We introduce the China Government Employee Database—Qing (CGED-Q), a new resource for the quantitative study of Qing officialdom. The CGED-Q details the backgrounds, characteristics and careers of Qing officials who served between 1760 and 1912, with nearly complete coverage of officials serving after 1830. We draw information on careers from the Roster of Government Personnel (jinshenlu), which in each quarterly edition listed approximately 12,500 regular civil offices and their holders in the central government and the provinces. Information about backgrounds and characteristics comes from such linked sources as lists of exam degree holders. In some years, information on military officials is also available. As of February 2020, the CGED-Q comprises 3,817,219 records, of which 3,354,897 are civil offices and the remainder are military. In this article we review the progress and prospects of the project, introduce the sources, transcription procedures, and constructed variables, and provide examples of results to showcase its potential.

The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this paper, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts.

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We introduce our approach to the nominative linkage of records of Qing officials who were included in the China Government Employee Datasets-Qing (CGED-Q) Jinshenlu (JSL) and Examination Records (ER). We constructed these datasets by transcription of quarterly rosters of civil and military officials produced by the government and by commercial presses, and records of examination degree holders. We assess each of the primary attributes available in the original sources in terms of their usefulness for disambiguation, focusing on their diversity and potential for inconsistent recording. For officials who were not affiliated with the Eight Banners, these primary attributes include surname, given name, and province and county of origin. For the small subset of officials who were affiliated with the Bannermen, we assess the available data separately. We also assess secondary attributes available in the data that may be useful for adjudicating candidate matches. We then describe the approach that we developed that addresses the issues we identified with the primary and secondary attributes. The issues we have identified and the approach that we have developed will be of interest to researchers engaged in similar efforts to construct and link datasets based on elite males in historical China.