates. Since most firms in the sample sell their products primarily in local markets, exchange rates have to be close to their equilibrium values in 2009 for these comparisons to be very accurate. If the exchange rate in a given country is over- or under-valued, the comparisons will under- or overstate the actual value of the performance measure for that country.

The individual measures are constructed in the following way

Value-added: Value-added is value of the goods and services that the firm produces less the cost of the raw materials (such as iron or wood) and intermediate inputs (such as engine parts or textiles) used to produce the output. Output is measured in local currency not in physical units. We subtract the cost of raw materials, intermediate inputs, electricity and fuel in local currency from output to get value-added. Firms report electricity and fuel costs separately from raw materials and intermediate inputs. Firms that do not report sales or raw materials are dropped for this measure. Electricity and fuel costs are treated as if they are zero for firms that do not report electricity or fuel costs (i.e., the firms are not dropped). This is done because dropping firms that do not report electricity or fuel costs would have a significant impact on sample size and because electricity and fuel costs are small relative to sales and raw materials.


Number of workers: The number of workers is the number of permanent and temporary full-time workers. Temporary workers are weighted by the average length of employment for these workers. For example, if the average length of employment for a temporary worker was six months, the weight for temporary workers would be half. Since data on employment was not collected in Tunisia, the weight is set to one for all firms in Tunisia. Data on part-time workers is not collected in most countries outside of Sub-Saharan Africa and so part-time workers are omitted for reasons of comparability. In practice, for countries with data on part-time workers, including these workers does not have a large effect on relative rankings. Firms that do not report permanent or temporary workers are dropped for measures that use workers (e.g., value-added per worker).

Labor Productivity: Value-added per worker is the basic measure of labor productivity used in this paper. It is value-added divided by the number of full-time workers in the firm. Firms that produce more output with less raw material and fewer workers have higher labor productivity.

Capital Intensity: The first measure is the book value of capital divided by the number of workers. For firms that keep detailed financial accounts, this measure should be the value of capital taken from those accounts. For other firms, it will either be omitted or estimated by the manager. As described in the ICA manual, this variable is:

The net book value represents the actual cost of assets at the time they were acquired, including all costs incurred in making the assets usable (such as transportation and installation) minus depreciation accumulated since the date of purchase. (World Bank, 2007b) The second measure is the sales value of capital divided by the number of workers. The manager is asked to estimate the value of the capital if sold in its current condition. Although this is probably closer to the true value of the capital, it has some shortcomings. In particular, when markets for capital equipment are thin, it might be difficult for the manager to give an accurate estimate. The implementation manual notes:

Ask the manager to estimate the market value of all of the equipment, land and buildings sold on the open market. If the respondent states that there is no market, ask how much the respondent would be willing to pay for the capital, knowing what it can produce in its current condition. Estimate how much it would cost to buy machinery in the current market which is similar in terms of age and characteristics. (World Bank, 2007b)

Firms that do not report these measures have to be dropped when calculating the respective measures of capital intensity.


Total Factor Productivity: This measure of productivity takes both labor and capital use into account. The methodology is described in below:

Labor costs per worker: The cost of labor is the cost of wages, salaries, bonuses, other benefits, and social payments for workers at the firm divided by the number of workers. The data is taken from the firms’ accounts. It includes wages and salaries paid to all workers and managers – not just production workers. Firms are only dropped from these averages when they do not report labor costs (or workers).

Unit labor costs: This measure is labor costs as a percentage of value-added. Although it is an approximation to true unit labor costs (i.e., it measures output in dollars rather than as physical measure of production), it can be calculated using information from the Enterprise Survey. Unit labor costs are higher when higher labor costs are not fully reflected in higher productivity.

Total Factor Productivity Estimation

This section describes how total factor productive is estimated for the cross-country comparisons of technical efficiency or total factor productivity and for the within country comparisons for firms with different characteristics.

Methodology

Technical Efficiency (TE) is calculated as a residual from a regression of the log of output (either value-added or revenue) on labor, capital, and other intermediate inputs.i Using a formulation based upon value-added, the estimation assumes a Cobb-Douglas Production function:ii

i i iY A K iLβα= (1)

Where Y is value-added for firm i, K is a measure of capital (e.g., the book value or replacement value of capital), L is the number of workers and A is total factor productivity or technical efficiency. Constant returns to scale are not imposed allowing the model to control for differences in productivity by firm size. The higher that A is, the more output the firm produces with the same amount of capital and labor. Taking natural logs of both sides implies that:

( ) ( ) ( )ln ln lni i i iy k lµ α β ε= + + + (2)

where

i iviA e eµ ε+= =


That is, the firm’s productivity is equal to a constant, μ, and an additional firm-specific measure of productivity, εi. It is easy to generalize this into a more general ‘augmented’ production function where the error term is:

i Ci iv Fµ ε= + ∂ + (3)

Where FCi is a vector of variables representing the characteristics of the firm or the characteristics of the investment climate that the firm faces. This implies that:

( ) ( ) ( ) iln ln ln FC i i i iy k lµ α β ε= + + + ∂ + (4)

To allow for productivity differences between countries, a vector of country dummies ( cµ ) can be included in the analysis. This implies that:

( ) ( ) ( ) iln ln ln FC i c i i iy k lµ α β ε= + + + ∂ + (5)

Under some assumptions, equation (4) can be estimated by Ordinary Least Squares (OLS). In particular, when firm characteristics are omitted (i.e., when equation (2) is estimated), the coefficients can be estimated with OLS if capital and labor are uncorrelated with the error term. That is, any shock or firm- specific factors that affect productivity must be uncorrelated with the firms’ decisions regarding capital and labor choices. This would be violated if, for example, managers were aware of something that affected productivity and allowed this to affect their hiring, firing or investment decisions. For example, this assumption would be violated if a firm received some technical advice from one of their suppliers or buyers that improved the firm’s productivity and then the manager decided to hire more workers to take advantage of this improved know-how.

Characteristics of the firm or the investment climate for the firm can also be directly included in the OLS regression as long as these characteristics are exogenous. For example, if becoming an exporter makes a firm more productive (e.g., through exposure to foreign markets) then a dummy variable indicating that the firm was an exporter could be included in the regression as long as changes in productivity did not affect the decision to become an exporter. If, for example, a firm became more productive and decided that this productivity boost meant that it could start exporting this assumption would be violated.iii

Rather than including firm or investment climate characteristics directly in the model, it is possible to first estimate equation (2) through OLS or another more


robust estimation method, obtain estimates of TE by calculating ε for each firm from equation (2) and then regress the residuals on the firm and investment climate characteristics (e.g., estimating equation [3]). An advantage of this approach is that if panel data is available it might be possible to estimate equation (2) using a robust technique such as the method suggested by Levinsohn and Petrin (2003) and then use something such as 2SLS in the second stage if one of the firm or investment climate characteristics were thought to be endogenous.iv

The drawback of this second approach is that if the firm level or investment climate characteristics are correlated with the amount of labor and capital that the firm uses (i.e., if the manager’s knowledge about the investment climate affects the firm’s use of labor or capital) then the estimates of the coefficients in equation (2) will be biased.v As a result, the ε’s will be estimated incorrectly and the coefficients from the second stage will be biased. It seems likely that this will often be the case. Escribano and Guasch (2005), argue that “this is almost always the case since the inputs are correlated with the Investment Climate (IC) variables and least squares estimators of [equation 2] are inconsistent and biased.” For this reason, estimation is done in a single step in this report.

One concern about OLS is that outliers can have a significant effect on OLS estimates. This can be dealt with in several ways. One possible way is to estimate the equation with a robust estimation method such as a Least Absolute Deviations (LAD) estimator. Another is to drop outliers. Due to concerns about outliers, LAD estimators are often used when estimating production functions.vi

In addition to concerns about outliers, a second set of concerns have been discussed in the literature on (i) the functional form of the error term, ε, and (ii) whether the error term is correlated with capital and labor. There are several methods that have been proposed regarding the functional form of the error term. In particular, stochastic frontier analysis allows for two error terms, a one-sided term assumed to have a half normal distribution, ν, representing technical efficiency and a two-sided normally-distributed error term, ε, representing temporary shocks to productivity and measurement error. The model is estimated using maximum likelihood estimation. In the analysis, the LAD estimators were used as the base estimators. The model was also estimated using standard OLS estimators and stochastic frontier analysis.

A broader problem is that things that affect productivity might affect firm managers’ choices regarding capital and labor. If this is the case, OLS,


stochastic frontier estimation and LAD estimation will all produce biased estimates of the coefficients. Although several methods have been proposed to deal with this, they require panel data (Levinsohn & Petrin, 2003; Olley & Pakes, 1996)—something that is not available for most Enterprise Surveys.

A final concern is that for analysis with firms from multiple sub-sectors of manufacturing, the analysis essentially assumes that firms use the same production technologies. Since the analysis in this chapter includes firms from more than one sub-sector of manufacturing, a more flexible estimation technique that allows firms in different sectors to use different production technologies is preferable. This can be done mechanically by including a full set of sector dummies and interacting these dummies with the measures of labor and capital to allow labor and capital intensities to differ in different sectors.

The augmented production function then becomes:

( ) ( ) ( )( )ij ijlog VA log log l FCij c j j ij j ij ijj

kµ α β γ ε ν= + + + + ∂ + +∑ (5)

The coefficients on labor and capital, β and γ, are assumed to vary between sectors. Sector dummies, α, are also included to allow for systematic differences in productivity across sectors. These models are sometimes referred to as the ‘unrestricted models’ while the models that assume identical production technologies are referred to as ‘restricted models’

Methodological Issues

There are some well-known problems, however, with this methodology.

For cross-country comparisons, value-added and capital have to 1. be denominated in a common currency (e.g., US dollars in these examples). Because these two variables are denominated in local currency in the survey, cross-country comparisons of TE are vulnerable to exchange rate fluctuations. If the exchange rate is overvalued relative to its long-run equilibrium then TE might look artificially high. Although this can make it difficult to interpret differences in TE between countries, it is important to note that this shouldn’t have a significant impact on the coefficients on firm-level variables.

The model essentially assumes that firms in different countries in the 2. same sector use similar technologies.

Capital is more difficult to measure than labor for both theoretical and 3. practical reasons. Since TE uses measured capital in its construction, it will generally be mis-measured when capital is mis-measured.


Ideally the measure of output would be a physical measure of output. 4. In practice, however, it is difficult to obtain physical measures of output and, instead, most analyses including those using Enterprise Survey data use sales (i.e., output multiplied by unit price) as the dependent variable (i.e., a sales generating function).vii With firms producing heterogeneous products, this can be problematic if some have market power. That is, firms with market power that charge high prices for their output (e.g., monopolists) would appear more productive than a similar firm in competitive markets that charges lower prices.viii

Because estimates are calculated in a regression framework, it is less 5. straightforward to calculate TE than labor productivity. One issue is that estimates of TE for groups of firms do not have natural units. For cross-country comparisons, TE is shown as % of TE in Tunisia. For other groups (e.g., exporters) differences are presented in terms of a base category (e.g., non-exporters).

As noted by Escribano and Guasch (2005), there is no single accepted 6. approach to estimating TE. For this reason, following Escribano and Guasch, the model was estimated in several different ways to check the robustness of results. We therefore estimate the model in various ways making different assumptions about the error terms (i.e., stochastic frontier estimation, OLS, and LAD estimators are presented).

Recent studies have noted that inputs in the production function 7. (labor and capital) are endogenous (Levinsohn & Petrin, 2003; Olley & Pakes, 1996) and this can affect the estimation of TE. With panel data, it is possible to control for this using sophisticated econometric techniques instrumenting for inputs with intermediate inputs or investment. In this case, without a long panel, these methodologies cannot be implemented.

There are some additional problems associated with including measures of the investment climate in TE regressions.

The impact that different aspects of the investment climate have 8. on firm productivity is likely to vary across countries—and results across countries have often varied in previous Investment Climate Assessments. For example, improvements in access to financing are likely to depend upon how developed financial markets are, how difficult it is to get access, how the financial sector is regulated, what informal institutions have evolved to make up for problems with formal credit markets, and many other factors. Because the study is primarily interested in the effect that investment climate variables have on productivity in Tunisia, the analysis was restricted into looking at


the how firm characteristics to affect productivity affect productivity in Tunisia. This ensures that the analysis is looking at differences in productivity as they apply to Tunisia.

Most firm-level variables and investment climate variables are 9. potentially endogenous.ix The results for the firm-level variables should therefore be treated with caution and should not be assumed to be causal. For the base estimates of country-level differences in productivity, the study will use the coefficients from the model without additional control variables.

Several papers have discussed these issues in more depth including Escribano and others (2005) and Dollar and others (2005).x Data is pooled for the sub-sectors of manufacturing for which enough data were collected to estimate productivity.xi

Referencesi. It is possible to make other assumptions about the functional form of the production function

(e.g., to assume a trans-log production function), although this does not appear to have a significant impact on results in most cases. See for example the analysis from the Investment Climate Assessment for Turkey (World Bank, 2007a).

ii. There is a large amount of literature on whether exporting improves performance (learning-by-exporting hypothesis) or whether only productive firms can export (self-selectivity hypothesis). The large literature on this topic is summarized in Tybout (2003) and Bernard and others (2007).

iii. Gatti and Love (2008) do this, allowing access to credit to be endogenous in the second step.iv. This is due to omitted variable bias. It is discussed in more detail in Chapter 7 in Kumbhakar

and Lovell (2000) and in Escribano and Guasch (2005).v. See, for example, Greene (2000, pp. 449-450).vi. See, for example, the discussion in Pakes (2008).vii. See, for example, the discussion by Levinsohn (2008) on the Escribano-Guasch methodology

(Escribano & Guasch, 2005; Escribano and others, 2008; Escribano and others, 2005).viii. For investment climate variables, many previous studies that use firm-level data from

Enterprise Surveys to look at the effect of the investment climate on various measures of firm performance control for this by replacing or instrumenting the potentially endogenous investment climate variables with region-industry averages.

ix. See also comments on the Escribano-Guasch methodology by Levinsohn (2008), Pakes (2008) and Verhoogen (2008). Although the analysis is not identical to the analysis that these authors are commenting on, it shares many characteristics with it. Previous investment climate assessments have used multiple firm-level observations in a single regression. In most cases, the data for the investment climate variables and many of the firm characteristics is cross-sectional. That is, it was only asked for a single year. In contrast, productivity data was collected for three years. By replicating the investment climate variables, which were only collected as a cross-section, for each year for which there was productivity data, it was possible to run random effects panel regressions. Fixed effects could not be included because these would collinear with the investment climate variables. In this case, productivity data were only collected for a single period making all analysis cross-sectional by necessity.

x. The econometric approach that is used to estimate TE is similar to the econometric approach used in other Investment Climate Assessments in several ways. See, for example, the Investment Climate Assessment for South Africa (2007) or Investment Climate Assessment for Turkey (World Bank, 2007a).

Annex: iii: icA enterprise survey sample composition & population Frame 103

icA enterprise survey sAmple composition & populAtion FrAme

ANNEx: III

SURVEY METHODOLOGY AND SAMPLE DESCRIPTION

The survey was undertaken throughout Tunisia by a survey team from IACE (Institute Arabe des Chefs d’Entreprises) and involved in-person interviews with senior managers or owners of businesses operating in Tunisia.

The survey targeted 600 firms operating in Tunisia in the manufacturing and service sectors across several regions. The survey covered several regions and is stratified across three main criteria: size, activity, and location. Since there is not one single reliable source from which to draw a sample containing the information needed, the sample was drawn from multiple sources, including the Social Security Administration, INS (the Statistical Agency) and the implementing agency’s own databases of firms.

The survey attempts to use parallel sampling and implementation methodology in order to facilitate comparability across countries and over time. The representative survey uses a standardized manufacturing and services questionnaire from the ICS core questionnaires with a number of additional questions designed to understand country specific issues. In Tunisia these included issues covering informality, the extent of discretion used in certain areas of the administration, and the relation between the administration and the firms.

The survey attempts to capture several dimensions of enterprise managers’ experience of the local investment climate:

Private managers’ relative priorities for reform of different investment �climate factors.

The private costs imposed by constraints in the enabling �environment and support systems (and/or their impact on firm-level performance).

The functioning of policies, as applied at the firm level and its unique �institutional environment.


The actual behavior of firms and their competitors, formal and �informal.

The survey field work began in June 2012 and was completed in December 2012. They survey covered four large regions including:

Region A (North East ): Coastal areas around Tunis including Bizerte �and Nabeul.

Region B (South East): Sfax, Gabes, Médenine, Tataouine. �

Region C (Center East): Covering the towns of Sousse, Monastir, �Mahdia and Kairouan.

Region D (West): Covering towns located in the western areas. �

Annex: iv: regression tables & Additional relevant data used in the innovation chapter 105

reGression tAbles & AdditionAl relevAnt dAtA used in the innovAtion chApter

ANNEx: IV

* probit new products dom 19nivetmoyen vocationaltrain b7anexprience ISO email web internet > internetRD patent RD e32adrd RD1 RD2 RD3

note: email omitted because of collinearitynote: internet omitted because of collinearitynote: RD omitted because of collinearitynote: RD3 omitted because of collinearityIteration 0: long likelihood = -78.579618Iteration 1: long likelihood = -72.817518Iteration 2: long likelihood = -72.708116Iteration 3: long likelihood = -72.701164Iteration 4: long likelihood = -72.701114Iteration 5: long likelihood = -72.701114

Probit regression Number of obs = 114

LR chi2 (10) = 11.76

Prob > chi2 = 0.3016

Log likelihood = -72.701114 Pseudo R2 = 0.0748

newproductsdom Coef. Std. Err. z P> | z | [95% Conf. Interval]

19nivetmoyen .1941986 .1246033 1.56 0.119 -.0500194 .4384166

vocational train .2883342 .2902776 0.99 0.321 -.2805994 .8572678

b7anexprience -.0025732 .0104523 -0.25 0.806 -.0230594 .0179129

ISO -.1913279 .2781869 -0.69 0.492 -.7365642 .3539084

email 0 (omitted)

web .023319 .3174206 0.07 0.941 -.598814 .645452

internet 0 (omitted)

internetRD .6382464 .6971106 0.92 0.360 -.7280653 2.004558

patent .4369838 .3294074 1.33 0.185 -.2086428 1.08261

RD 0 (omitted)

e32adrd 5.48e-08 7.07e-08 0.78 0.438 -8.36e-08 1.93e-07

RD1 -.1020597 .4893262 -0.21 0.835 -1.061121 .857002

RD2 .1317308 .5093153 0.26 0.796 -.8665088 1.12997

RD3 0 (omitted)

_cons -1.350208 .9706638 -1.39 0.164 -3.252675 .5522577

end of do-file

. do “C:\Users\wb417696\AppData\Local\Temp|STD05000000.tmp”

. mfx

Marginal effects after probit

y = Pr (newproductsdom) (predict) =.55351121

Variable dy/dx Std. Err z P> | z | [95% Conf. Interval] X

19nivetmoyen .076776 .04928 1.56 0.119 -.01981 .173362 3.53509

vocational train* .114231 .11467 1.00 0.319 -.110518 .33898 .675439

b7anexprience -.0010173 .00413 -0.25 0.806 -.009116 .007082 22.2193

ISO* -.0756104 .10967 -0.69 0.491 -.290562 .139341 .45614

web* .0092273 .12571 0.07 0.941 -.237155 .25561 .798246

internet~D* .2472042 .24861 0.99 0.320 -.240062 .734471 .964912

patent* .1669171 .11954 1.40 0.163 -.067371 .401205 .192982

e32adrd 2.17e-08 .00000 0.78 0.436 -3.3e-08 7.6e-08 765971

RD1* -.040287 .19277 -0.21 0.834 -.418119 .337545 .578947

RD2* .0518954 .19976 0.26 0.795 -.339626 .443417 .350877

( * ) dy/dx is for discrete change of dummy variable from 0 to 1

end of do-file


* pwcorr new products 19nivetmoyen vocationaltrain b7anexprience ISO email web internet > internetRD patent RD e32adrd RD1 RD2 RD3, sig star (.10)

newpro~r 19nive~n vocation b7anex~e ISO email webnewproduct 1.0000

19nivetmoyen 0.0781* 1.0000

0.0822

vocational~n 0.0999* 0.1019* 1.0000

0.0248 0.0145

b7anexprience -0.0029 -0.0165 0.0414 1.0000

0.9485 0.6939 0.3194

ISO 0.0541 0.0393 0.2391* 0.0686* 1.0000

0.2241 0.3496 0.0000 0.0986

email 0.0695 0.1083* 0.1646* -0.0611 0.1369* 1.0000

0.1172 0.0095 0.0001 0.1395 0.0009

web 0.1835* 0.0566 0.2004* 0.0459 0.2164* 0.2484* 1.0000

0.0000 0.1755 0.0000 0.2660 0.0000 0.0000

internet 0.0971* 0.1136* 0.1969* 0.1365* 0.1398* 0.4023* 0.2351*

0.0281 0.0064 0.0000 0.0009 0.0007 0.0000 0.0000

internetRD 0.1974* 0.1127* 0.1723* 0.0114 0.1323* 0.0564 0.2925*

0.0000 0.0123 0.0001 0.7991 0.0028 0.2046 0.0000

patent 0.1444* 0.0542 0.0726* 0.0131 0.2072* 0.0742* 0.0469

0.0014 0.2111 0.0911 0.7617 0.0000 0.0835 0.2738

RD 0.2099* -0.0175 0.1949* 0.0411 0.2446* 0.1289* 0.2219*

0.0000 0.6793 0.0000 0.3272 0.0000 0.0020 0.0000

e32adrd 0.0921 -0.0156 -0.1475* -0.0573 0.1439* . 0.0602

0.2918 0.8541 0.0776 0.4967 0.0853 . 0.4719

RD1 -0.490 -0.0324 0.0526 -0.0155 -0.0179 -0.0641 -0.1647*

0.5342 0.6711 0.4832 0.8364 0.8107 0.3910 0.0263

RD2 0.0178 -0.0301 -0.0256 0.0708 0.0642 0.0538 0.1590*

0.8220 0.6937 0.7333 0.3464 0.3902 0.4719 0.0320

RD3 0.0583 0.1118 -0.0502 -0.0938 -0.0788 0.0224 0.0213

0.4597 0.1420 0.5035 0.2118 0.2916 0.7646 0.7754

newpro 19nive vocation b7anex~e ISO email webnewproduct 1.0000

19nivetmoyen 0.0222 1.0000

0.6354

vocational 0.1116* 0.1019* 1.0000

0.0157 0.0145

b7anexprie~e 0.0023 -0.0165 0.0414 1.0000

0.9609 0.6939 0.3194

ISO 0.1390* 0.0393 0.2391* 0.0686* 1.0000

0.0025 0.3496 0.0000 0.0986

email 0.1499* 0.1083* 0.1646* -0.0611 0.1369* 1.0000

0.0011 0.0095 0.0001 0.1395 0.0009

web 0.2428* 0.0566 0.2004* 0.0459 0.2164* 0.2484* 1.0000

0.0000 0.1755 0.0000 0.2660 0.0000 0.0000

internet 0.1333* 0.1136* 0.1969* 0.1365* 0.1398* 0.4023* 0.2351*

0.0037 0.0064 0.0000 0.0009 0.0007 0.0000 0.0000

internetRD 0.1651* 0.1127* 0.1723* 0.0114 0.1323* 0.0564 0.2925*

0.0007 0.0123 0.0001 0.7991 0.0028 0.2046 0.0000

patent 0.1638* 0.0542 0.0726* 0.0131 0.2072* 0.0742* 0.0469

0.0005 0.2111 0.0911 0.7617 0.0000 0.0835 0.2738

RD 0.2729* -0.0175 0.1949* 0.0411 0.2446* 0.1289* 0.2219*

0.0000 0.6793 0.0000 0.3272 0.0000 0.0020 0.0000

e32adrd -0.0579 -0.0156 -0.1475* -0.0573 0.1439* . 0.0602

0.5126 0.8541 0.0776 0.4967 0.0853 . 0.4719

RD1 -0.0961 -0.0324 0.0526 -0.0155 -0.0179 -0.0641 -0.1647*

0.2236 0.6711 0.4832 0.8364 0.8107 0.3910 0.0263

RD2 0.0838 -0.0301 -0.0256 0.0708 0.0642 0.0538 0.1590*

0.2892 0.6937 0.7333 0.3464 0.3902 0.4719 0.0320

RD3 0.0283 0.1118 -0.0502 -0.0938 -0.0788 0.0224 0.0213

0.7206 0.1420 0.5035 0.2118 0.2916 0.7646 0.7754


Table 36: MANUFACTURING AND SERVICE COMPANIES BY REGION AND SECTOR

Table 35: MANFACTURING AND SERVICE COMPANIES BY SIZE AND SECTOR

REGION

Activités A B C D Total

Agro industry 1 36 14 16 22 88

Garments 2 23 43 11 20 97

Textiles 3 34 10 8 4 56

Electrical and Mechanical Engineering

4 45 7 22 14 88

Other 5 11 7 18 3 39

MCCV 6 15 12 12 13 52

Commerce 7 38 10 30 15 93

Tourism 8 17 13 0 16 46

ICT 9 16 7 4 3 30

Construction 10 1 1 1 1 4

Health 11 2 0 0 5 7

Total 238 124 122 116 600

SIZE

Sectorty Small Medium Large Total

Agro industry 21 48 19 88

Confectionaries 8 63 26 97

Textiles 8 27 21 56

Electrical and Mechanical Engineering 16 46 26 88

Others 7 20 12 39

MCCV 10 28 14 52

Commerce 60 24 9 93

Tourism 18 20 8 46

ICT 12 17 1 30

Construction 1 3 0 4

Health 1 6 0 7

Total 162 302 136 600

Table 34: MANUFACTURING AND SERVICE COMPANIES BY SIZE AND REGION

RéGION

Size A B C D Total

Small 50 26 48 38 162

Medium 115 66 55 66 302

Large 73 32 19 12 136

Total 238 124 122 116 600

Note: A higher percentage of firms that are part of a larger company innovate, supporting the findings that large firms have the highest level of innovation in Tunisia.


Figure 75: IS THE ESTABLISHMENT A PART OF A LARGER COMPANY, BY NON-INNOVATIVE VS. INNOVATIVE FIRMS

Figure 76: INNOVATIVE FIRMS BY AGE GROUP

5

1318

16

48

8

1621 20

36

0%

10%

20%

30%

40%

50%

60%

0-3 yrs 3-5 yrs 5-10 yrs 10-15 yrs 15+ yrs


24%

29%

76% 71%

0%

20%

40%

60%

80%

100%


Is the establishment a part of a larger company?

No

Yes% o

f �rm

s

Table 37: INDIRECT EXPORTS AND INPUT VARIABLESINDIRECT EXPORTS and input variables (correlation coefficient) Non-innovative Innovative

ISO certification-0.0645 -0.1225*

(0.3022) (0.0419)

Employees with university degree and higher-0.0907 -0.0985

(0.1496) (0.1082)

Email-0.074 0.0595

(0.2379) (0.3237)

Own website-0.0067 -0.0507

(0.9151) (0.4005)

Broadband Internet0.0336 0.003

(0.5905) (0.9603)

R&D-0.0001 -0.1591*

(0.9982) (0.008)

Patent-0.0204 -0.1542*

(0.7507) (0.0121)

Using Internet for R&D-0.0732 -0.1998*

(0.2809) (0.0015)

Note: * Significant at 10%; P value in parenthesis.

Table 38: DIRECT EXPORTS AND INPUT VARIABLES

DIRECT EXPORTS and input variables (correlation coefficient) Non-innovative Innovative

ISO certification0.2287* 0.2491*

(0.0002) (0.0000)

Employees with university degree and higher -0.1811* -0.1876*

(0.0038) (0.0021)

Email0.0685 0.1098*

(0.2759) (0.068)

Own website0.1027 0.1186*

(0.1005) (0.0487)

Broadband Internet0.1425* 0.083

(0.0221) (0.1676)

R&D0.1602* 0.1528*

(0.0104) (0.0109)

Patent0.1120* 0.0705

(0.0801) (0.2537)

Using Internet for R&D0.1864* 0.0136

(0.0058) (0.8309)

Note: * Significant at 10%; P value in parenthesis.


Figure 77: HAS THE ESTABLISHMENT BENEFITED FROM A FACILITY UPGRADE SCHEME?

Figure 78: INNOVATIVE VS. NON-INNOVATIVE FIRMS, BY INABILITY TO BENEFIT FROM FACILITY UPGRADE SCHEME

Figure 79: AVAILABILITY OF SCIENTIST AND ENGINEERS

0%

10%

20%

30%

40%

50%

60%

41%

Non-innovative

56%

Innovative

% o

f �rm

s re

por

ting

"yes

"Has the establishment bene�ted from a facility upgrade scheme?

19%16%

31%

2%

8%

39%

13%

29%

11%

21%

4% 4%

30%

16%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Lack

of i

nfor

mat

ion

Lack

of f

und

ing

Pro

ced

ures

War

rant

y co

nditi

ons

Elig

ibili

ty c

riter

ia

No

need

Oth

er

% o

f firm

s re

por

ting

"yes

"

Innovative vs. non-innovative firms by inability to benefit from facility upgrade scheme

Non-innovative

Innovative

99

63

44

40

36

35

30

20

8

0 20 40 60 80 100 120

Oman

Syria

Algeria

Egypt

Morocco

Turkey

Lebanon

Jordan

Tunisia

GCI Country ranking/142

Source: Global Competitive Index, 2012.

NOTES

NOTES

NOTES

World Bank Document...Co-Task Team Leader, Co-Author : Mehdi Benyagoub Contributing Authors : Gael Raballand, Randa Akeel, Laurent Gonnet, Lina Badawy, Phlippe Alby, George Clarke,

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