ECONOMETRIC MODELLING METHODS FOR ASSESSING THE INVESTMENT ATTRACTIVENESS OF INDUSTRIAL ZONES: A METHODOLOGICAL FRAMEWORK WITH EVIDENCE FROM UZBEKISTAN
DOI:
https://doi.org/10.5281/zenodo.23608240Abstract
Industrial zones, including special economic zones, small industrial zones, technoparks, and clusters, have become important spatial instruments of investment policy in many emerging economies. However, there is no universally accepted quantitative methodology for comparing their investment attractiveness. This paper systematises the econometric methods used to measure and explain the investment attractiveness of industrial zones and proposes an integrated, empirically testable modelling framework applicable to Uzbekistan’s industrial zone system. The proposed framework combines a composite investment attractiveness index constructed using principal component analysis (PCA) and cross-validated through data envelopment analysis (DEA); dynamic panel regression models employing fixed effects and the system generalised method of moments (system GMM); discrete-choice models, including conditional and nested logit models, to examine investors’ location decisions; and staggered-adoption difference-in-differences (DiD) methods to evaluate the causal effects of industrial zone status. Official statistics provide the empirical context: Uzbekistan had 766 industrial zones at the beginning of 2024 and 8,033 registered zone participants by October 2025, approximately 82% of whom were associated with small industrial zones and technoparks. Industrial output in special economic zones reached approximately EUR 3.12 billion during January–September 2025, representing a year-on-year increase of 42.7%. The methodological review demonstrates that no single econometric approach is sufficient to capture all dimensions of investment attractiveness. Composite indices facilitate comparisons and rankings of industrial zones, panel regression models explain investment inflows, discrete-choice models identify investor location preferences, and difference-in-differences methods estimate causal effects. Accordingly, an integrated modelling strategy incorporating appropriate diagnostic tests and robustness checks is recommended to support evidence-based assessments of industrial zone investment attractiveness.Keywords
investment attractiveness, industrial zones, special economic zones, panel data, system GMM, conditional logit, difference-in-differences, UzbekistanReferences
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