For a young person who has spent his or her whole life living in a village in rural Africa, moving out is often desirable in theory, but daunting in practice. From the life histories of migrants in Tanzania it becomes clear that a number of important resources are needed, which are typically scarce in supply, particularly within the village. These include, among others, cash to pay the bus fare and a familiar face at destination, professional skills to find meaningful employment, and the life skills to operate in the anonymous, cash-based urban environment. And just because of the particular challenge of getting these in the village, the first move becomes so special.
The importance of soil health in agrarian societies is indisputable – soil health has a direct relationship with agricultural productivity and sustainability. Yet, its highly complex nature renders it much more challenging to measure than other agricultural inputs, such as fertilizers or pesticides. Household surveys, particularly those in low-income contexts where agriculture is the primary means of livelihood, have generally relied on subjective assessments of soil health – and for good reason. Subjective assessment is relatively inexpensive, and alternative methodological options have historically been prohibitively expensive. Recent advances in rapid low-cost technologies, namely spectral soil analysis, however, have increased the feasibility of integrating objective plot-level soil health measurement in household surveys.
This new Guidebook provides practical guidance for survey practitioners aiming to implement objective soil health measurement via spectral analysis in household and farm surveys, particularly in low-income smallholder farmer contexts. Two methodological experiments, in Ethiopia and Uganda, provide the foundation for this Guidebook. In each study, plot-level soil samples were collected following best-practice protocols and analyzed using wet chemistry and spectral analysis methods at ICRAF’s Soil-Plant Diagnostics Laboratory, in addition to a subjective module of soil health questions asked of the plot manager. The Guidebook offers (i) a comparison of subjective farmer assessments of soil health with laboratory testing, and (ii) step-by-step guidance on how to implement spectral soil analysis in a household- or farm-level survey, from questionnaire design to soil sample collection, labeling, and processing.
The Guidebook is the result of collaboration between the World Bank's Living Standards Measurement Study (LSMS) team, the World Agroforestry Centre, the Central Statistical Agency of Ethiopia, and the Uganda Bureau of Statistics.
For practical advice on household survey design, visit the LSMS Guidebooks page: http://go.worldbank.org/0ZOAP159L0
This blog is part of a series using data from World Development Indicators to explore progress towards the Sustainable Development Goals and their associated targets. The new Atlas of Sustainable Development Goals 2017, published in April 2017, and the SDG Dashboard provide in-depth analyses of all 17 goals.
As Agriculture Economists who work on advancing the food and agriculture agenda, SDG 2 articulates much of our work in the Sustainable Development agenda and illustrates how food and agriculture are intertwined with poverty reduction. Goal 2 seeks to “End hunger, achieve food security and improve nutrition, and promote sustainable agriculture.”
Without making progress on Goal 2, we can’t achieve the Bank’s twin goals of ending poverty and boosting shared prosperity.
But what does Goal 2 mean, exactly? On the surface, it might seem to be a matter of producing more food in a sustainable way. But a deeper dive into this SDG reveals that it is not quite that simple.
Developing countries like Tanzania are experiencing an unforeseen youth bulge—a high proportion of young people aged 15 to 24. Sadly, this growth is not matched by an equivalent rise in economic opportunities for the youth. Thus, most youth are either unemployed or engaged in activities with low productivity. There are solutions to this problem.
Meet Ibrahim, 27, a 2015 Agronomy graduate from Tanzania’s Sokoine University of Agriculture, one of the leading agricultural colleges in Sub-Saharan Africa. You would expect him to be dressed in blue overalls, working on one of the largest plantations near Arusha, in Basutu or Ngarenairobi, where they grow barley and wheat.
However, Ibrahim sits in a comfy chair at his office in Morogoro, supervising three ICT graduates employed by his company. Indeed, it is becoming normal to major in chemistry at university only to practice “algebra”—as they say—in real life.
Agriculture is the backbone of many African economies, employing the most citizens in most countries, citizens who produce food for consumption and raw materials for industries. With the current data revolution, and the explosion of new data sources available in Tanzania, we can push for the integrated use of mechanization, fertilizers, and digital technologies to get more efficiency and productivity in our agriculture.
Many urban planners may know the success stories of Curitiba, Singapore or London realizing transit-oriented development (TOD). However, TOD is still very new in Sub-Saharan Africa. Although this concept of leveraging on major transit infrastructure to affect integrated land-use development for greater benefits may be gaining more recognition, there are few examples of successful TOD in Sub-Saharan Africa beyond a couple of South African cities, such as Cape Town and Johannesburg.
Dar es Salaam, the largest city in Tanzania with a population of 4.6 million, is expected to become a mega city by 2030 with a population over 10 million. However, its growth has been largely shaped by informality, coupled with a lack of hierarchy in roads and transit modes. It is increasingly difficult to get around the city without being stuck in traffic for hours. The complex and fragmented institutional structure of Dar es Salaam compounds the challenges, making management of the city complicated and less effective.
“Tell me where you live, and I can predict how well you’ll do in life.”
Does welfare vary largely across space?
Although I don’t have a crystal ball, I do know for a fact that location is an excellent predictor of one’s welfare. Indeed, a child born in Togo today is expected to live nearly 20 years less than a child born in the United States. Moreover, this child will earn a tiny fraction—less than 3%—of what his or her American counterpart will earn.
In our previous post, we explored how migration from rural to urban areas is not a one-step move, but rather a dynamic lifelong process that expands and modifies migrants’ action space and opportunities to improve their life conditions, and how the attraction of secondary towns could be partly understood within this framework because of their role as “action space” enhancers.
Yet, defining precisely what constitutes a town or a city is tricky, to the point that Wittgenstein found it even a useful analogy with which to demonstrate definitional conundrums more broadly. “And how many houses or streets does it take for a town to be a town?”, he rhetorically asks his readers, while discussing at what point a language should be considered complete in his Philosophical Investigations.
At the same time, the distinction between towns and cities is intuitively unambiguous to most non-experts. Asking how migrants themselves see the difference may further help understand why they often move to towns, while the income levels and amenities are higher in the cities. According to the conversations we had with 75 migrants from rural Kagera, Tanzania, three dimensions stand out: vibrancy, monetization and anonymity.
The 2015 Economic Report on Africa by the United Nations Economic Commission for Africa (UNECA) put Tanzania’s unemployment rate at 10.3 percent. It also reported that the number of unemployed women in the country is higher than that of unemployed men.
But there are a number of ways in which we can boost job opportunities for youth in Tanzania.