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Public Disclosure Authorized

Report No. 39672-CL

Report No. 39672-CL

Chile Investment Climate Assessment (In Two Volumes) Volume II: Background Chapters

Public Disclosure Authorized

April 16, 2007

Investment Climate Assessment Volume II

Public Disclosure Authorized

Chile

Public Disclosure Authorized

Finance and Private Sector Unit Poverty Reduction and Economic Management Unit Latin America and the Caribbean Region

Document of the World Bank

39672 v2

REPUBLIC OF CHILE - FISCAL YEAR (January 31 – December 31) CURRENCY EQUIVALENTS (Exchange Rate Effective as of 03/12/2007) Currency Unit = Chilean Peso ($/) US$1.00 = CLP $530

Vice President: Pamela Cox Country Director: Axel van Trostenburg Sector Director: Ernesto May Sector Manager: Lily Chu Sector Leader: James Parks Task Manager: Jose Guillherme Reis

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ABBREVIATIONS AND ACRONYMS ACCIF ACHEL AUGE CAI CAS CASEN CONAF CONICYT CORFO DIPRES DPR ENIA EU FDI FECU FEDIT FIA FIDES FOGAPE FONASA FONTEC FOSIS FSSA FTAs FTE GDP IMF INE ISP LAC LDC MIDEPLAN MINEDUC MOPTT MPG NIS NSF OECD OMIL PASIS PAYGO PDP PEE PEPs PISA PRI

Average Country Citation Impact Factor Asociacion Chilena de Leasing Plan of Universal Access with Explicit Guarantees Cuenta de Ahorro de Indemnización Country Assistance Strategy Chile’s National Household Survey Chile’s National Forest Corporation National Commission of Science and Technology Industrial Development Corporation Budget Office, Ministry of Finance Development Policy Review Encuesta Nacional Industrial Anual European Union Foreign Direct Investment Ficha Unica Codificada Uniforme Spanish Federation of Technological Innovation Entities Agrarian Innovation Foundation Fondos de Inversion de Desarrollo de Empresas Fondo de Garantia para la Pequeña Empresa Publicly Run Social Health Insurance System Fondo Nacional de Desarrollo Tecnológico y Productivo Social Development Fund Financed by Chilean Government Financial Systems Stability Assessment Free Trade Agreements Full-Time Equivalent Gross Domestic Product International Monetary Fund National Statistics Institute Public Health Institute Latin America and the Caribbean Least Developed Countries Chile’s Ministry of Planning and Cooperation Chile’s Ministry of Education Ministerio de Obras Públicas Minimum Pension Guarantee National Innovation system National Science Foundation (USA) Organization of Economic co-operation and Development Oficina Municipal de Intermediación Laboral Non Contributory, Social Assistance Benefit to Elderly Indigent Pay-as-you-go Financing for Social Insurance Institutions Programa Desarrollo de Proveedores Plan Especial de Empleo Public Employment Programs Program for International Student Assessment Public Research Institute

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SBIF SENCE SESMA SIC SII SIMCE SING SME SVS SUBTEL TFP TMC USD VAT WB

Superintendencia de Bancos e Instituciones Financieras Chile’s Social Risk Management Survey Chile’s Environmental Metropolitan health Service Sistema Interconectado Central Servicio de Impuestos Internos Subsidies for Employment Creation Sistema Interconectado del Norte Grande Small and Medium Enterprise Superintendencia de Valores y Seguros Telecommunications Sub-Secretariat Total Factor Productivity Tasa Máxima Convencional United Sates Dollar Value Added Tax World Bank

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ACKNOWLEDGEMENTS This report was prepared by a team led by José Guilherme Reis and comprised of Stefka Slavova (Business Regulation), Pablo Fajnzylber (Technology and Innovation), Tito Yepes (Infrastructure), and Emanuel Salinas (Finance). The productivity analysis is based on a background paper prepared by Alvaro Escribano, Jose Luis Guasch, Jorge Pena and Manuel de Orte. Leonid Koryukin also provided inputs for the productivity analysis and the analysis of the ENIA data base. Leyla Castillo, Mariam Dayoub and Patricia Melo provided valuable assistance in the preparation of the report. Peer reviewers Sara Calvo, William Maloney and Mary Hallward-Driemeier provided valuable comments and suggestions. Additional important inputs were received from Susan Goldmark, Marianne Fay, Jose Luis Guasch, Constantinos Stephanou, Raffaella Mota, James Parks, Daniel Oks, Phillippe Durand, Esperanza Lasagabaster, Juan Gaviria, Michael Goldberg and Francis J. Earwaker. We would like to acknowledge important contributions received from government officials in Chile, especially from the Subsecretaria de Economia, Ana Maria Correa, the former Subsecretario de Economia, Carlos Álvarez, as well as from Rosella Cominetti, Alberto Ergas, Jaime Gre, Marisa Ernst, Andres Sanfuentes and Isabel Zuñiga, all of them from Ministerio de Economia. The team also benefited from the insights provided in meetings with the Ministerio de Hacienda, led by the (then) economic policy coordinator, Marcelo Tokman, as well as with Comision Nacional de Energia, Ministerio de Obras Públicas, Transporte y Telecomunicaciones, Superintendencia de Electricidad y Combustibles (SEC), Fundación Chile, Corfo (Corporación de Fomento de la Producción) and Sofofa (private sector manufacturing association). The findings and views expressed here are exclusively those of the World Bank and do not represent the views of the government of Chile.

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TABLE OF CONTENTS Foreword .........................................................................................................................................................1 Chapter 1

- Investment Climate Assessment............................................................................................2

Chapter 2

– Chile: Recent Economic Trends ...........................................................................................6

Chapter 3 – Investment Climate in Chile ...............................................................................................14 Investment Climate and determinants of productivity (TFP) ....................................................................18 Chapter 4 – Finance ...............................................................................................................................32 I. Structure of the Financial System in Chile and the Sources of Finance.................................................32 II. Access to Credit in the Investment Climate Survey..............................................................................42 III. Major Constraints for Access to Credit ...............................................................................................50 IV. Conclusions and Policy Recommendations.........................................................................................54 Chapter 5 —Business and labor Regulations .........................................................................................57 Business-Government Relations................................................................................................................57 Contract Enforcement, the Judiciary and the Insolvency Regime .............................................................70 Labor Regulations .....................................................................................................................................77 Conclusions and Policy Recommendations...............................................................................................83 Chapter 6 – Innovation...........................................................................................................................86 I. Historical Background ...........................................................................................................................91 II. Current Policy Challenges ....................................................................................................................95 III. Evidence from Investment Climate Surveys in Chile and Selected Comparator Countries ................98 IV. Evidence from the Chile Investment Climate Survey .......................................................................102 V. Worker Education and Training .........................................................................................................107 VI. Links between Innovation Inputs and Outputs ..................................................................................112 VII. The Use of Innovation and Competitiveness Enhancing Government Programs ............................114 VIII. Conclusions and Policy Implications..............................................................................................116 Chapter 7 —Infrastructure ...................................................................................................................119 Introduction .............................................................................................................................................119 Electricity ................................................................................................................................................121 Transport .................................................................................................................................................129 Telecommunications................................................................................................................................133 Conclusions .............................................................................................................................................135 References ...............................................................................................................................................137 Annex—Investment Climate Determinants Of Productivity– Econometric Methodology .........................141 I. Introduction..........................................................................................................................................141 II. Econometric Methodology..................................................................................................................141

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LIST OF FIGURES Figure 2.1 GDP growth– International Comparison.......................................................................................6 Figure 2.2. Growth and output volatility in Latin America 1984- 2004..........................................................7 Figure 2.3. Chile: Net Public debt 1990-2004.................................................................................................7 Figure 2.4.Compared Trade openness – Chile, LAC, OECD..........................................................................8 Figure 2.5. Poverty - Recent Trends...............................................................................................................9 Figure 2.6. Inequality- International Comparison ...........................................................................................9 Figure 2.7. Chile— Productivity in the Manufacturing Sector......................................................................12 Figure 2.8. Chile— Productivity in the Manufacturing Sector......................................................................13 Figure 3.1. Entrepreneurs’ perceptions of Obstacles to growth.....................................................................16 Figure 3.2. Perceptions of Obstacles to Growth ............................................................................................16 Figure 3.3. Real Exchange Rate ....................................................................................................................16 Figure 3.4. Chile: Macroeconomic Uncertainty as a perceived Obstacle to growth......................................16 Figure 3.5. Profitability of the median firm in the manufacturing sector ......................................................17 Figure 3.6.Productivity elasticities and semi-elasticities with respect to IC variables ..................................20 Figure 3.7. Impact on Average Productivity of Investment Climate Variables.............................................20 Figure 3.8. Impact of IC variables on export Decisions ................................................................................25 Figure 3.9. Investment Climate Impact on Labor Demand ...........................................................................28 Figure 4.1. Domestic credit to private sector (% of GDP) ............................................................................33 Figure 4.2. Composition of the Banking Sector (2005).................................................................................34 Figure 4.3. Yearly growth of commercial and consumer lending .................................................................35 Figure 4.4. Growth of the leasing industry ....................................................................................................38 Figure 4.5. Financing provided by CORFO (US$ Million)...........................................................................39 Figure 4.6. Composition of industrial sectors supported by CORFO............................................................39 Figure 4.7. Allocation of FOGAPE guarantees by industry (number of operations in 2004) .......................40 Figure 4.8. Legal rights index........................................................................................................................42 Figure 4.9. Sources of funding for working capital and new investments.....................................................43 Figure 4.10. Obstacles for growth: Access to finance ...................................................................................44 Figure 4.11. Obstacles for growth: cost of finance........................................................................................44 Figure 4.12. Access to credit lines by size of company.................................................................................45 Figure 4.13. Access to credit lines in different regions of the country ..........................................................45 Figure 4.14. Access to credit lines by industry..............................................................................................46 Figure 4.15. Share of firms with loans (%), international comparison..........................................................46 Figure 4.16. Access to short-term credit and bank loans...............................................................................47 Figure 4.17. Distribution of companies according to their borrowing status ................................................47 Figure 4.18. Regional credit constraining and participation in Chile’s GDP (2003).....................................48 Figure 4.19. Credit constraining by industry .................................................................................................48 Figure 4.20. Cost of Financing as a Perceived Obstacle to Growth ..............................................................48 Figure 4.21. Cost of Financing as a Perceived Obstacle to Growth (by Size) ...............................................48 Figure 4.22. Use of trade credit by size of firms ...........................................................................................50 Figure 4.23. Trade credit balance (Trade credit provided to clients / Suppliers’ credit received).................50 Figure 4.24. Comparative collateral requirements.........................................................................................52 Figure 4.25. Obstacles for access to bank loans - Credit-constrained companies .........................................52 Figure 4.26. Percentage of companies with audited financial statements .....................................................54 Figure 4.27. Access to credit and availability of audited financials (selected industries) .............................54 Figure 5.1. Interpretations of government regulations are unpredictable, percentage of respondents sharing this view ........................................................................................................................................................58 Figure 5.2. Interpretations of government regulations are predictable, percentage of respondents sharing this view ........................................................................................................................................................58 Figure 5.3. The Time Tax: percent of senior management time devoted to government regulations, inspections, taxes, customs, etc. ....................................................................................................................59 Figure 5.4. The Time Tax in Chile: by Type of Firm....................................................................................60 Figure 5.5. Time to register or obtain license for firms registering for the first time between 2002 and 2004, all firms .........................................................................................................................................................60

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Figure 5.6. Re-registration for Firms Established Before 2002, manufacturing firms ..................................61 Figure 5.7. Time to Get Re-registered and Get Licenses with or without Use of Internet to Obtain Information and/or Apply, manufacturing firms ...........................................................................................61 Figure 5.8. Construction Permits Are Relatively Time-Consuming..............................................................62 Figure 5.9. Construction Permits: Time to Obtain by Type of Firm .............................................................63 Figure 5.10. Use of Online Procedures: Share of Chilean Manufacturing Firms Which Use Internet for Tax Payments (SII) and Social Security Payments (INP).....................................................................................64 Figure 5.11. Use of Online Procedure- Percent of Firms using electronic invoice .......................................64 Figure 5.12. Percent of Sales Reported to Tax Authorities: An International Comparison...........................65 Figure 5.13. Reported Sales: by Type of Firm ..............................................................................................66 Figure 5.14. Share of Firm Workforce Reported for Social Security and Other Regulatory Purposes .........66 Figure 5.15. Labor regulations, percent of firms which consider them no problem - Chilean firms are more satisfied than firms in other comparator countries.........................................................................................66 Figure 5.16. Inspections in Chile: Taxes, Public Health and Labor Inspections Have the Longest Duration .......................................................................................................................................................................67 Figure 5.17. Tax Inspections: Total Duration per Annum.............................................................................67 Figure 5.18. Bribes to “Get Things Done”: Percent of Total Annual Sales ..................................................69 Figure 5.19. Bribes to “Get Things Done”: Percent of Total Annual Sales, by type of firm.........................69 Figure 5.20. Bribes to get government contracts, % of contract value ..........................................................69 Figure 5.21. Trust in the Judiciary: percentage of respondents who believe that the courts will uphold their contractual and property rights ......................................................................................................................71 Figure 5.22. Trust in the Judiciary: percentage of respondents who do not believe that the courts will uphold their contractual and property rights..................................................................................................71 Figure 5.23. Share of firms using the courts to resolve at least some of their payment disputes ..................72 Figure 5.24.Number of Days to Resolve a Payment Dispute in Court, by Country ......................................72 Figure 5.25. Share of firms brining labor disputes over layoffs to the Labor Inspectorate, percent of firms that experienced disputes over layoffs...........................................................................................................74 Figure 5.26. Average Number of Labor Layoff Disputes Brought to the Labor Inspectorate, and Percent of Cases Resolved out of the Labor Inspectorate...............................................................................................74 Figure 5.27. Share of Sales Paid Before Delivery, at Delivery and Sold on Credit ......................................75 Figure 5.28. Share of Sales Paid Before Delivery, at Delivery and Sold on Credit, by Type of Firm ..........75 Figure 5.29. Years to Close an Insolvent Company, by Country and Region ...............................................76 Figure 5.30. Chile: Unemployment Rate, 1990-2005 (in percentage)...........................................................78 Figure 5.31. Chile: Real Wage Index, Real Labor Cost Index, GDP Growth Rate, and Unemployment Rate, 1997-2004......................................................................................................................................................78 Figure 5.32. Chile: Minimum Wage, Mean wage and Productivity ..............................................................80 Figure 5.33. Productivity and Labor Cost in Manufacturing.........................................................................81 Figure 5.34. Main Reasons for not changing the Size of the Labor Force ....................................................82 Figure 6.1. Chile’s Annual Trend GDP growth , 1990-2005 (%)..................................................................87 Figure 6.2. Chile’s Knowledge Economy Index relative to OECD and emerging countries.........................88 Figure 6.3. Chile’s relative R&D gap, 1979-2000.........................................................................................90 Figure 6.4. Total Chilean R&D Expenditures, 1975-2002 ............................................................................91 Figure 6.5. Chilean R&D Expenditures by Funding Sources........................................................................92 Figure 6.6. Historical Development of Chile’s Institutional Framework for Innovation ..............................94 Figure 6.7. The Chilean S&T policy institutional framework .......................................................................95 Figure 6.8. Manufacturing Firms Investing in R&D, Licensing, Joint Ventures and ISO Certification - Chile and Comparator Countries (%)......................................................................................................................98 Figure 6.9. Manufacturing Firms Investing in Fixed Capital, Worker Training, New Product Lines and Improved Product Lines - Chile and Comparator Countries (%) ..................................................................99 Figure 6.10. IT and Biotech Firms Investing in R&D, Licensing, Joint Ventures and ISO Certification Chile and China (%) ....................................................................................................................................100 Figure 6.11. IT and Biotech Firms Investing in Fixed Capital, Worker Training, New Product Lines and Improved Product Lines - Chile and China (%) ..........................................................................................100 Figure 6.12. Chilean Firms Investing in R&D, Technology Licensing, Joint Ventures and ISO Certification, by size (%)...................................................................................................................................................103

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Figure 6.13. Chilean Firms Investing in Fixed Capital, Worker Training, New Product Lines and Improved Product Lines, by size (%)...........................................................................................................................104 Figure 6.14. Chilean Firms Investing in R&D, Technology Licensing, Joint Ventures and ISO Certification, by sector (%) ...............................................................................................................................................104 Figure 6.15. Chilean Firms Investing in Fixed Capital, Worker Training, New Product Lines and Improved Product Lines, by sector (%) .......................................................................................................................104 Figure 6.16. Chilean Firms Cooperating with Universities and Technology Centers, by Size (%).............106 Figure 6.17. Chilean Firms Cooperating with Universities and Technology Centers, by Size (%).............107 Figure 6.18. Average years of schooling in 1960 and 2000 – International comparison.............................108 Figure 6.19. Educational distribution in 2000 – International comparison .................................................108 Figure 6.20. Share of Skilled workers by firm size (%) ..............................................................................110 Figure 6.21. Share of Skilled workers by sector (%)...................................................................................110 Figure 6.22. Share of Workers Trained, by firm size (%) ...........................................................................111 Figure 6.23. Share of Workers Trained, by sector (%)................................................................................112 Figure 6.24. Chilean Firms Investing in Innovation Inputs, by Innovation Output Status (%) ...................113 Figure 6.25. Usage, Value and Awareness of Government Programs (%)..................................................115 Figure 7.1. Composite Index of Productive Infrastructure ..........................................................................120 Figure 7.2. Growth of the Composite Index of Productive Infrastructure ...................................................120 Figure 7.3. Investment in Infrastructure ......................................................................................................120 Figure 7.4. Composition of private investment, 1990/00 ............................................................................121 Figure 7.5. Distribution of Electricity Generation Capacity........................................................................124 Figure 7.6. Node Electricity Prices..............................................................................................................124 Figure 7.7. Average Price of Electricity for Industry in 2004 ....................................................................125 Figure 7.8. Percentage of firms constrained by electricity issues................................................................125 Figure 7.9. Losses due to power outages.....................................................................................................125 Figure 7.10. Percentage of Firms owning a Generator ................................................................................126 Figure 7.11. Percentage of electricity from own generator, by Country .....................................................127 Figure 7.12. Percentage of electricity Consumption That Comes from own generator...............................127 Figure 7.13. Transmission and distribution losses......................................................................................128 Figure 7.14. Percentage of market served by private operators...................................................................128 Figure 7.15. Investment in Transport Infrastructure...................................................................................130 Figure 7.16. Road Network .........................................................................................................................131 Figure 7.17. Percentage of Firms that reported any breakage or theft in transit..........................................132 Figure 7.18. Custom delays for Exports ......................................................................................................132 Figure 7.19. Custom delays for Imports ......................................................................................................132 Figure 7.20. Percentage of firms that use their own logistics......................................................................133 Figure 7.21. Percentage of logistics costs over sales...................................................................................133 Figure 7.22. Private Sector Participation in Telecommunications in Latin America ..................................133 Figure 7.23. Mainlines per 100 Habitants, 2003 .........................................................................................135 Figure 7.24. Mobile Subscribers/100 Hab, 2003 .........................................................................................135 Figure 7.25. Internet Users / 100 Habitants, 2004 ......................................................................................135 Figure 7.26. Internet use by firms................................................................................................................135

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LIST OF TABLES Table 1.1 Description macro-zones by region and Sample Distribution .........................................................4 Table 2.1. Growth accounting .........................................................................................................................9 Table 2.2. Measures of TFP Growth .............................................................................................................11 Table 2.3. Doing Business 2007— Chile among top 30 economies on ease of doing business ...................13 Table 3.1. IC Elasticities and Semielasticities with respect to Productivity; Restricted Estimation. ............21 Table 3.2. IC Elasticities and Semielasticities with respect to Productivity; Unrestricted Estimation. .........22 Table 3.3.Two Stage Least Squares (2SLS) Estimation of Exporting Decisions; Coefficients and Percentage Impact on the Probability of Exporting. ........................................................................................................26 Table 3.4. Two Stage Least Squares (2SLS) Estimation of Employment Demand Equation; Coefficients and Percentage Contribution to the Average (log) Employment...................................................................29 Table 3.5. Summary of the Results of Chile's ICA; Marginal (or Direct) Effect of IC and C Variables.......30 Table 3.6. Summary of the Results of Chile's ICA; Net Effect of IC and C Variables. ................................31 Table 5.1. Correlation between the Time Tax and Unofficial Payments: Chile ............................................59 Table 5.2. Correlates of Some of the Regulatory and Contract Enforcement Variables ...............................67 Table 5.3. Correlation Matrix of Governance Variables ...............................................................................71 Table 5.4. Enforcing Contracts: Time, Cost and Procedures.........................................................................72 Table 5.5. Chile, Latin America, and OECD: Hiring and Firing Workers Component of the Ease of Doing Business Index...............................................................................................................................................81 Table 5.6 Correlates of Firms' decision on changing the number of workers ...............................................83 Table 6.1. Chilean R&D Expenditures in an International Context ..............................................................89 Table 6.2. Correlates of Innovation Inputs and Outputs in Chile and Comparator Countries .....................101 Table 6.3. Correlates of Innovation Inputs and Outputs in Chile ................................................................105 Table 6.4. Correlates of Innovation Inputs and Outputs in Chile, including the Share of Sales to Exporters and Foreign Firms .......................................................................................................................................105 Table 6.5. Correlates of Innovation Inputs and Outputs in Chile, including Education of the Workforce and Access to Finance ........................................................................................................................................106 Table 6.6. Correlates of Skilled Worker Employment and Worker Training..............................................111 Table 6.7. Links between Innovation Inputs, Outputs and Total Factor Productivity in Chile ...................113 Table 6.8.Table 6.8. Links between Training and Innovation Inputs in Chile, by Type of Training ..........114 Table 6.9. Correlates of Participation in Government Programs.................................................................115 Table 7.1. Structure of the Electricity Sector ..............................................................................................122 Table 7.2. Electrification Rates 2002 ..........................................................................................................128

LIST OF BOXES Box 1.1 What is an Investment Climate Assessment?.....................................................................................3 Box 4.1. The Proposed New Capital Markets Law .......................................................................................43 Box 4.2. Reverse Factoring ...........................................................................................................................53 Box 6.1 Chile’s multiyear Science and Technology Programs .....................................................................93

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FOREWORD The objective of this study is to evaluate constraints to private sector growth in Chile. Based on a survey covering 1,000 firms and other World Bank analytical work, this assessment focuses on the microeconomic and structural dimensions of the national business environment, viewed from an international perspective. To this end, the study examines in detail factors constraining the efficiency of product markets, financial and non-financial factor markets and infrastructure services including, in particular, weaknesses in the legal, regulatory and institutional framework. In the simplest terms, the investment climate establishes the rules of the game and the environment within which all firms must operate. This study focuses on policies and institutions that influence the rate of return and the risk associated with investment. It includes regulatory policies, administrative procedures, infrastructure, as well as institutional issues such as the security of property rights and the rule of law. This volume is divided in seven chapters. The first chapter provides an overview of the key questions and issues to be discussed, as well as the main characteristics of the survey. The second chapter presents recent economic trends in Chile, focusing on the excellent macroeconomic performance of the country in the last 25 years and in some microeconomic issues that may explain the performance of total factor productivity (TFP) in recent years. Chapter 3 presents a descriptive and econometric exploration of the data. It starts with a descriptive analysis of the main obstacles faced by Chilean firms, as reflected in entrepreneurs’ perceptions. We make extensive use of international comparisons, exploring a unique feature of this data base. A detailed econometric exercise focused on determinants of productivity is presented next. It shows that all investment climate areas are strongly correlated to TFP. Chapters 4 to 7 explore the different aspects of the investment climate in Chile: Finance is discussed in chapter 4, while Governance and Business Regulation, including issues related to labor regulation, a key obstacle from the point of view of Chilean businessmen, are presented in Chapter 5. Technology and innovation, issues that have been placed on the top of the agenda by Chilean authorities are discussed next, in Chapter 6, while the last chapter deals with infrastructure issues.

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CHAPTER 1 - INVESTMENT CLIMATE ASSESSMENT 1.1 The investment climate is recognized as a key pillar of World Bank Group work to promote economic growth and poverty alleviation in developing countries.1 A dynamic private sector, in which firms compete and seek to improve productivity by investing in human and physical capital as well as technological capacity, is the main propellant of sustained economic growth, a sine qua non condition to fight poverty and improve living standards. A sound business environment is essential to establish the appropriate incentives for firms to invest. ICAs mainly focus on the microeconomic and structural dimensions of a nation’s business environment from an international perspective. To this end, ICAs examine the factors constraining the effective functioning of product markets, financial and non-financial factor markets, and infrastructure services, including in particular weaknesses in an economy’s legal, regulatory and institutional framework. ICAs also provide the tools and analytical framework to identify reform priorities in a country’s investment climate, by linking constraints to firm-level costs and productivity.

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"The central challenge in reaping greater benefits from globalization lies in improving the investment climate—that is, in providing sound regulation of industry, including the promotion of competition; in overcoming bureaucratic delay and inefficiency; in fighting corruption; and in improving the quality of infrastructure. While the investment climate is clearly important for large, formal sector firms, it is just as important—if not more so—for small and medium enterprises (SMEs), the informal sector, agricultural productivity, and the generation of off-farm employment. For these reasons, the investment climate itself is a key issue for poverty reduction." Nicholas Stern, Chief Economist, March 22, 2001.

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Box 1.1 What is an Investment Climate Assessment?2 Investment climate assessments systematically analyze the conditions for private investment and growth, drawing on the experience of local firms to pinpoint areas where reform is most needed to enhance private sector productivity and competitiveness. The assessments provide a practical foundation for policy recommendations by involving local partners throughout the process. They are designed to give greater impetus to policy reforms that can speed private sector growth. 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 a survey of private enterprises that captures their experience in a range of areas — finance, governance, regulation, tax policy, labor relations, conflict resolution, infrastructure services, technology, and training, among others. These are all areas where inefficiencies can add substantially to the cost of doing business. The survey attempts to quantify costs related to bottlenecks in the investment climate. Using a standard methodology, the assessment then compares the survey findings with those in similar countries to evaluate how the private sector is competing. The findings of the survey, supplemented by information from other sources, provide a practical basis for identifying the most important areas for reform to improve the investment climate. The findings and policy recommendations emerging from the assessments are discussed extensively with the private sector and other stakeholders in the country. The broad dissemination of findings is aimed at engaging not only policymakers but also business leaders, investors, nongovernmental organizations, and the donor community in forging a consensus on the priorities for reform, and laying the groundwork for concrete responses to the problems identified. The ICA should be seen as complementary to another effort of the WB to evaluate the business climate, which is the Doing Business report. There are different ways to assess the business climate and how it affects firm behavior. One possibility is to analyze existing laws and regulations and their effect on a single hypothetical firm. This is the methodology adopted by the Doing Business report. A second alternative is to ask the enterprises about their experiences, deriving quantitative information on a large sample of firms. What do we mean by investment climate? The quantity and quality of investment flowing into a country or into any of its regions depend on the returns that investors expect and the uncertainties around those returns. These expectations depend in turn on three main features of a country’s policy and institutional environment: • Macro- or country-level issues: stability and openness. This group includes mainly national public policies that affect the country affect the country’s degree of political and economic stability and the extent to which the country is integrated in the global economy. These generally refer to macroeconomic, fiscal, monetary, trade, and exchange rate policies and political stability. • Efficacy of the regulatory framework. For firms, this relates to entry and exit, labor relations and flexibility in labor use, ownership transformation, efficiency and transparency of financing and taxation, and efficiency of regulations relating to health, safety, the environment, and other legitimate public interests. When it comes to regulation, the question is not whether to regulate or not, but whether regulations are designed in incentive-compatible ways, avoid adverse selection and moral hazard, serve the public interest, are implemented expeditiously without harassment 2

Adapted from World Bank (2005), El Salvador Investment Climate Assessment.

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and corruption, and facilitate efficient outcomes. While regulatory efficacy is difficult to measure, surveys clearly suggest that it varies widely across countries.

• Quality and quantity of physical, financial and technological infrastructure. This includes power, transport, telecommunications, banking and finance, and— given the imperfect mobility of skilled workers and the clustering of technology—the endowment of skills and technology. Entrepreneurs surveyed about their problems and bottlenecks often cite, as key determinants of their competitiveness and profitability, such issues as reliability of power, time and cost of transport, access to and efficiency of finance, the supply of skilled workers, and access to advanced technologies.

1.2 With the interest of collecting data through all the territory and prevent the concentration of data on the Metropolitan Region, the sample design included the concept of macro-zones. The macro zones were defined as auxiliary variables, taking into account the possibility of considering different sectors for which sample units are not of difficult access. Even though the sample does not have representation at the macro-zone level, the sample selection was done systematically within the strata (sectorsize), in which the geographic allocation of the different establishments was identified and organized, guaranteeing this way having disaggregated information at the national level for each of the activities considered in the survey. The distribution of regions included in each macro-zone are described as follows: Table 1.1 Description macro-zones by region and Sample Distribution MacroZone I II III

IV

V

Region I 5%

Region Tarapacá Antofagasta Atacama Coquimbo Valparaíso Libertador Bernardo O’higgins Maule Biobío La Araucanía Los Lagos Aisén del General Carlos Ibáñez del Campo Magallanes and Chilean Antartic Metropolitan Area of Santiago

Region II 7%

Region III 19.5% Region V 55%

Region IV 12.6%

Source: ICS

1.3 INE also provided the Bank with a ten year series of firm-level information originated from annual manufacturing surveys (ENIA). It covers quantitative information (production, employment, etc) for firms with more than 10 employees in the manufacturing sector, for the period 1992 on. Linking up the two data sets will greatly enhance the possibilities of assessment of total factor productivity dynamics. This is a distinguishing feature of this ICA report. Accordingly, the methodology developed by Escribano and Guasch (2004) to assess the impact of investment climate variables on firm productivity was adapted to the use of panel data.

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The richness of the data base allowed us to calculate TFP with much more precision, as well as to look at performance measures such as job creation and destruction rates, export performance, investment, etc.

5

CHAPTER 2 – CHILE: RECENT ECONOMIC TRENDS 2.1 Chile’s economic performance has been nothing less than impressive in the last Chile’s economic performance has been impressive in the last twenty years and output growth continues to surpass the rest of Latin America. Sustained growth rates show a resilient economy in a turbulent environment during this period. Chile’s per capita income was more or less stable at around 70% of the average per capita income of Latin America from 1960 to 1980. After 1985, however, consistently positive and high rates of GDP growth allowed Chile’s per capita income to grow at six times the Latin American average. More recently, Chile’s macroeconomic policies and strong fundamentals have largely protected it from regional crises. Economic performance compares reasonably well with growth rates of the world’s top performing region, East Asia and the Pacific (Figure 2.1).3 Figure 2.1 GDP growth– International Comparison Cumulative per Capita growth 1985-2004 250

200

%

150

100

50

0 Chile

Latin America & Caribbean

East Asia & Pacific

High income: OECD

World

Source: WDI, 2004

2.2 Chile’s economic performance is sustained by strong fundamentals and an ambitious reform agenda. A wide range of reforms covering most major areas has been put in place since the 1970s, including reforms in pensions and tax systems, trade liberalization, capital markets, etc. Among the various factors that explain Chile’s strong growth performance, the country’s continuous commitment to fiscal discipline, leading to a substantial reduction in the level of public indebtedness stands as one of the most important. Public indebtedness fell dramatically in the first half of the nineties, driven by 3

See World Bank (2005), page 1 and Cole, Ohanian, Riascos and Schmitz (2004), page 40.

6

the combination of large fiscal surpluses and accelerated economic growth, and has been hovering around 12% of GDP in recent years (Figure 2.3).4 More recently, in 2000, a structural budget surplus rule was introduced, allowing the government to formally pursue a counter-cyclical fiscal policy. Chile thus joined a limited group of countries throughout the world that are able to put in place such policy. Figure 2.2. Growth and output volatility in Latin America 1984- 2004 5.0 Chile

Average Growth 1980-2004

4.0

3.0

2.0 Latin America 1.0

0.0 0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-1.0

-2.0 Volatility-Standard Deviation

Source: WDI, 2004

Figure 2.3. Chile: Net* Public debt 1990-2004 Consolidated Net Public Debt (%GDP)

40 35 30 % GDP

25 20 15 10 5 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04

Source: Chile Central Bank *The consolidated data eliminates obligations between the government and the Central Bank.

2.3 Moreover, fiscal discipline has been combined with a flexible exchange rate regime and an inflation targeting framework implemented by an independent central bank, concurring to a sound macroeconomic management. The financial sector is healthy and solvent, having weathered well the economic slowdown of the 19984

See IMF (2004), page 40.

7

2003 period, as well as other emerging market crises. The robustness of the financial system in Chile is largely underpinned by a banking sector characterized by adequate supervision and capitalization. At the same time, a large pensions sector and ample deposit base provide the system with a high level of availability of funds that is well above that of other Latin American economies. 2.4 Trade openness also helps explain Chile’s positive economic performance. Trade reforms carried out in the 1970s aimed to make production for the external market more profitable, being crucial to create the conditions for an export-led growth model starting in the 1980s. Significant advances in negotiating free trade agreements have helped bolster its external sector5, and the value of Chilean exports has more than quadrupled since 1980. The structure of exports is heavily concentrated in natural resources, but there has been a gradual diversification during the last 30 years. Figure 2.4.Compared Trade openness – Chile, LAC, OECD Trade (%GDP) 80 70 Chile

% GDP

60 50 OECD

40 30

LAC 20 10 2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0

Source: WDI, 2004

2.5 Chile was also a precursor in Latin America in establishing a compromise between sound fiscal and monetary policies and the implementation of an ambitious social agenda. The adoption of well-targeted social policies coupled with the strong growth performance led to a significant reduction in poverty rates. Official headcount poverty has more than halved since 1990, falling from 38.6% in 1990 to 18.8% in 2003. Income inequality, however, remains high, with the Gini coefficient remaining at 0.56, among the highest in the world and comparable to the more highly unequal countries in Latin American and Africa. Inequality is today at the top of the agenda of the government’s new elected administration. 2.6 Exercises of growth accounting point to the conclusion that total factor productivity (TFP) growth has been the main driver of economic growth in the last 5

In the period 2002-2003, free trade agreements were concluded with the United States, the European Union and Korea. These agreements aim at improving market access for Chilean exports but also encouraging further diversification of exports toward high value added products (see WEF, 2005, p 115).

8

decades in Chile.6 Trade openness and privatization seem to have played a significant role to stimulate TFP growth since the 1980s. As Table 2.1 indicates, the share of TFP growth in total growth has been steadily increasing in Chile over the last four decades, which compares favorably to the rather chaotic behavior of TFP in other key LAC countries. Figure 2.5. Poverty - Recent Trends

Figure 2.6. Inequality- International Comparison

Poverty and Indigence Line

GINI Coefficient

45 40

70

Poverty Headcount

Indigence

60

30

50

25

40

20

30

15

20

10

10

5 1998

2000

2003

India (2000)

1996

United States (2000)

1994

China (2001)

1992

Argentina (2001)

1990

Mexico (2000)

Brazil(2001)

0

0

Chile (2000)

%

35

Source: WDI, 2005

Chile

Argentina

Brazil

Mexico

Peru

Table 2.1. Growth accounting 1961-70 1971-80 1981-90 1991-00 Annual GDP growth, average % 4.1 2.9 3.8 6.6 29.9 38.1 43.0 42.6 a % growth due to TFP 21.7 16.8 35.0 36.2 b c 5.2 8.2 40.5 Annual GDP growth, average % 3.9 3.0 -1.5 4.6 24.7 8.1 161.3 66.7 a % growth due to TFP* 5.7 -15.6 220.0 54.5 b c -16.3 160.0 80.5 Annual GDP growth, average % 6.1 8.5 1.6 2.7 30.7 36.8 -92.3 15.1 a % growth due to TFP 22.8 39.1 -138.7 -9.2 b c 41.8 -163.2 10.0 Annual GDP growth, average % 6.7 6.7 1.8 3.5 24.7 18.7 -101.7 11.7 a % growth due to TFP 10.3 2.2 -186.7 1.7 b c -180.7 3.4 Annual GDP growth, average % 5.3 3.6 -0.8 4.1 32.6 1.1 427.5 38.0 a % growth due to TFP 7.8 -36.6 440.0 12.0 b c -16.5 702.5 -12.0

Source: Loayza et. al. (2002)

Note: TFP impacts of over 100% are typical for periods of negative growth – and imply that declines were mostly driven by TFP, rather than physical factors. Estimates of TFP contribution were made using different models: option A is the traditional Solow residual, option B includes adjustments for human

6

See Loayza, Fajnzylber and Calderón (2002), and also Chumacero and Fuentes (2004), among other studies.

9

capital and option C includes adjustments for human capital and utilization of inputs.

2.7 A series of growth-accounting studies have shown a significant decline in the contribution of TFP to growth during the recent slowdown, in the late 1990s. Estimates presented by Beyer and Vergara (2002) and Fuentes, Larrain and SchmidtHebbel (2004) show a strong drop in the contribution of TFP to growth, from 3.70 and 4.40, respectively, in the first half of the decade, to values close to zero and even negative more recently (see Table 2.2). De Gregorio (2004) also shows an important, albeit more moderate, decline in the contribution of TFP to growth, from 2.7 to 1.3. The recovery in growth in 2003–2004 has been largely based on the rebound of investment, as well as private consumption. Employment expansion has been muted compared to previous periods. 2.8 The slowdown in TFP is now believed to be explained almost entirely by a cyclical component and linked mostly to external factors. In the manufacturing sector, our own7 calculations using different methods confirm the interruption in the upward trend in productivity in the mid 1990s and some recovery afterwards. Figure 2.7 below shows the evolution of two different measures of TFP as well as two measures of labor productivity from 1992 to 2002 in the manufacturing sector. It is clear the interruption in the increase in TFP that took place from 1996 up to 1999. The degree of recovery from that moment on is not clear, as one of the measures of TFP shows a one-off increase in 2000 and stability thereafter, while the other shows a more continuous rise starting in 1999. This pattern seems to be common for most of the manufacturing sectors, as depicted in Figure 2.8. The slowdown seems to be explained almost entirely by cyclical factors and linked mostly to the events that took place in the external front. Estimates from the Ministry of Finance show that potential output has been rising and is expected to grow at higher rates in the near future. However, there are other views for which there has been a structural break recently, with the long-term growth rate currently standing at the 4.5-5.0 percent range, compared to the 7.7 percent in the 1986-97 period, despite faster growth in the world economy (from 3.4% in 88-97 to 5.0% in 04-06). 2.9 Increasing TFP is the best way to create the conditions to long, sustained economic growth. In order to spur TFP growth in the future, there seems to be a consensus on the need for a second generation of reforms to foster high and sustained growth, with the emphasis being placed in how to improve education and increase innovation/technology adoption. The current diagnostic for this area in Chile stresses that despite important achievements in the incentive regime and in infrastructure for innovation, international comparisons suggest that the country still shows significant relative weaknesses in the areas of education and innovative capacity. For a natural resource-rich country like Chile, it is critical that the skills and technology are in place to exploit fully its natural resource advantages.

7

For detailed information on the methodology used for TFP calculations, please see the Annex of this report.

10

Table 2.2. Measures of TFP Growth Contribution of (%) Labor 2.50 1.50 0.50 0.10

Period 1986-1990 1991-1995 1996-2000 1998-2001

Output Growth 6.80 8.70 4.10 2.40

Capital 2.00 3.50 3.60 2.80

De Gregorio (2004)

1985-2004 1990-1994 1995-1999 2000-2004*

5.67 7.29 5.35 3.69

2.34 2.70 3.44 2.04

1.58 1.75 0.53 0.81

1.65 2.67 1.32 0.79

Fuentes et al. (2004)

1990-2003 1990-1997 1998-2003

5.18 7.14 2.08

1.76 1.61 1.48

0.81 1.14 0.16

2.61 4.40 0.44

Gallego and Loayza (2002)

1986-2003

6.64

2.46

2.22

1.95

Beyer and Vergara (2002)

TFP 2.30 3.70 0.10 -0.60

Notes: *Calculated using GDP estimates for 2004. Controls for input utlization and human capital are included by Fuetes et al (2004) and Gallego and Loayza (2002) studies. Beyer and Vergara (2002) and De Gregorio (2004) do not adjust for the utilization and quality of labor and capital.

Source: World Bank (2005)

2.10 Innovation is decisive, but other microeconomic reforms are also relevant for TFP growth. Since there is heterogeneity among firms in productivity levels and rates of growth, reallocation of resources from low-productivity to high-productivity firms leads to an increase in productivity level and rate of growth. As new firms enter and less efficient ones leave the market, a higher productivity is achieved. Fairly inefficient factor and product markets, as well as high costs of entry and exit may lead firms to incurring in otherwise unnecessary adjustment costs whenever a shock hits an economy. As discussed in the recent Development Policy Review,8 Chile needs to continue removing barriers to competition to increase microeconomic flexibility and thus reach higher levels of firm productivity and buffer the impact of shocks. The fall in microeconomic flexibility after the Asian crisis is seen as one of the elements that explain the decline in TFP growth over 1997-99 (Caballero, Engel and Micco, 2004). The recent Doing Business report helps to illustrate some structural dimensions of the business environment that deserve attention. Table 2.3 that Chile is ranked 28th in the world in the ease of doing business, a composite index based in the indicators of the DB data base covering facility of entry and exit of firms, labor regulations, access to credit, investor’s protection, contract enforcement and others.9 When Chile is compared to Latin American countries, its performance is outstanding in 2.11

8

World Bank (2005). There are different ways to assess the business climate and how it affects firm behavior. One possibility is to analyze existing laws and regulations and their effect on a single hypothetical firm. This is the methodology adopted by the Doing Business report. A second alternative is to ask the enterprises about their experiences, deriving quantitative information on a large sample of firms. This is the methodology used in the ICAs.

9

11

most of the indicators. However, one could argue that given the outstanding performance of the Chilean economy in the last 25 years, the appropriate comparators are no longer to be found among other Latin American economies, but instead in advanced economies with similar factor endowments and strong track records in raising income per capita, such as Finland, Ireland, Israel, New Zealand and South Korea. When the comparison is done with this group of countries, Chile actually performs worse than 3 of them and slightly better than Israel and Korea. 2.12 Building a sound environment for the private sector to operate requires the continuation of a program of reforms covering different areas. The country has succeeded in improving the business environment in the last 25 years, and has today a favorable investment climate relative to many other countries but the process can be improved in some areas, like labor regulation, competition policies, credit markets and overall business regulatory environment. This report tries to help identify remaining impediments to stronger growth by analyzing new evidence obtained through the Investment Climate Survey, which collected data from 1000 firms in 5 regions and 9 different industries in Chile. Figure 2.7. Chile— Productivity in the Manufacturing Sector

1992-2002 Chile: Productivity in Manufacturing (medians): 1992-2002 130

Index, 1995=100

120

110

100

90

80

70 1992

1993

1994

1995

1996

1997

1998

1999

2000

Real value added per employee-day Levinsohn-Petrin TFP estimate, value added-based OLS TFP estimate, value added-based Gross output per employee-day

Source: Own Calculations based on ENIA

12

2001

2002

Figure 2.8. Chile— Productivity in the Manufacturing Sector

1992-2002 140 Tex, A pparel, Leather

B asic M etal Industries

P aper, P rinting, P ublishing

No n-M etallic M in. P ro d.,

Index 1995=100

120

Fabricated M etal P ro ducts, M ach., Equip.

100

Chem., P et., Co al, Rubber, P lastic P ro d.

80

Fo o d, B ev., To b.

Wo o d, Wo o d P ro d.

60 1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Fo o d, B ev., To b.

Tex, A pparel, Leather

Wo o d, Wo o d P ro d.

P aper, P rinting, P ublishing

Chem., P et., Co al, Rubber, P lastic P ro d.

No n-M etallic M in. P ro d., except P et. & Co al

B asic M etal Industries

Fabricated M etal P ro ducts, M ach., Equip.

Source: Own Calculations based on ENIA

Table 2.3. Doing Business 2007— Chile among top 30 economies on ease of doing business

1. Singapore 2. New Zealand 3. United States 4. Canada 5. Hong Kong 6. United Kingdom 7. Denmark 8. Australia 9. Norway 10. Ireland 11. Japan 12. Iceland 13. Sweden 14. Finland 15. Switzerland

16. Lithuania 17. Estonia 18. Thailand 19. Puerto Rico 20. Belgium 21. Germany 22. Netherlands 23. Korea 24. Latvia 25. Malaysia 26. Israel 27. St. Lucia 28. Chile 29. South Africa 30. Austria

13

CHAPTER 3 – INVESTMENT CLIMATE IN CHILE 3.1 In this chapter we present evidence on the impact of the investment climate in Chile, using both qualitative and quantitative information. We start with a discussion of entrepreneurs’ perceptions about investment climate constraints to firms’ operations, followed by the presentation of the econometric analysis of the determinants of total factor productivity (TFP), as well as other variables. A technical annex describes the main features of the methodology. 3.2 In order to evaluate which are the most important investment climate constraints affecting Chilean firms, this report uses both subjective and objective indicators. The emphasis, however, is placed on the second type of information. Subjective indicators are based on the perceptions of the surveyed firms regarding the key factors that constrain their development. This approach provides valuable information on the priorities that entrepreneurs would adopt if faced with the task of designing policies to improve the investment climate. However, we argue that for policymaking purposes the emphasis should be placed on the analysis of objective and quantitative indicators regarding the costs associated with various investment climate problems. Indeed, the perceptions of the entrepreneurs may be biased by recent events reported in the media, and they may also reflect their specific cultural and socioeconomic background. For instance, managers of firms that concentrate on local as opposed to national or international markets may lack the necessary benchmarks to judge the severity of the problems existing in their cities or provinces, and compare them to national or international best practices. 3.3 The Investment Climate Survey inquires about entrepreneurs’ opinions of obstacles to their firms’ growth. Businessmen are asked to evaluate the severity of twenty potential obstacles to growth of their businesses. A five-point scale is used, ranging from extremely severe to not important. We examine the percentage of firms that consider the obstacles as “severe” and “extremely severe.” Whenever possible we compare the results for Chile with the correspondent figures for the countries selected as comparators in this report, Brazil, China, India, South Africa, Malaysia and Poland. 3.4 Chile compares extremely well with other countries where the World Bank has undertaken Investment Climate Assessments. Both quantitative data and business perceptions support that conclusion. When asked about the major obstacles to growth of their businesses in Chile, firms report fewer and less serious obstacles

compared with other countries.10 In general, the percentage of entrepreneurs that consider the investment climate to be a major constraint to grow was lower than in other countries. Labor regulation was the obstacle most frequently identified but, even so, it is still not perceived as a major problem by 75 percent of firms. The corresponding figure is much lower in comparator countries. Although it is difficult to compare perception-based measures across countries, this does suggest that enterprise managers are not greatly concerned about any of the 19 areas. Not surprisingly, the weights attached to the different obstacles to firms’ growth in Chile clearly differ from the usual pattern observed in developing countries and in Latin America in particular. A greater weight is attributed to problems linked to second generation of reforms. While possibly indicating that Chile has already successfully tackled a first round of micro reforms, this fact also highlights the more complex challenges that lie ahead for Chile to consolidate the process of economic growth. 3.5 Managers were more likely to be concerned about labor regulations, macroeconomic uncertainty, labor skills, informal practices, access to financing and tax rates than other aspects of the investment climate. Labor regulation emerges as the principal concern for Chilean managers, especially the managers of large firms. As discussed later in this report, firing costs are identified as the key constraint. This result seems to confirm the fact that labor market reforms are progressing only slowly in Chile and are constraining firms in their ability to compete in international and domestic markets. However, fact that labor regulations are cited as the most important obstacle to company growth may indicate that significant advances have already been made in other areas, such as regulatory policy and financial markets.11 Macroeconomic uncertainty and four other issues follow closely behind labor regulation as a concern for managers. Hence, it is difficult to single out one key issue perceived by firms to be an overriding concern. 3.6 Labor regulation emerges as a major concern for Chilean managers, being the first ranked perception as an obstacle for firms’ growth. The availability of skilled workers also concerns Chilean businessmen. This result seems to express the widely accepted fact that labor market reforms are progressing slowly in Chile, constraining firms – especially the large ones - in their ability to compete in international and in domestic markets. But it also may reflect the fact that significant advance was made in other areas in Chile, such as regulatory policy and financial markets. This is an indication of the challenges faced by the Chilean society in the area of education, innovation and technology. These issues have been placed at the top of the agenda by government officials when focusing on prospects for long term growth in Chile. 3.7 Even after a period of remarkably positive economic results, macroeconomic uncertainty ranks among the most severe perceived obstacles in 10

In order to evaluate which are the most important investment climate constraints affecting Chilean firms, this report uses both subjective and objective indicators. Greater emphasis is placed on the latter. Perceptions, however, can help identify some constraints. A five-point scale is used, ranging from extremely severe to not important. Figure 2 below sums up the findings, indicating the percentage of firms that consider the obstacle as “severe” and “extremely severe”. 11 Furthermore, the survey was made in 2004, when the issue of labor regulation was being widely debated.

15

Chile. This may simply reflect the fact that macroeconomic uncertainty increases risk perceptions of every single firm in the economy, while other obstacles may not be relevant for all firms. It may also reflect the perception of international volatility in an open economy such as Chile. The increased volatility of the real exchange rate since the end of the 1990s may be the source of macroeconomic-stability worries of the Chilean private sector, which is indeed higher among exporters (Figures 3.3 and 3.4). Last but not least, it could be simply an indication of the high standards implicitly adopted by Chilean businessmen. In this respect, it is worth noting that Chile was ranked first in the macroeconomic stability component of the World Economic Forum’s 2005-2006 Global Competitiveness Index. Figure 3.1. Entrepreneurs’ perceptions of Obstacles to growth

Figure 3.2. Perceptions of Obstacles to Growth Major Obstacle by Country

Labor Regulations Skilled Labor Force Macroeconomic Instability Anticompetitive practices Tax Rates Permits and licenses Electricity Transport Regulatory Uncertainty Tax Administration Acces to financing Crime, theft and disorder Int. Trade Regulations Legal System/ Conflict Resolution Corruption Telecommunications Cost of financing Acces to Land Customs Regulations

90

84

80 71 70 60 %

50 40

37

37

China - Tax Rates

India Corruption

35

26

30 20 10 0 5% 10% Large & Medium

15% 20% Micro & Small

25%

30%

Chile - Labor Regulation

35%

Brazil - Tax Rates

%

0%

South Af rica Poland - Tax - Skills of Rates Workers

Source:ICS

Source: Central Bank of Chile

Figure 3.3. Real Exchange Rate

Figure 3.4. Chile: Macroeconomic Uncertainty as a perceived Obstacle to growth

Real Exchange Index (Jun 1998=100)

35.0% 30.0%

160 25.0%

140

20.0% %

150

130

15.0%

120 10.0%

110 100

5.0%

Source: Central Bank of Chile

0.0%

Jan-06

Jan-05

Jan-04

Jan-03

Jan-02

Jan-01

Jan-00

Jan-99

Jan-98

Jan-97

Jan-96

Jan-95

Jan-94

Jan-93

Jan-92

Jan-91

Jan-90

Jan-89

Jan-88

Jan-87

Jan-86

90 All

Non-exporters

Exporters

Source:ICS

3.8 Risks, more than extra costs of production, seem to be the focus of the concerns of Chilean firms, which is confirmed by quantitative data. Firms in Chile report extra costs for their activity that are either in line or inferior to comparator countries. Losses with poor infrastructure, with crime and costs of security add up to 1.2% of total sales, roughly one third of the figures for countries like Brazil and China

16

and higher only than Poland. But businessmen in Chile are still concerned with risks. In the survey, firms reported significant risks to their businesses. The supply of energy, for instance, is a cause for concern and 35% of the sample reports having a private backup generator. The fact that macroeconomic instability was identified by firms as the second highest obstacle can also be interpreted as a concern with risks. Finally, even the emphasis placed on labor regulation seems to be more a concern with risks than with costs. When asked which aspect of labor regulation most prevents them from achieving their optimal level of employment, firms mention firing costs, and in most cases (80 percent) they express a desire to increase the labor force, not to decrease it. 3.9 Finally, the level of profitability in Chile does not seem to be out of line when compared to other middle income countries. The Investment Climate Surveys permits a comparison of profitability between countries. Profitability was estimated for manufacturing firms in four comparator countries excluding indirect costs and depreciation because this data were not available for all countries. For the median Chilean enterprise, profitability was about 24.2 percent in 2004, higher than in Poland (17.4 percent), but lower than in South Africa (25 percent), Brazil (29.4 percent) and India (34.4 percent). These are approximate numbers, but the important point to note is that profitability, while lower, is comparable to that of other middle income countries where Investment Climate Surveys have been completed. Figure 3.5. Profitability of the median firm in the manufacturing sector

Brazil*

South Africa

Poland*

India

Chile

0%

5%

10%

15%

20%

25%

30%

35%

40%

Profitability is calculated as (Total Sales – Total Material Cost -Labor Cost - Electricity Cost)/ Sales * Uses direct cost of materials instead of total material cost Source: ICS

17

INVESTMENT CLIMATE AND DETERMINANTS OF PRODUCTIVITY (TFP) 3.10 A number of investment climate indicators drawn from the Chile Investment Climate Survey were econometrically related to measures of productivity. Through the identification of statistically significant investment climate variables in these regressions we try to single out problems or to highlight positive elements that matter for the competitiveness of manufacturing firms in Chile. . The econometric methodology developed by Escribano and Guasch (2005) was employed in this analysis. Different specifications of the production function were examined in order to get robust empirical elasticities for policy analysis. For purposes of the econometric analysis, the investment climate (IC) was separated into four component parts: (i) infrastructure; (ii) governance (including red tape and crime); (iii) finance; and (iv) quality, innovation and skills. Data was drawn from firms having similar characteristics with respect to location, industry group, size, and other factors. The detailed results as well as details of the methodology are presented in the Annex. 3.11 A summary of the estimated values of the elasticities and semi-elasticities of productivity with respect to investment climate variables is provided in Figure 3.11. For each significant IC variable, we present the average values of the pooling OLS elasticity or semi-elasticity estimates given in Tables 3.1 and 3.2. In addition, each IC variable is listed under four thematic categories for analysis purposes. Following the methodology of Escribano and Guasch (2005), we use ten different productivity measures, showing that it is possible to get consistent and robust estimates (elasticities) of investment climate determinants of productivity. Most of the signs of the estimated coefficients are as expected. Obviously, the numerical values of those elasticities parameters vary from one productivity measure to the next, but the range of values is reasonable and significant in most cases.12 3.12 The overall results of the ICS show that all four component parts of investment climate affect the productivity of Chilean firms. The results indicate that three components of the investment climate in particular have a statistically significant association with productivity. Red tape, corruption and crime have, in general, a significant negative impact upon productivity. An increase of 1 percent in the number of inspections results in a decrease of 0.1 percent in productivity. Infrastructure shortcomings such as power outages and shipment losses have a strong negative effect. An increase of 1 percent in the number of days required for exports to clear customs reduces productivity by 0.1 percent. In the case of red tape, corruption and crime, the four significant variables affecting negatively productivity are: cost in security (equipment and staff), total number of inspections, cost of entry in terms of the number of days spent waiting for permissions and licenses and number of days lost in production due to absenteeism.

12

The variables in levels are used with logarithmic transformation of output, labor, intermediate materials and capital.

18

3.13 There is clear evidence that labor training and innovation — as measured by the share of staff engaged in R&D — increase productivity, with a strong positive impact in the case of internal training. The effects of the experience of the manager are also important. In the same sense, in respect to quality, innovation and labor skills, four variables are relevant to explain variation in productivity: firms report doing R&D activities, firms that provide internal training to its employees, percentage of the staff with at least one year of university studies and the experience of the manager in number of years. The latter showed the highest elasticity with 0.26. 3.14 Respect to finance, only one variable, having at the present time a financing line program (CORFO), has a positive impact on productivity. Finally, a group of other control variables were added to the analysis. Four variables have a positive effect on overall productivity such as having status of incorporated companies, firms receiving foreign direct investment, firms that export more than 10% of their sales and the percentage of the utilized capacity. In contrast, two variables have a negative impact on productivity: those firms that rent most of their land and the percentage of workers that belong to a trade union.13 3.15 Quality, innovation and labor skills variables have significant positive impact on average productivity. Figure 3.7 shows the impact of average IC variables on average (log) productivity. The average impact of IC variables on productivity of Chilean firms can be assessed by taking into account the elasticities and semielasticities of each variable evaluated at their sample means (see Annex for details). The results show that for infrastructure the two most important for average productivity are average duration of power outages (-6.98%) and shipment losses (-6.91). For governance and business regulations, in spite of the fact that cost of entry has the highest coefficient, it only represents -0.31% in terms of average productivity. Having a financing line program could represent 2% of average (log) productivity. Finally, regarding quality, innovation and labor skills, having staff with at least one year of university studies a represents 6.51% of average productivity, despite presenting a very low coefficient (0.005). In addition, having a manager with experience represents almost 30% of the average productivity. In the case of other control variables, the most significant positive impact is explained by capacity utilization, which represents 102% of average productivity, followed by the negative impact of belonging to a trade union (-11.73%).

13

Another exercise was done relating TFP and investment climate variables, the difference being that TFP measures were obtained from the ENIA dataset which is much more complete than the investment climate survey dataset. Details about the methodology and main results are presented in the Annex.

19

Figure 3.6. Productivity Elasticities and Semielasticities with Respect to IC Variables Infrastructures

Red Tape, Corruption and Crime

Finance

Quality, Innovation and Labor Skills

0.3

Other Control Variables

0.26 0.23

0.25

0.18

0.2 0.13

0.15 0.09

0.1 0.04

0.05

0.05

0.03

0.02

0.005

0 -0.01

-0.05 -0.1

-0.05

-0.07

-0.09 -0.12

-0.12

-0.15 -0.17

-0.2 1.1

1.2

1.3

1.4

2.1

2.2

-0.14 2.3

-0.12

2.4

3.1

4.1

4.2

1.1 Days to clear customs for exports. 1.2 Power outages. 1.3 Shipment losses. 1.4 Internet page. 2.1 Security. 2.2 Number of inspections. 2.3 Cost of entry. 2.4 Absenteeism. 3.1 Financing line program.

4.3

4.4

5.1

5.2

5.3

5.4

5.5

5.6

4.1 R + D. 4.2 Internal training. 4.3 University staff. 4.4 Experience of the manager. 5.1 Incorporated company. 5.2 Foreign direct investment. 5.3 Exporter. 5.4 Capacity utilization. 5.5 Rent land. 5.6 Trade union.

Elasticities are indicated by blue bars, semielasticities by yellow bars.

Source: Own Calculations based on ICS

Figure 3.7. Impact on Average Productivity of Investment Climate Variables Average Productivity Impact (Gains and Losses) of Investment Climate Variables; Aggregate Level.

% Red Tape, Corruption and Crime

Infrastructure

Finance

Quality, Innovation and Labor Skills

Other Control Variables

120 102.34

100 80 60 40

29.90

20 1.67

1.97

-0.31

2.00

4.23

6.51

1.71

1.99

2.37

0 -2.90

-20

-6.98

-6.91

-6.82

-4.38

-6.94

-

-18.59

-40 1.1

1.2

1.3

1.4

2.1

2.2

2.3

2.4

3.1

1.1 Days to clear customs to exports (log). 1.2 Average duration of power outages. 1.3 Shipment losses. 1.4 Internet page. 2.1 Security. 2.2 Number of inspections. 2.3 Cost of entry. 2.4 Absenteeism. 3.1 Financing Line Program.

4.1

4.2

4.3

4.4

5.1

4.1 R + D. 4.2 Internal training. 4.3 University staff. 4.4 Experience of the manager. 5.1 Incorporated company. 5.2 Foreign direct investment. 5.3 Exporter. 5.4 Capacity utilization. 5.5 Rent land. 5.6 Trade Union.

Source: Own Calculations based on ENIA

20

5.2

5.3

5.4

5.5

Table 3.1. IC Elasticities and Semielasticities with respect to Productivity; Restricted Estimation.

Explanatory variables Days to clear customs for exports Power outages Shipment losses Internet Page

Security Number of inspections Cost of entry Absenteeism

Two step estimation One step estimation Solow's Residual Cobb-Douglas Translog Pool OLS Random Efts. Pool OLS R. E. Pool OLS R. E. Dep. Var: Restr. Solow’s Resid. Dep. Var: log of sales. Infrastructure. -0.042 [0.083] -0.128** [0.054] -0.124*** [0.028] 0.036 [0.034] -0.053*** [0.020] -0.068 [0.087] -0.155*** [0.035] -0.126* [0.070]

Financing Line program

0.039** [0.019]

R+D

0.059 [0.038] 0.149*** [0.035] 0.006*** [0.001] 0.304*** [0.084]

Internal training University staff Experience of the manager

Incorporated company Foreign direct investment Exporter Capacity utilization Rent land Trade Union Observations R2

0.023 [0.036] 0.208*** [0.057] 0.156*** [0.051] 0.022*** [0.005] -0.163*** [0.041] -0.013*** [0.003] 2439 0.19

-0.042 -0.046 -0.036 [0.118] [0.082] [0.117] -0.128 -0.148*** -0.172** [0.082] [0.053] [0.081] -0.124*** -0.119*** -0.146*** [0.048] [0.028] [0.047] 0.036 0.035 0.064 [0.065] [0.035] [0.065] Red Tape, Corruption and Crime. -0.053* -0.046** -0.037 [0.028] [0.019] [0.028] -0.068 -0.091 -0.144 [0.133] [0.086] [0.132] -0.155** -0.139*** -0.152** [0.073] [0.038] [0.073] -0.126 -0.11 -0.132 [0.100] [0.069] [0.099] Finance and Corporate Governance. 0.039 0.033* 0.037 [0.029] [0.018] [0.029] Quality, Innovation and Labor Skills. 0.059 0.068* 0.112* [0.061] [0.039] [0.061] 0.149** 0.104*** 0.184*** [0.062] [0.038] [0.063] 0.006*** 0.005*** 0.005*** [0.001] [0.001] [0.001] 0.304** 0.261*** 0.276** [0.123] [0.082] [0.122] Other Control Variables 0.023 0.009 0.07 [0.067] [0.037] [0.067] 0.208** 0.212*** 0.304*** [0.081] [0.058] [0.082] 0.156** 0.152*** 0.270*** [0.075] [0.051] [0.076] 0.022*** 0.024*** 0.026*** [0.007] [0.005] [0.007] -0.163** -0.147*** -0.078 [0.069] [0.043] [0.070] -0.013* -0.014*** -0.011 [0.007] [0.003] [0.007] 2439 2439 2439 0.19 0.87 0.86

1

-0.068 [0.070] -0.206*** [0.047] -0.128*** [0.025] 0.009 [0.035]

-0.116 [0.106] -0.227*** [0.074] -0.166*** [0.043] 0.045 [0.058]

-0.051*** [0.018] -0.118 [0.077] -0.129*** [0.031] -0.092 [0.060]

-0.046* [0.025] -0.163 [0.119] -0.149** [0.066] -0.127 [0.090]

0.017 [0.016]

0.025 [0.026]

0.076** [0.034] 0.130*** [0.035] 0.004*** [0.001] 0.220*** [0.075]

0.119** [0.055] 0.199*** [0.057] 0.005*** [0.001] 0.265** [0.110]

0.044 [0.034] 0.148*** [0.051] 0.165*** [0.045] 0.023*** [0.005] -0.070* [0.039] -0.011*** [0.003] 2439 0.89

0.076 [0.061] 0.290*** [0.074] 0.250*** [0.069] 0.028*** [0.007] -0.043 [0.063] -0.01 [0.006] 2439 0.88

Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. The restricted Solow Residual is obtained using cost shares for inputs (labor, materials and capital) calculated as averages across all plants in the three countries in years 2001, 2002 and 2003 (excluding outliers). 3 The regressions include a constant, industry dummies and year dummies. 4 Hausman Tests for endogeneity of the regressors do not reject the null hypotheses (exogeneity) in all the cases except for exports. 2

21

Table 3.2. IC Elasticities and Semielasticities with respect to Productivity; Unrestricted Estimation.

Explanatory variables Days to clear customs for exports Power outages Shipment losses Internet Page

Security Number of inspections Cost of entry Absenteeism

Financing Line Program

R+D Internal training University staff Experience of the manager

Incorporated company Foreign direct investment Exporter Capacity utilization Rent land Trade Union Observations R2

Two step estimation One step estimation Solow's Residual Cobb-Douglas Translog Pool OLS Random Efts. Pool OLS R. E. Pool OLS R.E. Dep. Var: Restr. Solow’s Resid. Dep. Var: log of sales. Infrastructure. -0.049 [0.083] -0.135** [0.054] -0.109*** [0.029] 0.021 [0.034]

-0.049 -0.104 [0.116] [0.079] -0.135* -0.146*** [0.081] [0.050] -0.109** -0.119*** [0.047] [0.030] 0.021 0.05 [0.064] [0.033] Red Tape, Corruption and Crime. -0.052** -0.052* -0.062*** [0.021] [0.027] [0.020] -0.074 -0.074 -0.083 [0.087] [0.131] [0.083] -0.142*** -0.142** -0.123*** [0.036] [0.072] [0.036] -0.166** -0.166* -0.085 [0.072] [0.098] [0.061] Finance and Corporate Governance. 0.039** 0.039 0.032* [0.018] [0.029] [0.017] Quality, Innovation and Labor Skills. 0.073** 0.073 0.085** [0.037] [0.060] [0.038] 0.112*** 0.112* 0.085** [0.034] [0.061] [0.038] 0.005*** 0.005*** 0.003*** [0.001] [0.001] [0.001] 0.329*** 0.329*** 0.246*** [0.086] [0.121] [0.076] Other Control Variables 0.049 0.049 0.029 [0.035] [0.066] [0.035] 0.210*** 0.210*** 0.214*** [0.057] [0.080] [0.054] 0.152*** 0.152** 0.161*** [0.051] [0.074] [0.048] 0.022*** 0.022*** 0.028*** [0.005] [0.007] [0.006] -0.116*** -0.116* -0.081* [0.042] [0.068] [0.043] -0.014*** -0.014** -0.011*** [0.003] [0.007] [0.004] 2439 2439 2439 0.24 0.24 0.88

1

-0.019 [0.117] -0.206** [0.080] -0.154*** [0.047] 0.077 [0.064]

-0.124* [0.069] -0.177*** [0.043] -0.076*** [0.028] 0.056* [0.032]

-0.091 [0.104] -0.232*** [0.071] -0.097** [0.043] 0.046 [0.056]

-0.061** [0.028] -0.138 [0.130] -0.162** [0.071] -0.12 [0.099]

-0.033* [0.017] -0.176** [0.074] -0.072* [0.037] -0.083* [0.045]

-0.024 [0.025] -0.241** [0.114] -0.138** [0.063] -0.147* [0.089]

0.037 [0.029]

0.018 [0.014]

0.018 [0.025]

0.134** [0.060] 0.169*** [0.062] 0.005*** [0.001] 0.241** [0.120]

0.097*** [0.033] 0.070** [0.035] 0.003*** [0.001] 0.158*** [0.060]

0.135** [0.053] 0.135** [0.054] 0.004*** [0.001] 0.231** [0.107]

0.077 [0.066] 0.327*** [0.080] 0.237*** [0.076] 0.023*** [0.007] -0.039 [0.068] -0.01 [0.007] 2439 0.88

0.087*** [0.032] 0.147*** [0.050] 0.124*** [0.047] 0.019*** [0.005] -0.053 [0.038] -0.004 [0.003] 2439 0.91

0.094 [0.058] 0.295*** [0.071] 0.162** [0.067] 0.020*** [0.007] -0.034 [0.060] -0.006 [0.006] 2439 0.91

Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. The restricted Solow Residual is obtained using cost shares for inputs (labor, materials and capital) calculated as averages across all plants in the three countries in years 2001, 2002 and 2003 (excluding outliers). 3 The regressions include a constant, industry dummies and year dummies. 4 Hausman Tests for endogeneity of the regressors do not reject the null hypotheses (exogeneity) in all the cases except for exports. 2

22

TABLE 3.2B. IC ELASTICITIES AND SEMIELASTICITIES WITH RESPECT TO PRODUCTIVITY; TWO STAGE LEAST SQUARES (2SLS) ESTIMATION. Two step estimation Solow's Residual Restricted Unrestricted Explanatory variables Days to clear customs for exports Power outages Shipment losses Internet page

Security Number of inspections Cost of entry Absenteeism

Financing Line Program

R+D Internal training University staff Experience of the manager

Incorporated company Foreign direct investment Exporter Capacity utilization Rent land Trade Union Observations F test (p-values)

One step estimation Cobb-Douglas Translog Restricte Unrestricted Restricte Unrestricted d d Dep. Var: log of sales.

Dep. Var: Restr. Solow’s Resid. Infrastructure. -0.042 -0.059 -0.053 [0.082] [0.085] [0.083] -0.128** -0.134** -0.149*** [0.054] [0.053] [0.053] -0.124*** -0.109*** -0.118*** [0.028] [0.029] [0.028] 0.036 0.017 0.031 [0.034] [0.034] [0.035] Red Tape, Corruption and Crime. -0.053*** -0.052** -0.045** [0.020] [0.021] [0.019] -0.068 -0.084 -0.107 [0.086] [0.086] [0.086] -0.155*** -0.140*** -0.137*** [0.034] [0.036] [0.038] -0.126* -0.169** -0.115* [0.070] [0.072] [0.069] Finance and Corporate Governance. 0.039** 0.040** 0.034* [0.019] [0.018] [0.018] Quality, Innovation and Labor Skills. 0.059 0.070* 0.069* [0.038] [0.037] [0.039] 0.149*** 0.108*** 0.104*** [0.034] [0.034] [0.037] 0.006*** 0.004*** 0.005*** [0.001] [0.001] [0.001] 0.304*** 0.329*** 0.259*** [0.084] [0.086] [0.082] Other Control Variables

0.023 0.047 0.006 [0.036] [0.035] [0.037] 0.208*** 0.220*** 0.216*** [0.056] [0.058] [0.058] 0.156*** 0.164*** 0.169*** [0.051] [0.051] [0.051] 0.022*** 0.022*** 0.024*** [0.005] [0.006] [0.005] -0.163*** -0.116*** -0.142*** [0.041] [0.042] [0.042] -0.013*** -0.014*** -0.014*** [0.003] [0.003] [0.003] 2439 2421 2421 Instruments Evaluation (exports equation) 0.000 0.000 0.000 Instruments Evaluation (FDI equation) 0.000 0.000 0.000 0.605 0.379 0.564

-0.106 [0.081] -0.142*** [0.050] -0.116*** [0.030] 0.046 [0.033]

-0.072 [0.071] -0.204*** [0.046] -0.127*** [0.024] 0.008 [0.034]

-0.107 [0.069] -0.183*** [0.043] -0.074*** [0.028] 0.04 [0.031]

-0.064*** [0.020] -0.106 [0.082] -0.122*** [0.035] -0.088 [0.061]

-0.051*** [0.018] -0.121 [0.077] -0.129*** [0.030] -0.095 [0.060]

-0.036** [0.017] -0.205*** [0.074] -0.072* [0.039] -0.082* [0.044]

0.035** [0.017]

0.017 [0.016]

0.02 [0.014]

0.083** [0.037] 0.085** [0.038] 0.003*** [0.001] 0.232*** [0.076]

0.074** [0.033] 0.126*** [0.035] 0.004*** [0.001] 0.218*** [0.075]

0.084** [0.033] 0.068* [0.035] 0.003*** [0.001] 0.156*** [0.059]

0.026 [0.035] 0.216*** [0.054] 0.182*** [0.048] 0.027*** [0.006] -0.076* [0.042] -0.011*** [0.004] 2421

0.043 [0.034] 0 [0.000] 0.174*** [0.045] 0.023*** [0.005] -0.075* [0.039] -0.011*** [0.003] 2421

0.070** [0.031] 0 [0.000] 0.136*** [0.047] 0.019*** [0.005] -0.062 [0.038] -0.005 [0.003] 2421

0.000

0.000

0.000

F test (p-values) 0.000 0.000 0.000 Overidentifying Restrictions Hansen test 0.062 0.621 0.169 (p-values.) 1 Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. 1 Exports and FDI are endogenous and are instrumented with new technology purchased, days to clear customs for exports (industry-region averages), credit (i-r averages), derivatives, internal train, experience of the manager (i-r averages) and rent build. 3 All regressions include a constant, industry dummies and year dummies.

23

Impacts of Investment Climate on Exports and Labor Demand 3.16 We turn our attention now to employment and exports, two of the main objectives of economic policy. While productivity is the key variable to explain long term growth, employment generation and exports growth are also important objectives of economic policy and Chile is no different in this respect. As discussed in the precedent chapter, employment has not accompanied the good performance in economic growth in the last two decades. Regarding exports, although the country has performed extremely well, Chile’s exports remain concentrated in a few primary commodities. Hence, there is a vulnerability to changes in international prices. Diversifying and increasing the export base seems therefore an important goal. 3.17 The ICA allows us to show that both exports and labor demand are directly and indirectly affected by the investment climate. A simultaneous equation model was estimated to assess the impact of investment climate variables on a set of variables, including the probability of exporting, employment, the probability of foreign direct investment and wages. We report here the results obtained for the first two variables. Details can be obtained in the Annex. Robust results were found for the ten different productivity measures used. Therefore, we can concentrate on the analysis of only two of the productivity measures: the Solow’s residuals (TFP) from the restricted case and the unrestricted by industry. In the two-step estimation procedure, the Solow’s residuals with constant input-output elasticities are obtained at the aggregate level and in the second stage an equation is estimated to evaluate the impact of IC variables on those total factor productivity measures (TFP). Investment Climate and Productivity Effects on the Probability of Exporting 3.18 The results on the investment climate (IC) effects on the probability of exporting are as expected and the results are robust for both productivity (TFP) measures.14 The coefficient for productivity is significant and positive, indicating that improvements in productivity keeping the rest of the variables constant increases the probability of becoming an exporting firm. This evidence suggests that better-performing firms select themselves into exports. Since better investment climate conditions are associated with higher levels of productivity for both exporters and non-exporters, they should in turn lead respectively to an increase in the exported output shares of exporters, and to a boost in the rates of entry into exports for non-exporters. 3.19 Again, variables in all four areas are correlated with the probability of export. In terms of infrastructure, only one IC variable on infrastructure affects the probability of becoming an exporting firm. Those firms that use email have a higher probability of becoming exporting firms. On the other hand, firms that use bribes have a 14

Productivity is the endogenous variable and it instrumented by several IC variables where most of them are region-industry averages. The F-tests reject the null hypothesis of no correlation between productivity and the list of instruments with a p-value of 0. The over identification restrictions (Hansen test) are not rejected indicating that there is no evidence of correlation among the instruments minus one and the regression errors. The results of the 2SLS estimation of the estimated linear probability model, with heteroskedasticity-robust standard errors, are included in Table 3.3.

24

reduction in the probability of exporting. In respect to finance and corporate governance issues, three IC variables affect positively the probability of becoming exporting firms: firms that belong to any trade association or trade chamber, those that use financial derivatives to cover financial risks and those firms whose statements are externally audited. In addition, five IC variables related to quality, innovation and labor skills issues are affecting positively the probability of becoming an exporting firm, such as the fact the firm has received a quality certification, that it performs R+D activities, purchased any new technology, provides internal training to its employees or has a manager with experience increases the probability of exporting. It is thus clear that firms with higher levels skilled employment shares are more likely to enter the export market. Finally, two control variables are significant and positive for the probability of becoming an exporting firm. Those firms that are incorporated companies and those that rent almost all its buildings. However, older firms have lower probabilities of becoming exporting firms. Figure 3.8. Impact of IC variables on export Decisions Export Linear Probability Coefficients with Respect to IC Variables Productivity

Infrsts.

Re d Tape, Corr. & Crime

Finance and Corporate Governance

Q uality, Innovation and Labor Skills

O ther Control Variable

0.3 0.24 0.2

0.15

0.13

0.12 0.1

0.06

0.03

0.06

0.07

0.09

0.100

0.06

0.0 -0.001

0 -0.1 -0.2 -0.23 -0.3 1.1 1.1 2.1 3.1 4.1 4.2 4.3

2.1

3.1

4.1

4.2

4.3

5.1

Productivity. E-mail. Illegal payments for protection . T rade Association. Derivatives. External auditory.

5.2 5.1 5.2 5.3 5.4 5.5 6.1 6.2 6.3

Source: Own Calculations based on ICS

25

5.3

5.4

Quality certification. R + D. New technology purchased. Internal training. Experience of the manager. Incorporated company. Age. Rent buildings.

5.5

6.1

6.2

6.

Table 3.3.Two Stage Least Squares (2SLS) Estimation of Exporting Decisions; Coefficients and Percentage Impact on the Probability of Exporting. Productivity Measure Used:

Log of Productivity

E-mail

Illegal payments for protection

Trade association Derivatives External Auditory

Quality certification R+D New technology purchased Internal training Experience of the manager

Incorporated Company Age Rent buildings Observations F test (p-values) Overidentifying Restrictions Hansen Test (p-values)

Restricted Solow Residual % Probability Coefficients contribution Productivity 0.036*** 27.23 [0.011] Infrastructure 0.151*** 58.00 [0.023] Red Tape, Corruption and Crime -0.255*** -5.54 [0.045] Finance and Corporate Governance 0.296*** 64.78 [0.091] 0.126*** 6.11 [0.032] 0.066*** 16.47 [0.017] Quality, Innovation and Labor Skills 0.129*** 12.53 [0.023] 0.057*** 10.44 [0.018] 0.060*** 6.46 [0.019] 0.058*** 13.97 [0.017] 0.120*** 95.08 [0.028] Other Control Variables 0.100*** 31.71 [0.016] -0.001*** -13.21 [0.000] 0.031* 9.09 [0.018] 2319 Instruments Evaluation

Unrestricted Solow Residual % Probability Coefficients contribution 0.033*** [0.011]

24.11

0.152*** [0.024]

58.27

-0.202*** [0.044]

-4.38

0.175* [0.093] 0.111*** [0.032] 0.059*** [0.017]

38.26 5.40 14.65

0.126*** [0.023] 0.063*** [0.018] 0.072*** [0.019] 0.054*** [0.017] 0.063** [0.029]

12.23

0.099*** [0.016] -0.001*** [0.000] 0.031* [0.018] 2319

31.41

0.000

0.000

0.145

0.092

11.52 7.79 13.05 49.70

-11.08 9.22

1

Dependent variable is a dichotomous variable that takes value one if the firm exports at least the 10% of it production and zero otherwise (Exporter) (see equation ?.?). 2 Productivity is endogenous and is instrumented with: power outages (i-r averages), shipment losses (i-r averages), security (i-r averages), cost of entry, internal training, university staff, financing line program and rent land. 3 Specification also includes a constant term, industry and year dummies. 4 Significance is given by robust standard errors. *significant at 10%; ** significant at 5%; *** significant at 1%.

Investment Climate and Productivity Effects on Labor Demand 3.20 The results on the investment climate (IC) effects on employment demand are as expected and the results are robust for both productivity (TFP) measures. Productivity is the endogenous variable and it was instrumented by several IC variables

26

where most of them are region-industry averages.15 The coefficient for productivity is significant and negative, indicating that improvements in productivity keeping the rest of the variables constant decrease the demand for labor. The real wage, as expected, has a negative sign. 3.21 In terms of infrastructure, having access to internet affects positively the demand for labor. The other variable, power outages, has the expected negative coefficient, but it is only marginally significant in one of the equations. In the case of red tape, corruption and crime, the three significant variables affecting negatively employment are: total number of inspections, cost of entry in terms of the number of days spent waiting for permissions and licenses and number of days lost in production due to absenteeism. The cost of security (equipment and staff) has a positive impact on demand for labor. In respect with finance and corporate governance issues, two IC variables affect positively labor demand: firms that belong to any trade association or trade chamber and those firms whose annual statements are externally audited. 3.22 In addition, five IC variables related to quality, innovation and labor skills issues positively affect employment. The variables are the existence of a quality certification for the firm, the introduction of a new product, the provision of internal and external training to its employees and the percentage of the staff with at least one year of university studies. The experience of the manager in number of years has a negative impact on employment. Finally, six additional control variables are significant for the demand for labor. 3.23 It should be noted that, since productivity and wages have a negative sign in the labor demand equation, the net impact of the investment climate variables actually depends on the magnitude of the different impacts. Firms that adopt internal training, for instance, generate 24% more of employment, but the negative effect via productivity and wages reduce the net impact to 9.2%. In table 3.5 and 3.6 we present the direct and net impacts for a group of selected investment climate variables. 3.24 The conclusions of the econometric analysis are that investment climate variables significantly affect total factor productivity, the probability of firms to export and labor demand in Chile. In spite of the relatively good investment climate, the country would clearly benefit from improvements in all areas of the investment climate, as indicators pertaining to all four areas have shown to be related to productivity, probability of exports and labor demand. In the next four chapters, we discuss each of these areas of the investment climate: finance, governance and business regulation (including labor regulation), technology and innovation (including labor skills) and infrastructure. 15

The F-tests reject the null hypothesis of no correlation between productivity and the list of instruments with a p-value of 0. Again, the over identification restrictions (Hansen test) are not rejected indicating that there is no evidence of correlation among the instruments minus one and the regression errors. The results of the 2SLS estimation of the estimated linear probability model, with heteroskedasticity-robust standard errors, are included in Table 3.4.

27

Figure 3.9. Investment Climate Impact on Labor Demand Employment Elasticities and Semielasticities With Respect to IC Variables Prdvty.

Real Wage

Infrstrc.

Red Tape , Corruption and Crime

0.5 0.12

0.18

0.13

Q uality, Innovation and Labor Skills

Finance and Corp. Gov. 0.24

0.24 0.07

0.04

0.14

0.180 -0.003

O the r Control Variables

0.13

0.31 0.01

0.01

0 -0.09

-0.08 -0.27

-0.5

-0.28

-0.30 -0.77

-1 -1.5 -2

-1.89

-2.5 1.1 1.1 2.1 3.1 3.2 4.1 4.2 4.3 4.4 5.1 5.2

2.1

3.1

3.2

4.1

4.2

4.3

4.4

5.1

5.2

Productivity. Real wage per employee. Power outages. Internet page. Security. Number of inspections. Cost of entry. Absenteeism. T rade association. External auditory.

6.1

6.2

6.3 6.1 6.2 6.3 6.4 6.5 6.6 7.1 7.2 7.3 7.4 7.5 7.6

Elasticities are indicated by blue bars, semielasticities by yellow bars.

Source: Own Calculations based on ICS

28

6.4

6.5

6.6

7.1

Quality certification. New product. Internal training. External training. University staff. Experience of the manager. Incorporated company. Age. Exporter. T rade union. Small. Medium.

7.2

7.3

7.4

7.5

Table 3.4. Two Stage Least Squares (2SLS) Estimation of Employment Demand Equation; Coefficients and Percentage Contribution to the Average (log) Employment. Explanatory Variables Log of Productivity

Log of real wage

Power outages Internet page

Security Number of inspections Cost of entry Absenteeism

Trade association External Auditory

Quality certification New product Internal training External training University staff Experience of the manager

Incorporated company Age Exporter Trade union Small Medium Observations

Restricted Solow Residual Coefficient % Contribution Productivity -0.094*** -4.65 [0.027] Real Wage Per Employee -0.267*** -62.68 [0.019] Infrastructure -0.079 -1.83 [0.049] 0.124*** 2.43 [0.041] Red Tape, Corruption and Crime 0.133*** 23.63 [0.017] -0.295*** -10.81 [0.079] -0.779*** -0.77 [0.246] -0.275*** -7.37 [0.050] Finance and Corporate Governance 0.184*** 2.60 [0.038] 0.043*** 2.22 [0.016] Quality, Innovation and Labor Skills 0.233*** 1.49 [0.049] 0.070* 0.88 [0.037] 0.239*** 3.78 [0.040] 0.135*** 2.11 [0.041] -0.003*** -1.75 [0.001] 0.178*** 2.87 [0.037] Other Control Variables 0.130*** 2.72 [0.041] 0.008*** 5.30 [0.001] 0.312*** 2.05 [0.050] 0.014*** 6.73 [0.003] -1.898*** -21.37 [0.061] -1.007*** -6.01 [0.053] 2253 Instruments Evaluation 0.001

Unrestricted Solow Residual Coefficient % Contribution -0.078*** [0.028]

-3.72

-0.270*** [0.019]

-63.50

-0.081* [0.049] 0.119*** [0.042]

-1.88

0.134*** [0.017] -0.297*** [0.079] -0.752*** [0.244] -0.279*** [0.050]

23.85

2.35

-10.92 -0.74 -7.47

0.185*** [0.039] 0.043*** [0.016]

2.61

0.242*** [0.049] 0.073* [0.037] 0.235*** [0.040] 0.135*** [0.041] -0.003*** [0.001] 0.182*** [0.037]

1.55

0.132*** [0.041] 0.008*** [0.001] 0.312*** [0.050] 0.014*** [0.003] -1.884*** [0.061] -0.999*** [0.053] 2253

2.21

0.92 3.72 2.11 -1.90 2.93

2.75 5.32 2.05 6.64 -21.22 -5.97

F test (p-values) 0.000 Overidentifying Restrictions Hansen Test (p-values) 0.179 0.171 1 Dependent variable is defined as the total number of permanent workers total or part time (logs) (see equation ?.?). 1 Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. 2 Productivity is endogenous and is instrumented with: days to clear customs for exports (i-r averages), power outages (i-r averages), water outages (i-r averages), shipment losses (i-r averages), security (i-r averages), number of inspections (i-r averages), cost of entry, absenteeism (i-r averages). 3 All regressions include a constant, industry dummies and year dummies.

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Table 3.5. Summary of the Results of Chile's ICA; Marginal (or Direct) Effect of IC and C Variables Dependent variable Explanatory variable Productivity Employment Exports Productivity 0.000 -0.086 0.035 Real wage 0.000 -0.269 0.000 -0.066 0.000 0.000 Infrastructures Days to clear customs to export Days to clear customs to import 0.000 0.000 0.000 Av. duration of power outages 0.000 0.000 0.000 Power outages -0.170 -0.080 0.000 Shipment losses -0.123 0.000 0.000 E-mail 0.000 0.000 0.152 Internet page 0.041 0.122 0.000 -0.048 0.134 0.000 Red Tape, Security Corruption and Number of inspections -0.120 -0.296 0.000 Crime Cost of entry -0.138 -0.766 0.000 Absenteeism -0.123 -0.277 0.000 Illegal payments for protection 0.000 0.000 -0.225 0.031 0.000 0.000 Finance and Financing Line Program Corporate Trade Association 0.000 0.185 0.236 Governance Credit line 0.000 0.000 0.000 Derivatives 0.000 0.000 0.119 External auditory 0.000 0.043 0.063 0.000 0.238 0.128 Quality and Quality certification Innovation R + D 0.091 0.000 0.060 R+D new product 0.000 0.000 0.000 New Product 0.000 0.072 0.000 New technology purchased 0.000 0.000 0.066 Internal training 0.133 0.237 0.056 External training 0.000 0.135 0.000 University staff 0.005 -0.003 0.000 Experience of the manager 0.264 0.180 0.092 0.053 0.131 0.100 Other Control Incorporated company Variables Foreign direct investment 0.231 0.000 0.000 Age 0.000 0.008 -0.001 Exporter 0.178 0.312 0.000 Capacity utilization 0.023 0.000 0.000 Rent land -0.092 0.000 0.000 Rent buildings 0.000 0.000 0.031 Trade union -0.011 0.014 0.000 Small 0.000 -1.891 0.000 Medium 0.000 -1.003 0.000

30

Table 3.6. Summary of the Results of Chile's ICA; Net Effect of IC and C Variables. Dependent variable Explanatory variable Productivity Employment Exports Productivity 0.013 -0.088 0.035 Real wage 0.000 -0.269 0.000 Days to clear customs to export -0.066 0.013 -0.002 Infrastructures Days to clear customs to import -0.016 0.003 -0.001 Av. duration of power outages 0.000 0.000 0.000 Power outages -0.170 -0.046 -0.006 Shipment losses -0.123 0.025 -0.004 E-mail 0.027 -0.002 0.152 Internet page 0.041 0.113 0.001 -0.048 0.143 -0.002 Red Tape, Security Corruption and Number of inspections -0.120 -0.272 -0.004 Crime Cost of entry -0.138 -0.737 -0.005 Absenteeism -0.123 -0.252 -0.004 Illegal payments for protection -0.040 0.003 -0.226 0.031 -0.006 0.001 Finance and Financing Line Program Corporate Trade Association 0.053 0.179 0.237 Governance Credit line 0.034 -0.007 0.001 Derivatives 0.037 -0.005 0.120 External auditory 0.024 0.039 0.063 0.035 0.233 0.129 Quality and Quality certification Innovation R + D 0.102 -0.019 0.064 R+D new product 0.041 -0.009 0.001 New Product 0.000 0.072 0.000 New technology purchased 0.012 -0.001 0.066 Internal training 0.164 0.205 0.062 External training 0.000 0.135 0.000 University staff 0.005 -0.004 0.000 Experience of the manager 0.280 0.125 0.101 0.070 0.119 0.102 Other Control Incorporated company Variables Foreign direct investment 0.231 -0.047 0.008 Age 0.000 0.008 -0.001 Exporter 0.178 0.276 0.006 Capacity utilization 0.023 -0.005 0.001 Rent land -0.092 0.019 -0.003 Rent buildings 0.027 -0.005 0.032 Trade union -0.011 0.016 0.000 Small 0.000 -1.891 0.000 Medium 0.000 -1.003 0.000

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CHAPTER 4 – FINANCE 4.1 This chapter reviews the most important issues related to access to finance of Chilean firms in light of the information collected though the Investment Climate Survey (ICS). This chapter does not attempt to be an analysis of the corporate sector in Chile, nor an assessment of the participants in its financial system, or its strength. This chapter aims at identifying current obstacles to access finance leveraging on the information reported by companies surveyed. Recent reports and studies on the Chilean financial system are also used to contextualize issues raised by the survey. 4.2 This chapter has four mains sections. Section I presents an overview of the composition of the Chilean financial system. Section II includes an analysis of issues related to access to finance. Section III includes a review of the most important constraints for access to finance identified through the Investment Climate Survey and background literature. Finally, section IV presents the conclusions and policy recommendations. I. STRUCTURE OF THE FINANCIAL SYSTEM IN CHILE AND THE SOURCES OF FINANCE 4.3 The financial system in Chile is the most robust in Latin America. The banking sector is characterized by effective supervision, good capitalization and increasing sophistication of the participating institutions. Other sources of finance, including debt and equity markets, leasing and factoring companies and government finance programs provide further depth to the system. At the same time, a large pensions sector and a broad deposit base provide the system with an ample supply of funds, far greater than that of other Latin American economies. These factors result in a ratio of private-sector credit to GDP that compares very favorably with that of other economies in the region, and is currently on a par with that of middle income countries (although considerably below that of European countries such as Ireland and Spain). After a significant contraction following the financial crisis in 1983, domestic credit to the private sector has been growing steadily since the early 1990s. 4.4 A recent report by the World Bank and the IMF (Financial System Assessment Program, FSAP) reviewed in detail the Chilean financial system and identified some major issues related to access to finance of Chilean companies. This section draws mainly upon the conclusions of the FSAP report as a context for the analysis of the data collected through the ICS.

32

Figure 4.1. Domestic credit to private sector (% of GDP) Domestic Credit to the Private Sector (% GDP) Latin America

Domestic credit to the Private Sector (% GDP) 200

26

Poland

29

Costa Rica

31

Czech Rep

32

Slovak Rep

32

180 160 140

Brazil

120

(% GDP)

35

Mid Income

63

Chile

63

Greece

100 80

73

60

86

Italy Ireland

118

Spain

119

40 20

OECD

158

0 1960

0

50

100 (% of GDP)

1964

1968

1972

1976

1980

1984

1988

1992

1996

2000

2004

150 Chile

High income

Middle income

Latin America

Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. Source: World Bank WDI Database.

The Banking Sector 4.5 The Chilean banking sector is robust and stable, with high levels of capitalization, good asset quality, and high profitability. According to figures of the Superintendencia de Bancos e Instituciones Financieras (SBIF, the banking regulator) as of August 2005, the average capitalization in the sector stood at a comfortable 13.5 percent of risk weighted assets, well above that in peer Latin American economies. The asset quality in the sector was also good with a low 1.04 percent of past due loans16, which were amply covered by a level of provisioning of 166 percent. Similarly, profitability has been consistently robust, and currently stands at a high 15% of net profits over equity. 4.6 Even though competition in the banking sector increased in the last few years as a result of new entrants, it varies across different segments of the market. Importantly, the degree of competition is not even throughout the range of borrowers. Competition is intense in credit to large companies, which also have access to other funding sources such as capital markets and foreign finance. Similarly competition is high in consumer credit, especially from non-bank players such as department stores. 16

Note that the definition of past due loans in Chile differs from that of international standards outlined in the Basel II Accord from the Bank for International Settlements. The main difference stems from the fact that while international standards consider as past due the full amount of a loan when one or more installments go unpaid for more than 90 days, Chilean regulation considers only the amount of the past due installments, while the rest of the loan is considered as performing. This difference in treatment overestimates the quality of the loan portfolio of Chilean banks as compared to that of peers in other countries.

33

However, competition in lending to Small and Medium Enterprises (SME17) is much lower, and is mainly limited to small independent leasing and factoring companies. After a wave of mergers in the past few years, the system is fairly concentrated in 26 players (down from 40 institutions in 1992), of which 19 are local banks, 6 are branches of foreign banks and one is a government-owned. Out of these, the five largest commercial banks account for 62 percent of the total assets in the system (see figure 4.2). In terms of ownership, there is a strong foreign presence (almost 40% of sector), as well as significant government participation in commercial banking through BancoEstado (see paragraph 4.20). Figure 4.2. Composition of the Banking Sector (2005) Branches of Foreign Other local

Government ow nership, 15.6%

Banco Santander-Chile

Local Ow nership, 45.2%

Banco de Chile

Banco del Estado Corpbanca Banco Bilbao Vizcaya Argentaria, Chile

Banco de Crédito e Inversiones

Foreign Ow nership, 39.2%

Source: Author’s calculation based on data from SBIF

4.7 The strong and sustained commitment with the financial system from the Government strengthens the sector. The banking sector in Chile went through a financial crisis in 1982 – 1983. However, the strong government commitment on maintaining the integrity of the system, along with sound regulation and supervision, has translated into a steady trend of improvement of the sector. After the crisis, the banking sector showed high growth levels up to 1997, when deceleration in the economy translated into sluggish loan growth. 4.8 Banks’ consumer credit portfolio has increased substantially while commercial lending has lagged behind. Consumer credit (including housing) grew by almost 80 percent between 2000 and 2004. This is particularly noteworthy considering that it is in this segment of the market where Chilean banks have experienced considerable competition from non-bank credit providers such as department stores. On the other hand, during the same period, lending to companies increased by only 33 percent (see figure 4.3 below). That is due to the fact that large companies started to tap capital markets and external funding sources that offer cheaper finance than the domestic banking sector which, in turn, resulted in a significant reduction in bank lending. In addition, the banking sector has not been very active in seeking to increase lending to small and medium enterprises (SMEs).

17

The definition for firm size used in the present document corresponds to the standard World Bank classification based in the total number of permanent workers, i.e. large (250+ workers); medium (50-249), small (16-49) and micro ( 220 kV Lines 220 kV Lines < 220 kV Total length

Table 7.1. Structure of the Electricity Sector Magallanes SelfUnit SING SIC & AYSEN generators Generation # 6 23 2 MV 3,596 7,867 98 732 % 99.6 40.3 80.0 89.4 MV 1,567 5,431 50 GWh 12,330 36,259 291 3,013 % 99.5 42.5 71.0 92.5 Transmission # Kms. 408 878 na Kms. 3,204 3,465 na Kms. 1,278 4,403 na Kms. 4,890 8,745 na Distribution # 5 28 2 #000 253 4,237 71 GWh 11,240 34,602 271

Companies Regulated users Sales Ratios Demand Peak / Capacity 0.44 0.69 0.51 Sales / Generation 0.91 0.95 0.93 Source: National Commission of Energy

TOTAL 31 12,293 60.9 7,048 48,880 59.1 5 1,286 6,669 5,680 13,635 35 4,561 46,114 0.57 0.94

7.10 The government holds formal regulatory and control functions through CNE, and produces non compulsory plans for generation and transmission. There are two main regulatory bodies: (i) the CNE, which proposes sectoral policy and shares responsibility on regulated tariff setting with the Ministry of Economy; and (ii) the Superintendence of Electricity and Fuels (SEC), a multi-sectoral agency, in charge of regulating electricity, gas and other fuels. SEC collects data for enforcement and regulation purposes, monitors service quality and deals with customer complaints. The lack of independence of Chilean regulators may be the most striking feature of its institutional arrangements. Both regulatory bodies are subject to the operational control of the Ministry of Economy, whose oversight is not limited to tariff issues. There are two types of electricity customers regulated and unregulated. Unregulated customers are large

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consumers (with maximum demand above 500 kW92) that can contract directly with generators without the intervention of the regulator. On the other hand, the tariff for small customers is regulated and consists of the sum of the node price at which distribution companies buy energy from generators, and the remuneration to distributors called regulated VAD (value added of distribution). The current node pricing system is based on a four-year forward looking average of electricity prices adjusted every six months, and is thus suited to respond only to medium term information. 7.11 Tariff setting in Chile is based on an efficient utility model, which is based on the design of a model on how a representative, regulated utility could (and should) work. Such models are based on numerous assumptions and parameters fed by the information collected by SEC. This approach forces the regulator to micromanage the regulated utilities, with the burden of proof placed on the regulator to show that more efficiency is feasible. The literature shows that with asymmetric information, the company has ample opportunity to exploit the system or to convince members of the expert panel of its position by providing very detailed information regarding its operating environment, expenditures and other private information. An analysis of the rates of return of regulated utilities during the nineties showed rates systematically above 20 percent --even reaching 30 percent some years- in electricity distribution. Moreover, the regulated utilities in this sector earned higher rates of return than the electricity generating companies that operated in the competitive segment. However, an SEC official has reported significant improvements of the regulatory accounting. In fact, regulated rates of return in Chile have been reduced recently due to better to use of the available instruments for tariffs setting. 7.12 Chile recently needed to undertake a series of new reforms in the electricity sector to alleviate fears of unreliable supply. The Sistema Interconectado del Norte Grande (SING) is thermal (hence gas dependant) while the Sistema Interconectado Central (SIC) largely relies on hydro generation. The overall balance between the two (59% thermal, the balance hydro) is seen as sound and they are well interconnected. The installed capacity of 12.3 GV generated 49 GWh in 2004, 59 percent thermal, representing 57 percent of capacity used during peak demand. This overall balance is in the mid range of concentration between the hydro levels of Brazil and the thermal levels of China and India (see Figure 7.5). However recent events have resulted in nervousness among business regarding the reliability of electricity supply. A serious drought (the worst in 40 years) during the summer of 1998-99 disrupted hydro generation, causing serious outages across the country. This remains as a potential risk for SIC, even though the water cycles indicate low probability of a very dry year in the near future. In addition, recent problems with the provision of gas from Argentina have affected the SING area, which relies for about 60 percent of generation in on Argentine gas supply. This of course has also created difficulty for industrial use of natural gas. 7.13 New laws have corrected incentives for the needed investments. Investments in generation using coal, diesel or even LNG would not be viable given the probability of reestablishing natural gas supply from Argentina or even with Bolivia or Peru. (Diesel or 92

Level recently reduced from 2000kW in the Law Corta.

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LNG, are two to three times more expensive than natural gas.) Therefore, new plants could not offer energy at market prices if in the future competitors face unconstrained natural gas supply. The government has implemented regulatory measures through Laws Corta I and II, which attempt to clarify signals for new investments in transmission and generation capacity and encourage reliance on alternative energy sources for generation coal, diesel or even LNG. The Law Corta II was issued with the central objective of creating a price index to stabilize signals for investors. Weights in the index formula are shares of primary energy that generators would use for up to 15 years, which in turn will be defined by a bidding process. Different composition for each generator will allow a market price formation, while paying differential costs of primary energy. 7.14 While it is too early to evaluate the impact of the new regulations, there are no worries of shortages in the near term. In the short term, there is enough water in the multi-annual dams. In addition, new plants for a total of 421-MV generation capacity started operations in the SIC in 2005. There could be some room for uncertainty in the medium-term, between 2007 and 2009 or 2010, when new generation projects are expected to enter into operation. However, shortages would require a combination of severe drought plus severe restrictions in natural gas supply. Even in this case, while energy in general would become more expensive Chile would not face severe supply constraints. In the longer term, the impact of Law Corta II should be a more varied energy matrix as well as electricity generation system. 7.15 Electricity prices fell significantly from the beginning of the 1990s until 2002, reflecting declines in distribution costs and in the regulated node price (Figure 7.7). Generation costs led to reductions in node prices due to the introduction of combined cycle gas turbines and improvements in capacity utilization. Electricity prices started rising in 2003, responding to the higher cost of thermal generation. However, a reduction has been recently implemented in 2006 due to the indexation defined in the General Law of Electricity Services. Figure 7.5. Distribution of Electricity Generation Capacity

Figure 7.6. Node Electricity Prices SIC (Santiago) AISEN (Coihaique) PTO. NATALES

100

US$ / M W h

80

SING (Antofagasta) PUNTA ARENAS PTO. PORVENIR

60 40 20 0 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Source: International Energy Agency

Source: CNE

7.16 The combination of lower node prices and better regulation has translated into very competitive final user prices. In 2004, Chile’s electricity tariffs for industry

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were relatively low, compared to international standards. Current prices in Chile are lower than in most OECD countries. 7.17 Chilean firms rated electricity as a major infrastructure constraint for business. According to the IC survey, 18 percent of the firms reported being constrained by other issues associated with electricity provision. These issues included supply, access and quality. This response places the country in a mid range compared to other countries where the same question was asked. Figure 7.7. Average Price of Electricity for Industry in 2004

Source: Energy Prices and Taxes. Fourth Quarter, 2005. International Energy Agency

Figure 7.8. Percentage of firms constrained by electricity issues

Source: ICS

7.18 However, constraints associated with electricity seem to have more impact on perceptions than on real costs. Chilean firms reported 1.3 percent in sale losses due to electrical outages, with greater impacts on micro and small enterprises. Even though that percentage represents an important economic cost, it is similar to Brazil’s and China’s and below the Czech Republic’s. Figure 7.9. Losses due to power outages (% of sales)

Source: ICS

7.19 Firms have responded to uncertainty with precautionary investments in backup self-generation systems. The IC survey reports that 35 percent of Chilean firms have a generator - double the figure in Brazil where the quality of the public grid service is lower. Of firms with a generator, 60 percent bought their generator in or after the

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drought year of 1998. According to the IC survey more than 5 percent of electricity consumed by firms is self generated: a figure that is consistent with data from the National Commission of Energy (CNE).93 Self generated electric power in Chile is three times that in China and Brazil (but lower than in India). Figure 7.10. Percentage of Firms owning a Generator (a) Across countries (b) In Chile

Source: ICS

7.20 Although firms can balance energy costs by generating their own electricity at the margin, it is an activity that is not their core business. The cost of a generator is 0.9 percent of the total value of assets for an average manufacturing firm, rising up to 3.9 percent in micro-enterprises. The burden of self-generation is greater in the region of Valparaiso and Lagos. However, the highest percentage of firms owning a generator is located in Santiago. Installed self-generating capacity is greater in sectors with perishable products like farm-fishing and food and beverages, with levels over 50 percent. Also chemicals and paper products have substantial self-generating capacity. In general, it does not appear to be an issue of co-generation, which might be the case with wood products. Self generation is subject to economies of scale and ownership of a generator is positively correlated with firm size. Self generation may be an effective way of guaranteeing supply, but it is not efficient for the economy as a whole as firms are channeling financial and managerial resources to non-core activities. 7.21 As presented in the Government’s evaluation, quality of the electricity service is not the driver for that behavior. Transmission and distribution losses of 6 percent in Chile are aligned with the standard in well functioning markets. Most countries in the comparison group have losses ranging between 6 and 8 percent. These results are associated with good regulatory structures in pure market schemes, as is the case in Czech Republic, Malaysia, Thailand and South Africa. Electricity markets in China, Brazil, and India are completely different in supply structure and regulatory setup. In China, the low level of losses seems to be associated with the efficiency needed to cope with the demand. Energy is the most pressing need for China, where outages spread through regions due to an excess of peak demand over capacity, forcing rationing. Both Brazil and India are facing problems in regulating distribution companies in a highly decentralized scheme. 93

This is not driven by a few outliers. There are firms to be found along the entire range of self-generating capacity from zero to 100 percent of consumed electricity.

126

7.22 Access rates are the highest in Latin America, though still behind OECD standards in rural areas. Distribution companies are public service concessionaires, and are, therefore, accountable for universal coverage within their exclusive areas. Both urban and rural coverage rates are higher than the Latin American average and developing countries in general. However, 86 percent in rural electrification is still well below the average of 98 percent in the OECD. Figure 7.12. Percentage of electricity Consumption That Comes from own generator b) Distribution in Chile

0

.01

Firms Density .02 .03

.04

.05

Figure 7.11. Percentage of electricity from own generator, by Country a)

0

(c) Percentage of Firms owning a Generator, by region

Source: ICS

127

20 40 60 80 % of electricity consumption that comes from own generator

100

(d) Percentage of Firms owning a Generator, by sector

Figure 7.13. Transmission and distribution losses (% of output in 2002)

Source: WDI Table 7.2. Electrification Rates 2002 Urban Rural electrification rate electrification rate Chile 97% 86% Latin America 89% 61% Developing countries 66% 52% Transition economies and OECD 99.5% 98% Sources: IEA and CNE.

7.23 Both quality and access are result of the high involvement of private operators in all three segments of the market. As a result of the long running reform, the involvement of private sector is 100 percent in generation, transmission and distribution of electricity. These are the highest private participation rates in all Latin America. Generation

Figure 7.14. Percentage of market served by private operators Distribution Transmission

Source: Own elaboration

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TRANSPORT

7.24 Chile has a single entity to address issues and policies on transport, telecommunications and public works. In 1974 the Ministry of Transport and Telecommunications began operating as the country’s management and oversight body for the sectors. In 2000, it merged with the Ministry of Public Works, creating a new comprehensive entity -the Ministry of Public Works, Transport and Telecommunications (MOPTT) with a wider scope of intervention on infrastructure policies and regulation. The MOPTT also rules on water resources management. The Coordinación General de Concesiones, a separate agency within the MOPTT, grants and oversees concessions on roads, airports and ports, in addition to health and justice administration facilities. 7.25 Transport infrastructure has improved remarkably in the past decade due to the most successful concessions program in Latin America. The concessions program was created under the Concessions Law, enacted in 1991. It defined competitive bidding among national and international firms as the selecting mechanism for granting concessions. The Law provides a flexible guideline for the adjustment of user fees, public subsidies, income guarantees for the private sector, fees for preexisting facilities, distribution of risk during the construction and/or operation stages, standards on quality, and license fees. By 2004, the concessions had been granted for the most important highways, seaports, and airports, with an aggregate asset value of nearly US$6.7 billion. During the last five years each dollar of public investment in transport infrastructure has leveraged 1.1 dollar of additional private investment (see Figure 7.17). 7.26 The MOPTT has published its concessions portfolio for 2005-2007 including projects for $860 millions in roads and $100 millions in airports. The process of granting a concession begins with the issuing of call bonds by the bidders. Those bonds can be called by the government if the bidder cannot finance the project. Moreover, similar bonds are callable if construction targets are not achieved by predetermined timelines or quality standards are not met. In parallel, the Law establishes that banks are the only financial institutions permited to lend money to finance constructions. Therefore, concessionaires can issue bonds in the capital market only when the facilities are built. They can obtain resources backed by the revenues of the project up to 70 percent of the total debt. 7.27 The main virtue of the concessions program is to strengthen property rights94. Even in cases of renegotiated concessions, MOPTT has respected initial agreements following legal procedures for changes. Though there could be criticism, it has yielded stability for investors. Other virtues are that it is open to foreign companies, reducing risks of capture of the regulator and that most contracts are awarded without cost sharing by government, leveraging substantial investment resources. 7.28 There has been some concern about the lack of balance between development and regulation. On the contingent liabilities side there are major challenges. Engel et al (2003) point that MOPTT is being pressured by the need to deliver physical results while paying less attention to the regulation of contracts. Although this is conceptually 94

Engel, Fischer and Galetovic (2004) “Soft Budgets and Highway Franchising”

129

complex, its effects will not be observed until triggers are engaged. In normal conditions concessions should not reach minimum traffic guarantees, therefore eliminating costs for the government. Since Chile’s economy has been growing there should be no fears. Still, substantial contingent liabilities might be hiding in different contracts. The recent experience of Colombia with toll road concessions shows that it could be potentially expensive in fiscal terms. Chile’s government estimated the present value for minimum traffic guarantees and exchange rate guarantees at less than US$150 million, of which 78 percent correspond to one project. 95 Another concern raised by Engel et al (2003) is that MOPTT does not have an overseer who could actually help to reduce the risk of projects with low social rates of returns or reduce mismanaging in renegotiation processes. Since some cases had been brought to open discussion by the Comptroller, it would be worthwhile considering a strengthening of that component. 7.29 Public investment has continued focusing on upgrading secondary and unpaved roads. This joint effort is expected to increase the paved network up to 25,000 km by 2010 continuing the remarkable upward trend. 96 7.30 Considering the improved stock of infrastructure and the country’s remaining needs in terms of service quality, the sector institutions have ample room to improve their service orientation as well as the efficiency of the operations still financed by the public sector. For instance, the Ministry of Public Works could improve the social returns of its programs and the efficiency of service delivery through a more modern agency system based on performance based contracts and indicators rather than traditional unit cost contracts. Figure 7.15. Investment in Transport Infrastructure

Source: MOPTT

95 96

Ministerio de Hacienda “Presupuesto del Sector Público para el año 2006” Ebusinessforum.com “Chile travel: Transport and Communications” 12 July 2005.

130

Figure 7.16. Road Network

Source: WDI

7.31 Since 2000, the state-owned rail company (EFE) has been making new investments in rails. Ferronor, the other rail company in the northern region was privatized in November 1996 but the government’s plans to privatize all commercially viable EFE divisions were not successful. Instead, the government allocated about US$110m to EFE for 2000-2002 to modernize central and southern rail services between Santiago and Chillan. In 2003 the government also launched a rebuilding program for the entire EFE southern rail line up to Puerto Montt. Although service reached Temuco in January 2004, its efficiency has been unsatisfactory. Inhabitants in those areas of the country cannot afford the EFE fares and service has been reduced, resulting in operational losses. Although long distances make sense for multimodal transportation, Valparaiso, San Antonio and San Vicente ports are close enough to the main production centers. The combination of a slim market for passengers and reduced cargo in Puerto Montt make the project unlikely. Therefore, rail transportation would likely continue serving northern areas only. 7.32 The Chilean government has undertaken remarkable reform in ports infrastructure. In 1999 the four largest ports in the country, Valparaiso, San Antonio, San Vicente and Iquique were granted in concession. The privatizations achieved a reduction of 30 percent in port tariff rates while efficiency in the major four ports has been improved more than 100 percent. 7.33 Although transport is a less of a severe constraint to growth for Chilean business than is electricity, the quality of transport services varies according to destination, market and the size of firms. Transport for products shipped to international market is better than for products destined for the national market. However, the difference in overall cost is not substantial because the cost of addressing customer’s complaints is high in the international market. More than 17 percent of firms reported breakages in deliveries to international destinations, compared with 25 percent in national deliveries (Figure 24). The financial impact is greater for medium and large firms than for small and micro enterprises. However, the overall economic impact is not great.

131

Reported losses account for less than 1 percent of sales. Transport constraints were found to be higher in Chile than in China. Figure 7.17. Percentage of Firms that reported any breakage or theft in transit

Source: ICS

7.34 The customs component of logistics is well aligned with international standards. In the IC survey, firms were asked for the average and longest periods they had spent clearing customs. Figure 7.20 presents the average and longest periods reported by firms. Average days to clear customs for exports and imports are on a par with comparator countries, except for Brazil. The longest period associated with peaks in demand is relatively higher. Figure 7.18. Custom delays for Exports

Figure 7.19. Custom delays for Imports

Source: ICS

7.35 Small and micro enterprises use fewer third party services and their transport costs are 70 percent lower than large and medium firms. More than fifty percent of small and micro enterprises use their own means to transport products, while only 19 percent of large firms do so directly. By using their own transport, small and micro enterprises reduce direct transport costs, which are 70 percent lower than large and medium size firms.

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Figure 7.20. Percentage of firms that use their own logistics

Figure 7.21. Percentage of logistics costs over sales

Source: ICS

TELECOMMUNICATIONS

7.36 During the mid-1970s, Chile was the first country in Latin America to initiate privatization and liberalization in the Telecommunications sector. By the early 1990s Chile was among the highest ranked countries in South America in telecoms infrastructure. The main regulatory body, the Telecommunications Sub-Secretariat (SUBTEL), is part of the MOPTT. It is in charge of national policies for information and telecommunication technologies, administration and control of the radio-magnetic spectrum use, granting and overseeing of concessions and licenses, and following up of tariffs. Today Chile has a completely privatized telecommunications system as in most Latin American countries. The market is fully open to competition, and there are no restrictions for foreign investment. Following privatization the telecom industry has become one the Chile’s fastest growing sectors. Figure 7.22. Private Sector Participation in Telecommunications in Latin America Local

Long Distance

Cellular

Source: Own elaboration

7.37 The telecom sector regulatory framework includes a number of unique characteristics. First, a model of asymmetric regulation exists, whereby only the incumbent has its tariffs regulated. Second, there is freedom of entry as concessions are not exclusive. Third, incumbents must grant interconnection for new entrants at terms and

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rates fixed beforehand (compulsory connection). Fourth, rate freedom exists unless the competition authority states that for specific services there is not enough competition to warrant a free rate system and when such exception is invoked, rates are computed according to the costs of an “efficient” firm. Finally, reduction or elimination of cross subsidies and the existence of a universal access fund are aimed at stimulating private sector investment in rural areas, using a minimum subsidy mechanism. Rates of return in fixed telephones were also quite high during the nineties, with rates above 20 percent common and in some years above 30 percent or even 40 percent (Fisher and Serra 2002). 7.38 Despite positive results, there are some concerns in the regulation of the telecoms sector. The combination of deregulation, privatization and universal service has produced remarkable results in terms of expansion of lines, mobile phones and quality improvement. Outstanding concerns include: (i) Asymmetric regulation might have lead, in practice, to some cream skimming with effects on efficiency (Coloma and Tarziján 2004); (ii) Tariffs regulation has become increasingly complex over the years, and asymmetric regulation has prevented regulated operators from adequately competing and investing. 7.39 Regulatory needs for combining services differ from the old network regulatory schemes. While the old regulatory methods tried to stimulate the reduction of costs and development of an “efficient” company, new regulation should concentrate on promoting investment to reach more subscribers with broadband services. Chile is no exception. To respond to the challenges of VoIP technology, in July 2004, SUBTEL launched a public consultation, according to which if voice services are offered through the existing Public Switched Telephone Network (PSTN), the operator is required to comply with the regulations that apply to PSTN services. However, if services are provided over the internet, they are not subject to the same conditions, but the regulator is suggesting a broadband voice license. Considering the increasing convergence, the proposed solution appears to be problematic. A more flexible licensing regime, taking into account the influence of VoIP technology in the telecommunications industry is required to unleash the full potential of the technology. 7.40 In 2004 the number of mobile subscribers reached 62 percent, an important density rate comparing with main competitors like Malaysia and Thailand and by far superior to that of India and China. Penetration rates of mainlines had slowed as in most countries given the fast penetration of mobile telecommunications. 7.41 Access to the Internet by both households and firms is high in Chile. However, greater Internet access by households would improve competitiveness. At first glance, overall Internet access may not seem important for competitiveness since firms tend to have a higher rate of Internet and email use, compared with rest of the economy. More than 90 percent of firms use email to communicate with clients, and at least 70 percent of firms have set up their own internet page. On average these firms manage to sell about 6 percent via the Internet, especially small and micro enterprises. However, the overall Internet penetration could play an important role in competitiveness since it helps to build up the necessary skills for today’s production technologies. In 2004, Internet penetration in Chile was 28 users per 100 habitants, three times the level of

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India, China, and Thailand, but still below OECD countries (43) and other comparator countries. Figure 7.23. Mainlines per 100 Habitants, 2003

Figure 7.24. Mobile Subscribers/100 Hab, 2003

Source: ITU Figure 7.25. Internet Users / 100 Habitants, 2004

Source: ITU Figure 7.26. Internet use by firms

Source: ITU

Source: ICS

CONCLUSIONS

7.42 Although Chile lags behind fast growing economies in terms of productive infrastructure availability, the country’s story is one of a highly successful catch-up. Credible institutions and sustained financing levels have grounded strong foundations in electricity, telecommunications and transport sector, which allowed for turnaround in these sectors in terms of investment and service provision. 7.43 New laws have corrected incentives for the needed investments, as uncertainties in supply of primary energy sources led to fears of unreliable supply. Fears were caused by uncertainties in supply of primary energy sources. While it is too early to evaluate the impact of the new regulations, there are no worries of shortages in the near term. However, firms have responded to uncertainty with precautionary investment in self generation systems. Self generation may be an effective way of guaranteeing supply, but it is not efficient for the economy as a whole as firms are channeling financial and managerial resources to non-core activities. 7.44 Transport infrastructure has undergone remarkable improvement in the past decade due to the most successful concessions program in Latin America. Considering

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the improved stock of infrastructure and the country’s remaining needs in terms of service quality, the sector institutions have ample room to improve their service orientation as well as the efficiency of the operations still financed by the public sector. Although transport is a less severe restriction to growth for Chilean business compared to electricity, the quality of the logistics process varies across destination market and firm size. 7.45 Access to Internet by both households and firms is high in Chile; however it is worth promoting greater levels of Internet access by households as part of the strategy to improve competitiveness. 7.46 Signals given by the entrepreneurs along with the revision of sector issues suggest the following three actions: First, close follow up of the actual response of investors in generating capacity. Second, start up the transformation of the Ministry of Public Works towards service delivery oriented instead of the traditional works construction entity. Third, strengthen the strategy in the telecommunications sector to expand accessibility to Internet for all households.

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ANNEX—INVESTMENT CLIMATE DETERMINANTS OF PRODUCTIVITY– ECONOMETRIC METHODOLOGY I. INTRODUCTION

A.1 This annex summarizes the econometric methodology developed for the analysis of the impact of investment climate (IC) variables on manufacturing firm’s performance in Chile using the investment climate survey. It is based in Escribano, Guasch, Pena and Orte (2006), background paper prepared for this project. The analysis focuses on the impact of the investment climate variables on A.2 productivity. The methodology is largely based in Escribano and Guasch (2005), which has been widely used in investment climate reports. This methodology departs from the recognition that the production function is unknown, being necessary to adopt econometric procedures that give consistent and robust conclusions that do not depend on the specific measure of productivity used. It also proposes an alternative correction for the endogeneity of the investment climate variables, namely to use the region-industry average of the plant level investment climate variables, instead of the crude IC variables. II. ECONOMETRIC METHODOLOGY97

A.3 In previous robust ICA analysis done at the World Bank, for other Latin American countries, Escribano and Guasch (2005) proposed to pool observations across several countries when estimating productivity in levels (logs). In the case of Chile, to estimate the ICA elasticities and semi-elasticities on productivity, we pooled the observations from manufacturing industries, certain services and farm and fishing to estimate common IC coefficients. For the sector by sector evaluation we compute the impacts of IC variables on: the mean (log) productivity, the probability of exporting, the probability of receiving foreign direct investment, the mean (log) wages and on the mean (log) employment, as will be explained in the next sections. In all the panel data regressions we use several dummy variables (Dr, r = 1, 2, ...) A.4 and a constant term (intercept). In particular, in the regressions of Tables 3.1-3.2 in Chapter 3 we include seven dummy variables for the eight sectors, two year dummies for the three years of data and a constant term.

97

This section is based on Escribano, Guasch, Pena and Orte (2006).

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A.5 To address the endogeneity problem of the inputs we follow the approach proposed by Escribano and Guasch (2005). That is, we proxy the usually unobserved firm specific fixed effects (which are the main cause of the endogeneity of the inputs) by a long list of firm specific observed fixed effects coming from the investment climate information, see the list of investment climate variables (IC) and control variables (C) included in Tables A.2 (I-III). In particular the extended Cobb-Douglas production function estimated in 1-step becomes,

log Yit = α L log Lit + α M log M it + α K log K it + α ´IC ICi + α ´C Cit + α ´D Dit + α P + uit

(1)

where the variables IC, C and D are column vectors. With this specification we will test whether we have (at the aggregate and at the industry level) technologies with constant returns to scale

α +α +α =1

M K ( L ). If the production function is Translog, using similar arguments, we can consistently estimate by least squares, the following Translog extended production function in 1-step,

log Yit = α L log Lit + α M log M it + α K log Kit + 1 1 1 + α LL (log Lit )2 + α MM (log M it ) 2 + α KK (log Kit ) 2 + 2 2 2 + α LM (log Lit )(log M it ) + α LK (log Lit )(log K it ) + α MK (log M it )(log K it ) +

(2)

+ α ´IC ICi + α ´C Cit + α ´D Dit + α P + uit .

A.6 The Translog specification allows us also to test for constant returns to scale and to check whether the technology (at the aggregate level or at the industry level) is CobbDouglas. With both parametric specifications of the production function F(L,M,K), we can also test the constant returns to scale98 condition behind Solow´s residuals in levels ˆ

( log Pit ), see equation (3), under the condition that the shares are constant in time at the aggregate and at the industry level. Therefore, the third alternative methodology considered in this paper is to use a nonparametric or index number approach based on cost-shares from Hall(1990) to obtain the Solow´s residual in levels (logs) logˆ Pit = log Yit − sL log Lit − sM log M it − sK log K it

98

(3)

Remember, that CRS are satisfied if the coefficients of the inputs (L, M and K) in the Cobb-Douglas specification of the production function add up to one. Similar but more complicated coefficient restrictions apply for a CRS Translog production functions.

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sj =

sj

where is the aggregate average cost shares from the last two years99 given by j = L, M and K.

1 ( s j,t + s j,t −1 ) 2 for

A.7 The advantage of the Solow residuals, Solow (1957), is that it does not require the inputs (L, M, K) to be exogenous nor the input-output elasticities to be constant or homogeneous. The drawback is that it requires to have constant returns to scale (CRS) and at least competitive input markets. Two-step estimator: Once we have estimated productivity (1st-step) in equation A.8 (3) we can estimate from equation (4) the IC elasticities and semi-elasticities in the 2ndstep, logˆ Pit = α ´ IC ICi + α ´C Cit + α ´ D Dit + α P + uit .

(4)

A.9 Since there is no single salient measure of productivity (Pit), any empirical evaluation on the productivity impact of IC variables might critically depend on the particular way productivity is measured. Therefore, to get reliable empirical elasticities for policy analysis, Escribano and Guasch (2005) suggest searching for robust empirical results using several productivity measures. This is the approach followed for this ICA report of Chile.

A.10 Controlling for the largest set of investment climate (IC) variables and plant control (C) characteristics in equations (1) to (4) we can get, under standard regularity conditions, consistent and unbiased least squares estimators of the parameters of the production function and of the productivity equation. For example, we can run OLS from a one-step regression100 based on the extended production function (1). To estimate the IC-elasticities, we will do pooling OLS with robust standard errors and also random effects (RE) estimators, or generalized least squares (GLS) estimators, to control for the heterogeneity (heteroskedasticity) present in the regression errors. Endogeneity of the Explanatory Variables

A.11 Another econometric problem that we have to face when estimating (1), (2) and (4) is the possible endogeneity of IC variables and some C variables. In the productivity equations, the traditional instrumental variable (IV) approach is difficult to implement, given that we only have IC variables for one year and therefore we cannot use the natural instruments for the inputs, like those provided by their on lags, etc. As an alternative correction for the endogeneity of the IC variables, we use the region-industry average of the plant level investment climate variables ( IC ) instead of the crude IC variables, which 99

When there is only firm information about a single year we take the average cost share of the firms of that year. 100 Alternatively, we could have used an equivalent two-step control function approach procedure where we first estimate by OLS a regression of each of the inputs on all the IC and C variables (partialling out) and then running simple regressions including one by one the residuals of each estimated input equation, instead of the observed explanatory variables , in the equation.

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is a common solution in panel data studies at the firm level101. Furthermore, taking industry averages, and not the individual IC variables, is also useful to mitigate the effect of missing individual IC observations at the plant level. This is an important issue in most of the ICA surveys done in developing countries. However, to evaluate the productivity impact on wages, on labor demand, on the probability of becoming an exporting firm and the probability of receiving foreign direct investment (FDI), we instrument the productivity variable by using a selection of IC and C variables, as will be explained later on. Strategy for IC Variables’ Selection

A.12 The econometric methodology applied for the selection of the variables (IC and C) goes from the general to the specific. The otherwise omitted variables problem that we encounter, starting from a too simple model, generates biased and inconsistent parameter estimates. We include in tables 3.1 to 3.2 in chapter 3 the set of IC variables from Table A.2 (I-III) that were significant in at least one of the 10 productivity specifications estimated by using pooling OLS or random effects (RE). The list of significant IC variables for all the estimated equations in Chile is presented in Table B.3. These regression results of Tables 3.1 and 3.2 are consistent (robust) across the 10 productivity measures used, with equal signs and a reasonable range of parameter values. The detailed empirical results are explained in the next sections. Robustness of the Estimated Productivity-IC Elasticities and Semi-elasticities

A.13 For policy recommendations we want the elasticities, or semi-elasticities of IC variables on productivity to be robust (equal signs and of similar magnitudes) to the 10 productivity measures used. A.14 The alternative productivity measures considered in this paper come from considering: 1. different functional forms of the production functions (Cobb-Douglas and Translog), 2. different set of assumptions (technology and market conditions) to get consistent estimators based on Solow´s residuals, or OLS, RE, etc. and, 3. different levels of aggregation in measuring input-output elasticities (at the industry level or at the aggregate country level).

Table A.3 Summary of Productivity Measures and Estimated Investment Climate (IC) Elasticities 1.1.a OLS Two Step

101

1.1 Restricted Coef

1.1.b RE

2 (Pit) measures

This two step estimation approach has an instrumental variables ( 2SLS) interpretation.

144

1. Solow´s Residual

Estimation

1.2.a OLS 1.2 Unrestricted Coef

4 (IC) elasticities

1.2.b RE 2.1.a OLS

Single Step 2. Cobb-Douglas

2.1 Restricted Coef

Estimation 2.2 Unrestricted Coef

2.1.b RE

4 (Pit) measures

2.2.a OLS

4 (IC) elasticities

2.2.b RE 3.1.a OLS

Single Step 3. Translog

3.1 Restricted Coef

Estimation 3.2 Unrestricted Coef

3.1.b RE

4 (Pit) measures

3.2.a OLS

4 (IC) elasticities

3.2.b RE 10 (Pit) measures Total

12 (IC) elasticities

Restricted Coef.= Equal input-output elasticities in all industries Unrestricted Coef.= Different input output elasticities by industry OLS = Pooling Ordinary Least Squares estimation (with robust standard errors) RE = Random Effects estimation.

A.15 As mentioned in section 1, to reduce the simultaneous equation bias and the risk of getting reverse causality problems for those ICi variables that are endogenous, we use their region-industry average ( IC j). The productivity coefficients of investment climate ( IC i) variables and other plant-specific control (Cit) variables are maintained constant but we allow the production function elasticities, and therefore the productivity measures, to change for each functional form (Cobb-Douglas and Translog), and for each aggregation levels (industry and countries). Restricted estimation (equal input-output elasticities among industries) and unrestricted estimation (different input-output elasticities for each industry), are the two levels of aggregation considered in the inputoutput elasticities of the production functions. A.16 Moreover, we consider two different estimators (pooling OLS and random effects) for each productivity measure. Table 1, summarizes the list of productivity measures considered. Thus we obtain 10 different productivity measures (Pit) and we evaluate the impact of IC variables on each of them based on two estimation procedures, pooling OLS and RE. Table B.6 of the appendix reports the correlations for each of the

145

(log) productivity measures obtained from the eight production functions estimated in 1step and from the two Solow´s residuals respectively. A.17 The results for Chile are as follows: when we consider the correlations between the Solow residuals and the productivity measures that comes from estimating restricted production functions (see the second box of column one of Table B.6), the correlations are very similar in all the cases, ranging from 0.98 to 0.90. However, the unrestricted by industry production functions differ and therefore the correlations are much lower between those productivity measures (see the third box of column one of Table B.6), ranging from 0.76 in the unrestricted Cobb-Douglas case to a correlation of 0.09 in the unrestricted Translog OLS. With Solow´s unrestricted (by industry) productivity measures the correlations are much smaller. Therefore, the challenge is to get similar (robust) productivity elasticities for ICA variables even for those very different productivity profiles. The correlations between the Cobb-Douglas productivities and the Translog productivities are very high for the restricted aggregate case (the correlation is around 0.92) and lower for the unrestricted case, see the second column of Table B.6. A.18 The econometric analysis based on the 10 different productivity (P) measures is explained in the rest of this section. The units of measurement of each explanatory variable are included in Tables B.5.I and B.5.II of the appendix. But, before discussing the effects of different IC variables on productivity, it is important to take into account that the economic interpretation of each investment climate coefficient is contingent on the units of measurement of each IC variable and on the transformations performed on them (logs, fractions, percentages, qualitative constructions, etc.). Since productivity variables are always in logs, when the IC variable is expressed in log terms, the estimated coefficient is the constant productivity-IC elasticity; and when the IC variable is not expressed in log form, the estimated coefficient is generally described as a productivityIC semi-elasticity102. While the constant productivity-IC elasticity measures the percentage change in productivity induced by a percentage change in the IC variable, the semi-elasticity coefficient multiplied by 100, measures the percentage change in productivity induced by a unitary change in the IC variable. Notice that within each group, most of the IC variables of Tables 3.1 and 3.2 in chapter 3 have the expected signs and the estimated elasticities or semi-elasticities are within a reasonable range of values for the 10 productivity measures considered, as will be explained in subsection 2.4. The empirical results are robust since the signs of all of the ICA variables are equal and the range of values of the elasticities is reasonable. A.19 Two of the explanatory variables, exports and foreign direct investment, of the productivity equations are endogenous and could create important simultaneous equation biases and inconsistencies. To evaluate the magnitude and the implications in terms of the IC elasticity estimates we estimated those equations using instrumental variables. We use as instruments region industry averages of IC variables and other control variables. The selected instruments are correlated with the two endogenous variables since the null 102

While it is sometimes natural to express an IC variable in log form, for some types of IC variables it is more appropriate not to do so. For example, if IC variables are fractions or percentage numbers with some data equal to 0. However, expressing IC variables in fractions allow us to approximate their coefficients as constant elasticities and not as semi-elasticities.

146

hypothesis of uncorrelation is rejected (F-test) with p-value equal to 0. At the same time the overidentification restrictions are not rejected indicating that those instruments are not correlated with the regression errors. The Olley and Pakes Decomposition: IC Productivity Contribution to the Mean component and to the Allocative Efficiency component

A.20 The Olley and Pakes (1996) decomposition of productivity has two elements average productivity and an efficiency term or covariance term. As will become clear later on, it allows us to do a sector by sector evaluation of the impact of IC variables on average productivity and on efficiency. N jt

A.21

Let Pjt = ∑ sit Pj,it be the aggregate productivity of industry j at time t obtained as i =1

the weighted average of i-plant-level productivity in sector j at year t, where Njt is the number of firms in sector j where j = 1, ... ,8. The weights (sit) indicate the share of firm i sales in year t over the total sales of sector j of that year. N

1 jt ∑ log p j ,it be the sample average productivity of the firms of T i =1 sector j in year t. Then the annual aggregate productivity of industry j can be decomposed as in (8) where s% j,it = ( s j ,it − s j,i ) and log P%j ,it = (log Pj ,it − log Pj ,t ) are in deviations to the mean.

A.22

Let log Pjt =

The Olley and Pakes (1996) decomposition is given by: N jt

Pjt = log Pjt + ∑ s% j,it log P%j ,it .

(8)

i =1

A.23

The first term (log Pjt ) is the average productivity of industry j in year t and the N jt

second term ( ∑ s% j,it log P%j ,it )=Njt cov( s j,it , log Pj ,it ) , measures the allocative efficiency or i =1

covariance between sales shares and productivity, cov(sj,it, logPj,it) multiplied by the number of firms, Njt, that belong to sector j. If the covariance is positive, then the larger it is, the higher will be the share of sales that goes to more productive firms, allocation efficiency is increased and sector j productivity is enhanced. That is the most productive firms are the ones with larger market share. If the covariance is negative, there is allocation inefficiencies since the more negative the covariance is, the higher will be the share of output that goes to less productive firms, reducing sector j productivity. A.24 To complement the productivity analysis based on regression techniques we perform the allocation efficiency decomposition of Olley and Pakes (1996). This analysis

147

is especially interesting when the number of firms in some sectors have small number of observations on IC variables. In those cases, we cannot give much credibility to the sector-by-sector regression estimates of the impact of IC variables on productivity since they are based on very small samples. This decomposition provides additional information which is useful for analyzing the sector or industry efficiency allocation within each country as will become clear later on. A.25 For each aggregation level, we construct a measure of aggregate productivity and we apply the Olley and Pakes (O&P) decomposition. The particular productivity measure that we select is not important if the empirical results are robust for all the measures. In particular, we apply the Olley and Pakes productivity decomposition (8) to the Solow residuals, see equation (3), at five different levels of aggregation. ICA-Impact on Average (log) Productivity

A.26 Equation (4), estimated by least squares with a constant term, implies that the mean of the residuals is zero and therefore that we can evaluate the least squares estimation results of (3) at their sample mean values without including an error term. Therefore, following Escribano and Guasch (2005), the corresponding expression for the first term of Olley and Pakes decomposition becomes,

qIC

qC

r =1

r =1

logPit = ∑ αˆ IC ,r ICrit, + ∑ αˆ C ,r Crit, + αˆ P .

(9)

ˆ A.27 Dividing the whole expression by the dependent variable logPit and multiplying by 100, we get the contribution of each variable. That is,

qIC ⎛ αˆ IC ⎞ qC ⎛ αˆ C ⎞ ⎛ αˆ ⎞ 100 = ∑ ⎜ IC ,r rit, 100 ⎟ + ∑ ⎜ C ,r rit, 100 ⎟ + ⎜ P 100 ⎟ r =1 ⎝ log Pit ⎠ ⎠ r =1 ⎝ logPit ⎠ ⎝ logPit

(10)

which represents the sum of the percentage productivity gains and losses from all the explanatory variables of the regression, relative to the average productivity. In particular, the contribution of each IC variable to average (log) productivity is given by the term ⎛ αˆ IC ,r ICrit, ⎞ 100 ⎟ ⎜ ⎝ logˆ Pit ⎠.

A.28 If the average productivity is not calculated across all the firms of the country, but it is calculated industry by industry, or sector by sector, or by age, etc., then the sample mean of those residuals will not be exactly zero and the decomposition is not exact. In 148

this case the residual mean will also have a contribution (although marginal) to the average productivity. Out of the four infrastructure variables, the two most important for average productivity are average duration of power outages (-6.98%) and shipment losses (-6.91). b) Red Tape, Corruption and Crime. Notice that, contrary to what we might conclude by looking at elasticity and semielasticity results where cost of entry had the higher impact, the cost of entry only represents -0.31%. in terms of average productivity. c) Finance. Having a financing line program could represent 2% of average (log) productivity. d) Quality, Innovation and Labor skills. Notice that even if University staff had a very small impact on productivity (0.005) it represents 6.51% of average productivity. Having a manager with experience represents almost 30% of average (log) productivity. e) Other control variables. The most significant positive impact is due to the variable capacity utilization, which represents 102% of average (log) productivity, followed by the negative impact of belonging to a trade union (-11.73%).

Table B.1 Number of Firms that Enter into the IC Regressions by Sector and by Region Region Sector

Antofagasta

Valparaiso

Bio-Bio

La Araucania

Los Lagos

Santiago (Metropolitan Area)

Total

Food and Beverages

33

66

171

0

135

171

576

48

15

30

0

0

225

318

33

24

54

0

18

213

342

21

0

30

0

3

96

150

3

12

144

3

54

63

279

15

21

0

3

123

162

6

57

30

0

0

444

537

6

6

24

0

105

36

177

150

195

504

3

318

1371

2541

Chemicals Metal products Machinery and Equipment Wood and Cork products (excluded furniture) Paper products Information and Technology services Farmfishing Total

149

Table B.5 (I) List of Significant IC and C Variables, their Measurement Units, Equations in Which they are Significant and Form (Industry-Region Averages or not) in Which Each Variable Enters the Equations

Quality, Innovation and Labor Skills

Explanatory ICA Variables Quality certification

Measurement Units 0 or 1

Equation/s

Industry-Region Averages

Exp, L

No

0 or 1

L

No

0 or 1

P, Exp, Eff

No

R + D new product

0 or 1

FDI

Yes

New technology purchased Internal training

0 or 1

Exp

No

0 or 1

No

0 or 1

P, Exp, FDI, L, Eff L

0 or 1

Exp, W, L

No

Percentage

P, Exp, W, L, Eff

No

Logs

Exp, W, L, Eff

0 or 1

P, Exp, L, Eff

Yes in exports, productivity and efficiency eqs No

0 or 1

P, W

No

Years

Exp, W, L

No

0 or 1

P, L, Eff

No

Percentage

P

Yes

Rent land

0 or 1

P, Eff

No

Rent buildings

1 or 1

FDI

No

Percentage

P, W, L, Eff

Yes

0 or 1

Exp

No

0 or 1

L

No

0 or 1

L

No

New product R+D

External training

External Auditory University staff

Other Control Variables

Experience of the manager Incorporated company Foreign direct investment

Age Exporter Capacity utilization

Trade Union Income from manufacturing activities Small Medium

P: productivity equation. Exp: exports equation. FDI: foreign direct investment equation. W: wages equation. L: employment equation. Eff: efficiency equation.

150

No

Table C.1. Production Function Parameters from the Restricted Estimation

Cost-shares

Labor (L)

Materials (M)

Capital (K)

0.31

0.59

0.1

0.29*** 0.28***

0.51*** 0.49***

0.20*** 0.13***

L2

M2

K2

L*M

L*K

M*K

-

-

-

-

-

-

-

-

-

-

-

-

Cobb-Douglas Pool OLS RE Test for CRS

OLS

Prob > F = 0.934

RE

Prob > chi2 = 0.0000

Translog Pool OLS

1.27***

0.17

0.03

-0.09***

0.04***

0.01***

0.02

0.08***

RE

0.63***

0.24***

0.04

-0.07***

0.005*

0.02***

0.06***

0.03***

Test for CRS Test for Cobb-Douglas

OLS OLS

Prob > F = 0.0000 Prob > chi2 = 0.0000

RE RE

0.08*** 0.05***

Prob > F = 0.0000 Prob > chi2 = 0.0000

Notes: (1) Significance is given by robust standard errors.* significant at 10%; ** significant at 5%; *** significant at 1%. (2) The cost shares of labor, materials and capital are calculated as averages of the plant-level cost shares of labor, materials and capital across all plants in years 2001, 2002 and 2003 (excluding outliers). (3) The sample generating the sets of production function coefficients is constituted by all plants in years 2001, 2002 and 2003 (excluding outliers). Table C.2. Production Function Parameters from the Unrestricted Estimation by Industry; Cobb-Douglas Specification

151

Coefficients Food and beverages

Cost-share Pool OLS RE

Chemicals

Cost-share Pool OLS RE

Metal products (excluding machinery and equipment)

Cost-share

Machinery and equipment.

Cost-share

Pool OLS RE Pool OLS RE

Wood and cork products (excluding furniture)

Cost-share Pool OLS RE

Paper products.

Cost-share Pool OLS RE

IT-services

Cost-share Pool OLS RE

Farm-fishing

Cost-share Pool OLS RE

Labor

Materials

Capital

0.20 0.20*** 0.31*** 0.24 0.46*** 0.44 0.27 0.04** 0.18* 0.34 0.43** 0.20 0.26 0.47*** 0.41 0.21 0.26 0.14* 0.53 0.40** 0.27 0.32 0.07 0.22

0.69 0.58*** 0.50*** 0.66 0.42** 0.41** 0.64 0.78*** 0.56 0.57 0.51 0.49 0.64 0.53 0.48 0.67 0.55 0.68*** 0.39 0.39*** 0.41*** 0.58 0.50 0.61***

0.11 0.22*** 0.14*** 0.10 0.18 0.12 0.09 0.13* 0.11 0.09 -0.001*** 0.03** 0.09 0.05*** 0.06** 0.13 0.18 0.09 0.09 0.30 0.16 0.09 0.34* 0.13

Test for Constant Returns to Scale Prob>F=0.846 Prob > chi2 = 0.234 Prob>F=0.063 Prob > chi2 =0.67 Prob>F=0.028 Prob > chi2 =0.006 Prob>F=0.297 Prob > chi2 = 0.0005 Prob>F=0.166 Prob > chi2 =0.411 Prob>F=0.953 Prob > chi2 = 0.145 Prob>F=0.029 Prob > chi2 = 0.0000 Prob>F=0.009 Prob > chi2 = 0.3915

Notes:

(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. (2) The cost shares of labor, materials and capital are calculated as averages of the plant-level cost shares of labor, materials and capital for each industry using all plants for years 2001, 2002 and 2003 (excluding outliers). TABLE C.3. PRODUCTION FUNCTION PARAMETERS FROM THE UNRESTRICTED ESTIMATION BY INDUSTRY; TRANSLOG SPECIFICATION

152

L

M

K

L2

M2

K2

L*M

L*K

M*K

0.0004

1.37***

0.24

-0.01

0.04***

0.03

0.01

-0.67

1.69***

0.70***

0.02

-0.005 0.03***

0.05***

0.07***

-0.03*

0.08*** 0.09***

Pool OLS

0.54

1.28

RE

-0.56

1.27

Test for CD1

Test for CRS1

0.0000

0.011

0.0000

0.0000

Food Pool OLS RE Chemicals 0.45*** 0.22***

0.03

0.06***

0.03

-0.12***

0.07*

-0.07

0.0000

0.0013

0.07

0.04***

0.04

-0.09***

0.03*

-0.07*

0.0000

0.267

0.67*

-0.06

0.06**

0.04

0.06

0.004

-0.12

0.036

0.283

0.62

-0.08*

0.02**

0.05

0.12

-0.04

-0.10

0.0000

0.007

Metal Pool OLS

0.60

RE

0.85

0.10*** 0.03***

M&E -1.86**

1.23

0.02

0.16**

0.05**

0.0005***

-0.14***

0.04

-0.03

0.0000

0.0000

-1.21

1.17

-0.19

0.17*

0.04***

0.004***

-0.17***

-0.01

0.02***

0.014

0.001

Pool OLS

-0.18

0.97

0.24***

0.15***

0.03

-0.34

-0.03

0.0007

-0.87

1.99

0.25***

0.11***

0.02***

-0.36***

0.04**

-0.04** 0.03***

0.0000

RE

0.19 0.33***

0.0000

0

Pool OLS

0.34

0.06***

-0.09

-0.09

0.09***

0.03

0.02

0.14***

0.16***

0.0000

0.0001

RE

-0.19

0.22**

-0.07

-0.04

0.05***

0.03

0.03

0.07*

-0.11

0.0009

0.636

0.65

-0.10*

0.06***

-0.04***

-0.02***

0.10**

0.0000

0.0000

0.06***

-0.16***

0.02***

-0.01***

0.08

0.08***

-0.06 0.04***

0.0000

0.0000

0.39

-0.07

0.04

0.07

0.06

-0.02

-0.11

0.0000

0.049

0.73

-0.07*

0.01***

0.07*

0.13*

-0.11**

-0.08

0.0000

0.0000

Pool OLS RE Wood

Paper

IT Pool OLS

1.66**

RE

2.01***

0.14*** 0.38***

Farm-fish. Pool OLS RE

1.32 1.36**

0.30** 0.18***

Notes: 1 p-values. Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. The cost shares of labor, materials and capital are calculated as averages of the plant-level cost shares of labor, materials and capital for each industry using all plants for years 2001, 2002 and 2003 (excluding outliers).

153

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