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360° Technological change

Brittany Walters's picture
Young woman checks her phone.
For the World Bank, changes in the global landscape present a challenge in developing innovations and solutions that can address pressing issues around health, education, and social protection. (Photo: Simone D. McCourtie)

The way we communicate, produce, and relate to technology is evolving quickly.
 
Tell me something I don’t know, you’ll say.

That’s where Benedict Evans, a prominent tech guru from the venture capital firm Andreessen Horowitz ('a16z') in Silicon Valley, comes in. In a recent presentation at the World Bank (Mobile is Eating the World) Evans shared inspiring, and at times, unnerving insights on how technology is shaping our world and how it might impact the global development community.  Here are some key takeaways:    

The Future of Work: The number of jobs is not the only thing at stake

Siddhartha Raja's picture
Photo of computer lab. Technology is a great job-creating machine. But will these new jobs be better or worse?
Technology is a great job-creating machine. But will these new jobs be better or worse? (Photo: John Hogg / World Bank)

Most of the discussion about the future of work focuses on how many jobs robots will take from humans. But this is just a (small) part of the change to come. As we explained in our previous blog, technology is reshaping the world of work not only by automating production but also by facilitating connectivity and innovation. The changes that digital technology is introducing in the price of capital versus labor, the costs of transacting, the economies of scale, and the speed of innovation bring significant effects in three dimensions: the quantity, the quality, and the distribution of jobs. Let’s see them in detail.

The Middle East, version 2.0.

Bassam Sebti's picture


Let’s be honest. The Middle East and North Africa is burning, and in some areas it is literally burning. Conflict and fragility have long warped what once was the cradle of civilization and the inspiration for the many inventions we can’t live without today. However, in the midst of that fire hope rises, a driver of change that is transforming the ugly reality into a bright future.
 
After I fled the war in Iraq in 2006, I was pessimistic about what the future was holding for that region. Year after another, the domino-effect of collapse became a reality that shaped the region and its people. Yet, fast-forward to 2017, I have witnessed what I never thought I would see in my lifetime: the new renaissance in the Middle East and North Africa.
 
I have just recently come back from attending the World Economic Forum on the Middle East and North Africa at the Dead Sea in Jordan. This year, the Forum and the International Finance Corporation (IFC), the private sector arm of the World Bank Group, partnered to bring together 100 Arab start-ups that are shaping the Fourth Industrial Revolution.
 
There, the positive vibe was all around; no negativity, no pessimism. Instead there was a new sense of optimism and enthusiasm, hunger for change, and the will to take the region to a whole new future, away from conflict and the current norm of pessimism.

Mapping and measuring urban places: Are we there yet? (Part 2/2)

David Mason's picture
Photo by Anton Balazh via Shutterstock

My previous blog post surveyed some of the recent trends in developing global measures of urbanization. In this post, I want to turn to a brief discussion for scholars and practitioners on some possible applications and areas of focus for ongoing work:
 
[Download draft paper "Bright Lights, Big Cities: a Review of Research and Findings on Global Urban Expansion"]
 
While there are a number of different maps for documenting urban expansion, each has different strengths and weaknesses in application. Coarser resolution maps such as MODIS can be used for mapping the basic contours of artificial built-up areas in regional and comparative scales. On the other hand, high-resolution maps are best suited for individual cities, as algorithms can be used to identify and classify observed colors, textures, shading, and patterns into different types of land uses. These levels of detail are difficult to use for reliable comparisons between cities as the types of building materials, structure shapes, light reflectivity, and other factors can vary widely between countries and regions.
 
Nonetheless, there are a number of applications for policymakers in this regard, from identifying and mapping green spaces and natural hazard risks to identifying and tracking areas of new growth, such as informal settlements. However, such approaches to land use detection require careful calibration of these automated methods, such as cross referencing with other available maps, or by “ground truthing” with a sample of  street-level photos of various types of buildings and land cover as reference inputs for automation. One solution to this is the use of social media and geo-coded data to confirm and monitor changes in urban environments alongside the use of high-resolution satellite imagery.
 
Nighttime light maps also have gained traction as measures of urban extent and as ways to gauge changes in economic activity in large urban centers. They are probably less useful for documenting smaller settlements, which may be dimmer or have little significant variation in brightness. It is important to correct these types of maps for “overglow” measurement effects—where certain light may “bleed” or obscure the shapes and forms of very large, bright urban areas in relation to adjacent smaller and dimmer settlements (newer VIIRs maps have made some important advances in correcting this).

Mapping and measuring urban places: Are we there yet? (Part 1/2)

David Mason's picture
Source: Deuskar, C., and Stewart B.. 2016. “Measuring global urbanization using a standard definition of urban areas: Analysis of preliminary results” World Bank
This satellite image shows Sao Paolo's estimated “urban areas” based on a WorldPop gridded population layer. Areas in yellow are areas with at least 300 people per km2 and a known settlement size of 5,000 people. Red areas represent a population density threshold of at least 1,500 people per km2 and a known settlement size of 50,000 people.
There remains a surprising amount of disagreement over precisely what “urban” means despite the ubiquity of the term in our work. Are urban areas defined by a certain amount of artificial land cover such as permanent buildings and roads? Or are they more accurately described as spatially concentrated populations? The answer often depends on what country you are in, as their administrative definitions of urban areas can vary widely across and between these two dimensions.
 
Without a globally consistent measure of urban areas, it can be difficult to track changes in built-up areas (land surface coverage comprised of buildings and roads) and population growth across time and space. This impacts how policymakers may understand and prioritize the challenges cities face and what investments or reforms may be needed. In a new paper, “Bright Lights, Big Cities: a Review of Research and Findings on Global Urban Expansion,” I provide a brief introduction to some of the current approaches for measuring urban expansion and review the comparative findings of some recent studies.
The UN’s World Urbanization Prospects (WUP), perhaps the most comprehensive and widely cited measure of urbanization across the world, draws from a compilation of country-level population totals based on administrative definitions. A key weakness with this set is that since each country defines “urban” differently, it is difficult to accurately compare one country’s urbanization to another, as well as to estimate the urban population of a group of countries or the world itself. Recent work has provided more sophisticated ways to measure urban growth and expansion using both satellite map data and careful application of population data.

E-bureaucracy: Can digital technologies spur public administration reform?

Zahid Hasnain's picture

Editor's note: This blog post is part of a series for the 'Bureaucracy Lab', a World Bank initiative to better understand the world's public officials.

Photo: Arne Hoel / World Bank


“By introducing an automated customer management system we took a noose and put it around our own necks. We are now accountable!”

This reflection from a manager in the Nairobi Public Water and Sewerage utility succinctly captures the impact of MajiVoice, a digital system that logs customer complaints, enables managers to assign the issue to a specific worker, track its resolution, and report back to the customer via an SMS. As a result, complaint resolution rates have doubled, and the time taken to resolve complaints has dropped by 90 percent.

MajiVoice shows that digital technologies can dramatically improve public sector capacity and accountability in otherwise weak governance environments. But is this example replicable? Can the increasingly cheap and ubiquitous digital technologies—there are now 4.7 billion mobile phone users in the world—move the needle on governance and make bureaucrats more accountable?

Building a regional solution to bridge Eastern and Southern Africa’s science and technology gap

Xiaonan Cao's picture
 Sarah Farhat/World Bank


Like much of Sub-Saharan Africa, the Eastern and Southern Africa region has seen significant economic growth in recent years, largely relying on agriculture and extractives. However, it hasn’t been able to keep up with the skilled labor demanded by the region’s required economic transformation for further growth. Surveys reveal that firms in the region now face acute challenges in developing research and development (R&D) capacity and filling technical and managerial positions – not just due to inadequate production of college graduates that have been rising over the years, but also due to low quality and relevance of current education and training at the tertiary level.

Are tablets the best way to increase digital literacy in African countries?

Edward Amartey-Tagoe's picture
Credit: Arne Hoel


A good number of African governments have shown how technologically-forward thinking they are by announcing one-tablet-per-child initiatives in their countries. President John recently announced that tablets for Ghana’s schoolchildren were at the center of his campaign to improve academic standards. Last year, President Kenyatta of Kenya abandoned a laptop project for tablets.

How are future blue-collar skills being created?

Victor Mulas's picture



A technology bootcamp in Medellín, Colombia. © Corporación Ruta N Medellín/World Bank


The fourth industrial revolution is disrupting business models and transforming employment. It is estimated that 65 percent of children entering primary school today will, in the future, be working in new job types that do not exist today. These changes have been more noticeable in developed countries, with the 2008 financial crisis accelerating this transformation process. However, they are also affecting emerging economies that have traditionally relied on routine blue-collar jobs (e.g., textiles, manufacturing or business process outsourcing) for broad employment and economic development.

Start-ups are at the core of these disruptions in business models. In recent years, we have witnessed how completely new market categories have been created out of the blue, transforming entire sectors of the economy, including transportation, logistics, hospitality, and manufacturing. When start-ups disrupt a market, a new business category is created and new sources of growth and employment are generated.

When we think about start-ups and employment, the first thing that come to mind is the start-up founders, typically highly educated and motivated individuals. However, evidence from New York startup ecosystem, a testing ground of new jobs generated through technology after the financial crisis, suggests otherwise.

First, most of the jobs generated by the tech start-up ecosystem are not in start-ups but in the traditional industries that either are influenced or disrupted by start-up technologies (with over three times more employment generated in the non-tech traditional industry).

Second, more than 40 percent of these new jobs did not require bachelor’s degree skills or above. These are jobs like building a website, a basic database, a web or mobile app.

What are the skills needed to fill these categories — which we can call tech blue-collar skill jobs — and how people are being trained for them?

SDGs Made with Code: Giving women and girls the power to change the world

Mariana Dahan's picture
Increasingly more aspects in our lives are powered by technology, yet women aren’t represented in the roles that create this technology. In many places there are barriers to simply using technology, let alone, creating it. Women in India and Egypt are six times more likely than women in Uganda to say that internet use is not considered appropriate for them, and that their friends or family may disapprove. Learning to create with technology opens up opportunities for women to express themselves, have the ideas heard and contribute to shaping our future. Even though there’s so much more we need to do, we’re inspired to see the movement around the world to break down these barriers and start contributing their voices to the field of technology.

We recently met Mariana Costa from Laboratoria – a nonprofit that empowers young women by providing them access to the digital sector. In the next three years Laboratoria will train more than 10,000 young women as coders. This tech social enterprise located in Peru, Mexico and Chile, helps young women - who have not previously had access to quality education – enroll in an immersive five-month training program at Laboratoria’s Code Academy, where students achieve an intermediate level on the most common web development languages and tools. Their technical development is complemented with a personal development program that helps them build the soft skills needed to perform well at work. Successful graduates also receive mentoring and job placement and are usually able to pay-back the cost of the course during their first two years of employment. Most of the time, these young girls are the only breadwinners in their households.

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