Noyce Conference Room
Seminar
  US Mountain Time
Speaker: 
R. Maria del Rio Chanona (Complexity Science Hub Vienna)

Our campus is closed to the public for this event.

Abstract: We will focus on labor transformations in the first part of the talk. We will present a non-equilibrium and data-driven network model for understanding how workers adapt to changes in labor demand. In this model, workers move through an empirically derived occupational mobility network in response to automation scenarios. We find that the network structure is essential in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. Furthermore, in automation scenarios where low-wage occupations are more likely to be automated than high-wage occupations, the network effects are also more likely to increase the long-term unemployment of low-wage occupations. We will also discuss how the Covid-19 pandemic affected workers in the short term and how it led to the Great Resignation (i.e., the U.S. record high quit rates reached 2021) in the longer term. We use Reddit data and text analysis to show that mental health concerns have increased among the job quitting discourse since the start of the pandemic, likely contributing to the rise in quits. 

The second  part of the talk will be based on the paper “Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution”. It is well-known that value added per worker is extremely heterogeneous among firms, but relatively little has been done to characterize this heterogeneity more precisely. Here we show that the distribution of value-added per worker exhibits heavy tails, a very large support, and consistently features a proportion of negative values, which prevents log transformation. We propose to model the distribution of value added per worker using the four parameter Lévy stable distribution, a natural candidate deriving from the Generalised Central Limit Theorem, and we show that it is a better fit than key alternatives. Fitting a distribution allows us to capture dispersion through the tail exponent and scale parameters separately. We show that these parametric measures of dispersion are at least as useful as interquantile ratios, through case studies on the evolution of dispersion in recent years and the correlation between dispersion and intangible capital intensity. The paper is available at https://www.inet.ox.ac.uk/files/Manuscript.pdf

 

Speaker

R. Maria del Rio ChanonaR. Maria del Rio ChanonaJSMF Postdoctoral Research Fellow at the Complexity Science Hub Vienna
Purpose: 
Research Collaboration
SFI Host: 
J. Doyne Farmer