The Impact of Absent co-workers on Productivity in Teams: replication archive

We study how workers in production teams are affected by the temporary absence and replacement of a co-worker using data on injuries in the National Hockey League. We distinguish between the absence of a substitute worker, who performs the same tasks as the focal workers, and the absence of a complementary co-workers, who performs complementary tasks to the focal workers. When either type of co-worker is absent, remaining workers produce less output per working time. In the case of a substitute absentee, they compensate for this by increasing their working time at the expense of the (less able) replacement worker. This renders the output loss per remaining substitute worker to be insignificant. For the absence of a complementary worker, the productivity loss leads to a loss of total output per worker, because remaining workers cannot take over the absent co-worker’s tasks. Please read the Readme file which explains the empirical procedures and dataset. The dataset and R-files allow to replicate the results reported in the linked publication. The readme file exactly explains the correspondence between the results tables in the paper and the calculated results

Do-file and datasets of PlosOne publication "Common international trends in football stadium attendance"

Datasets and do-file for the paper "Common international trends in football stadium attendance" in PlosOne, 2021. For description of the dataset, please see the Readme-file

Replication archive Peeters and van Ours (2021) De Economist

This record contains the replication archive for the paper "Seasonal Home Advantage in English Professional Football; 1974–2018" by Thomas Peeters and Jan van Ours, which appeared in De Economist in 2021. Please read the explanation file first

Racial Bias in Newspaper Ratings of Professional Football Players

Data in STATA format with accompanying STATA do-file. Replicates the analysis in: Principe, F. and J.C. van Ours (202x) Racial Bias in Newspaper Ratings of Professional Football Players, European Economic Review, forthcoming

Estimation of the Vertical Land Motion from GNSS Time Series and Application in Quantifying Sea-Level Rise

Sea-level rise observed at tide gauges must be corrected for vertical land motion, observed with GNSS, to obtain the absolute sea-level rise with respect to the centre of the Earth. Both the sea-level and vertical position time series contain temporal correlated noise that need to be taken into account to obtain the most accurate rate estimates and to ensure realistic uncertainties. Satellite altimetry directly observes absolute sea-level rise but these time series also exhibit colored noise. In this chapter we present noise models for these geodetic time series such as the commonly used first order Auto Regressive (AR), the General Gauss Markov (GGM) and the ARFIMA model. The theory is applied to GNSS and tide gauge data from the Pacific Northwest coast