What Does Data Quality Tell Us? Learning Local Cultural Traits from Qing China Grain Prices
SSRN Electronic Journal
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We investigate the persistent impact of data misreporting from China's Qing Dynasty (1644-1912) on contemporary data quality in the country. We examine historical grain price data, collected monthly by Qing prefectural officials and reported to the central government. Using the absence of seasonal price fluctuations over extended periods as an indicator, we assess data quality. Employing an instrumental variable approach, we establish a causal link between data quality in the Qing Dynasty and data misreporting in modern China. Our findings indicate that a one-standard deviation increase in Qing data quality corresponds to a 0.16 standard deviation increase in modern data quality. This result holds under various alternative specifications, even when the analysis is limited to frequently traded major crop types from the Qing Dynasty and when different periods of modern data quality are used. Moreover, we find that the persistence of data misreporting remains evident even in prefectures that have undergone significant changes in geographical attributes. We further suggest that this persistent pattern of data misreporting is primarily influenced by cultural factors rather than geographical ones.
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