DOI: 10.5061/dryad.9cnp5hqvt
title: Behaviorally designed training leads to more diverse hiring

description: Many organizations are interested in increasing the diversity of their
 workforce and spend millions of dollars on diversity training. Yet there
 is little empirical evidence that such training increases diversity in
 organizations. We implemented a large-scale field experiment in a global
 telecommunications and engineering firm (n = 10,433) testing whether
 behaviorally designed training increases the diversity of who is hired. In
 particular, the diversity training was timely (delivered immediately
 before hiring managers shortlisted candidates), tailored to the hiring
 decision, delivered by senior members of the organization, and made
 diversity salient. Results show that behaviorally designed diversity
 training can positively influence the hiring of women and non-national
 applicants relative to business as usual. Our findings suggest that
 behaviorally designed diversity training can work to change the diversity
 of hires but that its success relies on carefully considered design
 choices and the decision context.
description: # Behaviorally designed training leads to more diverse hiring **Overview**
 The program (Stata do-files) for “Behaviorally designed training leads to
 diversity hiring” by Arslan, Chang, Chilazi, Bohnet, and Hauser, published
 in *Science* (2025). The program files run all the code to import raw data
 files (xlsx, csv), clean and generate the data (in dta format), prepare
 the data for the analysis, run the regression analyses, export output, and
 thus generate tables presented in the paper. The replicator should expect
 the code to run for up to 15 minutes. **Data Availability and Sharing**
 The organizational data used in this manuscript is of a proprietary
 nature. We, the authors of the manuscript, have legitimate access to and
 permission to use the data, but we are unable to make the data publicly
 available due to a strict data use agreement with our field partner
 (global telecommunications and engineering company). Interested
 researchers are encouraged to contact MoreThanNow to have a confidential
 conversation about data sharing. To request access to the data used, each
 institution has to submit a data access request form and needs to arrange
 signed Data Sharing/Use and Non-Disclosure agreements for all researchers
 to be covered under the agreements. Researchers from the same institution
 can be named on the same data request form. Once completed, please email
 the form and all attachments to
 [experiment@morethannow.co.uk](mailto:experiment@morethannow.co.uk). Upon
 submitting this request, MoreThanNow will share the request with the field
 partner. The field partner will review the request and decide whether they
 are willing to consider sharing their data with the named researchers. If
 they approve the request, the field partner will send Non-Disclosure and
 Data Sharing/Use Agreements with the legal contact listed above, which
 must be executed prior to data being shared. The ultimate decision
 authority on whether data requests are approved lies with the field
 partner. ## **Data Files** Main data files (administrative hiring and
 intervention tracking) are not available here and were obtained through
 institutional collaboration: Dataset #1 Treatments Tracking - Final.xlsx,
 Dataset #2 - Video - Final.xlsx, DatasetHiringMarch2023.xlsx,
 Dates_reqid_mid.xlsx (this is a template to be filled based on main data
 files). Auxiliary data files are shared publicly at Dryad:
 GenderEquality.xlsx (based on the Global Gender Gap Index 2023) and
 global_south_nationalities.csv (based on the Organization for Women in
 Science for the Developing World’s list) for heterogeneity analyses. -
 GenderEquality.xlsx: This file includes the variable
 'req_country' which refers to the country of job requisition
 raised during our study period, the variable 'gender_equality'
 which is the gender equality score (varying between 0 and 1) of the
 countries in the world (as obtained from the Global Gender Gap Index
 2023), the variable 'req_georegion' which refers to the
 geographic region of each country, the variable 'global_north'
 which takes the value 1 if the country of job requisition is a Global
 North country (e.g. United States, France, Australia) and 0 if the
 requisition country in the Global South (e.g. India, Colombia, Kenya). -
 global_south_nationalities.csv: The variable 'can_nationality'
 refers to the country of nationality of the job applicant and the variable
 'global_south' shows if the country of nationality of the
 candidate is a Global South country or not. It takes the value 1 if the
 candidate is a national of a country in the Global South (e.g. India,
 Colombia, Kenya) and 0 if the candidate is a national of a country in the
 Global North (e.g. United States, France, Australia). ## **Software
 Requirements** -Stata, code was last run with version 18. -The following
 packages will be installed in Stata: dropmiss, outreg2, ivreg2, ranktest
 -The approximate time needed to reproduce the analyses on a standard
 desktop machine is under 15 minutes (2024). **Description of
 programs/code** -DivExp_MasterDoFile.do will run all four do-files below
 which are necessary for data preparation and analysis.
 -DivExp_InitialCleaning.do will import raw files, clean, manipulate, and
 save the data in dta format. -DivExp_MergeFiles_Conditional.do will merge
 different data files (excluding some observations) and prepare the data
 for the analyses. -DivExp_MergeFiles.do will merge different data files
 and prepare the data for the analyses. This is the file generating the
 dataset for our experimental analyses. -DivExp_Analysis.do will run the
 regressions and generate the output (tables in Supplementary Materials).
 **Instructions** -Place all data files in the ‘data’ folder. There are
 some auxiliary data files we share, but the main data files will have to
 be obtained from the field partner. -Place code (all do-files) in the
 ‘do-files’ folder. -Change global ‘path’s in DivExp_MasterDoFile.do -Run
 program DivExp_MasterDoFile.do (which will run all the other do-files).
 -The provided code reproduces the regression results presented in the
 article. -The folder 'output' was left empty on purpose. This is
 where the regression results (article tables) will be exported when the
 program code (DivExp_Analysis.do) is run.