Worker inflow, outflow, and churning

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. ABSTRACT: 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. Terms of use: Documents inThe 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.Keywords: worker turnover, churning, employer-employee data JEL code: J23, J41, J63 EI-TEKNINEN TIIVISTELMÄ:Työntekijävirtoja on tutkittu yhdistettyjen työntekijä-työnantaja -aineistojen avulla. Tutkimuksessa käytetään toimipaikkatason paneeli-aineistoa, jossa on yli 219 000 havaintoa vuosilta 1991-97. Selittävinä muuttujina ovat työntekijöiden rekrytoinnit (sisäänvirtaus), työsuhteiden päättyminen (ulosvirtaus) sekä ns. "kirnuaminen" tai ylimääräinen vaihtuvuus. Kirnuamisella tarkoitetaan sellaista työntekijöiden samanaikaista (saman vuoden aikana tapahtuvaa) sisään-ja ulosvirtausta, joka ei liity toimipaikan työntekijä-määrän kasvuun tai supistumiseen. Nämä työntekijävirrat on ilmaistu asteina suhteuttamalla ne toimipaikan työntekijämäärään. Selittävinä tekijöinä on käytetty erilaisia toimipaikka-ja työn-tekijäominaisuuksia.Estimoinnit on tehty käyttämällä tilastollisia malleja (ns. tobit 2 malli), jois...

Hiring from unemployment and separation to unemployment

“…Most of them appear in small plants that have low weight in the estimation. Our previous experience (Ilmakunnas and Maliranta, 2003b) shows that weighted OLS using the whole sample gives practically the same results as the continuous part of the Tobit 2 model estimated with weighted maximum likelihood. Therefore, only the weighted OLS results are used in this study, using weighting by plant size (average of current and last year's employment).…”

“…(The determinants of these total inflow and outflow rates in Ilmakunnas and Maliranta, 2003b are examined.) Flows of workers that are hired and separated during the same year are not observed.…”

“…During the recovery, construction also had the highest inflow rate from unemployment, whereas in the 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 WIFU WOFU Net empl. change Type 2 Tobit models have been used for modeling total inflow and outflow rates in Ilmakunnas and Maliranta (2003b). The model allows the determinants of the probability of having a non-zero flow rate to differ from the determinants of the magnitude of the flow rates when they are non-zero.…”

The determinants of excess worker turnover in Luxembourg

“…In analyzing job and worker flows in Sweden for all industries except construction over the period 1986-1995, Persson (1998 finds that the average churning rate was high (about 27 percent over the period). Ilmakunnas and Maliranta (2005) use plant-level data on job and worker flows for Finland to analyze the determinants of churning flows. They too find that churning flows are related to such employer characteristics as size and age of establishment, but also to various worker characteristics such as age, education level, the percent of the workforce that is female, home-ownership rates, and wage levels.…”

Who Creates Jobs? Estimating Job Creation Rates at the Firm Level

“…Second, some authors argue that the DHS growth rate induces censoring at the interval [−2, 2] and, thus, apply Tobit estimation (see, e.g., Ibsen and Westergaard-Nielsen 2005;Ilmakunnas and Maliranta 2005;Guertzgen 2009). The Tobit model, however, is not appropriate in this case, since it assumes a distribution of firm level job creation rates with support outside the interval [−2, 2].…”