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Job Mobility and Wage Dynamics

Job Mobility and Wage Dynamics. Valerie Smeets Very preliminary – comments welcome Prepared for the Internal Seminar UC3M. Motivation. Job transitions (patterns) and wages Common framework is search and matching models Able workers should match with better jobs (Becker, 1973)

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Job Mobility and Wage Dynamics

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  1. Job Mobility and Wage Dynamics Valerie Smeets Very preliminary – comments welcome Prepared for the Internal Seminar UC3M

  2. Motivation • Job transitions (patterns) and wages • Common framework is search and matching models • Able workers should match with better jobs (Becker, 1973) • Workers change jobs to improve their match • Switching decision may be influenced by firm specific HC and switching costs • Firm specific HC destroyed for switchers • Workers with more firm specific human capital and better matches are less likely to leave

  3. Motivation (cont´d) • Number of past jobs • Ambiguous predictions about cross sectional correlation between the number of jobs a worker has held and his match quality • Workers who remain at their job for a long time have high matches while those who switch firms have low matches  # past jobs negatively related to wages • Workers take better matches at other firms as a substitute for acquiring specific capital  # past jobs positively related to wages

  4. Literature • Job seniority and wages • Large returns to firm tenure (2 to 3% per year) • Unobserved heterogeneity lead to biased returns estimates (Abraham and Farber (1987), Altonji and Shakotko (1987), Topel (1991), Abowd, Kramarz and Margolis (1999)) • The large firm tenure coefficients go away when industry, occupational or career tenure are included (Neal (1995), Kambourov and Manovskii (2002) and Pavan (2005))

  5. Job mobility and wages • Mincer (1986) • Short and long run wage gains from separations • PSID, 1970-1981 (all ages) • Positive wage gains in the short run • Movers never catch up higher wage of stayers (flatter life time trajectories) • Abowd, Kramartz and Roux (2006), Buchinsky, Fougere, Kramarz and Tchernis (2003) • Wage and mobility (firm policies, firm performance) • French matched employer-employee data • Includes # firm changes (more) to control for bias in worker heterogeneity wrt previous employment

  6. Job changes patterns and wages • Mincer-Jovanovic (1981) • National Longitudinal Survey 1966-1976 • No effect of past firm changes for young workers • Negative effect for old workers • Light and McGarry (1998) • NLSY (1979-1993), young white men only (8 first years of career) • Positive or no effect of # firm changes in first 2 years, negative effect of total # changes overall • Extremely small effects

  7. Outline • Estimation strategy • Data description, sum stats and results with Danish matched employer-employee data • Verification using US data (NSLY) – brief data description, sum stats and results with US data • Conclusions

  8. Estimation model • Correlation of variables with unobserved job or worker characteristics will produce biased OLS estimators of β

  9. Two empirical possibilities (I) • Consistent with costly search model of Jovanovic (1979) • No HC gain to accumulate experience in multiple firms • Workers who remain at their job for a long time have high matches while those who switch firms have low matches • Costly SHC only for workers planning to stay  • Firm tenure biased upward and firm changes biased downward (βft > 0 and βc < 0)

  10. Two empirical possibilities (II) • Some workers take better matches at other firms as a substitute for acquiring specific human capital • These workers may switch because of low search or switching costs, scare skills or plain luck at securing offers  • Firm tenure biased downward and firm changes biased upward (βft < 0 and βc > 0)

  11. Data - Denmark • Danish Integrated Database for Labor Market Research (IDA) – Statistics DK • Matched employer-employee dataset of entire Danish population (1980-2001) • Combination of 3 datasets • Common keys to match them back • Individual – personnel characteristics (age, sex, education, family status, location, occupation, etc.) • Job – plant, industry, location, # jobs, wage, etc., November data • Firms – nbr plants, old ID number, etc.

  12. Data - Denmark • Sample • Private sector, primary job only • Workers aged 25 to 44 • 25 - Avoid students • 45 - Data limitations in computation of labor history variables • 2001 cross section and 1996-2001 panel • 731,358 individuals or4,314,813 data year • Controls • Education (4 groups), part time, female, firm size, industry dummies, firm and worker fixed effect

  13. Data - Denmark • Human capital variables • Experience – computed back to 1969 by Stat DK • Firm tenure – using 1980-2001 history • Industry tenure - using 1980-2001 history • Firm changes variables • # past firm changes after last graduation • Decompose firm changes into exits and non exits • Intervals for firm changes [1-2, 3-5, 6-9, 10 up] to check for non linearity • Disaggregate exits and non exits by two age intervals: 25-34 and 35-44

  14. Histogram of firm changes (after last graduation)

  15. Histogram of exits and non exits

  16. Estimations • Wage (log) on HC variables, firm changes + controls • 2001 sample, men full time, firm worker fixed effects (1996-2001 sample) • Wage (log) on HC variables, exits and non exits + controls • 2001 sample, men full time, college, college men, manufacture, non manufacture • Wage growth (log) on HC variables, firm changes and controls • 1996-2001 sample, men full time, college, 2001 and 1996 firm fixed effects

  17. Data - US • National Longitudinal Survey of Youth (NLSY79) • Data prepared by Pavan (2005) • Computed workers labor history, including firm tenure, industry tenure and career tenure using NLSY work history files (1978-1994) • Sample • 1979-1993 • Full time male workers, primary job • Men only, age 25 and older [25,37] • 1935 individuals or 12,776 data year • Older sample than Light and McGarry (1998)

  18. Data - US • Firm changes variables • # past firm changes after age 25 • # exits and non exits after age 25 • Intervals for firm changes and non exits • Controls • Experience, firm tenure, industry tenure, career tenure, black, education • Estimations – log wage • Panel, without and with individual fixed effect • # past firm changes, exits and non exits (intervals)

  19. Summary statistics – US (NLSY)

  20. Histogram of firm changes (after 25)

  21. US Data (NLSY) - Wages and Exits vs. Non Exits

  22. Conclusions • Denmark • Cross section & panel - the effect of past firm changes increases with the number of changes • 2 to 7% in cross section • 17 to 23% in panel • US • Cross section - Only moving up to 3 times affect your wage (7-6%,3-6%) • Panel - the effect of past firm changes increases with the number of changes (13 to 23%)

  23. Conclusions • New empirical fact - workers who switch firms repeatedly in the past earn higher wages • Workers who move end up with better match than workers who do not move • Contredict Mincer- Jovanovic (1981) and Light McGarry (1999) • Different groups of workers have different effect of job transitions patterns on wages • Returns to firm changes dominate returns to SHC effect

  24. Conclusions • Correlation btw firm changes and wage become very large once workers fixed effects are introduced • Some negative selection in the time invariant ability of workers who switch firms • Robust to all specifications • Number of job changes, exits or non exits, age groups, fixed effect • Similar results for Denmark and the US • Policy implications for labor market flexibility

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