Working Papers

Internal Labor Markets: A Worker Flow Approach (with Andreas Kostøl, Jan Nimczik and Andrea Weber). Revise & resubmit, Journal of Econometrics.
Abstract: This paper develops a new method to study how workers’ career and wage profiles are shaped by internal labor markets (ILM) and job hierarchies in firms. Our paper tackles the conceptual challenge of organizing jobs within firms into hierarchy levels by proposing a data-driven ranking method based on observed worker flows between occupations within firms. We apply our method to linked employer-employee data from Norway that records fine-grained occupational codes and tracks contract changes within firms. Our findings confirm existing evidence that is primarily based on case studies for single firms. We expand on this by documenting substantial heterogeneity in the structure and hierarchy of ILMs across a broad range of large firms. Our findings on wage and promotion dynamics in ILMs are consistent with models of careers in organizations.

Hospital Queues, Patient Health and Labor Supply (with Anna Godøy, Venke F Haaland and Mark Votruba). Revise & resubmit, American Journal of Economics: Economic Policy.
Abstract: Long waits for health care raise concerns about the consequences of delayed treatment. We use variation in queue congestion to estimate effects of wait time for orthopedic surgery. We do not find that longer wait times lead to worse health outcomes. We do find persistent reductions in labor supply: long waits increase medium to long-term work absences and permanent disability receipt. The effect is driven by individuals who are already on sick leave at referral. Our results are consistent with patterns of state dependence, where extended periods of temporary disability while awaiting treatment create persistent barriers to returning to work.

Selection in Surveys (with Deniz Dutz, Santiago Lacouture, Magne Mogstad, Alex Torgovitsky, and Winnie van Dijk)
Abstract: We evaluate whether survey nonresponse affects conclusions drawn from survey data, measure the extent to which nonresponse bias is caused by observed or unobserved differences between participants and nonparticipants, and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the Covid-19 pandemic that used randomly assigned financial incentives to encourage participation. We link the survey data to administrative data sources, allowing us to observe a ground truth for participants and nonparticipants. We find evidence of large nonresponse bias, even after correcting for observable differences between participants and nonparticipants. We apply a range of existing methods that account for nonresponse bias due to unobserved differences, including worst-case bounds, bounds that incorporate monotonicity assumptions, and approaches based on parametric and nonparametric selection models.These methods produce bounds (or point estimates) that are either too wide to be useful or far from the ground truth. We show how these shortcomings can be addressed by modeling how nonparticipation can be both active (declining to participate) and passive (not seeing the survey invitation). The model makes use of variation from the randomly assigned financial incentives, as well as the timing of reminder emails. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods.

Work in Progress

Publications / Accepted Papers

  • Huitfeldt, I. (2021). Hospital Reimbursement and Capacity Constraints: Evidence from Orthopedic Surgeries. Health Policy.

  • Bensen, S. & Huitfeldt, I. (2020) Rumor has it: How do patients respond to patient-generated physician ratings? Journal of Health Economics.

  • Godøy, A. & Huitfeldt, I. (2020). Regional Variation in Health Care Utilization and Mortality. Journal of Health Economics.

  • S.A.C. Kittelsen et al. (2015). Costs and quality at the hospital level in the Nordic countries. Health Economics.