How Unemployment And Inflation Affect Gdp In Eu Economics Essay

Normally when the price of a good we want to buy goes up, it affects us. But why does the price increase? Is it because supply is lower than demand? Or, was it an increase in the oil price that affected the price? In order to answer these questions, we need to turn to macroeconomics.

Economists attempt to forecast economic conditions to help consumers, firms and governments make better decisions.

Consumers want to know how easy it will be to find work, how much it will cost to buy goods and services in the market, or how much it may cost to borrow money.

Businesses use macroeconomic analysis to determine whether to expand production or not. Will consumers be able to buy the products

A Government needs macro analysis when preparing a new budget, creating taxes, deciding on interest rates and making policy decisions.

Macroeconomic analysis mostly focuses on three things: Gross domestic product (GDP), unemployment and inflation.

Introduction

Normally when the price of a good we want to buy goes up, it affects us. But why does the price increase? Is it because supply is lower than demand? Or, was it an increase in the oil price that affected the price? In order to answer these questions, we need to turn to macroeconomics. Economists attempt to forecast economic conditions to help consumers, firms and governments make better decisions. Consumers want to know if it is easy to get a job, how much it will cost them to buy goods and services in the market, or how much it may cost them to borrow money. Businesses use macroeconomic analysis to determine whether to expand production or not. Will consumers be able to buy the products? A Government needs macro analysis when preparing a new budget, creating taxes, deciding on interest rates and making policy decisions. Macroeconomic analysis mostly focuses on three things: Gross domestic product (GDP), unemployment and inflation.

For these reasons, this empirical project will investigate how changes in the unemployment and inflation affect the gross domestic product per capita of European countries during the year of 2005.

The starting point of this work is the Okun’s Law model. This work will describe briefly the theory and show how unemployment and inflation are related to GDP.

The work explains the relevance of my research to the theme and briefly outlines the objectives and hypothesis to be tested. The expected results will also be mentioned.

After discussing the data, section 3 presents the econometric methodology used in the process and briefly describes the statistical model, tests and steps. Some time will then be spent, on the comments and interpretation of the results obtained by the tests and models.

Finally, provides the conclusions and highlights some topics for further research.

Literature review

In a research note, Alliance Bernstein economist Joseph Carson says job losses in prior downturns have been roughly proportional to the decline in gross domestic product. But in the current recession, the proportion of jobs lost is running about a third greater than the drop in real GDP.

The correlation between GDP growth and unemployment is called Okun’s Law, after the late economist Arthur Okun who documented it in the 1960s. But the numerical relationship that Okun estimated – and other economists have since refined – has broken down. His original estimate suggested about a 3% decline in GDP for every 1% increase in unemployment. Before joining the Fed, Ben Bernanke, working with Andrew Abel, figured more recent suggested about a 2% decrease in output for every 1% increase in unemployment.

Walterskirchen (1999), on “The Relationship between Growth, Employment and Unemployment in the EU” analyses the macroeconomic links between economic growth and the labour market. Two methods were adopted: time-series analysis for individual EU countries and international cross-country analysis for the period 1988-98.

Empirical test showed that there is still a strong and positive correlation between GDP-growth and the change in employment. But employment, of course, will rise only if economic growth rates are outstripping productivity gains.

He found and concluded that there is a strong negative correlation between real output growth and the change in the unemployment rates in time-series and in cross-country analyses. The simple-minded argument that there cannot be a negative relation between economic growth and unemployment, because both are rising in the long run, is of course completely wrong.

Arai, Kinnwall and Thoursie (2002), developed a model investigation conducted on “Cyclical and Causal Patterns of Inflation and GDP Growth”.

The views that high inflation impairs GDP growth are investigated using annual data for 115 countries over the period 1960-1995. They estimated dynamic panel-data models of the effects of inflation on growth taking into account that countries are heterogeneous and that there were time-specific symmetric shocks, as well as endogeneity of inflation and dynamics of GDP growth. They found no evidence supporting the view that inflation is in general hurtful to GDP growth. On the other hand, there is a negative correlation between contemporaneous intra-country inflation and growth for periods characterized by positive oil price shocks.

Economic theory

In economics, Okun's law is an empirically observed relationship relating unemployment to output. It states that for every 1% increase in the unemployment rate, a country's GDP will be an additional roughly 2% lower than its potential GDP. Another version describes the relationship between quarterly changes in unemployment and quarterly changes in real GDP. The name refers to economist Arthur Okun who proposed the relationship in 1962.

Okun's law is more precisely called "Okun's rule of thumb" because it is primarily an empirical observation rather than a result derived from theory. Okun's law is approximate because factors other than employment (such as productivity) affect output. In Okun's original statement of his law, a 3% increase in output corresponds to a 1% decline in the rate of unemployment; a .5% increase in labour force participation; a .5% increase in hours worked per employee; and a 1 % increase in output per hours worked (labour productivity). Okun's Law states that a one-percent decrease in unemployment is associated with two percentage points of additional growth in real GDP.

One implication of Okun's law is that an increase in labor productivity or an increase in the size of the labor force can mean that real net output grows without net unemployment rates falling.

Okun's law can be written as:

(\overline{Y}-Y)/\overline{Y} = c(u-\overline{u}), where:

\overline{Y}is potential output or GDP at full-employment

Y is actual output

\overline{u}is the natural rate of unemployment

u is actual unemployment rate

c is the factor relating changes in unemployment to changes in output

It is difficult to use in practice because \overline{Y}and \overline{u}can only be estimated, not measured. A more used form of Okun's law, known as the difference or growth rate form of Okun's law, relates changes in output to changes in unemployment:

\Delta Y/Y = k - c \Delta u\,, where:

Y and c are as defined above

ΔY is the change in actual output from one year to the next

Δu is the change in actual unemployment from one year to the next

k is the average annual growth rate of full-employment output

The Okuns can also be connected to inflation.

Macroeconomists try to forecast economic conditions to help consumers, firms and governments make better decisions. This project will investigate the following economic issues:

How much changes in unemployment affect GDP per capita

What is the effect of an increase in the overall price on the GDP per capita

Are inflation and unemployment correlated

How are these economic indicators related to each other

Data

The data set included 40 european countries over the year of 2005. I chose Europe due to nationality and also because European countries have almost the same level of development. The data came primarily from IMF World Economic Outlook (WEO) with the GDP per capita based on the purchasing power parity, unemployment rate measured in percentage of total labour force and inflation measured by the annual percentage change in the CPI index. This is widely accepted as a reliable source of information.

The selected 40 countries are presented in the table 2.

Gross Domestic Product based on purchasing power parity in ($), the inflation is in annual % growth; unemployment is in annual % of economically active population. I could have used percentage of real GDP per capita growth but because I am using cross sectional data, and not time series, it seemed more appropriate. Mean unemployment rate is about 8% and mean inflation rate is about 3.8%. The lowest unemployment rate was found in Belarus (1%) and the highest in Macedonia (34.9%). Lowest inflation rate was found in Poland (0.7%) and highest in Russia (10.9%). The Model and empirical results To begin with the methodology it is important to recall the main purpose of the research. The aim is to measure by how much changes in unemployment and inflation affect GDP per capita of European countries. To do that, GDP per capita will be the dependent variable, and unemployment and inflation will be the independent variables. My model is: GDP = β0+ β1unemployment+ β2inflation + ut Where GDP is the Gross Domestic Product based on purchasing power parity (PPP), Unemployment is the unemployment rate, Inflation is the annual inflation rate, exports is the total exports and ut is a disturbance term (other factors). I will carry out tests using STATA 11 stating the appropriate hypothesis to be tested. Firstly I will test for multicolinearity. Multicolinearity happens when 2 or more regressors are highly correlated. In the presence of multicolinearity, the estimate of one regressor's impact on y while controlling for the others tends to be less precise than if predictors were uncorrelated with one another. I will use a correlation matrix obtained from STATA to see the correlation between them. Secondly, I will do a T test for testing hypothesis relating to the regression coefficients. The statistics t-test allows us to determine a p-value that indicates how likely we could have gotten these results by chance. By convention, if there is a less than 5% chance of getting the observed differences by chance, we reject the null hypothesis (H0) and say we found a statistically significant difference between the two groups. Then, I will do an F test. = Finally, I will do the GoldFeld-Quandt test for heteroscedacity. The Goldfeld–Quandt test is a test for this type of heteroscedasticity. The sample is divided into three ranges containing the 3/8 of the observations with the smallest values of the X variable, the 3/8 of the observations with the largest values, and 1/4 in the middle. If the disturbance term is homoscedastic, there should be no systematic difference between RSS1 and RSS2. Empirical results In the table 1.5 the correlation between GDP per capita, unemployment and inflation is examined carefully. The correlation matrix measures the two way relation between GDP, unemployment and inflation. It can be seen that both unemployment and inflation share a negative relationship with GDP per capita. This negative relationship holds consistent with traditional Keynesian theory, Stockman’s neoclassical model and some endogenous growth theories, which imply that higher inflation, is negatively correlated to growth. Unemployment and inflation have a weak negative relationship, which means that there will be no risk of multicolinearity in this regression. The true model is: GDP= 42365.98 – 992.4unemployment - 3082.9inflation In the first regression (table 1.1), the results indicate that an increase of unemployment rate by 1% unit decreases GDP per capita by$992.4. On the other hand, an increase in the inflation rate by 1% unit decreases GDP per capita by \$3082.9.

R2 (=0.5199) indicate that the 2 independent variables (i.e. unemployment and inflation) account for 51.99% of the variation of the dependent variable (i.e. GDP per capita). If the model were derived from the population rather than a sample it would account for approximately 0.0259% less variance in the dependent variable as the adjusted R2 is less than R2 by this amount (0.5199– 0.4940=0.0259). Adjusted R2 is more accurate estimate of the proportion of variance explained for the sample that has been used to generalise to other populations and studies, particularly when comparisons are being made which have included either fewer or more variables.

T test

To test whether or not the slope coefficient or the intercept differ significantly from zero, the following hypothesis are employed at 1% significance level:

H0: coefficient of unemployment (B1) = 0

H1: coefficient of unemployment (B1) ≠ 0

Calculated t= (-992.39 – 0)/ 253.32= - 3.92

H0: coefficient of inflation (B2) = 0

H1: coefficient of inflation (B2) ≠ 0

Calculated t= (-3082.918 – 0)/ 572.28= -5.39

Critical t statistic at 1% =2.704

Both calculated t for B1 and B2 are greater than the critical value ate 1% level of significance. We reject the null hypothesis.

F test

H0: B1=B2 = 0

H1: at least one coefficient ≠ 0(i.e. B1≠0 or B2≠0)

α=0.01

Calculated F(2, 37)=( 0.5199/2)/ [ (1-0.5199)/37)]= 20.04

Critical value of F (2, 37) = 5.27

We reject H0 since calculated F is greater than the critical value at 1% significance level

Goldfeld-Quandt test for heteroscedasticity

After sorting the data in ascending order, the sample was divided in three subsamples omitting the middle one.

Central sample: ¼ of the observations in the middle of unemployment variable

First subsample: 3/8 of the observations with the smallest values

Third subsample: 3/8 of the observation with the largest values

OLS regression models on the first and third subsample were estimated according to table 1.4 and 1.5. we state the hypothesis:

H0: homoscedasticity of the error term (the variance is constant)

H1: heteroscedasticity of the error term( the variance is not constant)

F(13,13)=7

Since the calculated F-ratio of 0.366 is lower than the critical value at the 1% level of significance, then the null hypothesis of homosedasticity is not rejected at the mentioned level of significance. Therefore, the liner specification is definitely homoscedastic.

Conclusion

In general, the findings reveal that there is a negative relationship between inflation and GDP per capita that is statistically significant beyond 1% significance level and of an economically interesting magnitude.

The performance of the economy is important to all of us. We analyze the macro economy by primarily looking at national output, unemployment and inflation. Although it is consumers who ultimately determine the direction of the economy, governments also influence it through fiscal and monetary policy.

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