Bollywood Box Office Revenue Distributions (2005–2017): A Categorical and
Temporal Econophysics Analysis

Anhad Singh

doi:10.46609/IJSSER.2026.v11i07.047

Title: Bollywood Box Office Revenue Distributions (2005–2017): A Categorical and Temporal Econophysics Analysis
Authors: Anhad Singh
Journal: International Journal of Social Science and Economic Research (IJSSER) ISSN: 2455-8834 Volume: 11 Issue: 7 Publication Date: July 2026 DOI: 10.46609/IJSSER.2026.v11i07.047
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Anhad Singh Inventure Academy, Bengaluru, India
MLA 8 Singh, Anhad. "Bollywood Box Office Revenue Distributions (2005–2017): A Categorical and Temporal Econophysics Analysis." Int. j. of Social Science and Economic Research, vol. 11, no. 7, July 2026, pp. 3592-3603, doi.org/10.46609/IJSSER.2026.v11i07.047. Accessed July 2026. APA 6 Singh, A. (2026, July). Bollywood Box Office Revenue Distributions (2005–2017): A Categorical and Temporal Econophysics Analysis. Int. j. of Social Science and Economic Research, 11(7), 3592-3603. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i07.047 Chicago Singh, Anhad. "Bollywood Box Office Revenue Distributions (2005–2017): A Categorical and Temporal Econophysics Analysis." Int. j. of Social Science and Economic Research 11, no. 7 (July 2026), 3592-3603. Accessed July, 2026. https://doi.org/10.46609/IJSSER.2026.v11i07.047.
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ABSTRACT: Box office revenue is famously unequal: a few films earn enormous amounts while most earn very little. Econophysics has studied this kind of skew, and earlier work found that Indian film revenue follows a log-normal distribution rather than a power law. This study extends that result to a larger and more recent sample and asks a question earlier work did not: does the distribution depend on what kind of film it is? Using 1,694 Hindi-language films released between 2005 and 2017 from the Premkumar (2020) dataset, I fitted log-normal and power-law distributions by maximum likelihood and compared them with the Vuong test, across three categorical splits (release period, remake status, and franchise status) and two eras defined by the arrival of OTT streaming. I also measured concentration with the Gini coefficient and fitted a budget–revenue scaling law. Log-normal was favoured over power-law in every large subgroup and in both eras. Concentration rose over time, with the annual Gini increasing from 0.575 to 0.693 (Spearman rho = +0.93) and the top-decile revenue share crossing 50 percent, which matches Elberse's blockbuster prediction rather than Anderson's long tail. The budget–revenue exponent was 0.519 and stayed close to that value across every film type.

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