2.6.1 Multiple Correspondence Analysis
In this section, the results related to the MCA techniques. These are displayed in Table 2.2.
As a robustness check, Table 2.3 performs the same analysis using PCA. These are followed by additional robustness checks for the MCA technique, to examine how the weights differ with changing samples and if these regional differences are significantly different.
In this study, given that the first component was able to capture, on average, nearly 94% of the overall variation in the data, it is the only one that is utilized for the anal-ysis hereafter. Despite MCA being a better suited technique for categorical and binary data, there are still two countries that have around 23-25% of their variance unexplained (Moldova and Azerbaijan). Nonetheless, since this is approximately only 7% of our total sample of countries, they do not compromise the results in the paper.8 As mentioned, the use of MCA always leads to a weight ascribed to each of the categories within every indi-cator, which gives us 20 weights, one each for the 0 and the 1 category of the 10 indicators.
Since this different categorization is not so clear in terms of the overall contribution of each indicator, the weights that are shown in the table are produces after summing the values of the contribution of each of the two binary categories. Table A2.6 in the appendix lists the weights derived by each category of the binary indicators individually as well. These weights depict the weight that is laid upon deprived individuals versus non-deprived ones.
This varies largely amongst countries, where some have much larger contribution from the deprived individuals (most of the EU-west Asia region, Morocco, Bolivia, Nepal and Camodia), while several have the variation stemming mostly within the 0 binary category, i.e. the non-deprived (Congo DMR, Malawi and Niger).
7TableA2.1 in the appendix shows the years and phases of the DHS survey for each country in the sample.
8Since the variation explained is still very high, at around 75%, these results are still relevant and cannot be deemed incorrect or misleading in the first place.
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2RegionalPerspectivesontheMPI Table 2.2: The weights assigned to countries based on MCA
Years of Child Child Nutrition Electricity Sanitation Drinking Flooring Cooking Assets Variation
Schooling Enrolment Mortality Water Fuel Explained
Original 16.67 16.67 16.67 16.67 5.56 5.56 5.56 5.56 5.56 5.56
-Cameroon 9.40 3.80 1.20 0.40 27.10 8.00 0.20 22.90 13.00 14.00 93.77
Congo DMR 3.80 1.60 0.60 0.10 29.70 5.60 6.50 24.50 12.00 15.50 97.16
Congo Republic 3.40 0.70 0.50 0.40 24.80 6.90 10.80 16.70 13.70 22.20 96.48
Ethiopia 8.60 3.10 1.10 0.50 20.00 4.80 11.30 20.20 13.20 17.20 98.94
Ghana 9.10 4.00 1.40 1.90 24.20 16.60 3.60 7.70 22.10 9.40 89.83
Kenya 4.50 1.80 1.30 2.00 23.00 9.80 12.10 20.10 17.40 8.20 95.63
Liberia 11.00 0.20 0.00 0.10 10.00 19.60 5.30 25.20 0.30 28.40 93.94
Malawi 3.60 0.50 0.30 0.20 26.70 23.80 3.70 17.30 19.50 4.50 92.44
Mali 14.70 2.80 1.20 0.60 29.50 5.10 8.80 25.90 1.30 10.00 89.36
Morocco 8.30 4.90 1.50 0.50 18.20 14.30 11.90 15.20 8.10 17.00 98.70
Mozambique 13.60 1.90 0.50 0.60 24.90 11.40 10.90 19.00 9.00 8.10 94.40
Namibia 3.50 1.00 0.50 0.60 22.90 16.00 4.40 17.20 21.90 11.90 97.82
Niger 9.40 2.80 1.10 0.20 22.60 9.80 9.90 22.10 4.80 17.20 95.71
Nigeria 9.60 6.80 3.70 2.10 21.30 6.10 8.50 17.10 18.80 6.10 93.58
Swaziland 2.80 1.70 0.50 0.30 26.60 11.30 12.20 7.90 21.30 15.40 95.53
Zambia 3.40 1.00 0.10 0.20 27.00 9.70 9.80 18.10 21.80 8.90 96.94
Zimbabwe 1.10 1.10 0.40 0.30 27.70 5.50 9.10 14.30 26.70 13.90 97.56
Africa 7.05 2.34 0.94 0.65 23.89 10.84 8.18 18.32 14.41 13.41 95.16
Armenia 5.20 1.10 0.30 0.10 1.40 6.30 1.80 1.30 7.50 75.20 89.61
Azerbaijan 3.70 1.70 3.00 1.30 7.30 7.70 11.10 4.30 20.00 39.80 75.73
Moldova 4.40 1.60 0.10 0.00 5.00 3.00 1.30 10.00 17.00 57.50 76.82
Eu-West Asia 4.43 1.47 1.13 0.47 4.57 5.67 4.73 5.20 14.83 57.50 80.72
Bangladesh 8.60 0.50 1.10 3.50 20.20 9.30 0.50 26.90 15.90 13.50 93.68
Cambodia 10.70 3.20 1.30 0.30 29.30 24.80 4.80 0.10 14.50 11.00 92.51
India 7.90 3.00 2.00 2.70 14.70 12.40 2.20 19.70 19.70 15.80 97.80
Nepal 7.90 2.60 1.60 2.40 18.20 8.90 1.80 21.30 16.90 18.60 94.31
Asia 8.78 2.33 1.50 2.23 20.60 13.85 2.33 17.00 16.75 14.73 94.58
Bolivia 6.40 1.80 0.80 0.20 23.20 3.40 10.40 18.20 21.40 14.20 96.07
Dominican Republic 11.40 1.00 0.20 0.30 13.70 6.00 6.50 12.20 24.00 24.70 97.80
Haiti 14.40 3.50 1.10 0.10 23.30 6.00 4.20 19.10 4.40 23.90 97.15
Peru 5.30 0.50 0.40 0.00 18.00 11.90 8.10 16.50 19.60 19.50 98.21
Latin America 9.38 1.70 0.63 0.15 19.55 6.83 7.30 16.50 17.35 20.58 97.31
MCA Average 7.35 2.15 0.99 0.78 20.73 10.14 6.85 16.46 15.21 19.34 93.84
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2 Regional Perspectives on the MPI
When looking at the weights from Table 2.2, some patterns can be ascertained for countries that lie within each region. At the outset, it is clear that the analysis shows no data driven justification for equal weighting of the dimensions. The weight of the education dimension is around 10% on average, while health gets a relatively low weight of around 2%. In the education dimension, years of schooling seem to take precedence over child enrolment, with nearly 4 times the weight of the latter on average. In some cases, this was much larger (Liberia, Bangladesh and Dominican Republic), while in some it was less than double (Morocco). Child mortality is found to weigh higher on average in comparison to the nutrition indicator, although this is not such a big difference as in the case of the education indicators. However, the standard of living indicators account for more than 85% of the total weight on average and nearly 95% (or even more) in the case of certain countries such as Congo Republic, Democratic Republic of Congo, Namibia, Armenia, Azerbaijan and Peru, to name a few. Within the standard of living indicators, on average, electricity receives the largest weights in the Index (21%). Behind that, the highest weights are assigned to assets (19%), closely followed by flooring (16.5%).
With regards to regional trends, countries in Asia tend to have a much higher weight than normal for the nutrition indicators, except for the case of Cambodia (which falls more in the case of South East Asia and is not geographically/culturally as close to the other three countries). This is not surprising, given that these are countries, especially India, where there are a massive number of people, especially children, who suffer from malnourishment(FAO, 2015; Klasen, 2008). The African countries tend to have higher weights, in comparison to the other countries, for all of the standard of living indicators, most importantly for electricity, except in the case of assets. This is in stark contrast to the EU-West Asian region, where the weight allotted to the assets indicator is very large, and often more than one third of the overall weight itself. Since these largely different weights are quite unexpected, the next section derives weights using PCA, as a check on the MCA weights.
2.6.2 Principal Component Analysis
Table 2.3 depicts the weights that were derived using PCA, but only the first principal component for all the countries. The cronbach’s alpha for all the variables in the case of the following countries ranges between 0.65 and 0.8, except for certain countries such as Armenia and Azerbaijan, where it is as low as 0.25 and 0.29 even. All countries with an α-value less than 0.65 have been italicized (9 out of total 28 countries). This can be construed to imply that the variables are not suitable to construct a single latent variable that defines multidimensional poverty and perhaps more than one component is required for constructing an index on wellbeing. Alternatively, it can be concurred that PCA is not a suitable technique for these countries. The results of these countries should perhaps be considered in the context of this deficit of internal consistency.
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2RegionalPerspectivesontheMPI Table 2.3: The weights assigned to countries based on PCA
Years of Child Child Nutrition Electricity Sanitation Drinking Flooring Cooking Assets Cronbach’s Variation
Schooling Enrolment Mortality Water Fuel Alpha explained
Original 16.67 16.67 16.67 16.67 5.56 5.56 5.56 5.56 5.56 5.56
Cameroon 11.08 4.19 1.77 0.57 22.85 9.67 0.22 20.92 14.52 14.19 0.69 29.32
Congo DMR 5.09 2.33 0.77 0.25 23.79 7.34 8.44 21.69 13.70 16.61 0.64 28.75
Congo Republic 4.41 0.90 0.61 0.44 21.34 8.58 12.27 16.86 14.58 20.01 0.67 27.85
Ethiopia 9.80 3.85 1.51 0.62 18.14 5.90 12.20 18.21 13.62 16.14 0.80 41.47
Ghana 10.89 5.00 2.01 2.51 21.05 15.24 4.80 9.31 18.70 10.49 0.59 24.04
Kenya 5.37 2.17 1.74 2.72 19.10 10.90 13.10 18.60 16.65 9.65 0.75 32.86
Liberia 12.88 0.30 0.09 0.18 11.64 18.61 7.47 23.17 0.45 25.20 0.50 23.07
Malawi 5.16 0.81 0.52 0.46 24.18 20.83 5.49 17.77 18.25 6.53 0.50 25.53
Mali 15.92 3.67 1.25 0.91 24.32 6.97 11.08 22.65 2.04 11.19 0.58 23.90
Morocco 9.06 5.69 1.80 0.54 16.70 14.16 12.36 14.81 9.14 15.71 0.76 33.44
Mozambique 14.74 2.63 0.78 0.92 19.49 12.72 12.52 16.95 9.77 9.49 0.66 28.59
Namibia 3.96 1.43 0.70 0.97 20.40 16.46 5.81 17.26 19.98 13.04 0.77 35.53
Niger 10.66 3.57 1.16 0.34 19.76 11.24 11.14 19.49 6.09 16.55 0.72 34.41
Nigeria 10.69 7.14 4.07 2.52 18.24 7.26 9.60 16.25 17.41 6.81 0.73 30.92
Swaziland 3.49 2.48 0.70 0.47 22.29 12.98 13.68 9.78 19.03 15.10 0.69 28.57
Zambia 4.71 1.52 0.18 0.29 21.69 11.74 11.83 18.05 19.17 10.82 0.73 34.68
Zimbabwe 1.69 1.70 0.62 0.58 22.82 7.53 11.69 15.04 22.31 16.03 0.76 34.05
Africa 8.21 2.90 1.19 0.90 20.46 11.65 9.63 17.46 13.85 13.74 0.68 30.41
Armenia 9.57 4 1.27 0.05 4.24 15.5 6.92 6.38 17.31 34.75 0.25 13.77
Azerbaijan 4.45 2.92 4.95 2.39 7.33 10.59 13.48 7.28 17.16 29.45 0.29 14.08
Moldova 11,59 1.17 0.02 0.2 8.76 8.18 3.5 16.5 23.28 26.77 0.49 20.41
Eu-West Asia 7.01 2.70 2.08 0.88 6.78 11.42 7.97 10.05 19.25 30.32 0.34 16.09
Bangladesh 10.38 0.87 1.73 5.1 20.07 11.45 0.85 20.43 14.45 14.65 0.66 27.38
Cambodia 11.05 4.85 2.3 0.58 23.46 22.52 6.97 0.07 15.5 12.68 0.61 25.72
India 8.84 3.86 2.52 3.26 14.78 13.05 2.78 17.95 17.86 15.09 0.75 32.15
Nepal 8.74 2.85 1.87 2.41 16.88 9.46 6.12 18.68 15.86 17.14 0.71 30.24
Asia 9.75 3.11 2.11 2.84 18.80 14.12 4.18 14.28 15.92 14.89 0.68 28.87
Bolivia 7.33 2.09 0.97 0.24 20.65 4.62 11.99 17.79 19.61 14.71 0.73 32.21
Dominican Republic 12.78 1.48 0.33 0.5 14.62 7.79 8.06 11.79 21.23 21.45 0.65 26.31
Haiti 15.24 4.57 1.61 0.21 20.54 7.54 5.49 18.39 5.56 20.86 0.69 28.35
Peru 6.40 0.78 0.6 0.05 17.46 13.04 9.77 16.05 18.24 17.61 0.77 33.93
Latin America 10.44 2.23 0.88 0.25 18.32 8.25 8.83 16.01 16.16 18.66 0.71 30.20
PCA Average 8.68 2.82 1.37 1.08 18.45 11.50 8.56 16.00 15.05 16.38 0.65 28.63
MCA Average 7.35 2.15 0.99 0.78 20.73 10.14 6.85 16.46 15.21 19.34 93.80
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2 Regional Perspectives on the MPI
Since the indicators are binary variables and PCA is a method rather designed for continuous, normally distributed data, and additionally, very low variation is explained within the first component of the PCA. Nonetheless, I find that the results are generally similar to what was calculated using MCA, especially in terms of which dimension is assigned the largest weight. When looking at specific regions, Armenia, Azerbaijan and Moldova have a much higher weight than the average, for the assets indicator (although much lower in value in comparison to the MCA weights, reducing the overall assets average to 18% from 19%). Again, nutrition gets a very high weight for the Asian countries, with Bangladesh receiving the highest weight in this indicator, similar to the MCA analysis.
Electricity receives the highest average weights again, although this average might be drives by the relatively higher weights in the case of African countries. The fact that these two methods provided relatively similar results is not entirely surprising. There are several studies that point out the similarities between the two methods and show a high correlation between a PCA generated index versus a MCA generated one (Howe et al., 2008; Booysen et al., 2008). But so far, both methods suggest that if one considers multidimensional poverty to be a latent concept that can be captured by the given indicators, there seems to be merit in the idea of putting more weight on the standard of living dimension. At the very least, it is important to consider the possibility that the equal weights applied within the MPI are not reflecting the structural relationships of the information derived from the household survey for each country.
Although these are mostly visual comparisons, and they do not give a concrete value to the differences in weights, there do seem to be differences across certain countries and certain regions that can be perceived here. The next part of the analysis concentrates on the nature of these weights, and how clearly can regional patterns be identified using both techniques. Given that MCA is the preferred method of analysing weights derived from categorical and binary variables, the robustness checks would focus on these weights. The results for the PCA weights are also displayed in the appendix.