Abstract:
This project attempted to create the model to indicate the significant factors influencing the employment rate in countries. As the author was sceptical about the traditional macroeconomic concepts the new economic geography theory approach is used. European countries are assessed in this project as Europe has a flexible labour mobility and is more convenient to assess the impact of language speaking ability in labour market than the USA where majority of people speak English. The Employment Rate Index (ERI), the index of employment, was based on the exponential of the employment rate subtracting the minimum employment rate in the data and then multiplied with 10 in order to make a symmetric variable (The raw data for the employment rate was very asymmetric). There are two explanatory variables are used; one indicates the employment opportunity in the other countries, and the other indicates the advantage to speak English in trade in both with other countries and within a country. Generalised Least Squares (GLS) estimates showed these two variables are significant enough to explain about the employment rate in a country.
1. Introduction:
This research was carried out to investigate to explain how the employment rate changes in terms of the New Economic Geography theory approach.
2. The reason why the data sets in European countries are used:
Europe has a flexible labour mobility as same as the USA unlike Asia and South America where people rarely change their job in their life. Europe is more convenient to assess the impact of language speaking ability in labour market than the USA where majority of people speak English. The global research encounters with lack of data set for the employment rate figure.
3. The Simultaneous Equation Problem in the traditional Macroeconomic theories:
The traditional macroeconomic theories claim that the employment rate is negatively correlated with the real wage. However, this assumption encounters with the simultaneous equation problem. The real wage rate is highly affected by the employment rate itself. For example, when the employment rate decreases, the real wage starts being depreciated in order to encourage employers to employ labour more. A part of Keynesian wage theory claims that when the employment rate decreases, the nominal wage should increase in order to encourage employees to work more.
4. The significance of using Geographic data:
The best variable explaining the unemployment rate is considered as the Gross Domestic Product (GDP). There is a high demand for productions when the GDP rises so that the demand for labour rises whilst there is a low demand for productions when the GDP falls so that the demand for labour falls. Nonetheless, John Maynard Keynes (1936) claimed that the productivity and the demand of labour is not always positively correlated. When the productivity rises, the production method can alter the labour incentive to the capital incentive. In addition, whenever the employment rate (or any variable representing it) is regressed on the GDP, it causes the endogeneity problem. Therefore, the GDP hardly becomes the best explanatory variable.
Alternatively, geographical aspects are recommended to be used as explanatory variables. Any variables used in economics tend to be measured by a common measure such as money. All variables introduced in IS-LM model are correlated each other. For example, the investment rate, the consumption rate, and the money supply are highly correlated with the productivity, and the productivity is highly correlated with these variables as well. On the other hand, the variables representing geographical aspects are not affected by any economic data generally speaking although these geographic data may affect the economic data. For instance, the geographic distance between cities and latitude (not used in this project but commonly used in the NEG theory) are not modified by any social scientific data sets.
Instead of analysing by the real wage effect inside the countries, the real wage effect in outside the countries is used to analyse the employment rate. Focusing on the graph below, rise in the real wage implies either decrease in the labour supply or increase in the labour demand. When the labour supply decreases in a country, there is a lack of labour supply or labourers in this countries are reluctant to work anymore. Therefore, there is more employment potential for immigrant labourers from outside this country. When the labour demand increases in a country, there is also more employment potential for immigrant labourers from outside this country. By contrast, fall in the real wage implies the opposite effect to the rise in the real wage by referring to the graph below.
This project used the matrix algebra (Explained in Chapter 6) to explain the employment potential in the other countries. The variable representing this is called the Wage Potential Index (WPI) in this project. In order to show this potential, the minimum distance between capital cities is used. As the countries are closer each other the effect of the real wage on employment in a country is stronger whilst as the countries are farer each other the effect of the real wage on employment in a country is weaker. The matrix algebra enables to asses this effect of all the countries surrounding the country assessed by this analysis simultaneously.
5. Shared Language provides more employment opportunities
The NEG theory also uses a variable (variables) representing the human capital index (indices). This project focused on the effect of shared language in both an domestic and international trade. For both non-skilled and skilled workers, language skill is necessary to find a job opportunity. This project focused on English as it is the most commonly used shared language as a shared language in international academic and business activities. As many people speak English in a country, people there find more employment opportunities in the other countries trading with this country. As both a country and the other country trading with have more people speaking English it is more convenient to trade each other.
6. Formulae used:
* The Annual Inflation Rates are the average of the five years.
7. Regression Analysis:
The time periods used are 1995, 2000, and 2005. The countries used are United Kingdom, Ireland, Netherlands, Belgium, Luxembourg, France, Switzerland, Spain, Portugal, Germany, Austria, Czech Republic, Slovak Republic, Italy, Malta, Slovenia, Greece, Cyprus, Finland, Sweden, Norway, Denmark, and Iceland. The reason why the number of time periods and countries is restricted is due to the lack of data sets in some other countries not introduced in this project. But, the author's previously carried out research on the real GDP per capita in a global data showed it did not make a difference between using all countries in a globe and using some representative of the economic regions in a globe. Therefore, the author was confident enough to use the data set able to use as much as possible to analyse the employment in this project.
The Generalised Least Squares (GLS) was used because one of the explanatory variable, the LPI, does not vary across the time (The author could not find a data for this varying across the time), the fixed-effect estimator based the Ordinary Least Squares (OLS) could not be used due to the multicollinearity between the dummy variables used in the OLS and the variable not varying across the time. The pooled OLS should not be used as the unit specific effect in the countries is significant. There is a certain level of the employment rate fixed over the time period. Therefore, the unit specific effect is included in the dummy variable "inside the error term". The regression result is as follows:
Both the WPI and the LPI are significant and positively correlated. The Breusch-Pagan test indicates that the random effect estimate based on the GLS should be used, and the Pooled OLS is not appropriate to use. The Hausman test indicates that the hypothesis claiming there is not an endogeneity problem cannot be rejected. According to what this table shows, the GLS estimates are essential to do this regression, and there is not an endogeneity problem so that this regression analysis is consistent.
8. Conclution:
Having analysed the employment rate, the real wage in the other countries, which represents the potential for labourers in one country to be employed, the geographical figures (the geographical distance represented in this project), and learning English are significant factors influencing the employment rate. This project proved that the NEG theory is able to explain the employment rate in labour market.
Data Sources:
Gleditsch and Ward (2001) Minimum Distance Data // Kristian Skrede Gleditsch
http://pwt.econ.upenn.edu/php_site/pwt_index.php
http://www.imf.org/external/pubs/ft/weo/2010/01/weodata/weoselgr.aspx
http://en.wikipedia.org/wiki/List_of_countries_by_English-speaking_population
