Dataset for "E-Government for Corruption Control under the Moderating Role of Regulatory Quality: A Reagent–Flux Recrystallization Perspective"e

Dataset Description: E-Government, Regulatory Quality, and Corruption Control (2012–2024)

OverviewThis dataset supports the empirical analysis of the relationship between e-government development (EGOV) and Corruption Control (CC), specifically examining the moderating influence of Regulatory Quality (RQ). The data is organized as a balanced panel of 133 countries spanning 2012–2024.

Variable Operationalization

EGOV: Measured via the E-Government Development Index (EGDI) and its three pillars: Online Services (OS), Telecommunications Infrastructure (TI), and Human Capital (HC). Source: United Nations E-Government Knowledgebase (UN DESA)

Regulatory Quality (RQ): Sourced from the Worldwide Governance Indicators (WGI).

Corruption Control (Outcome): Operationalized through the Corruption Perceptions Index (CPI).Source: Transparency International.Source: Transparency International - Corruption Perceptions Index

Controls: Voice and Accountability (VA) Source: World Bank Worldwide Governance Indicators (WGI), Trade Openness (TO), Economic Development (Log GDP per capita) These are sourced from Source: World Bank World Development Indicators (WDI) and World Press Freedom Index (WPFI). Sourced from Reporters Without Borders.

Controls:  Press Freedom Index (WPFI). Reporters Without Borders.

Instruments: Secure internet servers and mobile penetration (used for IV estimation). Source: World Bank World Development Indicators (WDI)

File Structure and Model MappingThe dataset is provided in a single workbook with two distinct spreadsheets, structured to facilitate the sequential testing of five econometric models based on the temporal lag of the outcome variable (CPI):

Sheet 1 (Models 1, 2, & 3): Contains the primary panel data including the contemporaneous outcome variable (CPI) and short-term lags (CPI_t+1, CPI_t+2).

Sheet 2 (Models 4 & 5): Contains the extended longitudinal data specifically for models requiring longer-term lags (CPI_t+3 and CPI_t+4).

Methodological DesignThe data is formatted for Within–Between (Mundlak) decomposition, including both group-mean centered variables (within-country effects) and country-level means (structural between-country effects). This dual-sheet structure ensures the balanced panel remains robust even as the time-horizon for the "corruption gestation" period increases across the five models.