Identifying Kidney Trade Networks using Web Scraping Data

21 Pages Posted: 18 Aug 2021

See all articles by Meng-Hao Li

Meng-Hao Li

George Mason University

Abu Bakkar Siddique

Goerge Mason University

Brian Wilson

Systems Engineering and Operations Research

Amit Patel

Department of Public Policy and Public Affairs, McCormack Graduate School

Hadi El-Amine

Volgenau School of Engineering GMU

Naoru Koizumi

Schar School of Policy and Government

Date Written: March 01, 2021

Abstract

Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts of the world including South Asia, Central America, the Middle East, and East Asia. Kidney trade networks reported in these hotspots are often complex systems involving several players including buyers, sellers, and brokers operating across international borders. In particular, brokers are known to arrange buyers and sellers from different countries, mobilizing them to another country for surgery so that they can bypass domestic laws in sellers’ and buyers’ countries. The exact patterns of the country networks are, however, largely unknown due to the lack of a systematic approach to collect the data. Most of the kidney trade network information is currently available in the form of news articles, case studies, and reports, and no comprehensive database exists at this time. To this end, the present study explored online newspaper scraping to systematically compile the information of transnational kidney trade networks. Specific pieces of information about surgery locations as well as the nationalities of buyers and sellers were recorded to visualize the country networks of transnational kidney trade. The findings of the study suggest that newspaper scraping is a promising approach to compile such data especially in the dire shortage of empirical data.

Note: Funding: This work is funded by NSF - EAGER: ISN: / 1838306.

Declaration of Interests: None to declare.

Keywords: Kidney trafficking, Machine learning, Web-scraping data, Trafficking hub, South Asia, Newspaper articles

Suggested Citation

Li, Meng-Hao and Siddique, Abu Bakkar and Wilson, Brian and Patel, Amit and El-Amine, Hadi and Koizumi, Naoru, Identifying Kidney Trade Networks using Web Scraping Data (March 01, 2021). Available at SSRN: https://ssrn.com/abstract=3891901 or http://dx.doi.org/10.2139/ssrn.3891901

Meng-Hao Li

George Mason University

Founders Hall, Fifth Floor
3351 Fairfax Drive, MS 3B1
Arlington, VA 22201
United States

Abu Bakkar Siddique

Goerge Mason University ( email )

3351 Fairfax Dr.
Van Metre Hall, Room#526
Arlington, VA 22201
United States
15712745328 (Phone)

Brian Wilson

Systems Engineering and Operations Research ( email )

4400 University Drive
Fairfax, VA 22030
United States

Amit Patel

Department of Public Policy and Public Affairs, McCormack Graduate School ( email )

100 William T Morrissey Blvd
Boston, MA 02125
United States

Hadi El-Amine

Volgenau School of Engineering GMU ( email )

4400 University Drive
Fairfax, VA 22030
United States

HOME PAGE: http://https://volgenau.gmu.edu/profiles/helamine

Naoru Koizumi (Contact Author)

Schar School of Policy and Government

Founders Hall, Fifth Floor
3351 Fairfax Drive, MS 3B1
Arlington, VA 22201
United States

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