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01.12.2016 | Aufsätze

The contextualized index of statehood (CIS): assessing the interaction between contextual challenges and the organizational capacities of states

verfasst von: Oliver Schlenkrich, Lukas Lemm, B.A., Christoph Mohamad-Klotzbach, M.A.

Erschienen in: Zeitschrift für Vergleichende Politikwissenschaft | Ausgabe 3-4/2016

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Abstract

Although the measurement of the quality of statehood has become an important research field in Comparative Politics, most of the currently used indices are flawed by major weaknesses. To address such weaknesses, we developed the “Contextualized Index of Statehood” (CIS). This index is based on the two most essential dimensions of statehood: “monopoly on the use of physical force” and “administration”. Applying a new aggregation method called “variable threshold”, we highlight the interaction between the organizational capacities of a state and the contextual challenges it may face in order to measure the quality of statehood. First, we demonstrate the concept, measurement and aggregation. Then we investigate the validity of our index before presenting our empirical findings, including a model-based cluster analysis.

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See for example Berlin (http://www.sfb-governance.de/) and Bremen (http://www.sfb597.uni-bremen.de/).

For a good overview of strength and weaknesses of some of these indices see Bethke (2012), Mata and Ziaja (2009), and Ziaja (2012).

The steps of our presentation are based on the scheme for evaluating indices by Munck and Verkuilen (2002) (for a detailed discussion see Müller and Pickel 2007; Pickel et al. 2015). Even though they only refer to indices of democracy, these schemes are also usable for the description of indices of fragile states (Ziaja 2012).

This helps us to clarify our case selection: Though we are focusing on empirical statehood, we are not able to measure the empirical statehood of non-juridical states due to the lack of data.

For a discussion about the link between statehood and democracy see Erdmann (2014), Lauth (2004), Merkel et al. (2003, pp. 58–59) and Lauth and Kauff (2012, pp. 16–17).

Autocracies do not always have to be repressive. This may vary regarding the specific type of autocracy (Geddes et al. 2014; Hadenius and Teorell 2007; Kailitz and Köllner 2013; Schlenkrich and Mohamad-Klotzbach 2015).

Here also the regime type will decide, if this communication is mostly one sided/closed (autocratic regimes) or two sided/open (democratic regimes).

Our definition of the administration also includes some aspects of state as a service provider – a different understanding of the state (Lambach et al. 2015b).

In addition, we do not include the judiciary in the sense of the rule of law and political institutions in our definition as proposed by Ezrow and Frantz (2013) due to the mixing of regime and state characteristics.

Schneckener (2004, pp. 13–14) defines the scope of this function more widely: he additionally includes political rights or the rule of law. This is not justified because it further intertwines regime characteristics and statehood.

Bruce Gilley (2009, 2012) presents one possible way of measuring legitimacy on the basis of individual data, but his operationalization of state legitimacy also conflates characteristics of the state and the regime.

See for a deeper discussion Lambach (2008, pp. 36–42) and Marquez (2015).

It is possible that a state generates the contextual challenges due to its low organizational capacities. This could be considered as a problem of endogeneity. Nevertheless to function properly, a state has to overcome these challenges, even though it creates them.

See Abromeit (2004) and Stoiber (2008, 2011) who introduced a context-sensitive measurement of the quality of democracy. Grävingholt et al. (2012, p. 7) also address the context by stating that the “exact degree of authority required to maintain a stable state is dependent on context factors such as popular expectations and the strengths of rivals”. But they do not incorporate it in their overall concept.

It would be easy to carry the lists for each dimension forward; however, we confine ourselves to the ones which represent a quasi-consensus,.

See (Schneckener 2004, p. 18).

In addition, it seems that these contextual features play an important role for transformation countries – the type of countries which are mostly included in our dataset based on the Bertelsmann Transformation Index. But it would be easy to broaden the definition by including other contextual aspects as well (e. g. refugee crises, political culture) so that even non-transformation countries can be analyzed. We should be aware of a parsimonious selection of contextual factors so that a causal analysis is still possible.

A is a necessary condition or a precondition for B, if B cannot exist without the simultaneous existence of A. In other terms: B is a sufficient condition for A, but A can be in place without B. Thus, we argue that the monopoly on the use of physical force is only a necessary condition for the functioning of the administration – not a sufficient one. But a working administration is a sufficient condition for the monopoly on the use of force. It could be – and remains an empirical question – that the functioning of the monopoly on the use of force represents an obstacle for establishing an administration in some cases. But the critical point is that there cannot be a functioning of the administration without at least a moderate functioning of the monopoly on the use of force at the same time. The administration needs an institution which enforces its rules – and that is the monopoly on the use of force.

The data is drawn from the SIPRI Military Expenditure Database (SIPRI 2016), from the World Bank (World Bank 2016) and from GMI (2016).

UNODC has collected data related to the number of police personnel (criminal justice system resources), but the sample size is small and most data points are missing (especially for African countries), see http://www.unodc.org/unodc/en/data-and-analysis/statistics/crime.html.

We did a robustness check with the indicator V113 (Confidence in Police) of the World Value Survey Wave 6 (2010–2014). Due to the small number of countries surveyed by the WVS, the sample size consists of 44 to 46 cases. The results of the Pearson Correlation indicate a sufficient robustness (see Tab. 5 in appendix), although the Gallup World Poll performs rather poorly compared to the GCR-data.

The data can be obtained here: World Economic Forum (2016) and Kaufmann and Kraay (2015).

For a discussion of the methodology of the BTI see Lauth (2010).

Even though the BTI-instructions for this question exclude explicitly the evaluation of the quality of the administrative structures, some coders still include this aspect.

The Government Effectiveness Indicator actually covers more areas which help to measure our concept more accurately: quality of the primary education, health system, infrastructure and administration, see the detailed indicator description http://www.govindicators.org.

See for the BTI (Bertelsmann Stiftung 2014a) and for the WGI Kaufmann and Kraay (2015).

Another good indicator would be “MAGFIGHT” from the State Failure Problem Set by the Political Instability Task Force (Marshall et al. 2015). MAGFIGHT evaluates the number of rebel combatants and activists. This would be closer to our concept, but the number of coded cases is considerably smaller compared to the BTI.

We scaled the values twice: first for each year, and after that for all years to attain a better comparability across the years.

This is often done with indicators of military capacities, see Hendrix (2010).

The following countries are missing: Bhutan (2006–2012), Cuba (2006–2012), Eritrea (2006–2012), Guinea (2008–2012), Kosovo (2008−2012), Lesotho (2008), Mauritius (2008), Myanmar (2006–2012), North Korea (2006–2012), Oman (2008–2012), Panama (2006), Papua New Guinea (2006–2012), Somalia (2006–2012), South Sudan (2012), Sudan (2010–2012), Taiwan (2006–2012), Tajikistan (2008–2012), Togo (2006), Turkmenistan (2006–2012) and Uzbekistan (2006–2012). To exclude the possibility that missing data in the CIS-dataset is correlated with increasing state fragility, we compared our missing values to two other state fragility indices which both cover more cases – the BTI Indicator “monopoly on the use of force” and the WGI index “Political Stability”. It seems that the missing data in our dataset and state fragility are only weakly correlated (see Fig. 7 in the appendix).

See for different aggregation rules and their implications Munck and Verkuilen (2002, p. 24): “if the aggregation of two attributes is at issue and one’s theory indicates that they both have the same weight, one would simply add the scores of both attributes. If one’s theory indicates that both attributes are necessary features, one could multiply both scores, and if one’s theory indicates that both attributers are sufficient features, one could take the score of the highest attribute”.

Here is an example from our dataset: Nigeria in 2006 had a physical capacity value of 3.32 and a high contextual challenge value of 7.71, whereas Costa Rica in 2006 had similar physical capacities (3.33) but lower contextual challenges (2.00). This results in a low value for the functioning of the monopoly on the use force in Nigeria (0.53) and a rather high value for Costa Rica (1.68).

The more disaggregated levels (monopoly on the use of force, administration, physical capacities, administrative capacities and the physical and administrative challenges) will be available as well.

See the comments on the Combined Index of Democracy (CID), which uses a similar method (Lauth 2010).

We use the logged GDP per capita as a control variable.

We use the CID3D instead of the CID because the CID includes a measure for the quality of statehood. We tested this relationship with the WGI democracy index (“Voice and Accountability”) as well and gained the same results.

For more comfortable reading, some terms will be used synonymously: Physical challenges or conflict intensity, and administrative challenges or structural constraints; as well as physical capacities or security apparatus. The dimension’s designations will be abbreviated by using first (functioning of the monopoly on the use of force) and second (functioning of the administration).

Thus, as the transformation of the indicators’ scales for the challenges has resulted in no value lower than two, the classifying threshold (<2) does not apply here; instead the category of very low challenges is exclusively filled with the measurement value two.

Perhaps the effect of one context indicator changing on its counterpart is delayed.

That seems contradictory and could be declared with delayed effects or a strategy of restructuring the state’s capacities.

The distribution of an index should cover the complete scale of possible values and should not start or end abruptly (Ziaja 2012, p. 55): The CIS achieves the first but fails partly with regard to the second requirement. Nevertheless, this is mainly caused by the sample collection which is limited to transformation states. We would face a different picture if we could include all the OECD-states.

In addition, we have to mention that the strict hierarchy of aggregated total statehood scores match the hierarchy of the cluster membership in all but 78 cases.

It is also possible to perform model-based clustering with non-normal mixture distributions (Lee and McLachlan 2013), and it is not easy to decide which one should be used.

We will test an additional model-based cluster-analysis which is based on more disaggregated data such as the organizational capacities and contextual challenges in order to discover the relationship between them and gain a more detailed view.

As a conservative estimation, only cases below certainty values of 70% fall within a wrong group, therefore they are called uncertain cases.

Tab. 3 in the appendix serves as complement to Fig. 6 and provides descriptive statistics for our main indicators so that readers receive a more detailed picture of the clusters.

The model-based cluster analysis verifies our conceptual argument that the monopoly on the use of force is a precondition of the administration: the first two types with the lowest quality of statehood – the failed states and the failing ones – possess two symmetrically shaped dimensions: the monopoly on the use of force has roughly the same value as the administration. At the lower levels of statehood, there seems to be no empirical reason for an asymmetrical shape of these two dimensions to be possible. This is only the case for higher levels of statehood where the administration can in fact have higher values than the monopoly on the use of force (fortified administration states) – but here the monopoly on the use of force is functioning on a moderate level.

Not only the method produces such uncertainties, also reality is hard to catch with typologies using strict thresholds, which is why such frontier cases have to be checked case by case. In the following, cases belonging with a certainty below 60% to the given cluster and one higher than 40% belonging to another one at the same time, will be so called. We will only discuss frontier cases if necessary, but in terms of transparency their appearance will be noted; additionally, due to their defined nature, they overlap the clusters. This division between uncertain and frontier cases provides information about the internal coherence of the clusters.

As we described, they are relatives of the weak states due to their shared characteristics in the first dimension but their administration are pronounced, which is also possible because the mean of administrative challenges is nearly three points lower. Thus, the cluster of fortified administrative states is only a fuzzy mirror image of the weak states whereas it is a clear one of cluster five.

Oman in 2006 and Tunisia in 2008 are outliers in this cluster due to high assessments of the functioning of the administration differing from the other members but that would be apply more for cluster six which has a clearly higher level.

Kazakhstan (2006–2008) belongs with nearly the same probability to cluster three or five and a little to cluster four which is the most uncertain case of the method’s results but tends in the following years to become a physically pronounced state.

An interesting method would be configuration analyses (e. g. QCA) which are able to deal with the “equifinality” of causality in respect to the quality of statehood (Lambach et al. 2015a, p. 3).

Therefore, we invented a qualitative version of this concept which includes the strategies and perceptions of state actors vis-á-vis organizational capacities and contextual challenges (see Schlenkrich et al. 2016).

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Titel: The contextualized index of statehood (CIS): assessing the interaction between contextual challenges and the organizational capacities of states
verfasst von: Oliver Schlenkrich
Lukas Lemm, B.A.
Christoph Mohamad-Klotzbach, M.A.
Publikationsdatum: 01.12.2016
Verlag: Springer Fachmedien Wiesbaden
Erschienen in: Zeitschrift für Vergleichende Politikwissenschaft / Ausgabe 3-4/2016
Print ISSN: 1865-2646
Elektronische ISSN: 1865-2654
DOI: https://doi.org/10.1007/s12286-016-0312-y

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