1 THE REPUBLIC OF RWANDA INVESTMENT CLIMATE ASSESSMENT: Strategy for Sustained Employment and Export Growth February 2, 2009 World Bank Regional Program for Enterprise Development (RPED) Africa Finance and Private Sector (AFTFP) Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
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THE REPUBLIC OF RWANDA
INVESTMENT CLIMATE ASSESSMENT:
Strategy for Sustained Employment and Export Growth
February 2, 2009
World Bank Regional Program for Enterprise Development (RPED)
Africa Finance and Private Sector (AFTFP)
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THE REPUBLIC OF RWANDA
CURRENCY EQUIVALENTS
(Exchange Rate Effective as of 10/06/2008)
Currency Unit = Rwanda Franc (Rwf)
US$1.00 = 530.45 Rwf (need to update)
FISCAL YEAR
July 1‐June 30
ABBREVIATIONS AND ACRONYMS
AAA Analytical and Advisory Activities AIDS Acquired Immune Deficiency Syndrome BADEA Arab Bank for Economic Development in Africa BIZCLIR Business Climate Legal & Institutional Reform BNR Banque Nationale du Rwanda CAPMER Centre for Support to Small and Medium‐sized Enterprises in Rwanda CAS Country Assistance Strategy CEM Country Economic Memorandum COMESA Common Market for Eastern and Southern Africa CSO Civil Society Organization EAC East African Community EDPRS Economic Development and Poverty Reduction Strategy DB Doing Business DFID Department for International Development DRC Democratic Republic of Congo DPL Development Policy Lending EAC East African Community ESW Economic and Sector Work FDLR Forces Démocratiques de Libération du Rwanda FY Fiscal Year GDP Gross Domestic Product GNI Gross National Income GoR Government of Rwanda HIDA Human Resources and Institutional Capacity Development Agency HIPC Heavily Indebted Poor Countries (Initiative) HIV Human Immunodeficiency Virus ICA Investment Climate Assessment ICR Implementation Completion Report IDA International Development Association IEG Independent Evaluation Group IFC International Finance Corporation IMF International Monetary Fund
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LBICS Local Business Investment Climate Survey MDGs Millennium Development Goals MIGA Multilateral Investment Guarantee Agency MINECOFIN Ministry of Finance and Economic Planning
MINICOM Ministry of Commerce, Industry, Investment Promotion, Tourism and Cooperatives
NBR National Bank of Rwanda NGO Non‐Governmental Organization NIS National Institute of Statistics of Rwanda ODA Official Development Assistance PRSP Poverty Reduction Strategy Paper PSD Private Sector Development PSF Private Sector Federation RIEPA Rwanda Investment and Export Promotion Agency ROSC Report on the Observance of Standards and Codes RRA Rwanda Revenue Authority RWF Rwanda Francs RURA Rwanda Utilities Regulatory Agency SME Small and Medium Enterprise SOE State‐Owned Enterprise TA Technical Assistance USAID United States Agency for International Development UNDP United Nations Development Program VUP Vision 2020 Umurenge Program
Vice President: Obiageli K. Ezekwesili
Acting Country Director: Michel Wormser
Sector Director Marilou Jane D. Uy
Sector Manager: Gerardo Corrochano
Task Team Leader: Asya Akhlaque
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Table of Contents Table of Contents .....................................................................................................................................4
List of Figures............................................................................................................................................7
List of Tables .............................................................................................................................................9
List of Appendix ......................................................................................................................................11
Enterprise Performance in the Services Sector ......................................................................................48
Retail and IT Sector.................................................................................................................................49
Other Services: Construction and Transport, Hotels and Restaurants etc.............................................52
Chapter 3: Investment Climate and Regional Integration Opportunities and Challenges ......................... 55
Transport ................................................................................................................................................59
Legal system .........................................................................................................................................126
Macroeconomic Uncertainty and Political Instability ..........................................................................128
Entry and Exit........................................................................................................................................128
Factor Markets: Access to and Cost of Land.........................................................................................130
Figure 4: Unit Labor Cost ............................................................................................................................41
Figure 5: Capital Labor Ratio (sale value)....................................................................................................42
Figure 6: Total Factor Productivity: Percentage relative to Kenya .............................................................43
Figure 7: Productivity and Unit Labor Costs: Retail Sector .......................................................................51
Figure 8: Productivity and Unit Labor Costs: IT sector...............................................................................51
Figure 9: Business Constraints: Pct of firms ranking Problem as Major or Severe ....................................57
Figure 10: Biggest Constraints in Manufacturing and Services Sector ......................................................58
Figure 11: % of Firms Identifying Transportation as a Major Constraint....................................................60
Figure 12: Electricity Problems: Rwanda versus comparators...................................................................62
Figure 13: Energy Costs and Losses: East Africa Community.....................................................................63
Figure 14: Internet Usage ...........................................................................................................................64
Figure 15: Percentage of Firms Identifying Tax Rates as Major Constraint ...............................................66
Figure 16: Percent of Firms Identifying Tax Administration as Major Constraint .....................................67
Figure 17: Percent of Firms Expressing that a Typical Firm Report Less than 100% of Sales for Tax Purposes......................................................................................................................................................68
Figure 18: Percent of Firms Identifying Corruption as a Major Constraint.................................................72
Figure 19: Cross Country Comparisons of Informal Payments and Gift Giving .........................................73
Figure 20: % of Firms Identifying Crime, Theft and Disorder as Major Constraints*** ............................75
Figure 21: Bank Credit to Private Sector in Rwanda and Comparator Countries .......................................78
Figure 22: Cross‐Country Comparison of Interest Rates ............................................................................78
Figure 23: Bank Nonperforming loans in Rwanda and Comparator Countries ..........................................79
Figure 24: Cross‐Country Comparison of Access to Finance Obstacle and Credit Products Use................79
Figure 25: Cross‐Country Comparison of Sources of Working Capital and Investment Finance................80
Figure 26: Difference between micro enterprises and SML enterprises ....................................................81
Figure 27: Comparison of Access by Firm Size............................................................................................82
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Figure 28: Sources of Finance for Working Capital and Investment...........................................................83
Figure 29: Access Indicators by Formality and Age.....................................................................................85
Figure 30: Sources of Finance for Formality and Age .................................................................................86
Figure 31: Manufacturing Firms in Rwanda are in the middle of the pack with regards to perceptions of skills in the labor force. ...............................................................................................................................91
Figure 32: Manufacturing firms in Rwanda are at the bottom of the pack with respect to labor regulations. .................................................................................................................................................95
Figure 33: Doing Business ranks labor regulation to be particularly burdensome in Rwanda. .................96
Figure 34: Median monthly wages for production workers are higher in Rwanda than they are in India, Uganda and Tanzania..................................................................................................................................97
Figure 35: Median Monthly Wages in the Food and Garments Sectors.....................................................98
Figure 36: Unionization rates in Rwanda are very low ............................................................................100
Figure 37: Days lost due to illness in Rwanda are high.............................................................................101
Figure 38: Employment Growth in Manufacturing and Services have been robust in recent years.......102
Figure 39: Labor productivity of firms in the sample................................................................................105
Figure 40: Distributions of Labor Productivity: Rwanda and Comparators ..............................................106
Figure 41: Educational Distribution of Microenterprises: By Formality Status ........................................109
Figure 42: Ranking of Business Constraints: Formal vs. Informal Micro Firms.........................................111
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List of Tables Table 1: Structure of the Economy: Growth and Share of GDP..................................................................29
Table 2: Size Profile of Private Sector in Rwanda .......................................................................................33
Table 3 Sample size by stratum and sampling region.................................................................................34
Table 4: Rwanda ‐ Enterprise Productivity by Firm Characteristics............................................................40
Table 5: Technology and Learning Characteristics: Rwanda versus comparators......................................44
Table 6: Sample Description: Exporters and Importers .............................................................................46
Table 7: Characteristics of Exporters versus Non‐Exporters.......................................................................47
Table 8: Retail and Information Technology: Sample Characteristics .......................................................49
Table 9: Technology Characteristics of Retail and IT firms .........................................................................50
Table 10: Competition Characteristics of Retail Firms................................................................................50
Table 11: Sample Characteristics‐Residual Sector: Construction, Hotels and Restaurants........................52
Table 12: Labor Productivity and Unit Labor Costs in Service Sector ........................................................53
Table 13: Logistics Performance Index .......................................................................................................60
Table 14: Transport Costs and Losses: EAC................................................................................................61
Table 15: Medium and Large Enterprises ...................................................................................................65
Table 17: Doing Business: Trading Across Borders .....................................................................................70
Table 18: Informal Payments Characteristics across EAC...........................................................................73
Table 19: Kaufmann Kraay Rankings on Corruption ..................................................................................74
Table 20: Crime costs: Percentage lost due to property theft, % spent on Security..................................75
Table 21: Loan Providers and Loan Characteristics ....................................................................................83
Table 22: Reason for Lack of Loan Application ...........................................................................................84
Table 23: Investment in Rwanda................................................................................................................89
Table 24: Percent of firms reporting skills shortage as a major or severe constraint ...............................91
Table 25: Do reports of skill constraints vary by worker education ..........................................................92
Table 26: Do reports of skill constraints vary by training or employment growth.....................................92
Table 27: Percent of firms saying that the average worker in the firm has completed different levels of schooling .....................................................................................................................................................93
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Table 28:‐based training: Percent provide training and percent of workers trained.................................94
Table 29: Median Monthly Wages by Occupation in 2005 US Dollars ......................................................99
Table 31: Benefits of Formality .................................................................................................................110
Table 32: Costs of Formality .....................................................................................................................111
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List of Appendix Appendix 1. 1 Structure of the MSSE Sector, Classified by Rwandan System of Production ...................114
Appendix 1. 2 Population size by stratum and sampling region...............................................................116
Appendix 1. 3 Sample weights by stratum and sampling region..............................................................116
Appendix 2. 1 Total Factor Productivity Estimation Pooled Data.............................................................117
Appendix 2. 2 Probit Regressions: Decision to Export .............................................................................118
Appendix 2. 3 Sample Size: Residual and Retail Sectors..........................................................................119
Appendix 2. 4 Determinants of Productivity: Retail and IT Sectors..........................................................119
Appendix 2. 5 Determinants of Productivity: Services Sector .................................................................120
Appendix 3. 1 Business Constraints in Rwanda‐by firm Characteristics ...................................................123
Appendix 3. 2 Dealing with Licenses.........................................................................................................124
Appendix 3. 3 Functioning of Courts.........................................................................................................127
Appendix 3. 4 Educational Characteristics by Gender ..............................................................................131
Appendix 3. 5 Financial Characteristics of Firms: By Gender....................................................................132
Appendix Figure 3. 1 Challenges facing Rwandan businesses – Transport and Land are most commonly cited as major constraints.............................................................................................................................1
Appendix Figure 3. 2 Challenges facing Rwanda businesses – Businesses are generally positive about their future revenues and performance .......................................................................................................1
Appendix Figure 3. 3% of Firms Identifying Business Licensing and Permits as Major Constraint...........125
Appendix Figure 3. 4 Percent of Firms Identifying Labor Regulations as a Major Constraint ..................126
Appendix Figure 3. 5 Court Procedures and Costs....................................................................................127
Appendix Figure 3. 6 Duration (in days), Cost (% of income/capital) and Number of Procedures Required to Get Business Licenses – International Comparison..............................................................................129
Appendix Figure 3. 7 Enterprise Surveys: Connection Times ..................................................................130
Appendix Figure 3. 8 % of Firms owning Business Property.....................................................................131
Appendix Figure 3. 9 Ranking of Business Constraints by Gender ...........................................................132
Appendix Figure 3. 10 Microenterprises – Ranking of Constraints by Gender.........................................133
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Appendix 4. 1 Training Determinants: Firm Level.....................................................................................137
Appendix 4. 2 Training Determinants: Individual Level ...........................................................................138
Appendix 4. 3 Determinants of Average Wages Firm Level......................................................................139
Appendix 4. 4 Determinants of Worker Earnings .....................................................................................140
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Acknowledgments
The team gratefully acknowledges important contributions and support received from government officials of the Government of Rwanda. We would like to thank especially Minister Monique Nsanzabaganwa and State Minsiter Vincent Karega from the MINICOM; Ms. Marie Clare Akamanzi, Deputy Director, RIEPA; Mr. David Himbara, Head Strategy and Policy Unit of President’s office; and MINECOFIN. The preparation of the report benefited from valuable exchange of ideas with private sector representatives and development partners based in Rwanda, especially the Private Sector Federation (PSF) and participants of the Private Sector Cluster, led by Ms. P. Mujawayezu and Ryan Washburn respectively. OTF Group, specifically, James Foster, and the PSF were particularly helpful in collaboration with the PSF and OTF on the LBIC survey.
The task team was led by Asya Akhlaque (AFTFP). Core team members were Manju Shah (Consultant), Inessa Love (DECRG), and James Habyarimana (Consultant). The productivity analysis (chap. 2) was prepared by Manju Shah who led the write‐up of the Investment Climate chapter (3) and Microenterprises chapter (6). Other staff who contributed to the report were Mary Urujeni (Consultant) and Zachary A. Kaplan. Useful inputs and guidance was received from Ann Rennie, Melanie Mbuyi, Amadou Dem (AFTFP), Paramita Dasgupta (FIAS); David Blackden (IFC); and Kene Ezemenari (AFTP3).
The report was prepared under the guidance of Gerardo Corrochano and Victoria Kwaka. The team gratefully acknowledges the excellent administrative support provided by Josephine Ngou and Aline Dukuze. Jose Guilherme Reis and Jean Michel N. Marchat were the peer reviewers.
Foreword: Investment Climate Assessments vis‐à‐vis Doing Business
The World Bank has two powerful diagnostic tools that examine the business environment in a country and its impact on the growth and competitiveness of the private sector: the Investment Climate Assessments (ICAs) and the annual Doing Business Reports.
Investment Climate Assessments: based on local enterprise surveys, systematically analyze the conditions for private investment and enterprise growth in a country to pinpoint the areas where reform is most needed to improve the private sector’s productivity and competitiveness. By providing a practical foundation for policy recommendations and involving local partners throughout the process, the assessments are designed to give greater impetus to policy reforms that can speed the private sector’s growth, leading to faster economic growth and poverty reduction.
Produced by the World Bank Group in close partnership with a public or private institution in each country, the investment climate assessments are based on interviews with enterprise owners and managers, to identify what difficulties they encounter in starting and running a business—and, if the business fails, in exiting. The survey captures firms’ experience in a range of areas: financing, governance, regulation, tax policy, labor relations, conflict resolution, infrastructure services, supplies and marketing, technology, and training. All these are areas where difficulties can add substantially to the cost of doing business. The survey attempts to quantify these costs. Using a standard methodology, the assessment then compares the survey findings with those in similar countries to evaluate how the country's private sector is faring and how well it can compete.
Doing Business Report: based on expert surveys, the DB Report focuses on the policy, legal and regulatory framework across a vast number of countries, establishing comparative measures of the costs incurred by businesses to comply with existing laws and regulations. The policy, legal and regulatory framework measured by the indicators of the Doing Business report are an important part of a country’s investment climate because these impact how well firms can respond to changes in the economy. In dynamic economies, firms are forced to continually adapt to the changing market. However, if regulatory and legal requirements make the transactions needed to adapt to these market changes more cumbersome or impossible to undertake, add to costs, result in lengthy procedures and/or rule out the most adequate responses to economic changes, businesses may be forced to operate at lower efficiency, or slow in making productivity enhancing adaptations. Doing Business provides the tools to compare the regulatory burden across countries and identify areas requiring reform to reduce these transactions costs.
Value Addition of ICA to DB: The value addition of the ICA includes the following: (i) While the DB measures improvements to regulatory cost and burdens, the ICAs examine a broader range of drivers of competitiveness that impact macroeconomic outcomes. The ICA survey collects information on the investment climate areas such as infrastructure, crime, taxation, worker education and skills, and regulation and corruption. This is particularly valuable as firms’ investment decisions also depend on variables not measured by the DB indicators, such as the cost and access to infrastructure, labor skills and governance. (ii) More importantly, ICAs collect data that allows us to undertake firm productivity analysis. With ICA surveys undertaken across the vast majority of Bank’s client countries, the detailed data available allows us to identify Rwandan firm’s productivity and costs in a regional and cross‐country context, and analyze factors that determine these differences. Aside from productivity data, ICAs collect two types of information directly from entrepreneurs: (a) subjective or perception measures of what managers see as the major obstacles that their firm faces; and (b) objective indicators such as production lost due to power outages, and amount of time managers spend dealing with government regulations. (iii) Lastly, there are significant methodological differences: while ICA data is based on firm‐level survey, DB collects its information from expert informants in each country, mostly lawyers and accountants.
Complementary and Collaborative Partnership: Despite differences in approach, both ICA and DB provide complementary information and analysis. Consolidating the findings, the Bank Group works collaboratively to help clients in developing a more business friendly environment in the country.
Executive Summary
Rwanda’s long‐term development vision is articulated in the Rwanda Vision 2020 document (GoR 2000). The Vision sees Rwanda developed as a lower middle income economy, ‐ reaching US$900 per capita from the current US$320 per capita ‐ and positioned as a regional knowledge‐based service hub by 2020. To realize its growth ambition, the Government of Rwanda (GoR) is committed to facilitating the emergence of a strong and modern private sector which will drive growth, competitiveness, economic diversification, and export promotion. Manufacturing is envisaged as a major engine of sustainable growth in the medium term, producing high‐value goods for local, regional, and international markets (EDPRS, 2008‐2012, GoR).
Committed to a transparent and corruption‐free administration with business friendly policies, GoR has made great strides since 1994.1 Several macroeconomic and institutional reforms, leading to macro‐economic and exchange rate stability, have been instituted. Substantial reforms have also been undertaken to improve access to credit, contract enforcement, and cross‐border trading, moving Rwanda towards greater integration within the East African Community. Business reforms have led Rwanda to become on of the top 20 reformers globally.2 Good governance in Rwanda is reflected in the high rankings on International Governance indicators, where Rwanda fares far better than all other countries regionally.3
Challenges, nonetheless, remain. Economic growth has started to slow down – declining from an annual average of 10.8 percent between 1996‐2000 to 6.4 percent between 2001‐2006 ‐ and poverty remains pervasive, with 56 percent of Rwandan’s population below the national income poverty line. as the economy needs to grow at least 8 percent annually to make a significant dent on poverty4.
While the private Industrial sector is being given a central role in the country’s development efforts, its role and contribution in economic activity is limited: the economy remains dominated by agriculture, with industry contributing only 14% of GDP. Major economic activity remains in the informal sector, outside the purview of regulatory and tax authorities ‐ it is estimated that more than 60%‐80% of employment occurs in this sector.5 Although the GoR has pursued an aggressive liberalization policy to attract foreign investors, foreign direct investment stands at about 12% of GDP, well below the average of 29.5% for Africa as a whole.6 Limited increase in exports has been registered, from 3% of GDP in 2003 to 5% of GDP at present. Imports, nonetheless, have grown rapidly, currently constituting 15% of GDP. To reduce the trade imbalance and fuel higher economic growth, the next phase of economic reforms requires focus on sector‐specific constraints and structural impediments to sustainable growth.
As part of the World Bank Group’s continuing efforts to support GoR in its reform program, the Investment Climate Assessment (ICA) Report – the first ever for Rwanda ‐ provides a crucial diagnostic
1The civil conflict crisis in Rwanda culminated in the genocide of 1994 that led to the destruction of manpower, capital stock and state institutions. It is estimated that 800,000 people lost their lives between April‐June 1994 (United Nations, 1999).
2 Doing Business, 2009. Rwanda is now ranked 139 out of 175 countries surveyed, moving up from 158 in 2007.
3 Transparency International, 2008. Detailed rankings are presented in Chapter 3. 4 The high growth rates are required to keep pace with the rapid increase in population‐estimated to grow at
2.7% per annum (UNDP estimate for 2005‐2015). 5 Source: Rwanda Informal Sector Survey, 2005. 6 Source: UNCTAD,2008.
tool that identifies impediments to growth in the private sector, to structural transformation of the economy, and to the regional integration agenda, which together would help move Rwanda’s people out of poverty. Investment climate, broadly defined, includes a country’s unique attributes or “geography”, as well as the state of its infrastructure, economic, and social policy institutions, and governance mechanisms. The ICA covers a comprehensive investigation of the range of drivers of competitiveness, based on firm level surveys that affect macroeconomic outcomes. A thorough examination of these factors and the linkages with enterprise performance is critical to the design and implementation of growth oriented policies.
This Investment Climate Assessment is based on a survey of 340 enterprises in Kigali and Butare that includes micro‐enterprises with less than five employees, formal manufacturing firms, retail, construction, hotel and other enterprises. The ICA allows examination of differences in the investment climate across firms within and outside Rwanda. Similar surveys have been conducted in several countries in Africa and other regions which provide an opportunity to benchmark Rwanda’s investment climate vis‐à‐vis its global competitors. In this context, Rwanda is compared to two sets of countries: that of firms in the East African Community (EAC) ‐ Kenya, Tanzania, Uganda and Burundi, and a wider group that includes DRC and South Africa regionally, and India, China, Vietnam and Thailand, internationally. The analysis is carried out from the perspective of the regional trade and integration agenda of GoR. Faster and more comprehensive regional integration can help Rwanda overcome some of its inherent disadvantages of an adverse geography – including its landlocked location, limited natural resources, and small economy.
Key ICA Findings Manufacturing enterprises in Rwanda have much lower average productivity compared to other
countries regionally. Lower productivity is coupled with higher wage premiums from skill shortages, 7 resulting in higher unit labor costs, and reduced cost competitiveness of Rwandan firms in regional and global markets. Median Labor productivity, measured as value added per worker, in Rwanda is much lower than that of firms in Kenya and Tanzania, and is comparable to that of firms in Uganda and Burundi: value added is $2178 per worker, compared to $3395 per worker in Tanzania, and $6893 per worker in Kenya. ICA analysis of unit labor costs shows that labor costs are 44% of value added in Rwanda, compared to only 25% in Kenya. Unit labor costs are much lower in China (19%) and India (22%).
High labor costs in Rwanda are caused by a severe shortage of skills: Analysis of worker earnings data across comparator countries shows that educated workers receive much higher wages in Rwanda, compared to other countries regionally. An extra year of schooling increases earnings by about 12 to 13 percent in Rwanda ‐ one of the highest rates of return to education estimated based on investment climate data from Africa. Using a similar employee‐employer matched sample, returns to an extra year of schooling is only 4% in Uganda and 8% in Kenya.
7 The most obvious cause of the skills shortage in Rwanda is the 1994 genocide and, to a lesser extent, the post genocide flight of refugees and those attempting to escape retribution. There was some slight balancing of this loss of skills amongst those long‐term exiles and refugees who subsequently returned – some of them with qualifications, skills and experience gained in a diverse range of countries – but this could not compensate for the total devastation wrought on the country’s pool of educated, skilled and experienced personnel. The nature of Rwandan society at that time meant that the massacre included a large proportion of the most educated and skilled citizenry across a wide range of economic, political and cultural life. The National University of Rwanda, both staff and students, was a particular target in the genocide (DFID, 2003). Other contributing factors to the skills shortage may include public sector demand crowding out private sector needs with better wages for skilled workers (data are not available to confirm this apart from some anecdotal evidence). Lastly, for a small economy, Rwanda has grown fast, perhaps making it challenging for the supply side to keep pace adequately.
Few firms engage in any exporting; those that do, are not significantly more productive than others. Lack of correlation between exporting and productivity is related to the characteristics of exporting firms; exporting is concentrated in coffee and tea processing, with little product diversification into higher value‐added export baskets.8 ICA results reveal limited backward linkages with local suppliers of raw materials. Unlike other countries in the EAC, a vast majority of Rwandan firms use imported raw materials, which contribute to higher cost structures of manufacturing products.
Private sector productivity in Rwanda is constrained by relatively limited investments in technology, lack of foreign linkages through foreign technology licensing or foreign ownership, limited worker training programs, and slow uptake of ICT in the manufacturing sector. ICA analysis of across comparator countries reveals that fewer Rwandan firms have foreign linkages and access to other learning channels that augment productivity. These linkages, in turn, are key determinants driving export orientation within the EAC.
Employment Growth has been robust in the past few years. The ICA survey indicates that employment growth has been robust, with the manufacturing and services sectors registering annual growth rates of 16 percent or more between 2003 and 2006. Annual employment growth in the retail sector was slower, growing at an average of 4.7%.
Investments in fixed assets by existing firms have been limited. Although employment growth has been robust, only about half the firms made investment in fixed assets, with small firms more likely to invest – 67% of them report having made purchases of productive assets in last year. The amount of new purchases, however, is relatively small – about 4% of existing assets for small, medium and large firms, and at 8% for micro firms. Given that the rates of depreciation are around 6‐8%, the investment is relatively low. Further analysis indicates that access to credit is important for investment; firms with access to credit report a higher incidence of investment (significant at 5%) and higher amount of purchased assets relative to existing assets (only marginally significant at 11%). Those who report higher subjective obstacles have lower incidence of investment and relative size of investment (relative to sales).9
Access to credit: Majority of formal private sector firms in Rwanda have deposit accounts and access to borrowing in the form of loans or overdrafts. Firms in Rwanda have greater access to formal sector finance compared to all other countries in the region, except Kenya. Access to formal sector credit is skewed towards larger enterprises with high collateral‐to‐loan ratios; 73% of large firms have access to formal sector credit, compared to 31% of small firms. Loans are typically short term, with high interest rates. Lack of land ownership and audited accounts limits access to finance by smaller, newer enterprises.
Services sector in Rwanda: Retailing and IT firms have higher labor productivity and lower unit labor costs, compared to firms in other countries regionally. ICT uptake is also higher in this sector. The
8 Recent studies (Hausmann, Hwang and Rodrik,2006) have shown that positive externalities from export are based on what you export, rather than the magnitude of exports. A well diversified export basket with higher value added products creates positive externalities fostering economic growth. Other possible non‐firm level reasons may include differences in trade regime and cross‐border logistics gaps. While Rwanda is affected by both higher direct and indirect costs of transport, other EAC countries are similarly disadvantaged. As part of EAC, they operate under a customs union and are moving towards a common system of tariffs. 9 However, an alternative explanation could also be possible – that those firms with investment may have good growth potential and hence would be favored by banks. Without additional data or experimental design we can only estimate correlations, but cannot infer the causality of investment and access relationship.
analysis shows that this productivity difference is significant, even after controlling for other firm characteristics such as size, foreign ownership and entrepreneur education. Differences in performance are driven by high productivity of IT firms in particular, where labor productivity is much higher than that of firms in Kenya, Uganda, and Tanzania. At the same time, the role of government subsidies in the sector’s performance is unclear due to paucity of relevant information. The Rwanda Information Technology Authority (RITA) is actively investing in infrastructure that will enable a high‐speed internet network in the country, using fiber‐optic technology – this shall further augment the competitiveness of the IT firms regionally in the near future.
Business Constraints
The Rwandan Government has taken several measures to revamp its laws and regulations to make them more business friendly. In particular, business licensing and tax administration procedures have been streamlined and simplified. To help improve firm performance and productivity, GoR is cognizant that continued and concerted efforts are needed to improve the business climate. Enterprises were asked, via the ICA survey, to rank the severity of their main business constraints. These rankings are presented in the chart below.
More than 70% of firms in the manufacturing sector reported electricity to be a major constraint, followed by tax rates (50% of firms), and transport and access to finance (40% of firms). Overall, constraint rankings were lower for firms in the services sector where tax rates were reported as a major constraint by almost 50% of firms, followed by electricity and access to finance.
Rwanda IC Constraints: Pct of firms ranking Problem as Major or Severe
Source: World Bank Enterprise Survey
Rwandan firms continue to be adversely affected by both higher direct and indirect infrastructure costs, particularly electricity and transport. Detailed analysis of enterprise survey data, along with complementary evidence across comparator countries, shows that the direct costs of electricity and transport are amongst the highest in Rwanda. Indirect costs are higher in other countries; power outages are a much greater concern and more costly for enterprises in Uganda and Tanzania compared
to Rwanda. While direct transport costs are much higher in Rwanda and Uganda, enterprises in Kenya face a much greater burden in indirect transport costs, including the likelihood of theft during transport, road blocks, corruption, and inefficiency at check points and border posts.
Despite the GOR reforms, businesses in all sectors of the economy report tax rates to be one of the biggest constraints ‐ this ranking is higher than most other countries regionally, despite lower tax rates in Rwanda. Further investigation of the data reveals that these constraint rankings are related to levels of tax compliance. The effectiveness of the tax administration in collecting taxes from the formal sector, and severe penalties for tax avoidance are unique to Rwanda; while tax rates are similar across countries within EAC, tax evasion is rampant in other countries, leading to lower effective tax burden. Ceteris paribus, enterprises in these countries are likely to benefit from higher after‐tax profits compared to firms in Rwanda.
Enterprises in Rwanda report the lowest indirect costs due to bribe payments, inspector visits, theft in transit or property theft, compared to other countries regionally. International Governance indicators rank Rwanda highly for its effective governance and lack of corruption. These findings are corroborated by ICA firm level data. Good governance has a direct positive impact on enterprise costs and profitability. It also serves as a potential marketing tool for attracting foreign investment to Rwanda.
Informal sector: Compared to the formal sector, the informal sector has been growing rapidly in Rwanda. GoR is keen to encourage formalization of the informal sector firms. Examining differences between formal and informal microenterprises within Rwanda, and across comparator countries, the Enterprise Survey indicates that microenterprises in Rwanda have the lowest human capital compared with all other countries in the region.‐ this difference is particularly striking for informal microenterprises, where majority have only a primary school education.
Majority of microenterprises firms ‐ even in the smallest size class ‐ are required to pay some taxes.10 The effectiveness of the tax administration creates a disincentive for firms to set up operations separate from household finances, due to perceived tax burden.11
Lack of Public Services limits entry and formalization: While the majority of firms in the ICA sample are registered for tax purposes and report high compliance with tax laws, registration does not entitle firms to significantly greater access to infrastructure services such as electricity, water and public sewage facilities, which are lowest regionally. This limits entry of new firms into the formal microenterprise sector. Formal firms do have greater access to formal finance, but these are mainly through micro‐credit institutions and are uncorrelated with creditworthiness; less than 10% of firms have any audited accounts, a much lower proportion than all comparator countries.
Results from the ICA microenterprises indicate that regulatory efficiency has led to sorting of micro‐enterprises: those with greater productivity are most likely to register operations and comply with the tax codes. Unlike other countries, such as Kenya and South Africa, being formal does not entitle firms to better public services or banking sector borrowing in Rwanda. To foster formal sector growth, the GoR needs to improve education, and provide greater access to infrastructure and business support
10 Those outside the purview of the tax system are likely to be informal firms where household and enterprise finances cannot be separated. These enterprises were not included in our sample frame.
11 There is a 4% turnover tax imposed on microenterprises with sales less than 20 million RWF. Without company records and an inability to register for VAT, this translates into a much higher effective tax burden relative to those that pay the VAT (FIAS, 2006).
services (accounting, management, and networking) which will have a direct impact on mobilizing firms into the formal sector.
Conclusions and the Way Forward Overall, ICA results indicate that GoR’s regulatory and governance reforms have had a positive
impact in lowering enterprise costs. Business environment shall be further ameliorated once the on‐going reforms in the areas pertaining to access to electricity, transport and finance are completed. Given Rwanda’s Vision 2020, the GoR is cognizant that it needs to stay the course and at the same time, deepen the reforms to make the business environment more attractive for regional investment and trade. Strengthening its participation in regional infrastructure investment and services initiatives, including efforts to improve the functioning of cross‐border transit systems, power pools, and regional skills development programs, shall play a critical part.
Continuing and concerted efforts towards addressing these cross‐cutting business constraints is necessary. But are these measures sufficient for Rwanda to meet its development goals? The answer is most likely not, unless the more urgent and binding constraints that render Rwandan firms uncompetitive in regional markets are tackled aggressively. These include: increasing factory floor productivity of Rwandan firms, enhancing investments in new technology and machinery, facilitating export diversification; and encouraging entry of informal firms into the formal sector.
These challenges may call for additional and more strategic policy responses. New and dynamic mechanisms for addressing the issue of technological, informational, and coordination externalities need to be devised. As opposed to the earlier model of direct support to firms in the area of skills development, technology upgrades, and quality assurance, the GoR would play a more strategic role in coordinating and bringing together different public and private stakeholders to address these issues.
While the GoR is cognizant of the need for this parallel approach12, the current emphasis seems to be tilted towards addressing cross‐cutting constraints for PSD growth. This report underlines the need to recalibrate the balance between these twin approaches to help realize the Vision 2020 and the objectives laid out in EDPRS, 2008‐2012.
The priority areas and policy actions are presented in more depth in the policy recommendation matrix below. The policy actions identified are informed by: (i) the findings of the Enterprise Survey; (ii) Growth and competitiveness work and sector‐specific undertaken by Bank and non‐Bank studies; and (iii) Government’s priorities and on‐going policy dialogue based on the EDPRS. These are linked to the recent and on‐going GOR reforms in each of these areas, along with the support provided by Development Partners in Rwanda.
12 Recent initiatives introduced along these lines is a monthly Business Roundtable‐a structured Public Private Dialogue bringing together representatives from the Government, the Rwanda Private Sector Federation and the Rwanda Economic and Social Council, aimed at identifying key business constraints, and opportunities for strategic Government interventions to foster private enterprise growth. This is similar to public‐private dialogues pursued by many countries in East Asia that have resulted in successful
Priority Area of Investment Climate & its Diagnostic Policy Actions & Recommendations Recent and on‐going GOR reforms & Support from
Development Partners I. CROSS_CUTTING THEMES Infrastructure Power/Electricity Access to power/electricity identified as a key barrier to growth by Rwandan firms in manufacturing and services sector
Issues & Challenges
• Limited generation capacity • Cost of power high: US$0.22 compared to US$ 0.08‐
0.10 per Kwh in region • Low level of access to electricity; only 4.5% of HH
have access to electricity • Frequent supply interruptions • Low performance and inefficiency of the Electrogaz
utility
• Increase investment in energy diversification • Increase access to electricity for enterprises • Improve cost‐effectiveness and operational efficiency
by reducing unplanned outages • Define role of private sector in operations and
financing • Strengthen capacity and institutional framework
Note: Political commitment at highest level to implement the above key elements of reform already exists. Enhancing efforts, combined with ‘staying the course’ in terms of vigilant implementation and monitoring is critical for success.
GoR’s Reforms and Initiatives Underway
• Government is currently developing the Lake Kivu methane gas reserves for electricity generation, with private investment
• Jabana Thermal power plant (20 MW) in service by 2009
• Birembo sub‐station completed and in service by 2009 • SWAP in the energy sector in place and first
investment prospects financed by 2009 • Separation of ELECTROGAZ into two entities (water
and electricity) to improve performance • Improved tariff structure in place (i.e. eliminates cross‐
subsidies from water, reflects cost structure of electricity, and differentiates between different customer types) by end 2009
• Billing to supply ratio raised from 78% to 84% from 2009 onwards
WBG Support (Ongoing & Pipeline)
• Urgent Electricity Rehabilitation (FY05)incl. Nordic Development Fund
• PRSG IV (FY08) • Electricity Access (FY09) • Lake Kiwu Methane Guarantee (FY09) • PRSG V – VIII • Energy Supplement (FY12) • Regional: East Africa Power Market (FY11) • Regional: Rusumo Falls Hydro (FY11) • Urgent Electricity Rehabilitation (GEF) • IFC Lake Kivu Investment project
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Infrastructure Transportation and trade facilitation Transport identified as a key barrier to growth by Rwandan enterprises in both manufacturing and services sectors; Rwanda highly dependent on road transport Issues & Challenges
• Poor road conditions; only 23% of asphalt covered roads; 6% of secondary roads; and 5% of communal networks are in good condition
• High transport costs: transport cost per ton/km $165 compared to $95 in region
• High maintenance cost due to Topography, raising road construction and maintenance costs significantly
• Insufficient resources and capacity to develop and implement plans
• Improve transport links internally and internationally • Reduce and keep transport costs under control • Improve the institutional framework • Achieve sustainable financing of road maintenance,
including increasing private participation • Maintain the roads rehabilitated or constructed
GoR’s Reforms and Initiatives Underway
• 83 km of the Kigali‐Ruhengeri section of Kigali‐Gisenyi road rehabilitated
• 550 km of paved trunk roads maintained by 2011 • Complete and adopt the Transport Master Plan (TMP)
by 2010 WBG Support (Ongoing & Pipeline) Transport Sector Development Project (FY08) incl. Africa Catalytic Growth Fund Urban Infrastructure and City Management/PIGU (FY06) Regional: Transport Facility Project (FY01) Regional: East Africa Trade and Transport (FY06) PRSG IV Transport Sector Dev Additional Financing (FY09) Regional: East Africa Road Network (FY10) Rural Roads (FY10) PRSG V‐VIII IFC: TA for the privatization of Rwanda Air Other Development Partners Support EC, AFDB
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Access to finance (availability and cost); Firms cite lack of access to finance as one of the major constraints to business development Issues & Challenges
• Need to broaden and deepen access to financial services
• Weak supervision and management of finance institutions
Widen and deepen the financial sector by
• improving banking and access to credit • develop long‐term finance and capital markets • strengthen contractual savings regulation and
supervision • strengthen payments system • Strengthen the Pension Fund (Caisse Sociale du
Rwanda) and redefine its investment strategy • Implement international standards in accounting and
auditing (implement the recommendations of the 2008 World Bank ROSC)
• Improve the regulation and supervision of microfinance institutions (MFIs) and strengthen their capacity
• Improve the level of skills in the financial sector by creating the Banking Institute and strengthening the School of Finance and Banking
GoR’s Reforms and Initiatives Underway • All the banks, except one (Bank de Kigali), have been
privatized. Bank de Kigali is expected to be privatized soon.
• Capacity of the Association of Microfinance Institutions of Rwanda (AMIR) being strengthened
• Capacity of the Central Bank to supervise banks and non banks financial institutions being strengthened; Regulatory framework being strengthened
• Institute for Chartered Public Accountant in Rwanda (ICPAR) assisted in implementing new accounting law and standards by 2010
• Automated clearing house (ACH) and Real Time Gross Settlement implemented to modernize the payment system by 2010; Development of Rwanda Payment Card system ongoing
• Leasing law amendment adopted by cabinet by 2009 (8 Financial institutions launching leasing products by 2009)
WBG Support (Ongoing & Pipeline) PRSG IV (FY08) Competitiveness and Enterprise Dev. & Additional Financing (FY08) HRDP (FY04) PRSG V‐ VIII Skills Development (FY2011) EFA FTI (FY09) School Program Partial Risk Guarantee (IFC) (FY09) AAA: FIRST: second Phase Investment Climate Assessment Education Policy Analysis (Multi‐year) (FY09‐FY11) Advisory Services IFC:
1. Entrepreneurship and SME Development Program 2. Investment Climate Reform Project 3. Efficient Securities Markets Institutional Development
(ESMID) 4. Credit Reporting program: 5. Privatization of Rwanda Air
Other Development Partners’ Support AfDB, DfID, USAID, Netherlands
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Tax administration/Rates Tax rates are identified as part of top four major constraints by enterprises in manufacturing and services sector; Issues & Challenges
• Review tax incentive policies based on selective conditions; for example, indefinite Tax holidays for firms locating in SEZs may not be good practice as they hurt market efficiency and the overall investment environment, putting disproportionate burden on older existing firms
• Review 4% Turnover tax for Microenterprises; neighboring Kenya has instituted a 3% turnover tax; while simple to administer, it has a much higher effective tax burden, relative to VAT. But Microenterprises do not have the skills to opt into VAT.
• Provide clear guidelines to businesses on tax exemptions
• Improve education and training of RRA staff • Expand role of RRA to educate MSMEs on tax laws: in
particular relating to VAT.
GoR’s Reforms and Initiatives Underway WBG (Ongoing & Pipeline)
Develop skills and capacity for productive employment • Rwanda lacks most of the skills to support a modern,
growing & competitive economy; acute shortage of technical and managerial skills
• High physical, geographic and financial barriers to access high quality education at all levels
• Absence of an integrated policy framework for post‐basic education
• Increase the skills base to improve the productivity of the workforce
• Strengthen partnerships between higher education and the private sector through on‐the‐job training, to ensure that education system is producing graduates to meet the demand of the economy
• Increase the number of management training programs
• Conduct country‐wide skills assessment as means of formulating a plan for employment and skills development
• Increasing the coverage and quality of nine year basic education
• Improving the quality of higher education • Strengthening Technical and Vocational Education and
Training by changing model of delivery from supply to demand‐driven
• Increase Specialized Business Development Services for products and markets development for trade
GoR Reforms & Initiatives Underway
• TVET policy and 5 year TVET plan launched by 2009 • National Skills Development Policy, including post‐
Basic education policy in place by 2010 • National Skills audit completed by 2009 • Ongoing skills development strategy for the financial
sector WBG Support (Ongoing & Pipeline) PRSG IV (FY08) PSCBP (FY05) HRDP (FY04) Urban Development (FY06) E‐Rwanda (FY07) MDTF for PFM Reforms (FY05) PRSG V‐VIII Skills Dev elopement (FY2012) Other Development Partners’ Support
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II. SECTORAL THEMES Trade and export diversification Export‐led approach not taking off: few firms engage in exports; manufacturing contributes less than 5% of total exports; tea & coffee accounted over half of export revenues since last 5 years; volumes remain low.
Issues & Challenges
• little product diversification into high‐value goods • Low quality and variability in exports • Exporters not more productive than firms serving
domestic market • Poor infrastructure, compounded by its landlocked
status, affects competitiveness • Rwanda ranked 148 out of 150 economies on
Logistics Performance index that assesses logistics gap across countries along seven measures of performance indicators
• Poor custom and trade regulations: takes 42 days for a firm to clear its goods thru exports and imports,
Cross‐cutting initiatives/reforms
• Address key infrastructure, skills and technology constraints that will help both domestic and export producers
• Strengthen participation in regional infrastructure investment and services initiatives
• Strength the functioning of cross‐border transit systems, power pools and regional skills programs\Invest in extension and training in quality and handling
• Upgrade cold storage facilities at airport
Sector specific initiatives For non‐traditional and high value export products such as horticulture/floriculture, leather and textiles, processed fruits and vegetables, mining, tea and processed coffee:
• develop sector specific export promotion plan and use ‘cluster approach
• Support seller market linkage through Business Development Services that identifies and links exporters directly to external buyers; Support trade information system for product and market development
• Put in place an effective quality certification program (e.g. Horticulture, hides and skins
• Support technology and learning channels
GoR’s Reforms and Initiatives Underway
• Creation of the Rwanda Development Board to integrate all the export and investment promotion agencies
• Export Promotion action Plan completed and under implementation, but more emphasis needed on quality and standards
• Several positive regulatory reforms for ‘trading across borders are underway in legal reforms and institution building for trade (see Box. 3.1 in chapter 3)
• A possible PSD project that focus on trade and export diversification under discussion?
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Manufacturing
• Contribution to GDP on the decreasing trend; small formal manufacturing sector
• Issues & Challenges • Lack of adequate investment and energy shortages • Low availability of skilled labor • Lack of access to finance
Need to improve competitiveness and productivity by:
• Addressing the cross‐cutting IC constraints (outlined above), constraints – infrastructure (energy shortages; transport costs); skills gap; and access to finance
• Develop a value chain or cluster oriented development action plan for the manufacturing and industrial sector
• Strengthen options for establishing alternative financing and skills gaps mechanisms by facilitating partnerships between domestic players and foreign firms and venture capital incubators
• Identify measures to support on‐the‐job training and other good practices approaches including facilitating partnerships between domestic and foreign firms to transfer technology and skills
• High price and limited range of telecommunication services
• Low availability and quality of affordable telecom • Poor international connectivity • Lack of ICT trained personnel • Low supply of power • High mobile tariff despite reduction
• Invest in infrastructure to help widen access to ICT among the population
• Invest and build targeted ICT literacy • Promote ICT for education, for use by the private
sector, and e‐Governance • Identify quick wins and areas of support needed in
other sectors (i.e. MIS, information on agric. Prices; weather forecast for fishermen etc) to improve ICT use and literacy
• Address issues of technology compatibility raised by Rwandatel’s recent adoption of non‐GSM mobile network
GoR Reforms & Initiatives Underway Govt. privatized Rwandatel
• Technical, financial and corporate structure of Backbone system in place by 2010
• At least 10 public internet access points developed by Rwanda by 2010
• Price of wholesale international E1 capacity link decreased by 20% by 2010
WBG Support (Ongoing & Pipeline) eRwanda (FY06) PSCBP (FY05) RCIP II (FY09) Other Development Partners’ Support SIDA, EU, DfID, USAID, UNDP, Belgium, South Korea
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Microenterprises
• High degree of informality and increasing over time.
Issues & Challenges
• Disincentives exist to formalize • Lack of infrastructure access • Limited access to finance due to low or lack of assets • Poor business skills and training
• Improve infrastructure provision: access to electricity, water and public sewage facilities for micro‐enterprises will create incentives to formalize.
• Improve entrepreneur education, including education in accounting and financial management.
• Establish outreach mechanism to provide information on business registration and licensing (particularly for informal MSMEs)
• Develop tailored training to MSSE for business development and support
• Facilitate better access to finance and capital • Explore possibilities of developing different financial
products tailored to informal MSSEs
GoR Reforms & Initiatives Underway WBG (Ongoing & Pipeline)
Chapter 1: Introduction– Setting the Rwandan Context
Recent Economic Performance
Rwanda has made remarkable progress since the civil conflict crisis that culminated in the genocide of 1994,13 leading inevitably to the destruction of manpower, capital stock and state institutions. GDP declined by 30 percent following the 1994 genocide and it took 6 years of 14 percent average annual growth to reach pre‐genocide levels. Between 1994 and 2004, net official development assistance (ODA) to Rwanda averaged 29.7 percent of GDP.
Much of the growth observed in the past is attributed to the economy’s catch‐up to pre‐1994 GDP level (World Bank 2007). Continuation of the reforms initiated prior to 1994, nonetheless, contributed to the growth trends. In the last decade, the Government has promoted macroeconomic and institutional reforms, trade, pursued regional integration and initiated legislative reform. Substantial reforms have also been undertaken to improve access to credit contract enforcement, and cross‐border trading. Real GDP growth averaged 10 percent over the period 1995‐2005.
Figure 1: The Economy has grown steadily since 2003…
The economy has grown steadily since 2003..
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2001 2002 2003 2004 2005 2006
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tage
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Despite impressive progress and committed leadership, Rwanda remains a poor country, with 56% of its population below the national income poverty line.14 Inequality is high by international ‐
13 It is estimated that 800,000 people lost their lives between April and June 1994 (United Nations, 1999).
14 Refer to 2005/06 Rwanda Household Survey.
BASIC FACTS
Population: 9.7 million Population growth rate: 2.9% GNI per capita (2007): US$320 Region: Sub‐Saharan Africa Geography: Landlocked; Neighborhood: bordered by Uganda to the north, Tanzania to the east, Burundi to the south, and Democratic Republic of Congo to the west.
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including African ‐ standards. The Gini coefficient increased from 0.47 to 0.51 between two household surveys. The modest decline in poverty, coupled with population growth of 2.7 percent means the absolute number of poor people increased from 4.8 million to about 5.3 million between 2000/01 and 2005/06.
Moreover, growth has slowed down, and sustained and broad‐based growth remains a challenge. Rwanda appears to have fully exhausted the growth effects of the post‐conflict reconstruction leading to a considerable slow‐down in growth from 10.8 percent average achieved in the immediate aftermath of the genocide (see Table 1). Between 2001 and 2006, the economy grew at an average annual rate of 6.4 percent. The economy grew 5.5 percent in real terms in 2006 and an estimated 6 percent in 2007. On the supply side, this performance has been driven by the services and industry sectors which grew at an average of 7.4 percent and 8.1 percent annually respectively. In contrast, agriculture has performed poorly. On the demand side, the growth is explained by expansion in private sector consumption.
Table 1: Structure of the Economy: Growth and Share of GDP
Av. Annual Growth Share of GDP
1996‐2000 2001‐2006 1996‐2000 2001‐2006
GDP 10.8 6.4 100 100
Agriculture 9.5 4.8 37.7 36.4
Industry 7.5 8.1 15.1 14.2
Services 11.7 7.4 41.9 43.8
Source: Rwanda CAS, August 2008.
Rwanda’s economic structure makes it vulnerable to multifaceted domestic and external shocks. Continued dependence on traditional rain‐fed agriculture and the country’s geography makes it highly vulnerable to climatic changes. Rwanda continues to rely on traditional subsistence agriculture and mineral commodities which are subject to price instability. Agriculture sector grew only 1.1 percent in 2006 and contracted by almost 2 percent in 2007, driven by a 50 percent fall in coffee production, due to effect of poor rains on mature coffee bushes. There has been limited diversification into manufactured good and traded services or movement towards value addition to primary goods. Between 2001‐2006, agriculture contributed just over 36% share of GDP while services represented 44% share. Industry contributes approximately 14%. The narrow export base – with tea and coffee and minerals providing two‐thirds of total export revenue ‐ leave the economy vulnerable to shocks in global prices of primary commodities and to external debt crisis.15
Finally, with population growth of 2.7 percent annually, Rwanda is facing a growth deficit. The economy needs to grow at least 8 percent annually to make a significant dent on poverty. The fundamental challenge will be to speed up the process of economic diversification to allow the economy to become more resilient and less susceptible to some of these external and domestic shocks. While this may require growth in agriculture ‐ as currently it employs 80 percent of Rwandans ‐ the prospects for agriculture to grow at a dynamic and sustainable rate are not particularly bright, given the country’s vulnerability to climatic changes and exogenous shocks. A key element of the strategy for agricultural growth may involve diversification, and enhanced focus on the development of agro‐processing industries. A more dynamic and sustainable strategy for getting Rwanda out of the
15 See RIEPA, Investment and Export Performance Report, 2007.
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poverty trap calls for creating opportunities away from agriculture and toward the non‐agricultural sector, particularly manufacturing and services.
Government of Rwanda’s Development Vision At the core of long‐term development goals for Rwanda is the “Rwanda Vision 2020” document
(GoR 2000), that seeks to raise per capita GDP to $900 a year, and turning Rwanda into a lower middle income economy operating as a knowledge‐based service hub by 2020. The vision is built around six pillars: good governance and a capable State; human resource development, and a knowledge‐based economy; a private sector‐led economy; infrastructure development; productive and market oriented agriculture; and regional and international economic integration. As part of its development program (Vision 2020, GoR, 2000), export promotion is viewed as an important engine for growth.
To help realize its ambitious vision, the GoR has articulated its medium‐term policies in the second PRSP (also called the Economic Development and Poverty Reduction Strategy, EDPRS16, 2002‐2012, Govt. of Rwanda). The first PRSP focused on managing transitional period of rehabilitation and reconstruction, the second PRSP, seeks to achieve growth of about 8% annually, driven primarily by a strong and competitive private sector. The EDPRS’s strategic priorities are articulated through flagship programs: sustainable growth for jobs and exports; Vision 2020 Umurenge;17 and Governance. It assigns the highest priority to accelerating growth to create employment and generate exports. This is to be achieved through a three‐pronged strategy: systematic reduction in the cost of doing business; enhance the private sector’s capacity to innovate; and deepen and strengthen the financial sector. With regard to sectoral emphasis, the EDPRS states:18
“[t]he GoR regards the manufacturing sector as a major engine of sustainable growth and development. In the long run, the main source of job creation in Rwanda will pass from the primary to the secondary and tertiary sectors. To prepare for this transition, measures will be taken to enhance the production of competitive and high‐value manufacturing goods for sale in local, regional and international markets.
The New Industrial Policy (NEP) will focus on the following strategic objectives. The government will enhance the performance of the existing manufacturing sector, including textiles and food processing by promoting labor productivity and product quality. It will induce companies and startup SMEs to move up the value chain into higher value added activities that use domestically produced inputs, such as leather good production. The GoR will place a major effort on trade capacity building to enhance productivity and quality of exports and meeting import competition in the domestic market. This is essential for Rwandan companies to seize new opportunities in the COMESA and EAC trading associations.”
Regional Trade and Integration
Given Rwanda’s inherent disadvantages of an adverse geography – including its landlocked location and vulnerability to climatic changes, limited natural resources and small size of the economy
16 The EDPRS was launched in November, 2007.
17 This is a highly decentralized integrated rural development program that is designed to reduce extreme poverty in Rwanda.
18 Pl. refer to Government of Rwanda, “Economic Development and Poverty Reduction Strategy, 2008-2012. MFEP, July 2007.
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‐ the economic case for a more comprehensive regional trade and integration is easy to make.19 Indeed successful and rapid integration into Eastern and Southern Africa is critical for realizing Rwanda’s vision of becoming a regional service and trade hub as stated in the EDPRS.
Rwanda joined the East African Community (EAC) in 2007,20 signifying its commitment to regional integration of economic policy, regulatory frameworks, and trade in goods and services, among other facets. The EAC currently operates a customs union and planning a common system of tariffs as well as standard tourist visa. Given the interdependence of the EAC states, especially with respect to port access, the potential of streamlined administrative systems within the EAC to contribute to economic growth throughout the region is significant. Rwanda belongs to other regional economic communities (RECs) such as the Common Market for Eastern and Southern Africa (COMESA).21 With a population of 365 million, it is one of the largest trading arrangements in Africa. A COMESA free‐trade area (FTA) was launched in October 2000 which envisions an eventual creation of Customs Union. Eleven of its members, including Rwanda, have joined FTA and reduced their import tariffs to zero on a reciprocal basis. More recent and concerted efforts are underway towards establishing a larger single market by deepening the COMESA‐EAC‐SADC integration.22
At the operational level, Rwanda Investment and Export Promotion Agency (RIEPA)23 is mandated to promote and facilitate trade and investment in Rwanda. RIEPA’s efforts towards attracting investment ‐ both local and foreign ‐ opportunities have been slowly bearing fruit although increasing and diversifying exports remains a challenge. Total registered investment24 increased to US$494.4 million in 2007 from US$232 million in 2004. Of the total investment in 2007, foreign direct investment (FDI) accounted 46 percent. Telecommunications (23%), energy (22%) and hotel (14%) sectors together represented nearly 60 percent of all the registered investment, followed by tourism (7%), agribusiness (5%) and food processing (5%). Rwanda’s exports although steadily increasing over time – from US$75 million in 2002 to US$202 million in 2007 ‐ remain small. Imports have continued to grow disproportionately in comparison to exports. An area of additional concern is the dependence
19 Rwanda’s support for regional integration is fully aligned with the World Bank’s new Africa Regional Integration Assistance Strategy (RIAS), with the added benefit of increasing Rwanda’s access to IDA funds.
20 In June 2008, President Kagame was announced at the chairman of EAC, taking over from the Ugandan President. In addition to Rwanda, the four other EAC members include Uganda, Tanzania, Kenya, DRC and Burundi. Together, they have a combined population of about 100 million.
21 Currently there are multiple Regional Economic Communities (RECs), many of which have overlapping memberships. Established in 1993, COMESA has 20-member states. Rwanda joined in January 2004. COMESA is developing closer and more cooperative links with other regional organizations like EAC and Southern African Development Community (SADC). Rwanda is not a member of SADC.
22 On 22nd October 2008, the COMESA-EAC-SADC Tripartite Summit was held “[i]n pursuit of the broader objectives of the African Union to accelerate economic integration of the continent, with the aim to achieve economic growth, reduce poverty and attain sustainable economic development...”. Among other things, the final Communiqué issued by the Heads of State and Government agreed on a program of harmonization of trading arrangements among the three RECs, free movement of business persons, joint implementation of Inter-regional infrastructure programs as well as institutional arrangements on the basis of which the three RECs would foster cooperation. Specifically, in the area of trade, customs and economic integration, expeditious establishment of a Free Trade Area (FTA) - encompassing the member States of the 3 RECs - with the ultimate goals of establishing a single Customs Union, was approved.
23 RIEPA has recently merged with the newly formed Rwanda Development Bank (RDB). 24 Registered Investment means planned rather than actual investment in any given year. For further details and breakdown,
pl. refer to Investment and Export Performance Report, RIEPA, 2007; and Investment Guide to Rwanda: Opportunities and Conditions, United Nations and International Chamber of Commerce, October 2006.
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of exports on a narrow range of products i.e. coffee and tea and more recently, minerals – making it vulnerable to exogenous shocks. The government is actively pursuing a diversification strategy as part of its export promotion policy. Tourism, hotels and agribusiness are beginning to make inroads. FDI and exports, nonetheless, represent small shares of GDP and thus are not significant drivers of growth.
To fully realize its export‐led growth vision, Rwanda needs to speed up efforts to make the business environment more attractive for regional investment and trade; lower domestic costs of production so domestic firms can compete on the regional market; and strengthen its participation in regional infrastructure investment and services initiatives including efforts to strengthen the functioning of cross‐border transit systems, power pools and regional skills development programs. This shall be discussed in depth in chapter 3. The next section provides a profile of the private sector in Rwanda – the primary vehicle identified for accelerated and sustainable growth.
Status of the Private Sector in Rwanda
While a modern private sector is beginning to emerge in Rwanda, its role and contribution in economic activity is still limited. Private sector investment is only 12% of GDP compared to 14.4% in the region. FDI flows are on the rise but currently represent only 2% of GDP, compared to 3.9% and 3.4% in Uganda and Tanzania respectively.
The private sector in Rwanda is still overwhelmingly informal (Table 2). A recent business operators’ census conducted by the Rwandan Private Sector Federation identified about 73,000 operators. Individual enterprises account for about 96% of businesses; formally registered businesses account for less than 1% and cooperatives close to 3%. Close to 90% of operations are micro enterprises, employing between 1‐4 staff. Less than 0.5 percent of enterprises employ more than 50 employees. Total employment by these enterprises is about 198,000, of which 13% have secondary or higher level education.
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Table 2: Size Profile of Private Sector in Rwanda
Informal Informal/Formal Enterprises – Units in Transition to SMEs
Formal‐ organized enterprises
PSF Business Census: 25,000 businesses Family/Informal production Microenterprises Small Medium Large
Source: Local Business Investment Climate Survey Analysis, OTF Group‐ PSF, July 2008; Informal Sector Survey, OTF Group for FIAS, 2006, Manufacturing Sector Survey, 2004; PSF Business Census, 2007
Within the formal private manufacturing sector, large and medium size enterprises represent a small proportion of firms. Yet, these few firms control a vast majority of industrial output. Compared to other African countries, industry concentration is very steep in key sectors in Rwanda, perhaps underlying a weak competitive market structure. Firms predominantly supply to the local market. Only 14% of firms in manufacturing engage in any exporting; of those that do, only 20% of their output is exported.
To realize its growth ambitions, the GoR is committed to facilitating the emergence of a strong and modern private sector which will drive growth, competitiveness, diversification of the economy and export promotion. The first step in this direction is to identify key factors that constrain existing enterprises and promote entry of new firms, and subsequently design policy measures that will promote private sector growth and put Rwanda on a higher growth trajectory. The proposed Rwanda Investment Climate Assessment (ICA) will provide a crucial diagnostic of the business environment to the Government of Rwanda.
Survey Design and Coverage: The key survey instrument is the standard RPED/ICA questionnaire for the formal sector which is composed of four major parts:
The first part is designed for general managers or business owners and deals with the internal structure of businesses and the investment climate within which they operate, including bureaucratic obstacles and infrastructure constraints;
The second part deals with finances, production and markets and provides information on business performance which can be mapped to business characteristics and investment climate obtained in the first part of the questionnaire.
The third part of the questionnaire deals with human resources and labor market issues, particularly the effects of government labor regulations, as well as the cost and quality of the workforce;
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The last part includes a small questionnaire for a sample of up to 10 workers per business. This data facilitates an understanding of the interaction between firm performance/business climate and labor market outcomes.
Survey Sample: The World Bank Enterprise Survey undertaken in 2006 in Rwanda targeted 340 establishments located in Kigali and Butare, covering formal manufacturing firms, retail, construction, and hotels. The survey also sampled a selection of micro establishments (establishments with less than five employees) from the targeted universe, without stratification by industry. The final sample composition is presented in Table 3 below. Further details have been provided in Appendix 1. 2.
Table 3 Sample size by stratum and sampling region
Kigali Butare Total
Manufacturing 55 4 59
Food and beverages 19 2 21
Garments 5 0 5
Other manufacturing 31 2 33
Retail 41 3 44
Rest of the universe 96 13 106
Micro 116 12 121
TOTAL 308 32 340
In addition to collecting data on firm productivity, ICAs collect two types of information on the investment climate: (a) subjective or perception measures of what managers see as the major obstacles that their firm faces; and (b) objective indicators such as production lost due to power outages, and amount of time managers spend dealing with government regulations.
Preliminary findings of the ICA were shared with the stakeholders in Rwanda in June 2008. While endorsing the work, the team was advised to verify the validity of the survey conducted in 2006. In the first instance, the team consulted with the NIS, RRA and PSF regarding the current structure of industry in Rwanda. The team met with the PSF and obtained a master list of enterprises comprising the updated census to help benchmark representativeness of the ICA sample. At the time of the mission, the PSF in partnership with the OTF, were finalizing the survey instrument for the new 2008 Local Business Investment Climate Survey (LBICS). The OTF‐PSF team kindly agreed to incorporate several detailed questions on rankings of the business environment constraints in the final PSF instrument that would allow the two teams to capture changes in investment climate since the 2006 Bank ICA survey.25 The findings of the LBICS shall be presented in chapter 3.
Enterprise Perceptions provide a valuable starting point for analysis of the business climate. Firm managers probably know more about the immediate problems facing their businesses than government officials, researchers, or other experts. It therefore makes sense to take their concerns about the investment climate seriously. Nonetheless, there has been some debate on the use of perceptions based data in assessing the actual constraints faced by firms, with too much reliance on
25 Survey details are provided in the appendix. There exists a significant overlap in manufacturing firms surveyed by PSF-OTF (58 firms) and the Enterprise Survey (58 firms). The latter did not include professionals and household enterprises, which accounts for majority of the difference between the two surveys. The formal sector sample in the enterprise survey closely matches the PSF survey.
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perceptions can lead to misleading conclusion for several reasons. First, enterprise managers’ interests might not always be consistent with society’s interests. For instance, most managers would like subsidized credit or to have prices set below costs for power if they believed that the cost of providing these services would be borne by someone else. Second, the perceptions of managers of existing enterprises might not reflect all obstacles to private sector investment and growth. In addition, if investment climate constraints are particularly binding, then there might be very few firms that rely heavily upon that area of the investment climate.26 Third, firms’ benchmarks may differ by country‐‐ much as a poor family in an OECD country may feel “poorer” than a more deprived one in a low‐income country, a firm in South Africa may see corruption as a more serious problem than a firm in, say, Nigeria even if corruption is more endemic in the latter country. Benchmarks may be influenced by waves of pessimism and euphoria reflecting adverse or favorable trends. Since firms and entrepreneurs enter and exit in response to opportunities and constraints, they are endogenous to the investment climate and their opinions may not accurately reflect the severity of constraints as perceived by potential or discouraged entrants.
Despite these shortcomings it has been shown that perceptions data, collected through the World Bank Enterprise Surveys globally, are sensibly correlated with objective indicators of the business environment reported by firms, and can be used by policy makers to frame priorities for reforms and investments. (Gelb, Ramachandran, Shah) and they are increasingly complemented by “Doing Business” indicators based on expert surveys (Doing Business, 2004‐07). The latter provide a more comparable cross‐country perspective across a detailed range of regulation, but not a firm‐level view of the de facto severity of regulatory and infrastructural obstacles. In principle, such approaches are complementary, but combining approaches is only valuable if each supplies some useful information.27
Because of these concerns, the perception‐based information is supplemented with objective measures of the investment climate taken from the Enterprise Survey and other sources when appropriate. The additional objective data allow us to benchmark the investment climate in Rwanda against the investment climates in other countries. This analysis will provide new insights into impediments to growth and specific areas where policy interventions are required. Whenever possible, statistical results will be stratified. In effect, one of the unique features of the ICA data is the ability to analyze firm‐level variation of the experiences and perceptions by firms' size, crucial to an understanding of how to target or ensure coverage of Small and Medium Enterprises when designing policies.
Comparator Countries: The detailed data available from the cross country ICA surveys will allow us to identify Rwandan firm’s productivity and costs in a regional context, and factors that determine these differences. Firm performance and investment climate in Rwanda shall be compared with two set of countries. First, Rwanda shall be benchmarked against its neighbouring EAC countries with whom it currently directly competes with i.e. Uganda, Tanzania, Kenya, DRC and Burundi. This is
26 Hausmann and Velasco (2005) illustrate this point with an analogy to camel and hippos. They note that the few animals that you find in the Sahara will be camels, which have adapted to life in the desert, rather than hippos, which depend heavily upon water. Asking the camels about problems associated with life in the desert might not adequately represent the views of the missing hippos.
27 Details of this discussion are presented in “Do Enterprise Perceptions Matter? Evidence from African Firms”, Gelb, Ramachandran, Shah and Turner, 2007.
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particularly important as Rwanda seeks fuller integration into Eastern and Southern Africa through both its membership in COMESA and the recent membership in the East Africa Community (EAC). Furthermore, investment climate surveys in these countries were conducted at about the same time using a similar survey instrument, making cross‐national comparisons easier. Although regional comparisons are useful, as the world becomes more integrated, enterprises in Rwanda will increasingly find themselves competing with enterprises from outside of East Africa, both within Rwanda and in export markets. Therefore, along with regional comparators, Rwanda’s investment climate shall be compared with that of China, India, Vietnam and Thailand, with whom it aspires to compete with in the global market for export shares. Benchmarking investment climate in Rwanda to those faced by firms internationally helps highlights the areas in which Rwanda is already strong and areas where it might improve.
Special topics: The choice of themes to be examined in‐depth is being motivated by two criteria: (i) either they are based on robust theoretical or empirical groundings; and/or (ii) they are currently part of the policy dialogue with the government’s EDPRS. With regard to the latter, special emphasis shall be laid on regional trade and integration, access to finance and skills development issues that are priority cross‐sector areas highlighted in the EDPRS (2008‐2012, GoR). Given the limitations of the data, industry specific analysis is only possible for the food sector.
Links with other Bank and non‐Bank resources: The ICA goes well beyond the firm level survey results by placing them in the context of current analyses of the investment climate in Rwanda, taken from various sources, complementing firm‐level experiences and perceptions. It utilizes other data sources on the private sector from within the Bank, such as the Doing Business Indicators, and any existing work done by IFC and FIAS. We build on the broader development policy literature on Rwanda, Africa, and investment climate issues generally, including key Bank reports like the CEM (2007), FSAP (2006), and DTIS (2005). Lastly, we draw on work undertaken by other development partners, including the recently completed Business Climate Legal & Institutional Reform (BIZCLIR) report, sponsored by the USAID; Porter’s (July 2008) strategy work on competitiveness in Rwanda; and the output from the Commission on Growth and Development led by Michael Spence (2006‐2008).
Structure of the ICA Report
After setting the Rwandan context in chapter one, the next chapter two will analyze how enterprises in the formal sector in Rwanda perform relative to a set of comparator countries. Both labor productivity and total factor productivity (TFP) measures will be presented in a regional context. Chapter three, will examine reported investment climate constraints by existing enterprises in Rwanda, and benchmark them against regional and other international competitors. The discussion shall be framed from the vantage point of GoR’s regional integration agenda. Chapters four and five shall focus in depth on access and cost of finance, and labor market and skills development issues, respectively, given their central role in private sector development in Rwanda.
The informal sector has been growing rapidly in Rwanda, and understanding the characteristics of this sector, and its differences with the formal sector are important in developing policies that will encourage entry of these firms into the formal sector and fuel formal sector growth and employment. This shall form the focus of chapter six. Lastly, the priority policy actions and options necessary to address the top investment climate bottlenecks are presented in the Policy Matrix, as part of the Executive Summary.
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Chapter 2: Enterprise Performance in Rwanda
“For the fact is that the key both to long‐term economic growth and to sustained differences in economic performance between countries seems to be the ability to get more for less—to have output grow faster than input.” Paul Krugman (International Affairs, 1995; 71(4): 717–732)
Productivity gains are a key factor determining long term economic growth and improvement in living standards. Key performance indicators used globally to measure industry performance are labor productivity and total factor productivity (TFP).28 This chapter provides the first detailed assessment of enterprise productivity in Rwanda, benchmarking performance of Rwandan enterprises with that of firms relative in neighboring countries in the EAC and internationally, with special focus on manufacturing exports.29 Differences in labor productivity and TFP across countries are examined, along with factors (other than labor and capital) that determine productivity differentials within each country. Cost competitiveness of Rwandan firms is examined, using unit labor costs.30 Since labor costs in most industries comprise a major share of a firm’s costs ‐ which correspondingly determine product prices ‐ higher unit labor costs relative to productivity indicate higher prices and lower cost competitiveness of a country, compared to others.
To ensure that the results are comparable across countries, and because the standard methodology to estimate TFP is only appropriate for the manufacturing sector, results on TFP only cover formal firms in that sector.31 Only labor productivity measure is used to examine productivity in the retail and services sector. We begin by examining productivity characteristics of firms in manufacturing sector, followed by an analysis of productivity of firms in other sectors.
Enterprise Performance in the Manufacturing Sector
Characteristics of Manufacturing
The manufacturing sector in Rwanda is small and its contribution to GDP has been on the declining trend since the last decade – from nearly 11.5 percent in 1996 to 8.5 percent in 2006 and declining further to 6.1 percent in 2007 (CAS, 2008). At the same time, the growth rate of the manufacturing sector has fluctuated over time: from 2.3 percent in the period 1996‐2005; increasing
28 Labor productivity is measured as value added per worker. TFP is measured as value added per unit of combined inputs consisting of labor and capital. 29 As mentioned in chapter 1, given the small size and limited diversification of the manufacturing sector in Rwanda, its chief competitors in the export markets are companies in other countries within EAC. Rwanda also faces competition from regional imports that directly compete with domestic manufacturers. Lastly, Regional Integration is an element of GoR’s vision of becoming a regional service and trade hub. Lack of comparable accounting data limits comparison across other countries internationally.
30 Unit labor costs, capturing costs relative to productivity, are computed as a ratio of enterprise labor costs to value added.
31 Given the small size of the manufacturing sector and the corresponding sample size, all econometric analysis is conducted using a pooled sample of firms across EAC countries.
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to 13.5 percent in 2006; and then slowing to 7.9 percent in 2007 (CAS, 2008). According to the Rwanda Country Economic Memorandum,32 manufacturing in Rwanda is dominated by 20 firms, accounting 65% of total manufacturing output. Of this,, agro‐processing comprises 80 percent. Very few firms participate in export markets: In 2007, formal manufacturing sector contributed less than 5% of total exports.33
High electricity and transport costs, low availability of skills, and lack of access to finance, have been identified by the CEM (World Bank 2007) as the key problems faced by the private sector in Rwanda.34 The present analysis, based on detailed enterprise level survey,35 allows the examination of these issues in further depth. The availability of similar survey data for other countries in the EAC, within the same time frame, facilitates benchmarking of Rwandan firms in a broader regional context, and identifies further barriers to competitiveness.
Sample characteristics reflect the underlying population of firms: a manufacturing sector dominated by food processing, with a few large firms accounting for more than three quarters of manufacturing value added: This is presented in Figure 2 below. Further details of enterprise characteristics in manufacturing, and the sampling details, are presented in Appendix to Chapter .
32 “Rwanda: Towards Sustained Growth and Competitiveness”, CEM, World Bank, October 2007. 33 Source: http://www.rwandainvest.com/spip.php?article100, June 2007. 34 The CEM was based on an earlier Rwanda Industrial and Mining Survey (RIMS). It may be noted that the ICA survey differs from the earlier RIMS study in the following aspects: (i) survey timing: RIMS was conducted in 2004 while ICS was conducted in 2006; (ii) the ICA survey shows a census of 201 formal manufacturing establishments excluding mining sector but including microenterprises; RIMS reports a census of 85 manufacturing firms, including 26 mining firms and excluding microenterprises; and (iii) lastly, less than 30% firms overlap in the two samples.
35 The Enterprise Surveys exclude mining companies.
Figure 2 Sample Characteristics of the Manufacturing Sector
Source: World Bank Enterprise Survey
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The remainder of this chapter is organized as follows. We first examine labor productivity and unit labor costs in formal manufacturing enterprises. We then examine capital intensity in manufacturing. This is followed by an analysis of total factor productivity and its determinants across firms in Rwanda, comparing them to others within the EAC. A separate section on characteristics of exporters included thereafter. Lastly, we examine productivity characteristics of firms in service and retail sectors.
Labor Productivity
Labor productivity is a basic measure of firm productivity. Variation in labor productivity can be the result of differences in many factors such as technology, capacity utilization, energy and materials use, differences in organizational structure, worker skills, management ability, or the amount of capital that the firm uses. Labor productivity in most studies is positively correlated with total factor productivity and can be used as an initial measure of an economy’s competitiveness.
Median Labor productivity, measured as value added per worker, in Rwanda is much lower than that of firms in Kenya and Tanzania, and is comparable to that of firms in Uganda and Burundi.: Value added is $2178 per worker, compared to $3395 per worker in Tanzania and $6893 per worker in Kenya (Figure 3).
Figure 3 Labor Productivity
Source: World Bank Enterprise Survey
Labor Productivity in the Food‐Processing Sector is lowest amongst comparators, and is much lower than that of firms in Kenya and Tanzania. Manufacturing productivity may differ across countries due to differences in industry composition. Hence it also useful to examine differences in productivity for individual sectors. Rwandan manufacturing is concentrated in the food processing sub‐sector, hence we focus on this sector, along with overall manufacturing36.Labor productivity in the
36 As shown in Figure 1 above, a vast majority of firms in Rwandan manufacturing are concentrated in this sector, given the limited
number of firms in other sectors, it is not feasible to examine sector level productivity analysis in those sectors.
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food sector, at $1598 per worker, is one third that of firms in Tanzania, where median value added is $4508 per worker in food processing. Labor productivity in the food processing sector in Kenya is five times higher, at $7943 per worker. Differences in median labor productivity between Rwanda and DRC, Burundi and Uganda are statistically insignificant.
Focusing within Rwanda, larger firms have higher labor productivity than smaller enterprises, exporters are more productive than non‐exporters, and labor productivity is highest amongst firms older than ten years. These results are presented in Table 4 below. However, these variations do not control for other differences among different types of firms. For example, although exporters are, on average, more productive than non‐exporters, they are also more likely to be foreign‐owned and are larger than non‐exporters. Since size and ownership are correlated with export behavior, they might be more productive for these reasons rather than because of their export status. In the section on TFP, we examine the impact of these individual firm level fixed effects on enterprise performance.
Table 4: Rwanda ‐ Enterprise Productivity by Firm Characteristics
N Labor Productivity Capital Labor Ratio Unit Labor Costs
Overall 58 2178.92 2633.78 44%
Food 21 1558.92 899.38 44%
Small 43 2031.81 3508.79 44%Large 15 3294 2469.36 44%
A country’s competitiveness is not only determined by the productivity of its workforce, but also by the costs of its inputs used in the production process. A well known measure of competitiveness used globally is Unit Labor Costs (ULC), which combines labor costs and labor productivity into a simple measure of labor cost per unit of value added. ULC is widely used for international comparisons of cost competitiveness. The cost of labor in Rwanda37 is discussed in detail in the chapter on labor markets. In this chapter, we examine the cost competitiveness of firms in Rwanda by comparing ULCs across comparator countries, and also across firms within Rwanda.
Results, presented in Figure 4 below, indicate that manufacturers in Rwanda have much higher unit labor costs compared to Kenya and Tanzania within EAC: their ULCs are comparable to firms in Uganda. Labor costs are 44% of value added in Rwanda, compared to only 25% in Kenya. Unit labor costs are much lower in China (19%) and India (22%). ULCs are similar to that of firms in South Africa. In Rwanda, low labor productivity is not offset by low labor costs, leading to high ULCs. In South Africa, the high labor productivity is offset by much higher labor costs. Both indicate lower cost
37 The cost of labor includes wages, salaries, bonuses, other benefits, and social payments.
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competitiveness compared to other countries regionally and internationally. Patterns are similar within the food processing sector: firms in Rwanda have much higher unit labor costs compared to firms in Kenya, Tanzania and Burundi within EAC, and similar to that of firms in Uganda and South Africa.
Figure 4: Unit Labor Cost
Source: World Bank Enterprise Survey
Within Rwanda, unit labor costs are lowest amongst exporters38; and similar across other firm characteristics. These are presented in Figure 4 above. Higher labor productivity of exporting firms is not offset by higher wages, leading to cost competitiveness of these firms in regional and international markets. This is discussed further below, where the characteristics of exporters are examined in detail.
Capital Intensity
Variations in labor productivity often reflect differences in capital use: labor productivity can be increased with capital deepening. This section examines differences in capital use across firms within Rwanda, and across comparator countries. Before doing so, it is important to note that it is much more difficult to measure capital than labor.39 The analysis below is based on the sale value of machinery and equipment.
38 Due to the small sample size, these results need to be interpreted with caution. The result is driven by the dominance of a couple of coffee and tea processing firms among exporters ;-a sub-sector that consists of e large firms using labor intensive techniques to process these products.
39This is because most machinery has a long-life, it is difficult to obtain its current value from firm surveys, and measure its contribution to output in a single year. Several questions were asked in the enterprise survey to obtain a measure of capital stock. Firms were asked about the book value of capital stock, their replace cost (if purchased new) and also the sale value of capital. The book value of capital is often determined by accounting practices, and is difficult to use, without having additional data on age of capital stock and accounting depreciation rules. Hence this measure is not used for our comparative purposes. Replacement value of capital stock may differ significantly from current value, if machinery is old and dilapidated. The sale value of capital provides the closed approximation to
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Figure 5: Capital Labor Ratio (sale value)
Source: World Bank Enterprise Survey
The median firm in Rwanda uses about $2600 of capital per worker. This is much lower than that of firms in Kenya, similar to that of firms in Tanzania and Uganda, and higher than that of firms in Burundi (Figure 5). These differences across countries could be driven by several factors, including variation in sectoral composition of manufacturing, labor intensive methods of production, limited investments by firms, and lack of a secondary market for capital stock, leading to lower reported sales value.
Capital Labor Ratio is lowest amongst comparators in the food sector. Figure 5 also presents the capital labor ratios for the food sector. Results clearly indicate that enterprises in other countries are far more capital intensive than food processing firms in Rwanda. This suggests that the differences in labor productivity between these countries and Rwanda are at least partly due to differences in capital use, rather than to other differences. This will be investigated further in the next section on total factor productivity.
Total Factor Productivity
To get an overall assessment of productivity, it is necessary to take both capital and labor use into account. This can be done by calculating total factor productivity (TFP).40 Differences in TFP are differences in output after controlling for differences in the use of labor, capital and other intermediate inputs. Differences in TFP can be due to the quality of workers, the quality of management, the technology used (as long as it is not embodied in capital), or firm organization.41 In
current value, and is the measure we use here. In practice though, these measures are highly correlated: dispersions across firms and countries follow similar patterns irrespective of the capital used.
.
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this section, the relative performance of Rwandan firms versus those of firms in EAC comparators are compared first by estimating a basic production function. The production function is then augmented to include firm specific characteristics determining performance. Results from the first specification are presented in column 1 of Appendix Table 2.1.
Total factor productivity of Rwandan firms is less than half of firms in Kenya; firms in Tanzania are also considerably more productive, on average, with their productivity being almost 30% higher than that of firms in Rwanda. These estimates are based on the first specification, presented in Appendix 2. 1. These differentials are presented in Figure 6below.
Figure 6: Total Factor Productivity: Percentage relative to Kenya
Source: World Bank Enterprise Survey
What explains the lower productivity of Rwandan firms in a broader regional context? Several factors could drive productivity differentials. Factory floor productivity, measured here, is impacted by various management, technology and learning channels that affect each firm differentially. The role of an adverse business environment, which impacts firms by raising their transactions costs (for e.g. poor road conditions) or lowering their revenues (for e.g. losses due to power outages) are particularly important in Rwanda. These are discussed in greater detail in the next chapter on investment climate.
Table 5 below presents various firm management, technology and financial characteristics that may impact enterprise productivity.. The descriptive statistics indicate that fewer enterpreneurs/general managers of small firms in all comparator countries have foreign ownership. These linkages are more common in larger firms in all countries except Kenya, which has a large domestic entrepreneur base. 51% of all manufacturing firm managers in Rwanda have university education‐however, there is little sorting by firm size. In other countries, we see sharp differences in educational qualifications by firm size‐educated entrepreneurs are able to grow and run larger
41 At a technical level, TFP is calculated by estimating a Cobb‐Douglas production function, using data for enterprises from all manufacturing sub‐sectors, and looking at the residuals and coefficients on various variables in an augmented production function. To allow us to compare TFP between Rwanda and comparator countries, we pool the observations for all comparator countries into a single regression. The production function is estimated using a log‐linear OLS approach.
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enterprises. This is not true in Rwanda, where there is a much smaller difference between education across size classes..42
Examining technology characteristics, fewer large firms in Rwanda have internet connections, and are less likely to invest in worker training programs, compared to other large firms regionally. Forty percent of large firms in Rwanda have international ISO certification (predominant in the food industry) which is comparable to other countries in the region.
Examining finance and accounting characteristics, we see that Rwandan firms are less likely to have audited accounts compared to other large firms regionally. However, access to external finance, in the form of overdrafts and loans, is much higher for firms in Rwanda compared to other firms regionally. Implications of this are discussed in further detail in the finance chapter.
Table 5: Technology and Learning Characteristics: Rwanda versus comparators
Burundi Kenya Rwanda Tanzania Uganda MANAGEMENT CHARACTERISTICS
Small 20.83% 12.89% 11.63% 7.42% 14.95% Pct of Firms with some Foreign Ownership Large 50.00% 21.58% 40.00% 48.84% 46.15%
Small 17.0% 36.00% 48.48% 21.2% 34.4% Pct of Firms where Manager has University Ed. Large 64.0% 74.43% 56.00% 74.6% 70.2% TECHNOLOGY CHARACTERISTICS
Small 36.46% 58.98% 37.21% 31.88% 23.84% Pct. With Email Large 66.67% 89.21% 73.33% 97.67% 96.15% Small 20.83% 25.78% 18.60% 33.19% 29.18%
Pct with Training Program Large 33.33% 61.87% 53.33% 65.12% 65.38% Small 3.13% 9.77% 4.65% 16.59% 8.90%
Pct. Of Firms with ISO certification Large 33.33% 30.22% 40.00% 37.21% 57.69% FINANCIAL CHARACTERISTICS
Small 37.50% 43.75% 53.49% 15.28% 14.23% Pct. Of firms w/Overdrafts Large 83.33% 74.10% 80.00% 60.47% 57.69% Small 7.29% 82.81% 32.56% 50.66% 38.79% Pct. Of Firms with Loans Large 66.67% 98.56% 66.67% 95.35% 96.15% Small 12 21 13 14 13 Pct of firms with Audited Accounts Large 26 26 19 20 26
Source: World Bank Enterprise Survey
Next we examine the role of these factors in a multivariate context, using regression analysis. These results are presented in Appendix 2. 1. Due to the small sample size, data are pooled for all countries for econometric estimation.43 Since the ICA data are cross‐sectional, nothing can be inferred about causality. Nonetheless, significance in the correlations allow to determine which indicators have a significant or minimal impact on enterprise performance.44
42 Given the small size of the formal manufacturing sector, this difference may be due to entry barriers into formal manufacturing, and the self-selection of higher skilled entrepreneurs into the formal sector, and limited employment growth in existing firms in Rwanda. These issues are analyzed further in the chapters on labor and microenterprises.
43 Assuming differences all coefficients, the model was also estimated for Rwanda alone. While the significance levels were lower, significant factors determining productivity remained the same.
44 Multicollinearity problems may exist if we put in too many variables; too few variables may lead to an omitted variable bias. Alternate specifications include sequential elimination to minimize multicollinearity bias.
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Technology and Internet Use: Enterprises that have international ISO certification are much more efficient than others‐this difference is significant at the 1% level for all countries in EAC. Similarly, enterprises that use information technology to communicate with suppliers and clients are much more efficient than others, both within EAC countries as a whole, and in Rwanda in particular.
Globalization: Enterprises that are foreign owned, and those that export, are expected to be better performers than enterprises without these linkages. However, our results for firms within EAC shows that firms with some foreign ownership, those that export, are only marginally more productive than others producing for the domestic market‐differences in performance between these groups are not significant. This is discussed further in the section on exports below.
Access to Finance: In Rwanda, having access to formal financial sector borrowing is positively correlated with enterprise performance. Also, firms that have good accounting practices, and keep audited accounts are likely to be more efficient than others.
Finally, we see that even after controlling for skill and technology differences, Rwandan firms have lower TFP; it is much lower than Kenya and Tanzania, and equivalent to Uganda and Burundi.
These comparisons are made at the factory floor. If there are significant differences in transport costs and infrastructure bottlenecks, this will add to the cost disadvantages of Rwandan firms. In addition, costs due to regulation and governance add to overall firm costs and enterprise competitivenss regionally. A closer examination of these investment climate issues can provide insight into why Rwanda has been unable to attract more foreign investment in manufacturing, and the reasons behind productive inefficiency regionally. These are examined in the next chapter on investment climate.
Rwanda’s Manufacturing Exports
Exporting is an important channel for economic growth, particularly in countries where the domestic market for higher value added products is relatively small, private sector investment and growth of the manufacturing sector have to be fuelled by export demand. As described in chapter 1, the Government of Rwanda, in its Vision 2020 plan, has highlighted the strategic focus on manufacturing export growth in the medium term to accelerate economic growth in Rwanda. In this section, we review Rwanda’s export performance to date, and the possible reasons for the lack of export‐led growth and efficiency improvements in manufacturing
Performance to date: As discussed in the section above, very few firms in Rwanda export.45 Results from the productivity analysis show that Rwandan exporters are not likely to be more efficient than enterprises serving the domestic market, after controlling for other firm characteristics.
Recent empirical research has shown that what you export matters for successful development. Countries that are able to diversify into non‐traditional, higher productivity tradable have been shown to grow faster, due to the positive externalities from such activities. Hausmann, Hwang and Rodrik
45 The LBIC (2008) survey of 458 firms (this includes manufacturing and service sector enterprises) conducted in July 2008 found that less than 1% of firms export regionally within EAC, or outside EAC countries. In the ICA sample of 340 firms, 9 firms export - which constitute 2.6% of the total. Both studies point towards negligible export orientation of private sector firms in Rwanda.
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(2006) find that the sophistication of a country’s current export bundle is a good predictor of future growth, holding other things constant, such as human capital levels and the quality of institutions.
It is useful to highlight the lack of export diversification in Rwanda in this regard. The CEM (World Bank 2007) provided a detailed review of Rwandan exports. It noted that Rwanda’s export basket was far less diversified than neighboring countries, including landlocked Uganda, which has recently seen export success in areas such as fish and cut flowers. While there were several high‐value products in Rwanda’s export basket ‐ such as vegetables and fruits, tubers and foliage, and other materials of vegetable origin – their volumes were very low volume. Scaling up the miniscule quantities of these non‐traditional products remains a challenge. The study concluded that in addition to addressing the key infrastructure, skills and technology constraints within Rwanda’s manufacturing sector, Rwandan companies in non‐traditional export sectors such as horticulture/floriculture, leather and textiles, processed fruits and vegetables, mining, tea and processed coffee could benefit from a sector specific export promotion plan to identify and link directly to external buyers, as had been adopted for coffee by Starbucks and handicrafts for Macys.
In a recent GoR statement46 (2008) submitted to the IMF, the government noted that its export promotion strategy would continue to focus on enhancing productivity in the traditional sectors and diversifying the export base. The key sectors for focus included coffee, tea, mining and tourism. An Export Processing Zone is also in the process of being established.
RIEPA47, the key government agency mandated to increase investment and exports reported48 that investments have been increasing steadily since 2000. However, most recent investments have been in the hotels and tourism sectors ‐ very few firms have entered or made significant investments in non‐traditional exports of manufactures, while imports within manufacturing have been growing rapidly.
Similar patterns are observed in the survey sample. Table 6 below presents the sample characteristics of exporters and importers within EAC. Bearing in mind the small size of the manufacturing sector in Rwanda, we see that 15% of manufacturing firms export some part of their output‐this constitutes only 9 firms in the sample. The median share of output exported across all comparator countries ranges between 20‐30% indicating little export specialization by those that do export. However, Rwandan firms are much more import intensive compared to others in the region: more than 72% of firms use imported inputs; of those that use imported raw materials, median share of imported inputs is 90%,. In comparison, only half the firms in Kenya, and only 30% of firms in Tanzania and Rwanda use imported inputs‐import shares of those that do is also substantially less, at 60%.
Table 6: Sample Description: Exporters and Importers
Rwanda Burundi Kenya Tanzania Uganda
46 Letter of Intent, Memorandum of Economic and Financial Policies, and Technical MOU, May 29, 2008)
47 RIEPA has now merged with Rwanda Development Bank.
48 RIEPA, Investment and Export Performance Report, 2007
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Number of exporting firms 9 3 151 32 44 % of exporter firms 15.52% 2.94% 38.23% 11.76% 14.33% Median % of output exported 30% 25% 25% 20% 30% % of Firms importing Inputs 72.41% 70.59% 51.14% 39.71% 33.22% Median % of Inputs Imported 90% 70% 60% 60% 62% Number of Firms 58 102 395 272 307
Source: World Bank Enterprise Survey
Characteristics of exporters versus non‐exporters49 within EAC countries are presented in Table 7. Striking differences emerge between Rwanda and its competitors. We see that average foreign ownership in exporting firms is much higher than firms supplying to domestic markets in all countries except Rwanda‐average ownership is only 2.2% for firms in Rwanda, compared to more about 20% in Kenya, 28% in Tanzania and 41% in Uganda. No Rwandan exporter in our sample has foreign technology licenses, compared to 22% of firms in Kenya, 25% in Uganda and 12% in Tanzania. Similarly, we see that only 11% of exporting firms in Rwanda have ISO certification, compared to more than 20% in all other countries.
Table 7: Characteristics of Exporters versus Non‐Exporters
Median Firm Size 27 137 38 144 19 67 15 44 Number of Firms 244 151 49 9 240 32 263 44
Source: World Bank Enterprise Survey
How important are these linkages in determining the likelihood of exporting? We examine this in a multivariate framework, modeling the decision to export using probit regressions. Results are presented in Note: Robust standard errors in parentheses ***significant at 1%; ** significant at 5%, * significant at 10%. Excluded category for country dummies is Rwanda, for sectors is Food Processing.
Appendix 2. 2. We see that Kenyan firms are much more likely to export, compared to all other comparators within EAC, even after controlling for differences in firm characteristics. Larger firms are much more likely to export, compared to smaller enterprises. This positive association between firm size and exporting can be explained by economies of scale, and the fixed costs associated with exporting, due to bureaucratic procedures and the establishment of marketing channels. Foreign firms, with greater access to foreign technology, are more likely to export than local firms. In addition, firms that have international ISO certification are more likely to export, than others.
Overall, our enterprise survey data indicate that exporting within EAC is highly correlated with firm size, and foreign learning channels including foreign ownership, international certifications and foreign licensing. All these linkages are particularly poor in Rwanda. It is noteworthy in this context
49 These comparisons are made at the factory floor.
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to highlight RIEPA’s (now RDB) goals, “Rwanda needs to improve its export infrastructure and provide support to smaller indigenous investors who may lack skills information, or capacity to compete in regional and international markets.” Our results indicate that policies encouraging foreign linkages, those that invite large foreign investors who are able to achieve economies of scale are specific sub‐sector interventions are required to promote export led growth.
Further impediments to exporting from Rwanda are related to logistics costs‐including transport of goods to the ports, regulatory policies relating to exports and imports such as pre‐inspection clearance, warehousing fees, customs fees and so on. The Diagnostic Trade Integration Study (DTIS, 2007) for example, notes that Rwanda is an efficient coffee producer, but transport costs of coffee from the farm gate to the port of Mombasa add 80% to farm gate production prices; this cost is less than 50% of total costs in coastal economies. Apart from longer distances, landlocked countries such as Rwanda face added problems due to burdensome regulatory policies and corruption problems in neighboring countries‐costs they cannot fully control. These costs, and their impact on exporting, are examined in detail in the next chapter on the business environment.
The next section focuses on firm performance in the Service, Retail and IT sectors in Rwanda, a key priority area for GoR’s Vision of becoming a regional service and trade hub.
Enterprise Performance in the Services Sector
The GoR, in its Vision 2020 report, noted:
“As for services, in the medium to long term, this sector will become the most important engine of Rwanda’s economy. Since Rwanda is landlocked and has limited natural resources, the Government should take a lead role in designing policies geared towards encouraging investment in services, to acquire and maintain a competitive edge in the region.
It should be noted that the elaboration of such policies will not be sufficient to achieve a knowledge based economy. Major infrastructural investment will be required in the areas of energy, water, telecommunication and transport to reduce costs, whilst increasing their quality and reliability. Improvements in education and health standards will be crucial for providing an efficient and productive workforce.”
The Government’s EDPRS (2007) noted that:
“ Given the high priority assigned by Rwanda Vision 2020 to the development of the ICT sector, it is a matter of concern that not only were there no more professional and technical training centers in 2006 than in 2000, but that several of the existing centers are not adequately equipped and fully operational. The admission rate to tertiary education is also well below that required to create the knowledge base needed to accelerate growth of a skill‐intensive services sector”. It also highlighted that “expanding technical and vocational education (TVET) is of strategic importance if Rwanda is to become the ICT hub of the region. The absorption rate of TVET graduates in industry should have risen from around 25% in 2006 to 75% in 2012.”
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Contribution of Services: The contribution of the services sector to GDP in Rwanda has been on the rising trend since the last decade – from nearly 34.7 percent in 1996 to 37.8 percent in 2006 and 46 percent in 2007 (CAS, 2008). This trend has been matched by an increasing growth rate of the services sector over time: from 6.5 percent in the period 1996‐2005; increasing to 8.1 percent in 2006 and 9.2 percent in 2007 (CAS, 2008).
The World Bank Enterprise Surveys included a separate module for firms in the services sector, including construction and transport, hotels and services (termed residual sector) and firms in retail and information technology (retail sector)‐the questionnaire, while broadly similar to that used in the manufacturing surveys, was customized for each of these sectors.
The numbers of firms surveyed, by sub‐sector, across comparator countries, are presented in the Appendix 2. 3. The survey instrument for the residual sector was a subset of the questionnaire used for manufacturing firms. The Retail and IT questionnaire included additional questions not asked of manufacturing firms.
Retail and IT Sector
With the Government’s focus on the IT sector, and the goal to be the service hub in Sub‐Saharan Africa, it is instructive to review the status and nature of the current retail and IT sectors, and compares them across countries within the EAC market. An important caveat is the number of firms in the sample: while the observations are sufficient in retailing to draw conclusions about behavior of the underlying population, we can only make observational statements about the IT sector.
Table 8: Retail and Information Technology: Sample Characteristics
Burundi Kenya Rwanda Tanzania Uganda Average Firm Size 11 40 15 11 33
Average Firm Age 10.06 8.94 9.91 8.74 11.97
% with Foreign Ownership 16% 7% 21% 0% 18%
% with University Ed. 61% 48% 54% 37% 58%
% with Email 43% 41% 48% 37% 38%
% with Overdraft 18% 16% 38% 7% 23%
% with Current Loan 28% 21% 32% 14% 19%
% with Audited A/cs 15% 37% 45% 37% 53%
Number of Firms 89 150 56 70 124 Source: World Bank Enterprise Survey
Table 8 presents the characteristics of retail and IT firms across EAC countries. We find that average firm size in retailing is higher in Kenya and Uganda, compared to firms in Rwanda, where an average retail firm has 15 employees. Average enterprise age is 10 years, which is similar to that of firms in other countries. Twenty one percent of firms have at least some foreign ownership, which is higher than comparators. University education amongst owners is also similar across countries, with almost half the firms having managers with university education. Similarly, we see that almost half the firms in Rwanda have email connections, which is slightly higher than comparators.
Focusing on financial characteristics of Retail and IT firms, enterprises in Rwanda are more likely to have some access to loans from the formal financial sector, compared to firms in other countries. Roughly one‐third of firms have access to overdrafts; similarly for loans. Almost half the retailing firms have audited accounts, which are slightly lower than firms in Uganda, but higher than other countries.
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Internet Characteristics of firms in the Retail and IT sectors are presented in the Table 9 below. We see that retailing firms in Rwanda are very similar to those in other countries, with little uptake of internet technology. However, the IT firms in Rwanda are very similar to those in Kenya, with 42% having international certification, and majority with broadband internet. Firms in Rwanda are most concerned about internet security, and majority experienced internet unavailability in the past year, indicating the unstable network services.
Table 9: Technology Characteristics of Retail and IT firms
Pct. with International
Certification Pct with Broadband Internet connection
% concerned about internet security
% that experienced internet interruption
in past year Burundi Retail 5% 7% 4% 5%
Kenya Retail 6% 17% 2% 13%
Rwanda Retail 11% 25% 14% 16%
Tanzania Retail 3% 17% 5% 12%
Uganda Retail 12% 8% 2% 3%
Burundi IT 0% 46% 15% 8%
Kenya IT 38% 79% 38% 67%
Rwanda IT 42% 75% 58% 75%
Tanzania IT 0% 100% 40% 80%
Uganda IT 17% 17% 17% 17%
Source: World Bank Enterprise Survey
For enterprises in the retail sector, several questions were asked to assess the impact of domestic and foreign firms on enterprise pricing and competitiveness. Table 10 reports the competition characteristics of retail firms. We see that firms in Rwanda face the least competition in retailing compared to other firms in the region, with the smallest share of firms reporting that competition is important in determining prices and product lines, compared to other countries regionally.
Table 10: Competition Characteristics of Retail Firms
% reporting domestic competition important in determining prices
% reporting fgn. Competition impt in determining prices
% reporting domestic competition important in new product line
% reporting fgn competition important in new product line
% Reporting Competition from Informal Traders a major problem
Burundi 78% 42% 55% 41% 61%
Kenya 84% 26% 70% 31% 89%
Rwanda 55% 5% 34% 7% 45%
Tanzania 46% 26% 46% 28% 66%
Uganda 70% 41% 48% 35% 76%
Source: World Bank Enterprise Survey
Although most measures of firm performance cannot be calculated for firms in the retail trade and services sectors, it is possible to calculate labor productivity and unit labor costs for these firms. Median labor productivity and Unit Labor Costs for firms in Retail and IT sectors are presented in Figure 7 and Figure 8 below.
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Figure 7: Productivity and Unit Labor Costs: Retail Sector
Source: World Bank Enterprise Survey
Figure 8: Productivity and Unit Labor Costs: IT sector
Source: World Bank Enterprise Survey
Rwandan firms’ Labor productivity is highest amongst comparators in the IT sector. This higher productivity is not offset by higher wages‐unit labor costs are only 10% of Value added in the IT sector in Rwanda, compared to 22% in Kenya. In retailing, firms in Rwanda are more efficient than firms in Tanzania and Uganda and only slightly lower than firms in Kenya. Unit Labor costs are also lower in Rwanda compared to Kenya in the retail sector.
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Due to the small sample size in IT sector, the descriptive statistics presented above may not be robust to draw policy conclusions. To address this problem, we also examine productivity in retailing and IT sector using regression analysis for the pooled sample of firms, with labor productivity as a dependent variable, being our measure of performance.
Regression results are presented in the Appendix table 2.4 below. We see that firms in IT sector are 46% more efficient than firms in retailing, after controlling for country differences, and differences due to firm size and firm age. Firms in Rwanda are 85% more efficient than firms in Tanzania, while firms in Kenya are almost 60% more efficient than Tanzanian firms. Ugandan firms are not significantly different from firms in Tanzania.
The second model introduces foreign ownership as an additional explanatory variable, while Model 3 augments the second model by including education of the manager‐whether or not the manager has a university degree. Results indicate that foreign ownership is significant and positive in determining differences in value added, with foreign owned firms being 73% more efficient than local enterprises. Even after controlling for ownership differences, Rwandan firms are most efficient amongst comparator countries. In the third model, we see that university education is also significantly positive in determining differences in value added in the retailing and IT sector. While IT firms are still 25% more efficient than other retailers, the difference is no longer significant, indicating the correlation between education and ownership of IT firms.
Other Services: Construction and Transport, Hotels and Restaurants etc.
Characteristics of firms in the other sectors are presented in the Table 11 below. We find that firms in other service sectors in Rwanda are much smaller than firms in Kenya and Tanzania, and similar to firms in Uganda and Burundi. Other ownership and educational characteristics are similar across comparators. We see that more firms in Rwanda have access to loans and overdrafts compared to all other countries except Burundi. A smaller percentage of firms have internet connections in these sectors in Rwanda, compared to other countries.
Table 11: Sample Characteristics‐Residual Sector: Construction, Hotels and Restaurants
RESIDUAL SECTOR: CONSTRUCTION, HOTELS AND RESTAURANTS Burundi Kenya Rwanda Tanzania Uganda
Labor Productivity and Unit Labor cost comparisons show that firms in construction, and hotels and restaurants sectors in Rwanda are least competitive amongst comparators. These data are
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presented in Table 12 below. Labor productivity in Rwanda is the lowest amongst comparators in the growing hotels sector in Rwanda, this low labor productivity is not offset by lower wages, leading to highest unit labor costs in this sector.
Table 12: Labor Productivity and Unit Labor Costs in Service Sector
Construction and Transport Hotels and Restaurants Other Services
LABOR PRODUCTIVITY
Burundi2006 3249.1 2040.86 3009.67
Kenya2007 6149.08 4161.68 9124.27
Rwanda2006 2398.34 1754.79 3171.26
Tanzania2006 1368.41 2295.01 5055.41
Uganda2006 6816.54 2880.73 5297.63
UNIT LABOR COSTS
Burundi2006 15% 13% 15%
Kenya2007 15% 19% 25%
Rwanda2006 26% 32% 24%
Tanzania2006 11% 24% 12%
Uganda2006 16% 27% 15%
Source: World Bank Enterprise Survey
Regression results confirm the above univariate results. In all models, we find that firms in Rwanda are less efficient than comparators, while firms in Kenya have the highest efficiency. These are presented in appendix table xx. Foreign ownership and university education of managers are both positively correlated with enterprise performance‐low levels of these foreign linkages and university graduates in this sector in Rwanda impede sector performance.
Summary
Results from this chapter indicate that firms in Rwanda have low labor productivity, high unit labor costs due to higher wages relative to their productivity, and lower TFP compared to firms in Kenya and Tanzania. Factors associated with productivity differentials within Rwanda are manager education‐university educated managers have higher productivity than others; internet access, ISO certification and having formal sector loans. GOR’s focus on technology adoption and increase in education will have a medium term impact on productivity growth.
Examining the characteristics of exporters, we find that very few firms in Rwanda export; those that do are not more efficient than others. Government policies aimed at expanding the export basket towards higher value added products and directing domestic investment towards new sectors can have a long‐term impact on productivity growth.
Examining characteristics of the retail and services sector, we see that retail and IT firms in Rwanda are much more productive than other countries in the region: while the role of Government subsidies towards this sector is unclear, focused expansion of this sector through cost reductions and skills training, will provide the engine for future expansion.
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In the next chapter we assess how the different elements of investment climate affect enterprise competitiveness in Rwanda.
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Chapter 3: Investment Climate and Regional Integration Opportunities and Challenges
Introduction
This chapter compares the investment climate in Rwanda to two sets of countries: that of firms in Kenya, Tanzania, Uganda, Burundi and DRC regionally, and India, China, Vietnam and Thailand internationally. The analysis is carried out from the perspective of regional trade and integration agenda being espoused by the GoR. Faster and more comprehensive regional integration can help Rwanda overcome some of its inherent disadvantages of an adverse geography – including its landlocked location, limited natural resources and small economy. Successful integration is a pre‐requisite for realizing Rwanda’s vision of becoming a regional service and trade hub (EDPRS). To realize this vision, Rwanda needs to speed up efforts to make the business environment more attractive for regional investment and trade; lower domestic costs of production so domestic firms can compete on the regional market; and strengthen its participation in regional infrastructure investment and services initiatives including efforts to strengthen the functioning of cross‐border transit systems, power pools, and regional skills development programs.
Kenya, Tanzania, Uganda and Burundi are useful comparators because of their geographic proximity to Rwanda, and their joint membership of the East Africa Community. Furthermore, investment climate surveys in these countries were conducted at about the same time using a similar survey instrument, making cross‐national comparisons easier. Although regional comparisons are useful, as the world becomes more integrated, enterprises in Rwanda will increasingly find themselves competing with enterprises from outside of East Africa, both within Rwanda and in export markets. Therefore, in addition to comparing the investment climate in Rwanda with the investment climate in these regional comparators, the chapter also compares Rwanda’s investment climate with that of four countries in Asia: China, India, Vietnam and Thailand. Benchmarking investment climate in Rwanda to those faced by firms internationally helps highlights the areas in which Rwanda is already strong and areas where it might improve.
Productivity results indicate that Rwanda, remains an unattractive destination for manufacturing firms,50 compared to Kenya and Tanzania, even after controlling for skills and technology differences. Firm productivity in Rwanda is equivalent to Uganda and Burundi. Differences in the investment climate ‐ broadly defined as the state of a country’s infrastructure, economic, and social policy institutions, and governance mechanisms, as well as a country’s unique attributes or “geography” ‐ will also add to a company’s cost advantages or disadvantages and impact its overall competitiveness internationally. A closer examination of these issues may provide insight into why Rwanda has been unable to attract more foreign investment, and help prioritize reform initiatives. These issues are examined here.
This chapter is structured as follows. We first examine rankings of business constraint across all sectors. We then examine the main reported business constraints in Rwanda in detail. We examine issues in infrastructure provision and costs, transport, electricity, telecommunications including ICT. Next we examine issues in Government regulations We also examine the business constraints relating to
50 These comparisons are made at the factory floor.
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governance and service delivery: areas where Rwanda has made remarkable progress‐we examine how positive changes in these areas have impacted the competitiveness of Rwandan firms regionally and internationally. Details on other business environment constraints are presented in the chapter annex.
Throughout this chapter, enterprise survey results are supplemented with findings from other surveys conducted within Rwanda. In particular, to provide current context to the ICA findings, we report results from the recently completed PSF‐OTF’s Local Business Investment Climate (LBIC) survey,51 of private enterprises in Rwanda. Conducted in July 2008, the two teams worked closely to include several questions similar to those covered by the World Bank Enterprise Surveys. Despite differences in methodology, the results shed some light of the changes over time.
Business Environment – Key Findings
We start the analysis by reporting on data on perception related findings. It has been shown that perceptions data, collected through the World Bank Enterprise Surveys globally, are sensibly correlated with objective indicators of the business environment reported by firms, and can be used by policy makers to frame priorities for reforms and investments and they are increasingly complemented by “Doing Business” indicators based on expert surveys (Doing Business, 2004‐07).52 The latter provide a more comparable cross‐country perspective across a detailed range of regulation, but not a firm‐level view of the de facto severity of regulatory and infrastructural obstacles. Both approaches are complementary and are used in our analysis of Rwanda’s business environment.
The Enterprise survey of 340 firms in Rwanda includes formal manufacturing, microenterprises with less than five workers, and also firms in retailing and residual sectors such as construction, restaurants, hotels etc. Rankings across all sectors are presented in Figure 9 below:
51 The Local Business Investment Climate (LBIC) survey, July 2008, was sponsored by PSF and undertaken by the OTF Group.
52 Details of this discussion are presented in “Do Enterprise Perceptions Matter? Evidence from African Firms” by Gelb, Ramachandran, Shah and Turner, 2007.
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Figure 9: Business Constraints: Pct of firms ranking Problem as Major or Severe
Source: World Bank Enterprise Survey
More than 70% of firms in the manufacturing sector reported electricity to be a major constraint, followed by tax rates (50% of firms), and also transport and access to finance, (40% of firms) in our sample. Overall constraint rankings were lower for firms in the services sector: tax rates were reported as a major constraint by almost 50% of firms, followed by electricity and access to finance.
How do these constraint rankings compare with the findings of the most recent survey of the Private sector (PSF‐OTF LBIC survey, 2008)?53 The LBIC survey included several detailed questions on rankings of the business environment. Ranking of all constraints are presented in Appendix Table 3.1. We see that firms in Rwanda today are likely to report several areas of the business environment as major constraints‐chief amongst them being transport, access and cost of land, tax rates, finance and electricity and bureaucracy, which are reported as major constraints by more than 50% of enterprises in all categories. Some differences exist between manufacturing and services: other than corruption, constraints are more highly ranked by firms in manufacturing, with workforce skills being of particular importance to that group.
Comparing over time, we see that rankings of constraints identified by the Enterprise survey are similar to the current PSF‐OTF survey, with the exception of electricity. Only the importance of electricity as a major constraint has declined relative to other constraints‐constraints regarding access to finance, tax rates, transportation continue to be major constraints today as they were in 2006. Access and Cost of land feature as a major constraint in the preset survey: since this question on access to land
53 Survey details are provided in the appendix. There exists a significant overlap in manufacturing firms surveyed by PSF-OTF (58 firms) and the Enterprise Survey (58 firms). The latter did not include professionals and household enterprises, which accounts for majority of the difference between the two surveys. The formal sector sample in the enterprise survey closely matches the PSF survey.
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was asked differently across the surveys (unlike the LBIC survey, the Enterprise Survey only asked about access, rather than access and cost of land), so rankings for that question are not comparable.
Another approach in examining business environment constraints is to ask firms to pick a single constraint that can be considered the most serious obstacle to operation and growth. When examined in isolation, this may not be a useful indicator‐a firm’s choice of a particular constraint does not tell us how serious it is‐but, examined in conjunction with the ratings data above, it can provide a useful guide towards the binding constraints.
Figure 10 below presents the percentage ranking each problem to be their biggest constraint. We see that electricity and tax rates were most commonly ranked as the biggest obstacles to operations and growth in Rwanda.
Figure 10: Biggest Constraints in Manufacturing and Services Sector
Source: World Bank Enterprise Survey
Similar to the Enterprise Survey questions related to the constraint prioritization, the LBIC survey of 2008 also asked firms to choose which of the constraints should be prioritized. These results are presented in Appendix Table 2. More than 20% of firms in both the service and manufacturing sectors chose tax rates as a major priority, while land was of ranked as a highest priority constraint by 16% of firms in manufacturing and 9% in services. Finance was the greatest priority for about 9% of firms in both sectors.
The top ranking business constraints are examined in further detail below. We first examine constraints in infrastructure provision and costs, transport, electricity, telecommunications including ICT. Next we examine issues in Government regulations including Tax rates and Tax administration, and also constraints relating to governance‐while corruption and bribery are ranked very low in the order of business constraints, it is useful to compare these in a regional context and the corresponding competitive advantage of doing business in Rwanda. Analysis of other business constraints, which are not ranked as severe by a vast majority of firms, and where Rwanda’s ranking does not differ significantly from other countries regionally, are presented in the chapter Appendix.
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Infrastructure
Cost and access of infrastructure is one of the key factors that shall influence and drive Government of Rwanda’s (GoR) trade and regional integration agenda. It includes both direct and indirect costs related to electricity, transport, costs of clearing customs etc. A number of earlier studies have indicated that the high cost and access of infrastructure in Rwanda, combined with its landlocked position, increases trade costs and in turn impeding Rwanda’s competitiveness. This issue is examined in detail here.
Transport
The ability of a country to connect firms, suppliers and consumers to global supply chains efficiently is essential for its competitiveness. The lack of paved roads, freight and passenger transport systems through railways and air transport, all have a direct impact on business activity. Its first direct impact is on the level of business activity: it limits entry of enterprises. Economies with low cost, well functioning infrastructures encourage entry and growth of enterprises, its lack thereof limits entry to only those firms that either supply to fragmented regional markets, or those that can exploit market opportunities with profits high enough to cover the high transport costs. Transport bottlenecks are typically long‐term (unlike power, where the supply can improve or deteriorate rapidly): bad roads, limited rail and air linkages is well known to entrepreneurs when they start a business: to that extent we see a self‐selection of entrepreneurs into industry
Because Rwanda is landlocked and hilly, transport infrastructure needs to be highly efficient to offset its geographic disadvantages. This, however, is not the case. The CEM (World Bank, 2007) noted that rail and air transport are virtually non‐existent, poor road conditions and the long distance to the nearest port makes transport costs very high: these currently equal almost 50 percent of the value of goods depending on bulkiness and weight.
The poor quality of infrastructure is also noted in other studies. A recent study by the World Bank that is based on a large survey of freight forwarders and transport operators across the globe, provides seven measure of performance to assess the logistics gap across countries54. It ranked Rwanda 148 out of 150 economies, at the very bottom of the list, and well behind Kenya (76) and even landlocked neighbors such as Uganda (83) and Burundi (113). The lack of paved roads in much of the country outside Kigali, and the high costs of domestic and international transport, it noted, are the primary barriers to competitiveness. These rankings are presented in Table 13 below.
54 Connecting to People: Trade Logistics in the Global Economy, World Bank, 2007.
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Table 13: Logistics Performance Index
Country Ranking LPI Customs Infrastructure International shipments
Source: Arvis and Others (2007) Note: Scores are based on a five‐point scale based upon subjective assessments by freight forwarders and other logistics professionals.
Given high transportation costs and the poor quality of road infrastructure found in these country
surveys, it is not surprising that transportation was ranked as a serious concern for many firms in Rwanda in the Enterprise Survey. Cross‐Country rankings are presented in Figure 11 below. Almost 40% of firms in Rwanda said that transportation was a serious obstacle, higher than all comparators except Kenya, and much higher than in most of the successful manufacturing economies‐where less than 20% of firms rank transport was a serious obstacle.
Figure 11: % of Firms Identifying Transportation as a Major Constraint
Source: World Bank Enterprise Survey Note: Cross Country comparison are for manufacturing firms only.
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Do transport problems have differential impact on firms within Rwanda? Transport rankings may
differ across firm size if the economy is dualistic, with smaller firms supplying to local markets, and larger firms supplying nationally and internationally. It may also depend on a firm’s sector of operation, and also depend on whether a firm is exporting its products through ports in neighboring Kenya or Tanzania, or supplying locally. Rankings across firm characteristics are presented Appendix Table 3. 1. We see more 40% of firms in manufacturing rank transport to be a major constraint, compared to only about 20% of firms in services. Similarly‐44% of medium and large firms ranks transport to be a constraint, compared to 22% of small firms. Ranking by exporters and foreign owned firms is greater than that of domestic enterprises.
How do these rankings compare to actual transport costs incurred by firms? The survey provides us
with two sets of measures: the first asks firms directly about the amount spent on transporting goods in the past year; another question addresses transport problems by asking firms the percentage of sales lost due to theft or breakage in transit.
Our survey data, available for manufacturing firms across all EAC countries, are presented in Table 14 below. It shows that transport costs55 as a percentage of sales56, are much higher in Rwanda compared to Kenya and Tanzania‐two countries Rwandan companies directly compete with in the EAC market. These costs, for medium and large firms, comprise only 1.2% of sales in Kenya and 1.6% in Tanzania, compared to 3.3% in Rwanda. Across firm size, we see that small firms are likely to supply to local markets, face similar transport shares across all countries. However, medium and large firms in Rwanda are particularly disadvantaged‐firms also report 2% of sales lost due to breakage in transit. However, other countries in the region are likely to suffer higher losses due to theft in transit‐low incidents of crime (examined in further detail below) reduce indirect costs to firms due to these losses, unlike neighboring countries.
Table 14: Transport Costs and Losses: EAC
Direct Costs Indirect Costs
Firm Size Transport Costs/Sales
% Lost Due to Theft in Transit
% Lost Due to Breakage in Transit
Rwanda MLE 3.31% 0.08% 2.32%
Small 2.08% 0.03% 0.50% Burundi MLE 0.54% 0.23% 0.49% Small 1.99% 0.13% 0.22% Kenya MLE 1.21% 1.20% 1.18% Small 2.17% 0.82% 1.76% Tanzania MLE 1.66% 3.22% 3.06% Small 1.98% 0.69% 1.36% Uganda MLE 3.51% 1.68% 2.67% Small 2.61% 0.62% 1.13%
Source: World Bank Enterprise Survey
55 Collected from company accounting data: firms were asked to provide the cost of transport for goods (excluding fuel).
56 This serves as a proxy measure for returns to transport.
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Electricity
The problem of power outages in majority of countries in Sub‐Saharan Africa has been well documented. The recent Country Economic Memorandum57 for Rwanda examines this issue in depth, including the problems of underinvestment by Rwanda’s main power generation utility Electrogaz, and high electricity rates that are more than double that of Uganda or Tanzania. Our report examines this issue from an international perspective, and also extent of the power problem faced by enterprises within Rwanda, and its indirect cost burden on firms.
The ranking of electricity as a constraint to firm operations, and the extent of generator ownership, across comparator countries, are presented in Figure 12 below:
Figure 12: Electricity Problems: Rwanda versus comparators
Source: World Bank Enterprise Survey
We see that far more firms in Rwanda were likely to report electricity to be a major problem, compared to Asian comparators and South Africa. However, electricity problems are more severe in Tanzania and Uganda within EAC; problems in Rwanda are comparable to those of firms in Kenya.
Examining across firms within Rwanda (presented in Appendix Table 3), we see that electricity is a much bigger problem for firms in manufacturing compared to the service sector, and also for medium and large firms versus smaller enterprises. Generator ownership amongst formal manufacturing firms in Rwanda is high‐as shown in Figure 12 above, more than 60% of firms in manufacturing have generators:
57 “Rwanda: Towards Sustained Growth and Competitiveness”, October 2007.
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However, our data show that generator ownership across other sectors is minimal‐ only 6% of microenterprises owned or shared a generator in Rwanda, and none of the firms in the services sector had generators, although they all rank electricity to be a big problem for their operations.
Electricity rankings by businesses can be influenced by a combination of factors: the cost of available electricity, the problem of power outages, and also the high cost of alternative fuel, if a firm owns a generator. In Rwanda, the electricity costs from the public grid‐at 24 cents/kwh, are almost double that of other countries within EAC (ranging between 10c‐12c /kwh ) and four times that of firms in South Africa and China (4c/kwh) The cost of diesel fuel for generators is also correspondingly higher.
Figure 13: Energy Costs and Losses: East Africa Community
Source: World Bank Enterprise Survey
The high cost of electrical power is coupled with frequent power outages. Our data show that on average, enterprises in Rwanda reported power outages almost 15 days a month, averaging 2 hours per day. Firms were asked to estimate the percentage of sales lost due to outages. These data, along with enterprise energy cost shares, are presented in Figure 13 above. We see that energy shares for manufacturing are highest in Rwanda, comprising almost 6% of sales, compared to 4% in Kenya, less than 4% in Uganda, and less than 2% in Tanzania. Service firms also have much higher shares: more than 5% of sales, which is comparable to Tanzania, but much higher than firms in Kenya (2.5%) and Uganda (4%).
However, Uganda and Tanzania face more severe power problems compared to firms in Rwanda. Losses due to power outages average more than 10% in Tanzania, more than 8% in Uganda, compared to around 6% in Rwanda and Kenya. While losses are slightly lower than other countries regionally,
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these are offset by the higher energy costs for available electricity, which leads to higher cost structures of firms in Rwanda.
Information and Communications Technology
One of the most dramatic changes over the past decades has been the rapid spread of ICT in all parts of the world. In Vision 2020 the Government of Rwanda outlined its ambitious objective of being the ICT capital of Africa. Recent years have seen the expansion of investments in this sector; Rwanda has benefited from ICT based investments by international companies such as Microsoft, Nokia and Terracom. However, a majority of mobile phone, landline and internet access users are concentrated in Rwandan capital Kigali and surrounding area only.58 It is estimated that out of a population of 10 million, less than 30,000 have any internet access. Internet Infrastructure projects are also being undertaken: Rwanda Information Technology Authority (RITA) recently signed an agreement with Korea Telecom to have it set up the National Backbone Project which will enable high‐speed internet network in the country using fiber‐optic technology.59
Returns to investment in ICT are likely to be felt in the medium term, as more firms adopt these technologies. Our productivity analysis (Chapter 2) has shown that firms which adopt these technologies have higher efficiency. Current uptake is low: enterprises in our survey were asked whether they had access to the internet, and whether they had their own website. Comparing across countries (Figure 14), we see that less than 40% of firms in Rwandan manufacturing use email, less than 20% have their own website. This percentage is far lower than that of firms in Kenya, and other countries with successful manufacturing sectors60.
Figure 14: Internet Usage
Source: World Bank Enterprise Survey
58 Inter Press Service News “Rwanda Leading Africa in ICT revolution”, October 22nd, 2008 59 The New Times, Kigali, October 4th, 2008 60 Similar findings are reported in the PSF survey (July 2008): Only 17% of the respondents in their survey had
any access to internet or email.
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However, significant differences exist across firm size. As shown in Table 15 below, Medium and Large enterprises (MLEs) are much more likely to use ICT: More than three quarters of MLEs in EAC countries use email, while around a quarter of small firms have email access in all comparators.
Table 15: Medium and Large Enterprises
Burundi Kenya Rwanda Uganda Tanzania MLE Small MLE Small MLE Small MLE Small MLE Small
In summary, results from our enterprise survey findings on infrastructure constraints in Rwanda show that firms are severely constrained by the high cost of road transport and energy costs. These costs combined comprise almost 10% of sales, which is higher than all comparators except Uganda. Poor road conditions, leading to losses due to breakage in transit, and power outages, add to overall direct costs, which are particularly severe for small firms. In provision of telecommunications and ICT, Rwanda is at par with other countries regionally, but usage of ICT is only common in larger enterprises. The productivity chapter showed that these factors play an important role in reducing enterprise productivity‐improving provision and reducing costs will have a direct impact on private sector productivity and growth in Rwanda.
Taxes and Tax Administration
Government taxes and incentive policies are central in determining a country’s business climate. While taxes are essential for providing public services, they should be administered in a manner that is conducive to growth.61 This section examines the current tax system and tax administration in Rwanda, and whether it constrains business development. Rankings of taxation and tax administration by businesses are examined across countries, and across firm characteristics within Rwanda. These are evaluated along with actual tax rates and the administrative burden of tax compliance in Rwanda, to assess the role of taxes in impacting business climate in Rwanda.
Tax Rates
The Rwanda Revenue Authority (RRA) was established in 1997 as an independent body to administer the collection of taxes in Rwanda. Major revisions of the tax code were undertaken in 2005, leading to streamlined procedures, harmonization of tax incentives and reduction of the corporate income tax from 35 to 30%. Revenue collection in Rwanda under the RRA has increased significantly over time. However, the tax base remains small: as noted by the FIAS (2006) study, “There are fewer than 3000 registered companies paying national taxes in Rwanda of these, the top 13 companies are estimated to pay some 80% of all taxes collected in Rwanda, and the top 280 covered by the Large Taxpayers Department pay around 90%”. More recently, RRA reported that in 2008, one company:
61 Government taxation policies discussed in this section are obtained primarily from the FIAS report, “Sector Study of Effective Tax Burden in Rwanda”, FIAS, 2006.
66
Bralirwa, the sole Rwandan brewer, was the top tax payer, contributing $52m, equivalent to 14% of total tax revenue, followed by MTN Rwanda, which contributed $26.3 million.62
How do these taxes impact businesses? Our enterprise survey data show that almost 50% of businesses in Rwanda rated tax rates as a major or very severe constraint on enterprise performance and growth63– considerably more than rated any other obstacle as a major constraint, except electricity. Although the high level of concern about tax rates suggests that they are seen as a serious obstacle in Rwanda, it is important to note that tax rates are typically among the greatest concerns in Enterprises Surveys. Indeed, tax rates rank among the top three obstacles in over half of Enterprise Surveys in low‐income countries and in over two‐thirds of countries in Sub‐Saharan Africa (World Bank, 2004). In this respect, it is not surprising that they also rank among the top concerns in Rwanda.
However, we see that this concern is particularly high in Rwanda, versus comparator countries. Rankings across countries are presented in Figure 15 below. We see that more firms in Rwanda complain about tax rates compared to firms in Asia and South Africa, but the percentages are higher for Kenya and Uganda, while fewer firms in Tanzania and Burundi rank tax rates to be a major concern.
Figure 15: Percentage of Firms Identifying Tax Rates as Major Constraint
Source: World Bank Enterprise Survey
Reforms in the tax administration, noted above, significant publicity efforts to explain the role of taxes in promoting growth, and low corruption within the RRA have been noted in other studies. (FIAS, 2006, BIZClir 2008). Our survey data, presented in Figure 16 below, show that only 20% of firms in Rwanda rank tax administration to be a major constraint‐this is lower than all regional comparators, and most international comparators.
62 The Monitor, Kampala. 29 July 2008. 63 Almost 80% of firms in the BICS survey (July 2008) in both manufacturing and services sector reported tax rates to be a
major constraint.
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Figure 16: Percent of Firms Identifying Tax Administration as Major Constraint
Source: World Bank Enterprise Survey
Across firms within Rwanda (presented in Appendix 3. 3), we see that small firms are 20% more likely to rank tax rates to be a major concern than large firms, domestic firms, and firms that have domestic ownership, are 30% more likely to rank tax rates to be of major concern, compared to firms that export, or those that are foreign owned. In sharp contrast though, we see that exporters and foreign owned firms are more likely to rank tax administration to be a major problem, compared to domestic firms.
Although many enterprises may report tax rates as a major problem, this does not imply that tax rates on businesses should necessarily be reduced. Latest information provided by the Rwanda Revenue Authority64 (RRA) indicates that overall total tax rate has been reduced to 30% for corporations, which is lower than most regional comparators. Doing Business (2009) also ranks Rwanda fairly high in “ease of paying taxes” , with effective total tax rates on corporations being lower than all comparators, and much lower than rapidly growing countries such as India and China. These data are presented in the Table 16 below.
The impact of tax rates on firms across countries can differ considerably based on the actual tax code implementation and ability of firms to evade taxes. To measure the extent of tax evasion, enterprises were asked what percentage of total annual sales a typical establishment in their industry reported for tax purposes. Comparing across countries, presented in Figure 17 below, we see that firms in Rwanda are much less likely to report tax evasion by other firms –only 28% reported less than 100%: this is significantly lower than regional comparators such as Tanzania and Uganda, where more than 60% of firms report underreporting by others and more than 40% in Burundi and Kenya report the same.
Figure 17: Percent of Firms Expressing that a Typical Firm Report Less than 100% of Sales for Tax Purposes
Source: World Bank Enterprise Survey
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Given the low tax rates, our analysis suggests that this constraint is driven by the strict implementation of the tax code, and high penalties for non‐compliance. Compulsory audits by the RRA, particularly of large corporations which provide a significant share of the tax base, are also likely to be burdensome. This was also found in case study interviews, and is noted by BIZCLIR,65 “the tax rules are not yet sufficiently understood by businesses or tax collectors. Many of the rules are new, and few tax collectors have adequate understanding of business to properly apply rules related to deductions and depreciation. As a consequence, numerous businesses and banks complain that tax liabilities are uncertain because it is difficult to predict what will be allowed or disallowed. This raises business risks, which in turn raises the risk premium charged by banks.”
The effectiveness of the tax administration in collecting taxes from the formal sector, and the uncertainty imposed by the implementing body leads to lower net profitability and competitiveness of the Rwandan private sector compared to firms in Kenya, Uganda and Tanzania. In particular, we see above that tax evasion is commonly reported in Uganda and Tanzania‐coupled with low tax rates, firms in these countries are likely to enjoy higher after tax profits compared to firms in Rwanda, ceteris paribus.
The burden of taxes on formal businesses is also impacted by the existence and growth of the informal sector within Rwanda, which avoids taxes, but competes for consumers with the formal private sector that pays taxes. The growth of this sector (discussed in detail in Chapter 6) adversely impacts the formal sector. Although the benefits of some recent reforms in tax laws (FIAS 2006, BizClir 2008) are likely to be felt in the future, continued simplification in the tax code and bringing more firms into the formal sector could increase the tax base and reduce the burden of taxation on larger formal enterprises, that are currently reporting these as major constraints.
A second related issue, not captured through our survey, but noted elsewhere is that tax exemptions and incentives in some important sectors of the economy that are being designed and are favorable towards new businesses can impose disproportionate burden on existing firms. For example, the Government has created a Free Economic Zone: registered in this zone are entitled to an indefinite tax holiday, which is a suboptimal tax incentive as suggested by international best practice (FIAS, 2006).
Customs and Trade Regulations
The 2009 Doing Business ranks Rwanda lowest amongst comparators. It reports that it takes 42 days for a firm to clear its goods through exports or imports, at an exorbitant cost of $3275 per container for export, and $5070 for imports.
65 “Rwanda’s Agenda for Action”, USAID BIZCLIR Project, June 2008.
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Table 17: Doing Business: Trading Across Borders
China India Kenya Malaysia Rwanda Tanzania Thailand Uganda
Rank 48 90 148 29 168 103 10 145
Documents for export (number) 7 8 9 7 9 5 4 6
Time for export (days) 21 17 29 18 42 24 14 39
Cost to export (US$ per container) 460 945 2,055 450 3,275 1,262 625 3,090
Documents for import (number) 6 9 8 7 10 7 3 7
Time for import (days) 24 20 26 14 42 31 13 37
Cost to import (US$ per container) 545 960 2,190 450 5,070 1,475 795 3,290
Source: Doing Business, 2009
De Facto costs and delays are measured through enterprise level surveys. The most recent data on these are available from the BICS survey (PSF/BICS, July 2008) which reports that it takes 14 days for a container to leave the port in Mombasa after being readied for export in Rwanda at a total cost equivalent to 17% of the value of a container. However, only six firms in their survey reported, and no costs were provided for transport and port handling. Similarly, for the large share of manufacturing firms that use imported inputs, it would take 20 days for a container to be cleared from customs in Kigali after arriving in Mombasa at a total cost equivalent to 53% of the value of a container.
Similarly, anecdotal evidence points to the high cost of transport through Kenya. At the East African Community (EAC) heads of state summit in Kigali in June 2008, the President of Rwanda – who took over as the chairman of EAC – said that a researcher whom he sent on a truck to Mombasa was stopped at 47 roadblocks between Kigali and the Kenyan port (EIU, Rwanda Country Report, August 2008). Each roadblock provides an opportunity for corruption and increases cost of trade. However Rwanda is dependent on its neighboring countries to reduces these barriers
The BIZClir report, published in July 2008, reports that several positive changes are underway in legal reforms and institution building for trade: all point towards more positives than negatives within the trade regime. Box 3.1 highlights the important changes underway.
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Box 3.1: Trading Across Borders: Regulatory Reforms Underway
Source: CAS, 2007
Concrete reforms to enhance Trading Across Borders that have and are currently taking place include the following:
• Facilitation of document preparation for Customs by reducing the number of documents required to conform with international standards, placing some of the required documents on the Internet, and creating a one stop center for exports
• A speed‐up of inland transportation and handling with the aid of a one border post concept (negotiations of a draft agreement with Uganda for the establishment of such a post are well advanced), and the use of an electronic exchange of information system (RADDEX)developed by the East African Revenue Authorities (EARA), enabling the tracking of cargo information between Uganda, Kenya, and Rwanda• Improvement of customs clearance and technical control with the implementation of pre‐arrival clearance and a 24‐hour custom service
• Improved ports and terminal handling by opening offices of the Kenyan and Tanzanian port authorities in Kigali for cargo handling.189
Moreover, Rwandan Customs has increased the number of declaration acceptance points, thereby reducing the waiting time to submit declarations. An important administrative procedure recently implemented is the separation of files into those that require a physical check and those that do not. The latter groups are thus not delayed, since they do not have to wait behind those files that require physical checks. Overall, the reorganization of customs especially Kigali’s “Dry Harbor” should also lead to an improvement of procedures, especially with regard to greater transparency on the issuance of technical and health certificates.
Other important liberalization measures include the following:
• Abolition of export taxes in 1999
• Creation of the Rwanda Bureau of Standards (RBS)
• Rwanda's membership to the All Inter‐African Phytosanitary Council
• Creation of a draft law on traders and trade licensing for approval, which will regulate the commercial registry of such activities. Despite these important reforms, sectors that still need considerable attention are intellectual property, sanitary and phytosanitary measures, and government procurement.
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Governance and Corruption
The importance of good governance remains center‐stage in the discussion of economic growth. Much as been discussed recently about the low corruption levels and good governance in Rwanda. Much of what has been discussed is based on the traditional measures of corruption as the “abuse of public office for private gain”66. Behind this definition lies the image of a predatory state seen as a large “grabbing hand”, extorting firms for the benefit of politicians, high officials and bureaucrats. Much less has been examined in the investment climate studies about the role of firms in shaping the investment climate: where the choice is made to devise rules of the game that systematically benefit particular, privileged companies at the expense of society67, and the impact these have on industrial competition and economic growth. In this section we examine governance and corruption in Rwanda from both perspectives, and suggest new directions for policy based on these political economy issues.
The enterprise survey data on rankings of corruption as a major constraint across comparator countries is presented in Figure 18 below. Enterprises in Rwanda compare very favorably, with only 4.3% of firms reporting corruption to be a major constraint. This is much higher in all comparators, especially Kenya, where 73.8% of firms rank corruption to be major constraint.
Figure 18: Percent of Firms Identifying Corruption as a Major Constraint
Source: World Bank Enterprise Survey
These results are corroborated by objective indicators of corruption. Very few firms in Rwanda report informal payments and gift giving to tax officials, or to obtain a Government contract. These are lower than all comparators, except South Africa.
66 Definitions from Hellman and Kaufmann, 2002. 67 “Governing the Investment Climate” Development Outreach, March 2005
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Examining across firms within Rwanda, we see some dispersion across firm characteristics: 15% of exporters rank corruption to be a major problem, compared to less than 2% of domestic firms. More than 10% of manufacturing firms consider corruption to be a problem while less than 2% of service sector firms complain about corruption.
Enterprises were also asked about the magnitude paid in bribes: what percentage of sales that a typical enterprise had to pay to public officials (for customs, taxes, licenses, regulations etc) to get things done. Since exporters are the only firms in Rwanda (a subset of manufacturing) that are more likely to report corruption to be of major constraint, we examine data on bribe payments for this group, compared to others within EAC. These are presented in the Table 18 below.
Figure 19: Cross Country Comparisons of Informal Payments and Gift Giving
Source: World Bank Enterprise Survey
Table 18: Informal Payments Characteristics across EAC
Source: World Bank Enterprise Survey We see in Table 18 above, that 33% of exporting firms (almost all of which export through the
Mombasa port) report bribe payments are required, with losses averaging almost 3% of sales. These bribes are far more prevalent in other countries in EAC, whose impact is borne by firms in Rwanda.
Good governance has been highlighted in the Government’s Investment Guide, which notes that “Rwanda is also a country which current investors regard as being notably free of corruption, a feature that makes it stand out not only in its neighborhood, but in sub‐Saharan Africa”. However, as the BIZCLIR report (2008) notes,” Although blatant corruption among government inspectors and other officials is not widely regarded to be a problem, there is some indication that “preferred” business initiatives get regulated more favorably than others. This is an issue that requires continued scrutiny.”
These issues on governance are captured by the Kaufmann Kraay indicators, which are presented in Table 19 below. Countries are ranked on a score of 1‐100, with 100 being best practice countries. Rwanda ranks low on all measures of governance compared to international best practice, except in the control of corruption, in which its rankings are better than all countries regionally. Rankings on regulatory quality and voice and accountability are particularly low.
India 58.2 22.1 54 48.3 57.1 52.9 Kenya 43.8 15.4 28 44.9 15.7 16 Rwanda 14.4 27.4 39.8 25.4 34.3 55.8 South Africa 67.3 44.2 76.8 70.2 58.6 70.9 Tanzania 40.4 40.4 43.6 38 42.9 43.2 Thailand 32.2 16.3 64.9 62.4 55.2 50.5 Uganda 30.3 13.5 34.6 46.8 39 26.2 Vietnam 8.2 59.6 41.7 31.2 44.8 29.1 Source: World Bank, 2006
Crime
Good governance is also highlighted in low rankings on crime as a constraint to firm operations in Rwanda. Figure 20 below presents the rankings on crime across comparators. We see that only 4% of firms in Rwanda consider crime to be a major business constraint‐this is much lower than all comparators regionally, and also lower than all countries internationally.
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Figure 20: % of Firms Identifying Crime, Theft and Disorder as Major Constraints***
Source: World Bank Enterprise Survey
These rankings are corroborated by objective indicators. Rwandan firms report the lowest amount of sales lost due to property theft in the past year: a quarter of one percent of sales on average. The percentage of sales spent on security is also the lowest amongst comparators, with less than 1% being spent on security staff and equipment for business premises.
Table 20: Crime costs: Percentage lost due to property theft, % spent on Security
% Lost due to Theft % of Sales spent on Security
Rwanda 0.25 0.72
DRC 0.71 0.96
Kenya 0.85 1.04
Burundi 1.02 1.10
Uganda 0.53 1.27
South Africa 0.86 1.56
Tanzania 0.89 2.47 Source: World Bank Enterprise Survey
Summary
Infrastructure: Enterprise survey results show that Rwandan firms have much higher direct costs of transport and energy compared to other countries regionally. Transport costs are almost three times higher than that incurred by firms in Kenya, while energy costs are almost double that of Kenyan firms. Poor road conditions add to these costs, with losses reported due to breakage in transit. Uganda and Tanzania, however, face high losses due to chronic power outages‐the situation in Rwanda is better than
76
these countries with fewer firms reporting outages, and also corresponding losses, which are similar to that of firms in Kenya.
Regulations: We see that Rwanda has made rapid advances in regulatory reform in all areas
including tax regulations, customs regulations, business licensing and the legal system. However, with a small private sector tax base, we see that compliance costs are high, and older, larger firms report taxes to be a major constraint. Improvements in other areas have allowed Rwanda to move up on the scale of doing business rankings in these areas: however other countries, particularly Kenya, are also making rapid advances in some areas.
Governance: Rwanda has been a model of good governance. Enterprise data corroborate these
findings, with low reported corruption, petty crime and bribery payments. However, business‐political linkages remain, as noted by the Kaufmann Kraay indicators, and need to be addressed.
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Chapter 4: Access to Finance
Access to Finance in Rwanda: Investment Climate Assessment
Access to finance is critical for private sector development and economic growth. The financial sector is critical because it facilitates the reallocation of funds from economic agents with funds to agents with a deficit of funds. This function is particularly important when the mismatch between resources and opportunities is large. In broad terms, all measures of financial sector performance are measures of how well the sector intermediates, eliminates the mismatch, between these two types of economic agents.
This chapter contains analysis of access to finance based on the data from 2006 Enterprise Survey for Rwanda and a set of comparator countries and finds indications that financial development is not as high as is expected when comparing Rwanda to several of its cohorts.
This report also finds that the level of financial intermediation is very low, and the level of nonperforming loans is extremely high. Manufacturing firms have better access than retail or service firms and micro and small enterprises have lower access than medium and large enterprises. Micro enterprises rely primarily on non‐bank financial intermediaries to obtain loans, while small, medium and large firms use commercial banks. The loan maturity is short, especially for smaller firms and collateral requirements are relatively high for all firms. The interest rates are also relatively high and often cited as an obstacle.
Access to Finance in an International Perspective
One of the key indicators of financial sector development is the ratio of domestic credit to private sector to GDP. Domestic credit measures the extent of financial intermediation in the economy (Beck at al, 1999). This ratio is very low in Rwanda, only 13.5 percent (Figure 21, left panel), and it is among the lowest four out of eleven comparator countries.68
The Domestic Credit to private sector shows a small improvement since 2001 (Figure 21, right panel). On the other side, credit provided by banking sector, which is another commonly used measure of the extent of formal financial intermediation, has declined slightly in the years 2004‐2005 (no recent data is available in WDI database).
Interest rates are relatively high in Rwanda: firms report an average interest rate of about 15%, but inflation is high, so the real rate is close to 6%, which is about average among the comparator countries (Figure 22). However, the recent PSF survey reports much higher interest rates, which differ by type of finance. The interest rates reported for commercial bank finance are 34%, for savings circles 40%, for MFI’s 27% and cooperatives 24%.
68 The latest data available in WDI database for Rwanda is for year 2005.
78
Figure 21: Bank Credit to Private Sector in Rwanda and Comparator Countries
International Comparison: Domestic Credit to Private Sector, 2005
141.6
113.9
93.3
65.9
40.8
25.3
20.8
13.5
10.0
6.7
1.9
0 20 40 60 80 100 120 140 160
South Africa
China
Thailand
Vietnam
India
Kenya
Burundi
Rwanda
Tanzania
Uganda
Congo, Dem. Rep.
Percent of GDP
Bank Credit to the Private Sector, 2001-2005
0
2
4
6
8
10
12
14
16
2001 2002 2003 2004 2005
Perc
ent o
f GD
P
Credit provided by banking sector (% of GDP)Domestic credit to private sector (% of GDP)
Source: WDI Database
Figure 22: Cross‐Country Comparison of Interest Rates 69
Interest Rates
6%
10%
11%
12%
13%
14%
15%
19%
21%
0% 5% 10% 15% 20% 25%
China
Vietnam
Congo, Dem. Rep.
Tanzania
South Africa
Kenya
Rwanda
Burundi
Uganda
Real Interest Rates
14%
17%
-0.3%
-2%
8%
4%
6%
2%
6.3%
-3% 0% 3% 6% 9% 12% 15% 18% 21% 24%
Congo, Dem. Rep.
Kenya
Vietnam
China
Tanzania
Rwanda
South Africa
Uganda
Burundi
Source: World Bank Enterprise Survey and WDI Database
A more severe problem is shown by the non‐performing loans (Figure 23) – they are extremely high in Rwanda: 34% in 2005, which is much higher than all other comparator countries. Nonperforming loans is a significant problem that could lead to banking sector instability, crisis, and a large fiscal burden. This problem will need to be addressed to bring the banking sector to a sustainable role of provider of much needed financial intermediation.
69 For all countries inflation is measured as percent change in CPI, except DRC, for which no CPI data is available and GDP deflator is used instead.
79
Figure 23: Bank Nonperforming loans in Rwanda and Comparator Countries
International Comparison: Bank nonperfoming loans to total gross loans, 2005
34.1
11.1
10.5
5.2
5.2
1.5
0 5 10 15 20 25 30 35 40
Rwanda
Thailand
China
India
Kenya
South Africa
Percent
Bank nonperfoming loans to total gross loans (%)
0
10
20
30
40
50
60
70
2003 2004 2005
Perc
ent
Source: WDI Database
Despite low level of financial intermediation and high non‐performing loans, Rwanda scores relatively well among the selected set of comparator countries. Only 36% of all firms report access to be a major or severe problems, which is in the bottom half among other countries, and comparable to Tanzania and Kenya. The objective indicator of access shows a relatively similar picture – 45% of manufacturing firms use some credit products (such as overdraft, line of credit or a loan). This is about average relative to all comparator countries, and compares favorably to Kenya and Tanzania. More recent PSF BICS Survey 2008 shows access as the fourth obstacle to doing business in Rwanda.
It is important to note that for cross‐country comparability all firm‐level comparisons in Figures 24 and 25 are done only on manufacturing firms across all countries. Later section shows that manufacturing firms have significantly better access in Rwanda, which means that for an average firm Rwanda compares worse than reflected in the graphs and discussions above.
Figure 24: Cross‐Country Comparison of Access to Finance Obstacle and Credit Products Use
Percent reporting major/severe obstacle
58
51
42
38
36
19
14
8
0 10 20 30 40 50 60 70
Uganda
Burundi
Kenya
Tanzania
Rwanda
Vietnam
South Africa
Thailand
Percent of firms
Percent with overdraft or line of credit
86
64
51
46
45
35
31
28
25
0 10 20 30 40 50 60 70 80 90
Thailand
South Africa
Burundi
Vietnam
Rwanda
Kenya
China
Tanzania
Uganda
Percent of firms
Source: World Bank Enterprise Survey
External finance is used to finance firms’ working capital purchases (inventories, accounts receivables) and investments in productive assets (property, plant and equipment). In Rwanda manufacturing firms finance about 15% of working capital and 18% of investment from banks, which is again about average relative to other comparator countries – slightly better than Kenya, Tanzania and
80
Burundi and Uganda, but worse than China, Thailand or South Africa (on investment measure) (Figure 37, left panel).
In the recent PSF OTF Group BIC survey (July 2008) 27% of respondents report having used commercial banks to provide funding for their business. This number is comparable to ICA data, in which 32% of respondents report having used commercial banks. According to the same PSF survey, 39% of firms have used savings circles, 15% used MFI’s, 12% used cooperatives and 7% used friends. Unfortunately, PSF survey does not report what percent of total funds come from each source, only whether or not the firm has used each source.
Trade credit is an important source of financing for working capital in most countries, and Rwanda is not exception: trade credit comprises 15% of working capital finance, which is again about average relative to other comparator countries (Figure 25, right panel). Comparatively higher levels of trade credit indicate informal credit intermediation that provides viable substitutes to the lack of finance available from formal institutions. However, trade credit is often considered second‐best source of finance because it is generally more expensive and of short maturity (see Fisman and Love, 2003).
Often, in environment with lack of formal finance, firms have to rely on informal sources, such as finance from family, friends and money lenders. In Rwanda this source of capital is used to finance about 2.3% of working capital, which again is about average relative to other comparator countries.
Figure 25: Cross‐Country Comparison of Sources of Working Capital and Investment Finance
Total Banks
14
29
16
15
7 9
46
9
30
1822
2
1418
26
11
57
15
31
0
20
40
60
80
Burundi China Congo,Dem. Rep.
Kenya Rwanda SouthAfrica
Tanzania Thailand Uganda Vietnam
Perc
ent
Working Capital Investment
Working Capital
12
3
14
1715
23
19
12
21
9
12
7
23
0
5
23
5
2.3
0
5
10
15
20
25
30
Burundi China Congo,Dem. Rep.
Kenya Rwanda SouthAfrica
Tanzania Thailand Uganda Vietnam
Perc
ent
Trade credit Family, friends or informal sources
Source: World Bank Enterprise Survey
Because of the limited cross‐country data availability and for consistency, the above analysis was limited to manufacturing small, medium and large enterprises. For a limited set of countries we could conduct cross‐country comparison for micro enterprises, relative to non‐micro, i.e. small, medium and large enterprises, SML. Figure 26 reports the difference between SML firms and micro firms. In all countries considered SML firms are more likely to use checking accounts or credit products. The relative difference between micro and SML firms is about average in Rwanda.
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Figure 26: Difference between micro enterprises and SML enterprises
Difference between SML and Micro Firms
-38%
-18%
-39%
-14%-12%
-26%
-53%
-29%
-26%
-18%
-21%
-29%
-20%
-17%
-26%
-3%
-8% -7%
-60%
-50%
-40%
-30%
-20%
-10%
0%Kenya Rwanda South Africa Tanzania Uganda Zambia
% of firms with overdraft % having checking account% with loan
Source: World Bank Enterprise Survey
Lack of access can be attributed to weaknesses in the institutional and legal environment. (Djankov et al, 2007). A variety of measures of institutional and legal environment is now available in the Doing Business database. Rwanda, although improving, scores poorly on the Ease of Doing Business ranking, which ranks countries based on 10 indicators, such as starting a business, protecting investors, enforcing contracts, trading across borders, property rights, closing a business, etc. Rwanda ranks as number 138 among 178 countries considered. Only two countries among the comparators rank worse than Rwanda – Burundi and DRC.
Effect of size on Access to Credit
This section focuses on how access to credit varies within Rwanda. We start our analysis with focusing on three main categories of firms: micro enterprises (128 firms), small firms (151) and medium‐large firms (63).70
In most countries around the world smaller firms have more difficulties with access to finance. Rwanda is no exception. Comparison of micro, small and medium‐large firms shows that micro firms have least favorable access to finance, while medium‐large firms have the most favorable (Figure 27, left panel). Among micro firms 47% report access as one of the top 3 obstacles, while only 38% of small and 22% of medium‐large firms do so. Percent of firms with bank account also monotonically increasing in size, as is percent of firms with use of credit products (such as overdraft, line of credit or loans). There is larger difference in use of credit products between small and medium‐large firms, and less difference between micro and small firms. Thus, micro and small firms represent the disadvantaged groups.
These differences are not simply due to lack of demand, just the opposite: micro and small firms are less likely state “no need for loans” as reasons for lack of loan applications. This means they have larger
70 The data for micro enterprises are un-weighted because the weights for this subset of firms do not represent the whole population.
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self‐expressed demand for loans than medium‐large firms do, but less usage of credit products, lower application rates and higher rejection rates.
Interest rates are about the same for all 3 groups: 13% for micro and 15% for small and medium‐large groups. Lower rates for micro firms might be indicating directed or subsidized credit.
About half the firms make investment into productive assets, with small firms more likely to invest – 67% of them report having made purchases of productive assets in last years. However, the amount of new purchases is relatively small – about 4% of existing assets for small, medium and large firms, and larger –at 8% for micro firms. Given that the rates of depreciation are around 6‐8%, the investment is relatively low. Investment is necessary for sustainable growth and low investment is a reason for further investigation.
Figure 27: Comparison of Access by Firm Size
4752
2316
20
38
75
31 32
40
33
22
98
73
42
11
61
15
25
13 15
0
20
40
60
80
100
120
Percentreporting
finance accessas one of the
top 3 obstacles
Percent with abank account
Percent withcredit products
Applied for aloan
Rejectedapplication
No need for aloan
Interest Rate
Per
cent
Micro Enterprise Small Medium-Large
Source: World Bank Enterprise Survey
All around the world most enterprises rely primarily on retained earnings to finance working capital and investment. This is also the case in Rwanda (Figure 28). However, the same pattern described above is visible here as well ‐ micro and small enterprises have significantly less access to bank finance to finance their working capital and investment. Micro enterprises finance only about 1% of working capital and 3% of investment with bank funds, which increases to 10‐12% for small enterprises and 25%‐33% for medium and large enterprises. The proportion of bank finance in medium and large enterprises is fairly large, suggesting that these types of firms are hardly constrained in bank finance. But the story is different for small firms, and especially for micro firms, which are hardly able to get any bank finance.
Micro firms use less trade credit as a source of working capital finance than small and medium‐large firms. Thus, for them even informal finance form other firms is a challenge. They substitute funds form family and friends for some of the formal finance that other firms are able to obtain – they use 6% of this type of finance, more than any other firms. These patterns are common around the world and are signs of systematic difficulties with access that are more pronounced for smaller size firms.
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Figure 28: Sources of Finance for Working Capital and Investment
Working Capital
7868
1
1025
5 15 156
58
0.43
0%
20%
40%
60%
80%
100%
Micro Enterprise Small Medium-Large
Perc
ent
Retained earning Total BankSuppliers and customers Family, friends or informal sources
Investment
8978
312
33
314
64
13
0%
20%
40%
60%
80%
100%
Micro Enterprise Small Medium-Large
Per
cent
Retained earning Total BankSuppliers and customers Family, friends or informal sources
Source: World Bank Enterprise Survey
Characteristics of Loan Products
In the micro enterprise sample only 22 firms have any loans, while in small, medium large (SML) sample 74 firms do (Table 21). Most of the loans in the micro enterprise sample are given by non‐bank financial institutions, likely microfinance or special purpose financial institutions – 68% of all loans come from these types of providers. The situation is different in the SML sample, where 93‐94% of all loans are issued by private commercial banks. The public sector loans represent a small proportion of all loans – only 3% for SML sample and 14% for micro enterprise sample.
Table 21: Loan Providers and Loan Characteristics
Micro Enterprise Small Enterprises Medium‐Large Enterprises
No of Obs.
Percent No of Obs.
Percent No of Obs.
Percent
Loan Providers: Private commercial banks 4 18% 32 94% 37 93% State‐owned banks and/or government agency 3 14% 0 0% 2 5% Non‐bank financial institution 15 68% 2 6% 0 0% Other 0 0% 0 0% 1 3% Total 22 100% 34 100% 40 100% Loan Characteristics Mean Median Mean Median Mean Median Duration (month) 16 18 33 24 42 21 Collateral as % of Loan Value 138 100 165 145 158 150 Key Firm Characteristics Audited financial statements 7 5.5% 46 30% 38 62% Own Land 16 12.5% 39 26% 44 72% Source: World Bank Enterprise Survey
Loan maturity is also a function of firm size – micro firms have the shortest maturity – on average about 1.5 years or less, while small, medium and large firms have longer loan maturity – most firms have 2‐3 years maturity with a few firms with 10 years maturity (predominantly medium and large ones).
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Almost all firms were asked to post collateral (except 3 firms in micro sample and 2 firms in SML sample). Collateral requirements are relatively high – most firms are expected to post about 150% of collateral to the value of the loan, except micro firms are asked to post slightly less, on average.
Two important characteristics are often associated with financial access: audited financial statements and ownership of the land. Both of these are strongly correlated with size: only 5% of microenterprises and 30% of small enterprises have audited financial statements, while over 60% of large firms do. In the recent PSF report 22% of micro enterprises (with 0‐2 workers), 59% of small (3‐24 workers) and 64% of large firms (over 25 workers) report having audited financial statements. The qualitative patters are the same, while small differences might be explained by differences in size definitions and sample composition.
The patterns are similar with respect to ownership of the land: 12% of micr0, 26% of small and over 70% of large enterprises own land. The numbers are slightly higher in the recent PSF report: 37% of micro, 62% of small and 70% of large firms report having title to the land they occupy.71
Not surprisingly, both – audited statements and land ownership are strongly correlated with access. For example, partial correlation of audited statements and usage of any credit product is 0.37, and correlation between owned land and usage of any credit product is 0.26, both significant at 1%. In the regression annex we verify that these correlations hold up after controlling for firm size.
Next we look at the reasons firms did not apply for loans (Table 22). As discussed above, lack of demand is not the main reason for micro firms (only 20% of those who did not apply say they have no need for a loan), but it is the main reason for medium and large firms – 61% of them do not apply because they do not need loans. High interest rates and unattainable collateral requirements are cited by about a quarter of all micro firms and a fifth of small firms, but are not important reasons for medium and large firms.
These results are confirmed in the recent PSF OTF Group BIC survey ( July 2008): most businesses (81%) cite lack of collateral and 77% cite high interest rates as major problems among several finance and investment related issues. Lack of collateral was more important obstacle for larger firms (those with over 25 employees) and industrial sector firms. High interest rates were cited more by larger firms in firms in the professional sector. In the same survey, limited availability of leasing was taking a third place among the most severe finance issues, especially for medium firms and firms in tourism and industrial sectors.
Table 22: Reason for Lack of Loan Application
SML Enterprise Reason
Micro Enterprise Small Medium‐Large
No need for a loan 20% 33% 61% Application procedures are complicated 16% 21% 0% Interest rates are not favorable 25% 20% 8% Collateral requirement are unattainable 29% 21% 3% Size of loan and maturity are insufficient 1% 1% 3% Did not think it would be approved 4% 1% 5%
71 Here, the differences might also be explained by different wording of the questions: ICA survey asks about percent land owned, while PSF asks about having title to the land the firm occupies.
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Other 6% 3% 21% Total 100% 100% 100% Sample Size 108 104 38 Source: World Bank Enterprise Survey
Access for firms with different characteristics
Registration and Legal Status
In this section we consider another important aspect of access to credit – the degree of firm’s formality measured by a registration status for micro enterprises and legal status for small, medium and large firms. We separate limited liability companies, LLC, from unlimited liability firms, including sole proprietorships and partnerships. Limited liability can be seen as another step toward more formality as in involves further separation of individual ownership and the firm identity.
A subcategory of registered vs. unregistered firms was created based on firm’s actual registrations within the micro enterprise category. Firms are classified as “registered” if they have obtained a tax identification number from the tax administration or other agency responsible for tax registration. In Rwanda micro enterprise sample 23 firms are not registered for tax purposes, while the rest, i.e. 105, are. Among the SML sample, 65 firms have LLC status, while 145 firms have other legal forms.
These varying degrees of formality clearly reflected in the access to finance indicators. For example usage of bank accounts and credit products monotonically increasing in the degree of formality – informal micro enterprises have least access, followed by registered micro enterprises, followed by unlimited liability firms, with LLC firms on the top of the access ladder with the most access (Figure 29, left panel). Out of 23 informal micro enterprises no single one has any loans, overdraft or line of credit. Only one of these informal micro enterprises have applied for a loan and was rejected (hence the rejection rate is 100%). However, most all of these firms do not name finance as one of the top three obstacles for their business. Most likely these firms choose to stay informal and do not need to obtain formal finance.
Figure 29: Access Indicators by Formality and Age
4
17
04
18
6660
28
1821 21
36
75
3532
29 3233
99
65
42
32
61
100
13 15 15
0
20
40
60
80
100
120
Percentreporting
finance accessa major/severe
obstacle
Percent with abank account
Percent withcredit products
Applied for aloan
Rejectedapplication
No need for aloan
Interest Rate
Perc
ent
Informal Micro Enterprises Registered Micro EnterprisesLegal Status Unlimited Legal Status LLC
34
80
3339
2729
84
49
32
50
58
15
35
84
50
35
9
31
1515
26
0
10
20
30
40
50
60
70
80
90
Percentreporting
finance accessas one of the
top 3 obstacles
Percent with abank account
Percent withcredit products
Applied for aloan
Rejectedapplication
No need for aloan
Interest Rate
Perc
ent
1-5 yrs 5-10 yrs 10+ yrs
Source: World Bank Enterprise Survey
The sources of working capital and investment present similar picture: LLC firms use the most bank funds in working capital and investment, and micro enterprises use the least (Figure 30).
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Firm Age
It is widely documented that younger firms without a proven track record experience more severe financing constraints.72 These firms are more opaque because less information is available about them to the banks and often they are more risky (i.e., more likely to fail). To test whether age of the firm affects access to credit, the sample is divided into several groups of firms based on their age: firms that are 5 years old or younger; firms between 6 and 10 years old and firms that are more than 10 years old. In Rwanda about a third of the sample falls into one of these age categories.
There are no large differences in access indicators for firms in different age groups (Figure 29, right panel and Figure 30). The youngest firms have slightly less usage of credit products than older firms, but these differences are not statistically significant.
Figure 30: Sources of Finance for Formality and Age
Working Capital
81 7670
5568
61
110
22
17 11 18
45 12 21
11 16 18
3 8
66
0%
20%
40%
60%
80%
100%
No Yes Unlimited LLC 1-5 yrs 5-10 yrs 10+ yrs
Registered MicroEnterprise
Legal Status Firm Age
Perc
ent
Retained earning Total BankSuppliers and customers Family, friends or informal sources
Investment
92 8577
6779
64
1525
18 1324
1 6 2 30 4 4 3
77
7
0%
20%
40%
60%
80%
100%
No Yes Unlimited LLC 1-5 yrs 5-10 yrs 10+ yrs
Registered MicroEnterprise
Legal Status Firm Age
Perc
ent
Retained earning Total BankSuppliers and customers Family, friends or informal sources
Source: World Bank Enterprise Survey
Determinants of Access to Finance: Multivariate Analysis
In this section we perform multivariate analysis of access indicators to check the univariate results discussed above. Many firm characteristics are correlated, and thus one characteristic could proxy for the effect of another characteristic in univariate analysis. For example, LLC and foreign‐owned firms tend to be larger and older, while younger firms also tend to be smaller.
Appendix Table 4.2 reports regressions with several dependent variables used as indicators of access: two subjective indicators – i.e. whether the firm claims access is one of the top 3 obstacles, and whether the firm states “no need for loans” as a reason for lack of loan application and several objective indicators: whether the firm has any credit products (i.e. overdraft, line of credit or a loan), loan applications and rejections, percent of finance for working capital and investment and an interest rate. For comparison, micro enterprises are included in the same regression with SMLE.
The differences among subjective indicators are mostly not statistically significant across various categories of firms except for firms between 6 and 10 years, which are 20% more likely to report not
72 See for example, Love and Martinez Peria (2005) Demirguc-Kunt and Maksimovic (2004).
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needing a loan. The notable finding, confirming earlier results, is that micro enterprises and small firms are more likely to report financing as one of the top 3 obstacles, relative to medium and large firms. Furthermore, micro firms are significantly more constrained as small firms – the significance and the magnitude of the coefficient is almost twice as high for micro firms.
There are significant differences on objective indicators – specifically the use of any credit products and being rejected for a loan. Size has a significant impact on the usage of credit products – the omitted category is medium‐large firms and the regression coefficients are significant for micro and small firms. Micro and small firms have less usage of credit products and less bank finance for investment and working capital than medium‐large firms.
Micro and small firms are also substantially more likely to be rejected for a loan than medium‐large firms. However, micro firms report lower interest rates than medium and large firms (significant at about 5%). This might be due to subsidized interest rates through special targeted programs. The regression results confirm earlier findings that suggest micro and small firms to be more constrained in their access to finance relative to medium and large firms.
Two additional variables are included in regression analysis – whether the firm has an external auditor and whether or not the firm owns land. The external audit makes the firm’s financial statements more reliable and thus reduces the information asymmetry between the firm and financial institutions, and thus should improve firm’s access to finance. In Rwanda the presence of external auditor is associated with access to credit: firms with auditors report higher credit product usage and bank financing for investment. However, the causality with audit is not clear, as banks might require an external audit as a condition for a loan.
Own land could be used as collateral and should also be associated with increased access. In Rwanda, firms that own land are associated with higher usage of credit products and higher bank financing for working capital, although they also pay higher interest rates.73 These results on auditor and own land dummies are common across countries.
Among other firm characteristics, exporter status is associated with more bank capital in working capital finance and higher credit product usage. Ownership characteristics are no longer significant once size and other firm characteristics are taken into account. Legal status, interestingly, is associated with being more likely to have a loan rejected.
Manufacturing firms are more likely to report not needing a loan, less likely to report access as significant obstacle (only significant at 12%) and pay lower interest rates. Service firms are more likely to have applied for a loan and to be accepted for a loan. Between the two regions, Kigali is 20% more likely to report financing as one of the top 3 obstacles.
Investment
Investment into productive assets is one of the desirable outcomes of access to finance. Investment is important as a source of growth and efficient capital allocation. Table 23 reports regression analysis of
73 The sample size of interest rates is relatively small, so these results may not be robust.
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two measures of investment: an indicator for whether or not the firm has purchased any assets in 2006 and the ratio of Investment amount to sales.74 As control variables we use the same firm characteristics used in the access indicators regressions. In addition we include several access indicators to test whether access to finance is associated with higher incidence or larger amount of investment.75
We find that small firms are more likely to purchase assets relative to medium and large firms. Female owner firms, on the other hand, are less likely to make investments. Exporters and LLC firms also invest less relative to non‐exporters and non‐incorporated firms. Foreign owned and African owned firms are no different form others in their investment behavior, as are the firms in different age categories.76 Manufacturing and service firms are less likely to invest relative to “other” industries, which are an omitted category.
Access to credit is important for investment: firms with access to credit products report a higher incidence of investment (significant at 5%) and higher amount of purchased assets relative to existing assets (only marginally significant at 11%) Those who report higher subjective obstacles have lower incidence of investment and relative size of investment (relative to sales). Loan rejections are not significantly related to investment (but the sample is relatively small for this category).
This evidence is in line with the argument that access to credit helps increase investment. However, an alternative explanation could also be possible – that those firms with investment may have good growth potential and hence would be favored by banks. Without additional data or experimental design it is impossible to infer the causality of investment and access relationship.
74 The results with the ratio of investment to assets are similar, but have fewer observations due to missing assets and therefore are not reported here.
75 The regressions with Purchased Assets as dependent variable is estimated using Probit model and regressions with Investment amount as dependent variable is estimated by Tobit model with a lower bound of zero, to account for those firms which made no investments.
76 However, once we add rejected as a control, foreign owned firms invest more relative to non-foreign owned firms. The sample size drops significantly in those regressions, so the results are not likely to be representative of the whole populations.
Notes: Estimated by Probit (for Purchased Assets) and Tobit (Investment to sales, with lower limit of zero). * significant at 10%; ** significant at 5%; *** significant at 1%
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Chapter 5: Labor Markets and Human Capital
A well‐functioning labor market is vital to the success of the government’s policies to establish a globally competitive economy and provide jobs to a young and growing population. This chapter uses firm‐level data provided by the personnel managers of surveyed firms together with individual‐level employee survey data to describe the labor market in the manufacturing, retail and services sectors. The chapter starts with a broad description of firm perceptions on a variety of labor market constraints and firm responses to these constraints including training. The chapter then examines wage‐setting behavior using firm‐ and worker‐level data. Finally, the chapter discusses the impact of HIV/AIDS on firms and their responses to the crisis.
The data used for this chapter comes from a survey of 212 total firms, including 59 firms from the manufacturing 44 firms in retail, and 109 firms in services. The individual‐level data comes from 171 workers matched to the sampled firms in the manufacturing sector.
The average firm in sample employs 189 workers in the manufacturing sector, 15 in the retail sector and 19 in the services sector. Median firm size is considerably lower, with a median of 43 5 manufacturing, 9 in retail and 11 in the services sector. As is typical at this level of development, employment is dominated by a handful of very large firms. In Rwanda, the mean is skewed by one large privatized employing more than 5000 workers. In the manufacturing sector, firms in the food and chemicals sub‐sector are considerably larger than other firms with average employment of more than 200 workers.
The workforce of the typical firm in the manufacturing sector is 19 percent female, 6.5 percent part‐time and the typical worker has more than 9.5 years of schooling. The use of part‐time employment is quite extensive in the retail sector, where more than 17% of workers are part‐time employees.
Enterprises were asked to rank business constraints on a scale of 1 to 5. Two constraints are pertinent to this chapter: the extent to which an inadequately educated workforce and labor regulations constrain the growth and operations of enterprises. Labor market regulations are relatively low on firms’ lists of the impediments to productivity enhancing growth. None of the firms with over 50 employees rank this constraint to be major or severe constraint to growth and operation, while only 6% of small firms do. However, education and skills of the workforce are ranked as a major problem by 14% of the firms, with 20% of larger firms (with more than 50 employees) ranking it to be a problem, compared to 9% of small firms. Only 7% of firms in services (mainly hotels and restaurants) consider skills to be a problem, while 13% of firms in retailing consider it to be a major or severe problem.
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Worker Skills
Table 24: Percent of firms reporting skills shortage as a major or severe constraint
Firm Category Manufacturing Retail Services
Small (5‐49 employees) 6% 13% 7% Medium and Large (50 + employees) 20% Na 17%
Non‐exporter 14% 11% 7% Exporter 11% Na Na
Domestic 13% 9% 6% Foreign 18% 25% 18%
Source: Rwanda 2007 Investment Climate Survey
We advance three tentative explanations of why a small fraction of firms report being constrained by the level of formal education of the workforce. Firstly, it is possible that firms have made the necessary input‐mix adjustments that are compatible with an abundance of low skilled workers. Secondly, it is possible that the quality of formal training has risen sufficiently to match firm needs and that firm‐based training is a suitable substitute to inadequate formal education. Finally, it is possible that other binding constraints in the business environment dominate the importance of schooling. In other words, in an environment of low growth, with more urgent business environment concerns, we would not expect skills constraints to top firms’ lists of concerns. We use data on the average education level
Figure 31: Manufacturing Firms in Rwanda are in the middle of the pack with regards to perceptions of skills in the labor force.
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. The figure shows percentage of firms that report that skills shortage is a major/severe constraint to firm operation in all the countries shown
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of workers to examine the extent to which concerns about skills varies with observed skill intensity of operation. For the next two explanations, we can examine the extent to which training and employment growth affect perceptions of worker schooling.
Firms were asked to report the education level of the typical worker in the firm. We use this data to examine if perceptions of skill shortages are related to average skill intensity of the firm. The Table 25 below shows the percentage of firms reporting major or severe constraints due to skills shortages. Across all skill categories, a uniform percentage of firms report major or severe problems.
Table 25: Do reports of skill constraints vary by worker education
Average education level % Firms reporting Major/Severe impediment as a result of
skill shortages 0‐3 years na 4‐6 years 13% 7‐12 years 10% 13 years + 15%
Notes: The estimates shown above are restricted to the manufacturing sector.
If the provision of firm‐based training is associated with a lower likelihood of reporting skills shortages, then this is prima facie evidence that while formal skills might be inadequate, firms are able to compensate through firm based training. We explore this possibility below.
Table 26: Do reports of skill constraints vary by training or employment growth
Yes No
Does Firm provide training? 6% (0.25)
17% (0.37)
Firm employment growth above median
13.6% (0.35)
13.6% (0.35)
Notes: Standard errors in parentheses. The estimates shown above are restricted to the manufacturing sector. Median employment growth between 2003 and 2006 is 8.5%.
Table 26 above demonstrates that complaints about inadequately schooled workers are associated with whether the firm provides training. In particular, firms that do not provide training are nearly 3 times as likely to report being constrained by the quality of formal skills in the workforce. While it is possible that the direction of causation runs in the opposite direction: firms train because they are able to select the best workers, the association is strong enough to warrant an investigation into the determinants of firm‐based training.
Finally, we examine the extent to which reports of skills constraints are associated with how dynamic a firm is. Our measure of dynamism is the average growth rate of employment over the past 3 years.77 The table shows no difference in the percentage of firms reporting impediments across high and low growth firms.
77 This is not the best measure of dynamism; a firm constrained by skills is likely to grower slower than its potential employment growth rate. While a firm can substitute away for skilled labor with capital, this is less likely for a landlocked country such as Rwanda that faces high transport costs. Better measures of dynamism such as productivity are subject to greater measurement error.
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An alternative perspective on the extent of the inadequately schooled workers can be gleaned through an examination of the schooling attainment of a typical worker in the typical firm in international perspective. We acknowledge that cross‐country comparisons of schooling attainment could under‐ or over‐estimate differences in formal skills as a result of cross‐country differences in the quality of a year of education. As Table 27 shows below, the typical worker in the modal firm in Rwanda has between 7‐12 years of schooling. 36% of firms report that their typical worker has between 7‐12 years of schooling. This is the lowest share of firms relative to the other African comparators. However, the distribution of school attainment for the typical worker across firms is uniform with a third of the firms reporting that the average worker has more than 12 years of schooling. This is considerably higher than all the other comparator countries and is twice as high as Kenya and Uganda.
Table 27: Percent of firms saying that the average worker in the firm has completed different levels of schooling
0‐6 years 7‐12 years >12 years
Uganda 36 45 18 Tanzania 35 57 8 Kenya 15 68 17 South Africa 10 78 12 Rwanda 28 36 34 Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. Comparable data are unavailable for India and China.
While the foregoing focuses on the adequacy of the schooling system, an important avenue of human capital deepening is through purposeful firm‐based training. However, the ability of firms to undertake firm‐based or financed training will depend on a variety of factors that include the extent of firm‐level demand for skills development, the availability of external training by specialized firms, and financial and physical constraints at the firm level. In what follows, we examine the extent to which firms support skills development through on‐the‐job training. We abstract from implicit learning‐by‐doing (worker experience) and focus instead on formal on‐the‐job training programs.
About 28 percent of firms in Rwanda provide training to their workers. Of the firms that report providing training, 52% of production workers and 40% of non‐production workers received training.
The provision of training appears to be correlated with firm size: only 12% of firms with less than 50 employees provide training compared to 40% of firms with more than 50 employees. Similarly, training is more likely in exporting than non‐exporting firms with exporters nearly twice as likely to provide training as non‐exporters. Finally, a similar training gradient is observed across ownership categories with firms with foreign ownership more than twice as likely to provide training.
In order to establish the extent to which each of these three factors exert an independent effect on the likelihood of training provision, we carry out a probit estimation (results in Appendix 5. 1). While the sample size available for this estimation is considerably small, the results on firm size are fairly robust. Firms with more than 20 employees are about 20 percentage points more likely to provide training than firms with less than 20 employees, while firms with more than 100 employees are about 25 percentage points more likely to provide training than firms with 20‐99 employees. Controlling for the industry reduces the sample size and variation so that the firm size gradient is no longer statistically significant.
The estimation suggests that export status and some foreign ownership do not exert an independent effect on the likelihood of providing training. Similarly, firms that are active in HIV‐
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prevention or testing of their workers are not more likely to provide training than other firms. In related work (see Ramachandran, Shah and Turner, 2005) this variable has been assumed to measure the degree to which firms are sensitive to turnover of skilled workers and/or the skill intensity of production.
We can complement the firm level analysis with worker level data which allows us to estimate the effects of individual worker attributes on the likelihood of training. We examine both firm‐financed training and a combination of firm‐ and self‐financed training. Our findings indicate that formal schooling is an important complement of firm‐ and self‐financed training. An extra year of formal schooling is associated with a 3‐4 percentage point increase in the likelihood of receiving training (both firm‐ and self‐financed). The relationship is not as strong for firm‐financed training only. We find some evidence that firm‐based training is positively associated with experience over much of the range of the worker tenure observed in our sample. Further, we find evidence of a large negative gender gap in firm‐provided training: female workers are 10‐15 percentage points less likely to receive firm‐based training and union workers are more likely to receive firm‐based training. Similarly an employee that is a member of a union is about 10 percentage points less likely to receive firm‐based training. Controlling for firm fixed effects increases the size of these training gaps for both gender and union membership (see appendix for more details).
It is instructive to evaluate the extent of training provision in international perspective. Manufacturing firms in Rwanda lag behind the comparator countries with respect to on‐the‐job training (see Table 28 below). Only 28% of manufacturing firms provide training to their workers compared to over 70 percent of firms in China and over 60 percent in South Africa. Only India has a lower share of firms that provide training.
Conditional on providing training, firms in Rwanda provide training for half of all production workers and two‐fifths of non‐production workers. Particularly for production workers, this is lower than the proportion trained in the other East African neighbors. Once again, we interpret these estimates with caution since the data does not permit a discrimination in the quality of the training provided.
Table 28:‐based training: Percent provide training and percent of workers trained
Country % Firms Offer Training % Production workers
trained % Non‐production workers
Trained India 2005 16 7 6 Rwanda 2006 28 52 40 Uganda 2006 32 61 28 Kenya 2006 41 66 50 Tanzania 2006 42 69 31 South Africa 2003
64 45 47
China 2003 72 48 25 Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises.
Labor Regulation
Labor regulations govern the terms under which firms hire, utilize and fire workers. These terms include remuneration guidelines, leave and over‐time policies and separation policies. We investigate the extent to which this regulatory regime is an impediment to firm operation in Rwanda. Relative to
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concerns about the quality of the workforce, labor regulations are a not an impediment for firms in Rwanda.
Firms were asked to report an elasticity of employment with respect to two aspects of labor regulations: hiring and firing workers. Firms were asked if they would hire or fire more workers if the regulations governing both aspects were removed. In the manufacturing sector, 99% reported no changes: only one firm in the sample reported that its hiring and firing of workers are affected in a significant way due to regulations. 85% of firms in services sector said that regulations don’t impact their hiring/firing decisions. Regulations seem to have the largest impact in retailing, where 76% of firms said that their policies are not impacted by regulations; majority of others reported (11%) that it impacts their decisions to hire workers. In general, the regulatory regime governing the hiring, remuneration and firing of workers in Rwanda appears reasonable to firms in all three sectors.
However, the Doing Business survey finds that labor regulations are burdensome in Rwanda. This report collects detailed information on how labor regulations affect hiring, firing, and overall rigidity of employment. Based upon catalogued regulations, the DB report calculates objective measures that assess how strict labor regulation is in a country. Rwanda is ranked 106 out of 165 countries surveyed in 2006, the period of our survey. This ranking is considerably lower than many of its comparators and a great deal lower than Uganda. The low ranking appears to arise from a difficulty in hiring and rigidity in work hours. A possible resolution of this finding is that Doing Business places greater emphasis on existing laws than on enforcement. For Rwanda, it appears that the letter of the law and firms’ experiences are at variance perhaps due to an absence of enforcement.
Figure 32: Manufacturing firms in Rwanda are at the bottom of the pack with respect to labor regulations.
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. The figure shows percentage of firms that report that labor regulation is a major/severe constraint to firm operation in all the countries shown
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Wages
Assuming uniform worker productivity across countries, the level of wages paid to workers determines the competitiveness of the manufacturing sector in Rwanda. The level of wages and its growth trajectory is particularly important given that Rwanda is engaged in an ongoing strategy of attracting foreign direct investment. Given the advantages of a low‐regulatory burden, it is important that wage levels remain competitive to support an attractive low‐cost production environment. Rising wages that are not commensurate with productivity gains are likely to result in the flight of FDI to more favorable destinations and greater competitive pressure from imports.
CrossCountry Comparisons
This section compares median wages paid to various worker categories with wages in comparator countries. Once again, it is important to point out that these comparisons do not account for differences in human capital or the sectoral composition of manufacturing in the comparator countries. Figure 34 shows the median monthly wage in dollars paid to production workers.
Figure 33: Doing Business ranks labor regulation to be particularly burdensome in Rwanda.
Source: World Bank (2006a)
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The median monthly wage for a full‐time permanent production worker in Rwanda is $91. With the exception of Kenya, median monthly wages in Rwanda are generally higher than in other East African comparators.
A comparison with the economies that dominate global manufacturing is telling. Rwanda’s wages are considerably higher than wages in India, but only a shade higher than in China. The typical Indian production worker earns about 78% of the Rwandan worker’s wage. Given the high transport costs facing Rwanda’s manufacturing, even assuming uniform productivity across countries these wage comparisons underline issues of competitiveness that need to be addressed. Given that the aggregate numbers above conceal differences in sample composition, we restrict the analysis to the food and garments sectors for each of our comparators. Figure 35 below presents the results of such an analysis. Again the ordering of median monthly wages is unchanged. Focusing on the garments sector, the median monthly wage paid in Rwanda is nearly 60% higher than the wage paid in Tanzania. Median wages for garments firms in Uganda, China and India are also lower than wages in Rwanda. Of all the comparator countries, only Kenya and South Africa have higher wages than Rwanda.
Figure 34: Median monthly wages for production workers are higher in Rwanda than they are in India, Uganda and Tanzania
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. The figure shows median monthly wages in constant 2005 US$. Deflators and exchange rates are from World Bank (2007).
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Cross‐country comparisons of wages using median wages for full‐time permanent production
workers can be different from comparisons using average labor costs from the firm’s financial statements for several reasons. One notable difference between the two measures is that labor costs from the firms’ financial statements include wages for non‐production workers, managers, and professionals. Other things, including the ratio of production to non‐production workers, ratios of skilled to unskilled production workers, differences in average (relative to median) education levels, differences in ratios of full‐time and part‐time workers, differences in ratios of permanent and temporary workers, and many other factors, can also affect results.
Comparisons across firms in Rwanda
Understanding the wage setting mechanisms operating in the labor market is vital to the design of policies to improve the performance of the labor market. In this direction we examine the variation of wages across firm size, unionization rates and firm activity.
A wide range of wage‐setting mechanisms have been identified in the literature. The predominant explanations include efficiency wage motivations, the role of collective bargaining, search frictions and fairness norms. In order to identify which of these mechanisms best explains the wage patterns, the analysis looks at various factors to control for differences in monitoring costs, collective bargaining arrangements, and selective matching of high quality workers and firms. The Appendix presents detailed econometric results that test some of these mechanisms in a regression framework in which competing wage‐setting mechanisms are captured by one or more control variables.
Table 29 below indicates that while firms with between 20‐99 workers pay more than small firms, larger firms pay considerably less than even micro firms. The econometric analysis presented in the
Figure 35: Median Monthly Wages in the Food and Garments Sectors
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises in the Food and Garments sectors. The figure shows median monthly wages in constant 2005 US$. Deflators and exchange rates are from World Bank (2007)
99
appendix confirms the absence of a remuneration firm‐size gradient. Only the percentage of workers with more than 6 years of schooling appears to be associated with the level of wages paid. The same is true of non‐production worker remuneration. Evidence from worker‐level regressions, however, provides stronger evidence of a link after controlling for individual worker characteristics: a worker earns more in a larger firm.
Table 29: Median Monthly Wages by Occupation in 2005 US Dollars
Firm Category Production workers Non‐production workers <20 91 99 20‐99 99 151 >99 66 132 Non‐exporter 86 117 Exporter 148 170 Domestic 99 120 Foreign 66 214 Total 91 124 Note: All wages are converted to 2005 dollars using the Exchange Rate from the World Development Indicators.
While the bivariate relationship shown in the table above suggests that exporting firms pay considerably more for both production and non‐production workers, the econometric analysis suggests the absence of an independent effect of exporting.
Another interesting finding is that firms with access to external credit do not appear to pay higher wages than firms without access after controlling for other factors (see Appendix Tables 5.2‐5.4 with econometric results). Furthermore, we find that firms that use an external auditor (a factor that is crucial in accessing external finance) do not pay production workers more than non‐externally audited firms. However, externally audited firms pay nearly 50% for non‐production workers.
Finally, we find evidence for rent‐sharing: firms with higher profits pay more for both production and non‐production workers.
Firms that provide training to workers do not pay higher wages. There is little evidence to support the idea that collective bargaining has a large impact on wages rates. Firms with higher unionization rates do not appear to pay higher wages to production worker than firms that do not and union members do not appear to receive higher wages than other workers after controlling for other variables that might affect wages.
The finding that the rate of unionization is not associated with worker remuneration arises from the strength of unions in Rwanda (see Figure 36). 8.6% of workers in Rwanda’s manufacturing sector are members of a union. Amongst comparators, only India and Uganda have lower unionization rates. Unionization rates are the same in the services sector (8.6%) and considerably lower in the retail sector (0.6 percent). Unionization rates are higher in larger firms. About 20 percent of workers in manufacturing firms with more than 99 employees are unionized, compared to 4.3 percent in firms with less than 20 employees.
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While few of the firm level characteristics are associated with average worker remuneration, worker characteristics have a strong effect on individual wages. An extra year of schooling increases earnings by about 12 to 13 percent—this is one of the highest rates of return to education estimated using investment climate data from Africa.. Using a similar employee‐employer matched sample, returns to an extra year of schooling is only 4% in Uganda and 8% in Kenya. We also document high return to worker experience. Returns to an extra year in the labor market are positive at the beginning of a worker’s carrier and negative towards the end of the carrier. An additional year of experience increases wages by about 6 to 8 percent at the beginning of the carrier falling off as worker experience increases. While we find a negative coefficient on gender in most of our specifications, the estimate is not significant. Unlike the result for firm‐financed training, there is no evidence of gender discrimination in worker remuneration holding constant worker attributes. Despite the low rate of unionization in Rwanda, members of a union enjoy a premium in remuneration. While our estimates are not very robust, there is tentative evidence that the size of the premium is very high (> 30%). Workers who obtained their job through the network earn less than workers hired through more formal channels. However this result is not robust to controlling for firm‐fixed effects.
Individuals who receive training on the job earn considerably more than those who report no training. The point estimate suggests that a worker who has received training earns about 60% more than of the workers with no training.
Turning to firm characteristics, we investigate the role of size, export status, profits and foreign ownership. Unlike the findings above, we find that firm size exerts an independent effect on worker
Figure 36: Unionization rates in Rwanda are very low
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. The figure shows the percentage of workers in the manufacturing sector that are unionized
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remuneration. This firm‐size remuneration relationship differs from the standard positive gradient: while firms with more than 99 employees pay the most, the smallest firms pay more than firms with 20‐99 employees.
We find a large positive effect of exporting on worker remuneration: an exporting firm pays about 70% more than a non‐exporting firm. This result is consistent with the bi‐variate comparisons found in Table 4 above and suggest that precision in the firm level estimation is constrained by sample size.
Worker Absence
Workers miss an average of 0.8 days per month due to own illness and a further 0.2 days per month due to illness in the family. Figure 37 below shows the comparison in worker absenteeism across East African comparators and South Africa. Female workers report missing twice as many days to illness as male workers in Rwanda. Overall, only Uganda has higher days of work lost due to illness related absence. Assuming that improvements in health in Rwanda can be improved so that the estimates for South Africa are a reasonable standard, a firm in Rwanda loses about 8 days a year due to illness‐related absenteeism. This is equivalent to just over 3% of working time in a calendar year.78
78 This assumes a working calendar of just under 250 days.
Figure 37: Days lost due to illness in Rwanda are high.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Own Illness Family illness Own Illness Family illness Own Illness Family illness Own Illness Family illness Own Illness Family illness
Uganda Tanzania Kenya South Africa Rwanda
Day
s ab
sent
in la
st 3
0 da
ys
Male Female Total
Source: Investment Climate Surveys Note: Cross‐country comparisons are only for manufacturing enterprises. The figure shows the average number of days that a worker reports having been away from work due to own illness or illness in the family over the last 30 days.
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Employment Growth
The creation and maintenance of employment remains a major contribution of the private sector activity to social welfare. Employment growth consists of three components: firm creation, firm death and employment expansion of existing firms. This analysis cannot address the first two components; the creation of new firms that add workers and the destruction of employment when firms die. However, the data can be used to identify the sources and correlates of employment growth of existing firms.
Firms were asked to report their employment levels in 2003 and 2006. Figure 38 below shows average annual percentage growth in employment. As the figure shows, employment growth has been robust with the manufacturing and services sectors registering annual growth rates of 16 percent or more. Annual employment growth in the retail sector is only 4.7%. Export status appears to be negatively associated with net employment creation. However, it is important to bear in mind that exporting firms are considerably larger than non‐exporters: that the median size of an exporting firm in the manufacturing sector is more than 9 times as large as a non‐exporting firm. Given these size and growth differences, net employment creation is dominated by exporting firms.
Domestic firms dominate employment growth in the service sector and to a lesser extent in retail. Foreign owned firms experienced faster employment growth than domestically owned manufacturing firms.
Figure 38: Employment Growth in Manufacturing and Services have been robust in recent years.
Source: Investment Climate Surveys Note: The figure shows the annual percentage change in employment in manufacturing, retail and services sectors
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Chapter 6: Microenterprises in Rwanda
Background
The Enterprise Survey in Rwanda also included a separate survey of 128 microenterprises‐those with less than five workers. A subset of firms in our sample can be classified as informal, i.e. those that do not comply with the full extent of government regulations and do not pay all taxes. This sector has been rapidly growing in Rwanda. The Rwanda Informal Sector Survey, conducted between 2005 and 2006, reported the existence of 64,494 informal establishments in Rwanda. Informal enterprises, in this survey, were defined as those that are small in size in terms of investment and size of employment, they are not registered, and there is no clear distinction between enterprise and owner’s finances. The survey noted that in the case of Rwanda, the third criterion i.e. no clear distinction between enterprise’s and owners/household finances was the main reason for informality. Majority of these were located in the services sector. The Rwanda Country Economic Memorandum (World Bank, 2007) also noted the existence of 70,000 to 80,000 microenterprises, some of which are household enterprises, while others are considered “formal” enterprises, in transition to small and medium firms. Of these approximately 70,000 enterprises, only 1000 are registered with the Rwanda Revenue Authority for tax purposes.79
Operating in the informal economy negatively impacts society as a whole, especially the formal business sector, which has to compete with the informal sector for market share for its products, but bears the full burden of taxes to support the provision of public services. Reducing the share of the informal sector, and fostering formal private sector growth are key Government policy objectives.
Current taxation policy in Rwanda towards Micro and Small Businesses is defined by revenues or gross receipts. Any business with an annual turnover up to RWF 20 million (approximately $36,000) is defined as a small business. Businesses under this threshold do not have to register for VAT but has the option to register to claim input tax credits under VAT. A small business has the option of paying either corporate income tax of 30%, or the presumptive tax of 4% on turnover. Given their small size and difficulty in maintaining proper accounting records, there is an incentive to underreport turnover. It has been noted80 that the high turnover tax in Rwanda, and also tax compliance and recording requirements in Rwanda are strong deterrents to registering a business and paying taxes.
In this chapter, we examine the role and character of the microenterprise sector in Rwanda, and what drives the choice of formality/informality, and how is that decision impacted by existing Government regulations and policies towards the MSME sector, and by the characteristics of the firms themselves. How should these firms be considered – as a mainspring of future growth and employment generation or as a “survivalist” employment alternative? Are there implications for policymaking? These issues are discussed below.
79 Source: Designing a Tax System for Micro and Small Businesses: Guide for Practitioners. IFC, Dec. 2007. 80 Stern and Barbour, FIAS, 2005.
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Microenterprises: Seedbed for Larger Firms?
We begin first by examining the mobility patterns of larger enterprises in the formal sector of Rwanda. This is presented in Table 30 below:
Table 30. Startup Size
Current Size Micro(<5) Small(5‐49 workers) Medium (50‐99 ) Large (100+) N
A total of 216 formal sector firms reported data for both employment at start and current period. Classifying these firms into current size class (current size) we examine their startup size characteristics. We see that majority‐80.11% of formal sector firms that are presently small (with its 50 employees) started in the micro category of fewer than 5 employees and grew into the small size class. Almost all the remainder (18.75%) started in the small size class and remains presently small. Two firms (1.14%) started in the medium size category and downsized to small. About 41% of currently medium firms started as micro enterprises, while less than a quarter of firms in the largest size class (more than 100 employees) started as microenterprises. While some sorting of firms occurs at startup, with large firms starting as larger enterprises, a fair number of firms have grown into larger size classes from a microenterprise category.
It is clear that to grow into larger size classes, a firm must first formalize its operations by registering with tax authorities. The key question asked here is why some firms choose to formalize operations, while others choose to remain in the “shadow” or informal economy (Fafchamps, 1994).81 Firms that formalize face a tradeoff between the costs and benefits of formality. The costs of formality are related to compliance with all legal requirements. However, formal firms enjoy greater access to the banking sector, public services such as security, safety, amenities such as electricity, water etc and access to the courts system. Changes in any of these factors are likely to impact a firm’s decision to become formal, and will impact its future growth potential. By examining differences in characteristics between formal and informal microenterprises, and also between microenterprises and formal sector firms, we can make some inferences on the impediments to formality and growth. These are examined below.
For comparative purposes, our analysis looks at the microenterprise sectors in Uganda, Tanzania and Kenya: these are partner EAC countries with large informal sectors both in manufacturing and
81 Fafchamps (1994) discusses six factors that may govern this choice. Fafchamps argues that the observed informality is only a short‐run disequilibrium phenomenon; however, given that the number of firms in each of these countries in this sector has grown rapidly, this is unlikely to be the case. High transport costs may limit a firm’s market, it therefore produces on a small scale. However, this alone, cannot determine informality. Market failures, information asymmetries and management requirements play a role as well‐‐in each case, the demands are fewer on informal enterprises. Government policies and regulations such as registration procedures, costs, tax laws, labor regulations, worker safety laws can be avoided by informal firms. Informal firms, with flexible technologies, can adjust more easily to market demand. And finally, large scale production requires managerial skills that entrepreneurs may not have.
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services. It also compares microenterprises in Rwanda to those in South Africa, a country with well developed infrastructure and regulatory systems, but one which also faces a growing problem of informality.
Characteristics of Microenterprises and Small Formal Firms
As a first step towards looking at the differences between formal and informal firms, we look at a simple measure of productivity—value added per worker—of informal microenterprises, formal microenterprises and formal small and medium/large firms in each of the five countries in our sample.
Figure 39: Labor productivity of firms in the sample
Source: World Bank Enterprise Survey
We see that enterprises in Rwanda have the lowest labor productivity: while there is some difference across size categories, we see that average productivity is lowest amongst comparators and particularly low for the informal firms.
However, median values present only part of the picture. The dispersions around the median provide further information on the nature of enterprise performance, and competitiveness of the sector. Figure 40 shows kernel density estimations, which present the entire distribution of productivity in our sample, of labor productivity across the three types of firms in Rwanda, South Africa Kenya, Tanzania, Uganda and Rwanda.
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Figure 40: Distributions of Labor Productivity: Rwanda and Comparators
Informal microenterprises
Formal small enterprises
Formal microenterprises
0.2
.4.6
.8D
ensi
ty
4 6 8 10 12
RwandaKernel Density Estimate of Value Added Per Worker
Formal small enterprisesInformal microenterprises
Formal microenterprises
0.2
.4.6
.8D
ensi
ty
4 6 8 10 12
South AfricaKernel Density Estimate of Value Added Per Worker
Formal microenterprises
Formal small enterprises
Informal microenterprises
0.2
.4.6
.8D
ensi
ty
4 6 8 10 12
KenyaKernel Density Estimate of Value Added Per Worker
Formal microenterprises
Formal small enterprisesInformal
microenterprises
0.2
.4.6
.8D
ensi
ty
4 6 8 10 12
TanzaniaKernel Density Estimate of Value Added Per Worker
Informal microenterprises
Formal microenterprises
Formal smallenterprises
0.2
.4.6
.8D
ensi
ty
4 6 8 10 12
UgandaKernel Density Estimate of Value Added Per Worker
Source: World Bank Enterprise Survey
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A striking feature of the figures above is the bimodality of the probability density of labor productivity along the formal vs. informal firms divide in some countries but not in others, and the similarity between Rwanda and South Africa. Not only are the means lower for informal firms in these countries, the entire distribution, reflecting productivity of all firms in this group, are lower than that of firms in the formal microenterprise sector and formal small firm sector.
While it is clear that labor productivity is lower in informal firms on average in every country sample, the density of labor productivity in those firms also significantly overlaps the density for formal firms. As significantly, the degree of overlap also varies a great deal from country sample to country sample, being much smaller in South Africa and Rwanda, and much more in Kenya, Uganda and Tanzania. Since the sample designs are reasonably standardized across countries, It is likely that the smaller extent of overlap in labor productivity across the formal‐informal divide in South Africa and Rwanda may reflect differences in the structure of the economies and the regulatory framework in these countries. Specifically we hypothesize that it may indicate that governments in South Africa and Rwanda enforce business tax laws and codes of regulation more than their counterparts do in other Eastern African countries. These are examined in further detail below.
Sorting By Human Capital, Costs and Benefits of Formality
We first examine our data by formality status, by examining whether there is sorting by differences in the level of managerial talent, as proxied by educational attainment. Figure 41 compares the educational attainments of the entrepreneurs in the informal and formal sectors across the five countries. We are far more likely to see owners with university degrees operating firms in the informal sector in Uganda and Tanzania, compared to Kenya, Rwanda or South Africa, where firms are clearly sorted according to managerial talent.
We see that microenterprises in Rwanda have lowest levels of education amongst comparators, with more than 60% of firms having only primary school education in the informal sector, while more than 40% of formal microenterprises have primary school education only. There are clearer differences in education by formality status in Kenya, Rwanda and South Africa, while the distinctions are muted in Uganda and Tanzania.
From other studies across Africa, education is a key driver of rates of firm growth, except for certain “networked” firms owned and operated by ethnic minorities (Ramachandran and Shah, 2006). As discussed further below, there is also a strong relationship between educational attainment and productivity in South Africa, as well as between both of these variables and the likelihood of being formally registered. In contrast, there is little difference in education levels between formal and informal firms in Tanzania and Uganda, and also little productivity gap.
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Figure 41: Educational Distribution of Microenterprises: By Formality Status
Source: World Bank Enterprise Survey
Benefits (or the Opportunity Cost of Informality)
The benefits of formality lie in the access to public services: business support services, access to formal banking sector or micro finance, and availability of basic infrastructure facilities such as electricity, telephone, public sewage etc. In countries where these services are well developed, we would expect the informal sector to be smaller, and a sharper difference between the performance characteristics of informal versus formal microenterprises, ceteris paribus‐only firms that have very low productivity/profitability will choose informality, because the costs of formalizing outweigh its benefits.
Examining reported infrastructure access (electricity, water and sewage), presented in the charts below‐we see that for firms in all five countries, infrastructure constraints are in fact greater for informal firms, compared to formal enterprises. These results indicate that infrastructure access could provide incentives for firms to formalize. The benefits to formalization are clearly higher in terms of infrastructure for South Africa firms but much less so in Uganda and Tanzania. In Kenya, the difference in access to an electrical connection is fairly large.
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In Table 31 below shows that firms in Rwanda have very poor provision of infrastructure services: formal firms in Kenya and South Africa have significantly higher access to infrastructure provision: more than 70% of firms in both Kenya and South Africa, and almost 50% in Uganda and Tanzania have infrastructure provision: the percentages are much lower in Rwanda, with less than 15% of formal micros having access to water connection, and only about 30% having public sewage facilities.
Interestingly, there is sorting in the provision of formal finance, with about 20% of formal microenterprises having access to loans and overdrafts, and 60% having a checking account. Less than 20% of informal firms have checking accounts, while none have borrowing privileges.
Another key factor driving the decision to become formal is the costs of formality. A firm with a given level of productivity may choose informality in a country which has higher tax rates and much greater compliance costs, compared to countries where such costs are low. Table 32 shows that a very high share of both formal and informal firms in Kenya and Uganda pay bribes and a lower but equal share of firms in the formal and informal sector in Tanzania pay bribes as well. In South Africa, the share of firms making bribe payments in the informal sector is much higher than in the formal sector. A much smaller percentage of firms report bribe payments in Rwanda, and formal microenterprises also are more likely to report their entire income for tax purposes. A majority of firms in both groups are visited by tax inspectors, with informal firms being subject to more inspector visits. All these point towards the efficiency of the Rwandese Government in getting firms into the tax system. While informal firms in South Africa are shielded from inspector visits, this is not the case in Rwanda.
Another way to examine the differences in business environment between formal and informal microenterprises is by looking at differences in perceptions. Firms were asked to rank various areas of the investment climate to determine which constraints present the largest obstacles to enterprise operations. These rankings are presented in Figure 42 below, disaggregated by formal versus informal firms.
Figure 42: Ranking of Business Constraints: Formal vs. Informal Micro Firms
Source: World Bank Enterprise Survey
We see interesting dispersions across the two groups. Transport infrastructure problems are a bigger concern from informal firms, compared to formal microenterprises. Majority of formal microenterprises report access to finance, tax rates and macro instability to be their major constraints, while both groups find electricity to be a major business constraint.
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Our survey results show that micro‐enterprises in Rwanda are not a homogenous group of firms, all stagnant and survival enterprises. Some firms exhibit dynamism and have higher labor productivity which is comparable to firms in the formal sector. These enterprises are also the ones who are most likely to be more formalized operations.
Examining differences between formal and informal microenterprises within Rwanda, and also with comparator countries, we see that microenterprises in Rwanda have the lowest human capital compared to all other countries in the region. This difference is particularly stark for informal microenterprises, where majority have only a primary school education. While majority of firms are registered for tax purposes, and report high compliance with tax laws, we see that registration does not entitle firms to significantly greater access to infrastructure services such as water and public sewage facilities, which are lowest regionally. Firms do have greater access to formal finance, but these are mainly through micro‐credit institutions and uncorrelated with creditworthiness‐less than 10% of firms have any audited accounts: much lower than all comparators.
Results indicate that regulatory efficiency has led to sorting of micro‐enterprises: those with greater productivity are most likely to register their operations and comply with the tax codes. However, unlike other countries such as Kenya and South Africa, being formal does not entitle firms to better public services or banking sector borrowing: to foster formal sector growth, the Government needs to improve education, and provide greater infrastructure and business support services (accounting, management, networking) which will have a direct impact of mobilizing more firms into the formal sector.
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References
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‐‐‐‐‐‐‐‐. 2007. International Finance Statistics. Washington DC: International Monetary Fund.
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2005. "Measuring Governance Using Cross Country Perceptions Data." World Bank. Washington DC.
Stern, Nicholas. 2002a. A Strategy for Development. Washington DC: World Bank.
‐‐‐‐‐‐‐‐. 2002 b. "Development as a Process of Change." World Bank: Washington DC.
The Economist. 2007. "The Dark Continent." The Economist, August 16.
World Bank. 1997. World Development Report 1997: The State in a Changing World. New York: Oxford University Press.
‐‐‐‐‐‐‐‐. 2004. World Development Report 2005: A Better Investment Climate For Everyone. Washington DC: World Bank.
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‐‐‐‐‐‐‐‐. 2007. Rwanda: Toward Sustained Growth and Competitiveness (in Two Volumes), Report No. 37860‐RW.
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Appendix Chapter 1
The Structure of the Private Sector in Rwanda, reported in the Country Economic Memorandum, is as follows:
Appendix 1. 1 Structure of the MSSE Sector, Classified by Rwandan System of Production
Production System
Home‐based production Dispersed production — small scale Factory type — organized and
structured
Informal Informal’ formal enterprises — in transition to
small and medium Formal — organized enterprises
Family production Micro enterprises Small enterprises Medium‐scale enterprises
Based on available data for the population of firms in Rwanda, the World Bank Enterprise Survey in Rwanda targeted establishments located in Kigali and Butare in the following industries (according to ISIC, revision 3.1): all manufacturing sectors (group D), construction (group F), retail and wholesale services (sub‐groups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub‐group 72 of group K). For establishments with five or more full‐time permanent paid employees, this universe was stratified according to the following categories of industry:
1. Manufacturing: Food and Beverages (Group D, sub‐group 15); 2. Manufacturing: Garment (Group D, sub group 18); 3. Manufacturing: Other Manufacturing (Group D excluding sub‐groups 15 and 18); 4. Retail Trade: (Group G, sub‐group 52); 5. Rest of the universe, including:
• Construction (Group F); • Wholesale trade (Group G, sub‐group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub‐group 72).
The survey also sampled a selection of micro establishments (establishments with less than five full‐time permanent paid employees) from the targeted universe, without stratification by industry.
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Sampling methodology
Establishments with five or more fulltime paid permanent employees
The sample frame for establishments with five or more full‐time paid permanent employees consisted in a population of 600 establishments. This list of establishments was compiled from lists from different sources, including lists from the RRA, MINICOM, FRSP and CAPMER. The master list, once compiled and updated based on initial research in the field, was sent to EEC head office in Montreal. There, the number of establishments to be surveyed in each stratum and location was determined based on the sample frame and the World Bank’s guidelines on sampling for the global roll‐out of the ICS. Once the target number for each stratum and location was determined, a computer program selected at random establishments from the sample frame sufficient to fulfil the target, as well as a list of replacements.
These lists were then sent back to the EEC field team, who oversaw the enumerators in their attempts to survey the selected establishments.
Microestablishments
In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees. For many reasons including the small size of establishments, their expected high rate of turnovers, the high level of “informality” of establishments in many activities and consequently the difficulty to obtain trustworthy information from official sources, EEC Canada selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all regions of the survey.
First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.
The districts and the specific zones were selected at first according to our national sources. The EEC team then went in the field to verify these national sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.
At head office the following procedure was followed: The count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.
In Rwanda, enumerators were sent to each zone with instructions as to how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non‐participation.
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Appendix 1. 2 Population size by stratum and sampling region
Kigali Butare Total Manufacturing 176 25 201 Food and beverages 83 17 100 Garments 12 2 14 Other manufacturing 81 6 87 Retail 75 7 82 Rest of the universe 301 16 317 Micro 2017 312 2329
Total 2569 360 2929
Appendix 1. 3 Sample weights by stratum and sampling region
Kigali Butare Total Manufacturing 3.20 6.25 3.41 Food and beverages 4.37 8.50 4.76 Garments 2.40 0 2.80 Other manufacturing 2.61 3.00 2.64 Retail 1.83 2.33 1.86 Rest of the universe 3.14 1.23 2.99 Micro82 17.39 26.00 19.25
Total 8.34 11.25 8.61
82The weights for the Micro stratum represent only the areas for which we conducted an area sampling.
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Appendix Chapter 2 Appendix 2. 1 Total Factor Productivity Estimation Pooled Data
Note: Robust standard errors in parentheses ***significant at 1%; ** significant at 5%, * significant at 10%. Excluded category for country dummies is Rwanda, for sectors is Food Processing.
Appendix 2. 2 Probit Regressions: Decision to Export
Number of Observations 975 975 975 LogLikelihood ‐381.92 ‐361.92 ‐369.09
Note: Robust standard errors in parentheses ***significant at 1%; ** significant at 5%, * significant at 10%. Excluded category for country dummies is Rwanda, for sectors is Food Processing.
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Appendix 2. 3 Sample Size: Residual and Retail Sectors
Burundi Kenya Rwanda Tanzania Uganda
Residual
Construction and Transport 12 20 9 16 24
Hotels and Restaurants 30 56 79 47 75
Other 37 35 9 13 27
Retail 76 126 44 65 120
IT 13 24 12 5 6
Total Surveyed 168 261 153 146 252
Appendix 2. 4 Determinants of Productivity: Retail and IT Sectors
Determinants of Productivity: Retail and IT Sectors
Model 1 Model 2 Model 3 Intercept 7.19*** 7.24*** 7.03***
(0.259) (0.256) (0.258)
Log(Firmage) 0.27*** 0.25*** 0.26***
(0.100) (0.099) (0.097)
Size >20 employees 0.10 0.02 ‐0.03
(0.186) (0.186) (0.183)
Foreign Owned ─ 0.73*** 0.57***
─ (0.240) (0.240)
University Education ─ ─ 0.53***
─ ─ (0.141)
Uganda 0.13 0.04 ‐0.01
(0.211) (0.211) (0.207)
Kenya 0.59*** 0.55*** 0.53***
(0.202) (0.200) (0.197)
Rwanda 0.85*** 0.72*** 0.69***
(0.250) (0.251) (0.247)
IT sector 0.46** 0.39* 0.25
(0.225) (0.224) (0.223)
Note: Robust standard errors in parentheses ***significant at 1%; ** significant at 5%, * significant at 10%. Excluded category for country dummies is Tanzania.
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Appendix 2. 5 Determinants of Productivity: Services Sector
Model 1 Model 2 Model 3 Intercept 7.79*** 7.70*** 7.59***
(0.285) (0.278) (0.276)
Log(firmage) 0.30*** 0.28*** 0.28***
(0.093) (0.091) (0.090)
SME 0.28* 0.13 ‐0.04
(0.152) (0.152) (0.157)
Foreign Owned ─ 1.05*** 0.96***
─ (0.225) (0.223)
Univ. Ed ─ ─ 0.53***
─ ─ (0.147)
Uganda ‐0.04 ‐0.08 ‐0.18
(0.206) (0.201) (0.200)
Kenya 0.51*** 0.63*** 0.59***
(0.216) (0.212) (0.209)
Rwanda ‐0.38* ‐0.39* ‐0.40*
(0.220) ─ (0.211)
Hotel Dummy ‐0.58*** ‐0.51*** ‐0.50***
(0.150) (0.147) (0.145)
Note: Robust standard errors in parentheses ***significant at 1%; ** significant at 5%, * significant at 10%. Excluded category for country dummies is Tanzania.
Access to land 20.69 15.69 15.82 23.53 17.51 14.71 17.17 15.38
Access to finance (availability and cost) 41.38 31.37 33.90 35.29 35.03 29.41 33.84 38.46
NUMBER OF FIRMS 58 153 177 34 177 34 198 13
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Analysis of Other Business Constraints
Business Licenses
Rwanda has been on fast track towards reforming its business licensing structure. Doing Business (2009) ranks Rwanda 90th out of 181 countries surveyed. It is far lower than Tanzania (172), Burundi (173) India (136) and China (176). But, neighboring countries, in their bid to move up on the Doing Business rankings, have also made significant changes: Kenya is now ranked 9th, and Uganda 81st in these rankings.
The BizClir report (2008) noted that “Rwanda is beginning to apply the best practices for licensing regimes found in other business friendly countries, but it has not yet avoided certain pitfalls of the licensing function. For example, although blatant corruption among Government inspectors and other officials is not widely regarded as a problem with respect to obtaining a license, there is some indication that “preferred” business initiatives get treated more favorably than others. (Add our interview experiences?)
Although regulatory reform may lead to higher Doing Business rankings, their impact on firms may differ based on actual implementation. Rankings across countries, based on our survey, are presented in Figure 3.3 below.
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Appendix Figure 3. 3% of Firms Identifying Business Licensing and Permits as Major Constraint
Source: World Bank Enterprise Survey
We see that firms in Rwanda are much less likely to rank business licenses as a major constraint, compared to all other countries regionally, including Kenya, and most countries internationally. Examining across firm characteristics within Rwanda, presented in Appendix Table 3.3, we see some dispersion across groups, with small domestic firms more likely to rank it as a major problem compared to others. Overall, business licenses do not appear to be an obstacle to business development.
Labor Regulations
Comparing Rankings of Labor Regulations across countries, we see that firms in Rwanda are much less likely to report labor regulations to be a constraint to business operations, compared to most countries.
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Appendix Figure 3. 4 Percent of Firms Identifying Labor Regulations as a Major Constraint
Source: World Bank Enterprise Survey
Legal system
Secure property and contractual rights play a vital role in modern economies. When enterprise owners are unsure about the security of their property rights, they will be unwilling to risk investing their capital in projects that yield profits only in the medium‐ to long‐term.
The legal system in Rwanda is still in its infancy: the first commercial courts were only recently established in March 2008. Until these courts are well established over time, and contractual laws are enacted and operate appropriately, there will be no effective means for enforcing property and contractual rights. Bizclir, in its evaluation of the Doing Business rankings, also summarized that the framework laws dealing with contracts are archaic and ill suited to a modern commercial economy. Also, the court system is perceived neither to be efficient nor have the necessary capacity to deal with complex commercial transactions.
While these factors are likely to deter the entry of new firms, particularly large foreign investors, existing firms in Rwanda do not see the legal system as a serious obstacle to enterprise operations and growth. The investment climate assessments include several questions regarding perceptions about the legal system and the security of property rights. One question asked whether the enterprise managers believed that they could rely upon the courts to enforce contracts and uphold their property rights in business dispute. Rankings are presented in Appendix Table 3.3 below. We see that more than 60% of
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firms in Rwanda consider the court system to be fair, impartial and uncorrupted, affordable, quick and able to enforce its decision, which is much higher than other comparators.83
Appendix 3. 3 Functioning of Courts
Court System ‐ % Ranking it to be:
Fair, Impartial and
Uncorrupted Quick Affordable
Able to Enforce Decision
Rwanda 68.55 64.99 73.59 71.81
Burundi 44.72 37.59 73.96 70.02
Kenya 23.08 16.54 28.85 50.00
Tanzania 43.69 34.78 48.86 63.77
Uganda 43.63 29.82 43.73 63.41
Source: World Bank Enterprise Survey
Appendix Figure 3. 5 Court Procedures and Costs
Source: Doing Business, 2009
83 It is important to note however, that very few firms in all comparator countries have used the court system to resolve a conflict, these are general perceptions rather than based on actual experience.
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An alternate view of courts is obtained from the Doing Business indicators, presented in Appendix Figure 3.5 above. The survey is based on a survey of lawyers and legal experts, asked them how long it would take to get a court to recover a standardized debt contract (e.g., with respect to size, type of contract, nature of the parties being sued etc.) in their country.84 We see that while the number of procedures compare favorably with other countries, the cost (as a percentage of debt) is higher in Rwanda than comparators.
Macroeconomic Uncertainty and Political Instability
Good macroeconomic policies and political stability are necessary conditions to foster economic growth in any country. The US Department of State’s Investment Climate statement (2007) notes that Rwanda remains a stable country with little violence. A strong police and military provide an umbrella of security that continues to minimize criminal activity and political disturbances. Good macroeconomic policies by the GOR are highlighted in Chapter 1.
These have had a positive impact on business perceptions. We see that less than a quarter of firms in manufacturing rank macro economic uncertainty to be major constraint, while less than 10% of service sector firms find this to be a problem. Political instability is ranked as a major constraint by less than 10% of firms in both manufacturing and services.
It is important to note however, that these constraint rankings by existing firms may differ significantly from potential entrants. Rwanda’s very recent history of genocide, the perceptions of political control, and potential for future instability continue to deter foreign investors into Rwanda (BizClir,2008).
Entry and Exit
Regulations that affect entry and exit decisions have come under increased scrutiny in recent years. Because entry and exit restrictions affect the ability of new firms to enter, and of old inefficient firms to exit, they can reduce competition and productivity. Although some entry restrictions are necessary – for example, requiring enterprises to register with tax authorities – many entry restrictions in developing countries do not appear to solve market failures.
84 See World Bank, Doing Business (2009) for a complete description of the methodology
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Appendix Figure 3. 6 Duration (in days), Cost (% of income/capital) and Number of Procedures Required to Get Business Licenses – International Comparison
Source: Doing Business, 2009
Several recent empirical studies have shown that entry restrictions can have a large negative impact on economic performance. In addition to affecting productivity and investment, entry regulations can also lead to corruption and informality. In order to avoid the burden that entry restrictions impose upon new enterprises, entrepreneurs respond by either becoming informal (i.e., avoiding the high costs associated with formal entry) or bribing bureaucrats to speed the process up.
The Doing Business data, presented in Appendix Figure 3.6 above, show that Rwanda fares less favorably compared to Kenya and Uganda in terms of time required to obtain a business license. It has also much higher costs compared to Kenya, and higher number of procedures. However, rapid reforms are underway (BizClir 2008), and it is expected that business registration procedures will be further simplified and expedited in the near future.
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Appendix Figure 3. 7 Enterprise Surveys: Connection Times
Source: World Bank Enterprise Survey
Legal restrictions on entry are not the only barrier to entry that new firms face. Another barrier that can restrict entry is getting access to infrastructure. The biggest infrastructure related bottleneck for new firms in Rwanda is getting a telephone connection: it reportedly takes 50 days to obtain a new connection. However, current reforms underway are likely to improve access and time in the near future.
Access to and Cost of Land
The recent PSF survey shows that Access to and Cost of Land are reported as a major constraint by more than 50% of firms in Rwanda today. While access to land was ranked as a major constraint by less than 20% of firms in Rwanda, the question did not specifically address cost issues. Due to its small geographical area and high population density, land is recognized as a continued scarce resource which has to be allocated to its best use. The BIZclir Report (2008) notes that:
Across firm size within EAC, we see that land ownership remains low for small formal firms in all these countries, with less than 30% having a title to their business property, while more than 70% of medium and large firms in Rwanda own their business land.
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Appendix Figure 3. 8 % of Firms owning Business Property
Source: World Bank Enterprise Survey
Characteristics of Female Entrepreneurs in Rwandan Businesses
How different are firms owned by men vis‐à‐vis women in Rwanda? Of the 340 firms surveyed, 42% were owned by women. In this section, we examine the human capital and finance characteristics of a firm’s entrepreneurs by gender, and the reported business constraints thereafter. Worker earnings across gender are discussed in detail in the labor market chapter 5. Differences in education across gender are presented in the table below.
Appendix Table 3. 4 Educational Characteristics by Gender
University 70.27% 52.38% 14.08% 8.93% 37.25% 28.26% 60.53% 38.89%
Source: World Bank Enterprise Survey
Examining human capital and finance characteristics across gender, we find that in manufacturing, women own smaller enterprises ‐ with a median firm size of 33 workers ‐ compared to a firm size of 53 workers for firms owned by men. 52% of women entrepreneurs have a university degree, compared to 72% of male entrepreneurs in manufacturing. Median firm size for both men and women in the service
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sector is 6 employees: 20% of women have university degrees, compared to 32% of enterprises owned by men.
While educational qualifications are lower, on average, for women entrepreneurs, we find that access to finance does not substantially differ across gender lines in manufacturing. About 60% of both male and female owned firms in manufacturing had access to loans. 17% of women in services have current loans, while 39% of male entrepreneurs reported having a current loan.
Appendix Table 3. 5 Financial Characteristics of Firms: By Gender
Manufacturing Micro Residual Retail
Male Female Male Female Male Female Male Female
% with Bank Account 97.30% 85.71% 47.89% 56.14% 70.59% 63.04% 97.37% 83.33%
% with Land Ownership 72.97% 57.14% 7.04% 3.51% 27.45% 28.26% 18.42% 27.78%
% with Current Loan 62.16% 57.14% 21.13% 14.04% 21.57% 21.74% 39.47% 16.67%
Source: World Bank Enterprise Survey
Once a firm is established, irrespective of whether it is a male owned or female owned enterprise, we see that business constraint rankings do not differ. Rankings by gender are presented in the charts below.
Appendix Figure 3. 9 Ranking of Business Constraints by Gender
Source: World Bank Enterprise Survey
Examining business constraints in the formal sector, we see that tax rates, electricity and access to finance are reported as major constraints by both men and women entrepreneurs.
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Women comprise a major share of microenterprises in Rwanda. Again, rankings do not substantially differ across gender, except for transport, where firms owned by women are more likely to report transport to be a major concern, compared to men.
Appendix Figure 3. 10 Microenterprises – Ranking of Constraints by Gender
Source: World Bank Enterprise Survey
Overall, we see that there is negligible difference between the two groups: the sorting occurs at entry, with fewer women choosing to become business owners: those that do own smaller enterprises, have lower educational levels than their male counterparts. Once these factors are controlled for, we see that business constraints are similar. Based on the analysis above, there seems to be limited justification for targeted policy interventions to address constraints faced by women entrepreneurs in Rwanda.
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Appendix Chapter 4
Access to Finance: Other Firm Characteristics: Industry, Region, Exporters and Ownership
Among different industries the “other” category, which includes mostly service firms, reports the least availability of finance (Table 4). The highest percent of service firms report access to be one of the top 3 obstacles (40% for service vs. 24‐27% for other types of firms), the lowest percent use any credit products (34% service vs. 36% retail and 66% manufacturing). Service firms report lower proportion of bank finance for working capital and investment – twice lower than manufacturing firms. Thus, manufacturing firms have the most access among all industrial groups. This means that cross‐country comparison, presented in section I, which included only manufacturing firms, showed Rwanda in a better light than it is the case on average.
As in many other countries, foreign firms have preferential access – they have more usage of credit products and report lower obstacles; but they have about the same amount of bank capital in their working capital and investment. Exporters also have more access than non‐exporters. There is no significant difference in firms owned by males vs. females – they have about equal access indicators.
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There are no striking differences between firms in Kigali, vs. those in Butare.
Appendix 4. 1 Access and Firm Characteristics: Industry, Region, Exports and Ownership
Industry Ownership Owner
Manufacturing
Retail
Other*
Dom
estic
Foreign
Male
Sample Size 59 44 109 177 35 123
Percent reporting finance access as one of the top 3 obstacles 24% 27% 40% 34% 25% 31%
Percent with a bank account 93% 90% 74% 81% 93% 87%
Percent with overdraft, line of credit or loan 66% 36% 34% 41% 60% 45%
Percent ‐‐ Applied for a loan 47% 40% 27% 34% 41% 36%
R‐squared N/A N/A N/A N/A N/A N/A N/A 0.19 Notes: Estimated by probit (columns 1‐5), Tobit with lower limit of zero and upper limit of 100 (columns 6‐7) and linear regression (column 6). * significant at 10%; ** significant at 5%; *** significant at 1%.
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Appendix Chapter 5
Below we present the results of multivariate regressions to qualify the bivariate associations that we discuss in the chapter. Regressions are presented at the firm‐ and individual‐level for training and wages. For the training regressions, we estimate the likelihood that a firm provides on‐the‐job training to its workers. Typical controls include export status, ownership, firm size and vintage and sector. Using worker data we estimate the likelihood of receiving either firm‐ or self‐financed training using worker attributes such as gender, schooling, experience and the matched firm characteristics outlined above.
We use the firm‐ and individual‐level wage regressions to provide insights into the wage‐setting mechanisms as well as the returns to various worker attributes.
Notes: Robust standard errors in parentheses * significant at 5%; ** significant at 1%. Reported coefficients are probit estimates from a probit regression with the likelihood of providing training as the dependent variable. Last two rows report the results of a formal test that size is not associated with training provision. As the p‐values of this test indicate, we can reject this hypothesis as conventional levels of statistical significance.
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Appendix 5. 2 Training Determinants: Individual Level
Dependent variable: Worker received any kind of training
Dependent variable: Worker received firm financed training
Notes: Robust standard errors in parentheses * significant at 5%; ** significant at 1%. Reported coefficients are probit estimates from a probit regression with the likelihood of receiving training as the dependent variable. Specifications (4) and (8) include firm fixed effects.
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Appendix 5. 3 Determinants of Average Wages Firm Level
Dependent variable: Log Average wage for Production workers
Dependent variable: Log Average wage for Non‐Production workers
(1) (2) (3) (4) (5) (6) 20‐99 employees ‐0.058 0.001 ‐0.088 0.222 0.139 0.096 (0.206) (0.226) (0.207) (0.286) (0.302) (0.324) 100+ employees 0.109 ‐0.208 ‐0.598 0.515 0.275 0.044 (0.294) (0.306) (0.295)+ (0.508) (0.532) (0.691) Exports 0.322 0.317 0.276 0.311 0.274 0.373 (0.284) (0.267) (0.298) (0.309) (0.301) (0.347) Foreign owned ‐0.122 ‐0.142 ‐0.342 0.294 0.188 ‐0.040 (0.209) (0.226) (0.227) (0.296) (0.333) (0.340) Firm age 0.024 0.010 0.006 ‐0.014 ‐0.025 ‐0.017 (0.025) (0.024) (0.024) (0.026) (0.026) (0.030) Firm age sq ‐0.000 ‐0.000 ‐0.000 0.001 0.001 0.001 (0.000) (0.000) (0.000) (0.000) (0.000)+ (0.000) Typical worker has more than 6 years of school 0.499 0.521 0.387 0.419 0.367 0.248 (0.160)** (0.165)** (0.153)* (0.216)+ (0.215)+ (0.214) Skill ratio of production workers 0.289 0.226 0.251 0.094 0.107 0.170 (0.268) (0.260) (0.282) (0.335) (0.331) (0.351) % unionized ‐0.004 ‐0.003 ‐0.002 ‐0.003 ‐0.003 ‐0.001 (0.004) (0.004) (0.003) (0.005) (0.005) (0.005) Provides training 0.001 0.001 0.000 ‐0.002 (0.002) (0.003) (0.003) (0.002) Capacity utilization 0.010 0.005 0.000 ‐0.006 (0.006) (0.006) (0.008) (0.011) Access to External credit ‐0.052 ‐0.094 ‐0.084 ‐0.133 (0.152) (0.138) (0.240) (0.247) Access to Trade credit ‐0.229 ‐0.214 ‐0.065 ‐0.035 (0.171) (0.163) (0.235) (0.251) External audit 0.250 0.270 0.490 (0.193) (0.199) (0.255)+ Gross profit 0.000 0.000 (0.000)** (0.000)** Constant 5.609 5.108 5.536 6.009 6.111 6.640 (0.221)** (0.524)** (0.507)** (0.274)** (0.643)** (0.828)** Observations 58 58 57 53 53 52 R‐squared 0.40 0.50 0.60 0.53 0.58 0.61 F‐Test Firm Size Matters 0.16 0.24 2.06 0.58 0.19 0.04 prob>F 0.85 0.78 0.14 0.57 0.83 0.96 Notes: Robust standard errors in parentheses * significant at 5%; ** significant at 1%. Reported coefficients are an OLS regression with log of average worker earnings as the dependent variable. Last two rows report the results of a formal test that size is not associated with earnings. As the p‐values of this test indicate, we can reject this hypothesis as conventional levels of statistical significance only for the basic specifications (1) and (4).
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Appendix 5. 4 Determinants of Worker Earnings
Dependent Variable: Log Monthly Earnings (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Years of Schooling
Prob>F 0.04 0.00 0.00 0.00 0.00 Notes: Robust standard errors in parentheses * significant at 5%; ** significant at 1%. Reported coefficients are an OLS regression with log of average worker earnings as the dependent variable. Last two rows report the results of a formal test that size is not associated with earnings (cols (7)‐(11)). As the p‐values of this test indicate, we can reject this hypothesis as conventional levels of statistical significance. Specifications (2) and (12) reports the results of an OLS estimation controlling for firm fixed effects.
