This blog is the third of a series of blogs showcasing the different features of the Poverty and Inequality Platform (PIP).
Continuing with the video-blog series, this week’s video reviews PIP’s Poverty Calculator. Flexibility is this tool’s main trait. The Poverty Calculator allows for country-specific analyses and comparisons across countries, regions, and the world. Economies are easily added and dropped from the analysis.
The Poverty Calculator reports 9 different key development indicators. As in other parts of PIP, the poverty line value (right-hand side) and desired time range (bottom slider) can be adjusted. Results are plotted in four ways: a line graph, bar chart, map, and table. The data visualizations can be quickly shared on social media, and the underlying data can be easily downloaded in a CSV file.
The discontinuity in the line graph indicates breaks in comparability over time (for more information on the comparability of poverty estimates visit the “Comparability Over Time” dataset available in The Development Data Hub). Under the advanced options, it is possible to show the interpolated trend. For countries that do not have annual surveys, these interpolated numbers are used in the calculation of regional and global aggregates. The details of the methodology applied in this line-up estimate can be found in PIP’s Methodology Handbook. It is important to note that these interpolated values rely on additional methodological assumptions (such as that the growth in the household welfare can be approximated by growth in national accounts, and that the entire distribution grows at the same rate). Therefore, users are cautioned against using reference-year estimates for monitoring country trends over time.
We hope this third video tutorial was useful for your PIP experience. We will continue to explore the different functionalities of PIP in the following weeks. Stay tuned for a review of Statistics Online (SOL) next!
We gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. This work has also been supported by the World Bank’s Partnership Fund for Sustainable Development Goals (SDG Fund) and a World Bank trust fund with the Republic of Korea, acting through the Korea Development Institute School of Public Policy and Management (KDIS), for the KDI School Partnership for Knowledge Creation and Sharing.
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If theorized and micro: yes As long as an index and its underlying dimensions have a firm theoretical basis, and the indicators are valid and reliable, it may be quite informative. The index provides an overall assessment of the theoretical construct, while the scores on the sub dimensions and individual indicators allows one to understand group differences and changes over time. However, a good social... exclusion index should be based on microdata and citizen's involvement; and it may not be a very good idea to assume linear relationships (as in a factor analytic approach). Cf. http://link.springer.com/article/10.1007%2Fs11205-012-0138-1
Read more Read lessDear Maitreyi, Thank you so much for bringing forward this topic about measuring complex matters with simple concepts and single measures, when their content is incompatible and thus cannot be summarised. I am excited to study your methodology more in detail. There is increasing discomfort between methodologist of social research about construction of summary measures on qualitative social phenomena,... like happiness, quality of life, human development or social inclusion… Aggregation problem has been recognised as important methodological issues by Scriven, Munda, Virtanen and several other eminent evaluators. May I add also my belief that resolution of this question will be decisive in setting direction for future development of methodology of social research in “anti-postmodern fashion”, which is reconstructing holistic aspiration of modernity together with enriching postmodern capacity to deal with social diversity. Concerning weighting of input indices that set up composed index: not only this exposes index to subjectivity (which is nothing wrong) but also to relativism - which is very much wrong if one is concerned with measuring qualitative social issues (Sustainability, gender equality, cohesion, human development). Qualitative issues are: (i) equally important (equal weights) and (ii) few in numbers – the same qualities are not equally important everywhere but everywhere the most important issues are only few. This suggests that inquiry about methodology for construction compound indices of social qualities is not microscopic issue (weighting range of specific indices) not macroscopic (HDI as aggregate of three representative components) but mesoscopic. The methodological dilemma is not therefore between a summary measure or a dashboard; they are both inappropriate for the purpose (measuring social inclusion) from opposite reasons; micro reasoning is relativist in relation to qualitative concerns, macro reasoning is blind for context and diversity. The question therefore arises, how meso methodology is different from micro/macro methodology? Let me illustrate on the same case which you also apply – the HDI, in relation to your mentioning need for ‘multi-dimensional’ measure? What do you mean by ‘multi-dimensional’ measure? Is this only hierarchical structure so it allows for aggregation of detailed results from micro to macro or it includes also horizontal dimension, which denies possibility of full aggregation due to incommensurable values, contents? For instance, HDI is a vertical structure (indicator level – HDI index level) which on its way of construction kills its constitutive horizontal dimension (three domains of HDI) by their aggregation from component indices to a summary index. HDI methodology is developed precisely due to incommensurability of money with social qualities (health, education), but it nevertheless allows for aggregation of three components into summary measure of HD!? The problem with SPI is similar. In present form it degrades the qualitative into quantitative challenge instead of leading it to new qualitative level of consideration. Its methodology needs to be derived consistently in relation to the main challenge, to measure one aspect of social quality. Quality (SP) consists of qualities, not of measurable elements, so the components of a given qualitative phenomenon cannot be treated as commensurable by definition and so they cannot be aggregated into summary measure of SPI. The methodological challenge is not irresolvable, only conventional methodology is too narrow to deal with qualitative concerns. Summary conclusion is achievable on meso level – not as singular index but as a diagram (Venn’s), in graphical presentation that enables presenting diverse results of heterogeneous phenomenon (social inclusion), instead of aggregate of quantities. Please see two case studies for elaboration of methodological approach. Resolving aggregation problem: http://www.sdeval.si/attachments/article/105/sde_DZ_1-14_Apples%20and%20Oranges%20%2815feb2014%29.pdf Measuring Social inclusion (cohesion): http://www.sdeval.si/domov/39-objave/objave/490-divided-we-stand Background philosophical and theoretical text is also available on request. Kind regards, Bojan Radej, Slovenian Evaluation Society
Read more Read lessDear J. Cok Vrooman Thank you for your comment and for sharing your paper. I agree with you that theoretical grounding is very important, but so is data availability. In the paper that you have shared (which I look forward to reading), it appears that you collected primary data to test your theoretical construct and then came up with a studied and methodologically rigorous single measure. That is... not always possible and the issue of context specificity becomes really salient. Thanks very much for sharing your thoughts. Maitreyi
Read more Read lessDear Bojan Radej Thank you for your thoughts on measuring social inclusion. We do not espouse any particular methodology, but make a plea for framing the right questions and adapting data and methods to context. This does become problematic in data scarce environments and if we want to make comparisons across contexts. Thanks also for sharing your interesting papers. Both you and J. Cok Vrooman... have highlighted the importance of theorizing and of data, as do we in our report, “Inclusion Matters: The Foundation for Shared Prosperity”. Best, Maitreyi
Read more Read lessdear colleague, Following your response I am not sure any more that I properly understand your call for comments. My view is that SPI methodology is inconsistent from the same reasons as HDI. It does not matter if you lack data when your model is inconsistent in its core. When you receive a comment supported with arguments it is hardly appropriate to respond so lightly with 'hmm, this is interesting',... without any argument. I am dissapointed if this is the World Bank's level of discussion. Best, Bojan
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