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This repository contains replication files for "Network Cluster-Robust Inference."

Coded for Python 3.9.7. Packages used: numpy (v1.20.3), pandas (v1.3.4), scipy (v1.7.1), networkx (v2.6.3), sklearn (v0.24.2), matplotlib (v3.4.3), seaborn (v0.11.2).

The following can be done in any order.

• Run spectrum.py with make_plot=True to produce Figure 4 and images for Figure 1. Rerun with make_plot=False to produce Table 1.

• Run design1_size.py to produce Table 2. Run design1_power.py and then plot_power_design1.py to produce Figure 5.

• Download the Paluck et al. (2016) data from ICPSR, extract the contents, and then place the data file 37070-0001-Data.tsv into this directory. Run design2.py three times, one for each of network_model = 'RGG', 'RCM', 'config'. Then run tables_design2.py to produce Table 3.

  On the server I used, for RGG and RCM, design2.py completes in less than 24 hours with 32 cores (change the processes argument in the file to set number of cores). For config, I separated into two runs with num_schools=[1,2] and then num_schools=[4], with each completing in less than 24 hours.

• Download the Zacchia (2020) data from the publisher's website. Extract contents into a folder in this directory called 'zacchia'. Run application.py to produce Figure 2 and 3.