I have targeted to analyze the patterns of wage development in the changing macroeconomic conditions. According to the expectations the evolution of nominal and real wages is inter alia positively affected by the changes in labor productivity and negatively influenced by the deviation of unemployment rate from NAIRU. The evidence of procyclical behavior of real wages is not very robust as there was not found significant correlation in the time of decreasing real GDP. The most of results of the models applied on post Lehman Brothers period are consistent with the regressions based on complete data set. The chosen data proved to be not very suitable to show the extent of wage rigidity. The significant negative relationship between the development of real wages and changes in inflation rate implies the visibility of symptoms of wage rigidities even when we work with average macroeconomic variables.

It is a sandwich Thesis. The first and the second essay are joint works with my Supervisor, Dr. Ronald Balvers. The third essay is joint work with Fangxing Liu, a Ph.D. candidate (Finance) at DeGroote School of Business, where we have equally shared the work responsibility. / First essay deals with Productivity shocks. Productivity shocks transmitted from productivity leaders to trailing sectors are systematic sources of risk. Global technology and knowledge diffusion leads to predictable patterns in productivity dynamics across countries and industries. Productivity gaps determine the level of exposure to the systematic leader productivity shocks. Firms in a country-industry with larger productivity gaps relative to the world leader are more dependent on the leader's innovations compared to their own productivity improvements. They thus have higher loadings on the leader productivity shocks and higher average stock returns. For OECD panel data, a country-industry's productivity gap significantly predicts the stock returns of the country-industry: holding the quintile of country- industry portfolios with the largest gaps and shorting the quintile with the smallest gaps generates annual returns of 9.8% (6.7% after risk adjustment with standard factors). A factor associated with the productivity gap explains country-industry portfolio returns substantially better than standard factor models. Loadings on leader-country-productivity shocks are found to have substantial correlation with productivity gaps, and leader productivity shocks are more important for stock returns than idiosyncratic productivity shocks. These findings suggest that the productivity gaps and associated higher average returns are indeed tied to systematic risk. The second essay deals with Technology shocks. Technology shocks from technological frontier economies are a critical determinant of productivity shocks. These shocks spill over, pervading all lagging economies and are true systematic shocks. A country's aggregate technology gap with the frontier determines the potential for the systematic innovation shocks to affect it, but the country's absorption capacity determines its effective sensitivity to these shocks. We find conforming evidence that the technology gap, R&D intensity, and absorption capacity can explain stock returns. For OECD panel data, a one standard deviation increase in the technology gap increases excess stock returns by 0.578 percent per month. A one standard deviation increase in R&D intensity increases the excess return by 0.637 percent per month. An increase in absorption capacity of one standard deviation increases the excess return by 0.275 percent per month. When global FF factors are included, the results are diluted, which suggests that the FF factors may alias for the three variables associated with the systematic risk arising from frontier technology shocks. The third essay deals with Political risk. We find that the differences in Hassan et al. (2019) political Risk proxy derived from text processing of analyst transcripts can price cross-sectional returns after controlling for standard factor risks. A mimicking factor for the political risk measure, when added to the standard Fama French 5 factor model or the Q5 model, explains the test asset returns better than these models. In our limited sample, the changes in PRisk measure captures more information about political risk than the traditional measures from Baker et al. (2016), which suggests that one can start using changes in PRisk characteristic as a political risk proxy. / Thesis / Doctor of Philosophy (PhD)