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한국 2020 국회의원선거 부정 (Walter R. Mebane, Jr ) 논문 번역
Frauds in the Korea 2020 Parliamentary Election -Walter R. Mebane, Jr
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[전문공개] Frauds in the Korea 2020 Parliamentary Election -Walter R. Mebane, Jr.
편집국
승인 2020.04.30 23:25
Figure 4: Korea 2020 Fraud Plots , Democratic Party
Note: plots show turnout (number voting/number eligible) and vote proportions (number voting for Democratic Party/number eligible) for four subsets of observations: (a) district-level, election-day, not abroad (10 fraudulent, 318 not); (b) postal election-day (131 fraudulent, 14155 not); (c) abroad (0 fraudulent, 328 not); (d) pre-vote (1146 fraudulent, 2656 not). Plots show scatterplots with nonfraudulent observations in blue and fraudulent observations in red. 328 “abroad office” observations reported with zero eligible voters but often with a positive number of votes are omitted.
Figure 5: Korea 2020 Fraud Plots , Constituency Leaders
Note: plots show turnout (number voting/number eligible) and vote proportions (number voting for constituency-leading party/number eligible) for four subsets of observations: (a) district-level, election-day, not abroad (5 fraudulent, 323 not); (b) postal election-day (298 fraudulent, 13988 not); (c) abroad (0 fraudulent, 328 not); (d) pre-vote (860 fraudulent, 2942 not). Plots show scatterplots with nonfraudulent observations in blue and fraudulent observations in red. 328 “abroad office” observations reported with zero eligible voters but often with a positive number of votes are omitted.
I use a counterfactual method to calculate how many votes are fraudulent.4 Table 2 reports the observed counts of eligible voters, valid votes and votes for the (a) Democratic party and (b) constituency-leading party totaled over all units in the analysis, along with fraudulent vote count totals. The total of “manufactured” votes is reported separately from the total number of fraudulent votes: manufactured votes are votes that the model estimates should have been abstentions but instead were observed as votes for the leading party.
Both posterior means and 95% and 99.5% credible intervals are reported. The results show that for the Democratic Party focused specification over all about 1,491,548 votes are fraudulent, and of the fraudulent votes about 1,122,169 are manufactured (the remaining 369379 are stolen—counted for the leading party when they should have been counted for a different party).
Overall, according to the eforensics model, about 10.43% of the votes for the Democratic Party candidates are fraudulent. The results show that for the constituency-leading focused specification over all about 1,171,734 votes are fraudulent, and of the fraudulent votes about 910,444 are manufactured (the remaining 261,290 are stolen—counted for the leading party when they should have been counted for a different party). Overall, according to the eforensics model, about 7.26% of the votes for the constituency-leading candidates are fraudulent.
Fraudulent vote occurrence varies over constituencies.
Counts of frauds by aggregation unit appear in a supplemental file5, but I use the unit-specific fraudulent vote counts from the constituency-leader focused specification to assess whether the number of fraudulent votes is ever large enough apparently to change the winner of a constituency contest. For 236 constituencies it is not, but for 16 constituencies the number of fraudulent votes is large enough apparently to change the winner of the constituency contest. In 9 instances the apparently fraudulently winning party is the Democratic Party, in 6 instances it is the United Future Party and in the remaining instance it is an Independent candidate.
Given two specifications, which one is better?
Probably neither model is correct, strictly speaking, ven beyond the generality that no model is ever correct, but some are useful. If frauds only ever benefit the Democratic Party, then those frauds may have induced apparent frauds when we constrain frauds to benefit only constituency-leading candidates, because many of these do not affiliate with the Democratic Party.
Table 2: Korea 2020 eforensics Estimated Fraudulent Vote Counts
Similarly if only constituency-leading candidates benefit from frauds, then eforensics may be producing misleading results when we constrain frauds to benefit only the Democratic Party. Or perhaps other candidates—or several in each constituency—benefit from frauds and both specifications are producing misleading results. Possibly, of course, there are no frauds and something else is going on.
Caveats are many. The most basic caution is to keep in mind that “frauds” according to the eforensics model may or may not be results of malfeasance and bad actions.
If some normal political situation makes the apparently fraudulent aggregation units appear fraudulent to the eforensics model and estimation procedure, then the frauds estimates may be signaling that “frauds” occur where in fact something else is happening. In particular there maybe something benign that leads many of the pre-vote units to have a turnout and vote choice distribution that differs so much especially from the distribution for election-day postal units, the latter comprising the bulk of the data.
Likewise something benign may distinguish the election-day postal units that the eforensics model identifies as fraudulent.
Beyond that general caution, there may something about the particular data used for the analysis that triggers the “fraud” findings—for instance, the data appear to be missing about 100,000 votes and one entire constituency, and the vote totals in the data for constituency-leading candidates do not always match totals reported in “lists of winners.”
And there may be something about the model specification that should be improved that would produce different results.
Statistical findings such as are reported here should be followed up with additional information and further investigation into what happened. The statistical findings alone cannot stand as definitive evidence about what happened in the election.
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References
Ferrari, Diogo, Kevin McAlister and Walter R. Mebane, Jr. 2018. “Developments in Positive
Empirical Models of Election Frauds: Dimensions and Decisions.” Presented at the 2018
Summer Meeting of the Political Methodology Society, Provo, UT, July 16–18. (문건 끝)
Software Available for Downloading, with Documentation
Election Forensics R Package (eforensics tarball) and (eforensics GitHub). Diogo Ferrari, Kevin McAlister, Walter Mebane and Patrick Wu, 2019.
Robust Estimation Software (multinomRob). Walter R. Mebane, Jr., and Jasjeet S. Sekhon, 2003.
Genetic Optimization Using Derivatives for R (RGENOUD). Walter R. Mebane, Jr., and Jasjeet S. Sekhon, 2001. (The ancestral GENOUD C program from 1997 is here.)
Genetic Optimization and Bootstrapping of Linear Structures (GENBLIS). Walter R. Mebane, Jr., and Jasjeet S. Sekhon, 1998.
Papers Available for Downloading
Walter R. Mebane, Jr. 2020. `` Frauds in the Korea 2020 Parliamentary Election''
Walter R. Mebane, Jr. 2019. `` Evidence Against Fraudulent Votes Being Decisive in the Bolivia 2019 Election''
Walter R. Mebane, Jr. 2019. `` eforensics: A Bayesian Implementation of A Positive Empirical Model of Election Frauds''
Patrick Y. Wu, Walter R. Mebane, Jr., Logan Woods, Joseph Klaver, and Preston Due. 2019. `` Partisan Associations of Twitter Users Based on Their Self-descriptions and Word Embeddings'' Prepared for presentation at the 2018 Annual Meeting of the American Political Science Association, Washington, DC, Aug 29--Sep 1. 외 다수
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