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Poverty and vulnerability transitions in Myanmar: An analysis using synthetic panels

Over the past decade, Myanmar has experienced sustained economic growth and steady reductions in poverty. Between 2010 and 2015, the poverty rate fell from 42% to 32% of the population; just two years later, in 2017, it had dropped below 25%. In this paper, we provide new insights into household-level movements into and out of different states of poverty and vulnerability during the latter part of this period using national survey data from 2015 and 2017. Our findings reveal a reasonably high level of upward mobility, with just under half of all households that were poor in 2015 moving out of poverty two years later, and more than half of all ‘non poor’ vulnerable households experiencing an improvement in their circumstances.  

Ordinarily, detailed insights into poverty dynamics would require panel data (repeated observations on the same households over time), which is not readily available and is expensive to collect. Instead, our analysis employs innovative statistical approaches to create a ‘synthetic panel’ from cross-sectional household survey data (repeated observations on different households over time). This approach allows us to look at how the situation of different households might change over time and the characteristics associated with that change. By better understanding these dynamics, particularly in a time of relative stability, we can develop more effective poverty reduction strategies and responses in times of disruption. 

Our analysis finds that different transitions into or out of poverty may have been more or less likely for households that share certain characteristics – like the age of the household head or whether they have a valid ID card. For example, households headed by someone with no education are more likely than average to experience persistent poverty and considerably less likely than households with some education to escape poverty (by as much as 30 percentage points). And there is some overlap between these factors and the socioeconomic impacts of COVID-19 – such as months of missed schooling for millions of children.  


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