learning.<sup>14</sup> But this should not be seen as an indication that children should be learning outside the school system. As the COVID-19 school closures have shown, schools have several other functions to protect and improve children’s health and well-being,<sup>15</sup> such as safety, nutrition,<sup>16</sup> socialization, facilitate parents' labor market participation,<sup>17</sup> and at the macro level can help build social cohesion,<sup>18</sup> democracy and peace,<sup>19</sup> All those complementary functions mean that schooling has an intrinsic value over and above learning and justifies its deprivation as a relevant component of learning poverty.<sup>20</sup> Hence, learning poverty is multivariate and unidimensional, using a nested approach. Table 1 illustrates learning poverty classification using a 2 x 2 contingency table based on the two deprivations, schooling and learning.<sup>21</sup>

**Table 1 Contingency table of schooling and learning deprivations and learning poverty classification**

|||Learning<br>|deprivation<br>|
|---|---|---|---|
|||No|Yes|
|vation|No|_Not School Deprived_and_Not_<br>_Learning Deprived_<br>=|_Not School Deprived_and_Learning_<br>_Deprived_<br>_=_|
|depri||**Not Learning Poor**|**Learning poor**|
|Schooling|Yes|_School Deprived_and_Not Learning_<br>_Deprived_<br>_=_<br>**Learning poor**|_School Deprived_and_Learning Deprived_<br>_=_<br>**Learning poor**|

Note: Given the anchoring on the learning poverty measure on the SDGs, learning deprivation at the End of Primary is measured using schoolbased learning assessments. Hence, this measure requires the combination of two data sources, namely, school-based learning assessments used 14 World Bank 2018.

15 UNICEF 2020; WFP 2020.

16 Bhutta et al (2013) and Adelman et al (2008).

17 Blau and Currier (2006) and Blau and Robins (1988).

18 Easterly et al (2006).

19 Khan (2016).

20 Combing an ordinal (binary) and a cardinal variable always implicitly puts more weight on the ordinal data, since they always get the full score of 1. This is a problem since the measure always puts more weight on the deprivation in the ordinal variable. Alkire and Foster (2011) handle this problem by only using the ordinal form of the cardinal variable as well. The headcount measure of Learning Poverty uses the MPL to transform learning from cardinal to ordinal.

21 This measure combines a direct measure and an indirect measure to identify an underlying learning poverty concept, using two deprivation indicators, schooling and learning, in the space of reading at the end of primary. The direct measure identifies those whose basic need for schooling fails to meet the accepted minimum standard—to be enrolled in school when at the proper age to be at the end of primary. The indirect measure, learning, identifies those who do not to meet a minimum proficiency level as defined by their reading assessment for that age and grade. A learning poor child, on this approach, is one who is not in school or whose reading is not adequate to meet the specified minimum as defined by the GAML process. The indirect measure has the advantage of providing a metric of learning shortfalls through numerical distances from an agreed threshold—something the direct measure does not provide. The indirect method is stringent since it sets three preconditions for the exercise: (1) agreement on a common standard of reference, (2) the availability of large-scale learning assessments of sufficient quality, and (3) the benchmarking of the learning assessment’s competence levels against the standard agreed in the GAML process.

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