Working Inflow, Outflow, and Churning

Linked employer-employee data from the Finnish business sector is used in an analysis of worker turnover. The data is an unbalanced panel with over 219 000 observations in the years 1991-97. The churning (excess worker turnover), worker inflow (hiring), and worker outflow (separation) rates are explained by various plant and employee characteristics in type 2 Tobit models where the explanatory variables can have a different effect on the probability of the flow rates to be non-zero and on the magnitude of the flow rate when it is positive. Most of the characteristics are defined as 5-group categorical variables defined for each industry separately in each year. We compare the Tobit results to OLS estimates, and also use weighting by plant employment. It turns out that weighted OLS results are fairly close to Tobit results. The probabilities of observing non-zero churning, inflow, and outflow rates increase with plant size. The magnitudes of the non-zero churning and inflow rates depend positively on size, but the magnitude of outflow rate negatively. High-wage plants have low turnover, whereas plants with large within-plant variation in wages have high turnover. Average tenure of employees has a negative impact on turnover. High plant employment growth increases churning and separation but reduces hiring in the next year. We also control various other plant and average employee characteristics like average age and education, shares of women and homeowners, foreign ownership, ownership changes, and regional unemployment.http://deepblue.lib.umich.edu/bitstream/2027.42/39997/3/wp611.pd

Working Inflow, Outflow, and Churning

Linked employer-employee data from the Finnish business sector is used in an analysis of worker turnover. The data is an unbalanced panel with over 219 000 observations in the years 1991-97. The churning (excess worker turnover), worker inflow (hiring), and worker outflow (separation) rates are explained by various plant and employee characteristics in type 2 Tobit models where the explanatory variables can have a different effect on the probability of the flow rates to be non-zero and on the magnitude of the flow rate when it is positive. Most of the characteristics are defined as 5-group categorical variables defined for each industry separately in each year. We compare the Tobit results to OLS estimates, and also use weighting by plant employment. It turns out that weighted OLS results are fairly close to Tobit results. The probabilities of observing non-zero churning, inflow, and outflow rates increase with plant size. The magnitudes of the non-zero churning and inflow rates depend positively on size, but the magnitude of outflow rate negatively. High-wage plants have low turnover, whereas plants with large within-plant variation in wages have high turnover. Average tenure of employees has a negative impact on turnover. High plant employment growth increases churning and separation but reduces hiring in the next year. We also control various other plant and average employee characteristics like average age and education, shares of women and homeowners, foreign ownership, ownership changes, and regional unemployment.worker turnover, churning, employer-employee data

The micro-level dynamics of regional productivity growth: The source of divergence in Finland

Productivity growth of the Finnish regions in 13 manufacturing industries is decomposed into micro-level sources by using plant-level data from 1975 to 1999. There are substantial regional differences in the intensity of productivity-enhancing restructuring. Dynamic competition is more intensive in Southern Finland, where the productivity level is also high. In contrast, plants located in Eastern Finland are equipped with low-productivity technologies owing to persistently sluggish micro-level dynamics. Productivity dispersion between plants within industries is greatest in Southern Finland. We argue that intensive experimentation is a more reasonable interpretation of this finding than large static X-inefficiency in this high productivity region.

Is Inter-Firm Labor Mobility a Channel of Knowledge spillovers? Evidence from a Linked Employer-Employee Panel

An employer-employee panel is used to study whether the movement of workers across firms is a channel of unintended diffusion of R&D-generated knowledge. Somewhat surprisingly, hiring workers from others' R&D labs to one's own does not seem to be a significant spillover channel. Hiring workers previously in R&D to one's non-R&D activities, however, boosts both productivity and profitability. This is interpreted as evidence that these workers transmit knowledge that can be readily copied and implemented without much additional R&D effort.Labor Mobility, R&D Spillovers, Profitability, Linked Employer-Employee Data

The Roles of Employer and Employee Characteristics for Plant Productivity

Using a matched worker-plant data from Finnish manufacturing, the relationships of worker characteristics, wages, and productivity are examined. The process of linking various registers on employees and plants is described in detail. The final data set includes the characteristics of plants and their employees. The plant panel data is used for estimating productivity and wage profiles according to age and seniority. At low seniority productivity increases fast, but starts to decline early. Wage profiles are not related to productivity profiles, but continue to increase with seniority. These results support the hypothesis that human capital is not firm specific, and seniority related wages are used for incentive reasons. Various components of worker turnover have an impact on productivity growth.