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Birds of a feather flock together (or Networks of Mobility)

Oana Calavrezo, Richard Duhautois, Francis Kramarz. Birds of a feather flock together (or Networks of Mobility). Motivation.

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Birds of a feather flock together (or Networks of Mobility)

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  1. Oana Calavrezo, Richard Duhautois, Francis Kramarz Birds of a feather flock together (or Networks of Mobility)

  2. Motivation Most analyses of workers’ mobility episodes have been based on person-level data sources: the PSID in the U.S. (Abraham and Farber, 1987; Altonji et al., 1987; Topel, 1991; Buchinsky et al., 2005). The CWHS or the LEED data are “old” versions of tools that are being developed systematically now, called longitudinal matched employee-employer data sources (Topel and Ward, 1992; Anderson and Meyer, 1994). Longitudinal matched employee-employer data sources give information on both sides of the labor market. This paper belongs to the new wave of studies based on such tools (see Abowd, Kramarz and Roux, 2006; Beffy et al.,2004). COINVEST Conference, Lisbon 2010

  3. Issue The purpose of this study is to focus on the characteristics of firms when analyzing within- and between-industry mobilities (at two-digit level) between 1991 and 1999. Three original contributions • First, workers’ mobility within- and between-industries is a topic scarcely analyzed in the literature. • Second, the data used in this paper represent an original and rich statistical dataset.One of the most original features of our dataset is that it permits to examine the origin and destination of a representative sample of French workers. • And third, we lead an analysis in terms of networks of firms. COINVEST Conference, Lisbon 2010

  4. Presentation summary Section I: Introduction Section II: Data Section III: Econometric strategy Section IV: Results Section V: Conclusion COINVEST Conference, Lisbon 2010

  5. A Simple Framework for Firm Networks of Mobility (1/2) Starting with Granovetter’s (1973) observations, researchers have been extremely active in identifying the role of networks on the labor market. A flurry of papers shows how workers find jobs using friends, relatives (see for instance Munshi, 2003), or all sorts of ties available to them. Indeed most of the ties that were examined are person-based. Very little attention has been devoted to other links, in particular those stemming from the employing firm. Not surprisingly, this type of information is essentially unavailable. Here, we try to give a comprehensive view of various potential networks as well as measures of their prevalence and their respective weights in the mobility episodes. COINVEST Conference, Lisbon 2010

  6. A Simple Framework for Firm Networks of Mobility (2/2) Types of resources available in the data: Industry resources as measured by within industry (at two-digit level) mobility; Employer network as measured by the type of ownership: independent firm versus group (with affiliates); Local resources as measured by within region or “department” (county) mobility; Type of firm network as measured by firm size and by economic health. We define firm networks of mobility as follows: Two firms belong to the same network if they are similar according to certain attributes (industry, geographic location, ownership, size or economic health). For example, we consider that two firms belong to an industry network if they operate in the same industry. We study how the industry network of mobility is determined by other four networks of mobility: networks of size, department, ownership and economic health. COINVEST Conference, Lisbon 2010

  7. Within- and Between- Industry Mobility: Related Literature • Previous research on labor mobility has focused primarily on mobility between jobs (employers, firms) and on the relation between job seniority and earnings and worker-firm matching. • There are fewer studies that directly focus on industry mobility: • Parrado et al. (2007) use the PSID. They only use an industry variable (the size of industry) • McLaughlin and Bils (2001) also use the PSID. They do not use firm data to control for industry heterogeneity. • Shin (2007) uses the NSLY79 and he exclusively controls for worker variables. • Topel and Murphy (1987) use the CPS and show that both cyclical and secular increases in US male unemployment have been accompanied by declines in between-industry mobility. • Le Minez (2002) studies industry mobilities between 1968 and 1998 and she does not include firm variables. COINVEST Conference, Lisbon 2010

  8. Data • We used three data sets: • the “Déclarations Annuelles de Données Sociales ” (DADS) from 1991 to 1999: All workers born in October of an even year are included. The data include all private and semi-public employers. • the “Liaisons Financières” (LIFI) data base. For each head of group, all companies belonging to the head and most financial relationships between firms can be traced. • the “Bénéfices Réels Normaux” (BRN) files. These data give us measures of employment, value-added, profits… By merging the four data bases we finally work with more than 4 millions observations which cover the 1991-1999 period.We work with a data set containing observations only for people staying employed on the period of analysis. COINVEST Conference, Lisbon 2010

  9. Tree diagram of job mobility COINVEST Conference, Lisbon 2010

  10. Indicators • For the firm of origin (or the last firm where the worker was employed in), we construct the following variables: • Variables of individual characteristics: sex, age, skill, wage, number of previous mobilities and working time duration. • Variables of firm characteristics: industry, size, ownership, economic health, region. COINVEST Conference, Lisbon 2010

  11. Indicators • For the workers who are mobile during a year, we use information from firms of origin and destination. We construct the following variables: • Variables of individual characteristics: number of days of non-employment between two jobs, skill (sskill), wage (w+ and w-), working time duration (sduration and duration_sduration) • Variables of firm characteristics: industry (within), ownership (indep,indepd,indepf,mmg,m_grp), region (sdep,reg_sdep), size (ssize), economic health (q3q3,q3q1,q1q3,q1q1). COINVEST Conference, Lisbon 2010

  12. Econometric strategy • The idea that factors affecting selection into the sample may simultaneously affect the binary outcome of interest has been the motivation for the introduction of the probit sample selection model. • The model we use was initially developed by van De Ven and van Praag (1981). COINVEST Conference, Lisbon 2010

  13. COINVEST Conference, Lisbon 2010 Econometric strategy • The selection equation • The first equation describes the probability of selection: the probability that a worker changes job within the year. • z are explanatory exogenous variables : sex, age, skill, wage, working time duration, industry, size, ownership, economic health and previous number of mobilities.

  14. COINVEST Conference, Lisbon 2010 Econometric strategy • The within-industry mobility equation – it is defined only if mobile=1: • The probability of changing job within the same industry • The x explanatory variables are: sex, age, skill, wage, working time duration, industry, size, ownership, economic health, previous number of mobilities and dummies indicating the way the worker changes job between the firms of origin and destination (in terms of skill, wage, working time duration, firm size, firm ownership, firm economic health and firm location)

  15. COINVEST Conference, Lisbon 2010 Econometric strategy • δ and β are suitable vectors of unknown regression parameters • (ε1, ε2) is a zero-mean unit-variance bivariate normal random variable with corr (ε1, ε2) =ρ. • The contribution to the likelihood of the i-th unit of the sample can be written as follows:

  16. COINVEST Conference, Lisbon 2010 Descriptive results (1/2) 32,6 % of individuals are mobile at least once in any given year. Mobility occurs more often in services industries. Workers employed in independent (resp. group) firms have a tendency to move to independent (resp. group) firms. Workers tend to move more often in the Province rather than in the Paris region.

  17. COINVEST Conference, Lisbon 2010 Descriptive results (2/2) Mobility is decreasing with age and skills. 30% of moves between firms have no non-employment spell between the two jobs and another 30% need at least 6 months to find another job. Two-third of all episodes are made by high-mobility workers (an average of four episodes).

  18. COINVEST Conference, Lisbon 2010 Selection regression estimates for being mobile

  19. COINVEST Conference, Lisbon 2010 Main equation: estimates for within-industry mobility

  20. Conclusion • Our analysis permits to control, in the same time for individual and firm characteristics. • Robustness tests: • On different subsamples obtained by focusing at one year at the time or on extracting randomly from them 10% of their observations. • Results are identical • Our main result shows that the probability of being mobile within the same industry increases whether the worker changes job between two similar firms in terms of size, region, economic health or ownership. COINVEST Conference, Lisbon 2010

  21. Thank you very much for your attention! COINVEST Conference, Lisbon 2010

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