Estimation of Industrial Heterogeneity from Maximum Entropy and Zonotopes Using the Enterprise Surveys
Ting-Yen Wang

TL;DR
This paper develops a new framework combining maximum entropy estimation and Zonotope measures to better quantify industrial heterogeneity using firm-level survey data, surpassing traditional methods in capturing nuanced industry differences.
Contribution
It introduces a novel integration of maximum entropy and Zonotope-based measures for estimating industrial heterogeneity, validated with empirical data from multiple countries.
Findings
ME method outperforms traditional techniques in capturing heterogeneity
Zonotope-based Gini index provides complementary heterogeneity measure
Numerical simulation confirms ME regression's accuracy in approximating industry heterogeneity
Abstract
This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data from Bangladesh, Colombia, Egypt, and India. The study normalizes entropy values to quantify heterogeneity and compares these measures with a Zonotope-based Gini index. Results demonstrate the ME method's superiority in capturing nuanced, functional heterogeneity often missed by traditional techniques. Furthermore, the study incorporates a "Tangent Against Input Axes" method to dynamically assess technical change…
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Taxonomy
TopicsEconomic and Technological Innovation · Efficiency Analysis Using DEA · Economic Growth and Productivity
