Contract Cheating Statistics 2026: Global Data from the AMI

TL;DR

Contract cheating data from AMI v1.5 (May 2026). Demand signals captured via Google Trends. Countries with specific bans (Australia, UK, Ireland) score lowest on D1 (33 each). Highest scoring countries: Colombia, Argentina, Greece, Pakistan (100 each). Supply concentration in India, Kenya, Pakistan, Philippines.

Demand rankings — D1 score across 39 countries

Top 10 — highest D1 scores

Bottom 10 — lowest D1 scores

The pattern is clear: the three countries with specific bans (Australia, UK, Ireland) have D1 scores tied at 33, lower than all other countries in the dataset. Whether this reflects causation (bans reducing demand) or correlation (countries that legislated also had pre-existing lower demand) is harder to establish.

Regional patterns

Latin America

Spanish-language essay mill market is the most active globally. Colombia (D1=100) and Argentina (D1=100) anchor the highest demand signals. Mexico (67) and Brazil (67) show moderate-elevated demand. The Spanish-language market is structurally large — services target the whole Hispanophone academic market.

Middle East

Saudi Arabia (83), Egypt (67), Iran (67), Turkey (83) all show elevated demand. Arabic-language and Turkish-language essay mill markets operate alongside English-language services targeting the substantial English-medium higher education populations.

South Asia

Pakistan (100), India (83) — both high. Pakistan's score reflects domestic demand plus the substantial essay mill export industry operating from Pakistan. India similar — major export hub with significant domestic demand.

Europe — Q1 vs others

Q1 countries (UK 33, Ireland 33, Netherlands 50) score lower. Q3/Q4 European countries (Italy 83, Spain 83, France 67, Russia 83, Poland 67, Greece 100) show elevated demand. The Anglophone-Continental split is partly a language effect (English-language essay mills are largest globally) and partly a policy effect (the bans).

Anglophone

Australia 33, UK 33, Ireland 33 — the three lowest D1 scores in the dataset. US (50) and Canada (50) marginally higher; both lack specific essay mill bans. New Zealand 50.

Supply concentration

The AMI's D1 captures demand-side signals. Supply-side data is harder to measure systematically.

Lancaster (multiple publications) has documented supply concentration:

• India — largest essay mill industry globally; serves UK, Australian, US markets
• Pakistan — substantial export market; English-language proficiency advantage
• Kenya — Nairobi-based writers; significant Anglophone export
• Philippines — substantial export presence

These supply hubs also show elevated domestic D1 (India 83, Pakistan 100, Kenya 67, Philippines 67) — students in supply countries also purchase services, though the export market is the more globally significant feature.

Newton meta-analysis context

Newton (2018) [verify: "How common is commercial contract cheating in higher education and is it increasing? A systematic review"] systematic review of self-report studies estimated 15.7% of post-secondary students had paid for academic work. Applied to ~225 million global higher education students, this implies on the order of 30+ million students having used contract cheating services.

The 15.7% is an aggregate across studies of varying quality. Country variation is substantial — the McCabe and ICAI data shows rates ranging from below 5% in countries with strong infrastructure to over 30% in some samples from countries with weak infrastructure.

Trends over time

Pre-ChatGPT (before late 2022), contract cheating volume grew steadily through the 2010s, driven by:

• Internet enabling marketplace platforms connecting students and writers
• International student growth particularly in Anglophone markets
• Online and distance learning expansion (assessment authentication harder)

Post-ChatGPT, the picture is mixed:

• Some evidence of substitution toward free AI tools
• D1 demand signals have not collapsed
• Brand name searches for major essay mill services declined in 2023–2024 [verify trend specifics]

The substitution-vs-displacement balance is an active research question.

Sources

• AMI v1.5 dataset (D1 dimension data)
• Google Trends API (2022–2026), per-country signals
• Newton, P. M. (2018), Frontiers in Education
• Lancaster, T. (multiple), International Journal for Educational Integrity
• ICAI / McCabe survey data

Full methodology | Download dataset

Related

Explore the full dataset