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Africa

Qui apportera de la valeur ajoutée à l’Afrique ? Qui soignera ? Qui construira ?

Andreas Blom's picture
Also available in: English

 Dasan Bobo/World Bank​En tant qu’économiste, spécialisé dans le secteur de l’éducation à la Banque mondiale, je passe souvent en revue  de nombreuses stratégies pays ou sectorielles dissertant sur la meilleure façon de développer l’Afrique et d’y atteindre une croissance économique élevée.
 
Et à chaque fois je me demande: mais qui le fera ? Qui apportera de la valeur ajoutée aux exportations africaines ? Qui construira ? Qui inventera ? Qui soignera ?
La réponse est évidente : ce sont les jeunes fraîchement diplômés des universités africaines et des instituts de formation. Certes, mais dans ce cas nous avons un problème : il n’y a tout simplement pas assez de diplômés en sciences, en technologie, en ingénierie et en mathématiques (STIM) à l’heure actuelle sur le continent et la qualité des formations est très inégale.

Who will add value in Africa? Who will cure? Who will build?

Andreas Blom's picture
Also available in: Français

 Dasan Bobo/World Bank​From my seat as an Education economist at the World Bank, I go through a number of strategies from countries and sectors in Africa outlining how best to achieve economic growth and development. I am repeatedly struck by a key question: Who will do it? Who will add value to African exports? Who will build? Who will invent? Who will cure? The answer is, of course, that graduates from African universities and training institutions should do it. But the problem is one of numbers and quality—there are simply not enough graduates in science, technology, engineering and math (STEM), and programs are of uneven quality.
 

Africa’s big gender gap in agriculture #AfricaBigIdeas

Michael O’Sullivan's picture
Also available in: Français


Women are less productive farmers than men in Sub-Saharan Africa. A new evidence-based policy report from the World Bank and the ONE Campaign, Leveling the Field: Improving Opportunities for Women Farmers in Africa, shows just how large these gender gaps are. In Ethiopia, for example, women produce 23% less per hectare than men. While this finding might not be a “big” counter-intuitive idea (or a particularly new one), it’s a costly reality that has big implications for women and their children, households, and national economies.

The policy prescription for Africa’s gender gap has seemed straightforward: help women access the same amounts of productive resources (including farm inputs) as men and they will achieve similar farm yields. Numerous flagship reports and academic papers have made this very argument.

Learning from your peers: A lesson from Uganda and Senegal

Joseph Oryokot's picture

 Sarah Farhat, World Bank Group
















Despite Africa’s great diversity of cultures and climates, countries on the continent often speak the same language when it comes to tackling common development challenges. Senegal and Uganda recently did just that, teaming up to exchange best practices to boost agricultural productivity and employment on both sides of the continent.

I witnessed this knowledge exchange firsthand as I accompanied a Ugandan delegation led by Hon. Maria Kiwanuka, Uganda’s minister of finance, planning, and economic development, on its visit to Senegal. Their core mission was to seek out innovative ways to boost economic growth and create job opportunities for the country’s burgeoning youth, a challenge faced by Uganda and Senegal alike. As both countries continue to experience an increase in urbanization and population growth, and currently have economies that are predominantly based on agriculture, one common answer to this rising challenge is the enhancement of agricultural productivity and the development of agricultural value chains.

Big vs. small firms: one size does not fit all

Jacques Morisset's picture



Is bigger always better? Economists have long debated what size firms are more likely to drive business expansion and job creation. In industrial countries like the United States, small (young) firms contribute up to two-thirds of all net job creation and account for a predominant share of innovation. (Source: McKinsey, Restarting the US small-business growth engine, November 2012). In developing countries, evidence from Ethiopia, Ghana and Madagascar shows that the vast majority of small operators remain small, and so are unlikely to create many decent jobs over time [Source: World Bank, Youth Employment, 2014]. By contrast, ‘big’ enterprises are seen as the best providers of employment opportunities and new technologies.

The difference in role and performance of small firms in developing and industrial countries reflects to a large extent their owners’ characteristics. In the US, small firm owners are generally more educated and wealthier than the average worker, while the opposite is true in most developing countries. This point was emphasized by E. Duflo and A. Banerjee in their famous book ‘Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty’ (Penguin, 2011). Most business owners in developing countries are considered to be ‘reluctant’ entrepreneurs; essentially unskilled workers that are pushed into entrepreneurship for lack of other feasible options for employment.

This is also very much a reality in Tanzania where small business owners have few skills and limited financial and physical assets. Of the three million non-farm businesses operating in the country, almost 90% of business owners are confined in self-employment. Only 3% of business owners possess post-secondary level education. As a result, their businesses are generally small, informal, unspecialized, young and unproductive. They also tend to be extremely fragile with high exit rates, and operate sporadically during the year. Put simply, most small businesses are not well equipped to expand and become competitive.

Putting poverty on the map

Kathleen Beegle's picture

The expansion of household surveys in Africa can now show us the number of poor people in most countries in the region. This data is a powerful tool for understanding the challenges of poverty reduction. Due to the costs and complexity of these surveys, the data usually does not show us estimates of poverty at “local” levels. That is, they provide limited sub-national poverty estimates.
For example, maybe we can measure district or regional poverty in Malawi and Tanzania from the surveys, but what is more challenging is estimating poverty across areas within the districts or regions (known as “traditional authorities” in Malawi and “wards” in Tanzania).
 
To address this shortfall, several years ago a research team from the World Bank developed a technique for combining household surveys with population census data, and poverty maps were born.  Poverty maps can be used to help governments and development partners not only monitor progress, but also plan how resources are allocated. These maps depend on having access to census data that is somewhat close in time to the household survey data.  But what if there is no recent census (they are usually done every 10 years) or the census data cannot be obtained? (I will resist naming and shaming any specific country): we are left with no map.  Can we fill in the knowledge gaps in our maps?