Economics & Development

Integration into global value chains can drive rapid industrialization without necessarily building the technological capabilities needed for sustained upper-middle-income growth. Using Thailand as a diagnostic case and global country–product–year data, we link PATSTAT patent and citation records to HS6 trade flows through a probabilistic technology–product concordance and derive a structural-gravity measure of export capability. Three findings emerge. First, Thailand’s established manufacturing export base coexists with an innovation base concentrated in applied, downstream activities rather than upstream and science-intensive technologies. Second, innovation intensity is positively associated with exports and export capability, particularly in digital and semiconductor products. Third, in the log-export interactions, this relationship is stronger where human capital and logistics perform better and, in several technology-intensive product groups, where policy uncertainty is lower. Evidence from Thai electric-vehicle and electronics producers identifies certification, customs and logistics, documentation, skills, and dependence on external partners—rather than tariffs—as practical constraints on capability deepening. Although the results are descriptive rather than causal, they show the complementary conditions under which technological accumulation is more closely connected to export upgrading. For middle-income economies, the challenge after entering global value chains is therefore to build the domestic capabilities and institutions needed to move beyond assembly and sustain productivity growth.

Le Baromètre économique CEMAC est une publication semestrielle de la Banque mondiale qui présente un aperçu des développements récents et des perspectives économiques de la région CEMAC, suivi d’une brève évaluation au niveau national et d’une section consacrée à un sujet spécifique.

Air pollution imposes severe health and economic costs worldwide, yet ground-based monitoring data are difficult and expensive to collect, limiting the information available for policy analysis. This paper develops machine learning models to estimate daily air quality using satellite observations, meteorological data, emissions inventories, and geographic information. Data sources include IQAir ground station reports; atmospheric pollutant concentrations from the European Space Agency’s tropospheric monitoring instrument, TROPOMI; and meteorological data from the Copernicus ERA5 Land database. The paper estimates separate random forest models for fine particulate matter (PM2.5), ozone, nitrogen dioxide, and carbon monoxide, using a random forest algorithm that quantifies variable importance. The results are robust: TROPOMI dominates predictions for fine particulates (PM2.5), nitrogen dioxide, and carbon monoxide, with the Emissions Database for Global Atmospheric Research and meteorological variables providing secondary contributions. For ozone, the meteorological variables are most influential, followed by TROPOMI and the Emissions Database for Global Atmospheric Research. Geographic features matter across all pollutants—elevation, latitude, and coastal proximity consistently rank high, while Köppen-Geiger climate zones vary in importance by pollutant. Temporal tracking is validated by correlating daily monitored versus predicted pollutant levels across urban areas using IQAir ground station data. Median intertemporal correlations exceed 0.69 for all pollutants, with upper quartiles above 0.82 and lower quartiles above 0.53. Although the pilot uses data from April to November 2025, TROPOMI has been available since 2018 and the Copernicus ERA5 Land database provides matching coverage, enabling daily estimates from April 2018 onward on a global terrestrial grid at 0.1-degree resolution.