Understanding poverty dynamics and vulnerability in Tanzania: 2012–2018

Abstract

We study poverty dynamics and vulnerability in Tanzania between 2012 and 2018 using synthetic panel methods. Under the surface of apparent stability in aggregate poverty rates, and despite robust economic growth, households experienced strong fluctuations in consumption levels during this period: 12.5% of the population remained in persistent poverty, a further 30% experienced transient poverty, and one of five Tanzanians above the poverty line in 2012 was poor 6 years later. Education and employment in the nonfarm sector are particularly effective at shielding households from poverty, while rural and large households with many children are most likely to slip into poverty. Considerable differences exist between less‐deprived areas such as Dar es Salaam or Kilimanjaro and regions in the northwest, where persistent poverty is especially high. Looking ahead to the impact of COVID‐19, those households least prepared to take preventive measures against the virus suffer from more adverse poverty dynamics, while those involved in the sectors taking the hardest economic hit from the pandemic start from a better pre‐pandemic situation. This suggests that novel policies that specifically support this ‘new vulnerable’ need to be combined with redoubled efforts to address the structural causes of poverty and vulnerability.

Spatial variation and inequities in antenatal care coverage in Kenya, Uganda and mainland Tanzania using model-based geostatistics: a socioeconomic and geographical accessibility lens

“…Long distances, poor roads and high transport costs, poor services at the health facilities and lack of access to health-related information also impede women to utilize maternal services in this region [86]. Similar situation exists in North-western Tanzania which is poor and has low conditional probability of transitioning from poor to non-poor status [87]. Further, socio-cultural beliefs, distance, lack of transport, perceived poor quality of ANC services have been reported as barriers to ANC use in this region [88].…”

Assessment of the effects of targeted poverty alleviation policy on differently poverty‐stricken households: A multi‐propensity score weighting model

“…Model (1) showed the benchmark estimation results without the insignificant “Distance” covariate. The household characteristics were more strongly associated with positive or negative poverty dynamics (Aikaeli et al, 2021), so Model (2) added the covariate “EducationLevel,” which affected both income and causes of poverty. Generally speaking, the higher the education level, the higher the income (Lai et al, 2020).…”

The relationship between education and vulnerability to poverty in South Africa

“…The majority of the studies on poverty vulnerability only focus on the different estimation techniques (inter alia: Pham et al 2021 ; Aikaeli et al 2021 ). However, the studies that do control for education as a determinant of vulnerability to poverty usually take an aggregate approach (Pham et al 2021 ; Guo et al 2019 ).…”