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Development Analytics Supports UNICEF Country Offices for Estimating the Impact of COVID-19 on Child Poverty and Costing of Cash Transfer Scenarios Targeting Households with Children 

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Development Analytics Supports UNICEF Country Offices for Estimating the Impact of COVID-19 on Child Poverty and Costing of Cash Transfer Scenarios Targeting Households with Children 

Development Analytics has concluded three microsimulation projects to allow decision-makers to forecast the possible outcomes of various policy scenarios to alleviate the negative impact of the COVID pandemic. In this capacity, our team worked with a number of UNICEF country offices, including Georgia, St Lucia, and Turkey, to estimate the pandemic’s impact on the monetary poverty of children and the possible poverty-reducing impact of alternative cash transfer scenarios. Development Analytics also provided UNICEF country offices with interactive dashboards, making it easier to see the results dynamically and increasing the studies’ usefulness and effectiveness for government counterparts.

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Development Analytics was commissioned by the UNICEF Georgia Country Office to provide estimates on the impact of COVID-19 on household and child poverty through its effect on the labour market. The Development Analytics team provided inputs on the poverty impact of the COVID crisis, as well as the poverty-reducing impact of several cash transfer scenarios targeting different groups in the population and at varying benefit levels using the Georgia Welfare Monitoring Survey as the primary data source. This study has yielded important results on COVID-19 poverty impact and cash transfer policy scenarios. High COVID vulnerability households (which already have low expenditure levels) experienced the steepest percentage decline in expenditures. Poverty increased more in rural areas, and different poverty dynamics were observed in different regions, with some being affected more severely. In terms of the results of cash transfer scenarios, the Development Analytics team proposed that policies and transfers that widely target the bottom 40% of the distribution are more likely to have an impact on reducing poverty in a cost-effective manner rather than those that are very narrowly targeted (only current social assistance TSA beneficiaries), that target the unemployed or that are too widely distributed such as universal child grants. For detailed information about the project, you can read the project report at this link. The study is accompanied by an interactive microsimulation model, which can be viewed at this link.

In St. Lucia, the Development Analytics team was commissioned by the UNICEF Eastern Caribbean Office to provide estimates on the impact of COVID-19 on monetary and multi-dimensional poverty in collaboration with the Oxford Poverty and Human Development Initiative. Using the St Lucia Survey of Living Conditions and Household Budgets (SLC-HBS) as the primary data source, the team built the microsimulation models focusing on three main transmission mechanisms of the negative impact of COVID-19 on households: (i) labour demand, (ii) labour supply and (iii) health expenditures shocks.

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As a result of the simulated shocks that were experienced by individuals in the households, monthly per capita expenditure shrunk for households. Reductions in monthly household expenditure lead to significant increases in expenditure-based poverty. In terms of the results on COVID-19’s Multidimensional Poverty Impact, it was concluded that in the severe scenario, job quality deterioration could result in almost 91% of the population being multidimensionally poor.

The research team also modelled 12 different cash transfer scenarios with three different transfer levels (transfer levels 1, 2, 3) to combat the poverty impact of COVID-19. The team concluded that a scenario that targets households in the bottom 40% (that are not eligible under SLNET 3.0) is among the scenarios that lead to the highest poverty reduction after a severe shock while being the most cost-effective or among the most cost-effective scenarios depending on the transfer level. Development Analytics also conducted a capacity-building session with UNICEF St. Lucia and the Ministry of Equity, Social Justice and Empowerment to explain the methodology designed for the study and demonstrate the use of the interactive web application for practical use of policymakers. The study is accompanied by an interactive microsimulation model, which can be viewed at this link.

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The UNICEF Turkey Country Office also commissioned Development Analytics, in June 2020, to provide estimates on the impact of COVID-19 on poverty, child poverty and inequality in Turkey through its effects on the labour market using the Household Budget Survey (HBS) data. This microsimulation model focused on the transmission mechanisms through a loss of jobs and reduced labour income to show the impact of COVID-19 on households considering two different impact level assumptions (under a mild and severe shock). This study modelled seven different cash transfer scenarios with two different transfer levels (low and high). Regarding the cash transfer policy scenario results, the team found that among the seven scenarios considered, the universal child grant for 0-17 years old children had the highest poverty reduction impact overall while also being the most expensive scenario. Development Analytics also showed that targeting the bottom 20 percent of households was the most successful strategy in terms of being the most cost-effective after a mild or a severe shock. And the effect was also considerable in terms of the impact on poverty reduction. The results of this study were shared with the  Presidency of Strategy and Budget, DG Social Assistance at the Ministry of Family and Social Services and also published in the European Journal of Development Research. For further details about the findings, you can access the academic article following the link.

July 2021

Development Analytics holds a Long Term Arrangement (LTAS) with UNICEF on the Provision of High-Quality Technical Expertise on Child Poverty and Social Protection 2022-2025. If you work within or alongside UNICEF on these topics and would like to learn more about our microsimulation models, please get in touch with us at the following link