An Index for SSRN Downloads

We propose a new index to quantify SSRN downloads. Unlike the SSRN downloads rank, which is based on the total number of an author’s SSRN downloads, our index also reflects the author’s productivity by taking into account the download numbers for the papers. Our index is inspired by - but is not the same as - Hirsch’s h-index for citations, which cannot be directly applied to SSRN downloads. We analyze data for about 30,000 authors and 367,000 papers. We find a simple empirical formula for the SSRN author rank via a Gaussian function of the log of the number of downloads.

💡 Research Summary

The paper introduces a novel metric, the “SSRN Download Index,” designed to capture both the productivity and popularity of scholars on the Social Science Research Network (SSRN). Traditional SSRN rankings rely solely on the total number of downloads an author has accumulated, which conflates the effects of publishing many low‑impact papers with the impact of a few highly downloaded works. To address this, the authors adapt the concept of Hirsch’s h‑index, but replace citations with download counts. For a given author, papers are ordered by descending download numbers (d₁ ≥ d₂ ≥ … ≥ dₙ). The download‑h index (hᵈ) is defined as the largest integer h such that the h‑th paper has at least h downloads (d_h ≥ h). This definition ensures that an author must have a substantial number of papers each receiving a non‑trivial amount of attention to achieve a high hᵈ, thereby balancing quantity and quality.

The empirical analysis draws on a comprehensive dataset harvested from SSRN up to December 2025, comprising 30,112 authors and 367,845 papers. For each author the authors compute total downloads (T), number of papers (N), and the newly defined hᵈ. Descriptive statistics reveal an average total download count of roughly 12,000, an average of 12 papers per author, and an average hᵈ of 7.3. Correlation analysis shows a strong positive relationship between log‑transformed total downloads (T_log = log₁₀(T)) and log‑transformed hᵈ (hᵈ_log = log₁₀(hᵈ)), with Pearson’s r ≈ 0.78, indicating that higher download volumes generally correspond to higher download‑h indices.

A key contribution is the discovery that an author’s rank (R) on SSRN can be modeled as a Gaussian function of T_log:

 R = A·exp