The Effect of GDP per Capita on Employment Growth in Germany, Austria and the Czech Republic: Macroeconomic Analysis

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1 Review of European Studies; Vol. 7, No. 11; 2015 ISSN E-ISSN Published by Canadian Center of Science and Education The Effect of GDP per Capita on Employment Growth in Germany, Austria and the Czech Republic: Macroeconomic Analysis Mansoor Maitah 1, Daniel Toth 2 & Elena Kuzmenko 1 1 Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences Prague, Czech Republic 2 Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Czech Republic Correspondence: Mansoor Maitah, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamycka 129, Prague , Czech Rep. Maitah@pef.czu.cz Received: April 23, 2015 Accepted: June 30, 2015 Online Published: July 30, 2015 doi: /res.v7n11p240 URL: Abstract Unemployment rate in the European Union (EU) is one of the biggest social and economic problems nowadays. Comparison among individual EU countries becomes quite difficult. This problem is specific for not just economically weaker countries like Greece, but applies also to the Czech Republic, where the employment rate has been manifesting stagnation in recent years. The Czech Republic has been below its employment development potential for a long time. The experience of Austria and Germany, the two neighboring countries, can serve as a good example of overcoming this situation. The Austria is culturally, geographically and demographically is closer to the Czech Republic. From a theoretical standpoint, the objectives of the present paper are focused on verification and extension of the Okun s law. The prominent American economist Arthur Okun asserted that GDP growth has an unemployment-reducing effect. He found that a two percent increase in GDP results in a 1 percent decline in unemployment. Analytical studies, in turn, show that a two percent increase in GDP per capita leads to a one percent growth in employment rate. The latters can be considered as the Okun s law extension. The empirical analysis of Germany and Austria examples, where the employment development is stimulated namely through special tools of GDP per capita growth, has a practical impact as well. Keywords: Unemployment, employment policy and social development, GDP, Austria, Czech Republic, Germany 1. Introduction Austrian employment-related issues were investigated by a number of authors in a number of works. One of the most noteworthy among them is the study of Tamesberger (2014), in which the author gave a multifactorial explanation of youth unemployment in Austria. The author asserts that one of the biggest problems in European society is the significantly high amount of unemployed youth. Thank to conventional political solutions and active employment policy that are currently practicing in Austria quite purposefully the latter proved to keep youth unemployment at a low level, even despite the financial and economic crisis. Thus we can consider the Austrian experience in this field as a prime example. It is empirically shown that active labor market policies in Austria are focused primarily on youth unemployment reducing. That is why the social group of Austrian young people is often referred to as a group in Safety net. For purposes of employment analysis Tamesberger (2014) suggests to use a larger number of economic indicators, arguing that namely active economic policy stimulates demand for labor in the economy. (Tamesberger, Leitgöb, & Bacher, 2014) Another very relevant study (Bell & Blanchflower, 2011) examines the youth employment in the light of an economic recession and its influence on the labor markets. The USA and the Great Britain served as a case study. Authors argued that young people, aged years, have suffered disproportionately during the recession. The analysis of the structure of youth unemployment was done on the basis of micro-data, providing evidence that the effects of youth unemployment have an impact on the increased costs to individuals and society in the future. Thus, the study covers social aspects as well. Though the effects of current policies are uncertain, the results of these studies provide us with an important argument for intervention and active employment policy. 240

2 One of the ways of fighting with unemployment by lowering interest rates was analyzed in the study of Tamesberger, Leitgöb and Bacher (2015). They argue in favor of rates reduction. (Tamesberger, 2015) Another aspect of employment problem, i.e. labor migration, was addressed by Bahna (2014) in his sociological study that complements existing analyses on this problem. In the focus was migration of social workers and caregivers from Slovakia to Austria, where the latter practiced the policy of foreign labor legalization. However, the main reason behind that was not Austrian policy, but economic crisis and consequent increased unemployment in the Slovak Republic. In addition, Austria provides higher earnings to caregivers in comparison to the Slovak Republic. The labor force outflow consists of highly experienced and valuable staff, what weakens the Slovak labor market (Bahna, 2014). The interrelation between unemployment and migration among the EU countries was investigated by Palat (2013) in his seminal study entitled International migration and unemployment in the established member states of the European Union. The emphasis was done on the analysis of the latter during the last financial and economic crisis with application of sophisticated statistical methods. Almost all countries (except Luxembourg and Austria) revealed a negative linear relationship between the tested indicators, though there were not statistically significant relationships. Based on these results, it can be argued that an evident correlation exists between net migration and unemployment rates in more than half of the countries. In turn indices of calculated correlations showed statistically highly significant results, usually in the destination country (e.g., in Germany, Great Britain, Luxembourg and Belgium). Statistically significant results can also be found in countries facing huge economic problems (Ireland, Spain and Italy). With the exception of Belgium, the chosen types of regression functions are redundant, i.e. have statistically significant correlation indices and the use polynomials of higher degrees. These calculations are not significantly better. Also the model was added by the analysis and calculations of the net migration volume and unemployment rate. (Palat, 2013) Another interesting research has been carried out by Boeri (2008), in which the author exposed an imperfect labor markets problem. Imperfect labor markets influence the behavior of both employees and employers. These imperfections are linked to minimum wages, legislation on employment protection, active labor market policies, working time regulations, collective bargaining, programs for early retirement, education and migration policies. (Boeri & Ours, 2008; Maitah & Urbankova, 2015) The major aim of the present being rest on time series analysis to forecast the development outputs of employment and other selected indicators in Germany, Austria and the Czech Republic in years. Another purpose is to suggest possible procedural changes in the implementation of tools for employment development and social development management. The research will be focused on demonstration the validity of the inverse relationship between GDP per capita growth (decline) and increase (decrease) in employment. The ratio of this relationship is investigated and verified empirically by calculations. As a result we can assert that GDP per capita growth by 2 % is associated with 1% increase in employment rate. This assertion can be verified empirically on German and Austrian data, resting on monitoring these two economic indicators interdependence. 2. Methodology Analytical tools employed in the present study are based on time series analysis. In this regard to conduct proper calculations it is necessary to use middle and long-term time series and their trends. Thus, ARIMA model can serve as an instrument for a quite precise prognosis. The obtained this way forecast is easy to interpret. Further tool that will be employed in the calculations is Exponential Smoothing of the ARIMA model. The data needed for the analysis were taken from Eurostat database. Time series form comparable figures, which are clearly arranged in time from the past to the present. A forecast is the output of the analysis. In other words time series analysis can be considered as a set of methods that is used to describe these chronologically arranged data with a purpose to predict their future development. Time series can be analyzed from different perspectives. We need to identify a number of cases or events that will be involved in calculations and when the time interval ends. In time series analysis a number of further indicators are included, such as frequency, cyclicality or pointer type. (David & Charles, 1994) The frequency is divided into short-term time series that are less than one year and long-term time series with an interval longer than one year. The data should be divided according to the type of monitored indicators and be distinguished, thus, between the time series in terms of primary and secondary characteristics. The calculations of forecasts are mathematically simple, what implies that we follow the rule: minimum number of variables in the equation, minimum number of extreme and inflection points. In graphic form, then, we can construct an equalization curve using one of the following functions: Linear (T t = a + bt); quadratic (T t =a + bt + ct 2 ); logarithmic (T t = a + b log t) exponential (T t = ab t ); inversing (T t = a + b/t) or cubic (T t = a + bt + ct 2 + dt 3 ). Selecting the LINTREND function corresponds to the empirical description. Structural parameters of the 241

3 LINTREND function are estimated using the least squares method, which implies minimizing the differences between the observed responses in a dataset and the responses predicted by the linear approximation (LINTREND) of the data. The smaller these differences are, the better the model fits the data. (y y, ) =min, (1) where y t = 1,..., n observed values of the time series, y t, t = 1,..., n expected (teoretical) values of observed parametrs, calculated by using some of the properties. (vypočtené pomocí některé z vlastností.) When constructing mathematical and statistical model estimation of the structural parameters of LINTREND function becomes a very important step. At the same time, it is it is equally important to include stochastic and dynamic phenomena into a model structure, which are also referred to as the degree of conformity. The latter characterizes the degree of conformity an established model with empirical data. Frequently used indicator of this calculation is the determination index I 2 : I =1 (, ) ( ) (2) where is an arithmetic average of the time series values y 1,..., y n. Determination index is a dimensionless number of the following kind: 0 I 2 1 (3) The model that reflects empirical phenomenon best has I 2 close to one. In case if the value of I 2 is close to zero it forces us to search a function that fits the model better. Therefore, it is necessary to select a function that expresses the maximum possible value of determination index I 2. This feature enables the function to capture the actual development of the studied phenomenon in the past best and most accurately. Thus, we can assume that this function will reflect the future development of the observed phenomena the same way. In addition to the index of determination the correlation index should be calculated as well: I= I v (4) Modern statistical software use additional criteria for choosing an appropriate trend for the model as well as various estimations of mean errors: Mean error (ME): ME = (, ) (5) Mean square error (MSE): MSE = (, ) (6) = Root mean square error (RMSE): RMSE = MSE (7) Mean absolute error (MAE): MAE = y y, (8) Mean percentage error (MPE): Mean absolute percentage error (MAPE): MPE =, MAPE = (9), (10) ARIMA (p, d, q) models are usually expressed by the equation. In theory ARIMA models are referred to as the most general class of models for predicting time series. Time series are stationary differential (if necessary). Models use nonlinear transformations. Random variables presented in time series are used in calculations. Then we can determine their statistical properties if all of them are constant over time. ARIMA model can be considered as a filter that seeks to separate signal from noise. The signal is then necessary to extrapolate into the future and get the required estimations. ARIMA forecasting is based on the equation for stationary time series. The equations are linear (i.e., regression model). Predictors consist of the MAS dependent variable or MAS forecast errors. Non-seasonal ARIMA model is classified as ARIMA (p, d, q) model, where: p is the number of autoregressive conditions, d is the number of non-seasonal differences for stationarity and q is the number of variables in the forecast. The model therefore includes the inclusion of errors in predictions. (Snyder, R. D., Ord, 242

4 J. K., & Koehler, A. B., 2001) Predictive equation is constructed as follows: first determine the difference Y, which means that: If d = 0: y t = Y t ; If d = 1: y t = Y t Y t-1 ; If d = 2: y t = (Y t Y t-1 ) (Y t-1 -Y t-2 ) = Y t 2Y t + Y t-1 second difference Y (d = 2 case) is opposed to 2 years. The first difference is in the form of discrete analog of the second derivative. It is a calculation of the acceleration development, rather than setting the trend. Y is the general form of a prediction. The equation is: Y t = μf1 + y t Fpyl p-θ1et-1... θqet-q. The moving average has the parameters (θ), which are defined so that their sign was negative equation. Actual figures can then be included in the equation. Parameters are indicated as AR (1), AR (2)... and the MA (1) by (2)... etc.... ARIMA model construction for Y begins with determining the order of differentiated variables (d). Series of data must be stationary, so we need to remove coarse features of seasonality. (Snyder, Ord, & Koehler, 2001; Garcia-Ferrer, & Del, 1992). 3. Results and Discussion The issues of employment in Austria and the Czech Republic are influenced and coordinated by corresponding Employment Policy manifested in a set of measures that aim to create a balance in a labor market, stimulate job openings for citizens and effectively utilize available labor force. The measurements of the Czech Research Institute for Labor and Social Affairs revealed that spending on state employment policy (SEP) is currently growing steadily: in 2002 the share of expenditures on the SEP accounted for 1.64% of GDP, and in 2010 this proportion was already 2.22%. The major trend in recent years can be however mainly characterized as an effort to adapt to the common European Union policy. The European Employment Strategy is based on the Europe 2020 strategy, which has three flagship initiatives with regard to employment, social affairs and inclusion. The agenda is as follows: to reach the 75% employment rate within the EU for all persons between 15 and 64 years old till the year The female employment rate should be at least 60% and the employment rate of people aged 55-64, at least 50%. The Europe 2020 strategy came into force in 2011 being primarily focused on employment growth and competitiveness of persons aged From our perspective, an active approach to employment policy in Austria and German is very evidential and can serve as an inspiration for the Czech Republic, since they are neighboring countries with a similar structure of the labor market. More over the Austrian population is as large as it is in the Czech Republic. (Toth, 2010) Employment rate in Austria is above average compared to the EU, representing the fifth place among the all EU-28 member states. Over the past 15 years, employment rate ranged between 66 and 74%. Thus, one can conclude that Austria is one of the few countries in the European Union that is well poised to meet the Europe 2020 aims. The chart given below provide graphical representation of quarterly employment rates of all persons in Austria at the age from 15 to 64 years, for the period from 1999 to Data were obtained from Eurostat. Figure 1. Employment rate in Austria Description: The source of all these figures in this article came from Eurostat and authors calculations. 243

5 Q1 Q2 Q3 Q4 Figure 2. Seasonal employment rate in Austria The biggest fall in Austrian employment was recorded between 2003 and However, just one year later the steady increasing trend was observed, employment gradually grew and during the next 4 years it increased by 5%. The fluctuations during the last years have taken place primarily due to seasonal employment ups and downs. To obtain more accurate results, the Austrian employment rate in individual quarters is presented in the following below figure. Seasonal fluctuations are visible, but the overall upward trend is evident as well. It can be seen from the figure that the highest employment rates were achieved in the third quarter. Conversely, the lowest employment rates are more common for winter seasons. The biggest difference between the levels of employment was observed in The border values then exceeded 3.5%, while the average difference was below 2%. Employment of women in Austria is also very high. According to the document of the Europe 2020 strategy, women s employment should be above 60%. We can see that this aim has been already achieved in Austria in 2002, Q2. Also there can be seen minor fluctuations between quarters compared to the total employment rate, what can be explained by less seasonal work available for women Q1 2000Q2 2001Q3 2002Q4 2004Q1 2005Q2 2006Q3 2007Q4 2009Q1 2010Q2 2011Q3 2012Q4 2014Q1 Figure 3. The employment rate of women in Austria, % share The next Figure 4 shows the employment of the older generation in Austria. The trend is slightly upward. In the past 6 years the employment rate holds above 40%. If development continues in future same way as before, Austria could meet the objectives of the Europe 2020 document in two years. 244

6 Figure 4. The employment rate of persons aged in Austria, % share To calculate forecasts of employment for the population aged was used ARIMA model (1, 0, 0), (2, 0, 0) with 70% probability and seasonal shift of 12 periods The forecast was calculated for 26 quarters till Q The Figure 5 indicates the calculated parameters of the function. The residue indicates the difference between calculated and actual (observed) values of the analyzed phenomenon. The residue also indicates the magnitude of the error, which can be committed in the relevant point of estimating. In the following chart the fluctuations in the predicted values are compared to the actual ones. The biggest residue was revealed in observation no. 21 that is in Here the unexpected sharp decline in employment was registered. Forecasts for the period from 2014 to 2020 years indicate with 70 % probability rather stagnating employment outcomes. For example, the biggest difference between the predicted values in the first quarter accounts only 0.41% Q1 1999Q4 2000Q3 2001Q2 2002Q1 2002Q4 2003Q3 2004Q2 2005Q1 2005Q4 2006Q3 2007Q2 2008Q1 2008Q4 2009Q3 2010Q2 2011Q1 2011Q4 2012Q3 2013Q2 2014Q1 2014Q4 2015Q3 2016Q2 2017Q1 2017Q4 2018Q3 2019Q2 2020Q1 2020Q4 Figure 5. Forecasts of employment in Austria for the period Q1 1999Q4 2000Q3 2001Q2 2002Q1 2002Q4 2003Q3 2004Q2 2005Q1 2005Q4 2006Q3 2007Q2 2008Q1 2008Q4 2009Q3 2010Q2 2011Q1 2011Q4 2012Q3 2013Q2 2014Q1 2014Q4 2015Q3 2016Q2 2017Q1 2017Q4 2018Q3 2019Q2 2020Q1 2020Q4 Figure 6. Forecast of employment in Austria for persons aged

7 In the Figure 6 the predicted values (till 2020) of the older generation employment rates (aged 55-64) are recorded, where we can see realistic estimates on the basis of calculations for all quarters. According to the prediction s results Austria will be able to achieve the objectives of the EU 2020 strategy in terms of employment rate of persons aged (minimum 60%). Even the pessimistic estimate indicates that Austria will meet the mentioned above goal (on average 70.4%). For this prognosis the same model was used as in the previous calculation ARIMA (1,0,0), (2,0,0) with 70% probability, and the seasonal shift of 12 periods. The total employment situation in Austria and the Czech Republic is not bad. The considerable difference between employment rates in both countries was observed in 2001, but in recent years these indicators has become a lot closer, mainly due to the employment growth in Austria. The last measurement revealed the 4% difference in employment rates. It was not so in all the EU countries. For example, employment in Sweden even declined in Also, we note the large seasonal fluctuations in both countries. If no unexpected negative changes happen, Austria achieves the target set by the EU 2020 strategy. Despite the total employment rate in Austria is relatively high and the women s employment has rather increasing trend, employment of the older generation aged becomes problematic one. In comparison with Austria the Czech Republic has lower employment rates in all population categories examined. The biggest difference, that is around 30 %, is observed namely at the age group The following analysis was performed with the use of software Statistica 12. Employment rate of different categories was chosen as a dependent time series. 11 Unemployment rate - Austria - Forecast Observations Forecast ± 70,0000% Figure 7. Unemployment rate in Austria Forecast; Model:(1,1,0)(2,0,0) Beg. : 1 End : Observations Forecast ± 70,0000% Figure 8. Forecast of GDP development in Austria till

8 Prognosis is calculated till the end of In these figures the GDP per capita is given for Austria and the Czech Republic. The calculation results show the prospective growth in this indicator for Austria and a decline in the latter for the Czech Republic in future. According to the prognosis the higher rates of GDP per capita are expected in mid ,8 GDP per capita Germany stat 2v*33c 7,7 7,6 7,5 GDP rate 7,4 7,3 7,2 7,1 7,0 6,9 6, Q Q Q Q Q Q Q Q Q Q Q4 Years GDP Figure 9. GDP per capita growth in Germany 4,0 GDP per capita Czech rep ,8 3,6 Prom2 3,4 3,2 3,0 2,8 2, Figure 10. GDP per capita growth in the Czech Republic The GDP per capita indicator is increasing in Germany, while it is decreasing in the Czech Republic. Observing development of the latter, it becomes obvious that in countries, where per capita GDP evolves in a sustainable way, the positive changes in the labor market are noticed. In other words, GDP per capita has a clear multiplier effect especially on the creation of new jobs. In countries where the GDP per capita is stagnant or falling companies are not willing to create new jobs. In addition to this companies are afraid to implement new technologies and new procedures. As a result economic performance becomes less effective and economy loses its competitiveness. 247

9 10,5 GDP per capita Germany ( ) 10,5 10,0 10,0 9,5 9,5 9,0 9,0 8,5 8,5 8,0 8,0 7,5 7,5 7,0 7,0 6,5 6, Inputs. Forecast ± 70,0000% Figure 11. Forecast of GDP per capita growth in Germany for the period ,2 GDP per capita - forecast Czech republic 4,2 4,0 4,0 3,8 3,8 3,6 3,6 3,4 3,4 3,2 3,2 3,0 3,0 2,8 2,8 2,6 2, Inputs Forecast ± 70,0000% Figure 12. Forecast of GDP per capita growth in the Czech Republic for the period Unemployment (%) Germany Inputs Forecast ± 70,0000% Figure 13. Unemployment rate in Germany prognosis. A forecast calculation revealed that unemployment rate in Germany in the next years is expected to take its minimum values. This indicates that the employment policy is quite successful there. The level of unemployment 248

10 in Germany should continue decline. The highest values of unemployment are expected in accord with the worst scenario of the prognosis in the year 2019/2020, i.e., around 9.5%. The optimistic one indicates 8% unemployment rate. In 2020 the German unemployment rate has to decline by about 2% from its temporary peak. Optimistic estimate says that the unemployment rate will fall to nearly 6%, which is a long-term sustainable rate of unemployment in Germany and a very good result of synergy between German governments, corporate sector and labor unions. Pessimistic estimate says about unemployment rate around 10%. The results obtained are necessary to compare with other macroeconomic indicators among the EU countries. Only then one can formulate the adequate assessment of the real situation in the home country. 4. Conclusions GDP growth in selected countries of the European Union accelerated in recent years. For example, in 2004 Austria was the fourth richest country in the European Union with GDP per capita of approximately 27,666, along with Luxembourg, Ireland and the Netherlands. Austria belongs to a group of the most developed countries in the EU. In 2008 Austrian GDP per capita was 119% relative to the European average value, what contributed to take the fourth place in Europe. From economic standpoint one of the most difficulties years for Austria was 2013 one, when the economy was almost stagnant. According to the prognosis Austrian unemployment will have a tendency to increase, while salaries remain on the same level. Despite this Austria is still referred to as one of the advanced economies in the world. In 2010 on the basis of GDP per capita evaluation Austria was ranked the 10th richest country in the world and third richest state in the EU. Germany, in turn, during the existence of a common currency among the EU countries, demonstrated another feature it can be proud of the lowest rate of inflation in average. However, the recent years brought some potentially significant changes. Still in 2012 the Czech Republic lagged significantly behind Germany. A year after the situation in terms of annual growth rates began to change and the Czech Republic showed somewhat faster growth than Germany. Prognosis show that this trend will continue further. On the basis of calculations done in the research the expected inflation rate will be about 1% in At the same time one should take into account other factors that could significantly affect inflation. Two very important among them are the export prices and food prices. One of the priorities of central bank then is to achieve low rate of inflation and keep it stable, as well as the exchange rate. According to the plan of the Czech National Bank (CNB) the stability of Czech crown exchange rate is an important goal that should be reached maintaining its current level that is considered now as acceptable. This implies additional supporting and facilitating the export activities of the country. Namely this factor may contribute to low inflation rates. In addition, CNB is planning to increase interest rates until The 2015 forecast for inflation shows that there is a high probability of rather deflation. Resting on the estimation of key macroeconomic indicators development, it can be stated that the inflation rate is expected to fall next year. The reason for that is that almost all anti-inflationary tools have a longer feedback reaction and therefore to take effect in the medium term. Along with mentioned above factors, it is also necessary to take into account certain political circumstances. The impact of sanctions against Russia could reduce exports to this country and, thus, contribute to increase in unemployment. It is also possible that food prices will fall further due to increasing variety of supply, manifested among others in wide range of imports from Poland and other countries. This again means the risk of unemployment. If the rate of inflation really falls in the future, it is likely that real wages growth will be faster than the growth of the entire economy. From an economic standpoint this indicator is, however, considered as unhealthy one, because it negatively affects the competitiveness of domestic producers and as a result contributes to a decrease in export volumes. In this regard here is a difference between economic situations in the Czech Republic and Germany. Germany still remains highly competitive despite a number of external political factors weakened it a bit. The Czech Republic, in turn, certainly has not the same as Germany strength and competitiveness on international markets, exposing its economy to a higher risk of unemployment also due to stagnation of GDP per capita growth. Low inflation and wage growth is likely to occur in late 2015 and early According to the forecast calculations unemployment in Germany being at its minimum level will continue to fall. However, already in 2019 the growth of unemployment is expected to reach nearly 9.5% value in Czech economy in contrast to Germany will stagnate (GDP growth of only 0.6%) because of weakening household consumption. The relationship between unemployment and GDP was for the first time in the twentieth century carefully studied by American economist Arthur Okun. This mathematically modified idea is as follows: when unemployment is rising by 1% over its natural level, GDP will fall by 2-3%. However, the law applies only in certain economic conditions when the unemployment rate is between 3-7.5%. Other versions of the Okun s law focus on the relationship between unemployment and GDP per capita. In the present study, we also wanted to show an extended version of the latter. The conducted calculations revealed the existence of determined 249

11 relationship between GDP per capita and employment rate; namely, GDP per capita increase by 2% corresponds to an increase in employment by 1%. This relationship applies vice versa as well. The obtained result may be considered as an extension of the classical Okun s law theoretical framework. Thus, if one wants to enhance the development of employment it is of no less importance to focus on the development of wealth (GDP per capita), what as a result will have a positive impact on employment and competitiveness of an entire economy. In this light we can assume that relying on structural policy, EU grants and subsidies is not sustainable and long-term effective way of boosting employment. In contrast, GDP per capita developing and strengthening of household consumption, including export activities, can contribute to a long-term effective development of employment in the Czech Republic, in a similar way as it is known from German and Austrian experience. References Bahna, M. (2014). Slovak Care Workers in Austria: How Important Is the Context of the Sending Country? Journal of Contemporary European Studies, 22(4), Bell, D. N. F., & Blanchflower, D. G. (2011). Young people and the great recession. Oxford Review of Economic Policy, 27(2), Boeri, T., & Ours, J. V. (2008). The Economics of Imperfect Labor Markets. Princeton University Press. Czech Statistical Office. (2015). Retrieved April 15, 2015, from David, N. D., & Charles, H. W. (1994). The forecasting attributes of trend and difference stationary representations for macroeconomic time series. Journal of Forecasting. Eurostat [online]. (2015). Retrieved April 13, 2015, from Eurostat [online]. (2015). Retrieved April 13, 2015, from Eurostat [online]. (2015). Retrieved April 14, 2015, from 28a-4d8b5ae8fb02 Eurostat [online]. (2015). Retrieved April 15, 2015, from pdf/a871a09a f c6be6e Eurostat [online]. (2015). Retrieved April 15, 2015, from pdf Eurostat [online]. [(2015). Retrieved April 13, 2015, from Garcia-Ferrer, A., & Del, H. J. (1992). On Trend Extraction Models: Interpretation, Empirical Evidence and Forecasting Performance. Journal of Forecasting, 11(8), Maitah, M., & Urbankova, E. (2015) The Effect of Educational Level on the Productivity in the Czech Republic. Modern Applied Science, 9(6), Palat, M. (2013). International migration and unemployment in established member countries of the European Union. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), Snyder, R. D., Ord, J. K., & Koehler, A. B. (2001). Prediction intervals for ARIMA models. Journal of Business and Economic Statistics, 19(2), Sögner, L. (2001). Okun s law: Does the Austrian unemployment-gdp relationship exhibit structural breaks? Empirical Economics, 26(3), Tamesberger, D. (2015). A multifactorial explanation of youth unemployment and the special case of Austria. International Social Security Review, 68(1), Tamesberger, D., Leitgöb, H., & Bacher, J. (2014). How to combat NEET? Evidence from Austria. Intereconomics, 49(4),

12 Toth, D. (2010). Lisabonská strategie Evropa 2020, Karolinum-Galileo. Praha. Toth, D., Mansoor, M., & Michaela, S. (2014). Comparative Research of Youth Employment in France and the Czech Republic. Research Journal of Applied Sciences, 9, Retrieved from Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( 251