Inflation Persistence in the UK and Its Impact on Unemployment and Wage Rate - Research Proposal Example

No: {Insert your number here} inflation persistenCE IN THE UK AND ITS IMPACT ON UNEMPLOYMENT AND WAGE RATE project prepared within the EC3064 module Applied Econometric Project University of Leicester, 2015 Date of Submission: {Enter the date here in format DD/MM/2011} {Remember that the maximum length of the project is about 30 pages (with some tolerance for projects with a large number of relevant graphs and tables), including the references but excluding appendix} If appropriate, enter any acknowledgement or disclaimer here; OTHERWISE DELETE Executive Summary Inflation persistence is an important subject in the macroeconomics policy making process of a country. That is due to the effects that result from persistence inflation most of which are on the negative side. Policy makers concern is to determine the level of influence the inflation has on its key areas. This is significant in formulating the appropriate policies which will reduce the inflation rates and encourage economic growth. This study concerns itself with establishing the casual relationship between inflation and both the unemployment and wage rates in the United Kingdom. To establish such relationships the study employed the use of regression and correlation analysis. This was made in reference to the Philips curve and hypothesis which assumes there exists an inverse relationship between unemployment and inflation. The study was able to establish the existence of a causal relationship between the variables and proved this relationship to be positive in the long run. The level of persistence of inflation in the country was established to be minimal. 1. Introduction: Problems Addressed Inflation persistence can be described as the likelihood of having the average inflation rate of a country pushed away from its steady state and the targeted levels over a prolonged period. This is usually as a result of price shocks that tend to push up the inflation rate. It is of great interest to determine whether these shocks are stationary or whether they will change with time. Persistence inflation raises major concerns to the policy makers. This is because inflation rates have the capabilities of affecting several important areas of the economy. Inflation directly impacts on economic growth, unemployment levels and wage rates just to mention a few key areas. Knowing the level of persistence of inflation is useful in finding out the costs required to lower the inflation rates. Lower persistence will reflect the ability of the existing macroeconomic policies to cater for temporary shocks in the system. On the other hand, higher persistence indicates that there is a need for the policies to be adjusted to bring the inflation rates into equilibrium. Due to this the policy makers will require a good understanding of inflation persistence in the present and the future. They will need to establish the causal relationship between the inflation rates that have been prevailing in the country and the current situations in some of the areas in the economy. If a causal relationship has been established, their major concern will be determining whether the policies in place both monetary and fiscal have an impact on the persistence inflation. If they do, then it will be the responsibility of the policy makers to ensure such issues are resolved (Gali, 2008, p. 117). Inflation in the UK is determined using consumer price index (CPI). This measures the inflation at the retail level. Inflation rates above the targeted level have been experienced in the UK over time. The central bank has been targeting an inflation rate of 2%. The prices have been high above this level for a significant period. Currently, the country has 0% inflation but it is still important to find out the effects of the persistence inflation and determine if they are recurrent. In the period 2007 to 2015 the highest inflation rate were experience in 2008 and 2011. The rate was above 5.2% experienced in the month of September for both years. The other months experienced moderate inflation levels but which cannot be ignored because they still had an impact on the economy. The purpose of this study is to determine the effect of the persistent inflation experienced in the UK between the periods of 2008 and 2014 on the level of unemployment. The causal relationship between the two will be determined. Since employment levels in the country are directly related to the wage rates it will also be appropriate for the study to look at the causal relationship between inflation rates and the wage rates in the country. It is important to determine the causal relationship between inflation and unemployment levels because labour directly impacts on the country’s productivity. Lower levels of unemployment will mean less production in the country and consequently slowed economic growth while high employment levels will result in high productivity (Tucker, 2010, p. 81). The policy makers in the UK will be able to find out whether the persistent inflation rates are affecting their productivity negatively or positively. The study will also enable them to come up with appropriate policies in the future will enhance the economic development of the country (Mishkin, 2007, p. 47). 2. Economic Theory The original Phillips curve formulated by A. W. Phillips suggest that there is an inverse relationship between unemployment and inflation (Arnold, 2007, p. 31). He explained that there existed a trade-off between unemployment and inflation. For the government to get rid of one the other one had to increase (Dwivedi, 2010, p. 27).According Phillip there is a negative relationship between unemployment and the rate of change of wages (Barro, 2008, p. 64). High levels of unemployment will be associated with dropping level of wages, and low level of unemployment will lead to increased wages. This increase in wages would lead to increased prices because of the increased demand. That shows that in accordance with the Philip curve, the inflation rate is directly related to wage rates. That meant that governments would have to choose between targeting inflation or unemployment but not both. It was considered a causal relationship, which provided stable exchange between inflation and employment to the policy makers. The policy makers would target a specific level of unemployment and in return accept a certain level of inflation. To achieve the targeted unemployment level appropriate monetary and fiscal policies would have to be formulated (Carlberg, 2008, p. 104). On the other hand, policymakers could choose a low inflation level as their target and in this case they had to accept a higher unemployment rate. According to Friedman (2007) the estimates made using the Phillips curve did not produce satisfactory results. The inflation rate that would yield a certain level of unemployment did not remain fixed, but instead it changed with the situations at hand. An example of this is after the World War II period when countries were aiming to achieve full employment; the inflation rates produced varying results for different countries. It was also noted that inflation rates, which were expected to yield low levels of unemployment produced high unemployment rates. This introduced the doubts on the Philips curve of whether the negative relationship between inflation and unemployment were stable over time and with different circumstances. Friedman (2007, p. 93) further suggested that only surprises matter in determining the relationship between inflation and unemployment. If it was expected for the prices to rise, then this rise would be reflected in the future contracts and real wages, and so there would be no changes in the unemployment levels. The unanticipated change brought different reactions because of the imperfect knowledge that would exist especially if there were long-term contracts in place. This meant there would be a lagged adjustment of the prices and quantities that were in place. In the recent years, higher inflation rates have been associated with higher unemployment levels (Tucker, 2008, p. 72). This introduces the impact of other independent factors such as oil prices and economic stability on the trends of inflation and unemployment that were not experienced before (Tucker, 2010, p. 84). 3. Description of Data In macroeconomics, there is a strong relationship between the monetary economy and the real economy. Therefore, there is a need to explore this relationship with an aim of trying to solve the major macroeconomic problems. Philips Curve shows the relationship between inflation and unemployment that leads to the question of whether it is possible for the inflation rates play a part in the rates of unemployment thus productivity levels of a country. The challenges faced when understanding this model is that data collected of the unemployed may differ depending on the method of collection. The other challenge is the inflation expectations are available only in the short run and are heterogeneous between populations. These findings have led to a realization that Phillips curve can only be formulated in the short run. Some economists argue that a long run Phillips curve can also be formulated (Jha, 2008, p. 39). Arguments have erupted whether there is any relationship between unemployment and inflation rates. This study would like to resolve any dispute that is there as to whether the relationship exists. The analysis of UK comes in as an important analysis because of the following major reasons: The existence of a strong Monetary United Kingdom’s Union. UK has a liberal vibrant economy market (Croushore, 2007, p. 115). A time series data of both inflation and unemployment rates in the UK from the year 2007 to the year 2014 have been used in this project. The major questions which need to be answered is whether there can be a negative relationship between the two variables. It will also answer the question as to whether UK permanently trade off Inflation with Unemployment. Lastly, whether the relationship of the two variables is dependent on the macroeconomic factors or if it is a stable one. While trying to figure out the relationship between inflation and unemployment it will also be necessary to find out the relationship of the persistent inflation and the increasing wage rates. It will be important to find out if the inflation is the cause of the increase. The time series data of the minimum hourly wage rate from 2008 to 2014 will be used. Both correlation and Regression analysis will be done on these said data to give a deeper insight on the matters at hand. The Variables The independent variable which is also the external variable is the inflation rate (X) while the dependent variable (Y), is the unemployment rate. Therefore defining the model it is appropriate to say that the unemployment rate is a function of the inflation rates. Unemployment rates in the UK. (Y)=f *Inflation rates (X) Y=f (X) In the same light while trying to figure out the causal relationship between inflation and wage rate, the dependent variable will be the wage rate while the dependent variable will still be inflation. This can be represented in the linear equation: Y=f(X) However, in this case Y represents the wage rate. Empirical Analysis This paper uses the annual data of inflation, unemployment rates, and minimum hourly wage rate in the UK from the 2008 to the year 2014, with the respective data taken from the World Bank statistics. Understanding unemployment rate, the Oxford dictionary defines it us the total number of individuals with no employment as a percentage of the corresponding labour force while inflation rate is the change in the prices of commodities in the marketer relative to the original price. Minimum hourly wage rate, on the other hand, is the minimum amount, which should be paid for the labour provided by an employee per hour. Inflation rate in UK (2007-2014) Unemployment Rate in UK (2005-2014) The annualized time series data is given in the tables below: Table 4.1 Descriptive statistics from 2008 to 2014 Year Inflation rates (%) Unemployment rates (%) 2008 4.5 5.7 2009 3.3 7.7 2010 2.2 7.9 2011 3.6 8.1 2012 2.3 8.0 2013 2.3 7.6 2014 2.0 7.2 Source: Rate Inflation, www.rateinflation.com/inflation-rate/uk-historical-inflation-rate Year Minimum Hourly Wage 2008 5.52 2009 5.73 2010 5.8 2011 5.93 2012 6.08 2013 6.19 2014 6.31 4. Empirical Model(S) and Its (Their) Estimation A prerequisite for the use of this time series data is the knowledge of estimation problem of the estimation process (Mankiw, 2009, p. 65). The time series data analysis leads to both regression measurements. There are three useful assumptions that have to be observed during this computation. They are known as the Gauss Markov conditions, namely; a constant expected value over time, a constant variance over time, a covariance of a sole length of the lag, which is not dependent of time. If these three assumptions are met, it may be referred to as stationary, and the ordinary least square estimation provides right results In this analysis we use autoregressive process whereby we assume our first value is as a result of the previous period meaning there is an error term that has to be included in that process. This also means autocorrelation should be tested. Since there has to be a constant level of unemployment in the economy which is influenced by the inflation rate, it has to be accounted for. This constant unemployment level is the constant value (δ). In addition to lack of constant variance, there is also no constant expected value. In order to detect this, we use an accurate method represented by the Dickey-Fuller test which uses the OLS estimator method, formulating a null hypothesis of non-stationary and the opposite hypothesis as stationary. This enables the difference between the stationary and the non-stationary hypotheses to be found. Given that we are now in a position to make the non-stationary process identify, the next step involves trying to correct the data so as to make it usable. The time series subsequently consists of random process (δ). If the first difference is not stationary a further difference is formed and tested again until the desired stationary process is achieved. A further differentiation is done such that desired equations are made When Yt and Xt are integrated in this order, the residual in the equation should be integrated in order zero. One explanation for the deviations from the long run can be found in the different approaches of market imperfections. Furthermore, they also reflect why it is important to differentiate between short and long run effects of macroeconomic models. To get the co-efficient of βo and β1 matrix method is used. Empirical findings A first detailed analysis of the time series data shows that inflation as well as unemployment are non-stationary processes in both cases which lead to a high probability of them giving a problem of spurious regression. Regression Statistics Multiple R 0.512931 R Square 0.263099 Adjusted R Square 0.078873 Standard Error 0.313944 Observations 6 ANOVA df SS MS F Significance F Regression 1 0.140758 0.140758 1.428135 0.298079 Residual 4 0.394242 0.098561 Total 5 0.535 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7.086367 0.569918 12.434 0.000241 5.50402 8.668714 5.50402 8.668714 0.045 25.36177 21.22242 1.195046 0.298079 -33.5611 84.28464 -33.5611 84.28464 Regression Analysis between Inflation and Unemployment Rates From the above regression analysis between inflation and unemployment rates there exist a causal relationship between inflation rates and unemployment rates. R= 0.512931, this is the coefficient of correlation which is positive. Whereas R2, which is the coefficient of determination= 0.263099. R2 is 26%, meaning X variable explains only 26% of changes in X. Contrary to the theory of natural rate of unemployment, there is a long run relationship between the two variables. Regression Statistics Multiple R 0.596614433 R Square 0.355948781 Adjusted R Square 0.194935976 Standard Error 0.203078178 Observations 6 ANOVA df SS MS F Significance F Regression 1 0.09117 0.09117 2.210686 0.21126 Residual 4 0.164963 0.041241 Total 5 0.256133 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.540761615 0.368659 17.74206 5.93E-05 5.517201 7.564322 5.517201 7.564322 0.045 -20.4112719 13.72798 -1.48684 0.21126 -58.5262 17.7037 -58.5262 17.7037 Regression Analysis between Inflation and Minimum Hourly Wage Rate The table above represents the regression analysis of inflation and the minimum wage rate in the UK in the period 2007 and 2008. There exists a causal relationship of 0.596614433 as represented by R. The level of inflation in the country accounts for 35.5% of the changes that have occurred in the minimum wage rate in the period. In order to exclude the possibility of autocorrelation and ensure the robustness of the estimate, a dynamic estimation of the model was also be tested, this involved determining the autocorrelation of the time series data of inflation. This was established by performing a correlation analysis on the yearly inflation rates. The result reflected a correlation of 0.45 between the per annum rates. This means that the inflation rate is moderately serially correlated. A proof that the inflation in the UK is not stationary. 5. Conclusions The aim of the study was to determine the effects of persistent inflation rate on the unemployment and wage rates. To establish inflation indeed affects the two areas of the economy a causal relationship had to be established between the variables. The research depended on the hypothesis formulated by A.W. Philips which is represented by the Philips curve. The Philips curve illustrates the existence of an inverse relationship between inflation and the unemployment rate. The study also wanted to confirm if this hypothesis which has been disputed over time exists in the case of UK. Time Data on inflation, unemployment and time series were used to carry out regression and correlation analysis. Inflation and Unemployment Rates From the findings of the regression analysis it was determined that there exist a relationship between inflation rate and unemployment levels. But this relationship is not as it is expected from the Philips curve which require the relationship to be negative. In the UK case the relationship between inflation and unemployment can be described as one with a fair positive correlation. Meaning as inflation rate increases also the unemployment rate increases. This means that persistent inflation has a negative impact on the productivity of the country because it reduces the labour demand of the country and consequentially low production levels. Inflation and the Minimum Wage Rate It can be seen from the regression analysis carried out between inflation and minimum wage rate that there exist a positive correlation between the two variables. In a similar way like the case of unemployment the correlation is fairly positive. An increase in the inflation rate will lead to a moderate increase in the minimum wage rate. In both scenarios the change in inflation only contributes to a moderate change in the variables. This means that the Philips curve and hypothesis are only useful in understanding the short run dynamics of inflation. In the long run other factors come to play. The trend of unemployment levels increasing with increased inflation can be explained by recent events which took place such as increase in oil prices in 2008, the financial crisis which hit the country in the period between 2008 and 2011 (Bhar and Mallik, 2012, p. 1339). This trend also further confirms the relatively high degree of rigidities in the UK. From the autocorrelation analysis carried out, it shows that the level of persistence in the inflation rates in UK is moderately low. This means that the macroeconomic policies in place have the ability to accommodate the price shocks that are experienced in the economy though not to a larger extent. The analysis also indicates the inflation rates experienced are interdependent, and so there is a likelihood of inflation persistence to occur in the future, even though, the current inflation rate is at zero. Currently, the country is experiencing both lowest inflation and unemployment rates in the period. This further shows the positive relationship between the two variables. It will be important for the policy makers to establish appropriate measures to ensure the inflation rate is maintained at the set target all below the target over time. This is to keep unemployment levels down and improve the productivity of the country and, as a result, enhance the economic development of the country. References Arnold, R. A., 2007. Macroeconomics. Mason, OH, Thomson South-Western. Barro, R. J., 2008. Macroeconomics: a modern approach. Mason, Thomson. Benati, L., and Vitale, G., 2007. Joint estimation of the natural rate of interest, the natural rate of unemployment, expected inflation, and potential output. Frankfurt am Main, Germany: European Central Bank. Bhar, R., and Mallik, G.,2012. Inflation uncertainty, growth uncertainty, oil prices, and output growth in the UK. Empir Econ, 45(3), pp.1333-1350. Carlberg, M., 2008. Inflation and unemployment in a monetary union. Berlin: Springer. Croushore, D. D., 2007. Money and banking: a policy-oriented approach. Boston, MA, Houghton Mifflin Co. Dwivedi, D. N., 2010. Macroeconomics: theory and policy. New Delhi, Tata: McGraw Hill Education Pte Ltd. Forder, J., 2014. Macroeconomics and the Phillips curve myth. Oxford: Oxford University Press. Friedman, M., 2007. Price theory. New Brunswick, NJ, Transaction Publishers. http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk &AN=395716. Gali, J., 2008. Monetary Policy, Inflation, and the Business Cycle an Introduction to the New Keynesian Framework. Princeton: Princeton University Press. http://public.eblib.com/choice/publicfullrecord.aspx?p=475858. Jha, R., 2008. Contemporary macroeconomic theory and policy. New Delhi, Wiley Eastern Ltd. Mankiw, N. G., 2009. Principles of macroeconomics. Boston:  Cengage Learning. Mishkin, F. S., 2007. Monetary policy strategy. Cambridge, Mass. [u.a.], MIT Press. Tucker, I. B., 2008. Economics for today. Mason, Ohio: South-Western Cengage Learning. Tucker, I. B., 2010. Macroeconomics for today. Mason, OH, South-Western Cengage Learning. Tucker, I. B., 2014. Macroeconomics for today. Mason, OH: South-Western Cengage Learning. Appendix: Relevant Computer Output and Figures Table 1: Inflation rates in UK Year jan feb mar apr may jun jul aug sep oct nov dec ann 2015 0.3% 0% -0% 2014 1.9% 1.7% 1.6% 1.8% 1.5% 1.9% 1.6% 1.5% 1.2% 1.3% 1% 0.5% 1.5% 2013 2.7% 2.8% 2.8% 2.4% 2.7% 2.9% 2.8% 2.7% 2.7% 2.2% 2.1% 2% 2.5% 2012 3.6% 3.4% 3.5% 3% 2.8% 2.4% 2.6% 2.5% 2.2% 2.6% 2.6% 2.7% 2.8% 2011 4% 4.3% 4.1% 4.5% 4.5% 4.2% 4.5% 4.5% 5.2% 5% 4.8% 4.2% 4.5% 2010 3.4% 3% 3.4% 3.7% 3.3% 3.2% 3.1% 3.1% 3% 3.1% 3.2% 3.7% 3.3% 2009 3% 3.1% 2.9% 2.3% 2.2% 1.8% 1.7% 1.5% 1.1% 1.5% 1.9% 2.8% 2.2% 2008 2.2% 2.5% 2.4% 3% 3.3% 3.8% 4.4% 4.8% 5.2% 4.5% 4.1% 3.1% 3.6% 2007 2.7% 2.8% 3.1% 2.8% 2.5% 2.4% 1.9% 1.7% 1.7% 2% 2.1% 2.1% 2.3% 2006 1.9% 2.1% 1.8% 2% 2.2% 2.5% 2.4% 2.5% 2.4% 2.5% 2.7% 3% 2.3% 2005 1.6% 1.6% 2% 1.9% 1.9% 1.9% 2.4% 2.3% 2.4% 2.3% 2.1% 1.9% 2% Source: Office of national statistics. Table 2: Autocorrelation on inflation data Year Inflation Rates 2008 4.50% 2009 3.30% 2010 2.20% 2011 3.60% 2012 2.30% 2013 2.30% 2014 2.00%   0.045 0.045 1 Source: Computer Generated Table 3: Regression Analysis: Inflation and Unemployment. Regression Statistics Multiple R 0.512931 R Square 0.263099 Adjusted R Square 0.078873 Standard Error 0.313944 Observations 6 ANOVA   df SS MS F Significance F Regression 1 0.140758 0.140758 1.428135 0.298079 Residual 4 0.394242 0.098561 Total 5 0.535         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7.086367 0.569918 12.434 0.000241 5.50402 8.668714 5.50402 8.668714 0.045 25.36177 21.22242 1.195046 0.298079 -33.5611 84.28464 -33.5611 84.28464 Source: Computer Generated. Table 4: Regression Analysis: Inflation and wage rate. Regression Statistics Multiple R 0.596614433 R Square 0.355948781 Adjusted R Square 0.194935976 Standard Error 0.203078178 Observations 6 ANOVA   df SS MS F Significance F Regression 1 0.09117 0.09117 2.210686 0.21126 Residual 4 0.164963 0.041241 Total 5 0.256133         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.540761615 0.368659 17.74206 5.93E-05 5.517201 7.564322 5.517201 7.564322 0.045 -20.4112719 13.72798 -1.48684 0.21126 -58.5262 17.7037 -58.5262 17.7037 Source: Computer Generated. Read More