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Tanzania’s extreme poverty rate has remained stable, falling by just 6% between 2012 and 2018, compared to an 18% decline over the previous four years. Economic growth has been relatively high in Tanzania but this has not translated into sustained poverty reduction. Tanzania has one of the highest urbanisation rates in the world, is highly vulnerable to climate change and ranks low on indicators related to gender and social norms. DEEP is working on innovations in data to measure extreme poverty, analysing the main drivers of poverty in Tanzania, and studying what works to reduce extreme poverty.

Projects in the Country

Project 1: Evaluation of digital directory services in Northwest Tanzania

Mobile phones have proliferated without services like the Yellow Pages that allow users to find new phone numbers at low cost. Instead, most mobile phone users learn of new numbers by interacting directly with the party whose number they wish to acquire, or through their face-to-face network. The lack of systematic directory services reduces the productive benefits of the mobile phone revolution and skews them toward individuals with stronger pre-existing social networks or with the capacity to travel and gather numbers. This study addresses a number of key questions related to the distributional effects, scalability, and welfare consequences of providing access to telephone directory services. Our team will conduct a census of formal and informal enterprises in Northwest Tanzania for inclusion in the directory and conduct a series of small-scale RCTs that address key knowledge gaps. These include: how does receipt of a telephone directory impact income, consumption, and poverty status? How do the distributions of benefits and costs vary between paper and digital telephone directory deployment? How do directory recipients share directory information in their networks? Are there gender differences in the impact of the directory on households, and are those differences related to pre-existing gender differences in the size and scope of household social and professional networks? Do the benefits to listed firms come at the expense of unlisted firms in other villages (i.e., are there negative spill overs across villages)?


Project 2: Locating extreme poverty in Urban East Africa using advanced statistical methods

Exploring the extent to which information extracted from satellite images can help predict the distribution of extreme urban poverty in Ethiopia, Tanzania, and Mozambique.  Geo-referenced estimates of daily per capita consumption from nationally representative household surveys are matched with observable neighbourhood level characteristics, such as the average size, perimeter, and density of buildings, as well as distances to different types of road and other points of interest. By comparing cities across three countries with different poverty profiles and material characteristics, a broader range of possible outcomes can be described.

Project 3: What works to reduce extreme poverty?

A selective review of what works to reduce extreme poverty in five countries, undertaken as part of DEEP’s inception phase.

Project 4: Understanding the relationship between city size and poverty using small area estaimation in Tanzania

Earlier work by DEEP helped to locate extreme poverty in urban East Africa using satellite images. Interesting results emerged using available geo-spatial data derived from satellite images to predict poverty across three large urban areas, including Dar-es-Salaam. By focusing on characteristics at the sub-neighbourhood level, our work highlighted covariates of poverty found in the data and identified pockets of extreme poverty. There seems to be an inverse spatial relationship between places included in household surveys and the areas in which the bottom quintile of the welfare distribution reside. This project builds on and extends this small area estimation work and explores the relationship between city size and poverty using satellite imagery. Existing understanding is based on traditional survey based data and these are not well placed to shed light on the extent of poverty at the individual town and city level.  Moreover, we are investigating existing household surveys and their ability to capture the bottom tail of the welfare distribution.

Poverty Trends in Tanzania

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