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Financial repression and subsidies

University of Florida Institutional Repository
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PAGE 1

FINANCIAL REPRESSION AND SUBSIDIES: CAPITAL INVESTMENT IN RUSSIA, DECEMBER 1998 TO DECEMBER 2005 By M. YVONNE REINERTSON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2007 M. Yvonne Reinertson

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This dissertation is dedicated to the memory of my mother: A kinder spirit is not possible.

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iv ACKNOWLEDGMENTS I am grateful for the guidance of my Chair, Dr. Andy Naranjo, and my Cochair, Dr. Roy Crum, who made possible my experiences in Russia. I also thank my other committee members, Dr. Mahendrara Nimalendran, and Dr. Richard Beilock. Additionally, I would like to thank the following individuals: David Beim, Michael Bernstam, Charles Calomiris, and Alvin Rabushka. The Russian data used in this analysis would not have been available without the help of the following Russian friends: Andrei Betin, Victor Matiashvili, and Roman Vvedensky. I am greatly in their debt.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................................................................................. iv LIST OF TABLES ........................................................................................................... viii LIST OF FIGURES ........................................................................................................... ix ABSTRACT .........................................................................................................................x CHAPTER 1 INTRODUCTION ........................................................................................................1 1.1 Measuring Financial Repression .............................................................................3 1.2 Enterprise Network Socialism ................................................................................4 1.3 Focus of This Work ................................................................................................5 2 DIFFERENCES BETWEEN RUSSIA AND THE UNITED STATES.......................6 2.1 Economic, Banking, and Payment System Structures: Russia versus United States .........................................................................................................................6 2.1.1 Differences: Economic Structure .................................................................7 2.1.2 Differences: Central Bank Monetary Policy Channels ................................9 2.1.3 Differences: Central Bank Balance Sheet ..................................................13 2.1.4 Differences: Payment System .....................................................................14 2.2 Economic, Banking, and Payment System Structures: Role of Gazprom ............16 3 LITERATURE REVIEW ...........................................................................................24 3.1 Financial Repression on Investment: Chronological Changes .............................25 3.1.1 Literature Review: Pre-1998-Default .........................................................26 3.1.2 Literature Review: Post-1998-Default .......................................................28 3.2 Liquidity Constraints on Investment: Government Control of Banking ..............30 3.3 Monetary Policy Transmission on Investment: Primary Channels ......................31 3.3.1 Money Channel ..........................................................................................32 3.3.2 Bank-Lending Channel ...............................................................................32 3.4 Financing Constraints on Investment: Internal versus External Financing ..........33 3.5 Russian Governmental Subsidies .........................................................................38

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vi 4 TESTABLE HYPOTHESES ......................................................................................40 4.1 Hypothesis: Financial Repression .........................................................................40 4.2 Hypothesis: Enterprise Network Socialism ..........................................................42 4.3 Systemic Distortions: Gains Possible ...................................................................42 4.4 Analysis: General and Specific Questions ............................................................46 5 DATA .........................................................................................................................49 5.1 Descriptions: Variables .........................................................................................49 5.2 Sources: Data ........................................................................................................50 5.2.1 Russia: Financial Repression Measures and Aggregate Level Capital Investment ........................................................................................................50 5.2.2 Russia: Large Firm Level Capital Investment ............................................51 5.2.3 USA: Financial Repression Measuresand Aggregate Level Capital Investment ........................................................................................................51 5.2.4 USA: Large Firm Level Capital Investment ..............................................52 6 METHODOLOGY .....................................................................................................54 6.1 Model Notation Conventions ................................................................................56 6.2 Generalized Assumptions: Tested Per Analysis ...................................................58 6.3 MethodologyTime Series for Aggregate Level Investment: ARIMA [AR(1) Model] .....................................................................................................................59 6.4 MethodologyPanel Data for Firm Level Investment: GLS [Random-Effects Model] .....................................................................................................................60 6.5 MethodologyIncrease Data Interval Frequency: Proportional Denton Method .61 7 RESULTS ...................................................................................................................63 7.1 Aggregate Level InvestmentRussia: Descriptive Statistics and Correlations.....63 7.2 Aggregate Level InvestmentRussia: Time Series Results ..................................65 7.2.1 Results with Liquidity VariableRussia: ARIMA [AR(1)] ........................65 7.2.2 Results with Private Borrowing VariableRussia: ARIMA [AR(1)] .........66 7.3 Large Firm Level InvestmentRussia: Descriptive Statistics and Correlations ...67 7.4 Large Firm Level InvestmentRussia: Panel Data Results ..................................69 7.4.1 Results with Liquidity VariableRussia: GLS [Random-Effects] .............69 7.4.2 Results with Private Borrowing VariableRussia: GLS [Random-Effects] .............................................................................................................74 7.5 Aggregate Level InvestmentUSA: Descriptive Statistics and Correlations .......76 7.6 Aggregate Level InvestmentUSA: Time Series Results .....................................77 7.6.1 Results with Liquidity VariableUSA: ARIMA [AR(1)] ..........................77 7.6.2 Results with Private Borrowing VariableUSA: ARIMA [AR(1)] ...........78 7.7 Large Firm Level InvestmentUSA: Descriptive Statistics and Correlations ......79 7.8 Large Firm Level InvestmentUSA: Panel Data Results .....................................80 7.8.1 Results with Liquidity VariableUSA: GLS [Random-Effects] ................80 7.8.2 Results with Private Borrowing VariableUSA: GLS [Random-Effects] .82

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vii 7.9 Comparative Statics ..............................................................................................83 7.10 Results: General and Specific Questions ............................................................83 8 CONCLUSION.........................................................................................................101 APPENDIX A DATA JOURNAL ....................................................................................................105 B DATA: LARGE FIRMSRUSSIA AND UNITED STATES .................................110 LIST OF REFERENCES .................................................................................................112 BIOGRAPHICAL SKETCH ...........................................................................................118

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viii LIST OF TABLES Table page 5-1 Variable Definitions .................................................................................................53 7-1 Descriptive statistics and correlation Russia: Aggregate level ................................88 7-2 Estimation Russia: Aggregate level investmentwith liquidity variable .................89 7-3 Estimation Russia: Aggregate level investmentwith private borrowing variable ..90 7-4 Descriptive statistics and correlation Russia: Large firm level .............................91 7-5 Estimation Russia: Large firm level investmentwith liquidity variable ..............92 7-6 Estimation Russia: Large firm level investmentwith private borrowing variable .....................................................................................................................93 7-7 Descriptive statistics and correlation USA: Aggregate level ...................................94 7-8 Estimation USA: Aggregate level investmentwith liquidity variable ....................95 7-9 Estimation USA: Aggregate level investmentwith private borrowing variable ..96 7-10 Descriptive statistics and correlation USA: Large firm level ..................................97 7-11 Estimation USA: Large firm level investmentwith liquidity variable ...................98 7-12 Estimation USA: Large firm level investmentwith private borrowing variable ....99 7-13 Comparative statics Expected relationships to investment: Economies of industrialized and transitional countries .................................................................100 B-1 Data: Large firmRussia Russia ...............................................................................110 B-2 Data: Large Firms USA. ......................................................................................111

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ix LIST OF FIGURES Figure page 2-1 Monetary Measures from the Monetary Survey (Rubles; billion) ...........................18 2-2 Monetary Measures: M2 (broad) and M0 (narrow) from the CBR; Money and Quasi-money from the Monetary Survey (Rubles; billion)......................................19 2-3 Monetary Survey Components and CBR Total Liability (Rubles; billion) .............20 2-4 Sum of Monetary Survey Components with CBR Total Liability (Rubles; billion) ......................................................................................................................21 2-5 Ratio of Monetary Survey Components to CBR Total Liability .............................22 2-6 Relationship Diagram of the Enterprise Network Socialism ...................................23 4-1 Enterprise Receivables to M2 ..................................................................................47 4-2 Enterprise Receivables to M1 ..................................................................................48 7-1 Monetary Measures: M2 (broad) and M1 (narrow) from the Federal Reserve ........86 7-2 Potential Crowding Out: Private Credit and Total USA Credit ...............................86 7-3 Ratio of Private Credit to Total Credit .....................................................................87 7-4 Industrial Production to GDP ...................................................................................87

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x Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FINANCIAL REPRESSION AND SUBSIDIES: CAPITAL INVESTMENT IN RUSSIA, DECEMBER 1998 TO DECEMBER 2005 By M. Yvonne Reinertson May 2007 Chair: Andy Naranjo Cochair: Roy L. Crum Major: Business Administration The purpose of this dissertation is to assess potential systemic distortions on investment from governmental actions of (1) financial repression, and (2) subsidies in Russia. The extent to which the Russian government engages in policies that, in turn, stifle investments by firms and other market participants is explored using six individual proxies (real interest rates, reserve ratio, liquidity, private borrowing, bank lending, and stock market valuation), which are components of the Beim and Calomiris financial repression index. Then, subsidy based distortions, theorized in the work of Bernstam and Rabushka are examined using the results produced by the financial repression study. Using datasets on both aggregate and firm-economy, investment is shown to have a consistently negative association with an increase in the reserve requirement for both aggregate and large firm investments. Additionally, and specific to Russian large firms, the data show liquidity and bank

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xi lending are inversely related to capital investment changes. This last result is consistent with the existence of a subsidy system in Russia producing additional distortions to those created by the governmental use of financial repression. The principal objective of banking in any country should be to provide economic liquidity and investment intermediation. However, in countries where the banks are state controlled and financial repression is the norm, this objective becomes secondary to satisfying state budgetary and political goals. This appears to be the case in Russia. Moreover, the effects of financial repression are magnified by a subsidy system largely systemic distortions on investments. The extent to which investments in Russia are influenced by these effects are highlighted through comparisons with firms in the United States. While this research work is specific to Russia, its implications are not. This examination of financial repression produced distortions addresses an ongoing concern for many countries, firms, and governmental institutions around the world. Advisors responsible for designing policy to enhance global economic growth have an interest in determining the governmental actions that hamper investment. In many nations, primarily those which are underdeveloped or in transition, governments use repressed domestic financial systems to fund the state at the expense of private enterprise development.

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1 CHAPTER 1 INTRODUCTION ussian economy is strongly monopolized through its backbone companies and also Sberbank [nationalized savings bank], which is a monopoly [monopsony as a buyer of household deposits] in its sector. As a result, in every sector, there is little room for competition, and, hence, the economic growth rate is -Oleg Vyugin, former first deputy chairman of the Central Bank 1 Potential economic distortion through government manipulation of monetary policy, banking intermediation, and financial markets concerns all parties interested in the growth of transitional and developing countries. Assessing the reality of such assertions is one of goals. This will be investigated through development of financial repression measures for Russia, using both macroeconomic and microeconomic data (Beim and Calomiris 2001). Building on these results, the second goal will be addressed by seeking evidence on the possible existence and effects produced by an enterprise-driven subsidy system called (ENS) by Bernstam and Rabushka (1998, 2006). Financial repression can be loosely defined as governmental actions, either direct or indirect, taken to restrict or alter the flow-of-funds within an economy. A monopolized may control both money stocks and flows. At the start of 2003, state controlled banks in Russia, with Sberbank tail deposits, 34 percent of capital, 38 1 Notable Quotes, The Russia Journal, http://www.russiajournal.com (accessed November 2003)

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2 percent of assets and 39 percent of credit outstanding to the non-a potential benefit of this control is Sberbank funneling banking deposits into the Russian government treasury. To the assets are directed into government securities, fewer funds are left from Sberbank to flow into the private sector. Thus, while the death of the Soviet Union and the following industrial privatizations removed the extreme form of central control and reduced the monopolistic structure in many industries, it did not remove tight governmental ties to the banking industry. Concerning the banking structure and its effect on industrial Russia, Bernstam and Rabushka (1998, p. 52) have this to say: The banking systema winding maze of borrower ownership of banks, insider lending, rollover of bad loans, misallocation of credit, lack of competitive credit markets, and lack of long-term investment and credit-impeded the development of deprived productive users of credit and investment. A vicious circle developed that perpetuated bad credit, reinforced financial repression, and depressed the real sector. Most emerging private firms were forced to self-finance or organize informal arrangements with individuals. 2 While this research is specific to Russia, its implications are not. In many nations, primarily those which are underdeveloped or in transition, governments use repressed domestic financial systems to fund the state at the expense of private enterprise development. Ostensibly, the monetary controls that produce financial repression within a country are intended to facilitate realization of state goals, which may include programs that are intended to advance legitimate social and economic causes, such as regional 2 In addition to their book, Bernstam and Rabushka maintain an Web site of their research at www.russiaeconomy.org

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3 development. However, the controls limit the abilities for both financial institutions and financial markets to optimally allocate funds to private industry. 1.1 Measuring Financial Repression The extent to which governments engage in financially repressive policies that in turn stifle investments by firms and other market participants is an ongoing concern in many countries. Historically, the existence and extent of financial repression was gauged by the existence and degree to which real interest rates were negative. The definition of what constitutes financial repression has expanded to include an array of governmental actions that preserve tight control of both money stocks and flows and repress the development of financing sources other than the state controlled banks. These actions include placing ceilings on deposit interest rates, requiring high reserves from banks, directing bank credit, government ownership of banks, restricting financial industry development, and, finally, restraining international capital flows. Beim and Calomiris (2001) have created an index of financial repression based upon the following six financial repression proxies: real interest rates, reserve ratio, liquidity, private borrowing, bank lending, and stock market valuation. 3 Using these six proxies individually, it is possible to evaluate the degree of financial repression in Russia and to explore the effects of repression on company investment. -based economy, as in many other countries with developing economies, virtually all credit is only available to a few of the largest firms. In Russia, these large firms have publicly traded stock listed on the Russian Trading 3 In this research, economic liquidity is measured as M2/GDP. It is an aggregate liquidity measure and defined fully in Appendix A. For a complete explanation of each governmental action and the subsequent proxy, see Beim and Calomiris, 2001, pp. 47-66.

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4 System (RTS). The rest of the domestic economy consists of small and medium enterprises (SME) that are largely limited to internally produce funding sources, or less formal and usually exorbitant alternatives. 1.2 Enterprise Network Socialism The second purpose of this dissertation is to assess the potential for systemic distortions produced through enterprise-driven subsidies. nd their other works, Bernstam and Rabushka have studied extensively the relationship between the monopolized banking sector and excessive invoicing in a tax non-2006, chapter 2, p. 4). 4 The enterprises involved in the ENS subsidy constitute a network of the large and dominant firms in Russia. Specific to the enterprise network effects, Bernstam and Rabushka state: The enterprise network converts trade credit into a subsidy operation. Enterprises issue overdraft invoices in excess of cash flow available per regular payment period of about one month. Payments fall into arrears. The average length of trade payments expanded to about four months during 1992-1998 and shortened to under three months during 1999-2002. Arrears create the payment jam (2006, chapter 1 addendum, 1). [Additionally,] Payment arrears between enterprises force subsidies from the government. The more enterprises succeed at wringing the subsidy the more overdraft price increases. This reduces real spending during each given period of time, which expresses itself in payment arrears (2006, chapter 1 addendum, 2). [Specific to this research,] In Russia in 1999, the stock of receivables, which entailed cross-subsidies, was equal to 40 percent of GDP and some 27 percent of total sales, with the average length of payment about 3.5 months and the velocity (turnover) of 3.4 payments per year (2006, p. 23). 4 Enterprise Network Socialism theory by Bernstam and Rabushka (2006) describes a network of enterprises that have used enterprise receivables to redistributing national income.

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5 In a market based financial system driven by supply and demand, the subsidy network, and the gains received, would end. The existence of this system partly explains Russian entrepreneurial development stalling and the bank restructure process stagnating 15 years after the Soviet Union collapse. 1.3 Focus of This Work This dissertation examines the effects of financial repression and the ENS subsidy network on aggregate and firm-level investment in Russia. A dataset will be used which will permit exploration of the effects of both on aggregate and firm-level investments in For comparison, the same effects are explored using data from the United States. Moreover, a Denton proportional interpolation method is used to produce databases with increased interval frequency (e.g., annual interpolated to quarterly) for further comparison. This research fills the following gaps in the literature by investigating (1) financial repression on two levels of investment within one transitional country, and (2) Also, it indirectly adds to the studies of (3) systemic economic distortion produced through monetary policy manipulations from both government-instituted financial repression and enterprise-driven subsidies.

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6 CHAPTER 2 DIFFERENCES BETWEEN RUSSIA AND THE UNITED STATES There is a consistently high non-economic structure that makes the Russian payment system unusual. For this reason, a comparison with a developed Western economy, such as the United States, is appropriate. 2.1 Economic, Banking, and Payment System Structures: Russia versus United States economy is the high degrees to which non-monetary transactions permeate all sectors in Russia. 5 Previous studies have documented barter and trade credits being used in illiquid economies (Commander and Mumssen 2002). Additionally, in their barter research, Commander and Mumssen found that banking rollover of enterprise loan arrears made tracking both the information about the contract stipulations and the true levels of transactions involved difficult. The difficulty of the banking system being used to facilitate surrogate money transactions adds to the lack of transparency. That, along with the repeated exchange of debt contracts to offset taxes or expenses, sanctioned by Russian civil code, adds to doubts about the accuracy of information on the levels of non-monetary transactions (Commander and Mumssen 2002). An additional reason for concern about the quality of information on non-monetary transactions is that collecting data on this issue can be dangerous (Perotti 2003, p.3). For these reasons, inferences 5 Non-monetary transaction describes economic exchanges being made either through pure barter (exchange a good for another good), or through documents other than the government sanctioned currency. These documents have various IOU properties with specific financial requirements (e.g., maturity date and discount rate).

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7 about the actual levels of non-monetary transactions will have to be made from aggregate data. In Russia, non-monetary transactions take any of the following forms: (1) barter (goods for goods), (2) money surrogates (veksels, which are commodity or financial promissory notes issued by enterprises, banks or government), (3) mutual offsets (zachety, which are used to clear trade, or tax, obligations by exchanging debt for goods), and (4) debt swaps, sales and roll-overs of previously issued non-monetary documents (Commander and Mumssen 2002, p.2). Much of the existing research on illiquidity and chronic non-payment in a non-transparent structure, seen in Russia, is from the Karpov commission report completed in 1997. As an example, and quoted in Gaddy and Ickes (1998, p. 56), the Karpov pays in cash; where no one pays anything on time; where huge mutual debts are created s are declared and On the CBR balance sheet, non-monetary transaction totals are identified as -money. 6 2.1.1 Differences: Economic Structure Despite periodic increases in regulatory controls, the United States has overwhelmingly maintained an open market system based on private ownership. Since the fall of the Soviet Union, Russia has had a semi-capitalistic system which might be characterized as market-oriented socialism. Despite a general loosening of controls since the end of the Soviet Union, there still are high levels of centralized control through public ownership of property and production inputs. 6 In this dissertation, quasi-money describes the aggregate level listed on the CBR balance sheet, of which a large component measures non-monetary transactions. It is banking system deposits which are not directly used for effecting payments and are less liquid than money.

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8 The CBR and its precursor, Gosbank, have consistently played a dominant role in direct management of the centralized economic structures. Academic observers, watching (Gregory and Stuart 1980, p. 209). This monitoring and management by Gosbank was extensive with the requirement that enterprise investment funds actually flow through the state bank, which then directly affected the state budget. Additionally, Soviet firms were required to maintain accounts with Gosbank. These enterprise accounts allowed records of firm profits to flow through the state bank accounting and then through to the state budget (Gregory and Stuart 1980). Furthermore, these records of firm profits provided the taxation base. Thus, lack of independence has historically been an issue for the CBR, and it continues to be an issue in the post Soviet Union as the control shifts from parliamentary to presidential hands microeconomic involvement reaching down to enterprise levels unheard of in economically developed countries. The depth includes, and goes beyond, the recognized their governments by forcing both public and private banks to purchase government debt (Daniels and VanHoose 2002, p. 229). -turvy (e.g., net receivables in the flows of funds are counter-cyclical). Looks like enterprises make more sales and purchases when the economy contracts and less when the economy recovers, which would have been an

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9 obvious, even if ridiculous, interpretation in other economies, but in Russia it simply means that inflationary expectations and payments arrears went from high 7 Even at the point of deepest regulation in the economic history of United States, regulations, at most, restricted economic freedoms in isolated industries. In sharp contrast, the Russian tentacle-like economic structure, however, reaches deep into the operations of enterprises. For example, Beim and Calomiris (2001) note that the Russian tax collection process is arbitrary to the extent that some firms receive hidden subsidies while others, destructive vengeance. 2.1.2 Differences: Central Bank Monetary Policy Channels Countries with developed economic structures and financial markets often use indirect methods and channels to conduct monetary policy. In the United States, the usual channel is through the financial markets using open market operations where U.S. government securities are bought and sold. Countries with developing economic structures and emerging financial markets typically use more direct methods because the financial markets are not sufficiently developed. Argued in this dissertation and supporting the work of Beim and Calomiris, the insufficient development of financial market structures is caused, in part, from government use of financial repression to stymie financial industry growth, and thus subsequently creating systemic distortions. Furthermore, Roubini and Sala-i-Martin (1995) find support for the possibility of policy-induced distortions in the flow of savings to investment channel. When open market operations are not available, the more direct monetary policy channels are used by central banks. With respect to the money-creation channel and the 7 Email to author dated March 2005.

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10 bank-lending channel, the following are typical: (1) Reserve requirement adjustments to the portions of transactions (checking) and term deposits (time and savings) that banks must hold either as vault cash or as funds on deposit at the central bank, (2) Interest rate regulations on depositor funds, and (3) Direct credit controls that constrain the quantity of credit extended to individuals and firms by the banking system (Daniels and VanHoose 2002). However, atypical ones also apply. Specific to Russia, and applicable to other illiquid economies, the primary monetary transmission channel and money creation of currency in circulation is through debt issuance to the nationalized banks (Bernstam and Rabushka, 2006, chapter 1 addendum 1, p. 6). Empirically, both the direct and indirect transmission of monetary controls have been found to affect investment by slowing the economy and controlling both internal and external funding source availability (Schiantarelli 1996). Additionally, and supporting claims being made in this research, constrained investment, especially through the direct transmission in the bank lending channel, falls heavily on small firms. Because they are without access to other funding sources, small firms are more dependent on the intermediation provided by a sound commercial banking system (Kashyap and Stein 1994). To understand the structure of the money supply in Russia, a breakdown of the Monetary Survey components of money and quasi-money is useful. In Figure 2-1, the level and growth of quasi-money closely matches the same for the money measure. The Monetary Survey statistics differ from the usual statistics reported by country central

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11 banks, including the CBR, in that it specifically surveys for an approximation of the quasi-money used in Russia. 8 A further comparison between Monetary Survey components and the statistics typically reported by the CBR is shown in Figure 2-2. Note that while the monetary lower. An extension from this is to consider the actual ruble level within money to be lower than the M0 reported by the CBR. Thus, the true level of monies available for economic transactions could possibly be far less than is officially stated. As explained in Bernstam and Rabushka (2006), the Russian money supply has grown since 1998 from the increased repatriation requirement of export earnings mandated by the CBR. This requirement brought back export-generated dollars to be exchanged for rubles within Russia rather than maintaining the dollars abroad as foreign currency. With more export eresulting required purchase of rubles, there have been increases in the broad money supply. However, the increase in the liquid money supply of currency in circulation, which the small firms depend on for self-financing, is slight. Additionally and not often thought of as a usual monetary policy channel, there are central bank sterilization policies. 9 Russia, like China, has a managed exchange rate with an inconvertible currency and large inflows of U.S. dollars. However, unlike purchase U.S. treasuries, Russia is keeping the dollars obtained from the forced 8 The monetary survey methodology complies with the IMF Special Data Dissemination Standard (SDDS). 9 Monetary sterilization describes a form of monetary action in which a central bank attempts to insulate itself from the foreign exchange market to counteract the effects of a changing monetary base.

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12 repatriation requirement in the country. Following that, the chosen sterilization procedure by the CBR is to reduce the domestic money supply by selling, when possible, ruble denominated government bonds. This reduces the supply of rubles available to domestic firms. On the subject of sterilization, Bernstam wrotmethods in China vs. Russia is relevant and useful. But [also] that Russia accumulates U.S. treasuries in foreign exchange reserves, in addition to monetizing payments and sterilizing rubles. Both countries have net capitacapital outflow depends on the mandated repatriation rate and, as Ron McKinnon 10 Reflected in Figure 2-2., compared to the broader measures of aggregate money supply growth, the liquid ruble growth, M0, is far slower. This research documents that while the overall broad ruble money supply increases, the narrow money supply, and thus the source of cash in the Russian economy, has slower growth and a large non-monetary component. Therefore, cash in the economy, needed for self-financing investment through internally generated funds, suffers in a system which tends to keep money inflows as foreign exchange at the CBR. Concern for growth. Specifically, while the small firms are not privy to the subsidy network, the monetary environment produced affects them. In partially sterilizing the resulting inflowing funds to keep the U.S. dollars as foreign exchange, the Russian monetary authorities used ruble denominated bonds and other techniques to reduce the domestic 10 Email to author dated October 2005.

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13 money supply. A decrease of the domestic aggregate liquidity increases the use of non-monetary transactions and reduces the ability to finance capital investment internally. This supports the findings that available internal funds matter more to firms working in countries with poorly developed financial systems (Love and Zicchino 2002). The important point is that the primary negative effects to economic growth will be through the reduction of small firm growth. As is well-documented, small firms are crucial to economic growth for industrialized countries. In the United States as an example, small firms produce fifty percent of the output and account for over fifty percent of the employment (FED 2002). 2.1.3 Differences: Central Bank Balance Sheet The core difference between the balance sheet structures of Russia and the United States is the lack of a line item for quasi-money on the Federal Reserve balance sheet. Quasi-money is not listed on the CB balance sheet for any of the large developed countries (e.g., Japan, European System of Central Banks) (Daniels and VanHoose 2002, p.221). Liquid economies with relatively small black markets do not depend on IOUs being passed for economic transactions. Studying the quantity, and growth, of quasi-ide insight into the depth of involvement the banking system and non-monetary transactions have to enterprise operations and finance. As is shown in Figure 2.3, even after the increase of forced repatriated export earnings, the relative size of quasi-money to money has consistently been maintained by the parties to the Russian payment system. Focusing on the liability side of the CBR balance sheet, the growth of quasi-money to CBR total liabilities matches the growth of money to CBR total liability. Shown in Figure 2.4, and as a ratio in Figure 2.5, the sum of money and quasi-money

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14 actually exceeds system, Bernstam and Rabushka (2006) suspect that it is possibly common, albeit to a lesser degree, in the other former Soviet Union countries. Typical of central bank balance sheets, while the actual amounts will fluctuate over time, the proportions of liabilities and equity to assets remain stable (Daniels and VanHoose 2002). The majorsheet are governmental securities, and the majority of the liabilities and equity listed are the liabilities of the government sanctioned currency, with bank reserve deposits often being the next largest liability. In the case of the CBR, quasi-money is usually on a par with the Russian currency. The persistence of large volumes of quasi-money, both absolutely and relative to other liabilities, on the Central Bank balance sheet gives an indication of the persistence of non-monetary transactions. Additionally, and most importantly, a possible indicator of the level of non-monetary transactions being conducted outside the banking system can be seen in the spread between the sum of the monetary components and the CBR total liabilities shown in Figure 2-4. 2.1.4 Differences: Payment System Again, the principal difference between the United States and the Russian systems is the prevalence of non-monetary transactions in the latter. At all levels of economic activity, from government, banking, enterprises, and households, non-monetary documents are important to facilitate exchanging goods and services. Reproduced in Figure 2-6 is a flow diagram by Bernstam and Rabushka that aids in understanding the non-monetary transfer process. Figure 2-notational conventions are the following: (1) Relationships between flows are indicated

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15 by the plus and minus signs, (2) Red numbers indicate consumption flows, and (3) Blue numbers represent production flows. The flows relevant to production (blue numbered in Figure 2-6) taken by the parties to the Enterprise Network Socialism (ENS) are the following: 11 1. First flow: Trade credit separates from sales and production. Invoices outgrow payments when enterprises add a third party surcharge to the price and bill the government. 2. Second flow: The flow of receivables for many enterprises exceeds net income. They increase payables to prevent their net cash flows from turning negative. Aged receivables increase payment arrears and vice versa. Enterprises whose flow of receivables exceeds that of trade payables must increase tax payables. 3. Third flow: Enterprises do not remit taxes withheld from workers and collected from consumers. The government cannot enforce full tax remittance. 4. Fourth flow: The government is forced to issue debt (i.e., securitize tax non-remittance). 5. Fifth flow: To delay the default, the government is forced to monetize the budget deficit, that is, to monetize enterprise tax remittance. 6. Sixth flow: Banks transmit, extend, and roll over credit, which reduces aged receivables. 7. Seventh flow: Variable trade-offs between tax non-remittance and monetization of tax remittance, followed by credit rollover and extension, wind up in the self-enforceable subsidy. It sums up to the outstanding balances of receivables. A complementary array of cross-industry price subsidies accompanies this subsidy. 8. Eighth flow: [This flow] is identical to step 1. Stimulated by all these components, enterprises surcharge invoices with a network tax to extract the self-enforceable subsidy. This system becomes circular and self-reinforcing (Bernstam and Rabushka, 2006, chapter 1 addendum 1, p. 6). 9. Ninth flow: [Flows nine through eleven occur after the CBR increased the mandated repatriation of export earnings in late 1998.] Enterprise money balances in bank accounts expanded. 10. Tenth flow: Enterprise export earnings started to monetize tax remittance. 11 For details on the consumption (red numbered) flows, see Bernstam and Rabushka (2006).

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16 11. Eleventh flow: The link between monetization and the tax subsidy was weakened (Bernstam and Rabushka, 2006, chapter 1 addendum 1, p.33). Noted in the flow diagram is the importance the banking industry has in maintaining the ENS; banks, managed by the CBR, are directly involved in half of the flows. While bank discounting is one element, the other element is non-monetary documents, especially veksels, which allow barter exchanges in a manner which facilitates tax avoidance. Thus, while banking is an important part of the maintenance of ENS, it is equally important to realize that non-monetary exchanges also occur entirely outside of the banking system. These exchanges occur between all economic parties: Government, CBR, Banks (both private and public), enterprises, and households. An indicator of the size of the exchanges occurring outside of the banking system is the spread between the sum of monetary survey components and the CBR Total Liabilities in Figure 2-4. Explored in this dissertation is the systemic distortion still evident even after the increased repatriation requirement in late 1998 by the CBR. 2.2 Economic, Banking, and Payment System Structures: Role of Gazprom An additional primary player in maintaining the Russian subsidy system is Gazprom. Natural gas is important both as consumption good and as an industrial input. In the literature, Gazprom plays an extended role in that, like the Yukos oil, gas payables and receivables were often used as payment documents. Similar to price adjustments for veksels above the cash clearance price, gas payables and receivables were exchanged for elevated values. Through this mechanism, firms would able to increase prices charged over the price for cash (Commander and Mumssen 2002; Gaddy and Ickes 1998). Noted in the Karpov commission report, and cited since, is the liquidity issue within barter goods. The more liquid barter goods are, the more they are used in barter and as quasi-

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17 money and as offsets. Not surprisingly, therefore, non-monetary transactions have been particularly significant in the following: gas, electric power, ferrous metallurgy, chemistry, and machine-building (Gaddy and Ickes 1998). The Karpov commission results also show high quasi-monetary transactions in the petroleum, and extractive, industries. In fact, during the time of the Karpov commission research even Yukos engineers were being paid with containers of oil. 12 The non-monetary documents, such as veksels, allow Gazprom and other firms to change prices with them because they are not required to be cashed out. They can be used as negotiating documents with the value being adjusted to satisfy the parties. Often the stated value is adjusted up to adjust for the discounting banks will calculate when, or if, the veksel is cashed out (Commander and Mumssen 2002). Systemic distortion, produced by financial repression and subsidies of monetary or non-monetary values, will affect all levels of enterprises. Since small firms have more limited access to the commercial bank lending, they are doubly harmed in illiquid monetary environments. Research has shown that a liquidity squeeze and the resulting credit crunch can cause businesses and individuals to shift from monetary to non-monetary transactions (Commander and Mumssen 2002). 12 Personal communication with Russian individuals revealed to this author the level of barter conducted at the household level. Barter prevalence allowed continued sustenance.

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18 Figure 2-1. Monetary Measures from the Monetary Survey (Rubles; billion)

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19 Figure 2-2. Monetary Measures: M2 (broad) and M0 (narrow) from the CBR; Money and Quasi-money from the Monetary Survey (Rubles; billion)

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20 Figure 2-3. Monetary Survey Components and CBR Total Liability (Rubles; billion)

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21 Figure 2-4. Sum of Monetary Survey Components with CBR Total Liability (Rubles; billion)

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22 Figure 2-5. Ratio of Monetary Survey Components to CBR Total Liability

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23 Figure 2-6. Relationship Diagram of the Enterprise Network Socialism [Reprinted with permission from M. Bernstam, and A. Rabushka 2006. From Predation to Prosperity: How to Move from Socialism to Markets. Work in Progress (Page 67, Box 4). The Hoover Institution, Stanford, California.]

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24 CHAPTER 3 LITERATURE REVIEW The literature on capital account and financial market liberalization is extensive and decades long. The reciprocal literature for financial repression is chronologically shorter, but it is equally broad. Overwhelmingly, most of the work is on the investment or economic expansions possible from liberalizing the capital account and/or the financial markets. In developing countries, existing restrictions are often severe as can be seen in -sectional work. There is less research on the restrictions on investment produced by new constraints imposed by current or recent governments. It is critical to develop better knowledge about the mechanics of repressive policies, their effects, and the incentives structures that initiate and perpetuate them. These are the subjects of this research, and as such, are an extension to the literature started by Edward Shaw and Ronald McKinnon in the 1970s, and has since been expanded by the work of Ross Levine, Robert King, and William Easterly. The focus of this literature review will be on the following five topics as they apply to the investment theme: 1. Definitions and opinions of financial liberalization, and its reciprocal, financial repression, before and after the 1997-1998 Asian crisis and Russian default, 2. Primary monetary policy transmission channels on investment: Money (interest rate) and bank-lending, 3. Liquidity constraints on investment: Government control of banking, 4. Financing constraints on investment: Internal versus external financing, and 5. Government subsidies unique to Russia.

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25 Topics one and two describe the status of the current literature relative to investment in countries with emerging economies. Topics three and four are specific to the two most severe constraints of illiquidity and financial unavailability common to the less developed nations. In a sense, the financial repression indicators examined in this research also take this dichotomy. The first three variables ---real interest rates, reserve ratio, and liquidity ---are proxies for the governmental control over the monetary policy, which affects the financial system; the last three variables ---private borrowing, bank lending, and stock market valuation ---are proxies for external financing sources. Finally, the fifth topic describes the magnitude of the government subsidy in Russia. Liberalization and financial repression research almost exclusively relies on aggregate investment data in cross-country analyzes. Often absent from these studies are the transitional countries. This research closes a portion of this gap in the literature by studying one nation, Russia, and examines financial repression at both the macro and micro level. 3.1 Financial Repression on Investment: Chronological Changes Financial repression is the actions taken by a government to alter the flow of a often at the expense of domestic enterprise development. Over the years, the definition of financial repression has expanded from a monetary policy that produces negative real interest rates into a broader one that also covers manipulations of the different monetary policy variables previously stated, which includes interest rates. This broadening of actions which constitute financial repression is recognition that there are several possible approaches to pervert the financial sector.

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26 This study uses the broader definition of financial repression and focuses on the post-1998-default period. After that default, monetary policy recommendations took a dramatic turn. During the 1997-1998 Asian financial crisis and Russian default, the literature went from support for open flows of both goods and money, to support for moderation in money flows with the necessary legal and regulatory structure. 3.1.1 Literature Review: Pre-1998-Default The pre-default literature is supportive of fully liberalizing both the capital account and financial markets to fund investment and, thus, economic growth. In these regards, the s 1973 paper for the Brookings Institution and based on the narrow definition of financial repression. Both authors assert that deposit interest rates held below market level will produce an inadequate supply of savings to satisfy investment demand. They also claim that financial liberalization would allow available money to flow to its highest and best uses. Subsequent empirical evidence has tended to support the second claim; however, there is some evidence the first one does not hold once the negative real interest rates on suggests a contracting economy can occur even when real interest rates are high and positive. In thstill in doubt. In their study of banking and growth, Beck, Levine, and Loayza (2000) find savings allocation to have a large impact on real per capita GDP growth. However, like the earlier research, they find less of an association between the same growth measure and the quantity of savings deposited into banks. In such investigations, including this

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27 research, ing. While the interest rate ceiling on deposits harms savers and domestic investors, borrowers benefit from negative real interest rates on ruble deposits. This is particularly true of large firms in extractive industries as their outputs typically are dollarized. For these large firms, negative real interest rates mean extremely low costs of borrowing. Further, inflation from keeping a debased ruble creates conditions in which domestic trade suffers relative to the extractive export trade. Firms that export prefer a domestically weak currency. This preference influences the currency policy that a government maintains, even at the expense of the domestic economy, particularly small firms. The impacts on less favored borrowers (i.e., smaller firms) can be severe because the same policies which make borrowing so attractive to larger firms (particularly those focused on exporting) discourages savers from committing funds in the Russian banking system. In part, this may explain why so many manufacturing firms in Russia are still using aged and obsolete machinery. Two other pieces of pre-default literature are of interest to this research. The first one is the Roubini and Sala-i-Martin (1995) model of inflation, tax evasion, and financial repression. They show systems to access an easy source of funds for the public budget through the inflation tax created through seigniorage taxation. 13 And, the second piece is a paper by DeGregorio and Guidotti money controls and private sector growth. They claimed that bankers produced the crisis, 13 Seigniorage is the net revenue for a government when the money that is created is worth more than it costs to produce it; seigniorage can be seen as a form of tax levied on the holders of a currency, and as such a redistribution of resources to the issuer.

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28 in an unregulated financial system, by choosing projects for loans on the potential return without considering the connecting project risk. 3.1.2 Literature Review: Post-1998-Default Since the 1997-98 Asian financial crisis and the Russian default, there has been a shift in opinions from advising total capital and financial liberalizations to liberalization only under a structured system of governmental regulatory and legal reforms to ensure a stable flow of capital. Among the supporters for this new view is Joseph E. Stiglitz (1999, e has exhibited enormous Results from studies of previous financial crises generally indicate that financial liberalization can be destabilizing. The following six factors, mentioned in the literature, and cited by Beim and Calomiris (2001, p. 119), address potential problems with liberalization and list the solutions requiring financial restrictions in order to maintain financial system stability: 1. Controls on deposit interest rates to provide rents to banks. This makes the banks more profitable and arguably less vulnerable to failure, 2. Bank reserves to provide the central bank with revenues and a liquidity pool to help banks that get into trouble, 3. Government monitoring the bank lending decisions to prevent fraud and excessive risk taking, 4. Controls to prevent banks from becoming centers of industrial empires as bank owners use their lending power to buy recently privatized state owned enterprises (SOEs) for themselves, 5. Some controls should be in place to avoid having foreign banks out-compete domestic banks, leaving them seriously weakened, 6. Capital inflows, particularly short-term loans and portfolio flows, should be monitored with a view to avoiding serious reversals that could create a major liquidity crisis.

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29 Implicit in the preceding list of safeguards are the assumptions that governments are both technically competent and benevolent. Particularly in developing nations, the first assumption may not hold. Moreover, individuals, either within governments, or having influence over governments, may have incentives to redirect capital flows to either preserve or initiate repressive policies to fund government expenditures or provide favors to private individuals or enterprises. An important country comparison study, written pre-default but supportive of the post-default consensus, is by McKinnon. In 1994, McKinnon employed a ratio of a financial liberalization index to the inflation rate to compare China to some of the former Soviet Union republics. While financial liberalization in the former was moderate and controlled, the same process in the latter-was neither. In his results, McKinnon found China to have low inflation with high economic growth and the reverse in the former Soviet Union countries. An important segment of the post-default research has focused on bank fragility with the overall findings indicating reduced banking vulnerability is possible with regulatory environment, 14 (2) Societal respect for the rule of law, (3) Low-level of corruption, and (4) Binding contract enforcement (Demirg-Kunt and Detragiache 1998). Edison (2004) and his coauthors find in their meta-analysis on previous liberalization studies, government reputation, as a measure closely linked to quality of 14 In particular, Demirg-Kunt and Detragiache (1998) caution financial liberalization, even after macroeconomic stabilization has been obtained, when the following institutions have not been fully developed: laws to ensure contract enforcement, and regulations to produce effective banking sector and financial market supervision.

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30 institutions, to be a significant variable. Unfortunately, most of these studies, including -analysis, did not include former Soviet Union countries. 3.2 Liquidity Constraints on Investment: Government Control of Banking Liquidity does not have a single definition. In reference to financial securities, liquidity refers to the ability to trade a security without incurring large transaction costs. In monetary economics, it refers to the most tradable of financial assets-cash (or near-cash). In this research, the definition used describes the monetary aggregate ratio of money supply to an economic output measure, usually gross domestic product (GDP). This gauge for liquidity is often considered a reasonable measure of financial depth within an economy, typically associated with greater economic and financial system development (Beim and Calomiris 2001). With the numerator being determined by monetary policy, transmitted through the money and bank lending channels, bank ownership becomes critical in determining the funding available for enterprise investment. In this regard, in Russia the banks that matter tend to have high degrees of State ownership. Recent political theories concerning reasons for government ownership of banks kickbacks as the primary incentives. Furthermore, research on global banking finds government ownership to be positively associated with poorly operating financial systems and negatively correlated with efficient capital allocation to investments. In their research on the prevalence of government ownership in developing countries, La Porta, Lopez-De-Silanes, and Shleifer (2002) support these views. Additionally and specific to Russia, Laeven (2001) finds heavy insider lending and bribery compensation between banker and borrower. The findings contradict the previously theorized development view

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31 of governments principally directing scarce resources to productive enterprise projects in desired industries, and to then further ascribe positive social goals as the primary motivations for government bank ownership (Barth, Caprio, and Levine 1999; Wurgler 2000). This line of research supports the bank competition literature, which claims that competition, in either foreign or domestically owned banks, is necessary in generating investment and economic growth (Beck, Demirg-Kunt, and Maksimovic 2004c; Berger, Hasan, and Klapper 2004). Indicative of the extent of this effect is the finding by Smith (1998) that a country with a monopolistic banking system of any ownership type produces macroeconomic performance worse than an economy void of banks. In many developing nations, government ownership of banks is typically high, and the domestic money supply that reaches down to the household and enterprise level is often tight (Beim and Calomiris 2001). While the money supply growth is sufficient to produce significant inflation, the direction of the monetary growth is back to the government coffers. This can result from required purchase of government securities by the state owned banks. These characteristics are true for Russia. Additionally, it will be argued here that it is government control of the financial system, and banking in particular, that allows the Enterprise Network Socialism (ENS) subsidy network to work. 3.3 Monetary Policy Transmission on Investment: Primary Channels Developing economies are excellent testing grounds for monetary policy transmission research. In these countries, monetary controls generally are the tightest. Additionally, it is in these economies that the traditional interest rate effects in the money channel continue to be a mode of transmission in affecting the real interest rate, as well as

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32 from within the bank-lending channel. 15 This is possible because banking is typically the sole formal external financing form in these countries. The financial repression manifests itself through interest rates different from free market levels. Controls on interest rates are applied by and through the banking segment of the financial system. 3.3.1 Money Channel Two studies are chosen for review here for both the money and bank channels. In a study on Germany, Chirinko, and von Kalckreuth (2003) test the traditional interest rate effects in the money channel on business fixed investment. They find a statistically significant interest rate channel on a database of over 6,000 German businesses, and a credit channel for a subset of the companies. Honohan (1998), in a study of countries whose governments are liberalizing both the capital accounts and the financial markets, finds support for the money channel form of monetary policy transmission. Within this channel, he finds dramatic short-term volatility increases in the money market. Treasury bill interest rates, with bank spreads, tended to increase the most, implying that they were the securities most repressed. 3.3.2 Bank-Lending Channel Support is also found for the bank-lending channel as a monetary transmission mechanism. Kashyap and Stein (2000) find the impact of monetary policy through the bank-lending channel is greatest on small banks. In countries with developing economies where small banks dominate and the few large banks are State controlled, the bank-lending channel becomes a powerful conduit for monetary policy transmission. 15 For a detailed explanation of the various monetary policy channels, see chapter 25 of Mishkin (2003).

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33 Driscoll (2004) tests the relationship between individual U.S. state output and the supply of bank loans. He finds that the relationship is often statistically insignificant after controlling for shocks to the money demand. It is important to realize, however, that in developing economies, where government controls over the money supply is both tight and through banking, transaction money demand may take the form of plain barter. To continue, from these findings, Driscoll (2004, p. 469) suggests that when alternative sources of funding are available, and enterprises are no longer bank loan dependent, monetary policy transmission goes through a broader credit channel. The findings of Kashyap and Stein (2000), as well as, Driscoll, suggest that the bank-lending channel effect at the enterprise level is weaker for large firms that have alternatives to bank lending for project and investment funding. Additionally, the effect becomes progressively weaker as the firm is able to move up through bank sizes into using the international money and capital markets. 3.4 Financing Constraints on Investment: Internal versus External Financing 16 Early research in the internal versus external financing debate includes Dobrovolsky (1958), Cohen (1968), and Waite (1973). All found an increase in the external finance comparable in importance with internally derived funds. These early papers also noted differences in the funding sources available to large firms versus small firms. A chronological divide between internal and external financing is obvious in the literature. Research in the early part of the last decade is more specific to internal 16 For a comprehensive survey on business constraints (finance, taxation, and corruption) on global firms, see Batra, G., D. Kaufmann, A Firms Speak: What the World Business Environment Series

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34 financing constraints. Some of the works have included the difficulties of asymmetric information, capital market imperfections, and agency problems. Whited (1992) takes the early research on liquidity constraints and connects asymmetric information to it. From the findings, he states that adverse selection constraints that affect the bond market also expenditure generally find that internal financing takes a leading role because of capital market imperfections. This is especially true for small high-tech enterprises (Himmelberg, and Petersen 1994). Hubbard, Kashyap, and Whited (1995) find agency costs unimportant for business fixed investment of enterprises that have significant dividend payouts. However, agency costs do seem to be a factor for enterprises in a low-dividend-payout subsample. Some studies support the theoretical agency cost link between investment and internal finance. Considering asymmetrical information effects in their study, Calomiris and Hubbard (1990) find a high likelihood the interest rate does Bernanke and Gertler (1990) find that high agency costs produce low and inefficient investments for enterprises that have a heavy reliance on external finance. Finally, using hospital investment in support of the results found in the manufacturing sector, Calem, and Rizzo (1995) show a link between significant agency costs in capital markets and tight internal investment funds. Kaplan and Zingales (1997) find that less financially constrained enterprises are more sensitive to investment-cash flow changes than enterprises that are more financially constrained. In support of this research, Cleary (1999) finds enterprise investment to be

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35 directly related to financial factors with high credit rated enterprises being more sensitive to shifting internal funds than that of lower credit rated enterprises. Moving on to the external finance thread of the literature, Fazzari, Hubbard, and Petersen (1988) and Fazzari and Petersen (1993) examine the relationship between financial constraints and investment. These studies find strong impacts from finance constraints on growth and investment, and that internal finance and external finance are not perfect substitutes, especially in the short run. Additionally, Bond and Meghir (1994) developed a hierarchical finance model which predicts that in any period a subset of enterprises will have constrained investment from a lack of internally derived funds. Besides the agency theory aspect of this topic, legal structure, profitability, and enterprise size are also used as determining criteria of financing source availability. All three are found to be significant factors, with, not surprisingly, lower profitability indicating a need for external funding. In particular, Fauver, Houston and Naranjo (2003) find the value of firm diversification is related to the legal systems, as well as, to the depth and level of capital market development and international integration. Furthermore, they find optimal firm organizational structure may differ for firms operating in emerging market countries more than for firms operating in established market countries with international integration. Stock markets and banks differ in their external finance provisions and effects, and both are important (Demirg-Kunt, and Maksimovic 1998, and 2002; Beck, and Levine 2002; Claessen, Djankov, and Lang 2000). Love and other World Bank researchers find that capital account controls increase enterprise financing constraints, but that multinational firms are not constrained (Harrison, Love, and McMillan 2004).

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36 Further, Love (2003) finds that financial development (liberalization) influences growth by reducing financial constraints that would negatively affect investment. Additionally, using a vector autoregression technique, Love and Zicchino (2002) find financial constraints on enterprise level investment to be larger in countries with less developed financial systems. In addition to supporting the need for developed financial systems, financial intermediaries in particular, Levine, Loayza, and Beck (2000) find both strong legal and accounting systems to be positively associated with economic growth. Lastly, in studying the effects of monetary policy transmission on inventory investment, Kashyap, Lamont, and Stein (1994) find tight money supply to be positively associated to liquidity-constrained inventory investment. Again, as noted in the previous literature cited, transitional countries are absent from the databases used in these studies. In sector specific research, recent studies find support for higher growth of enterprises dependent on external finance when producing within countries with developed financial markets (Beck, Levine, and Loayza 2000; Galindo, Micco, and Ordoez 2002). Rajan and Zingales (1998) find the primary benefit of financial development regarding external finance to enterprises to be that of cost reduction. And, Galindo, Schiantarelli, and Weiss (2003) find most of financial reforms increased investment allocation efficiency within enterprises. Papers that study, and compare, small firm versus large firm reaction to monetary changes often confirm the suspicion that financial liberalization affects small and large firms differently. Since small firms do not have access to the financing choices available

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37 controls. Laeven (2003) found that larger firms may suffer relatively after liberalization. With the removal of controls, their comparative advantage over smaller firms in securing financing is reduced. Similarly, Beck, Demirg-Kunt, and Maksimovic (2004b) find small firms are most affected by financial constraints, as well as, legal and corruption issues. In their study of finance and growth, King and Levine (1993) affirm Joseph Schumpeter in his claim that financial intermediaries oiled the economic wheel. Sch-order relationship between financial development sh even skeptics toward the belief that the development of financial markets and institutions Recent research continues to search for the optimal allocation of financing in investment and economic growth. The optimal allocation work is in an attempt to determine when short-term funding is better than the longer-term for investment (Fisman and Love 2004). Most enterprises operating in countries with emerging markets typically have only the short-term funding variety available from local banks and so are vulnerable to constraints. Found to be resistant to funding constraints and currency crises are older, larger, and foreign owned enterprises (Beck, Demirg-Kunt, Laeven, and Maksimovic 2004a; Desai, Foley, and Forbes 2004). In developing countries with repressed financial markets, it is the entire investment environment that is constrained, not just industry segments. In these countries with cash based economies, often with cash shortages, the

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38 funding issue is not internal versus external financing of business as being optimal; instead, it is financing through barter. 3.5 Russian Governmental Subsidies others have studied the extensiveness of non-monetary economies and the use of subsidies within broader transitional country studies. 17 Teconomy began with the work by Gaddy and Ickes (1998). They observed that while the Russian economy seemed to be recovering from the initial fall in real GDP during the early 1990s, a non-monetary payment and subsidy system was also expanding. In the Russian studies, including the initial work by Gaddy and Ickes, authors repeatedly note the expanding tax arrears by the energy monopolies. The large extractive sector enterprises collect taxes from customers but fail to remit those taxes collected to the government. The arrears form an implicit subsidy and expand the tax delinquent level of the participant entfiscal accounts. Additionally, a repeated relationship feedback loop from setting economic goal to recovery from policy failure and then back again is noted (Pinto, Drebentsov, and Morozov 2001; Schaffer 1995). While macroeconomic stabilization and microeconomic minimization of social costs were the goals, policy failures involving chronic shortfalls in cash tax collections fed a feedback into a rise in public debt. The macroeconomic tools of fixed exchange rate and tight credit conflicted with the microeconomic minimization tools of avoiding mass enterprise bankruptcy through 17 The broad studies include publications by both IMF and The World Bank. In his work at the IMF, Stanley Fischer and coauthors produced a stream of research on economic growth within the transitional countries. They begin by painting a bleak picture of falling real GDP to rising inflation in their 1996 publication to reporting in a 2002 working paper that higher growth is dependent of structural reforms in general and privatization in particular. This same picture is echoed by the World Bank (1996).

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39 implicit subsidies. Thus, the feedback loop that Pinto, Drebentsov, and Morozov (2001) reported fed through enterprises as they faced liquidity and credit squeezes from the monetary policies, and then fed through to higher subsidies needed, which then fed the increasing public debt and tax revenue shortfalls, to then repeat the loopbut at a higher monetary layer. Finally, the subsidy literature supports the claim made in this research that it is the individuals with vested interests who are privy to the benefits that hold the subsidy structure in place. While the distortions have been well documented, the recommended economic and monetary restructuring have repeatedly, and deliberately, been stalled.

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40 CHAPTER 4 TESTABLE HYPOTHESES This research has two purposes. The first purpose is to examine the distortionary effects on Russian investment of financial repression, which is measured by the six financial repression components (real interest rates, reserve ratio, economic liquidity, private borrowing, bank lending, and stock market value). The second purpose is to explore the distortion arising from the Enterprise Network Socialism using inferences from the examination of the financial repression measures and effects. Thus, in this dissertation, potential systemic distortions on investment produced from governmental actions of both (1) financial repression, and (2) subsidies are examined. This examination is further subdivided into the following: (1) Within financial repression, how over the development of the last three, and (2) Within subsidies, how the system is bidirectional between government entities and enterprises. 4.1 Hypothesis: Financial Repression The first set of hypotheses address the influence of financial repression on Russian investment at the aggregate and firm levels after the 1998 Russian default. With index dropped from a near severe 48.1 measure in 1997 into a severe 36.8 by 1998 (Beim and Calomiris 2001). 18 This suggests that during the financial default, the Russian 18 For index details, see chapter 2 appendix in Beim and Calomiris (2001).

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41 government may have chosen to choke off funding from the entire country. Whether deliberate or otherwise, with the currency debased, liquidity plummeted and barter soared. Building on this line of reasoning, the following hypothesis is explored with regard to financial repression: In the literature, Beim and Calomiris (2001) found an increasingly repressive index for Russia. Extending their work, it is theorized in this research that repression impacts the levels and structure of investment. In other words, it is theorized that financial repression has a significant distortionary effect on the allocation of capital. An interesting subdivision in the financial repression hypothesis is a grouping that indicates possible effects to the last three financial repression indicators (private borrowing, bank lending and stock market valuation) from the actions taken by the CBR on the first three (real interest rates, reserve ratio, economic liquidity). The Central Bank has direct control on the levels of the first three financial repression indicators through direct changes to the interest rates and required reserve ratios. Those changes affect the money supply and thus, liquidity. The last three financial repression indicators are measures of the level of financial market development, which is affected by the direct control of the first three by the CBR. The first three components could be considered as three as external financing development indicators. Bank and financial market development are affected by the control of real interest rates, reserve ratio, economic liquidity. Within this division, the alternative to the typical null hypothesis of zero effects is that monetary policies that repress both the financial development of a country and the available external financing sources impact on aggregate economic and enterprise level investment.

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42 4.2 Hypothesis: Enterprise Network Socialism The Enterprise Network Socialism (ENS) hypothesis is based on the theory that firms were forced to selfproduce potential systemic distortions (e.g., unusual investment relationships to external funding measures) (Bernstam and Rabushka 1998, p. 52; and 2006, chapter 1 addendum, p. 2). The hypothesis explored in this dissertation is: In the literature, Bernstam and 2006) ENS subsidy system is theorized to distort economic growth in Russia. Extending their work to investment by using inferences drawn from the financial repression results, the hypothesis is that the ENS subsidy compounds the financial repression distortions. An interesting subdivision in the governmental subsidy distortion hypothesis is that the subsidy system unique to Russia is bidirectional between government entities and enterprises. Alluded to in the above quotes from Bernstam and Rabushka, Russian enterprises are able to force subsidies from government. Partly a holdover from procedures typical to the Soviet Union, and partly a recent creation produced during the privatization years when prices were released from central control, the system is self-enforcing and, as theorized by Bernstam and Rabushka (1998, 2006), can generate extreme outcomes. 4.3 Systemic Distortions: Gains Possible The gains from financial repression can pass to the Russian government. The potential

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43 ability to fund government Repression, with capital controls and regulations, allows the Russian government to restrict financial intermediation and prevent potential competition to the State-controlled banks from developing. 19 An added benefit to the government is that these actions are often inflationary, which allows the government to pay claims with cheaper rubles. Aptly stated and relevant to Russia, Roubini and Sala-i-Martin (1995, p. 277) claim that financial repression is associated with high inflation, high tax evasion, and low growth. governments to continue financial repression methods. Additionally, concerning financial repression, Roubini and Sala-i-Martin (1995, p. 277) state: It is our view that the main reason why governments stay in the way of private resources for the public budget. Governments have the power to follow policies of financial repression. By financial repression we mean that they have the option and capability of not allowing the financial sector to operate at its full potential by introducing all kinds of regulations, laws, and other nonmarket restrictions to the behavior of banks and other general financial intermediaries. Thus, the inflationary effect allows the government to pay the remaining fiscal expenses with a debased currency. The gains from the ENS subsidy system pass to the subsidized Russian large firms. As the Figures 4-1 and 4-2 show, the invoicing between Russian large firms and the Central Bank of Russia (CBR) is significant. The quantity of trade receivables submitted by the enterprise network equals, and often even exceeds, the sum of tax non-remittance and the money supply measures, M2 and M1. Since 2003, increases in 19 In basic banking, intermediation describes household savings efficiently allocated to economically productive private enterprise. However, iBernstam, and Rabushka 1998, p. 17).

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44 petroleum dollars continue to fill foreign reserves reflected in the broader measure M2; however, much slower growth in M1, see Figure 4-2, indicates an illiquid Russian economy. The subsidy process affects economic liquidity by being multiplied through the banking system as loans to the enterprises. The loans involved are both new and old, with the old ones rolled-over at maturity. An added distortion is attributed to many of the rolled-over loans being permanently non-performing. In Russia, the CBR and the large firms use the commercial banking system to re-intermediate the large firm subsidy. 20 Little retail banking, needed by small firms, occurs. Furthermore, at times the quantity of overAdditionally, the invoice pricing represents, to some extent, a recovery from the price caps that are still required by the Russian government for subsidized consumption to both businesses and households. The ENS subsidy firms submit the invoicing to receive the difference between the perceived market price and the price cap. This perceived market tolerance. Bernstam and Rabushka (2006) note that the massive size of the receivables has repeatedly caused financial havoc to the point of halting the economy by creating a payment jam in the credit system. This occurs when the mass of receivables expands in size and lengthens the payment arrears from buyer to seller. In effect the large firms in the receivables network have a degree of control over the tax base. 20 In Russian banking, re-intermediate describes hold funds to the government and government subsidies to enterprises, and Rabushka 1998, p. 20).

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45 In this research, it is argued that financial repression and the ENS subsidy are supportive of one another. The ENS network could not function in a country with a fully market-based intermediating banking system. Therefore, the argument explored in this research is that the financially repressive measures taken through the Russian monopolized banking and financial system sustain the ENS and its self-enforcing subsidy system. Furthermore, without the first distortion produced by financial repression, which benefits the Russia government, the second distortion produced by the subsidy system, which benefits the extracting sector enterprises, would end. Additionally, the combination of these two produce a third distortion with global implications: restricting money availability in a cash-based economy hampers small firm growth. Since financial repression produced by government actions coexist with the control over the tax base produced by the actions of the ENS subsidy network firms, both will be reviewed in the appropriate results section in this dissertation. Breaking apart the two effects is difficult. In this research, the assumption will be made that the statistical significance of a variable coefficient supports the possible effects of both financial repression and the ENS subsidy on investment. Coefficients counter intuitive to finance and economic development theory will be assumed to support the existence of distortions created by the forced subsidies. The dangers of systemic distortions to economic growth can be severe. important to economic growth has been supported in academic research (King and Levine anking system, as the first financial system to typically

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46 develop, is used to re-intermediate a subsidy rather than intermediate efficient allocation of capital, economic growth suffers. 4.4 Analysis: General and Specific Questions In exploring the stated hypotheses of potential systemic distortion from financial repression and governmental subsidies, the general questions of interest are the following: (1) Are there differences between Russia, with its emerging financial markets and systems, and the United States, with established financial markets and systems? (2) Within Russia, are there differences between the aggregate level and the large firm level? Further, are there differences when compared to the USA results? In further exploring the stated hypotheses, the specific research questions of interest in are the following: (1) Within Russia, which of the financial repression components (real interest rates, reserve ratio, economic liquidity, private borrowing, bank lending, and stock market value) matter to each level of investment? Further, are there differences when compared to the USA results? (2) Within Russia, is there evidence of systemic distortions consistent with the existence of the Russian ENS subsidy?

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47 Figure 4-1. Enterprise Receivables to M2 [Reprinted with permission from M. Bernstam, and A. Rabushka 2006. From Predation to Prosperity: How to Move from Socialism to Markets. Work in Progress (Page 62, Figure 13-1). The Hoover Institution, Stanford, California.]

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48 Figure 4-2. Enterprise Receivables to M1 [Reprinted with permission from M. Bernstam, and A. Rabushka 2006. From Predation to Prosperity: How to Move from Socialism to Markets. Work in Progress (Page 63, Figure 13-2). The Hoover Institution, Stanford, California.]

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49 CHAPTER 5 DATA Data for this research are used to estimate the degrees and dimensions of financial repression in Russia, as well as their determinants and investment effects at the aggregate and firm level in Russia. Inferences are drawn from the results to confirm or reject the hypotheses for financial repression and the Enterprise Network Socialism (ENS) subsidy system. For comparative purposes, a similar database for the U.S. is created. For both the Russia and USA analyses, a Denton proportional interpolation method is used to produce databases with increased frequencies (e.g., yearly data interpolated to quarterly). For interpolating variable with the source in all analyses being the respective central banks. 5.1 Descriptions: Variables 21 The variable consistently used across all analyses is the capital investment of fixed assets made at the aggregate and large firm economic levels of both the Russian and the United States economy. At the aggregate level, it is aggregate investment data as a component in the gross domestic product (GDP) equation. GDP measures production within the national boundaries, which includes foreign firms operating within the national borders. If purely foreign firms account for significant shares of domestic production, GDP could overstate progress by indigenous firms. However, this is less of a concern 21 See the Data Journal in Appendix A for details and glossary.

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50 than usual because the foreign direct investment in Russia has been consistently low, with most foreign investment restricted to joint ventures with Russian partners. At the firm level, a form of gross fixed expenditure is used. The investment data used is the change in plant, property, and equipment (PP&E) expenditure made by the large publicly traded firms in Russia, and by the Dow Jones 28 non-financial firms in the United States. The independent variables are the components that stem from the Beim and Calomiris (2001) financial repression index. These measures provide indications of the extent and types of repressive actions in which a government may engage. Assessment of these measures provides insights into the actions typically taken, or policies issued, by a wing variables are included to control for shocks to investment in Russia unrelated to repressive actions: Real price of oil, and winter temperature extremes. The petroleum extremes are a potential hindrance to industrial investment and production particularly in Russia. 5.2 Sources: Data 5.2.1 Russia: Financial Repression Measures and Aggregate Level Capital Investment The sample period of investigation for the Russian aggregate level analysis is from December 1998 through December 2005 (29 actual quarters, 85 Denton interpolated months). The data for all the financial repression measures, along with the aggregate capital investment measure, come from the Russian Economic Trends database (RET) database until June 2002. The Russian-European Center for Economic Policy (RECEP) produced the database for a project that began as the result of an original partnership

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51 between the European Community and the Stockholm School of Economics. The original mandate was to find and disseminate data that was difficult to get from the Russian government. The publishing outlet was the Russia Economic Trends (RET) quarterly journal, which originally began as a publication produced by the London School of Economics in 1992. However, the RET ceased to maintain their database in 2002. For this reason, data for this research after June 2002 was gathered from the original RET Russian sources: Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia). 5.2.2 Russia: Large Firm Level Capital Investment The sample period of investigation for the Russian large firm level analysis is from December 1998 through December 2005 (Unbalanced panel data: 8 to 5 actual years, 29 to 17 Denton interpolated quarters). The data for firm level capital investment come from the following sources: Worldscope, Global Reports, and Company financials. Firm investment data come from the Primark (now Thomson) Worldscope standardized company financials database. Most of the firm data are provided for by listings in Worldscope, supplemented with data gained directly from the original company documents. 5.2.3 USA: Financial Repression Measures and Aggregate Level Capital Investment The sample period of investigation for the USA aggregate level analysis is from December 1996 through December 2005 (37 actual quarters, 109 Denton interpolated months). The data for all the financial repression measures, with the aggregate capital investment measure, come from the following sources: (1) Federal Reserve, and (2) U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS).

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52 5.2.4 USA: Large Firm Level Capital Investment The sample period of investigation for the USA large firm level analysis is from December 1996 through December 2005 (37 actual quarters, 37 Denton interpolated quarters). The data for firm level capital investment come from Worldscope, SEC, and company financials.

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53 Table 5-1. Variable Definitions Variable Name: Description: Dependent Variable y1 INVESTMENT (y1) Aggregate: Gross fixed investment (logarithm); (y1) Large firms: Gross fixed investment (scaled by oneIndependent Variables Ly1 LAGGED Y1 (Ly1) One lag of y1 x1 REAL RATES (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag) x2 RESERVE RATIO (x2) Reserve ratio: Bank reserves / M2 (Russia: money plus (some) quasi-money; USA: standard M2) less M0 (one-period lag) x3 LIQUIDITY (x3) Liquidity: Short-term liquid liabilities M2 / GDP (one-period lag) x4 P. BORROWING (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag) x5B. LENDING (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag) x6 MARKET VALUE (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag) x7 F. CHARACTERISTIC (x7) Firm characteristic: Total revenue / Total assets (one-period lag) x8 OIL (x8) Real price for oil (one-period lag) x9 ACCELERATION PRINCIPLE (x9) Acceleration principle: One lag of revenue x10 SEASONAL (x10) Temperature for seasonal effects

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54 CHAPTER 6 METHODOLOGY As the first focus of this research is in the potential relationships of investment to financial repression components, the models take on some of the characteristics typical of models used in investment and financial repression studies. The main difference between investment and financial repression studies is in the scope. Where financial repression research focuses on a broader theme of aggregate growth using cross-sectional cross-country, the usual models in the investment literature are more narrow and specific. The most common types of investment models are Tobinand internal liquidity constraints. 22 Schiantarelli (1996) notes inconsistencies for both Tahmiscioglu (1997) study the internal liquidity constraint models, in addition to testing ed the models typical of the investment literature by the resulting statistical precision. In this ranking, Hsiao and Tahmiscioglu (1997) find the liquidity model to have 22 market value of assets divided by replacement value of assets. A Tobins Q ratio greater than 1 indicates the firm has done well with its investment decisions. Euler equation, as it applies to investment, discount factor is -order conditions form the same maximization problem used to derive Q 1996, p. 75). Internal liquidity constraints affect investment through low profits, or high dividend payouts, and external liquidity constraints describe the arbitrary limit on the amount a firm can borrow, or an arbitrary alteration in the interest rate they pay (Hsiao and Tahmiscioglu 1997).

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55 stock market, which is often not found in transitional countries, and the Euler equation may not be able to detect the unvarying financial constraints within a study of investment 1996, p. 77). Because of the difficulties found in these structured models, a more recent study tends to use a vector autoregression technique on unstructured and reduced form investment models (Love, and Zicchino 2002). The investment models examining liquidity constraints have a closer application to this research. The difference from many of the studies in the literature and this study is that the liquidity measure, as one of the financial repression constraints, is aggregate economic rather than internal to the enterprise as in Schiantarelli (1996), Chirinko, and Schaller (1995), and Hsiao and Tahmiscioglu (1997). Thus, unlike the investment models, but similar to the financial repression models, this research studies the macroeconomic constraints to aggregate and firm level investment produced by government control over the money supply, financial depth, and bank credit. These liquidity constraints are typical of countries with developing economies working under financial repression and tight monetary policy, Chirinko and Schaller (1995, p. 528) observed that be a significant determinant of investment even if firms do Finally, typical to investment models in general, the models in this research are all lagged models, with a t--to-for investment decisions.

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56 In both static and dynamic models, the base investment model performs as a function of the financial repression measures. (6-1) Further interpretation of the base investment function in this analysis is the following: (6-2) This division allows consideration of the possible distortionary effects on external financing of banking and financial market systems development which can be produced from the monetary policy controls over real interest rates, reserve ratio requirements, and economic liquidity. Additional models have exogenous variables added to the base model to control for possible fundamental effects important to investment in Russia: Real oil price, and temperature. Furthermore, in an attempt to model the acceleration principle, investment equations have included lagged revenue (Fazzari, Hubbard, and Petersen of product demand. 6.1 Model Notation Conventions Throughout this research, matrix notation to multiple regressions is used. And, with the exception of the vector xt being a row vector, all notations follow the recommendations made to the Royal Economic Society and stated in Abadir and

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57 23 In notational development from scalar to full matrix, the following conventions hold: Scalar Notation: For each model, the t subscript will be used to represent time, ; the i subscript will be added in the panel data analyses to index observations, with the n subscript denoting the sample size of those observations, ; and finally, with k to indicate parameters, The scalar model has a dependent variable for time t of and each as an independent variable with xt0 as unity; and finally, representing the population disturbance at time t. Vector Notation: With bolded lowercase, the scalar time-series model, applicable to the aggregate investment analysis in this research, becomes Each component, further defined, is the following: the xt is a 1 x K row vector of independent variables with xt0 reserved for the leading 1; the K x 1 parameters vector is Matrix Notation: When fully generalized, and with bolded uppercase, the same model becomes: The defined components then are the following: the T x 1 vector is observations on y, where an element of is yt,; a full T x K matrix, X, is observations on the independent variables, where the tth row of X consists of the vector xt, and where the element (t, k)th of X is xtk; with the K x 1 parameters vector becomes T x 1; and finally, t becomes a T x 1 vector of unobservable disturbances. 23 The econometric standards are attempting to structure standards that mimic those set by The International Organization for Standardization used by scientists in chemistry and physics (Abadir and Magnus 2002, p.76).

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58 does the same for the variance. Finally, the basic variable names will be used in the body of this paper with the appropriate time and firm level observation notation added when appropriate. 6.2 Generalized Assumptions: Tested Per Analysis Along with the assumption of linearity in the parameters, various diagnostics are used to test the following set of basic assumptions for the models, estimated in this research: Therefore, the error structure assumptions are the following: normality of errors, where the expectation of the error structure has a zero conditional mean and homoskedasticity, without serial correlation. In addition to the methods listed below, univariate, bivariate and multivariate diagnostics are completed for both specification and misspecification. To address the problems that are typical to the data and models used, the standard errors are adjusted for autocorrelation in the time-series models, and both autocorrelation and heteroskedasticity in the panel data models by using autocorrelation and heteroskedasticity-consistent analysis techniques. The diagnostics are correspondingly reported with the appropriate analysis results. Methodologies used are the following: (1) autoregressive integrated moving-average (ARIMA) technique for a first-order autoregressive process [AR (1)] is used for the aggregate investment time-series analyses. (2) Generalized least squares (GLS) random-effect is used for the firm level investment panel data analyses. (3) Proportional

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59 Denton method of interpolation is used for both aggregate and firm level investment analyses to determine consistency with data of increased periodic interval frequency (e.g., quarterly data interpolated to monthly). The ARIMA and GLS techniques were chosen over the more common ordinary least square (OLS) technique for two reasons. First, OLS estimation leads to biased and inconsistent results when the strict classical linear assumptions are violated in either time-series or panel data, and second, OLS is inefficient and biased with dynamic models (Greene, 2000). 6.3 MethodologyTime Series for Aggregate Level Investment: ARIMA [AR(1) Model] The Autoregressive integrated moving average (ARIMA) methodology, also called the Box and Jenkins technique, is used to analyze a structural first order autoregressive model: (6-3) While the ARIMA is frequently used on univariate models, it is also a useful technique to analyze multivariate structural models that have ARMA disturbances. Additionally, it is more flexible than either the older PraisWinsten or the more problematic CochraneOrcutt techniques. 24 The structural first-order autoregressive model [AR(1)] is determined by diagnostics, reported in the Results section, that rules out the moving-average parameter and eliminates the need for variable integration or first differencing. 24 CochraneOrcutt estimation omits the first observation.

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60 6.4 MethodologyPanel Data for Firm Level Investment: GLS [Random-Effects Model] (6-4) The advantage of using panel data is that it allows investigation of the effects, or change, of both within and between-firm units. The chosen random-effects model for the firm level analysis is based on the following: (1) The small number of time waves per panel precludes the number of indicator variables necessary for a fixed effect model, and (2) while the monetary policies are time-varying independent variables, because they are monetary policy variables, they do not vary by much. Still, and reported in the analysis results section, the Hausman test is used to determine the appropriateness of the random effects model to the fixed effects. Additionally, specific to this research, Hsiao and Tahmiscioglu (1997), in their firm level investment study, ran separate regressions on manufacturing firms in the United States to find out if heterogeneity among firms can be captured by firm specific intercepts in a fixed effect model. Their findings suggest that it cannot. However, Hsiao and Tahmiscioglu (1997) do add a lagged capital investment variable to capture the dynamics of firm investment. The random-effects model using either the GLS or the maximum likelihood (ML) method is considered to be a subject-specific model with a random constant term. Of the two techniques, GLS has the clear advantage over ML in this study in that it does not need the panels to be long to get consistent estimates of the betas. It also allows the regressors to be invariant, or in this research, to vary little. Additionally, the GLS version of the random-effects model is distribution free. Speculation of a distribution for the individual effect is not necessary (Hsiao 1986). In the correlation structure of the panel

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61 data analyses, both autocorrelation and heteroskedasticity typical to panel data analyses are addressed in the GLS random-effects models. 6.5 MethodologyIncrease Data Interval Frequency: Proportional Denton Method The reasons behind including the proportion Denton method in this research are: (1) Test for consistency across results, (2) Test for increased statistical precision with the increase in interval frequency, and thus, an increase in degrees of freedom, and (3) Test the method as a tool to be used in countries with developing economies where enterprise financial and operational data are typically only available in annual interval frequencies. Completed on all datasets, both time-series and panel data: The proportional Denton method produces an interpolation of an annual time series to quarterly by use of ed series obeys the annual total. The same procedure can be used for higher frequencies such as a quarterly series interpolated to monthly. 25 This method is the preferred one of the Denton family of least-squares-based benchmarking methods because it takes into account the series (Bloem, Dippelsman, and Maehle 2001, p. 84). 26 The appropriate industrial production measure is the indicator series used in this research for both the Russia and USA models. The proportional Denton method is a least squares approach: (1) the increased frequency estimates to be derived are the parameters, and (2) the sum of squares involved 25 Bloem, A., R. Dippelsman, and N National Accounts Manual: Concepts, Data See chapter VI on Benchmarking. 26 Benchmarking deals with the problems of combining a series of high-frequency data with a series of less frequent data (Bloem, Dippelsman, and Maehle 2001).

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62 are the first differences of the X/I ratio of the interpolated series (X) to the indicator series (I). Per Bloem, Dippelsman, and Maehle (2001), and in their notation, mathematically the proportional Denton formulation for an annual to quarterly interpolation is the following: (6-5) Each formulation is minimized under the restrictions that the sum of the quarters should be equal to the annual data for each year, and in doing so, the series proportions are maintained.

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63 CHAPTER 7 RESULTS This research examines the influence of the six financial repression measures on investment at both the aggregate and large firm level. Inferences drawn from the financial repression results are Network Socialism (ENS) subsidy hypothesis. On each model previously listed in the Methodology section, and in addition to the sensitivity analyses performed, the following diagnostic checks are completed for robustness: 1. The USA database matches the database for Russia aggregate investment and the large firms (Dow Jones 28 non-financial companies) for the United States. The same analyses are then explored on the U.S. data as a comparison test to the Russia research for robustness. 2. The proportional Denton procedure is completed on every database. While economists, working in developing countries, where data are scarce, use the proportional Denton method, it is not often used with financial data, largely because of the abundance of quality data provided by the financial markets in economically developed countries. The purposes for including the proportional Denton method here are: (1) Examine consistency in results for robustness checks, (2) Examine for increased statistical precision with the increase in both interval frequency and degrees of freedom, and (3) Examine the method as a tool to be used for financial economic research in developing market economies where data is scarce. 7.1 Aggregate Level InvestmentRussia: Descriptive Statistics and Correlations The aggregate level investment descriptive statistics and correlation analysis are summarized on Table 7-1. Worthy of note is the high positive correlation of 0.79 found between x3LIQUIDITY, and x4PRIVATE BORROWING. The tremendous size of the ENS subsidy

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64 along with the role banking has transmitting the non-monetary surrogate monies may explain the high correlation found. The Russian government monetizes the subsidy by issuing bank credit to the large firms. In this case, private borrowing does not have the bsidized by the government and the CBR, liquidity to private borrowing connection is possibly due to the use of the banking system to pay the subsidies in the form of loans and rolled-over loans. This tangled occurrence alone has the ability to distort. With that said, and for whatever the reason, the high correlation is examined here by having a dual set of models in each section. One set contains the x3LIQUIDITY measure, and the second set contains the x4PRIVATE BORROWING measure. 27 The time series analysis results, listed below, are produced by autocorrelation-consistent techniques to adjust standard errors. The sensitivity diagnostics completed on the data used in the models in this section are the following: Univariate diagnostics were used to test for skewness, data outliers, and stationarity of both the mean and the variance. The tests were not able to reject the null hypothesis of symmetrical distribution. The question of stationarity is of primary importance in using time-series data; therefore, the augmented Dickey-Fuller test is used. The assumption is that the variables indicate a stationary process because both the dependent and the independent variables exhibit slow change. This is especially true with the independent variables being calculated as ratios with monetary components that 27 In addition to the reported liquidity variable, M2/GDP, all analyses were reconfigured using the narrow money measures of M0, and M1, as well as the broadest of money measures, M3. All results are comparable to those reported using M2.

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65 typically have slow adjustments to economic changes. The Dickey-Fuller unit root tests show that the variables, including the real interest rate, are stationary. 28 Bivariate diagnostics were used to test sensitivity for correlation. Because of the unusually high correlation found between x3LIQUIDITY and x4PRIVATE BORROWING, a dual model structure was used throughout the research. Additionally, correlograms that were used to evaluate the first-order autocorrelation between current and lagged investment indicated white noise with no significant autocorrelation. Multivariate diagnostics were used to test sensitivity for specification, misspecification and precision. The Portmanteau statistic was calculated for correlation with the findings indicating white noise errors. The results of this final diagnostic are presented on the appropriate tables. 7.2 Aggregate Level InvestmentRussia: Time Series Results In most of the models in both the aggregate and large firm level investment the reserve requirement ratio is the statistically significant independent variable of prominence. It is the variable that seems to be of great importance across all of Russia. Other exogenous controls, however, did not indicate a statistical significance. Neither severe weather, x10SEASONAL, nor previous period petroleum price, x8OIL, mattered in the investment models for either the aggregate or the firm levels. 7.2.1 Results with Liquidity VariableRussia: ARIMA [AR(1)] (7-1) 28 The autocorrelation function and the partial autocorrelation function verify the Dickey-Fuller results.

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66 Per equation 7-1, within the aggregate level investment time series analysis, for the models that contain x3(t-1)LIQUIDITY as an independent variable, the variable of interest is the statistically significant x2(t-1)RESERVE RATIO. All insignificant variables are contained within which in this equation includes the x3(t-1)LIQUIDITY variable. Using Model 1 from Table 7-2, the independent variable x2(t-1)RESERVE RATIO has as coefficient of -2.75 for the Denton interpolated data (-3.34 for the actual data). This suggests that a 1-unit absolute increase in the required reserve ratio is associated with over a 2.75 relative decrease in y1(t)INVESTMENT (logarithm) in the following period. The significance of x2(t-1)RESERVE RATIO, suggests the primary importance this direct monetary control variable has in countries with underdeveloped financial markets. The potential for systemic distortion that the typically high reserve requirement has to investment and economic growth is indicated by having the reserve ratio statistically significant for all four Russian analyses. These results support the claim by Beim and Calomiris (2001, p. 51) that reserves paying zero7.2.2 Results with Private Borrowing VariableRussia: ARIMA [AR(1)] (7-2) Per equation 7-2, within the time series analysis, for the models that contain x4(t-1)PRIVATE BORROWING as an independent variable, the measure that matters to the aggregate level of investment in Russia is the statistically significant x2(t-1)RESERVE RATIO. The same relationship holds in this model as in the model with liquidity as an independent variable. Additionally, contains all insignificant variables including the x4(t-1)PRIVATE BORROWING

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67 variable in equation 7-2. In Rhaving potential effects on investment. Using Model 1 from Table 7-3, the independent variable x2(t-1)RESERVE RATIO has a coefficient of -1.45 for the Denton interpolated data (-1.59 for actual data). This suggests that a 1-unit absolute increase in the required reserve ratio is associated with over a 1.45 relative decrease in y1(t)INVESTMENT (logarithm) in the following period. The results from equation 7-2 with x4(t-1)PRIVATE BORROWING as an independent variable is comparable to the results from equation 7-1 containing the x3LIQUIDITY as an independent variable. Of all the financial repression variables, the reserve ratio as a measure of required reserves provides the most direct source of monies to a government purse. Beim and Calomiris (2001, p. 49) notes that having a high reserve requirement on commercial banks reduces 7.3 Large Firm Level InvestmentRussia: Descriptive Statistics and Correlations It is of interest to note on Table 7-4, the consistently negative real interest rates. Historically, this has been the narrow definition of financial repression. Both McKinnon and Shaw, beginning in their 1973 publications, focus on the interest rate variable from expected. They viewed the effects produced by deposit interest rate ceilings held below the level of inflation, as having a negative impact on economic growth, and they then forgo consumption for a negative real return on investment. However, while negative real interest rates are considered to be the original definition of financial repression, it is also an indication of the true cost of borrowing for firms. To any size firm, an increasing

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68 negative interest rate produces a decreasing true borrowing cost. Additionally, and relevant to Russia, the dominant extractive sector exporting firms gain by keeping production, and the resulting costs, in countries with inflationary economies. Throughout the research period of December 1998 to December 2005, Russian policy results in inflation and negative real interest rates by preserving a devalued currency and deposit interest rate ceilings below inflation. The government and the large firms in the ENS, nflation and negative real interest ratesreceive a huge subsidy via, inter alia, the negative real lending rates. This is a feature of est rates are low, and hence real interest rates are highly negative, credit rollover and extension represent a pure 29 The panel data analysis results, listed below, are produced by autocorrelation and heteroskedasticity-consistent techniques to adjust standard errors. The sensitivity diagnostics completed on the data used in the models in this section are the following: Univariate diagnostics were used to test sensitivity for skewness, data outliers, and stationarity of both the mean and the variance. The diagnostics used in this section address the same concerns with the time-series analysis but with the additional concern of heteroskedasticity. Because short panels tend to be stationary and with the variables the same as those used in the aggregate level analyses, the diagnostics produced similar results. The difference in this analysis is that it is firm-level investment being 29 Email to author dated October 2005.

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69 investigated; however, like the aggregate level investment, it also exhibits slow adjustment to economic changes. Bivariate diagnostics were used to test sensitivity for correlation. Again, the dual model structure is used in this research resulted from the finding of high correlation between x3LIQUIDITY and x4PRIVATE BORROWING. Multivariate diagnostics were used to test sensitivity for specification, misspecification and precision. The Hausman specification diagnostic indicated little difference in the fixed effect model and the random effect model. The final diagnostic, Collinearity Condition Number (CCN), measuring the eigenvalue and the determinant of the correlation matrix for each model is reported on the appropriate tables. 30 7.4 Large Firm Level InvestmentRussia: Panel Data Results Generally, like the aggregate level of investment, the reserve ratio is also indicating importance as a financial repression variable to the large firm level investment. However, in the large firm level results, other variables indicate possible ENS subsidy system effects. 7.4.1 Results with Liquidity VariableRussia: GLS [Random-Effects] (7-3) Per equation 7-3, within the large firm level investment analysis, for the models that contain x3(it-1)LIQUIDITY as an independent variable, the statistically significant financial repression measures are x2(it-1)RESERVE RATIO, x3(it-1)LIQUIDITY, x5(it-1)BANK LENDING, and x6(it-1)MARKET VALUE. 30 This analysis is an extension to a previous research of the same models using a database that ended June 2002. The negative coefficients exhibited are a consistent result across both studies.

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70 Using Model 3 from Table 7-5, the independent variable x2(it-1)RESERVE RATIO has as coefficient of -0.5 for Denton interpolated data. This suggests that a 1-unit increase in bank reserves relative to M2 is associated with over a 0.5 decrease in y1(it)INVESTMENT in the following period. In the large firm models, the importance of bank reserve requirements by the CBR to firm level investment indicates an understandably negative relationship, similar to the findings from aggregate level, but less pronounced. It would be expected that the increase in the reserve ratio would, by reducing the money supply available to satisfy consumption demand, negatively affect investment through a reduction in production. In addition to the previous period aggregate reserve ratio requirement from the banking system, the regression results also suggest an economically significant impact on investment from a change in previous period liquidity. Using Model 3 from Table 7-5, the independent variable x3(it-1)LIQUIDITY has as coefficient of -0.29 for Denton interpolated data. This suggests that a 1-unit increase in M2 relative to GDP is associated with over a 0.29 decrease in y1(it)INVESTMENT in the following period. The regression results also suggest an economically significant impact on capital investment to a change in bank lending. Using Model 3 from Table 7-5, the independent variable x5(it-1)BANK LENDING has as coefficient of -1.98 for the Denton interpolated data. This suggests that a 1-unit absolute increase in deposit bank assets to the sum of deposit bank assets and central bank assets is associated with a 1.98 decrease in y1(t)INVESTMENT in the following period. According to Bernstam and Rabushka (1998), in Russia the banking system does not intermediate between lender and borrower. Instead, the banks

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71 are used to re-intermediate the flow of invoicing from large firms to the CBR, and the return flow of the subsidy, in the form of loans, from the CBR back to the large firms. From the above results, the expected relationships on two of the six financial repression measures, liquidity and bank lending, are counter intuitive to the results typically observed in economically developed countries. Thus, besides testing the hypothesis. These subsidies are a throwback to the Soviet Union era, and are a substantial part of the Russian accounting standards. The massive subsidies that pass from the Russian Central Bank to firms are at least a partial explanation for, and possibly compound, the monetary distortions created by the financial repressive actions taken by the Russian government. The negative coefficient on liquidity and on bank lending indicates the possible systemic distortion financial repression and the ENS subsidy are capable of producing. Considering that the size of the receivables matches, and occasionally exceeds, the sum of the tax non-remittance and the most liquid money supply, M1, it is not surprising that variations in receivables affect the entire Russian economy. This continues to be true even after the increased petroleum dollars began to fill the foreign reserve vault. Per Bernstam, in a largely self-financing (also using a capital consumption allowance and government capital transfers) while the banking system chiefly recycles enterprise and household payments and deposits held for payments, re-intermediates rather than intermediates.

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72 When credit expansion is used for payments and, hence, to expand (and inflate) trade credit, investm 31 In this environment, x3(it-1)LIQUIDITY and x5(it-1)BANK LENDING have a monetary relationship, but not one of expanding the money supply through lending the deposit base. Instead, the relationship is the use of bank credit to deliver the subsidy. Similar to small firms in developed countries, self-financing from internally produced profits is a dominant form of business and project funding. In a country, like Russia, where external financing, including foreign direct investment, is available to only the largest firms, as a result, those firms indirectly control potential future competitors. This extra twist abets the monetary policies that support large firm interests, often at the expense of the financing sources needed by the small firms. 32 In this analysis, the stock market value variable is consistently important to the large firms. Using Model 3 from Table 7-5, the independent variable x6(it-1)MARKET VALUE has a coefficient of 0.87 for the Denton interpolated data. This suggests that a 1-unit increase in aggregate stock market capitalization relative to GDP is associated with over a 0.87 increase in y1(it)INVESTMENT in the following period. While the comparative static is as expected, it is important to realize that in Russia, the stock market is not the usual market valuation as an average percentage of GDP hovers around seven percent, and ownership is highly concentrated. Like the Korean chaebols and Japanese keiretsus 31 Email to author dated March 2005. 32 The importance of informal exchanges to the economic parties operating in illiquid Russia is further touched had featured portraits of 2000, p.320).

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73 before them, a small group of business conglomerates in Russia own and control large sections of the firms that make up the Russian economy. Additionally, like German banks, Russian banks own shares. Outside the state controlled banks, the conglomerates own many of the larger Russian commercial banks. Referred to in the popular press as monetary usefulness of the Russian stock market as a funding source for Russian publicly traded firms extends beyond the initial public offering. It is also the trading gains obtained in a manipulated market. 33 needed to collect tax arrears from the largest enterprise debtors, it did not approach enterprises but rather squeezed major banks (e.g., Uneximbank, Russian Credit, etc.) and The positive coefficient on the stock market valuation supports the ENS subsidy hypothesis. Assuis to restrict its growth and the resulting competition to the government controlled banking system as a financially repressed component, the stock market component of the Russian financial system is outside the re-intermediating payment cycle of the self-enforcing ENS subsidy. Thus, the coefficient is as expected, as probably would be the signs on x3(it-1)LIQUIDITY and x5(it-1)BANK LENDING if they were not involved in the subsidy cycle. Finally, the constant is consistently positive and significant in this set of models for the large firms. Using Model 3 from Table 7-0, has as estimated coefficient of 1.64 for the Denton interpolated data. This suggests that a 1-unit increase in 33 Claims made are based on personal observations by the author. It is a subject for future research.

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74 the constant, when all financial repression variables are held at zero, is associated with over a 1.64 increase in y1(it)INVESTMENT in the following period. Speculating on a possible explanation suggests external financing availability. The large Russian firms have funding choices outside the national borders. They are not dependent on the domestic financial markets and thus have a positive constant. Therefore, when the monetary components that make up the financial repression index are held to zero, the large firms can still fund business investment. 7.4.2 Results with Private Borrowing VariableRussia: GLS [Random-Effects] (7-4) Per equation 7-4, within the large firm level investment analysis, in the results for the models that contain x4(it-1)PRIVATE BORROWING as an independent variable, show x2(it-1)RESERVE RATIO and x5(it-1)BANK LENDING to be statistically significant. Using Model 3 from Table 7-6, the variable x2(it-1)RESERVE RATIO has as an estimated coefficient of -0.3 for the Denton interpolated data. This suggests that a 1-unit increase in required bank reserves is associated with over a 0.30 decrease in y1(it)INVESTMENT in the following period. Therefore, the result implies a negative relationship between x2(it-1)RESERVE RATIO and y1(it)INVESTMENT. An extra benefit to this research being conducted at both aggregate and large firm investment levels is that some tentative inferences on the monetary policy transmission channels can be made from the results of the previous period x2RESERVE RATIO being statistically significant in the Russian models. The results suggest a direct monetary policy control measure, frequently used by governments in developing countries, plays a significant part across all economic investment levels. As found historically in developed

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75 countries, internal finance is the dominant form of business and project funding for developing enterprises. Without funding alternatives, businesses in these countries often depend on available cash for survival, which is tied to the level of liquidity, which is then affected by the level of required reserves in the financial system. Additionally, it is already documented in the literature that in transitional countries there seems to be a strong connection between illiquidity and barter (Commander and Mumssen 2002). Using Model 3 from Table 7-6, the independent variable x5(it-1)BANK LENDING has as estimated coefficient of -2.05 for the Denton interpolated data. This suggests that a 1-unit increase in the nominal annual rate on deposit bank assets relative to total deposit bank and central bank assets is associated with over a 2.05 decrease in y1(it)INVESTMENT in the following period. The negative relationship between x5(it-1)BANK LENDING and y1(it)INVESTMENT suggests the distortion possible with the banking system being used as an ENS subsidy payment tool. Additionally, the bank ownership structure in Russia may facilitate these effects. In 2004, the bank sector capitalization was still only six percent of Of the primary state owned banks, Sberbank, Vneshtorgbank, Gazprombank, and Vneshekonombank, bank assets are dominated by the holdings of Russian government bonds as assets and not loans to private enterprise (World Bank 2004). This use of domestic lending to fund government expenses leads to a financial system dominated by the public sector. Thus, in Russia the commercial banks resemble individual central bank branches, rather than independent financial intermediation centers. Of the non-state-owned banks, the majority are dominated by banks owned by the large financial and industrial groups that are participants of the ENS subsidy system.

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76 Finally, the constant is positive and statistically significant in this analysis for Russian large firms, as it was for the previous models containing x3(it-1)LIQUIDITY as an independent variable. Using Model 3 from Table 7-6, the constant has an estimated coefficient of 1.48 for the Denton interpolated data. This suggests that a 1-unit increase in the constant, when all financial repression variables are held at zero, is associated with over a 1.48 increase in y1(it)INVESTMENT in the following period. A possible explanation is that large Russian firms have funding choices outside the national borders. 7.5 Aggregate Level InvestmentUSA: Descriptive Statistics and Correlations For comparison, the analysis of the USA database exactly matches that for the Russian database, including the separate models for liquidity and private borrowing. Of note on Table 7-7 is the high negative correlation of -0.87 between liquidity and private borrowing, a result suggestive of a crowding out effect. The USA results support the overall dissertation theme that systemic distortions are possible when governments intervene in the financial system. This is reflected in Figures 7-1 and 7-2. Similar to the Russia analyses, the time series analysis results, listed below, are produced by autocorrelation-consistent techniques to adjust standard errors. Additionally, the USA sensitivity diagnostics are the following with similar results: Univariate diagnostics were used to test sensitivity for skewness, data outliers, and stationarity of both the mean and the variance. Because the question of stationarity is of primary importance in using time-series data, the augmented Dickey-Fuller test is again used. In most countries, monetary variables typically exhibit slow adjustment to

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77 economic changes; thus, like Russia, the USA research variables indicate stationarity with the Dickey-Fuller unit root tests indicating stationary series. 34 Bivariate diagnostics were used to test sensitivity for correlation. The high correlation found between x3LIQUIDITY and x4PRIVATE BORROWING in the Russia diagnostics was also found in the USA data (positive for Russian; negative for USA); therefore, the dual model structure is continued. Multivariate diagnostics were used to test sensitivity for specification, misspecification and precision. The Portmanteau statistic was calculated for correlation with the findings indicating white noise errors, and the results of this final diagnostic are presented on the appropriate tables. 7.6 Aggregate Level InvestmentUSA: Time Series Results In general, and with respect to financial repression, Russian aggregate level investment indicated having a specific relationship to the required reserve ratio. In the USA, however, it is the lagged investment that is consistently important across all models in both investment levels. 7.6.1 Results with Liquidity VariableUSA: ARIMA [AR(1)] (7-5) Per equation 7-5, within the aggregate level investment time series analysis, for the models that contain x3(t-1)LIQUIDITY as an independent variable, the statistically significant measure is Ly1LAGGED Y1. 34 Similar to the Russia results, the autocorrelation function and the partial autocorrelation function verify the Dickey-Fuller results.

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78 Using Model 1 from Table 7-8, the independent variable Ly1LAGGED Y1 has a coefficient of 1.01 for the Denton interpolated data (1.04 for the actual data). This suggests that a 1-unit absolute increase in previous investment is associated with over a 1.01 relative increase in y1(t)INVESTMENT (logarithm) in the following period. The significance of Ly1LAGGED Y1 in the United States is consistent across all models in both the aggregate and large firm investment levels. 7.6.2 Results with Private Borrowing VariableUSA: ARIMA [AR(1)] (7-6) Per equation 7-6, within the aggregate level investment analysis, in the results summarized on Table 7-9 for the models that contain x4(t-1)PRIVATE BORROWING as an independent variable, the statistically significant variables of interest are x4(t-1)PRIVATE BORROWING and Ly1LAGGED Y1. Using Model 1 from Table 7-9, the independent variable x4(t-1)PRIVATE BORROWING has a coefficient of -0.14 for the Denton interpolated data (-0.67 for the actual data). This suggests that a 1-unit absolute increase in claims on private sector relative to total domestic credit is associated with over a 0.14 relative decrease in y1(t)INVESTMENT (logarithm) in the following period. The results indicate a negative relationship between in y1(t)INVESTMENT, and x4(t-1)PRIVATE BORROWING. Private Borrowing as claims on the private sector relative to total domestic credit has decreased relative to the growth of total domestic credit, which additionally contains the credit extended to government. The United States government expenditures increased and remained high during the 1996 to 2005 research period. Furthermore, these results hold in all but two models suggesting a potential relationship

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79 between investment and private borrowing, and thus indicating support for potential crowding out of private enterprise borrowing by increasing governmental borrowing. In these regards, Figures 7-3 and 7-4 are illustrative. Again lagged investment is found to be important. Using Model 1 from Table 7-9, the independent variable Ly1LAGGED Y1 has a coefficient of 0.99 for the Denton interpolated data (0.94 for the actual data). This suggests that a 1-unit absolute increase in previous investment is associated with over a 0.99 relative increase in y1(t)INVESTMENT (logarithm) in the following period. 7.7 Large Firm Level InvestmentUSA: Descriptive Statistics and Correlations Of note, on Table 7-10, is the small volume of investment from previous period by the USA large firms. This behavior mimics the situation for Russian large firms. Further inspection of the data indicates that the majority of the transnational firms were selling off assets during much of the research period, with the technology firms (e.g., Hewlett-Packard) selling off assets during the entire period. Using the same techniques in the USA analyses, the panel data analysis results, listed below, like those in the Russian research, are produced by autocorrelation and heteroskedasticity-consistent techniques to adjust standard errors. The USA sensitivity diagnostics are the following with similar results: Univariate diagnostics were used to test sensitivity for skewness, data outliers, and stationarity of both the mean and the variance. Because short panels tend to be stationary and with the independent variables the same as those used in the aggregate level analyses, the diagnostic results produce similar results. The difference in this analysis is that firm level investment being investigated. However, like the aggregate level investment, and in most countries, monetary variables typically exhibit slow

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80 adjustment to economic changes; thus, like Russia, the USA research independent variables indicate stationarity. Bivariate diagnostics were used to test sensitivity for correlation. In addition to using the USA results for comparison, with the high correlation found between x3LIQUIDITY and x4PRIVATE BORROWING in the USA data, the dual model structure is maintained. Multivariate diagnostics were used to test sensitivity for specification, misspecification and precision. The random effect model is chosen because of the panels being short; however, the Hausman specification diagnostic indicated little difference between the random effect model and the fixed effect model. As with the Russia analyses, the final diagnostic Collinearity Condition Number (CCN) measuring the eigenvalue and the determinant of the correlation matrix for each model are reported on the appropriate tables. 7.8 Large Firm Level InvestmentUSA: Panel Data Results Unlike the results for the Russian large firms, financial repression components do not seem to affect the transnational firms operating in the United States. 7.8.1 Results with Liquidity VariableUSA: GLS [Random-Effects] (7-7) Per equation 7-7, within the large firm level investment analysis, for the models that contain x3(it-1)LIQUIDITY as an independent variable, the statistically significant measures are x7(it-1)FIRM CHARACTERISTIC, and Ly1LAGGED Y1. Using Model 3 (dynamic) and Model 7 (static) from Table 7-11, the independent variable x7(it-1)FIRM CHARACTERISTIC has as estimated coefficient of 0.02 for the Denton

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81 interpolated data (0.02 for the actual data) in both the dynamic and static models. This suggests that a 1-unit increase in total revenue relative to total assets is associated with over a 0.02 increase in y1(it)INVESTMENT in the following period. The reaction the USA-based transnational firms have to the financial repression variables is quite different to their respective Russian firms. In the equivalent analysis on the large Russian firms x2(it-1)RESERVE RATIO, x3(it-1)LIQUIDITY, x5(it-1)BANK LENDING, and x6(it-1)MARKET VALUE are all statistically significant. Using Model 3 and from Table 7-11, the independent variable Ly1LAGGED Y1 has a coefficient of 0.21 for the Denton interpolated data (0.35 for the actual data). This suggests that a 1-unit increase in previous investment is associated with over a 0.21 increase in y1(it)INVESTMENT in the following period. While the Russian large firms were not affected by previous investment, the USA large firms are affected. The USA large firm results indicate that lagged investment and total revenue to total assets, x7(it-1)FIRM CHARACTERISTIC, matter to the large 28 non-financial firms in the Dow Jones index. With these results, there is an indication that central bank actions globally simply do not matter to these huge firms. Their funding sources extend beyond any one country. The results support claims made about international banking supply and demand developments. Daniels and VanHoose (2002, p.200) claim, The Eurocurrency markets -financed investment in the United States increasingly stems from loans by non-U.S. banks. The largest U.S. corporations on average use the services of more foreign banks than domestic

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82 megabanks based in many nations. Indeed, by the 1990s a typical multinational U.S. firm had accounts with at least as many banks abroad as it maintained with U.S.-based 7.8.2 Results with Private Borrowing VariableUSA: GLS [Random-Effects] (7-8) Per equation 7-8, within the large firm level investment analysis, in the results summarized on Table 7-12 for the models that contain x4(it-1)PRIVATE BORROWING as an independent variable, show the variables are x7(it-1)FIRM CHARACTERISTIC, and Ly1LAGGED Y1 as being statistically significant. Using Model 3 (dynamic) and Model 7 (static) from Table 7-12, the independent variable x7(it-1)FIRM CHARACTERISTIC has as coefficient of 0.02 for the Denton interpolated data (0.02 for the actual data) for both the dynamic and static models. This suggests that a 1-unit increase in total revenue relative to total assets is associated with over a 0.02 increase in y1(it)INVESTMENT in the following period. Using Model 3 and from Table 7-12, the independent variable Ly1LAGGED Y1 has a coefficient of 0.21 for the Denton interpolated data (0.37 for the actual data). This suggests that a 1-unit increase in previous investment is associated with over a 0.21 increase in y1(it)INVESTMENT in the following period. The same results found in the models with the liquidity independent variable hold in the models that contain private borrowing. Thus, consistently across the large transnational firms headquartered in the United States the most important investment determinants are firm characteristics and lagged investment.

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83 7.9 Comparative Statics While studying the distortions produced by the financial repression and the ENS subsidy, a useful comparison is the breakdown of expected comparative statics for economically developed and transitional countries to the results specific to Russia in this research. Shown on Table 7-13, with respect to investment, historically there would be expected to be positive statics on all three of the external finance variables x4PRIVATE BORROWING, x5BANK LENDING, x6MARKET VALUE, and on two of the monetary policy control variables x1REAL RATES, x3LIQUIDITY, and negative statics on the remaining government control variable x2RESERVE RATIO. In this research, the expected statics agree with theory in all but one variable: real interest rates. McKinnon and Shaw in 1973 viewed financial repression from an investor requiring a positive real interest rate, however, from the viewpoint of a borrower, the optimal expected static is for real interest rates to be negative representing a favorable real cost of borrowing. Comparing theoretical to actual statics, across all levels of investment, the findings suggest opposite relationships to investment for two of the financial repression measures: x3LIQUIDITY, and x5BANK LENDING. These findings signal the severity that the distortions produced by the current Russian financially repressive and governmental supported by the positive coefficient sign, which matches expectations, on x6MARKET VALUE. This indicates that the Russian stock market as a financial system is outside the ENS subsidy and payment cycle. 7.10 Results: General and Specific Questions Summarizing the results to the previously stated general and specific research questions find the follow implications:

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84 1. Generally, are there differences between Russia, with its emerging financial markets and systems, and the United States, with established financial markets and systems? The results of this study suggest interesting differences found in the descriptive statistics. A consistent negative real interest rate, which is the narrow definition of financial repression, is obvious in the Russia results. This narrow definition was first theorized by both McKinnon and Shaw, in their 1973 publications. They claimed that deposit interest rate ceilings held below the level of inflation would have a negative impact on economic growth by affecting an investment. However, it is theorized in this dissertation that a negative real interest rate is also an indication of the true cost of borrowing for firms. Thus, for companies, a negative real interest rate produces a decreasing true borrowing cost. This fact might help explain the persistence of negative real rates in developing countries. In comparison, the findings of the USA results reveal a fairly consistent positive average real interest rate in the United States. 2. Generally, and within Russia, are there differences between the aggregate level and the large firm level? The results of this study suggest the relationship of interest is the similarity across both investment levels, and not the differences. An important relationship of the reserve requirement ratio to all investments is found in both aggregate and large firm levels. In this study, it is dominant as a statistically significant financial repression measure across all four analyses. The reaction to government increases of the required reserves is negative across both investment levels. Thus, the results show possible distortion from financial repression measures produced by government actions. In comparison, the findings of the USA results show an important relationship between previous period investment and current investment. This relationship holds across both aggregate and large firm levels of investment. Completely absent from the results is the notion of financial repression measures distorting investment in the United States. Thus, the findings show that firms operating from a developed economy with an established financial system are not affected by financial repression. 3. Specifically, and within Russia, which of the financial repression components (real interest rates, reserve ratio, economic liquidity, private borrowing, bank lending, and stock market value) matter to each level of investment? The results of this study suggest the potential distortionary extent on efficient allocation of capital from governments conducting financial repression is high. This supports the findings of Beim and Calomiris (2001) when they found an increasingly repressive index for Russia. The results from this study support the idea that governmental actions that preserve tight control of both money stocks and flows, along with repressing the development of financing sources other than the state controlled banks hampers economic and investment growth. In Russia, and in addition to the required reserve ratio, liquidity, bank lending and market value financial repression measures potentially affect investment. In comparison, the findings of the USA results reveal the firm characteristic variable, which is a measure of total revenue to total assets, matter to the large firms operating out of

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85 the United States. At the firm level, this is in addition to the previous investment affecting current investment decisions. To these transnational companies that have money available to them from internationally established monetary markets, financial repression components are not a hindrance to investment. Thus, the assumption is that firms operating in countries with developed economies and established financial markets make investment decisions based on business growth strategies specific to the firm. 4. Specifically, and within Russia, is there evidence of systemic distortions consistent with the existence of the Russian ENS subsidy? The results of this study suggest the potential distortionary extent increases when governments add subsidies to a monetary policy mix that contains financial repression. This supports Bernstam and Rabushka (1998, 2006) claim that the ENS (Enterprise Network Socialism) possibly distorts economic growth in Russia. This study extends their work to investment by using inferences drawn from the financial repression results. In particular, and extrapolating from the financial repression results, the assumption is that the negative relationship large firm investment has to liquidity and bank lending indicates the possible additional distortion produced by the Russian ENS subsidy. Further, since firm investment has the expected positive relationship to the market value measure, it supports the findings. The supposition is that if the banking sector was not involved in the subsidy payment cycle, liquidity and bank lending would have the expected positive relationship; thus, their coefficients would be mimicking market value, which is a proxy for the Russian stock market. Unlike the banking sector, the Russian Trading System is not involved in distributing subsidies.

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86 Figure 7-1. Monetary Measures: M2 (broad) and M1 (narrow) from the Federal Reserve Figure 7-2. Potential Crowding Out: Private Credit and Total USA Credit

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87 Figure 7-3. Ratio of Private Credit to Total Credit Figure 7-4. Industrial Production to GDP

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88 Table 7-1. Descriptive statistics and correlation Russia: Aggregate level y1 Ly1 x1 x2 x3 x4 x5 x6 x8 x10 DESCRIPTIVE STATISTICS Mean 5.00 4.96 -0.10 0.34 0.68 1.36 0.64 0.08 723.04 4.44 Median 5.02 4.98 -0.08 0.33 0.64 1.34 0.64 0.08 692.13 1.21 Maximum 6.07 5.95 0.00 0.46 0.97 1.89 0.68 0.14 1534.95 20.76 Minimum 4.14 4.14 -0.65 0.22 0.43 0.83 0.62 0.02 50.24 -10.38 St. Deviation 0.54 0.50 0.11 0.06 0.15 0.25 0.01 0.03 326.60 10.04 Observations 29 28 29 29 29 29 29 29 29 29 CORRELATIONS y1 INVESTMENT 1.00 Ly1 LAGGED Y1 0.52 1.00 x1 REAL RATES 0.45 0.40 1.00 x2 RESERVE RATIO -0.44 -0.22 0.11 1.00 x3 LIQUIDITY 0.65 0.59 0.29 -0.53 1.00 x4 P. BORROWING 0.63 0.54 0.40 -0.30 0.79 1.00 x5 B. LENDING 0.39 0.49 0.28 -0.05 0.37 -0.21 1.00 x6 MARKET VALUE 0.61 0.53 0.44 -0.31 0.69 0.48 0.43 1.00 x8 OIL 0.64 0.65 0.46 -0.40 0.68 0.64 0.12 0.62 1.00 x10 SEASONAL -0.27 -0.53 0.01 0.15 0.14 -0.32 -0.17 0.13 -0.02 1.00 VARIABLES: (y1) Gross fixed investment (logarithm); (Ly1) One lag of y1; (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real ruble price for oil (one-period lag); (x10) Temperature for seasonal effects (degrees Celsius); RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-Russia) December 1998 to December 2005 (29 actual quarters, 85 Denton months); SOURCE ALL VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF INVESTMENT AND RATIO COMPONENTS: Rubles, billion

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89 Table 7-2. Estimation Russia: Aggregate level investmentwith liquidity variable ARIMA [AR (1)]: (1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6) Actual: Quarterly Denton: Monthly Constant 4.07 2.66 5.70 3.79 2.34 5.03 1.76 1.74 0.90 1.89 1.47 2.09 (p-value) (0.20) (0.44) (0.53) (0.24) (0.47) (0.18) (0.34) (0.35) (0.60) (0.32) (0.39) (0.30) x1 REAL RATES 0.95 0.71 1.70 1.15 0.75 1.03 0.89 0.88 0.68 2.10 1.10 2.14 (p-value) (0.54) (0.66) (0.52) (0.43) (0.61) (0.37) (0.51) (0.61) (0.56) (0.50) (0.67) (0.30) x2 RESERVE RATIO -3.34 -3.17 -0.56 -3.39 -3.22 -2.30 -2.75 -2.75 -1.64 -3.25 -2.29 -3.28 (p-value) (0.04) (0.04) (0.05) (0.02) (0.03) (0.03) (0.05) (0.05) (0.04) (0.04) (0.04) (0.03) x3 LIQUIDITY -1.29 -1.63 -2.17 -0.94 -1.63 -0.37 -0.26 -0.26 -0.20 -0.21 -0.31 -0.22 (p-value) (0.48) (0.41) (0.11) (0.53) (0.31) (0.34) (0.09) (0.09) (0.21) (0.30) (0.16) (0.29) x5 B. LENDING 1.96 4.74 2.40 3.56 5.82 2.58 0.89 0.92 1.62 1.10 2.47 1.86 (p-value) (0.61) (0.42) (0.60) (0.47) (0.29) (0.60) (0.66) (0.65) (0.53) (0.38) (0.30) (0.51) x6 MARKET VALUE 11.40 10.63 5.96 10.62 10.36 10.40 2.71 2.72 1.36 2.98 1.63 2.04 (p-value) (0.09) (0.14) (0.12) (0.09) (0.55) (0.06) (0.09) (0.17) (0.10) (0.08) (0.57) (0.06) x8 OIL 0.01 0.01 1.74 0.01 (p-value) (0.59) (0.42) (0.35) (0.40) x10 SEASONAL 0.04 0.01 0.01 0.01 (p-value) (0.09) (0.05) (0.06) (0.17) Ly1 LAGGED Y1 0.17 0.07 0.72 0.61 0.61 0.67 (p-value) (0.61) (0.86) (0.50) (0.41) (0.57) (0.53) Observations 28 28 28 28 28 28 84 84 84 84 84 84 Correlation 0.43 0.42 0.37 0.34 0.36 0.31 0.44 0.41 0.36 0.29 0.32 0.31 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (logarithm); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real ruble price for oil (one-period lag); (x10) Temperature for seasonal effects (degrees Celsius); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-Russia) December 1998 to December 2005 (29 actual quarters, 85 Denton months); SOURCE ALL VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Rubles, billion; ACRONYMS AND ABBREVIATIONS: (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (ARIMA) Autoregressive integrated moving average; DIAGNOSTICS-CORRELATION: Portmanteau Ho = White Noise; All standard errors are adjusted for potential autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of uarterly National Accounts Manual:

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90 Table 7-3. Estimation Russia: Aggregate level investmentwith private borrowing variable ARIMA [AR (1)]: (1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6) Actual: Quarterly Denton: Monthly Constant 9.43 9.16 5.23 4.24 4.28 3.31 1.33 1.30 0.58 1.50 1.61 1.61 (p-value) (0.33) (0.40) (0.34) (0.13) (0.16) (0.57) (0.46) (0.47) (0.42) (0.22) (0.35) (0.39) x1 REAL RATES 0.16 0.19 0.50 0.27 0.38 0.33 0.26 0.26 0.68 0.33 0.25 0.55 (p-value) (0.68) (0.66) (0.61) (0.66) (0.61) (0.63) (0.64) (0.65) (0.67) (0.58) (0.57) (0.65) x2 RESERVE RATIO -1.59 -1.65 -1.42 -1.71 -1.99 -1.70 -1.45 -1.43 -1.39 -2.84 -1.79 -2.84 (p-value) (0.08) (0.05) (0.05) (0.06) (0.06) (0.05) (0.06) (0.05) (0.05) (0.06) (0.05) (0.05) x4 P. BORROWING 2.05 2.08 1.62 1.20 1.42 1.09 0.14 0.14 0.13 0.19 0.17 0.19 (p-value) (0.20) (0.21) (0.32) (0.21) (0.33) (0.17) (0.28) (0.28) (0.36) (0.25) (0.39) (0.28) x5 B. LENDING 2.54 2.02 7.34 2.49 2.51 1.21 0.95 0.99 1.67 1.09 1.93 0.92 (p-value) (0.59) (0.60) (0.53) (0.15) (0.09) (0.16) (0.65) (0.65) (0.54) (0.07) (0.13) (0.09) x6 MARKET VALUE 1.65 2.07 5.16 2.77 4.22 3.46 0.70 0.68 1.59 1.09 1.49 1.08 (p-value) (0.66) (0.60) (0.25) (0.24) (0.49) (0.44) (0.41) (0.43) (0.35) (0.18) (0.53) (0.38) x8 OIL 0.01 0.01 0.01 0.01 (p-value) (0.60) (0.52) (0.65) (0.40) x10 SEASONAL 0.01 0.01 0.01 0.01 (p-value) (0.28) (0.67) (0.13) (0.65) Ly1 LAGGED Y1 0.48 0.46 0.58 0.60 0.60 0.66 (p-value) (0.40) (0.32) (0.53) (0.46) (0.38) (0.59) Observations 28 28 28 28 28 28 84 84 84 84 84 84 Correlation 0.44 0.41 0.39 0.33 0.35 0.32 0.43 0.42 0.37 0.31 0.33 0.31 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (logarithm); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real ruble price for oil (one-period lag); (x10) Temperature for seasonal effects (degrees Celsius); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-Russia) December 1998 to December 2005 (29 actual quarters, 85 Denton months); SOURCE ALL VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Rubles, billion; ACRONYMS AND ABBREVIATIONS: (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (ARIMA) Autoregressive integrated moving average; DIAGNOSTICS-CORRELATION: Portmanteau Ho = White Noise; All standard errors are adjusted for potential autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of uarterly National Accounts Manual: Concepts, Data Sources, an

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91 Table 7-4. Descriptive statistics and correlation Russia: Large firm level y1 Ly1 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 DESCRIPTIVE STATISTICS Mean 0.10 0.10 -0.10 0.34 0.67 1.35 0.64 0.08 1.33 693.03 23.78 4.40 Median 0.05 0.05 -0.08 0.34 0.64 1.33 0.64 0.08 0.91 684.13 6.25 1.21 Maximum 12.66 12.66 0.00 0.46 0.97 1.89 0.68 0.14 10.77 1534.95 422.84 20.76 Minimum -0.92 -0.92 -0.65 0.22 0.43 0.83 0.62 0.02 0.04 50.24 0.01 -10.38 St. Deviation 0.52 0.53 0.10 0.06 0.14 0.22 0.01 0.03 1.34 280.07 47.12 9.87 Observations 988 948 988 988 988 988 988 988 988 988 948 988 CORRELATIONS y1 INVESTMENT 1.00 Ly1 LAGGED Y1 0.14 1.00 x1 REAL RATES -0.07 -0.01 1.00 x2 RESERVE RATIO -0.01 -0.04 -0.28 1.00 x3 LIQUIDITY -0.07 -0.12 0.32 -0.55 1.00 x4 P. BORROWING 0.06 0.03 0.19 -0.36 0.76 1.00 x5 B. LENDING -0.14 -0.10 0.32 -0.14 0.45 -0.27 1.00 x6 MARKET VALUE -0.08 -0.16 0.39 -0.36 0.69 0.30 0.48 1.00 x7 F. CHARACTERISTIC 0.07 0.04 0.02 -0.05 0.08 0.06 0.02 0.06 1.00 x8 OIL -0.07 -0.12 0.31 -0.16 0.65 0.62 0.17 0.67 0.08 1.00 x9 A. PRINCIPLE 0.03 0.03 0.10 -0.48 0.22 0.19 0.04 0.18 -0.04 0.26 1.00 x10 SEASONAL 0.02 -0.11 -0.23 0.13 0.13 -0.43 -0.19 0.13 -0.01 -0.07 -0.05 1.00 VARIABLES: (y1) Gross fixed investment (scaled by one-Ly1) One lag of y1; (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some)quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets; (x8) Real ruble price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (x10) Temperature for seasonal effects (degrees Celsius); RESEARCH PERIOD & PERIODIC INTERVALS: (Russia-unbalanced) December 1998 to December 2005 (29 to 17 Denton quarters); FIRM COUNT (Russia): 40; SOURCE DEPENDENT VARIABLES: Worldscope, Global Reports, and Company Financials; SOURCE INDEPENDENT VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF INVESTMENT AND RATIO COMPONENTS: Rubles, billion; RUSSIA-UNBALANCED LARGE FIRM PANELS: 8 years (29 Denton interpolated quarters): 17 companies; 7 years (25 Denton interpolated quarters): 12 companies; 6 years (21 Denton interpolated quarters): 2 companies; 5 years (17 Denton interpolated quarters): 9 companies

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92 Table 7-5. Estimation Russia: Large firm level investmentwith liquidity variable GLS [Random Effects]: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Denton: Quarterly Constant 1.67 1.77 1.64 1.74 0.01 1.97 2.00 2.02 2.03 2.00 (p-value) (0.00) (0.00) (0.00) (0.00) (0.03) (0.00) (0.00) (0.00) (0.00) (0.00) x1 REAL RATES -0.01 -0.01 -0.01 -0.01 -0.01 -0.03 -0.01 -0.03 -0.05 -0.03 (p-value) (0.60) (0.64) (0.67) (0.66) (0.63) (0.50) (0.56) (0.47) (0.58) (0.51) x2 RESERVE RATIO -0.48 -0.51 -0.50 -0.48 -0.49 -0.46 -0.47 -0.43 -0.46 -0.46 (p-value) (0.01) (0.00) (0.01) (0.00) (0.02) (0.03) (0.01) (0.00) (0.00) (0.04) x3 LIQUIDITY -0.29 -0.18 -0.29 -0.31 -0.29 -0.35 -0.26 -0.34 -0.38 -0.36 (p-value) (0.01) (0.03) (0.00) (0.01) (0.01) (0.00) (0.00) (0.02) (0.02) (0.02) x5 B. LENDING -2.05 -2.21 -1.98 -2.15 -2.09 -2.52 -2.56 -2.63 -2.64 -2.56 (p-value) (0.02) (0.00) (0.01) (0.01) (0.01) (0.00) (0.00) (0.02) (0.01) (0.01) x6 MARKET VALUE 0.91 0.89 0.87 1.02 0.94 1.32 1.21 1.34 1.44 1.35 (p-value) (0.01) (0.01) (0.01) (0.00) (0.01) (0.00) (0.00) (0.01) (0.00) (0.01) x7 F. CHARACTERISTIC 0.01 0.02 (p-value) (0.61) (0.42) x8 OIL 0.01 0.01 (p-value) (0.09) (0.08) x9 A. PRINCIPLE -0.01 -0.01 (p-value) (0.68) (0.58) x10 SEASONAL 0.01 0.01 (p-value) (0.62) (0.52) Ly1 LAGGED Y1 0.01 0.01 0.01 0.01 0.01 (p-value) (0.85) (0.65) (0.72) (0.65) (0.93) Observations 908 908 908 908 908 948 948 948 948 948 CCN collinearity 6.02 6.80 6.05 6.03 6.10 5.88 6.74 5.93 5.90 5.97 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (scaled by one-assets); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets (one-period lag); (x8) Real ruble price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (x10) Temperature for seasonal effects (degrees Celsius); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (Russia-unbalanced) December 1998 to December 2005 (29 to 17 Denton quarters (Observations per group of 40 firms: 15 min 27 max for dynamic models and 16 min 28 max for static models); FIRM COUNT (Russia): 40; SOURCE DEPENDENT VARIABLES: Worldscope, Global Reports, and Company Financials; SOURCE INDEPENDENT VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Rubles, billion; ACRONYMS AND ABBREVIATIONS: (CCN) Collinearity Condition Number; (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (GLS) Generalized Least Squares; DIAGNOSTICS-CORRELATION: (CCN) Collinearity Condition autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating variable. See IMF manual: Bloem, A., R. Dippelsman, and International Monetary Fund; RUSSIA-UNBALANCED LARGE FIRM PANELS: 8 years (29 Denton interpolated quarters): 17 companies; 7 years (25 Denton interpolated quarters): 12 companies; 6 years (21 Denton interpolated quarters): 2 companies; 5 years (17 Denton interpolated quarters): 9 companies.

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93 93 Table 7-6. Estimation Russia: Large firm level investmentwith private borrowing variable GLS [Random Effects]: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Denton: Quarterly Constant 1.52 1.39 1.48 1.55 1.54 1.62 1.51 1.86 1.66 1.63 (p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) x1 REAL RATES -0.05 -0.03 -0.05 -0.04 -0.06 -0.03 -0.03 -0.01 -0.05 -0.03 (p-value) (0.61) (0.65) (0.63) (0.66) (0.57) (0.50) (0.56) (0.65) (0.36) (0.48) x2 RESERVE RATIO -0.30 -0.36 -0.30 -0.28 -0.30 -0.25 -0.27 -0.25 -0.26 -0.25 (p-value) (0.05) (0.02) (0.00) (0.04) (0.01) (0.01) (0.00) (0.01) (0.02) (0.01) x4 P. BORROWING 0.01 0.11 0.01 0.02 0.01 0.04 0.13 0.01 0.04 0.05 (p-value) (0.43) (0.10) (0.69) (0.53) (0.52) (0.15) (0.21) (0.58) (0.23) (0.10) x5 B. LENDING -1.01 -1.98 -2.05 -2.18 -2.16 -2.39 -2.27 -2.71 -2.48 -2.41 (p-value) (0.03) (0.00) (0.02) (0.00) (0.01) (0.02) (0.00) (0.02) (0.00) (0.01) x6 MARKET VALUE 0.17 0.40 0.20 0.12 0.13 0.02 0.52 0.16 0.02 0.02 (p-value) (0.42) (0.10) (0.50) (0.59) (0.53) (0.62) (0.04) (0.61) (0.61) (0.60) x7 F. CHARACTERISTIC 0.01 0.02 (p-value) (0.65) (0.54) x8 OIL 0.01 0.01 (p-value) (0.10) (0.09) x9 A. PRINCIPLE -0.01 -0.01 (p-value) (0.53) (0.26) x10 SEASONAL 0.01 0.01 (p-value) (0.40) (0.38) Ly1 LAGGED Y1 0.01 0.01 0.01 0.01 0.01 (p-value) (0.50) (0.64) (0.44) (0.69) (0.62) Observations 908 908 908 908 908 948 948 948 948 948 CCN collinearity 2.80 3.61 5.38 2.80 2.85 2.90 3.81 5.14 2.91 2.96 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (scaled by one-assets); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets (one-period lag); (x8) Real ruble price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (x10) Temperature for seasonal effects (degrees Celsius); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (Russia-unbalanced) December 1998 to December 2005 (29 to 17 Denton quarters (Observations per group of 40 firms: 15 min 27 max for dynamic models and 16 min 28 max for static models); FIRM COUNT (Russia): 40; SOURCE DEPENDENT VARIABLES: Worldscope, Global Reports, and Company Financials; SOURCE INDEPENDENT VARIABLES: Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Rubles, billion; ACRONYMS AND ABBREVIATIONS: (CCN) Collinearity Condition Number; (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (GLS) Generalized Least Squares; DIAGNOSTICS-CORRELATION: (CCN) Collinearity Condition errors are adjusted for potential panel heteroskedasticity and autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating variable. See IMF manual: Bloem, A., R. Dippelsman, and N. MInternational Monetary Fund; RUSSIA-UNBALANCED LARGE FIRM PANELS: 8 years (29 Denton interpolated quarters): 17 companies; 7 years (25 Denton interpolated quarters): 12 companies; 6 years (21 Denton interpolated quarters): 2 companies; 5 years (17 Denton interpolated quarters): 9 companies.

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94 Table 7-7. Descriptive statistics and correlation USA: Aggregate level y1 Ly1 x1 x2 x3 x4 x5 x6 x8 DESCRIPTIVE STATISTICS Mean 7.40 7.39 0.01 0.01 0.50 0.28 0.90 1.32 10.9 Median 7.38 7.38 0.01 0.01 0.51 0.30 0.90 1.33 9.5 Maximum 7.71 7.68 0.04 0.02 0.54 0.34 0.91 1.62 19.4 Minimum 7.14 7.14 -0.02 0.01 0.46 0.23 0.89 0.96 6.6 St. Deviation 0.13 0.13 0.01 0.01 0.02 0.03 0.01 0.17 3.6 Observations 37 36 37 37 37 37 37 37 37 CORRELATIONS y1 INVESTMENT 1.00 Ly1 LAGGED Y1 0.99 1.00 x1 REAL RATES -0.66 -0.64 1.00 x2 RESERVE RATIO -0.27 -0.30 0.41 1.00 x3 LIQUIDITY 0.59 0.57 -0.61 -0.56 1.00 x4 P. BORROWING -0.66 -0.63 0.60 0.36 -0.87 1.00 x5 B. LENDING 0.44 0.56 -0.26 -0.47 0.30 -0.39 1.00 x6 MARKET VALUE 0.10 0.07 0.65 0.21 -0.62 0.47 -0.10 1.00 x8 OIL 0.56 0.53 -0.66 -0.37 0.76 -0.66 0.17 -0.44 1.00 VARIABLES: (y1) Gross fixed investment (logarithm); (Ly1) One lag of y1; (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real dollar price for oil (one-period lag); RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-USA) December 1996 to December 2005 (37 actual quarters, 109 Denton months); SOURCE ALL VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF INVESTMENT AND RATIO COMPONENTS: Dollars, billion

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95 95 Table 7-8. Estimation USA: Aggregate level investmentwith liquidity variable ARIMA [AR (1)]: (1) (2) (3) (4) (1) (2) (3) (4) Actual: Quarterly Denton: Monthly Constant 0.64 0.49 5.21 5.38 0.36 0.32 8.01 9.87 (p-value) (0.48) (0.59) (0.15) (0.14) (0.09) (0.14) (0.14) (0.15) x1 REAL RATES -0.48 -0.37 -6.70 -6.34 -0.09 -0.12 -7.63 -7.31 (p-value) (0.28) (0.39) (0.10) (0.21) (0.43) (0.33) (0.11) (0.25) x2 RESERVE RATIO 1.53 1.73 10.78 10.77 1.17 1.20 11.26 10.53 (p-value) (0.18) (0.16) (0.07) (0.09) (0.19) (0.19) (0.12) (0.11) x3 LIQUIDITY 0.49 0.57 0.66 0.40 0.01 0.02 0.05 0.20 (p-value) (0.21) (0.11) (0.69) (0.80) (0.41) (0.23) (0.66) (0.30) x5 B. LENDING 0.73 0.48 12.71 13.00 0.48 0.39 7.24 7.39 (p-value) (0.39) (0.50) (0.20) (0.13) (0.07) (0.16) (0.23) (0.11) x6 MARKET VALUE 0.04 0.04 0.47 0.46 0.01 0.01 0.16 0.16 (p-value) (0.08) (0.09) (0.12) (0.13) (0.21) (0.37) (0.15) (0.13) x8 OIL 0.01 0.01 0.01 0.01 (p-value) (0.08) (0.33) (0.24) (0.21) Ly1 LAGGED Y1 1.04 1.03 1.01 1.01 (p-value) (0.00) (0.00) (0.00) (0.00) Observations 36 36 36 36 108 108 108 108 Correlation 0.28 0.16 0.16 0.11 0.33 0.35 0.17 0.12 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (logarithm); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real dollar price for oil (one-period lag); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-USA) December 1996 to December 2005 (37 actual quarters, 109 Denton months); SOURCE ALL VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Dollars, billion; ACRONYMS AND ABBREVIATIONS: (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (ARIMA) Autoregressive integrated moving average; DIAGNOSTICS-CORRELATION: Portmanteau Ho = White Noise; All standard errors are adjusted for potential autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating Manual: Concepts, Data Sources, and

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96 96 Table 7-9. Estimation USA: Aggregate level investmentwith private borrowing variable ARIMA [AR (1)]: (1) (2) (3) (4) (1) (2) (3) (4) Actual: Quarterly Denton: Monthly Constant 1.54 1.57 4.47 5.38 0.50 0.45 0.92 1.31 (p-value) (0.08) (0.02) (0.08) (0.09) (0.08) (0.09) (0.57) (0.43) x1 REAL RATES -0.65 -0.66 -0.44 -0.29 -0.07 -0.06 -0.03 -0.14 (p-value) (0.13) (0.12) (0.65) (0.72) (0.49) (0.58) (0.65) (0.74) x2 RESERVE RATIO 3.34 3.34 2.89 1.94 1.19 1.23 2.32 1.91 (p-value) (0.22) (0.24) (0.46) (0.63) (0.31) (0.28) (0.37) (0.47) x4 P. BORROWING -0.67 -0.68 -3.94 -4.18 -0.14 -0.12 -1.79 -2.91 (p-value) (0.01) (0.21) (0.01) (0.00) (0.04) (0.11) (0.03) (0.00) x5 B. LENDING 1.11 1.13 3.96 3.11 0.47 0.43 1.56 1.20 (p-value) (0.10) (0.10) (0.13) (0.22) (0.09) (0.14) (0.21) (0.25) x6 MARKET VALUE 0.02 0.02 0.31 0.31 0.01 0.00 0.12 0.12 (p-value) (0.56) (0.56) (0.09) (0.08) (0.60) (0.66) (0.10) (0.11) x8 OIL 0.01 0.01 0.01 0.01 (p-value) (0.67) (0.41) (0.69) (0.65) Ly1 LAGGED Y1 0.94 0.94 0.99 0.99 (p-value) (0.00) (0.00) (0.00) (0.00) Observations 36 36 36 36 108 108 108 108 Correlation 0.24 0.24 0.18 0.11 0.26 0.29 0.17 0.12 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (logarithm); ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8) Real dollar price for oil (one-period lag); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-USA) December 1996 to December 2005 (37 actual quarters, 109 Denton months); SOURCE ALL VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Dollars, billion; ACRONYMS AND ABBREVIATIONS: (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (ARIMA) Autoregressive integrated moving average; DIAGNOSTICS-CORRELATION: Portmanteau Ho = White Noise; All standard errors are adjusted for potential autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating variable. See IMF manual: Bloem, A., R. Dippelsman, and N. Maehle, 200

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97 Table 7-10. Descriptive statistics and correlation USA: Large firm level y1 Ly1 x1 x2 x3 x4 x5 x6 x7 x8 x9 DESCRIPTIVE STATISTICS Mean 0.03 0.03 0.01 0.01 0.50 0.28 0.90 1.32 1.08 10.90 3915.65 Median 0.01 0.01 0.00 0.01 0.51 0.30 0.90 1.33 0.87 9.55 2330.88 Maximum 6.42 6.42 0.04 0.02 0.54 0.34 0.91 1.62 6.43 19.46 25709.34 Minimum -0.84 -0.84 -0.02 0.01 0.46 0.23 0.89 0.96 0.06 6.60 230.41 St. Deviation 0.34 0.34 0.01 0.00 0.02 0.03 0.00 0.17 0.78 3.55 4172.27 Observations 1036 1008 1036 1036 1036 1036 1036 1036 1036 1036 1008 CORRELATIONS y1 INVESTMENT 1.00 Ly1 LAGGED Y1 -0.10 1.00 x1 REAL RATES 0.06 0.06 1.00 x2 RESERVE RATIO 0.04 0.03 0.42 1.00 x3 LIQUIDITY -0.06 -0.06 -0.62 -0.58 1.00 x4 P. BORROWING 0.06 0.05 0.61 0.38 -0.86 1.00 x5 B. LENDING 0.01 0.03 -0.25 -0.50 0.27 -0.35 1.00 x6 MARKET VALUE 0.05 0.06 0.68 0.20 -0.69 0.55 -0.15 1.00 x7 F. CHARACTERISTIC 0.21 0.07 0.01 0.02 -0.02 -0.01 -0.01 0.03 1.00 x8 OIL -0.05 -0.04 -0.66 -0.39 0.65 -0.65 0.14 -0.50 -0.01 1.00 x9 A. PRINCIPLE 0.02 0.01 -0.14 -0.08 0.13 -0.16 0.06 -0.06 -0.04 0.11 1.00 VARIABLES: (y1) Gross fixed investment (scaled by one(Ly1) One lag of y1; (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets; (x8) Real dollar price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue RESEARCH PERIOD & PERIODIC INTERVALS: (USA-balanced panels) December 1996 to December 2005 (37 actual quarters, 37 Denton quarters); FIRM COUNT (USA): 28; SOURCE DEPENDENT VARIABLES: (large firms-USA): Worldscope, SEC, and Company Financials; SOURCE INDEPENDENT VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF INVESTMENT AND RATIO COMPONENTS: Dollars, billion

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98 Table 7-11. Estimation USA: Large firm level investmentwith liquidity variable GLS [Random Effects]: (1) (2) (3) (4) (5) (6) (7) (8) (1) (2) (3) (4) (5) (6) (7) (8) Actual: Quarterly Denton: Quarterly Constant 0.43 0.31 0.76 0.41 1.26 1.22 2.25 1.28 0.03 0.07 0.16 0.03 0.23 0.22 0.49 0.22 (p-value) (0.47) (0.61) (0.34) (0.50) (0.40) (0.51) (0.50) (0.20) (0.60) (0.67) (0.67) (0.60) (0.23) (0.25) (0.16) (0.24) x1 REAL RATES -0.07 -0.02 -0.09 -0.08 -0.21 -0.16 -0.06 -0.21 -0.64 -0.69 -0.52 -0.64 -0.47 -0.47 -0.40 -0.47 (p-value) (0.69) (0.61) (0.68) (0.66) (0.33) (0.47) (0.65) (0.34) (0.60) (0.65) (0.66) (0.52) (0.43) (0.42) (0.63) (0.39) x2 RESERVE RATIO -0.50 -0.66 -0.13 -0.48 -0.46 -0.44 -1.04 -0.48 -1.50 -1.69 -1.85 -1.50 -0.37 -0.39 -0.79 -0.37 (p-value) (0.61) (0.50) (0.61) (0.63) (0.55) (0.57) (0.37) (0.54) (0.58) (0.50) (0.67) (0.69) (0.52) (0.50) (0.25) (0.52) x3 LIQUIDITY 0.44 0.39 0.51 0.46 0.19 0.15 0.25 0.22 0.51 0.48 0.41 0.51 0.23 0.23 0.27 0.34 (p-value) (0.09) (0.08) (0.09) (0.09) (0.25) (0.38) (0.33) (0.21) (0.10) (0.09) (0.13) (0.10) (0.20) (0.40) (0.27) (0.20) x5 B. LENDING 0.77 0.62 1.15 0.75 1.51 1.45 2.61 1.54 0.41 0.29 0.42 0.41 0.49 0.49 0.67 0.49 (p-value) (0.21) (0.31) (0.15) (0.22) (0.12) (0.06) (0.09) (0.10) (0.12) (0.28) (0.29) (0.12) (0.11) (0.07) (0.06) (0.11) x6 MARKET VALUE 0.01 0.01 0.01 0.01 0.01 0.01 0.03 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 (p-value) (0.69) (0.61) (0.58) (0.62) (0.66) (0.66) (0.60) (0.58) (0.47) (0.53) (0.44) (0.47) (0.61) (0.50) (0.55) (0.50) x7 F. CHARACTERISTIC 0.02 0.02 0.02 0.02 (p-value) (0.00) (0.01) (0.01) (0.01) x8 OIL 0.01 0.01 0.01 0.01 (p-value) (0.18) (0.34) (0.09) (0.36) x9 A. PRINCIPLE 2.83 1.93 2.99 2.00 (p-value) (0.10) (0.19) (0.09) (0.30) Ly1 LAGGED Y1 0.24 0.24 0.35 0.26 0.40 0.41 0.21 0.40 (p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Observations 980 980 980 980 1008 1008 1008 1008 980 980 980 980 1008 1008 1008 1008 CCN collinearity 8.28 9.06 8.28 8.31 7.36 7.36 6.71 6.74 8.31 9.09 6.76 6.76 7.37 7.37 6.76 6.76 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (scaled by one-ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets (one-period lag); (x8) Real dollar price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (USA-balanced panels) December 1996 to December 2005 (37 actual quarters, 37 Denton quarters); FIRM COUNT (USA): 28; SOURCE DEPENDENT VARIABLES: (large firms-USA): Worldscope, SEC, and Company Financials; SOURCE INDEPENDENT VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Dollars, billion; ACRONYMS AND ABBREVIATIONS: (CCN) Collinearity Condition Number; (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (GLS) Generalized Least Squares; DIAGNOSTICS-CORRELATION: heteroskedasticity and autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating variable. See ompilation,

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99 Table 7-12. Estimation USA: Large firm level investmentwith private borrowing variable GLS [Random Effects]: (1) (2) (3) (4) (5) (6) (7) (8) (1) (2) (3) (4) (5) (6) (7) (8) Actual: Quarterly Denton: Quarterly Constant 1.36 1.17 1.60 1.39 1.88 1.82 2.91 1.94 0.70 0.55 0.48 0.71 0.55 0.52 0.66 0.56 (p-value) (0.08) (0.06) (0.07) (0.08) (0.07) (0.06) (0.08) (0.09) (0.07) (0.07) (0.10) (0.09) (0.06) (0.06) (0.06) (0.08) x1 REAL RATES -0.01 -0.05 -0.03 -0.26 -0.02 -0.01 -0.01 -0.02 -0.41 -0.45 -0.39 -0.41 -0.28 -0.28 -0.28 -0.28 (p-value) (0.65) (0.65) (0.61) (0.66) (0.62) (0.67) (0.67) (0.63) (0.50) (0.52) (0.42) (0.48) (0.60) (0.61) (0.65) (0.58) x2 RESERVE RATIO -0.65 -0.43 -1.01 -0.73 -1.14 -1.07 -1.72 -1.22 -8.82 -0.63 -0.79 -1.01 -1.32 -1.37 -0.80 -1.32 (p-value) (0.45) (0.62) (0.40) (0.40) (0.12) (0.16) (0.15) (0.11) (0.51) (0.50) (0.30) (0.41) (0.10) (0.25) (0.15) (0.21) x4 P. BORROWING 0.29 0.25 0.19 0.31 0.21 0.19 0.18 0.23 0.23 0.20 0.16 0.23 0.22 0.11 0.15 0.13 (p-value) (0.23) (0.07) (0.30) (0.12) (0.06) (0.12) (0.33) (0.15) (0.21) (0.09) (0.20) (0.15) (0.08) (0.07) (0.36) (0.13) x5 B. LENDING 1.40 1.22 1.67 1.42 1.99 1.93 3.12 2.04 0.71 0.57 0.48 0.71 0.55 0.53 0.68 0.56 (p-value) (0.12) (0.06) (0.15) (0.09) (0.10) (0.16) (0.09) (0.13) (0.10) (0.06) (0.24) (0.10) (0.11) (0.11) (0.07) (0.19) x6 MARKET VALUE 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.02 (p-value) (0.27) (0.28) (0.23) (0.30) (0.32) (0.33) (0.69) (0.37) (0.30) (0.21) (0.11) (0.32) (0.30) (0.31) (0.52) (0.29) x7 F. CHARACTERISTIC 0.02 0.02 0.02 0.03 (p-value) (0.00) (0.01) (0.01) (0.00) x8 OIL 0.01 0.01 0.01 0.01 (p-value) (0.33) (0.61) (0.26) (0.66) x9 A. PRINCIPLE 1.12 2.14 1.21 2.45 (p-value) (0.07) (0.15) (0.08) (0.21) Ly1 LAGGED Y1 0.25 0.25 0.37 0.26 0.41 0.42 0.21 0.41 (p-value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Observations 980 980 980 980 1008 1008 1008 1008 980 980 980 980 1008 1008 1008 1008 CCN collinearity 7.08 7.85 7.08 7.11 6.95 7.77 6.95 6.99 7.10 7.86 7.10 7.12 6.96 7.77 6.97 6.98 ESTIMATED-DEPENDENT VARIABLE: (y1) Gross fixed investment (scaled by one-ESTIMATORS-INDEPENDENT VARIABLES: (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets (one-period lag); (x8) Real dollar price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (USA-balanced panels) December 1996 to December 2005 (37 actual quarters, 37 Denton quarters); FIRM COUNT (USA): 28; SOURCE DEPENDENT VARIABLES: (large firms-USA): Worldscope, SEC, and Company Financials; SOURCE INDEPENDENT VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Dollars, billion; ACRONYMS AND ABBREVIATIONS: (CCN) Collinearity Condition Number; (p-value) Two-k = 0; MODELS (SEE METHODOLOGY SECTION): (GLS) Generalized Least Squares; DIAGNOSTICS-CORRELATION: r potential panel heteroskedasticity and autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of industrial production is used as an interpolating variable. See IMF manual: Bloem, A., R. Dippelsman, and N. Maehle

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100 100 Table 7-13. Comparative statics Expected relationships to investment: Economies of industrialized and transitional countries Variable (proxy): Expected Relationship: Research Findings: Research Findings: Financial Repression Industrialized & Transitional USA Economy Aggregate: Large firm: Aggregate: Large firm: Aggregate: Large firm: x1REAL RATES (+) (+) (no) (no) (no) (no) x2 RESERVE RATIO (--) (--) (no) (no) (--) (--) x3 LIQUIDITY (+) (+) (no) (no) (no) (--) x4 P. BORROWING (+) (+) (--) (no) (no) (no) x5 B. LENDING (+) (+) (no) (no) (no) (--) x6 MARKET VALUE (+) (+) (no) (no) (no) (+) x7 F. CHARACTERISTIC () () (na) (+) (na) (no) x9 A. PRINCIPLE (+) (+) (na) (no) (na) (no) x8 OIL (--) (--) (no) (no) (no) (no) x10 SEASONAL () () (na) (na) (no) (no) Ly1 LAGGED Y1 () () (+) (+) (no) (no) VARIABLES (DEPENDENT): (aggregate): (y1) Gross fixed private investment; VARIABLES (DEPENDENT): (large firms): (y1) Gross fixed investment (scaled by one-VARIABLES (INDEPENDENT): (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (Russia: money plus (some) quasi-money; USA: standard M2) less M0 (one-period lag); (x3) Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x7) Firm characteristic: Total revenue / Total assets; (x8) Real ruble price for oil (one-period lag); (x9) Acceleration principle: One lag of revenue; (x10) Temperature for seasonal effects (degrees Celsius); (Ly1) One lag of y1; RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-USA) December 1996 to December 2005 (37 actual quarters, 109 Denton months); (aggregate-Russia) December 1998 to December 2005 (29 actual quarters, 85 Denton months); (large firm: USA-balanced panels) December 1996 to December 2005 (37 actual quarters, 37 Denton quarters); FIRM COUNT (USA): 28; (large firm: Russia-unbalanced) December 1998 to December 2005 (29 to 17 Denton quarters); FIRM COUNT (Russia): 40; SOURCE DEPENDENT VARIABLES (aggregate-USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); (aggregate-Russia): Russian Economic Trends database, Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); (large firms-USA): Worldscope, SEC, and Company Financials; (large firms-Russia): Worldscope, Global Reports, and Company Financials; SOURCE INDEPENDENT VARIABLES: (USA) Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); SOURCE INDEPENDENT VARIABLES: (Russia) Russian Economic Trends database; Central Bank of Russia and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Dollars, billion; Rubles, billion; NOTATION: (+) indicates a positive expected relationship, (--) indicates a negative expected relationship, () indicates uncertainty, (no) indicates insignificant findings, and (na) is not applicable; DEFINITIONS: Table Legend pertains to Research Findings; RUSSIA-UNBALANCED LARGE FIRM PANELS: 8 years (29 Denton interpolated quarters): 17 companies; 7 years (25 Denton interpolated quarters): 12 companies; 6 years (21 Denton interpolated quarters): 2 companies; 5 years (17 Denton interpolated quarters): 9 companies.

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101 CHAPTER 8 CONCLUSION the population is engaged in true enterprise. Most real entrepreneurs would happily pay their taxes and fulfill their social responsibility as soon as the government -Ajay Goyal, publisher of the Russia Journal 35 The research presented in this dissertation is consistent with the presence of systemic distortions produced by monetary manipulation through financial repression and a subsidy scheme involving the Central Bank of Russia and the commercial banking system. While the principal purpose of banking in any country should be to provide economic liquidity, and investment intermediation, in countries where the banks are state controlled and financial repression is the norm, this purpose is diverted to satisfy state needs and expenses. Additionally, under the Soviet Union economic and monetary system, the Central Bank controlled investment. Remnants of that system still weave throughout the current Russian structure. The banking industry in Russia still carries the Soviet legacy of government this context of government control over banking, it is not surprising that the combination of financial repression and the Russian subsidy system produces indications of systemic distortion. However, additionally indicated by the results of this research, the actions taken by the large firms in the ENS subsidy network create an extra layer of monetary 35 Notable Quotes, The Russia Journal, http://www.russiajournal.com (accessed November 2003)

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102 102 and economic distortion. There is an obvious advantage the Russian firms have in preserving the network. The subsidy produced through over-invoicing is self-enforcing and mandated by the firms involved. This is in stark contrast to the usual character of subsidies being determined, both in form and extent, by governments. An important and as yet unaddressed contribution would be to extend this research to examine the impacts of the Russian financial system on small firms. The potential for systemic distortion slowing growth in transitional countries stretches beyond the large enterprises to affect the smaller firms essential for future growth. The importance of entrepreneurial development cannot be overstated. In the United States, like Germany and many other economically developed countries, small firms of fewer than 500 employees employ more than one-half of the private-sector workers and produce more than one-half of private-sector output (FED 2002). The traditional funding sources for small firms working in countries with liberalized financial markets include a variety of credit products from an equally wide variety of financial institutions. Of a long list of funding sources available to small firms in the United States, commercial banks leads the list (FED 2002). The importance of commercial banking extends beyond loan issuance because many of the funding sources, like leasing, needs an underlying support structure from banks. In addition, mortgages, leases, and credit cards provide funding for projects. The United States example suggests that a variety of funding sources can contribute to an environment conducive to small firm development; these sources are often missing from countries with developing economies. Besides the lack of traditional external funding sources, business development, as a whole, is doubly hit by constrained internal funding in cash-based economies that often experience severe cash shortages.

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103 103 These financial system constraints are typically produced by financially repressive policies conducted through state-beneficial to large firms, illiquid monetary environments constrain other sources of funding. Furthermore, in addition to the other research validations, this study is important because it supports an infant literature that is beginning to reveal that groups in power, whether governments, monopolies, or dominant families, tend to direct economic policies to maintain their control. Specifically, recent research suggests the large monopoly firms may retard or even intentionally hamper domestic financial markets to the detriment of broad-based economic development. This, as an example, is what Guiso, Sapienza, and Zingales (2004) claim as a possible cause for underdevelopment of large areas throughout Italy 140 years after reunification. Thus, in addition to Russian entrepreneurial development stalling and bank structure revision stagnating 15 years after the Soviet Union collapse, this recent research suggests that the economic damage produced by perennial systemic distortion has the potential for longevity. This potential becomes alarming with the fact that while the Russian monopolistic petroleum sector firms produced twenty-five percent of the 2002 GDP they employ less than one percent of the population, (World Bank 2004).

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APPENDIX A DATA JOURNAL

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105 DATA JOURNAL Variable Name: Description and Source Time and Count: Russia RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate) December 1998 to December 2005 (29 actual quarters, 85 Denton months); RESEARCH PERIOD & PERIODIC INTERVALS: (firm-unbalanced panels) December 1998 to December 2005 (29 to 17 Denton quarters); FIRM COUNT (Russia): 40; RUSSIA-UNBALANCED LARGE FIRM PANELS: 8 years (29 Denton interpolated quarters): 17 companies; 7 years (25 Denton interpolated quarters): 12 companies; 6 years (21 Denton interpolated quarters): 2 companies; 5 years (17 Denton interpolated quarters): 9 companies. Time and Count: USA RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate) December 1996 to December 2005 (37 actual quarters, 109 Denton months); RESEARCH PERIOD & PERIODIC INTERVALS: (firm-balanced panels) December 1996 to December 2005 (37 actual quarters, 37 Denton quarters); FIRM COUNT (USA): 28. Model Variable: Dependent y1INVESTMENT (y1) Aggregate: Gross fixed investment (logarithm); (y1) Firms: Gross fixed investment (scaled by one-SOURCE: (aggregate-Russia) Russian Economic Trends database, and Central Bank of Russia; SOURCE: (firms-Russia) Worldscope and Company Financials; SOURCE: (aggregate-USA) U.S. Department of Commerce: Bureau of Economic Analysis; SOURCE: (firms-USA) Worldscope, SEC, and Company Financials; UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. Model Variable: Independent Ly1LAGGED Y1 (Ly1) One lag of y1; SOURCE: (aggregate-Russia) Russian Economic Trends database, and Central Bank of Russia; SOURCE: (firms-Russia) Worldscope and Company Financials; SOURCE: (aggregate-USA) U.S. Department of Commerce: Bureau of Economic Analysis; SOURCE: (firms-USA) Worldscope, SEC, and Company Financials; UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion.

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106 Variable Name: Description and Source x1REAL RATES (x1) Real interest rates: Nominal annual on bank deposits adjusted for realized annual inflation (one-period lag); SOURCE (Russia): Russian Economic Trends database; Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); SOURCE (USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS). x2RESERVE RATIO (x2) Reserve ratio: Bank reserves / M2 (Russia: money plus (some) quasi-money; USA: standard M2) less M0 (one-period lag); SOURCE (Russia): Russian Economic Trends database; Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); SOURCE (USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. x3LIQUIDITY (x3) Liquidity: Short-term liquid liabilities M2 / GDP (one-period lag); SOURCE (Russia): Russian Economic Trends database; Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); SOURCE (USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. x4P. BORROWING (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); SOURCE (Russia): Russian Economic Trends database; Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); SOURCE (USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion.

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107 Variable Name: Description and Source x5B. LENDING (x5) Bank lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); SOURCE (Russia): Russian Economic Trends database; Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); SOURCE (USA): Federal Reserve, U.S. Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics (BLS); UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. x6MARKET VALUE (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); SOURCE (Russia): Russian Economic Trends database, and the Russian Trading System (RTS); SOURCE (USA): New York Stock Exchange (NYSE); UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. x7F. CHARACTERISTIC (x7) Firm characteristic: Total revenue / Total assets (one-period lag); SOURCE (firms-Russia): Worldscope and Company Financials; SOURCE (firms-USA): Worldscope, SEC, and Company Financials; UNIT OF RATIO COMPONENTS: Rubles, or Dollars, respectively, billion. x8OIL (x8) Real price for oil (one-period lag); SOURCE: U.S. Department of Energy; UNIT: Rubles, or Dollars, respectively, per bbl. x9ACCELERATION PRINCIPLE (x9) Acceleration principle: One lag of revenue; SOURCE (firms-Russia): Worldscope and Company Financials; SOURCE (firms-USA): Worldscope, SEC, and Company Financials; UNIT: Rubles, or Dollars, respectively, billion. x10SEASONAL (x10) Temperature for seasonal effects: Average of three averages (Moscow, Saint Petersburg, Nizhny SOURCE: Space Monitoring station #26063, Nizhny Novgorod station #27459); UNIT: Degrees Celsius; CALCULATION: Author.

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108 Glossary: Gross Domestic Product GDP-Russia & USA: Measure of total economic activity within the national borders. Industrial Production IP-Russia & USA: Index measuring output of factories, mines, and utilities within the national borders Bank reserves Commercial Banks-deposited in the CBR (balances on correspondent accounts, required reserves, deposits, investments in the CBR bonds); Commercial Banks-USA: Deposits held in accounts at the Federal Reserve plus vault cash held at the banks. Claims on private sector Commercial Banks-Russia: Credits (including debt outstanding) plus the following: deferred interest on credits extended to non-financial nongovernmental enterprises (self-employed individuals and households), and credit institutions' investments in securities issued by private sector enterprises; Commercial Banks-USA: Credit from the financial system to individuals, and enterprises. Deposit bank assets (DBA) Commercial Banks-Russia & USA: Reserves and cash plus the following: Government securities, loans, other assets (physical capital). Central bank assets Central bank total assets-Russia: Precious metals, funds and securities denominated in foreign currency allocated to nonresidents, credits and deposits, of which: credits extended to resident credit institutions, for servicing foreign public debt, securities, of which: Russian Federation Government securities, other assets and fixed assets; Central bank total assets-USA: Government securities plus the following: Discount loans, gold and special drawing rights (SDR) certificate accounts, coin, cash items in process of collection, other assets and fixed assets.

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109 Domestic credit Domestic creditbonds. Interest rate on deposit Interest rates-Russia: Gross (excluding Sberbank) deposit rates because Sberbank has subsidized the interest rate offered to its depositors since Jan 2001; Interest rates-USA: Federal funds rate is the overnight loan rate between banks. M2 Broad Money-Russia: M0 plus demand deposits in rubles and some quasi-money (see listing below); Broad Money-USA: M1 plus the following: Savings deposits (including money market deposit accounts), small-denominations (under $100,000) time deposits issued by financial institutions, and shares in retail noninstitutional money market mutual funds (funds with initial investments under $50,000). M1 Narrow Money-USA: The sum of currency held outside the vaults of depository institutions, the Federal Reserve Banks, and the U.S. Treasury plus the following: Travdeposits issued by financial institutions (except demand deposits due to the Treasury and depository institutions) minus cash items in process of collection and Federal Reserve float. M0 Narrow Money-Russia: Comprises currency in circulation-held outside banks. Quasi-money Catchall on the CBR financials for the documents outside the usual balance sheet currency liability items including all forms of surrogate money (veksels, zachety): (1) Russian civil code allows debts (including tax debts) to be traded for non-cash items (goods, services, offsets) resulting in an expansion of the non-monetary system (Commander and Mumssen 2002), (2) While some quasi-money is captured in the official M2, used in this research, the total money measure calculated by the monetary survey methodology is consistently greater by approximately 15%. Stock market cap. Russia & USA: Total value of all listed stocks.

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110 APPENDIX B DATA: LARGE FIRMSRUSSIA AND UNITED STATES Table B-1. Data: Large firmRussia Russia Firms Panel Data Financial Reports: Unbalanced Bashkirenergo 1998-2005 (8 years) Gazprom 1998-2005 (8 years) MMC Norilsk Nickel 1998-2005 (8 years) Lukoil 1998-2005 (8 years) Mosenergo 1998-2005 (8 years) Novolipetsk Metallurgical Factory 1998-2005 (8 years) Rostelekom 1998-2005 (8 years) Sibneft-Siberian Oil 1998-2005 (8 years) Slavneft-Megionneftegaz 1998-2005 (8 years) Surgutneftegas 1998-2005 (8 years) Unified Energy Ststem of Russia 1998-2005 (8 years) Uralsvyazinform 1998-2005 (8 years) Vimpel-Communications 1998-2005 (8 years) Severstal 1998-2005 (8 years) Aeroflot 1998-2004 (8 years) Lenenergo 1998-2004 (8 years) Sibirtelecom 1999-2005 (8 years) Irkutskenergo 1998-2004 (7 years) Kamaz 1998-2004 (7 years) Krasnoyarskenergo 1998-2004 (7 years) Mobile Telesystems 1998-2004 (7 years) Moscow City Telephone Network 1998-2004 (7 years) North-West Telecom 1998-2004 (7 years) Primorsk Shipping 1998-2004 (7 years) Tatneft 1998-2004 (7 years) Trade House Gum 1998-2004 (7 years) Avtovaz 1998-2004 (7 years) Transneft 1999-2005 (7 years) Irkut 2000-2005 (6 years) Alrosa Company Ltd 1999-2004 (6 years) Sverdlovenergo 1998-2002 (5 years) Buryatzoloto 1998-2002 (5 years) NK Yukos 1998-2002 (5 years) Baltika Brewery 2001-2005 (5 years) Center Telecom 2000-2004 (5 years) Concern Kalina 2000-2004 (5 years) Wimm-Bill-Dann Foods 2001-2005 (5 years) Southern Telecommunications 2000-2004 (5 years) OMZ {was United Heavy Machinery} 2000-2004 (5 years)

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111 111 Table B-2. Data: Large Firms USA. Firms Panel Data Financial Reports: Balanced Annual 1996-2005; Quarterly 1996q4-2005q4 Alcoa Altria American Express American International Group Boeing Caterpillar Coca-Cola Disney DuPont Exxon Mobil General Electric General Motors Hewlett-Packard Home Depot Honeywell International IBM Intel Johnson & Johnson Merck Microsoft Pfizer Procter & Gamble AT&T Corp 3M United Technologies Verizon Wal-Mart

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114 114 Daniels, J., and D. VanHoose, 2002, International Monetary and Financial Economics. South-Western, Cincinnati. DeGregorio, J., and P. Guidotti, 1992, Financial Development and Economic Growth, IMF Working Paper Series 92/101. Demirg-Kunt, A., and E. Detragiache, 1998, Financial Liberalization and Financial Fragility, 1998 World Bank Annual Conference on Development Economics. Demirg-Kunt, A., and V. Maksimovic, 1998, Law, Finance, and Firm Growth, The Journal of Finance 53, 2107-2137. Demirg-Kunt, A., and V. Maksimovic, 2002, Funding growth in bank-based and market-based financial systems: evidence from firm-level data, Journal of Financial Economics 65, 337-363. Desai, M., C. Foley, and K. Forbes, 2004, Financial Constraints and Growth: Multinational and Local Firm Responses to Currency Crises, NBER Working Paper No. 10545. Dobrovolsky, S., 1958, Economics of Corporate Internal and External Financing, The Journal of Finance 13, 35-47. Driscoll, J., 2004, Does Bank Lending Affect Output? Evidence from the U.S. States, Journal of Monetary Economics 51, 451-471. Edison, H., M. Klein, L. Ricci, and T. Sloek, 2004, Capital Account Liberalization and Economic Performance: Survey and Synthesis, IMF Staff Papers 51, 220-256. Fauver, L., J. Houston, and A. Naranjo, 2003, Capital Market Development, International Integration, Legal Systems, and the Value of Corporate Diversification: A Cross-Country Analysis, Journal of Financial and Quantitative Analysis 38 No. 1. Fazzari, S., R. Hubbard, B. Petersen, 1988, Financing Constraints and Corporate Investment, Brookings Papers on Economic Activity 1988, 141-206. Fazzari, S., and B. Petersen, 1993, Working Capital and Fixed Investment: New Evidence on Financing Constraints, The RAND Journal of Economics 24, 328-342. FED, 2002, Report to Congress on the Availability of Credit to Small Businesses. Fischer, S., R. Sahay, and C. Vegh, 1996, Stabilization and Growth in Transition Economies: The Early Experience, The Journal of Economic Perspectives 10, 45-66.

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115 115 Fischer, S., R. Sahay, and C. Vegh, 2002, The Transition Economies After Ten Years, IMF Working Paper Series. Fisman, R., and I. Love, 2004, Financial Development and Growth in the Shortand Long-Run, World Bank Working Paper Series No. 3319. Gaddy, C., and B. Ickes, 1998, Russia's Virtual Economy, Foreign Affairs 77, 53-67. Galindo, A., A. Micco, and G. Ordoez,, 2002, Financial Liberalization and Growth: Empirical Evidence, Inter-American Development Bank Working Paper. Galindo, A., F. Schiantarelli, and A. Weiss, 2003, Does Financial Liberalization Improve the Allocation of Investment? Micro Evidence from Developing Countries, Inter-American Development Bank Working Paper. Greene, W., 2000, Econometric Analysis. Prentice-Hall, Upper Saddle River. Gregory, P., and R. Stuart, 1980, Comparative Economic Systems. Houghton Mifflin, Boston. Guiso, L., P. Sapienza, L. Zingales, 2004, Does Local Financial Development Matter? Quarterly Journal of Economics 929-969. Harrison, A., I. Love, and M. McMillan, 2004, Global Capital Flows and Financing Constraints, Journal of Development Economics 75, 269-301. Himmelberg, C., and B. Petersen, 1994, R&D and Internal Finance: A panel Study of Small Firms in High-Tech Industries, The Review of Economics and Statistics 76, 38-51. Honohan, P., 1998, How Interest Rates Changed under Financial Liberalization: A Cross-Country Review, World Bank Working Paper Series. Hsiao, C, 1986, Analysis of Panel Data. Cambridge University Press, Cambridge. Hsiao, C., and A. Tahmiscioglu, 1997, A Panel Analysis of Liquidity Constraints and Firm Investment, Journal of the American Statistical Association 92, 455-465. Hubbard, R., A. Kashyap, and T. Whited, 1995, Internal Finance and Firm Investment, Journal of Money, Credit and Banking 27, 683-701. Kaplan, S., and L. Zingales, 1997, Do Investment-Cash Flow Sensitivities Provide Useful Measures of Financing Constraints, The Quarterly Journal of Economics 112, 169-215.

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116 116 Kashyap, A., O. Lamont, and J. Stein, 1994, Credit Conditions and the Cyclical Behavior of Inventories, The Quarterly Journal of Economics 109, 565-592. Kashyap, A., and J. Stein, 1994, Monetary Policy. University of Chicago Press, Chicago. Kashyap, A., and J. Stein, 2000, What Do a Million Observations on Banks Say About the Transmission of Monetary Policy? The American Economic Review 90, 407-428. King, R., and R. Levine, 1993, Finance and Growth: Schumpeter Might be Right, The Quarterly Journal of Economics 108, 717-737. Laeven, L., 2001, Insider Lending and Bank Ownership: The Case of Russia, Journal of Comparative Economics 29, 207-229. Laeven, L., 2003, Does Financial Liberalization Reduce Financing Constraints? Financial Management 32. LaPorta, R., F. Lopez-De-Silanes, and A. Shleifer, 2002, Government Ownership of Banks, The Journal of Finance 57, 265-301. Levine, R., 1997, Financial Development and Economic Growth: Views and Agenda, Journal of Economic Literature 35, 688-726. Levine, R., N. Loayza, and T. Beck, 2000, Financial Intermediation and Growth: Causality and Causes, Journal of Monetary Economics 46, 31-77. Love, I., 2003, Financial Development and Financing Constraints: International Evidence from the Structural Investment Model, Review of Financial Studies 16, 765-791. Love, I., and L. Zicchino, 2002, Financial Development and Dynamic Investment Behavior: Evidence From Panel Vector Autoregression, World Bank Working Paper Series. McKinnon, R., 1973, Money and Capital in Economic Development, The Brookings Institution. McKinnon, R., 1994, Financial Growth and Macroeconomic Stability in China, 1978-1992: Implications for Russia and Other Transitional Economies, Journal of Comparative Economics 18, 438-469. Mishkin, F., 2003, The Economics of Money, Banking, and Financial Markets. Addison Wesley, New York. Perotti, E., 2003, Lessons from the Russian Meltdown: The Economics of Soft Legal Constraints, International Finance 5, 359-399.

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117 117 Pinto, B., V. Drebentsov, and A. Morozov, 2001, Dismantling Russia's Nonpayments System Creating Conditions for Growth, World Bank Working Paper Series. Rajan, R., and L. Zingales, 1998, Financial Dependence and Growth, The American Economic Review 88, 559-586. Rose, P., 2000, Money and Capital Markets: Financial Institutions and Instruments in a Global Marketplace. Irwin McGraw-Hill, New York. Roubini, N., and X. Sala-i-Martin, 1995, A Growth Model of Inflation, Tax Evasion, and Financial Repression, Journal of Monetary Economics 35, 275-301. Schaffer, M., 1995, Government Subsidies to Enterprises in Central and Eastern Europe: Budgetary Subsidies and Tax Arrears, in D. Newbery, ed.: Tax and Benefit Reform in Central and Eastern Europe. Center for Economic Policy Research, London. Schiantarelli, F., 1996, Financial Constraints and Investment: Methodological Issues and International Evidence, Oxford Review of Economic Policy 12, 70-89. Shaw, E, 1973, Financial Deepening in Economic Development. Oxford University Press, New York. Smith, R., 1998, Banking Competition and Macroeconomic Performance, Journal of Money, Credit and Banking 30, 793-815. Stiglitz, J., 1999, Reforming the Global Economic Architecture: Lessons from Recent Crises, Journal of Finance 54, 1508-1521. Tompson, W., 2004, Banking Reform in Russia: Problems and Prospects, OECD Econ. Dept. Working Paper No. 410. Waite, D, 1973, The Economic Significance of Small Firms, The Journal of Industrial Economics 21, 154-166. Whited, T., 1992, Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data, The Journal of Finance 47, 1425-1460. World Bank, 1996, World Development Report: From Plan to Market. Oxford University Press, New York. World Bank, 2004, Russian Economic Report. World Bank, Moscow. Wurgler, J., 2000, Financial Markets and the Allocation of Capital, Journal of Financial Economics 58, 187-214.

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118 BIOGRAPHICAL SKETCH Using experience in business and professional management strategy along with advanced degrees obtained from the University of Florida, a BA in economics (honors), and an MS in finance, M. Yvonne Reinertson has been able to launch her hobby of watching successful companies develop into a career. One additional experience has allowed her to take her hobby to the international level. From living abroad for a year in Nizhny Novgorod, Russia, Ms. Reinertson was able to watch financial markets and business tactics develop from within a transitional country.


Permanent Link: http://ufdc.ufl.edu/UFE0019700/00001

Material Information

Title: Financial repression and subsidies : capital investment in Russia, December 1998 to December 2005
Physical Description: Mixed Material
Language: English
Creator: Reinertson, M. Yvonne ( Dissertant )
Naranja, Andy ( Thesis advisor )
Crum, Roy ( Thesis advisor )
Nimalendran, Mahendrara ( Reviewer )
Beilock, Richard ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007
Copyright Date: 2007

Subjects

Subjects / Keywords: Business Administration Thesis, Ph.D.
Dissertations,Academic -- UF -- Business Administration
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
theses   ( marcgt )
Spatial Coverage: Russia

Notes

Abstract: The purpose of this dissertation is to assess potential systemic distortions on investment from governmental actions of (1) financial repression, and (2) subsidies in Russia. The extent to which the Russian government engages in policies that, in turn, stifle investments by firms and other market participants is explored using six individual proxies (real interest rates, reserve ratio, liquidity, private borrowing, bank lending, and stock market valuation), which are components of the Beim and Calomiris financial repression index. Then, subsidy based distortions, theorized in the work of Bernstam and Rabushka are examined using the results produced by the financial repression study. Using datasets on both aggregate and firm-level investments in Russia‟s economy, investment is shown to have a consistently negative association with an increase in the reserve requirement for both aggregate and large firm investments. Additionally, and specific to Russian large firms, the data show liquidity and bank lending are inversely related to capital investment changes. This last result is consistent with the existence of a subsidy system in Russia producing additional distortions to those created by the governmental use of financial repression. The principal objective of banking in any country should be to provide economic liquidity and investment intermediation. However, in countries where the banks are state controlled and financial repression is the norm, this objective becomes secondary to satisfying state budgetary and political goals. This appears to be the case in Russia. Moreover, the effects of financial repression are magnified by a subsidy system largely controlled by the country‟s largest firms. Not surprisingly, there is strong evidence of systemic distortions on investments. The extent to which investments in Russia are influenced by these effects are highlighted through comparisons with firms in the United States. While this research work is specific to Russia, its implications are not. This examination of financial repression produced distortions addresses an ongoing concern for many countries, firms, and governmental institutions around the world. Advisors responsible for responsible for designing policy to enhance global economic growth have an interest in determining the governmental actions that hamper investment. In many nations, primarily those which are underdeveloped or in transition, governments use repressed domestic financial systems to fund the state at the expense of private enterprise development.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi 188 p.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0019700:00001

Permanent Link: http://ufdc.ufl.edu/UFE0019700/00001

Material Information

Title: Financial repression and subsidies : capital investment in Russia, December 1998 to December 2005
Physical Description: Mixed Material
Language: English
Creator: Reinertson, M. Yvonne ( Dissertant )
Naranja, Andy ( Thesis advisor )
Crum, Roy ( Thesis advisor )
Nimalendran, Mahendrara ( Reviewer )
Beilock, Richard ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007
Copyright Date: 2007

Subjects

Subjects / Keywords: Business Administration Thesis, Ph.D.
Dissertations,Academic -- UF -- Business Administration
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
theses   ( marcgt )
Spatial Coverage: Russia

Notes

Abstract: The purpose of this dissertation is to assess potential systemic distortions on investment from governmental actions of (1) financial repression, and (2) subsidies in Russia. The extent to which the Russian government engages in policies that, in turn, stifle investments by firms and other market participants is explored using six individual proxies (real interest rates, reserve ratio, liquidity, private borrowing, bank lending, and stock market valuation), which are components of the Beim and Calomiris financial repression index. Then, subsidy based distortions, theorized in the work of Bernstam and Rabushka are examined using the results produced by the financial repression study. Using datasets on both aggregate and firm-level investments in Russia‟s economy, investment is shown to have a consistently negative association with an increase in the reserve requirement for both aggregate and large firm investments. Additionally, and specific to Russian large firms, the data show liquidity and bank lending are inversely related to capital investment changes. This last result is consistent with the existence of a subsidy system in Russia producing additional distortions to those created by the governmental use of financial repression. The principal objective of banking in any country should be to provide economic liquidity and investment intermediation. However, in countries where the banks are state controlled and financial repression is the norm, this objective becomes secondary to satisfying state budgetary and political goals. This appears to be the case in Russia. Moreover, the effects of financial repression are magnified by a subsidy system largely controlled by the country‟s largest firms. Not surprisingly, there is strong evidence of systemic distortions on investments. The extent to which investments in Russia are influenced by these effects are highlighted through comparisons with firms in the United States. While this research work is specific to Russia, its implications are not. This examination of financial repression produced distortions addresses an ongoing concern for many countries, firms, and governmental institutions around the world. Advisors responsible for responsible for designing policy to enhance global economic growth have an interest in determining the governmental actions that hamper investment. In many nations, primarily those which are underdeveloped or in transition, governments use repressed domestic financial systems to fund the state at the expense of private enterprise development.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi 188 p.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0019700:00001


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FINANCIAL REPRESSION AND SUBSIDIES:
CAPITAL INVESTMENT IN RUSSIA, DECEMBER 1998 TO DECEMBER 2005


















By

M. YVONNE REINERTSON


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2007






























2007 M. Yvonne Reinertson



























This dissertation is dedicated to the memory of my mother:
A kinder spirit is not possible.















ACKNOWLEDGMENTS

I am grateful for the guidance of my Chair, Dr. Andy Naranjo, and my Cochair, Dr.

Roy Crum, who made possible my experiences in Russia. I also thank my other

committee members, Dr. Mahendrara Nimalendran, and Dr. Richard Beilock.

Additionally, I would like to thank the following individuals: David Beim, Michael

Bernstam, Charles Calomiris, and Alvin Rabushka. The Russian data used in this

analysis would not have been available without the help of the following Russian friends:

Andrei Betin, Victor Matiashvili, and Roman Vvedensky. I am greatly in their debt.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST O F TA B LE S .............................. .... .. .................... .... .... ............. viii

LIST OF FIGURES ......... ............................... ........ ............ ix

A B S T R A C T ........................................... ... ......................... ................ .. x

CHAPTER

1 IN TROD U CTION ............................................... .. ......................... ..

1.1 M measuring Financial R epression....................................... .......................... 3
1.2 Enterprise N etw ork Socialism ........................................ .......................... 4
1.3 Focus of This W ork ................. .... ...... .................... .. ....... .......... ...... .

2 DIFFERENCES BETWEEN RUSSIA AND THE UNITED STATES...................6

2.1 Economic, Banking, and Payment System Structures: Russia versus United
States .............. ... .... ..... ......... ..... ................................. 6
2.1.1 Differences: Economic Structure .......................... .... ....................7
2.1.2 Differences: Central Bank Monetary Policy Channels .............................9
2.1.3 Differences: Central Bank Balance Sheet .............. ................................. 13
2.1.4 D differences: Paym ent System ............................................... ................ 14
2.2 Economic, Banking, and Payment System Structures: Role of Gazprom............16

3 L ITE R A TU R E R E V IE W ........................................ ............................................24

3.1 Financial Repression on Investment: Chronological Changes ...........................25
3.1.1 Literature Review: Pre-1998-Default ............. ..........................26
3.1.2 Literature Review : Post-1998-Default .................................................... 28
3.2 Liquidity Constraints on Investment: Government Control of Banking ..............30
3.3 Monetary Policy Transmission on Investment: Primary Channels ....................31
3.3.1 M oney C channel ................................... ................... ......... 32
3.3.2 B ank-L ending C hannel......................................................... ................ .... 32
3.4 Financing Constraints on Investment: Internal versus External Financing ..........33
3.5 Russian G overnm ental Subsidies ........................................ ...... ............... 38









4 TESTABLE HYPOTHESES ............................ .............. ............... 40

4.1 H ypothesis: Financial Repression.................................... ......................... 40
4.2 Hypothesis: Enterprise Network Socialism .......... ............ .......................42
4.3 System ic D distortions: Gains Possible ....................................... ............... 42
4.4 Analysis: General and Specific Questions ............... ...... ... ......................... 46

5 D A T A ................................................................................................................... 4 9

5.1 D descriptions: V ariables............................................... .............................. 49
5.2 Sources: D ata ................................ ... ................ ........ .... .................50
5.2.1 Russia: Financial Repression Measures and Aggregate Level Capital
Investm ent .....................................................50
5.2.2 Russia: Large Firm Level Capital Investment....................... ................51
5.2.3 USA: Financial Repression Measuresand Aggregate Level Capital
Inv estm ent ....................................... ............... .... ................ ... ..... 5 1
5.2.4 USA: Large Firm Level Capital Investment ...........................................52

6 M E T H O D O L O G Y ............................................................................ ................... 54

6.1 Model Notation Conventions................................................. 56
6.2 Generalized Assumptions: Tested Per Analysis .............. ............................58
6.3 Methodology-Time Series for Aggregate Level Investment: ARIMA [AR(1)
M o d e l] ..................................................................... ............................................ ...5 9
6.4 Methodology-Panel Data for Firm Level Investment: GLS [Random-Effects
M odel] ..................................... ... ................. ... ....................... .60
6.5 Methodology-Increase Data Interval Frequency: Proportional Denton Method .61

7 R E S U L T S .......................................................................... 6 3

7.1 Aggregate Level Investment-Russia: Descriptive Statistics and Correlations.....63
7.2 Aggregate Level Investment-Russia: Time Series Results............... ................65
7.2.1 Results with Liquidity Variable-Russia: ARIMA [AR(1)]........................65
7.2.2 Results with Private Borrowing Variable-Russia: ARIMA [AR(1)] .........66
7.3 Large Firm Level Investment-Russia: Descriptive Statistics and Correlations ...67
7.4 Large Firm Level Investment-Russia: Panel Data Results ................................69
7.4.1 Results with Liquidity Variable-Russia: GLS [Random-Effects] .............69
7.4.2 Results with Private Borrowing Variable-Russia: GLS [Random-
E ffe cts] .................................................. ............... ...... .................... 7 4
7.5 Aggregate Level Investment-USA: Descriptive Statistics and Correlations .......76
7.6 Aggregate Level Investment-USA: Time Series Results..................................77
7.6.1 Results with Liquidity Variable-USA: ARIMA [AR(1)] ........................77
7.6.2 Results with Private Borrowing Variable-USA: ARIMA [AR(1)] ..........78
7.7 Large Firm Level Investment-USA: Descriptive Statistics and Correlations......79
7.8 Large Firm Level Investment-USA: Panel Data Results...................................80
7.8.1 Results with Liquidity Variable-USA: GLS [Random-Effects] ..............80
7.8.2 Results with Private Borrowing Variable-USA: GLS [Random-Effects] .82









7.9 C om parative Statics ............. .................................................. ................... 83
7.10 Results: General and Specific Questions.......................................................83

8 CON CLU SION .................. ............ ................ ........... .. ............ 101

APPENDIX

A D A T A JO U R N A L ......... .................................. ................................................... 105

B DATA: LARGE FIRMS-RUSSIA AND UNITED STATES ...............................110

L IST O F R E FE R E N C E S ...................................................................... ..................... 112

BIOGRAPHICAL SKETCH ........................................................................118
















LIST OF TABLES


Table p

5-1 V ariab le D definition s ........................................................................ ...................53

7-1 Descriptive statistics and correlation Russia: Aggregate level .............................88

7-2 Estimation Russia: Aggregate level investment-with liquidity variable .................89

7-3 Estimation Russia: Aggregate level investment-with private borrowing variable..90

7-4 Descriptive statistics and correlation Russia: Large firm level ...........................91

7-5 Estimation Russia: Large firm level investment-with liquidity variable ..............92

7-6 Estimation Russia: Large firm level investment-with private borrowing
v a ria b le ........................................................................... 9 3

7-7 Descriptive statistics and correlation USA: Aggregate level .................................94

7-8 Estimation USA: Aggregate level investment-with liquidity variable ..................95

7-9 Estimation USA: Aggregate level investment-with private borrowing variable ..96

7-10 Descriptive statistics and correlation USA: Large firm level ................................97

7-11 Estimation USA: Large firm level investment-with liquidity variable ...................98

7-12 Estimation USA: Large firm level investment-with private borrowing variable ....99

7-13 Comparative statics Expected relationships to investment: Economies of
industrialized and transitional countries...................................... ............... 100

B-1 D ata: Large firm Russia Russia............. ......... .............................. ............... 110

B -2 D ata: Large Firm s U SA ...................... ..... ...... ........................... ............ 111
















LIST OF FIGURES


Figure p

2-1 Monetary Measures from the Monetary Survey (Rubles; billion) .........................18

2-2 Monetary Measures: M2 (broad) and MO (narrow) from the CBR; Money and
Quasi-money from the Monetary Survey (Rubles; billion)...................................19

2-3 Monetary Survey Components and CBR Total Liability (Rubles; billion) .............20

2-4 Sum of Monetary Survey Components with CBR Total Liability (Rubles;
b illio n ) ..............................................................................2 1

2-5 Ratio of Monetary Survey Components to CBR Total Liability .............................22

2-6 Relationship Diagram of the Enterprise Network Socialism .............................. 23

4-1 Enterprise Receivables to M 2 ............................................................................ 47

4-2 Enterprise R eceivables to M ............................................................................... 48

7-1 Monetary Measures: M2 (broad) and Ml (narrow) from the Federal Reserve........86

7-2 Potential Crowding Out: Private Credit and Total USA Credit.............................86

7-3 Ratio of Private Credit to Total Credit .............................. .....................87

7-4 Industrial Production to GDP ..................................................... ...................87















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

FINANCIAL REPRESSION AND SUBSIDIES:
CAPITAL INVESTMENT IN RUSSIA, DECEMBER 1998 TO DECEMBER 2005

By

M. Yvonne Reinertson

May 2007

Chair: Andy Naranjo
Cochair: Roy L. Crum
Major: Business Administration

The purpose of this dissertation is to assess potential systemic distortions on

investment from governmental actions of (1) financial repression, and (2) subsidies in

Russia. The extent to which the Russian government engages in policies that, in turn,

stifle investments by firms and other market participants is explored using six individual

proxies (real interest rates, reserve ratio, liquidity, private borrowing, bank lending, and

stock market valuation), which are components of the Beim and Calomiris financial

repression index. Then, subsidy based distortions, theorized in the work of Bernstam and

Rabushka are examined using the results produced by the financial repression study.

Using datasets on both aggregate and firm-level investments in Russia's

economy, investment is shown to have a consistently negative association with an

increase in the reserve requirement for both aggregate and large firm investments.

Additionally, and specific to Russian large firms, the data show liquidity and bank









lending are inversely related to capital investment changes. This last result is consistent

with the existence of a subsidy system in Russia producing additional distortions to those

created by the governmental use of financial repression.

The principal objective of banking in any country should be to provide economic

liquidity and investment intermediation. However, in countries where the banks are state

controlled and financial repression is the norm, this objective becomes secondary to

satisfying state budgetary and political goals. This appears to be the case in Russia.

Moreover, the effects of financial repression are magnified by a subsidy system largely

controlled by the country's largest firms. Not surprisingly, there is strong evidence of

systemic distortions on investments. The extent to which investments in Russia are

influenced by these effects are highlighted through comparisons with firms in the United

States.

While this research work is specific to Russia, its implications are not. This

examination of financial repression produced distortions addresses an ongoing concern

for many countries, firms, and governmental institutions around the world. Advisors

responsible for designing policy to enhance global economic growth have an interest in

determining the governmental actions that hamper investment. In many nations, primarily

those which are underdeveloped or in transition, governments use repressed domestic

financial systems to fund the state at the expense of private enterprise development.














CHAPTER 1
INTRODUCTION

"From the viewpoint of macrostructure, the Russian economy is strongly monopolized
through its backbone companies and also Sberbank [nationalized savings bank], which is
a monopoly [monopsony as a buyer of household deposits] in its sector. As a result, in
every sector, there is little room for competition, and, hence, the economic growth rate is
far below its potentially achievable level."

-Oleg Vyugin, former first deputy chairman of the Central Bank1

Potential economic distortion through government manipulation of monetary

policy, banking intermediation, and financial markets concerns all parties interested in

the growth of transitional and developing countries. Assessing the reality of such

assertions is one of this dissertation's two main goals. This will be investigated through

development of financial repression measures for Russia, using both macroeconomic and

microeconomic data (Beim and Calomiris 2001). Building on these results, the second

goal will be addressed by seeking evidence on the possible existence and effects

produced by an enterprise-driven subsidy system called "Enterprise Network Socialism"

(ENS) by Bernstam and Rabushka (1998, 2006).

Financial repression can be loosely defined as governmental actions, either direct

or indirect, taken to restrict or alter the flow-of-funds within an economy. A monopolized

or nationalized banking structure, in Russia's case Sberbank, may control both money

stocks and flows. At the start of 2003, state controlled banks in Russia, with Sberbank

being the largest, accounted for "...72 percent of retail deposits, 34 percent of capital, 38



1 Notable Quotes, The Russia Journal, hip \\ \\ %\ .russiajournal.com (accessed November 2003)









percent of assets and 39 percent of credit outstanding to the non-financial private sector"

(Tompson 2004). From the government's perspective, a potential benefit of this control is

Sberbank funneling banking deposits into the Russian government treasury. To the

extent that Sberbank's assets are directed into government securities, fewer funds are left

from Sberbank to flow into the private sector.

Thus, while the death of the Soviet Union and the following industrial

privatizations removed the extreme form of central control and reduced the monopolistic

structure in many industries, it did not remove tight governmental ties to the banking

industry. Concerning the banking structure and its effect on industrial Russia, Bernstam

and Rabushka (1998, p. 52) have this to say:

The banking system-a winding maze of borrower ownership of banks, insider
lending, rollover of bad loans, misallocation of credit, lack of competitive credit
markets, and lack of long-term investment and credit-impeded the development of
the new private sector... Misallocation of credit and depletion of real deposits
deprived productive users of credit and investment. A vicious circle developed
that perpetuated bad credit, reinforced financial repression, and depressed the real
sector. Most emerging private firms were forced to self-finance or organize
informal arrangements with individuals.2


While this research is specific to Russia, its implications are not. In many nations,

primarily those which are underdeveloped or in transition, governments use repressed

domestic financial systems to fund the state at the expense of private enterprise

development. Ostensibly, the monetary controls that produce financial repression within a

country are intended to facilitate realization of state goals, which may include programs

that are intended to advance legitimate social and economic causes, such as regional




2 In addition to their book, Bemstam and Rabushka maintain an Web site of their research at
www.russiaeconomy.org









development. However, the controls limit the abilities for both financial institutions and

financial markets to optimally allocate funds to private industry.

1.1 Measuring Financial Repression

The extent to which governments engage in financially repressive policies that in

turn stifle investments by firms and other market participants is an ongoing concern in

many countries. Historically, the existence and extent of financial repression was gauged

by the existence and degree to which real interest rates were negative. The definition of

what constitutes financial repression has expanded to include an array of governmental

actions that preserve tight control of both money stocks and flows and repress the

development of financing sources other than the state controlled banks. These actions

include placing ceilings on deposit interest rates, requiring high reserves from banks,

directing bank credit, government ownership of banks, restricting financial industry

development, and, finally, restraining international capital flows. Beim and Calomiris

(2001) have created an index of financial repression based upon the following six

financial repression proxies: real interest rates, reserve ratio, liquidity, private borrowing,

bank lending, and stock market valuation.3 Using these six proxies individually, it is

possible to evaluate the degree of financial repression in Russia and to explore the effects

of repression on company investment.

In Russia's largely cash-based economy, as in many other countries with

developing economies, virtually all credit is only available to a few of the largest firms.

In Russia, these large firms have publicly traded stock listed on the Russian Trading



3 In this research, economic liquidity is measured as M2/GDP. It is an aggregate liquidity measure and
defined fully in Appendix A. For a complete explanation of each governmental action and the subsequent
proxy, see Beim and Calomiris, 2001, pp. 47-66.









System (RTS). The rest of the domestic economy consists of small and medium

enterprises (SME) that are largely limited to internally produce funding sources, or less

formal and usually exorbitant alternatives.

1.2 Enterprise Network Socialism

The second purpose of this dissertation is to assess the potential for systemic

distortions produced through enterprise-driven subsidies. In "Fixing Russia's Banks: A

Proposal for Growth" (1998) and their other works, Bernstam and Rabushka have studied

extensively the relationship between the monopolized banking sector and excessive

invoicing in a tax non-remittance subsidy scheme that they call "Enterprise Network

Socialism" (ENS) (2006, chapter 2, p. 4).4 The enterprises involved in the ENS subsidy

constitute a network of the large and dominant firms in Russia. Specific to the enterprise

network effects, Bernstam and Rabushka state:

The enterprise network converts trade credit into a subsidy operation. Enterprises
issue overdraft invoices in excess of cash flow available per regular payment period
of about one month. Payments fall into arrears. The average length of trade
payments expanded to about four months during 1992-1998 and shortened to under
three months during 1999-2002. Arrears create the payment jam (2006, chapter 1
addendum, 1).

[Additionally,] Payment arrears between enterprises force subsidies from the
government. The more enterprises succeed at wringing the subsidy the more
overdraft invoices they issue in order to build up arrears. ...overdraft invoices carry
price increases. This reduces real spending during each given period of time, which
expresses itself in payment arrears (2006, chapter 1 addendum, 2).

[Specific to this research,] In Russia in 1999, the stock of receivables, which
entailed cross-subsidies, was equal to 40 percent of GDP and some 27 percent of
total sales, with the average length of payment about 3.5 months and the velocity
(turnover) of 3.4 payments per year (2006, p. 23).




4 Enterprise Network Socialism theory by Bernstam and Rabushka (2006) describes a network of
enterprises that have used enterprise receivables to redistributing national income.









In a market based financial system driven by supply and demand, the subsidy

network, and the gains received, would end. The existence of this system partly explains

Russian entrepreneurial development stalling and the bank restructure process stagnating

15 years after the Soviet Union's collapse.

1.3 Focus of This Work

This dissertation examines the effects of financial repression and the ENS subsidy

network on aggregate and firm-level investment in Russia. A dataset will be used which

will permit exploration of the effects of both on aggregate and firm-level investments in

Russia's economy. For comparison, the same effects are explored using data from the

United States. Moreover, a Denton proportional interpolation method is used to produce

databases with increased interval frequency (e.g., annual interpolated to quarterly) for

further comparison.

This research fills the following gaps in the literature by investigating (1)

financial repression on two levels of investment within one transitional country, and (2)

Russia's subsidy system to large firms. Also, it indirectly adds to the studies of (3)

systemic economic distortion produced through monetary policy manipulations from both

government-instituted financial repression and enterprise-driven subsidies.















CHAPTER 2
DIFFERENCES BETWEEN RUSSIA AND THE UNITED STATES

There is a consistently high non-monetary transaction component within Russia's

economic structure that makes the Russian payment system unusual. For this reason, a

comparison with a developed Western economy, such as the United States, is appropriate.

2.1 Economic, Banking, and Payment System Structures: Russia versus United States

The single biggest difference between Russia's economy and the United States'

economy is the high degrees to which non-monetary transactions permeate all sectors in

Russia.5 Previous studies have documented barter and trade credits being used in illiquid

economies (Commander and Mumssen 2002). Additionally, in their barter research,

Commander and Mumssen found that banking rollover of enterprise loan arrears made

tracking both the information about the contract stipulations and the true levels of

transactions involved difficult. The difficulty of the banking system being used to

facilitate surrogate money transactions adds to the lack of transparency. That, along with

the repeated exchange of debt contracts to offset taxes or expenses, sanctioned by

Russian civil code, adds to doubts about the accuracy of information on the levels of non-

monetary transactions (Commander and Mumssen 2002). An additional reason for

concern about the quality of information on non-monetary transactions is that collecting

data on this issue can be dangerous (Perotti 2003, p.3). For these reasons, inferences


5 Non-monetary transaction describes economic exchanges being made either through pure barter
(exchange a good for another good), or through documents other than the government sanctioned currency.
These documents have various IOU properties with specific financial requirements (e.g., maturity date and
discount rate).









about the actual levels of non-monetary transactions will have to be made from aggregate

data. In Russia, non-monetary transactions take any of the following forms: (1) barter

(goods for goods), (2) money surrogates (veksels, which are commodity or financial

promissory notes issued by enterprises, banks or government), (3) mutual offsets

(zachety, which are used to clear trade, or tax, obligations by exchanging debt for goods),

and (4) debt swaps, sales and roll-overs of previously issued non-monetary documents

(Commander and Mumssen 2002, p.2).

Much of the existing research on illiquidity and chronic non-payment in a non-

transparent structure, seen in Russia, is from the Karpov commission report completed in

1997. As an example, and quoted in Gaddy and Ickes (1998, p. 56), the Karpov

commission stated that "An economy is emerging where prices are charged which no one

pays in cash; where no one pays anything on time; where huge mutual debts are created

that also can't be paid off in reasonable periods of time; where wages are declared and

not paid, and so on...." On the CBR balance sheet, non-monetary transaction totals are

identified as 'quasi-money.'6

2.1.1 Differences: Economic Structure

Despite periodic increases in regulatory controls, the United States has

overwhelmingly maintained an open market system based on private ownership. Since

the fall of the Soviet Union, Russia has had a semi-capitalistic system which might be

characterized as market-oriented socialism. Despite a general loosening of controls since

the end of the Soviet Union, there still are high levels of centralized control through

public ownership of property and production inputs.

6 In this dissertation, quasi-money describes the aggregate level listed on the CBR balance sheet, of which a
large component measures non-monetary transactions. It is banking system deposits which are not directly
used for effecting payments and are less liquid than "money."









The CBR and its precursor, Gosbank, have consistently played a dominant role in

direct management of the centralized economic structures. Academic observers, watching

the stages of Russian economic transformation, called Gosbank an "...important

monitoring device..." used to manage the budgets and finances of most Soviet enterprises

(Gregory and Stuart 1980, p. 209).

This monitoring and management by Gosbank was extensive with the requirement

that enterprise investment funds actually flow through the state bank, which then directly

affected the state budget. Additionally, Soviet firms were required to maintain accounts

with Gosbank. These enterprise accounts allowed records of firm profits to flow through

the state bank accounting and then through to the state budget (Gregory and Stuart 1980).

Furthermore, these records of firm profits provided the taxation base. Thus, lack of

independence has historically been an issue for the CBR, and it continues to be an issue

in the post Soviet Union as the control shifts from parliamentary to presidential hands

(Rose 2000). Setting the independence issue aside, Russia's central bank has

microeconomic involvement reaching down to enterprise levels unheard of in

economically developed countries. The depth includes, and goes beyond, the recognized

"investment bank" role that central banks often play in emerging market countries for

their governments by forcing both public and private banks to purchase government debt

(Daniels and VanHoose 2002, p. 229).

Bernstam, in a personal correspondence, wrote "much if not everything in

Russia's economic system is topsy-turvy (e.g., net receivables in the flows of funds are

counter-cyclical). Looks like enterprises make more sales and purchases when the

economy contracts and less when the economy recovers, which would have been an









obvious, even if ridiculous, interpretation in other economies, but in Russia it simply

means that inflationary expectations and payments arrears went from high to low."7

Even at the point of deepest regulation in the economic history of United States,

regulations, at most, restricted economic freedoms in isolated industries. In sharp

contrast, the Russian tentacle-like economic structure, however, reaches deep into the

operations of enterprises. For example, Beim and Calomiris (2001) note that the Russian

tax collection process is arbitrary to the extent that some firms receive hidden subsidies

while others, destructive vengeance.

2.1.2 Differences: Central Bank Monetary Policy Channels

Countries with developed economic structures and financial markets often use

indirect methods and channels to conduct monetary policy. In the United States, the usual

channel is through the financial markets using open market operations where U.S.

government securities are bought and sold. Countries with developing economic

structures and emerging financial markets typically use more direct methods because the

financial markets are not sufficiently developed. Argued in this dissertation and

supporting the work of Beim and Calomiris, the insufficient development of financial

market structures is caused, in part, from government use of financial repression to

stymie financial industry growth, and thus subsequently creating systemic distortions.

Furthermore, Roubini and Sala-i-Martin (1995) find support for the possibility of policy-

induced distortions in the flow of savings to investment channel.

When open market operations are not available, the more direct monetary policy

channels are used by central banks. With respect to the money-creation channel and the


Email to author dated March 2005.









bank-lending channel, the following are typical: (1) Reserve requirement adjustments to

the portions of transactions (checking) and term deposits (time and savings) that banks

must hold either as vault cash or as funds on deposit at the central bank, (2) Interest rate

regulations on depositor funds, and (3) Direct credit controls that constrain the quantity of

credit extended to individuals and firms by the banking system (Daniels and VanHoose

2002). However, atypical ones also apply. Specific to Russia, and applicable to other

illiquid economies, the primary monetary transmission channel and money creation of

currency in circulation is through debt issuance to the nationalized banks (Bernstam and

Rabushka, 2006, chapter 1 addendum 1, p. 6).

Empirically, both the direct and indirect transmission of monetary controls have

been found to affect investment by slowing the economy and controlling both internal

and external funding source availability (Schiantarelli 1996). Additionally, and

supporting claims being made in this research, constrained investment, especially through

the direct transmission in the bank lending channel, falls heavily on small firms. Because

they are without access to other funding sources, small firms are more dependent on the

intermediation provided by a sound commercial banking system (Kashyap and Stein

1994).

To understand the structure of the money supply in Russia, a breakdown of the

Monetary Survey components of money and quasi-money is useful. In Figure 2-1, the

level and growth of quasi-money closely matches the same for the money measure. The

Monetary Survey statistics differ from the usual statistics reported by country central









banks, including the CBR, in that it specifically surveys for an approximation of the

quasi-money used in Russia.8

A further comparison between Monetary Survey components and the statistics

typically reported by the CBR is shown in Figure 2-2. Note that while the monetary

survey statistic 'money' is comparable to the CBR M2, as a measure of money, it is

lower. An extension from this is to consider the actual ruble level within money to be

lower than the MO reported by the CBR. Thus, the true level of monies available for

economic transactions could possibly be far less than is officially stated.

As explained in Bernstam and Rabushka (2006), the Russian money supply has

grown since 1998 from the increased repatriation requirement of export earnings

mandated by the CBR. This requirement brought back export-generated dollars to be

exchanged for rubles within Russia rather than maintaining the dollars abroad as foreign

currency. With more export earnings returning to Russia's economy, along with the

resulting required purchase of rubles, there have been increases in the broad money

supply. However, the increase in the liquid money supply of currency in circulation,

which the small firms depend on for self-financing, is slight.

Additionally and not often thought of as a usual monetary policy channel, there

are central bank sterilization policies.9 Russia, like China, has a managed exchange rate

with an inconvertible currency and large inflows of U.S. dollars. However, unlike

China's sterilizing technique of sending incoming U.S. dollars back out of the country to

purchase U.S. treasuries, Russia is keeping the dollars obtained from the forced



8 The monetary survey methodology complies with the IMF Special Data Dissemination Standard (SDDS).
9 Monetary sterilization describes a form of monetary action in which a central bank attempts to insulate
itself from the foreign exchange market to counteract the effects of a changing monetary base.









repatriation requirement in the country. Following that, the chosen sterilization procedure

by the CBR is to reduce the domestic money supply by selling, when possible, ruble

denominated government bonds. This reduces the supply of rubles available to domestic

firms. On the subject of sterilization, Bernstam wrote, "a comparison of sterilization

methods in China vs. Russia is relevant and useful. But [also] that Russia accumulates

U.S. treasuries in foreign exchange reserves, in addition to monetizing payments and

sterilizing rubles. Both countries have net capital outflow. China's is due to an

exceptionally high saving rate which exceeds its exceptionally high investment. Russia's

capital outflow depends on the mandated repatriation rate and, as Ron McKinnon

quipped, Russia's capital outflow is motivated by pure capital flight."'1 Reflected in

Figure 2-2., compared to the broader measures of aggregate money supply growth, the

liquid ruble growth, MO, is far slower.

This research documents that while the overall broad ruble money supply

increases, the narrow money supply, and thus the source of cash in the Russian economy,

has slower growth and a large non-monetary component. Therefore, cash in the economy,

needed for self-financing investment through internally generated funds, suffers in a

system which tends to keep money inflows as foreign exchange at the CBR. Concern for

the illiquid nature of Russia's economy extends to enterprises that will provide future

growth. Specifically, while the small firms are not privy to the subsidy network, the

monetary environment produced affects them. In partially sterilizing the resulting

inflowing funds to keep the U.S. dollars as foreign exchange, the Russian monetary

authorities used ruble denominated bonds and other techniques to reduce the domestic


10 Email to author dated October 2005.









money supply. A decrease of the domestic aggregate liquidity increases the use of non-

monetary transactions and reduces the ability to finance capital investment internally.

This supports the findings that available internal funds matter more to firms working in

countries with poorly developed financial systems (Love and Zicchino 2002). The

important point is that the primary negative effects to economic growth will be through

the reduction of small firm growth. As is well-documented, small firms are crucial to

economic growth for industrialized countries. In the United States as an example, small

firms produce fifty percent of the output and account for over fifty percent of the

employment (FED 2002).

2.1.3 Differences: Central Bank Balance Sheet

The core difference between the balance sheet structures of Russia and the United

States is the lack of a line item for quasi-money on the Federal Reserve balance sheet.

Quasi-money is not listed on the CB balance sheet for any of the large developed

countries (e.g., Japan, European System of Central Banks) (Daniels and VanHoose 2002,

p.221). Liquid economies with relatively small black markets do not depend on IOUs

being passed for economic transactions. Studying the quantity, and growth, of quasi-

money on the CBR's balance sheet can provide insight into the depth of involvement the

banking system and non-monetary transactions have to enterprise operations and finance.

As is shown in Figure 2.3, even after the increase of forced repatriated export earnings,

the relative size of quasi-money to money has consistently been maintained by the parties

to the Russian payment system.

Focusing on the liability side of the CBR balance sheet, the growth of quasi-

money to CBR total liabilities matches the growth of money to CBR total liability.

Shown in Figure 2.4, and as a ratio in Figure 2.5, the sum of money and quasi-money









actually exceeds the total liabilities of the CBR. While this is unique to Russia's payment

system, Bernstam and Rabushka (2006) suspect that it is possibly common, albeit to a

lesser degree, in the other former Soviet Union countries.

Typical of central bank balance sheets, while the actual amounts will fluctuate

over time, the proportions of liabilities and equity to assets remain stable (Daniels and

VanHoose 2002). The majority of the assets usually listed on a central bank's balance

sheet are governmental securities, and the majority of the liabilities and equity listed are

the liabilities of the government sanctioned currency, with bank reserve deposits often

being the next largest liability. In the case of the CBR, quasi-money is usually on a par

with the Russian currency. The persistence of large volumes of quasi-money, both

absolutely and relative to other liabilities, on the Central Bank balance sheet gives an

indication of the persistence of non-monetary transactions. Additionally, and most

importantly, a possible indicator of the level of non-monetary transactions being

conducted outside the banking system can be seen in the spread between the sum of the

monetary components and the CBR total liabilities shown in Figure 2-4.

2.1.4 Differences: Payment System

Again, the principal difference between the United States and the Russian systems

is the prevalence of non-monetary transactions in the latter. At all levels of economic

activity, from government, banking, enterprises, and households, non-monetary

documents are important to facilitate exchanging goods and services. Reproduced in

Figure 2-6 is a flow diagram by Bernstam and Rabushka that aids in understanding the

non-monetary transfer process. Figure 2-6 represents Russia's payment system and the

notational conventions are the following: (1) Relationships between flows are indicated









by the plus and minus signs, (2) Red numbers indicate consumption flows, and (3) Blue

numbers represent production flows.

The flows relevant to production (blue numbered in Figure 2-6) taken by the

parties to the Enterprise Network Socialism (ENS) are the following:11

1. First flow: Trade credit separates from sales and production. Invoices outgrow
payments when enterprises add a third party surcharge to the price and bill the
government.

2. Second flow: The flow of receivables for many enterprises exceeds net income.
They increase payables to prevent their net cash flows from turning negative.
Aged receivables increase payment arrears and vice versa. Enterprises whose flow
of receivables exceeds that of trade payables must increase tax payables.

3. Third flow: Enterprises do not remit taxes withheld from workers and collected
from consumers. The government cannot enforce full tax remittance.

4. Fourth flow: The government is forced to issue debt (i.e., securitize tax non-
remittance).

5. Fifth flow: To delay the default, the government is forced to monetize the budget
deficit, that is, to monetize enterprise tax remittance.

6. Sixth flow: Banks transmit, extend, and roll over credit, which reduces aged
receivables.

7. Seventh flow: Variable trade-offs between tax non-remittance and monetization
of tax remittance, followed by credit rollover and extension, wind up in the self-
enforceable subsidy. It sums up to the outstanding balances of receivables. A
complementary array of cross-industry price subsidies accompanies this subsidy.

8. Eighth flow: [This flow] is identical to step 1. Stimulated by all these
components, enterprises surcharge invoices with a network tax to extract the self-
enforceable subsidy. This system becomes circular and self-reinforcing (Bernstam
and Rabushka, 2006, chapter 1 addendum 1, p. 6).

9. Ninth flow: [Flows nine through eleven occur after the CBR increased the
mandated repatriation of export earnings in late 1998.] Enterprise money balances
in bank accounts expanded.

10. Tenth flow: Enterprise export earnings started to monetize tax remittance.

1 For details on the consumption (red numbered) flows, see Bernstam and Rabushka (2006).










11. Eleventh flow: The link between monetization and the tax subsidy was weakened
(Bernstam and Rabushka, 2006, chapter 1 addendum 1, p.33).

Noted in the flow diagram is the importance the banking industry has in

maintaining the ENS; banks, managed by the CBR, are directly involved in half of the

flows. While bank discounting is one element, the other element is non-monetary

documents, especially veksels, which allow barter exchanges in a manner which

facilitates tax avoidance. Thus, while banking is an important part of the maintenance of

ENS, it is equally important to realize that non-monetary exchanges also occur entirely

outside of the banking system. These exchanges occur between all economic parties:

Government, CBR, Banks (both private and public), enterprises, and households. An

indicator of the size of the exchanges occurring outside of the banking system is the

spread between the sum of monetary survey components and the CBR Total Liabilities in

Figure 2-4. Explored in this dissertation is the systemic distortion still evident even after

the increased repatriation requirement in late 1998 by the CBR.

2.2 Economic, Banking, and Payment System Structures: Role of Gazprom

An additional primary player in maintaining the Russian subsidy system is

Gazprom. Natural gas is important both as consumption good and as an industrial input.

In the literature, Gazprom plays an extended role in that, like the Yukos oil, gas payables

and receivables were often used as payment documents. Similar to price adjustments for

veksels above the cash clearance price, gas payables and receivables were exchanged for

elevated values. Through this mechanism, firms would able to increase prices charged

over the price for cash (Commander and Mumssen 2002; Gaddy and Ickes 1998). Noted

in the Karpov commission report, and cited since, is the liquidity issue within barter

goods. The more liquid barter goods are, the more they are used in barter and as quasi-









money and as offsets. Not surprisingly, therefore, non-monetary transactions have been

particularly significant in the following: gas, electric power, ferrous metallurgy,

chemistry, and machine-building (Gaddy and Ickes 1998). The Karpov commission

results also show high quasi-monetary transactions in the petroleum, and extractive,

industries. In fact, during the time of the Karpov commission research even Yukos

engineers were being paid with containers of oil.12

The non-monetary documents, such as veksels, allow Gazprom and other firms to

change prices with them because they are not required to be cashed out. They can be used

as negotiating documents with the value being adjusted to satisfy the parties. Often the

stated value is adjusted up to adjust for the discounting banks will calculate when, or if,

the veksel is cashed out (Commander and Mumssen 2002). Systemic distortion, produced

by financial repression and subsidies of monetary or non-monetary values, will affect all

levels of enterprises. Since small firms have more limited access to the commercial bank

lending, they are doubly harmed in illiquid monetary environments. Research has shown

that a liquidity squeeze and the resulting credit crunch can cause businesses and

individuals to shift from monetary to non-monetary transactions (Commander and

Mumssen 2002).












12 Personal communication with Russian individuals revealed to this author the level of barter conducted at
the household level. Barter prevalence allowed continued sustenance.






















4000 0


3500 0


3000 0


2500 0


2000 0


15000


10000


500 0


00




---Money ---Quasi-
(R, bn) money (R, bn)



Figure 2-1. Monetary Measures from the Monetary Survey (Rubles; billion)

















6000 000



5000 000



4000 000



3000000



2000000



1000000



0 000 ....................................................................................




---M2 (R, bn) -- Money Q-money (R, bn) MO (R, bn)
(R, bn)



Figure 2-2. Monetary Measures: M2 (broad) and MO (narrow) from the CBR; Money and

Quasi-money from the Monetary Survey (Rubles; billion)


















6000 0



5000 0



4000 0



30000



2000 0



10000








--Money ---Q-money (R, bn) CBR TL
(R, bn) (R, bn)



Figure 2-3. Monetary Survey Components and CBR Total Liability (Rubles; billion)








21







100000

9000 0

8000 0

7000 0

6000 0

5000 0

4000 0

3000 0----

20000

00 ----------------------------------------------------
10000

00



-- Money + -- CBRTL
Q-money (R, bn)
(R, bn)



Figure 2-4. Sum of Monetary Survey Components with CBR Total Liability (Rubles;
billion)


















1 70





1 65





1 60





155





150





145







Figure 2-5. Ratio of Monetary Survey Components to CBR Total Liability



































d_ I" I I I -


























permission from M. Bernstam, and A. Rabushka 2006. From Predation to
Prosperity: How to Move from Socialism to Markets. Work in Progress (Page
67, Box 4). The Hoover Institution, Stanford, California.]
V














.--













CHAPTER 3
LITERATURE REVIEW

The literature on capital account and financial market liberalization is extensive

and decades long. The reciprocal literature for financial repression is chronologically

shorter, but it is equally broad. Overwhelmingly, most of the work is on the investment or

economic expansions possible from liberalizing the capital account and/or the financial

markets. In developing countries, existing restrictions are often severe as can be seen in

Beim and Calomiris's cross-sectional work. There is less research on the restrictions on

investment produced by new constraints imposed by current or recent governments. It is

critical to develop better knowledge about the mechanics of repressive policies, their

effects, and the incentives structures that initiate and perpetuate them. These are the

subjects of this research, and as such, are an extension to the literature started by Edward

Shaw and Ronald McKinnon in the 1970s, and has since been expanded by the work of

Ross Levine, Robert King, and William Easterly.

The focus of this literature review will be on the following five topics as they

apply to the investment theme:

1. Definitions and opinions of financial liberalization, and its reciprocal, financial
repression, before and after the 1997-1998 Asian crisis and Russian default,

2. Primary monetary policy transmission channels on investment: Money (interest
rate) and bank-lending,

3. Liquidity constraints on investment: Government control of banking,

4. Financing constraints on investment: Internal versus external financing, and

5. Government subsidies unique to Russia.









Topics one and two describe the status of the current literature relative to

investment in countries with emerging economies. Topics three and four are specific to

the two most severe constraints of illiquidity and financial unavailability common to the

less developed nations. In a sense, the financial repression indicators examined in this

research also take this dichotomy. The first three variables ---- real interest rates, reserve

ratio, and liquidity ---- are proxies for the governmental control over the monetary policy,

which affects the financial system; the last three variables ---- private borrowing, bank

lending, and stock market valuation ---- are proxies for external financing sources.

Finally, the fifth topic describes the magnitude of the government subsidy in Russia.

Liberalization and financial repression research almost exclusively relies on

aggregate investment data in cross-country analyzes. Often absent from these studies are

the transitional countries. This research closes a portion of this gap in the literature by

studying one nation, Russia, and examines financial repression at both the macro and

micro level.

3.1 Financial Repression on Investment: Chronological Changes

Financial repression is the actions taken by a government to alter the flow of a

country's money supply to fund government expenses or finance favored investments,

often at the expense of domestic enterprise development. Over the years, the definition of

financial repression has expanded from a monetary policy that produces negative real

interest rates into a broader one that also covers manipulations of the different monetary

policy variables previously stated, which includes interest rates. This broadening of

actions which constitute financial repression is recognition that there are several possible

approaches to pervert the financial sector.









This study uses the broader definition of financial repression and focuses on the

post-1998-default period. After that default, monetary policy recommendations took a

dramatic turn. During the 1997-1998 Asian financial crisis and Russian default, the

literature went from support for open flows of both goods and money, to support for

moderation in money flows with the necessary legal and regulatory structure.

3.1.1 Literature Review: Pre-1998-Default

The pre-default literature is supportive of fully liberalizing both the capital

account and financial markets to fund investment and, thus, economic growth. In these

regards, the seminal works are McKinnon's 1973 paper for the Brookings Institution and

Shaw's book of the same year. Their works are based on the narrow definition of

financial repression. Both authors assert that deposit interest rates held below market

level will produce an inadequate supply of savings to satisfy investment demand. They

also claim that financial liberalization would allow available money to flow to its highest

and best uses.

Subsequent empirical evidence has tended to support the second claim; however,

there is some evidence the first one does not hold once the negative real interest rates on

deposits drop down to single digits. Further, evidence provided by Japan's lost decade

suggests a contracting economy can occur even when real interest rates are high and

positive.

In the more recent literature, the question of the "inadequate supply of savings" is

still in doubt. In their study of banking and growth, Beck, Levine, and Loayza (2000) find

savings allocation to have a large impact on real per capital GDP growth. However, like

the earlier research, they find less of an association between the same growth measure

and the quantity of savings deposited into banks. In such investigations, including this









research, the view should be from the borrower's viewpoint as a cost to borrowing. While

the interest rate ceiling on deposits harms savers and domestic investors, borrowers

benefit from negative real interest rates on ruble deposits. This is particularly true of

large firms in extractive industries as their outputs typically are dollarized. For these large

firms, negative real interest rates mean extremely low costs of borrowing. Further,

inflation from keeping a debased ruble creates conditions in which domestic trade suffers

relative to the extractive export trade. Firms that export prefer a domestically weak

currency. This preference influences the currency policy that a government maintains,

even at the expense of the domestic economy, particularly small firms. The impacts on

less favored borrowers (i.e., smaller firms) can be severe because the same policies which

make borrowing so attractive to larger firms (particularly those focused on exporting)

discourages savers from committing funds in the Russian banking system. In part, this

may explain why so many manufacturing firms in Russia are still using aged and obsolete

machinery.

Two other pieces of pre-default literature are of interest to this research. The first

one is the Roubini and Sala-i-Martin (1995) model of inflation, tax evasion, and financial

repression. They show that governments have incentives to repress a country's financial

systems to access an easy source of funds for the public budget through the inflation tax

created through seigniorage taxation.13 And, the second piece is a paper by DeGregorio

and Guidotti (1992) who found a negative association between liberalizing a country's

money controls and private sector growth. They claimed that bankers produced the crisis,



13 Seigniorage is the net revenue for a government when the money that is created is worth more than it
costs to produce it; seigniorage can be seen as a form of tax levied on the holders of a currency, and as such
a redistribution of resources to the issuer.









in an unregulated financial system, by choosing projects for loans on the potential return

without considering the connecting project risk.

3.1.2 Literature Review: Post-1998-Default

Since the 1997-98 Asian financial crisis and the Russian default, there has been a

shift in opinions from advising total capital and financial liberalizations to liberalization

only under a structured system of governmental regulatory and legal reforms to ensure a

stable flow of capital. Among the supporters for this new view is Joseph E. Stiglitz (1999,

p. 1509) who states that "the international financial architecture has exhibited enormous

fragility over the past quarter century." Results from studies of previous financial crises

generally indicate that financial liberalization can be destabilizing. The following six

factors, mentioned in the literature, and cited by Beim and Calomiris (2001, p. 119),

address potential problems with liberalization and list the solutions requiring financial

restrictions in order to maintain financial system stability:


1. Controls on deposit interest rates to provide rents to banks. This makes the banks
more profitable and arguably less vulnerable to failure,

2. Bank reserves to provide the central bank with revenues and a liquidity pool to
help banks that get into trouble,

3. Government monitoring the bank lending decisions to prevent fraud and excessive
risk taking,

4. Controls to prevent banks from becoming centers of industrial empires as bank
owners use their lending power to buy recently privatized state owned enterprises
(SOEs) for themselves,

5. Some controls should be in place to avoid having foreign banks out-compete
domestic banks, leaving them seriously weakened,

6. Capital inflows, particularly short-term loans and portfolio flows, should be
monitored with a view to avoiding serious reversals that could create a major
liquidity crisis.









Implicit in the preceding list of safeguards are the assumptions that governments

are both technically competent and benevolent. Particularly in developing nations, the

first assumption may not hold. Moreover, individuals, either within governments, or

having influence over governments, may have incentives to redirect capital flows to

either preserve or initiate repressive policies to fund government expenditures or provide

favors to private individuals or enterprises.

An important country comparison study, written pre-default but supportive of the

post-default consensus, is by McKinnon. In 1994, McKinnon employed a ratio of a

financial liberalization index to the inflation rate to compare China to some of the former

Soviet Union republics. While financial liberalization in the former was moderate and

controlled, the same process in the latter-was neither. In his results, McKinnon found

China to have low inflation with high economic growth and the reverse in the former

Soviet Union countries.

An important segment of the post-default research has focused on bank fragility

with the overall findings indicating reduced banking vulnerability is possible with

"structured" financial liberalization. These structures include: (1) Strong institutional and

regulatory environment,14 (2) Societal respect for the rule of law, (3) Low-level of

corruption, and (4) Binding contract enforcement (Demirgui-Kunt and Detragiache

1998). Edison (2004) and his coauthors find in their meta-analysis on previous

liberalization studies, government reputation, as a measure closely linked to quality of




14 In particular, Demirgtii-Kunt and Detragiache (1998) caution financial liberalization, even after
macroeconomic stabilization has been obtained, when the following institutions have not been fully
developed: laws to ensure contract enforcement, and regulations to produce effective banking sector and
financial market supervision.









institutions, to be a significant variable. Unfortunately, most of these studies, including

Edison's meta-analysis, did not include former Soviet Union countries.

3.2 Liquidity Constraints on Investment: Government Control of Banking

Liquidity does not have a single definition. In reference to financial securities,

liquidity refers to the ability to trade a security without incurring large transaction costs.

In monetary economics, it refers to the most tradable of financial assets-cash (or near-

cash). In this research, the definition used describes the monetary aggregate ratio of

money supply to an economic output measure, usually gross domestic product (GDP).

This gauge for liquidity is often considered a reasonable measure of financial depth

within an economy, typically associated with greater economic and financial system

development (Beim and Calomiris 2001). With the numerator being determined by

monetary policy, transmitted through the money and bank lending channels, bank

ownership becomes critical in determining the funding available for enterprise

investment. In this regard, in Russia the banks that matter tend to have high degrees of

State ownership.

Recent political theories concerning reasons for government ownership of banks

stress controlled investment of the country's enterprises and the subsequent political

kickbacks as the primary incentives. Furthermore, research on global banking finds

government ownership to be positively associated with poorly operating financial

systems and negatively correlated with efficient capital allocation to investments. In their

research on the prevalence of government ownership in developing countries, La Porta,

Lopez-De-Silanes, and Shleifer (2002) support these views. Additionally and specific to

Russia, Laeven (2001) finds heavy insider lending and bribery compensation between

banker and borrower. The findings contradict the previously theorized development view









of governments principally directing scarce resources to productive enterprise projects in

desired industries, and to then further ascribe positive social goals as the primary

motivations for government bank ownership (Barth, Caprio, and Levine 1999; Wurgler

2000).

This line of research supports the bank competition literature, which claims that

competition, in either foreign or domestically owned banks, is necessary in generating

investment and economic growth (Beck, Demirgiu-Kunt, and Maksimovic 2004c;

Berger, Hasan, and Klapper 2004). Indicative of the extent of this effect is the finding by

Smith (1998) that a country with a monopolistic banking system of any ownership type

produces macroeconomic performance worse than an economy void of banks.

In many developing nations, government ownership of banks is typically high,

and the domestic money supply that reaches down to the household and enterprise level is

often tight (Beim and Calomiris 2001). While the money supply growth is sufficient to

produce significant inflation, the direction of the monetary growth is back to the

government coffers. This can result from required purchase of government securities by

the state owned banks. These characteristics are true for Russia. Additionally, it will be

argued here that it is government control of the financial system, and banking in

particular, that allows the Enterprise Network Socialism (ENS) subsidy network to work.

3.3 Monetary Policy Transmission on Investment: Primary Channels

Developing economies are excellent testing grounds for monetary policy

transmission research. In these countries, monetary controls generally are the tightest.

Additionally, it is in these economies that the traditional interest rate effects in the money

channel continue to be a mode of transmission in affecting the real interest rate, as well as









from within the bank-lending channel.15 This is possible because banking is typically the

sole formal external financing form in these countries. The financial repression manifests

itself through interest rates different from free market levels. Controls on interest rates are

applied by and through the banking segment of the financial system.

3.3.1 Money Channel

Two studies are chosen for review here for both the money and bank channels. In

a study on Germany, Chirinko, and von Kalckreuth (2003) test the traditional interest rate

effects in the money channel on business fixed investment. They find a statistically

significant interest rate channel on a database of over 6,000 German businesses, and a

credit channel for a subset of the companies. Honohan (1998), in a study of countries

whose governments are liberalizing both the capital accounts and the financial markets,

finds support for the money channel form of monetary policy transmission. Within this

channel, he finds dramatic short-term volatility increases in the money market. Treasury

bill interest rates, with bank spreads, tended to increase the most, implying that they were

the securities most repressed.

3.3.2 Bank-Lending Channel

Support is also found for the bank-lending channel as a monetary transmission

mechanism. Kashyap and Stein (2000) find the impact of monetary policy through the

bank-lending channel is greatest on small banks. In countries with developing economies

where small banks dominate and the few large banks are State controlled, the bank-

lending channel becomes a powerful conduit for monetary policy transmission.


15 For a detailed explanation of the various monetary policy channels, see chapter 25 of Mishkin (2003).









Driscoll (2004) tests the relationship between individual U.S. state output and the

supply of bank loans. He finds that the relationship is often statistically insignificant

after controlling for shocks to the money demand. It is important to realize, however, that

in developing economies, where government controls over the money supply is both tight

and through banking, transaction money demand may take the form of plain barter. To

continue, from these findings, Driscoll (2004, p. 469) suggests that when alternative

sources of funding are available, and enterprises are no longer bank loan dependent,

monetary policy transmission goes through a broader credit channel. The findings of

Kashyap and Stein (2000), as well as, Driscoll, suggest that the bank-lending channel

effect at the enterprise level is weaker for large firms that have alternatives to bank

lending for project and investment funding. Additionally, the effect becomes

progressively weaker as the firm is able to move up through bank sizes into using the

international money and capital markets.

3.4 Financing Constraints on Investment: Internal versus External Financing16

Early research in the internal versus external financing debate includes

Dobrovolsky (1958), Cohen (1968), and Waite (1973). All found an increase in the

external finance comparable in importance with internally derived funds. These early

papers also noted differences in the funding sources available to large firms versus small

firms.

A chronological divide between internal and external financing is obvious in the

literature. Research in the early part of the last decade is more specific to internal



16 For a comprehensive survey on business constraints (finance, taxation, and corruption) on global firms,
see Batra, G., D. Kaufmann, A. Stone, 2003, "The Firms Speak: What the World Business Environment
Survey Tells Us about Constraints on Private Sector Development," World Bank Working Paper Series









financing constraints. Some of the works have included the difficulties of asymmetric

information, capital market imperfections, and agency problems. Whited (1992) takes the

early research on liquidity constraints and connects asymmetric information to it. From

the findings, he states that adverse selection constraints that affect the bond market also

affect an enterprise's ability to get needed funds. Studies of internal finance and R&D

expenditure generally find that internal financing takes a leading role because of capital

market imperfections. This is especially true for small high-tech enterprises

(Himmelberg, and Petersen 1994). Hubbard, Kashyap, and Whited (1995) find agency

costs unimportant for business fixed investment of enterprises that have significant

dividend payouts. However, agency costs do seem to be a factor for enterprises in a low-

dividend-payout subsample. Some studies support the theoretical agency cost link

between investment and internal finance. Considering asymmetrical information effects

in their study, Calomiris and Hubbard (1990) find a high likelihood the interest rate does

not reflect the "shadow price of credit." They also find that credit rationing is probable.

Bernanke and Gertler (1990) find that high agency costs produce low and inefficient

investments for enterprises that have a heavy reliance on external finance. Finally, using

hospital investment in support of the results found in the manufacturing sector, Calem,

and Rizzo (1995) show a link between significant agency costs in capital markets and

tight internal investment funds.

Kaplan and Zingales (1997) find that less financially constrained enterprises are

more sensitive to investment-cash flow changes than enterprises that are more financially

constrained. In support of this research, Cleary (1999) finds enterprise investment to be









directly related to financial factors with high credit rated enterprises being more sensitive

to shifting internal funds than that of lower credit rated enterprises.

Moving on to the external finance thread of the literature, Fazzari, Hubbard, and

Petersen (1988) and Fazzari and Petersen (1993) examine the relationship between

financial constraints and investment. These studies find strong impacts from finance

constraints on growth and investment, and that internal finance and external finance are

not perfect substitutes, especially in the short run. Additionally, Bond and Meghir (1994)

developed a hierarchical finance model which predicts that in any period a subset of

enterprises will have constrained investment from a lack of internally derived funds.

Besides the agency theory aspect of this topic, legal structure, profitability, and

enterprise size are also used as determining criteria of financing source availability. All

three are found to be significant factors, with, not surprisingly, lower profitability

indicating a need for external funding. In particular, Fauver, Houston and Naranjo (2003)

find the value of firm diversification is related to the legal systems, as well as, to the

depth and level of capital market development and international integration. Furthermore,

they find optimal firm organizational structure may differ for firms operating in emerging

market countries more than for firms operating in established market countries with

international integration.

Stock markets and banks differ in their external finance provisions and effects,

and both are important (Demirgtig-Kunt, and Maksimovic 1998, and 2002; Beck, and

Levine 2002; Claessen, Djankov, and Lang 2000). Love and other World Bank

researchers find that capital account controls increase enterprise financing constraints, but

that multinational firms are not constrained (Harrison, Love, and McMillan 2004).









Further, Love (2003) finds that financial development (liberalization) influences growth

by reducing financial constraints that would negatively affect investment. Additionally,

using a vector autoregression technique, Love and Zicchino (2002) find financial

constraints on enterprise level investment to be larger in countries with less developed

financial systems.

In addition to supporting the need for developed financial systems, financial

intermediaries in particular, Levine, Loayza, and Beck (2000) find both strong legal and

accounting systems to be positively associated with economic growth. Lastly, in

studying the effects of monetary policy transmission on inventory investment, Kashyap,

Lamont, and Stein (1994) find tight money supply to be positively associated to liquidity-

constrained inventory investment. Again, as noted in the previous literature cited,

transitional countries are absent from the databases used in these studies.

In sector specific research, recent studies find support for higher growth of

enterprises dependent on external finance when producing within countries with

developed financial markets (Beck, Levine, and Loayza 2000; Galindo, Micco, and

Ordofiez 2002). Rajan and Zingales (1998) find the primary benefit of financial

development regarding external finance to enterprises to be that of cost reduction. And,

Galindo, Schiantarelli, and Weiss (2003) find most of financial reforms increased

investment allocation efficiency within enterprises.

Papers that study, and compare, small firm versus large firm reaction to monetary

changes often confirm the suspicion that financial liberalization affects small and large

firms differently. Since small firms do not have access to the financing choices available

the larger firms, they are relatively more constrained because of a country's monetary









controls. Laeven (2003) found that larger firms may suffer relatively after liberalization.

With the removal of controls, their comparative advantage over smaller firms in securing

financing is reduced. Similarly, Beck, Demirgui-Kunt, and Maksimovic (2004b) find

small firms are most affected by financial constraints, as well as, legal and corruption

issues.

In their study of finance and growth, King and Levine (1993) affirm Joseph

Schumpeter in his claim that financial intermediaries oiled the economic wheel.

Schumpeter's claim is also supported in Levine's (1997, p. 688) later work with his

statement that there is a "positive, first-order relationship between financial development

and economic growth." He further states there is a "... growing body of work would push

even skeptics toward the belief that the development of financial markets and institutions

is a critical and inextricable part of the growth process...."

Recent research continues to search for the optimal allocation of financing in

investment and economic growth. The optimal allocation work is in an attempt to

determine when short-term funding is better than the longer-term for investment (Fisman

and Love 2004). Most enterprises operating in countries with emerging markets typically

have only the short-term funding variety available from local banks and so are vulnerable

to constraints. Found to be resistant to funding constraints and currency crises are older,

larger, and foreign owned enterprises (Beck, Demirgic-Kunt, Laeven, and Maksimovic

2004a; Desai, Foley, and Forbes 2004). In developing countries with repressed financial

markets, it is the entire investment environment that is constrained, not just industry

segments. In these countries with cash based economies, often with cash shortages, the









funding issue is not internal versus external financing of business as being optimal;

instead, it is financing through barter.

3.5 Russian Governmental Subsidies

In addition to Bernstam and Rabushka's work discussed throughout this research,

others have studied the extensiveness of non-monetary economies and the use of

subsidies within broader transitional country studies.17 The notion of a Russian "virtual"

economy began with the work by Gaddy and Ickes (1998). They observed that while the

Russian economy seemed to be recovering from the initial fall in real GDP during the

early 1990s, a non-monetary payment and subsidy system was also expanding.

In the Russian studies, including the initial work by Gaddy and Ickes, authors

repeatedly note the expanding tax arrears by the energy monopolies. The large extractive

sector enterprises collect taxes from customers but fail to remit those taxes collected to

the government. The arrears form an implicit subsidy and expand the tax delinquent level

of the participant enterprises by passing the related costs on to the Russian government's

fiscal accounts. Additionally, a repeated relationship feedback loop from setting

economic goal to recovery from policy failure and then back again is noted (Pinto,

Drebentsov, and Morozov 2001; Schaffer 1995). While macroeconomic stabilization and

microeconomic minimization of social costs were the goals, policy failures involving

chronic shortfalls in cash tax collections fed a feedback into a rise in public debt. The

macroeconomic tools of fixed exchange rate and tight credit conflicted with the

microeconomic minimization tools of avoiding mass enterprise bankruptcy through

1 The broad studies include publications by both IMF and The World Bank. In his work at the IMF,
Stanley Fischer and coauthors produced a stream of research on economic growth within the transitional
countries. They begin by painting a bleak picture of falling real GDP to rising inflation in their 1996
publication to reporting in a 2002 working paper that higher growth is dependent of structural reforms in
general and privatization in particular. This same picture is echoed by the World Bank (1996).









implicit subsidies. Thus, the feedback loop that Pinto, Drebentsov, and Morozov (2001)

reported fed through enterprises as they faced liquidity and credit squeezes from the

monetary policies, and then fed through to higher subsidies needed, which then fed the

increasing public debt and tax revenue shortfalls, to then repeat the loop-but at a higher

monetary layer.

Finally, the subsidy literature supports the claim made in this research that it is the

individuals with vested interests who are privy to the benefits that hold the subsidy

structure in place. While the distortions have been well documented, the recommended

economic and monetary restructuring have repeatedly, and deliberately, been stalled.














CHAPTER 4
TESTABLE HYPOTHESES

This research has two purposes. The first purpose is to examine the distortionary

effects on Russian investment of financial repression, which is measured by the six

financial repression components (real interest rates, reserve ratio, economic liquidity,

private borrowing, bank lending, and stock market value). The second purpose is to

explore the distortion arising from the Enterprise Network Socialism using inferences

from the examination of the financial repression measures and effects. Thus, in this

dissertation, potential systemic distortions on investment produced from governmental

actions of both (1) financial repression, and (2) subsidies are examined. This examination

is further subdivided into the following: (1) Within financial repression, how

government's direct control over the first three components affect the indirect control

over the development of the last three, and (2) Within subsidies, how the system is

bidirectional between government entities and enterprises.

4.1 Hypothesis: Financial Repression

The first set of hypotheses address the influence of financial repression on

Russian investment at the aggregate and firm levels after the 1998 Russian default. With

45 as the severity breakpoint, previous research finds that Russia's financial repression

index dropped from a near severe 48.1 measure in 1997 into a severe 36.8 by 1998 (Beim

and Calomiris 2001).ls This suggests that during the financial default, the Russian



18 For index details, see chapter 2 appendix in Beim and Calomiris (2001).









government may have chosen to choke off funding from the entire country. Whether

deliberate or otherwise, with the currency debased, liquidity plummeted and barter

soared. Building on this line of reasoning, the following hypothesis is explored with

regard to financial repression: In the literature, Beim and Calomiris (2001) found an

increasingly repressive index for Russia. Extending their work, it is theorized in this

research that repression impacts the levels and structure of investment. In other words, it

is theorized that financial repression has a significant distortionary effect on the

allocation of capital.

An interesting subdivision in the financial repression hypothesis is a grouping that

indicates possible effects to the last three financial repression indicators (private

borrowing, bank lending and stock market valuation) from the actions taken by the CBR

on the first three (real interest rates, reserve ratio, economic liquidity). The Central Bank

has direct control on the levels of the first three financial repression indicators through

direct changes to the interest rates and required reserve ratios. Those changes affect the

money supply and thus, liquidity. The last three financial repression indicators are

measures of the level of financial market development, which is affected by the direct

control of the first three by the CBR. The first three components could be considered as

indicators of a government's control over monetary policy, and the last three as external

financing development indicators. Bank and financial market development are affected

by the control of real interest rates, reserve ratio, economic liquidity. Within this division,

the alternative to the typical null hypothesis of zero effects is that monetary policies that

repress both the financial development of a country and the available external financing

sources impact on aggregate economic and enterprise level investment.









4.2 Hypothesis: Enterprise Network Socialism

The Enterprise Network Socialism (ENS) hypothesis is based on the theory that

an economic structure that allows a "...misallocation of credit and depletion of real

deposits deprived productive users of credit and investment," and "most emerging private

firms were forced to self-finance or organize informal arrangements with individuals,"

with "payment arrears between enterprises that force subsidies from the government" will

produce potential systemic distortions (e.g., unusual investment relationships to external

funding measures) (Bernstam and Rabushka 1998, p. 52; and 2006, chapter 1 addendum,

p. 2). The hypothesis explored in this dissertation is: In the literature, Bernstam and

Rabushka's (1998, 2006) ENS subsidy system is theorized to distort economic growth in

Russia. Extending their work to investment by using inferences drawn from the financial

repression results, the hypothesis is that the ENS subsidy compounds the financial

repression distortions.

An interesting subdivision in the governmental subsidy distortion hypothesis is

that the subsidy system unique to Russia is bidirectional between government entities and

enterprises. Alluded to in the above quotes from Bernstam and Rabushka, Russian

enterprises are able to force subsidies from government. Partly a holdover from

procedures typical to the Soviet Union, and partly a recent creation produced during the

privatization years when prices were released from central control, the system is self-

enforcing and, as theorized by Bernstam and Rabushka (1998, 2006), can generate

extreme outcomes.

4.3 Systemic Distortions: Gains Possible

The gains from financial repression can pass to the Russian government. The

potential advantage Russia's government has in preserving financial repression is the









ability to fund government expenses through control of the country's financial system.

Repression, with capital controls and regulations, allows the Russian government to

restrict financial intermediation and prevent potential competition to the State-controlled

banks from developing.19 An added benefit to the government is that these actions are

often inflationary, which allows the government to pay claims with cheaper rubles. Aptly

stated and relevant to Russia, Roubini and Sala-i-Martin (1995, p. 277) claim that

financial repression is associated with high inflation, high tax evasion, and low growth.

They also claim that the "potential source of easy resources" further encourages

governments to continue financial repression methods. Additionally, concerning financial

repression, Roubini and Sala-i-Martin (1995, p. 277) state:

It is our view that the main reason why governments stay in the way of private
financial evolution is that the financial sector is the potential source of "easy"
resources for the public budget. Governments have the power to follow policies of
financial repression. By financial repression we mean that they have the option
and capability of not allowing the financial sector to operate at its full potential
by introducing all kinds of regulations, laws, and other nonmarket restrictions to
the behavior of banks and other generalfinancial intermediaries.

Thus, the inflationary effect allows the government to pay the remaining fiscal expenses

with a debased currency.

The gains from the ENS subsidy system pass to the subsidized Russian large

firms. As the Figures 4-1 and 4-2 show, the invoicing between Russian large firms and

the Central Bank of Russia (CBR) is significant. The quantity of trade receivables

submitted by the enterprise network equals, and often even exceeds, the sum of tax non-

remittance and the money supply measures, M2 and Ml. Since 2003, increases in


19 In basic banking, intermediation describes household savings efficiently allocated to economically
productive private enterprise. However, in Russia, "...banks are akin to business enterprises but shuffle
financial instruments for their own profit instead of producing real goods and services" (Bernstam, and
Rabushka 1998, p. 17).









petroleum dollars continue to fill foreign reserves reflected in the broader measure M2;

however, much slower growth in Ml, see Figure 4-2, indicates an illiquid Russian

economy. The subsidy process affects economic liquidity by being multiplied through the

banking system as loans to the enterprises. The loans involved are both new and old, with

the old ones rolled-over at maturity. An added distortion is attributed to many of the

rolled-over loans being permanently non-performing. In Russia, the CBR and the large

firms use the commercial banking system to re-intermediate the large firm subsidy.20

Little retail banking, needed by small firms, occurs. Furthermore, at times the quantity of

over-invoiced receivables submitted by the enterprises for the subsidy exceed the "total

bank credit and the entire ruble money stock" (Bernstam, and Rabushka 1998, p. 11).

Additionally, the invoice pricing represents, to some extent, a recovery from the price

caps that are still required by the Russian government for subsidized consumption to both

businesses and households. The ENS subsidy firms submit the invoicing to receive the

difference between the perceived market price and the price cap. This perceived market

price is based on the amounts the firms consider the Russian government's price payment

tolerance.

Bernstam and Rabushka (2006) note that the massive size of the receivables has

repeatedly caused financial havoc to the point of halting the economy by creating a

payment jam in the credit system. This occurs when the mass of receivables expands in

size and lengthens the payment arrears from buyer to seller. In effect the large firms in

the receivables network have a degree of control over the tax base.



20 In Russian banking, re-intermediate describes "...channeling household funds to the government and
government subsidies to enterprises..." (Bernstam, and Rabushka 1998, p. 20).









In this research, it is argued that financial repression and the ENS subsidy are

supportive of one another. The ENS network could not function in a country with a fully

market-based intermediating banking system. Therefore, the argument explored in this

research is that the financially repressive measures taken through the Russian

monopolized banking and financial system sustain the ENS and its self-enforcing subsidy

system. Furthermore, without the first distortion produced by financial repression, which

benefits the Russia government, the second distortion produced by the subsidy system,

which benefits the extracting sector enterprises, would end. Additionally, the

combination of these two produce a third distortion with global implications: restricting

money availability in a cash-based economy hampers small firm growth.

Since financial repression produced by government actions coexist with the

control over the tax base produced by the actions of the ENS subsidy network firms, both

will be reviewed in the appropriate results section in this dissertation. Breaking apart the

two effects is difficult. In this research, the assumption will be made that the statistical

significance of a variable coefficient supports the possible effects of both financial

repression and the ENS subsidy on investment. Coefficients counter intuitive to finance

and economic development theory will be assumed to support the existence of distortions

created by the forced subsidies.

The dangers of systemic distortions to economic growth can be severe.

Schumpeter's view that developed financial systems of banking, bonds, and stocks, are

important to economic growth has been supported in academic research (King and Levine

1993). When a country's banking system, as the first financial system to typically









develop, is used to re-intermediate a subsidy rather than intermediate efficient allocation

of capital, economic growth suffers.

4.4 Analysis: General and Specific Questions

In exploring the stated hypotheses of potential systemic distortion from financial

repression and governmental subsidies, the general questions of interest are the

following: (1) Are there differences between Russia, with its emerging financial markets

and systems, and the United States, with established financial markets and systems? (2)

Within Russia, are there differences between the aggregate level and the large firm level?

Further, are there differences when compared to the USA results?

In further exploring the stated hypotheses, the specific research questions of

interest in are the following: (1) Within Russia, which of the financial repression

components (real interest rates, reserve ratio, economic liquidity, private borrowing, bank

lending, and stock market value) matter to each level of investment? Further, are there

differences when compared to the USA results? (2) Within Russia, is there evidence of

systemic distortions consistent with the existence of the Russian ENS subsidy?









47



4800 -4800
4600 -4600
44001 -4400
420 I.
400' I
380 T ..
360 ....
340
320 -
300 ,
280 I
260 -
240 '-
220 -
-J5
200 -. I
180 I I
160- .
140- I. I
120 -- I.
100 -I I
I., I
80
60
40 .
20 .




Note: 1 All data are denominated In billion 1998 noinal rubles
2 An increase ithe deposit nultiplier during 2000-2004, when tax non-rennttance decreased and becat negative and the subsidy to finance enterprise receivables decreased
accordingly nakes the monetary aggregate M2 less suitable than M1 (see figure 13 2) ir approxinnting the quasi-iscal conponaent ofthe subsidy, which together
with taxnon-remttance as a fiscal component, atches the outstanding balances ofetieprise receivables This change shows in the excess ofM2 over ecevables in 2002-2004
Sources: Receivables and taxnon-re ttance: Russian State Connittee on Statistics; uney. Central Bank ofRussia.

Figure 4-1. Enterprise Receivables to M2 [Reprinted with permission from M. Bernstam,

and A. Rabushka 2006. From Predation to Prosperity: How to Move from

Socialism to Markets. Work in Progress (Page 62, Figure 13-1). The Hoover

Institution, Stanford, California.]








48



3200 3200

3000- -3000

2800 M1 -2800

2600- Tax Non-Remittance -2600

2400 Receivables 2400

2200- -2200

2000- -2000

S1800- -1800

1600- -1600

1400- -1400

1200- -1200

1000- -1000

800 -800

600- -600








Note All data are denomnnated in billion 1998 noannal rubles.
Sources: Receivables and taxnon-renmttance Russian State Conmtttee on Stausncs.
Money: Central Bank ofRussia.

Figure 4-2. Enterprise Receivables to Ml [Reprinted with permission from M. Bernstam,
and A. Rabushka 2006. From Predation to Prosperity: How to Move from
Socialism to Markets. Work in Progress (Page 63, Figure 13-2). The Hoover
Institution, Stanford, California.]














CHAPTER 5
DATA

Data for this research are used to estimate the degrees and dimensions of financial

repression in Russia, as well as their determinants and investment effects at the aggregate

and firm level in Russia. Inferences are drawn from the results to confirm or reject the

hypotheses for financial repression and the Enterprise Network Socialism (ENS) subsidy

system.

For comparative purposes, a similar database for the U.S. is created. For both the

Russia and USA analyses, a Denton proportional interpolation method is used to produce

databases with increased frequencies (e.g., yearly data interpolated to quarterly). For

consistency, in all databases, the applicable country's industrial production is used as the

interpolating variable with the source in all analyses being the respective central banks.

5.1 Descriptions: Variables21

The variable consistently used across all analyses is the capital investment of

fixed assets made at the aggregate and large firm economic levels of both the Russian and

the United States economy. At the aggregate level, it is aggregate investment data as a

component in the gross domestic product (GDP) equation. GDP measures production

within the national boundaries, which includes foreign firms operating within the national

borders. If purely foreign firms account for significant shares of domestic production,

GDP could overstate progress by indigenous firms. However, this is less of a concern



21 See the Data Journal in Appendix A for details and glossary.









than usual because the foreign direct investment in Russia has been consistently low, with

most foreign investment restricted to joint ventures with Russian partners.

At the firm level, a form of gross fixed expenditure is used. The investment data

used is the change in plant, property, and equipment (PP&E) expenditure made by the

large publicly traded firms in Russia, and by the Dow Jones 28 non-financial firms in the

United States.

The independent variables are the components that stem from the Beim and

Calomiris (2001) financial repression index. These measures provide indications of the

extent and types of repressive actions in which a government may engage. Assessment of

these measures provides insights into the actions typically taken, or policies issued, by a

country's government to repress the financial system. Additionally, the following

variables are included to control for shocks to investment in Russia unrelated to

repressive actions: Real price of oil, and winter temperature extremes. The petroleum

sector is a dominant component of Russia's extractive sector economy, and weather

extremes are a potential hindrance to industrial investment and production particularly in

Russia.

5.2 Sources: Data

5.2.1 Russia: Financial Repression Measures and Aggregate Level Capital Investment

The sample period of investigation for the Russian aggregate level analysis is

from December 1998 through December 2005 (29 actual quarters, 85 Denton interpolated

months). The data for all the financial repression measures, along with the aggregate

capital investment measure, come from the Russian Economic Trends database (RET)

database until June 2002. The Russian-European Center for Economic Policy (RECEP)

produced the database for a project that began as the result of an original partnership









between the European Community and the Stockholm School of Economics. The original

mandate was to find and disseminate data that was difficult to get from the Russian

government. The publishing outlet was the Russia Economic Trends (RET) quarterly

journal, which originally began as a publication produced by the London School of

Economics in 1992. However, the RET ceased to maintain their database in 2002. For

this reason, data for this research after June 2002 was gathered from the original RET

Russian sources: Central Bank of Russia, and Goskomstat (State Statistical Agency of

Russia).

5.2.2 Russia: Large Firm Level Capital Investment

The sample period of investigation for the Russian large firm level analysis is

from December 1998 through December 2005 (Unbalanced panel data: 8 to 5 actual

years, 29 to 17 Denton interpolated quarters). The data for firm level capital investment

come from the following sources: Worldscope, Global Reports, and Company financial.

Firm investment data come from the Primark (now Thomson) Worldscope

standardized company financial database. Most of the firm data are provided for by

listings in Worldscope, supplemented with data gained directly from the original

company documents.

5.2.3 USA: Financial Repression Measures and Aggregate Level Capital Investment

The sample period of investigation for the USA aggregate level analysis is from

December 1996 through December 2005 (37 actual quarters, 109 Denton interpolated

months). The data for all the financial repression measures, with the aggregate capital

investment measure, come from the following sources: (1) Federal Reserve, and (2) U.S.

Department of Commerce: Bureau of Economic Analysis, and Bureau of Labor Statistics

(BLS).






52


5.2.4 USA: Large Firm Level Capital Investment

The sample period of investigation for the USA large firm level analysis is from

December 1996 through December 2005 (37 actual quarters, 37 Denton interpolated

quarters). The data for firm level capital investment come from Worldscope, SEC, and

company financial.











Table 5-1. Variable Definitions
Variable Name: Description:
Dependent Variable
y1 INVESTMENT (vl) Aggregate: Gross fixed investment (logarithm); (yl) Large firms:
Gross fixed investment (scaled by one-period lagged "PP&E" assets)


Independent Variables
Lyl LAGGED Y1

X REAL RATES


x2 RESERVE RATIO


x3 LIQUIDITY

x4 P BORROWING


X5B LENDING


x6 MARKET VALUE


x7 F CHARACTERISTIC

x8 OIL

x9 ACCELERATION PRINCIPLE

x1O SEASONAL


(Lyl) One lag ofyl

(xl) Real interest rates: Nominal annual on bank deposits adjusted for
realized annual inflation (one-period lag)

(x2) Reserve ratio: Bank reserves / M2 (Russia: money plus (some) quasi-
money; USA: standard M2) less MO (one-period lag)

(x3) Liquidity: Short-term liquid liabilities M2 / GDP (one-period lag)

(x4) Private borrowing: Claims on private sector / Total domestic credit
(one-period lag)

(x5) Bank lending: Deposit bank assets / Deposit bank assets plus central
bank assets (one-period lag)

(x6) Market Value: Aggregate stock market capitalization / GDP (one-
period lag)

(x7) Firm characteristic: Total revenue / Total assets (one-period lag)

(x8) Real price for oil (one-period lag)

(x9) Acceleration principle: One lag of revenue

(xlO) Temperature for seasonal effects















CHAPTER 6
METHODOLOGY

As the first focus of this research is in the potential relationships of investment to

financial repression components, the models take on some of the characteristics typical of

models used in investment and financial repression studies. The main difference between

investment and financial repression studies is in the scope. Where financial repression

research focuses on a broader theme of aggregate growth using cross-sectional cross-

country, the usual models in the investment literature are more narrow and specific.

The most common types of investment models are Tobin's Q, Euler equations,

and internal liquidity constraints.22 Schiantarelli (1996) notes inconsistencies for both

Tobin's Q and Euler equations in his investment constraint survey. Hsiao and

Tahmiscioglu (1997) study the internal liquidity constraint models, in addition to testing

the effects of Tobin's Q on capital investment. They then ranked the models typical of the

investment literature by the resulting statistical precision. In this ranking, Hsiao and

Tahmiscioglu (1997) find the liquidity model to have the highest ranking and the Tobin's

Q model the lowest. In addition to the inconsistencies, both Tobin's Q and the Euler

equation have recognized difficulties. Tobin's Q needs a functioning and liquid domestic



22 Tobin's Q describes the market value of assets divided by replacement value of assets. A Tobin's Q ratio
greater than 1 indicates the firm has done well with its investment decisions. Euler equation, as it applies to
investment, describes a firm's potential financing constraints by assuming that the firm's stochastic
discount factor is a function of the firm's financial position. Further, its relationship to Tobin's Q is a
"...rearrangement of the first-order conditions form the same maximization problem used to derive Q
equations" (Schiantarelli 1996, p. 75). Internal liquidity constraints affect investment through low profits,
or high dividend payouts, and external liquidity constraints describe the arbitrary limit on the amount a firm
can borrow, or an arbitrary alteration in the interest rate they pay (Hsiao and Tahmiscioglu 1997).









stock market, which is often not found in transitional countries, and the Euler equation

may not be able to detect the unvarying financial constraints within a study of investment

unless there is "... data over a period of time [that is] long enough to record changes in

individual firms' financial strength and overall macroeconomic conditions" (Schiantarelli

1996, p. 77). Because of the difficulties found in these structured models, a more recent

study tends to use a vector autoregression technique on unstructured and reduced form

investment models (Love, and Zicchino 2002).

The investment models examining liquidity constraints have a closer application

to this research. The difference from many of the studies in the literature and this study is

that the liquidity measure, as one of the financial repression constraints, is aggregate

economic rather than internal to the enterprise as in Schiantarelli (1996), Chirinko, and

Schaller (1995), and Hsiao and Tahmiscioglu (1997). Thus, unlike the investment

models, but similar to the financial repression models, this research studies the

macroeconomic constraints to aggregate and firm level investment produced by

government control over the money supply, financial depth, and bank credit. These

liquidity constraints are typical of countries with developing economies working under

financial repression and tight monetary policy, Chirinko and Schaller (1995, p. 528)

observed that "...liquidity is highly correlated with current, hence future, output, it could

be a significant determinant of investment even if firms do not face constraints." Finally,

typical to investment models in general, the models in this research are all lagged models,

with a t-I to denote the lag on each independent variable, which provides "time-to-plan"

for investment decisions.









In both static and dynamic models, the base investment model performs as a

function of the financial repression measures.

f ( real interest rates, reserve ratio, liquidity, 1
) private borrowing, bank lending, stock market value (6-1)
=f (xl, x2, x3, x4, x5, x6)

Further interpretation of the base investment function in this analysis is the following:

(first 3 variables measure government control over monetary policy, (6-2)
last 3 variables measure possibly effects to external financing

This division allows consideration of the possible distortionary effects on external

financing of banking and financial market systems development which can be produced

from the monetary policy controls over real interest rates, reserve ratio requirements, and

economic liquidity. Additional models have exogenous variables added to the base model

to control for possible fundamental effects important to investment in Russia: Real oil

price, and temperature. Furthermore, in an attempt to model the acceleration principle,

investment equations have included lagged revenue (Fazzari, Hubbard, and Petersen

1988). This principle tries to connect investment needs for capital goods to a firm's level

of product demand.

6.1 Model Notation Conventions

Throughout this research, matrix notation to multiple regressions is used. And,

with the exception of the vector x, being a row vector, all notations follow the

recommendations made to the Royal Economic Society and stated in Abadir and









Magnus's (2002) "Econometric Standardization."23 In notational development from

scalar to full matrix, the following conventions hold:

Scalar Notation: For each model, the t subscript will be used to represent time,

t=1, 2,...,T; the i subscript will be added in the panel data analyses to index

observations, with the n subscript denoting the sample size of those observations,

i =1, 2,...,n ; and finally, with k to indicate parameters, k = 1, 2,...,K. The scalar model

y,= = xto + Ax, + 2Xt2 +...+" xk + has a dependent variable for time t ofy,, and

each xtk, as an independent variable with xto as unity; and finally, 6, representing the

population disturbance at time t.

Vector Notation: With bolded lowercase, the scalar time-series model, applicable

to the aggregate investment analysis in this research, becomes y, = xp + c, t = 1, 2,...,T.

Each component, further defined, is the following: the xt is a 1 x K row vector of

independent variables x, =(1,x,,...,x,) with xto reserved for the leading 1; the K x 1


parameters vector p is p = (/, /,...,/P ) .

Matrix Notation: When fully generalized, and with bolded uppercase, the same

model becomes: y = X + E. The defined components then are the following: the Tx 1

vector y is observations on y, where an element of y is yt,; a full T x K matrix, X, is

observations on the independent variables, where the tth row of X consists of the vector xt,

and where the element (t, k)t of X is xtk; with the K x 1 parameters vector p, Xp

becomes Tx 1; and finally, Etbecomes E, a Tx 1 vector ofunobservable disturbances.

23 The econometric standards are attempting to structure standards that mimic those set by The International
Organization for Standardization used by scientists in chemistry and physics (Abadir and Magnus 2002,
p.76).









General notation: E(-) refers to the expectation of a random variable, while V(-)

does the same for the variance. Finally, the basic variable names will be used in the body

of this paper with the appropriate time and firm level observation notation added when

appropriate.

6.2 Generalized Assumptions: Tested Per Analysis

Along with the assumption of linearity in the parameters, various diagnostics are

used to test the following set of basic assumptions for the models, E(y|X)=Xp,

estimated in this research:

X is the T x K matrix with rank K, and thus no perfect collinearity,
having an error structure of e X ~ N(0, o2I), where E (e | X)= 0, E (s' I X) = o2I

Therefore, the error structure assumptions are the following: normality of errors, where

the expectation of the error structure has a zero conditional mean and homoskedasticity,

without serial correlation.

In addition to the methods listed below, univariate, bivariate and multivariate

diagnostics are completed for both specification and misspecification. To address the

problems that are typical to the data and models used, the standard errors are adjusted for

autocorrelation in the time-series models, and both autocorrelation and heteroskedasticity

in the panel data models by using autocorrelation and heteroskedasticity-consistent

analysis techniques. The diagnostics are correspondingly reported with the appropriate

analysis results.

Methodologies used are the following: (1) autoregressive integrated moving-

average (ARIMA) technique for a first-order autoregressive process [AR (1)] is used for

the aggregate investment time-series analyses. (2) Generalized least squares (GLS)

random-effect is used for the firm level investment panel data analyses. (3) Proportional









Denton method of interpolation is used for both aggregate and firm level investment

analyses to determine consistency with data of increased periodic interval frequency (e.g.,

quarterly data interpolated to monthly).

The ARIMA and GLS techniques were chosen over the more common ordinary

least square (OLS) technique for two reasons. First, OLS estimation leads to biased and

inconsistent results when the strict classical linear assumptions are violated in either time-

series or panel data, and second, OLS is inefficient and biased with dynamic models

(Greene, 2000).

6.3 Methodology-Time Series for Aggregate Level Investment: ARIMA [AR(1) Model]

The Autoregressive integrated moving average (ARIMA) methodology, also

called the Box and Jenkins technique, is used to analyze a structural first order

autoregressive model:

y, = xtP + u, where u, = p/u + ,, t = 1,2,..., T
(6-3)
and where p is the first-order autocorrelation parameter

While the ARIMA is frequently used on univariate models, it is also a useful

technique to analyze multivariate structural models that have ARMA disturbances.

Additionally, it is more flexible than either the older Prais-Winsten or the more

problematic Cochrane-Orcutt techniques.24 The structural first-order autoregressive

model [AR(1)] is determined by diagnostics, reported in the Results section, that rules out

the moving-average parameter and eliminates the need for variable integration or first

differencing.


24 Cochrane-Orcutt estimation omits the first observation.









6.4 Methodology-Panel Data for Firm Level Investment: GLS [Random-Effects Model]

y1, = xit+v, +,t, for i = 1,2,...,n and t =1,2,...,T
(6-4)
Where, v, is the random unit-specific residual and E (v,) = 0

The advantage of using panel data is that it allows investigation of the effects, or

change, of both within and between-firm units. The chosen random-effects model for the

firm level analysis is based on the following: (1) The small number of time waves per

panel precludes the number of indicator variables necessary for a fixed effect model, and

(2) while the monetary policies are time-varying independent variables, because they are

monetary policy variables, they do not vary by much. Still, and reported in the analysis

results section, the Hausman test is used to determine the appropriateness of the random

effects model to the fixed effects. Additionally, specific to this research, Hsiao and

Tahmiscioglu (1997), in their firm level investment study, ran separate regressions on

manufacturing firms in the United States to find out if heterogeneity among firms can be

captured by firm specific intercepts in a fixed effect model. Their findings suggest that it

cannot. However, Hsiao and Tahmiscioglu (1997) do add a lagged capital investment

variable to capture the dynamics of firm investment.

The random-effects model using either the GLS or the maximum likelihood (ML)

method is considered to be a subject-specific model with a random constant term. Of the

two techniques, GLS has the clear advantage over ML in this study in that it does not

need the panels to be long to get consistent estimates of the betas. It also allows the

regressors to be invariant, or in this research, to vary little. Additionally, the GLS version

of the random-effects model is distribution free. Speculation of a distribution for the

individual effect is not necessary (Hsiao 1986). In the correlation structure of the panel









data analyses, both autocorrelation and heteroskedasticity typical to panel data analyses

are addressed in the GLS random-effects models.

6.5 Methodology-Increase Data Interval Frequency: Proportional Denton Method

The reasons behind including the proportion Denton method in this research are:

(1) Test for consistency across results, (2) Test for increased statistical precision with the

increase in interval frequency, and thus, an increase in degrees of freedom, and (3) Test

the method as a tool to be used in countries with developing economies where enterprise

financial and operational data are typically only available in annual interval frequencies.

Completed on all datasets, both time-series and panel data: The proportional

Denton method produces an interpolation of an annual time series to quarterly by use of

an associated "indicator series," imposing the constraints that the interpolated series

obeys the annual total. The same procedure can be used for higher frequencies such as a

quarterly series interpolated to monthly.25 This method is the preferred one of the Denton

family of least-squares-based benchmarking methods because it takes into account the

"existence of any systematic bias [seasonal fluctuations, etc.] or lack thereof' in the

series (Bloem, Dippelsman, and Maehle 2001, p. 84).26 The appropriate industrial

production measure is the indicator series used in this research for both the Russia and

USA models.

The proportional Denton method is a least squares approach: (1) the increased

frequency estimates to be derived are the parameters, and (2) the sum of squares involved



25 Bloem, A., R. Dippelsman, and N. Maehle, 2001, "Quarterly National Accounts Manual: Concepts, Data
Sources, and Compilation" International Monetary Fund. See chapter VI on Benchmarking.

26 Benchmarking deals with the problems of combining a series of high-frequency data with a series of less
frequent data (Bloem, Dippelsman, and Maehle 2001).










are the first differences of the X/I ratio of the interpolated series (X) to the indicator

series (I).

Per Bloem, Dippelsman, and Maehle (2001), and in their notation, mathematically

the proportional Denton formulation for an annual to quarterly interpolation is the

following:

-2
min t- (6-5)



Each formulation is minimized under the restrictions that the sum of the quarters

should be equal to the annual data for each year, and in doing so, the series proportions

are maintained.














CHAPTER 7
RESULTS

This research examines the influence of the six financial repression measures on

investment at both the aggregate and large firm level. Inferences drawn from the financial

repression results are then used to examine, Bernstam and Rabushka's Enterprise

Network Socialism (ENS) subsidy hypothesis.

On each model previously listed in the Methodology section, and in addition to

the sensitivity analyses performed, the following diagnostic checks are completed for

robustness:


1. The USA database matches the database for Russia aggregate investment and the
large firms (Dow Jones 28 non-financial companies) for the United States. The
same analyses are then explored on the U.S. data as a comparison test to the
Russia research for robustness.

2. The proportional Denton procedure is completed on every database. While
economists, working in developing countries, where data are scarce, use the
proportional Denton method, it is not often used with financial data, largely
because of the abundance of quality data provided by the financial markets in
economically developed countries. The purposes for including the proportional
Denton method here are: (1) Examine consistency in results for robustness
checks, (2) Examine for increased statistical precision with the increase in both
interval frequency and degrees of freedom, and (3) Examine the method as a tool
to be used for financial economic research in developing market economies where
data is scarce.


7.1 Aggregate Level Investment-Russia: Descriptive Statistics and Correlations

The aggregate level investment descriptive statistics and correlation analysis are

summarized on Table 7-1. Worthy of note is the high positive correlation of 0.79 found

between X3LIQUIDITY, and X4PRIVATE BORROWING. The tremendous size of the ENS subsidy









along with the role banking has transmitting the non-monetary surrogate monies may

explain the high correlation found. The Russian government monetizes the subsidy by

issuing bank credit to the large firms. In this case, private borrowing does not have the

usual definition. "Credit to enterprises, subsidized by the government and the CBR,

consumed 99 percent of total credit" (Bernstam and Rabushka 1998, p. 41). Thus, the

liquidity to private borrowing connection is possibly due to the use of the banking system

to pay the subsidies in the form of loans and rolled-over loans. This tangled occurrence

alone has the ability to distort. With that said, and for whatever the reason, the high

correlation is examined here by having a dual set of models in each section. One set

contains the X3LIQUIDITY measure, and the second set contains the X4PRIVATE BORROWING

27
measure.

The time series analysis results, listed below, are produced by autocorrelation-

consistent techniques to adjust standard errors. The sensitivity diagnostics completed on

the data used in the models in this section are the following:

Univariate diagnostics were used to test for skewness, data outliers, and

stationarity of both the mean and the variance. The tests were not able to reject the null

hypothesis of symmetrical distribution. The question of stationarity is of primary

importance in using time-series data; therefore, the augmented Dickey-Fuller test is used.

The assumption is that the variables indicate a stationary process because both the

dependent and the independent variables exhibit slow change. This is especially true with

the independent variables being calculated as ratios with monetary components that


27 In addition to the reported liquidity variable, M2/GDP, all analyses were reconfigured using the narrow
money measures of MO, and M1, as well as the broadest of money measures, M3. All results are
comparable to those reported using M2.









typically have slow adjustments to economic changes. The Dickey-Fuller unit root tests

show that the variables, including the real interest rate, are stationary.28

Bivariate diagnostics were used to test sensitivity for correlation. Because of the

unusually high correlation found between X3LIQUIDITY and X4PRIVATE BORROWING, a dual

model structure was used throughout the research. Additionally, correlograms that were

used to evaluate the first-order autocorrelation between current and lagged investment

indicated white noise with no significant autocorrelation.

Multivariate diagnostics were used to test sensitivity for specification,

misspecification and precision. The Portmanteau statistic was calculated for correlation

with the findings indicating white noise errors. The results of this final diagnostic are

presented on the appropriate tables.

7.2 Aggregate Level Investment-Russia: Time Series Results

In most of the models in both the aggregate and large firm level investment the

reserve requirement ratio is the statistically significant independent variable of

prominence. It is the variable that seems to be of great importance across all of Russia.

Other exogenous controls, however, did not indicate a statistical significance. Neither

severe weather, XlOSEASONAL, nor previous period petroleum price, X8OIL, mattered in the

investment models for either the aggregate or the firm levels.

7.2.1 Results with Liquidity Variable-Russia: ARIMA [AR(1)]

yl, INVESTMENT = P2X2, RESERVE RATIO + X + (7-1)
p, = ppu, + E,, where p is the first-order autocorrelation parameter


28 The autocorrelation function and the partial autocorrelation function verify the Dickey-Fuller results.









Per equation 7-1, within the aggregate level investment time series analysis, for

the models that contain X3(t-1)LIQUIDITY as an independent variable, the variable of interest

is the statistically significant X2(t-)RESERVE RATIO. All insignificant variables are contained

within x which in this equation includes the x3(t-1)LIQUIDITY variable.

Using Model 1 from Table 7-2, the independent variable X2(t-)RESERVE RATIO has as

coefficient of -2.75 for the Denton interpolated data (-3.34 for the actual data). This

suggests that a 1-unit absolute increase in the required reserve ratio is associated with

over a 2.75 relative decrease in yl(t)INVESTMENT (logarithm) in the following period.

The significance of X2(t-1)RESERVE RATIO, suggests the primary importance this

direct monetary control variable has in countries with underdeveloped financial markets.

The potential for systemic distortion that the typically high reserve requirement has to

investment and economic growth is indicated by having the reserve ratio statistically

significant for all four Russian analyses. These results support the claim by Beim and

Calomiris (2001, p. 51) that reserves paying zero-interest are an "implicit tax on

banking," and thus the banking system as a whole provides an important source of

"government revenues."

7.2.2 Results with Private Borrowing Variable-Russia: ARIMA [AR(1)]

ylt INVESTMENT = 82X2t_, RESERVE RATIO + XP + t (7-2)
t = p/t_ + s,, where p is the first-order autocorrelation parameter

Per equation 7-2, within the time series analysis, for the models that contain x4(,

1)PRIVATE BORROWING as an independent variable, the measure that matters to the aggregate

level of investment in Russia is the statistically significant X2(t-)RESERVE RATIO. The same

relationship holds in this model as in the model with liquidity as an independent variable.

Additionally, x1 contains all insignificant variables including the X4(t-1)PRIVATE BORROWING









variable in equation 7-2. In Russia's aggregate level, only the reserve ratio holds as

having potential effects on investment.

Using Model 1 from Table 7-3, the independent variable X2(t-)RESERVE RATIO has a

coefficient of -1.45 for the Denton interpolated data (-1.59 for actual data). This suggests

that a 1-unit absolute increase in the required reserve ratio is associated with over a 1.45

relative decrease in yl(t)INVESTMENT (logarithm) in the following period. The results from

equation 7-2 with X4(t-1)PRIVATE BORROWING as an independent variable is comparable to the

results from equation 7-1 containing the X3LIQUIDITY as an independent variable. Of all

the financial repression variables, the reserve ratio as a measure of required reserves

provides the most direct source of monies to a government purse. Beim and Calomiris

(2001, p. 49) notes that having a high reserve requirement on commercial banks reduces

the amount of sovereign debt needed to be issued, and thus, "...reduces government debt

service costs."

7.3 Large Firm Level Investment-Russia: Descriptive Statistics and Correlations

It is of interest to note on Table 7-4, the consistently negative real interest rates.

Historically, this has been the narrow definition of financial repression. Both McKinnon

and Shaw, beginning in their 1973 publications, focus on the interest rate variable from

the investor's viewpoint where the return is to the investor and a positive coefficient is

expected. They viewed the effects produced by deposit interest rate ceilings held below

the level of inflation, as having a negative impact on economic growth, and they then

theorized that negative real rates would have an impact on an investor's willingness to

forgo consumption for a negative real return on investment. However, while negative real

interest rates are considered to be the original definition of financial repression, it is also

an indication of the true cost of borrowing for firms. To any size firm, an increasing









negative interest rate produces a decreasing true borrowing cost. Additionally, and

relevant to Russia, the dominant extractive sector exporting firms gain by keeping

production, and the resulting costs, in countries with inflationary economies. Throughout

the research period of December 1998 to December 2005, Russian policy results in

inflation and negative real interest rates by preserving a devalued currency and deposit

interest rate ceilings below inflation. The government and the large firms in the ENS,

Russia's two largest borrowers, are in the best position to benefit from inflation and

negative real interest rates. In a personal communication, Bernstam stated, "Enterprises

receive a huge subsidy via, inter alia, the negative real lending rates. This is a feature of

subsidized credit. ...when inflation is high and nominal interest rates are low, and hence

real interest rates are highly negative, credit rollover and extension represent a pure

subsidy."29

The panel data analysis results, listed below, are produced by autocorrelation and

heteroskedasticity-consistent techniques to adjust standard errors. The sensitivity

diagnostics completed on the data used in the models in this section are the following:

Univariate diagnostics were used to test sensitivity for skewness, data outliers,

and stationarity of both the mean and the variance. The diagnostics used in this section

address the same concerns with the time-series analysis but with the additional concern of

heteroskedasticity. Because short panels tend to be stationary and with the variables the

same as those used in the aggregate level analyses, the diagnostics produced similar

results. The difference in this analysis is that it is firm-level investment being


29 Email to author dated October 2005.









investigated; however, like the aggregate level investment, it also exhibits slow

adjustment to economic changes.

Bivariate diagnostics were used to test sensitivity for correlation. Again, the dual

model structure is used in this research resulted from the finding of high correlation

between x3 LIQUIDITY and X4PRIVATE BORROWING.

Multivariate diagnostics were used to test sensitivity for specification,

misspecification and precision. The Hausman specification diagnostic indicated little

difference in the fixed effect model and the random effect model. The final diagnostic,

Collinearity Condition Number (CCN), measuring the eigenvalue and the determinant of

the correlation matrix for each model is reported on the appropriate tables.30

7.4 Large Firm Level Investment-Russia: Panel Data Results

Generally, like the aggregate level of investment, the reserve ratio is also

indicating importance as a financial repression variable to the large firm level investment.

However, in the large firm level results, other variables indicate possible ENS subsidy

system effects.

7.4.1 Results with Liquidity Variable-Russia: GLS [Random-Effects]

y r -- -T- ir = ,0 + ,2,,_t RESERVE RATIO + 83Ax3,t_LIQUIDITY + A5X5it_ IBANKLENDING +

x6,it-1 MARKET VALUE + XP V, + + t, where v, is the random unit-specific residual and E (v) = (7

Per equation 7-3, within the large firm level investment analysis, for the models

that contain X3(zt-1)LIQUIDITY as an independent variable, the statistically significant

financial repression measures are X2(tI)RESERVE RATIO, X3(zt-1)LIQUIDITY, x5(zt-1)BANK LENDING,

and x6(zt-1)MARKET VALUE.


30 This analysis is an extension to a previous research of the same models using a database that ended June
2002. The negative coefficients exhibited are a consistent result across both studies.









Using Model 3 from Table 7-5, the independent variable X2(it-)RESERVE RATIO has

as coefficient of -0.5 for Denton interpolated data. This suggests that a 1-unit increase in

bank reserves relative to M2 is associated with over a 0.5 decrease in yl(t)INVESTMENT in

the following period. In the large firm models, the importance of bank reserve

requirements by the CBR to firm level investment indicates an understandably negative

relationship, similar to the findings from aggregate level, but less pronounced. It would

be expected that the increase in the reserve ratio would, by reducing the money supply

available to satisfy consumption demand, negatively affect investment through a

reduction in production.

In addition to the previous period aggregate reserve ratio requirement from the

banking system, the regression results also suggest an economically significant impact on

investment from a change in previous period liquidity. Using Model 3 from Table 7-5,

the independent variable X3(t_-)LIQUIDITY has as coefficient of -0.29 for Denton

interpolated data. This suggests that a 1-unit increase in M2 relative to GDP is associated

with over a 0.29 decrease in yl(t)NVESTMENT in the following period.

The regression results also suggest an economically significant impact on capital

investment to a change in bank lending. Using Model 3 from Table 7-5, the independent

variable x5(,t-)BANK LENDING has as coefficient of -1.98 for the Denton interpolated data.

This suggests that a 1-unit absolute increase in deposit bank assets to the sum of deposit

bank assets and central bank assets is associated with a 1.98 decrease in yl(t)INVESTMENT

in the following period. According to Bernstam and Rabushka (1998), in Russia the

banking system does not intermediate between lender and borrower. Instead, the banks









are used to re-intermediate the flow of invoicing from large firms to the CBR, and the

return flow of the subsidy, in the form of loans, from the CBR back to the large firms.

From the above results, the expected relationships on two of the six financial

repression measures, liquidity and bank lending, are counter intuitive to the results

typically observed in economically developed countries. Thus, besides testing the

components in Beim and Calomiris's financial repression index, the results of this

research are producing potential support for the Bernstam and Rabushka's ENS

hypothesis. These subsidies are a throwback to the Soviet Union era, and are a substantial

part of the Russian accounting standards.

The massive subsidies that pass from the Russian Central Bank to firms are at

least a partial explanation for, and possibly compound, the monetary distortions created

by the financial repressive actions taken by the Russian government. The negative

coefficient on liquidity and on bank lending indicates the possible systemic distortion

financial repression and the ENS subsidy are capable of producing. Considering that the

size of the receivables matches, and occasionally exceeds, the sum of the tax non-

remittance and the most liquid money supply, Ml, it is not surprising that variations in

receivables affect the entire Russian economy. This continues to be true even after the

increased petroleum dollars began to fill the foreign reserve vault. Per Bernstam, in a

personal correspondence, on liquidity as capital formation, "... enterprise investment is

largely self-financing (also using a capital consumption allowance and government

capital transfers) while the banking system chiefly recycles enterprise and household

payments and deposits held for payments, re-intermediates rather than intermediates.









When credit expansion is used for payments and, hence, to expand (and inflate) trade

credit, investment suffers."31

In this environment, x3(t-1)LIQUIDITY and X5(zt-)BANK LENDING have a monetary

relationship, but not one of expanding the money supply through lending the deposit

base. Instead, the relationship is the use of bank credit to deliver the subsidy. Similar to

small firms in developed countries, self-financing from internally produced profits is a

dominant form of business and project funding. In a country, like Russia, where external

financing, including foreign direct investment, is available to only the largest firms, as a

result, those firms indirectly control potential future competitors. This extra twist abets

the monetary policies that support large firm interests, often at the expense of the

financing sources needed by the small firms.32

In this analysis, the stock market value variable is consistently important to the

large firms. Using Model 3 from Table 7-5, the independent variable X6(it-1)MARKET VALUE

has a coefficient of 0.87 for the Denton interpolated data. This suggests that a 1-unit

increase in aggregate stock market capitalization relative to GDP is associated with over

a 0.87 increase in yl(zt)INVESTMENT in the following period. While the comparative static is

as expected, it is important to realize that in Russia, the stock market is not the usual

"initial public offering only" funding source for the large firms. The Russian stock

market valuation as an average percentage of GDP hovers around seven percent, and

ownership is highly concentrated. Like the Korean chaebols and Japanese keiretsus



31 Email to author dated March 2005.
32 The importance of informal exchanges to the economic parties operating in illiquid Russia is further
highlighted with the numerous stories of Siberian herders having the "last paper money they had physically
touched had featured portraits of Lenin" (Anderson 2000, p.320).









before them, a small group of business conglomerates in Russia own and control large

sections of the firms that make up the Russian economy. Additionally, like German

banks, Russian banks own shares. Outside the state controlled banks, the conglomerates

own many of the larger Russian commercial banks. Referred to in the popular press as

"pocket banks," these banks often own shares of the companies that own them. Thus, the

monetary usefulness of the Russian stock market as a funding source for Russian publicly

traded firms extends beyond the initial public offering. It is also the trading gains

obtained in a manipulated market.33 "In late 1997, when the government desperately

needed to collect tax arrears from the largest enterprise debtors, it did not approach

enterprises but rather squeezed major banks (e.g., Uneximbank, Russian Credit, etc.) and

got paid" (Bernstam and Rabushka 1998, p. 49).

The positive coefficient on the stock market valuation supports the ENS subsidy

hypothesis. Assuming that the Russian government's only concern with the stock market

is to restrict its growth and the resulting competition to the government controlled

banking system as a financially repressed component, the stock market component of the

Russian financial system is outside the re-intermediating payment cycle of the self-

enforcing ENS subsidy. Thus, the coefficient is as expected, as probably would be the

signs on X3(zt-_)LIQUIDITY and X5(zt-)BANK LENDING if they were not involved in the subsidy

cycle.

Finally, the constant is consistently positive and significant in this set of models

for the large firms. Using Model 3 from Table 7-5, the constant, 3o, has as estimated

coefficient of 1.64 for the Denton interpolated data. This suggests that a 1-unit increase in


33 Claims made are based on personal observations by the author. It is a subject for future research.









the constant, when all financial repression variables are held at zero, is associated with

over a 1.64 increase in yl(t)INVESTMENT in the following period.

Speculating on a possible explanation suggests external financing availability.

The large Russian firms have funding choices outside the national borders. They are not

dependent on the domestic financial markets and thus have a positive constant. Therefore,

when the monetary components that make up the financial repression index are held to

zero, the large firms can still fund business investment.

7.4.2 Results with Private Borrowing Variable-Russia: GLS [Random-Effects]

yl,t INVESTMENT = r0 + /2X21t-1 RESERVE RATIO + P5X5t-1 BANK LENDING
(7-4)
+x + v, + ,,t, where v is the random unit-specific residual and E (v) = 0

Per equation 7-4, within the large firm level investment analysis, in the results for

the models that contain X4(t_-)PRIVATE BORROWING as an independent variable, show x2(0,

1)RESERVE RATIO and X5(,t-)BANK LENDING to be statistically significant.

Using Model 3 from Table 7-6, the variable X2(it-)RESERVE RATIO has as an

estimated coefficient of -0.3 for the Denton interpolated data. This suggests that a 1-unit

increase in required bank reserves is associated with over a 0.30 decrease in

yl'(t)INVESTMENT in the following period. Therefore, the result implies a negative

relationship between X2(tI)RESERVE RATIO and yl(t)INVESTMENT.

An extra benefit to this research being conducted at both aggregate and large firm

investment levels is that some tentative inferences on the monetary policy transmission

channels can be made from the results of the previous period X2RESERVE RATIO being

statistically significant in the Russian models. The results suggest a direct monetary

policy control measure, frequently used by governments in developing countries, plays a

significant part across all economic investment levels. As found historically in developed









countries, internal finance is the dominant form of business and project funding for

developing enterprises. Without funding alternatives, businesses in these countries often

depend on available cash for survival, which is tied to the level of liquidity, which is then

affected by the level of required reserves in the financial system. Additionally, it is

already documented in the literature that in transitional countries there seems to be a

strong connection between illiquidity and barter (Commander and Mumssen 2002).

Using Model 3 from Table 7-6, the independent variable x5(t-1)BANK LENDING has as

estimated coefficient of -2.05 for the Denton interpolated data. This suggests that a 1-unit

increase in the nominal annual rate on deposit bank assets relative to total deposit bank

and central bank assets is associated with over a 2.05 decrease in yl(t)INVESTMENT in the

following period. The negative relationship between x5(t-1)BANK LENDING and

yl(t)INVESTMENT suggests the distortion possible with the banking system being used as an

ENS subsidy payment tool. Additionally, the bank ownership structure in Russia may

facilitate these effects. In 2004, the bank sector capitalization was still only six percent of

Russia's GDP (Tompson 2004). Of the primary state owned banks, Sberbank,

Vneshtorgbank, Gazprombank, and Vneshekonombank, bank assets are dominated by the

holdings of Russian government bonds as assets and not loans to private enterprise

(World Bank 2004). This use of domestic lending to fund government expenses leads to a

financial system dominated by the public sector. Thus, in Russia the commercial banks

resemble individual central bank branches, rather than independent financial

intermediation centers. Of the non-state-owned banks, the majority are dominated by

banks owned by the large financial and industrial groups that are participants of the ENS

subsidy system.









Finally, the constant is positive and statistically significant in this analysis for

Russian large firms, as it was for the previous models containing X3(zt-1)LIQUIDITY as an

independent variable. Using Model 3 from Table 7-6, the constant has an estimated

coefficient of 1.48 for the Denton interpolated data. This suggests that a 1-unit increase in

the constant, when all financial repression variables are held at zero, is associated with

over a 1.48 increase in yI(t)INVESTMENT in the following period. A possible explanation is

that large Russian firms have funding choices outside the national borders.

7.5 Aggregate Level Investment-USA: Descriptive Statistics and Correlations

For comparison, the analysis of the USA database exactly matches that for the

Russian database, including the separate models for liquidity and private borrowing. Of

note on Table 7-7 is the high negative correlation of -0.87 between liquidity and private

borrowing, a result suggestive of a crowding out effect. The USA results support the

overall dissertation theme that systemic distortions are possible when governments

intervene in the financial system. This is reflected in Figures 7-1 and 7-2.

Similar to the Russia analyses, the time series analysis results, listed below, are

produced by autocorrelation-consistent techniques to adjust standard errors. Additionally,

the USA sensitivity diagnostics are the following with similar results:

Univariate diagnostics were used to test sensitivity for skewness, data outliers,

and stationarity of both the mean and the variance. Because the question of stationarity is

of primary importance in using time-series data, the augmented Dickey-Fuller test is

again used. In most countries, monetary variables typically exhibit slow adjustment to









economic changes; thus, like Russia, the USA research variables indicate stationarity

with the Dickey-Fuller unit root tests indicating stationary series.34

Bivariate diagnostics were used to test sensitivity for correlation. The high

correlation found between X3LIQUIDITY and x4PRIVATE BORROWING in the Russia diagnostics

was also found in the USA data (positive for Russian; negative for USA); therefore, the

dual model structure is continued.

Multivariate diagnostics were used to test sensitivity for specification,

misspecification and precision. The Portmanteau statistic was calculated for correlation

with the findings indicating white noise errors, and the results of this final diagnostic are

presented on the appropriate tables.

7.6 Aggregate Level Investment-USA: Time Series Results

In general, and with respect to financial repression, Russian aggregate level

investment indicated having a specific relationship to the required reserve ratio. In the

USA, however, it is the lagged investment that is consistently important across all models

in both investment levels.

7.6.1 Results with Liquidity Variable-USA: ARIMA [AR(1)]

yl INVESTMENT = -, Lyl,_ LAGGED Y1 + X l +(75)
pt, = p,_1 + s,, where p is the first-order autocorrelation parameter

Per equation 7-5, within the aggregate level investment time series analysis, for

the models that contain X3(t-1)LIQUIDITY as an independent variable, the statistically

significant measure is LylLAGGED Y1.




34 Similar to the Russia results, the autocorrelation function and the partial autocorrelation function verify
the Dickey-Fuller results.









Using Model 1 from Table 7-8, the independent variable LylLAGGED Y1 has a

coefficient of 1.01 for the Denton interpolated data (1.04 for the actual data). This

suggests that a 1-unit absolute increase in previous investment is associated with over a

1.01 relative increase in yl(t)INVESTMENT (logarithm) in the following period. The

significance of LylLAGGED Y1 in the United States is consistent across all models in both

the aggregate and large firm investment levels.

7.6.2 Results with Private Borrowing Variable-USA: ARIMA [AR(1)]

yl INVESTMENT = 4X4t,_ PRIVATE BORROWING + A, Lylt_ LAGGED Y1 + X + Pl t ,
(7-6)
p~ = pp/, + E,, where p is the first-order autocorrelation parameter

Per equation 7-6, within the aggregate level investment analysis, in the results

summarized on Table 7-9 for the models that contain X4(t-1)PRIVATE BORROWING as an

independent variable, the statistically significant variables of interest are X4(t-)PRIVATE

BORROWING and Ly LAGGED Y1

Using Model 1 from Table 7-9, the independent variable X4(t1)PRIVATE BORROWING

has a coefficient of -0.14 for the Denton interpolated data (-0.67 for the actual data). This

suggests that a 1-unit absolute increase in claims on private sector relative to total

domestic credit is associated with over a 0.14 relative decrease in yl(t)INVESTMENT

(logarithm) in the following period.

The results indicate a negative relationship between in yl(t)INVESTMENT, and x4(t_

1)PRIVATE BORROWING. Private Borrowing as claims on the private sector relative to total

domestic credit has decreased relative to the growth of total domestic credit, which

additionally contains the credit extended to government. The United States government

expenditures increased and remained high during the 1996 to 2005 research period.

Furthermore, these results hold in all but two models suggesting a potential relationship









between investment and private borrowing, and thus indicating support for potential

crowding out of private enterprise borrowing by increasing governmental borrowing. In

these regards, Figures 7-3 and 7-4 are illustrative.

Again lagged investment is found to be important. Using Model 1 from Table 7-9,

the independent variable LylLAGGED Y1 has a coefficient of 0.99 for the Denton

interpolated data (0.94 for the actual data). This suggests that a 1-unit absolute increase in

previous investment is associated with over a 0.99 relative increase in yl(t)INVESTMENT

(logarithm) in the following period.

7.7 Large Firm Level Investment-USA: Descriptive Statistics and Correlations

Of note, on Table 7-10, is the small volume of investment from previous period

by the USA large firms. This behavior mimics the situation for Russian large firms.

Further inspection of the data indicates that the majority of the transnational firms were

selling off assets during much of the research period, with the technology firms (e.g.,

Hewlett-Packard) selling off assets during the entire period.

Using the same techniques in the USA analyses, the panel data analysis results,

listed below, like those in the Russian research, are produced by autocorrelation and

heteroskedasticity-consistent techniques to adjust standard errors. The USA sensitivity

diagnostics are the following with similar results:

Univariate diagnostics were used to test sensitivity for skewness, data outliers,

and stationarity of both the mean and the variance. Because short panels tend to be

stationary and with the independent variables the same as those used in the aggregate

level analyses, the diagnostic results produce similar results. The difference in this

analysis is that firm level investment being investigated. However, like the aggregate

level investment, and in most countries, monetary variables typically exhibit slow









adjustment to economic changes; thus, like Russia, the USA research independent

variables indicate stationarity.

Bivariate diagnostics were used to test sensitivity for correlation. In addition to

using the USA results for comparison, with the high correlation found between

X3LIQUIDITY and X4PRIVATE BORROWING in the USA data, the dual model structure is

maintained.

Multivariate diagnostics were used to test sensitivity for specification,

misspecification and precision. The random effect model is chosen because of the panels

being short; however, the Hausman specification diagnostic indicated little difference

between the random effect model and the fixed effect model. As with the Russia

analyses, the final diagnostic Collinearity Condition Number (CCN) measuring the

eigenvalue and the determinant of the correlation matrix for each model are reported on

the appropriate tables.

7.8 Large Firm Level Investment-USA: Panel Data Results

Unlike the results for the Russian large firms, financial repression components do

not seem to affect the transnational firms operating in the United States.

7.8.1 Results with Liquidity Variable-USA: GLS [Random-Effects]

ylzt INVESTMENT = f7x717-1 FRM CHARACTERISTIC + Lyl,,-1 LAGGED Y1 (
+xp + v, + t,, where v, is the random unit-specific residual and E (v) = 0

Per equation 7-7, within the large firm level investment analysis, for the models

that contain X3(t-1)LIQUIDITY as an independent variable, the statistically significant

measures are X7(zt-1)FIRM CHARACTERISTIC, and LylLAGGED Y1.

Using Model 3 (dynamic) and Model 7 (static) from Table 7-11, the independent

variable X7(1t-1)FIRM CHARACTERISTIC has as estimated coefficient of 0.02 for the Denton









interpolated data (0.02 for the actual data) in both the dynamic and static models. This

suggests that a 1-unit increase in total revenue relative to total assets is associated with

over a 0.02 increase in yl(zt)INVESTMENT in the following period. The reaction the USA-

based transnational firms have to the financial repression variables is quite different to

their respective Russian firms. In the equivalent analysis on the large Russian firms x2(,t

1)RESERVE RATIO, x3(zt-1)LIQUIDITY, X5(t-1)BANK LENDING, and X6(zt-1)MARKET VALUE are all

statistically significant.

Using Model 3 and from Table 7-11, the independent variable LylLAGGED Y1 has a

coefficient of 0.21 for the Denton interpolated data (0.35 for the actual data). This

suggests that a 1-unit increase in previous investment is associated with over a 0.21

increase in yl(zt)INVESTMENT in the following period. While the Russian large firms were

not affected by previous investment, the USA large firms are affected.

The USA large firm results indicate that lagged investment and total revenue to

total assets, X7(zt-1)FIRM CHARACTERISTIC, matter to the large 28 non-financial firms in the

Dow Jones index. With these results, there is an indication that central bank actions

globally simply do not matter to these huge firms. Their funding sources extend beyond

any one country.

The results support claims made about international banking supply and demand

developments. Daniels and VanHoose (2002, p.200) claim, "The Eurocurrency markets

are at the center of international banking activities.... Even bank-financed investment in

the United States increasingly stems from loans by non-U.S. banks. The largest U.S.

corporations on average use the services of more foreign banks than domestic

institutions." They also state, "Multinational businesses have relationships with









megabanks based in many nations. Indeed, by the 1990s a typical multinational U.S. firm

had accounts with at least as many banks abroad as it maintained with U.S.-based

banking institutions."

7.8.2 Results with Private Borrowing Variable-USA: GLS [Random-Effects]

yl,t INVESTMENT = f7X71 1 ,_FRM CHARACTERISTIC + f, Lyl,-1 LAGGED Y1 (7
(7-8)
+xp + v + t,, where v, is the random unit-specific residual and E (v) = 0

Per equation 7-8, within the large firm level investment analysis, in the results

summarized on Table 7-12 for the models that contain X4(t_-)PRIVATE BORROWING as an

independent variable, show the variables are X7(t-1)FIRM CHARACTERISTIC, and LylLAGGED Y1

as being statistically significant.

Using Model 3 (dynamic) and Model 7 (static) from Table 7-12, the independent

variable X7(t-1)FIRM CHARACTERISTIC has as coefficient of 0.02 for the Denton interpolated

data (0.02 for the actual data) for both the dynamic and static models. This suggests that a

1-unit increase in total revenue relative to total assets is associated with over a 0.02

increase inyl(zt)INVESTMENT in the following period.

Using Model 3 and from Table 7-12, the independent variable LylLAGGED Y1 has a

coefficient of 0.21 for the Denton interpolated data (0.37 for the actual data). This

suggests that a 1-unit increase in previous investment is associated with over a 0.21

increase inyl(zt)INVESTMENT in the following period.

The same results found in the models with the liquidity independent variable hold

in the models that contain private borrowing. Thus, consistently across the large

transnational firms headquartered in the United States the most important investment

determinants are firm characteristics and lagged investment.









7.9 Comparative Statics

While studying the distortions produced by the financial repression and the ENS

subsidy, a useful comparison is the breakdown of expected comparative statics for

economically developed and transitional countries to the results specific to Russia in this

research. Shown on Table 7-13, with respect to investment, historically there would be

expected to be positive statics on all three of the external finance variables X4PRIVATE

BORROWING, x5BANK LENDING, X6MARKET VALUE, and on two of the monetary policy control

variables XlREAL RATES, X3LIQUIDITY, and negative statics on the remaining government

control variable X2RESERVE RATIO. In this research, the expected statics agree with theory in

all but one variable: real interest rates. McKinnon and Shaw in 1973 viewed financial

repression from an investor requiring a positive real interest rate, however, from the

viewpoint of a borrower, the optimal expected static is for real interest rates to be

negative representing a favorable real cost of borrowing.

Comparing theoretical to actual statics, across all levels of investment, the

findings suggest opposite relationships to investment for two of the financial repression

measures: X3LIQUIDITY, and X5BANK LENDING. These findings signal the severity that the

distortions produced by the current Russian financially repressive and governmental

subsidy monetary system is having on the Russia's economy. This claim is further

supported by the positive coefficient sign, which matches expectations, on x6MARKET

VALUE. This indicates that the Russian stock market as a financial system is outside the

ENS subsidy and payment cycle.

7.10 Results: General and Specific Questions

Summarizing the results to the previously stated general and specific research

questions find the follow implications:









1. Generally, are there differences between Russia, with its emerging financial
markets and systems, and the United States, with established financial markets
and systems? The results of this study suggest interesting differences found in the
descriptive statistics. A consistent negative real interest rate, which is the narrow
definition of financial repression, is obvious in the Russia results. This narrow
definition was first theorized by both McKinnon and Shaw, in their 1973
publications. They claimed that deposit interest rate ceilings held below the level
of inflation would have a negative impact on economic growth by affecting an
investor's willingness to forgo consumption for a negative real return on
investment. However, it is theorized in this dissertation that a negative real
interest rate is also an indication of the true cost of borrowing for firms. Thus, for
companies, a negative real interest rate produces a decreasing true borrowing cost.
This fact might help explain the persistence of negative real rates in developing
countries. In comparison, the findings of the USA results reveal a fairly
consistent positive average real interest rate in the United States.

2. Generally, and within Russia, are there differences between the aggregate level
and the large firm level? The results of this study suggest the relationship of
interest is the similarity across both investment levels, and not the differences. An
important relationship of the reserve requirement ratio to all investments is found
in both aggregate and large firm levels. In this study, it is dominant as a
statistically significant financial repression measure across all four analyses. The
reaction to government increases of the required reserves is negative across both
investment levels. Thus, the results show possible distortion from financial
repression measures produced by government actions. In comparison, the findings
of the USA results show an important relationship between previous period
investment and current investment. This relationship holds across both aggregate
and large firm levels of investment. Completely absent from the results is the
notion of financial repression measures distorting investment in the United States.
Thus, the findings show that firms operating from a developed economy with an
established financial system are not affected by financial repression.

3. Specifically, and within Russia, which of the financial repression components
(real interest rates, reserve ratio, economic liquidity, private borrowing, bank
lending, and stock market value) matter to each level of investment? The results
of this study suggest the potential distortionary extent on efficient allocation of
capital from governments conducting financial repression is high. This supports
the findings of Beim and Calomiris (2001) when they found an increasingly
repressive index for Russia. The results from this study support the idea that
governmental actions that preserve tight control of both money stocks and flows,
along with repressing the development of financing sources other than the state
controlled banks hampers economic and investment growth. In Russia, and in
addition to the required reserve ratio, liquidity, bank lending and market value
financial repression measures potentially affect investment. In comparison, the
findings of the USA results reveal the firm characteristic variable, which is a
measure of total revenue to total assets, matter to the large firms operating out of









the United States. At the firm level, this is in addition to the previous investment
affecting current investment decisions. To these transnational companies that have
money available to them from internationally established monetary markets,
financial repression components are not a hindrance to investment. Thus, the
assumption is that firms operating in countries with developed economies and
established financial markets make investment decisions based on business
growth strategies specific to the firm.

4. Specifically, and within Russia, is there evidence of systemic distortions
consistent with the existence of the Russian ENS subsidy? The results of this
study suggest the potential distortionary extent increases when governments add
subsidies to a monetary policy mix that contains financial repression. This
supports Bernstam and Rabushka's (1998, 2006) claim that the ENS (Enterprise
Network Socialism) possibly distorts economic growth in Russia. This study
extends their work to investment by using inferences drawn from the financial
repression results. In particular, and extrapolating from the financial repression
results, the assumption is that the negative relationship large firm investment has
to liquidity and bank lending indicates the possible additional distortion produced
by the Russian ENS subsidy. Further, since firm investment has the expected
positive relationship to the market value measure, it supports the findings. The
supposition is that if the banking sector was not involved in the subsidy payment
cycle, liquidity and bank lending would have the expected positive relationship;
thus, their coefficients would be mimicking market value, which is a proxy for the
Russian stock market. Unlike the banking sector, the Russian Trading System is
not involved in distributing subsidies.



































1000





'I "o "o #0 "Q> "o # 0 ? 4

--M2 (bn) -U-M1 (bn)



Figure 7-1. Monetary Measures: M2 (broad) and Ml (narrow) from the Federal Reserve




7000


6000


5000


4000


3000


2000


1000







--- Private Credit (bn) -- Total Credit (bn)



Figure 7-2. Potential Crowding Out: Private Credit and Total USA Credit


















o400


0350


0300


0250


0 200


0 150


0 100


0 050


0 00




--- Private Credit/Total Credit



Figure 7-3. Ratio of Private Credit to Total Credit



0 0120



00100



0 0080



0 0060



0 0040



0 0020



0 0000






F-- IP/GDP


Figure 7-4. Industrial Production to GDP














Table 7-1. Descriptive statistics and correlation Russia: Aggregate level


yl Lyl xl x2 x3 x4 x5 x6 x8 xl1

DESCRIPTIVE STATISTICS
Mean 5.00 4.96 -0.10 0.34 0.68 1.36 0.64 0.08 723.04 4.44
Median 5.02 4.98 -0.08 0.33 0.64 1.34 0.64 0.08 692.13 1.21
Maximum 6.07 5.95 0.00 0.46 0.97 1.89 0.68 0.14 1534.95 20.76
Minimum 4.14 4.14 -0.65 0.22 0.43 0.83 0.62 0.02 50.24 -10.38
St. Deviation 0.54 0.50 0.11 0.06 0.15 0.25 0.01 0.03 326.60 10.04
Observations 29 28 29 29 29 29 29 29 29 29

CORRELATIONS
yl INVESTMENT 1.00
Lyl LAGGED Y1 0.52 1.00
xl REAL RATES 0.45 0.40 1.00
x2 RESERVE RATIO -0.44 -0.22 0.11 1.00 00
00
x3 LIQUIDITY 0.65 0.59 0.29 -0.53 1.00
x4 p BORROWING 0.63 0.54 0.40 -0.30 0.79 1.00
x5 B LENDING 0.39 0.49 0.28 -0.05 0.37 -0.21 1.00
x6 MARKET VALUE 0.61 0.53 0.44 -0.31 0.69 0.48 0.43 1.00
x8 OIL 0.64 0.65 0.46 -0.40 0.68 0.64 0.12 0.62 1.00
xO0 SEASONAL -0.27 -0.53 0.01 0.15 0.14 -0.32 -0.17 0.13 -0.02 1.00
VARIABLES: (yl) Gross fixed investment (logarithm); (Lyl) One lag ofyl; (xl) Real interest rates: Nominal annual on bank deposits adjusted for realized annual
inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less MO (one-period lag); (x3) Liquidity: Short term liquid
liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank lending: Deposit bank
assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period lag); (x8)
Real ruble price for oil (one-period lag); (xlO) Temperature for seasonal effects (degrees Celsius); RESEARCH PERIOD & PERIODIC INTERVALS: (aggregate-Russia)
December 1998 to December 2005 (29 actual quarters, 85 Denton months); SOURCE ALL VARIABLES: Russian Economic Trends database, Central Bank of
Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF INVESTMENT AND RATIO COMPONENTS: Rubles, billion














Table 7-2. Estimation Russia: Aggregate level investment-with liquidity variable


Constant
(p-value)
xl REAL RATES
(p-value)
x2 RESERVE RATIO
(p-value)
x3 LIQUIDITY
(p-value)
x5 B LENDING
(p-value)
x6 MARKET VALUE
(p-value)
x8 OIL
(p-value)
x10 SEASONAL
(p-value)
Lyl LAGGED Y1
(p-value)
Observations
Correlation


ARIMA [AR (1)]: y,
(1) (2)
Actual: Quarterly
4.07 2.66


(0.20)
0.95
(0.54)
-3.34
(0.04)
-1.29
(0.48)
1.96
(0.61)
11.40
(0.09)


0.17
(0.61)
28
0.43


(0.44)
0.71
(0.66)
-3.17
(0.04)
-1.63
(0.41)
4.74
(0.42)
10.63
(0.14)
0.01
(0.59)


0.07
(0.86)
28
0.42


xtP + ,,
(3)

5.70
(0.53)
1.70
(0.52)
-0.56
(0.05)
-2.17
(0.11)
2.40
(0.60)
5.96
(0.12)


0.04
(0.09)
0.72
(0.50)
28
0.37


At = P-1 + et


t = 1,2,..., T


(4) (5) (6) (1) (2)
Denton: Monthly
3.79 2.34 5.03 1.76 1.74


(0.24)
1.15
(0.43)
-3.39
(0.02)
-0.94
(0.53)
3.56
(0.47)
10.62
(0.09)


(0.47)
0.75
(0.61)
-3.22
(0.03)
-1.63
(0.31)
5.82
(0.29)
10.36
(0.55)
0.01
(0.42)


(0.18)
1.03
(0.37)
-2.30
(0.03)
-0.37
(0.34)
2.58
(0.60)
10.40
(0.06)


0.01
(0.05)


28 28 28
0.34 0.36 0.31


(0.34)
0.89
(0.51)
-2.75
(0.05)
-0.26
(0.09)
0.89
(0.66)
2.71
(0.09)


0.61
(0.41)
84
0.44


(0.35)
0.88
(0.61)
-2.75
(0.05)
-0.26
(0.09)
0.92
(0.65)
2.72
(0.17)
1.74
(0.35)


0.61
(0.57)
84
0.41


(3) (4) (5) (6)


0.90
(0.60)
0.68
(0.56)
-1.64
(0.04)
-0.20
(0.21)
1.62
(0.53)
1.36
(0.10)


0.01
(0.06)
0.67
(0.53)
84
0.36


1.89
(0.32)
2.10
(0.50)
-3.25
(0.04)
-0.21
(0.30)
1.10
(0.38)
2.98
(0.08)


1.47
(0.39)
1.10
(0.67)
-2.29
(0.04)
-0.31
(0.16)
2.47
(0.30)
1.63
(0.57)
0.01
(0.40)


2.09
(0.30)
2.14
(0.30)
-3.28
(0.03)
-0.22
(0.29)
1.86
(0.51)
2.04
(0.06)


0.01
(0.17)


84 84 84
0.29 0.32 0.31


ESTIMATED-DEPENDENT VARIABLE: (yl) Gross fixed investment (logarithm); ESTIMATORS-INDEPENDENT VARIABLES: (xl) Real interest rates: Nominal annual on bank
deposits adjusted for realized annual inflation (one-period lag); (x2) Reserve ratio: Bank reserves / M2 (money plus (some) quasi-money) less MO (one-period lag); (x3)
Liquidity: Short term liquid liabilities M2 / GDP (one-period lag); (x4) Private borrowing: Claims on private sector / Total domestic credit (one-period lag); (x5) Bank
lending: Deposit bank assets / Deposit bank assets plus central bank assets (one-period lag); (x6) Market Value: Aggregate stock market capitalization / GDP (one-period
lag); (x8) Real ruble price for oil (one-period lag); (xlO) Temperature for seasonal effects (degrees Celsius); (Lyl) One lag of yl; RESEARCH PERIOD & PERIODIC
INTERVALS: (aggregate-Russia) December 1998 to December 2005 (29 actual quarters, 85 Denton months); SOURCE ALL VARIABLES: Russian Economic Trends database,
Central Bank of Russia, and Goskomstat (State Statistical Agency of Russia); UNIT OF RATIO COMPONENTS: Rubles, billion; ACRONYMS AND ABBREVIATIONS: (p-value)
Two-tailed test Ho: k = 0; MODELS (SEE METHODOLOGY SECTION): (ARIMA) Autoregressive integrated moving average; DIAGNOSTICS-CORRELATION: Portmanteau Ho
= White Noise; All standard errors are adjusted for potential autocorrelation; PROPORTIONAL DENTON: Procedure is used to increase periodic interval; the index of
industrial production is used as an interpolating variable. See IMF manual: Bloem, A., R. Dippelsman, and N. Maehle, 2001, "Quarterly National Accounts Manual:
Concepts, Data Sources, and Compilation," International Monetary Fund.