Abstract
Various indicators have been developed to assess the sustainability of countries. However, it remains theoretically and practically unclear whether it is possible to include institutions as an element of the sustainability index. One of the main challenges is the substantial problem of missing data. Recent studies have shed light on the potential means to improve data collection and to construct better indicators for the quality of institutions and their use in theories of sustainability. However, the special nature of institutions and the time-trend effect make it difficult to develop an appropriate selection strategy, although a variety of imputation methods have nonetheless been developed in this field. This study addresses this problem by including variables that might theoretically be considered in a multiple imputation framework. We construct a panel dataset that covers approximately 190 countries for the 1980–2010 period. Based on this complete imputed dataset, we investigate the effects of institutions on the change in comprehensive wealth in a country, which is adjusted net savings, using the instrumental variable method. We also suggest a strategy for including institutional indicators in post-2015 sustainability index design.
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Notes
King et al. (2001) discussed when list wise deletion is preferable to the MI method. For instance, when the complete dataset is large, the sensitivity of the results of the imputed model maybe low. When the function form is known to be correctly specified, or when there are no unobserved omitted variables that affect the variable with missing values, the cost of listwise deletion is lower than the cost of the MI method.
Compared to MVN, MICE can handle different variable types because each variable in MICE is imputed using its own imputation model. However, the properties of MICE are not generally proven, and the justification of the MICE procedure has thus far rested on empirical studies (Kenward and Carpenter 2007). Empirical studies on countries with missing information are rare.
The 13 non-independent countries/areas that are excluded from the dataset are American Samoa, Aruba, Cayman Islands, French Polynesia, Greenland, Guam, Hong Kong, Isle of Man, Macao, New Caledonia, Northern Mariana Islands, Puerto Rico, Turks and Caicos Islands. The eight countries with poor data availability include the following: Channel Islands, Kosovo, Montenegro, Palestine, Virgin Islands (US), Bermuda, Faeroe Islands, and Monaco.
We also (1) compare real data from some domestic survey databases (published in domestic languages) with some of our imputed values and (2) generate randomly missing cells in our database; then, we impute them using the MI method. We compare the difference between the imputed values with real values, and the results indicate that the imputed values are close to reality.
After the Rio + 20 Conference, developing a set of sustainable development goals (SDGs) and indicators to improve global welfare became a hot topic. Whether and how to include institutions as a component of the sustainability index is also the topic of active discussion, as we state in the introduction.
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Acknowledgements
We thank Kagawa Shigemi, Yusuke Matsuki, Michiyuki Yagi and two reviewers for their comments on this paper. We also acknowledge the Project on Sustainability Transformation beyond 2015 from the Ministry of the Environment, Japan for providing funding for this research.
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Yang, J., Managi, S. & Sato, M. The effect of institutional quality on national wealth: an examination using multiple imputation method. Environ Econ Policy Stud 17, 431–453 (2015). https://doi.org/10.1007/s10018-014-0084-z
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DOI: https://doi.org/10.1007/s10018-014-0084-z