Essays on the Impact of Economic Freedom on Investment, Entrepreneurship, and Development

by

Gonzalo Macera M.S.

A Dissertation

In

Agricultural and Applied Economics

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Approved

Benjamin Powell Co-Chair of Committee

Eduardo Segarra Co-Chair of Committee

Jamie Bologna Pavlik

Jaime Malaga

Mark Sheridan Dean of the Graduate School

December 2020

Copyright 2020, Gonzalo Macera Texas Tech University, Gonzalo Macera, December 2020

ACKNOWLEDGMENTS Writing a dissertation was a challenging but rewarding experience. Certainly I could not have done it in solitude. Comments, criticism, and guidance have been central. Even though an exhaustive list of those who helped is not technically possible, I will try to acknowledge as many as possible. Some may go unmentioned, but they are not unappreciated.

I must begin by thanking Benjamin Powell. Because of him I came to Texas Tech in the first place, and he gave me a chance to undertake this project by offering me a fellowship at the Free Market Institute. He also made it possible for me to finish this by now and get beyond all the difficulties that arose along the way.

I also want to thank, along with Ben, the other members of the dissertation committee, Eduardo Segarra, Jamie Bologna Pavlik and Jaime Malaga. Their support, feedback, and comments were essential.

I must thank all of the people of the Free Market Institute. Adam Martin, Robert Murphy, Alex Salter, Kevin Grier, Andrew Young, Robin Grier and all other faculty gave me feedback and advice when I requested it, and they helped me a lot by giving me their opinion on how to navigate the path ahead at each stage.

To my fellow PhD students, Ray March, Audrey Redford, Glenn Furton, and Edwar Escalante, who all gave me a hand with their comments and suggestions whenever I needed them, thanks. As for the other peers I met in the department, Chandra Dhakal, Eric Asare, Maryam Almasifard, Ted Woolsey, and Ethan Sabala, we together took various courses and participated in seminars, always with great camaraderie.

The Department of Agricultural and Applied Economics was always very supportive in my research effort. Benaissa Chidmi and Chenggang Wang were always willing to give me a hand when I consulted them. The dissertation also couldn’t have

ii

Texas Tech University, Gonzalo Macera, December 2020

come to fruition if it weren’t for the support of Phil Johnson and DeeAnn Pruitt in navigating the administrative process.

My thanks also to the Institute for Humane Studies, which has provided me with financial and academic resources and organized events that have been of great help in working on my ideas.

Nor can I stop thanking Ed Stringham and the folks at the American Institute for Economic Research for their help. The institute hosted me on a couple of occasions during the time I was working on my dissertation. The academic and financial support, the numerous talks with Max Gulker and Phil Magness, and the atmosphere of camaraderie were important.

Nicolas Cachanosky, not only an outstanding academic but a lifetime friend, this would not have happened without you. Thank you.

Countless others deserve a thank-you for their encouragement and support. Alex Padilla, you have always provided me with feedback and support and friendship. Vincent Geloso, you’ve been there always as well; thank you.

I also must mention my professors back in Buenos Aires, Martin Krause, Juan Carlos Benitez, and Patricia Bonatti, who taught me and then gave me the chance to work with them at the university as a teacher. And to Harry David, a great editor that makes sense of my words when my non-native English gets in the way of clearly expressing the points I want to make, thank you.

And last but foremost, perhaps, there are my parents, without whom this would never have happened had they not encouraged me from my earliest days to improve myself in each stage of life and studies. I tragically lost you both in the middle of my time at Texas Tech and I still can’t fully process it. Wherever you are, and until we meet again, God willing, I thank you and miss you.

iii

Texas Tech University, Gonzalo Macera, December 2020

TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... ii ABSTRACT ...... v LIST OF TABLES ...... vii LIST OF FIGURES ...... viii I. ECONOMIC CALCULATION AND THE PRODUCTIVITY OF INVESTMENT ...... 1 Introduction ...... 1 Economic Calculation and the Creation of Capital ...... 3 Economic Freedom and the Productivity of Investment ...... 6 Conclusion ...... 12 II. INFLATION AND BANKRUPTCIES ...... 13 Introduction ...... 13 Entrepreneurship and interventionism ...... 15 Entrepreneurship and market distortions ...... 15 Cantillon effects and financial approaches ...... 17 Data ...... 19 Model ...... 22 Empirical results ...... 22 Conclusion ...... 26 III. THE 2001 DEBT DEFAULT: A SYNTHETIC-CONTROL ANALYSIS OF ARGENTINA’S CRISIS ...... 28 Introduction ...... 28 The 2001 Crisis and the Path Change ...... 30 The Pre-2001 Period and the Path to the Crisis ...... 30 The 2001 Postcrisis Scenario and Subsequent Policy Change ...... 32 Synthetic Control Method ...... 35 Control countries ...... 36 Synthetic Control on GDP per Capita ...... 36 Synthetic Control on Poverty ...... 43 Conclusions ...... 48 BIBLIOGRAPHY ...... 50 APPENDIX A ...... 56

iv

Texas Tech University, Gonzalo Macera, December 2020

ABSTRACT This dissertation, on the impact of economic freedom on investment, entrepreneurship, and development, attempts to evaluate the effects of different types of economic shocks. The first essay undertakes a theoretical analysis of the effect institutional quality has on the productivity of investments. The second essay looks at the effect of an inflationary shock on entrepreneurial activity. The third essay is a case study that analyzes an economic and institutional crisis followed by an economic policy shock.

One of the purposes of this research is to provide a better understanding of the dynamics of these shocks and their ultimate impact on individuals and communities. Institutions are essential because they generate outcomes in their interaction with politics. My analysis goes from the theoretical to the practical, assessing the different implications of the changes generated by modifying sets of institutions.

The first essay engages in a theoretical analysis building on previous empirical studies to analyze why an institutional change generates differences in the level and productivity of investments. It explains why institutional quality impacts the productivity of investments. The existing empirical literature finds that a given level of investment creates more economic growth in more economically free countries. Drawing on insights from Austrian economics, particularly as it pertains to the economic-calculation debate and associated knowledge problems, this essay provides a theoretical explanation for why entrepreneurs can find better investment opportunities in more economically free countries, leading to higher economic growth.

The second essay is an empirical study that analyzes nominal/inflationary changes in an unsound monetary regime that affect bankruptcies, which are one aspect of entrepreneurial error. It poses the question: does inflation impact entrepreneurial error? Using panel data and data from the Organisation for Economic Co-operation and Development on bankruptcies, it finds a positive relationship between inflation

v

Texas Tech University, Gonzalo Macera, December 2020

volatility and the number of bankruptcies, suggesting that inflation volatility may increase entrepreneurial error.

The third essay is a case study of Argentina after 2001. After a major crisis, the country faced a massive institutional and economic shock with a swift change in its economic policies. The changes in the policy regime generated an abrupt decrease in economic freedom, thus leading to an economic path less favorable than it would have been had the previous regime continued. I use the synthetic control method to study the impact of Argentina’s 2001 crisis on the economy’s path. More specifically, I compare outcomes under the new political-economic regime with a counterfactual of business as usual in control countries. Contrary to some scholars’ findings, I find no evidence of benefits of the new regime. Furthermore, some economic outcomes improved regionally at the time; we therefore cannot attribute the improvements in Argentina to the new regime.

vi

Texas Tech University, Gonzalo Macera, December 2020

LIST OF TABLES Table 2.1 Summary statistics ...... 19 Table 2.2 Changes in bankruptcies of enterprises ...... 23 Table 3.1 GDP/Capita SC – indicator variables...... 38 Table 3.2 GDP/capita SC – donor-country weights ...... 39 Table 3.3 Poverty SC – indicator variables ...... 44 Table 3.4 Poverty SC – donor country weights ...... 44 Table A.1 Correlation matrix ...... 56 Table A.2 GDP/capita SC, variables: treated vs. synthetic ...... 57 Table A.3 Poverty SC, variables: treated vs. synthetic ...... 60 Table A.4 GDP/capita SC donor-country weights, without Uruguay...... 62 Table A.5 GDP/capita SC, donor-country weights, without Mexico ...... 63 Table A.6 GDP/capita SC donor-country weights, without Chile ...... 64 Table A.7 Poverty SC, donor-country weights, without Uruguay ...... 66 Table A.8 Poverty SC, donor-country weights, without Colombia ...... 67 Table A.9 Poverty SC, donor-country weights, without Bolivia ...... 68

vii

Texas Tech University, Gonzalo Macera, December 2020

LIST OF FIGURES

Figure 3.1: Economic Freedom of the World Index – scores ...... 34 Figure 3.2: GDP/capita SC ...... 38 Figure 3.3 GDP/capita SC – historical: Argentina, main donor countries ...... 40 Figure 3.4 GDP/capita SC – placebo effects ...... 41 Figure 3.5 GDP/capita SC – effects ...... 42 Figure 3.6 GDP/capita SC – effects & P-Values ...... 43 Figure 3.7 Poverty gap $5.50/day SC ...... 45 Figure 3.8 Poverty gap 5.50$/day - historical: Argentina, donors and LA average ...... 46 Figure 3.9 Poverty SC - placebo effects...... 47 Figure 3.10 Poverty SC – effects & p-values ...... 48 Figure A.1 GDP/capita SC, without Uruguay ...... 62 Figure A.2 GDP/capita SC, without Mexico ...... 63 Figure A.3 GDP/capita SC, without Chile ...... 64 Figure A.4 GDP/capita SC, comparison ...... 65 Figure A.5 Poverty $5.50/day SC, without Uruguay ...... 66 Figure A.6 Poverty $5.50/day SC, without Colombia ...... 67 Figure A.7 Poverty $5.50/day SC, without Bolivia ...... 68 Figure A.8 Poverty SC, comparison ...... 69

viii

Texas Tech University, Gonzalo Macera, December 2020

I. ECONOMIC CALCULATION AND THE PRODUCTIVITY OF INVESTMENT*

Introduction Adam Smith appreciated that institutions were an important determinant of economic growth. More than 20 years before publishing the Wealth of Nations he wrote, “Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice: all the rest being brought about by the natural course of things.”1 Yet, neoclassical growth theory (Solow 1956) from the mid 20th century to the 1990s largely focused on inputs of physical and human capital and technology as the cause of growth, while ignoring institutions.

Following North’s (1990) contribution on the role of institutions in promoting long-run development more than 25 years of numerous empirical studies have examined the roles of geography, institutions, and inputs, such as investment in capital, in promoting long-run growth. However most of this empirical literature treated investment and institutions as independent contributors to growth. Yet, good institutions are likely to impact both the quantity and productivity of investment.

Gwartney, Holcombe, and Lawson (2006) are the first to recognize and attempt to correct for that fact that institutions could impact growth both directly and indirectly through their impact on investment. They use the Economic Freedom of the World Annual Report (Gwartney and Lawson 2003) to measure the quality of a country’s institutions. The index has been used in more than 100 papers finding that higher

* We thank the participants at the JBVELA conference hosted by Texas Tech’s Free Market Institute for helpful comments on an earlier draft of this paper. This chapter was co-authored with Benjamin Powell and originally published as: Powell, Benjamin and Gonzalo Macera. “Economic Calculation and the Productivity of Investment.” Journal of Business Valuation and Economic Loss Analysis 12 No. 1 (2017): pp-pp. 1 Lecture in 1755, quoted in Dugald Stewart, Account Of The Life And Writings Of Adam Smith LLD, Section IV, 25. 1

Texas Tech University, Gonzalo Macera, December 2020

levels of freedom and/or improvements in freedom are associated with higher levels income, growth, and a host of other improved economic outcomes (for surveys see: De Haan, Lundström, and Sturm (2006), Hall and Lawson (2014)). The index incorporates 43 variables across five broad areas: 1. Size of Government; 2. Legal Structure and Property Rights; 3. Access to Sound Money; 4. Freedom to Trade Internationally; and 5. Regulation of Credit, Labor, and Business. At its most basic level, the EFW index measures the extent to which individuals and private groups are free to buy, sell, trade, invest, and take risks without interference by the state. To score high on the EFW index, a nation must keep taxes and spending low, protect private property rights, maintain stable money, keep the borders open to trade and investment, and exercise regulatory restraint in the marketplace.

Gwartney, Holcombe and Lawson (2006) find that higher levels of economic freedom increase both the level of investment and the productivity of investment. In the latter case they find that

a one percentage point increase in private investment as a share of GDP on long-term growth was 74 percent greater in countries with EFW ratings of more than 7 (out of 10) than in those with ratings of less than 5. Similarly, private investment was, on average, 25 percent more productive in countries with EFW ratings above the median compared to those with ratings below the median (2006: 270). They also find that private investment, even in the least free countries, is more productive than public investment in creating growth. They offer a brief explanation of why countries with greater economic freedom might attract a greater quantity of investment noting that, “Investors will be reluctant to risk their capital when property rights are weak and poorly protected, and as a result, they fear that their returns may be appropriated by others” (2006: 255). But nowhere do they offer any account of why any given quantity of investment should produce more growth in countries that are more economically free than less free countries or why private investment is more productive than government investment.

2

Texas Tech University, Gonzalo Macera, December 2020

This paper contributes to the growth literature by explaining why investment is more productive in creating growth in more economically free countries than in less free countries. We draw on theoretical work on calculation and knowledge problems by Austrian school economist Ludwig Von Mises and Frederic Hayek to argue that entrepreneurs in less free economies 1) are more likely undertake investments that are privately profitable but socially destructive 2) are more likely make errors in investment evaluations that lead them to make private losses that are also socially destructive and 3) face more barriers to entry which allows existing firms to maintain less productive investments.

The next section briefly reviews Mises and Hayek’s contributions as they are relevant for this paper. Section III applies these theories to show how changes in economic freedom could impact investors’ net present value calculations leading them make errors or engage in socially destructive investments that would drive Gwartney, Holcombe, and Lawson’s empirical results. The final section concludes

Economic Calculation and the Creation of Capital2

The economic calculation problem stems from the fact that capital is both heterogeneous and multi-specific. Yet, standard neoclassical growth models treated capital as a homogenous unit of “K.” Similarly, the widely used “financing gap model” for calculating aid for investment acted as if increasing the quantity of some homogenous investment was all that was needed to promote development. The real economic problem, however, lies in creating the “right” heterogeneous capital that best compliments the existing capital stock and other resources to satisfy consumers’ most important demands.

The importance of heterogeneous capital for economic calculation first began to become apparent in the 1920s and 1930s during the debate surrounding the possibility of socialist planning. The Austrian economics Ludwig Von Mises

2 This section draws on Powell (2010). 3

Texas Tech University, Gonzalo Macera, December 2020

launched the debate with his famous 1920 article “Economic Calculation in the Socialist Common Wealth.”

Mises (1920) adopted the definition of socialism as collective ownership of the means of production. A postcard version of his argument reads:

• Socialism is the collective ownership of the means of production (MOP)

• Without private property in the MOP there is no market for the MOP

• Without a market for the MOP there are no prices for the MOP

• Without prices for the MOP there are no relative scarcity indicators for the MOP

• Without relative scarcity indicators economic calculation is impossible

o i.e. you have no way of knowing which capital goods to combine in which proportions to produce the final consumer goods most economically

Because socialism is defined as the collective ownership of the means of production, whether capital goods are homogeneous or heterogeneous is crucial because the economic calculation problem stems from the fact that we have no relative scarcity indicators for these capital goods.

If capital goods are all perfectly specific then no problem arises when you have no relative scarcity indicator for them. Each is only suitable to one task. An economy need only know the final consumer goods it wants and then the planner can choose to accumulate the capital necessary to make those goods. Similarly if all capital goods are perfectly homogeneous their relative scarcities do not matter. Each can be perfectly substituted for every other. A planner again only needs to know the desired type and quantity of consumer goods. Any structure of capital goods used to produce those consumer goods is equally efficient.

4

Texas Tech University, Gonzalo Macera, December 2020

Only with perfect capital specificity or perfect homogeneity the economic calculation problem collapses into a technical production problem. Schumpeter (1942: 175) argued that an economy could have economic calculation for factors of production without private property for the MOP because “[C]onsumers in evaluating (‘demanding’) consumers’ goods ipso facto also evaluate the means of production which enter into the production of these goods.” However, the “ipso facto” does not hold precisely because capital is heterogeneous and multi-specific. Hayek (1945) pointed out that Schumpeter’s ipso facto only holds if all the facts are given to one mind. Alternatively it is also accurate to say that with dispersed knowledge the ipso facto would hold only if all capital was perfectly specific or all capital was homogeneous.

The mainstream of the economics profession failed to appreciate the Austrians’ contribution to the socialist calculation debate both because of their preoccupation with equilibrium analysis and because of their tendency to model capital as homogeneous.

The economic calculation debate has relevance for evaluating the productivity of investment because economic calculation is not a binary yes-or-no variable. Although economic calculation under socialism is impossible, that fact does not imply that economic calculation in all non-socialist countries is equally accurate.

Prices must convey the real scarcities of resources, including capital goods created through investment, in alternative uses for the process of economic calculation to work well. When the process works well it mobilizes the tacit and inarticulate knowledge that is scattered across minds throughout society to reveal which resources should be put into what lines of production and what capital goods should be created. This requires free bidding between all entrepreneurs and would-be entrepreneurs, for natural resources, capital, and laborers. Only through the market’s process of competition do these real scarcities get imperfectly revealed.

5

Texas Tech University, Gonzalo Macera, December 2020

However, when the process of competition is interfered with and where resource prices are not allowed to be freely formed, the calculations made by investors and entrepreneurs will not reflect the real underlying scarcities. A socialist system is the ultimate stifling of this process that eliminates the ability to calculate altogether but each intervention that moves an economy farther away from a free market environment is also a step farther away from accurate economic calculation. This is important because each step away from accurate economic calculation will lower the productivity of investment in creating economic growth.

Economic Freedom and the Productivity of Investment Entrepreneurial activity in pursuit of profit can be socially beneficial or socially destructive. Baumol (1990) argued that a good institutional environment channels entrepreneurial activity toward socially productive entrepreneurship while a bad institutional environment promotes socially unproductive or destructive entrepreneurship. Our argument in this section is consistent with his observation but the mechanism through which entrepreneurship becomes destructive is different than in his rendering.

In Baumol’s account, a government that penalizes entrepreneurial success by taxing away profits or a culture that shuns profit making might discourage people from engaging in socially productive market place entrepreneurship and instead shift people into more rewarding and respected lines of work such as government service. This type of shift might decrease total investment but it wouldn’t necessarily decrease the productivity of what market place investment was undertaken. Similarly, Baumol recognizes that opportunities for rent seeking may divert entrepreneurs away from undertaking socially beneficial projects and instead divert them to pursuing zero or negative sum rent seeking projects. In either case, incentives are Baumol’s mechanism that drive differences in productive or unproductive entrepreneurship.

Our focus is on information rather than incentives because differences in the information available to entrepreneurs in different societies will also impact the

6

Texas Tech University, Gonzalo Macera, December 2020

productivity of entrepreneurs and the investments that they make. All entrepreneurial investments are necessarily speculative. Some result in profits while others result in losses. If the institutional environment creates additional uncertainty about future costs and revenues, a greater proportion of investments may turn out to create losses (i.e. be socially destructive) even though ex-ante they appeared profitable. Furthermore, if prices do not reflect the real scarcity of resources in society then investments that do create profits for an entrepreneur could create losses for society.

It is useful to look at this from the perspective of an entrepreneur deciding whether to make an investment by using a simple net present value calculation:

푇 푅 푁푃푉 = −푅 + ∑ 푡 0 (1 + 푖)푡 푡=1

The net present value (푁푃푉) of the investment is the summation of the initial outlay plus the net cash flows (푅) or revenues minus costs each period divided by the discount rate (푖). In a world where all prices convey the real relative scarcity of resources and the discount rate accurately reflects the opportunity cost of resources over time, and if entrepreneurs have perfect foresight, only those investments that are socially productive will result in positive net present values. In such an environment, investment would be maximally productive in generating economic growth. Of course, entrepreneurs do not have perfect foresight because the future is unknowable, so, even if all prices reflected real opportunity costs, some investment will be socially unproductive and result in losses for the entrepreneur and society.

The unknowability of the future is an epistemic constraint that entrepreneurs in all societies face, so it cannot explain differences in the productivity of investment across societies. Entrepreneurs do, however, encounter differences in economic freedom across societies. These differences in economic freedom can change the quantity of investment by artificially increasing costs that result in negative NPV values that should be positive. They also can artificially increase revenues or decrease costs to make investments privately profitable that are socially destructive, thus

7

Texas Tech University, Gonzalo Macera, December 2020

decreasing the average productivity of investment. They could distort the discount rate impacting both the quantity and productivity of investment. Additionally, if policy is not stable, the unpredictability of future prices caused by changes in economic freedom could create an additional epistemic hurdle that introduces more error into investment decisions both reducing the productivity of investment and decreasing the quantity of investment by increasing the required rate of return.

We next examine how each area of the Economic Freedom of the World index could impact NPV calculations in ways that would impact the productivity of investment.

Area 5 of the index measures the degree of regulation in credit markets, labor markets, and business regulation. The regulation of credit measures the percent of deposits held in government owned banks; the ratio of government borrowing to private borrowing; and the presence of interest rate controls. When government banks hold a larger share of the deposits they are also relatively more important in the credit allocation market. They often have soft budget constraints and respond to political incentives rather than profit incentives so they are more apt to loan to marginal borrowers that are politically connected rather than to unconnected but well qualified borrowers than private banks. This would lower the average productivity of investment. Similarly, interest rate controls could artificially decrease discount rates making long-run projects appear to be profitable even though the real social opportunity cost of undertaking the project is greater than appropriately discounted cash flows.

Many of the components within the labor market regulation category artificially increase the initial cost of hiring labor and the on-going cost of paying laborers. These sorts of regulations lower investment productivity by distorting the relative composition of investment to over bias capital investments when labor is more plentiful than its market price indicates. Additional regulations on maximum number of hours worked can limit investment productivity by prohibiting increased production

8

Texas Tech University, Gonzalo Macera, December 2020

through longer work hours in response to demand spikes and other unpredicted relative price fluctuations. Finally high mandated costs of worker dismissal regulations can lower investment productivity by keeping underperforming workers on the job rather than replacing them.

At first glance, the components of the business regulation part of index would appear to only decrease the quantity of investment rather than its productivity since most of these regulations would artificially increase either initial outlays or ongoing costs. The components of administrative requirements; bureaucracy costs; time, cost and capital requirements for start-up; bribes; licensing restrictions; and cost of tax compliance; all do decrease NPVs resulting in decisions not to invest when it otherwise would be profitable to do so. But the deterrence of entry means that entrenched companies face less competition and can afford to be less productive in running their existing business. Limitations on entry dull Schumpeter’s (1942) force of “creative destruction” and should result in a lower average productivity of investment.

Area 4 of the index measures the freedom to trade internationally. Many of the components from this area measure tariffs and other regulatory restrictions on international trade in goods. These restrictions result in local prices that are at odds with real international scarcities and thus lower the productivity of investment by misdirecting investments into industries that are not in a country’s comparative advantage. Additional measures in this category include restrictions on foreign ownership and investment; capital controls; and restrictions on foreigner visitors. All entrepreneurs need to be free to bid on resources and start businesses in order to maximize the use of knowledge in the global economy. In many cases people with productive investment ideas are born in a country other than the one in which their idea could be productive. Restrictions on foreign investment and visitation necessarily limit the ability of the market to discover the most productive investments.

9

Texas Tech University, Gonzalo Macera, December 2020

Area 3 of the index measures access to sound money. Variation of three of the four components in this area, money growth; standard deviation of inflation; and the most recent year’s inflation; can impact the productivity of investment. Money growth can artificially increase the supply of loanable funds lowering interest rates, which lowers firms’ discount rates resulting in projects showing positive net present values that should be negative. Higher standard deviation in the inflation rate increases uncertainty about what the correct discount rate should be. This decreases investment productivity by increasing entrepreneurial error through their mistaken forecasts of future inflation when discounting future cash flows. Finally, inflation itself does not instantly impact all prices equally. Inflation “ripples” through the economy one transaction at a time and therefore impacts the relative prices structure and thus lowers productivity by distorting the composition of investment.3

Area 2 of the index measures the legal structure and property rights. Most of the components in this portion of the index, judicial independence; impartial courts; protection of property rights; military interference in rule of law and politics; integrity of the legal system; legal enforcement of contracts; are indicators of how secure property rights are and whether the courts are fair and will enforce contracts. Additionally, measures on the reliability of police and costs of crime indicate other ways in which property rights may be unsecure. A lack of secure property rights can impact the productivity of investment in a few ways. First, not all forms of investment are equally at risk from government seizure, corrupt courts, or private crime. Some assets are harder to expropriate than others and in places where property rights are unsecure a disproportionate amount of capital will go into those hard to appropriate investments rather than those investments that would be most productive if property rights were reliably protected. Similarly, some contracts are more self-enforcing than others so they are less in need of potential court enforcement and thus a

3 Our argument here is consistent with Holcombe (2016) who argues that price and interest rate fluctuations associated with Austrian business cycle theory reduce the informational content of prices and cause more errors to be made even if actors have knowledge of the changes in money supply and business cycle theory. 10

Texas Tech University, Gonzalo Macera, December 2020

disproportionate share of investment will take place in industries where self- enforcement is possible rather than occurring where they could be most productive if contracts were reliably enforced. Second, insecure property rights increases the risk of investing and thus raises the discount rate required in order to undertake an investment. A higher discount rate not only decreases the quantity of investment but it can lower the productivity of investment because long-term more roundabout investment projects will be disproportionately impacted and thus investment will be biased towards short-term projects.4 Finally, the measure of the regulatory costs of the sale of real property in this area of the index would lower investment productivity by trapping real property ownership in the hands of people who cannot earn the greatest returns from the property yet are unable to sell to someone who could make better use of the property because of artificially high transaction costs.

Area 1 measures the size of government. The transfers and subsidies component within this area clearly can decrease the productivity of investment. Industries receiving transfers and subsidies would have future revenue forecasts greater than the true value that they create for society and thus overinvestment would occur in the subsidized industries.

The measure of government enterprises and investment within this area of the index is unlikely to impact the productivity of investment as measured by Gwartney, Holcombe, and Lawson (2006) since they explicitly separate private investment from this form of public investment. However, since they find that public investment is less productive than private investment, even in the least free countries, it is worth thinking about how the arguments made thus far relate to their finding. Clearly incentive problems could plague public investment more than private investment since the people making investment decisions are not residual claimants. However, information problems are also more severe since much public investment is not made on the basis

4For more on this point see Cachanosky, Nicolas (2016) “Austrian Economics, Market Process, and the EVA® Framework.” Journal of Business Valuation and Economic Loss Analysis. 11

Texas Tech University, Gonzalo Macera, December 2020

of profit and loss expectation. A government can know the cost of, for example, investing in building a bridge, but unless that bridge is sold on the market, they do not know the value it created for society. Thus both information and incentive problems can lead to the lower productivity of government investment.

In all five areas of the Economic Freedom of the World index we find multiple measures where decreases in economic freedom would distort entrepreneurs’ NPV calculations in ways that would decrease the average productivity of investment. The main channels that decreases in economic freedom decrease the productivity of investment are through 1) distorting resource prices in ways that make investments privately profitable that are socially destructive 2) increasing the uncertainty of future revenues and costs that result in greater entrepreneurial error that creates both private and social losses and 3) limiting entry which dulls the competitive forces of creative destructions and allows entrenched industries to become less productive in their own investment decisions.

Conclusion The positive relationship between economic freedom and higher levels of growth and development has many causes. This paper has argued that one channel through which greater economic freedom promotes development is through its impact on the productivity of investment. Prices better convey real scarcities and foster more accurate economic calculations in freer environments. This results in entrepreneurs making investment decisions where private profitability better matches social productivity. It allows entrepreneurs to make fewer errors that cost them losses and destroy resources for the rest of society. Finally, a free environment allows greater entry and experimentation with new ideas that continually challenges existing enterprises to become more productive.

12

Texas Tech University, Gonzalo Macera, December 2020

II. INFLATION AND BANKRUPTCIES

Introduction In his Resource Allocation and Entrepreneurship, economist James M. Buchanan writes that inflationary policies impair entrepreneurial efforts in that “opportunities for entrepreneurial profits emerge that do not necessarily generate increases in real value” (1980, p. 290). Monetary disturbances can have pernicious effects in any economic system. Whenever monetary policy is loose, distortions in relative prices arise and linkages in the chain of production across all sectors of an economy can break. The resulting losses from an increase in entrepreneurial error and malinvestments have direct impacts on the economy: slowed rates of growth; unintentional development of false path dependencies within specific sectors of the economy; and, of course, crises. The perspective that highlights the positive effects of inflation focuses on the sheer amount of economic activity and disregards the medium- or long-term economic effects.

The absence of a sound monetary environment has two conflicting effects on the level of entrepreneurial activity. First, the level may become higher than otherwise, a product of monetary illusion and problems of signal extraction that lead entrepreneurs to continue their plans even though they might not be generating value. Second, signal-extraction problems can increase uncertainty about the future and reduce the overall start-up rate, and an overall higher degree of entrepreneurial error than otherwise will increase the number of entrepreneurs going out of business.

Some authors, such as Mises (1949), Kirzner (1980), Rothbard (1973), Tullock (1974), and Buchanan (1980), provide theory connecting inflation and resource misallocation. A second literature, on bankruptcy and inflation (such as Wadhwani [1986], following Cohn and Lessard [1981], and Boeckh and Coghlan [1982]), analyzes this relationship broadly from the financial perspective, specifically via the interest rate mechanism for firms’ stocks and debt, some of the literature departing

13

Texas Tech University, Gonzalo Macera, December 2020

from the Modigliani and Cohn (1979) model. A third literature, while also following a standard macroeconomic approach, focuses on firm-level studies, discriminating by firms’ size and structure, such as Cabral (1993), Jovanovic (1982), Hopenhayn (1992), and Batthacharjee et al. (2009).

A fourth literature focuses more on the institutional, growth, and public policy margin, such as Bjornshov and Foss (2008), Audretsch and Thurik (2001), Aghion and Howitt (1992), and Powell and Weber (2013). This paper takes elements from the macroeconomic and institutional sets of models to empirically examine the impact of inflation on entrepreneurship while controlling for institutional factors. Using panel data on bankruptcies of enterprises, I consider the links between changes in entrepreneurial error, on the one hand, and, on the other, inflation-volatility measures, inflation levels, and a set of control variables. Entrepreneurial error can take many forms, many of which are not directly observable. In this paper I investigate merely enterprise bankruptcies as a measure of one aspect of entrepreneurial error.

Entrepreneurial error encompasses more than just the bankruptcies that result from unsound monetary policy. It also includes entrepreneurial mistakes and business failures that would happen regardless of the monetary problems. Moreover, other types of intervention can put an entrepreneur out of business; for example, a change in the regulatory environment could make a previously profitable business unviable. Also I am not looking at Type B errors, as Kirzner (1992) calls them: non-self-revealing, unperceived profit opportunities that require alertness to be discovered.

This paper proceeds as follows. Section 2 begins with an overview of the role of the entrepreneur and the difficulties that arise under the distorted market conditions caused by unsound monetary policies. It then takes theory and tools from a financial framework and situates itself relative to other macroeconomic approaches to the study of entrepreneurial incentives. Section 3 reviews the data and develops an empirical strategy. Section 4 presents the empirical findings and robustness measures. Section 5 concludes and suggests directions for future research.

14

Texas Tech University, Gonzalo Macera, December 2020

Entrepreneurship and interventionism

Entrepreneurship and market distortions Entrepreneurs need the price system and price signals to make their decisions. Prices must convey the real scarcities of resources, including capital goods created through investments. When the price system is distorted, resources are not efficiently allocated, and their alternative uses are not properly identified. When the resource- allocation process works well, individuals can weigh the costs associated with producing and consuming goods, and resources are allocated through a properly functioning price system. Railway lines are forged with steel, not with copper; “tin” cans are manufactured with aluminum, not silver or gold. The process requires free bidding among all entrepreneurs and would-be entrepreneurs for natural resources, capital, and labor. Only through market competition are real scarcities revealed.

Entrepreneurial activity in pursuit of profit can be socially beneficial or socially destructive contingent upon the institutional background. Baumol (1990) argues that good institutional environments drive entrepreneurial activity toward socially productive endeavors, while bad institutional environments promote socially unproductive or even destructive forms of entrepreneurship.

Monetary expansion works to alter price signals, distorting the information entrepreneurs use to find effective uses of resources. Differences in the information available to entrepreneurs in different societies impact the potential productivity of the investments they make. All entrepreneurial investments are necessarily speculative, which means additional uncertainty about future costs and revenues makes investment more difficult. During the initial investment period, a greater proportion of investments may turn out to create losses (i.e., be socially destructive) even though ex ante they appeared profitable. Moreover, if prices do not reflect the real scarcity of resources in society, then investments that do create profits for an entrepreneur can create losses for society.

15

Texas Tech University, Gonzalo Macera, December 2020

Powell and Macera (2017) show through a simple net-present-value calculation how various distortions to cash flows and discount rates, make the net present values diverge from ‘real’ societal NPV´s and thus lower the productivity of investment.

푇 푅 푁푃푉 = −푅 + ∑ 푡 0 (1 + 푖)푡 푡=1

The net present value (푁푃푉) of the investment is the summation of net cash flows (푅) and initial outlays, or revenues minus the costs of each period discounted by rate 푖. In a world where prices convey the real relative scarcity of resources and the discount rate accurately reflects the opportunity cost of resources over time, if entrepreneurs have perfect foresight, only socially productive investments will have positive net present values. In such an environment, the investments will be maximally effective in generating economic growth. Of course, entrepreneurs do not have perfect foresight. So, even if price distortions were nonexistent and prices accurately mirrored real opportunity costs, some investments would be socially unproductive and result in losses for entrepreneurs and society. Thus, I am not claiming that the optimal number of business bankruptcies is zero.

Different forms of economic interventions into the economy can artificially increase revenues or decrease costs, making some investments privately profitable while simultaneously socially destructive and thus decreasing the average productivity of investment. In this paper, the focus is on monetary interventions. Monetary distortions can make entrepreneurial forecasting more difficult, resulting a greater amount of entrepreneurial error than is optimal. There is no single measure of entrepreneurial error. In many cases errors may be paths never taken. However, enterprise bankruptcies are one measure of entrepreneurial error, since entrepreneurs certainly do not set out to go bankrupt. To the extent that this measure of entrepreneurial error correlates with other entrepreneurial errors, our subsequent empirical analysis is understating how much monetary distortions increase entrepreneurial error.

16

Texas Tech University, Gonzalo Macera, December 2020

Among the variables that are related to monetary intervention and impact investments are the variation of money growth, the standard deviation of inflation, and the most recent year’s inflation. Money growth can artificially increase the supply of loanable funds, thereby lowering interest rates and decreasing firms’ discount rates. Therefore, some investments exhibit positive net present values when the values should be negative. An increase in the standard deviation of the inflation rate raises uncertainty about the correct discount rate. This rise in uncertainty fosters mistaken forecasts of inflation when discounting cash flows, which increases entrepreneurial error and decreases investment productivity. It is important to understand that inflation does not affect all prices equally or at one time (Friedman 1969). Inflation ripples through the economy one transaction at a time and impacts relative-price structures differently, and thus it lowers productivity by distorting the makeup of investments.

Cantillon effects and financial approaches The non-neutral effects of an excess of money supply on relative prices are called Cantillon effects. More specifically, prices of goods change at different times and in different proportions. For example, if the monetary authority decides to expand the money supply and uses the newly printed money to buy Treasury bonds, then the Treasury receives newly printed money that has not been used yet. The government can benefit by using new money before prices rise. As the new money makes its way into the market through different agents’ actions, price variations arise throughout the economy at different points in time. The last person to receive her share of this once- new money does so after prices have risen. This creates a signal-extraction problem: the increase in the money supply does not provide us with any information about which prices will rise or when they will rise. The changes in the relative prices can affect how entrepreneurs value different project opportunities.

Problems with wrong valuations in unsound money environments can then lead to more bankruptcies—a higher degree of entrepreneurial error.

17

Texas Tech University, Gonzalo Macera, December 2020

This chapter relates to the business cycle literature by empirically examining the relationships between inflationary policy and production. The previous literature is largely divided on the role of inflation on macroeconomic volatilities. Some authors contend price discrepancies will be absorbed by market actors (Tullock 1988, 1989; Caplan 1997). Others contend they will not (Garrison 1991, 2001; Callahan and Horwitz 2010; O’Driscoll and Rizzo 1985; Carilli and Dempster 2001; Evans and Baxendale 2008). The latter perspective argues inflationary policy can produce a variety of disequilibrating trends. In the next section, I review several trends relevant to my hypothesis.

A strand of the financial literature (such as Wadhwani [1986], following Cohn and Lessard [1981], and Boeckh and Coghlan [1982]) approaches the relationship between bankruptcy and inflation via the interest rate mechanism for firms’ stocks and debt and finds that higher inflation implies higher bankruptcy risks, default premia, and higher liquidation rates (ratio of total number of compulsory liquidations and creditors’ voluntary liquidations divided by the total number of ‘live’ companies). Batthacharjee et al. (2009), following Cabral (1993), Jovanovic (1982), and Hopenhayn (1992), take a macroeconomic approach and focus on firm-level studies. Instead of a bankruptcy measure, they have a unified exit measurement that includes liquidations and acquisitions. However, the measurement is constrained by the scope of the market under consideration (UK) and having mostly mature and large firms in their dataset. They control for macroeconomic conditions, local and foreign GDP, and macroeconomic-instability measures. They consider changes in the exchange rate, price-instability measures, and the interest rate channel as well. Given their firm-level data, they include financial indicators on profits, liquidity, and leverage. Their results show impacts that depend on size, industry, and liquidity. This paper draws from these macroeconomic approaches and incorporates elements from other institutional studies of entrepreneurship to empirically examine the impact of inflation on this source of entrepreneurial error (enterprise bankruptcies). This allows us to have a better idea of what might be the effects of such exogenous shocks on the economy as regulations

18

Texas Tech University, Gonzalo Macera, December 2020

(factors that influence the ease of doing business), which can influence the level of entrepreneurship in a country and the way businesses are conducted.

Data To approximate one aspect of entrepreneurial error, I examine the changes in enterprise bankruptcies (BankEnt) captured in the Timely Indicators of Entrepreneurship series compiled by the Organization for Economic Cooperation and Development. The index covers 2006 to 2017, with the base year (=100) being 2007. The choice of the period and the countries in the sample5 is solely driven by the availability of the data.

Table 2.1 Summary statistics

Variable Obs Mean SD Min Max Bankruptcy 233 139.57 98.36 46.63 821.63 InfVol(t) 250 0.56 0.45 0.11 3.86 InfVol(t-1) 250 0.56 0.45 0.11 3.86 InfVol(t-2) 250 0.57 0.45 0.11 3.86 InfVol(t-3) 250 0.61 0.53 0.10 4.83 InfVol(t-4) 250 0.63 0.57 0.10 4.83 InfLvl(t) 250 2.68 3.20 -5.21 23.64 InfLvl(t-1) 250 2.80 3.32 -5.21 23.64 InfLvl(t-2) 250 2.95 3.46 -5.21 23.64 InfLvl(t-3) 250 3.10 3.63 -5.21 23.64 InfLvl(t-4) 250 3.22 3.73 -5.21 23.64 △GDP(t-1) 250 1.53 2.59 -8.27 9.43 △GDP(t-2) 250 1.68 2.63 -8.27 9.43 △GDP(t-3) 250 1.70 2.62 -8.27 9.43 △GDP(t-4) 250 1.73 2.66 -8.27 9.43 △WorldGDP 250 4.66 5.75 -5.42 12.65

5 The countries in the sample are Australia, Belgium, Brazil, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Norway, South Africa, Spain, Sweden, United Kingdom, United States. 19

Texas Tech University, Gonzalo Macera, December 2020

Table 2.1 Continued

RXrate 229 98.98 9.82 63.89 130.45 TradeOpen 247 70.72 33.33 22.11 169.40 △GpiComm 250 1.87 5.66 -8.80 9.81 EFW(Reg) 229 7.60 0.96 4.09 9.16 IntRate 238 3.72 2.43 -0.07 11.07

The main independent variables of interest are the level of inflation and the volatility of inflation. InfVol is a measure of inflation volatility constructed using monthly inflation data from the Organization for Economic Cooperation and Development (OECD) to generate a yearly standard deviation measure for each country. A contemporaneous measure, InfVol, and lagged variables are included

(InfVol(t-1) – InfVol(t-4)).

InfLvl and its subsequent lags (InfLvl(t-1) – InfLvl(t-4)) are the changes in the price level with respect to the previous period in each country. I include both level and variability (or volatility), following Grier (2004) and Wadwhani (1986), since it is not just levels of inflation, or the anticipation of them, that affect business, but the variability (or volatility). As Wadwhani suggests: “Most existing empirical evidence is concerned with the effect of inflation on the variability, as distinct from the uncertainty, of inflation” (1986, p. 128). I expect that the higher the volatility, the more entrepreneurial error we will observe.

Responding to the concerns of Bjørnskov and Foss (2007) and Audretsch and Thurik (2001) regarding the ties between entrepreneurship and growth, I control for each country’s GDP and world GDP. The control for each of the national economies uses GDP(t-1), the percentage change of GDP from two periods prior to one period prior; the same structure is followed for the subsequent lags (GDP(T-2) - GDP(T-4) ). GDP series are retrieved from the World Bank. The measure of the economy of each country is thus related to the amount of business activity in it. I address potential

20

Texas Tech University, Gonzalo Macera, December 2020

endogeneity concerns by using the change variation between the previous two periods and not the contemporaneous measure.

World growth affects the business opportunities and activity in each country. As a control for the state of the world’s economy, I add the WorldGDP independent variable, which accounts for the percentage change of the world’s gross domestic product between the previous and current periods. The data series comes from the World Bank World Development Indicators.

Following Bhattacharjee et al. (2009) and Chadha et al. (2000), I control for real exchange rates to measure the exchange rate and trade environment. Real effective exchange rates can affect the relative-price structure of an economy and therefore have different impacts on different economies. The variable RealXrate represents the real effective exchange rate (CPI-based) of the countries of the sample, gathered from Darvas and Zsolt’s (2012)6 study of 178 countries. These exchange rates affect the imports and exports of a country and therefore affect enterprises differently depending on their business sector and their use of resources. The net effect of changes is ambiguous; for example, appreciations might boost imports or import- related business, but the effect on the export-related counterpart on net is not a priori foreseeable. I control for the openness of the economy since the ratio of imports and exports to a country’s GDP influences the structure of its economy, specifically the way the economy reacts to shocks in relative prices and in the country’s level of business activity. Intrate is a measure for the interest rate, constructed from the OECD long-term (ten-year-bond) nominal-interest-rates database. TradeOpen is a variable acquired from the World Bank World Development Indicators related to trade openness for each country (trade as a percentage of GDP).

6 The real effective exchange rate (REER) is calculated from the nominal effective exchange rate (NEER) and a measure of the prices or costs in the country under study relative to those of its trading partners. The measures for adjusting prices and costs include consumer price indexes (CPI), producer price indexes (PPI), GDP deflators, and unit labor costs (ULC). 21

Texas Tech University, Gonzalo Macera, December 2020

Following Bhattacharjee and coauthors’ (2009) checks on price volatilities, I control for international prices’ variability since international prices of commodities can have diverse effects on different countries depending on the commodities’ share in total output. I include GPIComm, a variable constructed from the International Monetary Fund’s Global Price Index of All Commodities. The net effect of changes in commodity prices on businesses is hard to predict ex ante given that responses differ depending on businesses’ use of and dependence on commodities.

Institutional factors affect the performance of entrepreneurs. With that in mind, following Powell and Weber (2013) and Foss (2007), I include regulations, or the factors that influence the ease of doing business, that can influence the level of entrepreneurship in a country and the way businesses are conducted. EFW(Reg) controls for the impact of regulation on entrepreneurship. I obtain it from the regulations section of the Economic Freedom of the World Report published by the Fraser Institute. Other indicators such as the World Bank’s index on the ease of doing business were considered but not included because of their insufficient numbers of observations.

Model

Empirical results I start by examining the changes in enterprise bankruptcies with the following baseline specification:

퐵푎푛푘퐸푛푡 = 훽1퐼푛푓푉표푙(푡) + ⋯ + 훽5퐼푛푓푉표푙(푡−4)+훽6퐼푛푓퐿푣푙(푡) … + 훽10퐼푛푓퐿푣푙(푡−4)

+ 훽11퐺퐷푃(푡−1) + ⋯ + 훽14퐺퐷푃(푡−4) + 훽15WorldGDP

+ 훽16RealXrate + 훽17TradeOpen + 훽18GPIComm + 훽19EFW(Reg)

+ 훼푖 + 휀푖푡

22

Texas Tech University, Gonzalo Macera, December 2020

Table 2.2 Changes in bankruptcies of enterprises

(1) (2) (3) (4)

InfVol 40,2917*** 38,1118*** 31,2793** 18,7307

(13,9459) (14,233) (14,5749) (15,1862)

InfVol(t-1) 16,6711 11,1567 3,4248 8,4498

(14,788) (15,1559) (15,6034) (15,5182)

InfVol(t-2) 1,0443 -4,7597 4,2593

(15,2583) (15,6095) (14,7562)

InfVol(t-3) 5,6332 8,9468

(10,6235) (9,9657)

InfVol(t-4) 15,9973*

(9,698)

InfLvl -5,8723** -3,9248 -4,1728 -7,8968***

(2,6239) (2,6129) (2,7137) (2,7619)

InfLvl(t-1) -4,7877** -4,6653* -6,5985*** -8,6458***

(2,349) (2,4267) (2,4722) (2,5698)

InfLvl(t-2) -5,6267** -7,2943*** -8,4473***

(2,5679) (2,6014) (2,6336)

InfLvl(t-3) -6,7872*** -7,6832***

(2,4358) (2,4725)

InfLvl(t-4) -7,4738***

(2,3864)

△GDP(t-1) -10,0134*** -10,6122*** -9,2495*** -8,8767***

(1,9613) (2,0213) (2,0615) (2,1561)

△GDP(t-2) -8,8858*** -8,2202*** -7,1832***

(2,0403) (2,0611) (2,1104)

23

Texas Tech University, Gonzalo Macera, December 2020

Table 2.2 Continued

△GDP(t-3) -5,3602*** -4,6336**

(1,9578) (1,9973)

△GDP(t-4) -2,2630

(1,8616)

△WorldGDP -4,5899** -3,9964* -4,9886** -5,4118**

(2,0858) (2,1525) (2,1434) (2,2639)

RealXRate 0,6432 0,8291 0,9113 0,7449

(0,6315) (0,6511) (0,6741) (0,7114)

TradeOpen 0,8306 1,4734 2,0765** 2,5984***

(0,9058) (0,9195) (0,9316) (0,9827)

△GpiComm 1,7432 0,9397 1,4684 2,9973

(2,1902) (2,2465) (2,268) (2,3333)

EFW(Reg) -7,5463 -5.669622 -5,9147 1,2594

(13,7105) (14,1558) (14,7413) (15,3528)

IntRate 22,1710*** 20,8812*** 19,9226*** 17,6905***

(4,4896) (4,6215) (4,8073) (5,0192) s.e. in parentheses

Signif. codes: ‘***’ 0.01 ‘**’ 0.05 ‘*’0.1

R-square 0.5135 0.4678 0.4108 0.3224

R-square (Adj.) 0.3952 0.3514 0.2960 0.2058

Akaike Criterion 1952.512 1963.135 1975.942 1995.818

Bayesian Criterion 2020.139 2021.101 2024.247 2034.462

Infvol variables=0: Pr>F = 0.0012 Pr>F = 0.0108 Pr>F = 0.0703 Pr>F = 0.2030

InfLvl variables=0: Pr>F = 0.0000 Pr>F = 0.0000 Pr>F = 0.0001 Pr>F = 0.0001

Infvol & InfLvl Pr>F = 0.0000 Pr>F = 0.0001 Pr>F = 0.0005 Pr>F = 0.0005 variables=0:

24

Texas Tech University, Gonzalo Macera, December 2020

This model suffers, as we can see from the correlation matrix (appendix table 1), from weak to moderate correlation for several variables, which limits our ability to make inferences about the marginal effects of the individual coefficients. However, that does not invalidate the overall-significance analysis. Several tests (F-tests) show the inflation variables to be significant (table 2.2). The overall observed effect of volatility measures is consistent with the theory that predicts more bankruptcies as volatility increases, given the wrong signals embedded in prices and problems with discount rates for projects. Regarding inflation levels, the overall negative result might indicate that in this short-period analysis, the monetary-illusion effect dominates the negative effects of built-up errors caused by the inflation and the disruption to prices.

Table 2.2 shows the results of the baseline regressions (including fixed effects) of inflation volatility, inflation level, gross domestic product, world gross domestic product, trade openness, a global price index of commodities, and changes in regulation on enterprise bankruptcies. In regressions (1), (2), and (3), contemporaneous inflation volatility is statistically significant, positive, and large. It explains an increase of between 31.8 percent and 41.0 percent of a standard deviation of the bankruptcy index for each percentage-point change in the deviation of the inflation level from the mean. The subsequent lags appear to not have an effect on the bankruptcy index except for the fourth lag in regression (1), which explains 16.3 percent of a standard deviation of the index. Contemporaneous inflation level is statistically significant and negative in regressions (1) and (4), explaining a decrease of 4.0 to 9.1 percent of a standard deviation of the bankruptcy index. In regressions (2) and (3), the effect is of the same magnitude and sign but not statistically significant. In the subsequent lags of the variable, the effects are all negative and statistically significant and explain variations of between 4.7 and 8.8 percent.

The state of the economy explains a reduction of around 9.1 to 10.8 percent of a standard deviation of the bankruptcy index for each percentage-point increase, which

25

Texas Tech University, Gonzalo Macera, December 2020

is statistically significant in regressions (1) through (4); the subsequent lags up to the third lag also have a positive effect, accounting for 4.7 to 9.0 percent of a standard deviation in the bankruptcy index. The growth of the world economy explains a reduction of 4.0 to 5.6 percent of a standard deviation of the bankruptcy index per percentage-point increase (significant in all four regressions). International price variability shows a negative effect in all regressions; it increases bankruptcies between 1.0 and 1.8 percent, but the effect is not statistically significant. The interest rate has a strong effect on bankruptcies, as it is responsible for an 18.0 to 22.6 percent change in the index per percentage-point increase, a finding that is significant in all regressions. In regressions (1) through (3), the bankruptcy index decreases by 1.3 to 7.7 percent per percentage-point decrease in the regulatory burden, but the effect lacks statistical significance. Finally, the measures for real exchange rate do not show significant effects on bankruptcies.

Conclusion By looking at the overall significance of inflation variables, this study found a positive relationship between inflation volatility and the number of bankruptcies, suggesting that inflation volatility may increase entrepreneurial error, which is consistent with my theoretical framework and studies such as Grier (2004) and Wadwhani (1986). I found that the inflation level in the short term may decrease bankruptcies, which shows that in the short term, monetary-illusion effects might dominate the negative effects of built-up errors caused by the disruption to prices. Increases in the interest rate increase bankruptcies, which is consistent with the financial literature.

Correlation problems limit this model and make it difficult to make inferences about the marginal effects of the variables. Nevertheless, it seems the overall effect of economic-growth variables is to reduce the number of companies going out of business. The inclusion of an institutional variable was intended to allow for a more complete analysis of the factors at play. However, the institutional variable lacked

26

Texas Tech University, Gonzalo Macera, December 2020

significance. The variable’s limitations could be attributed to the fact that it cannot account for firm-level data as some other studies do, even though it has broader scope than its counterparts in the other studies (it covers all OECD countries and not just a single area).

The theory of entrepreneurship has been thoroughly developed, but measuring entrepreneurial error is difficult. Isolating each part of the total effect on it is not a simple task, and it is not always clear what to hold constant. Future research could separate and cluster countries into inflation-volatility brackets. Also, research could consider sector-specific price indexes and business failure. Similarly, the use of quasi- experimental methods to study the effects of unsound money on business failure in particular cases might be an option. Finally, the total-factor-productivity perspective, as in Hsieh and Klenow (2008), could be useful.

27

Texas Tech University, Gonzalo Macera, December 2020

III. THE 2001 DEBT DEFAULT: A SYNTHETIC-CONTROL ANALYSIS OF ARGENTINA’S CRISIS

Introduction During the 1990s, the Washington Consensus was a widely known set of policy recommendations implemented in several up-and-coming Latin American countries, such as Argentina. As an early participant in these economic reforms, Argentina was showcased as a successful case study7 until 2001, when economic crisis hit and the government defaulted on its debt. The convertibility-board era, which had constrained the monetary authority, also came to an end at that time. The postcrisis policy changes were mostly antiliberal and reactions to the Washington Consensus. While there have been criticisms of Argentina’s policy reforms—from Galiani and Heymann (2003) and Damil, Frenkel, and Maurizio (2002)—empirical analysis is still needed.

The political rhetoric during and after the crisis was mostly against the previous policy regime, blaming “neoliberalism” for all the macroeconomic evils. For example, in the beginning of his mandate in the middle of the crisis, former president Duhalde (2002–3) “made a promise regarding the protectionist measures for the local industry to begin to reverse the denationalization process of recent years, to which he attributed the responsibility for the severe crisis that the country is going through.”8

Or, from Duhalde’s minister of finance (2002–5), Roberto Lavagna (who would again be minister of finance for part of Nestor Kirchner’s term [2003-7]), praising the policy change after the crisis in December 2002: “I think the government was able to reverse the trend of decline in the level of production. Unemployment figures are looking very good, due to a drop in the rate and a rise in the economically

7 See Cavallo and Cottani (1997) for a defense of the Washington Consensus successes, published only a few years before Argentina’s 2001 economic collapse. 8 “Duhalde anuncia medidas proteccionistas y confirma la devaluación del peso,” El País, January 3, 2002, retrieved from https://elpais.com/internacional/2002/01/04/actualidad/1010098801_850215.html. 28

Texas Tech University, Gonzalo Macera, December 2020

active population seeking work, which we did not observe before. In terms of public debt, we returned USD 4.5 billion, and the debt negotiation we are facing does not imply an increase in its volume. And there have been six consecutive months of fiscal surplus.”9

Then president Nestor Kirchner, by the end of the year in which he took office (2003), bashed neoliberalism, saying in a speech to the Argentinian Chamber of Commerce, “It is not about continuing to coexist with the causes that dragged us to the situation we are in; it is about facing the most profound reforms to face Argentine problems at their roots. We cannot aim to coexist with misery, marginality, exclusion, and inequity, which resulted from the application of a unique position given the preeminence of neoliberal thought in the past ten years here and in the world.”10

I use the synthetic control method (SCM) to study the impact of the 2001 crisis on the Argentinian economy. I compare outcomes under the new political-economic regime with a business-as-usual counterfactual developed from countries with a similar profile to Argentina before the crisis to understand what would have occurred had the country continued along the previous path. I find that while some social outcomes improved after the regime change, they likely improved in the controls as well; thus, the improvements cannot be attributed to the economic reforms. Moreover, economic outcomes after the postcrisis recovery phase were unexceptional.

Section 2 describes Argentina’s political and economic situation. Section 3 describes the SCM analysis and my findings. Section 4 concludes.

9 “La entrevista. Roberto Lavagna: ‘Se puede vivir sin convertibilidad,’” La Nación, December 29, 2002, retrieved from https://www.lanacion.com.ar/opinion/roberto-lavagna-se-puede-vivir-sin- convertibilidad-nid462117/.

10 Casa Rosada, “Discurso del Presidente Néstor Kirchner en el 79° Aniversario de la Cámara de Argentina de Comercio,” December 11, 2003, retrieved from https://www.casarosada.gob.ar/informacion/archivo/24522-blank-13846532

29

Texas Tech University, Gonzalo Macera, December 2020

The 2001 Crisis and the Path Change

The Pre-2001 Period and the Path to the Crisis When President Carlos Menem took office in 1989, Argentina was a closed economy with a yearly inflation rate above 3,600 percent. His predecessor, Raúl Alfonsín, had utilized the country’s central bank (Banco Central de la República Argentina) to finance deficits to such an extent that it produced hyperinflation. The condition in which Menem received the country gave him no choice but to undertake institutional reforms.

Among Menem’s many changes, some had considerable impact on the economy. The first was the partial opening of the economy to international trade, although average tariffs remained at 14 percent, still high compared with other countries. Another crucial change was the privatization of national companies running deficits. Telecommunications companies, public utilities, and steel and mining conglomerates, among others, were sold, and the proceeds were used to help finance the fiscal deficit in the first stage of the Menem regime. The third reform was monetary: to stop hyperinflation and restore confidence in the currency, locally and internationally, a currency board was established in which the Argentinian peso became convertible at a rate of one Argentine peso per US dollar.

Kulkarni and James (2009) point to the convertibility board as one cause of the 2001 default. They argue that the currency board decreased Argentina’s exports by overvaluing the peso relative to the currencies of other countries in the region with which it traded, such as Brazil. The overvaluation made Argentinian exports much more expensive. Consequently, importers proceeded to buy from countries with more competitive prices. According to this argument, the shrinking volume of exports produced a shortage of reserves, which in turn was one cause of the 2001 crisis.

Kaminsky, Mati, and Choueri (2009) argue that other Argentinian policies caused the crisis. They show that inconsistent monetary and exchange rate policies encouraged speculative attacks on the peso. Also, controls on capital, interest rates,

30

Texas Tech University, Gonzalo Macera, December 2020

prices, and wages contributed to capital flight and depreciation, although they argue that capital inflows had already ceased by 1998. Moreover, the authors attribute the 2002 currency crisis (after Argentina abandoned the convertibility board) to the country’s contractionary monetary policy of 2000 in the midst of a deep recession.

Thomas and Cachanosky (2016) argue that even though all of the above arguments have some merit, debt default is mainly a fiscal problem. For Argentina to default, there must have been debt, and without deficits, there would not have been any debt. Menem did not solve the fiscal problem—that is, the structural deficit. Given that the country’s monetary authority was restricted by the convertibility board, the government had to resort to issuing debt and spending all extraordinary income, such as that stemming from the privatization of public companies.

Looking at the fiscal imbalance, from 1991 to 2000 Argentina’s gross domestic product (GDP) increased by 49.31 percent while government spending and public debt increased at a faster pace—90.76 and 91.60 percent, respectively. The debt was composed of credits from international governmental organizations and bonds issued in international markets under New York legislation.

Fernando de la Rúa replaced Menem in 1999, and, whether because of political constraints or lack of will, he did not undertake economic reforms, even with a crisis unfolding. To make things even worse, pressured in part by the International Monetary Fund, the Ministry of Finance pushed for a tax hike while the economy was in a contractionary phase, which inevitably worsened the recession (Hanke and Schuler, 2002). The deficit became unmanageable, and in December 2001 Argentina defaulted on $102 billion of debt. It abandoned the currency board in the first days of January 2002, after which GDP fell almost 11 percent that year.

Following the Mexican crisis, the Asian, Russian, and Brazilian crises of the late 1990s came. Studies show that, at least partially, Argentina’s recession may have been due to shocks emanating from these other crises (Calvo and Talvi, 2008; Hanke

31

Texas Tech University, Gonzalo Macera, December 2020

and Schuler, 2002). Thomas and Cachanosky (2016) argue that Argentina’s financial crisis was driven by a structural fiscal deficit and aggravated by these external shocks.

Because of the 1998 Russian crisis and Brazil’s 1999 devaluation, institutional investors engaged in a flight to quality, which made it prudent to take short positions on emerging markets, which included Argentina. Capital inflows to Argentina came to a stop, and asset values collapsed (Calvo and Talvi, 2008). Aggravating the problem of capital flight and the current-account problems is the fact that Argentina was relatively closed to international trade and held 80 percent of its debt in dollars. Thus the 2001 default was the result of a large fiscal deficit, which was aggravated but not initiated by external shocks.

The 2001 Postcrisis Scenario and Subsequent Policy Change The 2001 crisis had severe social, political, and economic consequences. Unemployment reached almost 19 percent, the currency was artificially overvalued, the domestic payments chain was broken, and there were riots in the streets. Much as in the 2010 Greek crisis, by the end of 2001 banks were not honoring their obligation to redeem deposits, and stores were being looted. President de la Rúa resigned after several violent incidents in December 2001. Because the elected vice president had already resigned over a previous scandal, it fell on Congress to select an interim president; it chose Adolfo Rodríguez Saá. He only served for a week, as some of his party’s members withdrew support. In those few days, however, he declared default on the public debt.

Congress again assembled and this time selected Eduardo Duhalde, who attempted to finish the de la Rúa’s term. His first executive order, in January 2002, was to immediately abandon the convertibility board, which led to a devaluation of the peso of almost 400 percent. The inflation rate hit 41 percent that year. Sandleris and Wright (2014) show that Argentina’s GDP fell by 25 percent, mostly because of productivity losses. More than half of that fall can be explained by inefficient resource allocation across manufacturing industries as the crisis unfolded.

32

Texas Tech University, Gonzalo Macera, December 2020

President Duhalde called for early elections in 2003. Nestor Kirchner (supported by Duhalde) was elected president and served until December 2007, when his wife, Cristina Kirchner, won office. She was reelected in 2011 and served until 2015.

Between 2003 and 2007, the economy recovered. GDP grew by 8–9 percent per year and unemployment fell from 16 to 8.5 percent. The official exchange rate stayed below 3.36 pesos per dollar, and the inflation rate ranged between 4 and 12 percent. The stock of central bank foreign reserves grew, and the current account was in surplus.

Even so, the conditions for future problems were in place. Price controls distorted relative prices—for example, in the energy and transportation sectors. Resources were significantly misallocated. Argentina went from being a net exporter of energy to being a net importer because of a lack of investment. Price controls in the utility sector—enacted because of the wealth losses after the 2001 crisis—were never updated to track inflation. Investments in energy, communications, and infrastructure halted, and a period of capital consumption began. Price controls were implemented, starting with utilities and spreading even to household items.

In the aftermath of the 2001 crisis, government spending increased significantly, at a 30 percent nominal yearly growth rate between 2003 and 2015 (5.5 percent in real terms). The tax burden increased massively from 26.1 percent of GDP in 2003 to above 51 percent in 2015 (including export taxes and the inflation tax). Argentina shifted to a dirty float of the peso and kept the devaluation rate beneath the inflation rate, producing real peso appreciation. Openness to trade declined. Many of the privatized companies were renationalized. Fiscal deficits increased from 0 percent at the end of the crisis to more than 6 percent by the end of 2015

Signs of macroeconomic imbalances started to appear by 2007. Beginning in 2007, private analysts started to estimate annual inflation to be higher than the official estimates. For example, in 2007 the official rate was 8.5 percent, but private

33

Texas Tech University, Gonzalo Macera, December 2020

estimations showed it was close to 26 percent. The rate never went down during the remainder of Nestor Kirchner’s term. Along with its misrepresentation of the inflation rate to the public, the government misrepresented other statistics, such as GDP, which has been overstated since 2007.

According to Economic Freedom of the World Index’s summary of the changes to the institutional framework of Argentina, in 2000 Argentina ranked 32nd out of 153 countries in economic freedom but by 2015 it was ranked 155th (see figure 3.1). This decline reflects the policy changes after the crisis, including the progressive abandonment of the structural reforms that had been undertaken in the 1990s.

Economic Freedom of the World Index Score Argentina 10

9

8

7

6

5

4

3

2

1

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

E F W Size of Gov´t Property Rights Sound Money Int´l Trade Regulation

Figure 3.1: Economic Freedom of the World Index – scores Grier and Grier (2020), who find positive and significant effects of the Washington Consensus reforms on living standards, argue that the Fraser Institute’s Economic Freedom of the World Index is a good proxy for the Washington Consensus. The index is on a continuum that reflects component policies on trade,

34

Texas Tech University, Gonzalo Macera, December 2020

property rights, government spending, and money, with higher values for countries with sounder monetary policy, stronger protection of property rights, more freedom to trade, and less government spending. All of these components map onto the Washington Consensus. Thus the downward turn in the index in Argentina after 2001 reflects the country’s gradual abandonment of the Washington Consensus after the crisis.

Synthetic Control Method I estimate the effect on income per capita and poverty of the post-2001 change in administration and policies. To do so, I need a control that tells us what those variables would have been had the 2001 crisis not occurred, the convertibility board not been abandoned, and the administration not changed. Because an exact control does not exist, I construct a control group by synthesizing the performance of countries similar to Argentina.

The SCM was developed by Abadie and Gardeazabal (2003) to study the conflict in the Basque Country. It has been used to study California’s tobacco-control programs (Abadie, Diamond, and Hainmueller 2010), the impact of Hugo Chávez on economic outcomes in Venezuela (Grier and Maynard 2016), the effect of German unification on economic growth in the former West Germany (Abadie, Diamond, and Hainmueller 2015), and the effect of immigration in Israel on economic freedom (Powell, Clark, and Nowrasteh 2017), among other topics.

The control group in this case, synthetic Argentina, is simply a weighted average of similar countries; for example, its per capita income is a weighted average of per capita income in the control countries. Because none of the control countries experienced the events in post-2001 Argentina (or similar events), I expect that the synthetic control’s behavior is independent of the events’ direct effects. To the extent that the synthetic control captures other influences on the Argentinian economy, it represents the counterfactual we need.

35

Texas Tech University, Gonzalo Macera, December 2020

Two important considerations are the choice of weights and the assignment of weights across the control countries. One approach is to choose weights that minimize the difference between the precrisis performance of the synthetic control and the precrisis performance of Argentina. In this approach, the weights on each country in the control must be non-negative and sum to one. This approach could put an unbalanced weight on countries that performed similarly to Argentina during the pretreatment period. But I wish to select countries that had similar economic processes to those of Argentina, so I look for countries with similar relationships between outcome variables, such as per capita income, and their determinants. To find them, I use time-invariant indicator variables and minimize differences between the weighted average of the variables for the synthetic control and the values of these variables for Argentina. Countries with more similar indicator variables to Argentina receive more weight in the control.

Control countries Abadie and Gardeazabal (2003) emphasize that the SCM depends on choosing countries that have similar economic processes to the country with the treatment. Furthermore, they note that restricting the size of the donor pool helps avoid overfitting. To capture similarities of geography, history, and culture, I use most of the Latin American countries with available data but exclude Venezuela, whose economic processes underwent major changes at this time. I study two outcome variables— poverty and per capita income—and create a synthetic control for each of these variables. A full list of countries in each synthetic-control group, along with the weights assigned, can be found in tables 3.2 and 3.5. In total, I consider fourteen potential control countries, although data are not available for all countries for both synthetic controls.

Synthetic Control on GDP per Capita For per capita income, I consider fourteen potential control countries. For indicator variables, I use income per capita from the precrisis years (1990–2001);

36

Texas Tech University, Gonzalo Macera, December 2020

average population growth rate (from the Penn World Tables [PWT]); openness to trade (1990–2001) (also from the PWT); and total years of schooling and years of primary schooling, again for the precrisis years (from the Barro and Lee [2018] database). (Table 3.1) Because income per capita and poverty are closely related, I use the same set of indicator variables for both. However, given the data limitations, the number of potential donors differs between the two controls.

Following the UN’s suggestions for measuring the dimensions of poverty,11 I include the Global Health Observatory’s measure of maternal mortality and, as a proxy for the size of the welfare state, which could have implications for poverty programs, I incorporate the ratio of government consumption to GDP adjusted by purchasing power (from the PWT).

The pretreatment data in Argentina start in 1990 since Argentina in the 1980s was not following the Washington Consensus. Fitting the pretreatment to both periods would not be a good way to get a counterfactual to indicate where Argentina was heading prior to the 2002 change. Also, data from the 1970s and 1980s in South America are not without problems given the occurrence of the 1970s energy crisis and 1980s debt crisis in several countries and especially given that Argentina had shocks of its own: the 1975–76 economic crisis and policy shocks, the Falklands conflict of 1982, and then the debt crisis of 1986–87 that would lead to several bouts of hyperinflation until the Washington Consensus reforms were implemented (see Di Tella 1989, and Della Paolera & Taylor 2003). The choice of 1990 thus makes sense since using a longer data series and going further back than 1990 would mean mixing two different Argentine policy regimes and would force the counterfactual to fit both.

11 I follow the recommendations from the United Nations Economic Commission for Latin America and the Caribbean in its publication “Measuring Poverty and Inequality and Indicators for Identifying Groups at Risk.” 37

Texas Tech University, Gonzalo Macera, December 2020

Table 3.1 GDP/Capita SC – indicator variables

Variable Years Description Source

rgdpPC 1990–2001 Real GDP per capita at constant 2011 national Penn World Table, prices (in mil. 2011 US$) v.9.1 pop growth 1990–2001 Population growth Penn World Table, v.9.1 Openness 1990–2001 (Exports + imports)/GDP Penn World Table, v.9.1 yr_sch 1990–2001 Avg. years of total schooling Barro & Lee (2018)

yr_sch_pri 1990–2001 Avg. years of primary schooling Barro & Lee (2018)

Gov_share 1990–2001 % government consumption at current PPP Penn World Table, v.9.0 MatlMortRatio 1990–2001 Maternal mortality ratio (p/100,000 live births) GHO Data Repository

Figure 3.2: GDP/capita SC Figure 3.2 plots actual and synthetic Argentinas over the precrisis period used to construct the control and over the postcrisis period, in which the control serves as

38

Texas Tech University, Gonzalo Macera, December 2020

our counterfactual. Synthetic Argentina tracks actual Argentina through the first half of the 1990s and tracks the downward turns during the 1995 Mexican crisis, 1998 Russian financial crisis, and 1998 Asian financial turmoil, all of which harmed emerging markets. Also, the two track one another through the downturn after 1998 and onward until the 2001 crisis. In the recovery stage, following the regional windfall in Latin America caused by commodity-price increases, they still track each other. Between 2005 and 2008, synthetic Argentina tracks the actual recovery process on a lower level. After the 2008 bump, the synthetic and actual Argentinas diverge, and by 2014 the difference in income per capita is almost 14 percent. One takeaway is that the post-2001 populist policies (and concomitant reduction of economic freedom) made the economic recovery unsustainable.

Table 3.2 GDP/capita SC – donor-country weights

Weight Country Weight Country

0.00% Bolivia 0.00% Honduras

0.00% Brazil 23.90% Mexico

7.10% Chile 0.00% Panama

0.00% Colombia 0.00% Peru

0.00% Costa Rica 0.00% Paraguay

0.00% Ecuador 0.00% El Salvador

0.00% Guatemala 69.00% Uruguay

39

Texas Tech University, Gonzalo Macera, December 2020

Real GDP (2011 USD)

23000

21000

19000

17000

15000

13000

11000

9000

7000 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Argentina Chile Mexico Uruguay

Figure 3.3 GDP/capita SC – historical: Argentina, main donor countries The SC algorithm creates a synthetic control composed of 23.9 percent Mexico, 69.0 percent Uruguay, and 7.1 percent Chile (table 3.2). When I use the synthetic control to predict Argentina’s GDP per capita from 1990 to 2000, the root mean squared percentage error (RMSPE) is close to 0.0337 (3.37 percent).12 (For a list of all of the variables, see the appendix.)

12 I repeated the experiment on real per capita income using the same data but dropping the maternal- mortality measure. The SC yields similar results. It seems that as a determinant indicator, it carries no weight in this particular analysis. 40

Texas Tech University, Gonzalo Macera, December 2020

Figure 3.4 GDP/capita SC – placebo effects Regarding per capita income, I develop a synthetic control for each of the control countries using data from the same period that I used for synthetic Argentina and compare the controls to the actual country in both the pre-treatment and post- treatment (intervention) periods. Figure 3.4 shows these placebo-test results. The black line represents the difference between observed income per capita in Argentina and the synthetic control; the synthetic control is normalized to zero. Gray lines represent the placebo tests (deviations between the synthetic control for the other countries in the dataset and the actual countries). The figure highlights the divergence of actual Argentina from where it would have been (as represented by synthetic Argentina) and makes it comparable to the divergences of the placebos. The results show that the post-event divergence in Argentina is larger than the divergence in most of the other countries, suggesting a significant effect in Argentina but little effect outside of Argentina.

41

Texas Tech University, Gonzalo Macera, December 2020

Figure 3.5 GDP/capita SC – effects Regarding the comparison between the synthetic control and actual Argentina, figure 3.5 shows the estimated effect of the treatment on per capita GDP for each period (before and after the treatment). We can observe a steady negative trend after 2008.

Figure 3.6 shows the p-values for the treatment effects. These values do not represent the canonical notion of p-values; instead, the process for estimating the p- values is to rank the absolute value of each period’s treatment effect relative to the absolute values of the period’s placebo effects. The number of placebos with a more extensive estimated effect divided by the total number of placebos provides the “p- value” for each post-treatment period. Observe that there is marginal significance in the first years after the crisis and then in the years after 2013.

42

Texas Tech University, Gonzalo Macera, December 2020

Effects - RealGDPC ; P-Values

2000

1000 0.38 0.46

0.46

0.92 0.85 0 1.00 0.54 0.54 0.31 -1000 0.62 0.62 0.23

-2000 0.31 0.23

0.31 0.31 -3000 2002 2005 2008 2011 2014 2017

Figure 3.6 GDP/capita SC – effects & P-Values

Synthetic Control on Poverty For the synthetic control on poverty, there are eleven potential Latin American control countries. For indicator variables, I use the same set that I used for the income synthetic control (table 3.4): income per capita from the pre-crisis years (1990–2001); average population growth rate and openness to trade from 1990 to 2001 (from the PWT); years of schooling and years of primary schooling from 1990 to 2001 (from the Barro and Lee [2018] database); a measure of maternal mortality (from the Global Health Observatory); and the ratio of government consumption to GDP adjusted by purchasing power (from the PWT).

43

Texas Tech University, Gonzalo Macera, December 2020

Table 3.3 Poverty SC – indicator variables Variable Years Description Source

rgdpPC 1990–2001 Real GDP per capita at constant 2011 national Penn World Table, prices (in mil. 2011 US$) v.9.1 pop growth 1990–2001 Population growth Penn World Table, v.9.1 Openness 1990–2001 (Exports + imports)/GDP Penn World Table, v.9.1 yr_sch 1990–2001 Avg. years of total schooling Barro & Lee (2018)

yr_sch_pri 1990–2001 Avg. years of primary schooling Barro & Lee (2018)

Gov_share 1990–2001 % government consumption at current PPP Penn World Table, v.9.0 MatlMortRatio 1990–2001 Maternal mortality ratio (p/100,000 live births) GHO Data Repository The pool of donor countries (table 3.5) for this synthetic control is predominantly composed of Uruguay (76.3 percent) and Colombia (23.1 percent), with a small amount of Bolivia (less than 1 percent). The root mean squared percentage error (RMSPE) of the synthetic control is close to 0.2404 (24.04 percent). For a list of all of the variables, see the appendix.

Table 3.4 Poverty SC – donor country weights Weight Country Weight Country

0.60% Bolivia 0.00% Ecuador

0.00% Brazil 0.00% Mexico

0.00% Chile 0.00% Panama

23.10% Colombia 76.30% Uruguay In figure 3.7, the synthetic control captures the general upward trend in the poverty rate over the 1990s, but it is unable to predict the massive shock of 2002 amid the crisis. However, after the shock, the synthetic and actual Argentinas behave similarly, showing downward trends reaching levels significantly below the crisis levels. This pattern is matched by the synthetic control, with the control lying, by 2010, slightly above the poverty rate in actual Argentina. This suggests that the

44

Texas Tech University, Gonzalo Macera, December 2020

reduction of poverty cannot be directly attributed to the change of policies after the crisis.

Figure 3.7 Poverty gap $5.50/day SC Of relevance to the criticisms of neoliberalism, such as Wylde (2016), that attribute poverty reduction to the post-2001 policies, figure 3.8 compares the poverty ratio of Argentina with those of donor countries and the Latin American average. It shows that the decrease in poverty followed a regional trend and was not a result of Argentinian policy.

These regional trends, which positively affected income and helped reduce poverty, were most likely driven by a combination of factors—namely, the increasing prices of commodities in this period and the lowering of international interest rates, which translated into capital inflows in the region (see Gerchunoff & Kacef 2016).

45

Texas Tech University, Gonzalo Macera, December 2020

Poverty Gap 5.50$/Day (%)

40

35

30

25

20

15

10

5

0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Argentina Bolivia Colombia Uruguay LA Avg.

Figure 3.8 Poverty gap 5.50$/day - historical: Argentina, donors and LA average Figure 3.9 shows the poverty placebo tests. The black line represents the difference between the poverty gap in actual Argentina and in the synthetic control; the synthetic control is normalized to zero. Gray lines represent the placebo tests (deviations from the synthetic control in the other countries in the dataset). To see the difference between actual and synthetic Argentinas, I run placebo tests just as I did for GDP per capita. Figure 3.9 illustrates that the divergence between actual and synthetic Argentinas is within the range of countries that did not experience the crisis and the change in policy regime. Therefore, there is no evidence that the treatment had a significant impact on the poverty rate in Argentina.

46

Texas Tech University, Gonzalo Macera, December 2020

Figure 3.9 Poverty SC - placebo effects The estimation of the p-values renders marginal results in some of the first years after the treatment and then in 2011. For the rest of the years the results are insignificant.

47

Texas Tech University, Gonzalo Macera, December 2020

Effects - PovertyGap 5.50/Day (%) ; P-Values

13 0

11

9

7

5 0.20 3

1 0.80

0.90 0.90 0.70 0.70 -1 1.00 0.70 0.80 0.60 0.70 0.40 0.40 0.50 0.30 -3 2002 2005 2008 2011 2014 2017

Figure 3.10 Poverty SC – effects & p-values

Conclusions Real-world controls for national economies are scarce. By using the SCM, I was able to compare actual with synthetic Argentina to see how the country would have performed had the policy regime not changed in 2002 after the crisis.

A wave of studies critiqued the Washington Consensus while it was falling out of fashion. But new studies show positive effects of the reforms on living standards in the short and medium runs.

Some people claim that the currency board was the main cause of the 2001 default (Kulkarni and James 2009). Others argue that monetary and exchange rate policies were inconsistent (Kaminsky, Mati, and Choueri 2009). Thomas and Cachanosky (2016) argue that debt default was mainly a fiscal problem. Regardless of which of those views is correct, I study how the government departed from Washington Consensus policies after the crisis and show that with this departure the economy did not improve any more than we would have expected without

48

Texas Tech University, Gonzalo Macera, December 2020

it. Furthermore, while the result is not statistically significant at conventional levels, I find some indication that the departure from Washington Consensus policies may have hurt economic performance in the longer run after the initial recovery.

I found indications that average incomes did rise after the initial shock, most likely because of a rebound effect and regional trends driven by commodity-price increases. But once policy errors were introduced into a fragile economy, Argentina began to lag far behind what might have been the case had the regime not changed. In other words, the expansion became unsustainable. There is no evidence that the initial recovery would have been weaker had Argentina practiced business as usual; if anything, the recovery was weaker because the policy changes reduced institutional quality and economic freedom.

I also find that the reductions in poverty follow a trend independent of the events in Argentina. No evidence suggests that the reductions would have been smaller in the absence of the policy changes.

49

Texas Tech University, Gonzalo Macera, December 2020

BIBLIOGRAPHY Abadie, A. A., Alexis Diamond, and Jens Hainmueller. “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association 105, no. 490 (2010): 493–505. Abadie, A., A. Diamond, and J. Hainmueller. “Comparative Politics and the Synthetic Control Method.” American Journal of Political Science 59, no. 2 (2015): 495–510. Abadie, A., and J. Gardeazabal. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review 93, no. 1 (2003): 113–32. Aghion, P., and P. Howitt. “A model of growth through creative destruction.” Econometrica 60, no. 2 (1992): 323–51. Audretsch, D. B., and R. Thurik. “Linking Entrepreneurship to Growth”, OECD Science, Technology and Industry Working Papers, 2001/2, OECD Publishing. Baumol, William. “Entrepreneurship: Productive, Unproductive, and Destructive.” Journal of Political Economy 98, no. 5 (1990): 892–921. Bhattacharjee, A., C. Higson, S. Holly, and P. Kattuman. “Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms.” Economica 76, no. 301 (2009): 108–31. Bjørnskov, C., and N. Foss. “Economic freedom and entrepreneurial activity: Some cross country evidence.” Public Choice 134, no. 3-4 (2008): 307–28. Boeckh, J. A. and Coghlan, R. T. The Stock Market and Inflation. Homewood, Illinois: Dow- Jones-Irwin, 1982. Buchanan, J. M. Resource Allocation and Entrepreneurship. Vol. 12 of The Collected Works of James M. Buchanan. Indianapolis: Liberty Fund, 1980 Caballero, R. J. “Macroeconomics after the Crisis: Time to Deal with the Pretense-of- Knowledge Syndrome.” Journal of Economic Perspectives 24, no. 4 (2010): 85–102. Cabral, L. “Experience Advantages and Entry Dynamics.” Journal of Economic Theory 59, no. 2 (1993). Cachanosky, Nicolás. “Expectation in Austrian Business Cycle Theory: Market Share Matters.” Review of Austrian Economics 28, no. 2 (2015): 151–65. Cachanosky, Nicolás, and Alexander W. Salter. “The View from Vienna: An Analysis of the Renewed Interest in the Mises-Hayek Theory of the Business Cycle.” Review of Austrian Economics 30, no. 2 (2017): 169–92.

50

Texas Tech University, Gonzalo Macera, December 2020

Cachanosky, Nicolás. “Austrian Economics, Market Process, and the EVA® Framework.” Journal of Business Valuation and Economic Loss Analysis 12, s1 (Forthcoming). Calcagno, A. y S. Manuelito (2001) “La Convertibilidad Argentina: un antecedente relevante para la dolarizacion de Ecuador?” Santiago: ECLAC-United Nations Callahan, G., & Horwitz, S. G. “The Role of Ideal Types in Austrian Business Cycle Theory.” Advances in Austrian Economics, 14(2010), 205–24. 0000014013. Calvo, G. A. “Puzzling over the Anatomy of Crises: Liquidity and the Veil of Finance.” Monetary and Economic Studies no. 31 November (2013): 39–63. Calvo, G. A., and E. Talvi. “Sudden Stop Financial Factors and Economic Collapse in Latin America: Learning from Argentina and Chile.” In The Washington Consensus Reconsidered: Towards New Global Governance, edited by N. Serra and J. E. Stiglitz, 119–49. New York: Oxford University Press, 2008. Caplan, B. “Why I Am Not an Austrian Economist: The Austrian Search for Realistic Foundations.” Southern Economic Journal 65, no. 4 (1999). Carilli, A. M., and G. M. Dempster. “Expectations in Austrian Business Cycle Theory: An Application of the Prisoner’s Dilemma.” Review of Austrian Economics 14, no. 4 (2001): 319–30. Cavallo, D. F., and J. A. Cottani. “Argentina’s Convertibility Plan and the IMF.” American Economic Review 87, no. 2 (1997): 17–22. Cohn, R., and D. Lessard. ‘The effect of inflation on stock prices: international evidence.’ Journal of Finance 36, no. 2 (1981): 277–89. Cowen, T. Risk and Business Cycles: New and Old Austrian Perspectives. New York: Routledge, 1997. Damill, M., R. Frenkel, and R. Maurizio. Argentina: Una década de convertibilidad. Un análisis del crecimiento, el empleo y la distribución del ingreso. Santiago de Chile: Oficina Internacional del Trabajo, 2002. Darvas, Z. ‘Real Effective Exchange Rates for 178 Countries: A New Database.’ Bruegel Working Paper, 2012. https://www.bruegel.org/wp- content/uploads/imported/publications/120315_ZD_REER_WP_01.pdf. De Haan, Jakob, Susanne Lundstrom, and Jan-Egbert Sturm. “Market-Oriented Institutions and Policies and Economic Growth: A Critical Survey.” Journal of Economic Surveys 20, no. 2 (2006): 157–91. Della Paolera, G., and A. M. Taylor. A New Economic History of Argentina. Cambridge University Press, 2003 Di Tella, G. The Political Economy of Argentina, 1946-83. University of Pittsburgh Press, 1989.

51

Texas Tech University, Gonzalo Macera, December 2020

Evans, A. J., and T. Baxendale. “Austrian Business Cycle Theory in Light of Rational Expectations: The Role of Heterogeneity, the Monetary Footprint, and Adverse Selection in Monetary Expansion.” Quarterly Journal of Austrian Economics 11, no. 2 (2008): 81–93. Friedman, M. “The Optimum Quantity of Money.” In The Optimum Quantity of Money and Other Essays. Chicago: Aldine Publishing Company, 1969. Galiani, S., and D. Heymann. “Missed Expectations: The Argentine Convertibility.” Working Papers 55, Universidad de San Andres, Departamento de Economia, 2003. Garrison, R. W. “New Classical and Old Austrian Economics: Equilibrium Business Cycle Theory in Perspective.” Review of Austrian Economics 5, no. 1 (1991): 91–103. Garrison, R. W. “Time and Money.” In The Macroeconomics of Capital Structure, edited by M. J. Rizzo & L. H. White. London: Routledge, 2001 Gerchunoff P., and O. Kacef. “Y ahora que hacemos?: La economía política del kirchnerismo.” Friedrich-Ebert-Stiftung – Análisis 9 (2016). Grier, K., and N. Maynard. “The Economic Consequences of Hugo Chavez: A Synthetic Control Analysis.” Journal of Economic Behavior & Organization 125 (2016): 1–21. Grier, K., and R. Grier. “The Washington Consensus Works: Causal Effects of Reform, 1970–2015.” Journal of Comparative Economics (2020). Grier, K., O. Henry, N. Oleklans, and K. Shields. “The Asymmetric Effects of Uncertainty on Inflation and Output Growth.” Journal of Applied Econometrics 19, no. 5 (2004): 551–65. Guido, P., and G. Lazzari. “Apuntes Sobre la Caída de la Economía Argentina: Se Puede Evitar todo Menos las Consecuencias.” Libertas 38 (May 2003): 107– 70. Gwartney, James D., and Robert A. Lawson. Economic Freedom of the World: 2002 Annual Report. Vancouver, Canada: Fraser Institute, 2003. Gwartney, James D., Randall G. Holcombe, and Robert A. Lawson. “Institutions and the Impact of Investment in Growth.” Kyklos 59, no. 2 (2006): 255–73. Hall, Joshua, and Robert Lawson. “Economic Freedom of the World: An Accounting of the Literature.” Contemporary Economic Policy 32, no. 1 (2013): 1–19. Hanke, S. H., and K. Schuler. “What Went Wrong in Argentina?” Central Banking Journal 12, no. 3 (2002): 43–48. Hayek, Friedrich A. “The Use of Knowledge in Society.” American Economic Review 35, no. 4 (1945): 519–30.

52

Texas Tech University, Gonzalo Macera, December 2020

Holcombe, Randall. “Malinvestment.” Review of Austrian Economics, no. 30 (2016): 153-67 Hopenhayn, H. “Entry, Exit and Firm Dynamics in Long Run Equilibrium.” Econometrica 60, no. 5 (1992): 1127–50. Hume, M., and A. Sentance. “The Global Credit Boom: Challenges for Macroeconomics and Policy.” Journal of International Money and Finance 28, no. 8 (2009): 1426–61. Jovanovic, B. “Selection and the Evolution of Industry.” Econometrica 50, no. 3 (1982): 649–70. Kaminsky, G., A. Mati, and N. Choueri. “Thirty Years of Currency Crises in Argentina: External Shocks or Domestic Fragility?” Journal of the Latin American and Caribbean Economic Association 10, no. 1 (2009): 81–119. Kaul, Ashok, Stefan Klößner, Gregor Pfeifer, and Manuel Schieler. “Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together with Covariates.” In MPRA Paper 83790. Germany: University Library of Munich, 2015. Kirzner, I. Competition and Entrepreneurship. Chicago: University of Chicago Press, 1973. Kirzner, I. M. The Meaning of Market Process. New York: Routledge, 1996. Lacoste, P. International Capital Flows in Argentina: Globalization and Monetary Policy in Emerging Markets. Basel, Switzerland: Bank of International Settlements, 2005. Lewin, P. Capital in Disequilibrium. Auburn, AL: Ludwig von Mises Institute, 1999. Lewin, P., and N. Cachanosky. “A Financial Framework for Understanding Macroeconomic Cycles.” Journal of Financial Economic Policy 8, no. 2 (2016): 268–80. Lewin, P., and N. Cachanosky. “The Average Period of Production: The History of an Idea.” SSRN Electronic Journal (2014). Lewin, P., and N. Cachanosky. “What Is Capital? (Again) Contributions from Finance and Economics.” SSRN Electronic Journal (2015). Liew, V. “Which Lag Length Selection Criteria Should We Employ?” Economic Bulletin 3, no. 33 (2004): 1–9. Mises, Ludwig von. Economic Calculation in the Socialist Commonwealth. Auburn, AL: Ludwig Von Mises Institute, 1990. Mises, Ludwig von. Human Action: A Treatise on Economics. New Haven, CT: Yale University Press, 1949.

53

Texas Tech University, Gonzalo Macera, December 2020

Mises, Ludwig von. The Theory of Money and Credit, translated by H. E. Batson. Indianapolis, IN: Liberty Fund, 1912. Modigliani, F., and R. Cohn. “Inflation, Rational Valuation and the Market.” Financial Analysts Journal 35, no. 2 (1979): 24–44. North, Douglas. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University. Press, 1990. O’Driscoll, G. P. J., and M. J. Rizzo. (1985). In M. J. Rizzo & L. H. White (Eds.), The economics of time and ignorance (1996th ed.). New York: Routledge. Powell, B. “Some Implications of Capital Heterogeneity.” In Handbook of Austrian Economics, edited by Peter Boettke, 124–35. Cheltenham, UK: Edward Elgar Publishing, 2010. Powell, B., and G. Macera. “Economic Calculation and the Productivity of Investment.” Journal of Business Valuation and Economic Loss Analysis 12, no. 1 (2017). Powell, B., and R. Weber. “Economic Freedom and Entrepreneurship: A Panel Study of the United States.” American Journal of Entrepreneurship 6, no. 1 (2013): 67–87. Powell, B., J. R. Clark, and A. Nowrasteh. “Does Mass Inmigration Destroy Institutions? 1990s Israel as a Natural Experiment.” Cato Working Papers 41, Cato Institute, 2017. Rodrik, Dani. “Goodbye Washington Consensus, Hello Washington Confusion? A Review of the World Bank’s Economic Growth in the 1990s: Learning from a Decade of Reform.” Journal of Economic Literature 44, no. 4 (2006): 973–87. Rosenbaum, M. “Lags in the Effect of Monetary Policy. Federal Reserve Bank of St. Louis.” Economic Review (1985): 11–85. Sandleris, G., and M. L. J. Wright. “The Costs of Financial Crises: Resource Misallocation, Productivity, and Welfare in the 2001 Argentine Crisis.” Scandinavian Journal of Economics 116, no. 1 (2014): 87–127. Sargent, T. J., and N. Wallace. “Some Unpleasant Monetarist Arithmetic.” Quarterly Review 5, no. 3 (1981): 1–17. Schumpeter, J. Capitalism, Socialism, and Democracy. New York: Harper & Row, 1942. Solow, R. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70, no. 1 (1956): 65–94. Thomas, C., and N. Cachanosky. “Argentina’s Post-2001 Economy and the 2014 Default.” Quarterly Review of Economics and Finance 60 (2016): 70–80.

54

Texas Tech University, Gonzalo Macera, December 2020

Tullock, G. “Reply to Comment by Joseph T. Salerno.” Review of Austrian Economics 3, no. 1 (1989): 147–49. Tullock, G. “Why the Austrians Are Wrong about Depressions.” Review of Austrian Economics 2, no. 1 (1988): 73–78. United Nations Economic Commission for Latin America and the Caribbean. Measuring Poverty and Inequality and Indicators for Identifying Groups at Risk. Vienna: United Nations, 2015. http://www.cepal.org/sites/default/files/project/files/annex_9_measuring_pover ty_and_inequality.pdf Wadhwhani, S. “Inflation, Bankruptcy, Default Premia and the Stock Market.” Economic Journal (London) 96, no. 381 (1986): 120–38. Wylde, C. “Post-neoliberal Developmental Regimes in Latin America: Argentina under Cristina Fernandez de Kirchner.” New Political Economy 21, no. 3 (2016): 322–41. Young, D. S., and S. E. O’Byrne. EVA and Value-Based Management. New York: McGraw-Hill, 2000.

55

Texas Tech University, Gonzalo Macera, December 2020

APPENDIX A Table A.1 Correlation matrix

e 1 t a R t n I ) 1 2 g 4 e 2 R 2 ( . 0 W F E 1 3 0 m 4 3 2 8 m 0 0 o . . 0 0 C - - i p G D 1 1 5 2 n e 3 8 9 p 2 3 0 3 0 3 O . . . e 0 0 0 d - a r T

1 5 8 3 2

e

4 5 8 4 t

a 5 2 3 4 r 4 2 0 0

. . . .

X

0 0 0 0

- - R 1 9 0 3 6 3 P 8 0 2 6 8 D 1 9 1 4 3 1 1 9 0 1 G . . . . . d 0 0 0 0 0 l - - r o W D ) 1 1 6 0 6 6 2 4 7 9 2 8 6 3 - t 6 6 4 0 6 2 ( 0 0 1 1 0 0 l ...... v 0 0 0 0 0 0 - - L f n I ) 1 0 3 3 5 0 0 8 3 6 8 9 2 6 0 4 - t 0 2 1 2 0 2 0 ( 0 0 0 0 0 0 0 l ...... v 0 0 0 0 0 0 0 - - L f n I ) 1 2 7 0 1 8 6 0 1 2 8 6 9 4 3 2 1 5 - t 5 7 1 1 0 5 7 0 ( 0 1 0 0 0 0 0 0 l ...... v 0 0 0 0 0 0 0 0 - - - - L f n I ) 1 0 2 4 5 0 0 6 1 8 1 5 2 5 3 2 0 6 3 0 - t 2 6 3 1 0 9 3 1 2 ( 0 1 0 0 0 0 0 0 1 l ...... v 0 0 0 0 0 0 0 0 0 ------L f n I ) 1 1 3 2 9 1 7 9 0 2 8 t ( 6 3 5 0 7 2 5 4 9 0 l 0 3 4 6 2 0 3 7 6 9 v 0 2 0 2 0 1 0 0 0 0 ...... L f 0 0 0 0 0 0 0 0 0 0 ------n I ) 1 6 3 6 7 6 8 3 3 4 4 9 4 7 3 9 1 8 2 2 4 0 8 8 - t 9 1 8 7 6 4 1 5 6 3 7 ( 0 0 0 1 0 0 0 0 0 0 0 ...... P 0 0 0 0 0 0 0 0 0 0 0 D ------G D

) 1 7 6 4 1 2 7 4 8 2 6 3 1

3 3 5 6 9 4 7 1 4 5 6 4 0 - t 3 8 1 1 3 5 4 2 3 6 1 4 ( 0 0 1 2 0 1 2 0 1 1 0 0

...... P

0 0 0 0 0 0 0 0 0 0 0 0

D ------G D ) 1 8 4 2 9 8 7 7 4 0 9 8 9 0 2 7 6 3 2 1 1 4 3 0 1 1 3 5 - t 2 7 0 1 2 3 3 0 4 0 9 8 0 ( 0 1 2 2 0 1 0 0 0 1 0 0 1 ...... P 0 0 0 0 0 0 0 0 0 0 0 0 0 D ------G D ) 1 9 9 9 4 3 2 5 3 2 7 2 2 6 0 1 9 1 4 7 4 5 9 5 1 7 5 5 9 7 - t 0 8 7 6 3 6 7 5 6 0 0 5 5 6 ( 0 1 0 1 1 0 0 0 1 1 1 1 0 0 ...... P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 D ------G D ) 1 3 5 1 3 8 0 5 5 6 0 1 7 9 7 0 4 2 0 4 7 0 9 0 9 6 6 5 6 9 0 6 - t 0 6 1 0 1 4 2 3 3 0 7 2 3 1 4 ( 0 0 0 3 1 0 0 0 2 0 0 1 0 0 0 l ...... o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ------V f n I ) 1 9 8 0 1 1 4 9 1 1 9 4 7 0 4 2 9 3 8 0 8 8 3 7 1 1 6 2 0 0 0 9 4 1 - t 7 2 3 5 3 2 4 6 8 7 0 8 9 1 2 1 ( 3 0 0 2 0 0 0 0 1 0 2 0 0 2 0 0 l ...... o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ------V f n I ) 1 2 7 7 0 9 1 9 1 1 4 5 2 0 3 0 6 8 2 9 3 7 0 4 3 1 3 9 7 8 3 6 9 2 4 8 - t 9 3 1 8 2 3 1 0 7 2 3 3 9 4 1 3 0 ( 3 1 1 2 0 0 0 0 2 0 0 1 0 0 2 1 1 l ...... o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - - - - - V f n I ) 1 7 1 1 4 5 3 0 6 0 6 1 1 0 0 9 0 4 7 1 7 3 6 3 6 9 7 6 1 8 0 0 9 8 8 2 9 1 - t 9 2 4 0 0 2 9 2 7 1 0 9 3 1 4 8 1 1 ( 2 1 0 4 0 0 0 0 2 0 1 0 1 0 0 1 0 0 l ...... o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ------V f n x I i r t ) a 1 1 4 5 6 0 9 2 5 7 2 1 0 8 3 8 1 7 0 1 t ( 3 0 2 5 9 9 8 1 3 6 1 8 3 8 7 0 5 8 7 l M 2 0 3 6 3 0 4 1 9 5 4 1 5 7 3 0 4 5 6

o 4 0 1 0 2 2 1 1 1 0 0 0 0 2 1 1 2 0 1 n ...... V o f 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 i ------t n I a l e r r P m o ) ) ) ) ) ) ) ) ) ) ) ) ) D n 1 2 3 4 1 2 3 4 g 1 2 3 4 e C s m ) ------) - - - - G e

e t t t t t t t t t p t t t t t o l ( ( ( ( ( ( ( ( ( d 1 ( ( ( ( ( e e R l l l l l l l l l l l O t b t C ( P P P P r e o o o o o e i a a v v v v v a l o i r D D D D d p W r V V V V V L L L L L R b a f f f f f f f f f f t a X a G G G G W r G F n n n n n n n n n n n T V I I I I I D D D D I I I I I D R T D E I 56

Texas Tech University, Gonzalo Macera, December 2020

Table A.2 GDP/capita SC, variables: treated vs. synthetic

Argentina Variable Treated Synthetic Pop_Growth 0.0125 0.0095 RealGdpPC_1990 9681.80 10350.39 RealGdpPC_1991 10548.41 10619.18 RealGdpPC_1992 11475.91 11307.40 RealGdpPC_1993 12030.43 11532.08 RealGdpPC_1994 12567.79 12150.30 RealGdpPC_1995 12057.83 11785.06 RealGdpPC_1996 12571.60 12365.30 RealGdpPC_1997 13434.16 12921.65 RealGdpPC_1998 13794.48 13389.18 RealGdpPC_1999 13179.65 13193.47 RealGdpPC_2000 12931.22 13147.56 RealGdpPC_2001 12224.51 12765.13 OpenTrade_1990 0.1734 0.2007 OpenTrade_1991 0.1624 0.1806 OpenTrade_1992 0.2046 0.2208 OpenTrade_1993 0.1960 0.2234 OpenTrade_1994 0.2025 0.2346 OpenTrade_1995 0.1855 0.2549 OpenTrade_1996 0.1680 0.2891 OpenTrade_1997 0.2003 0.3337 OpenTrade_1998 0.2155 0.3399 OpenTrade_1999 0.1962 0.3383 OpenTrade_2000 0.2190 0.3795 OpenTrade_2001 0.2185 0.3664 Yr_School_1990 8.37 7.28 Yr_School_1991 8.42 7.34 Yr_School_1992 8.48 7.40 Yr_School_1993 8.53 7.45 Yr_School_1994 8.59 7.51 Yr_School_1995 8.64 7.57 Yr_School_1996 8.66 7.66 Yr_School_1997 8.68 7.76

57

Texas Tech University, Gonzalo Macera, December 2020

Yr_School_1998 8.69 7.85 Yr_School_1999 8.71 7.95 Yr_School_2000 8.73 8.04 Yr_School_2001 8.86 8.07 Yr_School_Pri_1990 6.02 4.87 Yr_School_Pri_1991 6.06 4.88 Yr_School_Pri_1992 6.10 4.90 Yr_School_Pri_1993 6.15 4.92 Yr_School_Pri_1994 6.19 4.94 Yr_School_Pri_1995 6.23 4.95 Yr_School_Pri_1996 6.25 5.02 Yr_School_Pri_1997 6.27 5.08 Yr_School_Pri_1998 6.28 5.14 Yr_School_Pri_1999 6.30 5.20 Yr_School_Pri_2000 6.32 5.26 Yr_School_Pri_2001 6.38 5.29 MaternalMortalityRatio_1990 72.00 51.09 MaternalMortalityRatio_1991 70.80 50.18 MaternalMortalityRatio_1992 69.60 49.27 MaternalMortalityRatio_1993 68.40 48.36 MaternalMortalityRatio_1994 67.20 47.45 MaternalMortalityRatio_1995 66.00 46.54 MaternalMortalityRatio_1996 64.80 45.63 MaternalMortalityRatio_1997 63.60 44.72 MaternalMortalityRatio_1998 62.40 43.81 MaternalMortalityRatio_1999 61.20 42.90 MaternalMortalityRatio_2000 60.00 41.99 MaternalMortalityRatio_2001 59.47 40.59 gov_share_1990 0.0638 0.1848 gov_share_1991 0.0570 0.1817 gov_share_1992 0.0724 0.1735 gov_share_1993 0.0992 0.1714 gov_share_1994 0.1128 0.1752 gov_share_1995 0.1459 0.1863 gov_share_1996 0.2054 0.1848 gov_share_1997 0.1763 0.1668

58

Texas Tech University, Gonzalo Macera, December 2020

gov_share_1998 0.1589 0.1502 gov_share_1999 0.1536 0.1509 gov_share_2000 0.1409 0.1428 gov_share_2001 0.1341 0.1352

59

Texas Tech University, Gonzalo Macera, December 2020

Table A.3 Poverty SC, variables: treated vs. synthetic

Argentina Variable Treated Synthetic Pop_Growth 0.0125 0.0087 RealGdpPC_1990 9681.80 9206.95 RealGdpPC_1991 10548.41 9391.29 RealGdpPC_1992 11475.91 10060.36 RealGdpPC_1993 12030.43 10283.34 RealGdpPC_1994 12567.79 10891.48 RealGdpPC_1995 12057.83 10767.63 RealGdpPC_1996 12571.60 11195.80 RealGdpPC_1997 13434.16 11616.99 RealGdpPC_1998 13794.48 11964.52 RealGdpPC_1999 13179.65 11606.84 RealGdpPC_2000 12931.22 11414.78 RealGdpPC_2001 12224.51 11041.02 OpenTrade_1990 0.1734 0.1952 OpenTrade_1991 0.1624 0.1724 OpenTrade_1992 0.2046 0.1966 OpenTrade_1993 0.1960 0.2067 OpenTrade_1994 0.2025 0.2160 OpenTrade_1995 0.1855 0.2230 OpenTrade_1996 0.1680 0.2425 OpenTrade_1997 0.2003 0.2789 OpenTrade_1998 0.2155 0.2907 OpenTrade_1999 0.1962 0.2692 OpenTrade_2000 0.2190 0.2977 OpenTrade_2001 0.2185 0.2894 Yr_School_1990 8.37 7.11 Yr_School_1991 8.42 7.15 Yr_School_1992 8.48 7.19 Yr_School_1993 8.53 7.24 Yr_School_1994 8.59 7.28 Yr_School_1995 8.64 7.32 Yr_School_1996 8.66 7.41 Yr_School_1997 8.68 7.51

60

Texas Tech University, Gonzalo Macera, December 2020

Yr_School_1998 8.69 7.61 Yr_School_1999 8.71 7.70 Yr_School_2000 8.73 7.80 Yr_School_2001 8.86 7.79 Yr_School_Pri_1990 6.02 4.78 Yr_School_Pri_1991 6.06 4.78 Yr_School_Pri_1992 6.10 4.79 Yr_School_Pri_1993 6.15 4.79 Yr_School_Pri_1994 6.19 4.80 Yr_School_Pri_1995 6.23 4.80 Yr_School_Pri_1996 6.25 4.87 Yr_School_Pri_1997 6.27 4.94 Yr_School_Pri_1998 6.28 5.01 Yr_School_Pri_1999 6.30 5.08 Yr_School_Pri_2000 6.32 5.15 Yr_School_Pri_2001 6.38 5.16 MaternalMortalityRatio_1990 72.00 58.04 MaternalMortalityRatio_1991 70.80 57.04 MaternalMortalityRatio_1992 69.60 56.04 MaternalMortalityRatio_1993 68.40 55.05 MaternalMortalityRatio_1994 67.20 54.05 MaternalMortalityRatio_1995 66.00 53.05 MaternalMortalityRatio_1996 64.80 52.05 MaternalMortalityRatio_1997 63.60 51.06 MaternalMortalityRatio_1998 62.40 50.06 MaternalMortalityRatio_1999 61.20 49.06 MaternalMortalityRatio_2000 60.00 48.06 MaternalMortalityRatio_2001 59.47 46.69 gov_share_1990 0.0638 0.1811 gov_share_1991 0.0570 0.1766 gov_share_1992 0.0724 0.1681 gov_share_1993 0.0992 0.1682 gov_share_1994 0.1128 0.1736 gov_share_1995 0.1459 0.1794 gov_share_1996 0.2054 0.1815 gov_share_1997 0.1763 0.1657

61

Texas Tech University, Gonzalo Macera, December 2020

gov_share_1998 0.1589 0.1507 gov_share_1999 0.1536 0.1525 gov_share_2000 0.1409 0.1439 gov_share_2001 0.1341 0.1364

Figure A.1 GDP/capita SC, without Uruguay

Table A.4 GDP/capita SC donor-country weights, without Uruguay

Weight Country Weight Country 0.00% Bolivia 0.00% Guatemala 0.00% Brazil 0.00% Honduras 34.00% Chile 46.80% Mexico 19.20% Colombia 0.00% Panama 0.00% Costa Rica 0.00% Paraguay 0.00% Ecuador 0.00% Peru 0.00% El Salvador

62

Texas Tech University, Gonzalo Macera, December 2020

Figure A.2 GDP/capita SC, without Mexico

Table A.5 GDP/capita SC, donor-country weights, without Mexico

Weight Country Weight Country 0.00% Bolivia 0.00% Guatemala 0.00% Brazil 0.00% Honduras 16.20% Chile 0.00% Panama 0.00% Colombia 0.00% Paraguay 0.00% Costa Rica 0.00% Peru 0.00% Ecuador 83.80% Uruguay 0.00% El Salvador

63

Texas Tech University, Gonzalo Macera, December 2020

Figure A.3 GDP/capita SC, without Chile

Table A.6 GDP/capita SC donor-country weights, without Chile

Weight Country Weight Country 0.00% Bolivia 0.00% Honduras 0.00% Brazil 25.20% Mexico 0.00% Colombia 0.00% Panama 0.00% Costa Rica 0.00% Paraguay 0.00% Ecuador 0.00% Peru 0.00% El Salvador 74.80% Uruguay 0.00% Guatemala

64

Texas Tech University, Gonzalo Macera, December 2020

GDP/Capita SC Comparison

21000

19000

17000

15000

13000

11000

9000 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Argentina Synthetic ARG (No URU) Synthetic ARG (No MEX) Synthetic ARG (No CHI)

Figure A.4 GDP/capita SC, comparison

65

Texas Tech University, Gonzalo Macera, December 2020

Figure A.5 Poverty $5.50/day SC, without Uruguay

Table A.7 Poverty SC, donor-country weights, without Uruguay

Weight Country Weight Country 2.90% Bolivia 0.00% El Salvador 0.00% Brazil 0.00% Honduras 90.30% Chile 0.00% Mexico 0.00% Colombia 0.00% Panama 0.00% Costa Rica 6.80% Paraguay

66

Texas Tech University, Gonzalo Macera, December 2020

Figure A.6 Poverty $5.50/day SC, without Colombia

Table A.8 Poverty SC, donor-country weights, without Colombia

Weight Country Weight Country 19.50% Bolivia 0.00% Honduras 0.00% Brazil 0.00% Mexico 0.00% Chile 0.00% Panama 0.00% Costa Rica 0.00% Paraguay 0.00% El Salvador 80.50% Uruguay

67

Texas Tech University, Gonzalo Macera, December 2020

Figure A.7 Poverty $5.50/day SC, without Bolivia

Table A.9 Poverty SC, donor-country weights, without Bolivia

Weight Country Weight Country 0.00% Brazil 0.00% Honduras 0.00% Chile 0.00% Mexico 0.00% Colombia 23.90% Panama 0.00% Costa Rica 0.00% Paraguay 0.00% El Salvador 76.10% Uruguay

68

Texas Tech University, Gonzalo Macera, December 2020

Poverty SC Comparison

25

20

15

10

5

0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Argentina Synthetic ARG (No URU)

Synthetic ARG (No COL) Synthetic ARG (No BOL)

Figure A.8 Poverty SC, comparison

69