Mika Maliranta

The turnover of jobs and workers in a deep recession: evidence from the Finnish business sector

Job and worker flows in the Finnish business sector are studied during a deep recession in the early 1990s. The data set covers effectively the whole work force. The gross job and worker flow rates are fairly high. The evidence suggests that the adjustment of labor input has happened through a reduced hiring rate rather than through an increased separation rate. However, during the recession the group of declining plants included more and larger plants than before, which led to reduced employment. Excess worker turnover (churning) and excess job reallocation have been low during the recession. The evidence of the countercyclicality of job reallocation is mixed. The flows are calculated both for the whole business sector, and for seven main industries. Services have clearly higher flow rates than manufacturing, but the cyclical changes in the flows are fairly similar in all industries. To test the sensitivity of the results to data sources, job flows are calculated from three different statistics.

Labour characteristics and wage‐productivity gaps

We use plant-level linked employer-employee data from Finland to estimate production functions where also employee characteristics (average age and education, and sex composition) are included. We also estimate similar models for wages to examine whether wages are based on productivity. Our aim is to explain productivity besides manufacturing, also in services. For the service sector plants, no data on capital input, working hours, or value added is available, and productivity has to be measured by sales per employee. We use a stepwise procedure to examine whether the results for manufacturing are affected when less satisfactory data is used. Then we proceed to estimate the final model for manufacturing and services combined. The effect of age on productivity is small, but wages show strong age effects. Higher educational level leads to higher productivity and wage, but there is a clear productivity difference between non-technical and technical education. Wageproductivity gaps are positive except for the highest level of non-technical education. The share of female workers is negatively related to productivity. Also the wage effect is negative, but smaller in absolute value, leading to a positive female wageproductivity gap. However, the negative productivity effect disappears and the gap is negative if total factor productivity is used as the variable to be explained, or if the model is estimated with fixed plant effects.

Factors of Performance by Plant Generations. Some Findings from Finland

As plant-level data sets have become available to researchers, it has become possible to study some important factors of growth more comprehensively than before. This paper deals with such factors as technology vintages (or generations), learning by doing and spillovers. These are of interest, for example, when the process of the evolution among the new plants is explored.