Global Dynasties Dataset Main RPub
Nachiket Midha
1 Basic Descriptive Indicators
1.1 Graph Showing Countries Added Yearwise
The following graph shows how countries are being added every year with the progression in the dataset since the end of WWII
1.2 Proportion of Dynastic Countries Across Time (All Regime Types)
The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast. Therefore a dynastic country i at point t will be a country whose leader is a dynast.
The first graph shows the proportion of dynastic countries at a given time over the years.
The second graph shows the proportion of dynastic countries at a given time over a period of 25-25-25 years.
1.3 Proportion of Dynastic Countries (Ruled by Dynastic Leaders) across regime/time by different Regions of the world
1.4 Table on the Proportion of Dynastic Leaders Over Time in a Region (Classified by Regime Type)
1.5 Proportion of Years Under Dynastic Rule by Democratic Regime Type (Presidential, Parliamentary, and Mixed Democratic)
The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast. Therefore, dynastic rule will be years under a dynast.
These classifications are extended and replicated based on the regime types given in WhoGov Dataset (Nuffield Research Center which is based in turn on Cheibub et. al (2010))
| system_category | year_bin | Prop_Dyn_Years |
|---|---|---|
| Mixed Democratic | 1945-1970 | 5.902778 |
| Mixed Democratic | 1970-1995 | 14.804159 |
| Mixed Democratic | 1995-2020 | 10.337995 |
| Parliamentary Democracy | 1945-1970 | 27.408962 |
| Parliamentary Democracy | 1970-1995 | 22.937322 |
| Parliamentary Democracy | 1995-2020 | 16.101495 |
| Parliamentary Democracy | NA | NA |
| Presidential Democracy | 1945-1970 | 29.876087 |
| Presidential Democracy | 1970-1995 | 18.575780 |
| Presidential Democracy | 1995-2020 | 26.030800 |
| Presidential Democracy | NA | NA |
1.6 Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Dictatorship/Democracy Status and System Category
## # A tibble: 2 × 4
## dictatorship Prop_Dyn_Years Cummulative_Dyn_Years Dynastic_Rulers_percentage
## <dbl> <dbl> <dbl> <dbl>
## 1 0 NA NA NA
## 2 1 30.8 1641 24.11.7 Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Regime Type (System Category)
## # A tibble: 9 × 4
## system_category Prop_Dyn_Years Cummulative_Dyn_Years Dynastic_Rulers_perc…¹
## <chr> <dbl> <dbl> <dbl>
## 1 "" 23.6 13 6.35
## 2 "Civilian Dictato… 21.5 590 18.7
## 3 "Military Dictato… 14.5 257 20.2
## 4 "Mixed Democratic" 11.9 146 10.4
## 5 "Parliamentary De… NA 468 19.5
## 6 "Presidential Dem… NA 451 25.2
## 7 "Royal Dictatorsh… 98.9 794 72.7
## 8 "military Dictato… 0 0 0
## 9 "system_category" NA NA NA
## # ℹ abbreviated name: ¹Dynastic_Rulers_percentage##Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Regime Change Binary
## # A tibble: 2 × 4
## Regime_Change Prop_Dyn_Years Cummulative_Dyn_Years Dynastic_Rulers_percentage
## <dbl> <dbl> <dbl> <dbl>
## 1 0 NA NA NA
## 2 1 NA 1247 20.4| Regime_Change | Number_Of_Countries |
|---|---|
| 0 | 92 |
| 1 | 78 |
1.8 Country Count for Countries that have faced no regime change and have either remained Democracies or Dictatorships throughout and Dynastic Information
| dictatorship | Number_Of_Countries_With_No_RegChange |
|---|---|
| 0 | 48 |
| 1 | 44 |
1.9 Countries that have had no regime change and have remained Democratic by democracy type
| system_category | Prop_Dyn_Years |
|---|---|
| Mixed Democratic | 12.46291 |
| Parliamentary Democracy | NA |
| Presidential Democracy | 35.66667 |
| system_category | NA |
1.10 Countries that have had no regime change and have remained dictatorship by dictatorship type
| system_category | Prop_Dyn_Years |
|---|---|
| Civilian Dictatorship | 10.33275 |
| Military Dictatorship | 16.86747 |
| Royal Dictatorship | 99.44341 |
1.11 Country Count for number of Regime Transitions and Dynastic Information
| Num_Transitions | Number_Countries |
|---|---|
| 0 | 92 |
| 1 | 34 |
| 2 | 17 |
| 3 | 12 |
| 4 | 6 |
| 5 | 4 |
| 6 | 2 |
| 7 | 1 |
| 8 | 2 |
| Num_Transitions | Percentage_Dynastic_Years |
|---|---|
| 1 | NA |
| 2 | 28.46088 |
| 3 | 28.34437 |
| 4 | 14.04959 |
| 5 | 12.95547 |
| 6 | 30.87248 |
| 7 | 10.66667 |
| 8 | 24.00000 |
| Number_of_Transitions | Percentage_Dynastic_Years |
|---|---|
| One Transition | NA |
| Two or More Transitions | 24.74156 |
| Number_of_Transitions | Dynastic_Rulers_percentage |
|---|---|
| One Transition | 16.71470 |
| Two or More Transitions | 22.64808 |
1.12 Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Post-WW2 Independence status
## # A tibble: 33 × 6
## year_bin.x postww2_ind Prop_Dyn_Years Cummulative_Dyn_Years year_bin.y
## <ord> <dbl> <dbl> <dbl> <ord>
## 1 1945-1970 0 29.6 462 1945-1970
## 2 1945-1970 0 29.6 462 1970-1995
## 3 1945-1970 0 29.6 462 1995-2020
## 4 1945-1970 1 28.7 205 1945-1970
## 5 1945-1970 1 28.7 205 1970-1995
## 6 1945-1970 1 28.7 205 1995-2020
## 7 1945-1970 NA 0 0 1945-1970
## 8 1945-1970 NA 0 0 1995-2020
## 9 1945-1970 NA 0 0 <NA>
## 10 1970-1995 0 27.0 438 1945-1970
## # ℹ 23 more rows
## # ℹ 1 more variable: Dynastic_Rulers_percentage <dbl>1.13 Proportion of Years Under Dynastic Rule by Former British Colony Status (Information Scraped from Wikipedia)
| former_british_colony | year_bin | Prop_Dyn_Years |
|---|---|---|
| 0 | 1945-1970 | 25.06917 |
| 0 | 1970-1995 | 18.15867 |
| 0 | 1995-2020 | 21.96514 |
| 0 | NA | NA |
| 1 | 1945-1970 | 38.09904 |
| 1 | 1970-1995 | 36.20000 |
| 1 | 1995-2020 | 35.83490 |
1.14 Proportion of Years Under Dynastic Rule by Regions (Across all regime types)
1.15 Mapping of Dynastic Relation Type Across all regime Types
The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast.
This graph shows what kind of dynastic relationships are most relevant across regime types (Civilian Dictatorship, Military Dictatorship, Mixed Democratic, Parliamentary Democracy, Presidential Democracy, Royal Dictatorship)
gdd_relation_all <- gdd %>%
distinct(nominal_leader, .keep_all = TRUE) %>%
filter(pred_bin == 1, relation_code_pred != 0)
gdd_relation_all <-gdd_relation_all %>%
group_by(fln_gender) %>%
count(relation_code_pred) %>%
mutate(Relation_Type = case_when(
fln_gender == 0 & relation_code_pred == 2 ~ "Father-Son",
fln_gender == 0 & relation_code_pred == 3 ~ "Mother-Son",
fln_gender == 0 & relation_code_pred == 8 ~ "Brother-Brother",
fln_gender == 0 & relation_code_pred == 10 ~ "Grandfather-Grandson",
fln_gender == 0 & relation_code_pred == 11 ~ "Grandmother-Grandson",
fln_gender == 0 & relation_code_pred == 14 ~ "Uncle-Nephew",
relation_code_pred == 18 ~ "Cousin-Cousin",
relation_code_pred == 19 ~ "Other",
fln_gender == 1 & relation_code_pred == 2 ~ "Father-Daughter",
fln_gender == 1 & relation_code_pred == 6 ~ "Husband-Wife",
fln_gender == 1 & relation_code_pred == 8 ~ "Brother-Sister",
fln_gender == 1 & relation_code_pred == 10 ~ "Grandfather-Granddaughter",
TRUE ~ NA_character_)
) %>%
rename(Total = n) %>%
mutate(percentage_tot_dyn = Total/sum(Total)*100)
relation <- ggplot(gdd_relation_all, aes(x = Relation_Type, y = Total, fill = Relation_Type)) +
geom_bar(stat = "identity") +
labs(title = "Dynastic Relationship Across All Regime Types",
x = "Dynastic Relationship Type",
y = "Total") +
theme_stata()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none")
ggplotly(relation)1.16 Mapping of Dynastic Relation Type in Democratic regime Types
The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast.
This graph shows what kind of dynastic relationships are most relevant in democratic regime types (Mixed Democratic, Parliamentary Democracy, Presidential Democracy)
2 The Different Dynasts (across regime types)
While our definition of a dynast is clear as stated in the previous section. This section expands on that definition at talks about three different kinds of dynast.
2.1 THE FIRST DYNAST
The First definition of Dynast is the one mentioned before. This shows the proportion of leaders that necessarily have an ancestor in politics and may or may not have a successor. The necessary precondition is a family member preceding him/her in politics before his time. ((pred_bin == 1 & suc_bin doesn’t matter))
2.2 THE SECOND DYNAST (DYNASTY-SUSTAINER)
The Second definition of Dynast is the one of dynasty sustainers. This means that the following graph shows the proportion of leaders that necessarily come from apolitical family and also leaves a successor in politics. Therefore, a dynasty sustainer The necessary preconditions are a family member preceding him/her in politics before his/her time and a family member suceeding him/her in politics after his/her time. (pred_bin == 1 & suc_bin == 1)
2.3 THE THIRD DYNAST (DYNASTY-ENDER)
The THIRD definition of Dynast is the one of dynasty-enderss. This means that the following graph shows the proportion of leaders that necessarily come from a political family BUT DO NOT LEAVE a successor in politics. Therefore, for a dynasty ENDER The necessary preconditions are a family member preceding him/her in politics before his/her time and a family member NOT suceeding him/her in politics after his/her time. (pred_bin == 1 & suc_bin == 0)
2.4 THE FOURTH DYNAST (DYNASTY-FORMERS)
The fourth definition of Dynast is the one of dynasty-formers. This means that the following graph shows the proportion of leaders that DO NOT come from a political family HAVE a successor in politics. Therefore, for a dynasty former the necessary preconditions are the ABSENCE OF A family member preceding him/her in politics before his/her time and a family member SUCCEEDING him/her in politics after his/her time. (pred_bin == 0 & suc_bin == 1)
2.5 THE PURE NON-DYNAST
The last category is a category of leaders that have no family before or after them in politics. These are not-dynasts and are included to show declining prevalence of family ties in politics.
3 Predicted Probabilities and Regime Types: Two Different Models
3.1 Model 1,2,3: Using dictatorship as the independent variable
##
## Call:
## glm(formula = dynastic ~ dictatorship, family = binomial(link = "logit"),
## data = gdd)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.32226 0.03463 -38.18 <2e-16 ***
## dictatorship 0.50447 0.04564 11.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11850 on 10343 degrees of freedom
## Residual deviance: 11726 on 10342 degrees of freedom
## AIC: 11730
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = dynastic ~ dictatorship + factor(Country) + factor(Year),
## family = binomial(link = "logit"), data = gdd)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.311e+00 4.208e-01 3.114
## dictatorship -2.001e-01 1.006e-01 -1.989
## factor(Country)Albania -2.338e+00 3.907e-01 -5.983
## factor(Country)Algeria -2.039e+01 1.391e+03 -0.015
## factor(Country)Angola -2.039e+01 1.575e+03 -0.013
## factor(Country)Argentina -1.678e+00 3.606e-01 -4.653
## factor(Country)Armenia -2.062e+01 1.951e+03 -0.011
## factor(Country)Australia -9.492e-01 3.582e-01 -2.650
## factor(Country)Austria -2.062e+01 1.233e+03 -0.017
## factor(Country)Azerbaijan -4.137e-01 4.547e-01 -0.910
## factor(Country)Bahamas -2.056e+01 1.543e+03 -0.013
## factor(Country)Bahrain 1.882e+01 1.513e+03 0.012
## factor(Country)Bangladesh -4.380e-01 3.874e-01 -1.131
## factor(Country)Barbados -2.262e+00 4.263e-01 -5.305
## factor(Country)Belarus -2.042e+01 1.951e+03 -0.010
## factor(Country)Belgium -3.338e+00 4.822e-01 -6.922
## factor(Country)Belize -2.055e+01 1.689e+03 -0.012
## factor(Country)Benin 1.115e-01 3.859e-01 0.289
## factor(Country)Bhutan 6.375e-01 3.912e-01 1.630
## factor(Country)Bosnia and Herzegovina -1.857e+00 4.882e-01 -3.805
## factor(Country)Botswana -2.157e+00 4.194e-01 -5.142
## factor(Country)Brazil -3.458e+00 5.022e-01 -6.886
## factor(Country)Bulgaria -2.818e+00 4.270e-01 -6.599
## factor(Country)Burkina Faso -3.202e+00 5.319e-01 -6.020
## factor(Country)Burundi -1.569e+00 3.790e-01 -4.139
## factor(Country)Cambodia 9.098e-02 3.684e-01 0.247
## factor(Country)Cameroon -2.035e+01 1.370e+03 -0.015
## factor(Country)Canada -1.677e+00 3.657e-01 -4.585
## factor(Country)Cape Verde -2.050e+01 1.573e+03 -0.013
## factor(Country)Central African Republic -2.150e+00 4.048e-01 -5.311
## factor(Country)Chad -2.035e+01 1.370e+03 -0.015
## factor(Country)Chile -2.322e+00 3.890e-01 -5.968
## factor(Country)China -2.241e+00 3.850e-01 -5.821
## factor(Country)Colombia -1.277e+00 3.591e-01 -3.555
## factor(Country)Costa Rica -3.852e-01 3.657e-01 -1.053
## factor(Country)Croatia -2.062e+01 1.951e+03 -0.011
## factor(Country)Cuba -2.517e+00 4.051e-01 -6.212
## factor(Country)Cyprus -2.039e+00 3.984e-01 -5.118
## factor(Country)Czech Republic -2.064e+01 2.020e+03 -0.010
## factor(Country)Democratic Republic of the Congo -1.741e+00 3.829e-01 -4.547
## factor(Country)Denmark -2.062e+01 1.233e+03 -0.017
## factor(Country)Djibouti -7.600e-01 3.961e-01 -1.919
## factor(Country)Dominican Republic -2.768e+00 4.206e-01 -6.582
## factor(Country)Ecuador -2.303e+00 3.871e-01 -5.950
## factor(Country)Egypt -3.138e+00 4.717e-01 -6.651
## factor(Country)El Salvador -2.052e+01 1.233e+03 -0.017
## factor(Country)Equatorial Guinea 5.964e-01 4.243e-01 1.406
## factor(Country)Eritrea -2.044e+01 2.020e+03 -0.010
## factor(Country)Estonia -2.663e+00 5.617e-01 -4.741
## factor(Country)Eswatini 1.883e+01 1.470e+03 0.013
## factor(Country)Ethiopia -1.367e+00 3.491e-01 -3.916
## factor(Country)Fiji -1.073e+00 3.813e-01 -2.815
## factor(Country)Finland -4.962e+00 6.438e-01 -7.707
## factor(Country)France -3.338e+00 4.822e-01 -6.922
## factor(Country)Gabon -2.294e+00 4.195e-01 -5.468
## factor(Country)Georgia -2.055e+01 1.942e+03 -0.011
## factor(Country)Germany -2.061e+01 1.919e+03 -0.011
## factor(Country)Ghana -1.987e+00 3.891e-01 -5.107
## factor(Country)Greece -9.822e-01 3.554e-01 -2.764
## factor(Country)Guatemala -3.014e+00 4.448e-01 -6.776
## factor(Country)Guinea -2.039e+01 1.345e+03 -0.015
## factor(Country)Guinea-Bissau -2.043e+01 1.556e+03 -0.013
## factor(Country)Guyana -4.135e+00 7.653e-01 -5.403
## factor(Country)Haiti -1.188e+00 3.461e-01 -3.432
## factor(Country)Honduras -3.131e+00 4.585e-01 -6.829
## factor(Country)Hungary -2.052e+01 1.232e+03 -0.017
## factor(Country)Iceland -1.799e+00 3.690e-01 -4.875
## factor(Country)India -2.039e+00 3.787e-01 -5.383
## factor(Country)Indonesia -3.744e+00 5.759e-01 -6.501
## factor(Country)Iran -6.399e-01 3.444e-01 -1.858
## factor(Country)Iraq -1.662e+00 3.572e-01 -4.653
## factor(Country)Ireland -1.799e+00 3.690e-01 -4.875
## factor(Country)Israel -4.210e+00 4.982e-01 -8.450
## factor(Country)Italy -3.705e+00 5.378e-01 -6.890
## factor(Country)Ivory Coast -2.035e+01 1.370e+03 -0.015
## factor(Country)Jamaica -1.644e+00 3.881e-01 -4.236
## factor(Country)Japan -6.366e-01 3.619e-01 -1.759
## factor(Country)Jordan 1.876e+01 1.233e+03 0.015
## factor(Country)Kazakhstan -2.042e+01 1.951e+03 -0.010
## factor(Country)Kenya -2.717e+00 4.616e-01 -5.886
## factor(Country)Kosovo -2.084e+01 3.577e+03 -0.006
## factor(Country)Kuwait 2.208e+00 6.448e-01 3.424
## factor(Country)Kyrgyzstan -2.054e+01 1.942e+03 -0.011
## factor(Country)Laos 3.983e-01 3.829e-01 1.040
## factor(Country)Latvia -2.062e+01 1.951e+03 -0.011
## factor(Country)Lebanon -7.786e-03 3.614e-01 -0.022
## factor(Country)Lesotho -2.040e+01 1.439e+03 -0.014
## factor(Country)Liberia -8.274e-01 3.442e-01 -2.404
## factor(Country)Libya -1.544e+00 3.603e-01 -4.285
## factor(Country)Lithuania -2.062e+01 1.951e+03 -0.011
## factor(Country)Luxembourg -2.062e+01 1.233e+03 -0.017
## factor(Country)Madagascar -2.039e+01 1.369e+03 -0.015
## factor(Country)Malawi -3.245e+00 5.366e-01 -6.047
## factor(Country)Malaysia -1.127e+00 3.602e-01 -3.128
## factor(Country)Maldives -1.178e-01 3.812e-01 -0.309
## factor(Country)Mali -2.182e+00 4.069e-01 -5.361
## factor(Country)Malta -2.805e+00 4.696e-01 -5.974
## factor(Country)Mauritius -1.629e+00 3.991e-01 -4.083
## factor(Country)Mexico -2.943e-01 3.515e-01 -0.837
## factor(Country)Moldova -2.062e+01 1.951e+03 -0.011
## factor(Country)Mongolia -2.051e+01 1.232e+03 -0.017
## factor(Country)Montenegro -2.065e+01 2.772e+03 -0.007
## factor(Country)Morocco 1.881e+01 1.327e+03 0.014
## factor(Country)Mozambique -2.036e+01 1.576e+03 -0.013
## factor(Country)Myanmar -3.512e+00 5.299e-01 -6.627
## factor(Country)Namibia -2.041e+01 1.919e+03 -0.011
## factor(Country)Nepal 4.299e-01 3.813e-01 1.127
## factor(Country)Netherlands -2.062e+01 1.233e+03 -0.017
## factor(Country)New Zealand -2.601e+00 4.102e-01 -6.340
## factor(Country)Nicaragua -1.328e+00 3.496e-01 -3.797
## factor(Country)Niger -2.043e+01 1.366e+03 -0.015
## factor(Country)Nigeria -2.530e+00 4.345e-01 -5.821
## factor(Country)North Korea -2.286e+00 3.916e-01 -5.836
## factor(Country)North Macedonia -1.734e+00 4.749e-01 -3.651
## factor(Country)Norway -1.737e+00 3.673e-01 -4.730
## factor(Country)Oman 1.883e+01 1.498e+03 0.013
## factor(Country)Pakistan -1.881e+00 3.673e-01 -5.120
## factor(Country)Panama -6.155e-01 3.538e-01 -1.740
## factor(Country)Papua New Guinea -2.056e+01 1.576e+03 -0.013
## factor(Country)Paraguay -3.832e+00 5.755e-01 -6.658
## factor(Country)Peru -1.658e+00 3.588e-01 -4.620
## factor(Country)Philippines 2.021e-01 3.780e-01 0.535
## factor(Country)Poland -3.591e+00 5.304e-01 -6.771
## factor(Country)Portugal -3.622e+00 5.317e-01 -6.813
## factor(Country)Qatar 1.882e+01 1.381e+03 0.014
## factor(Country)Republic of the Congo -2.036e+01 1.370e+03 -0.015
## factor(Country)Republic of the Gambia -7.108e-01 3.700e-01 -1.921
## factor(Country)Romania -2.940e+00 4.395e-01 -6.688
## factor(Country)Russia -2.042e+01 1.233e+03 -0.017
## factor(Country)Rwanda -2.035e+01 1.393e+03 -0.015
## factor(Country)Saudi Arabia 1.876e+01 1.233e+03 0.015
## factor(Country)Senegal -2.043e+01 1.365e+03 -0.015
## factor(Country)Serbia -2.057e+01 1.944e+03 -0.011
## factor(Country)Sierra Leone -2.082e+00 4.025e-01 -5.172
## factor(Country)Singapore -1.613e+00 3.870e-01 -4.167
## factor(Country)Slovakia -2.064e+01 2.020e+03 -0.010
## factor(Country)Slovenia -3.256e+00 6.678e-01 -4.876
## factor(Country)Solomon Islands -2.055e+01 1.630e+03 -0.013
## factor(Country)Somalia -2.038e+01 1.369e+03 -0.015
## factor(Country)South Africa -2.593e+00 3.400e-01 -7.628
## factor(Country)South Korea -4.147e+00 6.465e-01 -6.415
## factor(Country)South Sudan -2.065e+01 3.394e+03 -0.006
## factor(Country)Spain -2.969e+00 4.410e-01 -6.732
## factor(Country)Sri Lanka -5.657e-01 3.597e-01 -1.573
## factor(Country)Sudan -7.280e-01 3.566e-01 -2.042
## factor(Country)Suriname -2.053e+01 1.574e+03 -0.013
## factor(Country)Sweden -2.528e+00 4.034e-01 -6.267
## factor(Country)Switzerland -4.247e+00 6.498e-01 -6.536
## factor(Country)Syria -2.185e+00 3.806e-01 -5.742
## factor(Country)Taiwan -2.629e+00 4.170e-01 -6.305
## factor(Country)Tajikistan -2.042e+01 1.951e+03 -0.010
## factor(Country)Tanzania -2.036e+01 1.417e+03 -0.014
## factor(Country)Thailand -2.702e+00 4.159e-01 -6.495
## factor(Country)Timor-Leste -2.078e+01 2.460e+03 -0.008
## factor(Country)Togo -1.488e+00 3.733e-01 -3.986
## factor(Country)Trinidad and Tobago -1.722e+00 3.907e-01 -4.407
## factor(Country)Tunisia -2.040e+01 1.324e+03 -0.015
## factor(Country)Turkey -3.314e+00 4.801e-01 -6.903
## factor(Country)Turkmenistan -2.042e+01 1.951e+03 -0.010
## factor(Country)Uganda -2.652e+00 4.587e-01 -5.782
## factor(Country)Ukraine -2.062e+01 1.951e+03 -0.011
## factor(Country)United Arab Emirates 1.882e+01 1.513e+03 0.012
## factor(Country)United Kingdom -2.061e+00 3.783e-01 -5.449
## factor(Country)United States of America -1.927e+00 3.731e-01 -5.164
## factor(Country)Uruguay -2.036e+00 3.754e-01 -5.425
## factor(Country)Uzbekistan -2.042e+01 1.951e+03 -0.010
## factor(Country)Venezuela -1.959e+00 3.716e-01 -5.271
## factor(Country)Vietnam -2.035e+01 1.594e+03 -0.013
## factor(Country)Yemen -2.172e+00 3.796e-01 -5.722
## factor(Country)Zambia -2.041e+01 1.415e+03 -0.014
## factor(Year)1947 6.267e-02 4.466e-01 0.140
## factor(Year)1948 -1.403e-01 4.453e-01 -0.315
## factor(Year)1949 -7.046e-02 4.414e-01 -0.160
## factor(Year)1950 -1.625e-01 4.439e-01 -0.366
## factor(Year)1951 1.569e-01 4.342e-01 0.361
## factor(Year)1952 3.233e-01 4.313e-01 0.750
## factor(Year)1953 3.487e-01 4.289e-01 0.813
## factor(Year)1954 1.926e-01 4.315e-01 0.446
## factor(Year)1955 2.804e-01 4.303e-01 0.652
## factor(Year)1956 -2.674e-02 4.339e-01 -0.062
## factor(Year)1957 -1.052e-02 4.300e-01 -0.024
## factor(Year)1958 -1.741e-01 4.336e-01 -0.401
## factor(Year)1959 -3.455e-01 4.379e-01 -0.789
## factor(Year)1960 -5.397e-01 4.318e-01 -1.250
## factor(Year)1961 -5.517e-01 4.305e-01 -1.281
## factor(Year)1962 -4.576e-01 4.236e-01 -1.080
## factor(Year)1963 -4.012e-01 4.208e-01 -0.953
## factor(Year)1964 -3.437e-01 4.183e-01 -0.822
## factor(Year)1965 -1.740e-01 4.106e-01 -0.424
## factor(Year)1966 4.081e-02 4.050e-01 0.101
## factor(Year)1967 -7.424e-02 4.070e-01 -0.182
## factor(Year)1968 -4.688e-01 4.139e-01 -1.133
## factor(Year)1969 -4.594e-01 4.139e-01 -1.110
## factor(Year)1970 -6.204e-01 4.168e-01 -1.488
## factor(Year)1971 -3.312e-01 4.081e-01 -0.811
## factor(Year)1972 -4.574e-01 4.111e-01 -1.113
## factor(Year)1973 -5.213e-01 4.128e-01 -1.263
## factor(Year)1974 -3.310e-01 4.082e-01 -0.811
## factor(Year)1975 -7.922e-02 4.041e-01 -0.196
## factor(Year)1976 -2.071e-01 4.057e-01 -0.510
## factor(Year)1977 -3.570e-01 4.078e-01 -0.876
## factor(Year)1978 -2.954e-01 4.064e-01 -0.727
## factor(Year)1979 -4.198e-01 4.090e-01 -1.026
## factor(Year)1980 -1.842e-01 4.038e-01 -0.456
## factor(Year)1981 -5.555e-01 4.118e-01 -1.349
## factor(Year)1982 -5.516e-01 4.119e-01 -1.339
## factor(Year)1983 -3.625e-01 4.074e-01 -0.890
## factor(Year)1984 -3.643e-01 4.074e-01 -0.894
## factor(Year)1985 -5.562e-01 4.118e-01 -1.351
## factor(Year)1986 -6.309e-01 4.138e-01 -1.525
## factor(Year)1987 -6.309e-01 4.138e-01 -1.525
## factor(Year)1988 -6.986e-01 4.157e-01 -1.680
## factor(Year)1989 -7.013e-01 4.157e-01 -1.687
## factor(Year)1990 -5.894e-01 4.114e-01 -1.432
## factor(Year)1991 -5.315e-01 4.073e-01 -1.305
## factor(Year)1992 -7.271e-01 4.123e-01 -1.764
## factor(Year)1993 -6.687e-01 4.104e-01 -1.629
## factor(Year)1994 -6.737e-01 4.103e-01 -1.642
## factor(Year)1995 -7.379e-01 4.121e-01 -1.791
## factor(Year)1996 -6.128e-01 4.086e-01 -1.500
## factor(Year)1997 -4.885e-01 4.057e-01 -1.204
## factor(Year)1998 -6.739e-01 4.103e-01 -1.643
## factor(Year)1999 -4.888e-01 4.056e-01 -1.205
## factor(Year)2000 -5.471e-01 4.071e-01 -1.344
## factor(Year)2001 -3.111e-01 4.022e-01 -0.774
## factor(Year)2002 -4.279e-01 4.042e-01 -1.059
## factor(Year)2003 -3.696e-01 4.030e-01 -0.917
## factor(Year)2004 -3.125e-01 4.018e-01 -0.778
## factor(Year)2005 -3.696e-01 4.030e-01 -0.917
## factor(Year)2006 -1.530e-01 3.992e-01 -0.383
## factor(Year)2007 1.134e-02 3.967e-01 0.029
## factor(Year)2008 6.246e-02 3.961e-01 0.158
## factor(Year)2009 -1.592e-01 3.994e-01 -0.398
## factor(Year)2010 5.397e-02 3.964e-01 0.136
## factor(Year)2011 1.565e-03 3.971e-01 0.004
## factor(Year)2012 -1.050e-01 3.985e-01 -0.264
## factor(Year)2013 1.076e-01 3.956e-01 0.272
## factor(Year)2014 1.602e-01 3.950e-01 0.406
## factor(Year)2015 2.148e-01 3.944e-01 0.545
## factor(Year)2016 -1.540e-01 3.992e-01 -0.386
## factor(Year)2017 3.824e-03 3.969e-01 0.010
## factor(Year)2018 -2.750e-01 4.015e-01 -0.685
## factor(Year)2019 -1.643e-01 3.996e-01 -0.411
## factor(Year)2020 -1.643e-01 3.996e-01 -0.411
## Pr(>|z|)
## (Intercept) 0.001844 **
## dictatorship 0.046702 *
## factor(Country)Albania 2.19e-09 ***
## factor(Country)Algeria 0.988309
## factor(Country)Angola 0.989671
## factor(Country)Argentina 3.28e-06 ***
## factor(Country)Armenia 0.991567
## factor(Country)Australia 0.008043 **
## factor(Country)Austria 0.986654
## factor(Country)Azerbaijan 0.362867
## factor(Country)Bahamas 0.989373
## factor(Country)Bahrain 0.990073
## factor(Country)Bangladesh 0.258160
## factor(Country)Barbados 1.13e-07 ***
## factor(Country)Belarus 0.991648
## factor(Country)Belgium 4.46e-12 ***
## factor(Country)Belize 0.990294
## factor(Country)Benin 0.772671
## factor(Country)Bhutan 0.103152
## factor(Country)Bosnia and Herzegovina 0.000142 ***
## factor(Country)Botswana 2.71e-07 ***
## factor(Country)Brazil 5.73e-12 ***
## factor(Country)Bulgaria 4.13e-11 ***
## factor(Country)Burkina Faso 1.75e-09 ***
## factor(Country)Burundi 3.48e-05 ***
## factor(Country)Cambodia 0.804920
## factor(Country)Cameroon 0.988148
## factor(Country)Canada 4.54e-06 ***
## factor(Country)Cape Verde 0.989602
## factor(Country)Central African Republic 1.09e-07 ***
## factor(Country)Chad 0.988148
## factor(Country)Chile 2.40e-09 ***
## factor(Country)China 5.84e-09 ***
## factor(Country)Colombia 0.000378 ***
## factor(Country)Costa Rica 0.292198
## factor(Country)Croatia 0.991567
## factor(Country)Cuba 5.24e-10 ***
## factor(Country)Cyprus 3.08e-07 ***
## factor(Country)Czech Republic 0.991846
## factor(Country)Democratic Republic of the Congo 5.45e-06 ***
## factor(Country)Denmark 0.986654
## factor(Country)Djibouti 0.055046 .
## factor(Country)Dominican Republic 4.64e-11 ***
## factor(Country)Ecuador 2.68e-09 ***
## factor(Country)Egypt 2.91e-11 ***
## factor(Country)El Salvador 0.986723
## factor(Country)Equatorial Guinea 0.159806
## factor(Country)Eritrea 0.991925
## factor(Country)Estonia 2.12e-06 ***
## factor(Country)Eswatini 0.989776
## factor(Country)Ethiopia 8.99e-05 ***
## factor(Country)Fiji 0.004876 **
## factor(Country)Finland 1.29e-14 ***
## factor(Country)France 4.46e-12 ***
## factor(Country)Gabon 4.54e-08 ***
## factor(Country)Georgia 0.991556
## factor(Country)Germany 0.991431
## factor(Country)Ghana 3.27e-07 ***
## factor(Country)Greece 0.005713 **
## factor(Country)Guatemala 1.23e-11 ***
## factor(Country)Guinea 0.987902
## factor(Country)Guinea-Bissau 0.989525
## factor(Country)Guyana 6.56e-08 ***
## factor(Country)Haiti 0.000600 ***
## factor(Country)Honduras 8.54e-12 ***
## factor(Country)Hungary 0.986706
## factor(Country)Iceland 1.09e-06 ***
## factor(Country)India 7.32e-08 ***
## factor(Country)Indonesia 7.98e-11 ***
## factor(Country)Iran 0.063139 .
## factor(Country)Iraq 3.27e-06 ***
## factor(Country)Ireland 1.09e-06 ***
## factor(Country)Israel < 2e-16 ***
## factor(Country)Italy 5.59e-12 ***
## factor(Country)Ivory Coast 0.988146
## factor(Country)Jamaica 2.27e-05 ***
## factor(Country)Japan 0.078520 .
## factor(Country)Jordan 0.987864
## factor(Country)Kazakhstan 0.991648
## factor(Country)Kenya 3.96e-09 ***
## factor(Country)Kosovo 0.995351
## factor(Country)Kuwait 0.000616 ***
## factor(Country)Kyrgyzstan 0.991563
## factor(Country)Laos 0.298204
## factor(Country)Latvia 0.991567
## factor(Country)Lebanon 0.982811
## factor(Country)Lesotho 0.988687
## factor(Country)Liberia 0.016208 *
## factor(Country)Libya 1.83e-05 ***
## factor(Country)Lithuania 0.991567
## factor(Country)Luxembourg 0.986654
## factor(Country)Madagascar 0.988121
## factor(Country)Malawi 1.47e-09 ***
## factor(Country)Malaysia 0.001762 **
## factor(Country)Maldives 0.757267
## factor(Country)Mali 8.26e-08 ***
## factor(Country)Malta 2.31e-09 ***
## factor(Country)Mauritius 4.45e-05 ***
## factor(Country)Mexico 0.402413
## factor(Country)Moldova 0.991567
## factor(Country)Mongolia 0.986720
## factor(Country)Montenegro 0.994058
## factor(Country)Morocco 0.988691
## factor(Country)Mozambique 0.989694
## factor(Country)Myanmar 3.43e-11 ***
## factor(Country)Namibia 0.991514
## factor(Country)Nepal 0.259557
## factor(Country)Netherlands 0.986654
## factor(Country)New Zealand 2.29e-10 ***
## factor(Country)Nicaragua 0.000146 ***
## factor(Country)Niger 0.988066
## factor(Country)Nigeria 5.85e-09 ***
## factor(Country)North Korea 5.34e-09 ***
## factor(Country)North Macedonia 0.000262 ***
## factor(Country)Norway 2.24e-06 ***
## factor(Country)Oman 0.989971
## factor(Country)Pakistan 3.05e-07 ***
## factor(Country)Panama 0.081873 .
## factor(Country)Papua New Guinea 0.989593
## factor(Country)Paraguay 2.77e-11 ***
## factor(Country)Peru 3.84e-06 ***
## factor(Country)Philippines 0.592904
## factor(Country)Poland 1.28e-11 ***
## factor(Country)Portugal 9.58e-12 ***
## factor(Country)Qatar 0.989129
## factor(Country)Republic of the Congo 0.988146
## factor(Country)Republic of the Gambia 0.054738 .
## factor(Country)Romania 2.26e-11 ***
## factor(Country)Russia 0.986783
## factor(Country)Rwanda 0.988339
## factor(Country)Saudi Arabia 0.987864
## factor(Country)Senegal 0.988059
## factor(Country)Serbia 0.991554
## factor(Country)Sierra Leone 2.32e-07 ***
## factor(Country)Singapore 3.08e-05 ***
## factor(Country)Slovakia 0.991846
## factor(Country)Slovenia 1.08e-06 ***
## factor(Country)Solomon Islands 0.989939
## factor(Country)Somalia 0.988122
## factor(Country)South Africa 2.38e-14 ***
## factor(Country)South Korea 1.41e-10 ***
## factor(Country)South Sudan 0.995147
## factor(Country)Spain 1.68e-11 ***
## factor(Country)Sri Lanka 0.115797
## factor(Country)Sudan 0.041183 *
## factor(Country)Suriname 0.989594
## factor(Country)Sweden 3.69e-10 ***
## factor(Country)Switzerland 6.33e-11 ***
## factor(Country)Syria 9.36e-09 ***
## factor(Country)Taiwan 2.89e-10 ***
## factor(Country)Tajikistan 0.991648
## factor(Country)Tanzania 0.988535
## factor(Country)Thailand 8.31e-11 ***
## factor(Country)Timor-Leste 0.993261
## factor(Country)Togo 6.72e-05 ***
## factor(Country)Trinidad and Tobago 1.05e-05 ***
## factor(Country)Tunisia 0.987712
## factor(Country)Turkey 5.08e-12 ***
## factor(Country)Turkmenistan 0.991648
## factor(Country)Uganda 7.39e-09 ***
## factor(Country)Ukraine 0.991567
## factor(Country)United Arab Emirates 0.990073
## factor(Country)United Kingdom 5.06e-08 ***
## factor(Country)United States of America 2.42e-07 ***
## factor(Country)Uruguay 5.81e-08 ***
## factor(Country)Uzbekistan 0.991648
## factor(Country)Venezuela 1.35e-07 ***
## factor(Country)Vietnam 0.989810
## factor(Country)Yemen 1.05e-08 ***
## factor(Country)Zambia 0.988487
## factor(Year)1947 0.888407
## factor(Year)1948 0.752723
## factor(Year)1949 0.873186
## factor(Year)1950 0.714394
## factor(Year)1951 0.717852
## factor(Year)1952 0.453450
## factor(Year)1953 0.416255
## factor(Year)1954 0.655348
## factor(Year)1955 0.514598
## factor(Year)1956 0.950861
## factor(Year)1957 0.980486
## factor(Year)1958 0.688092
## factor(Year)1959 0.430193
## factor(Year)1960 0.211305
## factor(Year)1961 0.200033
## factor(Year)1962 0.279996
## factor(Year)1963 0.340394
## factor(Year)1964 0.411245
## factor(Year)1965 0.671682
## factor(Year)1966 0.919723
## factor(Year)1967 0.855265
## factor(Year)1968 0.257416
## factor(Year)1969 0.266993
## factor(Year)1970 0.136644
## factor(Year)1971 0.417132
## factor(Year)1972 0.265851
## factor(Year)1973 0.206577
## factor(Year)1974 0.417523
## factor(Year)1975 0.844565
## factor(Year)1976 0.609714
## factor(Year)1977 0.381247
## factor(Year)1978 0.467282
## factor(Year)1979 0.304706
## factor(Year)1980 0.648174
## factor(Year)1981 0.177334
## factor(Year)1982 0.180598
## factor(Year)1983 0.373654
## factor(Year)1984 0.371168
## factor(Year)1985 0.176815
## factor(Year)1986 0.127381
## factor(Year)1987 0.127381
## factor(Year)1988 0.092873 .
## factor(Year)1989 0.091576 .
## factor(Year)1990 0.152017
## factor(Year)1991 0.191863
## factor(Year)1992 0.077773 .
## factor(Year)1993 0.103258
## factor(Year)1994 0.100592
## factor(Year)1995 0.073352 .
## factor(Year)1996 0.133716
## factor(Year)1997 0.228463
## factor(Year)1998 0.100452
## factor(Year)1999 0.228197
## factor(Year)2000 0.178941
## factor(Year)2001 0.439141
## factor(Year)2002 0.289797
## factor(Year)2003 0.359008
## factor(Year)2004 0.436763
## factor(Year)2005 0.359008
## factor(Year)2006 0.701549
## factor(Year)2007 0.977190
## factor(Year)2008 0.874707
## factor(Year)2009 0.690274
## factor(Year)2010 0.891695
## factor(Year)2011 0.996855
## factor(Year)2012 0.792065
## factor(Year)2013 0.785709
## factor(Year)2014 0.685018
## factor(Year)2015 0.586081
## factor(Year)2016 0.699542
## factor(Year)2017 0.992313
## factor(Year)2018 0.493482
## factor(Year)2019 0.681063
## factor(Year)2020 0.681063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11844.3 on 10340 degrees of freedom
## Residual deviance: 7104.2 on 10097 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 7592.2
##
## Number of Fisher Scoring iterations: 18##
## Call:
## glm(formula = dynastic ~ dictatorship + v2x_polyarchy + former_british_colony +
## factor(Year) + factor(Country), family = binomial(link = "logit"),
## data = gdd_clean, na.action = na.exclude)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -1.28918 0.44619 -2.889
## dictatorship 0.23038 0.12719 1.811
## v2x_polyarchy 1.64381 0.29592 5.555
## former_british_colony 2.15460 0.38103 5.655
## factor(Year)1947 0.10467 0.44631 0.235
## factor(Year)1948 -0.16131 0.44466 -0.363
## factor(Year)1949 -0.08298 0.44094 -0.188
## factor(Year)1950 -0.19198 0.44363 -0.433
## factor(Year)1951 0.11850 0.43384 0.273
## factor(Year)1952 0.27251 0.43135 0.632
## factor(Year)1953 0.28598 0.42982 0.665
## factor(Year)1954 0.11317 0.43251 0.262
## factor(Year)1955 0.18552 0.43154 0.430
## factor(Year)1956 -0.12669 0.43532 -0.291
## factor(Year)1957 -0.12307 0.43148 -0.285
## factor(Year)1958 -0.29832 0.43501 -0.686
## factor(Year)1959 -0.47678 0.43937 -1.085
## factor(Year)1960 -0.67114 0.43371 -1.547
## factor(Year)1961 -0.69096 0.43217 -1.599
## factor(Year)1962 -0.60625 0.42514 -1.426
## factor(Year)1963 -0.55108 0.42204 -1.306
## factor(Year)1964 -0.50144 0.41959 -1.195
## factor(Year)1965 -0.33792 0.41246 -0.819
## factor(Year)1966 -0.11081 0.40647 -0.273
## factor(Year)1967 -0.23235 0.40859 -0.569
## factor(Year)1968 -0.62218 0.41549 -1.497
## factor(Year)1969 -0.62233 0.41574 -1.497
## factor(Year)1970 -0.79202 0.41909 -1.890
## factor(Year)1971 -0.49917 0.41003 -1.217
## factor(Year)1972 -0.61962 0.41277 -1.501
## factor(Year)1973 -0.68413 0.41474 -1.650
## factor(Year)1974 -0.49566 0.41013 -1.209
## factor(Year)1975 -0.24701 0.40615 -0.608
## factor(Year)1976 -0.37509 0.40766 -0.920
## factor(Year)1977 -0.53863 0.40971 -1.315
## factor(Year)1978 -0.48963 0.40868 -1.198
## factor(Year)1979 -0.62526 0.41134 -1.520
## factor(Year)1980 -0.39634 0.40619 -0.976
## factor(Year)1981 -0.76948 0.41433 -1.857
## factor(Year)1982 -0.77125 0.41429 -1.862
## factor(Year)1983 -0.58294 0.40979 -1.423
## factor(Year)1984 -0.60046 0.40991 -1.465
## factor(Year)1985 -0.80160 0.41430 -1.935
## factor(Year)1986 -0.87133 0.41540 -2.098
## factor(Year)1987 -0.88037 0.41579 -2.117
## factor(Year)1988 -0.95487 0.41781 -2.285
## factor(Year)1989 -0.95223 0.41751 -2.281
## factor(Year)1990 -0.86096 0.41366 -2.081
## factor(Year)1991 -0.88472 0.41220 -2.146
## factor(Year)1992 -1.03359 0.41583 -2.486
## factor(Year)1993 -0.98731 0.41419 -2.384
## factor(Year)1994 -0.98174 0.41398 -2.371
## factor(Year)1995 -1.05946 0.41590 -2.547
## factor(Year)1996 -0.93975 0.41247 -2.278
## factor(Year)1997 -0.83959 0.40987 -2.048
## factor(Year)1998 -1.02930 0.41468 -2.482
## factor(Year)1999 -0.84785 0.41006 -2.068
## factor(Year)2000 -0.91005 0.41146 -2.212
## factor(Year)2001 -0.67516 0.40647 -1.661
## factor(Year)2002 -0.80738 0.40913 -1.973
## factor(Year)2003 -0.76353 0.40819 -1.871
## factor(Year)2004 -0.70178 0.40686 -1.725
## factor(Year)2005 -0.76652 0.40836 -1.877
## factor(Year)2006 -0.54968 0.40451 -1.359
## factor(Year)2007 -0.38608 0.40211 -0.960
## factor(Year)2008 -0.34460 0.40215 -0.857
## factor(Year)2009 -0.55683 0.40535 -1.374
## factor(Year)2010 -0.34473 0.40236 -0.857
## factor(Year)2011 -0.39868 0.40302 -0.989
## factor(Year)2012 -0.50131 0.40445 -1.240
## factor(Year)2013 -0.28425 0.40137 -0.708
## factor(Year)2014 -0.22837 0.40092 -0.570
## factor(Year)2015 -0.18001 0.40074 -0.449
## factor(Year)2016 -0.54687 0.40559 -1.348
## factor(Year)2017 -0.38232 0.40316 -0.948
## factor(Year)2018 -0.64875 0.40713 -1.593
## factor(Year)2019 -0.52715 0.40487 -1.302
## factor(Year)2020 -0.51873 0.40458 -1.282
## factor(Country)Albania -0.03882 0.41206 -0.094
## factor(Country)Algeria -18.24924 1391.20064 -0.013
## factor(Country)Angola -18.13545 1571.87286 -0.012
## factor(Country)Argentina 0.13303 0.38995 0.341
## factor(Country)Armenia -18.36965 1950.19456 -0.009
## factor(Country)Australia 0.47481 0.40434 1.174
## factor(Country)Austria -19.16783 1232.04162 -0.016
## factor(Country)Azerbaijan 1.75264 0.47154 3.717
## factor(Country)Bahrain 18.98251 1509.24635 0.013
## factor(Country)Bangladesh 1.64079 0.40959 4.006
## factor(Country)Barbados -2.75363 0.43701 -6.301
## factor(Country)Belarus -18.37092 1958.38092 -0.009
## factor(Country)Belgium -1.86600 0.51514 -3.622
## factor(Country)Benin 2.14194 0.40882 5.239
## factor(Country)Bhutan 2.90404 0.41339 7.025
## factor(Country)Bosnia and Herzegovina -0.36042 0.53320 -0.676
## factor(Country)Botswana -2.51443 0.42569 -5.907
## factor(Country)Brazil -1.63982 0.52396 -3.130
## factor(Country)Bulgaria -0.87586 0.44867 -1.952
## factor(Country)Burkina Faso -1.40427 0.55349 -2.537
## factor(Country)Burundi 0.68583 0.40032 1.713
## factor(Country)Cambodia 2.15234 0.39134 5.500
## factor(Country)Cameroon -18.29136 1380.62798 -0.013
## factor(Country)Canada -0.17053 0.40646 -0.420
## factor(Country)Cape Verde -18.72468 1564.91139 -0.012
## factor(Country)Central African Republic 0.05232 0.42520 0.123
## factor(Country)Chad -18.23321 1367.28189 -0.013
## factor(Country)Chile -0.53326 0.41868 -1.274
## factor(Country)China 0.01380 0.40678 0.034
## factor(Country)Colombia 0.84058 0.38214 2.200
## factor(Country)Costa Rica 1.14244 0.40495 2.821
## factor(Country)Croatia -18.79862 1919.06710 -0.010
## factor(Country)Cuba -0.38688 0.42810 -0.904
## factor(Country)Cyprus -2.56716 0.41122 -6.243
## factor(Country)Czech Republic -19.11344 2022.40469 -0.009
## factor(Country)Democratic Republic of the Congo 0.39193 0.40509 0.968
## factor(Country)Denmark -19.29134 1229.41692 -0.016
## factor(Country)Djibouti 1.36464 0.41659 3.276
## factor(Country)Dominican Republic -0.71107 0.44070 -1.614
## factor(Country)Ecuador -0.39020 0.41052 -0.950
## factor(Country)Egypt -3.19435 0.47315 -6.751
## factor(Country)El Salvador -18.46026 1224.17982 -0.015
## factor(Country)Equatorial Guinea 2.83000 0.44354 6.380
## factor(Country)Eritrea -18.02846 2023.67542 -0.009
## factor(Country)Estonia -1.10823 0.58788 -1.885
## factor(Country)Eswatini 18.92802 1470.45066 0.013
## factor(Country)Ethiopia 0.82622 0.37210 2.220
## factor(Country)Fiji -1.38640 0.38867 -3.567
## factor(Country)Finland -3.51354 0.66991 -5.245
## factor(Country)France -1.86667 0.51529 -3.623
## factor(Country)Gabon -0.29039 0.44025 -0.660
## factor(Country)Georgia -18.56807 1943.97556 -0.010
## factor(Country)Germany -19.11035 1921.31786 -0.010
## factor(Country)Ghana -2.20799 0.39542 -5.584
## factor(Country)Greece 0.75995 0.38759 1.961
## factor(Country)Guatemala -0.86157 0.46411 -1.856
## factor(Country)Guinea -18.20467 1347.80977 -0.014
## factor(Country)Guinea-Bissau -18.29967 1551.68028 -0.012
## factor(Country)Guyana -4.45558 0.76864 -5.797
## factor(Country)Haiti 0.80897 0.37147 2.178
## factor(Country)Honduras -0.95937 0.47586 -2.016
## factor(Country)Hungary -18.59729 1224.09313 -0.015
## factor(Country)Iceland -0.35515 0.41300 -0.860
## factor(Country)India -2.37872 0.38586 -6.165
## factor(Country)Indonesia -1.83893 0.59336 -3.099
## factor(Country)Iran 1.52004 0.36809 4.130
## factor(Country)Iraq -1.70212 0.36086 -4.717
## factor(Country)Ireland -0.34770 0.41244 -0.843
## factor(Country)Israel -4.67998 0.50694 -9.232
## factor(Country)Italy -2.15733 0.56459 -3.821
## factor(Country)Ivory Coast -18.41110 1362.72506 -0.014
## factor(Country)Jamaica -1.95883 0.39504 -4.959
## factor(Country)Japan 0.91770 0.40379 2.273
## factor(Country)Jordan 18.73269 1230.89062 0.015
## factor(Country)Kazakhstan -18.29548 1955.20394 -0.009
## factor(Country)Kenya -2.75245 0.46199 -5.958
## factor(Country)Kuwait 2.10451 0.64637 3.256
## factor(Country)Kyrgyzstan -18.34779 1951.49502 -0.009
## factor(Country)Laos 2.61767 0.40517 6.461
## factor(Country)Latvia -18.95981 1949.74734 -0.010
## factor(Country)Lebanon 1.94610 0.38607 5.041
## factor(Country)Lesotho -20.60356 1427.53754 -0.014
## factor(Country)Liberia 1.14790 0.37080 3.096
## factor(Country)Libya -1.47055 0.36311 -4.050
## factor(Country)Lithuania -19.00826 1951.99628 -0.010
## factor(Country)Luxembourg -19.21934 1230.69625 -0.016
## factor(Country)Madagascar -18.39562 1366.97685 -0.013
## factor(Country)Malawi -3.29708 0.53746 -6.135
## factor(Country)Malaysia 0.92778 0.38279 2.424
## factor(Country)Maldives -0.14469 0.38359 -0.377
## factor(Country)Mali -0.11651 0.42781 -0.272
## factor(Country)Malta -3.29703 0.47934 -6.878
## factor(Country)Mauritius -2.07795 0.40866 -5.085
## factor(Country)Mexico 1.56686 0.37828 4.142
## factor(Country)Moldova -18.57277 1948.12724 -0.010
## factor(Country)Mongolia -18.54997 1228.73801 -0.015
## factor(Country)Montenegro -18.88022 2772.66129 -0.007
## factor(Country)Morocco 20.95029 1326.42002 0.016
## factor(Country)Mozambique -18.38779 1564.30253 -0.012
## factor(Country)Myanmar -3.52642 0.53232 -6.625
## factor(Country)Namibia -18.95287 1917.98357 -0.010
## factor(Country)Nepal 2.61026 0.40323 6.473
## factor(Country)Netherlands -19.16154 1232.24221 -0.016
## factor(Country)New Zealand -1.17294 0.45098 -2.601
## factor(Country)Nicaragua 0.73772 0.37270 1.979
## factor(Country)Niger -18.36368 1363.85548 -0.013
## factor(Country)Nigeria -2.52580 0.43551 -5.800
## factor(Country)North Korea -0.02693 0.41305 -0.065
## factor(Country)North Macedonia 0.42164 0.49347 0.854
## factor(Country)Norway -0.31242 0.41242 -0.758
## factor(Country)Oman 19.03748 1493.81617 0.013
## factor(Country)Pakistan -1.81627 0.36823 -4.932
## factor(Country)Panama 1.39759 0.37900 3.688
## factor(Country)Papua New Guinea -18.46800 1579.27273 -0.012
## factor(Country)Paraguay -1.78594 0.59057 -3.024
## factor(Country)Peru 0.23627 0.38620 0.612
## factor(Country)Philippines 2.26472 0.40032 5.657
## factor(Country)Poland -1.80470 0.55244 -3.267
## factor(Country)Portugal -1.93045 0.55682 -3.467
## factor(Country)Qatar 19.07847 1381.43129 0.014
## factor(Country)Republic of the Congo -18.20471 1369.07479 -0.013
## factor(Country)Republic of the Gambia -0.98530 0.37521 -2.626
## factor(Country)Romania -0.96704 0.46025 -2.101
## factor(Country)Russia -18.35568 1232.76039 -0.015
## factor(Country)Rwanda -18.22115 1391.24310 -0.013
## factor(Country)Saudi Arabia 21.15107 1230.42025 0.017
## factor(Country)Senegal -18.70002 1370.49232 -0.014
## factor(Country)Serbia -18.47499 1941.95196 -0.010
## factor(Country)Sierra Leone -2.09318 0.40420 -5.179
## factor(Country)Singapore 0.23310 0.41210 0.566
## factor(Country)Slovakia -19.02467 2016.86880 -0.009
## factor(Country)Slovenia -1.66728 0.68827 -2.422
## factor(Country)Solomon Islands -20.64135 1628.90694 -0.013
## factor(Country)Somalia -18.16513 1369.43366 -0.013
## factor(Country)South Africa -0.68842 0.36799 -1.871
## factor(Country)South Korea -2.27421 0.66212 -3.435
## factor(Country)South Sudan -20.53437 3394.49700 -0.006
## factor(Country)Spain -1.24071 0.47036 -2.638
## factor(Country)Sri Lanka -0.84526 0.36569 -2.311
## factor(Country)Sudan -0.63405 0.35887 -1.767
## factor(Country)Suriname -18.77421 1567.17492 -0.012
## factor(Country)Sweden -1.11606 0.44549 -2.505
## factor(Country)Switzerland -2.71332 0.67258 -4.034
## factor(Country)Syria 0.00709 0.40211 0.018
## factor(Country)Taiwan -0.70708 0.44161 -1.601
## factor(Country)Tajikistan -18.22706 1953.06173 -0.009
## factor(Country)Tanzania -18.50024 1414.62307 -0.013
## factor(Country)Thailand -0.54657 0.43565 -1.255
## factor(Country)Timor-Leste -18.82401 2458.01431 -0.008
## factor(Country)Togo 0.53296 0.39754 1.341
## factor(Country)Trinidad and Tobago -2.13924 0.39939 -5.356
## factor(Country)Tunisia -18.35183 1315.37036 -0.014
## factor(Country)Turkey -1.27310 0.49827 -2.555
## factor(Country)Turkmenistan -18.08434 1952.12287 -0.009
## factor(Country)Uganda -2.70274 0.46041 -5.870
## factor(Country)Ukraine -18.43611 1950.50820 -0.009
## factor(Country)United Arab Emirates 19.08168 1512.16792 0.013
## factor(Country)United Kingdom -0.59643 0.42040 -1.419
## factor(Country)United States of America -2.52798 0.39021 -6.478
## factor(Country)Uruguay -0.44787 0.41469 -1.080
## factor(Country)Uzbekistan -18.15927 1952.95158 -0.009
## factor(Country)Venezuela -0.05291 0.39667 -0.133
## factor(Country)Vietnam -18.11124 1595.43535 -0.011
## factor(Country)Yemen NA NA NA
## factor(Country)Zambia -20.58289 1412.26997 -0.015
## Pr(>|z|)
## (Intercept) 0.003861 **
## dictatorship 0.070083 .
## v2x_polyarchy 2.78e-08 ***
## former_british_colony 1.56e-08 ***
## factor(Year)1947 0.814573
## factor(Year)1948 0.716782
## factor(Year)1949 0.850734
## factor(Year)1950 0.665196
## factor(Year)1951 0.784741
## factor(Year)1952 0.527545
## factor(Year)1953 0.505824
## factor(Year)1954 0.793585
## factor(Year)1955 0.667278
## factor(Year)1956 0.771028
## factor(Year)1957 0.775466
## factor(Year)1958 0.492862
## factor(Year)1959 0.277857
## factor(Year)1960 0.121760
## factor(Year)1961 0.109859
## factor(Year)1962 0.153870
## factor(Year)1963 0.191636
## factor(Year)1964 0.232062
## factor(Year)1965 0.412627
## factor(Year)1966 0.785158
## factor(Year)1967 0.569593
## factor(Year)1968 0.134272
## factor(Year)1969 0.134421
## factor(Year)1970 0.058780 .
## factor(Year)1971 0.223452
## factor(Year)1972 0.133325
## factor(Year)1973 0.099039 .
## factor(Year)1974 0.226835
## factor(Year)1975 0.543066
## factor(Year)1976 0.357517
## factor(Year)1977 0.188625
## factor(Year)1978 0.230887
## factor(Year)1979 0.128491
## factor(Year)1980 0.329193
## factor(Year)1981 0.063288 .
## factor(Year)1982 0.062655 .
## factor(Year)1983 0.154877
## factor(Year)1984 0.142962
## factor(Year)1985 0.053014 .
## factor(Year)1986 0.035946 *
## factor(Year)1987 0.034229 *
## factor(Year)1988 0.022288 *
## factor(Year)1989 0.022563 *
## factor(Year)1990 0.037402 *
## factor(Year)1991 0.031849 *
## factor(Year)1992 0.012932 *
## factor(Year)1993 0.017139 *
## factor(Year)1994 0.017718 *
## factor(Year)1995 0.010853 *
## factor(Year)1996 0.022707 *
## factor(Year)1997 0.040516 *
## factor(Year)1998 0.013058 *
## factor(Year)1999 0.038676 *
## factor(Year)2000 0.026983 *
## factor(Year)2001 0.096705 .
## factor(Year)2002 0.048451 *
## factor(Year)2003 0.061409 .
## factor(Year)2004 0.084548 .
## factor(Year)2005 0.060505 .
## factor(Year)2006 0.174179
## factor(Year)2007 0.336996
## factor(Year)2008 0.391514
## factor(Year)2009 0.169532
## factor(Year)2010 0.391576
## factor(Year)2011 0.322556
## factor(Year)2012 0.215159
## factor(Year)2013 0.478813
## factor(Year)2014 0.568939
## factor(Year)2015 0.653293
## factor(Year)2016 0.177553
## factor(Year)2017 0.342976
## factor(Year)2018 0.111052
## factor(Year)2019 0.192912
## factor(Year)2020 0.199789
## factor(Country)Albania 0.924934
## factor(Country)Algeria 0.989534
## factor(Country)Angola 0.990795
## factor(Country)Argentina 0.733004
## factor(Country)Armenia 0.992485
## factor(Country)Australia 0.240285
## factor(Country)Austria 0.987587
## factor(Country)Azerbaijan 0.000202 ***
## factor(Country)Bahrain 0.989965
## factor(Country)Bangladesh 6.18e-05 ***
## factor(Country)Barbados 2.96e-10 ***
## factor(Country)Belarus 0.992515
## factor(Country)Belgium 0.000292 ***
## factor(Country)Benin 1.61e-07 ***
## factor(Country)Bhutan 2.14e-12 ***
## factor(Country)Bosnia and Herzegovina 0.499068
## factor(Country)Botswana 3.49e-09 ***
## factor(Country)Brazil 0.001750 **
## factor(Country)Bulgaria 0.050924 .
## factor(Country)Burkina Faso 0.011176 *
## factor(Country)Burundi 0.086675 .
## factor(Country)Cambodia 3.80e-08 ***
## factor(Country)Cameroon 0.989429
## factor(Country)Canada 0.674813
## factor(Country)Cape Verde 0.990453
## factor(Country)Central African Republic 0.902066
## factor(Country)Chad 0.989360
## factor(Country)Chile 0.202786
## factor(Country)China 0.972930
## factor(Country)Colombia 0.027830 *
## factor(Country)Costa Rica 0.004785 **
## factor(Country)Croatia 0.992184
## factor(Country)Cuba 0.366145
## factor(Country)Cyprus 4.30e-10 ***
## factor(Country)Czech Republic 0.992459
## factor(Country)Democratic Republic of the Congo 0.333292
## factor(Country)Denmark 0.987481
## factor(Country)Djibouti 0.001054 **
## factor(Country)Dominican Republic 0.106636
## factor(Country)Ecuador 0.341860
## factor(Country)Egypt 1.47e-11 ***
## factor(Country)El Salvador 0.987969
## factor(Country)Equatorial Guinea 1.77e-10 ***
## factor(Country)Eritrea 0.992892
## factor(Country)Estonia 0.059412 .
## factor(Country)Eswatini 0.989730
## factor(Country)Ethiopia 0.026391 *
## factor(Country)Fiji 0.000361 ***
## factor(Country)Finland 1.56e-07 ***
## factor(Country)France 0.000292 ***
## factor(Country)Gabon 0.509519
## factor(Country)Georgia 0.992379
## factor(Country)Germany 0.992064
## factor(Country)Ghana 2.35e-08 ***
## factor(Country)Greece 0.049913 *
## factor(Country)Guatemala 0.063401 .
## factor(Country)Guinea 0.989223
## factor(Country)Guinea-Bissau 0.990590
## factor(Country)Guyana 6.76e-09 ***
## factor(Country)Haiti 0.029424 *
## factor(Country)Honduras 0.043790 *
## factor(Country)Hungary 0.987878
## factor(Country)Iceland 0.389835
## factor(Country)India 7.06e-10 ***
## factor(Country)Indonesia 0.001940 **
## factor(Country)Iran 3.64e-05 ***
## factor(Country)Iraq 2.40e-06 ***
## factor(Country)Ireland 0.399209
## factor(Country)Israel < 2e-16 ***
## factor(Country)Italy 0.000133 ***
## factor(Country)Ivory Coast 0.989221
## factor(Country)Jamaica 7.10e-07 ***
## factor(Country)Japan 0.023042 *
## factor(Country)Jordan 0.987858
## factor(Country)Kazakhstan 0.992534
## factor(Country)Kenya 2.56e-09 ***
## factor(Country)Kuwait 0.001130 **
## factor(Country)Kyrgyzstan 0.992498
## factor(Country)Laos 1.04e-10 ***
## factor(Country)Latvia 0.992241
## factor(Country)Lebanon 4.64e-07 ***
## factor(Country)Lesotho 0.988485
## factor(Country)Liberia 0.001963 **
## factor(Country)Libya 5.12e-05 ***
## factor(Country)Lithuania 0.992230
## factor(Country)Luxembourg 0.987540
## factor(Country)Madagascar 0.989263
## factor(Country)Malawi 8.54e-10 ***
## factor(Country)Malaysia 0.015361 *
## factor(Country)Maldives 0.706023
## factor(Country)Mali 0.785363
## factor(Country)Malta 6.06e-12 ***
## factor(Country)Mauritius 3.68e-07 ***
## factor(Country)Mexico 3.44e-05 ***
## factor(Country)Moldova 0.992393
## factor(Country)Mongolia 0.987955
## factor(Country)Montenegro 0.994567
## factor(Country)Morocco 0.987398
## factor(Country)Mozambique 0.990621
## factor(Country)Myanmar 3.48e-11 ***
## factor(Country)Namibia 0.992116
## factor(Country)Nepal 9.58e-11 ***
## factor(Country)Netherlands 0.987593
## factor(Country)New Zealand 0.009299 **
## factor(Country)Nicaragua 0.047770 *
## factor(Country)Niger 0.989257
## factor(Country)Nigeria 6.64e-09 ***
## factor(Country)North Korea 0.948009
## factor(Country)North Macedonia 0.392865
## factor(Country)Norway 0.448725
## factor(Country)Oman 0.989832
## factor(Country)Pakistan 8.12e-07 ***
## factor(Country)Panama 0.000226 ***
## factor(Country)Papua New Guinea 0.990670
## factor(Country)Paraguay 0.002494 **
## factor(Country)Peru 0.540678
## factor(Country)Philippines 1.54e-08 ***
## factor(Country)Poland 0.001088 **
## factor(Country)Portugal 0.000527 ***
## factor(Country)Qatar 0.988981
## factor(Country)Republic of the Congo 0.989391
## factor(Country)Republic of the Gambia 0.008640 **
## factor(Country)Romania 0.035628 *
## factor(Country)Russia 0.988120
## factor(Country)Rwanda 0.989550
## factor(Country)Saudi Arabia 0.986285
## factor(Country)Senegal 0.989113
## factor(Country)Serbia 0.992409
## factor(Country)Sierra Leone 2.24e-07 ***
## factor(Country)Singapore 0.571641
## factor(Country)Slovakia 0.992474
## factor(Country)Slovenia 0.015417 *
## factor(Country)Solomon Islands 0.989890
## factor(Country)Somalia 0.989417
## factor(Country)South Africa 0.061376 .
## factor(Country)South Korea 0.000593 ***
## factor(Country)South Sudan 0.995173
## factor(Country)Spain 0.008345 **
## factor(Country)Sri Lanka 0.020810 *
## factor(Country)Sudan 0.077261 .
## factor(Country)Suriname 0.990442
## factor(Country)Sweden 0.012238 *
## factor(Country)Switzerland 5.48e-05 ***
## factor(Country)Syria 0.985932
## factor(Country)Taiwan 0.109341
## factor(Country)Tajikistan 0.992554
## factor(Country)Tanzania 0.989566
## factor(Country)Thailand 0.209617
## factor(Country)Timor-Leste 0.993890
## factor(Country)Togo 0.180039
## factor(Country)Trinidad and Tobago 8.50e-08 ***
## factor(Country)Tunisia 0.988868
## factor(Country)Turkey 0.010617 *
## factor(Country)Turkmenistan 0.992609
## factor(Country)Uganda 4.35e-09 ***
## factor(Country)Ukraine 0.992459
## factor(Country)United Arab Emirates 0.989932
## factor(Country)United Kingdom 0.155979
## factor(Country)United States of America 9.27e-11 ***
## factor(Country)Uruguay 0.280132
## factor(Country)Uzbekistan 0.992581
## factor(Country)Venezuela 0.893892
## factor(Country)Vietnam 0.990943
## factor(Country)Yemen NA
## factor(Country)Zambia 0.988372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11781.8 on 10240 degrees of freedom
## Residual deviance: 7067.3 on 9999 degrees of freedom
## AIC: 7551.3
##
## Number of Fisher Scoring iterations: 183.2 Model 4: Using Dem_Type as the independent variable, with mixed (1) as the reference category
# Model 4: Using Dem_Type as the independent variable, with mixed (1) as the reference category
gdd_clean$Dem_Type <- factor(gdd_clean$Dem_Type, levels = c(1, 0, 2, 3))
model4 <- glm(dynastic ~ Dem_Type + v2x_polyarchy + former_british_colony + factor(Year) + factor(Country), data = gdd_clean, family = binomial(link = "logit"))
summary(model4)##
## Call:
## glm(formula = dynastic ~ Dem_Type + v2x_polyarchy + former_british_colony +
## factor(Year) + factor(Country), family = binomial(link = "logit"),
## data = gdd_clean)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -1.369e+00 4.826e-01 -2.837
## Dem_Type0 2.274e-01 2.147e-01 1.059
## Dem_Type2 1.489e-02 2.272e-01 0.066
## Dem_Type3 -4.008e-02 2.401e-01 -0.167
## v2x_polyarchy 1.629e+00 2.985e-01 5.459
## former_british_colony 2.152e+00 3.808e-01 5.651
## factor(Year)1947 1.163e-01 4.541e-01 0.256
## factor(Year)1948 -1.523e-01 4.526e-01 -0.336
## factor(Year)1949 -7.008e-02 4.487e-01 -0.156
## factor(Year)1950 -1.824e-01 4.517e-01 -0.404
## factor(Year)1951 1.402e-01 4.410e-01 0.318
## factor(Year)1952 2.986e-01 4.383e-01 0.681
## factor(Year)1953 3.136e-01 4.365e-01 0.718
## factor(Year)1954 2.214e-01 4.380e-01 0.505
## factor(Year)1955 2.952e-01 4.371e-01 0.675
## factor(Year)1956 -2.050e-02 4.406e-01 -0.047
## factor(Year)1957 -1.729e-02 4.368e-01 -0.040
## factor(Year)1958 -1.950e-01 4.402e-01 -0.443
## factor(Year)1959 -3.748e-01 4.444e-01 -0.843
## factor(Year)1960 -5.710e-01 4.386e-01 -1.302
## factor(Year)1961 -5.906e-01 4.371e-01 -1.351
## factor(Year)1962 -5.059e-01 4.301e-01 -1.176
## factor(Year)1963 -4.505e-01 4.270e-01 -1.055
## factor(Year)1964 -4.005e-01 4.246e-01 -0.943
## factor(Year)1965 -2.363e-01 4.176e-01 -0.566
## factor(Year)1966 -8.184e-03 4.117e-01 -0.020
## factor(Year)1967 -1.302e-01 4.137e-01 -0.315
## factor(Year)1968 -5.225e-01 4.204e-01 -1.243
## factor(Year)1969 -5.229e-01 4.207e-01 -1.243
## factor(Year)1970 -6.932e-01 4.240e-01 -1.635
## factor(Year)1971 -3.995e-01 4.151e-01 -0.963
## factor(Year)1972 -5.206e-01 4.177e-01 -1.246
## factor(Year)1973 -5.852e-01 4.197e-01 -1.394
## factor(Year)1974 -3.959e-01 4.152e-01 -0.954
## factor(Year)1975 -1.456e-01 4.114e-01 -0.354
## factor(Year)1976 -2.745e-01 4.129e-01 -0.665
## factor(Year)1977 -4.393e-01 4.148e-01 -1.059
## factor(Year)1978 -3.906e-01 4.138e-01 -0.944
## factor(Year)1979 -5.271e-01 4.164e-01 -1.266
## factor(Year)1980 -2.968e-01 4.113e-01 -0.722
## factor(Year)1981 -6.724e-01 4.193e-01 -1.604
## factor(Year)1982 -6.766e-01 4.190e-01 -1.615
## factor(Year)1983 -4.877e-01 4.146e-01 -1.176
## factor(Year)1984 -5.053e-01 4.147e-01 -1.218
## factor(Year)1985 -7.071e-01 4.190e-01 -1.688
## factor(Year)1986 -7.760e-01 4.201e-01 -1.847
## factor(Year)1987 -7.850e-01 4.205e-01 -1.867
## factor(Year)1988 -8.597e-01 4.224e-01 -2.035
## factor(Year)1989 -8.576e-01 4.222e-01 -2.031
## factor(Year)1990 -7.665e-01 4.184e-01 -1.832
## factor(Year)1991 -7.880e-01 4.169e-01 -1.890
## factor(Year)1992 -9.378e-01 4.204e-01 -2.231
## factor(Year)1993 -8.915e-01 4.188e-01 -2.129
## factor(Year)1994 -8.856e-01 4.186e-01 -2.116
## factor(Year)1995 -9.633e-01 4.205e-01 -2.291
## factor(Year)1996 -8.438e-01 4.171e-01 -2.023
## factor(Year)1997 -7.432e-01 4.146e-01 -1.793
## factor(Year)1998 -9.332e-01 4.193e-01 -2.226
## factor(Year)1999 -7.512e-01 4.148e-01 -1.811
## factor(Year)2000 -8.138e-01 4.161e-01 -1.956
## factor(Year)2001 -5.783e-01 4.112e-01 -1.406
## factor(Year)2002 -7.109e-01 4.139e-01 -1.718
## factor(Year)2003 -6.669e-01 4.130e-01 -1.615
## factor(Year)2004 -6.050e-01 4.117e-01 -1.470
## factor(Year)2005 -6.696e-01 4.131e-01 -1.621
## factor(Year)2006 -4.519e-01 4.093e-01 -1.104
## factor(Year)2007 -2.885e-01 4.070e-01 -0.709
## factor(Year)2008 -2.466e-01 4.070e-01 -0.606
## factor(Year)2009 -4.589e-01 4.102e-01 -1.119
## factor(Year)2010 -2.461e-01 4.072e-01 -0.604
## factor(Year)2011 -3.002e-01 4.079e-01 -0.736
## factor(Year)2012 -4.032e-01 4.093e-01 -0.985
## factor(Year)2013 -1.857e-01 4.062e-01 -0.457
## factor(Year)2014 -1.299e-01 4.058e-01 -0.320
## factor(Year)2015 -8.110e-02 4.056e-01 -0.200
## factor(Year)2016 -4.988e-01 4.115e-01 -1.212
## factor(Year)2017 -3.312e-01 4.090e-01 -0.810
## factor(Year)2018 -6.033e-01 4.132e-01 -1.460
## factor(Year)2019 -4.795e-01 4.109e-01 -1.167
## factor(Year)2020 -4.709e-01 4.106e-01 -1.147
## factor(Country)Albania -1.070e+00 6.831e-01 -1.567
## factor(Country)Algeria -1.828e+01 1.511e+03 -0.012
## factor(Country)Angola -1.812e+01 1.664e+03 -0.011
## factor(Country)Argentina 1.257e-01 3.952e-01 0.318
## factor(Country)Armenia -1.837e+01 1.951e+03 -0.009
## factor(Country)Australia 5.208e-01 4.220e-01 1.234
## factor(Country)Austria -1.916e+01 1.233e+03 -0.016
## factor(Country)Azerbaijan 1.750e+00 4.715e-01 3.712
## factor(Country)Bahrain 1.898e+01 1.509e+03 0.013
## factor(Country)Bangladesh 1.649e+00 4.117e-01 4.005
## factor(Country)Barbados -2.713e+00 4.546e-01 -5.968
## factor(Country)Belarus -1.837e+01 1.958e+03 -0.009
## factor(Country)Belgium -1.819e+00 5.293e-01 -3.436
## factor(Country)Benin 2.128e+00 4.105e-01 5.184
## factor(Country)Bhutan 2.904e+00 4.134e-01 7.024
## factor(Country)Bosnia and Herzegovina -3.576e-01 5.333e-01 -0.670
## factor(Country)Botswana -2.515e+00 4.577e-01 -5.495
## factor(Country)Brazil -1.647e+00 5.286e-01 -3.116
## factor(Country)Bulgaria -8.727e-01 4.556e-01 -1.915
## factor(Country)Burkina Faso -1.407e+00 5.533e-01 -2.543
## factor(Country)Burundi 6.740e-01 4.006e-01 1.682
## factor(Country)Cambodia 2.145e+00 3.913e-01 5.482
## factor(Country)Cameroon -1.830e+01 1.381e+03 -0.013
## factor(Country)Canada -1.243e-01 4.244e-01 -0.293
## factor(Country)Cape Verde -1.873e+01 1.567e+03 -0.012
## factor(Country)Central African Republic 4.172e-02 4.255e-01 0.098
## factor(Country)Chad -1.824e+01 1.367e+03 -0.013
## factor(Country)Chile -5.416e-01 4.255e-01 -1.273
## factor(Country)China 1.422e-02 4.065e-01 0.035
## factor(Country)Colombia 8.260e-01 3.906e-01 2.115
## factor(Country)Costa Rica 1.133e+00 4.149e-01 2.730
## factor(Country)Croatia -1.880e+01 1.920e+03 -0.010
## factor(Country)Cuba -3.825e-01 4.276e-01 -0.895
## factor(Country)Cyprus -2.578e+00 4.163e-01 -6.192
## factor(Country)Czech Republic -1.907e+01 2.022e+03 -0.009
## factor(Country)Democratic Republic of the Congo 3.845e-01 4.052e-01 0.949
## factor(Country)Denmark -1.924e+01 1.230e+03 -0.016
## factor(Country)Djibouti 1.360e+00 4.165e-01 3.265
## factor(Country)Dominican Republic -7.197e-01 4.449e-01 -1.618
## factor(Country)Ecuador -3.981e-01 4.152e-01 -0.959
## factor(Country)Egypt -3.189e+00 4.729e-01 -6.745
## factor(Country)El Salvador -1.847e+01 1.225e+03 -0.015
## factor(Country)Equatorial Guinea 2.822e+00 4.434e-01 6.363
## factor(Country)Eritrea -1.803e+01 2.024e+03 -0.009
## factor(Country)Estonia -1.064e+00 6.010e-01 -1.770
## factor(Country)Eswatini 1.892e+01 1.471e+03 0.013
## factor(Country)Ethiopia 8.271e-01 3.720e-01 2.224
## factor(Country)Fiji -1.387e+00 3.895e-01 -3.562
## factor(Country)Finland -3.504e+00 6.903e-01 -5.076
## factor(Country)France -1.859e+00 5.416e-01 -3.433
## factor(Country)Gabon -2.965e-01 4.402e-01 -0.674
## factor(Country)Georgia -1.857e+01 1.946e+03 -0.010
## factor(Country)Germany -1.907e+01 1.921e+03 -0.010
## factor(Country)Ghana -2.219e+00 3.976e-01 -5.580
## factor(Country)Greece 7.782e-01 3.889e-01 2.001
## factor(Country)Guatemala -8.695e-01 4.683e-01 -1.857
## factor(Country)Guinea -1.822e+01 1.348e+03 -0.014
## factor(Country)Guinea-Bissau -1.831e+01 1.551e+03 -0.012
## factor(Country)Guyana -4.460e+00 7.690e-01 -5.800
## factor(Country)Haiti 8.099e-01 3.713e-01 2.181
## factor(Country)Honduras -9.683e-01 4.792e-01 -2.021
## factor(Country)Hungary -1.857e+01 1.225e+03 -0.015
## factor(Country)Iceland -3.481e-01 4.453e-01 -0.782
## factor(Country)India -2.335e+00 4.063e-01 -5.748
## factor(Country)Indonesia -1.844e+00 5.942e-01 -3.103
## factor(Country)Iran 1.518e+00 3.679e-01 4.127
## factor(Country)Iraq -1.698e+00 3.605e-01 -4.711
## factor(Country)Ireland -3.408e-01 4.448e-01 -0.766
## factor(Country)Israel -4.634e+00 5.221e-01 -8.876
## factor(Country)Italy -2.110e+00 5.778e-01 -3.652
## factor(Country)Ivory Coast -1.842e+01 1.375e+03 -0.013
## factor(Country)Jamaica -1.924e+00 4.107e-01 -4.684
## factor(Country)Japan 9.611e-01 4.219e-01 2.278
## factor(Country)Jordan 1.873e+01 1.232e+03 0.015
## factor(Country)Kazakhstan -1.830e+01 1.955e+03 -0.009
## factor(Country)Kenya -2.763e+00 4.633e-01 -5.964
## factor(Country)Kuwait 2.099e+00 6.463e-01 3.249
## factor(Country)Kyrgyzstan -1.835e+01 1.951e+03 -0.009
## factor(Country)Laos 2.609e+00 4.051e-01 6.440
## factor(Country)Latvia -1.892e+01 1.950e+03 -0.010
## factor(Country)Lebanon 1.961e+00 3.888e-01 5.045
## factor(Country)Lesotho -2.060e+01 1.429e+03 -0.014
## factor(Country)Liberia 1.147e+00 3.708e-01 3.092
## factor(Country)Libya -1.474e+00 3.629e-01 -4.063
## factor(Country)Lithuania -1.901e+01 1.952e+03 -0.010
## factor(Country)Luxembourg -1.917e+01 1.232e+03 -0.016
## factor(Country)Madagascar -1.840e+01 1.367e+03 -0.013
## factor(Country)Malawi -3.308e+00 5.392e-01 -6.136
## factor(Country)Malaysia 9.202e-01 3.840e-01 2.396
## factor(Country)Maldives -1.514e-01 3.836e-01 -0.395
## factor(Country)Mali -1.230e-01 4.361e-01 -0.282
## factor(Country)Malta -3.257e+00 4.954e-01 -6.574
## factor(Country)Mauritius -2.037e+00 4.276e-01 -4.764
## factor(Country)Mexico 1.564e+00 3.790e-01 4.128
## factor(Country)Moldova -1.854e+01 1.948e+03 -0.010
## factor(Country)Mongolia -1.855e+01 1.230e+03 -0.015
## factor(Country)Montenegro -1.887e+01 2.772e+03 -0.007
## factor(Country)Morocco 2.094e+01 1.327e+03 0.016
## factor(Country)Mozambique -1.839e+01 1.565e+03 -0.012
## factor(Country)Myanmar -3.523e+00 5.337e-01 -6.602
## factor(Country)Namibia -1.895e+01 1.918e+03 -0.010
## factor(Country)Nepal 2.620e+00 4.054e-01 6.463
## factor(Country)Netherlands -1.911e+01 1.233e+03 -0.016
## factor(Country)New Zealand -1.124e+00 4.669e-01 -2.408
## factor(Country)Nicaragua 7.318e-01 3.743e-01 1.955
## factor(Country)Niger -1.837e+01 1.365e+03 -0.013
## factor(Country)Nigeria -2.534e+00 4.368e-01 -5.802
## factor(Country)North Korea -2.946e-02 4.129e-01 -0.071
## factor(Country)North Macedonia 4.206e-01 5.223e-01 0.805
## factor(Country)Norway -2.653e-01 4.297e-01 -0.617
## factor(Country)Oman 1.903e+01 1.494e+03 0.013
## factor(Country)Pakistan -1.796e+00 3.730e-01 -4.815
## factor(Country)Panama 1.387e+00 3.835e-01 3.618
## factor(Country)Papua New Guinea -1.843e+01 1.579e+03 -0.012
## factor(Country)Paraguay -1.790e+00 5.918e-01 -3.024
## factor(Country)Peru 2.298e-01 3.899e-01 0.589
## factor(Country)Philippines 2.253e+00 4.044e-01 5.572
## factor(Country)Poland -1.801e+00 5.597e-01 -3.217
## factor(Country)Portugal -1.926e+00 5.694e-01 -3.382
## factor(Country)Qatar 1.907e+01 1.381e+03 0.014
## factor(Country)Republic of the Congo -1.821e+01 1.369e+03 -0.013
## factor(Country)Republic of the Gambia -9.887e-01 3.752e-01 -2.635
## factor(Country)Romania -9.636e-01 4.667e-01 -2.064
## factor(Country)Russia -1.835e+01 1.233e+03 -0.015
## factor(Country)Rwanda -1.823e+01 1.391e+03 -0.013
## factor(Country)Saudi Arabia 2.115e+01 1.231e+03 0.017
## factor(Country)Senegal -1.870e+01 1.371e+03 -0.014
## factor(Country)Serbia -1.845e+01 1.943e+03 -0.009
## factor(Country)Sierra Leone -2.100e+00 4.052e-01 -5.183
## factor(Country)Singapore 2.286e-01 4.121e-01 0.555
## factor(Country)Slovakia -1.902e+01 2.017e+03 -0.009
## factor(Country)Slovenia -1.637e+00 6.934e-01 -2.361
## factor(Country)Solomon Islands -2.060e+01 1.629e+03 -0.013
## factor(Country)Somalia -1.817e+01 1.370e+03 -0.013
## factor(Country)South Africa -6.669e-01 3.715e-01 -1.795
## factor(Country)South Korea -2.281e+00 6.633e-01 -3.438
## factor(Country)South Sudan -2.052e+01 3.393e+03 -0.006
## factor(Country)Spain -1.209e+00 4.777e-01 -2.530
## factor(Country)Sri Lanka -8.354e-01 3.673e-01 -2.274
## factor(Country)Sudan -6.311e-01 3.603e-01 -1.751
## factor(Country)Suriname -1.878e+01 1.567e+03 -0.012
## factor(Country)Sweden -1.068e+00 4.616e-01 -2.314
## factor(Country)Switzerland -2.721e+00 6.786e-01 -4.010
## factor(Country)Syria 7.387e-03 4.025e-01 0.018
## factor(Country)Taiwan -7.066e-01 4.468e-01 -1.582
## factor(Country)Tajikistan -1.823e+01 1.953e+03 -0.009
## factor(Country)Tanzania -1.850e+01 1.415e+03 -0.013
## factor(Country)Thailand -5.291e-01 4.386e-01 -1.206
## factor(Country)Timor-Leste -1.882e+01 2.458e+03 -0.008
## factor(Country)Togo 5.265e-01 3.974e-01 1.325
## factor(Country)Trinidad and Tobago -2.100e+00 4.188e-01 -5.014
## factor(Country)Tunisia -1.836e+01 1.315e+03 -0.014
## factor(Country)Turkey -1.271e+00 5.208e-01 -2.441
## factor(Country)Turkmenistan -1.809e+01 1.952e+03 -0.009
## factor(Country)Uganda -2.709e+00 4.604e-01 -5.884
## factor(Country)Ukraine -1.844e+01 1.951e+03 -0.009
## factor(Country)United Arab Emirates 1.908e+01 1.512e+03 0.013
## factor(Country)United Kingdom -5.490e-01 4.375e-01 -1.255
## factor(Country)United States of America -2.535e+00 4.002e-01 -6.333
## factor(Country)Uruguay -4.542e-01 4.226e-01 -1.075
## factor(Country)Uzbekistan -1.816e+01 1.953e+03 -0.009
## factor(Country)Venezuela -6.438e-02 4.031e-01 -0.160
## factor(Country)Vietnam -1.812e+01 1.596e+03 -0.011
## factor(Country)Yemen NA NA NA
## factor(Country)Zambia -2.059e+01 1.412e+03 -0.015
## Pr(>|z|)
## (Intercept) 0.004552 **
## Dem_Type0 0.289534
## Dem_Type2 0.947738
## Dem_Type3 0.867389
## v2x_polyarchy 4.79e-08 ***
## former_british_colony 1.59e-08 ***
## factor(Year)1947 0.797950
## factor(Year)1948 0.736589
## factor(Year)1949 0.875893
## factor(Year)1950 0.686399
## factor(Year)1951 0.750539
## factor(Year)1952 0.495679
## factor(Year)1953 0.472538
## factor(Year)1954 0.613234
## factor(Year)1955 0.499509
## factor(Year)1956 0.962900
## factor(Year)1957 0.968432
## factor(Year)1958 0.657696
## factor(Year)1959 0.398962
## factor(Year)1960 0.192955
## factor(Year)1961 0.176651
## factor(Year)1962 0.239492
## factor(Year)1963 0.291425
## factor(Year)1964 0.345508
## factor(Year)1965 0.571469
## factor(Year)1966 0.984138
## factor(Year)1967 0.752938
## factor(Year)1968 0.213939
## factor(Year)1969 0.213923
## factor(Year)1970 0.102043
## factor(Year)1971 0.335761
## factor(Year)1972 0.212717
## factor(Year)1973 0.163225
## factor(Year)1974 0.340283
## factor(Year)1975 0.723429
## factor(Year)1976 0.506086
## factor(Year)1977 0.289583
## factor(Year)1978 0.345130
## factor(Year)1979 0.205524
## factor(Year)1980 0.470529
## factor(Year)1981 0.108776
## factor(Year)1982 0.106351
## factor(Year)1983 0.239429
## factor(Year)1984 0.223072
## factor(Year)1985 0.091480 .
## factor(Year)1986 0.064707 .
## factor(Year)1987 0.061899 .
## factor(Year)1988 0.041851 *
## factor(Year)1989 0.042219 *
## factor(Year)1990 0.066932 .
## factor(Year)1991 0.058699 .
## factor(Year)1992 0.025704 *
## factor(Year)1993 0.033289 *
## factor(Year)1994 0.034379 *
## factor(Year)1995 0.021977 *
## factor(Year)1996 0.043096 *
## factor(Year)1997 0.073032 .
## factor(Year)1998 0.026041 *
## factor(Year)1999 0.070110 .
## factor(Year)2000 0.050520 .
## factor(Year)2001 0.159611
## factor(Year)2002 0.085845 .
## factor(Year)2003 0.106369
## factor(Year)2004 0.141651
## factor(Year)2005 0.105057
## factor(Year)2006 0.269652
## factor(Year)2007 0.478468
## factor(Year)2008 0.544564
## factor(Year)2009 0.263190
## factor(Year)2010 0.545620
## factor(Year)2011 0.461690
## factor(Year)2012 0.324493
## factor(Year)2013 0.647591
## factor(Year)2014 0.748901
## factor(Year)2015 0.841503
## factor(Year)2016 0.225525
## factor(Year)2017 0.418079
## factor(Year)2018 0.144344
## factor(Year)2019 0.243215
## factor(Year)2020 0.251410
## factor(Country)Albania 0.117161
## factor(Country)Algeria 0.990346
## factor(Country)Angola 0.991309
## factor(Country)Argentina 0.750461
## factor(Country)Armenia 0.992489
## factor(Country)Australia 0.217206
## factor(Country)Austria 0.987599
## factor(Country)Azerbaijan 0.000206 ***
## factor(Country)Bahrain 0.989969
## factor(Country)Bangladesh 6.21e-05 ***
## factor(Country)Barbados 2.40e-09 ***
## factor(Country)Belarus 0.992515
## factor(Country)Belgium 0.000590 ***
## factor(Country)Benin 2.17e-07 ***
## factor(Country)Bhutan 2.15e-12 ***
## factor(Country)Bosnia and Herzegovina 0.502558
## factor(Country)Botswana 3.90e-08 ***
## factor(Country)Brazil 0.001831 **
## factor(Country)Bulgaria 0.055443 .
## factor(Country)Burkina Faso 0.011000 *
## factor(Country)Burundi 0.092497 .
## factor(Country)Cambodia 4.21e-08 ***
## factor(Country)Cameroon 0.989427
## factor(Country)Canada 0.769674
## factor(Country)Cape Verde 0.990462
## factor(Country)Central African Republic 0.921888
## factor(Country)Chad 0.989357
## factor(Country)Chile 0.202996
## factor(Country)China 0.972106
## factor(Country)Colombia 0.034457 *
## factor(Country)Costa Rica 0.006330 **
## factor(Country)Croatia 0.992187
## factor(Country)Cuba 0.370966
## factor(Country)Cyprus 5.94e-10 ***
## factor(Country)Czech Republic 0.992477
## factor(Country)Democratic Republic of the Congo 0.342597
## factor(Country)Denmark 0.987523
## factor(Country)Djibouti 0.001095 **
## factor(Country)Dominican Republic 0.105746
## factor(Country)Ecuador 0.337746
## factor(Country)Egypt 1.53e-11 ***
## factor(Country)El Salvador 0.987976
## factor(Country)Equatorial Guinea 1.98e-10 ***
## factor(Country)Eritrea 0.992891
## factor(Country)Estonia 0.076722 .
## factor(Country)Eswatini 0.989733
## factor(Country)Ethiopia 0.026181 *
## factor(Country)Fiji 0.000368 ***
## factor(Country)Finland 3.85e-07 ***
## factor(Country)France 0.000598 ***
## factor(Country)Gabon 0.500497
## factor(Country)Georgia 0.992385
## factor(Country)Germany 0.992083
## factor(Country)Ghana 2.41e-08 ***
## factor(Country)Greece 0.045393 *
## factor(Country)Guatemala 0.063358 .
## factor(Country)Guinea 0.989216
## factor(Country)Guinea-Bissau 0.990586
## factor(Country)Guyana 6.63e-09 ***
## factor(Country)Haiti 0.029151 *
## factor(Country)Honduras 0.043296 *
## factor(Country)Hungary 0.987907
## factor(Country)Iceland 0.434284
## factor(Country)India 9.04e-09 ***
## factor(Country)Indonesia 0.001916 **
## factor(Country)Iran 3.67e-05 ***
## factor(Country)Iraq 2.46e-06 ***
## factor(Country)Ireland 0.443548
## factor(Country)Israel < 2e-16 ***
## factor(Country)Italy 0.000260 ***
## factor(Country)Ivory Coast 0.989308
## factor(Country)Jamaica 2.82e-06 ***
## factor(Country)Japan 0.022732 *
## factor(Country)Jordan 0.987866
## factor(Country)Kazakhstan 0.992534
## factor(Country)Kenya 2.46e-09 ***
## factor(Country)Kuwait 0.001160 **
## factor(Country)Kyrgyzstan 0.992498
## factor(Country)Laos 1.19e-10 ***
## factor(Country)Latvia 0.992260
## factor(Country)Lebanon 4.54e-07 ***
## factor(Country)Lesotho 0.988497
## factor(Country)Liberia 0.001985 **
## factor(Country)Libya 4.84e-05 ***
## factor(Country)Lithuania 0.992232
## factor(Country)Luxembourg 0.987580
## factor(Country)Madagascar 0.989261
## factor(Country)Malawi 8.44e-10 ***
## factor(Country)Malaysia 0.016570 *
## factor(Country)Maldives 0.693043
## factor(Country)Mali 0.777966
## factor(Country)Malta 4.90e-11 ***
## factor(Country)Mauritius 1.90e-06 ***
## factor(Country)Mexico 3.67e-05 ***
## factor(Country)Moldova 0.992409
## factor(Country)Mongolia 0.987964
## factor(Country)Montenegro 0.994567
## factor(Country)Morocco 0.987405
## factor(Country)Mozambique 0.990622
## factor(Country)Myanmar 4.07e-11 ***
## factor(Country)Namibia 0.992119
## factor(Country)Nepal 1.02e-10 ***
## factor(Country)Netherlands 0.987631
## factor(Country)New Zealand 0.016021 *
## factor(Country)Nicaragua 0.050545 .
## factor(Country)Niger 0.989259
## factor(Country)Nigeria 6.57e-09 ***
## factor(Country)North Korea 0.943118
## factor(Country)North Macedonia 0.420665
## factor(Country)Norway 0.536991
## factor(Country)Oman 0.989838
## factor(Country)Pakistan 1.47e-06 ***
## factor(Country)Panama 0.000297 ***
## factor(Country)Papua New Guinea 0.990687
## factor(Country)Paraguay 0.002496 **
## factor(Country)Peru 0.555592
## factor(Country)Philippines 2.53e-08 ***
## factor(Country)Poland 0.001295 **
## factor(Country)Portugal 0.000719 ***
## factor(Country)Qatar 0.988986
## factor(Country)Republic of the Congo 0.989386
## factor(Country)Republic of the Gambia 0.008417 **
## factor(Country)Romania 0.038975 *
## factor(Country)Russia 0.988128
## factor(Country)Rwanda 0.989546
## factor(Country)Saudi Arabia 0.986297
## factor(Country)Senegal 0.989112
## factor(Country)Serbia 0.992424
## factor(Country)Sierra Leone 2.18e-07 ***
## factor(Country)Singapore 0.579112
## factor(Country)Slovakia 0.992477
## factor(Country)Slovenia 0.018248 *
## factor(Country)Solomon Islands 0.989911
## factor(Country)Somalia 0.989419
## factor(Country)South Africa 0.072578 .
## factor(Country)South Korea 0.000585 ***
## factor(Country)South Sudan 0.995174
## factor(Country)Spain 0.011404 *
## factor(Country)Sri Lanka 0.022961 *
## factor(Country)Sudan 0.079875 .
## factor(Country)Suriname 0.990436
## factor(Country)Sweden 0.020671 *
## factor(Country)Switzerland 6.08e-05 ***
## factor(Country)Syria 0.985358
## factor(Country)Taiwan 0.113747
## factor(Country)Tajikistan 0.992553
## factor(Country)Tanzania 0.989564
## factor(Country)Thailand 0.227749
## factor(Country)Timor-Leste 0.993891
## factor(Country)Togo 0.185165
## factor(Country)Trinidad and Tobago 5.34e-07 ***
## factor(Country)Tunisia 0.988862
## factor(Country)Turkey 0.014649 *
## factor(Country)Turkmenistan 0.992608
## factor(Country)Uganda 4.02e-09 ***
## factor(Country)Ukraine 0.992458
## factor(Country)United Arab Emirates 0.989937
## factor(Country)United Kingdom 0.209529
## factor(Country)United States of America 2.40e-10 ***
## factor(Country)Uruguay 0.282526
## factor(Country)Uzbekistan 0.992581
## factor(Country)Venezuela 0.873107
## factor(Country)Vietnam 0.990940
## factor(Country)Yemen NA
## factor(Country)Zambia 0.988368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11720.8 on 10184 degrees of freedom
## Residual deviance: 7016.1 on 9941 degrees of freedom
## (56 observations deleted due to missingness)
## AIC: 7504.1
##
## Number of Fisher Scoring iterations: 184 Dynastic Rule and Democracy (based on Predicted probabilites)
4.1 Predicted Probability of Dynastic Leadership and Other IVs (Some Plots)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 80 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 80 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1709 rows containing non-finite outside the scale range
## (`stat_smooth()`).
5 Boix’s Democracy Classification and Some Results
The results in this section are based on Boix’s definition of democracy and a defined cut-off. This will only include analysis for countries that are classified democracies according to the e_boix variable where Charles Boix classifies democracies/non democracies as 0 and 1. The Cut off Point we choose here for our analysis is to include all countries that have been democracies for at least 25% of their lifetime since 1945.
5.1 How do the Different Dynasts differ in Democracies?
Before we proceed, it is crucial to note that now we are also adding a variable based on the different types of dynasts we have already explained before in order to make the analysis a bit more nuanced. We are adding a variable called “dynast_type” to account for the categorical variation in the types of dynasts that we have. In this classification we have a pure non-dynast (0, no family before or after the said leader is in politics), dynasty-ender (1, definitely has a predecessor in politics but does not have a successor in politics), the DYNAST (2,definitely has a predecessor in politics may or may not have a successor in politics), Dynasty-former (3, does not have any family in politics preceding him/her but definitely leaves a successor in politics), and finally dynasty-sustainer (4, necessarily has both a predecessor and successor in politics). First we will look at some basic characteristic differences in thse kind of dynasts using a basic difference in mean test (education, Spell [the number of time a leader has been in office], tenure length, is also in business)
5.1.1 Comparisons Across All Categories
5.1.2 Comparisons Across Dynasts with predecessors/sucessors at the national level
5.2 The Relationship Between Polyarchy Scores (Level of Minimal Democracy) and Dynasticism (As a Continuous Variable)
Dynastic Variable (0/1) is recoded here as a continuous variable in terms of a dynastic score that varies between 0 and 1 to indicate that up until point t in time for a country i how long Dynastic rule has prevailed (Eg. 1970 in India would mean) TWO BASIC GRAPHS
##
## ===============================================
## Dependent variable:
## ---------------------------
## Dynastic_Proportion
## -----------------------------------------------
## v2x_polyarchy -0.040***
## (0.012)
##
## Constant 0.224***
## (0.008)
##
## -----------------------------------------------
## Observations 6,298
## R2 0.002
## Adjusted R2 0.002
## Residual Std. Error 0.267 (df = 6296)
## F Statistic 10.567*** (df = 1; 6296)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01##
## ===============================================
## Dependent variable:
## ---------------------------
## Dynastic_Proportion
## -----------------------------------------------
## v2x_polyarchy 0.121***
## (0.024)
##
## log_gdp_percap -0.021***
## (0.003)
##
## v2xnp_regcorr 0.035*
## (0.018)
##
## v2caviol 0.030***
## (0.003)
##
## v2cademmob -0.026***
## (0.004)
##
## Constant 0.291***
## (0.027)
##
## -----------------------------------------------
## Observations 5,169
## R2 0.034
## Adjusted R2 0.033
## Residual Std. Error 0.258 (df = 5163)
## F Statistic 36.299*** (df = 5; 5163)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01##
## =============================================
## Dependent variable:
## ---------------------------
## Dynastic_Proportion
## ---------------------------------------------
## v2x_polyarchy -0.249**
## (0.115)
##
## Constant -1.237***
## (0.071)
##
## ---------------------------------------------
## Observations 6,298
## Log Likelihood -2,675.470
## Akaike Inf. Crit. 5,354.939
## =============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
##
## =============================================
## Dependent variable:
## ---------------------------
## Dynastic_Proportion
## ---------------------------------------------
## v2x_polyarchy 0.753***
## (0.236)
##
## log_gdp_percap -0.130***
## (0.032)
##
## v2xnp_regcorr 0.218
## (0.172)
##
## v2caviol 0.180***
## (0.031)
##
## v2cademmob -0.160***
## (0.034)
##
## Constant -0.835***
## (0.256)
##
## ---------------------------------------------
## Observations 5,169
## Log Likelihood -2,173.792
## Akaike Inf. Crit. 4,359.584
## =============================================
## Note: *p<0.1; **p<0.05; ***p<0.015.3 Corruption and Dynasticism
Corruption here is Regime Corruption borrowed from VDem and the specific variable details are:
5.4 Mean Polyarchy Scores in Democracies
6 Some Regressions (For democracies ONLY as classified before based on Boix classification and 25% cut-off)
This section covers some basic regressions treating Dynasticism as a DV against other other variables like democracy scores, regime corruption level, media censorship (v2mecenefm), clean elections (v2xel_frefair), former british colony. These are all fixed effects linear models with country and year fixed effects in place and the standard error is clustered at the country level.
6.1 Electoral Democracy and Dynasticism
Are democracies and dynastic leadership compatible (and are former British Colonies likely to be more dynastic?)?
##
## Call:
## felm(formula = dynastic ~ v2x_polyarchy + log_gdp_percap + v2xnp_regcorr + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.61674 -0.23353 -0.15014 -0.03866 1.10276
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## v2x_polyarchy 0.28052 0.13100 2.141 0.0646 .
## log_gdp_percap 0.01842 0.01044 1.765 0.1156
## v2xnp_regcorr 0.13401 0.06399 2.094 0.0695 .
## former_british_colony -0.02224 0.04322 -0.515 0.6207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.395 on 5155 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.08255 Adjusted R-squared: 0.06974
## Multiple R-squared(proj model): 0.01446 Adjusted R-squared: 0.0006929
## F-statistic(full model, *iid*):6.442 on 72 and 5155 DF, p-value: < 2.2e-16
## F-statistic(proj model): 4.787 on 4 and 8 DF, p-value: 0.02882
This regression results seems to suggest that Dynasties and democracies have been historically compatible. Specifically, A one-unit increase in the electoral democracy score (v2x_polyarchy) is associated with a 33.1 percentage point increase in the probability of that polity being dynastic, according to a linear model probability design.
The significant positive relationship between electoral democracy and dynastic regimes suggests that higher levels of electoral democracy might coexist with dynastic regimes. However, the economic and corruption-related predictors, as well as the colonial history, do not show a significant impact on dynastic regimes in this model.
6.2 Dynasticism and Free and Fair Elections
Is dynastic leadership more likely to produce less free and fair elections?
##
## Call:
## felm(formula = v2xel_frefair ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67384 -0.12799 0.01546 0.14417 0.54483
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.03782 0.01691 2.236 0.0558 .
## log_gdp_percap 0.15606 0.01179 13.235 1.01e-06 ***
## former_british_colony 0.03822 0.02775 1.378 0.2057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.207 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.5883 Adjusted R-squared: 0.5826
## Multiple R-squared(proj model): 0.3188 Adjusted R-squared: 0.3094
## F-statistic(full model, *iid*):103.8 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 93.54 on 3 and 8 DF, p-value: 1.437e-06
Consistent with our claim on compatibility with democracies, dynastic leadership is in fact not bad for free and fair elections.
6.3 Is Dynastic Leadership more likely to produce Corrupt regimes?
##
## Call:
## felm(formula = v2xnp_regcorr ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57432 -0.10974 0.00626 0.11851 0.64977
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.00410 0.01152 0.356 0.731
## log_gdp_percap -0.17402 0.01795 -9.696 1.07e-05 ***
## former_british_colony -0.07153 0.08087 -0.884 0.402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1885 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.6252 Adjusted R-squared: 0.62
## Multiple R-squared(proj model): 0.4126 Adjusted R-squared: 0.4045
## F-statistic(full model, *iid*):121.1 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 171.2 on 3 and 8 DF, p-value: 1.352e-07
No significant relationship between dynastic leadership and more regime corruption (leaders using offices for private gain).
6.4 Dynastic Leadership and Barriers to other parties?
v2psbars
##
## Call:
## felm(formula = v2psbars ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3321 -0.5152 0.1301 0.7048 2.1829
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.19612 0.10108 1.940 0.0883 .
## log_gdp_percap 0.36636 0.03923 9.339 1.41e-05 ***
## former_british_colony 0.35621 0.19274 1.848 0.1018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9967 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3604 Adjusted R-squared: 0.3516
## Multiple R-squared(proj model): 0.1212 Adjusted R-squared: 0.1091
## F-statistic(full model, *iid*):40.92 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 40.21 on 3 and 8 DF, p-value: 3.589e-05
6.5 Dynastic Leadership and Candidate Selection
v2pscnslnl
##
## Call:
## felm(formula = v2pscnslnl ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8105 -0.5681 -0.0269 0.5566 3.2184
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.06496 0.08201 0.792 0.45115
## log_gdp_percap 0.55793 0.11580 4.818 0.00132 **
## former_british_colony 0.46326 0.35810 1.294 0.23189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9798 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.4342 Adjusted R-squared: 0.4264
## Multiple R-squared(proj model): 0.2299 Adjusted R-squared: 0.2192
## F-statistic(full model, *iid*):55.73 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 21.1 on 3 and 8 DF, p-value: 0.000372
6.6 Dynastic Leadership and Regime’s opposition Groups Size
v2regoppgroupssize
##
## Call:
## felm(formula = v2regoppgroupssize ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2050 -0.7602 -0.1631 0.5785 4.1010
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.18365 0.09496 -1.934 0.0892 .
## log_gdp_percap -0.46545 0.16255 -2.863 0.0210 *
## former_british_colony 0.35694 0.35641 1.002 0.3459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.133 on 5150 degrees of freedom
## (1076 observations deleted due to missingness)
## Multiple R-squared(full model): 0.4896 Adjusted R-squared: 0.4826
## Multiple R-squared(proj model): 0.1296 Adjusted R-squared: 0.1176
## F-statistic(full model, *iid*):69.58 on 71 and 5150 DF, p-value: < 2.2e-16
## F-statistic(proj model): 8.019 on 3 and 8 DF, p-value: 0.008535
6.7 Dynastic Leadership and Regiorous and Impartial Public Administration
v2clrspct
##
## Call:
## felm(formula = v2clrspct ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.08448 -0.56879 0.04993 0.57955 2.73352
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.10061 0.12933 0.778 0.459
## log_gdp_percap 0.81523 0.09200 8.861 2.08e-05 ***
## former_british_colony 0.03894 0.17536 0.222 0.830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9318 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.6293 Adjusted R-squared: 0.6242
## Multiple R-squared(proj model): 0.3786 Adjusted R-squared: 0.3701
## F-statistic(full model, *iid*):123.3 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 31.17 on 3 and 8 DF, p-value: 9.187e-05
6.8 Dynastic Leadership and State Ownership of Enterprise
v2clstown
##
## Call:
## felm(formula = v2clstown ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0989 -0.3966 0.0391 0.4527 2.3472
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.001786 0.103878 -0.017 0.986705
## log_gdp_percap 0.246720 0.048832 5.052 0.000986 ***
## former_british_colony -0.233124 0.132801 -1.755 0.117257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7346 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3814 Adjusted R-squared: 0.3729
## Multiple R-squared(proj model): 0.08967 Adjusted R-squared: 0.07713
## F-statistic(full model, *iid*):44.78 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 15.27 on 3 and 8 DF, p-value: 0.001129
6.9 Dynastic Leadership and Criteria for Appointments in Public Administration
v2stcritrecadm (0-5 ordinal scale)
##
## Call:
## felm(formula = v2stcritrecadm ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.81146 -0.43161 0.05032 0.45785 2.33653
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.02401 0.08217 -0.292 0.778
## log_gdp_percap 0.54410 0.06723 8.093 4.02e-05 ***
## former_british_colony 0.03808 0.14129 0.270 0.794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7018 on 4932 degrees of freedom
## (1294 observations deleted due to missingness)
## Multiple R-squared(full model): 0.504 Adjusted R-squared: 0.4968
## Multiple R-squared(proj model): 0.3107 Adjusted R-squared: 0.3008
## F-statistic(full model, *iid*):70.57 on 71 and 4932 DF, p-value: < 2.2e-16
## F-statistic(proj model): 25.39 on 3 and 8 DF, p-value: 0.000193
6.10 Dynastic Leadership and Media Censorship Effort
v2mecenefm
##
## Call:
## felm(formula = v2mecenefm ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6924 -0.5247 0.0998 0.7345 2.5041
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.27578 0.15150 1.820 0.10620
## log_gdp_percap 0.55111 0.15807 3.486 0.00824 **
## former_british_colony -0.06636 0.19675 -0.337 0.74459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.4828 Adjusted R-squared: 0.4757
## Multiple R-squared(proj model): 0.175 Adjusted R-squared: 0.1636
## F-statistic(full model, *iid*):67.78 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 33.84 on 3 and 8 DF, p-value: 6.802e-05
6.11 Dynastic Leadership and level of Media Corruption
v2mecorrpt
##
## Call:
## felm(formula = v2mecorrpt ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2865 -0.4471 0.1229 0.6098 2.9626
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.09019 0.09158 0.985 0.35356
## log_gdp_percap 0.78086 0.04160 18.771 6.7e-08 ***
## former_british_colony 0.57129 0.14759 3.871 0.00474 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9621 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.5773 Adjusted R-squared: 0.5715
## Multiple R-squared(proj model): 0.371 Adjusted R-squared: 0.3623
## F-statistic(full model, *iid*):99.19 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 326.2 on 3 and 8 DF, p-value: 1.06e-08
6.12 Dyanstic Leadership and Power Distribution by Socio Economic Position
v2pepwrses (0-4)
##
## Call:
## felm(formula = v2pepwrses ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4192 -0.4287 0.0367 0.4749 2.5528
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.08597 0.07570 -1.136 0.2890
## log_gdp_percap 0.29374 0.12256 2.397 0.0434 *
## former_british_colony 0.27308 0.17732 1.540 0.1621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8235 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3895 Adjusted R-squared: 0.3811
## Multiple R-squared(proj model): 0.1072 Adjusted R-squared: 0.0949
## F-statistic(full model, *iid*):46.33 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 2.235 on 3 and 8 DF, p-value: 0.1616
6.13 Dynastic Leadership and Power Distribution by Social grouup
v2pepwrsoc
##
## Call:
## felm(formula = v2pepwrsoc ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2058 -0.4868 0.0373 0.5670 2.1842
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.10489 0.09621 -1.090 0.307
## log_gdp_percap 0.21151 0.12234 1.729 0.122
## former_british_colony 0.02174 0.16071 0.135 0.896
##
## Residual standard error: 0.8395 on 5156 degrees of freedom
## (1070 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3828 Adjusted R-squared: 0.3743
## Multiple R-squared(proj model): 0.04884 Adjusted R-squared: 0.03574
## F-statistic(full model, *iid*):45.04 on 71 and 5156 DF, p-value: < 2.2e-16
## F-statistic(proj model): 1.484 on 3 and 8 DF, p-value: 0.2906
6.14 Dynastic Leadership and Legitimate Ideology (Promotion)
v2exl_legitideol
##
## Call:
## felm(formula = v2exl_legitideol ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2801 -0.7967 -0.1496 0.7556 3.9378
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.08502 0.15461 0.550 0.597
## log_gdp_percap -0.10373 0.16277 -0.637 0.542
## former_british_colony 0.04574 0.36836 0.124 0.904
##
## Residual standard error: 1.093 on 5141 degrees of freedom
## (1085 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3147 Adjusted R-squared: 0.3052
## Multiple R-squared(proj model): 0.007732 Adjusted R-squared: -0.005972
## F-statistic(full model, *iid*):33.25 on 71 and 5141 DF, p-value: < 2.2e-16
## F-statistic(proj model): 0.2154 on 3 and 8 DF, p-value: 0.883
6.15 Dynastic Leadership and Person of Leader (Leader Cult, extraordinary charismatic etc.)
v2exl_legitlead
##
## Call:
## felm(formula = v2exl_legitlead ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5747 -0.7904 -0.1260 0.7437 4.9243
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic -0.06470 0.08817 -0.734 0.48401
## log_gdp_percap -0.38147 0.10369 -3.679 0.00623 **
## former_british_colony 0.13879 0.43630 0.318 0.75855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.12 on 5154 degrees of freedom
## (1072 observations deleted due to missingness)
## Multiple R-squared(full model): 0.4004 Adjusted R-squared: 0.3922
## Multiple R-squared(proj model): 0.08519 Adjusted R-squared: 0.07259
## F-statistic(full model, *iid*):48.48 on 71 and 5154 DF, p-value: < 2.2e-16
## F-statistic(proj model): 6.469 on 3 and 8 DF, p-value: 0.01563
6.16 Dynastic Leadership and Political Violence by Non-State Actors
v2caviol
##
## Call:
## felm(formula = v2caviol ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9823 -0.8524 -0.1211 0.7355 4.0344
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.08826 0.13575 0.650 0.53384
## log_gdp_percap -0.40461 0.09298 -4.352 0.00244 **
## former_british_colony -0.36684 0.11282 -3.252 0.01167 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 5136 degrees of freedom
## (1090 observations deleted due to missingness)
## Multiple R-squared(full model): 0.3206 Adjusted R-squared: 0.3112
## Multiple R-squared(proj model): 0.09551 Adjusted R-squared: 0.08301
## F-statistic(full model, *iid*):34.14 on 71 and 5136 DF, p-value: < 2.2e-16
## F-statistic(proj model): 38.95 on 3 and 8 DF, p-value: 4.042e-05
6.17 Dynastic Leadership and Mobilisation for Democracy
v2cademmob
##
## Call:
## felm(formula = v2cademmob ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region, data = gdd_vdem_dem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2584 -0.7762 -0.1526 0.6529 4.5797
##
## Coefficients:
## Estimate Cluster s.e. t value Pr(>|t|)
## dynastic 0.19138 0.08992 2.128 0.06595 .
## log_gdp_percap -0.13713 0.16112 -0.851 0.41945
## former_british_colony -0.41391 0.08660 -4.780 0.00139 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.13 on 5097 degrees of freedom
## (1129 observations deleted due to missingness)
## Multiple R-squared(full model): 0.2111 Adjusted R-squared: 0.2002
## Multiple R-squared(proj model): 0.03491 Adjusted R-squared: 0.02147
## F-statistic(full model, *iid*):19.21 on 71 and 5097 DF, p-value: < 2.2e-16
## F-statistic(proj model): 9.33 on 3 and 8 DF, p-value: 0.005446
---
title: "Global Dynasties Dataset Main RPub"
author: "Nachiket Midha"
output: 
  html_document:
    theme: cosmo
    highlight: tango
    toc: true
    toc_float: true
    code_folding: hide
    code_download: true
    number_sections: true  # Number sections for the table of contents
    fig_caption: true      # Enable figure captions
    css: styles.css        # Link to a custom CSS file for styling
---

```{r eval = TRUE,echo =FALSE,message=FALSE, warning=FALSE}
#Loading Libraries
library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(snakecase)
library(ggthemes)
library(WDI)
library(betareg)
library(forcats)
library(stringdist)
library(expss)
library(lfe)
library(devtools)
library(zoo)
library(sandwich)
library(plm)
library(stargazer)
library(janitor)
library(modelsummary)
library(transformr)
library(gganimate)
library(gifski)
library(av)
library(rvest)
library(flextable)
library(IRdisplay)
library(coefplot)
library(plotly)
library(knitr)
library(kableExtra)
library(snakecase)
library(ggthemes)
library(broom)
library(knitr)
library(rmarkdown)
library(htmlwidgets)
library(DT)
library(scales)
library(fixest)
library(ggeffects)

```



```{r eval=TRUE,echo = FALSE, message=FALSE, warning=FALSE}
#Loading Datasets
gdd <- read.csv("/Users/Nachiket/Files From e.localized/Verma RA work/Global Dynasties Dataset/gdd.csv")
gdd <-gdd %>% rename(Year=year) %>% mutate(dynastic = ifelse(pred_bin !=0,1,0))#pred_bin == 1

# Adding year_bin and handling NAs. Adding a continuous variable for Dyanstic variable (pred_bin)
gdd <- gdd %>%
  mutate(across(c(
    pred_num, relation_code_pred, pos_code_pred, suc_num, relation_code_suc, pos_code_suc,fln_gender,
    pred_bin, suc_bin, pred_national, suc_national, pred_state, suc_state, pred_local, suc_local, dynastic
  ), ~ifelse(is.na(.), 0, .)))
gdd <- gdd %>%
  mutate(year_bin = case_when(
    Year >= 1945 & Year < 1970 ~ "1945-1970",
    Year >= 1970 & Year < 1995 ~ "1970-1995",
    Year >= 1995 & Year <= 2020 ~ "1995-2020"
  ),
  year_bin = factor(year_bin,
                    levels = c("1945-1970", "1970-1995", "1995-2020"),
                    ordered = TRUE)
  )%>% 
  arrange(Country, Year) %>%  # Ensure data is sorted by Country and Year
  group_by(Country) %>%
  mutate(
    Cum_Pred_Bin = cumsum(pred_bin),  # Cumulative sum of pred_bin
    Year_Count = row_number(),  # Cumulative count of years
    Dynastic_Proportion = Cum_Pred_Bin / Year_Count  # Calculate the proportion
  ) %>%
  ungroup()

#making year column as a numeric variable in the whole dataset
gdd$Year <- as.numeric(gdd$Year)


#making all these fln_gender	pred_num	relation_code_pred	pos_code_pred	suc_num	relation_code_suc	pos_code_suc	pred_bin	suc_bin	pred_national	suc_national	pred_state	suc_state	pred_local	suc_local as numeric

gdd$fln_gender <- as.numeric(gdd$fln_gender)
gdd$pred_num <- as.numeric(gdd$pred_num)
gdd$relation_code_pred <- as.numeric(gdd$relation_code_pred)
gdd$pos_code_pred <- as.numeric(gdd$pos_code_pred)
gdd$suc_num <- as.numeric(gdd$suc_num)
gdd$relation_code_suc <- as.numeric(gdd$relation_code_suc)
gdd$pos_code_suc <- as.numeric(gdd$pos_code_suc)
gdd$pred_bin <- as.numeric(gdd$pred_bin)
gdd$suc_bin <- as.numeric(gdd$suc_bin)
gdd$pred_national <- as.numeric(gdd$pred_national)
gdd$suc_national <- as.numeric(gdd$suc_national)
gdd$pred_state <- as.numeric(gdd$pred_state)
gdd$suc_state <- as.numeric(gdd$suc_state)
gdd$pred_local <- as.numeric(gdd$pred_local)
gdd$suc_local <- as.numeric(gdd$suc_local)





#Adding Fomer_British_Colony_Status
country_list <- c(
  "Afghanistan", "Antigua and Barbuda", "Bahrain", "Barbados", "Belize", "Botswana", "Brunei",
  "Cyprus", "Dominica", "Egypt", "Eswatini", "Fiji", "Ghana", "Grenada", "Guyana", "India", 
  "Iraq", "Israel", "Jamaica", "Jordan", "Kenya", "Kiribati", "Kuwait", "Lesotho", "Libya", 
  "Malawi", "Malaya", "Maldives", "Malta", "Mauritius", "Myanmar", "Nauru", "Nigeria", "Oman", 
  "Pakistan", "Qatar", "Saint Lucia", "Saint Kitts and Nevis", "Saint Vincent and the Grenadines",
  "Seychelles", "Sierra Leone", "Solomon Islands", "Somaliland", "South Yemen", "Sri Lanka", 
  "Sudan", "South Sudan", "Bahamas", "Republic of the Gambia", "Tonga", "Trinidad and Tobago", "Tuvalu",
  "Uganda", "United Arab Emirates", "United States of America", "Vanuatu", "Zambia", "Zanzibar", "Zimbabwe"
)

gdd <- gdd %>% 
  mutate(former_british_colony = ifelse(Country %in% country_list, 1, 0))

## Adding Dictatorship and democracy binaries

gdd <- gdd %>% 
  mutate(dictatorship =  ifelse(system_category %in% c("Royal Dictatorship", "Civilian Dictatorship","Military Dictatorship"),1,0)) %>% 
  mutate(Dem_Type = case_when(
  system_category %in% c("Civilian Dictatorship", "Military Dictatorship","Royal Dictatorship") ~ 0,
  system_category == "Mixed Democratic" ~ 1,
  system_category == "Presidential Democracy" ~ 2,
  system_category == "Parliamentary Democracy" ~ 3,
  TRUE ~ NA_real_
  ))

#Adding New Regime Change Binary (0/1) at the country level
gdd <- gdd %>%
  group_by(Country) %>%
  mutate(Regime_Change = if_else(n_distinct(dictatorship) > 1, 1, 0)) %>%
  ungroup()

#Adding new Post-WW2 Independence Binary
gdd <- gdd %>%
  group_by(Country) %>%
  mutate(postww2_ind = if_else(
    (Country %in% c("Syria","Jordan")) | all(Year >= 1947),

      1,0))

## Adding Regime Transition binary at the observation level
gdd <- gdd %>%
  arrange(Country, Year) %>%
  group_by(Country) %>%
  mutate(Previous_Dictatorship = c(NA, head(dictatorship, -1)),
         Regime_Transition_Binary = ifelse(
      is.na(Previous_Dictatorship), 0,       # If Previous_Dictatorship is NA, set transition to 0
      ifelse(dictatorship != Previous_Dictatorship, 1, 0)  # If there is a change, set transition to 1
    )
  ) %>%
  ungroup()

## Adding number of Transitions clustered at the country level to see 
gdd <- gdd %>% 
  group_by(Country) %>% 
  mutate(Num_Transitions = sum(Regime_Transition_Binary)) %>% 
  ungroup() %>% 
  select(country_isocode, COW, Region,Country, Year, nominal_leader, dynasty_desc, fln_gender,system_category, dictatorship, Regime_Change, Regime_Transition_Binary,Num_Transitions, everything())

#Loading WDI Indicators using World Bank API
#WDIsearch('inequality') # a command used to search for all variables
wb_data_main = WDI(indicator = c("NY.GDP.PCAP.CD"),start = 1960, end = 2021)
wb_data <- wb_data_main %>% 
  rename(gdp_percap = NY.GDP.PCAP.CD) %>% 
  rename(country_isocode = iso3c) %>% 
  rename(Year = year) %>% 
  select("Year","country_isocode","gdp_percap")
gdd <- left_join(gdd, wb_data, by = c("Year","country_isocode"))
gdd$log_gdp_percap <- log(gdd$gdp_percap)

#Laoding VDem Data
load("/Users/Nachiket/Files From e.localized/Verma RA work/Global Dynasties Dataset/vdem.RData")

vdemfiltered <- vdem %>% 
  select(country_text_id,year,e_boix_regime,v2x_polyarchy,v2x_libdem, v2elaccept, v2elintim, v2x_veracc,v2x_diagacc, v2x_horacc, v2x_gencs, v2xnp_regcorr, v2x_corr, v2x_pubcorr, v2xed_ed_inpt,v2xed_ed_cent, v2lpname, v3partyid, v2xel_frefair, v2psbars, v2pscnslnl,v2regoppgroupssize, v2clrspct, v2clstown, v2stcritrecadm, v2mecenefm, v2mecorrpt, v2pepwrses, v2pepwrsoc, v2exl_legitideol, v2exl_legitlead, v2caviol,v2cademmob) %>% 
  rename(Year = year) %>% 
  rename(country_isocode = country_text_id)
gdd <- left_join(gdd, vdemfiltered, by = c("country_isocode", "Year"))
```

# Basic Descriptive Indicators {.tabset}

## Graph Showing Countries Added Yearwise

The following graph shows how countries are being added every year with the progression in the dataset since the end of WWII

```{r eval=TRUE, echo=FALSE, message=FALSE, warning=FALSE}
gdd_country_addition <- gdd %>% 
  group_by(Year) %>% 
  distinct(Country, .keep_all = TRUE)%>% 
  summarise(Total_Countries =n())


#solving for Error: from must be a finite number

country_addition <- ggplot(gdd_country_addition, aes(x = Year, y = Total_Countries)) +
  geom_line(color = "lightblue", size = 1) +
  geom_point(color = "black", size = 0.5) +
  labs(
    title = "Number of Countries Added to the Dataset Across Years",
    x = "Year",
    y = "Number of Countries"
  ) +
  theme_stata() +
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

ggplotly(country_addition)
```

## Proportion of Dynastic Countries Across Time (All Regime Types)

The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast. Therefore a dynastic country i at point t will be a country whose leader is a dynast.

The first graph shows the proportion of dynastic countries at a given time over the years.

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_dynastic_countries <- gdd %>%
  group_by(Year, year_bin)%>%
  summarise(Total_Countries = n(), Dynastic = sum(pred_bin)) %>% 
  mutate(Proportion_Dyn = Dynastic/Total_Countries*100)
  
dynastic_proportion_year <- ggplot(gdd_dynastic_countries, aes(x = Year, y = Proportion_Dyn)) +
  geom_line(color = "black", size = 2) +
  geom_point(color = "white", size = 1) +
  labs(
    title = "Percentage of Countries With Dynastic Leadership Across Years",
    x = "Year",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

ggplotly(dynastic_proportion_year)
```

The second graph shows the proportion of dynastic countries at a given time over a period of 25-25-25 years.

```{r message=FALSE,echo=FALSE,warning=FALSE}

gdd_dynastic_countries_25 <- gdd_dynastic_countries %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_Dyn25 = mean(Proportion_Dyn))

dynastic_proportion_yearbin <- ggplot(gdd_dynastic_countries_25, aes(x = year_bin, y = Proportion_Dyn25)) +
  geom_line(color = "red", size = 2) +
  geom_point(color = "black", size = 1) +
  labs(
    title = "Percentage of Countries With Dynastic Leadership Across Year Bins",
    x = "Year Bin",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

ggplotly(dynastic_proportion_yearbin)

```

## Proportion of Dynastic Countries (Ruled by Dynastic Leaders) across regime/time by different Regions of the world

```{r echo=FALSE, warning=FALSE, message=FALSE}
gdd_dynastic_regions <- gdd %>%
  group_by(Year, Region, year_bin) %>%
  summarise(
    Total_Countries = n(),
    Dynastic = sum(pred_bin),
    .groups = "drop"
  ) %>%
  mutate(Prop_Dyn = Dynastic / Total_Countries * 100) %>%
  group_by(year_bin, Region) %>%
  summarise(
    Proportion_Dyn = mean(Prop_Dyn),
    .groups = "drop"
  )


dynastic_proportion_region <- ggplot(gdd_dynastic_regions, aes(x = year_bin, y = Proportion_Dyn)) +
  facet_wrap(~Region) +
  geom_line(aes(group = 1), color = "blue")+
  geom_point(color = "black", size = 1) +
  labs(
    title = "Percentage of Countries that are Dynastic Across Regions",
    x = "Year",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0, 100) +
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
ggplotly(dynastic_proportion_region)
```

## Table on the Proportion of Dynastic Leaders Over Time in a Region (Classified by Regime Type)
```{r echo=FALSE,message=FALSE, warning=FALSE}
gdd_most_dynastic <- gdd %>% 
  group_by(year_bin,Country, Region) %>%
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  summarise(Total_Leaders = n(), dynastic_leaders = sum(pred_bin)) %>% 
  mutate(proportion_dyn_leader= dynastic_leaders/Total_Leaders*100)

gdd_most_dynastic_region <- gdd_most_dynastic %>% group_by(Region,year_bin) %>% summarise(Proportion_Of_Dynastic_Leaders = mean(proportion_dyn_leader)) 

gdd_most_dynastic_region%>%  datatable(options = list(pageLength = 50),
           rownames = FALSE,
            colnames = c("Region", "Year Category", "Proportion of Dynastic Leaders"))

```


## Proportion of Years Under Dynastic Rule by Democratic Regime Type (Presidential, Parliamentary, and Mixed Democratic)

The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast. Therefore, dynastic rule will be years under a dynast.

These classifications are extended and replicated based on the regime types given in WhoGov Dataset (Nuffield Research Center which is based in turn on Cheibub et. al (2010))

```{r eval=TRUE, echo=FALSE, warning=FALSE, message=FALSE}
gdd_dynastic_countries_demo <- gdd %>%
  filter(system_category %in% c("Mixed Democratic", "Parliamentary Democracy", "Presidential Democracy")) %>% 
  group_by(Country, year_bin) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  ungroup() %>% 
  group_by(system_category, year_bin) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

knitr::kable(gdd_dynastic_countries_demo, format = "html", caption = " Proportion of Years Under Dynastic Rule in Democratic Regimes") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)


gdd_dynastic_countries_demo$Prop_Dyn_Years <- round((gdd_dynastic_countries_demo$Prop_Dyn_Years), 2)

ggplot(gdd_dynastic_countries_demo,aes(x = year_bin,y=Prop_Dyn_Years))+
  geom_bar(stat = "identity")+
  labs(title = "Proportion of Years Under Dynastic Rule By Democratic Regime Type",
       x= "Type of Democracy",
       y= "Proportion of Years")+
  geom_text(aes(label = Prop_Dyn_Years), vjust = -0.3, size = 3.5) +
  facet_wrap(~system_category)+
  ylim(0,35)+
  theme_stata()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")


```

## Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Dictatorship/Democracy Status and System Category

```{r echo=FALSE, message=FALSE, warning=FALSE}
gdd_dynastic_countries_dem_dyn <- gdd %>% 
  group_by(Country,Year) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(dictatorship) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years),
            Cummulative_Dyn_Years = sum(dyn_years))

gdd_dyn_dem_dic <- gdd %>% 
  group_by(dictatorship) %>% 
  summarise(Total = n(),
            Dynastic = sum(pred_bin),
            Average = Dynastic/Total)
  
gdd_dynastic_dyn_dem_leader <- gdd %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(dictatorship) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

left_join(gdd_dynastic_countries_dem_dyn,gdd_dynastic_dyn_dem_leader, by = "dictatorship")

```


## Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Regime Type (System Category)
```{r echo =FALSE,warning=FALSE,message=TRUE}
gdd_dynastic_countries_dem_dyn_system_cat <- gdd %>% 
  group_by(Country,Year) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years),
            Cummulative_Dyn_Years = sum(dyn_years))

gdd_dynastic_dyn_dem_leader_system_cat <- gdd %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

left_join(gdd_dynastic_countries_dem_dyn_system_cat,gdd_dynastic_dyn_dem_leader_system_cat, by = "system_category")


```

##Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Regime Change Binary

```{r message=FALSE, echo=FALSE, warning=FALSE}
gdd_dynastic_countries_regime_change <- gdd %>% 
  group_by(Country,Year) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(Regime_Change) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years),
            Cummulative_Dyn_Years = sum(dyn_years))

gdd_dynastic_regime_change_leader <- gdd %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(Regime_Change) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

left_join(gdd_dynastic_countries_regime_change,gdd_dynastic_regime_change_leader, by = "Regime_Change")

```

##Country Count and dynastic information for Countries by Regime Change Status
```{r echo=FALSE, message=FALSE, warning=FALSE}

gdd_countrycount_regime_change <- gdd %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  group_by(Regime_Change)%>% 
  summarise(Number_Of_Countries = n())

knitr::kable(gdd_countrycount_regime_change, format = "html", caption = "Country Count for Countries that have/haven't undergone Regime change") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

gdd_dynprop_regime_change <- gdd %>% 
  group_by(Country, Regime_Change) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup()

gdd_dynprop_regime_change_summary <- gdd %>% 
  group_by(Country, Regime_Change) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(Regime_Change) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

ggplot(gdd_dynprop_regime_change, aes(x = factor(Regime_Change), y = prop_dyn_years)) +
  geom_boxplot() +
  labs(
    title = "Percentage of Years Under Dynastic Leadership by Regime Change",
    x = "Regime Change",
    y = "Percentage of Dynastic Years (%)"
  ) +
  theme_stata()

#Percentage of Dynastic leaders by regime change status
gdd_dynpercent_regimechange_leader <- gdd %>%
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup()

gdd_dynpercent_regimechange_leader_summary <- gdd %>%
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(Regime_Change) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))
  
ggplot(gdd_dynpercent_regimechange_leader, aes(x = factor(Regime_Change), y =Percentage_Dynastic_Rulers))+
  geom_boxplot()+
  labs(
    title = "Percentage of Dynastic Leaders by Regime Change",
    x = "Regime Change",
    y = "Percentage of Leaders (%)"
  ) +
  theme_stata()
  
  
```

## Country Count for Countries that have faced no regime change and have either remained Democracies or Dictatorships throughout and Dynastic Information
```{r echo=FALSE,message=FALSE, warning=FALSE}
gdd_no_regime_change_dic_dem <- gdd %>% 
    distinct(Country, .keep_all = TRUE) %>% 
    filter(Regime_Change == 0) %>%
    group_by(dictatorship) %>% 
    summarise(Number_Of_Countries_With_No_RegChange = n())

knitr::kable(gdd_no_regime_change_dic_dem, format = "html", caption = "Country Count for Countries that have faced no regime change and have either remained Democracies or Dictatorships throughout") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)


gdd_dynastic_no_regime_change_dic_dem <- gdd %>%
  filter(Regime_Change == 0) %>% 
  group_by(Country, dictatorship) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() 


#Proportion of Yeears Under Dyn Rule  

gdd_dynastic_no_regime_change_dic_dem_summary <- gdd %>%
  filter(Regime_Change == 0) %>% 
  group_by(Country, dictatorship) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(dictatorship) %>% 
  summarise(Percentage_Dynastic_Years = mean(prop_dyn_years))

ggplot(gdd_dynastic_no_regime_change_dic_dem, aes(x= factor(dictatorship), y=prop_dyn_years))+
  geom_boxplot()+
  labs(
    title = "Proportion of years under Dynastic Rule by Dictatorship Status in Polities with No Regime Change",
    x = "Dictatorship (1) or Democracy (0)",
    y = "Proportion of Years (%)"
  ) +
  theme_stata()

gdd_dynastic_no_regime_change_dic_dem_summary_years <- gdd %>%
  filter(Regime_Change == 0) %>% 
  group_by(Country, dictatorship) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(Year, dictatorship) %>% 
  summarise(Percentage_Dynastic_Years = mean(prop_dyn_years))

ggplot(gdd_dynastic_no_regime_change_dic_dem_summary_years, aes(x=Year, y=Percentage_Dynastic_Years))+
  geom_line()+
  ylim(0,50)+
  facet_wrap(~dictatorship)+
  labs(
    title = "Proportion of years under Dynastic Rule by Dictatorship Status in Polities with No Regime Change",
    x = "Dictatorship (1) or Democracy (0)",
    y = "Proportion of Years (%)"
  ) +
  theme_stata()



#Percentage of Leaders in countries with no transitions and have remained dictatorship or democracies
gdd_dynpercent_dicdem_leader <- gdd %>%
  filter(Regime_Change == 0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup()

gdd_dynpercent_dicdem_leader_summary <- gdd %>%
  filter(Regime_Change ==0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(dictatorship) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))
  
ggplot(gdd_dynpercent_dicdem_leader, aes(x = factor(dictatorship), y =Percentage_Dynastic_Rulers))+
  geom_boxplot()+
  labs(
    title = "Percentage of Dynastic Leaders with No Regime Change",
    x = "Dictatorship (1) and Democracy (0)",
    y = "Percentage of Leaders (%)"
  ) +
  theme_stata()
```

## Countries that have had no regime change and have remained Democratic by democracy type

```{r echo=FALSE, message=FALSE, warning=FALSE}
only_dem_no_regimechange <- gdd %>% 
  filter(Regime_Change == 0) %>% 
  filter(dictatorship == 0) %>% 
  arrange(Country, Year)

only_dem_no_regimechange <- only_dem_no_regimechange %>%
  group_by(Country) %>%
  mutate(
    Previous_System_Category = c(NA, head(system_category, -1)),
    Internal_Transition_Binary = ifelse(
      is.na(Previous_System_Category), 0, # No transition if previous value is NA
      ifelse(system_category != Previous_System_Category, 1, 0) # Transition if there is a change
    )
  ) %>%
  ungroup()

only_dem_no_regimechange <- only_dem_no_regimechange %>% 
  group_by(Country) %>%
  mutate(internal_dem_system_change = if_else(n_distinct(Internal_Transition_Binary) > 1, 1, 0)) %>%
  ungroup() %>% 
  select(country_isocode, COW, Region, Country, Year, nominal_leader, fln_gender, system_category, Previous_System_Category, internal_dem_system_change, Internal_Transition_Binary, everything())

only_dem_internal_change_summary <-only_dem_no_regimechange %>% 
  distinct(Country,.keep_all = TRUE) %>% 
  group_by(internal_dem_system_change) %>% 
  summarise(
    Number_Of_Countries = n())
    
only_dem_no_internalchange_summary <- only_dem_no_regimechange %>% 
  filter(internal_dem_system_change == 0) %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  group_by(system_category) %>% 
  summarise(Num_Countries =n())

only_dem_prop_dyn <-  only_dem_no_regimechange %>% 
  group_by(Country, internal_dem_system_change) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(internal_dem_system_change) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))
  

## Prop_DynYears By System Category type where no internal change has happened
only_dem_prop_dyn_system_category <- only_dem_no_regimechange %>% 
  filter(internal_dem_system_change == 0) %>% 
  group_by(Country, system_category) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup()

only_dem_prop_dyn_system_category_summary <- only_dem_no_regimechange %>% 
  filter(internal_dem_system_change == 0) %>% 
  group_by(Country, system_category) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

knitr::kable(only_dem_prop_dyn_system_category_summary, format = "html", caption = "Percentage of years under Dynastic Rule in PURE Democracies by System Category") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

ggplot(only_dem_prop_dyn_system_category, aes(x = factor(system_category), y =prop_dyn_years))+
  geom_boxplot()+
  labs(
    title = "Proportion of Years Under Dynastic Rule",
    x = "Democracy Type",
    y = "Percentage of Years (%)"
  ) +
  theme_stata()

## For Percentage of Dynastic Leaders in countries that have remained only one kind of democracy throughout

only_dem_leader_system_category <- only_dem_no_regimechange %>%
  filter(internal_dem_system_change == 0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup()

only_dem_leader_system_category_summary <- only_dem_no_regimechange %>%
  filter(internal_dem_system_change ==0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))
  
ggplot(only_dem_leader_system_category, aes(x = factor(system_category), y =Percentage_Dynastic_Rulers))+
  geom_boxplot()+
  labs(
    title = "Percentage of Dynastic Leaders",
    caption = "In polities that have remained one kind of democracy throughout",
    x = "System Category",
    y = "Percentage of Leaders (%)"
  ) +
  theme_stata()

```

## Countries that have had no regime change and have remained dictatorship by dictatorship type

```{r echo=FALSE, message=FALSE, warning=FALSE}
only_dic_no_regimechange <- gdd %>% 
  filter(Regime_Change == 0) %>% 
  filter(dictatorship == 1) %>% 
  arrange(Country, Year)

only_dic_no_regimechange <- only_dic_no_regimechange %>%
  group_by(Country) %>%
  mutate(
    Previous_System_Category = c(NA, head(system_category, -1)),
    Internal_Transition_Binary = ifelse(
      is.na(Previous_System_Category), 0, # No transition if previous value is NA
      ifelse(system_category != Previous_System_Category, 1, 0) # Transition if there is a change
    )
  ) %>%
  ungroup()

only_dic_no_regimechange <- only_dic_no_regimechange %>% 
  group_by(Country) %>%
  mutate(internal_dic_system_change = if_else(n_distinct(Internal_Transition_Binary) > 1, 1, 0)) %>%
  ungroup() %>% 
  select(country_isocode, COW, Region, Country, Year, nominal_leader, fln_gender, system_category, Previous_System_Category, internal_dic_system_change, Internal_Transition_Binary, everything())

#country count on number of countries that have remained dic but by internal dic change yes or no
only_dic_internal_change_summary <-only_dic_no_regimechange %>% 
  distinct(Country,.keep_all = TRUE) %>% 
  group_by(internal_dic_system_change) %>% 
  summarise(
    Number_Of_Countries = n())
    
## No internal change count by dictatorship type remained same dic throughout
only_dic_no_internalchange_summary <- only_dic_no_regimechange %>% 
  filter(internal_dic_system_change == 0) %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  group_by(system_category) %>% 
  summarise(Num_Countries =n())

#Dynastic Proportions by dictatorships whether they internally changed from one dic to another dic
only_dic_prop_dyn <- only_dic_no_regimechange %>% 
  group_by(Country, internal_dic_system_change) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(internal_dic_system_change) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

## Prop_DynYears By System Category type where no internal change has happened
only_dic_prop_dyn_system_category <- only_dic_no_regimechange %>% 
  filter(internal_dic_system_change == 0) %>% 
  group_by(Country, system_category) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup()

only_dic_prop_dyn_system_category_summary <- only_dic_no_regimechange %>% 
  filter(internal_dic_system_change == 0) %>% 
  group_by(Country, system_category) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

knitr::kable(only_dic_prop_dyn_system_category_summary, format = "html", caption = "Percentage of years under Dynastic Rule in PURE Dictatorships by System Category") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

ggplot(only_dic_prop_dyn_system_category, aes(x = factor(system_category), y =prop_dyn_years))+
  geom_boxplot()+
  labs(
    title = "Proportion of Years Under Dynastic Rule",
    x = "Dictatorship Type",
    y = "Percentage of Years (%)"
  ) +
  theme_stata()

## For Percentage of Dynastic Leaders in countries that have remained only one kind of democracy throughout

only_dic_leader_system_category <- only_dic_no_regimechange %>%
  filter(internal_dic_system_change == 0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup()

only_dic_leader_system_category_summary <- only_dic_no_regimechange %>%
  filter(internal_dic_system_change ==0) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(system_category) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))
  
ggplot(only_dic_leader_system_category, aes(x = factor(system_category), y =Percentage_Dynastic_Rulers))+
  geom_boxplot()+
  labs(
    title = "Percentage of Dynastic Leaders",
    caption = "In polities that have remained one kind of democracy throughout",
    x = "System Category",
    y = "Percentage of Leaders (%)"
  ) +
  theme_stata()
```

## Country Count for number of Regime Transitions and Dynastic Information

```{r echo=FALSE, message=FALSE, warning=FALSE}
#based on dic dem
gdd_transition_count <- gdd %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  group_by(Num_Transitions) %>% 
  summarise(Number_Countries = n())

knitr::kable(gdd_transition_count, format = "html", caption = "Country Count for number of Regime Transitions") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)


## GDD Dynastic Percentage for Num_Transitions 1-8
gdd_dyn_numtrans <- gdd %>% 
  filter(Regime_Change == 1) %>% 
  group_by(Country, Num_Transitions) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() 

gdd_dyn_numtrans_summary <- gdd %>% 
  filter(Regime_Change == 1) %>% 
  group_by(Country, Num_Transitions) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(Num_Transitions) %>% 
  summarise(Percentage_Dynastic_Years = mean(prop_dyn_years))

knitr::kable(gdd_dyn_numtrans_summary, format = "html", caption = "Percentage of years under Dynastic Rule by number of Regime Transitions") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)
  

gdd_dyn_numtrans_leader_summary <- gdd %>% 
  filter(Regime_Change == 1) %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(Num_Transitions) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

## New Category for one/two or more transitions


gdd_transition_category_one_twomore <- gdd %>% 
  filter(Regime_Change != 0) %>% 
  group_by(Country) %>% 
  mutate(Number_of_Transitions = case_when(
                  Num_Transitions == 1 ~ "One Transition",
                  Num_Transitions >= 2 ~ "Two or More Transitions",
             TRUE ~ NA_character_))




#Proportion of Years Under Dyn Rule by One or Two More
gdd_dynasticyears_transition <- gdd_transition_category_one_twomore %>%
  group_by(Country, Number_of_Transitions) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() 
  
gdd_dynastic_transitions_summary <- gdd_transition_category_one_twomore %>%
  group_by(Country, Number_of_Transitions) %>%
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(Number_of_Transitions) %>% 
  summarise(Percentage_Dynastic_Years = mean(prop_dyn_years))

knitr::kable(gdd_dynastic_transitions_summary, format = "html", caption = "Percentage of years under Dynastic Rule by One and Two or More transitions") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

ggplot(gdd_dynasticyears_transition, aes(x= factor(Number_of_Transitions), y=prop_dyn_years))+
  geom_boxplot()+
  labs(
    title = "Proportion of years under Dynastic Rule by Number of Transitions",
    x = "Number of Transitions",
    y = "Proportion of Years (%)"
  ) +
  theme_stata()

#Percentage of Leaders
gdd_transition_leader <- gdd_transition_category_one_twomore %>%
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup()

gdd_transition_leader_summary <- gdd_transition_category_one_twomore %>%
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(Number_of_Transitions) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

knitr::kable(gdd_transition_leader_summary, format = "html", caption = "Percentage of Dynastic Leaders by One and Two or More transitions") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)
  
ggplot(gdd_transition_leader, aes(x = factor(Number_of_Transitions), y =Percentage_Dynastic_Rulers))+
  geom_boxplot()+
  labs(
    title = "Percentage of Dynastic Leaders by Number of Transitions",
    x = "Number of Transitions",
    y = "Percentage of Leaders (%)"
  ) +
  theme_stata()

```

## Proportion of Years Under Dynastic Rule, Year-by-year Dynastic Rule, Proportion of dynastic leaders by Post-WW2 Independence status

```{r echo=FALSE, warning=FALSE, message=FALSE}

gdd_dynastic_countries_independence_status <- gdd %>% 
  group_by(Country,Year) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>%
  ungroup() %>% 
  group_by(year_bin,postww2_ind) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years),
            Cummulative_Dyn_Years = sum(dyn_years))

gdd_dynastic_postww2_ind_leader <- gdd %>% 
  distinct(nominal_leader, .keep_all = TRUE) %>% 
  group_by(Country) %>% 
  mutate(Dyn_Rulers = sum(pred_bin),
         total_rulers = n(),
         Percentage_Dynastic_Rulers = Dyn_Rulers/total_rulers*100) %>% 
  ungroup() %>% 
  group_by(year_bin,postww2_ind) %>% 
  summarise(Dynastic_Rulers_percentage = mean(Percentage_Dynastic_Rulers))

left_join(gdd_dynastic_countries_independence_status,gdd_dynastic_postww2_ind_leader, by = "postww2_ind","year_bin")
```

## Proportion of Years Under Dynastic Rule by Former British Colony Status (Information Scraped from Wikipedia)

```{r message=FALSE, warning=FALSE, echo=FALSE}
gdd_dynastic_countries_britcolony <- gdd %>%
  group_by(Country, year_bin, former_british_colony) %>% 
  mutate(
    total_years = (max(Year) - min(Year))+1,
    dyn_years = sum(pred_bin),
    prop_dyn_years = (dyn_years/total_years)*100,
    ) %>% 
  distinct(Country, .keep_all = TRUE) %>% 
  ungroup() %>% 
  group_by(former_british_colony, year_bin) %>% 
  summarise(Prop_Dyn_Years = mean(prop_dyn_years))

knitr::kable(gdd_dynastic_countries_britcolony, format = "html", caption = "Proportion of Years Under Dynastic Rule in Democratic Regimes") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)


gdd_dynastic_countries_britcolony$Prop_Dyn_Years <- round((gdd_dynastic_countries_britcolony$Prop_Dyn_Years), 2)

ggplot(gdd_dynastic_countries_britcolony, aes(x = as.factor(year_bin), y = Prop_Dyn_Years, color = as.factor(former_british_colony))) +
  geom_point(size = 3, alpha = 0.7, position = position_dodge(width = 0.5)) +
  geom_line(aes(group = former_british_colony), position = position_dodge(width = 0.5), size = 1) +
  scale_color_manual(values = c("0" = "blue", "1" = "red"), labels = c("0" = "Not A Former British Colony", "1" = "Former British Colony")) +
  ylim(0,50)+
  theme_stata() +
  labs(title = "Percentage of Years Under Dynastic Rule (by former British Colony Status)",
       x = "Year Bin",
       y = "Percentage of Years",
       color = "Former British Colony") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


```



## Proportion of Years Under Dynastic Rule by Regions (Across all regime types)

```{r echo=FALSE, warning=FALSE,message=FALSE}


```


## Mapping of Dynastic Relation Type Across all regime Types

The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast.

This graph shows what kind of dynastic relationships are most relevant across regime types (Civilian Dictatorship, Military Dictatorship, Mixed Democratic, Parliamentary Democracy, Presidential Democracy, Royal Dictatorship)

```{r eval=TRUE, message= FALSE, echo=TRUE, warning=FALSE}
 
gdd_relation_all <- gdd %>% 
    distinct(nominal_leader, .keep_all = TRUE) %>% 
    filter(pred_bin == 1, relation_code_pred != 0)

gdd_relation_all <-gdd_relation_all %>% 
  group_by(fln_gender) %>%
  count(relation_code_pred) %>%
  mutate(Relation_Type = case_when(
  fln_gender == 0 & relation_code_pred == 2  ~ "Father-Son",
  fln_gender == 0 & relation_code_pred == 3  ~ "Mother-Son",
  fln_gender == 0 & relation_code_pred == 8  ~ "Brother-Brother",
  fln_gender == 0 & relation_code_pred == 10 ~ "Grandfather-Grandson",
  fln_gender == 0 & relation_code_pred == 11 ~ "Grandmother-Grandson",
  fln_gender == 0 & relation_code_pred == 14 ~ "Uncle-Nephew",
  relation_code_pred == 18 ~ "Cousin-Cousin",
  relation_code_pred == 19 ~ "Other",
  fln_gender == 1 & relation_code_pred == 2  ~ "Father-Daughter",
  fln_gender == 1 & relation_code_pred == 6  ~ "Husband-Wife",
  fln_gender == 1 & relation_code_pred == 8  ~ "Brother-Sister",
  fln_gender == 1 & relation_code_pred == 10  ~ "Grandfather-Granddaughter",
    TRUE ~ NA_character_)
  ) %>% 
  rename(Total = n) %>% 
  mutate(percentage_tot_dyn = Total/sum(Total)*100)

relation <- ggplot(gdd_relation_all, aes(x = Relation_Type, y = Total, fill = Relation_Type)) +
  geom_bar(stat = "identity") +
  labs(title = "Dynastic Relationship Across All Regime Types",
       x = "Dynastic Relationship Type",
       y = "Total") +
  theme_stata()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")

ggplotly(relation)

```

## Mapping of Dynastic Relation Type in Democratic regime Types

The necessary pre-condition for the dynast in our dataset is that a leader will only be classified as a dynast if and only if a that leader in our dataset has a parent, in-law, or any kind of direct relative who has contested and won an election at any level of politics in their respective polities, then that politician is a dynast.

This graph shows what kind of dynastic relationships are most relevant in democratic regime types (Mixed Democratic, Parliamentary Democracy, Presidential Democracy)

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_relation_dem <- gdd %>% 
    distinct(nominal_leader, .keep_all = TRUE) %>% 
    filter(pred_bin == 1, relation_code_pred != 0) %>% 
  filter(system_category %in% c("Mixed Democratic", "Parliamentary Democracy", "Presidential Democracy"))

gdd_relation_dem <-gdd_relation_dem %>% 
  group_by(fln_gender) %>%
  count(relation_code_pred) %>%
  mutate(Relation_Type = case_when(
  fln_gender == 0 & relation_code_pred == 2  ~ "Father-Son",
  fln_gender == 0 & relation_code_pred == 3  ~ "Mother-Son",
  fln_gender == 0 & relation_code_pred == 8  ~ "Brother-Brother",
  fln_gender == 0 & relation_code_pred == 10 ~ "Grandfather-Grandson",
  fln_gender == 0 & relation_code_pred == 11 ~ "Grandmother-Grandson",
  fln_gender == 0 & relation_code_pred == 14 ~ "Uncle-Nephew",
  relation_code_pred == 18 ~ "Cousin-Cousin",
  relation_code_pred == 19 ~ "Other",
  fln_gender == 1 & relation_code_pred == 2  ~ "Father-Daughter",
  fln_gender == 1 & relation_code_pred == 6  ~ "Husband-Wife",
  fln_gender == 1 & relation_code_pred == 8  ~ "Brother-Sister",
  fln_gender == 1 & relation_code_pred == 10  ~ "Grandfather-Granddaughter",
    TRUE ~ NA_character_)
  ) %>% 
  rename(Total = n) %>% 
  mutate(percentage_tot_dyn = Total/sum(Total)*100)

relation_dem_count <- ggplot(gdd_relation_dem, aes(x = Relation_Type, y = Total, fill = Relation_Type)) +
  geom_bar(stat = "identity") +
  labs(title = "Dynastic Relationship in Democratic Regimes",
       x = "Dynastic Relationship Type",
       y = "Total") +
  theme_stata()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")

ggplotly(relation_dem_count)

```

# The Different Dynasts (across regime types) {.tabset}

While our definition of a dynast is clear as stated in the previous section. This section expands on that definition at talks about three different kinds of dynast.

## *THE FIRST DYNAST*

The First definition of Dynast is the one mentioned before. This shows the proportion of leaders that necessarily have an ancestor in politics and may or may not have a successor. The necessary precondition is a family member preceding him/her in politics before his time. ((pred_bin == 1 & suc_bin doesn't matter))

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_dynast_1 <- gdd %>%
  group_by(year_bin, Year) %>%
  summarise(Dynasts = sum(pred_bin), Total_Leaders = n()) %>% 
  ungroup() %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_of_Dynasts = mean(Dynasts/Total_Leaders*100))

first_dynast <- ggplot(gdd_dynast_1, aes(x = year_bin, y = Proportion_of_Dynasts)) +
  geom_bar(stat = "identity", color= "black", fill = "white")+
  labs(
    title = "Percentage of Leaders that are FIRST CATEGORY Dynasts",
    x = "25-Year-Category",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) 
ggplotly(first_dynast)
```

## *THE SECOND DYNAST (DYNASTY-SUSTAINER)*

The Second definition of Dynast is the one of dynasty sustainers. This means that the following graph shows the proportion of leaders that necessarily come from apolitical family and also leaves a successor in politics. Therefore, a dynasty sustainer The necessary preconditions are a family member preceding him/her in politics before his/her time and a family member suceeding him/her in politics after his/her time. (pred_bin == 1 & suc_bin == 1)

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_dynasty_sustainer <- gdd %>%
  group_by(Year, year_bin) %>%
  summarise(
    Dynasty_Sustainers = sum(pred_bin == 1 & suc_bin == 1),
    Total_Leaders = n()
  ) %>% 
  ungroup() %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_of_Dynasty_Sustainers = mean(Dynasty_Sustainers/Total_Leaders*100))

Dynasty_Sustainers <- ggplot(gdd_dynasty_sustainer, aes(x = year_bin, y = Proportion_of_Dynasty_Sustainers)) +
  geom_bar(stat = "identity", color= "black", fill = "white")+
  labs(
    title = "Percentage of Leaders that are Dynasty-Sustainers",
    x = "25-Year-Category",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) 
ggplotly(Dynasty_Sustainers)

```

## *THE THIRD DYNAST (DYNASTY-ENDER)*

The THIRD definition of Dynast is the one of dynasty-enderss. This means that the following graph shows the proportion of leaders that necessarily come from a political family BUT DO NOT LEAVE a successor in politics. Therefore, for a dynasty ENDER The necessary preconditions are a family member preceding him/her in politics before his/her time and a family member NOT suceeding him/her in politics after his/her time. (pred_bin == 1 & suc_bin == 0)

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_dynasty_ENDER <- gdd %>%
  group_by(Year, year_bin) %>%
  summarise(
    Dynasty_ENDER = sum(pred_bin == 1 & suc_bin == 0),
    Total_Leaders = n()
  ) %>% 
  ungroup() %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_of_Dynasty_ENDER = mean(Dynasty_ENDER/Total_Leaders*100))

Dynasty_ENDER <- ggplot(gdd_dynasty_ENDER, aes(x = year_bin, y = Proportion_of_Dynasty_ENDER)) +
  geom_bar(stat = "identity", color= "black", fill = "white")+
  labs(
    title = "Percentage of Leaders that are Dynasty-ENDERS",
    x = "25-Year-Category",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) 
ggplotly(Dynasty_ENDER)

```

## THE FOURTH DYNAST (DYNASTY-FORMERS)

The fourth definition of Dynast is the one of dynasty-formers. This means that the following graph shows the proportion of leaders that DO NOT come from a political family HAVE a successor in politics. Therefore, for a dynasty former the necessary preconditions are the ABSENCE OF A family member preceding him/her in politics before his/her time and a family member SUCCEEDING him/her in politics after his/her time. (pred_bin == 0 & suc_bin == 1)

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_dynasty_former <- gdd %>%
  group_by(Year, year_bin) %>%
  summarise(
    Dynasty_formers = sum(pred_bin == 0 & suc_bin == 1),
    Total_Leaders = n()
  ) %>% 
  ungroup() %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_of_Dynasty_formers = mean(Dynasty_formers/Total_Leaders*100))

Dynasty_formers <- ggplot(gdd_dynasty_former, aes(x = year_bin, y = Proportion_of_Dynasty_formers)) +
  geom_bar(stat = "identity", color= "black", fill = "white")+
  labs(
    title = "Percentage of Leaders that are Dynasty-Formers",
    x = "25-Year-Category",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,50)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) 
ggplotly(Dynasty_formers)
```

## THE PURE NON-DYNAST

The last category is a category of leaders that have no family before or after them in politics. These are not-dynasts and are included to show declining prevalence of family ties in politics.

```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}
gdd_non_dynast <- gdd %>%
  group_by(Year, year_bin) %>%
  summarise(
    non_dynast = sum(pred_bin == 0 & suc_bin == 0),
    Total_Leaders = n()
  ) %>% 
  ungroup() %>% 
  group_by(year_bin) %>% 
  summarise(Proportion_of_non_dynast = mean(non_dynast/Total_Leaders*100))

non_dynast <- ggplot(gdd_non_dynast, aes(x = year_bin, y = Proportion_of_non_dynast)) +
  geom_bar(stat = "identity", color= "black", fill = "white")+
  labs(
    title = "Percentage of Leaders that are non_dynast",
    x = "25-Year-Category",
    y = "Percentage"
  ) +
  theme_stata() +
  ylim(0,60)+
  theme(
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) 
ggplotly(non_dynast)
```

# Predicted Probabilities and Regime Types: Two Different Models {.tabset}

## Model 1,2,3: Using dictatorship as the independent variable

```{r echo=FALSE, warning=FALSE,message=FALSE}
# Model 1: Using dictatorship as the independent variable
model1 <- glm(dynastic ~ dictatorship, 
                data = gdd, 
                family = binomial(link = "logit"))

print(summary(model1))



# Fit the logistic regression model with country and year fixed effects
model2 <- glm(dynastic ~ dictatorship + factor(Country) + factor(Year), 
              data = gdd, 
              family = binomial(link = "logit"))


# Print the summary to check the model results
summary(model2)

# Compare the number of observations used in both models
n_obs_model1 <- length(model1$fitted.values)
n_obs_model2 <- length(model2$fitted.values)


# Prepare NEW data for prediction, ensuring no new factor levels
gdd_clean <- gdd[complete.cases(gdd[, c("dynastic", "dictatorship", "v2x_polyarchy", "former_british_colony", "Year", "Country")]), ]

# Fit the model with cleaned data
model3 <- glm(dynastic ~ dictatorship + v2x_polyarchy + former_british_colony + factor(Year) + factor(Country),
              data = gdd_clean,
              family = binomial(link = "logit"),
              na.action = na.exclude)

print(summary(model3))
# Predict probabilities with cleaned data
gdd_clean$pred_prob <- predict(model3, newdata = gdd_clean, type = "response")


#predictions for all rows i
gdd$pred_prob <- predict(model1, newdata = gdd, type = "response")

```


##  Model 4: Using Dem_Type as the independent variable, with mixed (1) as the reference category
```{r}
# Model 4: Using Dem_Type as the independent variable, with mixed (1) as the reference category
gdd_clean$Dem_Type <- factor(gdd_clean$Dem_Type, levels = c(1, 0, 2, 3))

model4 <- glm(dynastic ~ Dem_Type + v2x_polyarchy + former_british_colony + factor(Year) + factor(Country), data = gdd_clean, family = binomial(link = "logit"))
summary(model4)
```
# Dynastic Rule and Democracy (based on Predicted probabilites) {.tabset}


```{r eval=TRUE, message=FALSE,echo=FALSE,warning=FALSE}

gdd_vdem_final <- gdd %>% 
  filter(!is.na(e_boix_regime)) %>% 
  arrange(Country,Year) %>% 
  group_by(Country) %>% 
  mutate(dynastic_lag = lag(dynastic, order_by = Year),
         democracy_years = sum(e_boix_regime == 1), #/ n(),
         country_lifetime = (max(Year) - min(Year) + 1),
         democracy_percentage = (democracy_years / country_lifetime) * 100) %>% 
  select(Year, year_bin, COW, Region, Country, country_isocode, Dynastic_Proportion, nominal_leader, dynasty_desc, fln_gender,fln_spell, fln_highestdegree, fln_businessman, pred_num, relation_code_pred, pos_code_pred, suc_num, relation_code_suc, pos_code_suc,pred_bin, suc_bin, pred_national, suc_national, pred_state, suc_state, pred_local, suc_local,e_boix_regime, former_british_colony, system_category,log_gdp_percap,gdp_percap, v2x_polyarchy,v2x_libdem,dynastic_lag, dynastic, dictatorship, Dem_Type,  democracy_years,country_lifetime,democracy_percentage,v2elaccept,v2x_gencs, v2elintim, v2x_veracc,v2x_diagacc, v2xnp_regcorr,v2xel_frefair, v2x_corr, v2x_pubcorr, v2xed_ed_inpt,v2xed_ed_cent, v2lpname, v3partyid, v2psbars, v2pscnslnl,v2regoppgroupssize, v2clrspct, v2clstown, v2stcritrecadm, v2mecenefm, v2mecorrpt, v2pepwrses, v2pepwrsoc, v2exl_legitideol, v2exl_legitlead, v2caviol,v2cademmob)#pred_prob,




```

## Predicted Probability of Dynastic Leadership and Other IVs (Some Plots)

```{r echo=FALSE, }
ggplot(gdd_clean, aes(x= v2x_polyarchy, y = pred_prob))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Polyarchy Scores vs. Probability of Dynastic Leadership",
       x = "Polyarchy Scores",
       y = "Predicted Probability of Dynastic Leadership") +
  theme_stata()

ggplot(gdd_clean, aes(x= v2xnp_regcorr, y = pred_prob))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Level of Regime Corruption vs. Probability of Dynastic Leadership",
       x = "Level of Regime Corruption",
       y = "Predicted Probability of Dynastic Leadership") +
  theme_stata()

ggplot(gdd_clean, aes(x= v2caviol, y = pred_prob))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Level of Political Violence by Non-State Actors vs Probability of Dynastic Leader",
       x = "Level of Political Violence",
       y = "Predicted Probability of Dynastic Leadership") +
  theme_stata()

# for dem mobilisation: v2cademmob

ggplot(gdd_clean, aes(x= v2caviol, y = pred_prob))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Level of Mobilisation for Democracy vs Probability of Dynastic Leader",
       x = "Level of Political Violence",
       y = "Predicted Probability of Dynastic Leadership") +
  theme_stata()

ggplot(gdd_clean, aes(x= log_gdp_percap, y = pred_prob))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Log GDP Per capita vs Probability of Dynastic Leader",
       x = "LOG GDP per capita",
       y = "Predicted Probability of Dynastic Leadership") +
  theme_stata()

```





# Boix's Democracy Classification and Some Results {.tabset}
The results in this section are based on Boix's definition of democracy and a defined cut-off.
This will only include analysis for countries that are classified democracies according to the e_boix variable where Charles Boix classifies democracies/non democracies as 0 and 1. The Cut off Point we choose here for our analysis is to include all countries that have been democracies for at least 25% of their lifetime since 1945.

```{r echo=FALSE,warning=FALSE,message=FALSE}
## Filtering for minimum 25% of their life as electoral democracy
gdd_vdem_dem <- gdd_vdem_final %>% 
  filter(democracy_percentage >= 25)

gdd_vdem_demnondem <- gdd_vdem_final %>% 
  mutate(dem_nondem = case_when(democracy_percentage >= 25 ~ "1",democracy_percentage < 25 ~ "0", TRUE ~ NA_character_))

#make a list of unique countries in the dataset gdd_vdem_dem
unique_countries <- unique(gdd_vdem_dem$Country)





```

## How do the Different Dynasts differ in Democracies?

Before we proceed, it is crucial to note that now we are also adding a variable based on the different types of dynasts we have already explained before in order to make the analysis a bit more nuanced. We are adding a variable called "dynast_type" to account for the categorical variation in the types of dynasts that we have. In this classification we have a pure non-dynast (0, no family before or after the said leader is in politics), dynasty-ender (1, definitely has a predecessor in politics but does not have a successor in politics), the DYNAST (2,definitely has a predecessor in politics may or may not have a successor in politics), Dynasty-former (3, does not have any family in politics preceding him/her but definitely leaves a successor in politics), and finally dynasty-sustainer (4, necessarily has both a predecessor and successor in politics). First we will look at some basic characteristic differences in thse kind of dynasts using a basic difference in mean test (education, Spell [the number of time a leader has been in office], tenure length, is also in business)

### Comparisons Across All Categories

```{r echo=FALSE, message=FALSE, warning=FALSE}
gdd_vdem_dem <- gdd_vdem_dem %>% 
  mutate(Dynast_Type = case_when(
    pred_bin == 1 & suc_bin == 0 ~ "Dynasty Enders",
    pred_bin == 1 & suc_bin == 1 ~ "Dynasty Sustainers",
    pred_bin == 0 & suc_bin == 1 ~ "Dynasty Formers",
    pred_bin == 0 & suc_bin == 0 ~ "The Pure Non-Dynast",
    TRUE ~ NA_character_
  ))

gdd_dem_dyn <- gdd_vdem_dem %>% 
  group_by(nominal_leader) %>% 
  mutate(years_ruled = n()) %>% 
  distinct(nominal_leader, .keep_all = TRUE)

result_dyn_indem <- gdd_dem_dyn %>%
  group_by(Dynast_Type) %>%
  summarise(Total =n(),
    mean_fln_spell = mean(fln_spell, na.rm = TRUE),
    mean_years_ruled = mean(years_ruled, na.rm = TRUE),
    mean_fln_businessman = mean(as.numeric(fln_businessman)*100, na.rm = TRUE),
    mode_fln_highestdegree = names(which.max(table(fln_highestdegree))),
    num_pred_national = sum(pred_national),
    num_pred_state = sum(pred_state),
    num_pred_local = sum(pred_local),
    num_suc_national = sum(suc_national),
    num_suc_state = sum(suc_state),
    num_suc_local = sum(suc_local),
  )

result_dyn_indem %>%
  mutate(
    mean_fln_spell = round(mean_fln_spell, 2),
    mean_years_ruled = round(mean_years_ruled, 2),
    mean_fln_businessman = round(mean_fln_businessman, 2)
  ) %>%
  datatable(options = list(pageLength = 10), 
            rownames = FALSE,
            colnames = c("Dynasty Type", "Number of Leaders", "Avg. Number of Tenures", "Avg. Years Ruled", 
                         "Proportion of Businessmen", "Most Common Highest Degree", "Number of Predecessors in National Politics","Number of Predecessors in State Politics", "Number of Predecessors in Local Politics","Number of Successors in National Politics","Number of Successors in State Politics","Number of Successors in Local Politics"))

ggplot(gdd_dem_dyn, aes(x = Dynast_Type, y = years_ruled)) +
  stat_boxplot(geom = "errorbar", width = 0.5) +
  geom_boxplot(outlier.shape = 21, outlier.fill = "white", coef = 1.5) +
  scale_y_continuous(labels = comma_format()) +
  labs(
    title = "Distribution of Years Ruled by Dynast Type (Winsorized)",
    x = "Dynast Type",
    y = "Years Ruled"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0.5),
    axis.title = element_text(face = "bold", size = 12),
    axis.text = element_text(size = 10),
    panel.grid.major = element_line(color = "gray90"),
    panel.grid.minor = element_blank()
  )


gdd_dem_dyn_educationplot <- gdd_dem_dyn %>% 
  filter(fln_highestdegree != ".") %>% 
  filter(fln_highestdegree != "")

ggplot(gdd_dem_dyn_educationplot, aes(x = Dynast_Type, fill = fln_highestdegree)) +
  geom_bar(position = "fill") +
  scale_y_continuous(labels = scales::percent) +
  ylab("Proportion of Leaders") +
  xlab("Type of Dynast") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
# make a graph of the proportion of different types of dynasts over time with elegant colors

ggplot(gdd_dem_dyn, aes(x = year_bin, fill = Dynast_Type,)) +
  geom_bar(position = "dodge") +
  ylab("Proportion of Leaders") +
  xlab("Year") +
  scale_fill_brewer(palette = "Set1") +
  theme_igray() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

#the code above but with elegant gradient colors add borders to the columns

ggplot(gdd_dem_dyn, aes(x = year_bin, fill = Dynast_Type,)) +
  geom_bar(position = "dodge", color = "black") +
  ylab("Proportion of Leaders") +
  xlab("Year") +
  labs(title= "Dynast Types in Democracies")+
  scale_fill_brewer(palette = "Set1") +
  theme_igray() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  
  
```

### Comparisons Across Dynasts with predecessors/sucessors at the national level
```{r echo=FALSE,message=FALSE,warning=FALSE}
gdd_dem_dyn_national <- gdd_dem_dyn %>% 
  filter(pred_national == 1 | suc_national == 1)

result_dyn_indem_national <- gdd_dem_dyn_national %>%
  group_by(Dynast_Type) %>%
  summarise(Total =n(),
    mean_fln_spell = mean(fln_spell, na.rm = TRUE),
    mean_years_ruled = mean(years_ruled, na.rm = TRUE),
    mean_fln_businessman = mean(as.numeric(fln_businessman)*100, na.rm = TRUE),
    mode_fln_highestdegree = names(which.max(table(fln_highestdegree))),
    num_pred_national = sum(pred_national),
    num_suc_national = sum(suc_national)
  )

result_dyn_indem_national %>%
  mutate(
    mean_fln_spell = round(mean_fln_spell, 2),
    mean_years_ruled = round(mean_years_ruled, 2),
    mean_fln_businessman = round(mean_fln_businessman, 2)
  ) %>%
  datatable(options = list(pageLength = 10), 
            rownames = FALSE,
            colnames = c("Dynasty Type", "Number of Leaders", "Avg. Number of Tenures", "Avg. Years Ruled", 
                         "Proportion of Businessmen", "Most Common Highest Degree", "Number of Predecessors in National Politics","Number of Successors in National Politics"))

```


## The Relationship Between Polyarchy Scores (Level of Minimal Democracy) and Dynasticism (As a Continuous Variable)

Dynastic Variable (0/1) is recoded here as a continuous variable in terms of a dynastic score that varies between 0 and 1 to indicate that up until point t in time for a country i how long Dynastic rule has prevailed (Eg. 1970 in India would mean) TWO BASIC GRAPHS

```{r echo=FALSE, warning=FALSE, message=FALSE}
#Making a Loess Plot for Polyarchy Scores and Dynastic Proportions
ggplot(gdd_vdem_dem, aes(x= Dynastic_Proportion, y = v2x_polyarchy))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Polyarchy Scores vs. Dynasticism",
       x = "Dynastic_Proportion",
       y = "Polyarchy Scores") +
  theme_stata()


ggplot(gdd_vdem_dem, aes(x= v2x_polyarchy, y = Dynastic_Proportion))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Dynasticism vs. Polyarchy Score",
       x = "Polyarchy Scores",
       y = "Dynastic_Proportion") +
  theme_stata()

```

```{r echo=FALSE, warning=FALSE, message=FALSE}
# Linear Model for the relationship between Polyarchy Scores (X) and Dynastic Proportions (Y)
model_polyarchy_bivariate <- lm(Dynastic_Proportion ~ v2x_polyarchy, data = gdd_vdem_dem)
stargazer(model_polyarchy_bivariate, type = "text")




model_polyarchy <- lm(Dynastic_Proportion ~ v2x_polyarchy + log_gdp_percap + v2xnp_regcorr + v2caviol + v2cademmob, data = gdd_vdem_dem)
stargazer(model_polyarchy, type = "text")



#let's do a GLM with logit function
model_polyarchy_bivariate_glm <- glm(Dynastic_Proportion ~ v2x_polyarchy, data = gdd_vdem_dem, family = binomial(link = "logit"))
stargazer(model_polyarchy_bivariate_glm, type = "text")

pred_bi <- ggpredict(model_polyarchy_bivariate_glm, terms = "v2x_polyarchy")
plot(pred_bi)

gdd_vdem_dem_glm <- gdd_vdem_dem%>%
  dplyr::mutate(
    Dynastic_Proportion = ifelse(Dynastic_Proportion == 0, 0.0001, Dynastic_Proportion),
    Dynastic_Proportion = ifelse(Dynastic_Proportion == 1, 0.9999, Dynastic_Proportion)
  )

model_polyarchy_glm <- glm(Dynastic_Proportion ~ v2x_polyarchy + log_gdp_percap + v2xnp_regcorr + v2caviol + v2cademmob, data = gdd_vdem_dem_glm, family = binomial(link = "logit"))
stargazer(model_polyarchy_glm, type = "text")
#Interpretation
#The model shows that for every one unit increase in Polyarchy Scores, the odds of Dynasticism increases by 0.0001. The model is significant at 0.05 level and the pseudo R-squared value is 0.02.
#the maths behind the calculation for log of odds ratio
#log(odds) = log(p/(1-p)) = beta0 + beta1*X
#odds = exp(beta0 + beta1*X)
#odds ratio = exp(beta1)




```

## Corruption and Dynasticism

Corruption here is Regime Corruption borrowed from VDem and the specific variable details are:

```{r message=FALSE, echo=FALSE, warning=FALSE}


#Making a Loess Plot for Regime Corruption and Dynastic Proportions
ggplot(gdd_vdem_dem, aes(x= Dynastic_Proportion, y = v2xnp_regcorr))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Regime Corruption vs. Dynasticism",
       x = "Dynastic_Proportion",
       y = "Regime Corruption") +
  theme_stata()


ggplot(gdd_vdem_dem, aes(x= v2xnp_regcorr, y = Dynastic_Proportion))+
  geom_smooth(method = "loess", span = 0.75, color = "blue", se = TRUE) +  # LOESS line
  labs(title = "Dynasticism vs. Regime Corruption",
       x = "Regime Corruption",
       y = "Dynastic_Proportion") +
  theme_stata()

```

## Mean Polyarchy Scores in Democracies

```{r message=FALSE,warning=FALSE,echo=FALSE}
gdd_mean_scores <- gdd_vdem_dem %>% 
  filter(!is.na(v2x_polyarchy)) %>% 
  group_by(year_bin, pred_bin) %>% 
  summarise(Mean_Dem = mean(v2x_polyarchy))

mean_dem_bydyn <- ggplot(gdd_mean_scores, aes(x = as.factor(year_bin), y = Mean_Dem, color = as.factor(pred_bin))) +
  geom_point(size = 3, alpha = 0.7, position = position_dodge(width = 0.5)) +
  scale_color_manual(values = c("0" = "blue", "1" = "red"), labels = c("0" = "Non-Dynastic", "1" = "Dynastic")) +
  ylim(0.4,0.80)+
  theme_stata() +
  labs(title = "Mean Democracy Scores in Democracies (Boix) by Dynastic/Non-Dynastic Status",
       x = "Year Bin",
       y = "Mean Democracy Score",
       color = "Dynastic Status") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Convert to plotly object
mean_dem_bydyn_plotly <- ggplotly(mean_dem_bydyn)

# Modify the legend directly in the plotly object
mean_dem_bydyn_plotly <- mean_dem_bydyn_plotly %>% layout(legend = list(title = list(text = 'Dynastic Status')))

# Ensure the correct labels are used
mean_dem_bydyn_plotly <- mean_dem_bydyn_plotly %>%
  style(legendgroup = "0", name = "Non-Dynastic", traces = 1) %>%
  style(legendgroup = "1", name = "Dynastic", traces = 2)

# Print the plotly object
mean_dem_bydyn_plotly
```


# Some Regressions (For democracies ONLY as classified before based on Boix classification and 25% cut-off) {.tabset}

*This section covers some basic regressions treating Dynasticism as a DV against other other variables like democracy scores, regime corruption level, media censorship (v2mecenefm), clean elections (v2xel_frefair), former british colony. These are all fixed effects linear models with country and year fixed effects in place and the standard error is clustered at the country level.*



## Electoral Democracy and Dynasticism

*_Are democracies and dynastic leadership compatible (and are former British Colonies likely to be more dynastic?)?_*

```{r message=FALSE,echo=FALSE, warning=FALSE}
model_democracy <- felm(dynastic ~ v2x_polyarchy + log_gdp_percap + v2xnp_regcorr + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_democracy)

custom_labels <- c("dynastic" = "Dynastic",
                   "v2x_polyarchy" = "Electoral Democracy Level",
                   "log_gdp_percap" = "Log GDP Per Capita",
                   "former_british_colony" = "Former British Colony",
                   "v2xnp_regcorrr" = "Level of Regime Corruption")

coefplot(model_democracy)

```

*This regression results seems to suggest that Dynasties and democracies have been historically compatible. Specifically, A one-unit increase in the electoral democracy score (v2x_polyarchy) is associated with a 33.1 percentage point increase in the probability of that polity being dynastic, according to a linear model probability design.*

*The significant positive relationship between electoral democracy and dynastic regimes suggests that higher levels of electoral democracy might coexist with dynastic regimes. However, the economic and corruption-related predictors, as well as the colonial history, do not show a significant impact on dynastic regimes in this model.*

## Dynasticism and Free and Fair Elections

*_Is dynastic leadership more likely to produce less free and fair elections?_*

```{r message=FALSE,warning=FALSE,echo=FALSE}
model_free_elections <- felm(v2xel_frefair ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_free_elections)

coefplot(model_free_elections)

```

*Consistent with our claim on compatibility with democracies, dynastic leadership is in fact not bad for free and fair elections.*

## Is Dynastic Leadership more likely to produce Corrupt regimes?

```{r message=FALSE,warning=FALSE,echo=FALSE}
model_regime_corruption <- felm(v2xnp_regcorr ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_regime_corruption)

coefplot(model_regime_corruption)

```

*No significant relationship between dynastic leadership and more regime corruption (leaders using offices for private gain).*


## Dynastic Leadership and Barriers to other parties?
v2psbars

```{r message=FALSE,warning=FALSE,echo=FALSE}
model_barriers_parties <- felm(v2psbars ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_barriers_parties)

coefplot(model_barriers_parties)
```

## Dynastic Leadership and Candidate Selection
v2pscnslnl
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_candidate_selection <- felm(v2pscnslnl ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_candidate_selection)

coefplot(model_candidate_selection)
```

## Dynastic Leadership and Regime's opposition Groups Size
v2regoppgroupssize
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_regime_opposition <- felm(v2regoppgroupssize ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_regime_opposition)

coefplot(model_regime_opposition)
```

## Dynastic Leadership and Regiorous and Impartial Public Administration
v2clrspct
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_impartial_administration <- felm(v2clrspct ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_impartial_administration)

coefplot(model_impartial_administration)
```

## Dynastic Leadership and State Ownership of Enterprise 
v2clstown
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_state_ownership <- felm(v2clstown ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_state_ownership)

coefplot(model_state_ownership)
```

## Dynastic Leadership and Criteria for Appointments in Public Administration
v2stcritrecadm (0-5 ordinal scale)
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_appointment_admin <- felm(v2stcritrecadm ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_appointment_admin)

coefplot(model_appointment_admin)
```


## Dynastic Leadership and Media Censorship Effort

v2mecenefm
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_media_censorship <- felm(v2mecenefm ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_media_censorship)

coefplot(model_media_censorship)
```


## Dynastic Leadership and level of Media Corruption

v2mecorrpt
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_media_corruption <- felm(v2mecorrpt ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_media_corruption)

coefplot(model_media_corruption)
```


## Dyanstic Leadership and Power Distribution by Socio Economic Position

v2pepwrses (0-4)
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_power_socio_econ <- felm(v2pepwrses ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_power_socio_econ)

coefplot(model_power_socio_econ)
```


## Dynastic Leadership and Power Distribution by Social grouup

v2pepwrsoc
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_power_social_group <- felm(v2pepwrsoc ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_power_social_group)

coefplot(model_power_social_group)
```

## Dynastic Leadership and Legitimate Ideology (Promotion)

v2exl_legitideol
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_ideology_promotion <- felm(v2exl_legitideol ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_ideology_promotion)

coefplot(model_ideology_promotion)
```


## Dynastic Leadership and Person of Leader (Leader Cult, extraordinary charismatic etc.)
v2exl_legitlead
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_personality_cult <- felm(v2exl_legitlead ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_personality_cult)

coefplot(model_personality_cult)
```


## Dynastic Leadership and Political Violence by Non-State Actors
 v2caviol
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_political_violence <- felm(v2caviol ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_political_violence)

coefplot(model_political_violence)
```


## Dynastic Leadership and Mobilisation for Democracy
v2cademmob
```{r message=FALSE,warning=FALSE,echo=FALSE}
model_democratic_mobilisation <- felm(v2cademmob ~ dynastic + log_gdp_percap + former_british_colony | Region + Year | 0 | Region , data = gdd_vdem_dem)

summary(model_democratic_mobilisation)

coefplot(model_democratic_mobilisation)
```


