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Australian industry is characterised by differences across firms, entry of new firms and exit of unsuccessful firms. These facts highlight the inappropriateness of measuring productivity using aggregate production functions based upon representative firms. In this study, we model heterogeneous firms which change over time. We model the interrelationship between productivity shocks, input choices and decisions to cease production. Firm‐level data provides production function estimates for 25 two‐digit Australian industries. A new aggregation method for industry‐level data allows us to separate productivity changes from output composition changes. Our study sheds new light on the Australian productivity performance.

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Abstract. With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.

Regional trade agreements concluded in recent years have increasingly included provisions on trade facilitation. Although ASEAN 1 FTAs vary in their scope, specificity and depth of commitments on trade facilitation, they tend to cover several core areas and affirm the application of international agreements, standards and instruments. A review of trade facilitation performance through a constructed Core Trade Facilitation Index shows that there are great disparities among ASEAN countries and their FTA partners. In considering the treatment of trade facilitation in a regional comprehensive economic partnership between ASEAN and its dialogue partners, attention could be given to defining a consistent set of trade facilitation principles, adopting specific measures, monitoring performance, implementing capacity-building measures and keeping abreast of developments in multilateral negotiations on trade facilitation.