12,003 research outputs found

    Exports, Imports and Wages:Evidence from Matched Firm-Worker-Product Panels

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    The analysis of the effects of firm-level international trade on wages has so far focused on the role of exports, which are also typically treated as a composite good. However, we show in this paper that firm-level imports can actually be a wage determinant as important as exports. Furthermore, we also find significant differences in the relationship between trade and wages across types of products. In particular, firms that increase their exports (imports) of high- (intermediate-) technology products tend to increase their salaries. Our analysis is based on unique data from Portugal, obtained by merging a matched firm-worker panel and a matched firm-transaction panel. Our data set follows the population of manufacturing firms and all their workers from 1995 to 2005 and allows for several control variables, including jobspell fixed effects.

    Using Generic Summarization to Improve Music Information Retrieval Tasks

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    In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most important information for the tasks may not be in those processed segments. In this paper, we leverage generic summarization algorithms, previously applied to text and speech summarization, to summarize items in music datasets. These algorithms build summaries, that are both concise and diverse, by selecting appropriate segments from the input signal which makes them good candidates to summarize music as well. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the performance obtained using summarized datasets against the performances obtained using continuous segments (which is the traditional method used for addressing the previously mentioned time constraints) and full songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based Centrality model improve classification performance when compared to selected 30-second baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin
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