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An investigation into the relationship between the Gross National Income of China, it’s Labour Force and the Number of Domestic Companies Registered on The Chinese Stock Exchange

Econometrics

Explain the causes of the

recession and analyse the

response of the UK capital and

money markets

Money, Banking & FinanceContemporary EconomicIssues

Game Theory, Evaluation of the

Market For Online Bookmakers

and Evaluation of an

International Issue

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To what extent has actual

economic growth in the UK been

below potential economic

growth since March 2009

Contemporary EconomicIssues

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linkedin.com/in/dansayerDANIEL SAYERBournemouth University Economics

Daniel Sayer Econometric Techniques

An investigation into the relationship between the Gross National Income of China, it’s Labour Force

and the Number of Domestic Companies Registered on The Chinese Stock Exchange

Abstract: The following paper will seek to explore links between the Gross National Income of China, the size of the countries workforce and the number of domestic companies on the Chinese Stock Exchange; displaying how it’s rapidly growing workforce could be a huge factor in the countries colossal economic growth. Using 11 years of data and methods of regression and other statistical analyses I have evaluated correlations between said variables and distinguished strong links between the independent and dependent variables; whilst displaying confidence in the bounds for error. 1) Introduction China’s extreme population growth has resulted in a rapidly expanding workforce that some may argue has had a positive effect on the economy; driving labour costs down and creating a larger pool of skilled workers.1 This newfound entrepreneurship has seen the number of start-up companies in the country soar and in this time we have seen the economy grow to become the second largest in the world.2 There is a clear link between population growth and size of the workforce3 and whilst there has not been substantial research on the effect of workforce growth and number of domestic companies, the relationships between population growth and economic development have been debated for a long time.4 Malthus (1826) suggested that population growth may have a negative impact on economic growth due to the fact that population, and thus the workforce, tends to grow geometrically whilst means of subsistence grows arithmetically. He states that output growth rate cannot keep the necessary pace to keep an equilibrium between output and consumption and this may have a negative effect on the economy.5 Solow (1956) agreed with this notion to an extent; in his paper he created a model using population growth rate as a constant, exogenous variable. His model illustrated that an increase in the population of a country will see absolute output increase due to the increase in labour supply, however it will reduce the physical capital stock per worker and as a result decrease productivity.6 1 Turner, A. (2009). Population Priorities: The Challenge of Continued Rapid Population Growth. Biological Sciences. 0183 (1), p2. 2 Shieber, K. (2010). As Internet IPOs Pop, Start-Up Prices Soar In China . Available: http://blogs.wsj.com/venturecapital/2010/12/16/as-internet-ipos-pop-start-up-prices-soar-in-china/. Last accessed 10th Apr 2014. 3 Bloom et al. (2007). I mplications of Population Aging for Economic Growth. PGDA Working Paper. 64 (1), p29. 4 Cincotta and Engleman. (1997). Economics and Rapid Change: The Influence of Population Growth. Occasion Paper. 3 (1), p5. 5 Malthus, T. (1826). An Essay on the Principle of Population. An Essay on the Principle of Population. 2 (1), p2. 6 Sollow, R. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics. 70 (1), p65-71.

In contrast Kuznets et al (1960) suggested a positive effect of population growth on business cycles and looked closely at how people produced, consumed and saved. Although in his original paper Kuznets provided no new aggregate production function the paper opened the door to further research on a topic that he later (1967) went on to provide more empirical evidence of a positive correlation.7 Galor and Weil (2000) proposed a unified model to explain the relation between population and economic growth. They suggested history showed population pressure on means of subsistence and slow technological progress to have a negative effect on economic performance, yet say that as we have moved into the technological age this may no longer be the case. They endorse Simon’s 1976 paper, suggesting that population growth has aided technological advances due to a growth of the workforce and more technological based companies and this in turn increases economic performance. Galor and Weil’s paper uses empirical data from the industrial revolution; criticising Malthus’ view and saying that new means of technology have allowed production to meet consumption;8 I believe that this is certainly the case when looking at China as a case study. Since initiating market reforms in 1978 China has shifted from a centrally planned economy to one that is market based; experiencing colossal growth in both economic and social terms in the process.9 Since the reform the countries Gross Domestic Product has consistently risen by around 10% per year and has contributed to lifting over 500 million people out of poverty, whilst we have also seen the population of the country boom – now standing at around 1.35 billion, making it by far the highest populated country in the world.10 Although China’s economy has grown to become the second largest in the world, it’s high population sees a GNI per capita that still classifies it as a developing country. This paper aims to evaluate whether China’s economic growth is partly down to the growing labour force and number of new companies or in spite of the stark overpopulation that has lead to a one birth per couple policy that was introduced in 1979 to “alleviate social, economic, and environmental problems in China.”11

7 Palumbo, L. (2012). Relations between the EU and the emerging global players. Göttingen Summer School. 1 (1), p4-5. 8 Galor and Weil. (2000). Population Technology and Growth From Malthusian Stagnation. Brown University Providence. 1 (1), p807-827. 9 Focus on China. (2014). Study in China. Available: http://www.focusonchina.cn/StudyinChina/2013/0626/12.html. Last accessed 10th Apr 2014. 10 World Bank. (2014). China - Overview. Available: http://www.worldbank.org/en/country/china/overview. Last accessed 10th Apr 2014. 11 Davis, B. (2013). Aging Population Could Trim 3% Off China GDP Growth . Available: http://blogs.wsj.com/chinarealtime/2013/10/23/report-aging-population-will-trim-3-off-chinas-gdp/. Last accessed 10th Apr 2014.

2) Methodology All data used in my observations was taken from the World Bank’s database12. The World Bank was used as it provides a trust-worthy dataset with an easy to use interface. The model uses eleven years worth of data, spanning from the year 2000 until 2010. The following model will see the Gross National Income of China used as a dependent variable, whilst independent variables will take the form of the size of labour force of China and the number of domestic companies floated on the Chinese stock exchange. In this case we are defining the labour force as people ages 15 and older who meet the International Labour Organization’s definition of the economically active population; this includes both the employed and the unemployed. 13 The system used to evaluate the data is a least squared linear regression model; this works by minimising the sum of the squares of vertical deviations between data points and the trend line. The standard regression model takes the form:

Y = β 0 + β 1X1 + β 2X2 + ε i

Linear regression models attempt to create a model to predict values by fitting the independent variables into a linear equation. Due to the fact that the model is making predictions there is an error term (ε i) present. This error/disturbance term is the unexplained component that deviates the predicted value from the real value. After the introduction of my variables the model takes the following form:

Y = β 0 + β 1(LabourForce) + β 2(ListedDomesticCompanies) + ε i

Where β 0 represents the constant parameter in the equation and β 1 and represent β 2 represent the parameters that take effect on labour force and listed domestic companies respectively.

3) Descriptive Statics and Analysis This section delves into the descriptive statistics of my model using IBM’s SPSS statistical analysis software. Figure 1 displays each central measures of tendency for the variables in my model along with the skewness. When data is unfairly skewed there it receives a number of either below -1 or above +1; skewness measures the asymmetry of the probability distribution of a real-valued random variable about it’s mean. If data is skewed then the median is the most suitable measure of central tendency as it disregards outliers whilst the mean factors these in.

12 World Bank. (2014). China - Overview. Available: http://www.worldbank.org/en/country/china. Last accessed 10th Apr 2014. 13 International Labour Organization. (2014). Main statistics (annual) – Economically Active Population. Available: http://laborsta.ilo.org/applv8/data/clc.html. Last accessed 10th Apr 2014.

Figure 1 – Descriptive Statics Table

Figure 1 shows the data for “Listed Domestic Companies” to be highly skewed, with a skew value of 1.026, in order to normalise my data logarithms were taken of all variables, this reduced the skewness to an acceptable value, as shown in figure 2.

Figure 2 – Descriptive Statics Table Adjusted For Normalised Data

As shown in figures 3, 4 and 5 all variables now carry a normal distribution and can now be used more effectively in the ordinary least squared regression model. After the introduction of my normalised data the model takes the following form:

Y = β 0 + β 1(LogLab) + β 2(LogDomCom) + ε i

Figure 3 – Histogram to show the skewness of LogDomCom

Figure 4 – Histogram to show the skewness of LogLab

Figure 5 – Histogram to show the skewness of LogGNI

4) Normality Testing Distribution of the dataset is critical for linear regression modeling as it affects a number of the Gauss Markov assumptions, which will be explained later in the paper. This would leave weaknesses in the model and leave it economically invalid. Normality can be determined by taking a further look at the Shapiro-Wilk significance level14 displayed in figure 6.

Figure 6 – Normality Tests

In this test we have a null hypothesis that the data is normally distributed and an alternate hypothesis that the data is not normally distributed. A significance level of greater than 5% sees us accept the null hypothesis and reject the alternate; as LogLab and LogGNI have a significance of 28.6% and 95.1% respectively one can say that all of our data carries normal distribution and can be used for my linear regression model. 5) Empirical Data and Analysis This section of the paper seeks to process and create the regression model and is completed in the following stages: Correlation The initial presumption of the regression analysis is that there is a strong relationship between the variables of interest. To review this one must look at the Pearson’s Correlation15 and its’ significance. Figure 7 shows a table of the Pearson’s Correlation of each variable along with their significance. In this instance our null hypothesis is that there is no correlation between variables whilst the alternate hypothesis is that variables are correlated. The lowest Pearson’s Correlation value shown in figure 7 is 0.922 (or 92.2%), this illustrates an extremely strong correlation , whilst p-values of all variables in the test stand at zero – this displays an extremely strong correlation between variables, thus we can reject H0 and accept H1.

14 Razali, N. (2011). Power comparisons of Shapir o-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests . Journal of Statistical Modeling and A nalytics . 2 (1), p25. 15 Pearson (1895). Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58 : p240–242.

Figure 7 – Pearson’s Correlation and Significance Levels

Creating the Model At this point we begin to create the regression model. Figure 8 displays an adjusted R2 value of 0.973. This describes my model as extremely accurate in predicting the dependent variable, with 97.3% of data being accounted for by LogDomCom and LongGNI.

Figure 8 – A Summary of the Model

As we now know that the model is going to be reliable one can begin to look at the coefficients table (Figure 9). This is the basis of building the model. Initially it is essential to look at the p-value of each variable and the constant, any values with a p-value of over 5% should be removed from the model. In this case significance levels are 0.3%, 0.2% and 1.6%, this shows all values to be significant to the model.

Figure 9 – Coefficients Table

From this table one can ascertain the linear equation of the model in full. The data shows β 0 to be equal to -86.516; this is the Y-intercept of the equation, thus when variables are equal to zero this is the value that the model predicts. It also shows β 0 and β 1 to be equal to 10.843 and 0.947 respectively. This completes the parameters of the equations and gives the linear equation:

Y = -86.516 +(10.843*LogLab) + (0.947*LogDomCom) + ε i By subtracting the predicted variables from the real values of LogGNI it is easy to obtain error values for the dataset (ε i) – these disturbances are illustrated in figure 10.

Figure 10 – Calculating the Error Term

Now that error terms have been calculated it is possible to begin testing the Gauss Markov Assumptions. The assumptions are a series of rules which assess whether or not the model suggests the Best Linear Unbiased Estimator, or in other words is “BLUE”. Meeting the Gauss Markov Assumptions16 The first assumption is that the population process is linear in parameters and that Y is found by following the process of

Y = β 0 + β 1X1 + β 2X2 + ε i As calculated earlier in the paper the equation of the regression model is:

ŷ = -86.516 + (10.843*LogLab) + (0.947*LogDomCom) + ε i

There are no multiplication of variables in the equation and as a result my model is shown to be linear, thus satisfies the first of the Gauss Markov assumptions. The second assumption is that the conditional mean of all errors is equal to zero. This is demonstrated in figure 11.

16 EconWeb. (2013). Gauss Markov Theorum. Available: http://econweb.rutgers.edu/tsurumi/blue1.pdf. Last accessed 10th Apr 2014.

LogGNI LogLab LogDomCom Yhat Error 12.47 8.86 3.04 12.43 0.04 12.52 8.86 3.06 12.5 0.02 12.56 8.87 3.09 12.57 -0.01 12.61 8.87 3.11 12.64 -0.03 12.67 8.88 3.14 12.71 -0.04 12.73 8.88 3.14 12.75 -0.02 12.79 8.88 3.16 12.79 0 12.86 8.89 3.18 12.84 0.02 12.91 8.89 3.21 12.88 0.03 12.95 8.89 3.23 12.92 0.03

13 8.89 3.31 13 0 Mean Error 0.003636364

Figure 11 – Table to show variable, values predicted by the model and the disturbance term Figure 11 shows the mean error to be 0.003636364, as this figure is negligible but not exactly zero one can presume that rounding errors have influenced the error figure and we can resultantly accept that the figure agrees with the Gauss Markov assumption. The third assumption is that all errors are homoscedastic. It states that all error terms must have a constant variance of σ ². Figure 12 displays an error variance that is negligible and again one can assess that this is due to rounding errors.

Figure 12 – Table to show variable, values predicted by the model and the variance of the disturbance term


13 8.89 3.31 13 0 Error Variance 0.000705455

However, this alone is not enough to completely decipher whether errors are homoscedastic or not. Figure 13 displays a scatter plot of regression standardised residuals against regression predicted values. The plot displays errors to be evenly distributed throughout; whilst the horizontal line of best fit with values plotted evenly either side allows one to confidently say that that the errors are homoscedastically distributed and thus meets the assumption.

Figure 13 – Scatter plot of standardised residuals.

The fourth assumption is that there is no perfect multicolinearity; this means that the independent variables cannot have a direct link between them – this is tested with reference to the Variance Inflation Factor (VIF). Figure 14 shows a VIF of 6.656, although this is relatively high I feel that by using a larger data-set in further tests this can be reduced due to the tenuous nature of the link between number of companies floated on the exchange and the number of people in the workforce. A VIF of greater than 10 suggests perfect multicolinearity, as 6.656 is below the threshold one can deduce that there is no perfect multicolinearity and thus the condition is satisfied.

Coefficientsa Collinearity Statistics Model

Tolerance VIF LogLab .150 6.656

1 LogDomCom .150 6.656 a. Dependent Variable: LogGNI

Figure 14 – The Variance Inflation Factor of the model.

By assessing the Durbin-Watson statistic we can check for the fifth assumption; that there is no serial correlation between the independent variables. Figure 15 displays a Durbin-Watson value of 0.629. One can then reference the Durbin-Watson table; by looking at the table of 5% significance values the lower and upper bounds of the 0.758 and 1.604 respectively can be ascertained.17 As the value of 0.629 falls below the lower bound one can reject the null hypothesis and state that there is no serial correlation between the independent variables.

Model Summaryb Model R R Square Adjusted R

Square Std. Error of the

Estimate Durbin-Watson

1 .989a .978 .973 .03032 .629 a. Predictors: (Constant), LogDomCom, LogLab b. Dependent Variable: LogGNI

Figure 15 – Summary of the model

Assumption six considers the correlation between the independent variables and the

error terms. In order to pass this assumption there must be no correlation between

either of the independent terms and the error term; this is to ensure that the error value

cannot be predicted as either positive or negative values no matter what it’s position

in the model. Figure 16 displays the Pearson’s correlation and significance level of

the test. The Pearson’s value of both independent variables relationship with the error

is zero and the significance stands at one, as a result this assumption is fulfilled.

17 Durbin-Watson Significance Tables. Available: https://www.msacl.org/short_course_docs/Lytle/07_Durbin_Watson_tables1.pdf. Last accessed 10th Apr 2014.

Figure 16 – Pearson’s correlation and significance levels

This is the final assumption; after meeting all Gauss Markov assumptions it is possible to say with confidence that the model is the Best Linear Unbiased Estimator. 6) Conclusion The model created has proved highly successful and has shown LogDomCom and LogLabour to explain 97.3% of LogGNI. Although data has been normalised the original regression model displayed an R2 value of 0.945, therefore explains 94.5% of all data – this is extremely strong for a predictive model. Since the model passed all Gauss Markov assumptions it is safe to say that it can be considered as the best linear unbiased estimator and thus provides a link and a concept that requires further investigation. Since only eleven years of data were used in the model it would be unwise to suggest that the model is fool-proof and a greater understanding could be gained by back-testing the data over a longer time-frame; for example over 100 years. If the model still proved to be successful further test could be carried out across multiple economies in order to gauge whether the model applied worldwide. As mentioned in the introduction to the paper there is certainly a link between the growth of the population and both the size of the workforce and number of domestic businesses no matter how tenuous. Although there will be a time lag as the population ages I believe that future models on this theory should factor in population growth as a variable and feel that with a time-lag the factoring in of a baby boom or bust as a dummy variable may prove to refine the model.

When looking into how useful the model proves to be if correct one must look at each of the variables individually. As both independent variables carry positively weighted slope coefficients it is clear that they both have a positive effect on Gross National Income. Government objectives state that a focus should be made on the growth of a countries economy; as GNI is a determinant of economic growth the model suggest that an emphasis should be put on increasing both the workforce and number of domestic companies. Policymakers should make it easier for people to start their own companies via means of easier loans or more support and economic performance in terms of GNI would improve in line with the model. However due to the low slope coefficient further tests would need to be done to determine whether this would be beneficial.

Appendix

Figure 1 – Descriptive Statics Table

Figure 2 – Descriptive Statics Table Adjusted For Normalised Data

Figure 6 – Normality Tests

Figure 3 – Histogram to show the skewness of LogDomCom

Figure 4 – Histogram to show the skewness of LogLab

Figure 5 – Histogram to show the skewness of LogGNI

Figure 7 – Pearson’s Correlation and Significance Levels

Figure 8 – A Summary of the Model

Figure 9 – Coefficients Table

Figure 10 – Calculating the Error Term

LogGNI LogLab LogDomCom Yhat Error

12.47 8.86 3.04 12.43 0.04 12.52 8.86 3.06 12.5 0.02 12.56 8.87 3.09 12.57 -0.01 12.61 8.87 3.11 12.64 -0.03 12.67 8.88 3.14 12.71 -0.04 12.73 8.88 3.14 12.75 -0.02 12.79 8.88 3.16 12.79 0 12.86 8.89 3.18 12.84 0.02 12.91 8.89 3.21 12.88 0.03 12.95 8.89 3.23 12.92 0.03

13 8.89 3.31 13 0 Mean Error 0.003636364

Figure 11 – Table to show variable, values predicted by the model and the disturbance term

Figure 12 – Table to show variable, values predicted by the model and the variance of the disturbance term


13 8.89 3.31 13 0 Error Variance 0.000705455

Figure 13 – Scatter plot of standardised residuals.

Coefficientsa Collinearity Statistics Model

Tolerance VIF LogLab .150 6.656

1 LogDomCom .150 6.656 a. Dependent Variable: LogGNI

Figure 14 – The Variance Inflation Factor of the model.

Model Summaryb Model R R Square Adjusted R

Square Std. Error of the

Estimate Durbin-Watson

1 .989a .978 .973 .03032 .629 a. Predictors: (Constant), LogDomCom, LogLab b. Dependent Variable: LogGNI

Figure 15 – Summary of the model

Figure 16 – Pearson’s correlation and significance levels

References:

Bloom et al. (2007). I mplications of Population Aging for Economic Growth. PGDA Working

Paper. 64 (1), p29.

Cincotta and Engleman. (1997). Economics and Rapid Change: The Influence of Population

Growth. Occasion Paper. 3 (1), p5.

Davis, B. (2013). Aging Population Could Trim 3% Off China GDP Growth . Available:

http://blogs.wsj.com/chinarealtime/2013/10/23/report-aging-population-will-trim-3-off-chinas-

gdp/. Last accessed 10th Apr 2014.

Durbin-Watson Significance Tables. Available:

https://www.msacl.org/short_course_docs/Lytle/07_Durbin_Watson_tables1.pdf. Last accessed

10th Apr 2014.

EconWeb. (2013). Gauss Markov Theorum. Available:

http://econweb.rutgers.edu/tsurumi/blue1.pdf. Last accessed 10th Apr 2014.

Focus on China. (2014). Study in China. Available:

http://www.focusonchina.cn/StudyinChina/2013/0626/12.html. Last accessed 10th Apr 2014.

Galor and Weil. (2000). Population Technology and Growth From Malthusian Stagnation. Brown

University Providence. 1 (1), p807-827.

International Labour Organization. (2014). Main statistics (annual) – Economically Active

Population. Available: http://laborsta.ilo.org/applv8/data/clc.html. Last accessed 10th Apr 2014.

Malthus, T. (1826). An Essay on the Principle of Population. An Essay on the Principle of

Population. 2 (1), p2.

Palumbo, L. (2012). Relations between the EU and the emerging global players. Göttingen

Summer School. 1 (1), p4-5.

Pearson (1895). Notes on regression and inheritance in the case of two parents. Proceedings of the

Royal Society of London, 58 : p240–242.

Razali, N. (2011). Power comparisons of Shapir o-Wilk, Kolmogorov-Smirnov, Lilliefors and

Anderson-Darling tests . Journal of Statistical Modeling and A nalytics . 2 (1), p25.

Shieber, K. (2010). As Internet IPOs Pop, Start-Up Prices Soar In China . Available:

http://blogs.wsj.com/venturecapital/2010/12/16/as-internet-ipos-pop-start-up-prices-soar-in-china/.

Last accessed 10th Apr 2014.

Sollow, R. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of

Economics. 70 (1), p65-71.

Turner, A. (2009). Population Priorities: The Challenge of Continued Rapid Population Growth.

Biological Sciences. 0183 (1), p2.

World Bank. (2014). China - Overview. Available: http://www.worldbank.org/en/country/china.

Last accessed 10th Apr 2014.

Daniel Sayer Contemporary Economic Issues

To what extent has actual economic growth in the UK been below potential economic growth since March 2009 and what, if anything, could have been done to reduce the

‘output gap’?

Introduction Economic growth is a key macroeconomic policy objective for governments world-wide and is a strong measure for the success of an economy. When looking at figures for economic growth one must gauge whether you are focusing on actual or potential economic growth. Actual economic growth is defined as the “measure of economic growth from one period to another expressed as a percentage… (it) is a measure of the rate of change that a nation's gross domestic product (GDP) experiences from one year to another.”1 There are three methods of deriving actual economic growth, these are the income method, the expenditure method and the output method – each of these are a measure of Gross Domestic Product and take into account differing factors. On the other hand, potential economic growth is defined as “the maximum output growth that an economy can sustain over the medium to long term without stoking inflation” 2 and is affected by availability of resources and productive efficiency. The difference between these two measures is a phenomenon known as the output gap. The 2008 UK recession has lead to the longest decline in Gross Domestic Product on record. A recession is be defined as a period of economic decline where trade and industrial activity are reduced – this is usually displayed by a fall in Gross Domestic Product (GDP) in two successive quarters3. The Bank of England does not have specific targets for economic growth, but it is commonly assumed that it should be around the long-run trend line of 2.5%. The first quarter of 2008 experienced minimal growth of 0.1%, with the following quarters seeing growth of -0.9%, -1.4% and -2.1% respectively meaning the country was officially in a recession. As illustrated by figure 1 this negative growth continued throughout 2009 and was a cause of major concern; leaving the country with a colossal output gap.

Figure 1: UK Gross Domestic Product growth as a percentage of the previous quarter (Office of National Statistics, 2013) Through a review of the time period since March this report will evaluate the extent of the output gap and identify the key policies that have had influence over it.

1 Investopedia. (2014). Real Economic Growth. Available: http://www.investopedia.com/terms/r/realeconomicrate.asp. Last accessed 26th May 2014. 2 The Economic Times. (2011). ET in the classroom: Potential growth rate. Available: http://articles.economictimes.indiatimes.com/2011-03-24/news/29181667_1_growth-rate-labour-productivity-indian-economy. Last accessed 26th May 2014. 3 Miles, D (2005). Macroeconomics: Understanding the Wealth of Nations. 2nd ed. West Sussex: John Wiley & Sons Ltd.. p348.

Extent of the Output Gap The United Kingdom has experienced real growth at an average of a minute positive value since the great recession of 2008; yet the stark reality is that this is the weakest post-war recovery in GDP growth in history.4 Since 2008 the UK has lost a significant amount of potential GDP due to the prolonged nature of the recession; this has left a considerably negative output gap, yet the actual size of this is a widely debated subject. HM treasury suggested that the output gap for 2012/13 was -2.7%,5 however others believe this underestimates the spare capacity in the UK economy. Bill Martin, professor of the Centre for Business Research at Cambridge University, argues that the UK’s spare capacity is closer to 10%6 - the difference between these two estimates is colossal and the actions taken to combat this will be dependent on the size of the output gap. A gap of around -2% suggests that moderate monetary policy would provide a solution, however a gap of -10% or more suggests insufficient aggregated demand is holding back the economy and puts forward a strong case for accommodative monetary and fiscal policy.7 Although the ONS conduct surveys on levels of spare capacity some may argue that due to the ambiguous nature of how capacity is defined it is very hard to make a clear prediction of what the output gap actually stands at. Figure 2 illustrates the stark variation in estimates of the output gap. It displays the National Institute of Economic & Social Research to have predicted a gap of -4%, whilst Schroders stated that it was nearing zero; there are a number of reasons why this prediction could be accurate.

4 British Chamber of Commerce. (2014). UK GDP to exceed pre-recession peak earlier than expected in 2014, says BCC. Available: http://www.britishchambers.org.uk/press-office/press-releases/uk-gdp-to-exceed-pre-recession-peak-earlier-than-expected-in-2014,-says-bcc.html. Last accessed 26th May 2014. 5 Office for Budget Responsibility. (2012). Economic & Fiscal Outlook. Office for Budget Responsibility. 8303 (1), p38. 6 Martin, B. (2011). Is the British Economy Supply Constrained?. Centre For Business Research. 1 (1), p1. 7 EconomicsHelp. (2012). What is the UK’s actual Output Gap?. Available: http://www.economicshelp.org/blog/6040/economics/what-is-the-uks-actual-output-gap/. Last accessed 26th May 2014.

Figure 2 – Estimates of the 2011 UK output gap

Unemployment is a strong indicator of lost potential output. In April 2014 the UK unemployment rate reached a five-year low of 6.9%,8 this gives credence to those predicting an output gap closer to zero. However, many have suggested that the fall in real wages had helped to contain the rise in unemployment, but the fall lead to demand deficiency in the economy. Another indicator, productivity (measured by output per worker), fell drastically in the years after the recession. Productivity in the private sector dropped to its lowest level in 7 years, as companies continued to employ new staff despite falling demand for goods and services. The output per hour of private-sector workers fell almost 4% between January and October 2012 and figures for the economy as a whole were not much better, with a 2.4% decline in productivity over the course of 2012.9 Although the first quarter of 2014 has displayed a 1.3% rise in productivity on last year’s figures there is a long way to go before it reaches pre-recession levels. Figure 3 displays labour productivity compared to each of the previous recessions, the sharp drop and sluggish recovery may be a good indicator that there is a significant loss of potential, thus a substantial output gap.

8 BBC News. (2014). UK unemployment falls to five-year low of 2.2m. Available: http://www.bbc.co.uk/news/business-27046681. Last accessed 26th May 2014. 9 Moulds, J. (2013). Fall in productivity takes shine off UK employment figures. Available: http://www.theguardian.com/business/2013/jan/03/business-productivity-declines-demand-falls. Last accessed 26th May 2014.

Figure 3 – Productivity compared to other recessions

A 2014 report by the Bank of England suggested that labour productivity was around 16% less than the pre-recession predicted level in the third quarter of 2013. This suggests a lack of supply-side growth and that the long run aggregate supply has increased at a far slower rate than predicted by trends. As a result one can gather that there could indeed be a substantial difference between actual and potential economic growth. Many people suggest that reasonably high inflation levels and growing employment gives evidence of a small output gap; however Bootle and Loynes (2012) argued that inflation was pushed up by temporary factors and has since eased, while domestically generated inflation remained low. They go on to suggest that the output gap is far larger than most people are predicting and that large-scale expansionary policies need to be implemented in order to close the gap. When assessing the output gap one must look closely at how potential growth is calculated. Potential economic growth is a measurement taken using time series data and trends to predict how well an economy can perform without stoking inflation. It is possible that the economy was overheating and unsustainable pre-recession and many people predicting the current output gap may not have taken this into account. The vast gap between potential and the pre-recession trend line is demonstrated in figure 4, where the output gap is the difference between the black and blue lines; rather than the black and red lines which is much larger and could be what some are predicting. The Office for Budget Responsibility suggests that business survey evidence and higher than average real wage growth implies that despite slowing inflation the economy was operating with a positive output gap of around 2% in 2007- this view is now unanimously held by the IMF and OECD alike.10 If these factors are not being taken into account when making predictions of the output gap then it is quite possible that the output gap is substantially less than it is being widely being predicted as the economy could be nearing full employment of resources.

10 Kaminska, I. (2012). Why the UK Output Gap Could Be a Chasm. Available: http://ftalphaville.ft.com/2012/10/04/1191411/why-the-uk-output-gap-could-be-a-chasm/. Last accessed 26th May 2014.

Figure 4 – Actual GDP, the pre-recession trend and the possible calculated potential

Assuming that there was a moderate positive output gap pre-recession and that the financial crisis dealt a permanent blow to the economy the predicted output gap should stand at around 6% of GDP. This suggests unnecessary fiscal consolidation of around £35 billion under current plans. A larger output gap may allow the UK to prosper from strong economic growth when demand recovers without inflation rocketing.11 Whilst there may have been a positive output gap in 2007, it is evident that inflation was low and relatively stable. Many hold the view that cheap imports from manufacturing countries such as China created a downward pressure on inflation that disguised an overheating economy. Since 2009 we have seen steady inflation that has remained above target; this has raised many questions with those who predicted a large negative output gap. In 2012 Clausen et al published a paper on behalf of the IMF that stated that with a negative output gap you would expect inflation to be well below target.12 This is totally contradictive of the current situation in the UK and suggests a complex economy where cost-push factors and government intervention may have played a part – quantitative easing and the like carried severe risk of hyperinflation and may have helped to disguise any inflationary problems.

11 Verma, S. (2012). Osborne’s nightmare, Labour’s ammunition and the Bank of England’s bind: the output gap question Full article: http://www.euromoney.com/Article/3098233/Osbornes-nightmare-Labours-ammunition-and-the-B. Available: http://www.euromoney.com/Article/3098233/Osbornes-nightmare-Labours-ammunition-and-the-Bank-of-Englands-bind-the-output-gap-question.html. Last accessed 26th May 2014. 12 Clausen et al. (2012). Simulating Inflation Forecasting in Real Time. Working Paper. 52 (1), p3.

Policy Implementation In 2009 the International Monetary Fund published a report identifying the three main objectives to ensure that financial sector health was restored. These were ensuring that financial institutions had access to liquidity; identifying and dealing with distressed assets and recapitalising the weak but viable institutions.13 The Bank of England responded by implementing a number of policies in order to battle against the wasted potential. At the beginning of 2006 interest rates were set at 4.5%; as the recession began and aggregate demand fell the Monetary Policy Committee felt it was necessary to decrease this rate to stimulate spending. On 5th March 2009 it was announced the rate had been lowered to 0.5% - the lowest rate in the history of the Bank of England.14 By cutting the rates it was hoped that commercial banks would begin to lend more money, this in turn would increase investment and resultantly aggregate demand. Amid fears of further recession it was proposed that this alone would not be significant enough. In an exchange of letters between the Treasury and the Bank of England, chairman Mervyn King stated that;

“Further rate cuts in the Bank Rate alone might not be enough to bring inflation in line with the Bank’s 2% target… The Bank of England remains committed to improving liquidity in credit markets that are not functioning normally.”15

In line with the IMF’s report and in order to ensure that financial institutions had access to liquidity the Monetary Policy Committee employed Quantitative Easing to allow the government to give money financial institutions money without reducing their balance sheet. In March 2009 the Bank of England said they would make £75 billion available for the purchase of government bonds and toxic assets in order to increase liquidity in the economy, boosting the flow of money. This saw a lot of money spent on existing Gilts and corporate bonds from financial institutions, creating immediate liquidity that could be leant to businesses and homeowners. The bank proposed to spend a total of £200 billion on asset purchases from which £197.275 million was spent on UK bonds and the rest on corporate papers.16 The purchase of toxic assets meant that not only was liquidity now readily available, but also that “distressed assets” had been identified and dealt with. Yet this was not the sole reason for these asset purchases; as the number of bonds available decreased the existing gilt prices rose, whilst yield interest adjusted downwards. This encouraged the financial institutions to invest heavily in assets with a high yield in attempt to rebalance their portfolio – this came in the form of investment in new businesses. As investment is a component of aggregate demand this saw GDP rise. Had Quantitative Easing not been implemented we may have seen an output gap far larger than it was. On the 14th May 2014 Mark Carney, governor of the Bank of England, stated that the UK economy was “heading back to normal”, forecasting real growth of 3.4% this year and 2.8% for 2016. This has lead to speculation that due to the recent strengths interest may be increased in the near future however Carney stated that rate “may stay at historically low levels for some time” and any increases in interest rates would be “gradual”.17 This forward guidance acts as an assurance for commercial banks as if they believe that they are going to be able to borrow money at a low rate for the foreseeable future they are more likely to lend to the general public. In turn this increases the disposable income available to the

13 The International Monetary Fund. (2009). World Economic Outlook: Crisis and Recovery. Available: http://www.imf.org/external/pubs/ft/weo/2009/01/pdf/text.pdf. Last accessed 26th May 2014. 14 Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 26th May 2014. 15 Cited - Kollewe, J. (2009). Bank of England cuts rates to 0.5% and starts quantitative easing. Available: http://www.theguardian.com/business/2009/mar/05/interest-rates-quantitative-easing. Last accessed 26th May 2014. 16 Bank of England. (2011). Quantitative Easing Explained. Available: http://www.bankofengland.co.uk/monetarypolicy/pages/qe/default.aspx. Last accessed 26th May 2014. 17 BBC News. (2014). http://www.bbc.co.uk/news/business-27407561. Available: http://www.bbc.co.uk/news/business-27407561. Last accessed 26th May 2014.

general public and the ideology behind it is that it will increase spending and subsequently increase GDP, thus closing the output gap.18 However, the use of the term “normal” by Carney is very vague and one must question what it is being compared to. Recoveries in prior recessions saw far different results – after the recession in the early 1980s annual GDP growth reached 6% before the end of the decade. The recession of the early 90s saw a recovery of 5% annual growth by 1993 and stayed above 3% for a considerable amount of time. These are illustrated in figure 1 by the black and red lines respectively. The post-2008 recovery has been far from “normal” compared to these with growth being consistently under the long-term trend rate of 2.5%, this is demonstrated by the blue line.19

Figure 5 – Year on year growth after recent recessions

The third point outlined in the IMF report was the need to recapitalise the weak but viable institutions. In 2008 the government spent £37 billion bailing out a selection of failed British banks. This increased the monetary base and ensured that the selected banks could operate normally and continue lending.20 These monetarist policies alone were not enough to pull through the recession and decrease the output gap and a number of fiscal policies were also implemented with these intentions. In 2010 George Osborne launched a round of austerity measures, expecting cuts to be completed within four years. The fiscal consolidation plan saw spending cuts of £81 billion in order to reduce the fiscal deficit and in turn the output gap. In 2010 the budget deficit stood at 9.4% of GDP. The budget aimed to cut this to 6% by 2013 by drastically cutting spending and increasing taxation. Income tax personal allowance was increased by £1000 with the aim of increasing disposable income and promoting spending, whilst

18 BBC News. (2014). Q&A: What is 'forward guidance'?. Available: http://www.bbc.co.uk/news/business-23145755. Last accessed 26th May 2014. 19 Roberts, M. (2014). US is not closing the gap – and neither is the UK. Available: http://thenextrecession.wordpress.com/2014/05/17/us-is-not-closing-the-gap-and-neither-is-the-uk/. Last accessed 26th May 2014. 20 BBC News. (2009). UK banks receive £37bn bail-out. Available: http://news.bbc.co.uk/1/hi/7666570.stm. Last accessed 26th May 2014.

the VAT rate rose by 5%, increasing government income by a colossal amount. Capital gains tax was raised by 10% for high earners to 28%, whilst public sector workers saw their wage frozen for two years.21 The IMF recently suggested that the UK is “still a long way from a strong and sustainable recovery” and suggested the economy may benefit more financing infrastructure projects and tax cuts for businesses rather than continuing with rapid austerity measures. Conclusion Conclusively, the predicted size of the output gap is extremely hard to measure due to ambiguous nature of “capacity” and the vast number of factors that affect it. However hard it may be to predict, it is essential that this prediction is as accurate as possible as the gap is used to calculate inflation and other economic variables; if these are poorly predicted then policies implemented could be catastrophic and extremely costly. The current plan of strong fiscal consolidation in order to reduce the budget deficit has been reinforced by the 2014 budget as further cuts have been rife. Valero et al (2014) of the London School of Economics suggested that a good alternative would be to slow down the pace of fiscal consolidation. They suggested that should the output gap be reaching zero we would see inflation rise and the desirability of this “Plan B” would be muted if “monetary policy was sufficient, if fiscal policy was ineffective or if markets would panic at any retreat from Plan A.” They go on to suggest that the government should be “pro-active in building human capital and infrastructure and supporting innovation” and state that there is an obvious need for austerity measures but suggest that the pace of the current measures is excessive whilst the “world economy is so fragile”.22 In previous recessions the output gap was eradicated within five to eight years however this time it looks highly unlikely keep to that time-scale. The economy seems to have wasted its potential and unless further policies are implemented along with austerity measures it will continue to fail to make up ground.

21 HM Treasury. (2010). Budget 2010. Available: http://www.direct.gov.uk/prod_consum_dg/groups/dg_digitalassets/@dg/@en/documents/digitalasset/dg_188581.pdf. Last accessed 26th May 2014. 22 Valero et al. (2014). The UK’s sustained growth between 1997 and 2008 was fuelled by the importance of skills and new technology. Rather than just austerity, the government should focus on building human capital and innova. Available: http://blogs.lse.ac.uk/politicsandpolicy/archives/17297. Last accessed 26th May 2014.

Appendix

Figure 1: UK Gross Domestic Product growth as a percentage of the previous quarter

(Office of National Statistics, 2013) Office for National Statistics. (2014). Economy tracker: GDP. Available:

http://www.bbc.co.uk/news/10613201. Last accessed 26th May 2014.

Figure 2 – Estimates of the 2011 UK output gap

Office for Budget Responsibility (2012). Why the UK Output Gap Could Be a Chasm.

Available: http://ftalphaville.ft.com/2012/10/04/1191411/why-the-uk-output-gap-could-be-a-chasm/. Last accessed 26th May 2014.

Figure 3 – Productivity compared to other recessions

Office for National Statistics. (2012). The Productivity Conundrum, Explanations and Preliminary Analysis. Available: http://www.ons.gov.uk/ons/rel/elmr/the-

productivity-conundrum/explanations-and-preliminary-analysis/art-explanations-and-preliminary-analysis.html?format=print. Last accessed 26th May 2014.

Figure 4 – Actual GDP, the pre-recession trend and the possible calculated potential

Roberts, M. (2014). US is not closing the gap – and neither is the UK. Available: http://thenextrecession.wordpress.com/2014/05/17/us-is-not-closing-the-gap-and-neither-is-

the-uk/. Last accessed 26th May 2014.

Figure 5 – Year on year growth after recent recessions

Roberts, M. (2014). US is not closing the gap – and neither is the UK. Available: http://thenextrecession.wordpress.com/2014/05/17/us-is-not-closing-the-gap-and-neither-is-the-uk/. Last accessed 26th May 2014.

References Bank of England. (2011). Quantitative Easing Explained. Available: http://www.bankofengland.co.uk/monetarypolicy/pages/qe/default.aspx. Last accessed 26th May 2014. Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 26th May 2014. BBC News. (2009). UK banks receive £37bn bail-out. Available: http://news.bbc.co.uk/1/hi/7666570.stm. Last accessed 26th May 2014. BBC News. (2014). http://www.bbc.co.uk/news/business-27407561. Available: http://www.bbc.co.uk/news/business-27407561. Last accessed 26th May 2014. BBC News. (2014). Q&A: What is 'forward guidance'?. Available: http://www.bbc.co.uk/news/business-23145755. Last accessed 26th May 2014. BBC News. (2014). UK unemployment falls to five-year low of 2.2m. Available: http://www.bbc.co.uk/news/business-27046681. Last accessed 26th May 2014. British Chamber of Commerce. (2014). UK GDP to exceed pre-recession peak earlier than expected in 2014, says BCC. Available: http://www.britishchambers.org.uk/press-office/press-releases/uk-gdp-to-exceed-pre-recession-peak-earlier-than-expected-in-2014,-says-bcc.html. Last accessed 26th May 2014. Business Research. 1 (1), p1. Cited - Kollewe, J. (2009). Bank of England cuts rates to 0.5% and starts quantitative easing. Available: http://www.theguardian.com/business/2009/mar/05/interest-rates-quantitative-easing. Last accessed 26th May 2014. Clausen et al. (2012). Simulating Inflation Forecasting in Real Time. Working Paper. 52 (1), p3. EconomicsHelp. (2012). What is the UK’s actual Output Gap?. Available: http://www.economicshelp.org/blog/6040/economics/what-is-the-uks-actual-output-gap/. Last accessed 26th May 2014. HM Treasury. (2010). Budget 2010. Available: http://www.direct.gov.uk/prod_consum_dg/groups/dg_digitalassets/@dg/@en/documents/digitalasset/dg_188581.pdf. Last accessed 26th May 2014. Investopedia. (2014). Real Economic Growth. Available: http://www.investopedia.com/terms/r/realeconomicrate.asp. Last accessed 26th May 2014. Kaminska, I. (2012). Why the UK Output Gap Could Be a Chasm. Available: http://ftalphaville.ft.com/2012/10/04/1191411/why-the-uk-output-gap-could-be-a-chasm/. Last accessed 26th May 2014. Martin, B. (2011). Is the British Economy Supply Constrained?. Centre For Miles, D (2005). Macroeconomics: Understanding the Wealth of Nations. 2nd ed. West Sussex: John Wiley & Sons Ltd.. p348. Moulds, J. (2013). Fall in productivity takes shine off UK employment figures. Available: http://www.theguardian.com/business/2013/jan/03/business-productivity-declines-demand-falls. Last accessed 26th May 2014. Office for Budget Responsibility. (2012). Economic & Fiscal Outlook. Office for Budget Responsibility. 8303 (1), p38.

Roberts, M. (2014). US is not closing the gap – and neither is the UK. Available: http://thenextrecession.wordpress.com/2014/05/17/us-is-not-closing-the-gap-and-neither-is-the-uk/. Last accessed 26th May 2014. The Economic Times. (2011). ET in the classroom: Potential growth rate. Available: http://articles.economictimes.indiatimes.com/2011-03-24/news/29181667_1_growth-rate-labour-productivity-indian-economy. Last accessed 26th May 2014. The International Monetary Fund. (2009). World Economic Outlook: Crisis and Recovery. Available: http://www.imf.org/external/pubs/ft/weo/2009/01/pdf/text.pdf. Last accessed 26th May 2014. Valero et al. (2014). The UK’s sustained growth between 1997 and 2008 was fuelled by the importance of skills and new technology. Rather than just austerity, the government should focus on building human capital and innova. Available: http://blogs.lse.ac.uk/politicsandpolicy/archives/17297. Last accessed 26th May 2014. Verma, S. (2012). Osborne’s nightmare, Labour’s ammunition and the Bank of England’s bind: the output gap question Full article: http://www.euromoney.com/Article/3098233/Osbornes-nightmare-Labours-ammunition-and-the-B. Available: http://www.euromoney.com/Article/3098233/Osbornes-nightmare-Labours-ammunition-and-the-Bank-of-Englands-bind-the-output-gap-question.html. Last accessed 26th May 2014.

Contemporary Economic Issues Game Theory

By Daniel Sayer

Introduction The following paper will seek to evaluate game theory and it’s different branches; exploring their real world application to issues faced by firms and states worldwide. Game theory is defined as a study of decision-making that allows one to evaluate real-life situations in order to make decisions that will benefit the user best. Roger Myerson (1991) described game theory as “the study of mathematical models of conflict and cooperation between intelligent rational decision-makers”1 which I am in agreement with, however one criticism is that in real life situations people are not always rational.2 There are many branches of game theory but the following paper will evaluate the concepts of “prisoners dilemma”, “battle of the sexes”, “matching pennies” and “hawke dove” games and display how each of these can be applied to real-life situations worldwide. Subsequently to this I will display data I have collected from bookmakers on the Cheltenham Neptune Chase; analysing to display the nature and type of competition in the online bookmakers market. The final part of my paper will evaluate the current crisis in Crimea through means of game theory in order to suggest the best possible outcome. 1) Real Life Applications of Game Theory The game of prisoner’s dilemma is the most widely-know concept within game theory. The game involves two prisoners who have to plead either guilty or not guilty to a crime. The decisions are made simultaneously; totally independent of each other and each prisoner does not know what the other is going to say. In this situation the police admit that they do not have the evidence to sentence both on the principal charge however plan to sentence both on a lesser charge. They also offer each prisoner the chance to confess to the primary crime; this will see them get a lower sentence should the other deny the charge; whilst the other may receive the maximum sentence if they do not confess. However, if both people cooperate with the police and confess each will receive a sentence that is higher than that of if they both deny all charges.3 Below I have created a table illustrating the prisoner’s dilemma:

Prisoner A Confess Deny

Confess 5 years / 5 years 1 year / 10 years Prisoner B Deny 10 years / 1 year 2 years / 2 years

Figure 1 – A standard view of a prisoner’s dilemma game. By looking at all possible outcomes we can decipher that the dominant strategy for both parties is to confess; giving an equilibrium of “confess, confess”. This is due to the fact that a confession sees the likely number of years in prison decrease; whilst a denial opens up the possibility that the prisoner could serve 10 years in prison.4 A real world application for prisoner’s dilemma can be seen in a country’s decisions on the production of nuclear weapons. If two nations enter a treaty with the main objective to eradicate the production of nuclear weapons the countries then have to make a decision of whether to keep to the treaty and not produce the weapons or to break the treaty and produce such weapons. This decision can be shown below:

1 Myerson, R (1991). Game Theory - Analysis of Conflict. United States: President & Fellows of Harvard College. p1. 2 Koslowski, P (2000). Contemporary Economic Ethics and Business Ethics. Hannover: Springer. 10. 3 Dixit, A. (2014). Prisoners Dilema. Available: http://www.econlib.org/library/Enc/PrisonersDilemma.html. Last accessed 13th Mar 2014. 4 CMSC. (2014). Game Theory. Available: http://www.cs.umd.edu/~nau/cmsc421/game-theory.pdf. Last accessed 12th Mar 2014.

Country A

Produce Nuclear Weapons

Don’t Produce Nuclear Weapons

Produce Nuclear Weapons Treaty broken, high

expense and chance of war high – Lose, lose

Treaty broken by B, country B win as have high

advantage in the chance of war. Country B


Treaty broken by A, country A win as have high advantage in the

chance of war.

Treaty remains intact, no expense, neither country produces and both win.

Figure 2 – A prisoner’s dilemma game with real world application. Although this is a very simplified model the purpose remains valid and this has to be taken into account when considering the possibility of producing nuclear weapons. Some may criticise the model suggesting that the country does not actually “win” in such situations as the country that has not broken the treaty has the prospect to legally repudiate its treaty obligations however as a basic model it is a good way of portraying a prisoner’s dilemma situation. The second of the games I will discuss is the battle of the sexes game. Battle of the sexes is a two-player co-ordination game that is commonly illustrated by the decisions of a husband and wife; in the instance of figure 3 they are unable to decide whether to go to the opera or to a boxing match. As both parties want to spend the night together we can say that both equilibria are in pareto optimal and each will receive no utility if they attend different events, however both have a strong preference to the event that they attend. As with the prisoner’s dilemma situation both decisions are made simultaneously and in figure 1 the male has a strong preference to attend a boxing event while the female is a lot keener to attend the opera. If the individual attends an event that they do not want to go to together they will receive a minimal pay-off, whilst attendance of their favoured event together sees the payoff maximized.5 This is illustrated below in figure 3:

Husband Boxing Opera

Boxing 5 / 10 0 / 0 Wife Opera 0 / 0 10 / 5

Figure 3 – A standard view of a battle of the sexes game. When looking at a battle of the sexes game one must decide whether to implement a pure strategy or a mixed strategy to solve the game. A pure strategy provides a complete definition of how a player will play a game. In this case there are two pure strategy Nash equilibria; one where both attend the boxing and one where both attend the opera. A mixed strategy assigns a probability to each pure strategy where each player works out the probability of the other choosing an event and the balance of the pay-off against it; it shows how sometimes a compromise may be the best possible strategy in order to have a net utility gain.6 Battle of the sexes can easily be applied to real-world economics by considering company mergers. Consider two companies planning to merge; each company has a director who would like to become CEO of the new merged company. If neither can agree who becomes the CEO then the merger is blocked, yet each may purposely risk impasse to get their preferred outcome. In this case Brown and Ayres suggest the best way to resolve this would be to appoint a mediator to make a recommendation before making their decision.7 5 Nisan et al (2007). Algorithmic Game Theory. New York: Cambridge University Press. p7-8. 6 Gameoptprakrita. (2013). Game Theory For Conservation of Natural Resources. Available: https://sites.google.com/site/gameoptprakrita/bos. Last accessed 12th Mar 2014. 7 Peer Zumbansen (2011). Law, Economics and Evolutionary Theory. Cheltenham: Edward Elger Publishing. p158.

The third game I will discuss is the game of matching pennies. Matching pennies is a basic game theory example that demonstrates how rational decision-makers seek to maximise their payoffs. The game involves two players who simultaneously place a penny on the table with pay-offs depending on whether they match or not. If both pennies match the first player wins, keeping the others coin. If they don’t match, the second player wins and keeps the other’s penny.8 Since matching pennies is a zero-sum game (one in complete pareto optimal) and there is an equal chance of choosing heads or tails there is no Nash equilibrium and neither player has an incentive to implement a different strategy. Whilst use of a mixed strategy equilibrium is best used if matching pennies is played repeatedly it may be possible to predict the opponent's move and choose accordingly. The concept of matching pennies is illustrated below in figure 4:

Person A Heads Tails

Heads -1 / 1 1 / -1 Person A Tails 1 / -1 -1 / 1

Figure 3 – A standard view of a matching pennies game. The main real world application of matching pennies is the options and futures markets. This is due to the fact that, excluding transaction costs, these can be regarded as zero-sum games. This is mainly due to the fact that for every person who gains on a contract there is an opposite who loses.9 The final theory I will explore is the hawk-dove game. This game had an initial name of the “chicken” game.10 This originates from a game where two drivers drive towards each-other on a collision course; one must swerve or else there will be a crash and players will be injured, thus both at a “loss”. However if one driver swerves and the other doesn’t said driver will be labelled a “chicken”, in other words a coward and will be at a loss; whilst the driver who carried on straight will “profit” as he will look strong.11 The name was adapted to hawk-dove by John Smith in his 1973 paper,12 "The logic of animal conflict" to display the relationship between animals in conflict, but still carries the same message. Figure 4 displays a standard “chicken” game which carries the same point as the hawk-dove game.

Player 1 Swerve Straight

Swerve -10 / 10 -10 / 10 Player 2 Straight 10 / -10 -100 / -100

Figure 4 – A standard view of a chicken game. A fantastic example of a hawk-dove game with real world application is the Cuban Missile Crisis.13 The Cuban missile crisis was instigated by the Soviet Union’s attempt to install missiles in Cuba that were capable of hitting a large portion of America. The United States’ goal was immediate removal of the missiles and the two main strategies considered were a naval blockade or an air strike to wipe out missiles followed by an invasion. The options of each country are displayed in figure 5. 8 Investopedia. (2014). Matching Pennies. Available: http://www.investopedia.com/terms/m/matching-pennies.asp. Last accessed 11th Mar 2014. 9 Investopedia. (2014). Zero-sum Games. Available: http://www.investopedia.com/terms/z/zero-sumgame.asp. Last accessed 11th Mar 2014. 10 Sugden, R. The Economics of Rights, Cooperation and Welfare 2 edition, page 132. Palgrave Macmillan, 2005. 11 GameTheory.net. (2006). Game of Chicken. Available: http://www.gametheory.net/dictionary/games/GameofChicken.html. Last accessed 12th Mar 2014. 12 Smith, J. (1973). The Logic of Animal Conflict. Nature. 246 (1), p1-4. 13 Russell, B (1959) p. 30.

Soviet Union Withdraw Maintain

Naval Blockade Compromise

5/5

Soviet Victory, US Defeat

-10 / 10 USA

Air Strike

US Victory, Soviet Defeat

10 / -10

Nuclear War

-100 / -100

Figure 5 – The chicken/hawk-dove game applied to the Cuban Missile Crisis. Although in one sense the United States "won" the game by making sure the Soviets withdrew their Missiles, Kruschev also got a promise from the American’s that they would not invade Cuba. This indicates that the eventual outcome was a compromise of sorts, however this is not the Hawk-Dove games’ solution as strategies associated with compromise do not constitute a Nash equilibrium.14

2) The Competitive Market Place for Online Bookmakers As stated in my introduction I will be analysing the online bookmakers market by studying price changes in the Cheltenham Neptune Chase. To determine the nature and type of competition in this market I will be analysing the data I have collected as well as looking at the number of firms operating in the market, barriers to entry and brand loyalty. Betting markets have experienced unprecedented growth over the past few years due to deregulation, abolition of national monopolies and the arrival of online gambling. The rise of online gambling has seen profits sore for bookmakers due to the far increased accessibility, with the annual turnover of the United Kingdom’s betting industry estimated to be £39 billion in 2007.15 Since it’s rise in the late 1990s16 the popularity of online gambling has shot through the roof; the market is now said to be worth over £2 billion per year.17 In order to be able to give a more in depth analysis of the market I selected two horses in the Neptune chase and recorded the prices over the course of two weeks; the selected horses were “Cup Final” and “Faugheen”. Structure of the Market Due to the low barriers to entry and supernormal profits since the rise of online gambling we have seen a multitude of online bookmakers form; yet the market seems a hard one to tap due to high brand loyalty and colossal advertising campaigns from the market-leaders and a lack of exposure for newcomers.18 When researching the market I have come to the conclusion that the online bookmaker’s market carries the characteristics of a monopolistic competition.

14 Brams, S. (2001). Game theory and the Cuban missile crisis. Available: http://plus.maths.org/content/game-theory-and-cuban-missile-crisis. Last accessed 12th Mar 2014. 15 Vlastakis, N. (2008). How Efficient is the European Football Betting Market? Evidence from Arbitrage and Trading Strategies. Journal of Forecasting. 28 (1), 2. 16 OnlineGambling.com. (2014). The History of Online Gambling. Available: http://www.onlinegambling.com/online-gambling-history.htm. Last accessed 12th Mar 2014. 17 Gallagher, P. (2013). Addiction soars as online gambling hits £2bn mark. Available: http://www.independent.co.uk/news/uk/home-news/addiction-soars-as-online-gambling-hits-2bn-mark-8468376.html. Last accessed 12th Mar 2014. 18 Martin, V. (2013). Investing The Hard Way: Barriers to Entry And US iGaming. Available: http://calvinayre.com/2013/04/08/business/barriers-to-enter-us-igaming/. Last accessed 12th Mar 2014.

There are five main characteristics of monopolistic competition are: • Product differentiation • Many firms • No entry and exit cost in the long run • Independent decision making • Buyers and Sellers do not have perfect information19

Whilst there is not strictly product differentiation there is a strong aspect of brand loyalty. Not only do lucrative advertising campaigns attract customers to the major players in the market, but many people will be reluctant to open multiple accounts; this severely impacts new firms entering the market and has lead to the majority of firms offering incentives to customers to join their site.20 It its an unquestionable point that there are an immense amount of firms in the market and a simple Google search will bring up thousands of results. This figure is rising as people try to gain entry into a hugely profitable market.21 In the online bookmakers market the exit costs are non-existent; whilst entry costs are minimal. The start-up costs for a firm are simply the building the website, whilst the main problem is raising the initial collateral to pay out any winners – however this does not effect the market’s status as a monopolistic competition.22 The matter of independent decision making is displayed by the primary data I have collected (see Appendix 1). This is demonstrated by Paddy Power’s decision to boost their odds on Faugheen to 4.00 despite others cutting prices. The final point is the matter of asymmetric information between buyers and sellers. This is clearly the case for bookmakers as not only are they more likely to incur inside information than the consumer – having access to jockeys and trainers but they also know how much customers are spending on each type of bet and can alter the market price accordingly. In conclusion it is clear to see that the online betting market is in monopolistic competition. Nature of the Market A 2012 report23 stated that the leading 6 players in the online sports betting market were Ladbrokes, Sky Bet, William Hill and Paddy Power in that order; whilst firms outside of the top 8 make up a meager 15.5% of the market. When looking deeper into the figures I have collected in Appendix 1 it is possible to come to the conclusion that there is asymmetric information between firms in the market as well as well as between firms and the customers. In the cases of both “Faugheen” and “Cup Final” it is clear to see that the five bigger firms seem to have prices that quickly move in line with eachother; whilst the two smaller firms (YouWin and Bet Internet) seem to have a time delay before prices come in line with the others. In the case of Faugheen it is wise to discard Paddy Power’s prices as an anomaly as this price was purely for a promotional offer. At price four Coral’s prices dropped from 2.75 to 2.25; almost immediately the prices of the other “big 5” firms dropped in line with this, whilst the prices of the smaller two take until price 8 to fall below 2.25. In this time the price of the majority has dropped a further two times; further investigation could possibly see that the companies with a larger market share are working together,

19 Preserve Articles. (2011). Most important characteristics features of monopolistic competition . Available: http://www.preservearticles.com/201106178092/6-most-important-characteristics-features-of-monopolistic-competition.html. Last accessed 13th Mar 2014. 20 Bridge, S. (2012). Ladbrokes and William Hill raise the stakes with punt on loyalty cards Read more: http://www.thisismoney.co.uk/money/markets/article-2103029/Ladbrokes-William-Hill-raise-stakes-punt-loyalty-cards.htm. Available: http://www.thisismoney.co.uk/money/markets/article-2103029/Ladbrokes-William-Hill-raise-stakes-punt-loyalty-cards.html. Last accessed 12th Mar 2014. 21 Top 100 Bookmakers. (2014). Top 100 Bookmakers. Available: http://www.top100bookmakers.com/. Last accessed 12th Mar 2014. 22 Martin, V. (2013). Investing The Hard Way: Barriers to Entry And US iGaming. Available: http://calvinayre.com/2013/04/08/business/barriers-to-enter-us-igaming/. Last accessed 12th Mar 2014. 23 Gambling Data. (2012). European Regulated Online Markets, Data Report. Regulated European Online Markets Data Report. 3 (1), p7-8.

informing each other of information that may lead to them cutting prices. Although this would benefit a competitor in the short-run; in the long run the benefits the companies could be colossal. To test the hypothesis that the larger firms are possibly helping each other to set prices I have taken the standard deviation of each price level twice; once with just the “big 5” firms and one with all of the firms. I then took an average of the two standard deviations – this is depicted in figure 6. Price Level Standard Deviation of “Big 5”

firms Standard Deviation of all 7 firms

Price 1 4.00 3.49927106111883 Price 2 4.00 3.49927106111883 Price 3 0 0 Price 4 2.8 2.44948974278318 Price 5 2.8 2.44948974278318 Price 6 6.36867333123626 8.58332507759989 Price 7 5.20 9.00566714998542 Price 8 0 0 Price 9 0 1.8070158058105 Price 10 0 0 Average Standard Deviation: 2.516867333 3.129352964 Figure 6 – A table to show the standard deviations of all 7 firms and the “big 5” firms. Whilst the deviation is noticeably higher when taking all seven firms into account it is not substantial enough to arouse suspicion of wrongdoing and further tests would need to be done to establish whether this is the case, however one can say that the firms with a larger market share seem to be the price-setters, whilst smaller firms and new entries to the market seem to take on the role of price-takers unless they try to undercut the market.

3) Evaluation of an International Issue – Russia and Crimea The Crimean peninsula is the focal point of the current crisis in Ukraine. By majority it is a pro-Russia part of Ukraine, which is separated from the rest of the country geographically and politically. Early March 2014 saw Russian troops take over a strategic position in Ukraine in order to “protect Russian interests”. The newly installed government in Kiev was left defenceless to react as Russian troops poured into the Crimean region.24 One can use game theory in order to evaluate the next possible move which Putin and Russia will make. The three main options for Russia now are to keep troops in place but not takeover, to provoke a fight and stage a full-scale invasion of the Crimea, or to reinstate a friendly government in the Crimea. The first option would see Russia remind Ukraine that they are strong and hold a strong influence over the region without provoking too much fighting back, which would be the case if they were to try and invade. They can do this by moving troops and army vehicles around the region; this is a low risk but high-profile move that would exert pressure without an overt takeover of the region. This tactic is likely to allow Putin to easily ramp up pressure without losing too much face if he were to withdraw. The second approach would see Putin pour more troops into the Crimea and other parts of Ukraine, provoking a fight-back to start more aggressive actions. This tactic is similar to the way Russia acted under Putin in Georgia; and once again, his military would be more than ready to respond and should easily defeat the Ukrainians.

24 Fowler, S. (2014). As it happened: Ukraine crisis. Available: http://www.bbc.co.uk/news/world-europe-26463731. Last accessed 11th Mar 2014.

The third option would see Putin try to reinstate a friendly regime in Ukraine, almost totally withdrawing his troops. He may feel that he has done enough in the Crimea to “flex his muscles” and show that he has what it takes to cause trouble; however under certain circumstances this could make him look weak.25 On the other side of the coin we have to evaluate the responses of the United States and other militaries worldwide. The two main options for these troops are to wage war with Russia, claiming that they will come to the aid of Ukraine and fight with Russia if needs be; or to take a back seat and see how the situation escalates. Figure 7, below, displays a pay-off matrix of the situation I have created:

USA & Other Troops

Wage War With Russia Take No Action & See What Happens

Moving Troops Around

USA receive extremely bad publicity for waging

a war before needed, whilst Russia waste

resources and don’t look any stronger

-5 / -5

Russia maintain their troops in the Crimea at a great

expense without achieving much, while there are no

immediate repercussions for the USA

-10 / 0 Russia

Start Invasion & Provoke Fight-back

Lose/Lose situation with a possible World War

-100 / -100

Russia Victory, US Defeat. Russia invade while the US

take a back seat and look extremely weak

50 / -20

Withdraw Troops

USA Victory, Russia Defeat. USA look strong whilst Russia loses face and backs down. USA achieve main goal of

removal from Crimea, but at the expense of

lives and money.

-20 / 50

Could be seen as a compromise. Putin shows he has some power while there are no immediate negative

repercussions and US achieve their goal of removal from Crimea

without wasting resources.

50 / 60 Figure 7 – A pay-off matrix of the Russia-Crimea Crisis By using iterated elimination of dominated strategies one can find the equilibrium strategy. We can assume that rational players would never play a dominated strategy; as a result we can say that Russia will never play “move troops around”, hence this can be disregarded. The USA would then play “take no action and see what happens” as it dominates “wage war with Russia”, hence Russia will play “withdraw troops” – this means that the equilibrium is at play “take no action and see what happens” for the USA “withdraw troops” for Russia. The best means of this would be some kind of peace treaty signed between the two parties as this will have let Putin show his strength, whilst eliminating any risk of a further war.

25 Popular Mechanics. (2014). 3 Russian Intervention Scenarios in Ukraine. Available: http://www.popularmechanics.com/technology/military/news/3-russian-intervention-scenarios-in-the-ukraine-16544025. Last accessed 13th Mar 2014.

Conclusion In conclusion, game theory is a highly complex notion that can be invaluable in real life situations. The “prisoners dilemma”, “battle of the sexes”, “matching pennies” and “hawke dove” games can be used to aid us making decisions in business and on an international political scale. Part two of my report allowed me to analyse the online bookmakers market; finding that it carries a monopolistically competitive structure with a number of firms fighting to make their way to the top of a highly lucrative market. I subsequently implemented game theory to make a decision on what I believed should be the best outcome of a high-profile political situation; allowing me to illustrate that game theory is a powerful tool that is used everyday throughout the world.26

26 Fisher, L (2008). Rock, Paper, Scissors: Game Theory in Everyday Life: Strategies for Co-operation. New York: Perseus Publishing. p2-6.

Appendix Appendix 1:

Table 1 - Prices for Faugheen - Decimal Odds

Bookmaker Coral Sky Bet Ladbrokes

Paddy Power

William Hill YouWin Bet Internet

Price 1 (Ante Post) 40.00 40.00 40.00 50.00 40.00 40.00 40.00

Price 2 40.00 40.00 40.00 50.00 40.00 40.00 40.00

Price 3 40.00 40.00 40.00 40.00 40.00 40.00 40.00

Price 4 33.00 40.00 40.00 40.00 40.00 40.00 40.00

Price 5 33.00 40.00 40.00 40.00 40.00 40.00 40.00

Price 6 33.00 20.00 20.00 33.00 20.00 40.00 40.00

Price 7 33.00 20.00 20.00 20.00 20.00 40.00 40.00

Price 8 20.00 20.00 20.00 20.00 20.00 20.00 20.00

Price 9 16.00 16.00 16.00 16.00 16.00 20.00 20.00 Price 10 (At the Post) 16.00 16.00 16.00 16.00 16.00 16.00 16.00

Average Price 30.40 29.20 29.20 32.50 29.20 33.60 33.60 Table 2 - Prices for Cup Final - Decimal Odds

Prisoner A Confess Deny

Confess 5 years / 5 years 1 year / 10 years Prisoner B Deny 10 years / 1 year 2 years / 2 years

Figure 1 – A standard view of a prisoner’s dilemma game.

Country A Produce Nuclear

Weapons Don’t Produce Nuclear

Weapons

Produce Nuclear Weapons Treaty broken, high

expense and chance of war high – Lose, lose

Treaty broken by B, country B win as have high

advantage in the chance of war. Country B


Treaty broken by A, country A win as have high advantage in the

chance of war.

Treaty remains intact, no expense, neither country produces and both win.

Figure 2 – A prisoner’s dilemma game with real world application.

Bookmaker Coral Sky Bet Ladbrokes Paddy Power

William Hill YouWin

Bet Internet

Price 1 (Ante Post) 2.75 2.75 2.75 2.75 2.75 3.50 3.00

Price 2 2.75 2.75 2.75 4.00 2.75 3.00 3.00

Price 3 2.75 2.75 2.75 4.00 2.75 3.00 3.00

Price 4 2.75 2.75 2.75 4.00 2.75 3.00 3.00

Price 5 2.25 2.75 2.75 4.00 2.75 3.00 3.00

Price 6 2.25 2.25 2.25 4.00 2.25 2.75 2.75

Price 7 2.00 2.00 2.00 4.00 2.00 2.75 2.75

Price 8 1.50 1.38 1.38 4.00 1.38 1.63 1.25

Price 9 1.50 1.50 1.50 4.00 1.50 1.10 1.50 Price 10 (At the Post) 1.50 1.50 1.50 4.00 1.50 1.10 1.50

Average Price 2.20 2.24 2.24 3.88 2.24 2.48 2.48

Husband Boxing Opera

Boxing 5 / 10 0 / 0 Wife Opera 0 / 0 10 / 5

Figure 3 – A standard view of a battle of the sexes game.

Player 1 Swerve Straight

Swerve -10 / 10 -10 / 10 Player 2 Straight 10 / -10 -100 / -100

Figure 4 – A standard view of a chicken game.

Soviet Union Withdraw Maintain

Naval Blockade Compromise

5/5

Soviet Victory, US Defeat

-10 / 10 USA

Air Strike

US Victory, Soviet Defeat

10 / -10

Nuclear War

-100 / -100

Figure 5 – The chicken/hawk-dove game applied to the Cuban Missile Crisis. Price Level Standard Deviation of “Big 5”

firms Standard Deviation of all 7 firms

Price 1 4.00 3.49927106111883 Price 2 4.00 3.49927106111883 Price 3 0 0 Price 4 2.8 2.44948974278318 Price 5 2.8 2.44948974278318 Price 6 6.36867333123626 8.58332507759989 Price 7 5.20 9.00566714998542 Price 8 0 0 Price 9 0 1.8070158058105 Price 10 0 0 Average Standard Deviation: 2.516867333 3.129352964 Figure 6 – A table to show the standard deviations of all 7 firms and the “big 5” firms.

References: Brams, S. (2001). Game theory and the Cuban missile crisis. Available: http://plus.maths.org/content/game-theory-and-cuban-missile-crisis. Last accessed 12th Mar 2014. Bridge, S. (2012). Ladbrokes and William Hill raise the stakes with punt on loyalty cards Read more: http://www.thisismoney.co.uk/money/markets/article-2103029/Ladbrokes-William-Hill-raise-stakes-punt-loyalty-cards.htm. Available: http://www.thisismoney.co.uk/money/markets/article-2103029/Ladbrokes-William-Hill-raise-stakes-punt-loyalty-cards.html. Last accessed 12th Mar 2014. CMSC. (2014). Game Theory. Available: http://www.cs.umd.edu/~nau/cmsc421/game-theory.pdf. Last accessed 12th Mar 2014. Dixit, A. (2014). Prisoners Dilema. Available: http://www.econlib.org/library/Enc/PrisonersDilemma.html. Last accessed 13th Mar 2014. Fisher, L (2008). Rock, Paper, Scissors: Game Theory in Everyday Life: Strategies for Co-operation. New York: Perseus Publishing. p2-6. Fowler, S. (2014). As it happened: Ukraine crisis. Available: http://www.bbc.co.uk/news/world-europe-26463731. Last accessed 11th Mar 2014. Gallagher, P. (2013). Addiction soars as online gambling hits Â£2bn mark. Available: http://www.independent.co.uk/news/uk/home-news/addiction-soars-as-online-gambling-hits-2bn-mark-8468376.html. Last accessed 12th Mar 2014. Gambling Data. (2012). European Regulated Online Markets, Data Report. Regulated European Online Markets Data Report. 3 (1), p7-8. Gameoptprakrita. (2013). Game Theory For Conservation of Natural Resources. Available: https://sites.google.com/site/gameoptprakrita/bos. Last accessed 12th Mar 2014. GameTheory.net. (2006). Game of Chicken. Available: http://www.gametheory.net/dictionary/games/GameofChicken.html. Last accessed 12th Mar 2014. Investopedia. (2014). Matching Pennies. Available: http://www.investopedia.com/terms/m/matching-pennies.asp. Last accessed 11th Mar 2014. Investopedia. (2014). Zero-sum Games. Available: http://www.investopedia.com/terms/z/zero-sumgame.asp. Last accessed 11th Mar 2014. Koslowski, P (2000). Contemporary Economic Ethics and Business Ethics. Hannover: Springer. 10. Martin, V. (2013). Investing The Hard Way: Barriers to Entry And US iGaming. Available: http://calvinayre.com/2013/04/08/business/barriers-to-enter-us-igaming/. Last accessed 12th Mar 2014. Martin, V. (2013). Investing The Hard Way: Barriers to Entry And US iGaming. Available: http://calvinayre.com/2013/04/08/business/barriers-to-enter-us-igaming/. Last accessed 12th Mar 2014. Myerson, R (1991). Game Theory - Analysis of Conflict. United States: President & Fellows of Harvard College. p1. Nisan et al (2007). Algorithmic Game Theory. New York: Cambridge University Press. p7-8. OnlineGambling.com. (2014). The History of Online Gambling. Available: http://www.onlinegambling.com/online-gambling-history.htm. Last accessed 12th Mar 2014. Peer Zumbansen (2011). Law, Economics and Evolutionary Theory. Cheltenham: Edward Elger Publishing. p158.

Popular Mechanics. (2014). 3 Russian Intervention Scenarios in Ukraine. Available: http://www.popularmechanics.com/technology/military/news/3-russian-intervention-scenarios-in-the-ukraine-16544025. Last accessed 13th Mar 2014. Preserve Articles. (2011). Most important characteristics features of monopolistic competition . Available: http://www.preservearticles.com/201106178092/6-most-important-characteristics-features-of-monopolistic-competition.html. Last accessed 13th Mar 2014. Russell, B (1959) p. 30. Smith, J. (1973). The Logic of Animal Conflict. Nature. 246 (1), p1-4. Sugden, R. The Economics of Rights, Cooperation and Welfare 2 edition, page 132. Palgrave Macmillan, 2005. Top 100 Bookmakers. (2014). Top 100 Bookmakers. Available: http://www.top100bookmakers.com/. Last accessed 12th Mar 2014. Vlastakis, N. (2008). How Efficient is the European Football Betting Market? Evidence from Arbitrage and Trading Strategies. Journal of Forecasting. 28 (1), 2.

Explain the causes of the recession, analyse the response of the UK capital and money markets and evaluate the monetary policy measures that the Bank of England used during this period. Introduction: A recession can be defined as a period of economic decline where trade and industrial activity are reduced – this is usually displayed by a fall in Gross Domestic Product (GDP) in two successive quarters1. In 2009 the UK saw negative growth of GDP in three consecutive quarters. Initially 2008 saw a minimal growth of 0.1%, the following three saw growth of -0.9%, -1.4% and -2.1% respectively meaning the country was officially in a recession; this negative growth continued throughout 2010 and was a cause of major concern; as displayed by figure 1.

Figure 1: UK Gross Domestic Product growth as a percentage of the previous quarter (Office of National Statistics, 2013) Starting in 2007, the financial crisis rapidly spread across the globe like an epidemic. The problems originated from the United States and grew from the bursting of the housing bubble to the worst world-wide recession the world has experienced since the 1930s.2 Through a review of the crisis in terms of causes and consequences this report will identify and evaluate the three key causes of the 2008 Great Recession; analyse the response of the UK markets and evaluate monetary policy measures undertaken by the Bank of England.

1 Miles, D (2005). Macroeconomics: Understanding the Wealth of Nations. 2nd ed. West Sussex: John Wiley & Sons Ltd.. p348. 2 Hall, R E (2013). Economics (Hall), 6ed: Principles & Applications. 6th ed. Ohio: Joe Sabatino. p720.

Causes: First and foremost the Great Recession was a global problem, so the United Kingdoms’ problems can largely be attributed to America; this is where the crisis originated. The old proverb that when the US catches a cold the rest of the world sneezes appeared to be vindicated as systemically countries all over the world seemed to collectively go into recession by 20083. US housing prices peaked in early 2006, started to decline throughout 2006 and 2007; reaching new all-time relative lows in 2012. In December 2008 the Case-Shiller home price index reported its largest price drop in history and the general consensus is that the credit crisis resulting from the bursting of the housing bubble is one of the main contributors to the start of recession in the US.4 Large increases in foreign investment into the USA saw the construction sector explode, with the supply of housing rising faster than ever. This lead to a mortgage companies sanctioning a plethora of new mortgage deals; all competing with each other to provide the best possible option for consumers. In the wake of the dot-com crash the Federal Reserve dramatically lowered interest rates to a record low from around 6.5% to just 1%, making it much easier for banks to make loans.5 By 2006 rates had increased to 5.25%, lowering the demand and increasing the monthly payments on mortgages which track the base rate. This lead to banks repossessing the houses of people unable to keep up with payments and selling them on to recoup what they had lent – the result was an increase in the supply of houses, dropping housing prices further. As the number of credit-worthy people taking mortgages began to dry up underwriting standards became severely lowered and as a result the number of subprime mortgages being sanctioned rose dramatically. 6

When US house prices declined rapidly after peaking in 2006, it became more difficult for borrowers to refinance their loans. As tracker mortgages rose to rates much higher than before causing higher monthly repayments the vast majority of subprime mortgages began to default on their loans, along with a number of normal mortgage holders. These securities lost most of their value and became toxic assets to financial firms all over the country. As a result global investors reduced purchases of mortgage-backed debt and concerns about the soundness of US credit market led to tightening credit around the world and slowing economic growth in the United States and across Europe.7 In 2008 Lehman Brothers investment bank faced shocking losses a result of having held on to large positions in subprime and other lower-rated mortgage tranches when securitising the underlying mortgages. Throughout the year multi-billion dollar losses were announced and the firm eventually filed for bankruptcy in September; it remains the largest bankruptcy filing in history with assets totalling over $600 billion.8 The domino effect began that day, with a global recession spreading like wildfire. This was a clear

3 Bernanke, B. (2010). Monetary Policy and the Housing Bubble. Annual Meeting of the American Economic Association, Atlanta, Georgia. 1 (1), p1-2. 4 Pachecker, H (2010). The Immigrant's Universe. United States: Xlibris Corperation. p212. 5 Nabudere, D (2009). The Crash of International Finance-Capital and Its Implications. Cape Town: Pambazuka Press. p197. 6 Baker, D. (2008). The housing bubble and the financial crisis. Real-world Economics Review. 46 (1), p75-76. 7 Maddaloni, A. (2010). Bank Risk-Taking, Securitization, Supervision and Low Interest Rates. European Central Bank Working Paper Series. 1248 (1), p14-15. 8 Mamudi, S. (2008). Lehman folds with record $613 billion debt. Available: http://www.marketwatch.com/story/lehman-folds-with-record-613-billion-debt?siteid=rss. Last accessed 3rd Dec 2013.

display of the power that expectation holds and is a strong case for the collapse of the United State’s housing bubble being a point from which the global recession stemmed from due to poor regulation of mortgage lenders.9 Secondly, monetary policy can have a huge influence on an economy. The Federal Reserve is in place in order to achieve price stability and the maximum sustainable employment. The Taylor rule is a monetary policy rule that specifies a ratio between how much a central bank should change its’ nominal base rate in accordance to a changes in inflation or other economic conditions. One particular aspect of the rule indicates that for each 1% increase in inflation in an economy, the central bank of said economy should increase the nominal interest rate by more than one percentage point.10 When policies are implemented by using rule-based decisions, the whole process is easier to make predictions upon. Policy makers can use equations to predict how their policy should act in the future in accordance with a large pool of previous data, which is readily available. On the other hand discretionary policies are far less predictable and focus more on short-term changes; they cannot normally be linked with much previous data as they are very dependent on the individual situation – this can leave a lot to go wrong. Although it is only a guideline, the use of Taylor rule throughout the early 1990s until the mid-2000s proved to be a success. Inflation and interest rate volatility fell drastically compared to the 1970s; while the volatility of GDP was over halved and unemployment declined.11As figure 2 displays, from 2001 to 2006 the Federal Reserve began to deviate from the Taylor rule by keeping interest rates much lower than the rule suggests they should be set at. Taylor (2007) condemns the Federal Reserve suggesting it kept the federal funds (base) rate far too low; while American Economist Ben Bernanke defends the policy12 stating that risks of deflation were far too prominent to raise the interest rate; however one could argue bias due to his position as Chairman of the Federal Reserve.

9 Pezzuto, I. (2012). MIRACULOUS FINANCIAL ENGINEERING OR TOXIC FINANCE? THE GENESIS OF THE U.S. SUBPRIME MORTGAGE LOANS CRISIS AND ITS CONSEQUENCES ON THE GLOBAL FINANCIAL MARKETS AND REAL ECONOMY. Journal of Governance and Regulation. 1 (3), p1. 10 Taylor, J . (1993). Discretion Versus Policy Rules In Practice. University of Stanford. 94305 (1), p202. 11 Taylor, J. (2011). The Cycle of Rules and Discretion in Economic Policy. National Affairs. 7 (1), p1-2. 12 Bernanke, B. (2010). Monetary Policy and the Housing Bubble. Annual Meeting of the American Economic Association, Atlanta, Georgia. 1 (1), p1-2.

Figure 2: The US Official Federal Funds Rate plotted against the Taylor Rule (US Economic Snapshot, 2013) The discretionary period between 2001 and 2007 showed a stark contrast; including the housing bubble collapse and, of course, the start of the great recession. While correlation does not substantiate causation one could argue that the timing of the crash is too much of a coincidence for the deviation from Taylor rule and implementation of discretionary policies to not have played a part in the start of the recession. A third major contributing factor was the stark rise in the price of oil between 2003 and 2008.13 Crude oil is an extremely volatile commodity; it is used heavily throughout both the production and transportation of goods and resultantly it is clear to see that a rise in oil prices can harm a countrys’ GDP due to the inelastic nature of its’ demand. In 2003 crude oil traded between $20 and $30 per barrel; this began to rise and by July 2008 the price had reached an all time high of $147.27 per barrel14 – this was around 35% more than the previous inflation adjusted high. As oil wells became exhausted supply of crude oil started to flatten and supply took a massive hit. In 2007 Saudi Arabia tried desperately to make up for the shortages, yet failed, seeing their output fall by 850,000 barrels per day in comparison to 2005.15

13 Hamilton, J. (2009). Causes and Consequences of the Oil Shock of 2007-08. NBER Working Paper Series. 15002 (1), p1-2. 14 Anonymous. (2008). Oil hits new high on Iran fears . Available: http://news.bbc.co.uk/1/hi/business/7501939.stm. Last accessed 3rd Dec 2013. 15 Hamilton, J. (2009). Oil Prices and the Economic Downturn . Testimony Prepared for the Joint Economic Committee of the U.S. Congress. 1 (1), p1.

Whilst supply fell globally, demand seemed to grow stronger and stronger; much of this can be attributed to the emergence of China as a major mass-production hub. The GDP of China has steadily grown, averaging at 7% growth per annum since 199016 and, as Figure 3 displays, oil consumption has shot through the roof. Chinas’ oil consumption is now second only to the United States. When we study the supply and demand of crude oil it is clear to see a combination of both demand-pull and cost-push inflation causing prices to shoot up between 2003 and 2005.

Figure 3: China’s oil consumption (EIA Short-Term Energy Outlook, 2011)

16 World Bank. (2013). GDP Statistics. Available: http://data.worldbank.org/country/china#cp_wdi. Last accessed 3rd Dec 2013.

Responses of Capital and Money Markets: The UK Foreign Exchange market was heavily impacted by the recession. At the start of 2006 the interest rate stood at 4.5000%, the Bank of England increased the rate to 4.7500% and 5.0000% on third of August and ninth of September respectably in order to combat the inflationary concerns in the UK.17 This lead to an appreciation of the pound against both the Euro and the dollar. On 7th November 2007 the UK reached its’ highest point against the dollar in 26 years when it hit $2.1161 per pound, yet as doubts grew about the recession and the UKs reliability on it’s hard-hit financial sector the Sterling Pound began to lose it’s value rapidly; reaching a 24-year low of $1.35 to the Pound in January 2009.18 The pound also began to weaken against the Euro – hitting an all time low of £1.0219 per Euro in December 2009. Figure 4 displays the indexed exchange rates of the BP, Euro and Yen plotted against the US dollar. From the graph one can deliberate that exchange rates remained relatively stable until November 2007 when the Euro began to appreciate against the dollar; there was also a slight depreciation of the pound. However, it is not until September 2008 that the Pound is deeply hit, as the depth of the problems British banks were facing came to light. As Lehman brothers crashed, so did the value of the pound, and to date it has not yet fully recovered. As Table 1 displays, the fall of Lehman Brothers also saw volatility of GBPUSD shoot up to 5500%, this sent transaction costs through the roof, causing less traders to take positions in that particular market.

Figure 4: Indexed exchange rates of the Great British Pound, Euro and Japanese Yen plotted against the U.S. dollar (Melvin Taylor, 2009)

17 Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 3rd Dec 2013. 18 Unknown. (2012). Influences on the Pound Sterling . Available: http://www.smx.com.sg/products/GBPUSD.aspx#tab3. Last accessed 3rd Dec 2013.

Table 1: The Increase in FX Transactions and Spreads; Pre-crisis to Post-Crisis (Melvin Taylor, 2009) The fall of Lehman Brothers was monumental, sending shockwaves throughout the world and the effect it had on consumer confidence was critical for the recession in the United Kingdom. Lehmans' bankruptcy sparked fears that other banks were just as vulnerable; grinding the inter-bank markets to a halt. The interbank market is fundamental to the entire financial system and is the main outlet for banks to gain short-term financing and loans. In the wake of the Lehman crash the over-night rate shot up, making loans far more expensive for banks, drying up money liquidity and credit availability.19 Confidence is key in any market and the lack of it highly discouraged any investment causing demand for shares to deplete and supply to grow as people and companies began to sell off their shares. Winnett (2008) states that:

“Share prices on the FTSE 100 index fell by 391 points - almost eight per cent - on Monday, as the turmoil gripping financial markets intensified. The London fall was the biggest points drop in history and the largest percentage drop since the stock market crash of 1987.” 20

This emphasises the true extent of the crash and demonstrates the excruciating effect that consumer confidence has. The domino effect had now commenced and Lehman Brothers file for bankruptcy on the 15th September 2008 saw Britain’s largest mortgage lending company HBOS have its’ shares collapse the next day; falling 34% by 12pm, eventually recovering to a fall of 17% at close.21

19 Elliot, L. (2013). Lehman Brothers collapse, five years on: 'We had almost no control'. Available: http://www.theguardian.com/business/2013/sep/13/lehman-brothers-collapse-five-years-later-shiver-spine. Last accessed 3rd Dec 2013. 20 Winnet, R. (2008). Financial crisis: London stock exchange suffers worst fall in history. Available: http://www.telegraph.co.uk/finance/financialcrisis/3147764/Financial-crisis-London-stock-exchange-suffers-worst-fall-in-history.html. Last accessed 3rd Dec 2013. 21 Treanor, J. (2008). HBOS shares plunge as investor confidence wanes. Available: http://www.theguardian.com/business/2008/sep/16/hbosbusiness.lehmanbrothers1. Last accessed 3rd Dec 2013.

Yet HBOS were not the only firm to be hit hard, as Figure 5 displays, on the 16th October 2008 the FTSE 100 hit its lowest point in more than five years as fears of a global recession heightened. Shares in the UK's leading companies closed down 5.35% at 3861, the FTSE's lowest point since April 2003, following another wave of selling by investors; with travel and oil companies being some of the hardest hit. Shares in holiday companies TUI Travel and Thomas Cook fell by over 20%; this was said to be due to shrewd investors calculating that as unemployment fell fewer people would be taking holidays; add this to the soaring oil prices at the time, investors predicted a recipe for disaster.22

Figure 5: FTSE 100 Over 2008 (Yahoo Finance, 2013) With the stock market flagging and the crisis in full swing, investors began to sell off any assets that they thought carried risk and investing in safer assets such as government bonds; this phenomena is known as flight-to-quality. Government bonds were seen as a safe-haven for investors as the risk of default is far lower than corporate bonds; this is due to the governments’ ability to raise capital at any given moment to fund bond payments in a number of ways, including taxation. Long-term government bonds became extremely favourable as although there was an opportunity cost of not having the money to spend, for large sums it was seen as safer than having your money looked after by a financial institution.23 On the other hand, corporate BAA bonds were looking far riskier since the recession and demand fell drastically. As risk increased yield had to follow on these bonds and as Figure 6 displays the bond-yield spread between 10-year government issued bonds and corporate BAA bonds grew drastically. The fourth quarter of 2008 shows a sharp increase in the yield of corporate bonds to just under 9%, this would have been a tool to stimulate low demand. Meanwhile the yield of government treasury bonds dropped to just below 3%, this connotes high demand. When looking closer at the dates it is plain to see that this coincides with the Lehman Brothers file for bankruptcy on the 15th September 2008 – once again this can be used as a measure to show that consumer confidence in commercial banks had dropped drastically. On the 5th March 2009 the 22 Milmo, D. (2008). FTSE 100 hits five-year low as world stockmarkets slump again. Available: http://www.theguardian.com/business/2008/oct/16/market-turmoil-recession. Last accessed 3rd Dec 2013. 23 Perry, B. (2011). Safety and Income: Bonds. Available: http://www.investopedia.com/university/safety-and-income/bonds.asp. Last accessed 3rd Dec 2013.

Monetary Policy Committee announced that it would decrease interest rates to 0.5%; bond yield and interest rates have an inverse relationship and the rate decrease is depicted on the graph as bind yield steadily falls from the second quarter of 2009 onwards.

Figure 6: BAA Corporate Bond Yield Vs 10-Year US Treasury Gilts (Econoday, 2013) Monetary Policy Measures of The Bank of England: The Monetary Policy Committee (MPC) is a group, independent to the government, which was established in by Gordon Brown in 1997 with the primary objective of keeping CPI inflation at 2%. When the recession hit the committee implemented a number of monetary policy measures with the aim of stabilising the economy of the United Kingdom; I will explore these in this section. At the start of 2006 the interest rate stood at 4.50%; as the recession set in and aggregate demand fell the Monetary Policy Committee felt that it was necessary to decrease the rate in order to stimulate spending. On 5th March 2009 it was announced that interest rates were set at the lowest level (0.5%) in the Bank of England’s history.24 The main reasons for the cuts were to try to encourage commercial banks to lend again, in order to increase investment and resultantly aggregate demand. Amid fears of a further recession it was proposed that this alone would not be significant enough. In an exchange of letters between the Treasury and the Bank of England, chairman Mervyn King stated that

“Further rate cuts in the Bank Rate alone might not be enough to bring inflation in line with the Bank’s 2% target… The Bank of England remains committed to improving liquidity in credit markets that are not functioning normally.”25

24 Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 3rd Dec 2013. 25 Cited - Kollewe, J. (2009). Bank of England cuts rates to 0.5% and starts quantitative easing. Available: http://www.theguardian.com/business/2009/mar/05/interest-rates-quantitative-easing. Last accessed 3rd Dec 2013.

Thus the MPC looked to Quantitative Easing as a solution to the problem. In March 2009 the Bank of England stated that they would make £75 billion available for the purchase of government bonds and toxic corporate assets in order to provide liquidity in the economy and boost the flow of money. The bank proposed to spend a total of £200 billion on asset purchases from which £197.275 million was spent on UK bonds and the rest on corporate papers.26 As the interest rate was now approaching zero cutting it further was not an option; the idea of Quantitative Easing allowed the government to give financial institutions money without reducing their balance sheet. As a result, much of the new £75 billion was spent on existing Gilts and corporate bonds from financial institutions, creating immediate liquidity that can be leant to businesses and homeowners. However, cleverly, these asset purchases also have an ulterior motive. As the number of bonds available decrease the existing gilt prices rice, whilst yield interest adjusts downwards. This encourages the financial institutions to invest more heavily in assets with a higher yield in order to rebalance their portfolio – this comes in the form of investment in new businesses. As investment rises the money is injected into the real economy and sees aggregate demand in the economy rise. When evaluating the effectiveness of quantitative easing we must look at the initial intentions of the policy makers and the Monetary Policy Committee to see if outcomes matched the intended consequences. Banks certainly saw the benefits, seeing a direct increase in available liquidity. Despite concerns about the transmission mechanism of credit, quantitative easing has been focused at the start of the supply chain; this means that those who dismissed the theory as “printing money” can be categorically rejected.27 Indeed the favoured effect was achieved, as Figure 7 displays there was a sharp increase in mortgage lending after quantitative easing commenced in March 2009; as a result of the multiplier effect we saw this translate into a growth in aggregate demand. As the worries about the inflation rate becoming extortionate were not vindicated, one can conclude that although slightly aggressive and unorthodox as a policy, in this instance Quantitative Easing proved to be a success.

26 Bank of England. (2011). Quantitative Easing Explained. Available: http://www.bankofengland.co.uk/monetarypolicy/pages/qe/default.aspx. Last accessed 3rd Dec 2013. 27 Flanders, S. (2009). Is quantitative easing really just printing money?. Available: http://www.bbc.co.uk/blogs/thereporters/stephanieflanders/2009/02/obtaining_the_right_to_print_m.html. Last accessed 3rd Dec 2013.

Figure 7: Mortgage Lending by Major UK Lenders (Bank of England) Prior to Quantitative Easing the MPC introduced the Special Liquidity Scheme in April 2008 to improve the liquidity position of the UK banking system. This scheme allowed banks and building societies to swap their high quality mortgage-backed securities for UK Treasury Bills. The scheme was put into place to help to finance some of the overhang of illiquid assets on bank balance sheets by swapping them temporarily for assets with more immediate liquidity. The Bank of England made £185 billon worth of treasury bills available to be lent under the scheme. The nominal value of collateral used by the banks stood at £287 billion, while the banks valuation of those securities stood at £242 billion; effectively a discount of 16%. The scheme was purely a temporary measure, giving banks the opportunity to diversify their funding source. The last of the swaps under the SLS expired in 2012, at which point the scheme was terminated. The scheme was successful in providing new liquidity for illiquid assets, but left credit options short when it expired. This credit shortage was slightly softened by Discount Window Facility, which was a permanent introduction in March 2008; providing liquidity insurance to the banking system. According to the Bank of England “the facility is explicitly designed to help contain system stress by providing financing against assets that may become illiquid in stressed conditions”, It allows eligible banks to borrow gilts against a wide range of collateral in order to provide immediate liquidity.28

28 Bank of England. (2009). Special Liquidity Scheme. Available: http://www.bankofengland.co.uk/markets/Documents/marketnotice090203c.pdf. Last accessed 3rd Dec 2013.

Conclusion: The BofE can be deemed to have had moderate success with their monetary policy implementations in the wake of the recession. Stock markets have seen a steady rise in participation and strength. After QE commenced the FTSE 100 saw a 42% increase to 5,188 – the Bank of England calculations suggest that this would only have risen around 8% without the use of QE; this statistic fundamentally shows that QE has had some success as an economys’ stock market can be a strong indication of the success of its’ economy. We have also seen recent GDP growth and a 1.3% fall in unemployment to 7.7%;29 these are both strong indications that the economy is continuing its’ recovery. The main fear amid the monetary policy implementation was the risk of super-normal inflation, this has been kept to a minimum and as a result I feel that the MPC have been successful in their quest to use monetary policy to drag the economy out of its’ slump.

29 Hamilton, S. (2013). U.K. Unemployment Rate Unexpectedly Declines to 7.7%. Available: http://www.bloomberg.com/news/2013-09-11/u-k-unemployment-unexpectedly-falls-to-eight-month-low-of-7-7-.html. Last accessed 4th Dec 2013.

Bibliography: Anonymous. (2008). Oil hits new high on Iran fears . Available: http://news.bbc.co.uk/1/hi/business/7501939.stm. Last accessed 3rd Dec 2013. Baker, D. (2008). The housing bubble and the financial crisis. Real-world Economics Review. 46 (1), p75-76. Bank of England. (2009). Special Liquidity Scheme. Available: http://www.bankofengland.co.uk/markets/Documents/marketnotice090203c.pdf. Last accessed 3rd Dec 2013. Bank of England. (2011). Quantitative Easing Explained. Available: http://www.bankofengland.co.uk/monetarypolicy/pages/qe/default.aspx. Last accessed 3rd Dec 2013. Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 3rd Dec 2013. Bank of England. (2013). Statistical Interactive Database - official Bank Rate history. Available: http://www.bankofengland.co.uk/boeapps/iadb/repo.asp. Last accessed 3rd Dec 2013. Bernanke, B. (2010). Monetary Policy and the Housing Bubble. Annual Meeting of the American Economic Association, Atlanta, Georgia. 1 (1), p1-2. Bernanke, B. (2010). Monetary Policy and the Housing Bubble. Annual Meeting of the American Economic Association, Atlanta, Georgia. 1 (1), p1-2. Elliot, L. (2013). Lehman Brothers collapse, five years on: 'We had almost no control'. Available: http://www.theguardian.com/business/2013/sep/13/lehman-brothers-collapse-five-years-later-shiver-spine. Last accessed 3rd Dec 2013. Flanders, S. (2009). Is quantitative easing really just printing money?. Available: http://www.bbc.co.uk/blogs/thereporters/stephanieflanders/2009/02/obtaining_the_right_to_print_m.html. Last accessed 3rd Dec 2013. Hall, R E (2013). Economics (Hall), 6ed: Principles & Applications. 6th ed. Ohio: Joe Sabatino. p720. Hamilton, J. (2009). Causes and Consequences of the Oil Shock of 2007-08. NBER Working Paper Series. 15002 (1), p1-2. Hamilton, J. (2009). Oil Prices and the Economic Downturn . Testimony Prepared for the Joint Economic Committee of the U.S. Congress. 1 (1), p1. Hamilton, S. (2013). U.K. Unemployment Rate Unexpectedly Declines to 7.7%. Available: http://www.bloomberg.com/news/2013-09-11/u-k-unemployment-unexpectedly-falls-to-eight-month-low-of-7-7-.html. Last accessed 4th Dec 2013.

Kollewe, J. (2009) – cited - . Bank of England cuts rates to 0.5% and starts quantitative easing. Available: http://www.theguardian.com/business/2009/mar/05/interest-rates-quantitative-easing. Last accessed 3rd Dec 2013. Maddaloni, A. (2010). Bank Risk-Taking, Securitization, Supervision and Low Interest Rates. European Central Bank Working Paper Series. 1248 (1), p14-15. Mamudi, S. (2008). Lehman folds with record $613 billion debt. Available: http://www.marketwatch.com/story/lehman-folds-with-record-613-billion-debt?siteid=rss. Last accessed 3rd Dec 2013. Miles, D (2005). Macroeconomics: Understanding the Wealth of Nations. 2nd ed. West Sussex: John Wiley & Sons Ltd.. p348. Milmo, D. (2008). FTSE 100 hits five-year low as world stockmarkets slump again. Available: http://www.theguardian.com/business/2008/oct/16/market-turmoil-recession. Last accessed 3rd Dec 2013. Nabudere, D (2009). The Crash of International Finance-Capital and Its Implications. Cape Town: Pambazuka Press. p197. Pachecker, H (2010). The Immigrant's Universe. United States: Xlibris Corperation. p212. Perry, B. (2011). Safety and Income: Bonds. Available: http://www.investopedia.com/university/safety-and-income/bonds.asp. Last accessed 3rd Dec 2013. Pezzuto, I. (2012). MIRACULOUS FINANCIAL ENGINEERING OR TOXIC FINANCE? THE GENESIS OF THE U.S. SUBPRIME MORTGAGE LOANS CRISIS AND ITS CONSEQUENCES ON THE GLOBAL FINANCIAL MARKETS AND REAL ECONOMY. Journal of Governance and Regulation. 1 (3), p1. Taylor, J . (1993). Discretion Versus Policy Rules In Practice. University of Stanford. 94305 (1), p202. Taylor, J. (2011). The Cycle of Rules and Discretion in Economic Policy. National Affairs. 7 (1), p1-2. Treanor, J. (2008). HBOS shares plunge as investor confidence wanes. Available: http://www.theguardian.com/business/2008/sep/16/hbosbusiness.lehmanbrothers1. Last accessed 3rd Dec 2013. Unknown. (2012). Influences on the Pound Sterling . Available: http://www.smx.com.sg/products/GBPUSD.aspx#tab3. Last accessed 3rd Dec 2013. Winnet, R. (2008). Financial crisis: London stock exchange suffers worst fall in history. Available: http://www.telegraph.co.uk/finance/financialcrisis/3147764/Financial-crisis-London-stock-exchange-suffers-worst-fall-in-history.html. Last accessed 3rd Dec 2013. World Bank. (2013). GDP Statistics. Available: http://data.worldbank.org/country/china#cp_wdi. Last accessed 3rd Dec 2013.

Appendix:

Figure 1: UK Gross Domestic Product growth as a percentage of the previous quarter (Office of National Statistics, 2013) Available: http://www.aftau.org/site/DocServer/TA_Notes_RIVLIN_FEB10_11.pdf?docID=13121. Last accessed 3rd Dec 2013.

Figure 2: The US Official Federal Funds Rate plotted against the Taylor Rule (US Economic Snapshot, 2013) Available: http://www.aftau.org/site/DocServer/TA_Notes_RIVLIN_FEB10_11.pdf?docID=13121. Last accessed 3rd Dec 2013.

Figure 3: China’s oil consumption (EIA Short-Term Energy Outlook, 2011) Available: http://www.examiner.com/article/eia-to-release-60-million-barrels-of-oil-from-strategic-stockpile. Last Accessed: 3rd Dec 2013

Figure 4: Indexed exchange rates of the Great British Pound, Euro and Japanese Yen plotted against the U.S. dollar (Melvin Taylor, 2009) Available: http://www2.warwick.ac.uk/fac/soc/wbs/subjects/finance/confpapers09/melvintaylor_paper.pdf. Last Accessed: 3rd Dec 2013

Table 1: The Increase in FX Transactions and Spreads; Pre-crisis to Post-Crisis (Melvin Taylor, 2009) Available: http://www2.warwick.ac.uk/fac/soc/wbs/subjects/finance/confpapers09/melvintaylor_paper.pdf. Last Accessed: 3rd Dec 2013

Figure 5: FTSE 100 Over 2008 (Yahoo Finance, 2013) Available: http://uk.finance.yahoo.com/echarts?s=^FTSE#symbol=^ftse;range=20071231,20081231;compare=;indicator=volume;charttype=area;crosshair=on;ohlcvalues=0;logscale=off;source=undefined;. Last Accessed: 3rd Dec 2013

Figure 6: BAA Corporate Bond Yield Vs 10-Year US Treasury Gilts (Econoday, 2013) Available: http://fidelityfiplus.econoday.com/byresource.aspx?cust=fidelityFIplus&year=2013&grp=3&rcrid=1098. Last Accessed: 3rd Dec 2013

Figure 7: Mortgage Lending by Major UK Lenders (Bank of England) Available: www.thisismoney.co.uk/money/news/article-2219598/Funding-Lending-Scheme-

scrutiny-lending-businesses-enters-nuclear-winter.html. Last Accessed: 3rd Dec 2013