claim: His large-scale big data analysis empirically demonstrates modern contracts are overwhelmingly unreadable, often requiring college-level comprehension. This pervasive incomprehensibility fundamentally challenges contract law's core assumptions about informed consent and the "meeting of minds," as most individuals cannot understand the terms binding them. Arbel suggests this "readability crisis," with readability often worsening over time, necessitates a reevaluation of legal doctrines and a push for greater contractual clarity to ensure fairness and true agreement in economic and social interactions.

evidence_quote: [p. 35] ARBEL, THE READABILITY OF CONTRACTS: BIG DATA ANALYSIS 35/55 CRM Flesch Kincaid Contract Type % % 2001 2022 2001 2022 Change Change Non-Competes, Confidentiality, - - 14.5 16.5 15.9 18.5 & Post-13.80% 16.40% Employment Insurance, Indemnity, and 17 19.2 18.5 25.1 13.00% 35.50% Coverage Ownership, - Trust, and 16.5 18.1-9.70% 18.9 23.2 23.30% Governance Mergers, Alliance, and-16.2 17.7-9.30% 18.9 22.2 Investment 18.70% Agreements Shares, Stocks, - Incentives, and 15.6 17.2-9.90% 17.4 19.3 10.90% Options Credit, Debt, - and Security 16.4 17.7-7.90% 18.8 22.3 18.60% Agreements Property, - 16 17.2-7.50% 18.1 20.9 Rights, and IP 15.50% Purchase or-Sale 16.2 17.3-6.80% 18.3 21 14.80%...

citation: Yonathan A. Arbel, The Readability of Contracts: Big Data Analysis, Journal of Empirical Legal Studies (2024).

confidence: machine-linked

limitations: Machine-linked claim. Use the evidence quote and PDF before treating it as a quotation or as a complete statement of the paper's position.


claim: Methodology and Data: his study employs a big data approach, utilizing an expansive and diverse dataset of over 1.2 million contracts sourced from public repositories like the SEC’s EDGAR database and the Consumer Financial Protection Bureau’s (CFPB) database, covering a wide variety of agreement types. He writes that these contracts underwent extensive cleaning to isolate substantive provisions for analysis using established readability metrics, primarily focusing on Flesch Reading Ease and Flesch-Kincaid Grade Level scores, chosen for their prevalence and validation as useful proxies for textual difficulty.

paper_title: The Readability of Contracts: Big Data Analysis

evidence_span: [p. 35] ARBEL, THE READABILITY OF CONTRACTS: BIG DATA ANALYSIS 35/55 CRM Flesch Kincaid Contract Type % % 2001 2022 2001 2022 Change Change Non-Competes, Confidentiality, - - 14.5 16.5 15.9 18.5 & Post-13.80% 16.40% Employment Insurance, Indemnity, and 17 19.2 18.5 25.1 13.00% 35.50% Coverage Ownership, - Trust, and 16.5 18.1-9.70% 18.9 23.2 23.30% Governance Mergers, Alliance, and-16.2 17.7-9.30% 18.9 22.2 Investment 18.70% Agreements Shares, Stocks, - Incentives, and 15.6 17.2-9.90% 17.4 19.3 10.90% Options Credit, Debt, - and Security 16.4 17.7-7.90% 18.8 22.3 18.60% Agreements Property, - 16 17.2-7.50% 18.1 20.9 Rights, and IP 15.50% Purchase or-Sale 16.2 17.3-6.80% 18.3 21 14.80%...