A 16-Year Panel Dataset on Human Capital Capitalization and Productivity (2010–2025).

Description

Dataset Description This dataset provides a comprehensive 16-year longitudinal panel (2010–2025) focused on the relationship between tertiary education investment, human capital capitalization, and labor productivity. The study covers four strategically selected emerging economies: Uzbekistan, Malaysia, Kazakhstan, and Vietnam, representing different institutional models of human capital development. Key features of the dataset include: HCI+ Integration: It is one of the first datasets to utilize the April 2026 World Bank Human Capital Index Plus (HCI+) expanded benchmarks, which move beyond traditional 0–1 indexing to a 0–325 scale of labor earnings potential. Multi-Pillar Analysis: Data is disaggregated into the specific pillars of the HCI+, including the On-the-Job Learning (OTJL) component, which serves as a proxy for the effectiveness of Dual Education systems. Harmonized Variables: The panel includes government expenditure on tertiary education (X1), gross school enrollment (X2), and Labor Productivity (Y) in constant 2021 PPP dollars. Accounting Framework: The data is structured to facilitate an Economic Audit of national human capital assets, identifying the "Quantity-Quality Gap" where rapid enrollment surges do not immediately translate into productivity gains. Methodology: The data were retrieved from the World Bank and IMF archival records, harmonized in Stata 18/BE, and treated for missing values using linear interpolation to ensure a balanced panel. All financial metrics have been adjusted for purchasing power parity (PPP) to ensure cross-country comparability.

Steps to reproduce

"The raw data was retrieved from the World Bank and IMF databases (2026 update). Data was imported into Stata 18/BE. Initial data showed scaling inconsistencies (e.g., productivity in $10^{15}$ units), which were corrected to standard PPP dollar units. Missing values between the 5-year benchmarks (2010, 2015, 2020, 2025) were treated using linear interpolation (ipolate) to create a balanced panel. Outliers were cross-referenced with national statistical reports from Uzbekistan and Malaysia. Investment ratios were audited to ensure zero-boundary consistency."

Institutions

• Samarkand branch of Tashkent State University of Economics

  Samarqand Region, Samarkand
• Lincoln University College

  Selangor, Petaling Jaya

Categories

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