文件列表:

01_data/ (0, 2023-09-06)
01_data/Check Archer.md (1926, 2023-09-06)
01_data/PublicDiplomacy/ (0, 2023-09-06)
01_data/PublicDiplomacy/AidData_Geocoding-Methodology-updated-2017-06.pdf (220481, 2023-09-06)
01_data/PublicDiplomacy/ChineseFinancialPublicDiplomacyProjectDetails.csv (2784952, 2023-09-06)
01_data/PublicDiplomacy/ChinesePublicDiplomacy.csv (37763, 2023-09-06)
01_data/PublicDiplomacy/Perceptions.csv (160, 2023-09-06)
01_data/PublicDiplomacy/WHO-WHERE-WHEN-dataset-v.1.1.xlsx (37808, 2023-09-06)
01_data/PublicDiplomacy/clean_diplomacy_data.csv (11305, 2023-09-06)
01_data/PublicDiplomacy/diplomacydata.md (1482, 2023-09-06)
01_data/aid_data/ (0, 2023-09-06)
01_data/aid_data/AidDataTUFF_Methodology_1.3.pdf (482947, 2023-09-06)
01_data/aid_data/AidDatasGlobalChineseDevelopmentFinanceDataset_v2.0.xlsx (16330668, 2023-09-06)
01_data/aid_data/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0.zip (17523613, 2023-09-06)
01_data/aid_data/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/ (0, 2023-09-06)
01_data/aid_data/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/.DS_Store (6148, 2023-09-06)
01_data/aid_data/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/AidData_TUFF_methodology_2_0.pdf (1384481, 2023-09-06)
01_data/aid_data/GlobalChineseOfficialFinanceDataset_v1.0.xlsx (3434741, 2023-09-06)
01_data/aid_data/__MACOSX/ (0, 2023-09-06)
01_data/aid_data/__MACOSX/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/ (0, 2023-09-06)
01_data/aid_data/__MACOSX/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/._.DS_Store (120, 2023-09-06)
01_data/aid_data/__MACOSX/AidDatas_Global_Chinese_Development_Finance_Dataset_Version_2_0/._AidDatasGlobalChineseDevelopmentFinanceDataset_v2.0.xlsx (231, 2023-09-06)
01_data/aid_data/clean_aid_data.csv (137821, 2023-09-06)
01_data/covariate_data/ (0, 2023-09-06)
01_data/covariate_data/QG_filtered.csv (208334, 2023-09-06)
01_data/covariate_data/UNvotes_filtered.csv (319973, 2023-09-06)
01_data/covariate_data/WB_fdi_filtered.csv (691903, 2023-09-06)
01_data/covariate_data/additional.md (2042, 2023-09-06)
01_data/covariate_data/atop.csv (8069178, 2023-09-06)
01_data/covariate_data/clean_covariate_data.csv (264741, 2023-09-06)
01_data/covariate_data/clean_covariate_data_imputed.csv (468814, 2023-09-06)
01_data/covariate_data/clean_covariate_data_nonimputed.csv (450194, 2023-09-06)
01_data/covariate_data/deaths-natural-disasters-ihme.csv (177059, 2023-09-06)
01_data/covariate_data/dist_cepii.dta (1909446, 2023-09-06)
01_data/covariate_data/prio_civilwars.csv (53841, 2023-09-06)
01_data/covariate_data/trade_data.csv (11359304, 2023-09-06)
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# Xinhua Coverage and Chinese Foreign Aid Contributors are: - Miles D. Williams - Lucie Lu # Think about future direction: - look at the sentiments for news articles covering existing Chinese aid programs - use the salience of countries in Xinhua's news articles to predict aid allocation in the upcoming years (win!) # Data analysis plan - Hypothesis: Chinese government suppresses news coverage over where they give aids. - Potential explanation: aids are not so popular among Chinese publics; maybe safer to keep it secret rather than promote it. - Conventional studies: more coverage -- > higher salience -- > justification of more aids to the recipient countries from the perspectives of aid-giving developed democratic countries; - China may be an odd case. So we want to loo at whether Chinese aid allocaion can predict a drop in the media coverage in the subsequent years. More aids -- > less coverage # Meeting memo (9/30/2021) - Look at summary statistics of average countries mentioned so we have a baseline of media coverage across aid-recipient countries - Try a bunch of prediction models to increase the accuracy - Think about other predictors we can put in X (more leeways in doing predictions) - A research design that speaks more directly to our theory # Meeting memo (10/28/2021) ## Design - subset of countries: African countries? - subset of news categories: economy? (need to revisit the dataset) ## Model specification - look at the distribution of data; outliers and stuff - measure of salience: counts; frequency; ranking - look at the preliminary results in the imputed data and the non-imputed raw data to decide whether we should try another imputation method - try different model specifications ## data sources - the aid data is also scrapped from the news, but we defend ourselves in saying those data not only use Xinhua source, so the data sources are not completely overlapping. # Meeting memo (12/3/2021) ## Miles: ### Analysis for getting around endogeneity issues - Lagged instruments -- 2LSL: regress coverage on a bunch of stuff and previous coverage; then regress predicted coverage on aid - Some alternative instrument - GMM - Just using lag of coverage ### Identification: - Between-recipient coverage at a given point in time (subset, African continents for example) - Within-recipient coverage over time (key recipients) ### other things - add some dummies - go back to redo the imputation and create alternative dataset ## Lucie: - update the literature list - write a rough draft of literature review - think about other models # Meeting Memo (1/20/2022) Aid data (https://www.aiddata.org/blog/call-for-papers-separating-fact-from-fiction-chinas-growing-global-influence-and-its-implications)

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