Abstract
This paper proposes a new index that focuses on capturing the extent of democracy in a country using not only the existence of rules but also the extent of their implementation. The measure, based on the axiomatically robust framework of (Alkire and Foster, J Public Econ 95:476–487, 2011), is able to moderate the existence of democratic rules by their actual implementation. By doing this we have a meaningful way of capturing the notion of a partial democracy within a continuum between non-democratic and democratic, separating out situations when the rules exist but are not implemented well. We construct our index using V-Dem data from 1900 to 2010 for over 100 countries to measure the process of democratization across the world. Our results show that we can track the progress in democratization, even when the regime remains either a democracy or an autarchy. This is the notion of partial democracy that our implementation-based index measures through a wide-based index that is consistent, replicable, extendable, easy to interpret, and more nuanced in its ability to capture the essence of democracy.
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Notes
Refer to https://freedomhouse.org/report/freedom-world/2019/democracy-retreat, last accessed on 23rd November 2021. Also see House (2017).
An alternative definition of democracy has focused on the existence of political elite. (Bollen, 1980) Lipset (1959) also argues that the construct should include more social requisites, though some of these may be categorized as outcomes of democracies as well. The Lipset hypothesis formed the basis of the theory of modernization which associates economic growth with democratic transitions of economies.
The partial aspect of democracy that the measure captures is still coming from objective data. As with any new concept, when we become more comfortable with the basic idea of partial democracies, then rather than just using objective indicators we could also conduct surveys which would enable researchers to use perception-based indicators of implementation. The theoretical framework of the index proposed in the paper is agnostic towards what type of data is used in the particular context.
The next segment of the paper discusses the existing measures and some of their inherent limitations.
According to Decancq and Lugo (2013) the drawbacks of using statistical methods include difficulty in interpreting results and a lack of transparency in a latent variable model. Secondly, such an empirical analysis relies on correlations between dimensions but as Nardo et al. (2005) points out correlations do not necessarily represent the real influence of the indicators on the variable of interest. This does not imply that empirical techniques should not be used at all. They are extremely useful in reducing the number of indicators and guiding choices of indicators to put into the index. This must always be done in conjunction with a theoretical measurement framework. Alkire (2013) asks for caution in using empirical methods to choose dimensions in multidimensional analysis, “For this reason the empirical approach should be used to provide informative support to participatory methods and deliberations, but should not constitute the sole basis for selecting dimensions.”.
The authors of the index recognize this as a possible drawback of their measure.
However, depending on the priorities of the analysis being conducted, the weights can be modified to give preference to the selected dimensions or indicators.
The dimensions are equally weighted (in this case, since there are 5 dimensions, it implies that each dimension has a weight of 1/5). Within each dimension, there are several sub-dimensions that are equally weighted within the dimension (say we have k sub-dimensions for a particular dimension, then each sub-dimension has 1/k weight). Therefore, the effective nested weight of the kth sub-dimension is 1\5 k. If there are say p indicators that are combined to get the rule part of the kth sub-dimension, then those p indicators each get 1/p weight and together add up for the rule part of the kth sub-dimension. Then if the implementation part of this sub-dimension has q indicators, then all get 1/q weight and are added up to get the implementation score of this sub-dimension. The rule and implementation together give the sub-dimension score which in the nested structure here has the 1/5 k weight.
However, the cut-offs used are for illustrative purposes only and can be varied depending on the purpose of the analysis and the context in which it is being applied.
Most of the studies on conflict use data from either the Correlates of War (CoW) project or from the Uppsala / PRIO dataset. The CoW dataset covers data for the period from 1930 to 2003. However, it only covers conflicts where there are more than 1000 battle deaths per year. Hence use of this dataset has been critiqued as it leaves out conflicts in many smaller countries / regions. The UCDP (Uppsala Conflict Data Program) / PRIO data covers data from the period 1946 to 2008. The UCDP / PRIO dataset from 1989 onwards includes data for conflicts where there are more than 25 battle deaths / year in addition to the 1000 battle deaths / year norm. From 2002-03 onwards, it also covers data on non-state conflict and one-sided violence.
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Acknowlegement
We are grateful to Tonmoy Islam for helpful comments and suggestions. Mitra would like to acknowledge the support of the Research Council of Norway grant no. 217995/V10. All remaining errors are solely ours.
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Basu, D., Mitra, S. & Purohit, A. Measuring Partial Democracies: Rules and their Implementation. Soc Indic Res 166, 133–155 (2023). https://doi.org/10.1007/s11205-022-03055-9
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DOI: https://doi.org/10.1007/s11205-022-03055-9