Machine Learning Career Path of Chinese Political Leaders

Recently JunYan Jiang published a Chinese Political Elite Database (CPED), which contains demographic and career information of Chinese political leaders from multiple levels. (ref https://www.junyanjiang.com/data.html)  It’s a very interesting dataset, here I will use unsupervised machine learning to explore the structure of the dataset and then applied a supervised learning model to predict the highest position of the political leaders with all their background information. All the codes are here  https://github.com/luciasalar/government_officials.git

Information in the CPED including: name, gender, ethnicity, birthday, birthplace, education back ground, whether the person join the army, whether the person has been expelled from the Communist Party of China(CPC), current position, whether the person commits a  crime, when does the person join CPC, how long does the person work in the government, when and where does the person been relocated, job grade, name of the position and so on.

Before I do any learning, I did a bit of work to process the dataset: 1) convert categorical variables to dummy variables.  2) I extended some variables based on the existing variables on the dataset. I added age by calculating the time difference between now and birthday; added frequency to be relocated  (time worked the in government divided by the number of times they were relocated); added number of times worked in the central government; added percentage of central government position to all the positions of each person; added number of times a person been relocated by a national institute or a central government; added number of years they have been working in the government; added number of years they have been in the CPC 3) I also recode the location of where they worked (developing countries and developed countries are assigned into two big groups). However, the location later turns out to be a confusing variable later in the cluster analysis. Maybe I need to identify the location they worked for the longest time and use GDP of the location as weights.

In order to know what variables can predict the highest position of a government official, I do a cluster analysis on the variables. Here I use model based clustering,. The advantage of model based clustering is that it can adapt to Gaussian with non-spherical variance. After a couple of attempts, I find all the extended variables I generated and the job grade produce 9 clusters. Adding other variables, especially the location only confounds the cluster results. 

Best BIC values:
             EEV,9     EEV,8     EEV,7
BIC      -40592.79 -44475.21 -48851.12
BIC diff      0.00  -3882.42  -8258.33
$mean
                            [,1]       [,2]       [,3]         [,4]        [,5]        [,6]       [,7]
central_freq          4.32237099  8.1333333  3.2921195 6.979184e-01  0.64672483  4.67187223  7.3296655
relocate_freq        12.61755297 19.6000000 18.5603415 1.779856e+01 13.79750420 19.30010818 19.5700083
nat_ins_relo        4.19084397  9.6000000  6.4393863 3.455870e+00  3.99058070  6.99442958  9.5765742
central_relo          0.00000000  1.8333333  0.5695582 6.904707e-04  0.18754764  0.33255111  1.9551820
级别_deputy_director  0.75474248  1.5333333  3.1303437 3.671810e+00  2.48911104  2.07742828  1.5326028
级别_deputy_leader    0.00000000  2.8666667  0.0000000 0.000000e+00  0.00000000  0.06536624  2.0542140
级别_deputy_dept      0.06884181  0.7333333  1.4554218 2.068585e+00  1.28341355  1.31928172  0.9915247
级别_vice_minister    2.49509706  3.2333333  3.2460501 5.323188e-01  0.32912791  4.29367926  3.5884937
级别_less_dept        0.61317397  2.0333333  2.7168478 3.333408e+00  2.39241514  2.71185414  2.4306439
级别_no_rank          0.89761799  1.6333333  1.7226345 1.670180e+00  1.90671226  1.70147894  1.6974206
级别_director         2.09337233  1.5333333  4.3141123 3.500887e+00  3.25762003  2.73690397  2.5425752
级别_national_leader  0.00000000  2.3666667  0.0000000 0.000000e+00  0.00000000  0.00000000  0.0000000
级别_dept             0.33755477  1.2666667  1.9749013 2.942800e+00  2.04231632  1.76069380  1.4218369
级别_minister         1.13393696  2.1333333  0.0000000 0.000000e+00  0.00000000  2.63342184  2.4670000
gov_working_yrs      34.77427264 89.1850000 52.8895299 5.021180e+01 51.41595474 70.20722067 65.0047612
age                  69.76954176 80.0189208 62.9543031 6.287416e+01 64.06984368 74.19215959 75.3007443
join_cpc             43.65866150 58.0666667 38.6672922 3.828230e+01 40.33759873 50.62669711 50.1089734
join_cpc_age         25.24244381 22.6220278 23.4355524 2.372276e+01 23.57874446 22.83489307 24.5402508
freq_change_pos_nor   3.02921911  4.9328471  2.9339499 2.935939e+00  4.01097470  3.75754392  3.4275268
central_freq_perce     0.36299367  0.4209026  0.1859861 3.710309e-02  0.04916415  0.25133780  0.3650817
                            [,8]         [,9]
central_freq         10.34535743  0.000000000
relocate_freq                19.34634107  6.950475840
nat_ins_relo       11.18794437  0.190331466
central_relo          0.56318475  0.164688275
级别_deputy_director  1.84379429  0.766288984
级别_deputy_leader    0.06257608  0.000000000
级别_deputy_dept      1.53382976  0.113210760
级别_vice_minister    3.28088365  0.609826329
级别_less_dept        1.31481036  0.081245263
级别_no_rank          1.65582924  0.130124281
级别_director         3.31431693  2.268522452
级别_national_leader  0.00000000  0.000000000
级别_dept             1.74969366  0.277415251
级别_minister         2.27820879  0.006609097
gov_working_yrs           47.96463094 17.907235176
age                  68.92297628 66.174523443
join_cpc               43.81333803 40.977279532
join_cpc_age             24.99038289 23.577696437
freq_change_pos_nor   2.51541676  3.108479572
central_freq_perce      0.53670229  0.000000000

The above table shows all the variables I used in clustering, freq = frequency, nat_ins_relo = relocated by national institute, central_relo = relocated by central government, gov_working_yrs: number of years working in the government, join_cpc: number of years they join CPC; join_cpc_age : age when they join CPC; central_freq : number of times they worked in the central government; freq_change_pos_nor: frequency of being relocated normalized by the number of years they work in the government; central_freq_perce: number of times working in the central government divided by the number of times being relocated.

It’s hectic work to see what information contained in these groups manually, so I wrote a function to see which cluster has the highest mean score in each variable. We can see that there are a few clusters that are quite important, group 2 contains most national leaders. Let’s call it ‘the leader group; group 3 has most directors let’s call it ‘director group’, group 4 has most deputy directors and deputy department heads, department heads, lower than department head positions, let’s call this group ‘department heads’; group 5 contains officials without rankings, let’s call it ‘no ranking’. group 6 is the ‘ministers’ group, group 8 contains people who works in the central government most number of times. We can also see that officials in the leader group have highest mean age.  Group 1 contains least high level government officials, officials in group 9 work in the government shortest period of time. Now we know that unsupervised learning managed to learn some patterns in these variables.

Now let’s do a regression and see if these variables can predict the job grade. We selected the highest job grade of each official as the job grade label. We can see that all the variables we selected are significant in the prediction.

lm(formula = job_grade ~ ., data = reg_fea)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2911 -0.4882 -0.0403  0.4308  5.1570 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          8.198359   0.159396  51.434  < 2e-16 ***
central_freq         0.098955   0.013897   7.121 1.27e-12 ***
Freq                -0.052931   0.005458  -9.697  < 2e-16 ***
nat_ins_relo         0.003095   0.006357   0.487 0.626389    
central_relo        -0.052582   0.014540  -3.616 0.000303 ***
time_diff           -0.019530   0.001595 -12.245  < 2e-16 ***
age                 -0.016151   0.005317  -3.038 0.002398 ** 
join_cpc            -0.016090   0.005559  -2.895 0.003817 ** 
join_cpc_age         -0.020595   0.006256  -3.292 0.001004 ** 
freq_change_pos_nor  0.182062   0.019311   9.428  < 2e-16 ***
central_freq_per    -2.759989   0.207682 -13.290  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8512 on 3906 degrees of freedom
Multiple R-squared:  0.4649,	Adjusted R-squared:  0.4635 
F-statistic: 339.3 on 10 and 3906 DF,  p-value: < 2.2e-16

Ok, the final part is machine learning. Here I produce a very very basic svm model with R. I’ll do a proper ML with Python on a lazy weekend. The basic model turns our really not bad! First, I recode the job grade to binary, with anyone under the minister level as 0, ministers and national leaders are 1.  We get a balanced set of data.

0    1 
1941 1982

The F1 score is

[1] 0.7790393

confusion matrix

    predictions
y     0   1
  0  477  97
  1  156 446

 

 

 

 

 

 

 

 

 

 

Author: Lucia

I am a psychologist and a PhD student in The University of Edinburgh, School of Informatics. My research interest is social media and psychological traits.

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