Fatores que influenciam a taxa de desemprego

Abstract

The main objective of this paper is to show the socioeconomic reality observed in several European countries, in particular to determine the factors that most influenced the evolution of the unemployment rate in Europe from 2006 to 2014. The National Institute of Statistics (INE) defines as an individual between the ages of 15 and 74 who, during a reference period, has no paid work or any other, is able and available to work immediately and looking for a job. The unemployment rate is therefore the indicator that measures the level of unemployment in an economy. This is calculated based on the quotient between the population and the active population. It should be noted that the expression being unemployed suggests an individual who is in a situation in which he has no official relationship with any employer and has no sources of income. Unemployment is one of the biggest problems at European level. The labor market is, in most countries, stagnant or even declining due to the low dynamism of the European economy and the greater volatility of the financial markets. However, it should be noted that although the unemployment rate is similar in different countries, this does not necessarily mean that the living conditions are the same. According to the latest statistics, Portugal has the sixth highest unemployment rate among the 28 countries of the European Union. One of the main causes for the level of unemployment is the number of young unemployed. As regards Europe as a whole, Spain is the country with the highest unemployment rate. In July 2016, the same rate at European level was the lowest ever. In the context of this article, data from 8 European countries (Belgium, Cyprus, Denmark, Spain, Estonia, Italy, the Netherlands and Portugal) were extracted from the PORDATA online platform, which, due to their geographical positions, showed different socio-economic realities. In order to reach the proposed objective, a longitudinal analysis of the data was carried out with R Studio software, since they configure observations of several differentiated factors for the eight countries over time. The variables studied were the unemployment rate, the proportion of the foreign population in the resident population, the population density, the gross emigration rate, the gross immigration rate, the household saving rate, the material deprivation of unexpected expenses, the population at risk of poverty, the gini index, the ratio of R & D people, the percentage of the population that does not ensure a healthy diet once in 2 days, and labor productivity. Several models of linear regression, fixed effects and random effects were applied in all its aspects, to determine the explanatory variables in the different models. After this first approach, it was concluded that only the variables crude gross immigration and material deprivation of payment of unexpected expenses are those that have a statistically significant impact on the unemployment rate. The necessary tests for the validation of the models were carried out and a comparison of the models was made, in order to determine the most appropriate. From the various models estimated, it was concluded that the Linear Regression model is the most indicated and that fills all the validation assumptions of the model. Moreover, it highlights a stable relationship between the dependent variable - unemployment rate - and the independent variables - gross immigration rate and material deprivation of payment of unexpected expenses.