A Panel Study of Factor Accumulation and Export Quality

Cross-sectional data show Global North countries export higher quality products at a point in time. Product-level panel data can address if countries improve their export quality over time. The literature has addressed this practically relevant panel question only in small samples over the short term. We addressed it for a large sample, over the long run, focusing on the hitherto overlooked endogeneity between export quality and factor accumulation and the role of export composition. We utilized a two-tiered panel: the panel of countries and the panel of products each country trades. We found some evidence that middle-income countries often upgrade export quality within the same product, but that high- and low-income countries do this less often. Our results appear to support product cycle theory: some countries climb the value ladder, others are competed off from the ladder’s top, and new countries enter markets. Technology appears to be a potential basis for consolidating trade competitiveness over time, as skill accumulation becomes more widespread across countries and loses significance as an explanatory variable. Our results provide some explanation of why Global North countries might resist sharing technology. This research is timely with deadlocked multilateral trade negotiations and looming trade wars. It attempts to contribute to an evidence-based guide to trade policy.

Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading

Grading is a vital process during the postharvest of horticultural products as it dramatically affects consumer preference and satisfaction when goods reach the market. Manual grading is time-consuming, uneconomical, and potentially destructive. A non-invasive automated system for export-quality banana tiers was developed, which utilized RGB, hyperspectral imaging, and deep learning techniques. A real dataset of pre-classified banana tiers based on quality and size (Class 1 for export quality bananas, Class 2 for the local market, and Class 3 for defective fruits) was utilized using international standards. The multi-input model achieved an excellent overall accuracy of 98.45% using only a minimal number of samples compared to other methods in the literature. The model was able to incorporate both external and internal properties of the fruit. The size of the banana was used as a feature for grade classification as well as other morphological features using RGB imaging, while reflectance values that offer valuable information and have shown a high correlation with the internal features of fruits were obtained through hyperspectral imaging. This study highlighted the combined strengths of RGB and hyperspectral imaging in grading bananas, and this can serve as a paradigm for grading other horticultural crops. The fast-processing time of the multi-input model developed can be advantageous when it comes to actual farm postharvest processes.