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

• Published date: 01 November 2021
• Author: Ines A. Ferreira,Vincenzo Salvucci,Finn Tarp
• Date: 01 November 2021
• DOI: http://doi.org/10.1111/rode.12836

Rev Dev Econ. 2021;25:1919–1944.

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1919

wileyonlinelibrary.com/journal/rode

Received: 17 March 2021

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Revised: 4 August 2021

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Accepted: 11 September 2021

DOI: 10.1111/rode.12836

SPECIAL ISSUE ARTICLE

Poverty and vulnerability transitions in

Myanmar: An analysis using synthetic panels

Ines A.Ferreira

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VincenzoSalvucci

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FinnTarp

Development Economics Research

Group (DERG), Department of

Economics, University of Copenhagen,

Copenhagen, Denmark

Correspondence

Vincenzo Salvucci, University of

Copenhagen, Øster Farimagsgade 5,

Building 26, DK- 1353 Copenhagen K,

Denmark.

Email: Vincenzo.Salvucci@econ.ku.dk

Funding information

This study was prepared under the

ExPov/DEEP project funded with UK aid

from the UK government and managed

by Oxford Policy Management (OPM).

Abstract

While Myanmar achieved distinct progress in economic

growth and poverty reduction over the past decade, ex-

treme natural events; economic, political, and social

crises; and the ongoing COVID- 19 shock pose serious

challenges. This study complements previous analy-

ses of poverty and vulnerability by providing a dynamic

perspective for the period 2015– 2017. Given the lack of

longitudinal household data, the analysis relies on the

synthetic panels approach to further our understanding

of transitions between different states— poverty, vulnera-

bility, non- poverty— and the characteristics of the house-

holds associated with these transitions. Among the main

results, we find that there was a relatively high probability

for people, who were poor in 2015 to exit poverty in 2017,

and that the probability of remaining in a vulnerable situ-

ation was non- negligible. Moreover, the results point to

important differences in the probability of transitioning

between different states depending on household and

location characteristics. While the COVID- 19 shock has

likely increased the proportion of households in the vul-

nerable and poor groups, these results highlight the need

to focus on households with specific characteristics that

make them more at risk of remaining or falling into pov-

erty than the rest of the population in a context of dimin-

ishing poverty rates and localized vulnerability pockets.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,

distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2021 The Authors. Review of Development Economics published by John Wiley & Sons Ltd.

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FERREIRA et al.

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INTRODUCTION

Myanmar achieved sustained economic growth and broad macroeconomic stability during the

past decade (Economist Intelligence Unit,2020). Poverty rates decreased from 2004/05 onwards,

and non- monetary dimensions of well- being also improved (CSO, UNDP, and World Bank, 2018,

2019b). Growth rates remained above 5% in the period 2011– 2018, and while it was relatively low

in 2016 (5.8%), it increased again in 2017– 2018 and 2018– 2019. Subsequently, Myanmar's econ-

omy suffered a significant slowdown in 2020 with the surge of the COVID- 19 crisis.2 The World

Bank's Economic Monitor estimates a growth rate of 1.7% in 2019/20 compared to a rate of 6.8%

in 2018/19 and projects a growth rate of 2% in 2020/21 (World Bank,2020a).

The current COVID- 19 crisis poses serious challenges for the future.3 It is expected to affect

poverty rates, reversing the country's recent progress. Under the macro- micro simulation model

developed by the World Bank (2020a),4 poverty rates will increase in 2020/21 and, without a

substantial policy response, to only return to their pre- crisis levels in 2021/22 or later. Moreover,

it seems to affect relatively more some categories of households that were not considered among

the most disadvantaged in the latest poverty assessments (CSO, UNDP, and World Bank, 2018,

2019b; MPF and World Bank, 2017). While it is important to study the actual or expected impact

of the COVID- 19 shock on household livelihood and poverty, no household data have been made

available after the surge that allow for rigorous evaluations of the effect of the shock as compared

to pre- crisis conditions.

In this study, we focus on poverty dynamics in the period 2015– 2017 and analyze the transi-

tions between states of poverty, vulnerability, and non- vulnerability during a “normal” or “mod-

erately high- growth” period, not affected by a major— but specific— nation- wide shock such as

the COVID- 19 pandemic. We recognize that the COVID- 19 shock may have influenced the pro-

files of poverty and vulnerability transitions obtained. Nevertheless, our premise is that analyz-

ing poverty dynamics during a time of general rising prosperity can offer useful insights into

those who are likely to be hit especially hard by the economic consequences of the COVID- 19

pandemic, given that the vulnerable and chronically poor in more prosperous times are likely to

be particularly affected by shocks. This is relevant as well given that, at the moment, nothing in-

dicates that those who were more at risk of being vulnerable or poor before the COVID- 19 shock

are now less at risk or in a better situation (see Diao & Mahrt,2020).

Given that recent, large- scale, nationally representative, longitudinal household data are not

available,5 we implement the synthetic panels approach introduced by Dang etal. (2014) and

Dang and Lanjouw (2013, 2014) to derive probabilities of transition at the national level and for

different household categories. We apply this methodology to the 2015 Myanmar Poverty and

Living Conditions Survey (MPLCS) and to the 2017 Myanmar Living Conditions Survey (MLCS).

As a result, this analysis of poverty and vulnerability transitions aims to supplement the infor-

mation from existing poverty assessments based only on cross- sectional data. Specifically, we (1)

KEYWORDS

Myanmar, poverty, poverty dynamics, synthetic panels,

vulnerability

JEL CLASSIFICATION

C53; I32