Online appendix for “Pandemic Pass? Treaty Derogations and Human Rights Practices During COVID-19”

(Research note)

Authors

Affiliations

Suparna Chaudhry

Lewis and Clark College

Audrey L. Comstock

Arizona State University

Andrew Heiss

Georgia State University

Latest version

July 25, 2024

Modeling approach

We use Stan 2.34.1 (Stan Development Team 2023) through R 4.4.0 (R Core Team 2023) and {brms} 2.21.0 (Bürkner 2017) to estimate our models. We generate 4 MCMC chains for each model with 2,000 iterations in each chain, 1,000 of which are used for warmup. All chains converge; we assess convergence with visual inspection.

Complete results from all the models, along with posterior predictive checks, goodness-of-fit measures, and prediction diagnostics are all available at a companion statistical analysis compendium at https://doi.org/10.17605/OSF.IO/7GVDA.

Priors

We follow the suggestion of Gelman et al. (2008) and use weakly informative priors for our logistic and ordered logistic regression models. For consistency with prior specification, and for computation efficiency, we mean-center all nonbinary variables so that parameter estimates represent changes from the mean. We use two general priors (see Figure 1):

For all $\beta$ terms, we use a Student t distribution with a mean of 0 and a standard deviation of 3. This keeps most parameter estimates around −5 to 5, with thicker tails that allow for some possibility of extreme values.
For $\sigma$ terms related to the variance or standard deviation of parameter distributions, which must be positive, we use a half Cauchy distribution, centered at 0 with a $\gamma$ of 1

These priors give more weight to realistic areas of parameter values and downweight values in unrealistic spaces. For instance, since logit-scale coefficient values greater than 4 or 5 are highly unlikely, our Student t prior puts more weight on smaller values. Additionally, weakly informative priors allow reasonable and considerable uncertainty in possible parameter estimates.

Figure A 1: Density plots of prior distributions for model parameters

Model definitions

H₁: Logistic regression

Figure A 2: Hierarchy of country-week data, showing location and frequency of measured variables

\[ \begin{aligned} &\ \mathrlap{\textbf{Binary outcome $i$ across week $t$ within each country $j$}} \\ \text{Outcome}_{it_j} \sim&\ \operatorname{Bernoulli}(\pi_{it_j}) \\[0.75em] &\ \textbf{Distribution parameters} \\ \pi_{it_j} =&\ (\beta_0 + b_{0_j}) + \beta_1 \text{Derogation in effect}_{it} + \\ &\ \beta_2\ \text{New cases}_{it}\ + \beta_3\ \text{Cumulative cases}_{it}\ + \\ &\ \beta_4\ \text{New deaths}_{it}\ + \beta_5\ \text{Cumulative deaths}_{it}\ + \\ &\ \beta_6\ \text{Past ICCPR derogation}_{it}\ + \beta_7\ \text{Past ICCPR action}_{it}\ + \\ &\ \beta_8\ \text{Rule of law index}_{it}\ + \beta_9\ \text{Civil liberties index}_{it}\ + \\ &\ \beta_{10}\ \text{Core civil society index}_{it}\ + \beta_{11}\ \text{Week number}_{it} \\ b_{0_j} \sim&\ \mathcal{N}(0, \sigma_0) \\[0.75em] &\ \textbf{Priors} \\ \beta_{0 \dots 11} \sim&\ \operatorname{Student\ t}(\nu = 1, \mu = 0, \sigma = 3) \\ \sigma_0 \sim&\ \operatorname{Cauchy}(x = 0, \gamma = 1) \text{, lower bound} = 0 \end{aligned} \]

The actual R code for these models is included in the replication code at https://doi.org/10.17605/OSF.IO/ANONYMIZED-FOR-NOW This is a simplified representation of the {brms} (Bürkner 2017) model code:

# H1: Logistic regression for binary outcomes
brm(
  bf(outcome ~ derogation_ineffect + 
      new_cases_z + cumulative_cases_z + 
      new_deaths_z + cumulative_deaths_z +
      prior_iccpr_derogations + prior_iccpr_other_action + 
      v2x_rule + v2x_civlib + v2xcs_ccsi + 
      year_week_num + (1 | country_name)),
  family = bernoulli(),
  prior = c(
    prior(student_t(1, 0, 3), class = Intercept),
    prior(student_t(1, 0, 3), class = b),
    prior(cauchy(0, 1), class = sd, lb = 0)),
  ...
)

H₂: Ordered logistic regression

Figure A 3: Hierarchy of region-quarter data, showing location and frequency of measured variables

\[ \begin{aligned} &\ \mathrlap{\textbf{Model of outcome level $i$ across quarter $t$ within each region $j$}} \\ \text{Outcome}_{it_j} \sim&\ \operatorname{Ordered\ logit}(\phi_{it_j}, \alpha_k) \\[0.75em] &\ \textbf{Models for distribution parameters} \\ \phi_{it_j} =&\ (\beta_0 + b_{0_j}) + \beta_1 \text{Derogation in effect}_{it} + \\ &\ \beta_2\ \text{New cases}_{it}\ + \beta_3\ \text{Cumulative cases}_{it}\ + \\ &\ \beta_4\ \text{New deaths}_{it}\ + \beta_5\ \text{Cumulative deaths}_{it}\ + \\ &\ \beta_6\ \text{Past ICCPR derogation}_{it}\ + \beta_7\ \text{Past ICCPR action}_{it}\ + \\ &\ \beta_8\ \text{Rule of law index}_{it}\ + \beta_9\ \text{Civil liberties index}_{it}\ + \\ &\ \beta_{10}\ \text{Core civil society index}_{it}\ + \beta_{11}\ \text{Quarter number}_{it} \\ b_{0_j} \sim&\ \mathcal{N}(0, \sigma_0) \\[0.75em] &\ \textbf{Priors} \\ \beta_{0 \dots 11} \sim&\ \operatorname{Student\ t}(\nu = 1, \mu = 0, \sigma = 3) \\ \sigma_0 \sim&\ \operatorname{Cauchy}(x = 0, \gamma = 1) \text{, lower bound} = 0 \\ \alpha_k \sim&\ \mathcal{N}(0, 1) \end{aligned} \]

# H2: Ordinal logistic regression for ordered outcomes
brm(
  bf(outcome ~ derogation_ineffect + 
      new_cases_z + cumulative_cases_z + 
      new_deaths_z + cumulative_deaths_z +
      prior_iccpr_derogations + prior_iccpr_other_action + 
      v2x_rule + v2x_civlib + v2xcs_ccsi + 
      year_quarter_num + (1 | who_region)),
  family = cumulative(),
  prior = c(
    prior(student_t(1, 0, 3), class = Intercept),
    prior(student_t(1, 0, 3), class = b),
    prior(cauchy(0, 1), class = sd, lb = 0)),
  ...
)

H₁: Derogations and emergency policies

Predicted probabilities and group contrasts

Table A 1: Summary of predicted probabilities and minimum and maximum contrasts between derogating and non-derogating countries for emergency policy models

	Predicted probabilities		Smallest difference			Largest difference
Derogation	March 2020	June 2021	Week	$\Delta$	p > 0	Week	$\Delta$	p > 0
No	0.98 [0.95–0.99]	0.93 [0.82–0.97]	2020-03-09	0.02 [0.01–0.05]	1.000	2021-06-28	0.07 [0.03–0.17]	1.000
Yes	1.00 [0.99–1.00]	1.00 [0.98–1.00]
No	0.91 [0.79–0.96]	0.89 [0.77–0.95]	2020-03-09	0.09 [0.04–0.21]	1.000	2021-06-28	0.11 [0.05–0.23]	1.000
Yes	1.00 [1.00–1.00]	1.00 [1.00–1.00]
No	0.49 [0.33–0.65]	0.32 [0.20–0.48]	2020-03-09	0.23 [0.15–0.32]	1.000	2021-03-29	0.25 [0.15–0.34]	1.000
Yes	0.73 [0.56–0.86]	0.58 [0.39–0.76]
No	0.66 [0.49–0.80]	0.18 [0.10–0.30]	2020-03-09	0.25 [0.15–0.36]	1.000	2021-02-01	0.39 [0.28–0.49]	1.000
Yes	0.91 [0.82–0.96]	0.54 [0.34–0.74]
No	0.99 [0.93–1.00]	1.00 [1.00–1.00]	2021-06-28	0.00 [0.00–0.00]	0.999	2020-03-09	0.01 [0.00–0.07]	0.999
Yes	1.00 [1.00–1.00]	1.00 [1.00–1.00]

Complete model results

Table A 2: Complete results from models showing relationship between derogations and emergency policies (H₁)

	Cancel Public Events	Gathering Restrictions	Close Public Transit	Movement	International Travel
Note: Estimates are median posterior log odds from ordered logistic and binary logistic regression models; 95% credible intervals (highest density posterior interval, or HDPI) in brackets. Total $R^2$ considers the variance of both population and group effects; marginal $R^2$ only takes population effects into account.
Derogation in effect	3.4	11	1.04	1.7	7.27
	[1.3, 5.9]	[3, 57]	[0.64, 1.45]	[1.2, 2.2]	[0.08, 40.80]
New cases (standardized)	2.19	8.5	-0.68	0.75	6.41
	[-0.53, 5.76]	[5.8, 11.4]	[-0.88, -0.48]	[0.31, 1.25]	[-0.34, 16.49]
Cumulative cases (standardized)	3.3	4.7	-0.367	-0.18	1.4
	[1.3, 5.6]	[2.9, 6.7]	[-0.810, 0.035]	[-0.57, 0.23]	[-6.0, 13.6]
New deaths (standardized)	8.2	2.4	1.24	0.74	-1.62
	[4.9, 11.3]	[1.1, 3.9]	[0.97, 1.54]	[0.38, 1.12]	[-3.24, -0.31]
Cumulative deaths (standardized)	-0.899	-2.7	0.70	0.17	6.07
	[-1.864, 0.089]	[-3.6, -1.8]	[0.29, 1.15]	[-0.23, 0.58]	[0.42, 11.59]
Past ICCPR derogation	0.38	-1.29	0.081	0.16	-1.2
	[-0.75, 1.63]	[-2.37, -0.19]	[-0.704, 0.837]	[-0.69, 0.93]	[-3.4, 1.0]
Past ICCPR action	-0.14	0.17	-0.18	0.072	0.31
	[-1.13, 1.00]	[-0.80, 1.10]	[-0.92, 0.56]	[-0.719, 0.812]	[-1.73, 2.47]
Rule of law	3.3	0.72	-0.81	-0.77	-0.066
	[1.3, 5.5]	[-1.18, 2.38]	[-2.15, 0.54]	[-2.12, 0.63]	[-4.058, 3.360]
Civil liberties	-3.99	1.2	1.1	-0.68	0.87
	[-7.70, -0.65]	[-1.4, 4.4]	[-1.3, 3.4]	[-3.10, 1.51]	[-4.33, 7.96]
Core civil society index	0.26	-0.022	-0.76	-0.57	-1.9
	[-1.90, 2.75]	[-2.017, 2.149]	[-2.58, 0.87]	[-2.22, 1.00]	[-7.7, 2.2]
Constant	8.1	4.8	1.29	3.6	10.8
	[6.6, 9.5]	[3.7, 6.0]	[0.47, 2.02]	[2.6, 4.4]	[7.3, 15.5]
Year-week	-0.021	-0.0030	-0.0103	-0.032	0.050
	[-0.026, -0.015]	[-0.0079, 0.0021]	[-0.0131, -0.0073]	[-0.035, -0.029]	[0.031, 0.068]
Country random effects σ	2.4	2.3	1.8	1.9	3.9
	[2.0, 2.9]	[2.0, 2.7]	[1.5, 2.1]	[1.7, 2.2]	[2.6, 5.5]
N	9453	9522	8832	9246	9591
$R^2$ (total)	0.31	0.41	0.36	0.40	0.32
$R^2$ (marginal)	0.01	0.03	0.07	0.12	0.00

H₂: Derogations and human rights

Predicted probabilities and group contrasts


		Predicted probabilities		Smallest difference			Largest difference
Derogation	Level	2020-Q2	2021-Q2	Week	$\Delta$	p > 0	Week	$\Delta$	p > 0
No	None	0.87 [0.79–0.93]	0.94 [0.89–0.97]	2021-Q2	0.02 [-0.04–0.06]	0.81600	2020-Q2	0.04 [-0.08–0.13]	0.81600
Yes	None	0.92 [0.78–0.98]	0.96 [0.89–0.99]
No	Minor	0.06 [0.03–0.10]	0.03 [0.01–0.05]	2020-Q2	-0.02 [-0.06–0.03]	0.18400	2021-Q2	-0.01 [-0.03–0.02]	0.18400
Yes	Minor	0.04 [0.01–0.10]	0.02 [0.00–0.06]
No	Moderate	0.01 [0.01–0.02]	0.01 [0.00–0.01]	2020-Q2	0.00 [-0.01–0.01]	0.18400	2021-Q2	0.00 [-0.01–0.00]	0.18400
Yes	Moderate	0.01 [0.00–0.02]	0.00 [0.00–0.01]
No	Major	0.05 [0.03–0.10]	0.02 [0.01–0.05]	2020-Q2	-0.02 [-0.06–0.03]	0.18400	2021-Q2	-0.01 [-0.03–0.02]	0.18400
Yes	Major	0.03 [0.01–0.10]	0.01 [0.00–0.05]
No	—	0.03 [0.01–0.06]	0.02 [0.01–0.04]	2021-Q2	0.00 [-0.02–0.04]	0.54075	2020-Q2	0.00 [-0.03–0.06]	0.54075
Yes	—	0.03 [0.01–0.10]	0.02 [0.00–0.07]
No	None	0.66 [0.55–0.76]	0.61 [0.49–0.71]	2020-Q2	0.22 [0.11–0.33]	0.99975	2021-Q2	0.25 [0.12–0.36]	0.99975
Yes	None	0.89 [0.76–0.95]	0.86 [0.71–0.94]
No	Minor	0.02 [0.01–0.03]	0.02 [0.01–0.03]	2020-Q2	-0.01 [-0.02–0.00]	0.00025	2021-Q2	-0.01 [-0.02–0.00]	0.00025
Yes	Minor	0.01 [0.00–0.02]	0.01 [0.00–0.02]
No	Moderate	0.32 [0.23–0.43]	0.37 [0.27–0.49]	2021-Q2	-0.24 [-0.35–-0.11]	0.00025	2020-Q2	-0.21 [-0.32–-0.10]	0.00025
Yes	Moderate	0.11 [0.04–0.22]	0.13 [0.05–0.28]
No	None	0.66 [0.55–0.75]	0.92 [0.88–0.95]	2020-Q2	-0.05 [-0.19–0.08]	0.23250	2021-Q2	-0.02 [-0.09–0.02]	0.23250
Yes	None	0.60 [0.44–0.76]	0.90 [0.81–0.95]
No	Minor	0.19 [0.15–0.24]	0.05 [0.03–0.08]	2021-Q2	0.01 [-0.01–0.05]	0.76750	2020-Q2	0.02 [-0.03–0.08]	0.76750
Yes	Minor	0.21 [0.14–0.28]	0.06 [0.03–0.12]
No	Moderate	0.11 [0.07–0.16]	0.02 [0.01–0.04]	2021-Q2	0.01 [-0.01–0.03]	0.76750	2020-Q2	0.02 [-0.03–0.08]	0.76750
Yes	Moderate	0.13 [0.07–0.21]	0.03 [0.01–0.06]
No	Major	0.04 [0.02–0.06]	0.01 [0.00–0.01]	2021-Q2	0.00 [0.00–0.01]	0.76750	2020-Q2	0.01 [-0.01–0.05]	0.76750
Yes	Major	0.05 [0.02–0.10]	0.01 [0.00–0.02]

Complete model results

Table A 3: Complete results from models showing relationship between derogations and human rights (H₂)

	Discriminatory Policy	Non-Derogable Rights	No Time Limit Measures	Abusive Enforcement
Note: Estimates are median posterior log odds from ordered logistic and binary logistic regression models; 95% credible intervals (highest density posterior interval, or HDPI) in brackets. Total $R^2$ considers the variance of both population and group effects; marginal $R^2$ only takes population effects into account.
Derogation in effect	-0.49	0.066	-1.4	0.22
	[-1.73, 0.59]	[-1.262, 1.174]	[-2.3, -0.6]	[-0.37, 0.76]
New cases (standardized)	0.26	0.051	-0.12	0.028
	[-0.49, 1.09]	[-1.596, 1.614]	[-0.98, 0.69]	[-0.510, 0.640]
Cumulative cases (standardized)	-0.11	-0.11	-0.49	0.17
	[-1.04, 0.98]	[-2.04, 1.78]	[-1.43, 0.40]	[-0.51, 0.95]
New deaths (standardized)	-0.27	-0.056	0.089	0.21
	[-1.16, 0.54]	[-1.223, 1.151]	[-0.526, 0.639]	[-0.33, 0.81]
Cumulative deaths (standardized)	0.12	-0.42	0.26	-0.29
	[-0.91, 1.13]	[-2.16, 0.94]	[-0.44, 0.99]	[-1.03, 0.39]
Past ICCPR derogation	0.95	0.36	0.13	0.457
	[0.41, 1.53]	[-0.42, 1.07]	[-0.32, 0.56]	[0.064, 0.852]
Past ICCPR action	0.27	1.38	-0.53	0.04
	[-0.27, 0.75]	[0.77, 2.01]	[-0.99, -0.12]	[-0.35, 0.42]
Rule of law	1.04	0.88	0.72	-0.75
	[-0.26, 2.38]	[-0.84, 2.59]	[-0.26, 1.67]	[-1.66, 0.14]
Civil liberties	1.2	-3.93	-1.39	0.24
	[-1.1, 3.7]	[-7.15, -0.89]	[-3.37, 0.46]	[-1.56, 2.01]
Core civil society index	-2.24	0.6	-0.28	-0.3
	[-3.92, -0.73]	[-1.5, 2.8]	[-1.57, 1.08]	[-1.6, 1.0]
Constant		-1.33
		[-2.59, -0.17]
Cut 1	1.26		0.54	-0.71
	[-0.25, 2.75]		[-0.50, 1.48]	[-1.53, 0.05]
Cut 2	1.98		0.62	0.38
	[0.46, 3.50]		[-0.43, 1.54]	[-0.41, 1.15]
Cut 3	2.20			1.9
	[0.52, 3.55]			[1.0, 2.7]
Region random effects σ	1.30	0.71	0.83	0.57
	[0.58, 2.60]	[0.17, 1.62]	[0.34, 1.67]	[0.23, 1.17]
N	834	834	834	834
$R^2$ (total)	0.15	0.11	0.07	0.15
$R^2$ (marginal)	0.08	0.08	0.04	0.10

Table A 4: WHO regions

AFRO: Regional Office for Africa
Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Cameroon, Central African Republic, Chad, Comoros, Congo - Brazzaville, Côte d’Ivoire, Congo - Kinshasa, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, St. Helena, São Tomé & Príncipe, Senegal, Seychelles, Sierra Leone, South Africa, South Sudan, Togo, Uganda, Tanzania, Zambia, and Zimbabwe
AMRO: Regional Office for the Americas
Anguilla, Antigua & Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, British Virgin Islands, Canada, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, French Guiana, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, St. Barthélemy, St. Kitts & Nevis, St. Lucia, Saint Martin (French part), St. Vincent & Grenadines, Sint Maarten, Suriname, Trinidad & Tobago, Turks & Caicos Islands, United States, Uruguay, and Venezuela
EMRO: Regional Office for the Eastern Mediterranean
Afghanistan, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Palestinian Territories, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, United Arab Emirates, and Yemen
EURO: Regional Office for Europe
Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia & Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Moldova, Romania, Russia, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Tajikistan, United Kingdom, Türkiye, Turkmenistan, Ukraine, and Uzbekistan
SEARO: Regional Office for South-East Asia
Bangladesh, Bhutan, North Korea, India, Indonesia, Maldives, Myanmar (Burma), Nepal, Sri Lanka, Thailand, and Timor-Leste
WPRO: Regional Office for the Western Pacific
American Samoa, Australia, Brunei, Cambodia, China, Cook Islands, Fiji, French Polynesia, Japan, Kiribati, Laos, Malaysia, Marshall Islands, Micronesia (Federated States of), Mongolia, Nauru, New Zealand, Palau, Papua New Guinea, Philippines, South Korea, Samoa, Singapore, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Vietnam

Table A 5: States that derogated from regional human rights conventions

States that derogated from the ACHR	Also submitted ICCPR derogation	States that derogated from the ECHR	Also submitted ICCPR derogation
ACHR = American Convention on Human Rights; ECHR = European Convention on Human Rights
Argentina	Yes	Albania	No
Bolivia	No	Armenia	Yes
Chile	Yes	Azerbaijan	Yes
Colombia	Yes	Estonia	Yes
Dominican Republic	Yes	Georgia	Yes
Ecuador	Yes	Latvia	Yes
El Salvador	Yes	Moldova	Yes
Guatemala	Yes	North Macedonia	No
Honduras	No	Romania	Yes
Jamaica	No	San Marino	Yes
Panama	No	Serbia	No
Paraguay	Yes
Peru	Yes
Suriname	No
Venezuela	No

Table A 6: Results from Chaudhry, Comstock, and Heiss (2024) showing the determinants of derogation

	Model 1	Model 2
Dependent variable = derogation submission
Logistic regression models; odds ratios (standard error)
* p < 0.1, p < 0.05, * p < 0.01
Intercept	0.00217 (0.00093)***	0.0044 (0.0021)***
Pandemic backsliding (PanBack)	7.1 (6.5)**
Pandemic violations of democratic standards (PanDem)		0.29 (0.29)
New cases (standardized)	0.33 (0.40)	0.42 (0.51)
New deaths (standardized)	1.66 (0.44)*	1.64 (0.43)*
Cumulative cases (standardized)	0.015 (0.033)*	0.0067 (0.0152)**
Cumulative deaths (standardized)	3.6 (1.7)***	4.3 (2.1)***
Rule of law index	1.99 (0.87)	1.27 (0.58)
N	9591	9591

References

Bürkner, Paul-Christian. 2017. “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software 80 (1): 1–28. https://doi.org/10.18637/jss.v080.i01.

Chaudhry, Suparna, Audrey L. Comstock, and Andrew Heiss. 2024. “Derogations, Democratic Backsliding, and International Human Rights During the COVID-19 Pandemic.” Working paper. https://doi.org/10.31235/osf.io/g3z6n.

Gelman, Andrew, Aleks Jakulin, Maria Grazia Pittau, and Yu-Sung Su. 2008. “A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models.” The Annals of Applied Statistics 2 (4). https://doi.org/10.1214/08-AOAS191.

R Core Team. 2023. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/.

Stan Development Team. 2023. “Stan Modeling Language Users Guide and Reference Manual.” Manual. http://mc-stan.org.

Modeling approach

Priors

Model definitions

H1: Logistic regression

H2: Ordered logistic regression

H1: Derogations and emergency policies

Predicted probabilities and group contrasts

Complete model results

H2: Derogations and human rights

Predicted probabilities and group contrasts

Complete model results

References

H₁: Logistic regression

H₂: Ordered logistic regression

H₁: Derogations and emergency policies

H₂: Derogations and human rights