Data for: Informal Networks and Influential Politicians in China: SNA-Study of Full CC CCP Members
Description
The dataset contains coded information on 204 full CC CCP members' biographies and professional ties among them. One spreadsheet has 3,488 rows, with each office held by a member of the Chinese elite having an individual row assigned to it; the other one has 4,610-row, each being a tie shaped by shared working experience between 1978 and 2020. We assume there is a tie between individuals who studied or worked together in a given year, or were born in the same location. Each professional tie is assigned a weight between 1 and 4 for working or studying in the same province, in the same locality, in one body or in one department. As far as localities are concerned, we take the closest level available, so maximum weight for working in the same place is two. While locality-wise there are few career intersections until the level of large district cities or provincial capitals, we consider working in the same province enough to form a tie. Birth ties have two weights – one is assigned for the same province; another is for the same county or city of birth. Given the overall design of the study, they do not significantly influence the result of the machine analysis. As for recording the ties, the dataset has certain exceptions. We did not take into account work ties between people working in Beijing. China’s capital has a host of party and government bodies that collaborate with one another with less frequency than provincial bodies. The tie was taken into account only if two persons worked in Beijing in the same body. Second, we did not count CC CCP membership as a tie since it is an elected body that does not function on a daily basis and its members can hold different offices in China’s other agencies and regions. Third, due to the design of the dataset, we could not emphathise patron-client relationships and ties with the preceding generation whose members brought to power many national leaders and are assisting the new generation in their career promotion. While we know of such relationships among several individuals in the dataset thanks to the expertise on Chinese politics, we would not be able to identify such ties among all of them, so partial inclusion of such ties – on top of those based on shared work and study experience – would skew the analysis. Hence, the ties between patrons and clients are counted by the same rule as any other tie in the dataset.
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Steps to reproduce
The first step was to collect biographical information. For that purpose, we used the official online resources of the CCP and Xinhua News Agency. Marginally, we used information from the ChinaVitae database compiled by the Carnegie-Tsinghua Center. When contradictory information was found, we gave preference to the data from official sources. Then we collected information on each member of China’s elite and entered it in a chronological table starting from 1978. Having verified the correctness of the data, we further arranged it. The following columns were used: name, year of birth, year of joining the CCP, native province, native city, official body, department, position, years of taking up and leaving a particular office. The second step was to develop a single system for encoding the biographical information of members of China’s elite. We decided to encode local authorities hierarchically: for instance, county-level committees were encoded differently from province-level ones. Original names of public organisations and corporations were retained to lower the risk of identifying false ties. Subsequently, we listed departments within a body when such information was available. Each office held by a member of the Chinese elite has an individual row assigned to it; the dataset has a total of 3,488 rows. Several offices held simultaneously were also noted. Additionally, we assigned individual columns to each year in office so as to be able to use filters to track offices held by a given member of the Chinese elite during any time period. Using additional filters, we identified ties between persons.