Vector based shortest path analysis in geographic information system (GIS) is well established for road networks. Even though these network algorithms can be applied to river layers, they do not generally consider the direction of flow. This paper presents a Python 3.7 program (upstream_downstream_shortests_path_dijkstra.py) that was specifically developed for river networks. It implements multiple single-source (one to one) weighted Dijkstra shortest path calculations, on a list of provided source and target nodes, and returns the route geometry, the total distance between each source and target node, and the total upstream and downstream distances for each shortest path. The end result is similar to what would be obtained by an “all-pairs” weighted Dijkstra shortest path algorithm. Contrary to an “all-pairs” Dijkstra, the algorithm only operates on the source and target nodes that were specified by the user and not on all of the nodes contained within the graph. For efficiency, only the upper distance matrix is returned (e.g., distance from node A to node B), while the lower distance matrix (e.g., distance from nodes B to A) is not. The program is intended to be used in a multiprocessor environment and relies on Python’s multiprocessing package.

We investigate the relationship between management practices and long working hours by combining large-scale establishment panel data on management practices with the corresponding employee data on overtime hours in the manufacturing sector. We find that the adoption of more structured bonus and promotion practices is correlated with an increasing probability of workers working more than short-to-medium overtime hours. In addition, the adoption of more structured production monitoring and targeting practices is associated with a lower probability of workers working long overtime hours, resulting in narrowing disparities in overtime hours across workers within establishments.

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