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Primary School Children are the most vulnerable to Covid-19

The Multidimensional Vulnerability Index for Bhutan reveals a host of stark realities for Bhutan that need to be considered immediately

By Chimi Wangmo

The Multidimensional Vulnerability Index 2021 published by the GNHC and UNICEF Bhutan Country Office has revealed that the COVID-19 pandemic is changing peopleโ€™s lives in multiple ways.

It has now become clear and more precise that the potential adverse effects of the pandemic are unevenly distributed. Some groups are being hit stronger depending either on individual characteristics (such as age, the sector of activity and type of occupation), family characteristics (like the number of earners in the family), or simply location.

For instance, families with employed members in the tourism sector immediately experienced the consequences of lower economic activity due to the closure of international borders.  Similarly, households with school aged children are now dealing with the education of their children.

This differentiated effect of the pandemic requires clear diagnostics to increase the impact of public interventions through targeted interventions addressed to the poorest and the most vulnerable members of the society.

Therefore, the study was aimed at providing an analytical tool to inform a broad set of public policies under a multidimensional framework.

โ€œThe analysis proposes the construction of a Multidimensional Vulnerability Index (MVI) to identify the most vulnerable population and hence to inform planning policies and envisage, or complement, current or prospective public interventions,โ€ it stated.

The study was designed in collaboration with the Gross National Happiness Commission (GNHC). Among others the study considered whether to use two nationally representative datasets, namely: the Bhutan Living Standard Survey (BLSS) 2017 and the Population and Housing Census of Bhutan (PHCB) 2017.

The GNHC selected the final indicators, and also indicators for disaggregation and results are based on PHCB (2017).

Bhutanโ€™s MVI was tailored to measure potential deprivations in the multiple domains affected by the pandemic. The index includes four dimensions: the first three relate to dimensions already included in Bhutanโ€™s official MPI namely health, education and living conditions.

The index then preserves the 13 indicators of Bhutanโ€™s MPI. In the case of poverty, a person who is deprived in 4/13 of the weighted indicators (30.7% of dimensions) is considered multidimensionally poor.

A fourth dimension was added to the current MPI structure to construct Bhutanโ€™s MVI. The household is considered vulnerable if they are deprived in nearly a quarter of all the weighted indicators to ensure comparability with poverty identification.

The MVI scenario in Bhutan

The study revealed that it is expected that the pandemic will produce different vulnerability profiles among the Bhutanese population. This vulnerability profiles can largely vary between rural and urban areas.

The rural vulnerability headcount ratio is much higher compared to the one for urban areas โ€“ 27% versus 3.6%, respectively. It is worth noticing that almost 62% of Bhutanโ€™s population live in rural areas (nearly 372 thousand population).

Nearly 10% of the population in Bhutan is 60 years or older but almost a third is multidimensionally vulnerable. This means that older population not only carries the largest risk in terms of the catastrophic consequences of getting infected, but also in terms of this multidimensional approach.

This information reveals that elder population requires priority actions as this is the age group more likely to be hit by the adverse effects of the pandemic.

Similarly, nearly one-third of the population of Bhutan – 32.5% – are children under 18 years of age. They are the second group most vulnerable social group in relative terms (as percentage of that population group). In particular the children of primary school age (0-9) are the most vulnerable among children.

Conversely, the less vulnerable group, again in relative terms, is the working-age population in particular those between 18-39. Things are slightly different, in absolute terms (number of people) since this population group is the largest in terms of number of vulnerable people -in particular young adults (18-39 years).

In Bhutan the majority of population is women (51.3%) and 18.6% of them is multidimensionally vulnerable. This is nearly 56.6 thousand vulnerable women.

In the case of men, the percentage of vulnerable population is slightly smaller 17.9%. The intensity of deprivation in both groups is slightly the same (A around 31%) and because of this the MVI for women population is slightly larger (0.058) than that for men (0.056).

The most multidimensionally vulnerable groups are households with an uneducated head (without no formal education) which covers 55% of the total population.

Nearly 28% of these households are vulnerable with an overall MVI of 0.091, which is almost twice as large as the national MVI (0.057). The group with the lowest levels of vulnerability is households with potential returnees living abroad (this is, households with family members living abroad). Nearly 8% of the Bhutanese population lives in a household with a potential returnee where roughly one out of 10 is MVI vulnerable.

Two of Dzongkhag with the highest vulnerability profile are in the west borders of Bhutan. In Gasa, 36% of the population is MVI vulnerable whereas in Samtse, over the Indian border, this percentage is nearly 31%.

These two Dzongkhag are respectively the least and the most populated Dzongkhag in Bhutan. Three Dzongkhag ranked third: Zhemgang, Dagana and Samdrup Jongkhar. In all of them the percentage of MVI population is nearly 30%. In the remaining 15 Dzongkhag the vulnerability incidence is below 25%.

The incidence of multidimensional vulnerability in large cities is below 10%. The largest incidence in relative terms is in Samdrup Jongkhar Thromde which is also the smallest city in terms of population (with respect of the three other Thromde. In this city the MVI (0.007) is considerably smaller than the national MVI. The bulk of vulnerable population is out of these large cities.

This study performed a detailed match between Gewogs with the 60 Towns and 4 Thromdes to account for all the national population.

The MVI values for the top 20 Gewogs range from 0.152 in Tashiding to 0.289 in Getana. This means that the most vulnerable Gewog is Getana is nearly five times larger than the national MVI.

In Getana, 78.6% if the population is vulnerable. The second largest MVI corresponds to Lunana with an MVI of 0.283; however, percentage of multidimensionally vulnerable population is slightly larger (80%) than in Getana.

The study reveals that with some exceptions, many of the Gewogs with the highest MVI levels are close to some borderline although it is also truth that some Gewogs over the Indian border have some of the lowest levels of MVI.

The lack of Connectivity for Education (a computer device and internet) appears amongst the most prevalent deprivation indicators.

The incidence of multidimensional vulnerability shows that nearly one-fifth of population is prone to be affected by the adverse effects of the pandemic (multidimensionally vulnerable).

In terms of age, the most vulnerable population groups are elder population and children of school age under 10.

The rural vulnerability headcount ratio is much higher than that for urban areas โ€“ 27% versus 3.6%, respectively. This pattern is relevant given that nearly 62% of the population live in rural areas.

The largest multidimensionally vulnerable population group is characterized by the households with an uneducated head This is relevant given that more than half of the population live in these households.

This study aims to provide an analytical tool to inform a broad set of public policies under a  multidimensional framework. The analysis proposes the construction of a Multidimensional  Vulnerability Index (MVI) to identify the most vulnerable population and hence to inform  planning policies and envisage, or complement, current or prospective public interventions.

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