Axel Gottfries 

Research Interests: Macroeconomics, Labor Economics, Economic Theory

Email: axel.gottfries'at'

CV: Link 

Abstract:    This paper studies the role of partial commitment in models with on-the-job search. Commitment is modeled as the frequency at which wages are renegotiated. This formulation nests earlier models in the literature as special cases when the frequency of renegotiation goes to zero (full commitment) or infinity (no commitment). In this setup, I first show that the degree of commitment is important for the share of the surplus captured by the worker. With no commitment, the worker value reflects only her bargaining power. With commitment, the worker receives a higher share of the surplus, because a higher wage increases the total surplus by increasing the length of the match. The length of the match is more responsive to the agreed wage when commitment is higher. When the model is calibrated, the value of the model primitives, e.g. the bargaining power of workers and the productivity distribution, differs starkly depending on the assumed degree of commitment. Second, I show that when the degree of commitment is endogenous, firms will in general choose an intermediate level of commitment, rather than a corner solution. I also show that the equilibrium wage distribution and the bargaining outcomes are unique.

Abstract:    A wide class of models with On-the-Job Search (OJS) predicts that workers gradually select into better-paying jobs. We develop a simple methodology to measure the position in the job ladder in these models using two sources of identification: (i) time-variation in job-finding rates and (ii) the time since the last lay-off. The measure can be used to control for match quality in reduced for regressions and also as a moment in structural estimation. This methodology is applied to the NLSY 79. The estimated standard deviation of the wage offer distribution is about 15%. Further, we find that OJS accounts for 30% of the experience profile, 9\% of the total wage dispersion and an average wage loss of 11% following a lay-off.

Abstract:    We consider a Burdett/Mortensen style wage posting model with aggregate shocks. We analyze the equilibrium under two alternative assumptions on wage setting in ongoing jobs: either fully flexible or downwardly rigid. In the model firms optimally pay only retention premiums. The equilibrium is characterized by a Taylor expansion. The model yields two simultaneous relations for wages and quits, of which the parameters are simple functions of three empirically observable arrival rates of: (i) jobs, (ii) lay offs, and (iii) aggregate shocks. Hence, there are over identifying restrictions, which are supported remarkably well by the data. We find strong evidence for wage downward rigidity and inefficiently low job-to-job transitions during the downturn. Furthermore, we find evidence that firms pay only retention premiums, not hiring premiums. A model with wage rigidity in ongoing jobs and OJS is therefore a useful benchmark for a wage equation in macro models.

"A job ladder model with stochastic employment opportunities", with Jake Bradley [coming soon]
Abstract:    The canonical equilibrium job ladder model is much used in labor research due to its ability to replicate empirical features relating to the wage distribution and employment dynamics. However, the search process for workers is highly stylized. The job offer arrival rate only varies by employment status and the distributions of accepted wages are independent of career histories. We extend the model so that the labor demand for a particular worker stochastically varies over time. The resulting model has on- and off-the-job search in a thin labor market while retaining analytical tractability. When the thickness of a worker's market is constant, we nest the standard job ladder model. Our estimates, suggest that the dynamics of the market thickness play a crucial role in explaining unemployment, worker values and the dispersion of wages. The model is estimated on U.S. survey data and identified using wage data and employment dynamics.