Can We Measure from the Bottom Up? Constructing an Index of Gas Station Infrastructure to Identify Regional Economic Development

Filkoski, Vasil and Tevdovski, Dragan (2025): Can We Measure from the Bottom Up? Constructing an Index of Gas Station Infrastructure to Identify Regional Economic Development.

Abstract

We develop a methodology that leverages open-source geospatial data on fuel station infrastructure and related services to construct the Gas Station Index (GSI), a novel indicator that augments official and alternative measures of regional economic development. Gas stations serve as consumer-facing infrastructure nodes, and their density and quality reflect local demand, purchasing power, and mobility. Using data on 19,033 stations across 62 regions in nine European countries, the GSI explains 64% of the cross-regional variation in GDP per capita - a notable result for a single-variable indicator. Beyond its statistical fit, the GSI uncovers meaningful economic patterns. It reflects diminishing returns to infrastructure, consistent with core economic theory; it maps spatial inequality both visually and statistically, highlighting clusters of prosperity in capitals, port cities, transit corridors, and tourist destinations; and it classifies regional development typologies through bivariate LISA analysis. The unexplained variation underscores the structural differences between infrastructure-based indicator and GDP per capita, driven by sectoral specialization, mobility patterns, and informal economic activity. The GSI should therefore be viewed not as a substitute for national accounts, but as a complementary indicator particularly relevant at the subnational level. Compared to existing indicators, it offers distinct advantages: GDP per capita is delayed and masks heterogeneity, while night-time lights suffer from saturation and rural undercoverage. By contrast, the GSI provides a ground-level, behaviorally grounded, and real-time measure of economic development. By capturing both infrastructure and consumption dynamics, it complements—and in certain respects surpasses—conventional indicators in tracing regional growth trajectories and spatial inequality.

• Item Type: MPRA Paper
• Language: English
• Keywords: regional income, regional inequality, economic development measurement, infrastructure, geospatial data, nowcasting.
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; E - Macroeconomics and Monetary Economics > E0 - General > E01 - Measurement and Data on National Income and Product Accounts and Wealth ; Environmental Accounts; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O18 - Urban, Rural, Regional, and Transportation Analysis ; Housing ; Infrastructure; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity

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