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Covenants, Constitutions, and Distinct Law Types: Investigating Governments’ Restrictions on CSOs Using an Institutional Approach

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Abstract

A growing number of researchers study the laws that regulate the third sector and caution the legal expansion is a global crackdown on civil society. This article asks two questions of a thoroughly researched form of legal repression: restrictions on foreign aid to CSOs. First, do institutional differences affect the adoption of these laws? Second, do laws that appear different in content also have different causes? A two-stage analysis addresses these questions using data from 138 countries from 1993 to 2012. The first analysis studies the ratification of the International Covenant on Civil and Political Rights (ICCPR) and constitution-level differences regarding international treaties’ status. The study then uses competing risk models to assess whether the factors that predict adoption vary across law types. The study finds that given ICCPR ratification, constitutions that privilege treaties above ordinary legislation create an institutional context that makes adoption less likely. Competing risk models suggest different laws have different risk factors, which implies these laws are more conceptually distinct than equivalent. Incorporating these findings in future work will strengthen the theory, methods, and concepts used to understand the legal approaches that regulate civil society.

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Fig. 1

Sources “Hands Off My Regime!” (Dupuy et al. 2016: Table 1); Values of Democracy (V-Dem); Freedom House

Fig. 2

Sources Dupuy et al. (2016, Table 1); United Nations Office of Legal Affairs; Comparative Constitutions Projects; United Nations Treaty Collection

Fig. 3

Sources: Appe and Marchesini da Costa (2017), Carothers and Brechenmacher (2014), Chikoto-Schultz and Uzochukwu (2016); Christensen and Weinstein (2013); Cunningham (2018); Dupuy and Prakash (2017); Dupuy et al. (2016); Gershman and Allen (2006); Gugerty (2017); Hodenfield and Pegus (2013); Kameri-Mbote (2002); Maru (2017); Mayhew (2005); Rutzen (2015); Salamon and Toepler (1997); Sidel (2017); Tiwana and Belay (2010); Wolff and Poppe (2015); World Bank (1997)

Fig. 4

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Notes

  1. Belize and Vietnam are not analyzed because they are not coded in several datasets. These countries adopted laws in 2003 and 2009, respectively.

  2. The ICCPR protects freedoms of expression and belief (Articles 18, 19, 27), rights to associate and organize (Articles 1, 18, 21, 22), rule of law and human rights (Articles 6, 7, 9, 14, 15, 16, 17, 25, 26), and personal autonomy and economic rights (Articles 1, 3, 8, 12, 22, 23, 25).

  3. Costa Rica was the first to ratify (November 29, 1968), and Fiji was the 172nd and most recent (August 16, 2018).

  4. Policy differences have theoretical implications for Kingdon’s politics and policies streams of the Multiple Stream Approach (Kingdon 1984) where various policies compete in the policy stream, and only proposals that successfully match the national mood, and the politics of policymaking get considered during the policy window (Zahariadis 2014). Through the theoretical lens of Punctuated Equilibrium Theory (Baumgartner and Jones 1991, 1993), strong, established interests in the policy subsystem make adoption of substantial policy changes less likely. Only in disequilibrium are entrenched players unable to railroad massive policy changes. In the Advocacy Coalitions Framework (Sabatier 1988; Sabatier and Jenkins-Smith 1993; Sabatier and Weible 2007), policies that are an affront to a coalition’s belief systems are met with stiff resistance, whereas minor changes may be the result of a cross-coalitional learning or the negotiated outcome of the dialogue with policymakers. While the above theories predict differences in policies affect policymaking, policy differences are shown to have different effects on public and elite opinion and the social construction of target groups (Ingram and Schneider 1990, 1991; Schneider et al. 2014) and affect the extent to which targeted groups participate in politics (MacLean 2011; Mettler and Soss 2004; Pierson 1993).

  5. Statistical software corrections—such as relogit or firthlogit in Stata 15—for analyzing rare events with logistic regressions are not yet available for panel data or analyze requiring clustered standard errors.

  6. This involves first collecting all the “events” and an equal number of randomly selected “non-events,” and continuing to add randomly sampled non-events and stop when the confidence intervals are sufficiently small for the substantive purposes at hand. In the rare events analysis, countries that adopt laws appear in all analyses (482 country-year observations).

  7. Belarus (2001, 2003); Indonesia (2004, 2008); and Uzbekistan (2003, 2004).

  8. The variable takes the value of 1 if at least one of the following nine provisions exists: “certain organizations are prohibited from receiving foreign funding”; “certain types of organizations are prohibited from receiving foreign funding”; “foreign-funded organizations prohibited from carrying out particular activities”; “foreign funding can be used only for certain purposes”; “foreign funding prohibited”; “foreign funding prohibited for certain activities”; “foreign-funded NGOs prohibited from working on certain issue areas”; “foreign-funded organizations prohibited from carrying out particular activities”; and “use of foreign funding prohibited for particular activities.”

  9. The variable equals 1 if at least one of the following twelve provisions exists: “government approval for foreign funding”; “government approval required for particular uses of foreign”; “government may cap the amount”; “government monitoring of NGO contracts financed with foreign funding”; “government restrictions on use and source”; “government restrictions on whether foreign funding can be received”; “other restrictions on use of foreign funding”; “requirements for how organizations can receive foreign funding”; “restrictions on certain types of organizations receiving foreign funding”; “restrictions on receipt and use of foreign funding”; “restrictions on sources from which foreign funding can be acquired”; and “restrictions on use of foreign funding.”

  10. The variable equals 1 if at least one of the following six provisions exists: “foreign funds are taxed”; “government notification of foreign funding required”; “organizations must report source of revenues”; “reporting and accounting requirements”; “reporting and accounting requirements for foreign funding”; and “reporting requirements.”

  11. Operationally, the additive index increases by 1 for each of the following binary variables present in the constitutional system as identified by CCP: (1) power to initiate legislation (coded 1 if head of state, head of government, or government can initiate legislation); (2) power to issue decrees (coded 1 if head of state or head of government can issue decrees); (3) power to declare emergencies (coded 1 if head of state, head of government, or government can declare emergencies); (4) power to propose amendments (coded 1 if head of state, head of government, or government can propose amendments to the constitution); (5) power veto legislation (coded 0 if no vetoes are possible or can be overridden by a plurality or majority in the legislature; coded 1 if vetoes are possible but require at least 3/5 supermajority of the legislature to override veto); (6) power to challenge the constitutionality of legislation (coded 1 if head of state, head of government, or government can challenge the constitutionality of legislation); (vii) power to dissolve the legislature (coded 1 if head of state, head of government, or government can dissolve the legislature).

  12. The logit models used here produce coefficients that represent the direction of the variable’s effect on the probability of adoption but are difficult to interpret or odds ratios whose substantive meanings depends on the specific value of the odds before they change (Long and Freese 2014: 228–235).

  13. According to the Cox model with time-varying coefficients (Table 8), the level of democracy has a negative sign for all types of laws but is statistically significant for highly-restrictive laws only (− 3.05, p < 0.05). A standard deviation increase in the level of democracy (approximately 2.85 points on a 10-point scale) at the beginning of the observation period, while all other variables are held constant, yields a hazard ratio equal to exp(− 3.05*2.85) = 0.0001. Thus, the rate of adoption decreases by (100% − 0.01%) = 99.99% with a standard deviation increase in democracy at the beginning of the observation period. The time-varying component of the level of democracy has a positive sign for all types of laws but is statistically significant for highly-restrictive laws only (0.18, p < 0.05). This suggests the level of democracy as a deterrent for adopting highly-restrictive declines with every unit of time. A standard deviation increase in the level of democracy at year 10 of the observation period, while all other variables are held constant, yields a hazard ratio equal to exp(− 3.05*2.85 + 0.18*2.85*10) = 0.0283. Thus, the rate of adoption decreases by (100%-2.89%) = 97.11%. While holding all other variables constant, this discrete change at year 20 yields a hazard ratio equal to exp(− 3.05*2.85 + 0.18*2.85*20) = 4.79, which increases the rate of adoption by (479% − 100%) = 379%. Holding all else equal, a positive and discrete change in democracy causes a decrease in the rate of adoption by 99.99% at the beginning of the observation period, 97% in year 10, and increases the rate of adoption by over 350% in year 20.

  14. According to the Cox model with time-varying coefficients (Table 8), voting alignment with Russia has a positive and statistically significant relationship with highly-restrictive laws (0.22, p < 0.01). A standard deviation increase in the voting alignment with Russia (approximately 11.75 points on a 100-point scale) at the beginning of the observation period, while all other variables are held constant, yields a hazard ratio equal to exp(0.22*11.75) = 13.26. Thus, the rate of adoption increases by 1226% with a standard deviation increase in voting alignment with Russia at the beginning of the observation period. The time-varying component has a negative and statistically significant relationship for highly-restrictive laws only (0.18, p < 0.05). This suggests the voting alignment with Russia is a propellant for adoption for highly-restrictive laws declines with every unit of time. A standard deviation in the factor, while all other variables are held constant, yields a hazard ratio equal to exp(0.22*11.75 + − 0.02*11.75*10) = 1.264. Thus, the rate of adoption increases by 26.4%. While holding all other variables constant, this discrete change at year 20 yields a hazard ratio equal to exp(0.22*11.75 + − 0.02*11.75*20) = 0.1206, which decreases the rate of adoption by (100% − 12.06%) = 87.93%. Holding all else equal, a positive and discrete change in voting alignment with Russia increases the rate of adoption by more than 1000% at the beginning of the observation period, 26% at year 10, and decreases the rate of adoption by 88% at year 20.

  15. The dictators’ dilemma of comparative politics stresses undemocratic leaders to use their tools of office—such as elections, laws, and policy implementation—to gather information that prolongs their stay in power and maximizes their gains from office.

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Acknowledgements

I am grateful to Jennifer Brass, Terrance Chapman, Sean Nicholson-Crotty, Michelle Reddy, Chrystie Swiney, Joey Carroll, Martin Delaroche, Renzo de la Riva Agüero, Laura Montenovo, the reviewers, and the journal’s editorial team for their written comments and constructive criticisms. My appreciation also extends to those who offered early input on the project at the International Society for Third-Sector Research, Brass Club, the Emory Conference on Institutions and Lawmaking, the Midwest Political Science Association, the International Spring School on Public Policy, and the Workshop on the Ostrom Workshop (WOW6).

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    Anthony J. DeMattee

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Appendix

Appendix

See Tables 5, 6, 7, 8, 9 and 10.

Table 5 Descriptive statistics of estimation sample
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Table 6 Pooled event history analysis (EHA) with logistic regression
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Table 7 Competing risk model with logistic regression (full table)
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Table 8 Cox with time-varying coefficients CRM (full model)
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Table 9 Rare events CRM (logistic regression, random 35% of sample)
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Table 10 Assessing conceptual equivalence and distinctness (all modeling strategies)
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DeMattee, A.J. Covenants, Constitutions, and Distinct Law Types: Investigating Governments’ Restrictions on CSOs Using an Institutional Approach. Voluntas 30, 1229–1255 (2019). https://doi.org/10.1007/s11266-019-00151-2

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