Jonas Cederlöf

PHD CANDIDATE · DEPARTMENT OF ECONOMICS · UPPSALA UNIVERSITY · jonas.cederlof@nek.uu.se

I’m a Ph.D candidate in economics at Uppsala University. My main research interests lies in policy relevant questions, first and foremost within labor and public economics. I have a special interest in both applied and theoretical econometrics.

I will be presenting in the Job Market session at the 31st EALE Conference 2019.

Research interests: Labor economics, Applied econometrics, Public economics


Job Market paper

Saved by Seniority? - Effects of Displacement for Workers at the Margin of Layoff

This paper takes a novel approach in estimating the effects of involuntary job loss on future earnings, wages and employment. Whereas previous literature has relied on mass layoffs and plant closures for exogenous variation in displacement, comparing adversely selected workers across firms, I exploit the fact that who is laid off within a downsizing establishment is often determined by seniority rules, specifically the last-in-first-out (LIFO) principle. Using matched employer-employee data from Sweden, with detailed information on job start and end dates, in combination with a unique individual level dataset on layoff notifications, I rank workers according to relative seniority and identify establishment/occupation specific discontinuities in the probability of displacement which I exploit in a regression discontinuity framework. I find that the average displaced worker suffer initial earnings losses of about 50-60 percent of prior levels but recover fully within 5-7 years. As the latter finding stands in contrast to previous literature, I unpack the earnings effect by taking advantage of worker heterogeneity in firm specific tenure, task content as well as the size and timing of the layoff across threshold. I show that persistent earnings losses can mainly be found in sub samples of workers with high tenure experiencing large layoffs which have been the focus of previous studies. This suggests that the consensus view of job loss creating lasting scars and not temporary blemishes should be complemented with the caveat of only being accurate for particular layoffs and groups of workers which are not representative for the average displaced worker.


Research

Saved by Seniority? - Effects of Displacement for Workers at the Margin of Layoff

This paper takes a novel approach in estimating the effects of involuntary job loss on future earnings, wages and employment. Whereas previous literature has relied on mass layoffs and plant closures for exogenous variation in displacement, comparing adversely selected workers across firms, I exploit the fact that who is laid off within a downsizing establishment is often determined by seniority rules, specifically the last-in-first-out (LIFO) principle. Using matched employer-employee data from Sweden, with detailed information on job start and end dates, in combination with a unique individual level dataset on layoff notifications, I rank workers according to relative seniority and identify establishment/occupation specific discontinuities in the probability of displacement which I exploit in a regression discontinuity framework. I find that the average displaced worker suffer initial earnings losses of about 50-60 percent of prior levels but recover fully within 5-7 years. As the latter finding stands in contrast to previous literature, I unpack the earnings effect by taking advantage of worker heterogeneity in firm specific tenure, task content as well as the size and timing of the layoff across threshold. I show that persistent earnings losses can mainly be found in sub samples of workers with high tenure experiencing large layoffs which have been the focus of previous studies. This suggests that the consensus view of job loss creating lasting scars and not temporary blemishes should be complemented with the caveat of only being accurate for particular layoffs and groups of workers which are not representative for the average displaced worker.

Extended Unemployment Benefits and the Hazard to Employment

Previous studies estimating the effect of generosity of unemployment insurance (UI) on unemployment duration has found that as job-seekers approach benefit exhaustion the probability of leaving unemployment increases sharply. Such ``spikes'' in the hazard rate has generally been interpreted as shirking among job-seekers timing their employment to coincide with benefit exhaustion. This, however, has been called into question by Card et al. (2007b) who claim that such spikes rather reflect flight out of the labor force as benefits run out. This paper revisits this debate by studying a 30 week UI benefit extension in Sweden and its effects on unemployment duration, duration on UI as well as the timing of employment. As the UI extension is predicated upon a job-seeker having a child below the age of 18 at the time of regular UI exhaustion this provides quasi-experimental variation which I exploit using a regression discontinuity design. I find that although increasing potential UI duration by 30 weeks increases actual take up by about 2.7 weeks, overall duration in unemployment and the probability of employment is largely unaffected. Moreover, I find no evidence of job-seekers manipulating the hazard to employment such that it coincides with UI benefit exhaustion.

Consequences of Mandatory Advance Layoff Notice for Workers and Firms

Employment protection rules are often criticized for creating inefficiently low labor turnover. However, such rules also provide insurance for displaced workers. This paper addresses the benefits and costs of advance notice of job loss for workers and firms, respectively, from an empirical and a theoretical perspective. Empirically, we use unique administrative data from Sweden on the exact dates of layoff notification as well as contracted notice periods, all at the individual-level. Discontinuities in notification time generated by employment legislation or collective bargaining agreements provide exogenous variation. Our regression-discontinuity estimates indicate that longer notice periods reduce the probability of non-employment and increase annual earnings during the first year after layoff notification. There is some evidence indicating that workers who get longer notification periods experience smaller falls in their reemployment wages. We also show that firms are willing to make – and workers accept – an upfront severance payment in order to avoid the notice period and that firms respond to longer notification periods by laying off fewer workers

Caseworkers – do they matter, why, and for whom? Exploring caseworker value-added using random variation


Curriculum Vitae


Resources

Stata Snippets

Gsample - Sampling by group
 
    
* Created by     : Jonas Cederlöf
* Date           : February 2017
* Contact        : jonas.cederlof@nek.uu.se
* Description    : Random sampling by group-var. Keeps all observations within 
*                  specified group while keeping keep(.%) of the population. 


cap program drop gsample
program define gsample , rclass
    syntax varlist [if] [in] , keep(numlist>0)
    
    qui count
    local xN = r(N)
    
    tempvar x_randid
    tempvar x_rand
    tempvar x_maxrand
    
    qui bys `varlist' : gen  `x_randid'  = `varlist'[_n==1]
    qui                 gen  `x_rand'      = runiform() if `x_randid'!=.
    qui bys `varlist' : egen `x_maxrand' = max(`x_rand')
    
    local temp = `keep'*100
    qui keep if `x_maxrand'< `keep'
    qui count 
    local xnewN = r(N)
    display "You have sampled `temp'% of the population by the variable(s) `varlist'."
    display "Number of remaning observations are `xnewN' out of the original `xN'." 

    
end program
    
    
Fastmax - 20 times faster then egen =max()
 
    
/*           FASTMAX PROGRAM
Creator:     Jonas Cederlöf
Date:        March 2017
Contact:     jonas.cederlof@nek.uu.se
Description: The program draws on the "fegen" package by Sergio Corriea.
             The purpose of the program is to speed up the max function
             in the much slower egen command.
*/
cap program drop fastmax
program define fastmax, rclass
    syntax varlist [if] [in] , [by(varlist)] name(string)

    tempvar maxvar
    clonevar `maxvar' = `varlist'

    if  "`by'" != "" {
        bys `by' :     replace `maxvar' = max( `maxvar'[_n-1], `maxvar') 
        bys `by' :     gen      `name'   =  `maxvar'[_N] 
    }
    else {
                    replace `maxvar' = max( `maxvar'[_n-1], `maxvar') 
                    gen      `name'   = `maxvar'[_N] 
}
end
    
    

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