Mismatch Explanations of European Unemployment, Softcover reprint of the original 1st ed. 1998 A Critical Evaluation Coll. European and Transatlantic Studies

The peristence of European unemployment stands in striking contrast to the cyclical pattern of unemployment in the US. Many people attribute the rise in European unemployment to increased imbalances between the pattern of labour demand and supply - in other words, to greater mismatch, but existing mismatch indicators do not support this view. However, the obvious inference is not legitimate because the evidence is based on trended data, and thus gives rise to spurious statistical results. To get around the problem, the author uses the dynamic flow approach to structural unemployment and disaggregated data. The reader will find new results on "non-spurious" mismatch tendencies, occupational reallocation, the matching of apprentices, and the importance of matching and mobility for wage differentials.

1. Introduction.- 1.1. European Unemployment and the Mismatch Hypothesis.- 1.2. The Conceptual Framework followed in this Book.- 1.3. Overview and Results.- 2. Do Aggregate Measures of Mismatch Measure Mismatch? A Time Series Analysis of Existing Concepts.- 2.1. Introduction.- 2.2. Some Puzzling Evidence.- 2.3. Measuring Mismatch: Theoretical Foundations.- 2.4. Measurement Problems.- 2.5. Disaggregation and Unobserved Heterogeneity.- 2.6. Time Series Analysis.- 2.7. Testing for Unit Roots.- 2.8. On the Magnitude of Drifts in Macroeconomic Mismatch Time Series.- 2.9. Some Simulation Experiments.- 2.10. Concluding Remarks.- Appendix A2.- 3. Disaggregate Matching Functions, Spurious Mismatch and Occupational Reallocation in Germany.- 3.1. Introduction.- 3.2. Aggregate Matching Functions: Theoretical Framework.- 3.3. Aggregate Matching Functions in West Germany.- 3.4. Spurious Matching Functions.- 3.4.1. The Econometric Theory of Spurious Matching Functions.- 3.4.2. Simulated Evidence.- 3.5. Disaggregate Matching Functions using a Panel of Occupational Groups.- 3.5.1. Occupational UV-curves.- 3.5.2. Nonsense Regressions with Panel Data?.- 3.5.3. Panel Data Estimations for Disaggregate Matching Functions.- 3.5.4. Substituting the Time Trend by Economic Variables.- 3.5.5. Period Effects and the Identification of Mismatch.- 3.6. On Occupational Reallocation: Theoretical and Empirical Aspects.- 3.6.1. Matching Functions and the Adjustment of Unemployment/Vacancy Ratios.- 3.6.2. Empirical Evidence.- 3.7. Understanding the Matching of Apprentices.- 3.7.1. The Economic Problem.- 3.7.2. Towards a Reduced-Form-Matching Function for Apprentices.- 3.7.3. Panel Estimations.- 3.7.4. Backward-Looking Behaviour and Future Rigidities.- 3.8. Concluding Remarks.- Appendix A3.- A3.I. Data.- A3.II. Spurious Regressions.- A3.III. List of Occupational Groups.- A3.IV. Panel Data Modelling.- 4. Matching and New Technologies: Does Unmeasured Ability Explain the Higher Wages of New-Technology Workers?.- 4.1. Introduction.- 4.2. Matching, Mobility, and Unmeasured Ability.- 4.3. The Micro Datasets: Presentation and Descriptive Analysis.- 4.3.1. INSEE Data Sources.- 4.3.2. The Definition of “New Technology” Categories.- 4.3.3. “New Technologies” and “Organization of the Work Place”.- 4.3.4. Variables Representing the Firm-Level Background of Individual Workers.- 4.4. Cross-Sectional Results.- 4.4.1. Specifying the Wage Equation.- 4.4.2. Some Theoretical and Empirical Reasons to include Firm-Level Variables.- 4.4.3. The Impact of New Technologies on Wages: Global Results.- 4.4.4. The Impact of New Technologies on Occupational Wages.- 4.4.5. Mismatch Related to New-Technology use.- 4.5. Wages and New Technologies: Evidence from Panel Data.- 4.5.1. Construction of Panel Data.- 4.5.2. The Impact of New Technologies on Wages: Fixed-Effect Modelling.- 4.6. Concluding Remarks.- Appendix A4.- A4.1. Descriptive Statistics.- A4.II. Definition of Educational Degrees.- A4.III. Additional Cross-Sectional Results.- 5. Conclusions.- 6. References.