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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

ECONOMIC ISSUES, PROBLEMS AND PERSPECTIVES

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ECONOMIC FREEDOM: CAUSES AND CONSEQUENCES

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ECONOMIC ISSUES, PROBLEMS AND PERSPECTIVES

ECONOMIC FREEDOM: CAUSES AND CONSEQUENCES

JOSHUA C. HALL AND

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

ROBERT A. LAWSON EDITORS

Nova Science Publishers, Inc. New York

Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Economic freedom : causes and consequences / editors, Joshua C. Hall, Robert A. Lawson. p. cm. Includes bibliographical references and index. ISBN HERRN 1. Free enterprise. 2. Economic development. 3. Democracy. I. Hall, Joshua C. II. Lawson, Robert, 1967HB95.E26 2011 330.1--dc22 2010048374

Published by Nova Science Publishers, Inc. † New York

Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook

CONTENTS Foreword

The Importance of Economic Freedom Edward P. Stringham and Gerald Gunderson

Preface

ix Introduction and Overview: What is Economic Freedom? Joshua C. Hall, James D. Gwartney and Robert A. Lawson

xi

Section 1

Economic Freedom and Growth

1

Chapter 1

The Two Freedoms in a Growth Model Wenbo Wu and Otto A. Davis Short Run Macroeconomic Performance and Economic Freedom: Can Economic Growth Rates be Higher and More Stable? Jody W. Lipford

3

Chapter 2

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vii

Section 2

Economic Freedom and Democracy

Chapter 3

Why Political and Civil Freedom are Fundamental to Economic Freedom, Reform, and Restructuring David A. Deese Economic Freedom, Democracy and Growth Abdiweli M. Ali The Relationship between Political and Economic Freedom Reconsidered Clifford F. Thies Are We Taking Certain Liberties by Assuming a Relationship Exists between Economic and Political Freedom? John A. Tures

Chapter 4 Chapter 5

Chapter 6

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39

41 55

67

81

vi Section 3

Economic Freedom and Other Values

Chapter 7

Economic Freedom, Globalization and Human Rights: Can We Have it All? Wesley T. Milner Male-Female Life Expectancy and Economic Freedom J. Wilson Mixon, Jr. and Gary H. Roseman The Impact of Economic Growth, Tax Policy and Economic Freedom on Income Inequality J.R. Clark and Robert A. Lawson

109

Section 4

Determinants of Economic Freedom

129

Chapter 10

You Get what You Vote for: Voter Preferences and Economic Freedom Eric Crampton Patent Rights and Economic Freedom: Friend or Foe? Walter G. Park Does Development Aid Lead to Economic Freedom? Benjamin Powell and Matt E. Ryan Social Cohesion and its Relationship to Endogenous Institutional Formation and Economic Growth Lauren R. Heller

Chapter 8

Chapter 9

Chapter 11 Chapter 12 Chapter 13

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Contents 91

93

121

131 145 165

177

Section 5

Economic Freedom: What do We Know?

193

Chapter 14

What Have We Learned from the Economic Freedom of the World Index? James Gwartney

195

Acknowledgments Index

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FOREWORD: THE IMPORTANCE OF ECONOMIC FREEDOM

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Edward P. Stringham and Gerald Gunderson1 Economic freedom makes a huge difference in everyone’s lives. It enables people to justly acquire property, voluntarily trade with others, and live their lives peacefully as they see fit. Economic freedom also fosters an atmosphere that rewards entrepreneurs who develop products and services that enrich the lives of their customers and create useful resources out of what would have otherwise been wasted. Research on economic freedom is, therefore, extremely important. Without an understanding of the linkages between economic freedom and various economic outcomes, voters are more likely to support economically unsound policies (Caplan and Stringham, 2005). Prior to the past two decades, many economists had theories about economic freedom, but none had measured its effect on economic outcomes. From their pathbreaking work in the early 1990s (Block, 1991; Gwartney, Lawson, and Block, 1994) to their continually updated and more refined work today (Gwartney, Lawson, and Hall, 2010) the authors of the Economic Freedom of the World Annual Report have created an entirely new field of research. Using this index, scholars have found that economic freedom is positively correlated with numerous desirable variables such as economic growth (Wu and Davis, 1999), life expectancy (Mixon, 2003), sanitation (Boettke, 2001), and human rights (Milner, 2000), and it is negatively correlated with numerous undesirable variables such as child mortality (Stroup, 2007), child malnutrition (Boettke, 2001), pollution (Stroup, 2008), and homicide (Stringham and Levendis, 2010). As editors of the Journal of Private Enterprise for more than two decades, we are pleased that our journal has become the main outlet for research on economic freedom. All chapters in this volume were initially published in the Journal of Private Enterprise, and it is gratifying to see them compiled for the first time here. We hope that this volume demonstrates the importance of this growing field and encourages others to join this valuable research program.

1

Stringham is the Lloyd V. Hackley Endowed Chair for Capitalism and Free Enterprise Studies, Fayetteville State University and has been Editor of the Journal of Private Enterprise since 2006. Gerald Gunderson is the Shelby Cullom Davis Professor of American Business and Economic Enterprise, Trinity College and was the Editor of the Journal of Private Enterprise from 1988 to 2006.

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We also hope that the lessons of this research reach the general public so that more people can appreciate the benefits of economic freedom. In recent times, statism, especially in the United States, has been on the march, and freedom has been under attack. But from a medium-term perspective, economic freedom has been increasing worldwide, including dramatic improvements in many countries. As Mises (1998a, p. 864) wrote, “The flowering of human society depends on two factors: the intellectual power of outstanding men to conceive sound social and economic theories, and the ability of these or other men to make these ideologies palatable to the majority.” With a better understanding of economic freedom, members of society will be better prepared and motivated to push for it.

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REFERENCES Block, Walter (ed.) 1991. Economic Freedom: Toward a Theory of Measurement. Vancouver: Fraser Institute. Boettke, Peter. 2001. Calculation and Coordination: Essays on Socialism and Transitional Political Economy. London: Routledge. Caplan, Bryan, and Edward Peter Stringham. 2005. “Mises, Bastiat, Public Opinion, and Public Choice.” Review of Political Economy, 17(1): 79-105. Gwartney, James D., Lawson, Robert A. and Block, Walter. 1996. Economic Freedom of the World: Annual Report. Vancouver: Fraser Institute. Gwartney, James D., Lawson, Robert A., and Hall, Joshua. 2010. Economic Freedom of the World: Annual Report. Vancouver: Fraser Institute. Milner, Wesley T. 2000. “Economic Freedom, Globalization and Human Rights: Can We Have It All?” Journal of Private Enterprise 15(2): 35-61. Mises, Ludwig von 1998. Human Action. Auburn: Mises Institute. Mixon, J. Wilson, Jr., and Gary H. Roseman. 2003. “Male-Female Life Expectancy and Economic Freedom.” Journal of Private Enterprise 19(1): 1-20. Stringham, Edward, and John Levendis. 2010. “The Relationship between Economic Freedom and Homicide.” In Economic Freedom of the World: 2010 Annual Report, ed. James D. Gwartney, Joshua C. Hall, and Robert Lawson, 203-217. Vancouver: Fraser Institute. Stroup, Michael D. 2007. “Economic Freedom, Democracy and the Quality of Life: An Empirical Analysis.” World Development 35(1): 52–66. Stroup, Michael D. 2008. “Separating the Influence of Capitalism and Democracy on Pollution in the OECD Countries.” Journal of Economic Behavior and Organization 67(3-4): 560-572. Wu, Wenbo, and Otto A. Davis. 1999. “The Two Freedoms in a Growth Model.” Journal of Private Enterprise 14(2): 115-146.

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PREFACE

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Since 1996, the Economic Freedom of the World (EFW) index has annually provided a measure of the consistency of a nation's policies and institutions with economic freedom. With data going back to 1970 and coverage of over 100 countries, the EFW index has become an important tool for scholars studying a wide range of economic and social outcomes around the world. The EFW index has over 300 citations in the academic literature, and has been used in studies of national income, economic growth, poverty, equality, financial returns, democracy and human rights, and many other areas. This volume highlights contributions made to this literature that were published originally in one of the most important journals in this field, the Journal of Private Enterprise, bringing together a wide variety of studies on the role of economic freedom on economic growth, income equality, political freedom, and other social goals. Also included are studies of the determinants of economic freedom itself. Students of the causes and consequences of economic freedom will find this compilation to be an indispensable addition to their library.

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INTRODUCTION AND OVERVIEW: WHAT IS ECONOMIC FREEDOM?1 Joshua C. Hall, James D. Gwartney and Robert A. Lawson

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INTRODUCTION It has been nearly a quarter of a century since Milton Friedman and Michael Walker hosted the initial meeting of a series of conferences that eventually led to the Economic Freedom of the World index. These conferences, held during 1986-1994, had a single objective: the development of a clearly defined measure of economic freedom for a large set of countries. Moreover, conference participants wanted the measure to be as objective and transparent as possible. The goals of this project have been unchanged during the 25 years of its existence. There was considerable debate about the nature of economic freedom at those early conferences (see, for example, Block (1991)), but a consensus emerged that the core concepts of economic freedom were self-ownership, non-interference, and the protection of people and their property from invasions by others. Self-ownership and non-interference imply that individuals have a right to choose for themselves—to decide how they will use their time, talents, and resources. On the other hand, they do not have a right to the time, talents, and resources of others. Put another way, individuals do not have a right to take things from others or demand that others provide things for them. Economic freedom is present when adults are free to produce, consume, and trade with others as long as their actions do not harm the person or property of others. Use of violence, theft, fraud, and physical invasions are not permissible, but otherwise, individuals who are economically free are free to choose and compete as they see fit.

1

This chapter was adapted from Chapter 1 of the 2010 edition of the Economic Freedom of the World report (Gwartney, Lawson, and Hall, 2010). We are grateful to the Fraser Institute for permission to use this material. Copies of the report can be found at www.freetheworld.com. Joshua Hall is an Assistant Professor of Economics at Beloit College. James Gwartney holds the Gus A. Stavros Eminent Scholar Chair at Florida State University. Robert Lawson is an Associate Professor of Finance at Auburn University and Director of the Economic Freedom Initiative at that institution’s Center for International Finance and Global Competitiveness.

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The Economic Freedom of the World index is designed to measure the consistency of a nation’s institutions and policies with this self-ownership concept. The key ingredients of economic freedom are

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   

personal choice, voluntary exchange coordinated by markets, freedom to enter and compete in markets, and protection of persons and their property from aggression by others.

These four cornerstones underpin the EFW index. Put simply, institutions and policies are consistent with economic freedom when they permit individuals to choose for themselves, enter into voluntary agreements with others, and protect individuals and their property from aggressors. In order to achieve a high EFW rating, a country must provide secure protection of privately owned property, evenhanded enforcement of contracts, and a stable monetary environment. It also must keep taxes low, refrain from creating barriers to both domestic and international trade, and rely more fully on markets rather than the political process to allocate goods and resources. During 1980-2007, there was a gradual but steady movement toward economic freedom. Numerous studies, a couple of which are reprinted here, have shown that countries with more economic freedom grow more rapidly and achieve higher levels of per capita income than those that are less free. Similarly, there is a positive relationship between changes in economic freedom and the growth of per capita income. Moreover, as per capita income has grown, the world’s poverty rate has declined and most of this progress has occurred in countries that have made substantial moves toward higher levels of economic freedom. Economic growth is primarily the result of gains from trade, capital investment, and the discovery of improved products, lower-cost production methods, and better ways of doing things. Given the sources of growth and prosperity, it is not surprising that increases in economic freedom and improvements in quality of life are closely related.

THE CONSTRUCTION OF THE INDEX The construction of the Economic Freedom of the World index is based on three important methodological principles. First, objective components are always preferred to those that involve surveys or value judgments. Given the multi-dimensional nature of economic freedom and the importance of legal and regulatory elements it is sometimes necessary to use data based on surveys, expert panels, and generic case studies. To the fullest extent possible, however, the index uses objective components. Second, the data used to construct the index ratings are from external sources such as the International Monetary Fund, World Bank, and World Economic Forum that provide data for a large number of countries. Data provided directly from a source within a country are rarely used, and only when the data are unavailable from international sources. Importantly, the value judgments of the authors or others in the Economic Freedom Network are never used to alter the raw data or the rating of any country. Third, transparency is present throughout. Each year, the full report provides information about the data sources, the methodology used to transform raw data into

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component ratings, and how the component ratings are used to construct both the area and summary ratings. Complete methodological details can be found in the Appendix of each annual report, which is freely available to interested scholars at www.freetheworld.com While the methodological principles of the EFW index have been consistent over time, the underlying structure and components of the index have changed over time as new data sources have become available. Currently, economic freedom is measured in five major areas:(1) Size of Government: Expenditures, and Taxes, Enterprises; (2) Legal Structure and Security of Property Rights; (3) Access to Sound Money; (4) Freedom to Trade Internationally; (5) Regulation of Credit, Labor, and Business. Within the five major areas, there are 23 components in the 2010 index (which reports on 2008, due to data lags). Many of those components are themselves made up of several sub-components. In total, the index comprises 42 distinct variables. Each component and sub-component is placed on a scale from 0 to 10 that reflects the distribution of the underlying data. The sub-component ratings are averaged to determine each component. The component ratings within each area are then averaged to derive ratings for each of the five areas. In turn, the five area ratings are averaged to derive the summary rating for each country. The following sections provide an overview of each of the five major areas.

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AREA 1: SIZE OF GOVERNMENT The four components of Area 1 indicate the extent to which countries rely on the political process to allocate resources and goods and services. When government spending increases relative to spending by individuals, households, and businesses, government decision-making is substituted for personal choice and economic freedom is reduced. The first two components address this issue. Government consumption as a share of total consumption (1A) and transfers and subsidies as a share of GDP (1B) are indicators of the size of government. When government consumption is a larger share of the total, political choice is substituted for personal choice. Similarly, when governments tax some people in order to provide transfers to others, they reduce the freedom of individuals to keep what they earn. The third component (1C) in this area measures the extent to which countries use private rather than government enterprises to produce goods and services. Government firms play by rules that are different from those to which private enterprises are subject. They are not dependent on consumers for their revenue or on investors for capital. They often operate in protected markets. Thus, economic freedom is reduced as government enterprises produce a larger share of total output. The fourth component (1D) is based on (Di) the top marginal income tax rate and (Dii) the top marginal income and payroll tax rate and the income threshold at which these rates begin to apply. These two sub-components are averaged to calculate the top marginal tax rate (1D). High marginal tax rates that apply at relatively low income levels are also indicative of reliance upon government. Such rates deny individuals the fruits of their labor. Thus, countries with high marginal tax rates and low income thresholds are rated lower. Taken together, the four components of Area 1 measure the degree to which a country relies on personal choice and markets rather than government budgets and political decisionmaking. Therefore, countries with low levels of government spending as a share of the total, a

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smaller government enterprise sector, and lower marginal tax rates earn the highest ratings in this area.

AREA 2: LEGAL STRUCTURE AND SECURITY OF PROPERTY RIGHTS Protection of persons and their rightfully acquired property is a central element of economic freedom and a civil society. Indeed, it is the most important function of government. Area 2 focuses on this issue. The key ingredients of a legal system consistent with economic freedom are rule of law, security of property rights, an independent judiciary, and an impartial court system. Components indicating how well the protective function of government is performed were assembled from three primary sources: the International Country Risk Guide, the Global Competitiveness Report, and the World Bank’s Doing Business project. Security of property rights, protected by the rule of law, provides the foundation for both economic freedom and the efficient operation of markets. Freedom to exchange, for example, is meaningless if individuals do not have secure rights to property, including the fruits of their labor. When individuals and businesses lack confidence that contracts will be enforced and the fruits of their productive efforts protected, their incentive to engage in productive activity is eroded. Perhaps more than any other area, this area is essential for the efficient allocation of resources. Countries with major deficiencies in this area are unlikely to prosper regardless of their policies in the other four areas.

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AREA 3: ACCESS TO SOUND MONEY Money oils the wheels of exchange. An absence of sound money undermines gains from trade. As Milton Friedman informed us long ago, inflation is a monetary phenomenon, caused by too much money chasing too few goods. High rates of monetary growth invariably lead to inflation. Similarly, when the rate of inflation increases, it also tends to become more volatile. High and volatile rates of inflation distort relative prices, alter the fundamental terms of longterm contracts, and make it virtually impossible for individuals and businesses to plan sensibly for the future. Sound money is essential to protect property rights and, thus, economic freedom. Inflation erodes the value of property held in monetary instruments. When governments finance their expenditures by creating money, in effect, they are expropriating the property and violating the economic freedom of their citizens. The important thing is that individuals have access to sound money: who provides it makes little difference. Thus, in addition to data on a country’s inflation and its government’s monetary policy, it is important to consider how difficult it is to use alternative, more credible, currencies. If bankers can offer saving and checking accounts in other currencies or if citizens can open foreign bank accounts, then access to sound money is increased and economic freedom expanded. There are four components to the EFW index in Area 3. All of them are objective and relatively easy to obtain and all have been included in the earlier editions of the index. The

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first three are designed to measure the consistency of monetary policy (or institutions) with long-term price stability. Component 3D is designed to measure the ease with which other currencies can be used via domestic and foreign bank accounts. In order to earn a high rating in this area, a country must follow policies and adopt institutions that lead to low (and stable) rates of inflation and avoid regulations that limit the ability to use alternative currencies.

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AREA 4: FREEDOM TO TRADE INTERNATIONALLY In our modern world of high technology and low costs for communication and transportation, freedom of exchange across national boundaries is a key ingredient of economic freedom. Many goods and services are now either produced abroad or contain resources supplied from abroad. Voluntary exchange is a positive-sum activity: both trading partners gain and the pursuit of the gain provides the motivation for the exchange. Thus, freedom to trade internationally also contributes substantially to our modern living standards. In response to protectionist critics and special-interest politics, virtually all countries adopt trade restrictions of various types. Tariffs and quotas are obvious examples of roadblocks that limit international trade. Because they reduce the convertibility of currencies, controls on the exchange rate also hinder international trade. The volume of trade is also reduced if the passage of goods through customs is onerous and time consuming. Sometimes these delays are the result of administrative inefficiency while in other instances they reflect the actions of corrupt officials seeking to extract bribes. In both cases, economic freedom is reduced. The components in this area are designed to measure a wide variety of restraints that affect international exchange: tariffs, quotas, hidden administrative restraints, and exchange rate and capital controls. In order to get a high rating in this area, a country must have low tariffs, a trade sector larger than expected, easy clearance and efficient administration of customs, a freely convertible currency, and few controls on the movement of capital.

AREA 5: REGULATION OF CREDIT, LABOR, AND BUSINESS When regulations restrict entry into markets and interfere with the freedom to engage in voluntary exchange, they reduce economic freedom. The fifth area of the index focuses on regulatory restraints that limit the freedom of exchange in credit, labor, and product markets. The first component (5A) reflects conditions in the domestic credit market. The first two subcomponents provide evidence on the extent to which the banking industry is dominated by private firms and whether foreign banks are permitted to compete in the market. The final two sub-components indicate the extent to which credit is supplied to the private sector and whether controls on interest rates interfere with the market in credit. Countries that use a private banking system to allocate credit to private parties and refrain from controlling interest rates receive higher ratings for this regulatory component. Many types of labor-market regulations infringe on the economic freedom of employees and employers. Among the more prominent are minimum wages, dismissal regulations, centralized wage setting, extension of union contracts to nonparticipating parties, and

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conscription. The labor-market component (5B) is designed to measure the extent to which these restraints upon economic freedom are present. In order to earn high marks in the component rating regulation of the labor market, a country must allow market forces to determine wages and establish the conditions of hiring and firing, and refrain from the use of conscription. Like the regulation of credit and labor markets, the regulation of business activities (component 5C) inhibits economic freedom. The sub-components of 5C are designed to identify the extent to which regulations and bureaucratic procedures restrain entry and reduce competition. In order to score high in this portion of the index, countries must allow markets to determine prices and refrain from regulatory activities that retard entry into business and increase the cost of producing products. They also must refrain from “playing favorites,” that is, from using their power to extract financial payments and reward some businesses at the expense of others.

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CONSTRUCTION OF AREA AND SUMMARY RATINGS Theory provides us with direction regarding elements that should be included in the five areas and the summary index, but it does not indicate what weights should be attached to the components within the areas or among the areas in the construction of the summary index. It would be nice if these factors were independent of each other and a weight could be attached to each of them. During the past several years, we have investigated several methods of weighting the various components, including principle component analysis and a survey of economists. We have also invited others to use their own weighting structure if they believe that it is preferable. In the final analysis, the summary index is not very sensitive to substantial variations in the weights. Furthermore, there is reason to question whether the areas (and components) are independent or work together like a team. Put another way, they may be linked more like the wheels, motor, transmission, drive shaft, and frame of a car. Just as it is the bundle of these factors that underlies the mobility of an auto, it may be a bundle of factors that underlies the composition of economic freedom. With regard to an automobile, which is more important for mobility: the motor, wheels, or transmission? The question cannot be easily answered because the parts work together. If any of these key parts break down, the car is immobile. Institutional quality may be much the same. If any of the key parts are absent, the overall effectiveness is undermined. As the result of these two considerations, we organize the elements of the index in a manner that seems sensible to us but we make no attempt to weight the components in any special way when deriving either area or summary ratings. Of course, the component and subcomponent data are available to researchers who would like to consider alternative weighting schemes and we encourage them to do so.

SUMMARY ECONOMIC FREEDOM RATINGS, 2008 The EFW index is calculated for as many as 141 countries and as far back as 1970 as the

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availability of data allows; see the full annual report which is available at www.freetheworld.com, for information from past years. Table 1 presents summary economic freedom ratings for the countries with the highest economic freedom for 2008, the most recent year for which comprehensive data are available. Hong Kong and Singapore, as is usual, occupy the top two positions. The other nations in the top 10 are New Zealand, Switzerland, Chile, United States, Canada, Australia, Mauritius, and the United Kingdom. The rankings of other major countries in 2008 include Germany (24th), Japan (24th), France (35th), Korea (37nd), Spain (39th), Italy (66th), Mexico (69th), China (82nd), Russia (84th), India (87th), and Brazil (102nd). The ten lowest-rated countries are Algeria, Democratic Republic of Congo, Burundi, Guinea-Bissau, Central African Republic, Republic of Congo, Venezuela, Angola, Myanmar, and again in last place, Zimbabwe. Table 1. Ten Highest Economic Freedom Scores, 2008

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Rank 1 2 3 4 5 6 7 8 9 10

Countries Hong Kong Singapore New Zealand Switzerland Chile United States Canada Australia Mauritius United Kingdom

2008 9.05 8.70 8.27 8.08 8.03 7.96 7.95 7.90 7.82 7.81

CONCLUSION Every year in the annual EFW report we conclude with some graphs illustrating simple relationships between economic freedom and various other indicators of human and political progress.2 To give but one example, there exists a clear and positive relationship between economic freedom and life expectancy. Many of these relationships reflect the impact of economic freedom as it works through increasing economic growth. In other cases, the observed relationships may reflect the fact that some of the variables that influence economic freedom may also influence political factors like trust, honesty in government, and protection of civil liberties. While those relationships do not automatically suggest a causal relationship, they do highlight some of the differences between market-oriented economies and those dominated by government regulation and planning. With the publication of the EFW index, we hoped to encourage others to study the relationship between economic freedom and other outcomes that individuals might value. As a consequence, hundreds of studies have been published over the past fifteen years using the EFW index, many of which appeared in the Journal of Private 2

We encourage the curious reader to read the most recent version of the Economic Freedom of the World report to see examples of these graphs.

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Enterprise. We bring these important articles together in this volume so that those interested in better understanding the causes and consequences of economic freedom can find them all in one place.

REFERENCES

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Block, Walter (ed.) 1991. Economic Freedom: Toward a Theory of Measurement. Vancouver: Fraser Institute. Gwartney, James D., Lawson, Robert A., and Hall, Joshua. 2010. Economic Freedom of the World: Annual Report. Vancouver: Fraser Institute.

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SECTION 1. ECONOMIC FREEDOM AND GROWTH

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In: Economic Freedom: Causes and Consequences Editors: Joshua C. Hall and Robert A. Lawson

ISBN 978-1-61122-970-7 © 2011 Nova Science Publishers, Inc.

Chapter 1

THE TWO FREEDOMS IN A GROWTH MODEL1 Wenbo Wu and Otto A. Davis

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INTRODUCTION With the publication of the Wealth of Nations, Adam Smith (1776) engaged a debate about the selection of policies which might better contribute to economic growth. Of course, Smith was arguing against the established policy of mercantilism and in favor of policies which became known as the doctrine of free trade. As Ricardo and Mill refined and elaborated his arguments from the principle of absolute into comparative advantage, and as modern theorists produced more complex extensions, the concern with growth remained—at least implicitly—but the argument largely became specialized into international trade theory and policy. Smith’s basic argument incorporated features which may have been not fully appreciated by modern theorists. Consider the following: It is the maxim of every prudent master of a family, never to attempt to make at home what it will cost him more to make than buy. The tailor does not attempt to make his own shoes, but buys them at the shoemaker. The shoemaker does not attempt to make his own clothes, but employs a tailor. The farmer attempts to make neither the one nor the other, but employs those different artificers. All of them find it for their interest to employ their whole industry in a way in which they have some advantage over their neighbors, and to purchase with a part of its produce, or what is the same thing, with the price of a part of it, whatever else they have occasion for. What is prudence in the conduct of every private family, can scarce be folly in that of a great kingdom. (Smith [1776], pp.424-6)

Thus Smith’s argument implicitly incorporated aspects of economic freedom: individuals are free to seek out their own advantages as they perceive them, and nations should do the same. 1

This article originally appeared as Wenbo Wu and Otto A. Davis, “The Two Freedoms in a Growth Model,” Journal of Private Enterprise 14, no. 2 (1999): 115-146. Wu and Davis were both affiliated with the John Heinz III School of Public Policy and Management at Carnegie Mellon University at the time of publication.

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Since those early times, it is only recently that there have been questions about the role of freedom in economic growth. The first concerns were largely over the issue of whether democracy itself encouraged growth. More recently, especially given the experiences of places such as Hong Kong and Singapore, the issue has shifted to whether it is economic freedom, rather than political freedom, which encourages growth. While the relationship between democracy and growth certainly of intellectual interest, the choice of a form of governance is hardly considered to be a mere matter of policy. This choice is far too fundamental. Advocacy for democracy is not likely to be contingent upon its relationship to growth. The very essence of economic freedom—the ability to seek out one’s own advantages and to dispose of the fruits of one’s labor as one wishes—is really defined by certain policies. Tax and expenditure policies define what portion of a person’s income might be kept or even redistributed. Smith’s concern with trade is also included. The relationship between these policies and growth is of both practical and intellectual interest. Further, if there is a strong positive relationship between economic freedom and growth, then advocate are likely to use this relationship to argue for the acceptance of those policies which are used in the definition and measurement of economic freedom. Given the importance of the topic, one hopes to see a variety of approaches used to explore the relationship between economic freedom and growth. This paper aims to develop one particular approach. We incorporate the two freedoms (political and economic) into the Solow growth model in a direct and straightforward manner. In this way, we can then use the historical experiences of many of the nations of the world to estimate the model and see how the two freedoms might have contributed to growth. The measure of economic freedom used here is adopted from the path breaking effort of Gwartney, Lawson and Block (1996) and explained in Wu and Davis (1998). One could, of course, use alternative measures, but these seem to be the most objective and systematic of those available and systematic comparisons would itself be the subject of another paper. The measure of political freedom combines two ratings published annually by Freedom House (Gastil and others, 1972-95). We begin by briefly reviewing some of the relevant literature and then proceed by developing the theoretical model. Next, we discuss aspects of the estimation since our data set consists of observations over time for a set of countries, and finally we present out estimates and attempt an interpretation of them. We conclude with some remarks about policy implications and alternatives.

LITERATURE REVIEW There is a vast literature concerning institutional determinants of economic growth. Empirical studies about the impact of political freedom on economic growth have long existed, and the findings in this literature are conflicting and inconclusive (Weede, 1983; Pourgerami, 1988; Scully, 1988; Glahe and Vorhies, 1989; Weede, 1993; Przeworski and Lomongi, 1993; Pastor and Sung, 1995; and De Haan and Siermann, 1996). The empirical results rang from positive to negative influences of political democracy on economic performance.

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Recently empirical modeling about possible impacts of aspects of economic freedom on economic growth is proliferating (Scully, 1992; De Vanssay and Spindler, 1994; Barro, 1995; Brunetti and Wedder, 1995; Knack and Keefer, 1995; Clague, Keefer, Knack and Olson, 1996; and De Haan and Siermann, 1998). However, these authors use various measurements of economic freedom. Barro (1995) used government consumption, the black-market premium and the rule-of-law index (measures of market distortions). Knack et al. (1995) used country risk evaluators, including evaluations of contract enforceability and risk of expropriation. In Clague et al. (1996), six different variables were used to describe property and contact rights which capture the core of economic freedom. The variables are: the amount of contract-intensive money, two indexes evaluating risks to foreign investors, a subject measure of default risk, currency depreciation, and the black market premium. A systematic measurement of economic liberty provided by Scully and Slottje (1991) is utilized in De Vanssay et al. (1994) and De Haan et al. (1998). Scully and Slottje constructed nine indices of economic liberty based on fifteen attribute variables. This kind of exercise of constructing a comprehensive measurement of economic freedom is also carried out by other groups (Gastil, 1982; Johnson and Sheehy, 1995 and 1996; and Gwartney, Lawson, and Block, 1996). Gwartney et al. selected 17 components as attribute variables related to economic freedom. These components are classified into four groups: (1) Money and inflation; (2) Government operations and regulation; (3) Takings and discriminatory taxation; (4) Restraints on international exchange. Wu and Davis (1998) performed a principle component analysis on the data set provided by Gwartney et al. to come up with measure of economic freedom which is statistically objective. It turned out that this index is highly correlated with three indices provided by Gwartney et al., which are a weighted sum of component variables. A final index of economic freedom is calculated by averaging the four indices with weights as loadings from a principle component analysis on the four indices. This final index of economic freedom is used in this paper. Differentiating economic freedom and political freedom in studies on institutional factors of economic growth is also of methodological significance. As is pointed out in Wu and Davis (1998), these two aspects of freedom have quite different natures and, hence, have different implications for economic growth. In their study, Wu and Davis built a log-linear model on categorical data of four variables: economic freedom, political freedom, the level of income, and the rate of economic growth for a panel for about 100 countries during the period from 1975 to 1992. One major finding is that economic freedom, but not political freedom, is strongly associated with economic growth. This suggests that economic freedom might be a significant determinant of economic growth, and that political freedom might not be relevant to economic growth. To explicitly, explore the effects of economic and political freedoms upon economic growth is the aim of this paper. We carry out our analysis in a variety of Solow growth models, including those augmented with the two institutional variables: economic freedom and political freedom. The Solow model provides a good framework for our endeavor. It has simple and elegant assumptions and structure, and has been widely utilized by researchers of economic growth. Especially, Solow models have regained attention in recent years in a wave of new growth studies (see Temple [1998] for an excellent survey). Mankiw, Romer and Weil (1992) asserted that the Solow model’s theoretical predictions are consistent with the empirical evidence. They augmented a standard Solow model to include human capital

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accumulation, and used a cross-sectional approach to estimate the augmented model which explains about 80 percent of the cross-country variation in per capita income. However, one major drawback of the cross-sectional approach employed by Mankiw et al. (1992), Barro and Sala-I-Martin (1992), and many other studies is that it ignores individual effects of countries. Econometrically, the cross-sectional approach implicitly assumes that unobserved factors that are contained in the initial level of technology of an individual country are uncorrelated with explanatory variables. However, this assumption is not likely to be held in the reality. That is why cross-sectional models rarely give unbiased estimates. Panel data models can be employed to solve this problem. Knight, Loayza, and Villanueva (1993), and Islam (1995) both applied the panel data method suggested by Chamberlain (1983) to estimate augmented Solow models.2 A common finding in these studies is that the estimated speed of convergence is faster than that estimated by Mankiw et al. (1992). This is due to the fact that a cross-sectional approach does not take into account the correlation between individual effects of countries and the independent variables in the growth equation. A more rigorous panel data method that can be applied to studies of economic growth is a dynamic panel data model as utilized in Caselli, Esquivel and Lefort (1996). They used GMM (generalized method of moments) to estimate the dynamic panel data model by first differencing to eliminate the effect of the initial level of technology and then using lags of the variables as instruments (see Section 4). In addition to the advantage that a panel data model can control for unobserved country-specific effects, a dynamic panel data model can alleviate other econometric problems presented in cross-sectional models such as measurement error and endogeneity by using lags of the regressors as instruments in the estimation. This paper assesses the distinctive impacts of economic and political freedoms on per capita income in the framework of a Solow model, and estimates the dynamic panel data model with a GMM method outlined in Arellano and Bond (1991).

THE THEORETICAL MODEL Consider the following Cobb-Douglas production function: 𝑌(𝑡) = 𝐾 𝑎 (𝑡)(𝐴(𝑡)𝐿(𝑡))1−𝑎 , where Y is output, K is capital stock, L is labor, A is labor-augmenting factor which reflects the level of technology and efficiency, and t refers to time in years. It is assumed that 0F

0.025 4.84††† 0.474 2.31†† 146 0.179 0

0.025 4.65††† 0.481 2.18†† 146 0.2202 0

0.025 4.78††† 0.389 2.30†† 146 0.2429 0

Reg 6

0.023 2.30††

Initial Right-Left

Year

Reg 5 -0.118 7.52†††

0.025 3.79††† -0.438 2.64††† 146* 0.1730** 0

0 0.3 0.012 2.15†† 0.026 4.91††† 0.37 2.10†† 146 0.2173 0

0.013 2.47†† 0.025 3.79††† -0.44 2.62†† 146* 0.1477*** 0.0001

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* (25 groups, avg 5.8 obs/group, max 6, min 4) **(within = .1745, between = .0.2204) ***(within = 0.145, between = 0.316) Note: coefficient estimates are followed by robust t statistics. † denotes significance at the 10% level; †† denotes the 5% significance level; ††† denotes the 1% significance level.

In all specifications, initial levels of economic freedom enter significantly and negatively. Countries that were freer in 1970 showed lower increases in economic freedom than lowerranked countries; countries ranked highly in 1970 had little upward room to move. Stronger baseline preferences for economic freedom result in larger increases in economic freedom, though the measure is rarely statistically significant. Baseline Conservatism, however, does not significantly affect a country’s economic freedom. Changes in Conservatism measure have some predictive power in fixed effects estimation of changes in economic freedom. The time trend proves positive and significant in all specifications; economic freedom increased overall in the sample of countries examined, for reasons not captured in the other independent variables. Changes in preferences for economic freedom prove significant in all specifications. Evaluating at sample means12, a standard deviation increase in median voter preference for economic freedom yields a 0.21 standard deviation increased change in economic freedom – the expected change in economic freedom rises from 0.19 to 0.33. Specifications using the disaggregated Freedom Preference measure have slightly more predictive power than specifications using the Right-Left measure.

12

Using results from Specification 2

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Table 3. Voter Preferences, Economic Freedom, and Legal Institutions Dependent Variable: Δ Freedom

No institution German Legal Origin Scandinavian Legal Origin English Legal Origin French Legal Origin Supreme Court Tenure Administrative Court Tenure S.C. Control over A.C. A.C. power over Executive Case Law Constitutional Rigidity Judiciary Review

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Federal System

Initial Freedom

Initial Pref.

Δ Freedom Preference

-0.127 6.34††† -0.126 5.45††† -0.127 5.55††† -0.13 5.51††† -0.13 5.47††† -0.128 5.59††† -0.127 6.01††† -0.139 5.63††† -0.135 5.70††† -0.125 5.75††† -0.123 5.14††† -0.128 5.60††† -0.124 4.48†††

0.005 2.00† 0.007 1.88† 0.004 0.97 0.006 1.67 0.005 1.90† 0.005 1.8 0.003 1.2 0.005 1.72† 0.006 1.87† 0.006 1.98†† 0.005 1.67 0.005 1.76† 0.005 1.62

0.025 2.94††† 0.028 2.17†† 0.031 3.59††† 0.025 2.33†† 0.02 2.48†† 0.025 2.87††† 0.081 2.49†† 0.026 2.89††† 0.026 2.90††† 0.045 2.30†† 0.025 2.87††† 0.025 2.86††† 0.023 2.12††

Institution

-0.052 1.77† 0.013 0.38 0.08 1.72† -0.068 1.51 0.007 0.21 -0.016 0.24 0.082 1.92† 0.068 1.78† 0.088 2.65†† -0.023 0.76 -0.006 0.21 -0.026 0.49

Institution * Δ Freedom Preference -0.007 0.54 -0.018 1.06 0.003 0.21 0.035 1.29

-0.032 1.87† ‡

-0.024 1.14‡

0.01 0.76

N

R2

146

0.22

140

0.223

140

0.227

140

0.225

140

0.234

140

0.222

140

0.241

140

0.224

140

0.224

140

0.231

140

0.222

140

0.222

140

0.223

Notes: all specifications include the Year variable (not reported). Each row reports results from separate specifications where each institutional variable is tested in turn. Coefficient estimates are followed by robust t statistics. † denotes significance at the 10% level; †† denotes the 5% significance level; ††† denotes the 1% significance level. ‡ denotes that the interaction term correlates with Δ Freedom at 0.70 or higher. The interaction term is not dropped in these two cases of high correlation as it does not unduly magnify the standard error of Δ Freedom.

It seems clear that changes in voter preferences lead to changes in economic freedom outcomes. I move on to examine whether these results are robust to the addition of the institutional variables suggested by La Porta et. al. (2002), who find supreme court tenure, administrative court tenure, power of administrative law judges over the executive, case law, constitutional rigidity, judicial review, federalism, and legal origin to be significant determinants of economic freedom in a cross-section of countries. Regression 2, above, was re-run sequentially adding each of the La Porta variables in turn. Where La Porta tests the effect of these institutions on levels of economic freedom in a cross-section of countries, I test the effects of these variables on changes in economic freedom. Changes in median voter preferences translate into changes in economic freedom outcomes only as mediated by these institutions; consequently, interactions between the Δ Freedom Preference and each institutional variable are also tested. The results are reported in Table 3. The first important result is the overwhelming robustness of the Δ Freedom Preference variable. It remains significant at the 1% level and the coefficient remains quite stable in the

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majority of specifications. Exceptions occur when the interaction term is highly collinear with the Δ Freedom Preference measure. In most cases where the interaction term correlated with the Δ Freedom Preference measure at 0.70 or higher, the typical effects of collinearity were evidenced. The interaction term was dropped in those specifications where it correlated strongly with the voter preference term and where the voter preference term and the interaction term showed inflated standard errors. The variables that La Porta finds significant in determining levels of economic freedom prove less significant in determining changes in economic freedom. Four of these variables prove significant and have the expected sign, one is significant but with the opposite sign, one is statistically insignificant but has the expected sign, and five are insignificant and have the opposite sign. English Legal Origin, Supreme Court Control over Administrative Courts, Administrative Court Power over Executive and Case Law prove significant in increasing economic freedom over time. Interaction effects generally proved insignificant, and many of the interaction terms correlated too strongly with the Δ Freedom Preference measure to provide meaningful results.13 A negative coefficient indicates that the institution tends to mute the effects of changes in voter preferences while a positive coefficient suggests that the institution tends to amplify changes in voter sentiment. Only the Administrative Court Tenure variable proves statistically significant in interaction with Δ Freedom Preference; longer administrative court tenure reduces the effect of changes in voter preferences on economic freedom outcomes. Table 4. Voter Preferences, Economic Freedom, and Political Institutions Dependent Variable: Δ Freedom

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No institution Presidential System Assembly-elected President Parliamentary System Plurality System Proportional Representation

Initial Freedom Initial Preference

Δ Freedom Preference

-0.145 4.03††† -0.138 3.84††† -0.150 4.13††† -0.137 3.83††† -0.153 3.90††† -0.152 3.93†††

0.037 3.65††† 0.035 3.36††† 0.037 3.58††† 0.147 2.37†† 0.037 3.60††† 0.037 3.58†††

0.004 0.42 0.001 0.16 0.005 0.56 0.002 0.27 0.003 0.36 0.004 0.41

Institution

0.265 0.99 -0.178 0.24 -0.078 0.35 0.030 0.26 0.049 0.35

Institution * Δ Freedom N Preference

0.077 1.10 0.209 0.54 -0.113 1.80†‡

R2

121

0.396

121

0.415

121

0.405

121

0.416

121

0.398

121

0.398

Note: all specifications include the Year variable (not reported). Each row reports results from separate specifications where each institutional variable is tested in turn. Coefficient estimates are followed by standard t statistics. † denotes significance at the 10% level; †† denotes the 5% significance level; ††† denotes the 1% significance level. ‡ denotes that the interaction term correlates with Δ Freedom at 0.70 or higher. The interaction term is not dropped in this case of high correlation as it does not unduly magnify the standard error of Δ Freedom.

13

The interaction of Constitutional Rigidity and Δ Freedom Preference correlates with Δ Freedom Preference at 0.9438, for example.

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The institutions listed by La Porta et. al. may be important in determining overall levels of economic freedom, but do relatively little to explain changes in economic freedom over time. Only the Case Law variable proves significant at anything more than the ten percent level. And, importantly, the political institutions tested seem to do little either to augment or to mitigate the effects of changes in popular sentiment; when they can be tested, the interaction effects are statistically insignificant. Electoral institutions seem another plausible mechanism by which voter preferences are translated into political outcomes. Persson, Roland and Tabellini (2000) find that electoral mechanisms correlate with policy outcomes. In particular, they find that presidential regimes enjoy smaller governments than parliamentary regimes. Beck et. al. (2001) provide a useful database of worldwide political institutions. As the Database of Political provides observations for 1975 to 1997, I truncate my dataset to eliminate observations from 1999. Results of specifications incorporating electoral institutions are reported in Table 4. None of the institutional terms tested prove significant in explaining changes in economic freedom, either alone or in interaction with the Δ Freedom Preference measure. As was the case in Table 3, interaction terms correlating strongly with the voter preference measure and causing collinearity problems were dropped. Electoral institutions seem not to affect changes in economic freedom scores. More careful analysis of these results is warranted however; in particular, alternate methods should be explored to resolve the collinearity problems encountered in these specifications. The Δ Freedom Preference measure proved quite robust to alternate institutional specifications and to the truncation of post-1997 observations. It remained very stable and highly significant in all specifications tested, with the exception of those in which it was strongly collinear with an interaction term. Changes in voter preferences for economic freedom prove an important determinant of changes in economic freedom rankings.

CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH Economic freedom correlates with desirable outcomes on several dimensions, including per capita GDP, economic growth, poverty abatement, human development, literacy and life expectancy. Given the beneficent effects of economic freedom, we should expect that many studies examine the causes of economic freedom. While several studies have examined economic freedom in cross-sections of countries, there has been relative little work examining changes in economic freedom over time. This paper provides an initial exploration of the determinants of changes in economic freedom over time in the developed world. I construct a panel measure of voter preferences for economic freedom and find that changes in my measure of voter preference correlates strongly with changes in economic freedom. These results are robust to multiple specifications, estimation techniques, and varying institutional control variables. These results are, of course, rather preliminary and many avenues remain to be explored. Other variables, including the cultural variables examined by Norton, have been identified as contributing to economic freedom and should be tested against the voter preference measure. Further institutional variables need to be tested, and more refined estimation techniques

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should be applied. Examination of the causes of changes in economic freedom remains an area almost wholly open for new study. While keeping in mind that much work remains to be done, these preliminary results are rather striking. Voter preferences matter greatly. Increasing economic freedom, and thereby achieving the beneficial outcomes identified by Grubel and others, depends in no small part on changing voter attitudes towards economic freedom.

APPENDIX DATA

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Issue Categories included in Budge et. al. Foreign Special Relationships: Positive Foreign Special Relationships: Negative Anti-Imperialism Military: Positive Military: Negative Peace Internationalism: Positive European Community: Positive Internationalism: Negative European Community: Negative Freedom and Human Rights Democracy Constitutionalism: Positive Constitutionalism: Negative Decentralisation Centralisation Governmental and Administrative Efficiency Political Corruption Political Authority Free Enterprise Incentives Market Regulation Economic Planning Corporatism Protectionism: Positive Protectionism: Negative Economic Goals Keynesian Demand Management

Productivity Technology and Infrastructure Controlled Economy Nationalisation Economic Orthodoxy Marxist Analysis Anti-Growth Economy Environmental Protection Culture Social Justice Welfare State Expansion Welfare State Limitation Education Expansion Education Limitation National way of life: Positive National Way of Life: Negative Traditional Morality: Positive Traditional Morality: Negative Law and Order Social Harmony Multiculturalism: Positive Multiculturalism: Negative Labour Groups: Positive Labour Groups: Negative Agriculture and Farmers Middle Class and Professional Groups Underprivileged Minority Groups Non-economic demographic groups

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Right/Left Issue Categories RIGHT Military: Positive Freedom and Human Rights Constitutionalism: Positive Political Authority Free Enterprise Incentives Protectionism: Negative Economic Orthodoxy Welfare State Limitation National way of life: Positive Traditional Morality: Positive Law and Order Social Harmony

LEFT Anti-Imperialism Military: Negative Peace Internationalism: Positive Market Regulation Economic Planning Protectionism: Positive Controlled Economy Nationalisation Welfare State Expansion Education Expansion Labour Groups: Positive Democracy

Economic Freedom Right/Left categories RIGHT: PRO MARKET Free Enterprise Incentives Protectionism: Negative Economic Orthodoxy Welfare State Limitation

LEFT: PRO-GOVERNMENT Market Regulation Economic Planning Protectionism: Positive Controlled Economy Welfare State Expansion Nationalisation

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Countries included in the Manifesto data set Australia (1946-1998, 22 elections) Austria (1949-1995, 15 elections) Belgium (1946-1995, 17 elections) Canada (1945-1997, 17 elections) Denmark (1945-1998, 22 elections) Finland (1945-1995, 15 elections) France (1946-1997, 14 elections) Germany (1949-1998, 14 elections) Greece (1974-1996, 9 elections) Iceland (1946-1995, 16 elections) Ireland (1948-1997, 16 elections) Israel (1949-1996, 14 elections) Italy (1946-1996, 14 elections)

Japan (1960-1996, 13 elections) Luxembourg (1945-1994, 12 elections) The Netherlands (1946-1998, 16 elections) New Zealand (1946-1996, 18 elections) Norway (1945-1997, 14 elections) Portugal (1975-1995, 9 elections) Spain (1977 – 1996, 7 elections) Sweden (1948-1998, 17 elections) Switzerland (1947-1995, 13 elections) Turkey (1950-1995, 12 elections) Great Britain (1945-1997, 15 elections) United States (1948-1996, 13 elections)

REFERENCES Ali, Abdiweli M. 1997. “Economic Freedom, Democracy and Growth.” Journal of Private Enterprise 13 (Fall): 1-20.

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Beck, Thorsten, George Clarke, Alberto Groff, Philip Keefer and Patrick Walsh. 2001. “New tools in comparative political economy: The Database of Political Institutions.” World Bank Economic Review, 15:1 (September), 165-76. Budge, Ian. 1999. “Estimating Party Policy Positions: From Ad Hoc Measures to Theoretically Validated Standards.” Workshop paper presentation at the Joint Sessions of the European Consortium for Political Research, Workshop #5: “Estimating the policy positions of political actors”. http://www.essex.ac.uk/ecpr/jointsessions/Manpapers/w5/ w5home.htm Budge, Ian, Hans-Dieter Klingemann, Andrea Volkens, Judith Bara, and Eric Tanenbaum, (eds.), 2001. Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945-1998. Oxford: Oxford University Press. Castles, F. G. and P. Mair. 1984. “Left-right political scales: Some ‘expert’ judgments”, European Journal of Political Research 12: 73-88. Caudill, Steven B., Fernando C. Zanella and Franklin G. Mixon, Jr., 2000. “Is Economic Freedom One Dimensional? A Factor Analysis of Some Common Measures of Economic Freedom”, Journal of Economic Development, 25:1 (June), 17-40 Dawson, John W., 1998. “Institutions, Investment, and Growth: New Cross-Country and Panel Data Evidence”, Economic Inquiry, 36 (October), 603-19. De Haan, Jakob and Jan-Egbert Sturm, 2000a. “On the Relationship between Economic Freedom and Economic Growth”, European Journal of Political Economy, 16:2 (June), 215-41. De Haan, Jakob and Jan-Egbert Sturm, 2000b. “Does more democracy lead to greater economic freedom? New evidence for developing countries”, working paper. De Haan, Jakob and Jan-Egbert Sturm, 2001. “How Robust is the Relationship between Economic Freedom and Economic Growth?”, Applied Economics, 33:7 (June), 839-44. De Soto, Hernando. 2000. The Mystery of Capital. New York, Basic Books. Easton, Steven T., and Michael A. Walker (1997). “Income, Growth, and Economic Freedom”, American Economic Review 87:2 (May), 328-32. Grubel, Herbert G., 1998. “Economic Freedom and Human Welfare: Some Empirical Findings”, Cato Journal, 18:2 (Fall), 287-304. Gwartney, James D., Robert A. Lawson, and Randall G. Holcombe, 1999. “Economic Freedom and the Environment for Economic Growth”, Journal of Institutional and Theoretical Economics, 155:4 (December) 643-63. Gwartney, James D., Robert A. Lawson, 2001. “Economic Freedom of the World 2001 Annual Report”. Vancouver, B.C.: Fraser Institute. Heckelman, Jac C., 2000. “Economic Freedom and Economic Growth: A Short-Run Causal Investigation”, Journal of Applied Economics, 3:1 (May), 71-91. Hofferbert, Richard I. and Ian Budge. 1992. “Party Mandate and the Westminster Model: Election Programmes and Government Spending in Britain, 1948-85”, British Journal of Political Science, 22:3 (April) 151-82. Kim, Hee-Min and Richard C. Fording. 1998. “Voter Ideology in Western Democracies, 1946-1989”, European Journal of Political Research, 33: 73-97. Kim, Hee-Min and Richard C. Fording. 2001a. “Extending Party Estimates to Governments and Electors”, pp.157-177 (Chapter 8) in Budge et al., 2001.

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Kim, Hee-Min and Richard C. Fording. 2001b. “Voter Ideology, the Economy, and the International Environment in Western Democracies, 1952-1989”, Political Behavior 23:1 (March) 120-40. La Porta, Rafael, Florencio López-de-Silanes, Christian Pop-Eleeches, and Andrei Shleifer, 2002. “The Guarantees of Freedom”, working paper. Available at http://pos t.economics.harvard.edu/faculty/laporta/papers/freedom.pdf Laver, Michael and Ian Budge, (ed.). 1992. Party Policy and Government Coalitions. London: St. Martin’s Press. Laver, Michael and Ian Budge. 1993. Party Policy and Coalition Government in Western Europe. London: Macmillan. Laver, Michael and John Garry. 2000. “Estimating Policy Positions from Political Texts”, American Journal of Political Science 44:3 (July) 619-34. Norton, Seth W. 2002. “Ethnicity, Religion, and Aggregate Property Rights: A CrossNational Study”, working paper. Available at www.isnie.org/ISNIE98/Norton.doc Stevenson, Randolph T. 2001. “The Economy and Policy Mood: A Fundamental Dynamic of Democratic Politics?”, American Journal of Political Science, 45: 620-33. Stimson, J.A. 1991. Public Opinion in America: Moods, cycles, and swings. Boulder, CO: Westview Press.

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Chapter 11

PATENT RIGHTS AND ECONOMIC FREEDOM: FRIEND OR FOE?1 Walter G. Park

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INTRODUCTION The purpose of this paper is to examine the relationship between patent rights and economic freedom. Are patent rights associated with a higher or lower level of economic freedom? What does this association or relationship depend on? How do patent rights affect the functioning of markets? How might patent systems be reformed so as to be more conducive to economic freedom? These questions are of interest in light of the changes in patent laws that are occurring around the world (as nations reform patent systems in accordance to international agreements2 and in response to new technological developments (in computer software, internet, biotechnology, and so forth)). A concern is that a strengthening of patent systems comes at a cost - namely reduced market competition and distorted prices (above marginal costs), among other things. But free enterprise and economic freedom are also valued for their contributions to economic efficiency and welfare. Thus, to the extent that patent rights detract from economic freedom, some tradeoffs must be accepted (it is argued) as far as technological progress is concerned. Hence, the issue is whether in exchange for technological progress markets must be less free. There is very little research on this topic,3 particularly empirical analysis. This paper exploits empirical measures of economic freedom and patent protection to examine their relationship. The purpose is not so much to draw strong conclusions about causality as to pave the way for more research and analysis. The subject of the relationship between economic freedom and patent rights is controversial, yet has not been formally investigated This aricle originally appeared as Walter G. Park, “Patent Rights and Economic Freedom: Friend or Foe?” Journal of Private Enterprise vol. 18, no. 1 (2002): 84-121. The author is an associate professor of economics at American University. 2 For example, the World Trade Organization’s Trade-Related Intellectual Property Rights Agreement (TRIPs). 3 With the exception of Bethune (1993), though his focus is on copyrights - not patents. 1

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(despite the availability of data). Is it a forgone conclusion that patent rights vary inversely with economic freedom? This paper argues that this is not the case: the two are found to be complements (not substitutes). The paper is organized as follows: the next section discusses what patent rights are and what they are not. There are several misconceptions about patent rights which lead to a misunderstanding of how they affect markets. The section following it provides the empirical analysis. The analysis centers around correlations between measured levels of economic freedom and patent rights (including some tests of sensitivity to alternative measures of economic freedom and to third factor influences). The last section summarizes the results and provides some thoughts on where reforms in patent systems are needed. The importance of this last discussion is in suggesting where economic freedom could be adversely affected by inefficiencies in the patent system, thereby shifting the focus away from whether intellectual property rights should be granted (i.e. the “why”) to the manner in which such property rights are granted (i.e. the “how”).

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PATENT RIGHTS: SOME MISCONCEPTIONS First, it is important to recognize that patent rights are one of several kinds of intellectual property rights (IPRs). It is not uncommon in the literature to find the terms ‘patent rights’ and ‘IPRs’ erroneously used interchangeably. There are different kinds of intellectual property rights (such as copyrights, trademark rights, geographic indications, industrial designs, and so forth) that perform different functions. The focus in this paper is on patent rights, which typically protect inventions (or ideas that produce a “technical effect”). The logic of patent protection is discussed extensively in the literature.4 It is therefore best here to clarify a few points. First, in the absence of a patent system, markets for ideas would be ‘missing’ due to the public good nature of knowledge. A patent system therefore creates a market which would otherwise not exist. The patent right provides three essential ways for patent owners to profit from their inventions: the first is the exclusive rights to manufacture or use their inventions; the second is the right to license others to manufacture or use the invention; and the third is to sue for damages if infringement occurs. The market in question, however, will not be perfectly competitive, given that the patent owner has exclusive rights to the invention. There is no free entry and exit of other suppliers to drive prices down to marginal costs. Without positive economic profits, however, inventors might not otherwise be able to recoup their upfront fixed research and development (RandD) costs, given how relatively cheaply the output can be reproduced by imitators. Hence the classic tradeoff between technology creation and diffusion: patent systems must provide on the one hand adequate incentives for technology creation and on the other hand opportunities for competitive, efficient diffusion. Thus dynamic efficiency (i.e. a positive long run rate of innovation) is achieved at the expense of static inefficiency (i.e. non-competitive pricing and supply). Unfortunately, this characterization of the patent system often creates some misconceptions. The first is that patents create monopolies, in the traditional sense of a single firm in an industry. Rather, the patent gives the holder the right to exclude others from using 4

See, for example, Kaufer (1989).

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the new idea commercially; it does not allow the holder to exclude other firms from the industry in which it serves. A more appropriate paradigm would be that of a monopolistic competitive industry, as Romer (1990) models innovation, where there are many agents in an industry, each producing a differentiated innovation. Indeed, it is relatively infrequent for an agent to have patent rights over an entire (self-contained) product - for example, a cell phone, television, motor vehicle, and so forth. Most inventors own patent rights to pieces that make up a product (or process of production) - for example, technological components. Exceptions exist of course; for example, pharmaceutical firms do often own patents to an entire product (e.g. Prozac, Claritin, etc.). In such industries, there is likely to be free entry and exit in the sense that these differentiated inventions compete with one another (say solve similar problems in different ways). They may also compete against old goods. Consumers, for example, may not upgrade to the new technology if the price gap does not justify the quality difference. The exclusive right is temporary. In most jurisdictions, the maximum length of protection is twenty years from the date of filing a patent application. But most patent lives are much less than that. The reason is that patent owners must renew their patent rights at particular time intervals (e.g. every year, other year, five years, etc. depending on the jurisdiction). The patent right lapses unless it is renewed with fees paid. In practice, most patent rights are not renewed beyond ten years from the date of application because of such factors as technological obsolescence and falling market value (Cornelli and Schankerman, 1999).5 A second misconception is that the tradeoff is between technology creation and knowledge diffusion; rather it is between the former and the diffusion (or supply) of output embodying the new knowledge. Patents do not restrict the diffusion of knowledge; rather, they help diffuse it. The reason is that, in exchange for patent protection, inventors must publicly disclose their new knowledge. In some countries, the disclosure occurs 18 months after an inventor files for a patent, and in other countries, the disclosure occurs after - and only if - the patent is granted. For this reason, it is inaccurate to suggest that patents restrict access to knowledge. On the contrary, patent databases exist all over the world for researchers and practitioners to access. The databases are filled with detailed technical information (including drawings) about previous patents, and the information is supplied both publicly by patent offices and privately by firms that specialize in database services. Because of the internet, access to patent information is much easier and cheaper than it used to be. A third misconception is that patent protection may impede future scientific research because, even though technical information is fully disclosed, the fact is that only a few (patent owners) have the right to use it. This view comes from a failure to distinguish between basic RandD and applied RandD. Of course granting protection very broadly to basic scientific knowledge can impact negatively on future research. After all, basic research is the foundation for applied research as well as for future basic research. Applied research can also generate future knowledge externalities, but to a lesser extent than basic research. However, in actuality, basic scientific discoveries, theoretical concepts, and mathematical principles are

5

Pharmaceutical patents, however, can last more than 20 years, since some drug manufacturers receive extensions. They argue that it takes considerable time to obtain marketing approval from drug regulatory agencies -- say 8 years -- time which otherwise reduces their effective patent life.

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not patentable anyway. It is the commercially or industrially applicable ideas developed out of that research or knowledge that are patentable.6 A fourth misconception relates to the use of the term ‘imitation’ in the literature on innovation versus imitation (Helpman, 1993 and Segerstrom, 1991). Innovation is regarded as an activity that generates new knowledge while imitation is an activity that helps diffuse that knowledge. Given scarce resources, it is often suggested that there exists a tradeoff between innovation and imitation. Both are ways for nations to acquire technology. As far as stimulating economic development and/or maximizing social welfare is concerned, it is argued that there exists some “optimal” mixture of innovation and imitation. This mixture varies upon country characteristics and preferences. Hence, to enable the optimal level of imitation, the strength of patent protection needs to be adjusted accordingly. Aside from the ethical and moral issues of treating ‘imitation’ on a par with innovation (which will be taken up below), a definitional issue arises. What is meant by imitation? This is not always clear in the debate. In some respects, the innovation versus imitation dichotomy is a false one. That is, one cannot have innovation without imitation; what makes a work original (whether it is an invention, song, or economics journal article) is not the whole of the work but the value added part. There will always be some part of past knowledge incorporated in the work. Moreover, imitation (to some people) is the very essence of learning (the way people learn their alphabets and periodic tables). The real issue is not innovation versus imitation but rather innovation versus infringement - the misappropriation of someone else’s idea or creation, or the exploitation, or free-riding off, of someone else’s effort. Of course it is cheaper to infringe than to innovate to take someone else’s output rather than to work and buy one’s own - but is this the most conducive to economic development or social welfare? Should infringement or theft be characterized as part of an alternative economic development model? This is the implied reference in the innovation versus imitation (a.k.a. infringement) debate that has some moral and ethical implications, particularly the treatment of property rights infringement or expropriation as just another policy tool. The “wrong” ethical message is sent in suggesting that the acquisition of technological capacity can occur through infringement. Of course it can, but it is an inefficient strategy of economic development. A general mood of disrespect for law and order is not conducive to market development. What is likely to occur (and what seems to be assumed away) is an inward shift in society’s production possibilities frontier, rather than a movement along a fixed frontier as the mixture of innovation and infringement in society is varied. Some practical issues should also be considered. If individuals or firms, say, in a developing nation have the capacity to infringe, they typically have the capacity to innovate. While duplicating cassette tapes or video tapes is a simple task, certain other activities like reverse engineering a patented technology requires some technical sophistication. Thus developing nations (such as Brazil, India, or China) that succeed in producing and distributing patent-infringing goods usually reveal a capacity to innovate but also reveal an environment 6

This is not to say that no strategic abuses of patent rights occur. Firms may file numerous patents around a technology just to pre-empt rivals from developing competing technologies. Such patents are known as ‘blocking’ patents. However, this concern is a “red herring.” It is not about patent laws per se but about firm behavior. Abuses can occur with a variety of business strategies, such as advertising, vertical restraints, RandD, etc. Such practices are typically a matter for, and governed by, competition policy laws, which lie outside the scope of this paper.

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where the relative economic rewards of infringement are greater than that from innovation (say because the punishment is too weak and/or the protection afforded innovative works too low). In other words, the net social gain from innovation may be positive, but the private incentives to infringe are greater than those to innovate. Another practical factor is that the patent-infringing sectors are not always competitive, but oligopolistic. Several studies find that weaker patent protection does not necessarily result in lower prices (Sherwood, 2000). To summarize the discussion thus far, patent protection exists to create and facilitate a market for innovation. Many of the concerns about the effects of patent protection on monopoly power, knowledge diffusion, future research, and technological acquistion, transfer, and development are generally misconceptions. The next section turns to some empirical observations on how patent rights and economic freedom relate.

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PATENT RIGHTS AND ECONOMIC FREEDOM The availability of data on both patent rights and economic freedom permits an investigation of how the two measures relate across countries. Many researchers have exploited the data separately to study the effects on economic development, growth, trade, and other variables, but have not studied the interrelationship between the two. Given both their importance to the functioning of markets, it would be useful to fill this void in the literature. The objective here is not to provide a comprehensive analysis, but to help initiate further inquiry. A detailed description of the economic freedom index can be found in Gwartney and Lawson (2000), and a detailed description of the patent rights index can be found in Ginarte and Park (1997) and Park, Vijaya, and Wagh (2000). The patent rights index is a measure of the strength of patent protection, not the quality of patent regimes. (It is possible that stronger need not imply better, from a social welfare point of view. The welfare effects are the subject of much controversial debate.) The patent rights index is also largely a measure of the statutory level of protection. However, even then, statutes (or laws on the books) are found to play a role -- even if a ‘signaling role’. Specifically, strong laws provide signals to inventors of the system’s willlingness and capacity to enforce patent rights. Ex post failures to enforce rights diminish the credibility and reputation of the legal authorities, as information on deviations from statutory protection becomes widely known and incorporated by market participants. Secondly, the statutory measures of protection tend to be highly correlated with measures of enforcement based on experiences and expert opinion, as obtained through surveys of firms (see Mansfield, 1994, Park, 2001a, and Sherwood, 1997). The index of patent rights for each country varies from zero to five, with higher values indicating stronger levels of protection. The index contains five categories, each of which is scored from zero to one. The score reflects the percentage or fraction of legal features in that category that are available in the country. The five categories are: (i) coverage, (ii) membership in international treaties, (iii) restrictions, (iv) enforcement mechanisms, and (v) duration of protection. Briefly, coverage refers to the type of inventions that are patentable; membership in international treaties refers to whether a country is a signatory to some of the key international agreements in patent law; restrictions refer to whether limitations on the exercise of exclusive rights exist (such as compulsory licensing); enforcement deals with the

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different legal mechanisms for enforcing patent rights; and duration refers to the maximum length of protection.

Descriptive Statistics and Correlations Tables 1 and 2 show the values of the economic freedom and patent rights index for 99 countries for the years 1980 and 1995. In Table 1, the countries are sorted in ascending order of Gross Domestic Product (GDP) per person (averaged 1988-1992), and are placed into three groups (low income, medium, and high income).7 In Table 2, the countries are sorted in ascending order of growth rate during the sample period, and are placed into three groups (slow growth, medium, and fast growth).8 In each table, and for each subgroup of countries, the following sample statistics are provided: means, standard deviations, skewness, and coefficients of variation.9 Table 3 provides measures of the simple correlation between patent rights and economic freedom, and the level of statistical significance of those correlations (based on regression analyses).

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Table 1. Economic Freedom and Patent Rights Grouped by Income (GDP per capita) LOW-INCOME Chad Malawi Niger Mali Tanzania Myanmar Uganda Burundi C. African Rep. Togo Madagascar Rwanda Haiti Sierra Leone Ghana Kenya Benin Nigeria Senegal Zimbabwe Cameroon Guyana

PAT80 2.71 3.04 2.24 1.90 2.90 0.00 2.57 2.86 2.57 2.24 1.86 2.52 3.19 2.52 2.90 2.57 2.52 3.05 2.24 2.90 2.57 1.42

PAT95 2.71 3.24 2.57 2.57 2.90 0.00 2.90 2.86 2.57 2.57 2.27 2.86 3.19 2.52 2.07 2.90 2.86 3.05 2.57 2.90 2.57 1.42

ECON80 4.16 5.14 5.45 4.21 2.63 2.64 3.31 5.24 4.12 3.98 3.62 5.24 4.17 1.95 4.58 5.29 3.07 4.88 4.29 5.08 4.15

ECON95 5.28 4.09 4.04 5.43 4.89 2.23 4.86 3.64 4.60 5.11 4.44 3.91 5.59 3.77 6.00 5.73 4.87 3.42 3.71 5.42 5.30 5.19

GDPC 409 516 521 525 534 536 548 554 563 611 657 751 842 874 881 909 946 981 1144 1170 1211 1230

7 The GDP per capita figures are in real 1985 PPP U.S. dollars. The source is Summers et. al. (1996), Penn World Tables, Version 5.6a. The reason the GDP per capita figures are averaged is to smooth out business cycles. The data in Summers et. al. (1996) go up to 1992. 8 The growth rate refers to the average annual growth rate of GDP per capita (in real 1985 U.S. dollars) during the period 1975-1992. 9 The coefficient of variation is the standard deviation divided by the mean, and measures the relative variability in the data. The skewness helps measure the extent to which the data are concentrated above or below the mean: Skewness = (Mean - Median)/Standard Deviation. A negative skew indicates that the sample is concentrated near the top of the distribution (e.g. in a large class of students, most students earn A’s while few earn C’s); a positive skew indicates that the sample is concentrated near the bottom of the distribution (say, where most students earn C’s and few earn A’s).

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Patent Rights and Economic Freedom: Friend or Foe? LOW-INCOME Cote d'Ivoire India Nicaragua China Honduras Pakistan Bangladesh Pap. New Guinea Bolivia Philippines El Salvador Mean Std Dev Skewness Coef of Variation

PAT80 2.52 1.62 0.92

MEDIUM-INCOME Egypt Indonesia Romania Paraguay Sri Lanka Guatemala Morocco Dominican Rep. Peru Botswana Jamaica Algeria Ecuador Tunisia Panama Jordan South Africa Colombia Iran Costa Rica Thailand Turkey Gabon Fiji Syria Brazil Poland Chile Uruguay Argentina Malaysia Hungary Mexico Mean Std Dev Skewness Coef of Variation

PAT80 1.99 0.33

HIGH-INCOME Mauritius Bulgaria Malta

PAT80 2.89

1.76 1.99 1.99 0.00 1.98 2.67 2.19 2.22 0.76 -1.57 0.34

1.80 2.79 1.08 2.38 2.41 1.02 1.90 2.86 3.38 1.54 1.90 2.41 1.86 3.57 1.12 2.38 1.94 1.85 1.80 2.57 2.01 2.46 1.85 2.41 2.26 2.26 2.57 1.40 2.07 0.67 -0.21 0.32

1.89

151

PAT95 2.52 1.51 0.92 1.55 2.10 1.99 2.32 0.00 2.31 2.67 2.86 2.21 0.76 -0.90 0.34

ECON80 4.75 4.39 3.70 3.21 5.43 3.46 2.88 4.63 4.12 5.03 3.70 4.14 0.90 -0.48 0.22

ECON95 5.46 4.49 5.55 5.09 7.20 5.26 4.22 5.92 7.71 7.21 7.86 5.07 1.21 0.38 0.24

GDPC 1246 1247 1349 1375 1395 1398 1409 1546 1682 1712 1843 1004 407 0.29 0.41

PAT95 1.99 1.24 2.71 2.80 3.12 1.08 2.38 2.41 2.71 1.90 2.86 3.38 2.71 1.90 3.52 2.19 3.57 2.57 2.38 1.80 2.24 1.80 2.57 2.01 2.46 3.05 2.90 3.07 2.60 3.19 2.85 3.37 2.86 2.55 0.60 -0.48 0.24

ECON80 4.03 5.15

ECON95 5.34 6.83 4.23 7.40 6.23 7.89 6.21 6.04 7.19 6.13 6.98 3.35 6.70 5.42 8.16 5.83 5.99 5.30 4.27 7.12 7.34 6.24 5.32 6.02 3.52 5.02 6.28 7.93 6.90 7.52 7.44 6.88 7.01 6.24 1.20 -0.71 0.19

GDPC 1899 1935 2066 2096 2112 2157 2171 2253 2284 2304 2490 2766 2808 2877 2984 3141 3241 3299 3411 3462 3500 3609 3678 3739 3946 4082 4082 4410 4783 4987 5085 5219 5803 3293 1058 0.60 0.32

PAT95 2.89 2.57 1.89

ECON80 4.53

ECON95 7.49 5.08 6.73

GDPC 5873 6055 6373

6.06 4.30 6.46 3.87 5.42 3.34 4.99 3.91 4.37 5.86 4.45 6.66 5.38 6.00 4.50 3.35 5.43 5.81 3.57 3.57 5.16 3.67 4.21 6.00 6.22 4.66 7.03 4.52 5.07 4.94 1.03 0.20 0.21

5.06

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Table 1. (Continued) HIGH-INCOME South Korea Venezuela Greece Portugal Trinidad and Tob. Cyprus Ireland Israel Spain New Zealand Singapore Italy Austria Netherlands United Kingdom Belgium Iceland Finland France Denmark Germany Japan Sweden Australia Norway Hong Kong Switzerland Luxembourg Canada United States Mean Std Dev Skewness Coef of Variation

PAT80 3.28 1.35 2.46 1.98 3.01 2.24 2.99 3.57 3.29 3.32 2.57 3.71 3.81 4.24 3.57 3.38 2.12 2.95 3.90 3.62 3.86 3.94 3.47 3.23 3.29 2.24 3.80 3.05 2.76 4.19 3.12 0.71 -0.62 0.23

PAT95 4.20 2.90 2.65 2.98 3.35 2.24 3.32 3.57 4.05 3.86 3.90 4.19 4.57 4.38 3.57 3.90 2.45 4.19 4.05 4.05 3.86 3.94 4.24 3.86 3.90 2.57 3.91 3.05 3.57 4.86 3.56 0.71 -0.57 0.20

ECON80 5.78 6.97 5.66 5.64 4.80 5.74 6.58 3.59 6.15 6.38 8.04 5.56 6.67 7.75 6.60 7.84 5.34 6.91 6.31 6.49 7.69 7.48 6.10 7.37 6.03 9.65 8.32 8.87 7.90 8.35 6.63 1.32 0.07 0.20

ECON95 6.96 4.19 7.19 7.92 6.84 6.29 8.58 5.85 7.96 8.97 9.36 7.22 7.64 8.42 8.72 8.17 7.86 7.93 7.88 8.00 8.02 8.06 7.87 8.39 7.86 9.73 8.28 8.30 8.04 8.74 7.71 1.12 -1.25 0.15

GDPC 6406 6488 6686 6688 7977 8278 8946 9339 9430 11439 11592 12395 12545 12837 12994 13050 13259 13265 13719 13850 14218 14247 14440 14543 14954 15030 16161 16180 16937 17880 11639 3562 -0.28 0.31

ALL COUNTRIES Mean Std Dev Skewness Coef of Variation

PAT80 2.48 0.85 -0.41 0.34

PAT95 2.81 0.88 -0.42 0.31

ECON80 5.24 1.51 0.50 0.29

ECON95 6.34 1.60 -0.21 0.25

GDPC 5312 5054 1.01 0.95

Notes: PAT__ – Index of Patent Rights in Year 1980 or 1995. ECON__ – Index of Economic Freedom in Year 1980 or 1995. GDPC – GDP per capita in real 1985 U.S. PPP dollars (Average of 1988-1992).

The focus of this section is on the correlation between patent rights and economic freedom, but the distributional characteristics of each index and the trends in each index are worth examining first. Generally the richer economies have higher levels of patent protection and economic freedom. The fastest growing economies do not, however, have the highest average levels of patent rights and economic freedom. The fastest growing economies are the smaller, medium income countries that are largely growing faster because they have a smaller stock of accumulated capital (i.e. less diminishing returns have set in).

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The gap or variation in patent rights and economic freedom across countries has generally decreased over the period (judging by the coefficients of variation) for the sample as a whole. The reduction in the variability of economic freedom is more prominent, suggesting a greater ‘catch-up’ in levels of economic freedom than in patent rights. The variation in economic freedom is lowest among high-income countries. The variation in patent rights is greatest among medium-income countries. This group is interesting because the countries it consists of are likely to be at the stage of development where crucial choices must be made between innovation and imitation. Thus it should not be too surprising to find a greater mixture of countries that pursue either the innovation route (and defend patent rights relatively strongly) or the imitation route (and provide patent rights relatively weakly). According to the skewness estimates, countries are concentrated at relatively high levels of patent rights. That is, most countries are providing levels of protection above the mean (both in 1980 and 1995). However, in terms of economic freedom, most countries had levels below the mean in 1980 and above the mean in 1995, which is consistent with the earlier observation that there has been greater growth and catching up in economic freedom levels.10 Table 2. Economic Freedom and Patent Rights, Grouped by Growth Rate

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SLOWEST Guyana Nicaragua Cote d'Ivoire Iran Gabon Chad Madagascar Nigeria Sierra Leone C. African Rep. Peru Niger Argentina Trinidad and Tob. Venezuela Poland El Salvador Bolivia Pap. New Guinea Guatemala Haiti Togo Benin Ghana Uganda Jamaica Ecuador South Africa Costa Rica Honduras Zimbabwe 10

PAT80 1.42 0.92 2.52 2.38 2.57 2.71 1.86 3.05 2.52 2.57 1.02 2.24 2.26 3.01 1.35 2.19 1.98 0.00 1.08 3.19 2.24 2.52 2.90 2.57 2.86 1.54 3.57 1.94 1.76 2.90

PAT95 1.42 0.92 2.52 2.38 2.57 2.71 2.27 3.05 2.52 2.57 2.71 2.57 3.19 3.35 2.90 2.90 2.86 2.31 0.00 1.08 3.19 2.57 2.86 2.07 2.90 2.86 2.71 3.57 1.80 2.10 2.90

ECON80 4.15 3.70 4.75 3.35 3.57 3.98 3.07 4.17 5.24 3.34 5.14 4.66 4.80 6.97 3.70 4.12 4.63 6.46 5.24 4.12 5.29 1.95 2.64 3.91 5.86 6.00 5.43 5.43 4.29

ECON95 5.19 5.55 5.46 4.27 5.32 5.28 4.44 3.42 3.77 4.60 7.19 4.04 7.52 6.84 4.19 6.28 7.86 7.71 5.92 7.89 5.59 5.11 4.87 6.00 4.86 6.98 6.70 5.99 7.12 7.20 5.42

GROWTH -4.536 -4.320 -3.853 -3.467 -3.427 -3.077 -3.015 -2.797 -2.169 -2.161 -2.084 -1.722 -1.633 -1.601 -1.528 -1.315 -1.283 -1.258 -1.239 -1.115 -0.851 -0.812 -0.793 -0.536 -0.514 -0.494 -0.477 -0.464 -0.337 -0.316 -0.263

Within income-groups, however, economic freedom is skewed positively; that is, a few countries in each group have levels of economic freedom well above the within-group average.

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Table 2. (Continued) SLOWEST Syria Philippines Mean Std Dev Skewness Coef of Variation

PAT80 2.46 2.67 2.21 0.76 -0.90 0.34

PAT95 2.46 2.67 2.47 0.72 -1.65 0.29

ECON80 3.67 5.03 4.47 1.09 0.04 0.24

ECON95 3.52 7.21 5.74 1.30 0.01 0.23

GROWTH -0.256 -0.213 -1.63 1.25 -0.85 -0.77

MEDIUM Malawi Panama Senegal Mali Kenya Uruguay Brazil Tanzania Paraguay Fiji Dominican Rep. Algeria Hungary Rwanda Mexico Jordan New Zealand Burundi Myanmar Netherlands Greece Sweden United States Colombia Switzerland Turkey Australia Morocco France Cameroon Israel Denmark Belgium Mean Std Dev Skewness Coef of Variation

PAT80 3.04 2.41 2.24 1.90 2.57 2.26 1.85 2.90 1.80 2.01 2.41 3.38 2.52 1.40 1.86 3.32 2.86 0.00 4.24 2.46 3.47 4.19 1.12 3.80 1.80 3.23 2.38 3.90 2.57 3.57 3.62 3.38 2.64 0.93 -0.48 0.35

PAT95 3.24 3.52 2.57 2.57 2.90 2.60 3.05 2.90 2.80 2.01 2.41 3.38 3.37 2.86 2.86 2.19 3.86 2.86 0.00 4.38 2.65 4.24 4.86 2.57 3.91 1.80 3.86 2.38 4.05 2.57 3.57 4.05 3.90 3.05 0.90 -0.81 0.30

ECON80 4.16 6.66 4.88 5.45 4.58 6.22 4.21 4.21 6.06 5.16 5.42 4.37 4.52 3.62 5.07 5.38 6.38 3.31 2.63 7.75 5.66 6.10 8.35 4.50 8.32 3.57 7.37 3.87 6.31 5.08 3.59 6.49 7.84 5.37 1.47 0.39 0.27

ECON95 4.09 8.16 3.71 5.43 5.73 6.90 5.02 4.89 7.40 6.02 6.04 3.35 6.88 3.91 7.01 5.83 8.97 3.64 2.23 8.42 7.19 7.87 8.74 5.30 8.28 6.24 8.39 6.21 7.88 5.30 5.85 8.00 8.17 6.27 1.75 -0.39 0.28

GROWTH -0.189 -0.038 0.002 0.245 0.341 0.350 0.360 0.410 0.412 0.456 0.528 0.559 0.631 0.701 0.754 0.804 0.820 1.197 1.252 1.292 1.388 1.457 1.480 1.521 1.536 1.550 1.558 1.569 1.664 1.696 1.798 1.823 1.835 0.96 0.61 -0.16 0.63

FASTEST Canada Tunisia Germany Spain Austria Egypt United Kingdom Chile Iceland

PAT80 2.76 1.90 3.86 3.29 3.81 1.99 3.57 2.41 2.12

PAT95 3.57 1.90 3.86 4.05 4.57 1.99 3.57 3.07 2.45

ECON80 7.90 4.45 7.69 6.15 6.67 4.03 6.60 6.00 5.34

ECON95 8.04 5.42 8.02 7.96 7.64 5.34 8.72 7.93 7.86

GROWTH 1.886 1.907 1.930 1.972 2.009 2.154 2.180 2.183 2.215

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Patent Rights and Economic Freedom: Friend or Foe? Italy Finland Norway Bangladesh Pakistan Ireland Luxembourg Portugal Mauritius Botswana Sri Lanka Romania India Bulgaria Japan Malaysia Malta China Indonesia Thailand Cyprus Singapore Hong Kong South Korea Mean Std Dev Skewness Coef of Variation

3.71 2.95 3.29 1.99 1.99 2.99 3.05 1.98 2.89 1.90 2.79

5.56 6.91 6.03 2.88 3.46 6.58 8.87 5.64 4.53 4.99 4.30

0.33 1.85 2.24 2.57 2.24 3.28 2.59 0.80 -0.35 0.31

4.19 4.19 3.90 2.32 1.99 3.32 3.05 2.98 2.89 1.90 3.12 2.71 1.51 2.57 3.94 2.85 1.89 1.55 1.24 2.24 2.24 3.90 2.57 4.20 2.92 0.91 0.03 0.31

ALL COUNTRIES Mean Std Dev Skewness Coef of Variation

PAT80 2.48 0.85 -0.41 0.34

PAT95 2.81 0.88 -0.42 0.31

1.62 3.94 2.57 1.89

7.48 7.03 5.06 3.21 5.15 5.81 5.74 8.04 9.65 5.78 5.87 1.59 0.29 0.27

7.22 7.93 7.86 4.22 5.26 8.58 8.30 7.92 7.49 6.13 6.23 4.23 4.49 5.08 8.06 7.44 6.73 5.09 6.83 7.34 6.29 9.36 9.73 6.96 7.02 1.44 -0.41 0.21

2.335 2.389 2.458 2.639 2.662 2.728 2.875 2.897 2.915 3.003 3.016 3.034 3.091 3.466 3.495 3.750 3.980 4.122 4.566 4.659 5.001 5.096 5.851 6.452 3.18 1.18 1.16 0.37

ECON80 5.24 1.51 0.50 0.29

ECON95 6.34 1.60 -0.21 0.25

GROWTH 0.84 2.23 -0.12 2.67

4.39

155

Notes: GROWTH – Average Annual Growth Rate of Real GDP per capita in real 1985 PPP U.S. dollars) 1975 – 1992. For all other variables, see Notes to Table 1.

For the sample as a whole, economic freedom and patent rights are positively correlated (with a correlation coefficient of 0.396 in 1980 and 0.477 in 1995 -- see Table 3). Of course, this indicates nothing about the direction of causality. But what is interesting is that the correlation between the two indexes varies by country grouping. For instance, if countries are grouped according to income, the correlation is negative for the medium-income group. However, as Part B of Table 3 indicates, these correlations (by income group) are not statistically significant. This conclusion is based on running regressions of the economic freedom index on the patent rights index.11 In this two-variable regression, the estimated coefficients mirror the simple correlations. In Table 3, part B, only the estimated slopes, tstatistics, and goodness-of-fit are reported. For all countries (pooled), the correlation between patent rights and economic freedom is positive and statistically significant at conventional levels. This is the case in 1980 and in 1995. But when the sample is grouped by income level,

11

If the regression is reversed -- i.e. patent rights are regressed on economic freedom -- the results are qualitatively similar.

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the within-group correlations between the two indexes (whether positive or negative) are weak (except in the case of high-income countries in 1995). Table 3. Correlation between Economic Freedom and Patent Rights A. Simple Correlations All Countries By Income Level: Low Medium High By Growth Rate: Slowest Medium Fastest

1980 0.396

# of Obs. 93

1995 0.477

# of Obs. 99

0.079 -0.02 0.169

31 30 32

0.74 -0.015 0.375

33 33 33

-0.169 0.587 0.443

31 32 30

-0.086 0.675 0.591

33 33 33

R2 0.157

1995 0.264

(5.34)

R2 0.227

0.006 0.0004 0.029

0.048 -0.007 0.239

(0.41) (-0.08) (2.25)

0.006 0.0002 0.14

0.029 0.344 0.196

-0.048 0.349 0.372

(-0.48) (5.09) (4.08)

0.008 0.456 0.349

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B. Regression Analysis Regression Equation: Econ___= a +  Pat___ + error Estimates of  (T-Statistic) by year and associated R2: 1980 All Countries 0.244 (4.11) By Income Level: Low 0.067 (0.43) Medium -0.013 (-0.11) High 0.09 (0.94) By Growth Rate: Slowest -0.119 (-0.93) Medium 0.367 (3.97) Fastest 0.23 (2.61)

Note: These are the same grouping of countries (by income level or growth rate) used in previous tables. In Part B, the coefficient estimates of the constant are not reported; only the slope  and its t-statistic (in parentheses). The number of observations in each regression would be the same as indicated in part A. Econ__ and Pat__ are the respective indexes in year 1980 or 1995. The results are qualitatively the same if the LHS and RHS variables are switched.

However, when the sample is grouped by growth rates, the within-group correlation between economic freedom and patent rights is positive and statistically significant for both the fast-growth economies and medium-growth economies. This is shown in both Parts A and B of Table 3. For the slow-growth countries, the measured correlation is negative but not statistically significant. Thus for countries with medium to fast GDP growth rates during the sample period, patent rights and economic freedom co-vary positively. Why the two different ways of grouping countries (either by income level or by growth rate) produce different results is of interest. First, the two ways of grouping do not perfectly overlap: a number of low and medium income countries are among the fastest growing economies.12 Secondly, the co-movement of patent rights and economic freedom must have more to do with economic expansion than with levels of economic development - that is, with factors such as savings, investment rates, human capital accumulation, and so forth, rather

12

This is consistent with the “convergence” literature which finds that conditional on human capital, and other factors, the growth rate is inversely related to the level of economic development (see Mankiw et. al. (1992)).

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than with perhaps the more complex, heterogeneous circumstances or historical processes which brought economies to their current state.13 Thus, though more rigorous study is desired, the results seem to suggest that in regions where economic freedom and patent protection go together, countries grow faster. Again this says nothing about causality, about which further research is needed, particularly theoretical analyses to guide the empirical research. The exercise thus far has been ‘measurement without theory’. In any event, one possibility is that economic growth, by expanding national production possibility frontiers, relaxes resource constraints and enables countries to “afford” an expansion in both patent rights and economic freedom. The other (reverse) possibility is that an increase in both patent rights and economic freedom together stimulate economic growth. That is, as determinants of growth, the two contribute as complementary factors. Patent rights, for example, may protect inventors from the misappropriation of their results, while economic freedom may enable them to better market their inventions, raise capital, and so forth. The complementarity between patent rights and economic freedom might also account for why in slow growing economies, the correlation between economic freedom and patent rights is not significant. In general, the slow-growth economies tend not to invest in both economic freedom and patent rights, if at all.

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Sensitivity Analyses Thus far, economic freedom and patent rights are found to vary positively, at least among countries with positive growth rates during the sample period. This section is devoted to examining the sensitivity of this result to (a) alternative measures of economic freedom and (b) third factor influences. The idea behind the latter is that the observed correlations between economic freedom and patent rights may be spurious if there exist other variables (‘third factors’) that drive both economic freedom and patent rights First, consider two alternative measures of economic freedom: the first is the Heritage Foundation Index of Economic Freedom (henceforth denoted by HERIT) and the second is the Freedom House Index of Economic Freedom (henceforth denoted by FH). All three indexes (including the Gwartney and Lawson index, denoted by ECON) are independently assessed measures of economic freedom. Though there are some overlapping components, somewhat different factors are also incorporated (O’Driscoll et. al., 2001 and Messick, 1996). Thus far this paper focused on the Gwartney and Lawson index instead of the Freedom House version because the former contains more observations, and instead of the Heritage Foundation index because the Gwartney and Lawson measure goes back farther in time. Table 4, part A presents some sample statistics. There is indeed a high correlation among all three indexes of economic freedom (HERIT, FH, and ECON).14 The main differences are that 13

14

Indeed, in Park (2001b), it is pointed out that both economic freedom and patent rights are ‘flow’ variables - as opposed to stocks. That is, they represent levels in specific time periods, not the cumulative levels (since some earlier time period). Hence, it may not be surprising that these flow variables are related to flow measures like growth rates. Note that the inverse of the original Heritage Foundation index is used so that higher values could indicate greater levels of economic freedom. Moreover, the 1996 values of HERIT are considered because the 1995 data are quite sparse. Of the observations that are available, small differences exist between the index values of those two years.

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the FH measure has greater variability and the HERIT measure is positively skewed (so that a greater proportion of countries are below the mean). Table 4. Alternative Measures of Economic Freedom A. Correlation Among the Indexes of Economic Freedom ECON HERIT ECON 1 HERIT 0.862 1 FH 0.810 0.722

HERIT FH

1

Mean

St. Dev.

Skew

0.362 11.030

0.096 3.966

1.68 -0.76

B. Correlation Between HERIT and the Index of Patent Rights (PAT) Corelation Coefficent All Countries 0.552 * By Income Level: Low 0.263 Medium 0.045 High 0.169 ** By Growth Rate: Slowest 0.163 Medium 0.730 * Fastest 0.482 *

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FH

C. Correlation Between FH and and the Index of Patent Rights (PAT) Corelation Coefficent All Countries 0.713 * By Income Level: Low 0.506 * Medium 0.449 * High 0.422 * By Growth Rate: Slowest 0.112 Medium 0.872 * Fastest 0.735 *

Coef. Of Variation 0.25 0.37

# of Observations 91 27 33 29 29 31 31

# of Observations 60 14 20 26 15 21 24

Notes: These are the same grouping of countries (by income level or growth rate) used in previous tables. HERIT denotes the Heritage Foundation Index of Economic Freedom in 1996. (Values for 1996 were used instead of values for 1995 since the sample size is very limited for 1995.) Moreover, the inverse of the original Heritage Foundation Index is used so that higher values could indicate greater levels of economic freedom. FH denotes the Freedom House Index of Economic Freedom 1995-1996. ECON, as before, denotes the Gwartney and Lawson Index of Economic Freedom. In the correlation tables, * denotes statistical significance at conventional levels, ** at the 12% level of significance. As shown in Table 3, the statistical significance is based on tstatistics of a bivariate regression (not reported) between the patent rights index and the index of economic freedom.

Part B of Table 4 repeats the analysis in Table 3 for the HERIT index. The patent rights index and the Heritage Foundation index of economic freedom are also positively correlated (for all countries pooled). This correlation coefficient is significant at conventional levels. (This is based again on the results of a bivariate regression, the results of which are not reported to avoid cluttering up the table). By income group the correlation remains positive

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but is not significant at conventional levels. The correlation is significant at the 12% level for the top-third of countries. However, when countries are grouped according to their growth rates (of GDP per capita), the Heritage Foundation measure and the patent rights index are positively and significantly correlated among the medium- to fastest-growth economies. Part C of Table 4 repeats the analysis with the Freedom House measure. Nearly the same pattern emerges. For all countries pooled and for almost all sub-groups, a positive and significant correlation exists between patent rights and economic freedom. The exception is the slowest-growth group where the correlation is positive but not significant at conventional levels. Thus these results support rather than contradict the findings in Table 3. Table 5. Partial Correlation Between Economic Freedom and Patent Rights A. Regressions

Constant Political Freedom Index Govt. Spending as a % of GDP Openness Index Lagged GDP per capita

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Adj. R-squared No. of Observ.

Dependent Variable ECON 1.528 * (0.304) 0.092 * (0.031) -0.097 * (-0.046) 0.222 * (0.038) 0.056 * (0.025) 0.55 177

B. Correlation Between Residuals of Above Two Equations Overall: 0.171 By Growth Rate: Slowest 0.036 Medium 0.459 Fastest 0.061

PAT 0.470 (0.419) 0.050 (0.043) 0.001 (0.063) 0.034 (0.052) 0.103 (0.035) 0.21 177

*

*

*

C. Summary Statistics Political Freedom Index Openness Govt. Spending as a % of GDP

Mean 3.44 0.45 18.10

St. Dev. 2.11 0.49 7.76

Min 1 0 4.6

Max 7 1 42.5

Notes: Estimation is by ordinary least squares, pooling the 1980 and 1995 samples. All variables, except the Openness Index and the constant, are logged. Standard errors are in parentheses and * denotes statistically significant at conventional levels. The political freedom index was inverted (so that higher values would indicate greater freedom). Lagged GDP refers to the average of the five years preceding.

The next test is to determine whether the correlation between economic freedom and patent rights remains positive even after netting out the influences of third factors. For this, the analysis returns to the Gwartney and Lawson index (ECON), and pools the 1980 and 1995 samples. The aim here is to examine partial correlations. That is, to run regressions of each index (i.e. economic freedom index (ECON) and the patent rights index (PAT)) on several independent variables, and then to compute the correlation between the residuals from each

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regression. The residuals have the interpretation of being that part of the index (whether ECON or PAT) that is not explained by those independent variables. Hence the partial correlation between ECON and PAT gives the correlation between the two indexes net of the influences from other variables. The issue then is the selection of independent variables to be used. Thus far limited empirical work has been done on the determinants of economic freedom and patent rights. Ginarte and Park (1997), for example, study the determinants of patent rights, such as a country’s level of economic development, political freedom, government policy, and degree of openness to international trade. Other studies, such as Alesina and Wacziarg (1997), Bhalla (1994), and Gwartney et. al. (1998) investigate the interrelationships among economic freedom, political freedom, economic development, openness, and government size. Of course, it is likely that there are endogenous interactions among all these variables. Moreover, there is simply no received structural model that identifies the linkages. But given the mutual interdependence – or interrelatedness – among these variables, it is conceivable that these variables would appear in a reduced form equation for economic freedom and patent rights.15 Part A of Table 5 shows the results of regressing each index (of economic freedom and patent rights) on GDP per capita (which is used to proxy for the level of economic development), share of government spending in GDP (which is used to proxy for government size), index of openness to international trade, and index of political freedom.16 Part C of Table 5 displays the descriptive statistics behind the additional variables. As the regression results show, levels of economic freedom are inversely related to government size and positively related to per capita GDP, political freedom, and openness to trade – and all statistically significantly. Patent rights are also positively related to these variables, but only GDP per capita is statistically significantly at conventional levels. Thus, if GDP per capita rises, the levels of both economic freedom and patent rights would rise, giving the appearance of a positive correlation between them. However, as part B of Table 5 shows, the residuals from these two reduced-form regressions are positively correlated and statistically significant. For the sample as a whole, the correlation coefficient is 0.171. This indicates that even after netting out third factor influences, both patent rights and economic freedom are correlated. When the sample is broken down by growth-rate groups, the correlation is also positive and statistically significant for the medium-growth economies. This group consists of a number of OECD (industrialized) economies, such as the U.S. The partial correlation between patent rights and economic freedom is positive for the other two groups, but is not statistically significant at conventional levels. To summarize, the positive correlation between patent rights and economic freedom does not disappear once third factor influences are controlled for. Thus the evidence overall is favorable to the view that economic freedom and patent rights move together across countries. There is no finding here of any statistically significant negative association between economic freedom and patent protection. Of course, some of the positive covariation between patent rights and economic freedom is driven by per capita GDP, but the latter may also be determined in turn by the level of patent rights and economic freedom

15

To avoid simultaneity between the indexes and these other (RHS) variables, lagged (five year averaged) values of these other variables are used. 16 The equation is estimated by OLS in log-linear form. The openness index is a 0-1 dummy, and is not logged.

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(through the stimulation of investment and innovation). Thus, all three factors (patent rights, economic freedom, and economic development) are likely to interact endogenously. Future work could try to ascertain the underlying structural model that generates these co-variations.

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FUTURE OF PATENT RIGHTS Debate will continue as to whether patent protection undermines free markets or enhances them. The traditional view that markets depend for their smooth functioning on the clear delineation and enforcement of property rights is considered by some not to extend to intellectual creations or output. This paper has responded to this claim in two ways. First, it presented many misconceptions about patent rights. For instance, patent rights do not extend to knowledge but to the products or services based on that knowledge. The knowledge is still available for others to exploit (but not to market the same products or services for which others have acquired a (temporary) exclusive right). Secondly, the paper has provided an empirical look at the relationship between patent rights and economic freedom. To the extent that the two indexes are reliable (in measuring what they are supposed to measure), the evidence does not suggest that the two are at odds. In fact, patent rights are found to be positively associated with economic freedom, particularly in regions experiencing positive growth rates. This would not have been observed in the data if patent rights largely distorted and restricted markets. The result also holds under different measures of economic freedom and even after controlling for other variables. Nonetheless, the remainder of this paper provides some thoughts on patent reform, since there is still room for improvements in the patent system - improvements towards making patent rights more conducive to economic freedom. For the ensuing discussion, it is necessary to shift attention away from how patent rights affect markets to what goes on within patent systems (i.e. the internal factors). The transactions costs of obtaining patent protection are high. Typically, to apply for patent rights, patent applicants must pay various fees; for example, official fees (application fees, search and examination fees, taxes, etc.), legal fees, and if applicants seek global protection, translation fees. The fees are considered high in the sense that many of these costs are redundant and unnecessary burdens (Park, 1999). For example, official fees are high because patent office surpluses (that is, their net incomes) are transferred to national treasuries - surpluses that could otherwise be passed on to inventors in the form of lower fees or be used to hire more examiners and thereby process patents more expeditiously. The official fees are also high because nations conduct duplicative searches and examinations (to determine if an invention is novel, non-obvious, and industrially applicable); they do not always accept the search and examination results of foreign offices - though this situation is improving (with electronic networking). The legal fees are high because patent applicants must hire a patent attorney or agent for even routine things like filing applications, paying renewal fees, and filing translations. Legal fees are also high because inventors must hire a local legal professional in every country in which they wish to obtain patent protection. Foreign legal representation is rarely permitted. Moreover, with high entry barriers into the profession, the supply of patent attorneys and agents is quite scarce (especially in Japan). Finally translation costs are high because every jurisdiction insists on a translation of the

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patent in order for it to have legal force (even if many researchers, scientists, and inventors are fluent in English). Moreover, translations are handled by the law firms (on the grounds that the patent is as much a legal document as it is a scientific document, and must be precisely worded). For many patent law firms, translation work is their main source of income. Thus relaxing the translation requirement will mean a loss of business for them. There are just a sample of factors that contribute to the transactions costs of obtaining a patent, and of where there is scope for reform. These transactions costs reduce the ability of inventors to obtain property rights for their ideas. There is also a long queue for those waiting for patent protection. The average time to process patent applications has increased over time while the average time an examiner spends on each application has decreased over time. This raises concerns about examination quality and the implications for errors (granting protection too broadly or too narrowly). Again the problem can be traced to some internal workings of patent systems, such as resource allocation problems. The patent offices are public enterprises. They do not price their services competitively; for example, they do not charge fees according to the marginal costs of services. They charge the same fees to all customers (i.e. patent applicants). Thus fees do not vary with the complexity of inventions or with the length of time it takes to process applications. The U.S. patent office, for instance, does not recover costs from the unsuccessful applicants or from the roughly 40% of applicants who simply abandon their applications.17 Patent examination time and resources are scarce, yet applicants under the current system have no incentive to economize on their use of those resources. Correcting this failure should help improve the allocation of resources for examining and processing applications. To conclude, the effect of increased transactions costs and burdens on patent system resources will be a diminishing of patent rights, not strengthening. Strong patent laws are not very useful if it is too costly for individuals to acquire their patent rights. In other words, property rights must not only exist and be enforced, but also be accessible. Thus, to the extent that patent rights and economic freedom are positively related, the efficiency of markets may be reduced not because of a strengthening of patent rights but because of a decrease in effective patent rights.

REFERENCES Alesina, Alberto and Wacziarg, Roman. 1997. Openness, Country Size, and Government. National Bureau of Economic Research Working Paper No. 6024. Bethune, John J. 1993. Towards an Austrian View of Copyright. Studies in Economic Analysis, Vol. 15, No. 1, pp. 51-71. Bhalla, Surjit S. 1994 “Freedom and Economic Growth: A Virtuous Cycle?” in Axel Hadenius, ed. Democracy’s Victory and Crisis. Cambridge University Press. Cornelli, Francesca and Schankerman, Mark. 1999. Patent Renewal and RandD Incentives. Rand Journal of Economics, Vol. 50, No. 2, pp. 197-213. Freedom House. 2001. Freedom in the World: Annual Survey of Political Rights and Civil Liberties 2000-2001. Freedom House, New York. 17

See U.S. GAO (1997).

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Ginarte, Juan Carlos and Park, Walter G. 1997. Determinants of Patent Rights: A CrossNational Study. Research Policy, Vol. 26, pp. 283-301. Gwartney, James and Lawson, Robert (with Dexter Samida). 2000. Economic Freedom of the World: 2000 Annual Report. Vancouver, B.C.: The Fraser Institute. Gwartney, James, Holcombe, Randall, and Lawson, Robert. 1998. The Scope of Government and the Wealth of Nations. CATO Journal, Vol. 18, No. 2, pp. 163-190. Helpman, Elhanen. 1993. Innovation, Imitation, and Intellectual Property Rights. Econometrica, Vol. 61, No. 6, pp. 1247-1280. Kaufer, Eric. 1989. The Economics of the Patent System. Harwood Academic Publishers, New York. Mankiw, Gregory, Romer, David, and Weil, David. 1992. A Contribution to the Empirics of Economic Growth. Quarterly Journal of Economics, Vol. 107, pp. 407-437. Mansfield, Edwin. 1994. Intellectual Property Protection, Foreign Direct Investment, and Technology Transfer. International Finance Corporation Discussion Paper No. 19, Washington, D.C. Messick, Richard, ed. 1996. World Survey of Economic Freedom. Freedom House, New York. O’Driscoll, Gerald Jr., Holmes, Kim, and O’Grady, Mary, eds. 2001. Index of Economic Freedom. Heritage Foundation, Washington, D.C. Park, Walter G. 1999. Highlights and Perspectives on Patent Cost Containment. Proceedings of the Fourth International Symposium on Reducing Patent Costs. American Intellectual Property Law Association (AIPLA) and the Federation Internationale des Conseils en Propriete Industrielle (FICPI), Hague, Netherlands, July 1999. Park, Walter G., Vijaya, Ramya, and Wagh, Smita. 2000. Patent Rights: An Update. Unpublished. Park, Walter G. 2001a. Intellectual Property and Patent Regimes. Chapter 4 in Gwartney, James and Lawson, Robert, eds. Economic Freedom of the World: 2001 Annual Report. Vancouver, B.C.: The Fraser Institute, pp. 95-112. Park, Walter G. 2001b. Do Intellectual Property Rights Stimulate RandD and Productivity Growth? Evidence from Cross-National and Manufacturing Industry Data. Forthcoming Industry Canada Conference Volume on Intellectual Property and Innovation in the Knowledge-Based Economy. Romer, Paul. 1990. Endogenous Technological Change. Journal of Political Economy, Vol. 98, No. 5, pp. S71-S102. Sachs, Jeffery D. and Warner, Andrew. 1995. Economic Convergence and Economic Policies. Brookings Papers on Economic Activity, Vol. 1, pp. 1 - 95. Segerstrom, Paul S. 1991. Innovation, Imitation, and Economic Growth. Journal of Political Economy, Vol. 99, No. 4, pp. 807-827. Sherwood, Robert M. 1997. Intellectual Property Systems and Investment Stimulation: The Rating of Systems in Eighteen Developing Countries. IDEA: The Journal of Law and Technology, Vol. 37, No. 2, pp. 261-370. Sherwood, Robert M. 2000. The TRIPS Agreement: Benefits and Costs for Developing Countries. International Journal of Technology Management, Vol. 19, No. 112, pp. 5776.

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Summers, Robert, Heston, Alan, Nuxoll, Daniel, and Aten, Bettina. 1996. Penn World Tables, Mark 5.6a. National Bureau of Economic Research, Cambridge, MA. U.S. General Accounting Office. 1997. Intellectual Property: Fees Are Not Always Commensurate With the Costs of Services. GAO/RCED-97-113, Washington, D.C.

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Chapter 12

DOES DEVELOPMENT AID LEAD TO ECONOMIC FREEDOM?1 Benjamin Powell and Matt E. Ryan

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INTRODUCTION For more than 50 years individual countries and international agencies have given aid to third world countries to promote economic development, yet poverty still persists in many recipient nations. In recent years development economists and agencies have increasingly recognized the importance of institutions that support property rights and economic freedom in promoting development. Nevertheless, nations continue to provide aid to less developed countries. This raises two important questions. Does the receipt of aid lead to increases or decreases in economic freedom, and are increases in economic freedom rewarded with more aid or punished with reduced aid? The failure of aid to directly promote development in impoverished countries is well documented. Boone (1996), Vasquez (1998), Easterly and Levine (2001), and Ovaska (2003) all find that development aid fails to systematically improve growth rates. Easterly (2001) summarizes World Bank aid policies that have failed to promote development. Although aid has generally failed to promote development, Burnside and Dollar (2000) argue that aid can be effective if the correct policy environment is in place. This leads us to examine the characteristics of the correct policy environment and whether aid has been directed to countries with it. Since the development of the indexes of economic freedom in 1995, a substantial body of literature has related economic freedom to growth rates and standards of living. The Economic Freedom of the World annual report is the most comprehensive index measuring

1

This article originally appeared as Benjamin Powell and Matt E. Ryan, “Does Development Aid Lead to Economic Freedom?” Journal of Private Enterprise vol. 22, no. 1 (2006): 1-21. The authors thank Scott Beaulier, Christopher Coyne, Bob Lawson, Peter Leeson, Tom Means, Edward Stringham, Robert Subrick, Alex Tabarrok, an anonymous referee, and the participants at the 2005 Association of Private Enterprise conference for helpful comments on earlier drafts. The usual disclaimer applies. Powell is an Assistant Professor of Economics at Suffolk University and Ryan is an Assistant Professor of Economics at Duquesne University.

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economic freedom. It looks at 37 distinct pieces of data in five major categories: government size; legal structure and security of property rights; sound money; free international trade; and regulation of credit, labor and business. The index ranks countries back to 1970 over five-year intervals, allowing time series analyses to be conducted. Research by Dawson (1998, forthcoming), Gwartney, Lawson and Holcombe (1999), de Haan and Sturm (2000, 2001), Adkins, Moomaw and Savvides (2002), Pitlik (2002), and Weede and Kampf (2002) all find that increases in economic freedom are positively correlated with increases in economic growth rates. Other research indicates that higher levels of economic freedom in a country also increase growth rates, though some research debates whether this is statistically significant. Berggren (2003) provides a comprehensive summary of the literature on economic freedom. While much has been written on the relationship of economic freedom to growth rates and standards of living, little has been written on the relationship between aid and economic freedom. Vasquez (1998), Ovaska (2003) and Heckelman and Knack (2005) are exceptions; they examine both economic freedom and aid. Ovaska looks at whether the quality of governance (economic freedom) affects the results of aid, while we look at whether aid affects governance and if governance affects aid flows. Heckleman and Knack (2005) examine whether aid impacts freedom and the effect it had on growth over 10 and 20 year periods (1980-2000). They find aid negatively impacts overall economic freedom. Our study expands on their results by looking at a total time span of 30 years (1970-2000) and by analyzing the impact of aid flows on freedom scores in smaller five and ten year time periods. Our study also differs by examining changes in freedom scores and changes in flows aid from period to period instead of just levels of aid flows. Vasquez (1998) examines what happens to the flow of aid when economic freedom changes. He observed a limited number of both countries and years; we expand on his work by looking at a larger set of countries over a 30 year period. The paper is organized as follows. The second section examines whether aid flows increase or decrease economic freedom. The third section of this paper then investigates whether good policy (i.e., policies that increase freedom) is rewarded with more aid. The fourth section of the paper concludes.

HAS FOREIGN AID INFLUENCED ECONOMIC FREEDOM? P.T. Bauer long contended that intergovernmental aid increases the size and scope of recipient governments, contributes to the politicization of life, negatively impacts economic performance, and is, on balance, an anti-market force (1971, 1978, 1984, 1990).2 His long list of anti-market policies to which aid contributes includes expulsion of productive groups, suppression of private trade, restriction of the inflow of foreign capital, confiscation of property, forced collectivization, takeover of foreign enterprises, discouragement of agriculture, support of unviable projects, and import substitution (1990: 46). The economic freedom index accounts for many of the policies Bauer lists. Analyzing measures of aid and

2

Similarly, Casella and Eichengreen (1996) argue that aid may retard development if it delays the adoption of policy reforms.

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economic freedom scores allows us to examine if Bauer’s anecdotal evidence characterizes most countries receiving aid in recent years.3 To examine the relationship between aid and freedom, we averaged annual aid data from the World Bank’s World Development Indicators over 5-year periods (1970-74, 1975-79, etc.) to create an independent variable. Economic freedom scores from the Economic Freedom of the World annual report were our dependent variable. Since a given amount of aid is likely to have a bigger impact on freedom in a small economy than a large economy, we examine how aid as a percent of GNI and as a percent of government expenditure impacts economic freedom.4 Although not all countries had complete data sets, the maximum span of the aid data was from 1965 to 2000, and the maximum span for the freedom scores were from 1970 to 2000. The vast majority of data periods calculated contained five individual years of foreign aid data.5 Non-Recipients of Aid

Recipients of Aid

10.0

9.0

8.0

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Economic Freedom

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0 1970

1975

1980

1985

1990

1995

2000

Figure 1. Average Economic Freedom of Recipients vs. Non-Recipients of Foreign Aid.

We first examined the relationship between the amount of aid given to a country and its level of economic freedom. Figure 1 compares levels of economic freedom in countries

3

Alesina and Weder (2002) find that increases in aid are associated with increases in corruption. Although they are not directly addressing the policy environment as we are, their results are clearly consistent with Bauer’s claims. 4 Bauer claimed that aid would have little impact on growth since aid would likely be small relative to the size of the economy. Aid could have a much bigger impact on the politicization of economic life, however, because aid as percent of government spending is much greater than aid as percent of GDP. 5 In cases in which five years of data were not available, the average of the available years was used.

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receiving and not receiving aid at the end of each five-year period.6 We find that countries receiving aid have lower levels of economic freedom than those that do not. Although this could be because aid promotes statism, it could also simply result from the fact that poor countries are more likely to both have low freedom scores and to receive aid. We next look at only those countries that receive aid to determine if the level of aid they receive as a percent of national income impacts their level of economic freedom. We examine 5 year aid flows and the level of freedom a country had at the conclusion of the 5 year period. We use a simple regression model of:

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Freedomit = α + β1Aidit+ β2ExComit + β3GNIperCapit + εit with the subscripted t denoting the final year of both the aid flows and the economic freedom scores. Aidit represents the average foreign aid flow per year to the country over the 5 year span. ExComit is a dummy variable capturing whether a country is a former communist regime.7 GNIperCapit controls for the average GNI per capita for the respective 5- or 10-year period. We included fixed effects for years in all regressions.8 We attempted to add data from the World Value Survey, which measures social and political beliefs in nearly 80 countries, so we could have an independent variable to proxy statist vs. classical liberal ideology.9 Presumably, in addition to aid flows, a population’s ideology should influence their level of freedom. We tried three different questions from the survey but unfortunately, we were unable to find any significant relationship between value survey score and freedom nor any significant impact on the other coefficients so we do not report the results with the World Value Survey data.10 We think the insignificance of ideology is a reflection of the WVS data failing to adequately capture a nation's dominant ideology, not a rejection of the significance of ideology impacting freedom. Finally, since aid may impact freedom more slowly than 5-year periods, regressions 2 and 4 repeat the above procedures using 10-year time periods. Regressions 3 and 4 use aid as a percent of government expenditure instead of aid as a percent of GNI.11 Table 1 contains our regression results.

6

Aid flows are averaged over the five years preceding the economic freedom score and year indicated in the figure. Many countries that desocialized their economies experienced increases in economic freedom, as well as receiving heavy aid support from western economies. This effect is worth separating from other forms of aid procurement. Countries designated as previously communist are: Albania, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Russia, Slovakia, Slovenia, and Ukraine. 8 We also ran the regressions without the ExCom and GNI per capita control variables without added results. 9 The World Values Survey has conducted four waves of surveys since 1981; we used data from the third and fourth waves, 1995 and 1999-2001, respectively because the data from the earlier waves were too limited. By using the WVS variable, our regression model was limited to 45 countries over the time period 1990-2000. 10 We used the questions: A) On a scale of 1 to 10, with 1 indicating “People” and 10 indicating “The government,” where would you place your views on the following statement: ________ should take more responsibility. B) On a scale of 1 to 10, with 1 indicating “Private” and 10 indicating “Government,” where would you place your views on the following statement: ________ ownership of business should be increased. C) On a scale of 1 to 10, with 1 indicating “good” and 10 indicating “harmful,” where would you place your views on the following statement: Competition is ________. 11 We also used figures for total aid and aid per capita. Most signs were the same, although these regressions were less statistically and economically significant. We also added fixed effects for countries (requiring us to drop the former communist dummy) and had similar results, though less significant. 7

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Table 1. Regression Results for the Effects of Foreign Aid on Economic Freedom Aid 1. Aid as a % of GNI, 5-year

-0.031

(N = 476)

(-4.924)

2. Aid as a % of GNI, 10-year

-0.025

(N = 232)

(-2.802)

3. Aid as a % of Government Expenditure, 5-year

-0.007

ExCom ***

***

***

(-4.811)

-0.760

***

1.059 x 10-5

(-3.777)

(8.342)

-0.264

1.037 x 10-4

(-0.938)

(5.597)

-0.740

R2

GNI per capita

***

9.308 x 10-5

(-3.549)

(7.100)

-0.299

8.358 x 10-5

(-0.933)

(4.140)

***

0.366

***

0.390

***

0.394

***

0.424

(N = 373) 4. Aid as a % of Government Expenditure, 5-year

-0.01 (-4.113)

***

(N = 173)

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Note: Fixed year effects added for each regression. T-statistics in parentheses. *, **, and *** denote significance at the .10, .05, and .01 levels, separately. Significant coefficients in italic.

We find that the coefficients on aid as a percent of GNI and aid as a percent of government expenditure are negative and significant at the 99% level in all 4 analyses. The economic effects could become significant in the largest cases. Aid is less than 25% of GNI in 97% of our data; the median term is roughly 2.25%. Using regression #1, our model predicts a drop in economic freedom of slightly less than 0.1 due to the median level of aid in our data set, yet suggests a drop in freedom of nearly 0.8 when foreign aid reaches 25% of GNI. This is a nontrivial effect on economic freedom levels. The coefficient for communism is consistently negative, indicating that former communist countries tend to have lower levels of freedom than those never controlled in a command-style economy. Higher levels of per capita income are associated with higher freedom scores though the coefficient is small. Our main finding here is that after controlling for years, whether a country was formerly communist, and per capita income, the amount of aid a country received over both five and ten year periods adversely impacted economic freedom just as Bauer predicted. We next examine how levels of aid impact changes in countries’ freedom scores. We look at the change in freedom score over each five-year period, from 1975 to 1980, for example, and the level of aid received in that five-year period. Our model is: ΔFreedomit = α + β1Aidit + β2ExComit + β3GNIperCapit + εit We use the same variables and format as used in regressions 1-4. Table 2 contains the results.

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Table 2. Regression Results for the Effects of Foreign Aid on Changes in Economic Freedom

5. Aid as a % of GNI, 5-year (N = 395) 6. Aid as a % of GNI, 10year (N = 182)

Aid 0.008 (1.639) 0.018

*

(1.853)

7. Aid as a % of Government Expenditure, 5-year (N = 329)

-0.001 (-0.705)

8. Aid as a % of Government Expenditure, 5-year (N = 139)

-0.002 (-0.869)

ExCom 0.726 (4.327) 0.942

***

GNI per capita -8.398 x 10-7 (-0.088)

R2 0.185

**

6.051 x 10-7

0.254

(2.345) ***

(0.034)

0.746 (4.245)

***

-1.872 x 10-6 (-0.183)

0.190

0.812 (1.827)

*

-3.101 x 10-6 (-0.162)

0.229

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Note: Fixed year effects added for each regression. T-statistics in parentheses. *, **, and *** denote significance at the .10, .05, and .01 levels, separately. Significant coefficients in italic.

Here our results are mixed. The coefficient of aid is never significant at the 99 percent level, and in only once is it significant at the 90 percent level. In that case (regression 6) the coefficient is small but positive, indicating that increases in the size of an aid flow over a 10 year period increase economic freedom. The dummy variable for former communist countries was positive indicating that they were increasing economic freedom faster than other countries. This is unsurprising since they were transforming towards capitalist economies. National income per capita failed to be significant. In measuring the impact of aid flows on changes in freedom it is difficult to say that aid has much of a positive or negative effect on freedom. This could be because prior aid flows were similar to those in the period observed. If aid does adversely impact economic freedom, we may be observing countries where previous aid has already decreased freedom such that the current, near-constant aid levels cause freedom scores to remain poor but not change significantly. To address this, we next looked at how changes in aid flows impact changes in freedom. Any one pair of aid and economic freedom figures spans ten years. As an example, the change in the average amount of aid from the span 1970-1975 to the span 1975-1980 was paired with the change in freedom from 1975 to 1980. This model allows previous aid to be incorporated into the initial freedom score so that we can see how the change in aid flow affects economic freedom over the next five-year period. Our regression model is: ΔFreedomit = α + β1[ΔAidit] + β2ExComit + β3[ΔGNIperCapit] + εit Again, the same fixed effects and other independent variables were used as in previous regressions. We were only able to observe 5 year changes because 10 years of data were required to examine each five year change. Regressions matching the longer periods tested in

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regressions 2, 4, 6, and 8 were not undertaken because they require 20 years of data, and the number of observations for which this was possible was too limited. Table 3 reports our results. Table 3. Regression Results for the Effects of Changes in Foreign Aid on Changes in Economic Freedom Aid

ExCom

9. Aid as a % of GNI, 5year

-0.010

0.900

(N = 380)

(-1.004)

(4.443)

10. Aid as a % of Government Expenditure, 5-year

-0.002 (-1.309)

***

1.029 (4.494)

***

GNI per capita

R2

1.636 x 10-5

0.175

(0.575) ***

5.112 x 10-5

0.171

(1.598)

(N = 274)

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Note: Fixed year effects added for each regression. T-statistics in parentheses. *, **, and *** denote significance at the .10, .05, and .01 levels, separately. Significant coefficients in italic.

In both regressions, aid had a negative sign, indicating that increases in aid flows from one period to the next decreased freedom scores over that time. However, aid failed to be statistically significant in any of the regressions. Our highest level of confidence attained in these two regressions was from regression 10, at 80.8 percent for the aid term. The coefficient on communist countries was again positive and significant, indicating that their freedom improved more rapidly than other countries. Income per capita again failed to be significant. Overall, our regression results are mixed but on balance tend to support Bauer’s claim that aid promotes statism. We find that countries receiving aid are less economically free than those that do not. This could be a confirmation of Bauer’s claim that aid harms economic freedom, or it could simply be an indication that countries with less freedom are more likely to get aid. Our strongest regression results find that countries that receive larger amounts of aid over a five year period, even after controlling for their level of income, have a lower freedom score at the conclusion of that period. When we compare the level of aid a country receives to how its freedom changes over five years, we find a very slight positive relationship in some cases. But this could be because the country already had a low level of economic freedom since it was receiving large amounts of aid, and that a constant, high aid flow does not cause economic freedom to continue to worsen. Examining changes in aid flows and freedom scores eliminates some of the above problem. We find that increases in aid negatively impact changes in economic freedom, but the results are not statistically significant. This section is neither a resounding confirmation nor rejection of Bauer’s claims. The one clear result that emerges is that aid is unlikely to increase economic freedom. There is some indication that aid actively harms freedom but the result is not perfectly clear.

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IS GOOD POLICY REWARDED WITH AID? The economics literature and aid agencies have recognized that an environment of economic freedom and property rights is necessary for economic development. Research by Burnside and Dollar (2000) claims that although aid in general cannot promote growth, if given to countries with the right policy environment the aid can be effective. Their research is disputed, however. Easterly, Levine and Roodman (2003) and Brumm (2003) find that aid has a negative impact even with good policy, while other research—Hansen and Tarp (2000), Dalgaard and Hansen (2001), and Guillaumont and Chauvet (2001)—also casts doubts on the Burnside and Dollar results. Despite debate, many in the policy world believe aid can work if the right policies are in place. The World Bank has written, “There is no value in providing large amounts of money to a country with poor policies” (1998: 13). Similarly, the millennium challenge account has been set up to direct U.S. foreign aid to countries with better policies in the hope that aid will be more effective. Thus, since good policy is necessary for economic growth, and at least some researchers believe aid can be effective if a country has good policies in place, the question that we address in this section is “Have increases in economic freedom been rewarded with increased aid?” Vasquez (1998) found the opposite, that the greater the reductions in economic freedom, the greater the increases in aid. Vasquez only looked at 20 countries from 1985 to 1990, however, and 24 countries from 1990 to 1995. Our study expands on his by including data for 96 countries over thirty years to see if his result can be generalized.12 We used a model similar to one in section 2 to determine if improvements in freedom were rewarded with increases in aid. In this case, since freedom is the independent variable and aid flows are dependent, we simply used aid flows instead of adjusting the figure to reflect aid as a percent of income or expenditure. We are only concerned with whether policy changes that increase freedom are rewarded with more aid. Since freedom scores are available at five year intervals, aid flows were again averaged over the same five year periods. Thus, we now compare the change in freedom over a five year period with the change in aid from that period to the subsequent five year period. For example, the difference in freedom scores from 1980 to 1985 becomes the independent variable (ΔFreedom), and the difference in aid from 1981-1985 to 1986-1990 becomes the dependent variable (ΔAid).13 Our regression model is: ΔAidit' = α + β1(ΔFreedom)it + β2GNIperCapit + εit with the subscript t referring to the last year of the summed aid flows. The subscripted t' is the last year of the freedom scores, which lags 5 years behind that of the aid flows. We again control for income per capita. Table 4 contains our results.

12

Alesina and Weder (2002) is again related. They examine corruption instead of economic freedom and find no significant correlation between the level of corruption in a country and the amount of aid they receive. 13 This is not simply the inverse of the regressions in the prior section because we are now matching aid flows to prior freedom scores where before we were matching freedom scores to prior aid flows.

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Table 4. Regression Results for the Effects of Changes in Economic Freedom on Foreign Aid Flows

11. Freedom and GNI per capita on Total Aid Flows

Change in Freedom

GNI per capita

R2

-2.442 x 108

-3.204 x 104

0.029

**

(-2.296)

(-1.527)

(N = 284)

12. Freedom and GNI per capita on Total Aid Flows (90s)

-1.129 x 108

***

(-0.607)

2.477 x 104

0.012

(0.791)

(N = 81)

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T-statistics in parentheses. *, **, and *** denote significance at the .10, .05, and .01 levels, separately. Significant coefficients in italic.

We found that the coefficient of freedom was negative and statistically significant. A one point rise in freedom will lower subsequent aid flows by $240 million. Increases in economic freedom—the addition of proper policies and implementation of sound institutions—were not rewarded with more foreign aid. In fact, steps in the right direction were punished by decreases in foreign aid. This occurred even though discussion of property rights and economic freedom in development agencies increased over the time period considered. When we examined the last period with complete data available in isolation, the 1990s, improvements in economic freedom continued to be punished with lower levels of aid; the results were not statistically significant, however. The debate that has ensued since the publication of Burnside and Dollar (2000) has led to doubts that foreign aid can promote development even if it goes to countries with good policies. However, even if Burnside and Dollar are right, we find that aid agencies have not rewarded good policy with increased aid. They punish improvements in economic freedom by decreasing aid.

CONCLUSION Although many articles examine whether aid promotes development and whether economic freedom promotes development, little empirical research has been written on the relationship between foreign aid and economic freedom. We examined both how aid influences economic freedom and if increasing economic freedom has been rewarded with aid. No clear cut theoretical case proves that aid either increases or decreases freedom. It could be that governments in impoverished countries receiving more aid will not need to tax their own citizens as much to obtain a given level of spending. If governments held spending constant, then aid could conceivably increase economic freedom. Alternatively, as P.T. Bauer has argued, aid could lead to the expansion of the public sector relative to the private sector

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and thus decrease economic freedom. Our paper examined this question, with mixed results. Our regressions give some indication that aid decreases economic freedom. Our findings clearly can cast serious doubt on the proposition that aid increases freedom in poor countries. Given the World Bank’s mission of promoting economic growth in poor countries and the strong empirical literature on the importance of economic freedom for growth, our paper indicates that since aid cannot be shown to have a positive influence on freedom, aid is unlikely to lead to development in poor countries. The second question we addressed is whether improvements in economic freedom were rewarded with increased aid. While there has been debate as to aid’s effectiveness if a country has good policy, we find that good policy has not been rewarded. Increases in economic freedom have been punished by decreases in the amount of aid a country receives.

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REFERENCES Adkins, L., Moomaw, R., and Savvides, A. (2002) “Institutions, Freedom and Technical Efficiency.” Southern Economic Journal 69, 92-108. Alesina, A., and Dollar, D. (2000) “Who Gives Foreign Aid to Whom and Why?” Journal of Economic Growth 5, 33-63. Alesina, A., and Weder, B. (2002) “Do Corrupt Governments Receive Less Foreign Aid?” American Economic Review 92(4), 1126-37. Bauer, P. (1971) Dissent on Development. Cambridge MA: Harvard University Press. _____. (1978) “Hostility to the Market in Less-Developed Countries.” in The First World and the Third World: Essays on the New International Economic Order. ed Karl Brunner. Rochester NY: University of Rochester Policy Center Publications. _____. (1984) Reality and Rhetoric. Cambridge MA: Harvard University Press. _____. (1991) The Development Frontier. Cambridge MA: Harvard University Press. Berggren, N. (2003) “The Benefits of Economic Freedom,” The Independent Review 8(2), 193-211. Boone, P. (1996). “Politics and the Effectiveness of Foreign Aid,” European Economic Review 40, 289-329. Burnside, C., and Dollar, D. (2000). “Aid, Policies, and Growth,” The American Economic Review 90(4), 847-868. Brumm, H. (2003). “Aid, Policies and Growth: Bauer Was Right,” Cato Journal, 23(2), 167174. Casella, A. And Eichengreen, B. (1996) “Can Foreign Aid Accelerate Stabilisation?” Economic Journal 106 (436): 605-19. Dalgaard, C., and Hansen, H. (2001). “On Aid, Growth, and Good Policies,” Journal of Development Studies 37(6), 17-41. Dawson, J. (1998) “Institutions, Investment, and Growth: New Cross-Country and Panel Data Evidence,” Economic Inquiry 36, 603-19. _______. (Forthcoming) “Causality in the Freedom-Growth Relationship,” European Journal of Political Economy. Easterly, W. (2001) The Elusive Quest for Growth. Cambridge MA. MIT Press.

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Easterly, W., Levine, R., and Roodman, D. (2003) “New Data, New Doubts: A Comment on Burnside and Dollar’s ‘Aid, Policies, and Growth’.” NBER Working Paper 9846. Cambridge, MA: National Bureau of Economic Research. Guillaumont, P., and Chauvet, L. (2001) “Aid and Performance: A Reassessment,” Journal of Development Studies 37(6), 66-92. Gwartney, J., and Lawson, R. (2004) Economic Freedom of the World: 2004 Annual Report. Vancouver: Fraser Institute. Gwartney, J., Holcombe, R. and Lawson, R. (1999) “Economic Freedom and the Environment for Economic Growth,” Journal of Institutional and Theoretical Economics 155(4), 1-21. Hann, J. and Sturm, J. (2000) “On the Relationship Between Economic Freedom and Economic Growth," European Journal of Political Economy 16(2), 215-41. _____. (2001) "How Robust is the Relationship Between Economic Freedom and Economic Growth?” Applied Economics 33(7), 839-44. Hansen, H., and Tarp, F. (2000) “Aid and Growth Regressions,” Journal of Development Economics 64, 547-570. Heckelman, J. and Knack, S. (2005) “Foreign Aid and Market-Liberalizing Reform,” World Bank Policy Research Working Paper, WPS3557. Ovaska, T. (2003) “The Failure of Development Aid,” Cato Journal 23(2), 175-188. Pitlik, H. (2002) “The Path of Liberalization and Economic Growth,” Kyklos 55(1), 57-80. Vasquez, I. (1998) “Official Assistance, Economic Freedom, and Policy Change: Is Foreign Aid Like Champagne?” Cato Journal 18(2), 275-286. Weede, E. And Kampf, S. (2002) “The Impact of Intelligence and Institutional Improvements on Economic Growth,” Kyklos 55(3), 361-80. World Bank (1998) Assessing aid: What works, what doesn’t, and why. Washington, DC: Oxford University Press. _____. (2005) World Development Indicators Online. www.worldbank.org/data/wdi.

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In: Economic Freedom: Causes and Consequences Editors: Joshua C. Hall and Robert A. Lawson

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Chapter 13

SOCIAL COHESION AND ITS RELATIONSHIP TO ENDOGENOUS INSTITUTIONAL FORMATION AND ECONOMIC GROWTH1 Lauren R. Heller

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INTRODUCTION Despite an increasing global awareness of worldwide poverty, living standards for the majority of developing country populations continue to fall persistently below those of richer nations. This begs the question of why many countries are unable to utilize existing resources to reduce endemic poverty. The importance of institutional quality is one well known predictor of a country's ability to increase per capita growth and elevate living standards. Given that the importance of sound governance is a recognized fact, why have so many countries been unable to reach significant levels of institutional maturity? One possibility is that policymakers within these countries face serious barriers to implementing necessary reforms. If this is true, then the persistence of corruption and inefficiency that results from bad policy will prevent any well intentioned development strategies from accomplishing their intended objectives. Understanding the ways in which developing country governments are constrained from instituting reform is a worthwhile goal. To this end, this paper argues that the social and cultural dynamics existing within a country are an important determinant of a policymaker's ability to affect change. The paper builds upon previous work to examine the idea that social cohesion, or the ability of a society to coalesce in pursuit of needed reforms, is at least a partial determinant of institutional development and maturity. Empirically, the work will test the hypothesis that growth is a function of endogenous institutional quality, using measures of social cohesion as an identification strategy.

1

This article originally appeared as Laureny R. Heller, “Social Cohesion and Its Relationship to Endogenous Institutional Formation and Economic Growth,” Journal of Private Enteprise vol. 25, no. 1 (2009): 81-104. The author is an Assistant Professor of Economics at Berry College.

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In the process of developing an empirical model with which to test this hypothesis, the work will also be able to examine some of the usual assumptions made in the literature regarding institutional and cultural characteristics. While it is widely accepted that institutional quality is positively correlated with growth rates, discussions of the attributes of a society that contribute to institutional development are far less common in the literature than might be expected. Though it is recognized that the magnitude of the effect of legal and judicial quality on living standards is important, it is equally valuable to examine the underlying characteristics of a nation that encourage positive institutional outcomes. The results of estimation indicate that a more cohesive society tends to provide a beneficial environment for policymakers to implement reform, though the magnitude of these effects tend to vary according to the institutional types being examined. The argument is developed using the structure that follows. Section II reviews the current literature and discusses a few possibilities for future progress in this area. Section III is a discussion of the ideas surrounding community fractionalization and institutions, respectively. The reasoning primarily examines whether these measures are accurate indications of the evolution of social relationships, culture, and the ability of an economy to advance. Additionally, the analysis will apply these relationships to economic growth as it relates to developing countries, with a critical survey of the theories discussed in the past. Section IV builds on this analysis to propose a new model of economic development that more fully captures these relationships. Section V discusses the data, Section VI presents the results of estimation, Section VII performs various robustness checks to ensure the model's validity, and Section VIII concludes the paper.

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THE CURRENT LITERATURE One of the most recent and comprehensive papers involving social cohesion and the endogeneity of institutions has been published by Easterly, Ritzen, and Woolcock in the July 2006 issue of Economics and Politics, hereafter referred to as ERW. Within the article, it is argued that one of the main explanations for the enactment of bad policies around the world, even among seasoned politicians, is that these politicians face a variety of social and cultural constraints to initiating reform. The authors formally model this relationship using a system of equations in which social cohesion, as measured by ethnic fractionalization and inequality, contributes to the development of sound institutions. In turn, these institutions provide a suitable environment for growth promotion. Subsequently, the ERW paper uses a crosssectional dataset comprised of country-specific averages to measure the extent to which the model captures its predicted effects. In addition to the ERW work, there is a variety of other literature examining the relationship between institutional quality and growth2. There are only a select number of papers, however, that take steps to study the determinants of institutions and politics within this context. In contrast to the research citing social cohesion as a mechanism for institutional modernization, a related literature links economic crises to subsequent policy change. Using some of the same economic freedom measures employed within this paper, Pitlik and Wirth

2

See, for example, Acemoglu, Johnson, and Robinson, 2005b; Barro, 1997; and Dawson, 1998, among others.

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(2003) find that severe economic downturns have the potential to hasten reform by raising the costs of bad policy and weakening the power of political coalitions. Proponents of this "crisis hypothesis" provide indirect support for the social cohesion approach. If social cohesion is indeed a constraint on policymakers to reform, then the weakening of political and economic blocks created by such crises may expedite change within developing country governments. In this way, the crisis hypothesis recognizes the role of political structures in determining institutional change. Taking a different approach to the study of institutional causation, other work has exploited historical differences between countries to examine the determinants of good governance. This line of research is primarily associated with the work of Daron Acemoglu. Acemoglu (2003) refutes previous notions that geography is a primary determinant of institutional and economic development, arguing that the mere correlation between climate differentials and standards of living does not imply a causal relationship . Rather than using social cohesion as an exogenous source of variation, Acemoglu views 15th century colonization as a "natural experiment" through which to separate the endogenous relationship between institutions and growth into two distinct channels. He finds that, rather than geographic factors contributing to a country's potential for future growth, a history of colonization serves as a far better indicator of future progress. In a later work, Acemoglu, Johnson, and Robinson (2005a) assert that the significant expansion of trade volume among countries along the Atlantic enhanced the ability for colonialism to flourish, entrenching these institutional differences even further. Recognizing the potential barriers created by colonialism could be beneficial to the determination of the optimal course of action for former colonies to take moving forward. Unfortunately, though a history of colonization can be taken into account when formulating policy, that history can hardly be changed. Thus, there exists a limitation as to the potential benefits that can be drawn from such a conclusion. The discovery of other, more contemporary determinants of an effective policy environment with the potential to be changed in the future can build on Acemoglu's findings. In addition to historical analyses involving colonialism, there are other possible explanations of institutional differences across nations that relate primarily to factor endowments. Sokoloff and Engerman (2000) argue that differences in initial resource allocations are a primary explanation of the persistence of inequalities across time. Such inequalities contributed to the dominance of a particular cultural or ethnic group that installed institutions to ensure their persistence, subsequently decreasing growth. Like Acemoglu, Sokoloff and Engerman agree that initial colonization patterns still contribute to the crosscountry differences observed today. Their work is also very much related to the social cohesion arguments presented here and in the ERW paper, because it is the divide between those with power and those without power that causes bad policies to persist. In this way, the analysis presented in this paper aligns well with the Sokoloff and Engerman hypothesis, as it examines the divisions among people that lead institutional inequalities to be sustainable over the long run. All of the research discussed above recognizes that it is not only a discussion of institutional impacts on growth that is necessary, but also an examination of the best mechanisms to initiate and prolong good governance. The avenues for further analysis, however, are far from exhausted. There are a variety of ways that this research could be

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expanded in order to increase its applicability to broader development goals and program evaluation. The use of cross-sectional averages in place of panel data is common in the literature when examining the causal impacts of social norms on institutional formation. Examples of the use of cross-sectional data can be found in the ERW paper, as well as research of a similar nature by Knack and Keefer (1997). While cross-sectional analysis allows the researcher to examine the long run impacts usually associated with the creation of institutions, the use of this type of data is not without its drawbacks. If there are persistent unobservable country characteristics that occur simultaneously with random shocks through time, for example, there could be some doubt as to the consistency of a cross-sectional model's estimates. Additionally, the use of panel data allows for lagged variables to mitigate problems of reverse causality and aid inference. For these reasons, the measurement of social cohesion effects using panel data, as carried out in this paper, could serve as a robustness check to satisfy the concerns surrounding cross-sectional analysis, lending support to previous results. In addition to the use of cross-sectional data, most papers in this area employ a set of institutions that are generally accepted by the majority of economists, but do not attempt to justify the inclusion of particular institutional characteristics specifically. Thus, to provide clarity and expand understanding, a discussion of the measurement of a variety of institutional characteristics will be undertaken in Section III of this paper. Specifically, the analysis will break down political structures and characteristics of governance into specific subgroups in order to compare outcomes across institutional types. As a final consideration, the straightforward relationships described by the ERW model can be expanded to incorporate the effects of education into the ideas of social cohesion. Incorporating education into a model of social cohesion will broaden the scope of the current discussion and augment the ways in which the discipline thinks about social networks. In summary, while it is recognized that the previous work in this literature is both important and unique, there are still enhancements to be made that, if successful, will strengthen these arguments.

SOCIAL COHESION AND INSTITUTIONAL QUALITY The definition of social cohesion seems quite nebulous at first, and is interpreted in a variety of ways by authors using the term in different contexts. Researchers using social cohesion to examine distributional consequences usually define it in terms of income distribution and other inequalities. In contrast, those using the term to describe a "willingness to work together" to create bonds within communities or a level of personal investment in society often look at variables such as rates of civic participation and levels of trust of other people (Beauvais and Jenson, 2002). Regardless of the context in which it is used, the commonality between these measures is that they promote the idea that there are important links between an individual's place within a society and the aggregate outcomes of the society itself. It is therefore important to define what one means when using these terms, especially for the purposes of economic and political research. When examining the contribution of social cohesion to institutional development around the world, both the distributional aspects of the

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term and a civic participation component become important. In this light, I define social cohesion as those attributes that contribute to a breakdown of economic, social, and political barriers to reform within a society. Measures of this quality can be manifested in several forms. To measure the extent to which a population feels a certain level of economic inclusion, income inequality can provide a suitable mechanism for evaluation. Income inequality has been cited as an important measure of social cohesion by a variety of authors (e.g., Easterly et al., 2006; Kawachi and Kennedy, 1997). Notions of inclusion could also manifest themselves through education and civic participation, as people who are more equipped with basic skills may be more civically engaged. To this end, the adult literacy rate of a society could provide a valuable indicator of progress. To gauge the degree of social and political divisions, indices of ethnolinguistic fractionalization can be of use. All of these potential variables will be discussed in greater detail in the fourth section of this paper. Though these measures are by no means perfect, they provide good proxies with which to measure social divisiveness or inclusion. Unlike the general pattern of consensus that seems to have emerged with respect to measures of social cohesion, when thinking about the qualities of a good economic or political institutions, there are a wide variety of views about which characteristics should dominate the discourse. Many times institutions are consolidated into groups without identifying or ranking their individual attributes. In a recent paper by Acemoglu and Johnson (2005), the authors argue that this approach masks the fundamental economic ideas that institutions are thought to promote3. To examine this hypothesis, the authors divide institutions into two categories: those supporting private contracts between individuals, and those preventing inappropriate government expropriation or exploitation of the powerless by the powerful. Using an instrumental variables approach, they find that while the property rights-type of institutions have a positive impact on economic growth, the contracting or private institutional varieties seem to matter much less. As will soon be shown, by making distinctions between various institutional types within the data, this paper will be able to provide an indirect test of the Acemoglu and Johnson hypothesis. One additional type of institution that is often overlooked in the literature is the methods by which the political and bureaucratic organizations within a country promote or discourage production and innovation. For example, a country could have well defined property rights and limited corruption, but a plethora of "red tape" that makes entry into an industry difficult by a relatively new or small firm. These barriers to entry can place constraints on competition, diminishing the potential for positive spillovers, and creating social losses from inefficiency. Several prominent economists have recognized the significance of these qualities, and have taken steps to measure its importance in national economies. The Global Competitiveness Report, published annually and edited by Xavier Sala-i-Martin and colleagues (2004), is an attempt to measure both the macroeconomic policies that create a suitable environment for prosperity and the ability of firms to generate wealth at the microeconomic level. The indices recognize that the ease of doing business in a country can dramatically affect its potential to grow, and that measuring the degree to which this occurs is important for understanding long run patterns of economic progress.

3

The 2005 paper builds upon previous work by these authors in 2002, where they examine the "institutional reversal" caused by European intervention in colonial investment markets. (Acemoglu, Johnson, and Robinson, 2002)

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The previous discussion indicates the need to establish an intellectual "common ground" when discussing the potential impacts of institutional quality on growth. Without making clear distinctions about the nature and types of institutional quality, difficulties can arise when interpreting the estimation results of such complex relationships. Focusing on these issues prior to an empirical analysis greatly simplifies subsequent work, making the data easier to understand, and simplifying formal economic modeling.

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THE EMPIRICAL MODEL With these ideas in mind, a basic dual-equation econometric model is presented that will support and extend the previous work established in this area. The form of the model builds upon an original model presented by ERW, where ethnolinguistic fractionalization and middle class income share are used as instruments for institutions in a bivariate per-capita growth equation. The ethnolinguistic fractionalization variable used in that paper is measured as the probability that two randomly drawn individuals from a population will belong to different ethnolinguistic groups, as derived by Easterly and Levine (1997). However, as is noted by Alesina and colleagues (2003), since these data are largely based on linguistic distinctions, it can mask other important differentiating information such as racial origins or skin color. In contrast, measuring fractionalization with respect to religious differences has been shown to have the opposite of the paper's hypothesized effect on political institutions, because freer and more tolerant societies also tend to have a large number of religious groups. Fortunately, Alesina and his coauthors have improved upon these measures by separating fractionalization measures into three distinct groups pertaining to ethnicity, language, and religion, respectively. Given the problems pertaining to the use of the language and religion measures described above, the empirical analysis shown here will make use of the ethnic fractionalization component of the work, while recognizing that future work with the other two measures and more direct metrics pertaining to political participation could be a useful extension of the current project. As previously mentioned, the ERW paper uses the income share of the middle class within a society as a measure of economic cohesion. The authors suggest that this measure of economic divisions is preferred to household-level measures such as the gini coefficient. The use of the "middle class share" variable, however, is not available as a panel during the measured time frame, necessitating that 20-year cross-sectional averages be used in the ERW paper when measuring inequality. As noted above, this approach, while offering some substantial benefits, is unable to account for any time-varying characteristics that affect institutional quality and inequality simultaneously. The authors note that they are "well aware of the limitations of cross-country regressions", and hope to improve upon this potential for bias in future work (Easterly, Ritzen, and Woolcock, 2006, p.104). Fortunately, by using household-level income measures of the gini coefficient instead of middle class share, this paper is able to construct a panel data set that can accurately capture the potential for heterogeneity of inequality measures across a short time span, and improve upon the work in a manner in which the authors had hoped.

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In addition to the previous concerns surrounding the measures of fractionalization and inequality, the empirical specification of the ERW model suggests that inequality is assumed to be uncorrelated directly with growth, implying that it only affects the institutional variable. This exclusion restriction seems to be only marginally valid in the paper for some of the institutional specifications, as the test statistics of overidentification are fairly large with small p-values. Unfortunately, recent econometric research has shown that the power of these overidentification tests against plausible alternatives is relatively minor, necessitating that small test statistics (or large p-values) be obtained to have confidence in this type of exclusion restriction (Cameron and Trivedi, 2005, pp. 105-107). To further investigate the robustness of these restrictions, overidentification tests were conducted on the ERW model using the panel data from this paper, replacing the "middle class share" measure with the gini coefficient variable. The results (not shown here) indicated that the gini coefficient measure, which is known to be highly correlated with variables measuring middle class share, does not meet the requirements to be validly excluded from the growth equation. For this reason, the model estimated in this paper includes the inequality measure in the growth equation. Given these considerations, the hypothesized model of the paper is as follows:

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𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠𝑖,𝑡−1 = 𝛾0 + 𝛾1 (𝐸𝑡ℎ𝑛𝑖𝑐𝐹𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛)𝑖,𝑡−1 + 𝛾2 (𝐼𝑛𝑒𝑞𝑢𝑎𝑙𝑖𝑡𝑦)𝑖,𝑡−1 + 𝛾3 (𝐴𝑑𝑢𝑙𝑡𝐿𝑖𝑡𝑒𝑟𝑎𝑐𝑦)𝑖,𝑡−1 + 𝜇𝑖,𝑡−1 𝑅𝐺𝐷𝑃𝑃𝐶𝐺𝑅𝑖𝑡 = 𝛾4 + 𝛾5 (𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠)𝑖,𝑡−1 + 𝛾6 (𝐼𝑛𝑒𝑞𝑢𝑎𝑙𝑖𝑡𝑦)𝑖,𝑡−1 + 𝜂𝑖𝑡 here RGDPPCGR represents the annual growth in real GDP per capita for country i at time t4. There are several aspects of this specification that require explanation in order to interpret the model's predictions. First, it is important to note that adult literacy is included as a new measure of social cohesion that was not previously incorporated into the ERW model. The addition of this variable is believed to be an improvement because it measures an aspect of social inclusion that is not already captured by ethnic fractionalization or income inequality. The ability to read enables an individual to participate more fully in society on a variety of different levels. Not only is literacy a good proxy for basic educational attainment, it also reflects the ability of an individual to engage in simple political and social functions, such as voting, applying for government programs and services, and asserting certain rights that may be provided by the current political system. Recent World Bank projects have recognized the importance of the relationship between educational outcomes and social cohesion, especially when that education is tailored to foster a sense of inclusion among members of a society (Roberts-Schweitzer, 2006). From an econometric perspective, the use of this variable also contributes an additional instrument to control for the endogeneity of institutions, allowing the model to remain overidentified5. For these reasons, it is believed that the inclusion of the literacy variable captures components of social cohesion that may not already be taken into account.

4

The model specification as presented here is similar to the structure of the ERW model. Though, for purposes of brevity, the exact equations of that model are not included here, a comparison of the two approaches may prove useful to the reader. 5 This analysis assumes that the adult literacy rate of a country can be validly excluded from the growth equation. The validity of such an assumption will be examined when discussing estimation results.

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In addition to the inclusion of an additional explanatory variable, the use of panel data allows a number of flexibilities specific to the empirical estimation of the model. For example, because of the potential for correlation between institutions, inequality, and the error term, the independent variables in the growth equation are lagged one year. Though this does not necessarily eliminate any remaining endogeneity problems entirely, it has the potential to improve upon the case where a lag does not occur by minimizing the potential for a bidirectionally causal relationship. In addition, though institutional characteristics were not able to be obtained for each year measured, two waves of data were used to incorporate timevarying characteristics partially.

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DATA AND ESTIMATION Descriptive statistics for the data used in estimation are given in Table 2. The data spans 111 countries over the years 1992-19996. Observations with missing or inconsistent estimates were dropped, leaving 419 total observations for estimation purposes. Because results were not available in all cases for the Law and Order variable, the estimates using that measure are limited to 398 observations. To check for potential selection bias from dropped observations, observed country characteristics were compared between the original sample and the sample used for estimation. The means for all observed characteristics do not differ significantly between the two groups of observations. This provides some evidence that sample selection issues are unlikely to be driving the estimation results7. As noted in Table 1, institutional characteristics for each country were obtained from the 2004 annual report of the Economic Freedom of the World, created by James Gwartney and Robert Lawson, as well as the 2004 edition of the Global Competitiveness Report. Using a variety of indicators, the reports create indices of the quality of specific institutional groups that range from 1 to 10, where 10 is indicative of the highest level of institutional quality possible. For comparison with previous work, the Property Rights and Enforcement variable used in this paper most closely corresponds with Acemoglu and Johnson's definition of "property rights" institutions, while the Law and Order variable can be classified in the "contracting" category. The Ease of Doing Business indicator incorporates administrative obstacles for new businesses as well as time spent dealing with bureaucracy and bribes for government officials, the proverbial "greasing the wheels" of corruption (Gwartney and Lawson, 2007; Sala-i-Martin et al., 2004). This measure is similar to the Doing Business indicators compiled by the World Bank, though the years spanned by the World Bank data are not sufficient for use in this paper8. The empirical model is estimated using two stage least squares (2SLS) with robust standard errors. This technique shares the advantage of three stage least squares (3SLS) in that it is able to use the independent variables as instruments to correct the endogeneity of institutions in the growth equation. By using the Huber-White correction of the variance6

It should be noted that 1996 is excluded from estimation as a result of complications in the procurement of reliable data for that year. 7 It is recognized, however, that the potential for bias from selection on unobservables remains. The striking similarities between the two samples, however, provide evidence that this risk is minimal. 8 See Djankov et al. (2002) for the background paper discussing the business initiation measures of the Doing Business data.

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covariance matrix, the 2SLS estimator also allows for potentially heteroskedastic errors in each equation. Table 1. Data Sources Source Growth of Real GDP per capita Institutional Measures Property Rights and Enforcement Law and Order Ease of Doing Business Measures of Social Cohesion Ethnic Fractionalization Inequality (gini) Adult Literacy Rate

United Nations Human Development Report (2006)

Gwartney and Lawson (2004) Gwartney and Lawson (2004) Sala-i-Martin et al. (2004) Alesina et al. (2003) UTIP Estimated Household Income Inequality Data Set (2007), United Nations WIDER World Income Inequality Database (2005), and Deininger and Squire (2007) United Nations Human Development Report (2006)

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It is important to note, however, that 3SLS estimation will provide increased efficiency over 2SLS estimates if the error terms are homoskedastic but correlated across equations (Cameron and Trivedi, 2005, p. 214). For this reason, the model was also estimated using 3SLS as a robustness check. As expected, coefficient estimates were quite similar across estimation methods while standard errors varied slightly. The signs, magnitude, and significance level of all relevant variables remained unchanged between each technique. Because of the additional flexibility and ease of interpretation allowed by 2SLS estimation, results from this estimation method are primarily reported in the paper unless otherwise noted. To ensure the model's validity, various specification tests and other checks will be discussed in Section 7 of the paper. Table 2. Descriptive Statistics Mean Growth of Real 0.0315 GDP per capita Institutional Measures Property Rights 6.116 and Enforcement Law and Order 7.039 Ease of Doing 5.814 Business Measures of Social Cohesion Ethnic 0.4184 Fractionalization Inequality (gini) 42.56 Adult Literacy 81.70 Rate

Std. Dev.

Obs.

Minimum

Maximum

0.1555

538

-0.3952

0.8019

1.733

570

2.6

9.6

2.448

534

0

10

1.182

570

2.7

8.8

0.2451

666

0.002

0.8635

8.125

581

20.6

74.3

19.87

658

25.6

99.7

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RESULTS The results of the estimation of the model are given in Tables 3 and 4. Observing the estimates of the institutional equation given in Table 3, we can see that the hypotheses of the economic model relating to the first stage are generally confirmed. Looking at each equation individually, the negative and statistically significant coefficients of the ethnic fractionalization measure indicate that a more ethnically fractionalized society indeed seems to place constraints on policymakers to develop the types of institutions specified in the top row of the table. It also appears that fractionalization tends to have a larger impact on government expropriation and contractual regulations than on the ease of doing business in a society. This seems to make some intuitive sense, as a society run by a particular group (or set of groups) may be more eager to expropriate the wealth of other groups if the society is more dispersed along ethnic lines. Without a large coalition able to fight against government intrusion, it may be easier for unequal policies to persist. For these same reasons, it also makes sense that inequality is a far better indicator of property rights and the rule of law than the ease of doing business within a country. Even more interesting for the purposes of this paper are the results pertaining to the adult literacy rate. To take the estimate pertaining to property rights as an example, the coefficient indicates that, ceteris paribus, a single percentage point increase in the literacy rate will increase the average rating of property rights in a country by .0159. The magnitude of the estimated coefficient may seem tiny at first, but it is important to remember that since this political indicator is only ranges from 1 to 10 (and from 2.6 to 9.6 among the countries of the sample), even a small change in this indicator can indicate a relatively large change in the policy environment of a nation. In addition, it is important to note that, when viewed individually, these variables are not intended to capture the entire degree of social cohesion within a country, per sé. Rather, the main purpose of these measures is to serve as indicators for the general social structure and cultural interactions of a particular environment. When viewed in this light, the results of the political institutions equation can generally be considered to support the theory implicit in the economic model of this paper. Table 3. 2SLS Results: Institutional Equation

Ethnic Fractionalization Inequality (gini) Adult Literacy Rate Constant F-Statistic Adjusted R2 N

Property Rights and Enforcement -1.579*** (0.3557) -0.730*** (0.0137) 0.0159*** (0.0047) 8.572*** (0.7595) 30.17 0.3593 419

Law and Order

Ease of Doing Business

-1.801*** (0.566) -0.1013*** (0.0203) 0.0217** (0.0092) 10.320*** (1.289) 11.27 0.3102 398

-0.5957** (0.2606) 0.0022 (0.0087) 0.0192*** (0.0036) 4.326*** (0.5729) 30.56 0.1266 419

*, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively. Standard errors are given in parentheses, and are corrected for heteroskedasticity.

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Looking at Table 4, the results of the growth equation are somewhat consistent with that of the previous literature. Unlike previous studies, however, the structure of the empirical model in this paper allows for a more nuanced interpretation of the effects of social indicators on a country's growth prospects. Comparing the coefficients of all three specifications, it seems clear that, rather than having a direct effect on growth rates, measures of social cohesion affect growth through mechanisms relating to institutional quality. Moreover, since the paper differentiates among institutional types, even more information can be obtained from the data. The estimation indicates that the property rights-type indicator of institutional quality has large and significant effects on economic growth. To wit, an increase in these types of political foundations is associated with an increase in growth of over four percent. This estimate is so large, in fact, that it instigates questions as to other factors correlated with these types of institutions that may be driving such results. An investigation into these issues is reserved for future work, but the story of this coefficient remains clear: Protecting the rights of citizens and their property from seizure by others is a crucial element in the promotion of economic growth and stability. Table 4. 2SLS Results: Growth Equation

Institutional Measure Inequality (gini)

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Constant Sargan Statistic (p-value) N

Property Rights and Enforcement 0.0471*** (-0.0137) 0.0024

0.0422*** (0.0140) -0.0033

Ease of Doing Business 0.0467*** (0.0182) -0.0016

(0.0019)

(0.0024)

(0.0010)

-0.3639** (0.1625) 0.458 (0.498) 419

-0.4083** (0.2002) 0.067 (0.796) 398

-0.1716 (0.1374) 5.347 (0.020) 419

Law and Order

*, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively. Standard errors are given in parentheses, and are corrected for heteroskedasticity.

Similar inferences can be obtained when examining the estimates pertaining to the ease of doing business. It seems that easing the ability of a firm to conduct business within a society also has large impacts on future growth, a result that has been well documented by other sources. The unique contribution presented here, however, arises when connecting these results to that of Table 2. Examining the result of both equations simultaneously, it is evident that social cohesion measures such as ethnic fractionalization and the adult literacy rate are major contributors to the promotion of entrepreneurship, subsequently affecting growth outcomes. One of the most interesting pieces of information obtained from Table 4 is the significance and magnitude for the variables involving law and order. The distinction drawn between the Property Rights and Enforcement and Law and Order variables can be directly related to Acemoglu's arguments that the property rights components of institutions matter far more than contractual or private concerns. Acemoglu's emphasis on property protection does not seem to be upheld by these results, however, as the coefficient representing the effect of property rights is not significantly larger than the coefficient relating to law and order. This

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result suggests that the colonization arguments put forth in previous work may not always hold under alternative specifications. This implies that even more empirical work is needed in this area in order to dissect the relative importance of social cohesion versus colonization effects with respect to institutional outcomes. It is also interesting to note that the effects of inequality, as measured by the gini coefficient, are insignificant across all institutional specifications, even though overidentification tests indicate that the variable cannot validly be excluded from the growth equation. A possible explanation for this outcome relates to the Kuznets (1955) hypothesis, which dictates that inequality will vary along stages of a country's growth path. If the relationship between inequality and growth is nonlinear in nature, a linear specification may yield insignificant coefficients even if the two variables are directly related. This brings forth the possibility for the incorporation of a nonlinear specification of inequality as a potentially fruitful avenue for future work.

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SPECIFICATION TESTS AND ROBUSTNESS CHECKS As noted above, the use of 2SLS in estimation requires two main assumptions; that measures of social cohesion are highly correlated with institutional outcomes, and that at least one of these social cohesion measures can be validly excluded from the growth equation. The large F-statistics reported for all three institutional equation specifications in Table 3 indicate that these instruments are strong predictors of institutional quality. The importance of instrument strength has been increasingly emphasized in the literature. According to Staiger and Stock (1997, p. 557), the value of the F-statistic should exceed ten in regressions of this type, while values below five suggest serious problems relating to finite-sample bias (Cameron and Trivedi 2005, p. 105). It is encouraging, then, to observe that the possibility of inconsistent estimates resulting from irrelevant instruments do not appear to threaten the validity of this work. These large F-statistics and reasonable R2 values indicate that the first of the two main assumptions of 2SLS is likely to hold. The task remains, then, to examine whether the exclusion restrictions of the model hold under scrutiny. Intuitively, theories of social cohesion generally support the idea that ethnic fractionalization should only have a significant and direct impact on growth through its effect on institutional characteristics. Social environments that foster an increased willingness for citizens to work together should promote positive governmental reforms, but may not directly impact a country's growth rate. A plausible argument can also be made for the exclusion of the adult literacy rate as a direct cause of economic growth. While an increase in the number of literate citizens per capita certainly affects the communication and networking ability of a population, it is unclear that this component will also directly affect growth outcomes in a given year. Additionally, since both the adult literacy and ethnic fractionalization variables are lagged one year with respect to current growth, there is minimal risk of feedback effects of an economic boom on past outcomes. To examine the validity of excluding both ethnic fractionalization and adult literacy from the growth equation from an econometric perspective, Sargan tests of overidentifying restrictions were conducted for each growth equation estimated, and the results of these tests

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are reported in Table 4. Note that a rejection of the null hypothesis of a Sargan test implies that the joint validity of the instrument moment conditions does not hold, thus rejecting the model specification (Verbeek, 2004, p.147). It is therefore encouraging to observe that for all specifications with the exception of the Ease of Doing Business regression, the test strongly supports the overidentification conditions. The p-values corresponding with each of these two statistics are especially high, helping to mitigate concerns of a lack of power for this test. As an additional check, separate 2SLS regressions were run including either ethnic fractionalization or literacy in the second stage. In all institutional specifications, an exceptionally small and insignificant coefficient on adult literacy was obtained when including the variable in the second stage. Similarly, no significant effect was found when including ethnic fractionalization in the growth equation, except for the case when the Ease of Doing Business indicator was used as the instrumental variable. Though these are informal tests, they do contribute additional support to the assumption of validly excluded instruments. The only concerns regarding overidentification arise when examining the Sargan statistic for the Ease of Doing Business regression. A Sargan statistic of 5.347 indicates that exclusion can only fail to be rejected at the two percent level. This implies that one or both of the identifying social cohesion variables may need to be incorporated into the growth equation in this case. As noted above, inclusion of adult literacy in the growth equation when Ease of Doing Business is used as an institutional measure yields small and insignificant results for that variable9. This confirms, albeit informally, that adult literacy is a validly excluded instrument. Inclusion of the ethnic fractionalization measure in the second stage, however, does yield significant results for this single specification. Because these results indicate that ethnic fractionalization may not be validly excluded in all cases, the model is re-estimated including this variable in both equations to test the susceptibility of the results to identification bias10. The results of this estimation are reported in Table 5. While the coefficient for ethnic fractionalization does appear to be significant, the inclusion of this variable sweeps out any potential significance from the other explanatory variables. It appears that even though the overidentification restriction for ethnic fractionalization may only weakly hold in one of the model's specifications, it generally seems true that social cohesion affects growth indirectly through institutional outcomes rather than encouraging growth directly. Even if the results of all regressions involving the ease of doing business within a society were dismissed, a strong case could still be made for the links between social structure, institutions, and subsequent growth outcomes using these results. The specification tests and other checks performed within this section generally support that the economic model of the paper is a valid representation of the relationship between social structures, institutional change, and economic outcomes. As with any empirical work, there are some flaws in estimation that cannot be avoided. In general, however, these tests show that the reader can be reasonably confident in the results generated here.

9

Specifically, the coefficient of adult literacy in the growth equation is -0.0045 when using the Ease of Doing Business measure, with a standard error of 0.0043. 10 The inability to test the remaining exclusion restriction in an exactly identified model of this type is recognized, but the importance of including the fractionalization measure in the growth equation seems to outweigh the costs of this potential weakness. This is especially true when considering that all results point to adult literacy as a validly excluded variable.

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Lauren R. Heller Table 5. 2SLS Results: Growth Equation, Exactly Identified Model

Coefficient (Standard Error) Ease of Doing Business 0.0220 (0.0203) Inequality (gini) -0.0011 (0.0009) Ethnic Fractionalization -0.0848** (0.0347) Constant -0.0166 (0.1457) 2 Adjusted R 0.0413 N 419 ** indicates a significance level of 5%. All standard errors are corrected for heteroskedasticity.

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CONCLUSION This paper serves as an initial step in what is hoped will be a comprehensive examination of the relationships between social cohesion, institutions, and growth by the discipline. By disaggregating the notion of institutions, the paper has shown that such distinctions matter in the discussions of growth and living standards. The first stage results lend support to the idea that a more ethnically fractionalized society constrains policymakers in their ability to promote institutional maturity within a country's borders. When dissecting these institutional characteristics into specific groups, social cohesion variables seem to have a larger impact on government expropriation and contractual regulations than on the ease of doing business in a society. Adult literacy rates are also shown to have a strong effect on the ability for these institutional characteristics to take hold among a given population. When examining the results of the second stage, however, the similarly positive effects of both Property Rights and Law and Order types of institutions on growth rates seem to refute previous arguments that the property rights components of institutions matter far more than contractual considerations. In this way, the results here have been able to contribute to a more nuanced discussion of the relationship between social variables, institutions, and growth outcomes. In addition, the paper has made significant headway in extending previous models of relationships between social cohesion and growth to analyze the theoretical underpinnings of these relationships in further detail. Despite this additional level of complexity, the results shows here should reinforce a central conclusion of the development literature: Supporting a country's ability to develop sound institutions is a key determinant of future economic growth and prosperity. Though there is certainly room for improvement in some aspects of the analysis, it is believed that the work presented here is an important beginning to the encouragement of dialogue surrounding appropriate policy recommendations for lesser-developed countries. By deconstructing previous notions of social cohesion, institutions, and growth, it is hoped that the paper is able to shed a little more light on the ways in which the developing world can begin to attain standards of living not yet seen in these areas.

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REFERENCES Acemoglu, Daron. 2003. "Root Causes: A Historical Approach to Assessing the Role of Institutions in Economic Development." Finance and Development: 27-30. Acemoglu, Daron and Simon Johnson. 2005. "Unbundling Institutions." Journal of Political Economy, 113(5): 949-995. Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2002. "Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution." The Quarterly Journal of Economics, 117(4): 1231-1294. Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2005a. "The Rise of Europe: Atlantic Trade, Institutional Change, and Economic Growth." American Economic Review, 95(3): 546-579. Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2005b. "Institutions as the Fundamental Cause of Long-Run Growth." In Philippe Aghion and Steve Durlauf, editors, Handbook of Economic Growth, Volume 1a. Amsterdam: Elsevier. Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat, and Romain Wacziarg. 2003. "Fractionalization." NBER Working Paper 9411. Barro, Robert J. 1997. Determinants of Economic Growth: A Cross-Country Empirical Study. Cambridge, Massachusetts: MIT Press. Beauvais, Caroline and Jane Jenson. 2002. "Social Cohesion: Updating the State of the Research." CPRN Discussion Paper F22. Cameron, A. C. and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications, 1st ed. New York: Cambridge University Press. Dawson, John W. 1998. "Institutions, Investment, and Growth: New Cross-Country and Panel Data Evidence." Economic Inquiry, 36(4): 603-619. Deininger, Klaus W. and Lyn Squire. 1997. "Measuring Income Inequality Database." Macroeconomic and Economic Growth and Poverty. Washington, DC: World Bank. Djankov, Simeon, Rafael L. Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2002. "The Regulation of Entry." The Quarterly Journal of Economics, 117(1): 1-37. Easterly, William and Ross Levine. 1997. "Africa's Growth Tragedy: Policies and Ethnic Divisions." Quarterly Journal of Economics, 112(4): 1203-1250. Easterly, William, Jozef Ritzen, and Michael Woolcock. 2006. "Social Cohesion, Institutions, and Growth." Economics and Politics, 18(2): 103-120. Gwartney, James and Robert Lawson. 2004. Economic Freedom of the World: 2004 Annual Report. Vancouver: The Fraser Institute. Kawachi, Ichiro and Bruce P. Kennedy. 1997. "Health and Social Cohesion: Why Care about Income Inequality?" British Medical Journal, 314(7086): 1037-1040. Knack, Stephen and Philip Keefer. 1997. "Does Social Capital have an Economic Payoff? A Cross-Country Investigation." The Quarterly Journal of Economics, 112(4): 1251-1288. Kuznets, Simon. 1955. "Economic Growth and Income Inequality." American Economic Review, 65(1): 1-28. Manning, Richard. 2008. "Summary in English." In OECD Journal on Development: Development Co-Operation Report 2007, 1-16. France: OECD.

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Pitlik, Hans and Steffen Wirth. 2003. "Do Crises Promote the Extent of Economic Liberalization?: An Empirical Test." European Journal of Political Economy, 19(3): 565581. Roberts-Schweitzer, Eluned. 2006. "Introduction." In Promoting Social Cohesion through Education: Case Studies and Tools for using Textbooks and Curricula, ed. Eluned Roberts-Schweitzer, Vincent Greaney, and Kreszentia Duer, 1-5. Washington, DC: The World Bank. Sala-i-Martin, Xavier, Michael Porter, Klaus Schwab, and Augusto Lopez-Carlos, ed. 2004. The Global Competitiveness Report, 2003-2004. New York: Oxford University Press. Sokoloff, Kenneth L. and Stanley L. Engerman. 2000. "Institutions, Factor Endowments, and Paths of Development in the New World." Journal of Economic Perspectives, 14(3): 217232. Staiger, Douglas and James H. Stock. 1997. "Instrumental Variables Regression with Weak Instruments." Econometrica, 65(3): 557-586. United Nations Development Programme. 2006. Human Development Report 2006. New York: Palgrave Macmillan. University of Texas Inequality Project. 2007. "Estimated Household Income Inequality Dataset." http://utip.gov.utexas.edu/data.html. Verbeek, Marno. 2004. A Guide to Modern Econometrics, 2nd ed. London: John Wiley and Sons. World Institute for Development Economics Research (WIDER). June, 2005. "World Income Inequality Database", 2(a). http://www.wider.unu.edu/research/Database/en_GB/ database/.

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SECTION 5. ECONOMIC FREEDOM: WHAT DO WE KNOW?

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Chapter 14

WHAT HAVE WE LEARNED FROM THE ECONOMIC FREEDOM OF THE WORLD INDEX?1 James Gwartney

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INTRODUCTION When you have a co-author, such as Bob Lawson, introducing you and who expects to continue working with you in the future, he can really make the introduction sound good. I really appreciate and am deeply honored to be the recipient of this year’s Adam Smith award. Reviewing the list of people who have won this award in the past many of them are my heroes: Of course, people like Jim Buchanan and Doug North, but also Armand Alchian, Harold Demsetz, Vernon Smith and so on. It’s hard to imagine my name being added to that list. I am deeply honored by it. My co-authors deserve a big share of this award. I’ve co-authored so many things. I guess that is the secret to getting things done — work with a lot of smart co-authors. Many of them are here—Rick Stroup, Russ Sobel, and Bob Lawson, as well as Michael Walker with whom I have worked closely on a number of projects. It’s a special honor to receive this award at the same time Mike will be receiving the Thomas Jefferson Award for his contribution to economic freedom and good government. I would also like to express my congratulations to the Association of Private Enterprise Education for the growth of this organization. I believe that the first annual meeting I attended was no more than half this size, maybe not even that large. Jeff Clark has done a fantastic job with this organization over the last 14 years and it has become the leading organization in the area of economic education and free enterprise economics. I congratulate Jeff for that achievement. Also, I would be remiss if I didn’t mention another person who has contributed so much to any achievements that I might have made: my wife Amy. We have been married 41 years 1

The address that Professor Gwartney made on accepting the Association of Private Enterprise Education’s Adam Smith Award in Nassau in the Bahamas, April 2004. It appeared as James Gwartney, “What Have We Learned from the Economic Freedom of the World Index?” Journal of Private Enterprise 20, no. 1 (2004): 1-8. The author is holder of the Gus A. Stavros Eminent Scholar Chair at Florida State University.

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and she is still my best friend. When we came to the Bahamas the immigration form asked why you were coming here and one of the options to check off was honeymoon, and we both decided it would be appropriate to check that option. Amy is my helper, chauffeur, and proofreader. I feel like some of the things I write should say “if there are any remaining errors, they are the responsibility of my wife Amy.” But that would hardly seem an appropriate way to express one’s gratitude. As many of you know, I spent two years working for the government at the Joint Economic Committee. Please forgive me for that shortcoming. I went to Washington being very cynical and skeptical and expecting things to be bad, and it turned out to be even worse than I had expected. While I was there, among the responsibilities that Amy had was that she was my unpaid intern. That led to an interesting event. Bill Clinton gave me a call one time and said he heard that I was sleeping with my intern and would like me to join one of the largest clubs in Washington DC—federal government workers who are sleeping with their interns. I explained to him, “Mr. President, my intern is my wife. Thus, I am merely sleeping with my wife.” He hung up on me at that point. In any case, after-dinner speeches are typically too long and the speaker nearly always tells you more than you really wanted to know. I told Jane Shaw early on that my plan was to avoid both of these mistakes.

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LESSONS FROM THE EFW PROJECT I would like to think a little bit with you this evening about what we’ve learned from the Economic Freedom of the World (EFW) project. When we set out on this project 15 years ago, our objective was to develop to the fullest extent possible a comprehensive and objective measure of economic freedom. We recognized that if we could develop an accurate measure of economic freedom it would allow us to investigate more directly a large number of unsettled questions. I am delighted that many of the papers that will be presented at this conference will be using the index to tackle many of these issues. A great deal of research has already been conducted in this area and we have learned quite a number of things. I was tempted to show a few slides, but I thought that a blind man explaining graphics might be a bit much. So you’ll have to wait for the slide show tomorrow. Even without the slides, I’d like to emphasize a few things I think we have learned as a result of using the freedom index to analyze economic performance. Most of you are probably familiar with the quintile analysis of the relationship between per capita GDP and economic freedom. There is a strong positive relationship between the two: countries with more economic freedom have higher levels of per capita GDP. The same is true for economic growth: economically free countries grow more rapidly. However, it is important to look at these relationships for longer periods of time. For example, when you consider economic freedom and per capita income, what really matters is the type of institutional arrangements a country has had over the last several decades. The EFW measure provides us with good data for about 100 countries back to 1980. When you look at the relationship between average EFW ratings from 1980 through 2000 and the 2000 per capita GDP, the results are pretty amazing. About two-thirds of the cross-country differences in per capita GDP are explained by just this one variable. Of course, the EFW

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index is a comprehensive measure of institutional quality and the consistency of policies with economic freedom. Thus, it registers the impact of many factors operating in unison. Similarly, economic freedom is a major determinant of cross-country differences in economic growth. For example, there is a strong positive relationship between the 1980-2000 average EFW rating and the growth of per capita GDP over that two-decade period. The economic freedom-growth relationship remains strong, even when an extensive set of control variables, including those stressed by Jeffery Sachs, such as tropical location and percentage of the population living within a hundred kilometers of a coastline, are incorporated into the model. I would also like to mention a couple of other important things that I think we have learned. Free economies have substantially higher investment rates. This is particularly true for private investment. One way of analyzing this relationship would be to break countries into three different categories: those with an average 1980-2000 EFW rating of greater than 7.0, between 5.0 and 7.0, and less than 5.0. These three groups might be thought of as economies that are persistently free, a middle group, and those that are persistently unfree. For countries with EFW ratings above 7.0, private investment averaged about 18% of GDP, compared to 9.6 percent of GDP for countries with EFW ratings of less than 5.0. Thus, the private investment rate in countries in the persistently free category was almost twice that of the persistently unfree group. Of course we know that investment is a major determinant of growth but economic freedom is, in turn, a major determinant of investment, particularly private investment. The foreign direct investment figures shine additional light on the linkage between economic freedom and private investment. Foreign direct investment is almost entirely private. This category of investment reflects that foreigners think highly enough of an economy to place their funds at risk. The average annual foreign direct investment per worker in the freest economies (EFW ratings of 7 or more) during 1980-2000 was $3,117, compared to $444 for the middle group, and $68 for the less free group. Thus, over the two-decade period, the foreign direct investment per worker of the persistently free economies was an astonishing 45 times the rate for the persistently unfree group. This huge difference illustrates the importance of sound institutions and policies in which people have confidence and expect to remain in place over an extended period of time. The strong relationship between economic freedom and investment highlights an important point that is generally overlooked by researchers in this area: regression models that include investment along with various measures of institutional quality will systematically understate the impact of economic freedom and sound institutions. This occurs because when investment is included as an independent variable, the indirect effects of institutional factors that affect the level of investment are ignored. Put another way, when institutional factors are a major determinant of investment, the usual methodology excludes their indirect impact through investment. Economic freedom also influences the productivity of investment. We estimated the impact of a 1 percentage point increase in investment as a share of GDP on the growth of per capita GDP for the three EFW groups. A percentage point change in investment as a share of GDP increased the annual rate of growth during the two decades by 70 percent more for the persistently free (EFW greater than 7) than for the persistently unfree (EFW less than 5) group. The bottom line here: economic freedom not only influences the level of investment; it also influences the productivity of that investment. We were also able to calculate the

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productivity of government investment. Interestingly, the productivity of government investment was even lower than that of private investment in the unfree group. In addition to the cross-country differences in levels of economic freedom, changes in EFW also exert a positive impact on long-term growth. We have examined the impact of changes in EFW during the 1980s and the 1990s. In both cases, there was a positive and significant relationship between changes in EFW and long-term growth. The countries that moved toward economic freedom grew more rapidly. Even when the level of economic freedom was initially low, increases in EFW were associated with more rapid growth. Some have argued that the association between economic freedom and growth may reflect “reverse causality.” Perhaps, countries that grow more rapidly are more likely to adopt institutions and policies more consistent with economic freedom. This is a reasonable argument and we examined the data in an effort to test its validity. First, we examined the relationship between changes in EFW during the 1980s and 1990s and the growth of per capita GDP during the 1990s. There was a positive and significant relationship between the changes in EFW during each of the decades and growth during the 1990s. This was true both with and without a wide set of control variables. This indicates that changes in EFW are not only associated with higher growth rates, they also exert an independent impact on growth during the subsequent decade. In order to address the direction of causality, we then reversed the situation and made the growth rate during a decade an independent variable and changes in EFW during the following decade the dependent variable. I anticipated that the relationship would be random, that is, I did not think there would be a significant relationship between changes in growth during one decade (the 1980s) and changes in EFW during the following decade (the 1990s). I was correct about the relationship not being positive, but wrong about the random relation. The data indicates that rapid growth during a decade exerts a negative impact on changes in EFW during the following decade. Put another way, rapid growth during a decade actually reduces the likelihood of an increase in economic freedom during the following decade. Clearly, this undermines the reverse causality theory. When you think about it, these findings are not too surprising. They suggest that, reforms consistent with economic freedom are more likely to be adopted when things are going bad. If the economy has been a basket case during the last five or ten years, political decision-makers may well be more receptive to constructive reforms. On the other hand, when things are going pretty good and growth rates are relatively strong the pressure for reform is minimal. Confronted with the evidence on the positive impact of economic freedom on growth and income, some will respond, “Okay, but what about the distribution of income? Won’t markets generate more income inequality?” The EFW data also facilitate the examination of this charge. The evidence indicates that there is no relationship between economic freedom and income inequality. In fact, in the case of less developed countries, there is a modest tendency for more economic freedom to be associated with less income inequality. The argument that more economic freedom leads to greater inequality is simply a myth. The data do not support that proposition. How about the overall quality of life? How does economic freedom influence things like environmental quality, life expectancy, and the access to health care? Again, when you look at the relationship between economic freedom and various indicators of quality of life, broadly defined, economic freedom exerts a positive impact on these variables. The environment in economically free countries is cleaner than in those that are less free. The life

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expectancy at birth is about 20 years longer in the freest quintile of countries than in the least free quintile. Of course, the positive impact of economic freedom on various quality of life measures is largely due to its positive impact on income. But this should not detract from the positive effects. Today the case for economic freedom is stronger than ever. Without exception the fifteen countries with the highest economic freedom ratings, have both high per capita income levels and attractive growth rates. On the other hand, all of the countries with low levels of economic freedom have low per capita incomes, and many have experienced reductions in per capita real GDP during the last two decades. The only possible exception to this proposition is Israel. Israel is in the bottom third in terms of economic freedom, but its per capita GDP is relatively high. But given the outside aid Israel has received from both private parties and governments over the last several decades, it is something of a special case.

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CONCLUSION The bottom line is clear: compared to those that are less free, economies with more economic freedom attract more private investment, generate greater productivity from that investment, and achieve both more rapid growth and higher levels of income. But you will never hear this on the nightly news. Instead, the media will try to convince you that markets need direction from government, all of the good jobs are leaving the country, or that people would starve if it were not for government programs. I fear that we are once again falling into the romantic trap of the 1960s. I know that was before many of you were born, but I remember it well. During that era, there was a love affair with government. Government was going to wipe out poverty, trade-off a little inflation for a reduction in unemployment, provide free health care for everyone that needed it, and so on. Government was perceived of as a corrective device. Essentially, it was as if you had the winning run on third base in the bottom of the 9th and you could bring government in as a pinch hitter that would always deliver the game-winning hit. Of course this is mythology, as Jim Buchanan, Gordon Tullock, and the work of the public choice school has shown so convincingly. Obviously, we have a lot of work to do in terms of economic education and communicating the realities of economic freedom. This is the focus of APEE and I am sure this conference will provide all of us with helpful information and ideas. As we look forward to our meeting here, I am impressed with the quality of the program and I am deeply honored to be chosen as the recipient of the Adam Smith Award. Thank you so much.

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ACKNOWLEDGMENTS

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Joshua Hall would like to thank the Social Philosophy and Policy Center at Bowling Green State University for the support of their Visiting Scholar program during Summer 2009. Both editors would like to thank Edward Stringham and Gerald Gunderson to permission to reprint the articles contained in this volume from the Journal of Private Enterprise.

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INDEX

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A abatement, 131, 139 access, xiv, 16, 27, 29, 35, 59, 84, 104, 109, 112, 114, 122, 123, 147, 198 accommodation, 26 accountability, 43, 44 actual growth rate, 26 Adam Smith, 3, 19, 195, 199 adjustment, 27, 28, 29, 30, 116 adult literacy, 181, 183, 186, 187, 188, 189 Africa, 12, 41, 44, 50, 51, 53, 54, 57, 73, 74, 75, 77, 89, 106, 111, 119, 151, 153, 191 age, 12, 13, 61, 94, 111, 113, 114, 115 agencies, 54, 94, 147, 165, 172, 173 aggregate demand, 26, 27 aggregate supply, 26 AIDS, 111, 119 Albania, 49, 96, 168 Algeria, xvii, 12, 47, 49, 106, 111, 113, 151, 154 American Economic Association, 24 amplitude, 32 analytical framework, 94 Argentina, 12, 46, 47, 48, 52, 106, 151, 153 Asia, 25, 28, 41, 42, 51, 53, 74, 77, 118, 119 Asian countries, 28, 73, 75, 76 assessment, 25, 33, 68, 69, 101, 131, 133 Association of Private Enterprise Education, 67, 195 asymmetric information, 29 atheists, 76 atmosphere, vii attachment, 42 Austria, 106, 141, 152, 154 authoritarianism, 42, 44 authorities, 149 authority, 69, 84, 110 autonomy, 42, 69, 70 awareness, 177

B Bahrain, 48, 49, 52, 72 Bangladesh, 12, 52, 106, 110, 116, 117, 118, 119, 151, 155 banking industry, xv bankruptcy, 28 banks, xv, 29, 35, 54 bargaining, 28, 35, 70, 98, 110 barriers, xii, 28, 35, 161, 177, 179, 181 barriers to entry, 181 basic education, 183 basic needs, 94, 98, 101, 102 basic research, 147 behavioral change, 115 Belarus, 114 Belgium, 106, 141, 152, 154 belief systems, 77 benefits, viii, 34, 35, 110, 179, 182 bias, 95, 124, 182, 184, 188, 189 biotechnology, 145 births, 94, 112, 114, 115, 116 black market, 5, 29, 35, 59, 105 black-market premium, 5 blame, 25 Bolivia, 12, 16, 48, 52, 106, 151, 153 bonds, 180 Botswana, 106, 111, 151, 155 Brazil, xvii, 12, 49, 52, 106, 148, 151, 154 breakdown, 44, 110, 181 Bretton Woods system, 96 bribes, xv, 184 Britain, 141, 142 brutality, 104 Bulgaria, 52, 106, 151, 155, 168 bureaucracy, 36, 184 Burundi, xvii, 12, 49, 106, 150, 154 business cycle, 24, 29, 32, 34, 150

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204

Index

business environment, 29 businesses, xiii, xiv, xvi, 29, 35, 36, 59, 70, 104, 184

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C Cambodia, 96 Cameroon, 12, 47, 49, 106, 150, 154 candidates, 84 capital accumulation, 6, 156 capital controls, xv, 35, 96 capital flight, 25 capital flows, 25, 96 capital markets, 35 capitalism, 23, 24, 29, 33, 82, 102 causal relationship, xvii, 53, 82, 179, 184 causality, 20, 44, 53, 68, 73, 78, 145, 155, 157, 180, 198 causation, 179 Central African Republic, xvii, 12 central bank, 26, 28 Central Europe, 50 Chad, 12, 106, 113, 150, 153 Chicago, 36, 78, 79, 107 child mortality, vii childrearing, 111 children, 70, 113, 114, 115 Chile, xvii, 12, 46, 47, 48, 52, 102, 106, 151, 154 China, xvii, 12, 41, 47, 52, 88, 114, 115, 116, 117, 119, 148, 151, 155 Christianity, 76 Christians, 76 CIA, 76 cities, 110 citizens, xiv, 16, 35, 59, 61, 70, 98, 102, 104, 105, 173, 187, 188 civil liberties, xvii, 12, 13, 15, 16, 19, 45, 58, 60, 63, 69, 84, 97, 98 civil rights, 55 civil society, xiv, 42, 43, 45 Civil War, 100, 101, 108 classification, 57, 58 climate, 26, 122, 179 clothing, 94 clustering, 135 coding, 50, 84, 95, 104, 133 coefficient of variation, 150 coercion, 67, 114 Cold War, 93, 96, 100 collective bargaining, 28, 35, 70 Colombia, 12, 52, 151, 154 combined effect, 35 commerce, 42 commercial, 23, 43, 44, 83 commodity, 27, 28

common law, 132 communication, xv, 95, 96, 188 communism, 169 communist countries, 169, 170, 171 Communist Party, 23 communities, 132, 180 community, 95, 96, 102, 103, 178 comparative advantage, 3, 28, 33 compatibility, 55 competition, xvi, 28, 29, 35, 145, 148, 181 competition policy, 148 competitiveness, 56 compilation, ix complementarity, 157 complexity, 162, 190 complications, 126, 184 composition, xvi, 69, 123 computer, 95, 145 computer software, 145 conference, xi, 165, 196, 199 conflict, 44, 55, 67, 98, 99, 111 Congo, xvii, 12, 47, 49, 106, 113 Congress, iv consensus, xi, 26, 43, 55, 82, 93, 181 constant prices, 30 Constitution, 79 construction, xii, xvi, 60, 132 consumers, xiii consumption, xiii, 5, 24, 34, 55, 57, 58, 59, 63, 99, 104, 109, 123, 124 content analysis, 104 contingency, 85 contraceptives, 111, 112 controversial, 94, 145, 149 convergence, 6, 7, 16, 17, 20, 62, 123, 124, 135, 156 correlation, 6, 9, 13, 14, 15, 16, 17, 18, 31, 45, 56, 64, 68, 72, 73, 74, 75, 109, 117, 137, 138, 150, 152, 155, 156, 157, 158, 159, 160, 172, 179, 184 correlation coefficient, 31, 64, 117, 155, 158, 160 correlations, 131, 146, 150, 155, 157, 159 corruption, 59, 69, 167, 172, 177, 181, 184 cost, xii, xvi, 3, 61, 114, 145 Costa Rica, 12, 48, 106, 151, 153 covering, 98, 101 credit market, xv crises, 23, 29, 42, 96, 178 Cuba, 85, 113, 117 culture, 69, 178 currency, xv, 5, 28, 35, 59, 104, 132 customers, vii, 162 cycles, 143, 150 Cyprus, 12, 52, 106, 152, 155 Czech Republic, 168

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Index

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D data set, 4, 5, 13, 45, 67, 101, 133, 141, 167, 169, 182 database, 139, 147, 192 death rate, 109 deaths, 94, 111, 112 decentralization, 41 decision-making process, 69 decreasing returns, 6 democracy, ix, 4, 15, 16, 19, 20, 21, 41, 42, 43, 44, 54, 55, 56, 57, 58, 61, 73, 81, 82, 83, 84, 85, 88, 98, 99, 100, 103, 131, 142 Democrat, 83 democratic elections, 82 Democratic Republic of Congo, xvii democratization, 41, 42, 43, 44, 45, 54, 100, 103 democrats, 82, 85, 88 demonstrations, 60 Denmark, 106, 141, 152, 154 dependent variable, 10, 41, 43, 46, 76, 82, 102, 115, 135, 167, 172, 198 deposits, 29, 35 depreciation, 5, 7 depression, 25 developed countries, 17, 18, 56, 109, 111, 132, 165, 190, 198 developing countries, 12, 13, 16, 17, 18, 19, 29, 41, 45, 46, 47, 50, 53, 57, 81, 82, 85, 86, 88, 95, 109, 112, 115, 131, 132, 142, 178 developing nations, 102, 148 deviation, 25, 30, 33, 35, 59, 61, 104, 124, 136, 150 dichotomy, 148 diffusion, 146, 147, 149 diminishing returns, 152 direct investment, 25, 197 disclosure, 147 diseases, 112 distortions, 5 distribution, xiii, 11, 21, 53, 94, 99, 102, 121, 122, 124, 150, 180, 198 distribution of income, 121, 122, 198 diversification, 31 domestic economy, 30 domestic markets, 30 dominance, 179 Dominican Republic, 12, 16, 106

E earnings, 109, 110 earnings data, 109 East Asia, 25, 28, 41, 42, 43, 49, 51, 53, 56, 119

205

Eastern Europe, 88 economic activity, 55 economic boom, 188 economic change, 44, 46, 51 economic development, 15, 20, 21, 41, 43, 44, 46, 54, 56, 68, 73, 76, 77, 83, 97, 99, 100, 101, 102, 103, 148, 149, 156, 160, 161, 165, 172, 178, 179 economic development model, 148 economic downturn, 179 economic efficiency, 145 Economic Freedom of the World (EFW), ix, 123, 196 economic growth, vii, ix, xvii, 3, 4, 5, 6, 12, 15, 16, 19, 20, 21, 24, 27, 41, 43, 45, 55, 56, 58, 60, 61, 63, 67, 68, 76, 77, 79, 82, 97, 99, 102, 121, 122, 124, 125, 126, 131, 139, 157, 166, 172, 174, 178, 181, 187, 188, 190, 196, 197 economic growth rate, 21, 166 economic institutions, 7, 27 economic liberalization, 44 economic performance, 4, 20, 24, 30, 32, 33, 42, 43, 55, 63, 121, 166, 196 economic policy, 42, 43, 121, 122, 124 economic power, 24 economic progress, 50, 109, 181 economic reform, 41, 43, 44, 45, 50, 54 economic reforms, 41 economic theory, 122 economics, 42, 143, 145, 148, 172, 195 economies of scale, 30 Ecuador, 12, 52, 106, 151, 153 editors, iv, vii, 191, 201 education, 25, 44, 60, 63, 70, 102, 109, 180, 181, 183, 195, 199 educational attainment, 183 Egypt, 12, 52, 76, 106, 112, 151, 154 El Salvador, 12, 48, 52, 106, 151, 153 election, 58, 69, 84 emergency, 44 empirical studies, 56, 60 employment, 20, 28, 29, 70, 131 encouragement, 134, 190 endogeneity, 6, 56, 178, 183, 184 endowments, 179 energy, 99 energy consumption, 99 enforcement, xii, 29, 114, 149, 161 engineering, 148 environment, xii, 29, 55, 56, 63, 93, 113, 123, 133, 148, 165, 167, 172, 178, 179, 181, 186, 198 environmental degradation, 24 environmental issues, 133 environmental quality, 33, 198

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Index

epidemic, 23 equal opportunity, 70 equality, ix, 42, 59, 70, 94, 102, 122, 124, 126 equity market, 24 estimation problems, 19 Europe, 28, 50, 51, 53, 74, 77, 88, 143, 191 European Community, 140 evidence, xv, 5, 15, 16, 18, 19, 20, 21, 24, 25, 27, 29, 31, 33, 42, 53, 55, 56, 68, 76, 77, 78, 82, 102, 109, 112, 113, 116, 117, 118, 124, 142, 160, 161, 167, 184, 198 evolution, 93, 178 examinations, 161 exchange controls, 36 exchange rate, xv, 24, 25, 27, 28, 29, 35, 59, 105, 111 exchange rate policy, 29 exclusion, 183, 188, 189 execution, 44, 95 exercise, 5, 149, 157 exile, 59, 70 expenditure switching, 28 expenditures, xiv, 59, 61, 104, 113, 123 exploitation, 70, 148, 181 exporters, 29 exports, 28, 31, 35, 59, 61, 63, 96, 105 expulsion, 166 external shocks, 28, 29, 33 externalities, 147

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F factor analysis, 133 factor endowments, 179 federal government, 196 federalism, 137 female partner, 111 fertility, 110, 111, 112, 114, 116, 117 fertility rate, 110, 111, 112 Fiji, 12, 48, 52, 106, 151, 154 financial, ix, xvi, 24, 25, 29, 43, 44, 53, 54, 62, 95, 96, 97, 99, 134 financial crisis, 24, 43, 95 financial intermediaries, 29 financial markets, 24 Finland, 106, 118, 141, 152, 155 fiscal policy, 26, 63 fixed effect model, 9 floating exchange rates, 28 fluctuations, 24, 26, 27, 28, 29 food, 44, 94 force, 28, 35, 43, 44, 51, 97, 110, 123, 162, 166 foreign aid, 167, 168, 169, 172, 173 foreign banks, xv, 35

foreign direct investment, 25, 197 foreign policy, 103 formation, 42, 122, 180 formula, 50, 133 foundations, 187 France, xvii, 106, 141, 152, 154, 191 Fraser Institute Economic Freedom Index, 132 fraud, xi, 97 free trade, 3, 15, 18, 19, 59 fruits, xiii, xiv, 4 full employment, 29 funds, 197

G Gabon, 12, 52, 106, 151, 153 GAO, 162, 164 GATT, 96 GDP, xiii, 12, 13, 15, 16, 17, 25, 27, 28, 30, 31, 32, 33, 34, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 70, 75, 76, 77, 97, 104, 105, 122, 123, 125, 131, 139, 150, 152, 155, 156, 159, 160, 167, 183, 185, 196, 197, 198, 199 GDP deflator, 63 GDP per capita, 13, 15, 30, 57, 58, 60, 61, 67, 68, 75, 76, 123, 125, 150, 152, 155, 159, 160, 183, 185 gender equality, 70 gender inequality, 109 General Accounting Office, 164 geographic indications, 146 geography, 63, 179 Germany, xvii, 106, 141, 152, 154 Gini coefficients, 122 Global Competitiveness Report, xiv, 181, 184, 192 global economy, 28 global markets, 43 globalization, 25, 93, 94, 96, 99, 102 GMM (generalized method of moments), 6 GNP, 57, 94, 97, 98, 99, 100, 101, 102 goods and services, xiii, xv, 28, 97 governance, 4, 15, 166, 177, 179, 180 government budget, xiii government expenditure, 61, 167, 168, 169 government intervention, 131 government policy, 26, 29, 117, 160 government spending, xiii, 82, 124, 160, 167 governments, xiii, xiv, 54, 68, 72, 81, 82, 94, 95, 101, 114, 139, 166, 173, 177, 179, 199 Great Britain, 141 Great Depression, 24, 29, 36, 37 Greece, 16, 52, 106, 141, 152, 154 Gross Domestic Product, 150 gross national product, 94, 96, 99

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Index growth rate, 7, 11, 13, 17, 21, 25, 26, 30, 33, 57, 58, 59, 60, 61, 76, 103, 104, 150, 156, 157, 158, 159, 161, 165, 166, 178, 187, 188, 190, 198, 199 growth theory, 121, 123 Guatemala, 12, 48, 52, 106, 151, 153 guidance, 112 guidelines, 114 Guinea, xvii, 151, 153 Guyana, 150, 153

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H Haiti, 47, 49, 52, 106, 150, 153 health, 25, 82, 94, 109, 113, 114, 115, 117, 198, 199 health care, 82, 94, 113, 115, 117, 198, 199 health condition, 109 heterogeneity, 16, 182 heteroskedasticity, 30, 115, 186, 187, 190 hiring, xvi, 35 history, 15, 41, 71, 104, 118, 179 HIV, 110, 111, 116, 119 HIV/AIDS, 111, 119 HIV-1, 119 homicide, vii homogeneity, 132 House, 4, 13, 20, 21, 45, 46, 47, 48, 49, 51, 58, 61, 68, 69, 70, 71, 76, 78, 84, 85, 86, 87, 97, 98, 157, 158, 159, 162, 163 human, vii, viii, ix, xvii, 5, 6, 33, 41, 42, 56, 61, 63, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 111, 115, 121, 124, 131, 139, 156 human capital, 5, 6, 61, 63, 115, 121, 124, 156 human development, 131, 139 Human Development Index, 109 Human Development Report, 76, 79, 119, 185, 192 human right, vii, ix, 93, 95, 96, 98, 99, 100, 101, 102, 103 human rights, vii, ix, 93, 95, 96, 98, 99, 100, 101, 102, 103 human welfare, 33, 111 Hungary, 12, 52, 106, 151, 154, 168 hypothesis, 13, 16, 25, 30, 32, 34, 50, 60, 62, 82, 85, 86, 95, 96, 98, 115, 116, 118, 123, 124, 177, 178, 179, 181, 188, 189

I Iceland, 12, 76, 102, 106, 141, 152, 154 ideal, 13, 110 identification, 97, 177, 189 imagination, 42 IMF, 25, 35, 43, 62, 95, 96 imitation, 148, 153 immigration, 196

207

import prices, 31 import substitution, 166 imports, 31, 35, 59, 61, 96, 105 imprisonment, 59, 70, 95, 104 improvements, viii, xii, 47, 96, 102, 118, 161, 172, 173, 174 incidence, 112, 114 income, ix, xii, xiii, 4, 5, 6, 7, 8, 13, 21, 24, 29, 33, 34, 57, 60, 73, 102, 105, 109, 110, 111, 113, 114, 116, 117, 121, 122, 123, 124, 125, 126, 150, 152, 153, 155, 156, 158, 162, 168, 169, 170, 171, 172, 180, 181, 182, 183, 196, 198, 199 income distribution, 21, 121, 124, 180 income inequality, 24, 33, 121, 122, 124, 125, 126, 181, 183, 198 income tax, xiii, 34, 123, 126 independence, 27, 29, 34, 132 independent variable, 6, 10, 13, 14, 17, 31, 46, 74, 82, 85, 94, 115, 135, 136, 159, 160, 167, 168, 170, 172, 184, 197, 198 India, xvii, 12, 47, 52, 97, 106, 114, 115, 116, 117, 148, 151, 155 indirect effect, 197 indirect measure, 96 individuals, xi, xii, xiii, xiv, xvii, 3, 9, 42, 44, 95, 97, 148, 162, 181, 182 Indonesia, 12, 44, 48, 49, 52, 106, 151, 155 industrial designs, 146 industrial policy, 25 industrialized countries, 45, 109, 112 industries, 28, 147 industry, xv, 3, 23, 42, 146, 181 inefficiency, xv, 146, 177, 181 inequality, 24, 33, 102, 109, 121, 122, 124, 125, 126, 178, 181, 182, 183, 184, 186, 188, 198 infant mortality, 94, 112, 114 infants, 115, 116, 118 inferences, 102, 187 inflation, xiv, xv, 5, 16, 26, 27, 34, 45, 59, 60, 61, 63, 82, 97, 104, 132, 199 inflation target, 27 infrastructure, 25 ingredients, xii, xiv initiation, 184 institutional change, 179, 189 institutions, ix, xii, xv, 7, 12, 26, 27, 29, 33, 44, 45, 56, 59, 63, 70, 84, 98, 122, 134, 137, 139, 165, 173, 178, 179, 180, 181, 182, 183, 184, 186, 187, 189, 190, 197, 198 insulation, 56 integration, 96, 99, 102 integrity, 29, 94, 95, 98, 99, 101, 102 intellectual property, 29, 34, 146

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Index

intellectual property rights, 29, 146 interaction effect, 139 interaction effects, 139 interdependence, 95, 96, 160 interest rates, xv, 16, 29, 35, 59, 104 interference, xi, 34, 123 intermediaries, 29 International Monetary Fund, xii international trade, xii, xv, 3, 35, 45, 59, 105, 160, 166 International Trade, 70, 123, 124 internationalization, 96 interrelatedness, 160 intervention, 126, 131, 181 inventions, 146, 147, 149, 157, 162 inventors, 146, 147, 149, 157, 161 investment, xii, 6, 12, 20, 28, 29, 34, 55, 60, 61, 68, 115, 122, 123, 124, 156, 161, 180, 181, 197, 199 investment rate, 156, 197 investors, xiii, 5, 24, 61 Iran, 12, 47, 49, 102, 106, 151, 153 Ireland, 72, 106, 113, 141, 152, 155 Islam, 6, 20 isolation, 173 Israel, 12, 97, 106, 141, 152, 154, 199 issues, 13, 24, 26, 60, 62, 96, 98, 102, 110, 133, 148, 182, 184, 187, 196 Italy, xvii, 106, 113, 141, 152, 155

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J Jamaica, 12, 46, 52, 102, 106, 151, 153 Japan, xvii, 102, 106, 118, 141, 152, 155, 161 Joint Economic Committee, 196 Jordan, 12, 52, 106, 112, 151, 154 Journal of Private Enterprise, vii, viii, ix, xviii, 3, 23, 37, 41, 55, 67, 81, 88, 93, 109, 121, 131, 141, 145, 165, 195, 201 judiciary, xiv, 34, 59, 70, 104 jurisdiction, 69, 72, 147, 161 justification, 102

K Kazakhstan, 114 Kenya, 12, 106, 150, 154 Keynesian, 24, 29, 33, 140 Keynesians, 29 Korea, xvii, 12, 25, 44, 48, 50, 52, 82, 85, 96, 106, 152, 155

L labor force, 28, 35, 110 labor force participation, 110

labor market, xvi, 110 labor markets, xvi labour market, 134 landscape, 118 Latin America, 28, 41, 42, 53, 57, 58, 65, 95 Latvia, 114, 168 laws, 59, 69, 145, 148, 149, 162 LDCs, 57 lead, xiv, xv, 29, 35, 41, 43, 95, 112, 113, 117, 122, 131, 133, 134, 137, 142, 146, 165, 173, 179 leadership, 43 learning, 148 legal protection, 29 legality, 34 lending, 29, 180 liberalism, 19, 95 liberalization, 24, 25, 44, 103 liberty, 5, 20, 21, 45, 49, 57, 60, 61, 63, 64, 67, 68, 71, 84, 93, 131 license fee, 35 life expectancy, vii, xvii, 24, 94, 109, 110, 111, 113, 114, 116, 117, 118, 131, 139, 198 light, 103, 121, 145, 181, 186, 190, 197 linear model, 5 liquidate, 28 literacy, 24, 33, 61, 94, 131, 139, 181, 183, 186, 187, 188, 189, 190 literacy rates, 24, 33, 190 Lithuania, 168 loans, 29, 35, 43 local government, 84 longevity, 109, 110, 112, 113, 115, 116, 117, 118

M macroeconomic policies, 181 macroeconomics, 24, 26 magnitude, 124, 178, 185, 186, 187 majority, viii, 24, 72, 96, 138, 167, 177, 180 Malaysia, 12, 43, 44, 48, 52, 102, 106, 151, 155 malnutrition, vii, 54 man, 49, 131, 196 management, 23, 29, 33, 36 Manifesto Research Group, 133 marginal costs, 145, 146, 162 market capitalism, 23, 24, 29, 102 market economy, 25, 102 Mauritania, 113 Mauritius, xvii, 12, 46, 47, 48, 52, 102, 106, 151, 155 measurement, 4, 5, 6, 13, 20, 61, 67, 68, 76, 83, 85, 94, 96, 97, 134, 157, 180 measurements, 5, 13, 15, 19, 103, 134 media, 59, 70, 93, 199

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Index median, 110, 132, 133, 134, 135, 136, 137, 169 membership, 85, 96, 149 mercantilism, 3 methodological principles, xii, xiii methodology, xii, 25, 30, 69, 197 Mexico, xvii, 12, 28, 48, 52, 106, 151, 154 Michael Walker, xi, 69, 195 middle class, 42, 44, 182, 183 Middle East, 51, 53, 119 military, 35, 59, 69, 72, 105 Milton Friedman, xi, xiv, 69, 81 minimum wage, xv, 28, 35 minimum wages, xv, 28 minority groups, 59, 69 misconceptions, 146, 149, 161 mission, 174 misunderstanding, 146 model specification, 24, 63, 183, 189 models, 5, 6, 9, 17, 19, 31, 32, 62, 99, 102, 124, 147, 190, 197 modernization, 41, 178 momentum, 50 monetary policy, xiv, xv, 26, 27, 63, 124 money supply, 34, 59, 104 monopoly, 82, 115, 149 monopoly power, 149 Moon, 94, 95, 98, 101, 103, 107, 108 Morocco, 12, 106, 112, 151, 154 mortality, vii, 94, 109, 111, 112, 114, 115, 116, 119 mortality rate, 111, 114, 115 motivation, xv multiple regression, 125 murder, 104 Muslims, 75, 76, 77 Myanmar, xvii, 150, 154 mythology, 199

N national income, ix, 123, 168 national income accounts, 123 national product, 57, 94, 96, 99, 157 natural resources, 28, 63 negative influences, 4 negative relation, 56 neglect, 114 Nepal, 49, 106 Netherlands, 72, 106, 113, 141, 152, 154, 163 networking, 161, 188 neutral, 115 New England, 110 New Zealand, xvii, 72, 76, 106, 113, 141, 152, 154 Nicaragua, 12, 47, 52, 102, 106, 151, 153 Nigeria, 12, 49, 52, 106, 150, 153

209

non-wage benefits, 110 North Korea, 85, 96 Norway, 106, 141, 152, 155 null, 82, 85, 86, 189 null hypothesis, 82, 85, 86, 189 nutrition, 114

O obstacles, 184 officials, xv, 70, 184 open economy, 97 openness, 24, 25, 26, 31, 33, 44, 54, 69, 83, 96, 97, 99, 126, 160 operations, 5, 45, 59, 82, 97 opportunism, 84 opportunities, 34, 43, 69, 117, 146 opportunity costs, 111 optimism, 71, 78 ores, 86, 87 Organization for Economic Cooperation and Development, 85 organize, xvi, 58, 59, 60, 69 overlap, 45, 71, 156 ownership, xi, xii, 23, 25, 28, 70, 168

P Pacific, 51 Pakistan, 12, 52, 102, 106, 151, 155 Panama, 12, 48, 106, 151, 154 Paraguay, 12, 48, 106, 151, 154 parallel, 42, 45 parameter estimates, 9, 101 participants, xi, 23, 67, 149, 165 patents, 145, 146, 147, 148, 161 payroll, xiii, 34 Penn World Tables (PWT), 12 per capita GNP, 97, 102 per capita income, xii, 6, 13, 24, 33, 60, 102, 111, 116, 169, 196, 199 personal autonomy, 70 personal choice, xii, xiii, 25 personal contact, 69 Peru, 12, 48, 106, 151, 153 pharmaceutical, 147 Philadelphia, 108 Philippines, 12, 46, 47, 48, 52, 106, 151, 154 Physical Quality of Life Index, 94, 99, 108 platform, 134 playing, xvi Pol Pot, 96 Poland, 12, 47, 106, 151, 153, 168 police, 36, 70

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210

Index

policy, xiv, xv, 3, 4, 6, 15, 24, 25, 26, 27, 28, 29, 32, 33, 42, 43, 45, 54, 63, 69, 93, 99, 102, 103, 110, 113, 114, 115, 117, 121, 122, 123, 124, 125, 132, 133, 134, 139, 142, 148, 160, 165, 166, 167, 172, 173, 174, 177, 178, 179, 186, 190 policy makers, 102 policy reform, 43, 166 policy responses, 24 policy variables, 6, 124, 125 policymakers, 177, 178, 179, 186, 190 political democracy, 4, 57, 98 political leaders, 43 political opposition, 58 political participation, 84, 182 political parties, 59, 60, 69 political party, 133 political power, 59, 98 political system, 56, 58, 84, 183 population, 11, 12, 13, 15, 16, 17, 24, 30, 31, 32, 33, 54, 60, 61, 94, 99, 101, 103, 110, 112, 114, 115, 117, 118, 123, 168, 181, 182, 188, 190, 197 population density, 32 population growth, 13, 15, 16, 17, 30, 31, 33, 60, 61, 99, 103 population size, 114, 117 Portugal, 46, 47, 52, 102, 106, 141, 152, 155 positive correlation, 56, 72, 73, 160 positive relationship, xii, xvii, 4, 56, 171, 196, 197 potential benefits, 179 poverty, ix, xii, 24, 33, 131, 139, 165, 177, 199 pregnancy, 111, 115 price stability, xv, 18, 83 primary school, 61 principal component analysis, 132 principles, xii, xiii, 19, 147 private banks, 29 private enterprises, xiii private firms, xv, 16 private investment, 197, 198, 199 private ownership, 23, 25 privatization, 25 probability, 182 product market, xv production function, 6, 122 production possibility frontier, 157 profit, 146 progressive income tax, 126 progressive tax, 121, 122, 126 project, xi, xiv, 68, 182, 196 property rights, xiv, 24, 25, 26, 29, 55, 97, 123, 124, 126, 132, 146, 148, 161, 162, 165, 166, 172, 173, 181, 184, 186, 187, 190

Property Rights, xiii, xiv, 34, 36, 37, 70, 143, 145, 163, 184, 185, 186, 187, 190 proposition, 55, 110, 174, 198, 199 prosperity, xii, 55, 122, 181, 190 protection, xi, xii, xvii, 29, 36, 55, 59, 71, 95, 98, 145, 146, 147, 148, 149, 152, 153, 157, 160, 161, 162, 187 protectionism, 28, 134 Prozac, 147 public enterprises, 162 public goods, 61 public health, 94 public investment, 6 public sector, 54, 173 punishment, 149 purchasing power, 30 purchasing power parity, 30 Purchasing Power Parity exchange rate, 111 P-value, 14, 16, 17, 18

Q qualitative differences, 63 quality of life, xii, 21, 97, 99, 101, 102, 198 quotas, xv

R rating scale, 98 realism, 95 reality, 6 reallocation of resources, 28 reasoning, 96, 133, 178 recession, 26, 29 redistribution, 15, 16, 63, 103 reform, 25, 41, 42, 43, 44, 45, 50, 53, 54, 145, 161, 162, 177, 178, 179, 181, 198 Reform, v, 37, 41, 51, 52, 175 reforms, 41, 43, 44, 45, 46, 47, 82, 84, 146, 166, 177, 188, 198 regression, 16, 25, 30, 32, 61, 63, 73, 75, 76, 77, 85, 99, 102, 113, 124, 125, 134, 135, 150, 155, 156, 158, 160, 168, 169, 170, 171, 172, 189, 197 regression analysis, 16, 85, 99 regression equation, 113 regression model, 102, 168, 170, 172, 197 regulations, xv, xvi, 25, 29, 35, 59, 61, 82, 97, 104, 123, 186, 190 regulatory agencies, 147 rejection, 13, 16, 82, 168, 171, 189 relative prices, xiv relatives, 71 relevance, 18 religion, 60, 76, 132, 182

Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook

Index repression, 57, 95, 101 researchers, xvi, 5, 42, 45, 147, 149, 162, 172, 197 reserves, 28 residuals, 12, 13, 14, 16, 17, 18, 159, 160 resistance, 114 resource allocation, 110, 162, 179 resources, vii, xi, xii, xiii, xiv, xv, 15, 16, 18, 23, 25, 27, 28, 29, 31, 33, 61, 63, 97, 102, 148, 162, 177 response, xv, 29, 33, 118, 134, 145 restrictions, xv, 10, 13, 14, 16, 17, 96, 114, 134, 149, 183, 188 rights, iv, vii, ix, xiv, 5, 12, 13, 20, 24, 25, 26, 29, 45, 55, 57, 58, 63, 84, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 110, 123, 124, 126, 131, 132, 145, 146, 147, 148, 149, 150, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 165, 166, 172, 173, 181, 183, 184, 186, 187, 190 risk, 5, 184, 188, 197 Romania, 49, 106, 151, 155, 168 rule of law, xiv, 25, 34, 42, 70, 71, 104, 186 rule-of-law index, 5 rules, xiii, 84 rural women, 110 Russia, xvii, 49, 50, 76, 110, 113, 114, 118, 168 Rwanda, 12, 49, 106, 112, 150, 154

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S safety, 109 sample mean, 136 sample survey, 114 sanctions, 58, 96 savings, 15, 61, 156 savings rate, 15 scarce resources, 61, 148 school, 61, 199 school enrollment, 61 schooling, 63, 112, 123 science, 81 scientific knowledge, 147 scope, 15, 34, 45, 124, 148, 162, 166, 180 security, xiv, 26, 29, 70, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 166 security forces, 70 seizure, 187 sensitivity, 146, 157 sequencing, 50 services, iv, vii, xiii, xv, 28, 97, 147, 161, 162, 183 showing, 33, 53, 113, 133 Sierra Leone, 12, 150, 153 signals, 149 significance level, 135, 136, 137, 138, 185, 186, 187, 190 signs, 135, 168, 185

211

Singapore, xvii, 4, 12, 43, 44, 48, 49, 72, 102, 106, 112, 113, 152, 155 skewness, 150, 153 skin, 182 Slovakia, 168 social indicator, 187 social network, 180 social norms, 180 social relations, 178 social relationships, 178 social structure, 186, 189 social welfare, 148, 149 socialism, 71, 82 society, viii, xiv, 23, 42, 43, 45, 95, 97, 121, 148, 177, 178, 180, 181, 182, 183, 186, 187, 189, 190 Sound Money, xiii, xiv, 34, 123 South Africa, 12, 89, 106, 111, 151, 153 South Asia, 51, 53 South Korea, 12, 25, 44, 48, 50, 52, 82, 106, 152, 155 Southeast Asia, 28 Soviet Union, 113 Spain, xvii, 12, 52, 106, 141, 152, 154 specialization, 32 specifications, 12, 68, 116, 118, 124, 135, 136, 137, 138, 139, 183, 187, 188, 189 spending, xiii, 25, 29, 34, 82, 123, 124, 133, 160, 167, 173 spillovers, 181 Spring, 67, 88 Sri Lanka, 12, 52, 106, 151, 155 stability, xv, 15, 17, 18, 24, 25, 27, 28, 29, 31, 33, 34, 62, 63, 83, 187 standard deviation, 25, 30, 33, 61, 124, 136, 150 standard error, 14, 17, 18, 124, 135, 137, 138, 184, 185, 189, 190 Stanford University, 44 state, 7, 16, 26, 28, 29, 43, 59, 60, 67, 69, 70, 83, 93, 96, 110, 123, 134, 157 state control, 110 state-owned enterprises, 28 states, 41, 44, 53, 56, 83, 93, 96 statistic test, 14, 17, 18 statistics, 9, 13, 14, 16, 17, 31, 62, 73, 75, 99, 124, 135, 136, 137, 138, 150, 155, 157, 158, 160, 169, 170, 171, 173, 183, 184, 188, 189 statutes, 149 structure, xiii, xvi, 5, 12, 26, 29, 45, 124, 166, 178, 183, 186, 187, 189 style, 169 subgroups, 180 subjectivity, 109 sub-Saharan Africa, 119

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Index

subsistence, 93, 99, 102, 103 substitutes, 146 substitution, 166 suppliers, 44, 146 suppression, 166 survival, 54, 114 susceptibility, 31, 75, 189 Sweden, 89, 97, 106, 141, 152, 154 Switzerland, xvii, 72, 106, 113, 141, 152, 154 symmetry, 115 Syria, 12, 47, 49, 106, 151, 154

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T Taiwan, 12, 25, 44, 48, 50, 52, 76, 82, 106 takeover, 166 Tanzania, 52, 106, 111, 150, 154 target, 27 tariff, 28, 29, 35 tariff rates, 28, 35 tax policy, 121, 122, 124 tax rates, xiii, xiv, 123, 124, 126 tax reform, 25 tax system, 121 taxation, 5, 82, 97, 121, 122, 126, 132, 134 taxes, xii, 25, 35, 61, 121, 122, 123, 126, 161 technical efficiency, 30 techniques, 13, 83, 85, 139 technological advances, 27, 121, 122 technological developments, 95, 145 technological progress, 7, 145 technology, xv, 6, 28, 95, 146, 147, 148 tenure, 137, 138 Thailand, 12, 16, 48, 50, 52, 106, 151, 155 theft, xi, 97, 148 Third World, 174 thoughts, 25, 146, 161 threshold level, 46 time frame, 56, 182 time periods, 9, 30, 31, 103, 157, 166, 168 time series, 30, 34, 45, 74, 75, 103, 132, 166 Togo, 12, 52, 106, 113, 150, 153 torture, 70, 95, 104 trade, vii, xi, xii, xiv, xv, 3, 4, 6, 15, 18, 19, 25, 26, 28, 31, 33, 35, 45, 59, 60, 61, 63, 70, 83, 94, 95, 96, 99, 102, 105, 122, 124, 126, 131, 149, 160, 166, 179, 199 trade liberalization, 25 trade policy, 124 trade union, 59, 70 trademark rights, 146 trade-off, 94, 102, 122, 199 trading partners, xv traditions, 29

trajectory, 77 transactions, 59, 83, 97, 105, 161, 162 transformation, 19, 43 translation, 161 transmission, xvi transparency, xii, 25, 43, 69 transportation, xv, 123 treaties, 93, 149 treatment, 96, 113, 114, 116, 148 trial, 104 Trinidad, 12, 106, 152, 153 Trinidad and Tobago, 12 Turkey, 52, 72, 73, 76, 106, 112, 141, 151, 154

U UK, 119 Ukraine, 49, 113, 114, 168 UN, 94, 111 UNESCO, 94 uniform, 102 unions, 59, 70 United, viii, xvii, 72, 79, 82, 102, 106, 109, 111, 113, 119, 122, 141, 152, 154, 185, 192 United Kingdom, xvii, 72, 102, 106, 152, 154 United Nations, 79, 109, 111, 119, 185, 192 United Nations Development Programme, 109, 119, 192 United States, viii, xvii, 72, 82, 106, 111, 113, 122, 141, 152, 154 urban, 110, 112 urban areas, 110, 112 urbanization, 112, 116 Uruguay, 12, 48, 52, 106, 151, 154 USSR, 116, 118

V variables, vii, xiii, xvii, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 26, 27, 30, 31, 33, 43, 60, 61, 63, 68, 74, 75, 76, 77, 81, 82, 83, 84, 85, 95, 99, 101, 102, 103, 110, 112, 113, 115, 116, 117, 124, 125, 132, 135, 136, 137, 138, 139, 149, 155, 156, 157, 159, 160, 161, 168, 169, 170, 180, 181, 183, 184, 185, 186, 187, 188, 189, 190, 197, 198 variance-covariance matrix, 185 variations, xvi, 93, 96, 110, 161 Venezuela, xvii, 12, 47, 49, 103, 106, 152, 153 violence, xi, 70 vision, 24 volatility, 26, 28, 29, 30, 73 vote, 56, 59, 69, 84, 134 voters, vii, 133 voting, 133, 183

Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook

Index

W

working population, 11, 12, 13, 17 World Bank, xii, xiv, 12, 21, 43, 95, 96, 119, 122, 127, 142, 165, 167, 172, 174, 175, 183, 184, 191, 192 World Development Report, 43 World Economic Forum, xii World Trade Organization, 145 worldwide, viii, 45, 54, 95, 96, 109, 111, 139, 177

Y Yale University, 37 yield, 134, 188, 189

Z Zimbabwe, xvii, 12, 16, 49, 106, 113, 150, 153

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wages, xvi, 26, 28, 35, 110 war, 70, 99, 133 Washington, 20, 25, 37, 43, 79, 119, 127, 163, 164, 175, 191, 192, 196 Washington Consensus, 25 water, 24, 94 weakness, 189 wealth, 42, 56, 71, 98, 110, 115, 181, 186 welfare, 33, 56, 109, 110, 111, 113, 134, 145, 148, 149 welfare state, 134 well-being, 109, 110, 114, 118 Western Europe, 143 WHO, 113 workers, 196

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Economic Freedom: Causes and Consequences : Causes and Consequences, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook