The Economic Impact of Non State Actors on National Failure [PDF]

The Economic Impact of Non State Actors on National Failure Colombia: A Case Study And An Economic Regression of Social Development Indicators As Indicative of National Failure

LtCol Leslie N. Janzen, United States Marine Corps Midn 2/c Alpa Patel, United States Navy

Department of Economics United States Naval Academy 589 McNair Road Annapolis, Maryland 21402 United States of America

The views expressed in this paper are those of the authors and do not reflect the official policy or position of the Department of Defense or the United States Government.

Part I: Impact of Non State Actors National failure is something of a “buzz word” these days among the policy makers and defense policy analysts in the United States. The definition of national failure, however, is much less clear. From the political perspective, examining issues of statehood and the definition of nation states, it can be defined at its simplest terms as the breakdown in the social contract between the governed citizens and the governing body. The state has “rights, abilities, and obligations.”1 States fail when they no longer possess and can no longer fulfill the obligations, of statehood. Leaders of failed states do not have the wherewithal to compel internal order, or to deter or repel external aggression, or they cannot sufficiently provide for the common weal to attract minimal domestic support.2 No longer can the citizens of a nation rely upon the government and national institutions to provide them with basic services, within the capabilities of the government. Education becomes a privilege, not a right. The basic social welfare provisions so commonly accepted in the industrialized world today are unimaginable in the failed state. Internal and external actors, both state and non-state, impact the state today. While state actors are fairly well defined, non-state actors may be as widely diverse as environmental groups, pushing for international standards that will guarantee protection of endangered species, or criminal elements that use the state as a staging ground for their activities and thus have a vested interest in a weak state government. They may have positive or negative spillover effects on citizens of nations and their neighbors. They may seek to provide public goods which the state is not providing, such as education or health care, or they may seek self-aggrandizement and engage in criminal activities. They may be non-profit, legitimate organizations, or they may be rentiers who generate revenue from illicit activities. The reaction to the perceived impact of non-state actors covers a wide spectrum, from concern or approval of environmental and worker safety standards to fear of loss of national identity due to the cultural impact of mass media. The recent demonstrations in Seattle, Washington, DC, and Davos, Switzerland are indicative of the wide ranges of response from people and organizations of all ideologies to the perceived threat generated by legitimate corporate, non-governmental, or governmental entities which are now nonstate actors. The economic impact of entities that are far less benign than juridical, charitable, corporate, environmental, or social organizations is widely recognized but far less well understood. Their impact on national failure is traditionally underreported and only now beginning to be examined in depth in various literatures. In part this is due to the availability, or lack thereof, and veracity of data from nations that could be considered to be failures. Many nations report data that is, at best, a guess. In some nations there is no functioning bureaucracy that can collect, correlate and analyze data to provide it to international bodies. In others, the data is self serving and inaccurate. In some cases, it simply does not exist. Additional complications arise when attempting to examine 1

Douglas Dearth, “Redefining the State,” paper presented at InfoWarCon 97, page 2, accessed via www.infowar.com/iwcon/iwcon_dearth1.html-ssi, accessed 10 Jan 01. 2 Ibid, page 7.

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black/gray market activities. It is highly unlikely that the Mafiya in Russia would allow economists and business analysts a look at its financial data. Similar concerns exist for the diamond smugglers in Rwanda, Sierra Leone, Liberia, and Uganda and narcotraffickers in Central America. The term “Failed state” brings images of ethnic and religious warfare, of governments under siege from insurgencies, whether ideologically or economically grounded. The economic impact of an insurgency is traumatic on the state but relatively well understood. More and more monies must be spent to combat it, bringing a greater and greater percentage of the federal budget into the arena of military/law enforcement and, if productivity is not increased, leaving less for social programs. Insurgents may have significant impact on economic production. An example is the decline in oil production in Colombia, where the FARC and ELN have been able to attack oil pipelines. Colombian oil production fell 15.7% in the year 2000, due to attacks by the rebel groups and depletion of existing stores. While Colombia has large oil deposits, the major oil companies are reluctant to undertake exploration. The risks to their physical and personnel assets are huge. Since 1986, FARC and the ELN have bombed the country’s main oil pipelines more than 1,000 times and earned roughly $150 million a year in ransom payments and extortions. Local contractors hired by the oil companies to repair the damage are also taxed by the rebels.3 Kidnapping is a major method of finance for the rebel groups and the oil companies are prime targets. Kidnapping in Colombia has gone from 100 reported in 1990 to 3,706 in 2000. Seventy percent of abductions are performed by guerillas. Ironically, it is preferable to be kidnapped by guerillas, as those persons abducted by paramilitaries are prone to “disappearing.”4 Forty three percent of Colombians fear being kidnapped. 5 Additionally, over 1 million Colombians have left their native land in the past 5 years. 6 Combining criminal activity with civil war increases the economic risk to the state. The impact of criminal activity can act as a magnifier lens or multiplier effect for the other difficulties the state faces. On the economic side, monies get diverted into private, unnumbered accounts in other nations, to the black or gray market, into private investments outside the nation, or simply squandered. Imports shift from capital goods, which will assist in economic development, to luxury goods, benefiting only a small percentage of the population. Foreign reserves go to pay for luxury goods when they could be used better elsewhere. National revenue may never be collected in the first place. Tax monies, which might have gone into the nation’s coffers, are instead used to pay for protection from criminal elements’ more unpleasant activities. Conversely, those revenues may be paid to non-state actors to provide services which the state can no longer provide, whether due to lack of control over an area or a breakdown of institutional capability. While arguably the first example, kidnapping, is, from the citizen’s perspective, the least optimal, both impact the nation-state negatively across

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“Rebels Aiming at Colombian Economy,” Stratfor.com, 5 March 2001, accessed via ebird.dtic.mil, 6 March 2001. 4 Marc Cooper, “Plan Colombia,” The Nation, 9 March 2001, accessed via ebird.dtic.mil, 5 March 01 5 Ibid. 6 Arnaud de Bourchgrave, “Four Way Civil War Makes Colombia A Nightmare,” Washington Times, 13 February 2001, accessed via ebird.dtic.mil, 14 February 2001.

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many spectrums. Revenues which are paid to employees of criminal activities for their work, such as coca farmers, never make their way into the state’s coffers. The impact of the results of this criminal activity on the legitimate business sector can be staggering. For instance, the laundering of money on a large scale as a result of criminal activity has a significant negative impact of the business, banking, and financial sector of a nation. The high ethics and integrity required to maintain faith in these institutions is seriously undermined. The attitudes of regulatory authorities, and ordinary customers, can be seriously damaged if it is believed that a financial institution is involved in money laundering. Between $590 billion and $1.5 trillion, or approximately between 2 and 5 percent of the world’s Gross Domestic Product (GDP), is believed to have been laundered in 1998, roughly the total output of an economy the size of Spain.7 Inexplicable changes in money demand and supply, particularly the M1, and increased volatility in capital flows and exchange rates due to unanticipated cross-border asset transfers can all help undermine a state’s fiscal and economic stability.8 Economic productivity can be undermined as well, dropping off due to payments of “protection money” or fear/intimidation. Workers may be reluctant to be productive if they are worried about their safety and that of their families based on their work related activities. Those concerns aside, the impact of non-state actors can be examined conceptually and intuitively from a basic supply and demand perspective. Economics is the study of scarce resources and their allocation among various users. The most common framework in which economics is studied in the international arena is with the use of money as a medium of exchange. In part this is because money is portable, divisible, holds wealth over time, and serves as a constant method of accounting.9 It is also because we speak, when we speak of the wealth or poverty of nations, of issues that can be primarily, measured using money. Among these are such things as Gross Domestic and National Product (GNP/GDP), trade deficits or imbalances, tax revenue, government expenditures, percentages of population living above or below the poverty line, percent of national budget spent to pay interest on foreign debt and on arms/national defense, and so forth. In other words, when we think about economics, and talk about economics, we use, and speak in, the language of money. We chose Colombia as a case study because of our professional concern – the increasing United States involvement in Plan Colombia may someday impact the United States military far more than it has thus far – and because we feel that the increased linkages between the illegal narcotics industry and the revolutionaries (FARC and ELN) have had and will continue to have a great impact on the nation’s economy and its success as a viable government. The ceding of large tracts of territory by Andres Pastrana, president of Colombia, to the FARC and the illegal narcotics industry is indicative of a failure of the nation state to control the territory which it views as an integral part of itself. One could use William Zartman’s analogy of national failure being like a car going over a cliff – it doesn’t occur all at once but in several steep sliding motions. We are concerned that Colombia has taken the first slide down to the cliff’s edge. What the beleaguered government and citizens of that nation will do, or even can do, to stop the slide will remain to be seen. Perhaps the best quote to describe the 7

“Money Laundering,” OECD Observer Policy Brief, OECD, 1999, page 2. Ibid, page 3 9 Irvin Tucker, Survey of Economics, Cincinnati, South-Western College Publishing, 2000, pages 396-397. 8

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situation in Colombia is given in an article in The Nation, 9 March 2001, by Marc Cooper: “What we have in Colombia isn’t a civil war. What we have in Colombia is a war of armed actors against a civil society.”10 The human rights implications of the conjoining of guerillas and narco traffickers, and the subsequent reaction by the nation’s armed forces, police, and paramilitaries, is great – in an effort to control both, the government has not acted with restraint. In an effort to fund operations, the guerillas have expanded kidnappings and trading cocaine and drugs for weapons.11 The spraying of exfoliates over coca growing regions in an effort to prevent production is a double edged sword – the chemicals will kill the coca, but also the legitimate crops such as beans, corn, plantain, maize, etc. The farmers are left with little or nothing, and so far the economic and developmental aid to the farmers, which must be a part of any successful eradication plan, appears to be slow in coming. Issues of human migration also occur. Fall out of the “war” on the narco traffickers impacts Ecuador in the form of refugees from Colombia.12 In Venezuela, cattle ranchers announced, on 29 January 2001, the intent to form an armed group whose objective was to drive Colombian guerillas out of the border. The guerillas, however, are claiming that they have an agreement with Venezuelan President Chavez “…not to attack the Venezuelan armed forces and not to kidnap. But we are allowed to extort protection money.”13 (President Chavez’ recent declaration of Venezuelan neutrality vis-à-vis the Colombian conflict has, if anything, exacerbated the situation.) And while Bolivia has been successful in eradicating coca as an unenviable export, the cultivation of coca has shifted more and more to Colombia, thus proving that spillover effects of a program must indeed be taken into consideration with any eradication program.14 Panama appears to be particularly vulnerable given that it has no standing army and the border area is a favorite route for the smuggling of drugs.15 The regional situation is not unlike the analogy of driving the drug dealers out of one neighborhood – they simply go to another. However, Colombia’s problems cannot be blamed on the narcotics traffickers and guerrillas individually. The linkages between them appear to be strong and growing stronger.16 The heated debate on the issue of illegal drug use in the United States and Europe simply reflects the concern on the matter – we can attempt to control supply by controlling the production of such narcotics as cocaine and heroin, or we can attempt to control demand by instituting better treatment and education programs, or the developed world can attempt to do both. Either way, the demand for illegal drugs in the developed world is fueling the success of the narcotics traffickers and guerillas who are undermining the state in Colombia. 10

Cooper, op. cit Larry Rohter, “Rebels linked to Drug Trade,” New York Times, 4 March 2001, accessed via ebird.dtic.mil, 5 March 2001. 12 Patrice M. Jones, “Colombia’s Drug War Spills Into Ecuador,” Chicago Tribune, 13 February 2001, accessed via ebird.dtic.mil 14 February 2001. 13 Phil Gunson, “Frontier Clash Pits Cattlemen Against Guerillas,” Christian Science Monitor, 8 March 2001, accessed via ebird.dtic.mil, 9 March 2001. 14 Christopher Marquis, “U.S. Finds That Coca Cultivation is Shifting Sharply to Colombia,” New York Times, 2 March 2001, accessed via ebird.dtic.mil, 2 March 2001. 15 “The Colombian Cauldron,” Jane’s Foreign Report, 5 October 2000, accessed via Lexus-Nexus Academic Universe, 13 Jan 01. 16 Rohter, op. cit. 11

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When examining economic aspects of national failure, the overarching impact of criminal non-state actors can be difficult to assess in some areas and easier in others. We can postulate that the following will occur or be observed when criminal non-state actors are present, and that such occurrences will worsen when an insurgency is also present: 1. The state will spend a greater portion of its federal budget on defense/military/law enforcement in an effort to control a separatist insurgency and criminal activities, thus leaving a smaller percentage of revenue available to provide social services or for legitimate policing of criminal activities. 2. Fewer social services will be provided by the state. If the non-state actors then provide these, the population has little incentive to pay legitimate taxes to the state. 3. The more of the state’s tax revenue that is diverted immediately by the payer to non state actors who have assumed the traditional provision of social services, and who may be the main employer of a significant percentage of the population in an area, the less legitimacy the state government will have in that location and the more heavily taxed will be the population in the area where it still holds sway. 4. The increased perceived legitimacy of the non-state actors providing social services (see point 2 above) will act as disincentive for the ceding of tax revenue to the legitimate state by the affected population. 5. The area the state controls will diminish as it struggles to provide basic services and is required to devote more and more resources to controlling its population and crushing the non-state actors. 6. Corruption will increase as the non-state actors influence, by monetary and threat means, the direction of the state’s enforcement efforts. Money laundering will increase and negatively impact the financial systems and institutions of the state. 7. The issue of jurisprudence, from the cop on the beat to the judge in the courtroom, will become critical. The rule of law will be more and more difficult for the state to enforce as the non-state actors influence everything from court case outcomes to legislation to law enforcement. Corruption of officials undermines the legitimacy of the entire process. Additionally, the courts may be used themselves by the criminal actors, either through threatening judges and officials or through corruption of such officials. 8. Income disparity will increase, either because of the increased taxation efforts of the state or because chaos increases in the areas controlled by the non-state actors, driving the rural poor into the urban areas, usually the last place the state loses control, where they become a part of the large numbers of unemployed/underemployed – all of whom fled the same thing and sought the same thing as this latest rural economic refugee influx. The rich will barricade themselves into walled, gated communities, and the poor will live in increasingly chaotic areas. 9. Unemployed youth are prone to radicalization and seek outlets for frustration, thus increasing crime and unrest in the cities. Raised

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expectations caused by mass media, which it seems is inescapable these days, increase that frustration. The state must now spend even more of its scarce resources in an effort to control not only the non-state actors but also its discontented, radicalized poor. The radicalized poor are prone to seek violent political solutions to their problems and thus provide fertile ground for further activities of non-state actors. The non-state actors, in the mean time, are growing wealthy and will engage in rent seeking behavior, thus becoming quasi-legitimate actors on the stage of the state’s economy. The non-state actors are the importers of luxury goods, the builders of schools and roads and harbors and airports, of clinics for their workers. Their employees and advocates serve as the mayors of towns, as the town’s teachers and doctors, as the enforcers of social customs and the regulators of behavior of the residents of the area. They obtain income, invest it in legitimate enterprises, and in addition to the monies gained from illegal activities, also now have legitimate business concerns. These legitimate business concerns allow the non state actor rentiers to move into social and business circles which allow them to broaden their legitimate business and political influence, thus worsening the cycle of corruption and violence and reinforcing the futility of the state’s efforts to quash activities. Part II A Statistical Regression

Originally, we decided to undertake a project which would attempt to correlate some conditions that are indicative of national failure with the presence of criminal nonstate actors. 17 Our idea was to attempt to expand the CIA’s State Failure Task Force Project’s findings and focus on economic aspects to find out if there was or was not a single criterion which could be indicative of the economic failure of a nation.18 While some of the CIA’s variables can be quantified according to accepted measures, others are more difficult to assess. Infant mortality is an easily quantifiable variable – in cases where it is accurately reported. The same holds for trade deficits and tariffs/quotas. However, the variable levels of democracy have no such commonly accepted definition and economic measurements. Having said that, the work in the CIA’s State Failure Task

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We would like to thank Assistant Professors M. Baker and S. McCoskey from the United States Naval Academy Department of Economics for their assistance and guidance in the structuring of this project. Thanks are also owed to Midshipmen 2/c Awad and 2/c Olivier in the Honors program of the Economics Department for their assistance in “crunching” the data. 18 Esty, et.al. “Working Papers: State Failure Task Force Report. McLean, VA: Science Applications International Corporation, 30 November 1995 and State Failure Task Force Report: Phase II Findings, 31 July 1998. Documents were used in toto.

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Force Project’s Phase II Findings was invaluable as a conceptual starting place for this work. The points above provide us a working start for the examination of the economic impact of non-state actors on national failure. It can be approached from a supply and demand perspective. The supply of revenue to the nation is limited by the amount of revenue that would otherwise go into national coffers being diverted into the gray/black economy and to non-state actors to provide the functions the nation has traditionally provided, such as schools and roads. The demand for revenue by the state is approached by an examination of how much money the nation must spend to combat the non-state actors and their impact on the activities of the nation. The greater the impact on the state by the non-state actors, the more money needed to combat their impact – either militarily or legally. Would there be a correlation that could be determined that would actually let us see the impact of non-state actors on national revenue and expenditures? It was at this point that we realized that we had encountered a problem, or, to use naval parlance, had “struck a reef.” Data availability and a limited time frame did not allow us to actually examine the funding of Colombia’s state government, its revenues and expenditures. Nor, if we were to examine military spending, would we necessarily be able to judge its relevance as a regressor in determining national failure. A state may have relatively high levels of military spending because it is developed and a superpower, or a regional power. It may be in alliance with other nations and spend monies to attain parity. Or, conversely, a state may spend high levels of its budget on military spending to put down an internal rebellion or because its military is integrated into its economy, such as Zaire or China. Given this difficulty, and the distortion of data which may be encountered as a result of lack of transparency, we realized quickly that as much as we would like to use military spending as a part of a regression, the results would be subject to interpretation and present some difficulties. What, therefore, could be used? Were there models which would let us still examine issues of economically quantifying failure, with or without the impact of non-state actors? We attempted to correlate various data to determine if there was a single criterion that would act as a benchmark to predict national failure. We theorized that there may or may not be one single deciding factor which will predict national failure from an economic perspective. Various social development indicators reflect certain economic data, and from certain economic data, other things could be construed. A classic example is number of doctors per 1,000 persons, which leads to certain expectations about a nation’s health care system. We used Colombia as a case study and compared it to several other nations across a spectrum of data, both economic and social. The countries to which we compared it were Argentina, Brazil, Chile, France, Germany, Jordan, Netherlands, Russian Federation, Thailand, the United States, and Venezuela. We felt that this was an interesting cross section of developed and developing nations, of nations which had been in crisis both economically and politically in the past and currently (Argentina, Brazil, Chile, Russian Federation), and that the United States would provide a “bench mark” for success as a nation or not. While we admit that this is a nationalist perspective, we also accept the fact that we are able to find data more easily for the United States and feel that the United States’ impact on the world justifies its use as a target/model. We tried to present a cross section of geographic regions and areas as well, but found ourselves

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constrained by lack of data on some nations. For example, we wanted to include several nations from Sub Saharan Africa, as we felt that these would provide us with some additional data samples, which would provide more balanced and rich statistical findings, but the lack of data availability precluded their use. Data was obtained from the World Bank World Development Indicators 2000 data set.19 A range from 1985 to 1997 was utilized for twelve nations. This should have yielded approximately 180 observations, though through a lack of data, we were forced to work with roughly 59. The software used was SPSS. The formula we used for regression analysis was different in terms of the “y” or dependent variable. We intended to utilize the dependent variable as an indicator of state failure. We created an index where a higher value of “y” indicates a more stable or successful nation. In order to truly capture the notion of failed nations, we decided that it was necessary to have more than one indicator represented in the dependent variable. With that, we used a combination of indicators that were weighted to reduce their disproportionate impact on the findings. Our “x” or independent regressors that in theory are to explain the variations in “y” are listed below. General Government Consumption (% of GDP) General government consumption includes all current spending for purchases of goods and services (including wages and salaries). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. Industry, value added (% of GDP) Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 2. Net Income (BoP, current US$) Net income refers to receipts and payments of employee compensation paid to nonresident workers and investment income (receipts and payments on direct investment, portfolio investment, other investments, and receipts on reserve assets). Income derived from the use of intangible assets is recorded under business services. Data are in current U.S. dollars. Consumer price index (1995 = 100) Consumer price index reflects changes in the cost to the average consumer of acquiring a fixed basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. 19

The World Bank, World Development Indicators, 2000, Washington, DC, The World Bank, in toto. Both print and CD ROM versions were used.

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Aid (% of GNP) Official development assistance and net official aid record the actual international transfer by the donor of financial resources or of goods or services valued at the cost to the donor, less any repayments of loan principal during the same period. Aid dependency ratios are computed using values in U.S. dollars converted at official exchange rates. Bank and trade-related lending (PPG + PNG) (NFL, current US$) Bank and trade-related lending covers commercial bank lending and other private credits. Data are in current U.S. dollars. Export duties (% of exports) Export duties include all levies collected on goods at the point of export. Rebates on exported goods--that is, repayments of previously paid general consumption taxes, excise taxes, or import duties--should be deducted from the gross receipts of the appropriate taxes, not from export duty receipts. Data are shown for central government only. Net foreign assets (current LCU) Net foreign assets are the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities. Data are in current local currency. Official exchange rate (LCU per US$, period average) Official exchange rate refers to the actual, principal exchange rate and is an annual average based on monthly averages (local currency units relative to U.S. dollars) determined by country authorities or on rates determined largely by market forces in the legally sanctioned exchange market. Taxes on international trade (% of current revenue) Taxes on international trade include import duties, export duties, profits of export or import monopolies, exchange profits, and exchange taxes. Current revenue includes all revenue from taxes and nonrepayable receipts (other than grants) from the sale of land, intangible assets, government stocks, or fixed capital assets, or from capital transfers from nongovernmental sources. It also includes fines, fees, recoveries, inheritance taxes, and nonrecurrent levies on capital. Data are shown for central government only. Public spending on education, total (% of GNP, UNESCO) Public expenditure on education (total) is the percentage of GNP accounted for by public spending on public education plus subsidies to private education at the primary, secondary, and tertiary levels. Subsidies and other current transfers (% of total expenditure) Subsidies and other current transfers include all unrequited, nonrepayable transfers on current account to private and public enterprises, and the cost of covering the cash operating deficits of departmental enterprise sales to the public by departmental enterprises. Data are shown for central government only.

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Foreign direct investment, net inflows (% of GDP) (BX.KLT.DINV.DT.GD.ZS) Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. Gross domestic fixed investment (% of GDP) Gross domestic fixed investment includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including commercial and industrial buildings, offices, schools, hospitals, and private residential dwellings. Private consumption (annual % growth) Annual growth of private consumption based on constant local currency. Aggregates are based on constant 1995 U.S. dollars. Private consumption is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers) purchased or received as income in kind by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. In practice, private consumption may include any statistical discrepancy in the use of resources relative to the supply of resources. Illiteracy rate, adult total (% of people aged 15 and above) Adult illiteracy rate is the percentage of people aged 15 and above who cannot, with understanding, read and write a short, simple statement on their everyday life. We initially picked these x regressors because we felt that they would adequately explain the variations found in the y. In the first models, Y consisted of the stability of trade openness, an idea generated from the findings from the CIA Task Force, and military expenditures. As mentioned above, the ambiguity inherent in the interpretation of military expenditures presented us with difficulties in analyzing the results of the model. Models One and Two utilized military expenditures and trade openness as indicators of national failure. We felt these two variables would allow us to see if a correlation existed between the impact of these factors on a nation’s economy and our index of national failure. Model One consisted of a y variable that had military expenditures and trade openness weighted equally, at .5. Military expenditure, however, was given a negative value of the weight. We felt, after much debate, that a nation that spent a great deal of money towards bolstering its armed forces most likely had some internal conflict and was subsidizing its military in order to protect the state. For that reason we gave this indicator a negative weight. This initial model made sense conceptually, however, we soon realized that this formula presented additional difficulties in analysis. Putting aside the vagueness of interpretation of military expenditures, we realized that the magnitude of the data presented for trade openness far exceeded that of the other indicator. In other words, the dollar volume of trade openness far exceeded that of military expenditures.

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As a result of this the findings from Model One were skewed. Military expenditure was not represented at all because the data for trade openness overshadowed it. With that, we proceeded with Model Two. In Model Two we still held firm with our two indicators, however, we corrected the overrepresentation of trade openness by weighting the variables differently. Our dependent variable consisted of trade openness being given the weight of .1, and military expenditure was weighted at -.9. The results continued to contain ambiguity. Analyzing Model One’s results led us to believe that we had again “struck a reef.” Certain x regressors that we intuitively believed would affect the indicators positively had in fact a negative effect according to the regression. Bank and trade related lending, for example, displayed a negative value for its coefficient. This did not make sense. We expected that in a stable nation where y would be increasing, bank and trade related lending would also be positive. The same intuitive analysis caused us to second guess the findings for foreign assets. A stable nation that engages in world trade will have foreign assets as its corporations and private individuals seek a globally based portfolio and engage in direct foreign investment. Additionally, foreign assets help pay for investment. This directly correlates to the nation’s level of openness to trade, one of our y indicators. In Model One, we saw the coefficient for this regressor to be negative. This did not make sense to us. The biggest anomaly that we discovered from Model One’s regression was the negative value for the coefficient of private consumption. This negative value was clearly illogical given our assumptions. We would have expected private consumption to increase as a nation became more stable. In a stable nation, its people enjoy a higher standard of living and have access to a wider variety of goods. The regression showed that though foreign assets was statistically significant to a good degree in explaining y, the regressors of bank and trade related lending and private consumption were not. This again did not make sense. These discrepancies led us believe that our indicators were flawed and required modification. Accordingly, we redeveloped our model. In Model Two, we still felt that military expenditure was a viable indicator of stability. We therefore kept it in the model but this time weighted it differently. We noticed that the dollar volume of trade openness overwhelmed the data presented for military expenditure. The new model’s weights of .9 and .1 for military expenditures and trade openness respectively we felt would provide more accurate results and perhaps reduce the anomalies seen with Model One. However, we were again faced with data that did not match intuition. Although some of Model One’s discrepancies were corrected, Model Two presented new discrepancies. General government consumption assumed a negative value for its coefficient. We would have expected that an increase in the weight of military expenditures would have resulted in an increase in general government consumption. As spare parts, pieces, maintenance, and personnel are fielded to meet the demands of increased military expenditures, government consumption increases. The data showed differently. Also, of our three models, this model was the only one in which subsidies and other transfers assumed a negative value. Although not statistically significant, this single change in value and other issues with the findings made us question the validity of the model. From here, we proceeded to Model Three where we began to truly question the value of using military expenditures as an indicator for state stability. Given the

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problems of explaining the impact of military expenditures as mentioned above, we decided to replace this indicator with that of illiteracy. In our opinion, illiteracy was good replacement for military expenditures in that it would be an indicator of the standard of living engendered by government expenditures. We also felt that this was in keeping with the CIA’s Task Force findings; data on infant mortality was not available, but we felt that illiteracy would capture the same level of state success or failure as infant mortality. The same issues of public health and education are inherent in both. In Model Three, we weighted illiteracy with -.8 due to its inverse relationship with state success. We gave trade openness a weight of .2; again, the disparity in magnitudes in the data forced us to compensate with uneven weightings. This model proved to be the most successful of the three. With an R square adjusted value of .916, it demonstrated that the x regressors were indeed significant in affecting the y, our indicator of stability. Though this R square adjusted value is not as high as that found in Model One (.945) we felt that the ambiguity in the interpretation of military expenditures significantly impacted the validity of that model. Again, however, anomalies occurred with this new model that disproved intuition. Government consumption continued to have a negative coefficient. The official exchange rate did not correspond to our expectation as well. Its positive coefficient implied that a more stable nation had a weaker currency. This surprised us. As a side note, we were also surprised by the negative coefficient value given to foreign assets in all three models. Intuition would lead us to believe that as stability increases, so would the amount of foreign assets held by corporations and private individuals. After further introspection, however, we realized that another interpretation of this regressor could explain the negative coefficient. In failing nations, the population has a tendency to move its wealth outside of the country into more stable countries where value of assets will be maintained and potentially increased. With that logic, the negative value does not appear to be so erroneous. This leads us to believe that there are many alternate interpretations of not only the indicators, but the regressors as well. Having explored these three models we have realized that further research needs to be done in this area. The initial concept of doing a regression analysis using indicators for state failure as dependent variables is valid. The issue lies in properly identifying the indicators and the regressors that should best fit the model. This task becomes difficult due to the inability to truly quantify state failure. While economic variables and social development indicators can be used, they are not always the most accurate tools to measure underlying faults in national stability. One of the main detriments to a purely statistical analysis of state failure lies in the inadequate amount of data present, particularly for developing and failing nations. Initially our original data set included figures for the Czech Republic and South Korea; we were not able to use these nations due to a lack of data across variables and years. We were also forced to throw away many x regressors, such as research and development and private investment, for the same reason. In order to address the shortcomings with the models, we will require more data across a wider spectrum of nations and years. We will also require more detailed data concerning such issues as terrorism, a more detailed breakdown of federal spending, a more detailed breakdown of federal revenue sources, and other issues presented in the conceptual portion of this paper. The data for these potential regressors, however, is not

13

available to us at this time. We feel that were such data included in our regression we would have an outstanding opportunity to attempt a statistically valid and significant partial economic quantification of state failure and of the economic impact of non state actors on a nation. Economic data is easily quantified, although qualitative analysis is more difficult. That is where history and politics enter the model. Their impact on economic issues is indisputable and highly relevant. If we think of state failure as resting on a triangular base consisting of history, politics, and economics, only one is easily quantifiable. We can only seek to address the other two sides of the base, and to attempt quantification, at a later date.

14

APPENDIX A: BIBLIOGRAPHY

Ades, Alberto, and Rafael Di Tella. “Rents, Competition, and Corruption.” American Economic Review, September 1999 Brogan, Patrick J. World Conflicts. London: Bloomsbury Publishing. 1998. Dunnigan, James F., and Austin Bay. A Quick and Dirty Guide to War, 3d Ed. New York: William Morrow and Company. 1996. Baron, Javier Guerrerro. “Is The War Ending? Viewing the Conflict in Colombia.” Latin American Perspectives, Issue 116, Vol. 28, No.1. Jan 2001. Buckman, Robert T. Latin America 2000. The World Today Series, 34th Edition. Harpers Ferry, WV: Stryker-Post Publications. August 2000. Central Intelligence Agency. “Global Trends 2015: A Dialogue About The Future With Nongovernment Experts.” McLean, VA. December 2000 Cooper, Marc. “Plan Colombia.” The Nation. 19 March, 2001. Accessed via ebird.dtic.mil, 5 March 2001. DeBourchgrave, Arnaud. “Four Way Civil War Makes Colombia A Nightmare.” Washington Times. 13 February 2001. Accessed via ebird.dtic.mil, 14 February 2001. Esty, Daniel, Jack Goldstone, Ted Robert Gurr, Pamela Surko, and Alan Unger. Working Papers: State Failure Task Force Report. McLean, VA: Science Applications International Corporation, 30 November 1995. Esty, Daniel, Jack Goldstone, Ted Robert Gurr, Barbara Harff, Marc Levy, Geoffrey Dabelko, Pamela Surko, and Alan Unger. State Failure Task Force Report: Phase II Findings. McLean, VA: Science Applications International Corporation, 31 July 1998. Forero, Juan. “Colombian Paramilitaries Adjust Attack Strategies.” New York Times. 22 Jan 2001. Gunson, Phil. “Frontier Clash Pits Cattlemen Against Guerillas.” Christian Science Monitor. 8 March 2001. Accessed via ebird.dtic.mil, 9 March 2001. “The Colombian Cauldron.” Jane’s Foreign Report. 5 October 2000. Accessed via Lexus-Nexus Academic Universe, 13 Jan 2001.

15

Marquis, Christopher. “U.S. Finds that Coca Cultivation is Shifting Sharply To Colombia.” New York Times. 2 March 2001. Accessed via ebird.dtic.mil, 2 March 2001. Mendonca, Maria Luisa. “Debt, Drugs, and Democracy: An Interview with Noam Chomsky.” NACLA Report on the Americas. July/Aug 1999. Accessed on line 8 June 2000. OECD Financial Action Task Force “Money Laundering.” OECD Observer Policy Brief. 1999. Onis, Ziya, and Ahmet Faruk Aysan. “Neoliberal Globalization, The Nation-State and Financial Crisis in the Semi-Periphery: A Comparative Analysis.” Third World Quarterly. Volume 21, Issue 1, Feb 2000. Accessed via proquest.umi.com, 6 August 00. Political Risk Yearbook. Volume 3. Political Risk Services. 1999. Robinson, Linda. “Where Angels Fear To Tread: Colombia and Latin America’s Tier of Turmoil.” World Policy Journal. Vol. XVI no. 4, winter 1999/2000. Accessed via CIAO online, 13 Jan 01. “Rebels Aiming at Colombian Economy.” Stratfor.com. 5 March 2001. Accessed via www.stratfor.com, 6 March 2001 Rohter, Larry. “Rebels Linked to Drug Trade By Arrests in Colombia.” New York Times. 4 March 2001. Accessed via ebird.dtic.mil, 5 March 2001. Sanin, Francisco Gutierrez. Translated by Richard Stoller. “The Courtroom and the Bivouac.” Latin American Perspectives, Issue 116, Vol. 28, No. 1, Jan 2001. Tucker, Irvin. Survey of Economics. 3rd Edition. Cincinnati, Ohio: South-Western College Publishing. 2001. The World Bank. World Development Indicators, 2000. Washington, DC: The World Bank

16

APPENDIX B: 1996 STATISTICS 1996 Statistics Colombia

Thailand

France

General Gov't Consumption

x1

15.50173

39.27618

19.5511

United States 15.588898

Industry

x2

27.97394

40.91249

26.14446

26.4022

Net Income

x3

-2.10E+09

-3.40E+09

-2.70E+09

1.72E+10

CPI

x4

120.2387

105.8129

102.0084

102.9312

% Aid

x5

0.0197575

0.468563

0.055571

0.055571

Bank and Trade Related Lending

x6

2.32E+09

6.04E+09

7.21E+08

7.21E+08

Export Duties

x7

0

0.10632

0

0

Foreign Assets

x8

8.29E+12

-8.10E+10

3.69E+11

-1.30E+11

Official Exchange Rate

x9

1036.686

25.34268

5.115529

1

Tax on International Trade

x10

7.578021

14.62896

0.003062

1.245727

Spending on Public Education

x11

4.12

4.82

6.04

6.04

Subsidy

x12

42.10115

7.121607

64.61277

59.25074

Foreign Investment

x13

3.143382

1.287387

1.427866

1.199375

South American

x14

1

0

0

0

Fix Investments

x15

18.537

41.0707

17.4301

17.30627

Private Consumption

x16

1.666

6.332878

1.977186

3.673123

Illiteracy

x17

9.4

5.6

8.6

8.6

Bold = data unavailable for country; data from Venezuela stats used

Model One ^Y = -71.88 + .595x1 + 1.269x2 + 1.879E-09x3 + .163x4 + .81x5 + -4.270E+10x6 + .404x7 + -3.799E-12x8 + 2.879E-02x9 + .556x10 + 2.193x11 + .169x12 + -3.349x14 + .561x15 + -.194x16 + .597x17 Model Two ^Y = -11.901 + -1.88x1 + .256x2 + 3.773E-11x3 + 5.124E-02x4 + 6.038E-02x5 + -4.441E-12x6 + 4.797E-02x7 + -1.071E-12x8 + 8.241E-.03x9 + .125x10 + .638x11 + -5.894E-02x12 + .196x13 + 2.177x14 Model Three ^Y = -26.22 + -3.190E-02x1 + .51x2 + 1.187E-09x3 + .122x4 + .153x5 + 5.39E-11x6 + .745x7 + -2.582E-12x8 + 1.691E-02x9 + .141x10 + 1.22x11 + 4.513E-02x12 + .253x13 + -2.789x14 Columbia

France

Model One = 18.7188

Model One = -3.07867E+19

Model Two = -24.2464

Model Two = -1.01875E+06

Model Three = 1.5985

Model Three = 5.15574

Thailand

United States Model One = -3.07867E+19

Model One = -2.57867E+20 Model Two = -1.28288E+06

Model Two = -27.6265

Model Three = 11.5686

Model Three = 30.2417

17

APPENDIX C: MODELS Model One YWEIGHT2 -0.5milit + 0.5trade Constant

Model Two YWEIGHT4 -0.9milit + 0.1trade

Model Three YILLIT -0.8illit + 0.2trade

-71.881*** -3.864

-11.901** -2.545

-26.622*** -3.516

0.595

-1.88***

-3.190E-02

1.366

-2.813

-0.295

1.269*** 3.872

0.256*** 3.206

0.51*** 3.936

1.879E-09*** 3.104

3.773E-11 -0.334

1.187E-09*** 6.499

CPI

0.163** 2.271

5.124E-02*** 4.048

0.122*** 5.931

% Aid

0.81*** 3.264

6.038E-02 0.844

0.153 1.322

-4.270E-10 -0.719

-4.441E-12 -0.026

5.398E-11 0.197

0.404 0.259

4.797E-02 0.263

0.745** 2.523

Foreign Assets

-3.799E-12** -2.477

-1.071E-12*** -3.257

-2.582E-12*** -4.85

Official Exchange Rate

2.879E-02***

8.241E-03***

1.691E-02***

3.196

3.515

4.455

Tax on International Trade

0.556*

0.125*

0.141

2.002

1.753

1.216

Spending on Public

2.193***

0.638***

1.22***

General Government Consumption Industry

Net Income

Bank and Trade Related Lending Export Duties

18

Education

3.063

3.02

3.569

Subsidy

0.169 0.924

-5.894E-02 -1.236

4.513E-02 0.585

0.196

0.253

0.926

0.741

-3.349

2.177

-2.789

0.433

0.294

-0.84

Foreign Investment

South American

Fix Investments

0.561** 2.477

Private Consumption

-0.194 -1.538

Illiteracy

0.597 1.172

Rsquare

0.961

0.78

0.936

Adj Rsquare

0.945

0.712

0.916

54

59

59

n

First statistic reported = coefficient Second statistic reported = tstatistic * = p < 0.1 ** = p < 0.05 *** = p < 0.01

19