Replication data and code for "Resources, Institutions and Foreign Direct Investment in Emerging and Developing Economies: Evidence from the Emerging Markets Investment Attractiveness Score (EMIAS) Framework"
Description
Research hypothesis: This dataset supports a study testing whether a multi-dimensional composite index of emerging-market attractiveness (EMIAS) — combining natural-resource endowment, political/institutional environment, macroeconomic and business-risk conditions, growth and aspiration indicators, and market/infrastructure factors — better predicts foreign direct investment (FDI) inflows than natural-resource endowment alone. The competing hypothesis is that a single-factor natural-resources model is at least as predictive, meaning the added complexity of a composite index would not be empirically justified for FDI-location decisions in emerging markets. What the data shows: Across a panel of emerging-market countries (2019–2023), natural resources is the only pillar with a consistent, statistically significant association with FDI inflows; the other four EMIAS pillars are not individually significant. A five-pillar corrected model (176 observations, 59 countries) confirms this pattern (NR coefficient = 0.333, p < .001), and dropping natural resources from the model collapses its explanatory power (joint F test not significant). Five-fold country-grouped cross-validation, computed with training-only preprocessing to avoid leakage, shows the natural-resources-only model achieves the lowest prediction error (RMSE = 0.842) versus the traditional four-pillar (0.888) and full five-pillar (0.909) models. The result holds under multiple imputation of missing natural-resource data (292 observations, 60 countries), country-clustered and wild-cluster bootstrap inference, regional and annual subsamples, and z-score-vs-min-max standardization checks. The original six-pillar confirmatory sample (445 observations, 89 countries) is included alongside the corrected sample for full transparency. How the data was gathered: Indicators were compiled from publicly available international sources, principally World Bank World Development Indicators and World Governance Indicators, IMF macroeconomic series, and recognized country-risk/political-risk indices, merged into a country-year panel and standardized into pillar scores. Missing natural-resource-rent values were multiply imputed (20 imputations, Rubin's rules) rather than dropped, to preserve sample size and avoid selection bias from listwise deletion. How to interpret and use the files: EMIAS_Phase3_panel_with_scores_FROZEN.xlsx used for every reported result. emias_strengthening_pipeline_FIXED.py — the complete, path-portable analysis script; running it reproduces every table and figure from the frozen panel. 25 excel sheets outputs (model results, coefficients, diagnostics, robustness checks) generated directly by the script, so results can be inspected without rerunning Python. Manuscript and tables files — the accompanying article text and formatted tables these data support. Requirements numpy==2.4.4 pandas==3.0.2 scipy==1.17.1 openpyxl>=3.1
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Institutions
- University of South FloridaFlorida, Tampa