Viz Hand Spring 2013

Fall 2013 | The Visible Hand | 1

Volume xxii. issue i.

The Visible HandEmerging from Crisis

Cornell Economics SocietyVolume XXII

2 | The Visible Hand | Fall 2013


The Visible HandISSN: 1559-8802

Editor-in-Chief:Peter Park

Executive Board: Junsuk Ahn

Matt BensonAlina Dvorovenko

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Table of Contents

3 Editorial Peter Park

4 Comparative Growth Analysis: Research & Development in Germany and Spain Conrad M. Brown

12 Does Foreign Direct Investment Boost Host-Country Innovation? Winston Soh

18 Challenging the Economics of Transactions: Bitcoin - A Network Regulated Crypto-currency Abhishek Gupta

22 Why am I Still Here? : The Impact of Cadet Academic GPA on Officer Retention Rates Mark Van Benschoten

29 Politics and Privatization: How Political Economy Factors Influence Service Provision in County Governments Diane Shahan

42 China, Russia and Other External Factors’ Economic Influence on Mongolian Economy Sukhbat Lkhagvadorj

Issues of The Visible Hand are archived at http://rso.cornell.edu/ces/publications.html

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In the wake of the financial crisis, today’s economy faces a lot of novelties and uncertainties that exacerbate the difficulty of making the right choices for all economical entities, from individuals to countries. As individuals are faced with the implementation of Obamacare, companies confronted with the prospect of Fed tapering, and countries uncertain of what routes they can take to fully recover from the crisis and get back on the track for GDP growth, a critical and economic review of all facets of life is inevitable. This issue of The Visible Hand seeks to provide exactly that; critical analyses both of the evidence that have emerged since the crisis and of newly developing trends across the world. The journal starts with Conrad Brown’s “Comparative Growth Analysis: Research & Development in Germany and Spain”, looking at the effect of R&D on economic growth. Using Germany and Spain as the focal points, the paper demonstrates a significant correlation between R&D and economic growth. Consequently, the paper suggests government policies that could utilize the correlation by boosting R&D. Winston Soh’s “Does Foreign Direct Investment Boost Host-Country Innovation?”, similarly, looks to take the existing literature on the role of Foreign Direct Investment on host-country economic growth and job creation a step further by testing the effect of FDI on innovation. Given the importance of innovation for a country’s success at global competition, this again is a question that could be the harbinger of future research that would serve to inform governments in policy decisions for the outcomes of economic growth and international competency. Moving away from papers that are concerned with using historical evidence to aid decision-making at the country level, Abhishek Gupta’s “Challenging the Economics of Transactions: Bitcoin – a Network Regulated Crypto-currency!” provides insight into the fascinating emergence of bitcoins as a potential next-generation currency. In the aftermath of Fed chief Bernanke’s favorable comment on the long-term promise of bitcoins, the article presents a wholesome look at the latest development. Mark Van Benschoten’s “Why am I Still Here?: The Impact of Cadet Academic GPA on Officer Retention Rates” follows, tackling the issue of officer

retention dependent on academic GPA. The paper does find a statistically significant correlation, but not enough economic significance to justify any policy recommendations. Overall, the article serves as an interesting foreground for a more critical review of how private sector opportunities impact officers’ judgments to either remain in or leave the army, as well as whether this issue should be of concern to the army. At the municipal level, Diane Shahan’s “Politics and Privatization: How Political Economy Factors Influence Service Provision in County Governments” tests how political pressures, rather than efficiency concerns, may affect the decisions of county governments. While the paper is inconclusive as to whether the found effect of political pressure on governmental decisions is necessarily undesirable, as the malleability of the government to political pressure from constituents is exactly what we would hope for in a democratic society, it nonetheless introduces an interesting means of verifying an idea that has been troubling the minds of constituents and political scientists alike for a long time. Finally, the journal comes full cycle with Sukhbat Lkhagvadorj’s “China, Russia and Other External Factors’ Economic Influence on Mongolian Economy”, which takes a look at Mongolia, a country that has experienced truly exceptional growth in recent years with exports of coal from large-scale mining projects. The author concludes by providing insight into Mongolia’s heavy dependence on its two neighboring countries, China and Russia, and suggests further diversification of revenue sources by means of an expansion in the customer base. We would like to thank Professor Jennifer Wissink for her continued support and guidance of The Visible Hand, the Cornell Economics Society for their dedication to our journal, and The Student Assembly Finance Commission for their generous financial support. We would also like to extend our gratitude for students across the board who have contributed to the journal this semester. Their hard work and dedication continues to make The Visible Hand possible, year after year.

Peter ParkEditor-in-Chief



Comparative Growth Analysis:Research & Development in Germany and Spain

Conrad M. BrownMassachusetts Institute of Technology

This paper seeks to analyze the comparative growth over the last two decades between Ger-many and Spain. Through graphical analysis, re-sults show a strong correlation between GDP per capita and research and development spending several years prior. This paper then seeks to out-line recent changes in government policy that could stimulate economic growth through the support of research and development.

I. Summary Starting with the Solow Growth Model for capital expenditure, this paper seeks to analyze the comparative growth over the last two decades between Germany and Spain. The Solow growth model uses levels of productivity, depreciation, and population growth to predict a country’s steady-state capital to labor ratio. A recession will often cause shocks to productivity, which will be amplified by lowered levels of investment. When productivity returns to its pre-recession level, out-put, investment, and consumption take time to return to their respective pre-recession levels. The analysis then uses a number of variables correlat-ed with GDP in attempt to find a causal relation-ship between the variables and GDP per capita as an economic indicator. Through graphical analy-sis, results show a strong correlation between GDP per capita and research and development spend-ing several years prior; this is useful for the com-parison because Germany spends a significantly greater percentage of their GDP on research and development than does Spain.

II. Introduction The recession of the modern century has had a very harmful effect on the world economy. Nu-merous developed nations are experiencing high levels of debt and stagnated levels of growth, and many among them are members of the European Union. Germany and Spain in particular are highly sensitive to diverse variables, and shocks in any number of those variables (employment levels, capital investment levels, government policies, in-

ternational trade policies, etc.) can result in high fluctuations in production. Germany and Spain are highly developed, modernized countries. Both were also highly influenced by Communist ide-als in the twentieth century, are part of the Eu-ropean Union, and have high standards of living on a global scale. Regardless of these similarities, Germany and Spain were affected very differently by the recent recession. This is due to the clear difference in policy between the two countries; Germany’s policy has supported industrial re-search and growth, while Spain’s has allowed for high levels of unemployment and unstable levels of foreign dependence. Germany and Spain have gross domestic products that reflect their indus-trial spending, as can be expected; in addition, these countries are positively reactive to increases in spending in research and development, and it seems feasible that such spending can stimulate domestic growth as well as stabilize an economy to avoid high exposure to great changes in the world economy.

III. Data The following tables give data on the changes over time of variables specifically related to the Solow model: GDP Per Capita and Capital Forma-tion Growth. The time span is specifically set to in-clude both the recession and the property bubble implosion in Spain. Tables including data with a greater time span are included in the data work-sheet. The Solow analysis focuses on two key vari-ables: growth in gross domestic product per capita and growth in capital stock per capita. The model summarizes a number of key variables in addition to the change in capital stock and the change in GDP. We can take into account the sav-ings rates and population growth rates across the countries. As a percentage of the total GDP, both countries have saved around 20% in the past de-cade. Spain has around a 1% population growth rate, which is significantly larger than Germany’s. The Solow model predicts that there is a negative



relationship between population growth and cap-ital stock. This is shown in the data. Note that capital growth rates in both countries have been highly variable during this recession, going from an average of 8.5% in 2006 to -16% in 2009. According to the Solow model, the capi-tal growth per capita should approach the lin-ear function of depreciation rate and population growth. The depreciation rate is the rate at which capital like machines and buildings lose their val-ue. In general, the capital depreciation rate and population growth do not change drastically in a short time period.

[See Table 1 in the Appendix] It is clear that Germany is very productive, with a GDP per capita in the top twenty coun-tries in the world, and it is likely their business-supportive policy that leads them to have a sig-nificantly higher GDP per capita. Other variables can be compared between the two countries, though, to determine whether they may have a strong correlation with GDP per capita. A number of variables likely characterize the development level and policy tendencies of a country; many of these variables also tend to follow or cause trends in GDP per capita. These variables include: popula-tion growth, industry value added (in percentage growth), research and development expenditure, and unemployment rate. Research and develop-ment rates are all taken as a percentage of the re-spective year’s GDP.

[See Table 2 in the Appendix]This data set was gathered over a 15-year time span because the data along the past twenty years can show trends due to the recession and any other business cycles that occur. The focus of this portion is to study both the static policy of each individual country and how the country has re-acted to the recession. From the increase of its un-employment and the greater drop in the growth of GDP per capita, it is clear that Spain has been more greatly affected by the recession. Spain’s unemployment, as explained earlier, is highly vari-able and likely indicates a lack in productivity.

IV. Graphical Analysis To analyze the effects of certain key variables on these economies, real GDP per capita growth is a strong indicator of success. Variations in capital formation growth and population growth are ac-counted for in the Solow Model. Unemployment rate is not included, but is a good indication of productivity in any economy.

V. Spain[See Table 3 in the Appendix]

The above graph shows a plot of Spain’s GDP per capita growth, unemployment, and gross capital formation growth percentages deviating from their respective 20-year mean. Variations in Spain’s gross capital formation growth trend with changes in GDP per capita growth, as the Solow model predicts. Changes in unemployment have opposing trends to the changes in GDP growth, which is to be expected as well. High unemploy-ment levels - a characteristic of Spain’s economy - lead to low levels of output; a worse performing economy with also likely have less jobs available. The variation for Spain was negative between 1999 and 2008, but it subsequently grew positive in the past five years as Spain initiated policies to dampen high variations in employment.

[See Table 4 in the Appendix] This graph depicts the variation of GDP per capita growth with changes in population growth in Spain. Population growth levels were below the 20-year average until the turn of the century, where they trended upwards. Population shows less covariance with GDP growth levels except during the recession, where high levels of emigra-tion were experienced. This assumption is not sup-ported by the Solow model, which predicts that growth in population lowers the respective capi-tal available per worker and increases the margin-al return of capital. Analyzing a greater data set, however, results in an often ambiguous response of gross domestic product growth to population growth. A number of things may be responsible for this inconsistency. First, there could simply be a selection bias in the countries with data available, meaning that even though the Solow assump-tion of population growth is accurate in general, it may not apply to these specific countries. Another potential reason could in fact be an error in the Solow model in its shortness, which could be ex-plained in terms of multiple variables both affect-ing and being affected by population growth. The way population growth would fit the Solow model is that an increase in citizens would lower the ef-fective level of capital stock per person, therefore lowering the GDP. A counterargument that should be considered is actually a problem that Spain has experienced in times of high unemployment rate; when Spain’s unemployment is high, they often experience increased levels of emigration, which consequently lowers the effective population growth rate, as evidenced in the 1970’s following



the Spanish Miracle.

VI. Germany[See Table 5 in the Appendix]

The above graph shows a plot of GDP growth, unemployment, and gross capital formation growth percentages deviating from their respec-tive 20-year mean. Like Spain, variation in Ger-many’s gross capital formation follows (or leads) trends in variation of GDP per capita. But unlike Spain, Germany’s unemployment levels do not vary as much with changes in GDP per capita. Germany has high productivity levels regardless of employment levels, but as expected, unem-ployment growth fell along with GDP growth at the end of the last decade, as shown by the above graph. Employment rates began to lose their posi-tive slope in 2005 before the GDP downturn in 2007, which could potentially indicate a causal re-lationship. This could be explained by Germany’s high rate of return per worker, as Germany has such in-depth vocational education policies cre-ated by the Hartz concept.

[See Table 6 in the Appendix] This graph depicts the variation of GDP per capita growth with changes in population growth in Germany. The population growth of Germany was positive following Reunification, but then trended downwards to its current, slightly nega-tive levels. There are slight indications that popu-lation growth has changed with the recent reces-sion, depicted on the graph by the negative trend of both. This is likely an effect of economic down-turn.

VII. Research and Development Effects Unfortunately, neither result shows a strong difference between the tendencies of the Spanish and German economies. The economies have very different levels of GDP per capita, but the only dif-ference we have found in these two regressions is that Spain tends to react more to changes in popu-lation and unemployment. Spain has higher levels of both population growth and unemployment, which could have some indication of the differ-ence. A possible problem with this data is that the variables we are considering are very likely to be resultant of the GDP growth, not causal variables. The data found over the past 20 years does not prove otherwise. In order to find a solution, we will analyze a variable that has very different levels in Spain and Germany: research and development spending. As a percentage of total GDP, Germany spent 2.8

percent on research and development projects in 2009, while Spain only spent 1.4 percent. Both countries have been increasing their spending on research and development spending over the past decade. Research and development is a broad title for a spectrum of expenditures made by both the government and private sector to increase knowl-edge and productivity.

[See Table 7 in the Appendix] A by-country analysis of research and devel-opment indicates a key difference in the German and Spanish economies. Above are two graphs showing the variations in GDP per capita, industry value growth, and research and development as a percentage of GDP spending in Spain and Ger-many. In both countries, value added in industry is procyclical with changes in the GDP growth. There is not an immediately visible correlation between research and development spending and the other two variables in either country, but the deviation in Germany’s research and develop-ment spending (0.165) is lower than that of Spain (0.193).

[See Table 8 in the Appendix] Above are the same two graphs as before, with a distinct difference: variations of R&D spend-ing are adjusted for lag effects. This accounts for the idea that research and development spend-ing does not have an immediate effect on the economy, but rather takes effect in the future. The maturation effect is strongest in Germany after 2 years’ time, as shown; the lagged effect is stron-gest in Spain in 5 years’ time. Through this data, we prove that spending on research in development has a strong effect on GDP growth in the future. This is important because it means that even dur-ing peaks of business cycles, the GDP growth likely reflects a high level of spending two or five years in the past. With the previous analysis on popu-lation growth and unemployment, the variables were found to correlate the most with GDP growth when analyzed in the same year. Since this analy-sis is run with the R&D levels two and five years in the past, it is less likely to be an effect of the state of the economy and more likely to be a cause. VIII. Summary It is clear that research and development spending is a particulary good source of growth for developed countries like Spain and Germa-ny, since Germany was able to continue regular growth within two years of the beginning of the shock and maintain a positive trade surplus with EU countries like Spain. Germany’s maturation



time of reasearch and development is significantly smaller than Spain’s, likely a sign of successful en-deavors and strong development infrastructure. In addition, Germany is less exposed to shocks in the economy because their high industrial spend-ing allows them to have high levels of productivity and less sensitive employment levels. In order to strengthen their economy, Spain must continue to increase investment and reduce dependence on foreign support. Through such investments, their industrial structure will be developed, con-sequently leading to an increase in employment levels. Research and development is a unique trait of developed nations that supports competition, productivity, and growth. Growth in research and development can indicate as well as boost pro-ductivity in both the private and public sectors. To this end, patent protection and goverment support of research and development should be championed as having the potential to lead to re-fined programs that are highly productive in dis-covering new technologies.

References:“Current Economic Policy.” BMWi. Federal Ministry of Economics and Technology, n.d. Web. 29 Mar. 2013.

De Guindos, Luis. “Spain’s Economic Policy Strat-egy.” Oct. 2012. Speech.

Donnerstag. “Regierung Will Arbeitslosengeld II-Regelsatz Erhöhen.” FOCUS Online. N.p., 13 Sept. 2012. Web. 04 Apr. 2013.

Eichengreen, Barry. The European Economy since 1945: Coordinated Capitalism and Beyond, (2008) pp 64-73)

Gavin, Mike (23 September 2010). “Germany Has 1,000 Market-Leading Companies, Manager-Mag-azine Says”. Businessweek (New York). Retrieved 27 March 2011

Jensen, Geoffrey. “Franco: Soldier, Commander, Dictator”. Washington D.C.: Potomac Books, Inc., 2005. p. 110-111.

Kulish, Nicholas. “German Growth Bolsters Its Stance on Recession.” The New York Times. The New York Times, 14 Aug. 2010. Web. 25 Mar. 2013 Thesing, Gabi. German Economy Enters Worst

Recession in 12 Years, Bloomberg http://www.bloomberg.com/apps/news?pid=newsarchive&sid=aV1q1nQoldKc&refer=home

The World Factbook. Central Intelligence Agency. ISSN 1553-8133 ISSN 1553-8133. Retrieved 4 April 2013.



Appendix:

Table 1

Table 2



Table 4

Table 3

Table 5



Table 6

Table 7



Table 8



I. Introduction Foreign Direct Investment (FDI) has been widely recognized as a key driver of economic growth and employment creation in the host country. This is especially evident in the rapid rise of the Four Asian Tigers – Hong Kong, South Ko-rea, Singapore, and Taiwan – and other develop-ing economies (Subramaniam, 2008). Innovation has been recognized as an important factor in maintaining a comparative advantage over other economies in today’s highly competi-tive global economy (Sala-i-Martin et al., 2011). Foreign direct investment, defined as “an invest-ment in which the investor acquires a substantial controlling interest in a foreign firm or sets up a subsidiary in a foreign country” (Subramaniam, 2008), has been attributed as a possible driver of increases in the level of innovation in the host-country (Blind and Jungmittag, 2004). Yet, questions remain as to the overall net ef-fect of FDI on host-country innovation. Does FDI not only increase innovation in foreign-invested firms, but also in domestic firms? Or does FDI have a negative impact on innovation, due to the sub-stitution of innovation with the import of technol-ogies and processes? Is there a generalization that can be made globally with regards to the relation-ship? This paper is interested in analyzing the net impact of the direct and indirect effects of FDI on the level of innovation in the host-country.

II. Theory Innovation can increase in the host-country as a result of either the direct or indirect effects of FDI. The direct effects of FDI on innovation occur when foreign-invested firms conduct innovation in order to modify pre-existing products to better suit the local market, or to create products specific to local market conditions. Beyond such basic in-novation, some foreign firms choose to conduct their research and development (R&D) in the host country in order to take advantage of the lower cost of high-skilled human capital, as is evident in

the case of India’s thriving high-technology sec-tors (Zhao, 2006). In addition to the lower cost of human capital, it has been argued that another im-portant pre-condition for foreign firms to conduct R&D in the host-country is an acceptable level of intellectual property rights (IPR) protection that ensures that innovations are not easily copied by competitors (Brandstetter et al., 2006). While the relationship between stronger IPR protection and higher levels of investment in R&D by foreign in-vestors seems logical, however, it has been found that large foreign firms have identified means with which to protect themselves even in states with weak IPR protection. FDI also has indirect effects on innovation in the form of spillovers. The entrance of FDI can in-crease innovation in domestic firms through dif-ferent channels, such as the competition effect, the demonstration effect, labor mobility, and sup-plier-customer relationship (Cheung & Lin, 2003 and Günther, 2002). I aim to conduct a more general analysis of the relationship between FDI and host-country innovation, with a global approach that is lacking in the current literature. I am interested in find-ing out if there exists a generalization that can be made across different economies in the world and if FDI-centric policies can be used as a gen-eral tool to induce higher levels of innovation in the host-country. Some policymakers possess a protectionist mindset motivated by a fear that FDI may strangle the domestic firms’ ability to survive in a constantly changing and rapidly innovating global economy. Instead, the competition and spillovers from FDI may stimulate domestic firms to innovate and perform better, thereby moving the host-country up in the global value chain. This study will thus help to inform policymakers about the effects of FDI on the host country’s innovation and to prevent any decision-making that is based more on fear than evidence.

Does Foreign Direct InvestmentBoost Host-Country Innovation?

Winston SohWesleyan University



III. Data and Methodology My hypothesis is that increases in inward FDI is correlated with increases in the level of innovation in the host-country. Based on the literature review, I believe that not only will FDI directly contribute to high-quality innovation in the host-country through foreign-invested firms, but the indirect effects of FDI will also induce high-quality innova-tion in domestic firms, resulting in an overall in-crease in the level of host-country innovation.Taking into account the various explanatory vari-ables that affect high-quality innovation besides FDI and how it requires more than a year for any variable to fully demonstrate their effects, I have chosen to conduct a panel regression over a five-year period. Data over a five-year period, starting from 2006 and ending in 2010, was extracted for as many countries as possible. I then eliminated some countries from being included in this study for their missing data points for any of the vari-ables used. I was left with 61 countries that had complete data points (Table 1). In total, there are 305 observations from the panel dataset that con-sists of five-year data points. While there may be concerns of a skewed dataset because of the elim-ination of many countries, there is still a balance of developed and developing countries within the sufficiently large sample size. 29 of the countries in the study are Organization for Economic Co-oper-ation and Development (OECD) countries, and the other 32 countries are non-OECD. The model used for the panel least squares regres-sion is as follows:

Yi = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7

+ β8X8 + β9X9 + β10X10 + β11X11 + β12X12 + εi

The number of patents granted in a country will serve as a proxy for the level of high-quality innovation, which will be the response variable (Yi) in this model. Number of patents granted was selected as a proxy, given that all patents are granted by a single organization, ensuring a glob-al standard with no overlaps. The data is obtained from the World Intellectual Property Organization, the official organization governing patents in the world. A deliberate choice was made in choosing the number of granted patents over the number of patent applications, as granted patents are bet-ter testimonies to the quality of an innovation in a

country. As larger and more developed countries tend to have more patents granted such that the distribution is not normal among countries, a log of the number of patents granted will be used to correct for it. The first explanatory variable (X1) is FDI as a percentage of gross domestic product (GDP), to test its correlation to the level of high-quality in-novation. The relationship is expected to be posi-tive, as explained in section II, with higher levels of FDI correlating with higher levels of innovation. The second explanatory variable (X2) is FDI as a percentage of GDP with a lag of one year, in or-der for us to illuminate any causality it has with re-spect to the level of innovation which X1 is unable to show. Like X1, the relationship is expected to be positive. The third explanatory variable (X3) is log of GDP growth per capita. It is adjusted for size of each country in terms of population, and also put into a log form to achieve a more normal dis-tribution. FDI and GDP growth has been shown to share a positive relationship (Borensztein, 1998). These three variables are taken from the World Bank database. The following two explanatory variables ac-count for factors affecting direct effects of FDI on the level of high-quality innovation. The fourth ex-planatory variable (X4) is an index that measures IPR protection. It is derived from the Global Com-petitiveness Index and ranked on a scale of 1 (very weak) to 7 (very strong). The effects of IPR on inno-vation levels remain ambiguous. While some have found it to be an important factor that affects the decision of foreign firms to locate any of their R&D facilities in the host-country (Brandstetter et al, 2006), others have found that large corporations have alternate ways to protect their innovations in countries with weak IPR protection, thus deeming it a less important factor than previously thought (Zhao, 2006). The fifth explanatory variable (X5) is an index that measures the quality of education system, in particular, how well it meets the needs of a com-petitive economy. It is ranked by the Global Com-petitiveness Index on a scale of 1 (not well at all) to 7 (very well). Foreign firms that invest in a country with the intention of innovating will require high-skilled human capital that is capable of conduct-ing R&D, and as such, the quality of human capital is necessarily an important factor for the direct effects of FDI (Borensztein et al., 1998). A country with a higher quality of education is thus assumed to be able to produce a higher level of human



capital suitable for the creation of innovation. An increase in the quality of educational system is ex-pected to be associated with a higher level of in-novation. The next three explanatory variables rep-resent factors affecting the competition effect caused by FDI. The sixth explanatory variable (X6) is an index that measures the intensity of local competition. It is ranked by the Global Competi-tiveness Index on a scale of 1 (limited in most in-dustries) to 7 (intense in most industries). The re-lationship is expected to be positive, as the more intense competition is, the more domestic firms are stimulated to innovate in order to secure mar-ket share and increase efficiency, as the literature suggests. The seventh explanatory variable (X7) mea-sures the domestic market size. It is an index de-rived from the Global Competitiveness Index and based on the sum of gross domestic product and the value of imports of goods and services, minus the value of exports of goods and services, nor-malized on a 1-7 (best) scale. The eighth explana-tory variable (X8) measures the foreign market size. This again is an index derived from the Global Competitiveness Index, based on the value of ex-ports of goods and services and normalized on a 1-7 (best) scale. These two variables are expected to be positively associated with the level of inno-vation. It has been found that firms that have ac-cess to a larger market size have more incentive to innovative given the lower cost of innovation per unit (Guadalupe et al, 2010). Also, exports by for-eign-invested firms in China to international mar-kets have shown to have significant and positive effects on domestic patent applications (Cheung and Lin, 2004). In sum, larger domestic or foreign market sizes should result in higher levels of inno-vation. The ninth explanatory variable (X9) is an index measuring financial markets efficiency, on a scale of 1 to 7, with 7 being the most efficient. Taken from the Global Competitiveness Index, the index considers sub-factors of the availability of finan-cial services, the affordability of financial services, the ease of financing through local equity market, the ease of access to loans, the availability of ven-ture capital, the soundness of banks, the regula-tion of securities exchange, and the strength of legal rights. A sound financial market with easy access to domestic finance has been found to be an important pre-requisite for spillovers to occur, as innovation activities by domestic firms requires

funding (Alfaro et al., 2004 and Girma et al., 2008). A more efficient financial market is expected to be associated with a higher level of innovation. The tenth explanatory variable (X10) is an in-dex derived from the Global Competitiveness In-dex measuring the efficiency of the labor markets, on a scale of 1 (least efficient) to 7 (most efficient). The index combines factors of the cooperation in labor-employer relations, the flexibility of wage determination, hiring and firing practices, redun-dancy cost, pay and productivity, the reliance on professional management, brain drain, and fe-male participation in the work force. With labor being a main instrument of knowledge transfers when individuals move amongst firms, in this case from foreign-invested firms to domestic firms, a more efficient labor market can be expected to correlate with higher innovation levels. The eleventh explanatory variable (X11) is an index of measuring business freedom, ranked on a scale of 0-100 with 100 being the freest and 0 being the least. It is taken from the Heritage Foun-dation, which considers the procedure, time and cost in starting a business, obtaining a license, and closing a business. With an easier environment in business creation, entrepreneurs are encouraged to utilize the knowledge they have picked up from working in foreign-invested firms to start new do-mestic firms that contribute to innovation. The re-lationship is thus expected to be positive. Lastly, a dummy variable for OECD member status is included. OECD member status is used as a proxy for developed economies. A value of ‘0’ represents non-OECD members, and a value of ‘1’ represents OECD member status. This variable will help us see if there exist any distinct disparities be-tween developed and developing countries in our study of the relation of FDI and level of innovation.Combining the 12 explanatory variables that at-tempt to represent factors determining the direct and indirect effects of FDI, the final model is:

LogPatents = β0 + β1 FDI + β2 FDI(T-1) + β3 LogGDP + β4 IntellectualPropertyRights

+ β5 EducationQuality + β6 LocalCompetition + β7 DomesticMarketSize + β8 ForeignMarketSize + β9 FinancialMarketEfficiency + β10 LaborMar-ketEfficiency + β11 BusinessFreedom + β12 OECD

+ εi

While there are other factors affecting inno-vation such as the spillover channels of supplier-customer relationships and the demonstration



effect, these factors cannot be clearly accounted for in this model using a variable. The use of panel regression, however, would help to control for un-observed variables that have not been included, which may differ across countries but are constant over the five-year period. There were minor multi-collinearity issues amongst the 12 explanatory variables. First, the variable for domestic market size (X7) and the variable for foreign market size (X8) have a high correlation coefficient of 0.8431, given the over-lap in the construction of both indexes (Refer to Table 2). Therefore, these two variables will be subsequently tested separately, in models (3), (4), (5) and (6). Second, the variable for IPR protection (X4) has high correlation coefficients with log GDP per capita (X3), Quality of Educational System (X5), and Financial Markets Efficiency (X9). Given the ambiguous effect of the variable in addition to its high correlation to other variables, I have decided to remove it from models (2), (3), and (5).

IV. Results In the first comprehensive model that in-cludes all 12 explanatory variables, the R-squared value for the panel regression suggests that the model can be use to explain 77.3% of the variation in the number of patents granted, a proxy for level of innovation. The high R-squared value indicates that the model is indeed useful in learning about innovation in the host-country. The result for FDI as a percentage of GDP has a small but positive coefficient, which translates to a positive relationship with innovation. The relation-ship, however, is not significant, suggesting that FDI as a percentage of GDP is not an explanatory factor for variations in innovation. In contrast, the variable of lag FDI as a percentage of GDP has a negative coefficient, with a 1% increase in FDI(T-1) being correlated with a 0.973% decrease in level of innovation. It is also significant at the 10% level (Refer to Table 3). While I noted these two variables as having an insignificant correlation coefficient, I tested both of them separately in different mod-els, and the negative coefficient of FDI(T-1) still persisted. The result goes against the hypothesis that higher levels of FDI will result in higher levels of innovation, though there may be more underly-ing the relationship between FDI and innovation given the opposing signs of the coefficients of FDI and FDI(T-1). The variable of log GDP growth per capita is also insignificant, suggesting that innovation is

not correlated to the rate of GDP growth. What this means is that rapidly growing economies do not necessary possess higher levels of innovation. Also, both the IPR variable and quality of educa-tional system variable, which are included in the model to represent factors affecting the direct effects of FDI, are statistically insignificant. While the results suggest that these two variables may not be strong determinants of innovation, it does not tell us that the direct effects of FDI in contrib-uting to innovation are significant. Instead, there may be other variables that are unaccounted for in this model. As mentioned earlier, the literature has indicated that the variable for IPR protection has ambiguous effects. In addition, it is also highly correlated with three other variables in the mod-el. However, when the IPR variable is removed in Model (2), there are insignificant differences in the R-squared value and the values of other variables, indicating that the IPR variable hardly accounts for variations in the level of innovation. The labor market efficiency variable is statisti-cally significant at the 1% level in all of the models, suggesting that labor is indeed a main instrument of knowledge transfer in the economy. The easier it is for individuals to bring with them what they learned from foreign firms to other firms within the economy when they change employment, the higher the level of innovation in the host-country. On the other hand, the financial markets efficiency variable and the business freedom variable, both of which were included to account for the ease with which new innovative domestic firms could be founded, are insignificant in all of the models. With the financial markets efficiency variable and the business freedom variable being insignificant in all models, the importance of entrepreneurship in creating innovation through harnessing the indirect spillovers of FDI does not appear to be important. Instead, the transfusion of knowledge through labor mobility appears to be one of the most important factors in contributing to a higher level of innovation. The intensity of local competition variable is mostly insignificant in all models, suggesting that intensified competition with the entrance of new firms does not play a big part in stimulating in-novation amongst firms. The competition effect is thus disputed. However, with the exclusion of the domestic market size variable in models (3) and (4), the intensity of local competition variable becomes statistically significant at the 10% level and exhibits a negative relationship with innova-



tion. This backs up the opposing claim that FDI is indeed detrimental to host-country innovation. With the exclusion of the domestic market size variable and with only the inclusion of the foreign market size variable, however, the coefficient and significance of the intensity of local competition variable may be representative of the impact of export-oriented FDI and not market-seeking FDI. That would explain the negative relationship with innovation given that export-oriented FDI focuses on reducing cost of production more than on in-novating. The following two variables that represent market access are significant in all models. The domestic market size variable is significant at the 1% level in all the models it is included in. The foreign market size variable exhibits statisti-cal significance at the 5% level in models (1) and (2), as well as at the 1% level in models (3) and (4) in which the domestic market size variable is not included. These results suggest that market sizes are important factors in the determination of the level of innovation. This could be due to cheaper costs of innovation given the larger spread with a higher volume of sales. While we cannot predict the magnitude of change from the coefficient giv-en that these variables are indexes normalized on a scale of 1 to 7, the domestic market size has over three times the impact of the foreign market size in models (1) and (2), once again suggesting that market-seeking FDI may have a stronger impact on host-country innovation levels than export-oriented FDI. To address the multi-collinearity issue be-tween the domestic market size variable and the foreign market size variable, models (3) and (4) exclude the former, while models (5) and (6) ex-clude the latter. In models (3) and (5), the IPR vari-able is also excluded for reasons that have been explained earlier. In models (3) and (4), with the exclusion of the domestic market size variable, the FDI variable becomes significant at the 5% level and demonstrates a positive relationship with in-novation, supporting the initial hypothesis. The intensity of local competition variable also be-comes significant at the 10% level, but has a nega-tive relationship with levels of innovation. As has been explained earlier, this could be due to the FDI being export-oriented, which is more focused on the reduction of cost of production than on in-novation. In addition, the OECD dummy-variable, which is insignificant in all the other models, be-comes statistically significant at the 1% level given

the exclusion of the domestic market size variable. Some of these changes as a result of the exclusion of the domestic market size variable require more investigation, as the relationships are not obvious. It is important to note that with the exclusion of the domestic market size variable, the R-squared value of the model falls from 0.773 to 0.664 as we can see from the results in models (1), (3) and (4), while the exclusion of the foreign market size variable has a much smaller impact, with the R-squared value falling from 0.773 in model (1) to 0.766 in model (5) and 0.767 in model (6). The do-mestic market size variable is thus a strong explan-atory variable for level of innovation and needs to be included in the model. The foreign market size variable is also not negligible, but relatively weak-er than the domestic market size variable. All in all, model (1) still presents us with the best explana-tion of variations in the level of innovation. Overall, the results have strong R-squared values and have proven to be useful in providing a general understanding of the impact of FDI on the level of innovation in the host-country. Based on model (1), is the most comprehensive of all models attempted, the negative coefficient of the FDI(T-1) variable, which shows causality and is sta-tistically significant, debunks the initial hypothesis that inward FDI provide a boost to innovation in the host-country. Instead, inward FDI weakens the innovation marginally. Results point to the impor-tance of market sizes, whether foreign or domes-tic, and the importance of an efficient labor mar-ket, in contributing to higher levels of innovation.

V. Conclusion This paper has successfully managed to pro-vide a big picture approach that is lacking in the literature on inward FDI and host-country pat-ented innovation. According to the paper’s find-ing of a negative relationship between inward FDI and host-country innovation, a FDI-focused policy cannot be the only resolution in the search for higher levels of innovation. It is important to note that with the need to maintain generality in this paper, nuances with regards to individual countries and regions are lost. More factors should be considered to ensure the capability of FDI in in-creasing host-country innovation, an issue that in-numerable policymakers are concerned about. In addition, it would be interesting and informative to further investigate the impact of specific types of FDI on innovation, as well as explore the results that suggest the shift of focus to market sizes and



labor market efficiency in understanding varia-tions on the level of host-country innovation.

References:Alfaro, Laura, Areendam Chanda, Sebnem Kalemli-Ozcan, and Selin Sayek. “FDI and economic growth: the role of local financial markets.” Journal of Inter-national Economics 64, no. 1 (2004): 89-112.

Blind, Knut, and Andre Jungmittag. “Foreign direct investment, imports and innovations in the ser-vice industry.” Review of Industrial Organization 25, no. 2 (2004): 205-227.

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tion activities within domestic firms-The case of Central East European transition economies. No. 162. Halle Institute for Economic Research, 2002.

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Subramaniam, T. “The Dynamic interactions among FDI, Unemployment, economic growth and exports: Evidence from Malaysia,” Jati, Vol. 13, December 2008, pages 35-48.

Vahter, Priit. “Does FDI spur innovation, productiv-ity and knowledge sourcing by incumbent firms? Evidence from manufacturing industry in Estonia.” The University of Tartu Faculty of Economics and Business Administration Working Paper No. 69 (2010).

Zhao, Minyuan. “Conducting R&D in countries with weak intellectual property rights protection.” Management Science 52, no. 8 (2006): 1185-1199.



Abhishek GuptaMcGill University

The concept of bitcoin as “crypto-currency” stems from the idea that since money is nothing more than a means of transaction, it is very possi-ble to carry out transfers using only online records. It builds on the notion of utilizing cryptography for the creation of this medium of exchange. Bitcoins are an example of crypto-currency which since the recent ‘Silk Road’ incident, have grasped the imagination of the public (note: since both the protocol and the currency itself have the same name, the protocol is referred to by capital-izing the first letter and the units of currency are referred to by using all lowercase letters). The soft-ware for bitcoins was started by an individual or a group of individuals who go under the pseud-onym Satoshi Nakamoto. The smallest unit of bit-coin is named ‘satoshi’ in reference to its founder. The original 50 bitcoins generated by Nakamoto are called “the genesis block” and are considered to be of higher value than all other bitcoins due to their uniqueness. At present, Nakamoto remains anonymous despite the many attempts made by the New Yorker and the Fast Company to uncover its true identity. Though the New Yorker proposed numerous possible identities, one of whom was Michael Clear, a graduate student studying cryp-tography at Trinity College, Dublin, all denied be-ing Nakamoto . Bitcoins are also referred to as a peer-to-peer currency because they are not regulated by a central authority, and instead rely on network-de-signed controls for regulation and management. Transactions are made secure by using public-key cryptography which involves having two separate keys, one public and one private. As is true for any currency, the limited availability of the bitcoins is what lends it its value. Thus, if there is too much

of the currency in circulation, there is a decline in its value. The total bitcoin count is permanently capped at 21 million units . Bitcoins are created by a process known as “bitcoin mining” which is the use of software to solve extremely complex math problems and then earning a reward in terms of bitcoins for coming up with the right solution. As time progresses and there are more successful mining attempts, the mathematical problems involved in the process become harder and after certain intervals, the rewards associated with a successful attempt are halved. The difficulty of these problems is modified on a regular basis to regulate the number of bit-coins that can be brought into existence through the bitcoin mining process and hence have some sort of control on inflation. The network strives to keep this figure to an average of six per hour. The idea that has made the bitcoin gain popu-larity is that it allows both the buyer and the seller to be anonymous in any transaction. Anyone who wishes to use bitcoins can do so by creating one or more Bitcoin addresses which are tracked to-gether in a “wallet” that has a public address and a private key. Though some may argue that this is also possible through the use of cash, the ace up the sleeve for this crypto-currency is the fact that it is not regulated by any government or central authority. Past attempts to establish a similar sys-tem of transactions such as Ecash, LiteCoin, Fre-icoin or PPCoin never found widespread accep-tance, a necessity for anything to act as a medium of exchange. One of the main reasons that people were skeptical of this type of currency is the prob-lem of “double spending,” in this case using the same piece of code to make multiple payments. This was resolved in part by maintaining a central

Abstract: In the age of the internet, with e-transactions infiltrating all aspects of our lives from online banking to buying things on Amazon, there is an emerging trend towards using a different form of money – one that is anonymous, unregulated by a central authority and managed by a distributed network running algorithms to solve complex mathematical problems. Bitcoin, a crypto-currency, is seeking to challenge all our notions about what money is and how it is used in carrying out transactions. This article aims to introduce the con-cept behind bitcoins and explore some of the current trends associated with it.

Challenging the Economics of Transactions – Bitcoin – a 3 Network Regulated

Crypto-currency!



ledger, but that created the need for a third party mediator. Bitcoin does away with that problem by distributing the entire ledger to almost all the us-ers of the network. The mechanics of how bitcoins function and how transactions take place are extremely inter-esting, but it is even more fascinating to examine the way in which it is changing the economics of transactions and the world of currencies. Milton Friedman once said, “I’ve always been in favor of abolishing the Federal Reserve and sub-stituting a machine program that would keep the quantity of money going up at a steady rate” . In a sense, his idea has come to fruition with bitcoins, which increase at an almost steady rate and are controlled by machine programs. To analyze why bitcoins increased in popularity, one can look back to the period around 2008 - the entire world was in a state of panic and people were losing faith in central authorities that had let the financial crisis take the shape that it did in the United States, the effects of which percolated into significant parts of the world. Nakamoto, at the time, had just pub-lished his paper on Bitcoin (the protocol). Faith was to be placed only in the elegant algorithms responsible for generating bitcoins, making it a seemingly more reliable source than central banks and governments. The financial crisis though itself not responsible for the spectacular rise of bitcoins, was to a large extent an accelerator pushing the ideology that currency not regulated by central authorities can become a store of value and a me-dium of exchange. Bitcoin has experienced a meteoric rise since its start when it had almost no value and was distributed largely to the enthusiasts who would further publicize it. However, in a short amount of time, its value shot up tremendously. One of the first transactions made using bitcoins was made for the purchase of a home delivered Papa John pizza using about 1000 bitcoins . Soon, the value of 1000 bitcoins exceeded $200,000. Economic agents with speculative interests saw the rise in the value of bitcoins as an opportunity to make some money on the side. This led to wild fluctua-tions in their value within short durations of time. With wild fluctuations becoming the norm in the world of bitcoins and interest in the move-ments of its value rising, the private keys for bit-coins increasingly became a commodity in need of careful guarding. People were trusting mas-sive amounts of money with third party handlers that offered to keep the bitcoins. Sure enough,

a hacker broke into the exchange called Mt Gox which handled about 90% of the bitcoin transac-tions. This break-in triggered a sell-off leading to the prices plummeting to almost zero. The hacker then withdrew substantial amounts of bitcoins at throwaway rates, but with the market forces in ac-tion, speculators trying to make a buck drove up the prices in a few hours and it was almost back to what it was before. This incident shook the con-fidence of the entire community and the prices met the ceiling of $17 per bitcoin for some time that followed. Recent trends and trading price figures, however, have shown otherwise – at the beginning of October, 2013, the price of a bitcoin was close to $200, but as of November 26, 2013, it closed at a high of $984 for every bitcoin – it has more than quadrupled its value in less than two months, almost doubling in value in a two week period from mid-November to the end of the month . One of the reasons for the remarkable suc-cess of bitcoins is that since it is unregulated and anonymous, it is conducive to carrying out illegal activities; in fact, a whole new online market was founded where bitcoins serve as a medium of ex-change for trading illicit goods. With the recent turn of events, “Silk Road” has gained a new meaning. It no longer only refers to the trade route that linked China to the Mediterra-nean Sea, but now also refers to the black market website that served as a platform to exchange il-licit goods using this crypto-currency. The FBI ar-rested the man behind “Silk Road,” Ross Ulbricht, also known as Dread Pirate Roberts, and seized a stash containing 144,336 bitcoins (as of publica-tion, roughly $150M). With such a seizure, the FBI is faced with a problem that is unprecedented – in previous drug seizures, there were drugs and cash that were physical entities which could be appro-priately handled. In this case though, the FBI has its hands tied in terms of being able to liquidate all the bitcoins. Current estimates put this amount that the FBI has at approximately 1.5%4 of the number of bitcoins available. With so much scruti-ny and attention on bitcoins, some other interest-ing revelations have unfolded. Two Israeli math-ematicians, Dorit Ron and Adi Shamir (founder of the RSA encryption standard) have found startling connections linking Ulbricht and Nakamoto . They traced a transfer of 1000 bitcoins to Ulbricht’s ac-count from an account that was created approxi-mately a week after bitcoins came into existence. The mathematicians pointed out that this was



highly unusual and a deeper search was spurred that revealed that the two might indeed be linked, sparking a vigorous debate in the community. Despite all these controversies surround-ing bitcoins, there is an increasing trend towards more companies and places accepting bitcoins as a means of payment. The main concern with such a volatile currency being used as a means of pay-ment is the constant re-evaluation of the product prices to keep them in sync with the changing value of the bitcoins in the market. But even then there has been movement in the direction of mak-ing bitcoins mainstream, the most recent addition being Virgin Galactic which has now begun ac-cepting bitcoins as a means of payment for pur-chasing a space flight ticket. They have even had a first customer, a flight attendant from Hawaii who has made use of this unique opportunity. The Uni-versity of Nicosia in Cyprus has also announced that it will be accepting bitcoins towards the pay-ment of tuition. This announcement comes at an interesting time when Cyprus has been reeling under austerity measures as outlined in the bail-out from the European Union . Entities from the Hudson Eatery in New York to Amazon.com now accept bitcoins as valid means of payments. As bizarre as this may sound, one can even pay for coffee in bitcoins in the high-tech zone of Beijing. China has rapidly adapted bitcoins and seems to be pushing very strongly to drive bitcoins into the mainstream. Real-estate developers in Shanghai, online retailers, and traders in Tinanmen Square all have at least one thing in common – they ac-cept bitcoins as a mode of payment. With the larg-est population in the world gravitating towards this new form of exchange, bitcoins are surely are on the path to becoming commonplace. Considering all the recent interest in this cryp-to-currency and many more merchants and sellers joining the ranks of those accepting bitcoins every day, there is an unequivocal movement towards bitcoins becoming a mainstream currency. A cur-rency that is truly independent of government/central controls and one that seeks to maintain anonymity for both parties involved is an idea that is grasping public attention and is a trend to watch in coming years.

References:Davis, Joshua. “The Crypto-Currency.” The New Yorker. The New Yorker, 10 Oct. 2011. Web. 08 Oct. 2013.

Wallace, Benjamin. “The Rise and Fall of Bitcoin.” Wired.com. Conde Nast Digital, 23 Nov. 2011. Web. 8 Oct. 2013. <http://www.wired.com/maga-zine/2011/11/mf_bitcoin/all/>.

Steadman, Ian. “ArsTechnica.” Ars Technica. Ars Technica, 11 May 2013. Web. 26 Nov. 2013. <http://arstechnica.com/business/2013/05/wary-of-bit-coin-a-guide-to-some-other-cryptocurrencies/>. Roberts, Russell. “An Interview with Milton Fried-man.” Milton Friedman, Library of Economics and Liberty, 4 Sept. 2006. Web. 08 Oct. 2013. Nakamoto, Satoshi. “Bitcoin: A Peer-to-Peer Elec-tronic Cash System.” N.p., n.d. Web. 22 Nov. 2013. <http://bitcoin.org/bitcoin.pdf>. Wallace, Benjamin. “The Rise and Fall of Bitcoin.” Wired.com. Conde Nast Digital, 23 Nov. 2011. Web. 8 Oct. 2013. <http://www.wired.com/maga-zine/2011/11/mf_bitcoin/all/>.

Source for figures is the Mt. Gox exchange for bit-coins that accounts for the most substantial trad-ing volume of bitcoins - http://bitcoincharts.com/charts/mtgoxUSD#rg730zczsg2013-10-2zeg2013-11-27ztgSzm1g10zm2g25zv Greenberg, Andy. “FBI Says It’s Seized $28.5 Million In Bitcoins From Ross Ulbricht, Alleged Owner Of Silk Road.” Forbes. Forbes Magazine, 25 Oct. 2013. Web. 23 Nov. 2013. <http://www.forbes.com/sites/andygreenberg/2013/10/25/fbi-says-its-seized-20-million-in-bitcoins-from-ross-ulbricht-alleged-owner-of-silk-road/>. Wile, Rob. “There Is A ‘Very Surprising’ Connection Between Bitcoin’s Creator And The Alleged Found-er Of The Silk Road.” Business Insider. Business In-sider, 25 Nov. 2013. Web. 26 Nov. 2013. <http://www.businessinsider.com/satoshi-nakamoto-and-silk-road-link-2013-11>. Shan Li November. “Virgin Galactic to Accept Bit-coin as Payment for Space Flights.” Los Angeles Times. Los Angeles Times, 26 Nov. 2013. Web. 26 Nov. 2013. <http://www.latimes.com/business/money/la-fi-mo-virgin-galactic-branson-bit-coin-20131125,0,3879223.story?track=rss>.

Shan Li November. “Bitcoin Now Accepted as Tuition Payment at a Cyprus University.”Los An-



geles Times. Los Angeles Times, 21 Nov. 2013. Web. 24 Nov. 2013. <http://www.latimes.com/business/money/la-fi-mo-cyprus-university-bit-coin-20131120,0,3194094.story>. Chang, Gordon G. “A China Triangle: Bitcoin, Baidu And Beijing.” Forbes. Forbes Magazine, 24 Nov. 2013. Web. 24 Nov. 2013. <http://www.forbes.com/sites/gordonchang/2013/11/24/a-china-tri-angle-bitcoin-baidu-and-beijing/>.



and pay system is based on seniority and length of service, younger officers may not be fully re-warded according to their ability or productivity. These officers may anticipate better opportuni-ties and career options outside the military and face higher opportunity costs by remaining in the army. This greater opportunity cost disincentiviz-es high performing officers from remaining in the army. Therefore, the expectation is that a higher academic GPA will have a negative impact on the probability that the officer will remain on active duty past their initial terms of service. This paper will investigate this hypothesis utilizing data from West Point graduates between the years of 1997-2004 and using linear regres-sion analysis to determine the effect of academic GPA on retention decisions. The analysis will begin with an initial background information sec-tion, including a short review of previous litera-ture written on the subject to ascertain previous theories, analyses, and findings. The background section will be followed by a discussion of the data used for this project, as well as the summary statistics for final observations. The economic theory motivating this hypothesis will be ex-plained in greater detail, as well as the specific regression equation chosen. A section devoted to the regression analysis findings will be fol-lowed by a discussion of the potential problems with the findings, and whether or not causality is established. In conclusion, the paper will reiter-ate the findings, possible policy implications, and

Why am I Still Here?:The Impact of Cadet Academic GPA on Officer Retention Rates

Mark Van BenschotenUnited States Military Academy

Abstract: This project examined how a USMA cadet’s academic GPA affects his/her probability to remain in the army beyond his/her Initial Active Duty Service Obligation (IADSO). Economic theory suggests that offi-cers with higher GPAs would be less likely to remain in the army because they face a greater opportunity cost of potential higher earnings in the civilian world. If this theory is true, the Army may be losing higher perform-ing officers and thus decreasing its overall effectiveness, as it seems reasonable that GPA and performance are positively correlated. The data used was pooled cross section data with observations from 1997-2004 graduation years of USMA, excluding those who were automatically committed to a longer initial term of service. The findings demonstrate that a higher academic GPA at USMA does decrease the probability that the officer will remain in the army. This finding is statistically significant, but not economically significant. Because of violations to MLR.4, causality cannot be determined. However, higher GPAs remain correlated with lower retention rate.

I. Introduction Due to the combined effects of the with-drawal of United States combat forces from Iraq in December 2011, the continued drawdown of US forces in Afghanistan to be completed by 2014, and the projected budget cuts of up to 600 billion dollars in its defense spending, the US army is preparing to reduce its active duty service members from its current 570,000 to 490,000 by 2017. While downgrading its personnel by 80,000, the army must ensure it is retaining its best and most productive soldiers and officers, both in this current drawdown and beyond. How-ever, high performing soldiers and officers are in high demand in the civilian labor market and therefore might be the most likely to leave the service because of their high opportunity cost of forgone income. Numerous factors, from the months the officer spent deployed to the post he or she is currently stationed at, can impact an officer’s decision to stay or leave the army. This paper will specifically examine the question of how a graduated USMA officer’s academic GPA affects his or her probability of remaining in the Army past the initial service obligation.In theory, officers with higher academic GPAs are more productive, demonstrate greater problem-solving ability, and exhibit the ability to handle complex and demanding leadership and tech-nical assignments in the army. It is these skills which civilian employers seek as they search to fill job vacancies. Because the Army’s promotion



topics for future research.

II. Background Commissioned officers are selected from four sources. The first source is the Officer Candidate School (OCS) in which enlisted non-commis-sioned officers are trained to become commis-sioned officers. The second source is the Reserve Officer Training Corps (ROTC), in which the army fully or partially pays for a student to receive an undergraduate degree at a civilian university, fol-lowed by the student’s commissioning and join-ing the army as an officer for a pre-determined time following graduation. The third source, termed direct commissioning, involves civilians with specialized degrees or skill sets, such as doc-tors and lawyers, offered entrance directly into the officer corps. The fourth source is the United States Military Academy, whereby students attend the academy for four years and receive a bachelor’s degree and military training in exchange for a commitment of at least five years of active duty as an officer upon graduation. This commitment is known as the Initial Active Duty Service Obligation (IADSO). After the completion of their IADSO, officers are free to leave the active duty army and either join the civilian workforce or remain on active duty. It is this decision that forms the focus of this paper.

III. Literature Review In 2010, Colonel Wardynski and Major Lyle from the Office of Economic and Manpower Analysis at USMA examined the phenomenon of low retention rates among junior officers to see if the army was incentivizing its best officers to stay in the army instead of just adequate officers. Us-ing officer data from the 1980s to the time of the article, the authors contended that officers who received their degree due to a scholarship, i.e. USMA and ROTC scholarships “are disproportion-ately likely to possess conceptual and problem-solving talent… [and their] talents align extreme-ly well with complex jobs at senior company and field grade levels.” It is these officers whom civilian employers attempt to hire away from the army. However, the authors further determined that graduates from USMA with the highest potential and performance, tend to “remain in the Army to their 10th year of service at higher rates” than their classmates. While the article does not directly address this paper’s inquiry, it did find that those with higher academic perfor-

mance were more likely to remain in service for 10 years, which contradicts this project’s findings. However, the article did support the argument that academic potential/ability is correlated with greater productivity, a crucial link for this proj-ect’s economic theory. In 1992, Mairs and Makin from the SAG Cor-poration examined Air Defense Artillery officers’ decision to remain in the army. Using a panel probit framework, the authors determined that greater civilian job opportunities and income compared to military salaries had a large effect on the officers’ retention rates. They also con-cluded that race, gender, and marital status were all significant factors as well. While this article only dealt with ADA officers, it does support the theory that financial incentives in the outside world affect an officer’s decision for reenlistment. As the study indicated, race, gender and marital status are all factors that should be included in this project’s regression. John Warner, Curtis Simon, and Deborah Payne (2003) examined the question of why the US military faced declining high-quality recruit-ment rates in the 1990’s. They applied aggregate monthly data by state between the years 1989-1997 with economic factors, recruiting inputs, enlistment incentives, and demographic factors. They concluded that “high-quality recruiting is significantly related to military recruiters, adver-tising, and enlistment incentives”, and somewhat related to relative pay. Although not specific to GPA or officers, the article does discuss the link between financial opportunities and the choice between being in the military and being a civil-ian.

IV. Data and Sample Evolution The data and observations in this paper were compiled by the Office of Economic and Manpower Analysis at West Point (OEMA). Each observation consists of information from an officer who graduated from USMA between the years 1997-2004 with his or her academic, mili-tary, and physical grades, along with whether or not he/she was a recruited athlete and whether or not he/she remained in the Army 72 months past graduation. Since the officers who are com-missioned into the Aviation, Medical Services, or Medical Corps branches automatically agree to longer service obligation, and anyone who branched into something other than the basic branches is unable to leave for 72 months, they



were dropped from the dataset. Below is a table explaining the evolution of the dataset and the final number of observations chosen (6129): In the appendix, Table 1 contains the sample summary statistics. The first column contains the means and standard deviations for all the variables for the entire sample. The second column presents the summary statistics only for those officers who remained on active duty past their IADSO. The third column displays the summary statistics only for those officers that chose to leave the army after their IADSO. The summary statistics show an almost even split between those who stay and those who leave the army with 50.3% staying 72 months past gradu-ation. On the surface, the summary statistics point against academic GPA being a factor in determining retention rates, as the cumulative academic performance score (academic GPA) is essentially the same between the two groups. The difference between the two is only .017 of a point and well within one standard deviation of both means. However, there are several variables that appear significantly different. The first is the number of months deployed; those who did not stay in the army had, on average, an additional three months of deployment than their counter-parts who remained in the army. In addition, over 57% of the officers who stayed in the army were married by the completion of their IADSO versus 46.5% of those who left. Furthermore, recruited athletes were significantly more likely to leave the army (773), than remain on active duty (488). Overall, the data used is adequate to examine how academic GPA affects an officer’s decision to remain in the army. The data contains a large number of the variables that affect retention rates and are related to academic GPA. The num-ber of observations is more than sufficient, with over 6000 observations. One potential shortcom-ing is that the findings only apply to West Point graduates who did not branch into aviation or go to medical school. It is not a representative random sample for all officers, or even all officers who graduated from West Point. Therefore, the findings cannot be applied to officers from other commissioning sources, or Army officer avia-tors or doctors. However, for West Point gradu-ates who did not branch into one of the above mentioned branches, the data appears to be a representative random sample.

V. Economic Theory

The economic theory behind this project is that if cadets with higher academic GPAs are more productive, better developed problem solvers, or simply work harder, these attributes offer them better career options outside the military. Because all officers of the same rank are paid the same in the army regardless of ability or productivity, better officers do not receive com-pensation for their extra output. As such, at the end of their IADSO, officers with higher GPAs will have better financial opportunities in the civilian labor market. Consequently, they face a higher opportunity cost of remaining in the army and therefore will be more likely to leave the service. The linear regression used should account for as many variables as possible that are likely to affect an officer’s decision to remain in the army and that affect the officer’s academic GPA. Below is the regression specification with the variable of interest being Academic GPA (AGPA). It is expect-ed that β1 will be negative.

Regression specificationInat72=β0+β1AGPA+β2MGPA+β3PGPA+β4monthsdeployed+β5SAT+δ1female+ δ2black+δ3hispanic+ δ4other+ δ5married+ δ6combatarms+ δ7recruit+

δ8priorservice+uInat72 = Indicator for if the officer is on active duty

as an officer after 72 months past graduation.AGPA = Officer’s cumulative academic GPA at West

PointMGPA = Officer’s cumulative military GPA at West

PointPGPA = Officer’s cumulative physical GPA at West

PointSAT = Officer’s cumulative SAT score out of 1600 monthsdeployed= Number of months spent on

deployment since graduationFemale = Indicator for if the officer is female

Black = Indicator for if the officer is BlackHispanic = Indicator for if the officer is Hispanic

Other = Indicator for if the officer is not Hispanic, Black or White

Married = Indicator for if the officer is married by the end of their IADSO

Combatarms = Indicator for if the officer branched combat arms

Recruit = Indicator for if the officer was a recruited athlete at West Point

Priorservice = Indicator for if the officer was prior service before attending West Point



The dependent variable is an indicator for if the officer is on active duty after 72 months, or six years, not the 5 years of the IADSO. 72 months were used because there can be many reasons why an officer who wishes to leave the army may be unable to do so immediately following his or her IADSO, such as being in mid-deployment or wanting to finish a command position. By al-lowing an additional 12 months past IADSO, it is more likely that an officer who wishes to leave is able to leave. The regression specification chosen above attempts to account for as many measur-able variables as possible that are expected to have a significant effect on remaining on active duty such as months deployed and combat arms, variables that have previously been determined to be significant such as gender and race vari-ables, and variables that are related to the deci-sion to remain on active duty and academic GPA such as SAT, Military GPA, and Recruit. While there are many additional factors that affect this deci-sion, many are not measurable or are unrelated to academic GPA. Year controls were used for all of the regressions as well.

VI. Results Table 2 in the appendix shows the empirical findings from the linear regressions. 10 mod-els were run, beginning with the simple linear regression model of AGPA on Inat72 and pro-gressing up to all variables included in model 10. The coefficient in model 10 for cumulative academic performance score (academic GPA) is -.061. This means that all other controlled variables constant, a 1 point increase in a cadet’s academic GPA decreases the probability that they will remain in the army after 72 months by 6.1%. In other words, a cadet with a 4.0 cumulative academic performance score is 6.1% less likely to remain in the army than a cadet with a 3.0 cumu-lative academic performance score and is 12.2% less likely than a cadet with a 2.0, all else equal. The coefficient for the variable of interest is negative for all of the models except for model one. The negative sign supports the hypothesis that higher GPAs are linked to lower retention rates. The coefficient is also statistically significant for all of the models except for model one, when it was the only explanatory variable except for year controls. The likely reason the coefficient is positive and insignificant is because cumulative military performance score (military GPA) and cumulative academic performance score are very

positively correlated and military GPA has a much larger positive effect on the dependent variable. This positive correlation likely leads to a large positive bias in model 1 because of the omit-ted variable. Once the SAT score was added to the regression, the regression became relatively stable, fluctuating by less than .030 for the next 6 models. Overall, this is not a very economically significant coefficient; as already stated, a 1 point difference in academic GPA only changes the of-ficer’s probability of staying by about 6%, a small number given such a large difference in GPA. For the regression, an F-test was used to confirm that race controls and year controls were jointly significant when added to the regression. Also, the regression model correctly predicted if an officer was going to stay in the army past 72 months, 66.8% of the time. This prediction was calculated by determining the predicted y-value of the observation and positing that the officer would stay in the army if y-hat was equal to or greater than .500 whereas the officer would leave the army if y-hat was equal to or lesser than .500. These predicted values were then compared to the officers’ actual decision to reenlist. Overall, there were 168 observations out of the 0-1 prob-ability range; it is interesting to note that all of them had prediction values of less than 0. This could reflect the fact that there are omitted vari-ables with a positive effect on remaining in the army, explaining why over 150 observations have a negative probability. From the R-squared in the regression results, it can be seen that the model does not explain a majority of the variation in the data. Even after all of the controls were included, the model only explains 13% of variation. However, this should not be surprising, as there are a multitude of fac-tors that affect an officer’s decision to remain in the military. These omitted variables only matter if they affect the dependent variable and are cor-related to academic GPA. Known as the omitted variable bias, this biases the coefficient and its standard errors. One omitted variable is ability. A higher abili-ty likely leads to a higher GPA and likely also has a positive coefficient because greater ability means a greater opportunity cost of staying in the army. These two factors lead to positive bias in the co-efficient in academic GPA. In this regression, SAT was used as a proxy for ability, which should par-tially remove the bias, but the idea that SAT is a perfect proxy for ability is tenuous at best. Other



omitted variables that may cause bias include: whether or not the officer grew up in a military home, job satisfaction and job performance as an officer, and having a toxic commander. There are few sources of measurement error in the data as most of the data is relatively easy to measure accurately (i.e. is the officer in the army or not, what was his/her GPA, etc.) How-ever, one source of measurement error could be the months deployed measurement. Months deployed is determined by whether or not an officer received hostile fire pay for a particular month, but an officer need not have been de-ployed for the entire month to receive this pay. If this measurement error is uncorrelated with the unobserved actual number of months deployed, then there are classical errors-in-variables and the coefficients are biased toward zero. Because this is a linear probability model, heterosdedasticity is present by definition; this was corrected by us-ing robust standard errors in all the regressions. Because of the likelihood of omitted variable bias given above, MLR 4, the zero conditional mean assumption, is not met and therefore no causal relationship can be established. It can, however, be demonstrated that an officer’s academic GPA and the probability of his or her remaining in the army post IADSO are negatively related.

VII. Conclusion This project determined that a higher aca-demic GPA corresponds to a lower probability that an officer will remain in the army past 72 months after graduation. This effect is statically significant, but not economically significant. The theory behind this phenomenon is that officers with higher GPAs have better opportunities for employment in the private sector and therefore face higher opportunity cost to remain in the army. Since this effect is small and there are still many unexplained factors that determine reten-tion rates, no policy recommendations can be provided at this point. Potential further research includes examin-ing to see if the effect of GPA on retention rates is constant; that is to say, an increase in a cadet’s GPA from 2.5 to 3.0 might not affect his/her deci-sion as much as an increase from 3.5 to 4.0, as this increase might send a stronger signal to potential civilian employers of the officer’s productivity. Another potential point of future research is to further substantiate the link between GPA and officer performance in the army. If it is the case

that those with lower GPAs are just as competent as those with higher GPAs, it does not matter if officers with higher GPAs are leaving, given that those who are left can still perform the job just as well.

References:Mackin, Patrick C., Paul F. Hogan, and Lee S. Mairs. A Multiperiod Model of U.S. Army Officer Reten-tion Decisions. Http://sagcoup.com. N.p., n.d. Web. 24 Sept. 2012. http://sagcorp.com/publica-tions/2.pdf

Milano, James. “Army to Stay Strong While Down-sizing.” www.Army.mil. United States Army, 08 Mar. 2012. Web. 04 Dec. 2012. <http://www.army.mil/article/75278/Army_to_stay_strong_while_downsizing/>.

Simon, Curtis J., and John T. Warner. “ARMY RE‐ENLISTMENT DURING OIF/OEF: BONUSES, DEPLOYMENT, AND STOP‐LOSS.” Defence and Peace Economics 21.5-6 (2010): 507-27. Taylor & Francis Online. Web. 24 Sept. 2012. <http://www.tandfonline.com/doi/full/10.1080/10242694.2010.513488>.

Stewart, James B., and Juanita M. Firestone. “Looking for a Few Good Men: Predicting Pat-terns of Retention, Promotion, and Accession of Minority and Women Officers.” American Journal of Economics and Sociology 51.4 (1992): 435-58. Web. 24 Sept. 2012. <http://www.jstor.org/stable/3487452>.

Wardnski, Casey, David Lyle, Michael J. Colarusso. “Towards a U.S. Army Officer Corps Strategy for Success: Retaining Talent.” Strategic Studies Insti-tute (2010). 1-64. <http://www.strategicstudiesin-stitute.army.mil/pubs/display.cfm?pubID=965>.

Warner, Jon, Curtis Simon, and Deborah Payne. “The military recruiting productivity slowdown: The roles of resources opportunity cost and the tastes of youth.” Defence and Peace Economics 14:5, 329-342.



Appendix:





categories of potential influences: i.) political pa-tronage, ii.) voter ideologies and pressures, and iii.) budget constraints. The first category tries to tease out what as-pects of the provision decision are influenced by political patronage, wherein officials favor their supporters in political appointments and posi-tions. As described by Lopez-de-Silanes et. al (1997), patronage is one of the benefits of inhouse provision, allowing officials more opportunities to hire their allies, and could lead decision-makers to champion this medium of service distribution. There is no clear way of measuring the influence of patronage; however, a number of state-wide laws regulating elected official conduct could the-oretically constrain the ability of these officials to use inhouse service placements as political prizes. Laws that mandate ethics codes, transparency in county decision making, and hiring standards for public employees, among others, all have the po-tential to inhibit political patronage. Thus, study-ing the effects of these laws on the probability of privatization could indicate how large a role, if any, is played by patronage in deincentivizing pri-vate provision – if these laws are associated with large increases in privatization, it would seem that political favoritism is an important incentive for inhouse provision of services. Another political aspect of the provision deci-sion is in regards to voter ideologies. Officials must cater to the demands of their constituents, and the political leanings of this group could easily af-fect a government’s attitude toward privatization. Furthermore, the degree of pressure imposed on an official to please his constituents is not always uniform and will certainly influence how con-strained he is by the opinions of his voters. Such factors as how highly contested the county is, the government structure of the county, and the ac-tivism of voters (measured in terms of voter turn-out) would reasonably alter how strictly he would feel the need to adhere to the voice of his con-stituents. In addition, aspects of the county po-litical environment could manifest themselves in

Politics and Privatization:How Political Economy Factors Influence Service Provision in County Governments

Diane ShahanPrinceton University

I. Introduction County governments in the United States face the task of providing a range of basic services, in-cluding programs as diverse as crime prevention and fire suppression to street repair and parking lot operation. Although these services were tradi-tionally provided “inhouse”, that is, supplied by the government via its salaried employees, the end of the last century has been marked by a push to-ward contracting these services out to the private sector (Lopez-de-Silanes, Shleifer, & Vishny, 1997). Ensuing research that investigates the respective merits and drawbacks of the two modes of pro-vision has consistently found that procuring ser-vices privately is less expensive than doing so in-house, yet inhouse provision continues to account for the lion’s share of public services (Gerber, Hall, & Hines Jr., 2004). This begs the question, how do county officials decide whether or not to contract out services, and why do they continue to furnish the majority of them inhouse? A plethora of pos-sible answers have been raised to this question, citing, for example, social goals, desired availabil-ity of services, fear of changing the status quo, and quality concerns as possible justifications for public provision (Gerber, Hall and Hines Jr. 2004; Lopez-de-Silanes, Shleifer and Vishny 1997). One potentially crucial influence that I intend to inves-tigate in this paper is that of the political economy environment; how factors such as state laws, the structure of individual county governments, and political sentiments drive a county’s privatization decisions. According to Gerber et al.’s 2004 policy report, “little research has been conducted to date that explores the political environment which shapes service provision decisions.” Not only is this a ne-glected aspect of the privatization literature, it is an extremely relevant factor at the level of county governments. Choices at this level about services are often made autonomously by elected or ap-pointed officials who thus are subject to political pressures that could easily affect their decisions. In particular, I focus my study around three main



a government’s decisions more indirectly; for ex-ample, a high unemployment rate may encourage officials to “create” jobs by contracting services out to the public sector with the goal of pleasing his voting bloc. Finally, as indicated by Lopez-de-Silanes et. al, varying degrees of strictness in regards to coun-ty budget constraints, as imposed by the state, could affect how services are provided. Since pri-vate provision is historically more cost-effective, it seems reasonable to hypothesize that stricter budget constraints would be correlated with in-creased privatization, and this result has indeed been found in similar previous research. My analysis aims to establish the direction, magnitude, and significance of the variables with-in these three broad categories. I find that among the first group, those state laws relating to limits on patronage, three of the five tested laws are sig-nificant: laws mandating ethics codes for county officials, laws requiring county decisions be made in a public forum, and laws allowing the recall of the county official. The first of these, however, is surprisingly associated with a decrease in priva-tization, contrary to both my predictions and the hypotheses raised by previous authors, while the latter two are both positively correlated as ex-pected. Among the variables related to voter ide-ologies, the structure of the county government appears very significant; those counties with elected, as opposed to appointed, officials saw less privatization. Both measures of the fraction of Republican votes entered the regression with negative coefficients on private provision, which also coincides with some previous literature on the subject. However, when considering the joint effects between Republican votes and elected of-ficials, I find that if the county official is elected, a higher fraction of Republican votes is robustly correlated with more privatization. A highly politi-cally contested population is also very significant and associated with more privatization – addition-ally, interacting this variable with the presence of an elected official reveals a robust result for even more privatization when both the official is elect-ed and the population is contested. Voter turnout is insignificant and higher unemployment rates are unexpectedly associated with more privatiza-tion. Finally, the state-mandated budget laws as a group are robust, with four out of the five show-ing up significant, and all but one behaving as predicted to increase the probability of private provision, although in some cases contrasting the

results of previous literature.

II. Literature Review Privatization of services has frequently been linked with government cost savings, lower bud-get deficits, and consumer cost savings through-out the world. Kodrzycki finds such results in an empirical paper particularly relevant to my analy-isis in its use of local U.S. governments (Kodrzycki, 1994). Examining public service provision in New England cities and counties with a series of regres-sions, the paper concludes unequivocally that “state and local governments can achieve sav-ings, without sacrificing quality, by privatizing the delivery of services through judicious use of pri-vate contractors.” Numerous other papers report identical findings, including Babitsky and Perry’s study of urban mass transit systems, which found privatization more efficient on a number of fronts, including cost reduction, output per dollar, and revenue generation (Perry & Babitsky, 1986). The argument about service privatization is not, however, one-sided, and several scholars have suggested that the associated cost savings are re-sults of diminished quality, oftentimes to quality levels significantly below the efficient level. This is the stance advocated by Blank in her article ex-amining various situations in which government provision might be preferrable to private (Blank, 2000). She presents a theoretical model in which it is only advantageous to offer services through the private sector when it is easy to monitor the quality of the service; otherwise, quality stan-dards will only be met through inhouse provision. Economists such as Blank posit that the reason for continued support of publicly-provided services, despite the empirically demonstrated budget improvements associated with privatization, is that governments are working to preserve qual-ity standards. In this model of the world, efficiency concerns, rather than political pressures, motivate governments’ provision decisions. By examining the politics behind these decisions, my paper tests this view, attempting to establish what part, if any, of the decision is made for political, as opposed to economic, reasons. Indeed, the significant influence of politi-cized decision-making on government officials is grounded in the politics literature. The ideas of po-litical patronage and the importance of contested governments that will be analyzed extensively in this paper find a theoretical backdrop in Thayer’s philosphical analysis of corruption and its relation-



ship with electoral competition (Thayer, 2000). He argues that corruption, specifically in the form of political patronage, is the inevitable result of com-petition between political parties. Although he doesn’t go so far as to assert a linkage between more competition and more corruption, this is a logical extension that I will explore. If more politi-cal competition, such as a highly contested local government, fosters patronage, one would expect to see a positive relationship between competi-tion and public provision. The merits of Thayer’s argument have not gone uncontested, however, and in particular, a response counters that compe-tition instead forces more government transpar-ency, which would inhibit patronage and lead to the opposite result, so there is room for analysis on the role of these factors (Maletz, 2002). Further, there is the question of whether such influences, if they exist, would in fact manifest themselves in the probability of privatization. This does not seem unlikely given that recent international work in the field of politics has es-tablished a number of linkages between the po-litical stance of governments and their preferred mode of provision. Work by Sundell and Lapuente (2012) has found that in Sweden, governments to the right are more likely to privatize municipal services. This leads the authors to push the hy-pothesis that right-leaning governments privatize not only for ideological reasons, but also to gain political support from the private sector. Doyle (2012) also suggests that political and ideological motives impact the privatization decision; he ex-amines political pressures to privatize in eighteen Latin American countries indebted to the IMF. The empirical findings of this paper again reveal that right-of-center governments have a propensity for privatization, while governments further to the left will resist this process, even under pres-sure from the IMF. While there is an extensive amount of internation-al literature regarding private vesus public provi-sion of services, only a few at local U.S. govern-ments and almost none attempt to analyze what political factors could be at play in those govern-ments. The exception to this trend is a paper by Lopez-de-Silanes et. al, which considers, among other variables, how three state “clean-govern-ment” laws (such as laws mandating merit sys-tems for public hiring to prevent patronage), pub-lic union presence, five state-mandated budget constraints, and regional ideology influence the mode of provision in U.S. county governments,

using data primarily from the 1987 Census of Gov-ernments. This data, however, is outdated and lim-ited in that it only includes information for twelve service types, a small subset of the wide variety of services provided by local governments. They find that some state level clean-government laws as well as laws restricting the budget of county governments are correlated with more private provision, suggesting that these political factors, including laws limiting patronage or hardening a government’s budget constraints, affect a govern-ment’s decision on what mode to use for services. A subsequent study by Levin and Tadelis (2010) focuses mainly on contracting difficulty and its effect on provision by U.S. city governments, but they also replicate some of the political economy variables considered by Lopez-de-Silades et. al for the city level while taking into account a third type of provision. This third mode is a mix of in-house provision and contracting out, where the city government contracts out to another public agency rather than to a private enterprise. Since they study contracting difficulty, this distinction between types of contracting is very relevant to their analysis. Interestingly, while Levin and Tadel-is are able to replicate some of Lopez de Silades et. al’s results, they also find many contrasting results. In particular, they report negative coefficients on the effects on privatization of state laws requiring merit systems, prohibiting strikes by public em-ployees, and permitting take-over of city finances, all of which contradict earlier findings. Also of in-terest, they find a negative coefficient for the ef-fect of Republican voters on privatization, a find-ing contrary to their predictions and the results in Lopez-de-Silades et al.’s work, as well as in the analgous international cases discussed earlier. Since the Republican platform generally advo-cates limited government involvement and in-creased privatization, the sign on this coefficient is intriguing and suggests that more sensitive mea-sures of political sentiment than their admittedly “rough” metric could yield a different outcome. My paper addresses the lack of a comprehen-sive analysis of the political economy components in the privatization literature, as well as adds to the sparse amount of local level empirical work. By combining a richer and more up to date data set with a greater variety of political economy variables, it attempts to shed light on the highly contrasting results between the two papers by Lopez-de-Silades et. al and Levin and Tadelis dis-cussed above, as well as to illuminate aspects of



the broader debate about privatization decisions: whether these choices are made with the intent of maximizing quality or, at least partly, because of other, more political, factors at play.

III. Data My main data source is the International City/County Management Association (ICMA) 2007 Al-ternative Service survey, which solicited informa-tion from local officials about which mode of pro-vision their government uses to provide a variety of services. To my knowledge, this particular data set has not been used for any kind of similar analy-sis, although Levin and Tadelis used city-level data from an earlier wave of the same survey in their paper on contracting difficulty. This data is an im-provement on the Census of Governments data used by Lopez-de-Silanes et. al not only because it is more recent (by twenty years), but also be-cause it includes information on sixty-seven ser-vices as opposed to the meager twelve covered in the Census of Governments. While it contains in-formation for both county and city governments, I restrict my analysis to county governments be-cause most political economy data, especially vot-ing information, is compiled at a county level. The data contains information for 355 U.S. counties, with responses marking services as pro-vided entirely by government employees, partly by government employees, by another govern-ment, private for profit, private nonprofit, fran-chises/concessions, subsidies, or volunteers. Sum-mary statistics can be found in Tables 1 and 2 of the Appendix. In accordance with expectations, the vast majority (80.07%) of services in this sam-ple are provided publicly. As mentioned above, Levin and Tadelis draw a distinction between ser-vices contracted out to another government and inhouse provision, however, for my analysis the differentiation isn’t necessary since, as far as po-litical factors are concerned, which government agency provides the service isn’t relevant so much as whether it is provided by the public sector or the private sector. For example, a Republican of-ficial trying to limit government involvement would likely oppose inhouse service provision regardless of whether it is through his own ad-ministration or contracted out to another public agency. Therefore, I consider the category “pro-vided by another government” as a form of pub-lic provision, as done by Lopez-de-Silanes et. al. Although the response rate to the ICMA survey is only about 26.2%, there doesn’t seem to be much

potential for response bias, as noted by Levin and Tadelis, in regards to private or inhouse provision, although the northeast and smaller counties are slightly underrepresented in the sample. For the political economy component of my research, the data comes from a variety of sources. For testing certain state laws, I use the same data used by Lopez-de-Silanes et. al, which is the U.S. Advisory Commission on Intergovernmental Re-lations (ACIR) report “State Laws Governing Local Structure and Administration”. As its name would suggest, this data records information about state laws that dictate the structure of local govern-ments. For my purposes, it lists which states have enacted a number of relevant laws, including the eight clean-government and budget constraining laws tested in Lopez-de-Silanes et. al’s research, as well as two others of interest that I will include in my analysis. As to the political leanings of coun-ties, I will be using voting data from the David Leip U.S. Elections Atlas for the 2006 House of Repre-sentatives elections and from the Inter-Univer-sity Consortium for Political and Social Research (ICPSR) Report on County Characteristics for the 2004 Presidential election. The ICPSR report also provides a number of control variables including county demographic information, unemployment rates, incomes, crime rates, government revenues and expenditures, and economy-type. Population and geographic data come from the ICMA survey.

IV. Methodology

4.1 Basic Regression I use a probit regression model to analyze the effects of the political economy factors on priva-tization, similar to the model used by Lopez-de-Silanes et. al., with a binary dependant variable measuring the form of service provision, either privately provided or publicly provided. My ob-servations are county-service pairs (n=8553, summary statistics can be found in Table 2 of the Appendix), so that I was able to eliminate those observations where the service is not provided or is provided by a means other than the private or public sector (i.e. vounteering, franchising, a com-bination of the two sectors, etc.). Thus, there are multiple observations for each county, since most counties provide a range of services. My actual equation is:

P (private) = Φ(α0 + α1E + α2T + α3Ri + α4Sj + α5Bk + α6Z + α7Cm +



α8P + α9X + ε) (1) In the equation, P(private) is the probabil-ity of providing the service privately and Φ is the standard normal cumulative distribution func-tion used in probit regressions. E is a dummy vari-able specifying whether the county official(s) in charge of service decisions is(are) elected or ap-pointed (where E=1 specifies an elected official). T is a variable representing a county’s voter turn-out in the most recent House of Representatives election. The Ri variables (i=1,2) label the fraction of Republican votes in a county; R1 in the presi-dential election, R2 in the House election. Vari-ables Sj (j=1,..,5) are five dummy variables for the presence of state laws limiting patronage in local governments (respectively, whether or not a local official may be recalled, whether local govern-ment decisions must be made publicly, whether there exists a code of ethics for local government officials, whether government employees are banned from political activity, and whether state law requires a merit system for hiring). Similarly, Bk (k=1,...,5) represents five state-mandated bud-get constraints, (whether short-term borrowing is allowed, whether there is an imposed debt limit, whether there is a mandated balanced budget, whether a public hearing is required for budget approval, and whether the state may take over the county’s finances), which, considering the re-lationship between privatization and lower bud-gets, could influence service provision. The Z vari-able labels the county’s unemployment rate, and Cm (m=1,2) are generated dummy variables that represent whether or not the county population is highly politically contested; they equal one if the percentage of Republican votes was between 45-55% in the most recent House of Representa-tives or presidential election, respectively. Finally, X takes the place of almost thirty control variables, including geography, county median household income, and population characteristics such as age, gender, and ethnic composition. One drawback to this approach, as discussed by Lopez-de-Silades et. al, is that there is a po-tential correlation among the error terms in the regression. By using county-service pairs as my observations, it is conceivable that unobservable county-specific factors, such as a particular county manager’s personal feelings toward privatization, could lead to a correlation between the various private/public observations within a county. Simi-larly, observations within the same state could potentially be correlated, a result of, for example,

state-wide sentiments towards privatization. Con-sequently, the error terms in my regression could be underestimated, leading to results that appear more significant than they actually are. The inclu-sion of so many control variables is an attempt to mitigate this effect by picking up aspects of the county environment that could otherwise lead to error term correlation. Lopez-de-Silades et. al combatted this effect by running regressions for a few individual services across all of the counties in the sample, using the same variables as their other regressions, and comparing the results. They found the same signs on the coefficients, al-though some of them were slightly less significant than previously estimated.

4.2 Interaction Terms After studying my intial regression outputs based on Equation (1) as described above, I noted that the signs on both measures of Republican voters were negative, results that seem counterin-tuitive given my analysis. For this reason, I wanted to consider certain potential interaction effects, specifically how the effects of a contested gov-ernment or a more Republican population might depend on the presence of an elected, rather than appointed, official. I hypothesized that interact-ing these terms with the dummy variable for an elected official could yield interesting results. For example, if a population is highly Republican-leaning, but the county official is appointed, the political stance of his constituents may have little or no influence on that official’s decisions. Similar-ly, a contested government will likely only add po-litical pressures to an official if he is elected. These considerations led to a new equation, as follows:

P (private) = Φ(α0 + α1E + α2T + α3Ri + α4Sj + α5Bk + α6Z +

α7Cm + α8P + α9X + α10E*R1 +α11E*C1 + ε) (2)

The first eight variables have the same inter-pretation given previously. However, I have added the two interaction terms E*R1 and E*C1. Intui-tively, when considering the interaction between the Elected Official dummy variable and Fraction of Republican Votes or Contested County, it seems more reasonable to use the measure of votes from the House of Representatives election, as this is on a more local level that would likely be a bet-ter stand-in for the political pressure on a local county official. Comparing regressions using this



measure versus the presidential measure affirmed this intuition with more robust and meaningful re-sults, so that is the variable used in the interaction terms in these regressions.

V. Results

5.1 Complete Regression Outputs Table 3 in the Appendix contains the full re-sults of four regressions derived from equation (1) and (2) discussed above, using various combina-tions of certain related variables. Regressions (A) through (C) include different specifications from equation (1), based on which measure of Repub-lican votes is used. The resulting regression using the two interaction terms added in equation (2) is displayed in regression (D) of Table 3. The Pseudo R-squared in this regression shows an increase of .2 percentage points indicating that the two sig-nificant variables help explain more of the varia-tion than in the previous regressions. We can reject the null hypothesis that none of the considered variables is significant with al-most certainty (Prob > X2 = 0.000), indicating that political economy factors are important. Further-more, F-tests for each category of variables con-sidered (political patronage, voter ideologies and pressures, and budget constraints) also reject the null hypotheses that the variables within these categories are all insignificant. The results of these F-tests are displayed at the bottom of Table 3.

5.2. Political Patronage The results for the state clean government laws are mixed. The most significant outcome is for laws mandating ethics codes for public offi-cials, although the sign is negative, which is the opposite of what I predicted. A similar result was found by Levin and Tadelis for city governments and was given the explanation that ethics codes improve the overall quality and performance of public employees, making public provision a bet-ter option for service quality and cost. State laws allowing the recall of public officals and requiring county decisions be made publicly both enter the regression with positive coefficients, as expected, and are significant (only at the 10% level) in three of the four regressions shown in Table 3. Neither of these laws had previously been studied, but, like the other laws tested by Lopez-de-Silanes et al., it seems reasonable to assume that both in-crease the transparency and accountability of a county official’s administration, making the prac-

tice of patronage more difficult. Therefore, the fact that both are correlated with more privatization implies that patronage does potentially play a role in the decision-making process of county officials. The two other laws tested were not significant, despite having been significant and conflicting in previous studies. It is possible that the inclusion of other political economy variables in my regression eliminated some aspect of omitted variable bias which had previously led these laws to appear sig-nificant. The overall significance of political patronage as a component of inhouse provision is not con-clusively established, although it cannot be ruled out since some of the state clean government laws were significant and F-tests indicate that the variables on the whole are significant. Morevoer, these laws are clearly imperfect proxies. The other less significant regression results could as easily reflect the laws’ ineffectiveness at limiting patron-age than that patronage does not influence the privatization decision.

5.3 Voter Ideologies and Pressures In this category, all but one of the variables (voter turnout) were significant. Higher unem-ployment rates showed a very robust correlation with more privatization, which could be explained in a number of ways. One possibility is a type of reverse causality; that is, privatization tends to use fewer employees (Stolt, Blomqvist, & Winblad, 2011), and as such, counties that provide the ma-jority of their services privately will see more un-employment. It seems unlikely, however, that this effect alone could explain the large demonstrated result. Additionally, counties with chronically high unemployment rates are likely also to have more impoverished governments, which may in turn lead to a stronger emphasis on cost-saving, and thus privatization. Although this variable has been overlooked in previous work, the presence of an elected versus an appointed official seems to explain a large part of the provision decision. It enters all four regres-sions with a negative coefficient that is statistically significant at the 1% level. The sign on this coeffi-cient can be explained in a way that corroborrates the theories about patronage discussed above. That is, elected officials who tend to be more po-liticized than their appointed counterparts are more likely to wish to engage in patronage by re-warding their supporters and allies through pub-lic placements. An appointed official seemingly



would have less need to court and later reward donors and political advocates. Thus, there would be an increase in inhouse provision when there is an elected position and vice versa. The importance of this political susceptability is further demonstrated with the interaction term “Elected Offical * Fraction of Republican Votes.” As mentioned earlier, both measures of Republi-can votes were associated with less privatization, similar to findings by Levin and Tadelis (and con-trasting other related studies’ results). However, considering how the effect of Republican votes might depend on the presence of an elected of-ficial, through the interaction term, reveals that when the county official is elected, a stronger Republican presence is robustly correlated with more privatization. Moreover, the magnitude of this increase is substantially larger than that of the decrease associated with Republican votes in general. This result helps to explain the confusing and conflicting signs found in previous papers by revealing how the political stance of a county population works its way into the decision pro-cess through the mechanism of an elected official. The variables for a contested county popula-tion are also significant, although interestingly, the presidential election variable shows a much stronger effect than the variable derived from the House of Representatives election. Though the meaning of the direction of these signs is not immediately clear, these variables lend support to the hypothesis that the political climate influ-ences decisions. As noted in Section 2, political theory has argued both that political competition encourages patronage and that it discourages patronage by increasing transparency. My results could be used to bolster the latter argument; the fact that a more contested government is correlat-ed with less inhouse provision might reflect fewer possibilities for patronage in a politically competi-tive environment. Yet it is important to keep in mind in this case that a number of other possible explanations could also justify such a correlation. By interacting the House of Representatives con-tested county variable with the elected official dummy (using the variable “Elected Official * Con-tested County”), we see that the original positive effect of a contested population is even stronger in the presence of an elected official and explains essentially all of the contested population vari-able’s previous significance. Clearly, a politically competitive environment is much more relevant of a factor in situations where the government is

elected, as one would expect. While none of these variables involving con-tested populations have been previously tested, it is clear that they are important factors in the allot-ment of services between the private and public sectors, and furthermore, that they are strong in-dicators of political influence in these decisions.

5.4 Budget Constraints Of the five budget constraints, four were significant, although only one was significant at the 10% level. Laws that allow states to take over county finances in cases of mismanagement or distress were highly robust, with positive coef-ficients on private provision. This sign contrasts with Lopez-de-Silanes et. al’s previous finding, which indicated a negative coefficient on this vari-able, as well as Levin and Tadelis’s outcome, which was insignificant. It does, however, corroborrate the hypothesis that increased budget pressure from the state, in this case in the form of the threat of state takeover, encourages cost-reducing priva-tization. Laws placing debt limits on counties and laws requiring budget hearings before passage of budgets both enter significantly and positively, lending further weight to this logic. Only laws re-quiring balanced budgets break the trend, with a negative coefficient; this outcome, however, mir-rors the sign determined in both previous papers.

VI. Conclusion As a society, we rely on our government to make decisions that are in our best interest, allo-cating our scarce resources to their best uses, es-pecially in cases where regular markets fall short. However, it has long been known that political sys-tems and, particularly, the individuals that make them up are fallible. Susceptible to any number of self-promoting actors, the idea that public of-ficials could fall prey to some of these influences is hardly a precarious hypothesis. This paper has attempted to analyze some of these political fac-tors that might affect how local officials decide to provide services. For governments as well as for their constituents, the results of such an analy-sis have important implications for the future of service provision. As a whole, my analysis shows that the decision-making process is not made in a quality-maximizing, cost-minimizing vacuum; political influences can and do affect the outcome of these decisions. Corroborating previous find-ings in this overarching conclusion, my paper also extends and clarifies these findings. Particularly,



the analysis reveals how the processes of political patronage, voter ideologies and pressures, and buget constraining laws manifest themselves in the ultimate distribution of services between the public and private sectors. Further study would be required to establish what role, if any, is played by political patronage in the decision process, since the results of this anal-ysis are predominantly inconclusive in that regard. As to voter ideologies and pressures, it seems clear that these influences factor into a decision-mak-er’s considerations; the political composition of a county’s population and its government structure are especially relevant. Additionally, the degree of leniency permitted by the state in terms of county finances also alters the medium of service provi-sion in a predictable way. Posed against the relevant literature, these findings question the argument that the consid-erable preference for inhouse provision amongst counties is entirely founded in quality concerns. A number of political variables weigh in, with po-tentially many more that were not tested in this analysis. The importance of budget laws under-scores that cost-savings are certainly a consider-ation, and state legislation mandating constraints might consider their indirect effects in either en-couraging or discouraging privatization. While this paper establishes the existence of political effects on the privatization decision, it is important to note that it stops short of present-ing a normative position on the consequences of such effects. The reader may too easily jump to the conclusion that these political influences are necessarily “bad”, when in fact it is possible to con-struct an argument that emphasizes the postive outcomes resulting from such influences or even argues the necessity of these forces for govern-ment efficiency. The result that an elected official is more heavily influenced by the political make-up of his constituency could as easily be con-strued as a pro than as a con; perhaps those who are elected should be more easily swayed in cater-ing to the wishes and needs of the constituents. Seen this way, the significant coefficents on these variables can represent the county population holding their local government in check; a con-tested government or an elected official afford us an even tighter control, diminishing the mutation that occurs between our voice and its realization through the arm of the government. Even political patronage which seems inherently inefficient has been given a positive spin with arguments that it

promotes trust and cooperation amongst officials, lending efficiency to operations, as noted by Fee-ney and Kingsley (2008). The importance, then, of these findings lies in the fact that they assert the existence of such factors in service allottment; it is up to further research to determine whether these should be encouraged or eliminated and, ultimately, what significance they hold for public policy.

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Appendix:









country’s economic and environmental sectors suggests that Mongolian dependency on Chinese goods and the Chinese market is growing.” Mongolia’s impressive economic growth is driven by China’s economy’s huge demand, and there is a danger in being dependent on just one particular economy. However, according to Bill Bikales, Chief Economist for the United Nations Development Programme (UNDP) in China, Mongolia and China’s geographic proximity and mutually beneficial economic system together constitute a framework for potential economic exchange that could develop into a US-Canada type of trading relationship. Therefore, this paper will emphasize the strength of China’s and Russia’s economic influences as well as the effect of other factors, such as commodity prices, on Mongolia’s economy.

II. Literature Review Before Mongolia revolutionized from a communist to a free market-based economy in 1991, the country was highly dependent on Russia. Over 720 housing projects, Darkhan and Erdenet’s power stations, the Baganuur, Aduunchuluun and Sharyn Gol coal mines, and over 1,000-kilometers of roads were built using 11.4 billion in convertible rubles of Soviet loan, which was forgiven with less than $300 million to clear the debt on 1st of January, 2014 (FT, January 2004, p. 7). However, in the beginning of the 1990s, Russia’s influence on Mongolia slightly weakened, and Mongolia announced itself as a liberal economy and an open country to the outside world. Subsequently, Mongolia started to promote the “third” economic neighbor countries in order to sustain a more healthy economy, and hence achieved great economic growth of over 10% per year (Blagov 2012). However, during the decade following Mongolia’s revolution in 1991, the world trade of liberal market economies increased by 177% and intra-regional trade in East Asia grew 304%, which implies that most new trade in East Asia was among East Asian states

China, Russia and Other External Factors’ Economic Influence on Mongolian Economy

Sukhbat LkhagvadorjWesleyan University

I. Introduction Mongolia has recently been one of the world’s fastest growing economies, primarily due to a large-scale mining project called Oyu Tolgoi. Oyu Tolgoi, one of the world’s largest undeveloped high grade copper-gold mining projects, is projected to last for at least 50 years of excavation (www.ot.mn/en/about-us). Tavan Tolgoi, the second largest mine in Mongolia, is a 125 billion metric ton coal mining project (www.en.tavantolgoi.mn). According to the IMF, Mongolia’s real GDP growth was 12% in 2012 and it is projected to be 11.8% in 2013 (IMF, World Economic Outlook, October 2013). This rapid GDP increase is the result of a few mining projects such as Oyu Tolgoi. Many international and Mongolian scholars such as Amarjargal Rinchinnyam and Malin Samuelsson have expressed concern at the possibility of Mongolia falling victim to the “wealth curse” or “Dutch disease” (http://www.globaltrends.com/blog/entry/the-resource-curse-and-development-mongolia-revisited). These economic afflictions affect countries that depend substantially on mining tax revenue; the country’s economy becomes fragile and commodity prices have a significant effect on the economy. Furthermore, fast growing mining industries absorb most of the skilled workers, and thus other sectors of the economy lack potential growth because of a shortage of experts. Moreover, there is a risk in having just one main export partner because in the long run a monopolized market can shift the prices of minerals. In fact, 84% of Mongolia’s total exports go to China (Mongolian National Statistical Office, Statistical Yearbook 2010). The Economist magazine also emphasizes that in an attempt to achieve such “dreams under your feet” as Mongolia has, it is letting itself be dug up and sold to China. Mining resource exports already exceed 80% of its total exports according to 2012 The Economist article “Booming Mongolia.” Jeffrey Reeves claimed in his doctoral thesis at the London School of Economics in 2010: “Indeed, examination of the



(Ohashi 2006). Hence, in both the old and recent history of Mongolia, it is an inescapable truth that Mongolia’s macro economy is highly dependent on its two neighbors, China and Russia. In fact, Mongolia imports nearly 60% of its total goods from its two dominating neighbors; the main export partner is China -- 84% of Mongolia’s total exports go to China (National Statistical Office of Mongolia, Statistical Yearbook 2010). The doctoral thesis of Jeffrey Reeves at the London School of Economics highlights that China’s huge economy’s “unconscious power” over Mongolia’s comparatively tiny economy has been increasing significantly in recent years. In fact the Chinese government has employed what Alden, in his work on Sino-African relations, describes as a “traditional strategy of linking investment to tie-in projects and providing lower labor costs in the form of less costly managerial staff and introducing their own contract workers” (Alden 2007). From the recent actions of China, it is clear that China is trying to increase its “unconscious power” on Mongolia, while at the same time Russia does not want to lose its power. In 2006, the Chinese and Mongolian governments, under the auspices of the Asian Development Bank and CAREC, signed a $5 million Trade Facilitation Project meant to foster bilateral trade by improving cooperation, infrastructure, and transportation (Asian Development Bank 2007). Mongolia imports nearly all its gas from Russia, although today Mongolia is intensively trying to find another gas-supplying partner. Mongolia’s national railway system was built with the help of their Russian brothers and Russia owns a 50% stake in this railway system. Although a number of academic articles and papers claim that Mongolia is heavily dependent on its two adjacent countries, there is not yet a paper which empirically evaluates these three countries’ macroeconomic correlations, which can be illustrated by the most common macroeconomic index, GDP. The most extensive research among the available resources online was done by Jeffrey Reeves at the London School of Economics although it emphasized Mongolia and China’s relationship more in terms of political and cultural aspects, and one possible reason why research studies on Mongolia were lacking in the past could be the result of a lack of government-collected statistical information. However, recently the National Statistical Office of Mongolia has developed several methods to estimate

macroeconomic indices with the help of the World Bank, and statistical information has now become more common and easier to collect. Thus, this paper could be one of the first papers, at least among the available online sources, which tries to empirically estimate how strongly neighboring countries and other external environmental factors influence or correlate with Mongolia’s economy.

III. Methodology Several macroeconomic indicators, such as GDP, CPI, unemployment rate, and others can illustrate any particular country’s overall macroeconomic performance. This paper will use Mongolia’s GDP as the main macroeconomic indicator. Although Mongolia’s macro economy has its own internal factors and other external environmental factors, this paper will explore the strength of Russia and China’s influence on Mongolia. Although it is hard to measure other countries’ economic influence on Mongolia’s economy, the main macroeconomic indices provide a rough picture. The hypothesis of this particular research paper is that Mongolia’s macroeconomy is highly dependent on its two neighboring countries, Russia and China, because these countries are Mongolia’s only trading partners for practical purposes. Mongolia’s economy is becoming increasingly dependent on commodity prices as well, because of its new massive mining project Oyu Tolgoi. Russia, China and Mongolia’s quarterly GDP growth would be the ideal variables for a regression analysis equation. Only Russia’s quarterly GDP growth is available, but from the raw data on Mongolia’s and China’s quarterly GDPs, seasonally-adjusted GDP growth rate is derivable. However, differences in calculation methodology present a problem. For example, according to Wall Street Journal - China , China started to use a quarter-on-quarter-based GDP calculation method only in May of 2011 (The Wall Street Journal, Q-on-Q Seasonally Adjusted Annualized GDP – It’s Important, 2011). Russia, on the other hand, uses a year-on-year method to calculate quarterly GDP growth. Hence taking the natural logs of these quarterly GDPs could provide comparatively unbiased data of change in GDP growths. The model assumes Mongolia’s quarterly GDP in tugriks (the national currency of Mongolia) as a main economic indicator for Mongolia, which is the dependent variable Y in the regression analysis



equation. The data used in this study is collected from the National Statistical Office of Mongolia, which has been releasing the seasonally-adjusted quarterly GDP since 2008. Fortunately seasonally-adjusted quarterly GDP data from 2002 to 2011 is currently available at the National Statistical Office website (www.nso.mn). Thus, the natural logs of quarterly GDPs of Mongolia, Russia and China will be used in the regression analysis equation and the total sample size will be 40 -- four quarters over 10 years. Although Mongolia’s economy was mainly dependent on Russia especially before 1991, the dependency on Russia has been shifting to China over the last 20 years making the dependency comparatively equal. Hence the natural log of China’s quarterly nominal GDP in US dollars is the first/main independent variable – that is, X1 for the regression analysis equation. Subsequently, China’s corporate goods index, which includes the price indexes of coal, oil and electricity, will be the second control variable (X2) in order to test how well China’s corporate goods price index ties up with Mongolia’s GDP. Russia’s nominal quarterly GDP in US dollars is the third independent variable (X3), and Russia’s corporate good index (electricity, gas and water supply) will be the fourth control variable (X4). Even though this research paper mainly emphasizes Mongolia’s two neighboring economies’ influence on Mongolia’s macro economy, other external variables will be tested as well, such as world copper and gold prices, because of Mongolia’s furiously increasing mining sector. In fact, the mining and quarrying sector of GDP has increased over twelvefold from 2002 to 2010 (National Statistical Office of Mongolia, Statistical Yearbook 2010). Since 2010, the Oyu Tolgoi mining project officially started construction, and plans to work at full efficiency by 2013. Hence fifth and sixth control variables are respectively world grade “A cathode” copper price index and world gold price indexes (both of them measured in US dollars). Thus we will be able to see which macroeconomic index is most closely correlated with Mongolia’s GDP. Then our regression analysis equation will be:

Y = B0 + B1*X1 + X2*B2 + X3*B3 + X4*B4 + X5*B5 + X6*B6

GDP of Mongolia = B0 + B1*(Natural log of GDP of China) + B2*(Corporate goods price index

of China) + B3*(Natural log of GDP of Russia) + B4*(Energy price index of

Russia) + B5*(World copper price index) + B6*(World gold price index)

It is assumed these indicators are highly, linearly and positively correlated with the economy of Mongolia but interacted and other versions of the model will be tested as well in order to see interaction effects of the variables. In fact, even though Russia and China’s GDPs are highly correlated with Mongolia’s GDP, Russia and China’s GDPs are highly correlated too. Hence, the high correlation between Russia and China’s GDPs causes the “multicollinearity” problem. There are several ways to solve this problem, but in this case the simplest method to remedy this problem is to drop one of the Russia’s GDP and China’s GDP variables in order to get a more reliable model. The most reliable version of the model will be chosen based on the R-squared coefficients, but other versions of the model will be emphasized in the results section as well.

IV. Results As illustrated in the following Eviews table (Table 1), the initial model works fine with nearly 89% of adjusted R-squared and the probability of F-statistics at almost 0. Some of the control variables have extremely high P-values, which means they failed to confirm that these variables’ coefficients are not equal to 0, but the main control variables have close to 0 P-values.

Dependent Variable: MGDP Method: Least Squares Date: 05/13/12 Time: 22:17 Sample: 1 40 Included observations: 40

MGDP – Natural log of Mongolia’s GDPC – Constant variable

CGDP – Natural log of China’s GDPCCORP – China’s Corporate Goods Index (Coal, oil

and electricity)RGDP – Natural log of Russia’s GDP

RNRG – Russia’s Energy Price Index (Electricity, gas and water supply)

WCOP – World Copper Price IndexWGOLD – World Gold Price Index

MGDP – Natural log of Mongolia’s GDPC – Constant variable

CGDP – Natural log of China’s GDPCCORP – China’s Corporate Goods Index (Coal, oil

and electricity)RGDP – Natural log of Russia’s GDP

RNRG – Russia’s Energy Price Index (Electricity,



Interestingly, as shown in the Eviews estima-tion output table (Table 1) above, Russia’s GDP is negatively correlated with Mongolia’s GDP, which does not really make sense in this case. Although there can be several explanations for this phe-nomenon, one could be “multicollinearity,” which is a statistical phenomenon in which two variables in a multiple regression model are highly correlat-

ed but it might not show a valid result. As shown in the following correlation table (Table 3), Russia and China’s GDPs are highly correlated at 95% but Mongolia and Russia’s GDPs are only slightly cor-related at 77%.





One can see that the GDPs of Russia and China seem non-linearly correlated with Mongolia’s GDP, because we can draw concave-like left skewed curves on the scatter plot. In fact, that is true and if we try a non-linear model and run a regression analysis on Eviews, we will get:

MGDP = 5.736195 + 0.121339*CGDP^2 + 0.008930*CCORP -0.069282*RGDP^2

-2.377419*RNRG + 0.001212*WCOP - 0.000111*WGOLD

Adjusted R-squared = 0.892099Prob (F-statistic) = 0.000000

Although we have a slightly higher adjusted R-squared value and still almost 0% probability of

F-statistics, in this paper we will use the simplest model (Table 2) in order to keep the model sim-pler and easier to understand and to explain. Fi-nally, if we drop the GDP of Russia from our model in order to solve “multicollinearity,” our new model becomes somewhat better and the results are:

Y = B0 + B1*X1 + B2*X2 + B3*X3 + B4*X4 + B5*X5Dependent Variable: MGDP Method: Least Squares Date: 05/18/12 Time: 00:23 Sample: 1 40 Included observations: 40



As shown above (Table 2), the most highly correlated variable with Mongolia’s GDP is the Chinese GDP, as projected in the initial hypothesis earlier on. The low probability (F-statistic) and rel-atively high (0.818185) coefficient of China’s GDP (CGDP) implies that this variable fits the model al-most perfectly. For instance, every one unit of a natural log of China’s GDP implies 0.818 units of natural log of Mongolia’s GDP. In other words, $10 million of China’s GDP correlates with $5,001,000 (6.576578 million tugriks, 1 USD = 1.315 tugrik) of Mongolia’s GDP. Furthermore the next control variable China’s corporate goods index is also a really good variable for our regression model be-cause this P-value is the 9.3% that represents a two tailed test, while this variable’s P-value was 93% in our initial old model that includes the Russia’s GDP. In other words, if we conduct our hypoth-esis for China’s corporate goods index with 95% confidence and run a one tailed test because we assume it is negatively correlated, we have suffi-cient evidence to reject the null hypothesis, which implies China’s corporate goods index is a pretty good variable for our regression model. Then ev-ery 1% decrease in China’s corporate goods index correlates with $1,568.4 decrease in Mongolia’s GDP. The sub-control variable of the Russian effect on Mongolia is the energy price index of Russia (RNRG for the model, Table 2), which is also highly correlated with the GDP of Mongolia with a coef-ficient of (-2.463) and P-value of 0.0084. In other words, 1% of increase in the energy price index in Russia correlates with $220,838 decrease in Mon-golia’s GDP, which makes sense because Mongo-lia imports almost all its total gas consumed from Russia. If the gas price goes up in Russia, it will af-fect Mongolia’s gas prices as well. Hence, almost every Mongolian good and service is somehow correlated with gas prices, and service and goods in Mongolia becomes more expensive, which can lead to a decrease in GDP. The fourth control variable is the world copper price (denoted as WCOP in the model and Table 1). While the initial hypothesis claimed that Mongo-lia’s GDP is becoming highly dependent on mining because of its huge mining projects, the P-value of the world copper price is 49.42%, which is com-paratively high even if we divide by 2 in order to convert it into a one-tailed test, and the coefficient of this variable is 0.000741, which is close to 0 and has little correlation with the dependent variable. The next control variable is the world gold price

(denoted as WGOLD in the initial model and Table 2). This control variable’s P-value is extremely high (90.1%) and negatively correlates with the depen-dent variable, Mongolia’s GDP, which implies that world gold prices and Mongolia’s GDP are not sta-tistically correlated. One reason could be that one of Mongolia’s largest two mining projects, Tavan Tolgoi, has not officially started yet. Although the mining and quarrying sector of Mongolia have in-creased extremely in the last 10 years, the goods and services sector of GDP is still higher than any other sector of GDP. V. Conclusion Since the sample used in this study covers the last 10 years (2002 – 2011), the weak performanc-es of world gold and copper prices indexes were expected. In fact, the mining project Oyu Tolgoi is planned to start by 2013. I assume the usefulness of the world gold and copper prices indices will in-crease in the near future for Mogolia, and as such should not be omitted from the model. None-theless, the data confirms that Mongolia’s macro economy, which is quantified by its GDP, is highly correlated with its two neighbor countries’ econo-mies, which are also quantified by their GDPs. In recent years, China’s GDP as well as the Chinese corporate goods index are highly and positively correlated Mongolia’s GDP. There is a weak corre-lation between world copper and gold price index and Mongolia’ GDP. One explanation could be that China is Mongolia’s long standing trading partner; even before Mongolia was known to the world for its huge mining projects, China was the only trad-ing partner in coal and other commodities. In the near future, when Tavan Tolgoi starts to work at full capacity, China’s corporate goods index may become even more correlated with Mongolia’s GDP. While the relationship between Mongolia’s GDP and Russia’s GDP is not fully explained, it is true that they are at least somehow highly corre-lated with each other (Table 2). The insufficient ex-planation of this issue might be the weakest part of this particular model. Despite this shortcoming of the model on this issue, Mongolia is still highly dependent on Russia’s energy price index and especially negatively-correlated with this index meaning that the Mongolian economy benefits when Russian energy prices decrease. That find-ing reaffirms what was proposed in the initial hy-pothesis, that Mongolia is highly dependent on its northern neighbor in the sense that Mongolia



imports all of its gas from Russia. Specifically, the correlation coefficient between the natural log of Mongolia’s GDP and Russia’s energy price index is -2.463 with P-value of 0.0084. In conclusion, Mongolia is highly dependent on its two neighbor countries and its GDP is es-pecially negatively correlated with the Russian en-ergy price index, positively with China’s GDP, and China’s corporate goods price index. These rela-tionships imply Mongolia is highly dependent on its two neighboring countries’ gas and coal prices. In order to sustain a more independent and di-versified Mongolian economy, Mongolia should reaffirm what they started 20 years ago: open its doors to the world and attract “third” neighbor countries.

ReferencesEconomist magazine, Booming Mongolia – Mine, All mine, 2012

Federal Reserve Bank of New York, Buffalo, China Statistical Information and Consultancy Center, China – GDP, Nominal, US Dollars, 2012

Federal State Statistics Service, Energy Industry Price Index, 2012

International Monetary Fund, Copper, grade A – Spot (Cash Price), 2012International Monetary Fund, Gold at market Pric-es, May 9th of 2012

International Monetary Fund, World Economic Outlook, 2012

Jeffrey Reeves, Mongolia State Weakness, Foreign Policy, and Dependency on the People Republic of China, 2010

Library of Congress, A Country Study: Mongolia, 1990

National Statistical Office of Mongolia, Mongolia Statistical YearBook, 2005

National Statistical Office of Mongolia, Monthly Bulletin of Statistics, April 2011

National Statistical Office of Mongolia, Monthly Bulletin of Statistics, January 2012

People’s Bank of China, Corporate Goods Price In-dices, 2012Rogier van den Brink, Munkhnasan Narmandakh, Tehmina Khan and Altantsetseg Shiilegmaa, Mon-golia’s Quarterly Update, World Bank, 2011

State Committee of the Russian Federation on Sta-tistics, Russia – GDP, Nominal, US Dollars, 2012

Ulaanbaatar Post, Mongolian Current Business and Economic Giants: Their Influence, 2012



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