Analysis of Key Macroeconomic Indicators and Their Relationship to Unemployment in the Czech Republic

Abstract

The Czech Republic boasts one of the lowest unemployment rates in the European Union, indicating a demand-supply imbalance in the labour market. This situation has implications for other labour market-related variables, such as nominal wages and labour productivity. In 2021, there was a significant increase in the inflation rate, reaching 15.1%. This development also impacted the trajectory of real wages. This article uses statistical analysis to predict the future economic trends of essential macroeconomic indicators related to the labour market. The dependence of individual variables on the unemployment rate will be determined through correlation analysis. Additionally, the study aims to assess the degree of independence between the unemployment rate and selected economic indicators, including nominal wages, labour productivity, real wages, and inflation. Future developments will be forecasted based on linear time series regression models for each examined variable. The statistical results will be economically interpreted in light of the current economic situation. Overall, the results of the correlation analysis indicate a significant statistical relationship between unemployment and nominal wages, suggesting a strong negative correlation between these variables. This finding aligns with macroeconomic expectations that an increase in unemployment leads to a decrease in the rate of nominal wage growth. The correlation analysis between unemployment and inflation reveals a moderate indication of a negative relationship between the two variables. However, the correlation analysis between unemployment and labour productivity and between unemployment and real wages indicates weak and insufficient evidence of any significant relationship between these variables. Therefore, further analyses and consideration of additional factors are necessary to obtain a more comprehensive and reliable understanding of these relationships.