Replicating Kavasoglu (2022)
Ozlem Tuncel
2026-01-10
1 Introduction
I gathered all country-year observations in Kavasoglu (2022) paper. Originally, the paper spans from 1970 to 2019. However, I limit this data to 1990 to 2019 for two reasons: - There are not many cases of co-optation between 1970-1990 – only Malaysia. - There are a lot of post-USSR countries excluded from the analysis with V-Dem variables. So, starting from 1990 makes sense.
According to Kavasoglu (2022), there could be three kinds of co-optation:
- cabinet appointment,
- electoral support,
- parliamentary support.
Relying on party-level information in his data, I generated country-level information. This led to several variables of interest:
n_coopt: count of any co-optation in that country-year observationn_cabinet: count of cabinet appointment happened in that country-year observationn_parl: count of parliamentary support happened in that country-year observationn_elec: count of electoral support happened in that country-year observationany_coopt: binary, any co-optation occured in that country-year observationany_cabinet: binary, any cabinet appointment occured in that country-year observation
To test our estimation model:
\[ Co-optation_i = \beta_0 + \beta_1 Corruption_i + \beta_2 Judicial Indep_i + \beta_3 Corruption \times Judicial Indep + \epsilon_i \]
I relied on some V-Dem variables:
- v2jupack: court packing
- v2juhcind: judicial independence
- v2x_execorr: executive corruption index (reverse coded)
- v2xnp_regcorr: regime corruption index
We can use these variables interchangibly. I decided to use judicial independence and executive corruption index.
# Load data
my_data <- read.csv("analysis_data.csv")
my_data$any_coopt <- as.factor(my_data$any_coopt) # any form of cooptation
my_data$any_cabinet <- as.numeric(my_data$n_cabinet > 0) # any form of cabinet appointment
names(my_data)## [1] "X" "country_name" "COWcode"
## [4] "year" "n_coopt" "n_cabinet"
## [7] "n_parl" "n_elec" "any_coopt"
## [10] "polyarchy" "v2x_polyarchy_codelow" "v2x_polyarchy_codehigh"
## [13] "v2x_polyarchy_sd" "e_v2x_polyarchy_3C" "e_v2x_polyarchy_4C"
## [16] "e_v2x_polyarchy_5C" "court_pack" "v2jupack_codelow"
## [19] "v2jupack_codehigh" "v2jupack_sd" "v2jupack_osp"
## [22] "v2jupack_osp_codelow" "v2jupack_osp_codehigh" "v2jupack_osp_sd"
## [25] "v2jupack_ord" "v2jupack_ord_codelow" "v2jupack_ord_codehigh"
## [28] "v2jupack_mean" "v2jupack_nr" "jud_ind"
## [31] "v2juhcind_codelow" "v2juhcind_codehigh" "v2juhcind_sd"
## [34] "v2juhcind_osp" "v2juhcind_osp_codelow" "v2juhcind_osp_codehigh"
## [37] "v2juhcind_osp_sd" "v2juhcind_ord" "v2juhcind_ord_codelow"
## [40] "v2juhcind_ord_codehigh" "v2juhcind_mean" "v2juhcind_nr"
## [43] "regime_corrupt" "v2xnp_regcorr_codelow" "v2xnp_regcorr_codehigh"
## [46] "v2xnp_regcorr_sd" "pol_corrupt_index" "v2x_corr_codelow"
## [49] "v2x_corr_codehigh" "v2x_corr_sd" "e_v2x_corr_3C"
## [52] "e_v2x_corr_4C" "e_v2x_corr_5C" "exec_corrupt_index"
## [55] "v2x_execorr_codelow" "v2x_execorr_codehigh" "v2x_execorr_sd"
## [58] "e_v2x_execorr_3C" "e_v2x_execorr_4C" "e_v2x_execorr_5C"
## [61] "any_cabinet"1.1 Check data
subset_data <- my_data |>
select(country_name, COWcode, year, n_coopt:any_coopt, any_cabinet, polyarchy,
court_pack, jud_ind, regime_corrupt, pol_corrupt_index, exec_corrupt_index)
skim(subset_data)| Name | subset_data |
| Number of rows | 1890 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 1 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country_name | 0 | 1 | 4 | 32 | 0 | 63 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| any_coopt | 0 | 1 | FALSE | 2 | 0: 1782, 1: 108 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| COWcode | 0 | 1.00 | 482.65 | 197.53 | 41.00 | 373.00 | 481.00 | 615.00 | 840.00 | ▂▁▇▃▃ |
| year | 0 | 1.00 | 2004.50 | 8.66 | 1990.00 | 1997.00 | 2004.50 | 2012.00 | 2019.00 | ▇▇▇▇▇ |
| n_coopt | 0 | 1.00 | 0.10 | 0.46 | 0.00 | 0.00 | 0.00 | 0.00 | 5.00 | ▇▁▁▁▁ |
| n_cabinet | 0 | 1.00 | 0.05 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| n_parl | 0 | 1.00 | 0.06 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 5.00 | ▇▁▁▁▁ |
| n_elec | 0 | 1.00 | 0.02 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 | ▇▁▁▁▁ |
| any_cabinet | 0 | 1.00 | 0.03 | 0.17 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| polyarchy | 32 | 0.98 | 0.40 | 0.18 | 0.07 | 0.26 | 0.37 | 0.53 | 0.86 | ▃▇▆▃▂ |
| court_pack | 31 | 0.98 | 0.16 | 1.19 | -4.23 | -0.80 | 0.41 | 1.16 | 1.85 | ▁▁▅▆▇ |
| jud_ind | 31 | 0.98 | -0.31 | 1.20 | -3.00 | -1.13 | -0.27 | 0.64 | 2.35 | ▃▆▇▇▂ |
| regime_corrupt | 31 | 0.98 | 0.66 | 0.23 | 0.01 | 0.52 | 0.70 | 0.86 | 0.97 | ▁▂▅▆▇ |
| pol_corrupt_index | 31 | 0.98 | 0.67 | 0.22 | 0.01 | 0.54 | 0.72 | 0.85 | 0.97 | ▁▂▅▅▇ |
| exec_corrupt_index | 31 | 0.98 | 0.65 | 0.24 | 0.01 | 0.49 | 0.70 | 0.86 | 0.97 | ▁▃▃▇▇ |
We have some missing values in certain countries, especially in 1990s. Almost all of these are due to post-USSR countries not being established yet. For instance, Croatia in 1990 is missing.
1.2 Quick descriptives
Let’s look at correlations and distributions. All of our variables of interest are left-skewed. Most V-Dem variables are close to normal distribution. I do not see any possible multicollinearity issue. Of course, measures like regime corruption index and executive corruption index are highly correlated. So, substituing one for the another makes sense.
subset_data |>
select(-any_coopt, -country_name) |>
chart.Correlation()
2 Logistic models: co-optation and cabinet appointment
Let’s start with the naive logistic regression models. Here, we can test two things:
- Do corruption and judicial independence explain any form of cooperation in these countries?
- Do corruption and judicial independence explain any cabinet appointment in these countries?
I am using fixest package to run binomial logistic regression with country-fixed effects, and clustered standard errors. I also add polyarchy (democracy) score as a control.
Small note 1: Executive corruption index is inverse coded. That is, lower scores indicate a normatively better situation (e.g. more democratic) and higher scores a normatively worse situation (e.g. less democratic).
Small note 2: Fixest requires dependent variable to be numeric, although in glm() world it has to be a factor variable.
# Recode variables
subset_data$num_any_coopt <- as.numeric(subset_data$any_coopt)
subset_data$num_any_coopt <- ifelse(subset_data$num_any_coopt == 2, 1, 0)
# Helper function to run the three models
run_models <- function(outcome) {
f0 <- as.formula(paste0(outcome, " ~ jud_ind + exec_corrupt_index | country_name"))
f1 <- as.formula(paste0(outcome, " ~ jud_ind * exec_corrupt_index | country_name"))
f2 <- as.formula(paste0(outcome, " ~ jud_ind * exec_corrupt_index + polyarchy | country_name"))
m0 <- feglm(f0, data = subset_data, family = binomial("logit"), vcov = "cluster")
m1 <- feglm(f1, data = subset_data, family = binomial("logit"), vcov = "cluster")
m2 <- feglm(f2, data = subset_data, family = binomial("logit"), vcov = "cluster")
etable(m0, m1, m2)
}2.1 Any form of cooperation?
run_models("num_any_coopt")## m0 m1 m2
## Dependent Var.: num_any_coopt num_any_coopt num_any_coopt
##
## jud_ind -0.0265 (0.1286) -0.1968 (0.8682) 0.2192 (0.9300)
## exec_corrupt_index 3.753*** (1.140) 3.844** (1.444) 2.958* (1.490)
## jud_ind x exec_corrupt_index 0.2325 (1.191) 0.1147 (1.171)
## polyarchy -4.023* (1.708)
## Fixed-Effects: ---------------- ---------------- ---------------
## country_name Yes Yes Yes
## ____________________________ ________________ ________________ _______________
## S.E.: Clustered by: country_name by: country_name by: country_n..
## Observations 1,230 1,230 1,229
## Squared Cor. 0.03982 0.04020 0.04133
## Pseudo R2 0.06560 0.06566 0.07393
## BIC 989.61 996.68 997.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Judicial independence is not statistically significant across our models. Corruption index is significant across different models suggesting that in less democratic contexts, we are likely to see more cases of co-optation of the opposition actors. Finally, the interaction is significant. Let’s look at the average marginal effects.
m1_glm <- glm(num_any_coopt ~ jud_ind * exec_corrupt_index, data = subset_data,
family = binomial(link = "logit"))
meff <- margins(m1_glm)
summary(meff)## factor AME SE z p lower upper
## exec_corrupt_index 0.0701 0.0348 2.0156 0.0438 0.0019 0.1383
## jud_ind -0.0137 0.0055 -2.5078 0.0121 -0.0244 -0.0030tidy_marginal_predictions(m1_glm)## variable term estimate std.error
## 1 jud_ind:exec_corrupt_index -3.004 * 0.012 6.347504e-01 0.2891425443
## 2 jud_ind:exec_corrupt_index -3.004 * 0.489 2.665047e-01 0.1048112571
## 3 jud_ind:exec_corrupt_index -3.004 * 0.703 1.525703e-01 0.0352911980
## 4 jud_ind:exec_corrupt_index -3.004 * 0.855 9.856176e-02 0.0212319048
## 5 jud_ind:exec_corrupt_index -3.004 * 0.971 6.953109e-02 0.0222842468
## 6 jud_ind:exec_corrupt_index -1.128 * 0.012 5.147362e-02 0.0256373312
## 7 jud_ind:exec_corrupt_index -1.128 * 0.489 7.049302e-02 0.0148148539
## 8 jud_ind:exec_corrupt_index -1.128 * 0.703 8.098921e-02 0.0090597862
## 9 jud_ind:exec_corrupt_index -1.128 * 0.855 8.929064e-02 0.0093714089
## 10 jud_ind:exec_corrupt_index -1.128 * 0.971 9.613446e-02 0.0139195987
## 11 jud_ind:exec_corrupt_index -0.271 * 0.012 1.101509e-02 0.0053549837
## 12 jud_ind:exec_corrupt_index -0.271 * 0.489 3.574827e-02 0.0067519794
## 13 jud_ind:exec_corrupt_index -0.271 * 0.703 5.978600e-02 0.0064974104
## 14 jud_ind:exec_corrupt_index -0.271 * 0.855 8.532280e-02 0.0111736641
## 15 jud_ind:exec_corrupt_index -0.271 * 0.971 1.110873e-01 0.0197148890
## 16 jud_ind:exec_corrupt_index 0.639 * 0.012 2.068378e-03 0.0015765242
## 17 jud_ind:exec_corrupt_index 0.639 * 0.489 1.704306e-02 0.0050313373
## 18 jud_ind:exec_corrupt_index 0.639 * 0.703 4.303006e-02 0.0070768524
## 19 jud_ind:exec_corrupt_index 0.639 * 0.855 8.128519e-02 0.0161658850
## 20 jud_ind:exec_corrupt_index 0.639 * 0.971 1.291540e-01 0.0344552022
## 21 jud_ind:exec_corrupt_index 2.348 * 0.012 8.811086e-05 0.0001329483
## 22 jud_ind:exec_corrupt_index 2.348 * 0.489 4.143483e-03 0.0025082713
## 23 jud_ind:exec_corrupt_index 2.348 * 0.703 2.291734e-02 0.0074008153
## 24 jud_ind:exec_corrupt_index 2.348 * 0.855 7.417033e-02 0.0264552561
## 25 jud_ind:exec_corrupt_index 2.348 * 0.971 1.698205e-01 0.0776719327
## statistic p.value s.value conf.low conf.high df
## 1 2.1952854 2.814313e-02 5.1510734 0.0680414353 1.2014593816 Inf
## 2 2.5427110 1.099962e-02 6.5064028 0.0610784438 0.4719310220 Inf
## 3 4.3231826 1.537943e-05 15.9886380 0.0834008159 0.2217397701 Inf
## 4 4.6421534 3.447968e-06 18.1458223 0.0569479897 0.1401755273 Inf
## 5 3.1201903 1.807342e-03 9.1119144 0.0258547692 0.1132074115 Inf
## 6 2.0077606 4.466874e-02 4.4845906 0.0012253769 0.1017218687 Inf
## 7 4.7582663 1.952628e-06 18.9661515 0.0414564401 0.0995296004 Inf
## 8 8.9394173 3.912278e-19 61.1486250 0.0632323547 0.0987460639 Inf
## 9 9.5279844 1.603666e-21 69.0791166 0.0709230137 0.1076582616 Inf
## 10 6.9064104 4.970705e-12 37.5496867 0.0688525499 0.1234163743 Inf
## 11 2.0569790 3.968825e-02 4.6551442 0.0005195136 0.0215106641 Inf
## 12 5.2944875 1.193507e-07 22.9982902 0.0225146342 0.0489819072 Inf
## 13 9.2015114 3.529499e-20 64.6190987 0.0470513054 0.0725206861 Inf
## 14 7.6360624 2.239658e-14 45.3437147 0.0634228169 0.1072227754 Inf
## 15 5.6346915 1.753720e-08 25.7650062 0.0724468445 0.1497277892 Inf
## 16 1.3119864 1.895247e-01 2.3995420 -0.0010215524 0.0051583091 Inf
## 17 3.3873809 7.056335e-04 10.4687933 0.0071818159 0.0269042959 Inf
## 18 6.0803948 1.198870e-09 29.6356775 0.0291596807 0.0569004325 Inf
## 19 5.0281931 4.951232e-07 20.9457091 0.0496006387 0.1129697437 Inf
## 20 3.7484607 1.779232e-04 12.4564578 0.0616230169 0.1966849276 Inf
## 21 0.6627451 5.074938e-01 0.9785378 -0.0001724631 0.0003486848 Inf
## 22 1.6519276 9.854932e-02 3.3430103 -0.0007726389 0.0090596040 Inf
## 23 3.0965970 1.957558e-03 8.9967290 0.0084120107 0.0374226736 Inf
## 24 2.8036141 5.053335e-03 7.6285484 0.0223189791 0.1260216774 Inf
## 25 2.1863814 2.878771e-02 5.1184031 0.0175862796 0.3220546612 Infplot_marginal_predictions(m1_glm, pred = "exec_corrupt_index", at = list(jud_ind = c(0, 0.5, 1)))## [[1]]
This is a bit hard to read – continuous to continous interactions are often tricky. So, we have to find a better ways to present these results.
2.2 Any form of cabinet appointment?
run_models("any_cabinet")## m0 m1 m2
## Dependent Var.: any_cabinet any_cabinet any_cabinet
##
## jud_ind 0.0638 (0.2239) -1.702* (0.7986) -1.557. (0.8309)
## exec_corrupt_index 3.801** (1.328) 4.721** (1.446) 4.122** (1.434)
## jud_ind x exec_corrupt_index 2.411* (1.104) 2.529* (1.139)
## polyarchy -2.854 (2.110)
## Fixed-Effects: --------------- ---------------- ----------------
## country_name Yes Yes Yes
## ____________________________ _______________ ________________ ________________
## S.E.: Clustered by: country_n.. by: country_name by: country_name
## Observations 870 870 870
## Squared Cor. 0.03337 0.03582 0.03677
## Pseudo R2 0.06848 0.07305 0.07715
## BIC 611.72 616.51 621.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Across models, we have consistent findings like any form of cabinet appointment, like we observe in any form of cooperation. Let’s look at the marginal effects.
m2_glm <- glm(factor(any_cabinet) ~ jud_ind * exec_corrupt_index, data = subset_data,
family = binomial(link = "logit"))
meff2 <- margins(m2_glm)
summary(meff2)## factor AME SE z p lower upper
## exec_corrupt_index 0.0468 0.0280 1.6747 0.0940 -0.0080 0.1017
## jud_ind -0.0020 0.0045 -0.4368 0.6623 -0.0109 0.0069tidy_marginal_predictions(m2_glm)## variable term estimate std.error
## 1 jud_ind:exec_corrupt_index -3.004 * 0.012 9.456387e-01 8.925283e-02
## 2 jud_ind:exec_corrupt_index -3.004 * 0.489 2.760906e-01 1.395626e-01
## 3 jud_ind:exec_corrupt_index -3.004 * 0.703 6.429631e-02 2.167587e-02
## 4 jud_ind:exec_corrupt_index -3.004 * 0.855 1.993548e-02 7.997765e-03
## 5 jud_ind:exec_corrupt_index -3.004 * 0.971 7.969551e-03 4.740229e-03
## 6 jud_ind:exec_corrupt_index -1.128 * 0.012 6.989712e-02 4.345187e-02
## 7 jud_ind:exec_corrupt_index -1.128 * 0.489 5.114252e-02 1.335010e-02
## 8 jud_ind:exec_corrupt_index -1.128 * 0.703 4.437214e-02 6.683649e-03
## 9 jud_ind:exec_corrupt_index -1.128 * 0.855 4.009165e-02 6.909262e-03
## 10 jud_ind:exec_corrupt_index -1.128 * 0.971 3.709409e-02 8.906546e-03
## 11 jud_ind:exec_corrupt_index -0.271 * 0.012 6.209575e-03 3.928983e-03
## 12 jud_ind:exec_corrupt_index -0.271 * 0.489 2.157286e-02 5.365669e-03
## 13 jud_ind:exec_corrupt_index -0.271 * 0.703 3.736956e-02 5.086194e-03
## 14 jud_ind:exec_corrupt_index -0.271 * 0.855 5.483611e-02 8.827994e-03
## 15 jud_ind:exec_corrupt_index -0.271 * 0.971 7.307613e-02 1.640458e-02
## 16 jud_ind:exec_corrupt_index 0.639 * 0.012 4.452575e-04 4.748529e-04
## 17 jud_ind:exec_corrupt_index 0.639 * 0.489 8.462111e-03 3.504644e-03
## 18 jud_ind:exec_corrupt_index 0.639 * 0.703 3.110032e-02 6.351164e-03
## 19 jud_ind:exec_corrupt_index 0.639 * 0.855 7.599686e-02 1.801615e-02
## 20 jud_ind:exec_corrupt_index 0.639 * 0.971 1.443089e-01 4.713951e-02
## 21 jud_ind:exec_corrupt_index 2.348 * 0.012 3.124516e-06 6.757558e-06
## 22 jud_ind:exec_corrupt_index 2.348 * 0.489 1.433532e-03 1.213987e-03
## 23 jud_ind:exec_corrupt_index 2.348 * 0.703 2.196681e-02 8.927394e-03
## 24 jud_ind:exec_corrupt_index 2.348 * 0.855 1.367417e-01 5.865978e-02
## 25 jud_ind:exec_corrupt_index 2.348 * 0.971 4.129279e-01 1.817436e-01
## statistic p.value s.value conf.low conf.high df
## 1 10.5950557 3.141539e-26 84.7186592 7.707064e-01 1.120571e+00 Inf
## 2 1.9782563 4.789980e-02 4.3838364 2.552919e-03 5.496283e-01 Inf
## 3 2.9662620 3.014436e-03 8.3738963 2.181238e-02 1.067802e-01 Inf
## 4 2.4926309 1.268006e-02 6.3012948 4.260145e-03 3.561081e-02 Inf
## 5 1.6812588 9.271265e-02 3.4310900 -1.321126e-03 1.726023e-02 Inf
## 6 1.6086103 1.077016e-01 3.2148888 -1.526697e-02 1.550612e-01 Inf
## 7 3.8308712 1.276903e-04 12.9350631 2.497680e-02 7.730824e-02 Inf
## 8 6.6389096 3.160122e-11 34.8812289 3.127243e-02 5.747185e-02 Inf
## 9 5.8025950 6.529636e-09 27.1903503 2.654975e-02 5.363356e-02 Inf
## 10 4.1648122 3.116090e-05 14.9699036 1.963758e-02 5.455060e-02 Inf
## 11 1.5804534 1.140031e-01 3.1328554 -1.491091e-03 1.391024e-02 Inf
## 12 4.0205347 5.806621e-05 14.0719417 1.105634e-02 3.208937e-02 Inf
## 13 7.3472544 2.023196e-13 42.1684295 2.740080e-02 4.733832e-02 Inf
## 14 6.2116163 5.244237e-10 30.8285481 3.753356e-02 7.213866e-02 Inf
## 15 4.4546171 8.404304e-06 16.8604402 4.092374e-02 1.052285e-01 Inf
## 16 0.9376746 3.484117e-01 1.5211352 -4.854371e-04 1.375952e-03 Inf
## 17 2.4145418 1.575501e-02 5.9880452 1.593134e-03 1.533109e-02 Inf
## 18 4.8967910 9.741435e-07 19.9693623 1.865227e-02 4.354837e-02 Inf
## 19 4.2182634 2.461912e-05 15.3098615 4.068586e-02 1.113079e-01 Inf
## 20 3.0613154 2.203669e-03 8.8258770 5.191716e-02 2.367006e-01 Inf
## 21 0.4623736 6.438135e-01 0.6352854 -1.012005e-05 1.636909e-05 Inf
## 22 1.1808462 2.376638e-01 2.0730059 -9.458387e-04 3.812902e-03 Inf
## 23 2.4606069 1.387023e-02 6.1718650 4.469437e-03 3.946418e-02 Inf
## 24 2.3310978 1.974821e-02 5.6621347 2.177063e-02 2.517128e-01 Inf
## 25 2.2720348 2.308441e-02 5.4369374 5.671688e-02 7.691389e-01 Infplot_marginal_predictions(m2_glm, pred = "exec_corrupt_index", at = list(jud_ind = c(0, 0.5, 1)))## [[1]]
Again, hard to read, but we can figure this issue later.
3 Count models: cabinet appointments, electoral support, parliamentary support
To confirm, let’s look at distributions. All left-skewed. More importantly, zero-inflated (in my opinion – we have to do diagnostics before deciding on this).
hist(subset_data$n_coopt)
hist(subset_data$n_cabinet)
hist(subset_data$n_elec)
hist(subset_data$n_parl)
3.1 Establish models: Poisson and Negative Binomial
outcomes <- c("n_cabinet", "n_coopt", "n_elec", "n_parl")
pois_models <- list()
nb_models <- list()
for (y in outcomes) {
f0 <- as.formula(paste0(y, " ~ jud_ind + exec_corrupt_index | country_name"))
f1 <- as.formula(paste0(y, " ~ jud_ind * exec_corrupt_index | country_name"))
f2 <- as.formula(paste0(y, " ~ jud_ind * exec_corrupt_index + polyarchy | country_name"))
pois_models[[paste0(y, "_p0")]] <- feglm(f0, data = subset_data, family = poisson(), vcov = "cluster")
pois_models[[paste0(y, "_p1")]] <- feglm(f1, data = subset_data, family = poisson(), vcov = "cluster")
pois_models[[paste0(y, "_p2")]] <- feglm(f2, data = subset_data, family = poisson(), vcov = "cluster")
nb_models[[paste0(y, "_nb0")]] <- fenegbin(f0, data = subset_data, vcov = "cluster")
nb_models[[paste0(y, "_nb1")]] <- fenegbin(f1, data = subset_data, vcov = "cluster")
nb_models[[paste0(y, "_nb2")]] <- fenegbin(f2, data = subset_data, vcov = "cluster")
}3.1.1 Count of total co-opations
Poisson and negative binomial models for total number of co-optations are not substantively differnet. Still, corruption is significant across our models. Effect size differs. So, probably negative binomial’s fit is better than Poisson (see BIC scores).
etable(pois_models$n_coopt_p0, pois_models$n_coopt_p1, pois_models$n_coopt_p2,
nb_models$n_coopt_nb0, nb_models$n_coopt_nb1, nb_models$n_coopt_nb2)## pois_models$..0 pois_models$n..1 pois_models$n..2
## Dependent Var.: n_coopt n_coopt n_coopt
##
## jud_ind 0.0409 (0.1136) -0.4599 (0.7784) -0.1295 (0.8510)
## exec_corrupt_index 3.269** (1.014) 3.546** (1.352) 2.825* (1.399)
## jud_ind x exec_corrupt_index 0.6898 (1.093) 0.5880 (1.085)
## polyarchy -3.202* (1.527)
## Fixed-Effects: --------------- ---------------- ----------------
## country_name Yes Yes Yes
## ____________________________ _______________ ________________ ________________
## Family Poisson Poisson Poisson
## S.E.: Clustered by: country_n.. by: country_name by: country_name
## Observations 1,230 1,230 1,229
## Squared Cor. 0.06310 0.06433 0.06412
## Pseudo R2 0.10819 0.10877 0.11446
## BIC 1,399.5 1,405.9 1,405.7
## Over-dispersion -- -- --
##
## nb_models$n_..0 nb_models$n_..1 nb_models$n_..2
## Dependent Var.: n_coopt n_coopt n_coopt
##
## jud_ind 0.0058 (0.1400) -0.1852 (1.021) 0.2478 (1.068)
## exec_corrupt_index 4.056** (1.258) 4.130** (1.545) 3.054. (1.619)
## jud_ind x exec_corrupt_index 0.2625 (1.417) 0.1925 (1.357)
## polyarchy -5.102* (2.015)
## Fixed-Effects: --------------- --------------- ---------------
## country_name Yes Yes Yes
## ____________________________ _______________ _______________ _______________
## Family Neg. Bin. Neg. Bin. Neg. Bin.
## S.E.: Clustered by: country_n.. by: country_n.. by: country_n..
## Observations 1,230 1,230 1,229
## Squared Cor. 0.05824 0.05863 0.05192
## Pseudo R2 0.05926 0.05930 0.06634
## BIC 1,248.7 1,255.8 1,255.6
## Over-dispersion 0.15909 0.15930 0.16132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.2 Count of cabinet appointments
Substantively similar results!
etable(pois_models$n_cabinet_p0, pois_models$n_cabinet_p1, pois_models$n_cabinet_p2,
nb_models$n_cabinet_nb0, nb_models$n_cabinet_nb1, nb_models$n_cabinet_nb2)## pois_models$..0 pois_models$n_..1
## Dependent Var.: n_cabinet n_cabinet
##
## jud_ind 0.0655 (0.2383) -2.565** (0.8400)
## exec_corrupt_index 2.920. (1.654) 4.253* (1.676)
## jud_ind x exec_corrupt_index 3.641** (1.253)
## polyarchy
## Fixed-Effects: --------------- -----------------
## country_name Yes Yes
## ____________________________ _______________ _________________
## Family Poisson Poisson
## S.E.: Clustered by: country_n.. by: country_name
## Observations 870 870
## Squared Cor. 0.06475 0.07248
## Pseudo R2 0.12151 0.13339
## BIC 794.16 793.03
## Over-dispersion -- --
##
## pois_models$n_..2 nb_models$n_..0 nb_models$n_c..1
## Dependent Var.: n_cabinet n_cabinet n_cabinet
##
## jud_ind -2.438** (0.8112) 0.1195 (0.2796) -2.773* (1.083)
## exec_corrupt_index 3.670* (1.507) 4.367* (1.727) 5.521*** (1.673)
## jud_ind x exec_corrupt_index 3.726** (1.212) 3.952** (1.524)
## polyarchy -2.627 (1.833)
## Fixed-Effects: ----------------- --------------- ----------------
## country_name Yes Yes Yes
## ____________________________ _________________ _______________ ________________
## Family Poisson Neg. Bin. Neg. Bin.
## S.E.: Clustered by: country_name by: country_n.. by: country_name
## Observations 870 870 870
## Squared Cor. 0.07431 0.05529 0.06510
## Pseudo R2 0.13708 0.07328 0.08043
## BIC 797.35 726.88 729.66
## Over-dispersion -- 0.16150 0.17398
##
## nb_models$n_..2
## Dependent Var.: n_cabinet
##
## jud_ind -2.534* (1.092)
## exec_corrupt_index 4.818** (1.616)
## jud_ind x exec_corrupt_index 4.026** (1.532)
## polyarchy -3.733. (2.203)
## Fixed-Effects: ---------------
## country_name Yes
## ____________________________ _______________
## Family Neg. Bin.
## S.E.: Clustered by: country_n..
## Observations 870
## Squared Cor. 0.06085
## Pseudo R2 0.08466
## BIC 734.07
## Over-dispersion 0.17353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.3 Count of electoral support
Interestingly, we do not find any support for number of electoral support as a dependent variable. I guess this is a super rare case? Or, something else is going on (look at the N).
etable(pois_models$n_elec_p0, pois_models$n_elec_p1, pois_models$n_elec_p2,
nb_models$n_elec_nb0, nb_models$n_elec_nb1, nb_models$n_elec_nb2)## pois_models$n..0 pois_models$..1 pois_models..2
## Dependent Var.: n_elec n_elec n_elec
##
## jud_ind -0.0401 (0.2580) 0.6536 (1.188) 0.9667 (1.284)
## exec_corrupt_index 1.677 (1.579) 1.726 (1.368) 1.176 (1.534)
## jud_ind x exec_corrupt_index -0.9427 (1.636) -1.010 (1.559)
## polyarchy -2.977 (3.181)
## Fixed-Effects: ---------------- --------------- --------------
## country_name Yes Yes Yes
## ____________________________ ________________ _______________ ______________
## Family Poisson Poisson Poisson
## S.E.: Clustered by: country_name by: country_n.. by: country_..
## Observations 450 450 450
## Squared Cor. 0.09339 0.09159 0.09448
## Pseudo R2 0.14275 0.14391 0.14830
## BIC 388.85 394.57 399.22
## Over-dispersion -- -- --
##
## nb_models$n_e..0 nb_models$n..1 nb_models$n..2
## Dependent Var.: n_elec n_elec n_elec
##
## jud_ind -0.0404 (0.2348) 1.250 (1.626) 1.632 (1.746)
## exec_corrupt_index 2.118 (1.908) 1.994 (1.869) 1.348 (2.036)
## jud_ind x exec_corrupt_index -1.793 (2.294) -1.894 (2.202)
## polyarchy -3.647 (3.745)
## Fixed-Effects: ---------------- -------------- --------------
## country_name Yes Yes Yes
## ____________________________ ________________ ______________ ______________
## Family Neg. Bin. Neg. Bin. Neg. Bin.
## S.E.: Clustered by: country_name by: country_.. by: country_..
## Observations 450 450 450
## Squared Cor. 0.09183 0.08840 0.08855
## Pseudo R2 0.07332 0.07578 0.08022
## BIC 356.42 361.86 366.75
## Over-dispersion 0.15677 0.15660 0.15667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13.1.4 Count of parliamentary support
Again, number of parliamentary support as a dependent variable is not significant across models. I am guessing that this might be a rare case as well.
etable(pois_models$n_parl_p0, pois_models$n_parl_p1, pois_models$n_parl_p2,
nb_models$n_parl_nb0, nb_models$n_parl_nb1, nb_models$n_parl_nb2)## pois_models$n..0 pois_models$..1 pois_models$..2
## Dependent Var.: n_parl n_parl n_parl
##
## jud_ind -0.0219 (0.1102) 0.6671 (1.125) 1.294 (1.246)
## exec_corrupt_index 3.827* (1.753) 3.530. (1.924) 2.766 (2.037)
## jud_ind x exec_corrupt_index -0.9501 (1.542) -1.283 (1.534)
## polyarchy -4.762. (2.512)
## Fixed-Effects: ---------------- --------------- ---------------
## country_name Yes Yes Yes
## ____________________________ ________________ _______________ _______________
## Family Poisson Poisson Poisson
## S.E.: Clustered by: country_name by: country_n.. by: country_n..
## Observations 750 750 749
## Squared Cor. 0.06833 0.06788 0.07407
## Pseudo R2 0.11276 0.11385 0.12251
## BIC 804.67 810.52 810.74
## Over-dispersion -- -- --
##
## nb_models$n_p..0 nb_models$n..1 nb_models$n_..2
## Dependent Var.: n_parl n_parl n_parl
##
## jud_ind -0.0045 (0.1146) 0.9212 (1.176) 1.410 (1.233)
## exec_corrupt_index 4.095* (1.791) 3.800* (1.923) 2.837 (2.079)
## jud_ind x exec_corrupt_index -1.279 (1.623) -1.408 (1.492)
## polyarchy -5.291. (3.085)
## Fixed-Effects: ---------------- -------------- ---------------
## country_name Yes Yes Yes
## ____________________________ ________________ ______________ _______________
## Family Neg. Bin. Neg. Bin. Neg. Bin.
## S.E.: Clustered by: country_name by: country_.. by: country_n..
## Observations 750 750 749
## Squared Cor. 0.06489 0.06488 0.07073
## Pseudo R2 0.07011 0.07150 0.07826
## BIC 744.94 750.71 753.00
## Over-dispersion 0.23544 0.23639 0.24470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14 Diagnostics tests
Let’s perform overdispersion test to see which model is the best. With overdispersion, we compare the residual deviance to the degrees of freedom for the Poisson model. If the ratio is much larger than 1, you have overdispersion, favoring the NB model.
check_overdispersion(pois_models$n_cabinet_p2, nb_models$n_cabinet_nb2)## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length## Warning in (insight::get_response(x, verbose = FALSE) - yhat)/sqrt(yhat):
## longer object length is not a multiple of shorter object length## # Overdispersion test
##
## dispersion ratio = 4.219
## Pearson's Chi-Squared = 3653.434
## p-value = < 0.001## Overdispersion detected.check_overdispersion(pois_models$n_coopt_p2, nb_models$n_coopt_nb2)## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length
## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length## # Overdispersion test
##
## dispersion ratio = 4.287
## Pearson's Chi-Squared = 5252.124
## p-value = < 0.001## Overdispersion detected.check_overdispersion(pois_models$n_elec_p2, nb_models$n_elec_nb2)## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length
## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length## # Overdispersion test
##
## dispersion ratio = 3.242
## Pearson's Chi-Squared = 1446.047
## p-value = < 0.001## Overdispersion detected.check_overdispersion(pois_models$n_parl_p2, nb_models$n_parl_nb2)## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length
## Warning in insight::get_response(x, verbose = FALSE) - yhat: longer object
## length is not a multiple of shorter object length## # Overdispersion test
##
## dispersion ratio = 3.783
## Pearson's Chi-Squared = 2818.230
## p-value = < 0.001## Overdispersion detected.AIC(pois_models$n_cabinet_p2, nb_models$n_cabinet_nb2)## [1] 639.9855 576.7087AIC(pois_models$n_coopt_p2, nb_models$n_coopt_nb2)## [1] 1175.585 1025.487AIC(pois_models$n_elec_p2, nb_models$n_elec_nb2)## [1] 321.1437 288.6792AIC(pois_models$n_parl_p2, nb_models$n_parl_nb2)## [1] 676.7920 619.0517These tests confirm that negative binomial performs better – almost always!
5 Zero-inflated models
I suspect that zero-inflated models might be a better suit here. So, I am going to run them as well. I will be running zero-inflated models, and zero-inflated negative binomial models.
5.1 Establish models
# Manually create country fixed effects
subset_data <- subset_data |> mutate(country_fe = factor(country_name))
# outcome vars
outcomes <- c("n_cabinet", "n_coopt", "n_elec", "n_parl")
zip_models <- list()
for (y in outcomes) {
f0 <- as.formula(paste0(
y, " ~ jud_ind + exec_corrupt_index + country_fe |
jud_ind + exec_corrupt_index"
))
f1 <- as.formula(paste0(
y, " ~ jud_ind * exec_corrupt_index + country_fe |
jud_ind * exec_corrupt_index"
))
f2 <- as.formula(paste0(
y, " ~ jud_ind * exec_corrupt_index + polyarchy + country_fe |
jud_ind * exec_corrupt_index + polyarchy"
))
zip_models[[paste0(y, "_zip0")]] <- zeroinfl(f0, data = subset_data, dist = "poisson")
zip_models[[paste0(y, "_zip1")]] <- zeroinfl(f1, data = subset_data, dist = "poisson")
zip_models[[paste0(y, "_zip2")]] <- zeroinfl(f2, data = subset_data, dist = "poisson")
}Let’s check the results.
For all models, none of our variables are statistically significant!
# Total cooptation
summary(zip_models$n_coopt_zip0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.355e-01 -2.806e-01 -1.700e-01 -4.107e-05 6.611e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -2.934e+00 1.314e+00 -2.232
## jud_ind 1.698e-01 2.144e-01 0.792
## exec_corrupt_index 3.688e+00 1.492e+00 2.472
## country_feAlgeria 9.994e-01 1.041e+00 0.960
## country_feAngola -1.839e+01 3.756e+03 -0.005
## country_feArmenia 7.385e-01 9.365e-01 0.789
## country_feAzerbaijan -1.337e+00 1.232e+00 -1.085
## country_feBangladesh -6.967e-01 1.158e+00 -0.602
## country_feBelarus 6.195e-03 1.048e+00 0.006
## country_feBurkina Faso 8.766e-01 9.490e-01 0.924
## country_feCambodia -7.865e-01 1.113e+00 -0.706
## country_feCameroon -1.379e+00 1.242e+00 -1.110
## country_feCentral African Republic -6.966e-01 1.230e+00 -0.566
## country_feCroatia -1.696e+01 4.084e+03 -0.004
## country_feDemocratic Republic of the Congo 4.386e-01 1.043e+00 0.421
## country_feDjibouti 4.145e-02 1.124e+00 0.037
## country_feEgypt 1.407e+00 1.037e+00 1.357
## country_feEquatorial Guinea -1.336e+00 1.291e+00 -1.035
## country_feEthiopia -1.701e+01 4.542e+03 -0.004
## country_feGabon -2.347e-01 9.656e-01 -0.243
## country_feGeorgia 2.475e-01 9.383e-01 0.264
## country_feGhana -1.297e+00 1.357e+00 -0.956
## country_feGuinea -2.087e+00 1.428e+00 -1.461
## country_feGuinea-Bissau -2.319e-01 1.005e+00 -0.231
## country_feGuyana -1.756e+01 4.170e+03 -0.004
## country_feHaiti 6.526e-01 9.841e-01 0.663
## country_feIvory Coast -1.228e+00 1.359e+00 -0.904
## country_feKazakhstan 3.990e-01 1.091e+00 0.366
## country_feKenya -1.802e+01 4.100e+03 -0.004
## country_feKyrgyzstan 1.042e+00 9.467e-01 1.101
## country_feLesotho -1.717e+01 4.523e+03 -0.004
## country_feMadagascar -1.092e+00 1.415e+00 -0.772
## country_feMalaysia 1.478e+00 8.605e-01 1.717
## country_feMauritania 8.905e-01 1.070e+00 0.832
## country_feMexico -1.729e+01 4.266e+03 -0.004
## country_feMozambique -1.748e+01 4.366e+03 -0.004
## country_feNamibia -1.664e+01 5.073e+03 -0.003
## country_feNicaragua -1.780e+01 3.672e+03 -0.005
## country_feNiger 7.797e-01 1.030e+00 0.757
## country_feNigeria -1.949e+00 1.362e+00 -1.431
## country_fePanama -1.713e+01 4.589e+03 -0.004
## country_feParaguay -1.832e+01 3.919e+03 -0.005
## country_fePeru -1.720e+01 3.968e+03 -0.004
## country_fePhilippines -1.777e+01 4.222e+03 -0.004
## country_feRussia 1.370e+00 9.742e-01 1.406
## country_feRwanda 2.252e+00 1.158e+00 1.946
## country_feSenegal 5.826e-01 1.480e+00 0.394
## country_feSerbia -8.616e-01 1.164e+00 -0.740
## country_feSierra Leone -3.443e-01 1.226e+00 -0.281
## country_feSingapore -1.559e+01 5.378e+03 -0.003
## country_feSouth Korea -1.652e+01 4.642e+03 -0.004
## country_feSri Lanka 7.133e-01 9.885e-01 0.722
## country_feTaiwan -1.640e+01 4.996e+03 -0.003
## country_feTajikistan -8.630e-01 1.125e+00 -0.767
## country_feTanzania -1.677e+01 4.811e+03 -0.003
## country_feThe Gambia -1.783e+01 4.078e+03 -0.004
## country_feTogo -9.755e-01 1.186e+00 -0.822
## country_feTürkiye -1.764e+01 4.146e+03 -0.004
## country_feTurkmenistan -2.045e+00 1.522e+00 -1.343
## country_feUganda -3.455e-01 1.021e+00 -0.339
## country_feUzbekistan 4.333e-02 1.185e+00 0.037
## country_feVenezuela -5.826e-01 1.239e+00 -0.470
## country_feZambia -1.682e+01 4.410e+03 -0.004
## country_feZimbabwe -6.982e-01 1.187e+00 -0.588
## Pr(>|z|)
## (Intercept) 0.0256 *
## jud_ind 0.4285
## exec_corrupt_index 0.0134 *
## country_feAlgeria 0.3370
## country_feAngola 0.9961
## country_feArmenia 0.4303
## country_feAzerbaijan 0.2777
## country_feBangladesh 0.5474
## country_feBelarus 0.9953
## country_feBurkina Faso 0.3556
## country_feCambodia 0.4799
## country_feCameroon 0.2671
## country_feCentral African Republic 0.5712
## country_feCroatia 0.9967
## country_feDemocratic Republic of the Congo 0.6740
## country_feDjibouti 0.9706
## country_feEgypt 0.1748
## country_feEquatorial Guinea 0.3008
## country_feEthiopia 0.9970
## country_feGabon 0.8079
## country_feGeorgia 0.7919
## country_feGhana 0.3389
## country_feGuinea 0.1440
## country_feGuinea-Bissau 0.8175
## country_feGuyana 0.9966
## country_feHaiti 0.5073
## country_feIvory Coast 0.3662
## country_feKazakhstan 0.7146
## country_feKenya 0.9965
## country_feKyrgyzstan 0.2709
## country_feLesotho 0.9970
## country_feMadagascar 0.4403
## country_feMalaysia 0.0859 .
## country_feMauritania 0.4052
## country_feMexico 0.9968
## country_feMozambique 0.9968
## country_feNamibia 0.9974
## country_feNicaragua 0.9961
## country_feNiger 0.4493
## country_feNigeria 0.1524
## country_fePanama 0.9970
## country_feParaguay 0.9963
## country_fePeru 0.9965
## country_fePhilippines 0.9966
## country_feRussia 0.1596
## country_feRwanda 0.0517 .
## country_feSenegal 0.6939
## country_feSerbia 0.4592
## country_feSierra Leone 0.7789
## country_feSingapore 0.9977
## country_feSouth Korea 0.9972
## country_feSri Lanka 0.4705
## country_feTaiwan 0.9974
## country_feTajikistan 0.4429
## country_feTanzania 0.9972
## country_feThe Gambia 0.9965
## country_feTogo 0.4109
## country_feTürkiye 0.9966
## country_feTurkmenistan 0.1791
## country_feUganda 0.7350
## country_feUzbekistan 0.9708
## country_feVenezuela 0.6382
## country_feZambia 0.9970
## country_feZimbabwe 0.5562
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9737 0.7293 2.706 0.0068 **
## jud_ind 0.1999 0.1364 1.466 0.1427
## exec_corrupt_index -0.4031 0.9563 -0.422 0.6734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 43
## Log-likelihood: -450 on 67 Dfsummary(zip_models$n_coopt_zip1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.286e-01 -2.783e-01 -1.741e-01 -4.591e-05 6.574e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -3.89417 1.48531 -2.622
## jud_ind -1.76084 1.21930 -1.444
## exec_corrupt_index 5.44904 1.91947 2.839
## country_feAlgeria 0.46476 1.21797 0.382
## country_feAngola -18.30232 3249.68574 -0.006
## country_feArmenia 0.36975 1.12567 0.328
## country_feAzerbaijan -1.27653 1.36243 -0.937
## country_feBangladesh -0.91522 1.29967 -0.704
## country_feBelarus -0.46744 1.23114 -0.380
## country_feBurkina Faso 0.71440 1.08597 0.658
## country_feCambodia -0.81492 1.26525 -0.644
## country_feCameroon -1.29710 1.37426 -0.944
## country_feCentral African Republic -0.88825 1.35923 -0.653
## country_feCroatia -16.84510 3628.11571 -0.005
## country_feDemocratic Republic of the Congo -0.11469 1.24423 -0.092
## country_feDjibouti -0.32049 1.26811 -0.253
## country_feEgypt 2.06102 1.14457 1.801
## country_feEquatorial Guinea -1.04165 1.43190 -0.727
## country_feEthiopia -17.04094 3777.37643 -0.005
## country_feGabon -0.69854 1.16724 -0.598
## country_feGeorgia -0.28058 1.17380 -0.239
## country_feGhana -1.65207 1.48779 -1.110
## country_feGuinea -2.01006 1.54656 -1.300
## country_feGuinea-Bissau -0.73927 1.20931 -0.611
## country_feGuyana -17.49128 3703.19909 -0.005
## country_feHaiti 0.33336 1.14245 0.292
## country_feIvory Coast -1.53867 1.50498 -1.022
## country_feKazakhstan 0.63663 1.22958 0.518
## country_feKenya -17.98749 3475.09168 -0.005
## country_feKyrgyzstan 0.85422 1.11626 0.765
## country_feLesotho -16.93659 4706.77930 -0.004
## country_feMadagascar -1.29786 1.51633 -0.856
## country_feMalaysia 1.19423 1.03806 1.150
## country_feMauritania 0.41020 1.24055 0.331
## country_feMexico -17.14485 3950.13900 -0.004
## country_feMozambique -17.38346 3986.93058 -0.004
## country_feNamibia -15.98383 8137.06164 -0.002
## country_feNicaragua -17.66662 3255.14403 -0.005
## country_feNiger 0.62445 1.16321 0.537
## country_feNigeria -2.62851 1.55694 -1.688
## country_fePanama -16.98543 4488.99139 -0.004
## country_feParaguay -18.33420 3086.61257 -0.006
## country_fePeru -17.05179 3477.77361 -0.005
## country_fePhilippines -17.71883 3642.70089 -0.005
## country_feRussia 0.92221 1.16649 0.791
## country_feRwanda 1.49094 1.30245 1.145
## country_feSenegal 0.55853 1.53732 0.363
## country_feSerbia -1.00379 1.30704 -0.768
## country_feSierra Leone -0.67561 1.35255 -0.500
## country_feSingapore -15.64198 4670.11417 -0.003
## country_feSouth Korea -16.14091 5143.82882 -0.003
## country_feSri Lanka 0.58360 1.13463 0.514
## country_feTaiwan -15.93853 5217.10252 -0.003
## country_feTajikistan -0.71828 1.26239 -0.569
## country_feTanzania -16.41138 5868.90883 -0.003
## country_feThe Gambia -17.78995 3472.78674 -0.005
## country_feTogo -1.22424 1.32010 -0.927
## country_feTürkiye -17.53361 3561.84162 -0.005
## country_feTurkmenistan -1.44172 1.67255 -0.862
## country_feUganda -0.69797 1.18550 -0.589
## country_feUzbekistan 0.58176 1.37132 0.424
## country_feVenezuela -0.02365 1.45060 -0.016
## country_feZambia -16.62237 4514.57122 -0.004
## country_feZimbabwe -1.16313 1.37587 -0.845
## jud_ind:exec_corrupt_index 2.65092 1.65771 1.599
## Pr(>|z|)
## (Intercept) 0.00875 **
## jud_ind 0.14870
## exec_corrupt_index 0.00453 **
## country_feAlgeria 0.70277
## country_feAngola 0.99551
## country_feArmenia 0.74256
## country_feAzerbaijan 0.34878
## country_feBangladesh 0.48131
## country_feBelarus 0.70418
## country_feBurkina Faso 0.51063
## country_feCambodia 0.51953
## country_feCameroon 0.34524
## country_feCentral African Republic 0.51344
## country_feCroatia 0.99630
## country_feDemocratic Republic of the Congo 0.92656
## country_feDjibouti 0.80048
## country_feEgypt 0.07175 .
## country_feEquatorial Guinea 0.46695
## country_feEthiopia 0.99640
## country_feGabon 0.54954
## country_feGeorgia 0.81108
## country_feGhana 0.26682
## country_feGuinea 0.19371
## country_feGuinea-Bissau 0.54099
## country_feGuyana 0.99623
## country_feHaiti 0.77044
## country_feIvory Coast 0.30660
## country_feKazakhstan 0.60463
## country_feKenya 0.99587
## country_feKyrgyzstan 0.44412
## country_feLesotho 0.99713
## country_feMadagascar 0.39204
## country_feMalaysia 0.24996
## country_feMauritania 0.74090
## country_feMexico 0.99654
## country_feMozambique 0.99652
## country_feNamibia 0.99843
## country_feNicaragua 0.99567
## country_feNiger 0.59138
## country_feNigeria 0.09136 .
## country_fePanama 0.99698
## country_feParaguay 0.99526
## country_fePeru 0.99609
## country_fePhilippines 0.99612
## country_feRussia 0.42918
## country_feRwanda 0.25232
## country_feSenegal 0.71637
## country_feSerbia 0.44250
## country_feSierra Leone 0.61742
## country_feSingapore 0.99733
## country_feSouth Korea 0.99750
## country_feSri Lanka 0.60701
## country_feTaiwan 0.99756
## country_feTajikistan 0.56937
## country_feTanzania 0.99777
## country_feThe Gambia 0.99591
## country_feTogo 0.35372
## country_feTürkiye 0.99607
## country_feTurkmenistan 0.38869
## country_feUganda 0.55602
## country_feUzbekistan 0.67140
## country_feVenezuela 0.98699
## country_feZambia 0.99706
## country_feZimbabwe 0.39790
## jud_ind:exec_corrupt_index 0.10979
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.3870 1.0911 1.271 0.204
## jud_ind -0.3326 0.8789 -0.378 0.705
## exec_corrupt_index 0.3816 1.4475 0.264 0.792
## jud_ind:exec_corrupt_index 0.7202 1.1625 0.620 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 44
## Log-likelihood: -448.6 on 69 Dfsummary(zip_models$n_coopt_zip2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.302e-01 -2.760e-01 -1.706e-01 -3.328e-05 5.998e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -1.71848 1.94713 -0.883
## jud_ind -0.90841 1.34476 -0.676
## exec_corrupt_index 4.17616 1.98822 2.100
## polyarchy -4.83014 2.68863 -1.797
## country_feAlgeria 1.22375 1.02788 1.191
## country_feAngola -18.68220 4001.26627 -0.005
## country_feArmenia 1.05974 0.88104 1.203
## country_feAzerbaijan -0.93669 1.18527 -0.790
## country_feBangladesh -0.60434 1.06379 -0.568
## country_feBelarus 0.26481 1.03760 0.255
## country_feBurkina Faso 1.58557 0.92508 1.714
## country_feCambodia -0.14446 1.07838 -0.134
## country_feCameroon -0.66290 1.20524 -0.550
## country_feCentral African Republic -0.25219 1.18634 -0.213
## country_feCroatia -16.53232 4329.00029 -0.004
## country_feDemocratic Republic of the Congo 0.40584 0.96844 0.419
## country_feDjibouti -0.17616 1.06715 -0.165
## country_feEgypt 0.92315 1.29852 0.711
## country_feEquatorial Guinea -0.80817 1.28713 -0.628
## country_feEthiopia -17.62331 4696.01539 -0.004
## country_feGabon -0.36872 0.89545 -0.412
## country_feGeorgia 0.31432 0.89052 0.353
## country_feGhana -1.49599 1.28264 -1.166
## country_feGuinea -1.54161 1.40252 -1.099
## country_feGuinea-Bissau -0.07786 0.97383 -0.080
## country_feGuyana -17.41243 4837.64558 -0.004
## country_feHaiti 1.11871 0.94574 1.183
## country_feIvory Coast -1.13965 1.35229 -0.843
## country_feKazakhstan 1.22904 1.05816 1.161
## country_feKenya -18.01212 4525.35882 -0.004
## country_feKyrgyzstan 1.49290 0.88115 1.694
## country_feLesotho -17.26255 4874.19132 -0.004
## country_feMadagascar -0.59938 1.35631 -0.442
## country_feMalaysia 1.37205 0.77804 1.763
## country_feMauritania 1.14445 1.04022 1.100
## country_feMexico -16.87190 5258.50553 -0.003
## country_feMozambique -17.60943 5038.86763 -0.003
## country_feNamibia -16.05354 8621.77542 -0.002
## country_feNicaragua -16.98721 4183.61214 -0.004
## country_feNiger 0.90214 0.91625 0.985
## country_feNigeria -2.06450 1.35188 -1.527
## country_fePanama -16.26807 5945.87396 -0.003
## country_feParaguay -17.87780 4459.12889 -0.004
## country_fePeru -16.93496 4166.44228 -0.004
## country_fePhilippines -17.56476 4935.43365 -0.004
## country_feRussia 1.68727 0.96990 1.740
## country_feRwanda 1.29063 1.17239 1.101
## country_feSenegal 1.62579 1.47746 1.100
## country_feSerbia -0.57272 1.11062 -0.516
## country_feSierra Leone -0.06225 1.17626 -0.053
## country_feSingapore -16.06231 5739.94013 -0.003
## country_feSouth Korea -15.64802 6695.73365 -0.002
## country_feSri Lanka 1.05804 0.92529 1.143
## country_feTaiwan -15.95335 5580.27961 -0.003
## country_feTajikistan -0.51761 1.06534 -0.486
## country_feTanzania -16.87334 6515.66808 -0.003
## country_feThe Gambia -18.12821 4363.59856 -0.004
## country_feTogo -0.49681 1.15121 -0.432
## country_feTürkiye -17.69827 4544.00717 -0.004
## country_feTurkmenistan -1.26167 1.59124 -0.793
## country_feUganda -0.52745 0.95880 -0.550
## country_feUzbekistan 0.78361 1.24838 0.628
## country_feVenezuela 1.19271 1.44593 0.825
## country_feZambia -16.91098 5587.58161 -0.003
## country_feZimbabwe -1.10078 1.15473 -0.953
## jud_ind:exec_corrupt_index 1.82818 1.76911 1.033
## Pr(>|z|)
## (Intercept) 0.3775
## jud_ind 0.4993
## exec_corrupt_index 0.0357 *
## polyarchy 0.0724 .
## country_feAlgeria 0.2338
## country_feAngola 0.9963
## country_feArmenia 0.2290
## country_feAzerbaijan 0.4294
## country_feBangladesh 0.5700
## country_feBelarus 0.7986
## country_feBurkina Faso 0.0865 .
## country_feCambodia 0.8934
## country_feCameroon 0.5823
## country_feCentral African Republic 0.8317
## country_feCroatia 0.9970
## country_feDemocratic Republic of the Congo 0.6752
## country_feDjibouti 0.8689
## country_feEgypt 0.4771
## country_feEquatorial Guinea 0.5301
## country_feEthiopia 0.9970
## country_feGabon 0.6805
## country_feGeorgia 0.7241
## country_feGhana 0.2435
## country_feGuinea 0.2717
## country_feGuinea-Bissau 0.9363
## country_feGuyana 0.9971
## country_feHaiti 0.2369
## country_feIvory Coast 0.3994
## country_feKazakhstan 0.2454
## country_feKenya 0.9968
## country_feKyrgyzstan 0.0902 .
## country_feLesotho 0.9972
## country_feMadagascar 0.6585
## country_feMalaysia 0.0778 .
## country_feMauritania 0.2712
## country_feMexico 0.9974
## country_feMozambique 0.9972
## country_feNamibia 0.9985
## country_feNicaragua 0.9968
## country_feNiger 0.3248
## country_feNigeria 0.1267
## country_fePanama 0.9978
## country_feParaguay 0.9968
## country_fePeru 0.9968
## country_fePhilippines 0.9972
## country_feRussia 0.0819 .
## country_feRwanda 0.2710
## country_feSenegal 0.2712
## country_feSerbia 0.6061
## country_feSierra Leone 0.9578
## country_feSingapore 0.9978
## country_feSouth Korea 0.9981
## country_feSri Lanka 0.2528
## country_feTaiwan 0.9977
## country_feTajikistan 0.6271
## country_feTanzania 0.9979
## country_feThe Gambia 0.9967
## country_feTogo 0.6661
## country_feTürkiye 0.9969
## country_feTurkmenistan 0.4278
## country_feUganda 0.5822
## country_feUzbekistan 0.5302
## country_feVenezuela 0.4094
## country_feZambia 0.9976
## country_feZimbabwe 0.3404
## jud_ind:exec_corrupt_index 0.3014
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7022 1.2341 1.379 0.168
## jud_ind -0.2127 0.9064 -0.235 0.814
## exec_corrupt_index 0.3570 1.5025 0.238 0.812
## polyarchy -1.1846 1.9097 -0.620 0.535
## jud_ind:exec_corrupt_index 0.5066 1.1860 0.427 0.669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 48
## Log-likelihood: -446.7 on 71 Df# Cabinet
summary(zip_models$n_cabinet_zip0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.726e-01 -2.038e-01 -4.445e-05 -1.506e-05 6.855e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) 1.592e+00 2.428e+00 0.656
## jud_ind 1.494e+00 4.311e-01 3.465
## exec_corrupt_index -5.585e+00 3.141e+00 -1.778
## country_feAlgeria 3.054e+00 1.642e+00 1.860
## country_feAngola -1.821e+01 2.292e+04 -0.001
## country_feArmenia 3.978e+00 1.344e+00 2.959
## country_feAzerbaijan -1.820e+01 2.771e+04 -0.001
## country_feBangladesh 2.026e+00 1.446e+00 1.401
## country_feBelarus -1.760e+01 1.474e+04 -0.001
## country_feBurkina Faso 2.194e+00 1.372e+00 1.600
## country_feCambodia 3.652e+00 1.685e+00 2.167
## country_feCameroon -1.829e+01 3.939e+04 0.000
## country_feCentral African Republic -1.789e+01 2.041e+04 -0.001
## country_feCroatia -1.699e+01 5.256e+03 -0.003
## country_feDemocratic Republic of the Congo 2.499e+00 1.582e+00 1.580
## country_feDjibouti -1.770e+01 1.458e+04 -0.001
## country_feEgypt -1.789e+01 9.759e+03 -0.002
## country_feEquatorial Guinea -1.832e+01 5.190e+04 0.000
## country_feEthiopia -1.698e+01 7.277e+03 -0.002
## country_feGabon 2.650e+00 1.306e+00 2.029
## country_feGeorgia 2.061e+00 1.728e+00 1.193
## country_feGhana -1.792e+01 8.606e+03 -0.002
## country_feGuinea 2.691e+00 1.883e+00 1.429
## country_feGuinea-Bissau 2.730e+00 1.399e+00 1.952
## country_feGuyana -1.753e+01 7.532e+03 -0.002
## country_feHaiti 3.369e+00 1.299e+00 2.592
## country_feIvory Coast 3.885e-01 1.528e+00 0.254
## country_feKazakhstan -1.818e+01 4.825e+04 0.000
## country_feKenya -1.791e+01 1.163e+04 -0.002
## country_feKyrgyzstan 4.130e+00 1.394e+00 2.964
## country_feLesotho -1.717e+01 5.570e+03 -0.003
## country_feMadagascar -1.741e+01 1.132e+04 -0.002
## country_feMalaysia 3.617e+00 1.202e+00 3.009
## country_feMauritania 3.254e+00 1.611e+00 2.020
## country_feMexico -1.727e+01 6.399e+03 -0.003
## country_feMozambique -1.743e+01 7.905e+03 -0.002
## country_feNamibia -1.671e+01 3.386e+03 -0.005
## country_feNicaragua -1.769e+01 1.548e+04 -0.001
## country_feNiger 8.744e-01 1.714e+00 0.510
## country_feNigeria 8.884e-01 1.635e+00 0.543
## country_fePanama -1.711e+01 6.318e+03 -0.003
## country_feParaguay -1.818e+01 1.419e+04 -0.001
## country_fePeru -1.723e+01 5.207e+03 -0.003
## country_fePhilippines -1.770e+01 8.998e+03 -0.002
## country_feRussia 1.697e+00 1.706e+00 0.995
## country_feRwanda 2.391e+00 1.750e+00 1.367
## country_feSenegal -6.482e-02 2.062e+00 -0.031
## country_feSerbia 3.072e+00 2.134e+00 1.439
## country_feSierra Leone 2.253e+00 1.521e+00 1.481
## country_feSingapore -1.572e+01 2.699e+03 -0.006
## country_feSouth Korea -1.661e+01 3.484e+03 -0.005
## country_feSri Lanka 3.147e+00 1.279e+00 2.461
## country_feTaiwan -1.648e+01 3.131e+03 -0.005
## country_feTajikistan -1.828e+01 3.413e+04 -0.001
## country_feTanzania -1.681e+01 4.326e+03 -0.004
## country_feThe Gambia -1.773e+01 1.138e+04 -0.002
## country_feTogo 2.426e+00 1.530e+00 1.586
## country_feTürkiye -1.762e+01 6.887e+03 -0.003
## country_feTurkmenistan -1.821e+01 8.160e+04 0.000
## country_feUganda 1.745e+00 1.306e+00 1.336
## country_feUzbekistan 4.046e+00 2.162e+00 1.871
## country_feVenezuela 3.780e+00 2.127e+00 1.777
## country_feZambia -1.687e+01 4.781e+03 -0.004
## country_feZimbabwe 3.442e+00 1.905e+00 1.807
## Pr(>|z|)
## (Intercept) 0.51191
## jud_ind 0.00053 ***
## exec_corrupt_index 0.07540 .
## country_feAlgeria 0.06293 .
## country_feAngola 0.99937
## country_feArmenia 0.00308 **
## country_feAzerbaijan 0.99948
## country_feBangladesh 0.16121
## country_feBelarus 0.99905
## country_feBurkina Faso 0.10967
## country_feCambodia 0.03026 *
## country_feCameroon 0.99963
## country_feCentral African Republic 0.99930
## country_feCroatia 0.99742
## country_feDemocratic Republic of the Congo 0.11417
## country_feDjibouti 0.99903
## country_feEgypt 0.99854
## country_feEquatorial Guinea 0.99972
## country_feEthiopia 0.99814
## country_feGabon 0.04241 *
## country_feGeorgia 0.23302
## country_feGhana 0.99834
## country_feGuinea 0.15297
## country_feGuinea-Bissau 0.05097 .
## country_feGuyana 0.99814
## country_feHaiti 0.00953 **
## country_feIvory Coast 0.79932
## country_feKazakhstan 0.99970
## country_feKenya 0.99877
## country_feKyrgyzstan 0.00304 **
## country_feLesotho 0.99754
## country_feMadagascar 0.99877
## country_feMalaysia 0.00262 **
## country_feMauritania 0.04340 *
## country_feMexico 0.99785
## country_feMozambique 0.99824
## country_feNamibia 0.99606
## country_feNicaragua 0.99909
## country_feNiger 0.60996
## country_feNigeria 0.58697
## country_fePanama 0.99784
## country_feParaguay 0.99898
## country_fePeru 0.99736
## country_fePhilippines 0.99843
## country_feRussia 0.31984
## country_feRwanda 0.17167
## country_feSenegal 0.97492
## country_feSerbia 0.15007
## country_feSierra Leone 0.13856
## country_feSingapore 0.99535
## country_feSouth Korea 0.99620
## country_feSri Lanka 0.01384 *
## country_feTaiwan 0.99580
## country_feTajikistan 0.99957
## country_feTanzania 0.99690
## country_feThe Gambia 0.99876
## country_feTogo 0.11285
## country_feTürkiye 0.99796
## country_feTurkmenistan 0.99982
## country_feUganda 0.18152
## country_feUzbekistan 0.06130 .
## country_feVenezuela 0.07559 .
## country_feZambia 0.99718
## country_feZimbabwe 0.07073 .
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.8340 0.9377 5.155 2.53e-07 ***
## jud_ind 1.0959 0.3602 3.042 0.00235 **
## exec_corrupt_index -4.2341 1.3758 -3.077 0.00209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 38
## Log-likelihood: -246.1 on 67 Dfsummary(zip_models$n_cabinet_zip1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.444e-01 -2.010e-01 -3.001e-05 -1.851e-05 6.762e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -6.6262 3.8607 -1.716
## jud_ind -4.4098 3.4990 -1.260
## exec_corrupt_index 6.5851 4.8291 1.364
## country_feAlgeria 1.2774 1.7059 0.749
## country_feAngola -17.7874 7634.0825 -0.002
## country_feArmenia 2.2118 1.3631 1.623
## country_feAzerbaijan -17.2758 8834.3841 -0.002
## country_feBangladesh 1.1674 1.5225 0.767
## country_feBelarus -17.5742 8139.7598 -0.002
## country_feBurkina Faso 2.0926 1.4389 1.454
## country_feCambodia 1.5641 1.6212 0.965
## country_feCameroon -17.2769 9243.0564 -0.002
## country_feCentral African Republic -17.4967 8464.6359 -0.002
## country_feCroatia -16.3217 7428.6010 -0.002
## country_feDemocratic Republic of the Congo 0.3149 1.5264 0.206
## country_feDjibouti -17.5999 8045.9571 -0.002
## country_feEgypt -17.9379 6265.1042 -0.003
## country_feEquatorial Guinea -16.9163 10084.3460 -0.002
## country_feEthiopia -17.1287 8524.6844 -0.002
## country_feGabon 1.0401 1.3459 0.773
## country_feGeorgia -0.1088 1.6709 -0.065
## country_feGhana -17.8054 6640.6166 -0.003
## country_feGuinea 0.8407 1.8563 0.453
## country_feGuinea-Bissau 0.6931 1.4019 0.494
## country_feGuyana -17.0694 7334.8176 -0.002
## country_feHaiti 2.2991 1.3628 1.687
## country_feIvory Coast 0.1586 1.6982 0.093
## country_feKazakhstan -16.9145 10148.7529 -0.002
## country_feKenya -17.6794 7523.4381 -0.002
## country_feKyrgyzstan 2.8148 1.4027 2.007
## country_feLesotho -15.9644 8925.9594 -0.002
## country_feMadagascar -17.4306 7777.5546 -0.002
## country_feMalaysia 3.1678 1.2951 2.446
## country_feMauritania 1.3480 1.6925 0.796
## country_feMexico -16.5160 7958.8837 -0.002
## country_feMozambique -16.9274 8377.2211 -0.002
## country_feNamibia -13.4150 14664.9906 -0.001
## country_feNicaragua -17.3160 8382.0025 -0.002
## country_feNiger 0.9288 1.6477 0.564
## country_feNigeria -1.3051 1.7063 -0.765
## country_fePanama -16.3668 9483.2419 -0.002
## country_feParaguay -18.2521 6507.2393 -0.003
## country_fePeru -16.4072 6586.9855 -0.002
## country_fePhilippines -17.3899 7368.3895 -0.002
## country_feRussia 0.2474 1.7817 0.139
## country_feRwanda 2.5161 1.7404 1.446
## country_feSenegal 2.3698 1.9131 1.239
## country_feSerbia 0.8358 1.7127 0.488
## country_feSierra Leone 1.2221 1.5691 0.779
## country_feSingapore -16.0136 10207.8011 -0.002
## country_feSouth Korea -14.8055 8355.6370 -0.002
## country_feSri Lanka 2.0469 1.3788 1.485
## country_feTaiwan -14.9413 8512.6382 -0.002
## country_feTajikistan -17.2555 8985.2490 -0.002
## country_feTanzania -14.9425 11861.5476 -0.001
## country_feThe Gambia -17.4449 7842.5951 -0.002
## country_feTogo 0.9416 1.5460 0.609
## country_feTürkiye -17.0701 6409.8675 -0.003
## country_feTurkmenistan -16.3472 12994.6079 -0.001
## country_feUganda 1.0535 1.3688 0.770
## country_feUzbekistan 2.1937 2.2049 0.995
## country_feVenezuela 2.1885 2.6514 0.825
## country_feZambia -15.7682 8954.8135 -0.002
## country_feZimbabwe 0.5735 1.5747 0.364
## jud_ind:exec_corrupt_index 6.6930 4.3722 1.531
## Pr(>|z|)
## (Intercept) 0.0861 .
## jud_ind 0.2076
## exec_corrupt_index 0.1727
## country_feAlgeria 0.4540
## country_feAngola 0.9981
## country_feArmenia 0.1047
## country_feAzerbaijan 0.9984
## country_feBangladesh 0.4432
## country_feBelarus 0.9983
## country_feBurkina Faso 0.1459
## country_feCambodia 0.3346
## country_feCameroon 0.9985
## country_feCentral African Republic 0.9984
## country_feCroatia 0.9982
## country_feDemocratic Republic of the Congo 0.8366
## country_feDjibouti 0.9983
## country_feEgypt 0.9977
## country_feEquatorial Guinea 0.9987
## country_feEthiopia 0.9984
## country_feGabon 0.4397
## country_feGeorgia 0.9481
## country_feGhana 0.9979
## country_feGuinea 0.6506
## country_feGuinea-Bissau 0.6210
## country_feGuyana 0.9981
## country_feHaiti 0.0916 .
## country_feIvory Coast 0.9256
## country_feKazakhstan 0.9987
## country_feKenya 0.9981
## country_feKyrgyzstan 0.0448 *
## country_feLesotho 0.9986
## country_feMadagascar 0.9982
## country_feMalaysia 0.0144 *
## country_feMauritania 0.4257
## country_feMexico 0.9983
## country_feMozambique 0.9984
## country_feNamibia 0.9993
## country_feNicaragua 0.9984
## country_feNiger 0.5730
## country_feNigeria 0.4443
## country_fePanama 0.9986
## country_feParaguay 0.9978
## country_fePeru 0.9980
## country_fePhilippines 0.9981
## country_feRussia 0.8896
## country_feRwanda 0.1483
## country_feSenegal 0.2155
## country_feSerbia 0.6255
## country_feSierra Leone 0.4360
## country_feSingapore 0.9987
## country_feSouth Korea 0.9986
## country_feSri Lanka 0.1377
## country_feTaiwan 0.9986
## country_feTajikistan 0.9985
## country_feTanzania 0.9990
## country_feThe Gambia 0.9982
## country_feTogo 0.5425
## country_feTürkiye 0.9979
## country_feTurkmenistan 0.9990
## country_feUganda 0.4415
## country_feUzbekistan 0.3198
## country_feVenezuela 0.4091
## country_feZambia 0.9986
## country_feZimbabwe 0.7157
## jud_ind:exec_corrupt_index 0.1258
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7948 3.7396 0.213 0.832
## jud_ind -1.1359 3.1877 -0.356 0.722
## exec_corrupt_index 1.0052 4.7478 0.212 0.832
## jud_ind:exec_corrupt_index 1.9792 3.9078 0.506 0.613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 44
## Log-likelihood: -245.9 on 69 Dfsummary(zip_models$n_cabinet_zip2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.315e-01 -1.980e-01 -4.953e-05 -2.205e-05 7.608e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -2.738e+00 2.794e+00 -0.980
## jud_ind -5.764e+00 2.304e+00 -2.502
## exec_corrupt_index 7.203e+00 2.537e+00 2.839
## polyarchy -1.142e+01 3.793e+00 -3.011
## country_feAlgeria -2.179e-02 1.865e+00 -0.012
## country_feAngola -1.814e+01 4.594e+03 -0.004
## country_feArmenia 1.708e+00 1.276e+00 1.339
## country_feAzerbaijan -1.740e+01 4.687e+03 -0.004
## country_feBangladesh 6.469e-02 1.481e+00 0.044
## country_feBelarus -1.757e+01 4.126e+03 -0.004
## country_feBurkina Faso 2.282e+00 1.308e+00 1.745
## country_feCambodia 9.756e-01 1.579e+00 0.618
## country_feCameroon -1.714e+01 5.389e+03 -0.003
## country_feCentral African Republic -1.746e+01 4.868e+03 -0.004
## country_feCroatia -1.619e+01 4.565e+03 -0.004
## country_feDemocratic Republic of the Congo -4.799e-01 1.459e+00 -0.329
## country_feDjibouti -1.790e+01 4.143e+03 -0.004
## country_feEgypt -1.863e+01 4.720e+03 -0.004
## country_feEquatorial Guinea -1.701e+01 4.676e+03 -0.004
## country_feEthiopia -1.765e+01 3.352e+03 -0.005
## country_feGabon 8.869e-02 1.283e+00 0.069
## country_feGeorgia -3.272e-01 1.585e+00 -0.206
## country_feGhana -1.771e+01 6.309e+03 -0.003
## country_feGuinea -3.336e-02 1.926e+00 -0.017
## country_feGuinea-Bissau 5.839e-01 1.242e+00 0.470
## country_feGuyana -1.700e+01 7.534e+03 -0.002
## country_feHaiti 1.961e+00 1.281e+00 1.531
## country_feIvory Coast -3.592e-01 1.574e+00 -0.228
## country_feKazakhstan -1.680e+01 4.868e+03 -0.003
## country_feKenya -1.770e+01 6.049e+03 -0.003
## country_feKyrgyzstan 2.448e+00 1.376e+00 1.778
## country_feLesotho -1.632e+01 4.967e+03 -0.003
## country_feMadagascar -1.728e+01 4.244e+03 -0.004
## country_feMalaysia 1.999e+00 1.281e+00 1.560
## country_feMauritania -6.021e-02 1.934e+00 -0.031
## country_feMexico -1.627e+01 7.682e+03 -0.002
## country_feMozambique -1.709e+01 5.945e+03 -0.003
## country_feNamibia -1.328e+01 7.017e+04 0.000
## country_feNicaragua -1.677e+01 6.655e+03 -0.003
## country_feNiger 4.228e-01 1.559e+00 0.271
## country_feNigeria -1.202e+00 1.644e+00 -0.731
## country_fePanama -1.576e+01 2.528e+04 -0.001
## country_feParaguay -1.788e+01 8.276e+03 -0.002
## country_fePeru -1.640e+01 4.391e+03 -0.004
## country_fePhilippines -1.725e+01 8.397e+03 -0.002
## country_feRussia -3.690e-01 1.944e+00 -0.190
## country_feRwanda -3.301e-01 2.144e+00 -0.154
## country_feSenegal 3.821e+00 1.953e+00 1.956
## country_feSerbia 4.204e-02 1.707e+00 0.025
## country_feSierra Leone 8.413e-01 1.502e+00 0.560
## country_feSingapore -1.643e+01 5.361e+03 -0.003
## country_feSouth Korea -1.433e+01 1.982e+04 -0.001
## country_feSri Lanka 1.861e+00 1.269e+00 1.467
## country_feTaiwan -1.514e+01 3.915e+03 -0.004
## country_feTajikistan -1.738e+01 4.671e+03 -0.004
## country_feTanzania -1.524e+01 1.572e+04 -0.001
## country_feThe Gambia -1.775e+01 4.328e+03 -0.004
## country_feTogo 4.696e-01 1.515e+00 0.310
## country_feTürkiye -1.722e+01 5.915e+03 -0.003
## country_feTurkmenistan -1.640e+01 4.252e+03 -0.004
## country_feUganda 2.524e-02 1.351e+00 0.019
## country_feUzbekistan 2.976e-01 2.403e+00 0.124
## country_feVenezuela 1.692e+00 2.019e+00 0.838
## country_feZambia -1.594e+01 8.799e+03 -0.002
## country_feZimbabwe -7.121e-01 1.469e+00 -0.485
## jud_ind:exec_corrupt_index 7.869e+00 2.952e+00 2.666
## Pr(>|z|)
## (Intercept) 0.32711
## jud_ind 0.01235 *
## exec_corrupt_index 0.00452 **
## polyarchy 0.00260 **
## country_feAlgeria 0.99068
## country_feAngola 0.99685
## country_feArmenia 0.18060
## country_feAzerbaijan 0.99704
## country_feBangladesh 0.96517
## country_feBelarus 0.99660
## country_feBurkina Faso 0.08106 .
## country_feCambodia 0.53666
## country_feCameroon 0.99746
## country_feCentral African Republic 0.99714
## country_feCroatia 0.99717
## country_feDemocratic Republic of the Congo 0.74216
## country_feDjibouti 0.99655
## country_feEgypt 0.99685
## country_feEquatorial Guinea 0.99710
## country_feEthiopia 0.99580
## country_feGabon 0.94491
## country_feGeorgia 0.83648
## country_feGhana 0.99776
## country_feGuinea 0.98618
## country_feGuinea-Bissau 0.63826
## country_feGuyana 0.99820
## country_feHaiti 0.12581
## country_feIvory Coast 0.81953
## country_feKazakhstan 0.99725
## country_feKenya 0.99767
## country_feKyrgyzstan 0.07534 .
## country_feLesotho 0.99738
## country_feMadagascar 0.99675
## country_feMalaysia 0.11876
## country_feMauritania 0.97516
## country_feMexico 0.99831
## country_feMozambique 0.99771
## country_feNamibia 0.99985
## country_feNicaragua 0.99799
## country_feNiger 0.78623
## country_feNigeria 0.46475
## country_fePanama 0.99950
## country_feParaguay 0.99828
## country_fePeru 0.99702
## country_fePhilippines 0.99836
## country_feRussia 0.84943
## country_feRwanda 0.87766
## country_feSenegal 0.05044 .
## country_feSerbia 0.98036
## country_feSierra Leone 0.57550
## country_feSingapore 0.99755
## country_feSouth Korea 0.99942
## country_feSri Lanka 0.14237
## country_feTaiwan 0.99692
## country_feTajikistan 0.99703
## country_feTanzania 0.99923
## country_feThe Gambia 0.99673
## country_feTogo 0.75654
## country_feTürkiye 0.99768
## country_feTurkmenistan 0.99692
## country_feUganda 0.98510
## country_feUzbekistan 0.90142
## country_feVenezuela 0.40195
## country_feZambia 0.99855
## country_feZimbabwe 0.62782
## jud_ind:exec_corrupt_index 0.00769 **
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.143 2.571 -0.444 0.6567
## jud_ind -3.668 2.340 -1.568 0.1169
## exec_corrupt_index 6.291 3.691 1.704 0.0883 .
## polyarchy -7.524 4.620 -1.629 0.1034
## jud_ind:exec_corrupt_index 4.701 2.935 1.602 0.1092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 57
## Log-likelihood: -243.6 on 71 Df# Electoral support
summary(zip_models$n_elec_zip0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.267e-01 -2.007e-05 -1.625e-05 -1.288e-05 5.673e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -2.491e+01 1.254e+04 -0.002
## jud_ind 2.238e-01 3.324e-01 0.673
## exec_corrupt_index 6.459e+00 2.952e+00 2.188
## country_feAlgeria 1.951e+01 1.254e+04 0.002
## country_feAngola -3.206e-01 1.646e+04 0.000
## country_feArmenia 6.576e-03 1.696e+04 0.000
## country_feAzerbaijan -3.648e-01 1.615e+04 0.000
## country_feBangladesh -5.314e-02 1.712e+04 0.000
## country_feBelarus 1.956e+01 1.254e+04 0.002
## country_feBurkina Faso 1.988e+01 1.254e+04 0.002
## country_feCambodia -4.315e-01 1.622e+04 0.000
## country_feCameroon 1.796e+01 1.254e+04 0.001
## country_feCentral African Republic 1.920e+01 1.254e+04 0.002
## country_feCroatia 4.719e-01 1.859e+04 0.000
## country_feDemocratic Republic of the Congo -3.943e-01 1.652e+04 0.000
## country_feDjibouti 1.870e+01 1.254e+04 0.001
## country_feEgypt 2.199e+01 1.254e+04 0.002
## country_feEquatorial Guinea 1.875e+01 1.254e+04 0.001
## country_feEthiopia 4.047e-01 1.910e+04 0.000
## country_feGabon -1.312e-01 1.716e+04 0.000
## country_feGeorgia 1.891e+01 1.254e+04 0.002
## country_feGhana 1.890e+01 1.254e+04 0.002
## country_feGuinea -4.170e-01 1.614e+04 0.000
## country_feGuinea-Bissau 1.837e+01 1.254e+04 0.001
## country_feGuyana 2.001e-01 1.810e+04 0.000
## country_feHaiti -1.362e-01 1.713e+04 0.000
## country_feIvory Coast 2.896e-03 1.762e+04 0.000
## country_feKazakhstan -4.062e-01 1.594e+04 0.000
## country_feKenya -6.499e-02 1.724e+04 0.000
## country_feKyrgyzstan -2.180e-01 1.660e+04 0.000
## country_feLesotho 4.235e-01 1.896e+04 0.000
## country_feMadagascar 1.136e-01 1.720e+04 0.000
## country_feMalaysia 2.152e+01 1.254e+04 0.002
## country_feMauritania -4.918e-02 1.697e+04 0.000
## country_feMexico 3.322e-01 1.831e+04 0.000
## country_feMozambique 2.267e-01 1.810e+04 0.000
## country_feNamibia 7.456e-01 2.082e+04 0.000
## country_feNicaragua -6.997e-02 1.660e+04 0.000
## country_feNiger 1.979e-01 1.798e+04 0.000
## country_feNigeria -2.808e-01 1.716e+04 0.000
## country_fePanama 4.031e-01 1.888e+04 0.000
## country_feParaguay -2.091e-01 1.708e+04 0.000
## country_fePeru 3.802e-01 1.803e+04 0.000
## country_fePhilippines 9.292e-02 1.782e+04 0.000
## country_feRussia 2.016e+01 1.254e+04 0.002
## country_feRwanda 5.114e-01 2.020e+04 0.000
## country_feSenegal 7.506e-01 2.227e+04 0.000
## country_feSerbia 4.498e-02 1.711e+04 0.000
## country_feSierra Leone -5.306e-02 1.698e+04 0.000
## country_feSingapore 1.139e+00 3.182e+04 0.000
## country_feSouth Korea 7.589e-01 2.064e+04 0.000
## country_feSri Lanka 1.946e+01 1.254e+04 0.002
## country_feTaiwan 8.205e-01 2.158e+04 0.000
## country_feTajikistan -4.133e-01 1.607e+04 0.000
## country_feTanzania 6.261e-01 2.008e+04 0.000
## country_feThe Gambia -2.917e-03 1.714e+04 0.000
## country_feTogo -2.360e-01 1.657e+04 0.000
## country_feTürkiye 1.999e-01 1.821e+04 0.000
## country_feTurkmenistan -4.714e-01 1.571e+04 0.000
## country_feUganda 8.764e-02 1.763e+04 0.000
## country_feUzbekistan -4.441e-01 1.578e+04 0.000
## country_feVenezuela 1.935e+01 1.254e+04 0.002
## country_feZambia 5.675e-01 1.950e+04 0.000
## country_feZimbabwe -5.873e-02 1.740e+04 0.000
## Pr(>|z|)
## (Intercept) 0.9984
## jud_ind 0.5008
## exec_corrupt_index 0.0287 *
## country_feAlgeria 0.9988
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9988
## country_feBurkina Faso 0.9987
## country_feCambodia 1.0000
## country_feCameroon 0.9989
## country_feCentral African Republic 0.9988
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9988
## country_feEgypt 0.9986
## country_feEquatorial Guinea 0.9988
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9988
## country_feGhana 0.9988
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9988
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9986
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9987
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 1.0000
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9988
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9988
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5959 2.3598 -0.676 0.499
## jud_ind 0.4389 0.3246 1.352 0.176
## exec_corrupt_index 4.6185 3.2122 1.438 0.150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 27
## Log-likelihood: -119.5 on 67 Dfsummary(zip_models$n_elec_zip1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.502e-01 -3.699e-05 -1.448e-05 -1.171e-05 6.823e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -1.961e+01 1.572e+04 -0.001
## jud_ind 4.658e+00 2.211e+00 2.107
## exec_corrupt_index -2.163e+00 4.296e+00 -0.503
## country_feAlgeria 2.052e+01 1.572e+04 0.001
## country_feAngola -4.740e-01 2.153e+04 0.000
## country_feArmenia -3.585e-02 2.150e+04 0.000
## country_feAzerbaijan -6.481e-01 2.013e+04 0.000
## country_feBangladesh -1.190e-01 2.149e+04 0.000
## country_feBelarus 2.067e+01 1.572e+04 0.001
## country_feBurkina Faso 2.005e+01 1.572e+04 0.001
## country_feCambodia -6.767e-01 2.097e+04 0.000
## country_feCameroon 1.816e+01 1.572e+04 0.001
## country_feCentral African Republic 1.975e+01 1.572e+04 0.001
## country_feCroatia 2.544e-01 1.776e+04 0.000
## country_feDemocratic Republic of the Congo -5.225e-01 2.244e+04 0.000
## country_feDjibouti 1.935e+01 1.572e+04 0.001
## country_feEgypt 2.015e+01 1.572e+04 0.001
## country_feEquatorial Guinea 1.939e+01 1.572e+04 0.001
## country_feEthiopia 5.795e-01 2.222e+04 0.000
## country_feGabon -1.421e-01 2.293e+04 0.000
## country_feGeorgia 2.031e+01 1.572e+04 0.001
## country_feGhana 1.924e+01 1.572e+04 0.001
## country_feGuinea -6.990e-01 2.041e+04 0.000
## country_feGuinea-Bissau 1.928e+01 1.572e+04 0.001
## country_feGuyana 8.604e-02 1.950e+04 0.000
## country_feHaiti -1.688e-01 2.251e+04 0.000
## country_feIvory Coast 5.818e-03 2.230e+04 0.000
## country_feKazakhstan -7.657e-01 1.958e+04 0.000
## country_feKenya -1.154e-01 2.185e+04 0.000
## country_feKyrgyzstan -3.323e-01 2.161e+04 0.000
## country_feLesotho 1.679e-01 1.819e+04 0.000
## country_feMadagascar 1.250e-01 2.070e+04 0.000
## country_feMalaysia 2.219e+01 1.572e+04 0.001
## country_feMauritania 3.319e-02 2.344e+04 0.000
## country_feMexico 1.562e-01 1.868e+04 0.000
## country_feMozambique 1.616e-01 2.021e+04 0.000
## country_feNamibia 1.954e-02 1.652e+04 0.000
## country_feNicaragua -1.604e-01 2.075e+04 0.000
## country_feNiger 1.670e-01 2.069e+04 0.000
## country_feNigeria -2.388e-01 2.538e+04 0.000
## country_fePanama 3.227e-01 1.946e+04 0.000
## country_feParaguay -2.384e-01 2.299e+04 0.000
## country_fePeru -2.172e-02 1.689e+04 0.000
## country_fePhilippines 2.647e-02 2.076e+04 0.000
## country_feRussia 2.107e+01 1.572e+04 0.001
## country_feRwanda 8.126e-01 2.240e+04 0.000
## country_feSenegal 8.510e-01 1.975e+04 0.000
## country_feSerbia -7.827e-02 2.041e+04 0.000
## country_feSierra Leone -6.158e-02 2.217e+04 0.000
## country_feSingapore 1.523e+00 2.111e+04 0.000
## country_feSouth Korea 2.502e-01 1.659e+04 0.000
## country_feSri Lanka 1.777e+01 1.572e+04 0.001
## country_feTaiwan 2.148e-01 1.617e+04 0.000
## country_feTajikistan -7.344e-01 1.991e+04 0.000
## country_feTanzania 2.756e-01 1.750e+04 0.000
## country_feThe Gambia -6.667e-02 2.068e+04 0.000
## country_feTogo -3.549e-01 2.158e+04 0.000
## country_feTürkiye -1.808e-02 1.864e+04 0.000
## country_feTurkmenistan -9.810e-01 1.870e+04 0.000
## country_feUganda 9.216e-02 2.185e+04 0.000
## country_feUzbekistan -9.035e-01 1.887e+04 0.000
## country_feVenezuela 1.837e+01 1.572e+04 0.001
## country_feZambia 3.655e-01 1.816e+04 0.000
## country_feZimbabwe -1.008e-01 2.156e+04 0.000
## jud_ind:exec_corrupt_index -6.440e+00 3.077e+00 -2.093
## Pr(>|z|)
## (Intercept) 0.9990
## jud_ind 0.0352 *
## exec_corrupt_index 0.6147
## country_feAlgeria 0.9990
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9990
## country_feBurkina Faso 0.9990
## country_feCambodia 1.0000
## country_feCameroon 0.9991
## country_feCentral African Republic 0.9990
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9990
## country_feEgypt 0.9990
## country_feEquatorial Guinea 0.9990
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9990
## country_feGhana 0.9990
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9990
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9989
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9989
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 0.9999
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9991
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9991
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
## jud_ind:exec_corrupt_index 0.0364 *
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.199 2.340 1.367 0.1716
## jud_ind 2.613 1.342 1.946 0.0516 .
## exec_corrupt_index -2.714 3.536 -0.768 0.4427
## jud_ind:exec_corrupt_index -3.637 1.728 -2.105 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 34
## Log-likelihood: -119.1 on 69 Dfsummary(zip_models$n_elec_zip2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.896e-01 -2.385e-05 -1.488e-05 -1.115e-05 6.445e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -1.851e+01 1.563e+04 -0.001
## jud_ind 3.848e+00 1.972e+00 1.952
## exec_corrupt_index 2.139e-01 3.257e+00 0.066
## polyarchy -8.118e+00 3.922e+00 -2.070
## country_feAlgeria 2.054e+01 1.563e+04 0.001
## country_feAngola -7.810e-01 2.049e+04 0.000
## country_feArmenia 1.501e-01 2.041e+04 0.000
## country_feAzerbaijan -6.906e-01 1.958e+04 0.000
## country_feBangladesh -8.742e-02 2.100e+04 0.000
## country_feBelarus 2.071e+01 1.563e+04 0.001
## country_feBurkina Faso 2.058e+01 1.563e+04 0.001
## country_feCambodia -5.596e-01 1.927e+04 0.000
## country_feCameroon 1.799e+01 1.563e+04 0.001
## country_feCentral African Republic 1.969e+01 1.563e+04 0.001
## country_feCroatia 6.593e-01 2.006e+04 0.000
## country_feDemocratic Republic of the Congo -7.497e-01 2.044e+04 0.000
## country_feDjibouti 1.904e+01 1.563e+04 0.001
## country_feEgypt 1.918e+01 1.563e+04 0.001
## country_feEquatorial Guinea 1.867e+01 1.563e+04 0.001
## country_feEthiopia 1.180e-01 2.480e+04 0.000
## country_feGabon -2.871e-01 2.127e+04 0.000
## country_feGeorgia 1.998e+01 1.563e+04 0.001
## country_feGhana 1.926e+01 1.563e+04 0.001
## country_feGuinea -6.452e-01 1.928e+04 0.000
## country_feGuinea-Bissau 1.903e+01 1.563e+04 0.001
## country_feGuyana 1.900e-01 2.103e+04 0.000
## country_feHaiti -2.696e-01 2.132e+04 0.000
## country_feIvory Coast -1.033e-01 2.168e+04 0.000
## country_feKazakhstan -5.322e-01 1.902e+04 0.000
## country_feKenya -1.270e-01 2.117e+04 0.000
## country_feKyrgyzstan -2.295e-01 1.993e+04 0.000
## country_feLesotho -1.159e-01 2.024e+04 0.000
## country_feMadagascar 4.265e-01 2.040e+04 0.000
## country_feMalaysia 2.180e+01 1.563e+04 0.001
## country_feMauritania 3.314e-01 2.204e+04 0.000
## country_feMexico 4.675e-01 2.035e+04 0.000
## country_feMozambique -5.450e-02 2.149e+04 0.000
## country_feNamibia -3.075e-03 1.751e+04 0.000
## country_feNicaragua 4.386e-01 1.958e+04 0.000
## country_feNiger 2.857e-01 2.175e+04 0.000
## country_feNigeria -3.498e-01 2.231e+04 0.000
## country_fePanama 1.015e+00 2.301e+04 0.000
## country_feParaguay 2.031e-01 2.241e+04 0.000
## country_fePeru 2.128e-01 1.826e+04 0.000
## country_fePhilippines 1.941e-01 2.154e+04 0.000
## country_feRussia 2.106e+01 1.563e+04 0.001
## country_feRwanda 2.003e-01 2.620e+04 0.000
## country_feSenegal 1.417e+00 2.539e+04 0.000
## country_feSerbia -6.072e-03 2.026e+04 0.000
## country_feSierra Leone 9.623e-02 2.173e+04 0.000
## country_feSingapore 1.258e+00 2.882e+04 0.000
## country_feSouth Korea 7.838e-01 1.787e+04 0.000
## country_feSri Lanka 1.891e+01 1.563e+04 0.001
## country_feTaiwan 4.294e-01 1.715e+04 0.000
## country_feTajikistan -7.768e-01 1.931e+04 0.000
## country_feTanzania -9.668e-02 1.967e+04 0.000
## country_feThe Gambia -3.322e-01 2.110e+04 0.000
## country_feTogo -2.185e-01 2.007e+04 0.000
## country_feTürkiye -1.320e-01 1.982e+04 0.000
## country_feTurkmenistan -9.904e-01 1.946e+04 0.000
## country_feUganda -2.581e-01 2.174e+04 0.000
## country_feUzbekistan -8.233e-01 1.913e+04 0.000
## country_feVenezuela 1.962e+01 1.563e+04 0.001
## country_feZambia 1.498e-01 2.048e+04 0.000
## country_feZimbabwe -5.888e-01 2.136e+04 0.000
## jud_ind:exec_corrupt_index -5.081e+00 2.649e+00 -1.918
## Pr(>|z|)
## (Intercept) 0.9991
## jud_ind 0.0510 .
## exec_corrupt_index 0.9476
## polyarchy 0.0385 *
## country_feAlgeria 0.9990
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9989
## country_feBurkina Faso 0.9989
## country_feCambodia 1.0000
## country_feCameroon 0.9991
## country_feCentral African Republic 0.9990
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9990
## country_feEgypt 0.9990
## country_feEquatorial Guinea 0.9990
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9990
## country_feGhana 0.9990
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9990
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9989
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9989
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 1.0000
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9990
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9990
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
## jud_ind:exec_corrupt_index 0.0551 .
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.064 2.925 1.731 0.0834 .
## jud_ind 2.242 1.427 1.571 0.1162
## exec_corrupt_index -2.087 4.000 -0.522 0.6019
## polyarchy -7.900 4.764 -1.658 0.0973 .
## jud_ind:exec_corrupt_index -3.181 1.907 -1.669 0.0952 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 39
## Log-likelihood: -117.1 on 71 Df# Parliamentary support
summary(zip_models$n_parl_zip0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.450e-01 -2.300e-01 -3.995e-05 -2.553e-05 5.964e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -5.3951 1.9535 -2.762
## jud_ind 0.1246 0.2611 0.477
## exec_corrupt_index 6.9794 2.1817 3.199
## country_feAlgeria 0.5257 1.4729 0.357
## country_feAngola -19.1869 4231.0144 -0.005
## country_feArmenia -1.5698 1.7210 -0.912
## country_feAzerbaijan -2.0491 1.6328 -1.255
## country_feBangladesh -18.6319 5016.5822 -0.004
## country_feBelarus -0.2238 1.4812 -0.151
## country_feBurkina Faso -0.6039 1.6454 -0.367
## country_feCambodia -1.4969 1.5393 -0.972
## country_feCameroon -2.0908 1.6361 -1.278
## country_feCentral African Republic -18.8124 4480.6332 -0.004
## country_feCroatia -17.5484 5496.2288 -0.003
## country_feDemocratic Republic of the Congo -0.9077 1.5015 -0.604
## country_feDjibouti -0.7159 1.5241 -0.470
## country_feEgypt 2.2122 1.4338 1.543
## country_feEquatorial Guinea -19.4477 3749.6672 -0.005
## country_feEthiopia -17.5347 6605.9796 -0.003
## country_feGabon -18.8136 5121.0088 -0.004
## country_feGeorgia -0.7322 1.4459 -0.506
## country_feGhana -18.5311 6349.7684 -0.003
## country_feGuinea -19.3583 3973.6169 -0.005
## country_feGuinea-Bissau -19.1484 4705.0855 -0.004
## country_feGuyana -18.1319 6025.4563 -0.003
## country_feHaiti -18.8285 5069.5038 -0.004
## country_feIvory Coast -18.5386 5638.3064 -0.003
## country_feKazakhstan -0.3290 1.5468 -0.213
## country_feKenya -18.6696 5236.3063 -0.004
## country_feKyrgyzstan -0.2424 1.4466 -0.168
## country_feLesotho -17.6275 7074.7166 -0.002
## country_feMadagascar -1.5205 1.7700 -0.859
## country_feMalaysia 1.6211 1.3246 1.224
## country_feMauritania -0.1637 1.5243 -0.107
## country_feMexico -17.8305 5940.1978 -0.003
## country_feMozambique -18.0177 6254.1163 -0.003
## country_feNamibia -16.9267 9175.4232 -0.002
## country_feNicaragua -18.6125 4105.5348 -0.005
## country_feNiger 0.4791 1.4181 0.338
## country_feNigeria -19.1803 5007.3154 -0.004
## country_fePanama -17.6056 6963.7092 -0.003
## country_feParaguay -19.0164 4865.9179 -0.004
## country_fePeru -17.8042 5153.8249 -0.003
## country_fePhilippines -18.3512 5909.8178 -0.003
## country_feRussia 1.0119 1.4278 0.709
## country_feRwanda -17.2769 6946.3249 -0.002
## country_feSenegal -16.7777 8215.3210 -0.002
## country_feSerbia -1.8268 1.7234 -1.060
## country_feSierra Leone -18.5706 4887.5122 -0.004
## country_feSingapore -15.8671 10509.8901 -0.002
## country_feSouth Korea -16.9092 7546.9277 -0.002
## country_feSri Lanka 0.8453 1.3824 0.611
## country_feTaiwan -16.7217 8491.8548 -0.002
## country_feTajikistan -1.5974 1.5616 -1.023
## country_feTanzania -17.1508 8027.3126 -0.002
## country_feThe Gambia -18.4985 5093.5081 -0.004
## country_feTogo -18.9855 4456.1066 -0.004
## country_feTürkiye -18.1970 5852.0309 -0.003
## country_feTurkmenistan -3.0103 1.8941 -1.589
## country_feUganda -18.3337 5456.6473 -0.003
## country_feUzbekistan -1.0735 1.6352 -0.656
## country_feVenezuela -1.9346 1.7144 -1.128
## country_feZambia -17.2998 6896.3240 -0.003
## country_feZimbabwe -18.7134 5077.2597 -0.004
## Pr(>|z|)
## (Intercept) 0.00575 **
## jud_ind 0.63323
## exec_corrupt_index 0.00138 **
## country_feAlgeria 0.72116
## country_feAngola 0.99638
## country_feArmenia 0.36170
## country_feAzerbaijan 0.20948
## country_feBangladesh 0.99704
## country_feBelarus 0.87992
## country_feBurkina Faso 0.71359
## country_feCambodia 0.33084
## country_feCameroon 0.20128
## country_feCentral African Republic 0.99665
## country_feCroatia 0.99745
## country_feDemocratic Republic of the Congo 0.54552
## country_feDjibouti 0.63855
## country_feEgypt 0.12286
## country_feEquatorial Guinea 0.99586
## country_feEthiopia 0.99788
## country_feGabon 0.99707
## country_feGeorgia 0.61257
## country_feGhana 0.99767
## country_feGuinea 0.99611
## country_feGuinea-Bissau 0.99675
## country_feGuyana 0.99760
## country_feHaiti 0.99704
## country_feIvory Coast 0.99738
## country_feKazakhstan 0.83154
## country_feKenya 0.99716
## country_feKyrgyzstan 0.86693
## country_feLesotho 0.99801
## country_feMadagascar 0.39032
## country_feMalaysia 0.22099
## country_feMauritania 0.91447
## country_feMexico 0.99761
## country_feMozambique 0.99770
## country_feNamibia 0.99853
## country_feNicaragua 0.99638
## country_feNiger 0.73547
## country_feNigeria 0.99694
## country_fePanama 0.99798
## country_feParaguay 0.99688
## country_fePeru 0.99724
## country_fePhilippines 0.99752
## country_feRussia 0.47852
## country_feRwanda 0.99802
## country_feSenegal 0.99837
## country_feSerbia 0.28914
## country_feSierra Leone 0.99697
## country_feSingapore 0.99880
## country_feSouth Korea 0.99821
## country_feSri Lanka 0.54090
## country_feTaiwan 0.99843
## country_feTajikistan 0.30633
## country_feTanzania 0.99830
## country_feThe Gambia 0.99710
## country_feTogo 0.99660
## country_feTürkiye 0.99752
## country_feTurkmenistan 0.11199
## country_feUganda 0.99732
## country_feUzbekistan 0.51153
## country_feVenezuela 0.25913
## country_feZambia 0.99800
## country_feZimbabwe 0.99706
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1691 1.4663 0.115 0.9082
## jud_ind 0.4201 0.2109 1.992 0.0463 *
## exec_corrupt_index 1.8026 1.9194 0.939 0.3477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 37
## Log-likelihood: -272.3 on 67 Dfsummary(zip_models$n_parl_zip1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.500e-01 -2.307e-01 -4.044e-05 -2.349e-05 5.865e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -5.5940 2.0631 -2.711
## jud_ind -0.4133 1.2912 -0.320
## exec_corrupt_index 7.3788 2.4359 3.029
## country_feAlgeria 0.3724 1.6562 0.225
## country_feAngola -19.2903 4370.3232 -0.004
## country_feArmenia -1.6405 1.8582 -0.883
## country_feAzerbaijan -2.0147 1.7866 -1.128
## country_feBangladesh -18.6951 5004.8095 -0.004
## country_feBelarus -0.3475 1.6618 -0.209
## country_feBurkina Faso -0.6484 1.7749 -0.365
## country_feCambodia -1.4928 1.7010 -0.878
## country_feCameroon -2.0569 1.7916 -1.148
## country_feCentral African Republic -18.8989 4577.3545 -0.004
## country_feCroatia -17.8131 6123.4004 -0.003
## country_feDemocratic Republic of the Congo -1.0263 1.6820 -0.610
## country_feDjibouti -0.8103 1.6799 -0.482
## country_feEgypt 2.3601 1.5940 1.481
## country_feEquatorial Guinea -19.8234 4781.7253 -0.004
## country_feEthiopia -17.4674 6053.0475 -0.003
## country_feGabon -18.8071 4822.3246 -0.004
## country_feGeorgia -0.8662 1.6640 -0.521
## country_feGhana -18.5663 6183.9947 -0.003
## country_feGuinea -19.5928 4527.9831 -0.004
## country_feGuinea-Bissau -19.1321 4377.4203 -0.004
## country_feGuyana -18.2739 6340.7445 -0.003
## country_feHaiti -18.8409 4858.5063 -0.004
## country_feIvory Coast -18.5374 5343.2890 -0.003
## country_feKazakhstan -0.2496 1.7143 -0.146
## country_feKenya -18.7144 5155.8322 -0.004
## country_feKyrgyzstan -0.2779 1.6176 -0.172
## country_feLesotho -17.9555 8761.9265 -0.002
## country_feMadagascar -1.5426 1.8925 -0.815
## country_feMalaysia 1.5377 1.5090 1.019
## country_feMauritania -0.2981 1.6975 -0.176
## country_feMexico -18.0530 6601.5675 -0.003
## country_feMozambique -18.1335 6572.2430 -0.003
## country_feNamibia -17.7897 17954.6050 -0.001
## country_feNicaragua -18.6703 4229.0561 -0.004
## country_feNiger 0.4439 1.5655 0.284
## country_feNigeria -19.0820 4365.8680 -0.004
## country_fePanama -17.7771 7745.0366 -0.002
## country_feParaguay -19.0020 4547.6729 -0.004
## country_fePeru -18.1956 6131.6032 -0.003
## country_fePhilippines -18.4315 5965.2335 -0.003
## country_feRussia 0.8899 1.6092 0.553
## country_feRwanda -17.0971 5815.6863 -0.003
## country_feSenegal -16.8472 8527.2168 -0.002
## country_feSerbia -1.8565 1.8664 -0.995
## country_feSierra Leone -18.5937 4757.3716 -0.004
## country_feSingapore -15.7391 9436.3203 -0.002
## country_feSouth Korea -17.5145 11109.7177 -0.002
## country_feSri Lanka 0.8067 1.5586 0.518
## country_feTaiwan -17.4073 13140.8651 -0.001
## country_feTajikistan -1.5347 1.7226 -0.891
## country_feTanzania -17.6238 11429.7545 -0.002
## country_feThe Gambia -18.5730 5090.1681 -0.004
## country_feTogo -19.0812 4575.2062 -0.004
## country_feTürkiye -18.4101 6304.4742 -0.003
## country_feTurkmenistan -2.8246 2.0712 -1.364
## country_feUganda -18.3463 5261.6658 -0.003
## country_feUzbekistan -0.9213 1.8218 -0.506
## country_feVenezuela -1.7740 1.8915 -0.938
## country_feZambia -17.6012 8336.8712 -0.002
## country_feZimbabwe -18.7296 4784.9212 -0.004
## jud_ind:exec_corrupt_index 0.7395 1.7286 0.428
## Pr(>|z|)
## (Intercept) 0.00670 **
## jud_ind 0.74889
## exec_corrupt_index 0.00245 **
## country_feAlgeria 0.82208
## country_feAngola 0.99648
## country_feArmenia 0.37734
## country_feAzerbaijan 0.25944
## country_feBangladesh 0.99702
## country_feBelarus 0.83436
## country_feBurkina Faso 0.71486
## country_feCambodia 0.38017
## country_feCameroon 0.25094
## country_feCentral African Republic 0.99671
## country_feCroatia 0.99768
## country_feDemocratic Republic of the Congo 0.54177
## country_feDjibouti 0.62955
## country_feEgypt 0.13872
## country_feEquatorial Guinea 0.99669
## country_feEthiopia 0.99770
## country_feGabon 0.99689
## country_feGeorgia 0.60270
## country_feGhana 0.99760
## country_feGuinea 0.99655
## country_feGuinea-Bissau 0.99651
## country_feGuyana 0.99770
## country_feHaiti 0.99691
## country_feIvory Coast 0.99723
## country_feKazakhstan 0.88426
## country_feKenya 0.99710
## country_feKyrgyzstan 0.86362
## country_feLesotho 0.99836
## country_feMadagascar 0.41502
## country_feMalaysia 0.30820
## country_feMauritania 0.86061
## country_feMexico 0.99782
## country_feMozambique 0.99780
## country_feNamibia 0.99921
## country_feNicaragua 0.99648
## country_feNiger 0.77676
## country_feNigeria 0.99651
## country_fePanama 0.99817
## country_feParaguay 0.99667
## country_fePeru 0.99763
## country_fePhilippines 0.99753
## country_feRussia 0.58026
## country_feRwanda 0.99765
## country_feSenegal 0.99842
## country_feSerbia 0.31990
## country_feSierra Leone 0.99688
## country_feSingapore 0.99867
## country_feSouth Korea 0.99874
## country_feSri Lanka 0.60473
## country_feTaiwan 0.99894
## country_feTajikistan 0.37297
## country_feTanzania 0.99877
## country_feThe Gambia 0.99709
## country_feTogo 0.99667
## country_feTürkiye 0.99767
## country_feTurkmenistan 0.17264
## country_feUganda 0.99722
## country_feUzbekistan 0.61305
## country_feVenezuela 0.34830
## country_feZambia 0.99832
## country_feZimbabwe 0.99688
## jud_ind:exec_corrupt_index 0.66882
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1009 1.6280 0.062 0.951
## jud_ind 0.2856 1.1360 0.251 0.802
## exec_corrupt_index 1.9061 2.1891 0.871 0.384
## jud_ind:exec_corrupt_index 0.1849 1.4817 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 39
## Log-likelihood: -272.2 on 69 Dfsummary(zip_models$n_parl_zip2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "poisson")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.776e-01 -2.276e-01 -2.773e-05 -1.578e-05 6.812e+00
##
## Count model coefficients (poisson with log link):
## Estimate Std. Error z value
## (Intercept) -2.596e+00 2.566e+00 -1.012
## jud_ind 2.471e-01 1.297e+00 0.190
## exec_corrupt_index 6.452e+00 2.316e+00 2.786
## polyarchy -8.977e+00 3.600e+00 -2.494
## country_feAlgeria 1.403e+00 1.136e+00 1.235
## country_feAngola -1.991e+01 6.981e+03 -0.003
## country_feArmenia -8.292e-01 1.394e+00 -0.595
## country_feAzerbaijan -1.506e+00 1.340e+00 -1.124
## country_feBangladesh -1.871e+01 7.060e+03 -0.003
## country_feBelarus 7.352e-01 1.160e+00 0.634
## country_feBurkina Faso 4.224e-01 1.412e+00 0.299
## country_feCambodia -5.984e-01 1.194e+00 -0.501
## country_feCameroon -1.314e+00 1.311e+00 -1.002
## country_feCentral African Republic -1.880e+01 6.390e+03 -0.003
## country_feCroatia -1.735e+01 7.526e+03 -0.002
## country_feDemocratic Republic of the Congo -1.708e-01 1.092e+00 -0.156
## country_feDjibouti -5.292e-01 1.220e+00 -0.434
## country_feEgypt 9.500e-01 1.603e+00 0.593
## country_feEquatorial Guinea -2.010e+01 6.621e+03 -0.003
## country_feEthiopia -1.819e+01 8.404e+03 -0.002
## country_feGabon -1.908e+01 6.941e+03 -0.003
## country_feGeorgia -9.292e-02 1.008e+00 -0.092
## country_feGhana -1.848e+01 8.254e+03 -0.002
## country_feGuinea -1.969e+01 6.386e+03 -0.003
## country_feGuinea-Bissau -1.920e+01 6.462e+03 -0.003
## country_feGuyana -1.815e+01 8.444e+03 -0.002
## country_feHaiti -1.906e+01 6.980e+03 -0.003
## country_feIvory Coast -1.876e+01 7.310e+03 -0.003
## country_feKazakhstan 5.391e-01 1.243e+00 0.434
## country_feKenya -1.879e+01 7.138e+03 -0.003
## country_feKyrgyzstan 5.260e-01 1.035e+00 0.508
## country_feLesotho -1.855e+01 1.051e+04 -0.002
## country_feMadagascar -7.123e-01 1.488e+00 -0.479
## country_feMalaysia 1.845e+00 9.244e-01 1.995
## country_feMauritania 7.896e-01 1.180e+00 0.669
## country_feMexico -1.759e+01 9.349e+03 -0.002
## country_feMozambique -1.849e+01 8.954e+03 -0.002
## country_feNamibia -1.791e+01 2.735e+04 -0.001
## country_feNicaragua -1.792e+01 5.513e+03 -0.003
## country_feNiger 1.071e+00 1.044e+00 1.026
## country_feNigeria -1.931e+01 6.655e+03 -0.003
## country_fePanama -1.671e+01 1.338e+04 -0.001
## country_feParaguay -1.834e+01 6.360e+03 -0.003
## country_fePeru -1.802e+01 7.717e+03 -0.002
## country_fePhilippines -1.817e+01 8.134e+03 -0.002
## country_feRussia 1.845e+00 1.036e+00 1.781
## country_feRwanda -1.806e+01 8.522e+03 -0.002
## country_feSenegal -1.591e+01 1.627e+04 -0.001
## country_feSerbia -1.410e+00 1.425e+00 -0.989
## country_feSierra Leone -1.847e+01 6.824e+03 -0.003
## country_feSingapore -1.604e+01 1.354e+04 -0.001
## country_feSouth Korea -1.672e+01 1.990e+04 -0.001
## country_feSri Lanka 1.435e+00 1.016e+00 1.413
## country_feTaiwan -1.721e+01 1.501e+04 -0.001
## country_feTajikistan -1.303e+00 1.246e+00 -1.046
## country_feTanzania -1.825e+01 1.389e+04 -0.001
## country_feThe Gambia -1.907e+01 7.453e+03 -0.003
## country_feTogo -1.899e+01 6.328e+03 -0.003
## country_feTürkiye -1.864e+01 8.543e+03 -0.002
## country_feTurkmenistan -2.508e+00 1.800e+00 -1.393
## country_feUganda -1.892e+01 7.535e+03 -0.003
## country_feUzbekistan -6.936e-01 1.440e+00 -0.482
## country_feVenezuela -3.055e-01 1.577e+00 -0.194
## country_feZambia -1.796e+01 1.101e+04 -0.002
## country_feZimbabwe -1.951e+01 7.669e+03 -0.003
## jud_ind:exec_corrupt_index 2.283e-01 1.665e+00 0.137
## Pr(>|z|)
## (Intercept) 0.31161
## jud_ind 0.84892
## exec_corrupt_index 0.00534 **
## polyarchy 0.01264 *
## country_feAlgeria 0.21699
## country_feAngola 0.99772
## country_feArmenia 0.55197
## country_feAzerbaijan 0.26095
## country_feBangladesh 0.99789
## country_feBelarus 0.52621
## country_feBurkina Faso 0.76486
## country_feCambodia 0.61637
## country_feCameroon 0.31611
## country_feCentral African Republic 0.99765
## country_feCroatia 0.99816
## country_feDemocratic Republic of the Congo 0.87569
## country_feDjibouti 0.66456
## country_feEgypt 0.55331
## country_feEquatorial Guinea 0.99758
## country_feEthiopia 0.99827
## country_feGabon 0.99781
## country_feGeorgia 0.92658
## country_feGhana 0.99821
## country_feGuinea 0.99754
## country_feGuinea-Bissau 0.99763
## country_feGuyana 0.99829
## country_feHaiti 0.99782
## country_feIvory Coast 0.99795
## country_feKazakhstan 0.66464
## country_feKenya 0.99790
## country_feKyrgyzstan 0.61114
## country_feLesotho 0.99859
## country_feMadagascar 0.63209
## country_feMalaysia 0.04599 *
## country_feMauritania 0.50348
## country_feMexico 0.99850
## country_feMozambique 0.99835
## country_feNamibia 0.99948
## country_feNicaragua 0.99741
## country_feNiger 0.30512
## country_feNigeria 0.99769
## country_fePanama 0.99900
## country_feParaguay 0.99770
## country_fePeru 0.99814
## country_fePhilippines 0.99822
## country_feRussia 0.07497 .
## country_feRwanda 0.99831
## country_feSenegal 0.99922
## country_feSerbia 0.32252
## country_feSierra Leone 0.99784
## country_feSingapore 0.99905
## country_feSouth Korea 0.99933
## country_feSri Lanka 0.15777
## country_feTaiwan 0.99908
## country_feTajikistan 0.29568
## country_feTanzania 0.99895
## country_feThe Gambia 0.99796
## country_feTogo 0.99761
## country_feTürkiye 0.99826
## country_feTurkmenistan 0.16347
## country_feUganda 0.99800
## country_feUzbekistan 0.63013
## country_feVenezuela 0.84640
## country_feZambia 0.99870
## country_feZimbabwe 0.99797
## jud_ind:exec_corrupt_index 0.89092
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.2371 1.8474 0.670 0.5031
## jud_ind -0.4794 1.2091 -0.396 0.6918
## exec_corrupt_index 2.9465 2.3733 1.242 0.2144
## polyarchy -7.0390 4.2321 -1.663 0.0963 .
## jud_ind:exec_corrupt_index 1.0938 1.5644 0.699 0.4844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Number of iterations in BFGS optimization: 55
## Log-likelihood: -269.7 on 71 Df5.2 Zero-inflated negative binomial models
zinb_models <- list()
for (y in outcomes) {
f0 <- as.formula(paste0(
y, " ~ jud_ind + exec_corrupt_index + country_fe |
jud_ind + exec_corrupt_index"
))
f1 <- as.formula(paste0(
y, " ~ jud_ind * exec_corrupt_index + country_fe |
jud_ind * exec_corrupt_index"
))
f2 <- as.formula(paste0(
y, " ~ jud_ind * exec_corrupt_index + polyarchy + country_fe |
jud_ind * exec_corrupt_index + polyarchy"
))
zinb_models[[paste0(y, "_zinb0")]] <- zeroinfl(f0, data = subset_data, dist = "negbin")
zinb_models[[paste0(y, "_zinb1")]] <- zeroinfl(f1, data = subset_data, dist = "negbin")
zinb_models[[paste0(y, "_zinb2")]] <- zeroinfl(f2, data = subset_data, dist = "negbin")
}## Warning in sqrt(diag(vc)[np]): NaNs produced
## Warning in sqrt(diag(vc)[np]): NaNs produced
## Warning in sqrt(diag(vc)[np]): NaNs produced
## Warning in sqrt(diag(vc)[np]): NaNs produced
## Warning in sqrt(diag(vc)[np]): NaNs producedWE HAVE CONVERGENCE ISSUE HERE – NANS produced. I have to check why this is happening.
Let’s see the results. – Nothing is statistically significant across models.
# Total cooptation
summary(zinb_models$n_coopt_zinb0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.355e-01 -2.806e-01 -1.700e-01 -4.107e-05 6.611e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -2.934e+00 1.314e+00 -2.232
## jud_ind 1.698e-01 2.144e-01 0.792
## exec_corrupt_index 3.688e+00 1.492e+00 2.471
## country_feAlgeria 9.994e-01 1.041e+00 0.960
## country_feAngola -1.839e+01 3.756e+03 -0.005
## country_feArmenia 7.386e-01 9.365e-01 0.789
## country_feAzerbaijan -1.337e+00 1.232e+00 -1.085
## country_feBangladesh -6.967e-01 1.158e+00 -0.602
## country_feBelarus 6.282e-03 1.048e+00 0.006
## country_feBurkina Faso 8.767e-01 9.490e-01 0.924
## country_feCambodia -7.865e-01 1.113e+00 -0.706
## country_feCameroon -1.379e+00 1.242e+00 -1.110
## country_feCentral African Republic -6.967e-01 1.230e+00 -0.566
## country_feCroatia -1.696e+01 4.084e+03 -0.004
## country_feDemocratic Republic of the Congo 4.387e-01 1.043e+00 0.421
## country_feDjibouti 4.133e-02 1.124e+00 0.037
## country_feEgypt 1.407e+00 1.037e+00 1.357
## country_feEquatorial Guinea -1.336e+00 1.291e+00 -1.035
## country_feEthiopia -1.701e+01 4.547e+03 -0.004
## country_feGabon -2.347e-01 9.656e-01 -0.243
## country_feGeorgia 2.476e-01 9.384e-01 0.264
## country_feGhana -1.297e+00 1.357e+00 -0.956
## country_feGuinea -2.087e+00 1.428e+00 -1.461
## country_feGuinea-Bissau -2.319e-01 1.005e+00 -0.231
## country_feGuyana -1.756e+01 4.170e+03 -0.004
## country_feHaiti 6.526e-01 9.841e-01 0.663
## country_feIvory Coast -1.228e+00 1.359e+00 -0.904
## country_feKazakhstan 3.990e-01 1.091e+00 0.366
## country_feKenya -1.802e+01 4.102e+03 -0.004
## country_feKyrgyzstan 1.042e+00 9.468e-01 1.101
## country_feLesotho -1.717e+01 4.524e+03 -0.004
## country_feMadagascar -1.092e+00 1.415e+00 -0.772
## country_feMalaysia 1.478e+00 8.605e-01 1.717
## country_feMauritania 8.905e-01 1.070e+00 0.832
## country_feMexico -1.729e+01 4.267e+03 -0.004
## country_feMozambique -1.748e+01 4.366e+03 -0.004
## country_feNamibia -1.664e+01 5.523e+03 -0.003
## country_feNicaragua -1.780e+01 3.672e+03 -0.005
## country_feNiger 7.798e-01 1.030e+00 0.757
## country_feNigeria -1.949e+00 1.362e+00 -1.431
## country_fePanama -1.713e+01 4.589e+03 -0.004
## country_feParaguay -1.832e+01 3.919e+03 -0.005
## country_fePeru -1.720e+01 3.968e+03 -0.004
## country_fePhilippines -1.777e+01 4.222e+03 -0.004
## country_feRussia 1.370e+00 9.742e-01 1.406
## country_feRwanda 2.252e+00 1.158e+00 1.946
## country_feSenegal 5.827e-01 1.480e+00 0.394
## country_feSerbia -8.617e-01 1.164e+00 -0.740
## country_feSierra Leone -3.442e-01 1.226e+00 -0.281
## country_feSingapore -1.559e+01 5.379e+03 -0.003
## country_feSouth Korea -1.652e+01 4.643e+03 -0.004
## country_feSri Lanka 7.133e-01 9.885e-01 0.722
## country_feTaiwan -1.640e+01 4.996e+03 -0.003
## country_feTajikistan -8.629e-01 1.125e+00 -0.767
## country_feTanzania -1.677e+01 4.811e+03 -0.003
## country_feThe Gambia -1.783e+01 4.078e+03 -0.004
## country_feTogo -9.755e-01 1.186e+00 -0.822
## country_feTürkiye -1.764e+01 4.146e+03 -0.004
## country_feTurkmenistan -2.045e+00 1.522e+00 -1.343
## country_feUganda -3.455e-01 1.021e+00 -0.339
## country_feUzbekistan 4.339e-02 1.185e+00 0.037
## country_feVenezuela -5.824e-01 1.239e+00 -0.470
## country_feZambia -1.682e+01 4.411e+03 -0.004
## country_feZimbabwe -6.983e-01 1.187e+00 -0.588
## Log(theta) 1.670e+01 6.955e+01 0.240
## Pr(>|z|)
## (Intercept) 0.0256 *
## jud_ind 0.4285
## exec_corrupt_index 0.0135 *
## country_feAlgeria 0.3370
## country_feAngola 0.9961
## country_feArmenia 0.4303
## country_feAzerbaijan 0.2777
## country_feBangladesh 0.5474
## country_feBelarus 0.9952
## country_feBurkina Faso 0.3556
## country_feCambodia 0.4800
## country_feCameroon 0.2671
## country_feCentral African Republic 0.5712
## country_feCroatia 0.9967
## country_feDemocratic Republic of the Congo 0.6740
## country_feDjibouti 0.9707
## country_feEgypt 0.1748
## country_feEquatorial Guinea 0.3008
## country_feEthiopia 0.9970
## country_feGabon 0.8080
## country_feGeorgia 0.7919
## country_feGhana 0.3389
## country_feGuinea 0.1440
## country_feGuinea-Bissau 0.8175
## country_feGuyana 0.9966
## country_feHaiti 0.5072
## country_feIvory Coast 0.3662
## country_feKazakhstan 0.7146
## country_feKenya 0.9965
## country_feKyrgyzstan 0.2710
## country_feLesotho 0.9970
## country_feMadagascar 0.4403
## country_feMalaysia 0.0859 .
## country_feMauritania 0.4051
## country_feMexico 0.9968
## country_feMozambique 0.9968
## country_feNamibia 0.9976
## country_feNicaragua 0.9961
## country_feNiger 0.4492
## country_feNigeria 0.1524
## country_fePanama 0.9970
## country_feParaguay 0.9963
## country_fePeru 0.9965
## country_fePhilippines 0.9966
## country_feRussia 0.1596
## country_feRwanda 0.0517 .
## country_feSenegal 0.6938
## country_feSerbia 0.4592
## country_feSierra Leone 0.7789
## country_feSingapore 0.9977
## country_feSouth Korea 0.9972
## country_feSri Lanka 0.4705
## country_feTaiwan 0.9974
## country_feTajikistan 0.4429
## country_feTanzania 0.9972
## country_feThe Gambia 0.9965
## country_feTogo 0.4109
## country_feTürkiye 0.9966
## country_feTurkmenistan 0.1791
## country_feUganda 0.7350
## country_feUzbekistan 0.9708
## country_feVenezuela 0.6383
## country_feZambia 0.9970
## country_feZimbabwe 0.5562
## Log(theta) 0.8102
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9736 0.7294 2.706 0.00681 **
## jud_ind 0.1999 0.1364 1.466 0.14271
## exec_corrupt_index -0.4030 0.9564 -0.421 0.67347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 17952595.2154
## Number of iterations in BFGS optimization: 104
## Log-likelihood: -450 on 68 Dfsummary(zinb_models$n_coopt_zinb1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.286e-01 -2.783e-01 -1.741e-01 -4.591e-05 6.574e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -3.89413 1.48700 -2.619
## jud_ind -1.76076 1.21955 -1.444
## exec_corrupt_index 5.44883 1.92305 2.833
## country_feAlgeria 0.46495 1.21799 0.382
## country_feAngola -18.30243 3250.19002 -0.006
## country_feArmenia 0.36989 1.12586 0.329
## country_feAzerbaijan -1.27625 1.36272 -0.937
## country_feBangladesh -0.91506 1.29975 -0.704
## country_feBelarus -0.46728 1.23118 -0.380
## country_feBurkina Faso 0.71450 1.08595 0.658
## country_feCambodia -0.81475 1.26558 -0.644
## country_feCameroon -1.29680 1.37451 -0.943
## country_feCentral African Republic -0.88793 1.35933 -0.653
## country_feCroatia -16.84519 3628.53374 -0.005
## country_feDemocratic Republic of the Congo -0.11452 1.24461 -0.092
## country_feDjibouti -0.32024 1.26816 -0.253
## country_feEgypt 2.06101 1.14466 1.801
## country_feEquatorial Guinea -1.04142 1.43217 -0.727
## country_feEthiopia -17.04103 3777.74643 -0.005
## country_feGabon -0.69841 1.16744 -0.598
## country_feGeorgia -0.28042 1.17420 -0.239
## country_feGhana -1.65221 1.48794 -1.110
## country_feGuinea -2.00979 1.54676 -1.299
## country_feGuinea-Bissau -0.73911 1.20960 -0.611
## country_feGuyana -17.49136 3703.53374 -0.005
## country_feHaiti 0.33347 1.14253 0.292
## country_feIvory Coast -1.53871 1.50507 -1.022
## country_feKazakhstan 0.63692 1.22980 0.518
## country_feKenya -17.98759 3475.50533 -0.005
## country_feKyrgyzstan 0.85441 1.11653 0.765
## country_feLesotho -16.93665 4707.02751 -0.004
## country_feMadagascar -1.29762 1.51636 -0.856
## country_feMalaysia 1.19435 1.03809 1.151
## country_feMauritania 0.41042 1.24058 0.331
## country_feMexico -17.14492 3950.50713 -0.004
## country_feMozambique -17.38354 3987.26980 -0.004
## country_feNamibia -15.98385 8136.99840 -0.002
## country_feNicaragua -17.66673 3255.66048 -0.005
## country_feNiger 0.62447 1.16321 0.537
## country_feNigeria -2.62843 1.55719 -1.688
## country_fePanama -16.98549 4590.28230 -0.004
## country_feParaguay -18.33432 3087.04220 -0.006
## country_fePeru -17.05189 3478.19946 -0.005
## country_fePhilippines -17.71892 3643.05655 -0.005
## country_feRussia 0.92242 1.16653 0.791
## country_feRwanda 1.49102 1.30249 1.145
## country_feSenegal 0.55844 1.53743 0.363
## country_feSerbia -1.00383 1.30728 -0.768
## country_feSierra Leone -0.67545 1.35259 -0.499
## country_feSingapore -15.64204 4670.30489 -0.003
## country_feSouth Korea -16.14096 5144.07392 -0.003
## country_feSri Lanka 0.58371 1.13470 0.514
## country_feTaiwan -15.93857 5217.35455 -0.003
## country_feTajikistan -0.71802 1.26264 -0.569
## country_feTanzania -16.41141 5869.05977 -0.003
## country_feThe Gambia -17.79005 3473.23793 -0.005
## country_feTogo -1.22416 1.32025 -0.927
## country_feTürkiye -17.53371 3562.19873 -0.005
## country_feTurkmenistan -1.44135 1.67272 -0.862
## country_feUganda -0.69785 1.18558 -0.589
## country_feUzbekistan 0.58207 1.37149 0.424
## country_feVenezuela -0.02344 1.45072 -0.016
## country_feZambia -16.62243 4514.84015 -0.004
## country_feZimbabwe -1.16299 1.37610 -0.845
## jud_ind:exec_corrupt_index 2.65087 1.65805 1.599
## Log(theta) 18.18411 20.97220 0.867
## Pr(>|z|)
## (Intercept) 0.00882 **
## jud_ind 0.14880
## exec_corrupt_index 0.00461 **
## country_feAlgeria 0.70266
## country_feAngola 0.99551
## country_feArmenia 0.74250
## country_feAzerbaijan 0.34899
## country_feBangladesh 0.48142
## country_feBelarus 0.70429
## country_feBurkina Faso 0.51057
## country_feCambodia 0.51972
## country_feCameroon 0.34544
## country_feCentral African Republic 0.51362
## country_feCroatia 0.99630
## country_feDemocratic Republic of the Congo 0.92669
## country_feDjibouti 0.80064
## country_feEgypt 0.07178 .
## country_feEquatorial Guinea 0.46713
## country_feEthiopia 0.99640
## country_feGabon 0.54968
## country_feGeorgia 0.81125
## country_feGhana 0.26683
## country_feGuinea 0.19382
## country_feGuinea-Bissau 0.54118
## country_feGuyana 0.99623
## country_feHaiti 0.77038
## country_feIvory Coast 0.30661
## country_feKazakhstan 0.60453
## country_feKenya 0.99587
## country_feKyrgyzstan 0.44413
## country_feLesotho 0.99713
## country_feMadagascar 0.39214
## country_feMalaysia 0.24992
## country_feMauritania 0.74077
## country_feMexico 0.99654
## country_feMozambique 0.99652
## country_feNamibia 0.99843
## country_feNicaragua 0.99567
## country_feNiger 0.59137
## country_feNigeria 0.09142 .
## country_fePanama 0.99705
## country_feParaguay 0.99526
## country_fePeru 0.99609
## country_fePhilippines 0.99612
## country_feRussia 0.42909
## country_feRwanda 0.25231
## country_feSenegal 0.71643
## country_feSerbia 0.44256
## country_feSierra Leone 0.61752
## country_feSingapore 0.99733
## country_feSouth Korea 0.99750
## country_feSri Lanka 0.60696
## country_feTaiwan 0.99756
## country_feTajikistan 0.56958
## country_feTanzania 0.99777
## country_feThe Gambia 0.99591
## country_feTogo 0.35382
## country_feTürkiye 0.99607
## country_feTurkmenistan 0.38886
## country_feUganda 0.55612
## country_feUzbekistan 0.67127
## country_feVenezuela 0.98711
## country_feZambia 0.99706
## country_feZimbabwe 0.39804
## jud_ind:exec_corrupt_index 0.10987
## Log(theta) 0.38591
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.3871 1.0918 1.270 0.204
## jud_ind -0.3326 0.8792 -0.378 0.705
## exec_corrupt_index 0.3816 1.4484 0.263 0.792
## jud_ind:exec_corrupt_index 0.7202 1.1629 0.619 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 78933038.1259
## Number of iterations in BFGS optimization: 66
## Log-likelihood: -448.6 on 70 Dfsummary(zinb_models$n_coopt_zinb2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.302e-01 -2.760e-01 -1.706e-01 -3.329e-05 5.999e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -1.71848 1.94695 -0.883
## jud_ind -0.90831 1.34472 -0.675
## exec_corrupt_index 4.17705 1.98813 2.101
## polyarchy -4.83066 2.68880 -1.797
## country_feAlgeria 1.22338 1.02786 1.190
## country_feAngola -18.68228 4000.25827 -0.005
## country_feArmenia 1.05918 0.88100 1.202
## country_feAzerbaijan -0.93724 1.18520 -0.791
## country_feBangladesh -0.60495 1.06375 -0.569
## country_feBelarus 0.26437 1.03757 0.255
## country_feBurkina Faso 1.58519 0.92505 1.714
## country_feCambodia -0.14514 1.07834 -0.135
## country_feCameroon -0.66357 1.20519 -0.551
## country_feCentral African Republic -0.25277 1.18630 -0.213
## country_feCroatia -16.53239 4328.01021 -0.004
## country_feDemocratic Republic of the Congo 0.40525 0.96837 0.418
## country_feDjibouti -0.17653 1.06710 -0.165
## country_feEgypt 0.92269 1.29838 0.711
## country_feEquatorial Guinea -0.80900 1.28708 -0.629
## country_feEthiopia -17.62337 4695.20198 -0.004
## country_feGabon -0.36924 0.89539 -0.412
## country_feGeorgia 0.31372 0.89047 0.352
## country_feGhana -1.49679 1.28266 -1.167
## country_feGuinea -1.54230 1.40249 -1.100
## country_feGuinea-Bissau -0.07837 0.97379 -0.080
## country_feGuyana -17.41249 4836.32772 -0.004
## country_feHaiti 1.11829 0.94571 1.182
## country_feIvory Coast -1.14049 1.35233 -0.843
## country_feKazakhstan 1.22832 1.05811 1.161
## country_feKenya -18.01218 4524.20434 -0.004
## country_feKyrgyzstan 1.49227 0.88110 1.694
## country_feLesotho -17.26260 4872.68837 -0.004
## country_feMadagascar -0.59988 1.35625 -0.442
## country_feMalaysia 1.37161 0.77797 1.763
## country_feMauritania 1.14405 1.04019 1.100
## country_feMexico -16.87194 5257.03861 -0.003
## country_feMozambique -17.60948 5037.49543 -0.003
## country_feNamibia -16.05355 8617.53592 -0.002
## country_feNicaragua -16.98728 4182.47269 -0.004
## country_feNiger 0.90160 0.91620 0.984
## country_feNigeria -2.06514 1.35190 -1.528
## country_fePanama -16.26810 5944.19052 -0.003
## country_feParaguay -17.87786 4463.89415 -0.004
## country_fePeru -16.93503 4165.42361 -0.004
## country_fePhilippines -17.56481 4934.11163 -0.004
## country_feRussia 1.68686 0.96987 1.739
## country_feRwanda 1.29051 1.17235 1.101
## country_feSenegal 1.62524 1.47746 1.100
## country_feSerbia -0.57351 1.11059 -0.516
## country_feSierra Leone -0.06274 1.17623 -0.053
## country_feSingapore -16.06235 5739.22881 -0.003
## country_feSouth Korea -15.64804 6693.50941 -0.002
## country_feSri Lanka 1.05742 0.92524 1.143
## country_feTaiwan -15.95339 5578.93088 -0.003
## country_feTajikistan -0.51831 1.06528 -0.487
## country_feTanzania -16.87337 6513.18445 -0.003
## country_feThe Gambia -18.12828 4362.58839 -0.004
## country_feTogo -0.49746 1.15120 -0.432
## country_feTürkiye -17.69833 4542.81685 -0.004
## country_feTurkmenistan -1.26239 1.59115 -0.793
## country_feUganda -0.52797 0.95875 -0.551
## country_feUzbekistan 0.78279 1.24830 0.627
## country_feVenezuela 1.19178 1.44581 0.824
## country_feZambia -16.91102 5585.83566 -0.003
## country_feZimbabwe -1.10131 1.15466 -0.954
## jud_ind:exec_corrupt_index 1.82806 1.76905 1.033
## Log(theta) 18.64300 16.20430 1.150
## Pr(>|z|)
## (Intercept) 0.3774
## jud_ind 0.4994
## exec_corrupt_index 0.0356 *
## polyarchy 0.0724 .
## country_feAlgeria 0.2340
## country_feAngola 0.9963
## country_feArmenia 0.2293
## country_feAzerbaijan 0.4291
## country_feBangladesh 0.5696
## country_feBelarus 0.7989
## country_feBurkina Faso 0.0866 .
## country_feCambodia 0.8929
## country_feCameroon 0.5819
## country_feCentral African Republic 0.8313
## country_feCroatia 0.9970
## country_feDemocratic Republic of the Congo 0.6756
## country_feDjibouti 0.8686
## country_feEgypt 0.4773
## country_feEquatorial Guinea 0.5296
## country_feEthiopia 0.9970
## country_feGabon 0.6801
## country_feGeorgia 0.7246
## country_feGhana 0.2432
## country_feGuinea 0.2715
## country_feGuinea-Bissau 0.9359
## country_feGuyana 0.9971
## country_feHaiti 0.2370
## country_feIvory Coast 0.3990
## country_feKazakhstan 0.2457
## country_feKenya 0.9968
## country_feKyrgyzstan 0.0903 .
## country_feLesotho 0.9972
## country_feMadagascar 0.6583
## country_feMalaysia 0.0779 .
## country_feMauritania 0.2714
## country_feMexico 0.9974
## country_feMozambique 0.9972
## country_feNamibia 0.9985
## country_feNicaragua 0.9968
## country_feNiger 0.3251
## country_feNigeria 0.1266
## country_fePanama 0.9978
## country_feParaguay 0.9968
## country_fePeru 0.9968
## country_fePhilippines 0.9972
## country_feRussia 0.0820 .
## country_feRwanda 0.2710
## country_feSenegal 0.2713
## country_feSerbia 0.6056
## country_feSierra Leone 0.9575
## country_feSingapore 0.9978
## country_feSouth Korea 0.9981
## country_feSri Lanka 0.2531
## country_feTaiwan 0.9977
## country_feTajikistan 0.6266
## country_feTanzania 0.9979
## country_feThe Gambia 0.9967
## country_feTogo 0.6656
## country_feTürkiye 0.9969
## country_feTurkmenistan 0.4276
## country_feUganda 0.5818
## country_feUzbekistan 0.5306
## country_feVenezuela 0.4098
## country_feZambia 0.9976
## country_feZimbabwe 0.3402
## jud_ind:exec_corrupt_index 0.3014
## Log(theta) 0.2499
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7016 1.2341 1.379 0.168
## jud_ind -0.2132 0.9065 -0.235 0.814
## exec_corrupt_index 0.3581 1.5027 0.238 0.812
## polyarchy -1.1853 1.9099 -0.621 0.535
## jud_ind:exec_corrupt_index 0.5072 1.1861 0.428 0.669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 124896718.7799
## Number of iterations in BFGS optimization: 65
## Log-likelihood: -446.7 on 72 Df# Cabinet
summary(zinb_models$n_cabinet_zinb0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.726e-01 -2.038e-01 -4.445e-05 -1.506e-05 6.855e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) 1.592e+00 2.428e+00 0.656
## jud_ind 1.494e+00 4.311e-01 3.465
## exec_corrupt_index -5.585e+00 3.141e+00 -1.778
## country_feAlgeria 3.054e+00 1.642e+00 1.860
## country_feAngola -1.821e+01 2.292e+04 -0.001
## country_feArmenia 3.978e+00 1.344e+00 2.959
## country_feAzerbaijan -1.820e+01 2.771e+04 -0.001
## country_feBangladesh 2.026e+00 1.446e+00 1.401
## country_feBelarus -1.760e+01 1.474e+04 -0.001
## country_feBurkina Faso 2.195e+00 1.372e+00 1.600
## country_feCambodia 3.652e+00 1.685e+00 2.167
## country_feCameroon -1.829e+01 3.939e+04 0.000
## country_feCentral African Republic -1.789e+01 2.041e+04 -0.001
## country_feCroatia -1.699e+01 5.256e+03 -0.003
## country_feDemocratic Republic of the Congo 2.499e+00 1.582e+00 1.580
## country_feDjibouti -1.770e+01 1.458e+04 -0.001
## country_feEgypt -1.789e+01 9.759e+03 -0.002
## country_feEquatorial Guinea -1.832e+01 5.190e+04 0.000
## country_feEthiopia -1.698e+01 7.277e+03 -0.002
## country_feGabon 2.650e+00 1.306e+00 2.029
## country_feGeorgia 2.061e+00 1.728e+00 1.193
## country_feGhana -1.792e+01 8.604e+03 -0.002
## country_feGuinea 2.691e+00 1.883e+00 1.429
## country_feGuinea-Bissau 2.730e+00 1.399e+00 1.952
## country_feGuyana -1.753e+01 7.532e+03 -0.002
## country_feHaiti 3.369e+00 1.299e+00 2.592
## country_feIvory Coast 3.885e-01 1.528e+00 0.254
## country_feKazakhstan -1.818e+01 4.825e+04 0.000
## country_feKenya -1.791e+01 1.163e+04 -0.002
## country_feKyrgyzstan 4.130e+00 1.394e+00 2.964
## country_feLesotho -1.717e+01 5.568e+03 -0.003
## country_feMadagascar -1.741e+01 1.132e+04 -0.002
## country_feMalaysia 3.617e+00 1.202e+00 3.009
## country_feMauritania 3.254e+00 1.611e+00 2.020
## country_feMexico -1.727e+01 6.399e+03 -0.003
## country_feMozambique -1.743e+01 7.897e+03 -0.002
## country_feNamibia -1.671e+01 3.386e+03 -0.005
## country_feNicaragua -1.769e+01 1.548e+04 -0.001
## country_feNiger 8.744e-01 1.714e+00 0.510
## country_feNigeria 8.884e-01 1.635e+00 0.543
## country_fePanama -1.711e+01 6.318e+03 -0.003
## country_feParaguay -1.818e+01 1.419e+04 -0.001
## country_fePeru -1.723e+01 5.207e+03 -0.003
## country_fePhilippines -1.770e+01 8.998e+03 -0.002
## country_feRussia 1.697e+00 1.706e+00 0.995
## country_feRwanda 2.391e+00 1.750e+00 1.367
## country_feSenegal -6.479e-02 2.062e+00 -0.031
## country_feSerbia 3.072e+00 2.134e+00 1.439
## country_feSierra Leone 2.253e+00 1.521e+00 1.481
## country_feSingapore -1.572e+01 2.696e+03 -0.006
## country_feSouth Korea -1.661e+01 3.484e+03 -0.005
## country_feSri Lanka 3.147e+00 1.279e+00 2.461
## country_feTaiwan -1.648e+01 3.131e+03 -0.005
## country_feTajikistan -1.828e+01 3.413e+04 -0.001
## country_feTanzania -1.681e+01 4.325e+03 -0.004
## country_feThe Gambia -1.773e+01 1.138e+04 -0.002
## country_feTogo 2.426e+00 1.530e+00 1.586
## country_feTürkiye -1.762e+01 6.880e+03 -0.003
## country_feTurkmenistan -1.821e+01 8.160e+04 0.000
## country_feUganda 1.745e+00 1.306e+00 1.336
## country_feUzbekistan 4.046e+00 2.162e+00 1.871
## country_feVenezuela 3.780e+00 2.127e+00 1.777
## country_feZambia -1.687e+01 4.781e+03 -0.004
## country_feZimbabwe 3.442e+00 1.905e+00 1.807
## Log(theta) 1.379e+01 NaN NaN
## Pr(>|z|)
## (Intercept) 0.51191
## jud_ind 0.00053 ***
## exec_corrupt_index 0.07539 .
## country_feAlgeria 0.06293 .
## country_feAngola 0.99937
## country_feArmenia 0.00308 **
## country_feAzerbaijan 0.99948
## country_feBangladesh 0.16121
## country_feBelarus 0.99905
## country_feBurkina Faso 0.10966
## country_feCambodia 0.03026 *
## country_feCameroon 0.99963
## country_feCentral African Republic 0.99930
## country_feCroatia 0.99742
## country_feDemocratic Republic of the Congo 0.11417
## country_feDjibouti 0.99903
## country_feEgypt 0.99854
## country_feEquatorial Guinea 0.99972
## country_feEthiopia 0.99814
## country_feGabon 0.04241 *
## country_feGeorgia 0.23301
## country_feGhana 0.99834
## country_feGuinea 0.15297
## country_feGuinea-Bissau 0.05096 .
## country_feGuyana 0.99814
## country_feHaiti 0.00953 **
## country_feIvory Coast 0.79931
## country_feKazakhstan 0.99970
## country_feKenya 0.99877
## country_feKyrgyzstan 0.00304 **
## country_feLesotho 0.99754
## country_feMadagascar 0.99877
## country_feMalaysia 0.00262 **
## country_feMauritania 0.04340 *
## country_feMexico 0.99785
## country_feMozambique 0.99824
## country_feNamibia 0.99606
## country_feNicaragua 0.99909
## country_feNiger 0.60994
## country_feNigeria 0.58695
## country_fePanama 0.99784
## country_feParaguay 0.99898
## country_fePeru 0.99736
## country_fePhilippines 0.99843
## country_feRussia 0.31983
## country_feRwanda 0.17167
## country_feSenegal 0.97493
## country_feSerbia 0.15005
## country_feSierra Leone 0.13857
## country_feSingapore 0.99535
## country_feSouth Korea 0.99620
## country_feSri Lanka 0.01384 *
## country_feTaiwan 0.99580
## country_feTajikistan 0.99957
## country_feTanzania 0.99690
## country_feThe Gambia 0.99876
## country_feTogo 0.11285
## country_feTürkiye 0.99796
## country_feTurkmenistan 0.99982
## country_feUganda 0.18151
## country_feUzbekistan 0.06130 .
## country_feVenezuela 0.07558 .
## country_feZambia 0.99718
## country_feZimbabwe 0.07073 .
## Log(theta) NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.8340 0.9377 5.155 2.53e-07 ***
## jud_ind 1.0959 0.3602 3.042 0.00235 **
## exec_corrupt_index -4.2341 1.3759 -3.077 0.00209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 976531.4287
## Number of iterations in BFGS optimization: 38
## Log-likelihood: -246.1 on 68 Dfsummary(zinb_models$n_cabinet_zinb1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.444e-01 -2.010e-01 -3.001e-05 -1.851e-05 6.762e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -6.6264 3.8622 -1.716
## jud_ind -4.4100 3.5000 -1.260
## exec_corrupt_index 6.5853 4.8311 1.363
## country_feAlgeria 1.2774 1.7059 0.749
## country_feAngola -17.7874 7634.1376 -0.002
## country_feArmenia 2.2119 1.3631 1.623
## country_feAzerbaijan -17.2758 8834.4556 -0.002
## country_feBangladesh 1.1674 1.5225 0.767
## country_feBelarus -17.5742 8139.7992 -0.002
## country_feBurkina Faso 2.0927 1.4389 1.454
## country_feCambodia 1.5641 1.6212 0.965
## country_feCameroon -17.2769 9243.1264 -0.002
## country_feCentral African Republic -17.4967 8464.7001 -0.002
## country_feCroatia -16.3217 7428.6855 -0.002
## country_feDemocratic Republic of the Congo 0.3149 1.5264 0.206
## country_feDjibouti -17.5999 8046.0279 -0.002
## country_feEgypt -17.9379 6265.1649 -0.003
## country_feEquatorial Guinea -16.9163 10084.4322 -0.002
## country_feEthiopia -17.1287 8524.7960 -0.002
## country_feGabon 1.0401 1.3459 0.773
## country_feGeorgia -0.1088 1.6709 -0.065
## country_feGhana -17.8054 6640.7022 -0.003
## country_feGuinea 0.8407 1.8563 0.453
## country_feGuinea-Bissau 0.6931 1.4020 0.494
## country_feGuyana -17.0694 7334.9254 -0.002
## country_feHaiti 2.2992 1.3628 1.687
## country_feIvory Coast 0.1586 1.6982 0.093
## country_feKazakhstan -16.9145 10148.8318 -0.002
## country_feKenya -17.6794 7531.5338 -0.002
## country_feKyrgyzstan 2.8148 1.4027 2.007
## country_feLesotho -15.9644 8926.1716 -0.002
## country_feMadagascar -17.4306 7777.6090 -0.002
## country_feMalaysia 3.1679 1.2951 2.446
## country_feMauritania 1.3481 1.6925 0.796
## country_feMexico -16.5160 7959.0046 -0.002
## country_feMozambique -16.9274 8377.3616 -0.002
## country_feNamibia -13.4150 14665.8376 -0.001
## country_feNicaragua -17.3160 8382.0724 -0.002
## country_feNiger 0.9288 1.6477 0.564
## country_feNigeria -1.3051 1.7063 -0.765
## country_fePanama -16.3668 9483.4521 -0.002
## country_feParaguay -18.2521 6507.2889 -0.003
## country_fePeru -16.4072 6587.0556 -0.002
## country_fePhilippines -17.3899 7368.4864 -0.002
## country_feRussia 0.2474 1.7817 0.139
## country_feRwanda 2.5161 1.7404 1.446
## country_feSenegal 2.3699 1.9133 1.239
## country_feSerbia 0.8358 1.7127 0.488
## country_feSierra Leone 1.2221 1.5691 0.779
## country_feSingapore -16.0136 10208.0222 -0.002
## country_feSouth Korea -14.8055 8355.8313 -0.002
## country_feSri Lanka 2.0470 1.3788 1.485
## country_feTaiwan -14.9413 8512.8254 -0.002
## country_feTajikistan -17.2555 8985.3216 -0.002
## country_feTanzania -14.9425 11862.0175 -0.001
## country_feThe Gambia -17.4449 7842.6701 -0.002
## country_feTogo 0.9416 1.5460 0.609
## country_feTürkiye -17.0701 6423.6832 -0.003
## country_feTurkmenistan -16.3472 12994.7421 -0.001
## country_feUganda 1.0535 1.3688 0.770
## country_feUzbekistan 2.1937 2.2050 0.995
## country_feVenezuela 2.1886 2.6515 0.825
## country_feZambia -15.7683 8955.0242 -0.002
## country_feZimbabwe 0.5735 1.5747 0.364
## jud_ind:exec_corrupt_index 6.6932 4.3735 1.530
## Log(theta) 15.7563 184.2390 0.086
## Pr(>|z|)
## (Intercept) 0.0862 .
## jud_ind 0.2077
## exec_corrupt_index 0.1729
## country_feAlgeria 0.4540
## country_feAngola 0.9981
## country_feArmenia 0.1047
## country_feAzerbaijan 0.9984
## country_feBangladesh 0.4432
## country_feBelarus 0.9983
## country_feBurkina Faso 0.1459
## country_feCambodia 0.3346
## country_feCameroon 0.9985
## country_feCentral African Republic 0.9984
## country_feCroatia 0.9982
## country_feDemocratic Republic of the Congo 0.8366
## country_feDjibouti 0.9983
## country_feEgypt 0.9977
## country_feEquatorial Guinea 0.9987
## country_feEthiopia 0.9984
## country_feGabon 0.4397
## country_feGeorgia 0.9481
## country_feGhana 0.9979
## country_feGuinea 0.6506
## country_feGuinea-Bissau 0.6210
## country_feGuyana 0.9981
## country_feHaiti 0.0916 .
## country_feIvory Coast 0.9256
## country_feKazakhstan 0.9987
## country_feKenya 0.9981
## country_feKyrgyzstan 0.0448 *
## country_feLesotho 0.9986
## country_feMadagascar 0.9982
## country_feMalaysia 0.0144 *
## country_feMauritania 0.4257
## country_feMexico 0.9983
## country_feMozambique 0.9984
## country_feNamibia 0.9993
## country_feNicaragua 0.9984
## country_feNiger 0.5729
## country_feNigeria 0.4444
## country_fePanama 0.9986
## country_feParaguay 0.9978
## country_fePeru 0.9980
## country_fePhilippines 0.9981
## country_feRussia 0.8896
## country_feRwanda 0.1483
## country_feSenegal 0.2155
## country_feSerbia 0.6255
## country_feSierra Leone 0.4360
## country_feSingapore 0.9987
## country_feSouth Korea 0.9986
## country_feSri Lanka 0.1377
## country_feTaiwan 0.9986
## country_feTajikistan 0.9985
## country_feTanzania 0.9990
## country_feThe Gambia 0.9982
## country_feTogo 0.5425
## country_feTürkiye 0.9979
## country_feTurkmenistan 0.9990
## country_feUganda 0.4415
## country_feUzbekistan 0.3198
## country_feVenezuela 0.4091
## country_feZambia 0.9986
## country_feZimbabwe 0.7157
## jud_ind:exec_corrupt_index 0.1259
## Log(theta) 0.9318
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7947 3.7409 0.212 0.832
## jud_ind -1.1360 3.1888 -0.356 0.722
## exec_corrupt_index 1.0054 4.7494 0.212 0.832
## jud_ind:exec_corrupt_index 1.9793 3.9090 0.506 0.613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 6963950.9151
## Number of iterations in BFGS optimization: 47
## Log-likelihood: -245.9 on 70 Dfsummary(zinb_models$n_cabinet_zinb2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -6.042e-01 -1.951e-01 -3.388e-05 -1.782e-05 8.904e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -4.5940 3.2669 -1.406
## jud_ind -4.3120 2.8928 -1.491
## exec_corrupt_index 3.1679 4.3907 0.722
## polyarchy 2.4367 4.2818 0.569
## country_feAlgeria 1.2759 2.2515 0.567
## country_feAngola -18.1401 8875.8321 -0.002
## country_feArmenia 2.2700 1.9565 1.160
## country_feAzerbaijan -17.4003 11174.2010 -0.002
## country_feBangladesh 1.3847 1.9899 0.696
## country_feBelarus -17.5706 8854.5558 -0.002
## country_feBurkina Faso 1.6360 1.9094 0.857
## country_feCambodia 2.1555 2.0871 1.033
## country_feCameroon -17.1361 12794.0015 -0.001
## country_feCentral African Republic -17.4561 8967.5781 -0.002
## country_feCroatia -16.1898 6509.5735 -0.002
## country_feDemocratic Republic of the Congo 0.3027 2.0583 0.147
## country_feDjibouti -17.9039 7913.1468 -0.002
## country_feEgypt -18.6295 5460.5638 -0.003
## country_feEquatorial Guinea -17.0131 19622.9442 -0.001
## country_feEthiopia -17.6538 6745.3026 -0.003
## country_feGabon 0.8621 1.8880 0.457
## country_feGeorgia -0.1737 2.1853 -0.079
## country_feGhana -17.7107 5721.3664 -0.003
## country_feGuinea 1.4940 2.2161 0.674
## country_feGuinea-Bissau 0.6958 1.9827 0.351
## country_feGuyana -16.9965 6216.4550 -0.003
## country_feHaiti 2.0190 1.9073 1.059
## country_feIvory Coast -0.2959 2.0650 -0.143
## country_feKazakhstan -16.7982 15634.2638 -0.001
## country_feKenya -17.6996 6816.9149 -0.003
## country_feKyrgyzstan 2.6311 1.9917 1.321
## country_feLesotho -16.3156 6217.9174 -0.003
## country_feMadagascar -17.2828 6949.5511 -0.002
## country_feMalaysia 2.8916 1.7235 1.678
## country_feMauritania 1.6076 2.2411 0.717
## country_feMexico -16.2744 6909.1665 -0.002
## country_feMozambique -17.0902 6784.2001 -0.003
## country_feNamibia -13.2803 8742.3780 -0.002
## country_feNicaragua -16.7653 8724.0918 -0.002
## country_feNiger 0.7801 2.0387 0.383
## country_feNigeria -1.4074 2.1968 -0.641
## country_fePanama -15.7623 7776.8109 -0.002
## country_feParaguay -17.8806 6421.4591 -0.003
## country_fePeru -16.4038 5963.8834 -0.003
## country_fePhilippines -17.2457 6431.3851 -0.003
## country_feRussia 0.1972 2.3006 0.086
## country_feRwanda 1.3537 2.2210 0.609
## country_feSenegal 1.4125 2.6822 0.527
## country_feSerbia 1.0741 2.1882 0.491
## country_feSierra Leone 1.3321 2.0294 0.656
## country_feSingapore -16.4332 6514.3438 -0.003
## country_feSouth Korea -14.3320 6767.8147 -0.002
## country_feSri Lanka 1.5238 1.8994 0.802
## country_feTaiwan -15.1362 5764.5162 -0.003
## country_feTajikistan -17.3801 11835.6732 -0.001
## country_feTanzania -15.2427 7523.8534 -0.002
## country_feThe Gambia -17.7477 7746.6297 -0.002
## country_feTogo 1.0722 2.0686 0.518
## country_feTürkiye -17.2217 5561.0090 -0.003
## country_feTurkmenistan -16.3973 40721.9651 0.000
## country_feUganda 0.4945 1.8154 0.272
## country_feUzbekistan 3.5501 2.3782 1.493
## country_feVenezuela 4.6590 2.6716 1.744
## country_feZambia -15.9383 6698.4072 -0.002
## country_feZimbabwe 0.1333 2.0156 0.066
## jud_ind:exec_corrupt_index 7.3679 3.7173 1.982
## Log(theta) 16.9161 NaN NaN
## Pr(>|z|)
## (Intercept) 0.1597
## jud_ind 0.1361
## exec_corrupt_index 0.4706
## polyarchy 0.5693
## country_feAlgeria 0.5709
## country_feAngola 0.9984
## country_feArmenia 0.2460
## country_feAzerbaijan 0.9988
## country_feBangladesh 0.4865
## country_feBelarus 0.9984
## country_feBurkina Faso 0.3916
## country_feCambodia 0.3017
## country_feCameroon 0.9989
## country_feCentral African Republic 0.9984
## country_feCroatia 0.9980
## country_feDemocratic Republic of the Congo 0.8831
## country_feDjibouti 0.9982
## country_feEgypt 0.9973
## country_feEquatorial Guinea 0.9993
## country_feEthiopia 0.9979
## country_feGabon 0.6479
## country_feGeorgia 0.9366
## country_feGhana 0.9975
## country_feGuinea 0.5002
## country_feGuinea-Bissau 0.7256
## country_feGuyana 0.9978
## country_feHaiti 0.2898
## country_feIvory Coast 0.8861
## country_feKazakhstan 0.9991
## country_feKenya 0.9979
## country_feKyrgyzstan 0.1865
## country_feLesotho 0.9979
## country_feMadagascar 0.9980
## country_feMalaysia 0.0934 .
## country_feMauritania 0.4732
## country_feMexico 0.9981
## country_feMozambique 0.9980
## country_feNamibia 0.9988
## country_feNicaragua 0.9985
## country_feNiger 0.7020
## country_feNigeria 0.5217
## country_fePanama 0.9984
## country_feParaguay 0.9978
## country_fePeru 0.9978
## country_fePhilippines 0.9979
## country_feRussia 0.9317
## country_feRwanda 0.5422
## country_feSenegal 0.5984
## country_feSerbia 0.6235
## country_feSierra Leone 0.5116
## country_feSingapore 0.9980
## country_feSouth Korea 0.9983
## country_feSri Lanka 0.4224
## country_feTaiwan 0.9979
## country_feTajikistan 0.9988
## country_feTanzania 0.9984
## country_feThe Gambia 0.9982
## country_feTogo 0.6042
## country_feTürkiye 0.9975
## country_feTurkmenistan 0.9997
## country_feUganda 0.7853
## country_feUzbekistan 0.1355
## country_feVenezuela 0.0812 .
## country_feZambia 0.9981
## country_feZimbabwe 0.9473
## jud_ind:exec_corrupt_index 0.0475 *
## Log(theta) NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0141 2.7008 -0.005 0.9958
## jud_ind -1.4756 2.1869 -0.675 0.4998
## exec_corrupt_index -0.8861 3.0694 -0.289 0.7728
## polyarchy 6.1814 3.6629 1.688 0.0915 .
## jud_ind:exec_corrupt_index 2.8467 2.8707 0.992 0.3214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 22211694.3377
## Number of iterations in BFGS optimization: 77
## Log-likelihood: -243.7 on 72 Df# Electoral support
summary(zinb_models$n_elec_zinb0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.267e-01 -2.007e-05 -1.625e-05 -1.288e-05 5.673e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -2.491e+01 1.254e+04 -0.002
## jud_ind 2.239e-01 3.323e-01 0.674
## exec_corrupt_index 6.459e+00 2.952e+00 2.188
## country_feAlgeria 1.951e+01 1.254e+04 0.002
## country_feAngola -3.207e-01 1.646e+04 0.000
## country_feArmenia 6.563e-03 1.696e+04 0.000
## country_feAzerbaijan -3.648e-01 1.614e+04 0.000
## country_feBangladesh -5.316e-02 1.711e+04 0.000
## country_feBelarus 1.956e+01 1.254e+04 0.002
## country_feBurkina Faso 1.988e+01 1.254e+04 0.002
## country_feCambodia -4.315e-01 1.622e+04 0.000
## country_feCameroon 1.796e+01 1.254e+04 0.001
## country_feCentral African Republic 1.920e+01 1.254e+04 0.002
## country_feCroatia 4.719e-01 1.859e+04 0.000
## country_feDemocratic Republic of the Congo -3.943e-01 1.652e+04 0.000
## country_feDjibouti 1.870e+01 1.254e+04 0.001
## country_feEgypt 2.199e+01 1.254e+04 0.002
## country_feEquatorial Guinea 1.875e+01 1.254e+04 0.001
## country_feEthiopia 4.047e-01 1.910e+04 0.000
## country_feGabon -1.312e-01 1.716e+04 0.000
## country_feGeorgia 1.891e+01 1.254e+04 0.002
## country_feGhana 1.890e+01 1.254e+04 0.002
## country_feGuinea -4.170e-01 1.614e+04 0.000
## country_feGuinea-Bissau 1.837e+01 1.254e+04 0.001
## country_feGuyana 2.001e-01 1.809e+04 0.000
## country_feHaiti -1.362e-01 1.713e+04 0.000
## country_feIvory Coast 2.886e-03 1.762e+04 0.000
## country_feKazakhstan -4.063e-01 1.594e+04 0.000
## country_feKenya -6.500e-02 1.724e+04 0.000
## country_feKyrgyzstan -2.180e-01 1.660e+04 0.000
## country_feLesotho 4.235e-01 1.895e+04 0.000
## country_feMadagascar 1.136e-01 1.720e+04 0.000
## country_feMalaysia 2.152e+01 1.254e+04 0.002
## country_feMauritania -4.919e-02 1.697e+04 0.000
## country_feMexico 3.322e-01 1.831e+04 0.000
## country_feMozambique 2.267e-01 1.810e+04 0.000
## country_feNamibia 7.456e-01 2.082e+04 0.000
## country_feNicaragua -6.998e-02 1.660e+04 0.000
## country_feNiger 1.979e-01 1.797e+04 0.000
## country_feNigeria -2.808e-01 1.716e+04 0.000
## country_fePanama 4.031e-01 1.888e+04 0.000
## country_feParaguay -2.091e-01 1.708e+04 0.000
## country_fePeru 3.802e-01 1.803e+04 0.000
## country_fePhilippines 9.291e-02 1.782e+04 0.000
## country_feRussia 2.016e+01 1.254e+04 0.002
## country_feRwanda 5.114e-01 2.020e+04 0.000
## country_feSenegal 7.506e-01 2.227e+04 0.000
## country_feSerbia 4.496e-02 1.711e+04 0.000
## country_feSierra Leone -5.307e-02 1.698e+04 0.000
## country_feSingapore 1.139e+00 3.183e+04 0.000
## country_feSouth Korea 7.589e-01 2.064e+04 0.000
## country_feSri Lanka 1.946e+01 1.254e+04 0.002
## country_feTaiwan 8.205e-01 2.158e+04 0.000
## country_feTajikistan -4.133e-01 1.607e+04 0.000
## country_feTanzania 6.261e-01 2.008e+04 0.000
## country_feThe Gambia -2.929e-03 1.714e+04 0.000
## country_feTogo -2.360e-01 1.657e+04 0.000
## country_feTürkiye 1.999e-01 1.821e+04 0.000
## country_feTurkmenistan -4.714e-01 1.571e+04 0.000
## country_feUganda 8.763e-02 1.762e+04 0.000
## country_feUzbekistan -4.441e-01 1.578e+04 0.000
## country_feVenezuela 1.935e+01 1.254e+04 0.002
## country_feZambia 5.675e-01 1.950e+04 0.000
## country_feZimbabwe -5.874e-02 1.739e+04 0.000
## Log(theta) 1.862e+01 NaN NaN
## Pr(>|z|)
## (Intercept) 0.9984
## jud_ind 0.5004
## exec_corrupt_index 0.0287 *
## country_feAlgeria 0.9988
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9988
## country_feBurkina Faso 0.9987
## country_feCambodia 1.0000
## country_feCameroon 0.9989
## country_feCentral African Republic 0.9988
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9988
## country_feEgypt 0.9986
## country_feEquatorial Guinea 0.9988
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9988
## country_feGhana 0.9988
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9988
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9986
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9987
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 1.0000
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9988
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9988
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
## Log(theta) NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5963 2.3598 -0.676 0.499
## jud_ind 0.4389 0.3246 1.352 0.176
## exec_corrupt_index 4.6190 3.2121 1.438 0.150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 122551011.3995
## Number of iterations in BFGS optimization: 52
## Log-likelihood: -119.5 on 68 Dfsummary(zinb_models$n_elec_zinb1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.502e-01 -3.699e-05 -1.448e-05 -1.171e-05 6.823e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -1.961e+01 1.572e+04 -0.001
## jud_ind 4.658e+00 2.211e+00 2.106
## exec_corrupt_index -2.162e+00 4.297e+00 -0.503
## country_feAlgeria 2.052e+01 1.572e+04 0.001
## country_feAngola -4.740e-01 2.153e+04 0.000
## country_feArmenia -3.587e-02 2.150e+04 0.000
## country_feAzerbaijan -6.481e-01 2.013e+04 0.000
## country_feBangladesh -1.190e-01 2.149e+04 0.000
## country_feBelarus 2.067e+01 1.572e+04 0.001
## country_feBurkina Faso 2.005e+01 1.572e+04 0.001
## country_feCambodia -6.767e-01 2.097e+04 0.000
## country_feCameroon 1.816e+01 1.572e+04 0.001
## country_feCentral African Republic 1.975e+01 1.572e+04 0.001
## country_feCroatia 2.543e-01 1.776e+04 0.000
## country_feDemocratic Republic of the Congo -5.225e-01 2.244e+04 0.000
## country_feDjibouti 1.935e+01 1.572e+04 0.001
## country_feEgypt 2.015e+01 1.572e+04 0.001
## country_feEquatorial Guinea 1.939e+01 1.572e+04 0.001
## country_feEthiopia 5.795e-01 2.223e+04 0.000
## country_feGabon -1.421e-01 2.292e+04 0.000
## country_feGeorgia 2.031e+01 1.572e+04 0.001
## country_feGhana 1.924e+01 1.572e+04 0.001
## country_feGuinea -6.990e-01 2.041e+04 0.000
## country_feGuinea-Bissau 1.928e+01 1.572e+04 0.001
## country_feGuyana 8.602e-02 1.950e+04 0.000
## country_feHaiti -1.688e-01 2.251e+04 0.000
## country_feIvory Coast 5.808e-03 2.230e+04 0.000
## country_feKazakhstan -7.657e-01 1.958e+04 0.000
## country_feKenya -1.154e-01 2.184e+04 0.000
## country_feKyrgyzstan -3.323e-01 2.161e+04 0.000
## country_feLesotho 1.678e-01 1.819e+04 0.000
## country_feMadagascar 1.250e-01 2.070e+04 0.000
## country_feMalaysia 2.219e+01 1.572e+04 0.001
## country_feMauritania 3.318e-02 2.344e+04 0.000
## country_feMexico 1.562e-01 1.868e+04 0.000
## country_feMozambique 1.615e-01 2.021e+04 0.000
## country_feNamibia 1.944e-02 1.652e+04 0.000
## country_feNicaragua -1.604e-01 2.075e+04 0.000
## country_feNiger 1.669e-01 2.069e+04 0.000
## country_feNigeria -2.388e-01 2.538e+04 0.000
## country_fePanama 3.227e-01 1.947e+04 0.000
## country_feParaguay -2.384e-01 2.299e+04 0.000
## country_fePeru -2.178e-02 1.689e+04 0.000
## country_fePhilippines 2.646e-02 2.076e+04 0.000
## country_feRussia 2.107e+01 1.572e+04 0.001
## country_feRwanda 8.126e-01 2.240e+04 0.000
## country_feSenegal 8.509e-01 1.975e+04 0.000
## country_feSerbia -7.828e-02 2.041e+04 0.000
## country_feSierra Leone -6.159e-02 2.217e+04 0.000
## country_feSingapore 1.523e+00 2.111e+04 0.000
## country_feSouth Korea 2.501e-01 1.659e+04 0.000
## country_feSri Lanka 1.777e+01 1.572e+04 0.001
## country_feTaiwan 2.147e-01 1.617e+04 0.000
## country_feTajikistan -7.344e-01 1.991e+04 0.000
## country_feTanzania 2.756e-01 1.750e+04 0.000
## country_feThe Gambia -6.669e-02 2.068e+04 0.000
## country_feTogo -3.549e-01 2.158e+04 0.000
## country_feTürkiye -1.810e-02 1.864e+04 0.000
## country_feTurkmenistan -9.810e-01 1.870e+04 0.000
## country_feUganda 9.215e-02 2.185e+04 0.000
## country_feUzbekistan -9.035e-01 1.887e+04 0.000
## country_feVenezuela 1.837e+01 1.572e+04 0.001
## country_feZambia 3.655e-01 1.816e+04 0.000
## country_feZimbabwe -1.008e-01 2.156e+04 0.000
## jud_ind:exec_corrupt_index -6.439e+00 3.078e+00 -2.092
## Log(theta) 1.688e+01 3.385e+01 0.499
## Pr(>|z|)
## (Intercept) 0.9990
## jud_ind 0.0352 *
## exec_corrupt_index 0.6149
## country_feAlgeria 0.9990
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9990
## country_feBurkina Faso 0.9990
## country_feCambodia 1.0000
## country_feCameroon 0.9991
## country_feCentral African Republic 0.9990
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9990
## country_feEgypt 0.9990
## country_feEquatorial Guinea 0.9990
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9990
## country_feGhana 0.9990
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9990
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9989
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9989
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 0.9999
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9991
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9991
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
## jud_ind:exec_corrupt_index 0.0364 *
## Log(theta) 0.6181
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.199 2.340 1.367 0.1717
## jud_ind 2.613 1.342 1.946 0.0516 .
## exec_corrupt_index -2.714 3.537 -0.767 0.4428
## jud_ind:exec_corrupt_index -3.637 1.728 -2.104 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 21328138.3958
## Number of iterations in BFGS optimization: 48
## Log-likelihood: -119.1 on 70 Dfsummary(zinb_models$n_elec_zinb2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -4.896e-01 -2.385e-05 -1.488e-05 -1.115e-05 6.445e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -1.851e+01 1.563e+04 -0.001
## jud_ind 3.849e+00 1.971e+00 1.952
## exec_corrupt_index 2.134e-01 3.257e+00 0.066
## polyarchy -8.118e+00 3.922e+00 -2.070
## country_feAlgeria 2.054e+01 1.563e+04 0.001
## country_feAngola -7.810e-01 2.049e+04 0.000
## country_feArmenia 1.501e-01 2.042e+04 0.000
## country_feAzerbaijan -6.906e-01 1.958e+04 0.000
## country_feBangladesh -8.743e-02 2.101e+04 0.000
## country_feBelarus 2.071e+01 1.563e+04 0.001
## country_feBurkina Faso 2.058e+01 1.563e+04 0.001
## country_feCambodia -5.596e-01 1.927e+04 0.000
## country_feCameroon 1.799e+01 1.563e+04 0.001
## country_feCentral African Republic 1.969e+01 1.563e+04 0.001
## country_feCroatia 6.593e-01 2.006e+04 0.000
## country_feDemocratic Republic of the Congo -7.497e-01 2.044e+04 0.000
## country_feDjibouti 1.904e+01 1.563e+04 0.001
## country_feEgypt 1.918e+01 1.563e+04 0.001
## country_feEquatorial Guinea 1.867e+01 1.563e+04 0.001
## country_feEthiopia 1.180e-01 2.480e+04 0.000
## country_feGabon -2.871e-01 2.128e+04 0.000
## country_feGeorgia 1.998e+01 1.563e+04 0.001
## country_feGhana 1.926e+01 1.563e+04 0.001
## country_feGuinea -6.452e-01 1.928e+04 0.000
## country_feGuinea-Bissau 1.903e+01 1.563e+04 0.001
## country_feGuyana 1.900e-01 2.104e+04 0.000
## country_feHaiti -2.696e-01 2.132e+04 0.000
## country_feIvory Coast -1.033e-01 2.168e+04 0.000
## country_feKazakhstan -5.322e-01 1.902e+04 0.000
## country_feKenya -1.270e-01 2.117e+04 0.000
## country_feKyrgyzstan -2.295e-01 1.993e+04 0.000
## country_feLesotho -1.159e-01 2.024e+04 0.000
## country_feMadagascar 4.265e-01 2.040e+04 0.000
## country_feMalaysia 2.180e+01 1.563e+04 0.001
## country_feMauritania 3.314e-01 2.204e+04 0.000
## country_feMexico 4.675e-01 2.035e+04 0.000
## country_feMozambique -5.452e-02 2.149e+04 0.000
## country_feNamibia -3.111e-03 1.751e+04 0.000
## country_feNicaragua 4.385e-01 1.959e+04 0.000
## country_feNiger 2.857e-01 2.176e+04 0.000
## country_feNigeria -3.498e-01 2.231e+04 0.000
## country_fePanama 1.015e+00 2.301e+04 0.000
## country_feParaguay 2.030e-01 2.241e+04 0.000
## country_fePeru 2.127e-01 1.826e+04 0.000
## country_fePhilippines 1.941e-01 2.154e+04 0.000
## country_feRussia 2.106e+01 1.563e+04 0.001
## country_feRwanda 2.003e-01 2.620e+04 0.000
## country_feSenegal 1.417e+00 2.539e+04 0.000
## country_feSerbia -6.086e-03 2.026e+04 0.000
## country_feSierra Leone 9.622e-02 2.173e+04 0.000
## country_feSingapore 1.258e+00 2.883e+04 0.000
## country_feSouth Korea 7.838e-01 1.788e+04 0.000
## country_feSri Lanka 1.891e+01 1.563e+04 0.001
## country_feTaiwan 4.293e-01 1.715e+04 0.000
## country_feTajikistan -7.768e-01 1.931e+04 0.000
## country_feTanzania -9.669e-02 1.967e+04 0.000
## country_feThe Gambia -3.322e-01 2.110e+04 0.000
## country_feTogo -2.185e-01 2.007e+04 0.000
## country_feTürkiye -1.320e-01 1.982e+04 0.000
## country_feTurkmenistan -9.904e-01 1.946e+04 0.000
## country_feUganda -2.582e-01 2.175e+04 0.000
## country_feUzbekistan -8.233e-01 1.913e+04 0.000
## country_feVenezuela 1.962e+01 1.563e+04 0.001
## country_feZambia 1.498e-01 2.048e+04 0.000
## country_feZimbabwe -5.888e-01 2.136e+04 0.000
## jud_ind:exec_corrupt_index -5.081e+00 2.648e+00 -1.919
## Log(theta) 1.684e+01 NaN NaN
## Pr(>|z|)
## (Intercept) 0.9991
## jud_ind 0.0509 .
## exec_corrupt_index 0.9478
## polyarchy 0.0385 *
## country_feAlgeria 0.9990
## country_feAngola 1.0000
## country_feArmenia 1.0000
## country_feAzerbaijan 1.0000
## country_feBangladesh 1.0000
## country_feBelarus 0.9989
## country_feBurkina Faso 0.9989
## country_feCambodia 1.0000
## country_feCameroon 0.9991
## country_feCentral African Republic 0.9990
## country_feCroatia 1.0000
## country_feDemocratic Republic of the Congo 1.0000
## country_feDjibouti 0.9990
## country_feEgypt 0.9990
## country_feEquatorial Guinea 0.9990
## country_feEthiopia 1.0000
## country_feGabon 1.0000
## country_feGeorgia 0.9990
## country_feGhana 0.9990
## country_feGuinea 1.0000
## country_feGuinea-Bissau 0.9990
## country_feGuyana 1.0000
## country_feHaiti 1.0000
## country_feIvory Coast 1.0000
## country_feKazakhstan 1.0000
## country_feKenya 1.0000
## country_feKyrgyzstan 1.0000
## country_feLesotho 1.0000
## country_feMadagascar 1.0000
## country_feMalaysia 0.9989
## country_feMauritania 1.0000
## country_feMexico 1.0000
## country_feMozambique 1.0000
## country_feNamibia 1.0000
## country_feNicaragua 1.0000
## country_feNiger 1.0000
## country_feNigeria 1.0000
## country_fePanama 1.0000
## country_feParaguay 1.0000
## country_fePeru 1.0000
## country_fePhilippines 1.0000
## country_feRussia 0.9989
## country_feRwanda 1.0000
## country_feSenegal 1.0000
## country_feSerbia 1.0000
## country_feSierra Leone 1.0000
## country_feSingapore 1.0000
## country_feSouth Korea 1.0000
## country_feSri Lanka 0.9990
## country_feTaiwan 1.0000
## country_feTajikistan 1.0000
## country_feTanzania 1.0000
## country_feThe Gambia 1.0000
## country_feTogo 1.0000
## country_feTürkiye 1.0000
## country_feTurkmenistan 1.0000
## country_feUganda 1.0000
## country_feUzbekistan 1.0000
## country_feVenezuela 0.9990
## country_feZambia 1.0000
## country_feZimbabwe 1.0000
## jud_ind:exec_corrupt_index 0.0550 .
## Log(theta) NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.064 2.925 1.732 0.0833 .
## jud_ind 2.242 1.427 1.571 0.1161
## exec_corrupt_index -2.088 4.000 -0.522 0.6017
## polyarchy -7.899 4.763 -1.658 0.0973 .
## jud_ind:exec_corrupt_index -3.182 1.906 -1.669 0.0952 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 20593968.1762
## Number of iterations in BFGS optimization: 58
## Log-likelihood: -117.1 on 72 Df# Parliamentary support
summary(zinb_models$n_parl_zinb0)##
## Call:
## zeroinfl(formula = f0, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.450e-01 -2.300e-01 -3.995e-05 -2.553e-05 5.964e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -5.3951 1.9535 -2.762
## jud_ind 0.1246 0.2611 0.477
## exec_corrupt_index 6.9793 2.1816 3.199
## country_feAlgeria 0.5257 1.4729 0.357
## country_feAngola -19.1870 4231.3125 -0.005
## country_feArmenia -1.5697 1.7210 -0.912
## country_feAzerbaijan -2.0490 1.6327 -1.255
## country_feBangladesh -18.6320 5016.8484 -0.004
## country_feBelarus -0.2237 1.4811 -0.151
## country_feBurkina Faso -0.6039 1.6454 -0.367
## country_feCambodia -1.4968 1.5393 -0.972
## country_feCameroon -2.0907 1.6360 -1.278
## country_feCentral African Republic -18.8125 4480.9003 -0.004
## country_feCroatia -17.5485 5496.4504 -0.003
## country_feDemocratic Republic of the Congo -0.9075 1.5015 -0.604
## country_feDjibouti -0.7159 1.5241 -0.470
## country_feEgypt 2.2122 1.4337 1.543
## country_feEquatorial Guinea -19.4478 3749.9673 -0.005
## country_feEthiopia -17.5347 6606.1255 -0.003
## country_feGabon -18.8137 5121.2864 -0.004
## country_feGeorgia -0.7321 1.4459 -0.506
## country_feGhana -18.5311 6350.0392 -0.003
## country_feGuinea -19.3583 3973.9179 -0.005
## country_feGuinea-Bissau -19.1484 4705.3869 -0.004
## country_feGuyana -18.1319 6025.6958 -0.003
## country_feHaiti -18.8285 5069.7827 -0.004
## country_feIvory Coast -18.5386 5638.5688 -0.003
## country_feKazakhstan -0.3289 1.5467 -0.213
## country_feKenya -18.6696 5236.5732 -0.004
## country_feKyrgyzstan -0.2423 1.4466 -0.168
## country_feLesotho -17.6275 7074.8970 -0.002
## country_feMadagascar -1.5205 1.7700 -0.859
## country_feMalaysia 1.6212 1.3245 1.224
## country_feMauritania -0.1637 1.5242 -0.107
## country_feMexico -17.8305 5940.4210 -0.003
## country_feMozambique -18.0177 6254.3255 -0.003
## country_feNamibia -16.9267 9175.4824 -0.002
## country_feNicaragua -18.6126 4105.8065 -0.005
## country_feNiger 0.4792 1.4181 0.338
## country_feNigeria -19.1803 5007.6227 -0.004
## country_fePanama -17.6056 6963.8661 -0.003
## country_feParaguay -19.0164 4866.2171 -0.004
## country_fePeru -17.8042 5154.0733 -0.003
## country_fePhilippines -18.3512 5910.0669 -0.003
## country_feRussia 1.0119 1.4278 0.709
## country_feRwanda -17.2769 6946.4419 -0.002
## country_feSenegal -16.7777 8215.3642 -0.002
## country_feSerbia -1.8267 1.7234 -1.060
## country_feSierra Leone -18.5707 4887.7656 -0.004
## country_feSingapore -15.8671 10509.7540 -0.002
## country_feSouth Korea -16.9092 7547.0636 -0.002
## country_feSri Lanka 0.8454 1.3824 0.612
## country_feTaiwan -16.7217 8491.9103 -0.002
## country_feTajikistan -1.5973 1.5615 -1.023
## country_feTanzania -17.1508 8027.4174 -0.002
## country_feThe Gambia -18.4985 5093.7590 -0.004
## country_feTogo -18.9856 4456.3887 -0.004
## country_feTürkiye -18.1970 5848.9054 -0.003
## country_feTurkmenistan -3.0102 1.8940 -1.589
## country_feUganda -18.3338 5456.8969 -0.003
## country_feUzbekistan -1.0734 1.6352 -0.656
## country_feVenezuela -1.9346 1.7144 -1.128
## country_feZambia -17.2998 6896.4871 -0.003
## country_feZimbabwe -18.7135 5077.5494 -0.004
## Log(theta) 16.5575 NaN NaN
## Pr(>|z|)
## (Intercept) 0.00575 **
## jud_ind 0.63325
## exec_corrupt_index 0.00138 **
## country_feAlgeria 0.72113
## country_feAngola 0.99638
## country_feArmenia 0.36172
## country_feAzerbaijan 0.20949
## country_feBangladesh 0.99704
## country_feBelarus 0.87994
## country_feBurkina Faso 0.71361
## country_feCambodia 0.33085
## country_feCameroon 0.20128
## country_feCentral African Republic 0.99665
## country_feCroatia 0.99745
## country_feDemocratic Republic of the Congo 0.54556
## country_feDjibouti 0.63857
## country_feEgypt 0.12284
## country_feEquatorial Guinea 0.99586
## country_feEthiopia 0.99788
## country_feGabon 0.99707
## country_feGeorgia 0.61261
## country_feGhana 0.99767
## country_feGuinea 0.99611
## country_feGuinea-Bissau 0.99675
## country_feGuyana 0.99760
## country_feHaiti 0.99704
## country_feIvory Coast 0.99738
## country_feKazakhstan 0.83158
## country_feKenya 0.99716
## country_feKyrgyzstan 0.86697
## country_feLesotho 0.99801
## country_feMadagascar 0.39032
## country_feMalaysia 0.22095
## country_feMauritania 0.91449
## country_feMexico 0.99761
## country_feMozambique 0.99770
## country_feNamibia 0.99853
## country_feNicaragua 0.99638
## country_feNiger 0.73543
## country_feNigeria 0.99694
## country_fePanama 0.99798
## country_feParaguay 0.99688
## country_fePeru 0.99724
## country_fePhilippines 0.99752
## country_feRussia 0.47848
## country_feRwanda 0.99802
## country_feSenegal 0.99837
## country_feSerbia 0.28915
## country_feSierra Leone 0.99697
## country_feSingapore 0.99880
## country_feSouth Korea 0.99821
## country_feSri Lanka 0.54086
## country_feTaiwan 0.99843
## country_feTajikistan 0.30634
## country_feTanzania 0.99830
## country_feThe Gambia 0.99710
## country_feTogo 0.99660
## country_feTürkiye 0.99752
## country_feTurkmenistan 0.11199
## country_feUganda 0.99732
## country_feUzbekistan 0.51154
## country_feVenezuela 0.25914
## country_feZambia 0.99800
## country_feZimbabwe 0.99706
## Log(theta) NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1691 1.4663 0.115 0.9082
## jud_ind 0.4201 0.2109 1.992 0.0463 *
## exec_corrupt_index 1.8027 1.9194 0.939 0.3476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 15518332.6601
## Number of iterations in BFGS optimization: 57
## Log-likelihood: -272.3 on 68 Dfsummary(zinb_models$n_parl_zinb1)##
## Call:
## zeroinfl(formula = f1, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.500e-01 -2.307e-01 -4.044e-05 -2.349e-05 5.865e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -5.5939 2.0631 -2.711
## jud_ind -0.4134 1.2913 -0.320
## exec_corrupt_index 7.3790 2.4360 3.029
## country_feAlgeria 0.3722 1.6564 0.225
## country_feAngola -19.2904 4370.0114 -0.004
## country_feArmenia -1.6407 1.8585 -0.883
## country_feAzerbaijan -2.0150 1.7868 -1.128
## country_feBangladesh -18.6952 5004.4132 -0.004
## country_feBelarus -0.3477 1.6621 -0.209
## country_feBurkina Faso -0.6486 1.7751 -0.365
## country_feCambodia -1.4930 1.7013 -0.878
## country_feCameroon -2.0571 1.7918 -1.148
## country_feCentral African Republic -18.8990 4577.0041 -0.004
## country_feCroatia -17.8131 6122.8566 -0.003
## country_feDemocratic Republic of the Congo -1.0265 1.6823 -0.610
## country_feDjibouti -0.8106 1.6802 -0.482
## country_feEgypt 2.3599 1.5942 1.480
## country_feEquatorial Guinea -19.8234 4781.3490 -0.004
## country_feEthiopia -17.4675 6052.5185 -0.003
## country_feGabon -18.8072 4821.9644 -0.004
## country_feGeorgia -0.8664 1.6643 -0.521
## country_feGhana -18.5663 6183.5022 -0.003
## country_feGuinea -19.5928 4527.6420 -0.004
## country_feGuinea-Bissau -19.1322 4377.1236 -0.004
## country_feGuyana -18.2740 6340.2068 -0.003
## country_feHaiti -18.8410 4858.1394 -0.004
## country_feIvory Coast -18.5375 5342.8710 -0.003
## country_feKazakhstan -0.2498 1.7146 -0.146
## country_feKenya -18.7145 5155.4212 -0.004
## country_feKyrgyzstan -0.2781 1.6179 -0.172
## country_feLesotho -17.9555 8761.1270 -0.002
## country_feMadagascar -1.5428 1.8927 -0.815
## country_feMalaysia 1.5375 1.5093 1.019
## country_feMauritania -0.2983 1.6977 -0.176
## country_feMexico -18.0530 6600.9789 -0.003
## country_feMozambique -18.1335 6571.6647 -0.003
## country_feNamibia -17.7897 17952.9897 -0.001
## country_feNicaragua -18.6703 4228.7591 -0.004
## country_feNiger 0.4437 1.5657 0.283
## country_feNigeria -19.0821 4365.5858 -0.004
## country_fePanama -17.7771 7744.3310 -0.002
## country_feParaguay -19.0020 4547.3556 -0.004
## country_fePeru -18.1956 6131.0597 -0.003
## country_fePhilippines -18.4315 5964.7368 -0.003
## country_feRussia 0.8897 1.6094 0.553
## country_feRwanda -17.0971 5815.1832 -0.003
## country_feSenegal -16.8472 8526.4542 -0.002
## country_feSerbia -1.8567 1.8667 -0.995
## country_feSierra Leone -18.5938 4756.9997 -0.004
## country_feSingapore -15.7391 9435.5348 -0.002
## country_feSouth Korea -17.5145 11108.6803 -0.002
## country_feSri Lanka 0.8065 1.5588 0.517
## country_feTaiwan -17.4073 13139.6295 -0.001
## country_feTajikistan -1.5349 1.7228 -0.891
## country_feTanzania -17.6239 11428.7034 -0.002
## country_feThe Gambia -18.5731 5089.7544 -0.004
## country_feTogo -19.0812 4574.8615 -0.004
## country_feTürkiye -18.4101 6303.9466 -0.003
## country_feTurkmenistan -2.8248 2.0714 -1.364
## country_feUganda -18.3464 5261.2471 -0.003
## country_feUzbekistan -0.9215 1.8220 -0.506
## country_feVenezuela -1.7742 1.8917 -0.938
## country_feZambia -17.6013 8336.1112 -0.002
## country_feZimbabwe -18.7297 4784.5762 -0.004
## jud_ind:exec_corrupt_index 0.7396 1.7288 0.428
## Log(theta) 17.2655 15.1150 1.142
## Pr(>|z|)
## (Intercept) 0.00670 **
## jud_ind 0.74886
## exec_corrupt_index 0.00245 **
## country_feAlgeria 0.82223
## country_feAngola 0.99648
## country_feArmenia 0.37733
## country_feAzerbaijan 0.25945
## country_feBangladesh 0.99702
## country_feBelarus 0.83428
## country_feBurkina Faso 0.71480
## country_feCambodia 0.38018
## country_feCameroon 0.25095
## country_feCentral African Republic 0.99671
## country_feCroatia 0.99768
## country_feDemocratic Republic of the Congo 0.54175
## country_feDjibouti 0.62949
## country_feEgypt 0.13879
## country_feEquatorial Guinea 0.99669
## country_feEthiopia 0.99770
## country_feGabon 0.99689
## country_feGeorgia 0.60267
## country_feGhana 0.99760
## country_feGuinea 0.99655
## country_feGuinea-Bissau 0.99651
## country_feGuyana 0.99770
## country_feHaiti 0.99691
## country_feIvory Coast 0.99723
## country_feKazakhstan 0.88418
## country_feKenya 0.99710
## country_feKyrgyzstan 0.86353
## country_feLesotho 0.99836
## country_feMadagascar 0.41500
## country_feMalaysia 0.30835
## country_feMauritania 0.86053
## country_feMexico 0.99782
## country_feMozambique 0.99780
## country_feNamibia 0.99921
## country_feNicaragua 0.99648
## country_feNiger 0.77690
## country_feNigeria 0.99651
## country_fePanama 0.99817
## country_feParaguay 0.99667
## country_fePeru 0.99763
## country_fePhilippines 0.99753
## country_feRussia 0.58041
## country_feRwanda 0.99765
## country_feSenegal 0.99842
## country_feSerbia 0.31990
## country_feSierra Leone 0.99688
## country_feSingapore 0.99867
## country_feSouth Korea 0.99874
## country_feSri Lanka 0.60490
## country_feTaiwan 0.99894
## country_feTajikistan 0.37297
## country_feTanzania 0.99877
## country_feThe Gambia 0.99709
## country_feTogo 0.99667
## country_feTürkiye 0.99767
## country_feTurkmenistan 0.17265
## country_feUganda 0.99722
## country_feUzbekistan 0.61302
## country_feVenezuela 0.34829
## country_feZambia 0.99832
## country_feZimbabwe 0.99688
## jud_ind:exec_corrupt_index 0.66879
## Log(theta) 0.25334
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1008 1.6280 0.062 0.951
## jud_ind 0.2855 1.1360 0.251 0.802
## exec_corrupt_index 1.9064 2.1891 0.871 0.384
## jud_ind:exec_corrupt_index 0.1850 1.4818 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 31501403.6354
## Number of iterations in BFGS optimization: 64
## Log-likelihood: -272.2 on 70 Dfsummary(zinb_models$n_parl_zinb2)##
## Call:
## zeroinfl(formula = f2, data = subset_data, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -5.776e-01 -2.276e-01 -2.773e-05 -1.578e-05 6.813e+00
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value
## (Intercept) -2.595e+00 2.566e+00 -1.011
## jud_ind 2.474e-01 1.297e+00 0.191
## exec_corrupt_index 6.451e+00 2.316e+00 2.785
## polyarchy -8.978e+00 3.600e+00 -2.494
## country_feAlgeria 1.403e+00 1.136e+00 1.235
## country_feAngola -1.991e+01 6.980e+03 -0.003
## country_feArmenia -8.293e-01 1.394e+00 -0.595
## country_feAzerbaijan -1.506e+00 1.340e+00 -1.124
## country_feBangladesh -1.871e+01 7.060e+03 -0.003
## country_feBelarus 7.351e-01 1.160e+00 0.634
## country_feBurkina Faso 4.223e-01 1.412e+00 0.299
## country_feCambodia -5.985e-01 1.194e+00 -0.501
## country_feCameroon -1.314e+00 1.311e+00 -1.003
## country_feCentral African Republic -1.880e+01 6.390e+03 -0.003
## country_feCroatia -1.735e+01 7.526e+03 -0.002
## country_feDemocratic Republic of the Congo -1.709e-01 1.092e+00 -0.157
## country_feDjibouti -5.295e-01 1.220e+00 -0.434
## country_feEgypt 9.494e-01 1.603e+00 0.592
## country_feEquatorial Guinea -2.010e+01 6.621e+03 -0.003
## country_feEthiopia -1.819e+01 8.403e+03 -0.002
## country_feGabon -1.908e+01 6.940e+03 -0.003
## country_feGeorgia -9.301e-02 1.008e+00 -0.092
## country_feGhana -1.848e+01 8.253e+03 -0.002
## country_feGuinea -1.969e+01 6.386e+03 -0.003
## country_feGuinea-Bissau -1.920e+01 6.462e+03 -0.003
## country_feGuyana -1.815e+01 8.443e+03 -0.002
## country_feHaiti -1.906e+01 6.980e+03 -0.003
## country_feIvory Coast -1.876e+01 7.310e+03 -0.003
## country_feKazakhstan 5.390e-01 1.243e+00 0.433
## country_feKenya -1.879e+01 7.138e+03 -0.003
## country_feKyrgyzstan 5.260e-01 1.035e+00 0.508
## country_feLesotho -1.855e+01 1.051e+04 -0.002
## country_feMadagascar -7.123e-01 1.488e+00 -0.479
## country_feMalaysia 1.844e+00 9.244e-01 1.995
## country_feMauritania 7.896e-01 1.180e+00 0.669
## country_feMexico -1.759e+01 9.349e+03 -0.002
## country_feMozambique -1.849e+01 8.953e+03 -0.002
## country_feNamibia -1.791e+01 2.734e+04 -0.001
## country_feNicaragua -1.792e+01 5.513e+03 -0.003
## country_feNiger 1.070e+00 1.044e+00 1.025
## country_feNigeria -1.931e+01 6.656e+03 -0.003
## country_fePanama -1.671e+01 1.338e+04 -0.001
## country_feParaguay -1.834e+01 6.362e+03 -0.003
## country_fePeru -1.802e+01 7.717e+03 -0.002
## country_fePhilippines -1.817e+01 8.134e+03 -0.002
## country_feRussia 1.845e+00 1.036e+00 1.781
## country_feRwanda -1.806e+01 8.520e+03 -0.002
## country_feSenegal -1.591e+01 1.627e+04 -0.001
## country_feSerbia -1.410e+00 1.425e+00 -0.989
## country_feSierra Leone -1.847e+01 6.823e+03 -0.003
## country_feSingapore -1.604e+01 1.354e+04 -0.001
## country_feSouth Korea -1.672e+01 1.990e+04 -0.001
## country_feSri Lanka 1.435e+00 1.016e+00 1.412
## country_feTaiwan -1.721e+01 1.501e+04 -0.001
## country_feTajikistan -1.303e+00 1.246e+00 -1.046
## country_feTanzania -1.825e+01 1.389e+04 -0.001
## country_feThe Gambia -1.907e+01 7.452e+03 -0.003
## country_feTogo -1.899e+01 6.328e+03 -0.003
## country_feTürkiye -1.864e+01 8.543e+03 -0.002
## country_feTurkmenistan -2.508e+00 1.800e+00 -1.394
## country_feUganda -1.892e+01 7.534e+03 -0.003
## country_feUzbekistan -6.937e-01 1.440e+00 -0.482
## country_feVenezuela -3.055e-01 1.577e+00 -0.194
## country_feZambia -1.796e+01 1.101e+04 -0.002
## country_feZimbabwe -1.951e+01 7.668e+03 -0.003
## jud_ind:exec_corrupt_index 2.279e-01 1.665e+00 0.137
## Log(theta) 1.661e+01 2.271e+01 0.731
## Pr(>|z|)
## (Intercept) 0.31180
## jud_ind 0.84872
## exec_corrupt_index 0.00535 **
## polyarchy 0.01264 *
## country_feAlgeria 0.21701
## country_feAngola 0.99772
## country_feArmenia 0.55194
## country_feAzerbaijan 0.26091
## country_feBangladesh 0.99789
## country_feBelarus 0.52625
## country_feBurkina Faso 0.76493
## country_feCambodia 0.61636
## country_feCameroon 0.31609
## country_feCentral African Republic 0.99765
## country_feCroatia 0.99816
## country_feDemocratic Republic of the Congo 0.87562
## country_feDjibouti 0.66443
## country_feEgypt 0.55357
## country_feEquatorial Guinea 0.99758
## country_feEthiopia 0.99827
## country_feGabon 0.99781
## country_feGeorgia 0.92651
## country_feGhana 0.99821
## country_feGuinea 0.99754
## country_feGuinea-Bissau 0.99763
## country_feGuyana 0.99829
## country_feHaiti 0.99782
## country_feIvory Coast 0.99795
## country_feKazakhstan 0.66469
## country_feKenya 0.99790
## country_feKyrgyzstan 0.61116
## country_feLesotho 0.99859
## country_feMadagascar 0.63205
## country_feMalaysia 0.04601 *
## country_feMauritania 0.50348
## country_feMexico 0.99850
## country_feMozambique 0.99835
## country_feNamibia 0.99948
## country_feNicaragua 0.99741
## country_feNiger 0.30520
## country_feNigeria 0.99769
## country_fePanama 0.99900
## country_feParaguay 0.99770
## country_fePeru 0.99814
## country_fePhilippines 0.99822
## country_feRussia 0.07498 .
## country_feRwanda 0.99831
## country_feSenegal 0.99922
## country_feSerbia 0.32248
## country_feSierra Leone 0.99784
## country_feSingapore 0.99905
## country_feSouth Korea 0.99933
## country_feSri Lanka 0.15781
## country_feTaiwan 0.99908
## country_feTajikistan 0.29563
## country_feTanzania 0.99895
## country_feThe Gambia 0.99796
## country_feTogo 0.99761
## country_feTürkiye 0.99826
## country_feTurkmenistan 0.16345
## country_feUganda 0.99800
## country_feUzbekistan 0.63006
## country_feVenezuela 0.84642
## country_feZambia 0.99870
## country_feZimbabwe 0.99797
## jud_ind:exec_corrupt_index 0.89110
## Log(theta) 0.46451
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.2370 1.8475 0.670 0.5031
## jud_ind -0.4793 1.2091 -0.396 0.6918
## exec_corrupt_index 2.9468 2.3734 1.242 0.2144
## polyarchy -7.0393 4.2321 -1.663 0.0963 .
## jud_ind:exec_corrupt_index 1.0938 1.5644 0.699 0.4845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 16338728.7736
## Number of iterations in BFGS optimization: 70
## Log-likelihood: -269.7 on 72 Df