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This paper aims to examine whether integration of national markets fosters innovation in the technologically inferior country.

The authors examine a simple game theoretic framework where a technologically backward home firm and a technologically advanced foreign firm compete in both qualities and prices in an integrated market. They formalize the speed of response to market integration of the two countries as a first mover advantage in the R&D stage.

The authors find that the outcome depends on the speed of response of the two firms and their initial technological distance. If the domestic firm is not too far behind the foreign firm to begin with, and if it responds faster, then the technological gap may get reversed. Further, the authors find that integration may be welfare improving for both the countries. There are, however, distributional implications. While the consumers always gain from such integration the firms may not.

This paper contributes to the debate on the implication of market integration. In contrast to the literature the authors use a model of vertical product differentiation which is relatively less explored in the literature and formalize the speed of response to market integration as a first mover advantage in R&D. This allows them to establish several interesting results with interesting policy implications.

With product labelling has already been in vogue in the developed countries to distinguish products produced by child labour from those that are “child labour free” to influence the buyers’ choice, one might be tempted to suggest that such labels should contain more information regarding the extent to which child labour has been used in a product. But, as this chapter shows, if labels contain such information and the buyers react to this by offering increasingly lower prices for the varieties containing more child labour per unit of output, it may do more harm than helping the cause. That is, a trade sanction against child labour content of a good exported by developing countries may not be a good idea since it may, in fact, raise the incidence of child labour in those countries. This result is established in a simple general equilibrium framework where parents are altruist.

This study aims to propose a novel deep learning (DL)-based framework, iRelevancy, for identifying the disaster relevancy of a social media (SM) message.

It is worth mentioning that a fusion-based DL model is introduced to objectively identify the relevancy of a SM message to the disaster. The proposed system is evaluated with cyclone Fani data and compared with state-of-the-art DL models and the recent relevant studies. The performance of the experiments is assessed by the accuracy, precision, recall, f1-score, area under receiver operating curve and precision–recall curve score.

The iRelevancy leads to a better performance in accuracy, precision, recall, F-score, the area under receiver operating characteristic and area under precision-recall curve, compared to other state-of-the-art methods in the literature.

The predictive performance of the proposed model is illustrated with experimental results on cyclone Fani data, along with misclassifications. Further, to analyze the performance of the iRelevancy, the results on other cyclonic disasters, i.e. cyclone Titli, cyclone Amphan and cyclone Nisarga are presented. In addition, the framework is implemented on catastrophic events of different natures, i.e. COVID-19. The research study can assist disaster managers in effectively maneuvering disasters during distress.