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Emily C. Lawler | Associate Professor | University of Georgia — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Economy →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
nathanschiff.com ↗	71 match 1 shared topics	Nathan Schiff \| Associate Professor, Alberta School of Business, University of Alberta	en	economy WordPress	robotsllmsaihumans	emailphone	none
kevindevereux.com ↗	69 match 1 shared topics	Kevin Devereux – Department of Economics at the Chinese University of Hong Kong	en	economy WordPress	robotsllmsaihumans	emailphone	none
miristryjan.com ↗	69 match 1 shared topics	Miri Stryjan \| Economics \| Aalto University	en	economy	robotsllmsaihumans	emailphone	none
mbinder.net ↗	69 match 1 shared topics	Prof. Dr. Martin Binder \| Professor of Socio-Economics, Universität der Bundeswehr München	en	economy WordPress	robotsllmsaihumans	emailphone	none
simonloertscher.net ↗	68 match 1 shared topics	Simon Loertscher - Professor of Economics	en	economy WordPress	robotsllmsaihumans	emailphone	none
moritzschularick.com ↗	67 match 1 shared topics	Home - Moritz Schularick \| Professor of Economics	United States~ en	economy Jimdo	robotsllmsaihumans	emailphone	none
moritzschularick.net ↗	67 match 1 shared topics	Home - Moritz Schularick \| Professor of Economics	United States~ en	economy Jimdo	robotsllmsaihumans	emailphone	none
zhouyc.com ↗	67 match 1 shared topics	Christy Zhou \| John E. Walker Department of Economics, Clemson University	en	economy WordPress	robotsllmsaihumans	emailphone	none
kfisrael.com ↗	67 match 1 shared topics	Dr. habil. Karl-Friedrich Israel \| Professor of Economics at UCO Angers \| Director of the Scientific Committee of IREF	en	economy WordPress	robotsllmsaihumans	emailphone	none
adamsmithprogram.org ↗	67 match 1 shared topics	Adam Smith Program · Department of Economics · George Mason University	United States~ en	economy	robotsllmsaihumans	emailphone	none
ben-faber.com ↗	67 match 1 shared topics	Benjamin Faber - UC Berkeley Economics Professor \| Globalization & Economic Development	United States~ en	economy	robotsllmsaihumans	emailphone	none
franciscoamaral.net ↗	67 match 1 shared topics	Francisco Amaral – Welcome! I’m a PhD candidate in Economics at the University of Bonn, affiliated with Bonn’s MacroFinance Lab and a member of ECONtribute: Markets & Public Policy, a Cluster of Excellence under Germany’s Excellence Strategy.	en	economy WordPress	robotsllmsaihumans	emailphone	none
denvermcasey.com ↗	66 match 1 shared topics	Denver M. Casey, Ph.D. – The personal site for the professor of economics	en	economy WordPress	robotsllmsaihumans	emailphone	none
nathandeutscher.com ↗	66 match 1 shared topics	Nathan Deutscher \| Australian National University \| University of Technology Sydney	en	economy WordPress	robotsllmsaihumans	emailphone	none
nacimocan.com ↗	66 match 1 shared topics	Economics Professor, Baton Rouge, LA \| Naci Mocan	en	economy WordPress	robotsllmsaihumans	email phone	none
aibe.website ↗	66 match 1 shared topics	AIBE - Italian-Brazilian Association of Economics	en	economy	robotsllmsaihumans	emailphone	none
mnikiforos.net ↗	66 match 1 shared topics	Michalis Nikiforos	en	economy	robotsllmsaihumans	emailphone	none
nikhil-datta.com ↗	66 match 1 shared topics	Home - Nikhil Datta	en	economy WordPress WooCommerce	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.