Syndicate content

Information and Communication Technologies

The Middle East and North Africa cannot miss the Fourth Industrial Revolution

Ferid Belhaj's picture

The traditional route of industrialization for developing countries may no longer be available for the Middle East and North Africa (MENA) region. This should not be a source of regret, as the aspirations of the region’s young and well-educated population extend far beyond auto assembly lines. Furthermore, the repetitive work of an assembly line will increasingly be performed by machines rather than people. The rapid pace of technological change that is propelling this process, dubbed the "Fourth Industrial Revolution," offers new opportunities for developing countries. Opportunities the MENA region cannot afford to miss. 

E-commerce for poverty alleviation in rural China: from grassroots development to public-private partnerships

Xubei Luo's picture
A young woman is selling products on-line. Photo: Xubei Luo/World Bank

China’s rapid development of e-commerce has begun to reshape production and consumption patterns as well as change people’s daily lives. In 2016, the World Bank and the Alibaba Group launched a joint research initiative to examine how China has harnessed digital technologies to aid growth and expand employment opportunities through e-commerce development in rural areas. The research seeks to distill lessons and identify policy options to enhance the positive effect of e-commerce on the reduction of poverty and inequality. Emerging findings from that research show that rural e-commerce evolves from grassroots development to become a potential tool for poverty alleviation with public-private partnerships.

E-commerce has grown quickly in China. Total e-commerce trade volume increased from less than 1,000 billion yuan (US$120.8 billion) in 2004 to nearly 30,000 billion yuan (US$4.44 trillion) in 2017. While e-commerce is more developed in urban areas, online retail sales in rural areas have grown faster than the national average. From 2014 to 2017, online retail sales in rural China increased from RMB 180 billion to 1.24 trillion, a compound annual growth rate of 91%, compared to 35% nationally.

Demystifying machine learning for disaster risk management

Giuseppe Molinario's picture

To some, artificial intelligence is a mysterious term that sparks thoughts of robots and supercomputers. But the truth is machine learning algorithms and their applications, while potentially mathematically complex, are relatively simple to understand. Disaster risk management (DRM) and resilience professionals are, in fact, increasingly using machine learning algorithms to collect better data about risk and vulnerability, make more informed decisions, and, ultimately, save lives.

Artificial intelligence (AI) and machine learning (ML) are used synonymously, but there are broader implications to artificial intelligence than to machine learning. Artificial (General) Intelligence evokes images of Terminator-like dystopian futures, but in reality, what we have now and will have for a long time is simply computers learning from data in autonomous or semi-autonomous ways, in a process known as machine learning.

The Global Facility for Disaster Reduction and Recovery (GFDRR)’s Machine Learning for Disaster Risk Management Guidance Note clarifies and demystifies the confusion around concepts of machine learning and artificial intelligence. Some specific case-studies showing the applications of ML for DRM are illustrated and emphasized. The Guidance Note is useful across the board to a variety of stakeholders, ranging from disaster risk management practitioners in the field to risk data specialists to anyone else curious about this field of computer science.

Machine learning in the field

In one case study, drone and street-level imagery were fed to machine learning algorithms to automatically detect “soft-story” buildings or those most likely to collapse in an earthquake. The project was developed by the World Bank’s Geospatial Operations Support Team (GOST) in Guatemala City, and is just one of many applications where large amounts of data, processed with machine learning, can have very tangible and consequential impacts on saving lives and property in disasters.

The map above illustrates the “Rapid Housing Quality Assessment”, in which the agreement between ML-identified soft-story buildings, and those identified by experts is shown (Sarah Antos/GOST).

Join sector and communication specialists for a leadership, strategy and stakeholder analysis training course

Umou Al-Bazzaz's picture
Flora Bossey, center, Communication Officer, Edo SEEFOR Project, Nigeria, attended in 2015. © World Bank
Flora Bossey, center, Communication Officer, Edo SEEFOR Project, Nigeria, attended in 2015.
© World Bank

When Amr Abdellah Aly, a department manager at the Electricity Ministry in Egypt, returned home from the Summer Institute in California training course at last year, his first question to his supervisors was if they had a communications strategy in place for the efforts of reforms in the electricity sector. His goal was to stress on the important role of communications throughout the reform process, something he had just learned from the course.
 
Each summer, the World Bank collaborates with the Annenberg School for Communication at the University of Pennsylvania and the Annenberg School for Communication and Journalism at the University of Southern California to offer the executive education course on reform communication: Leadership, strategy and stakeholder alignment. 

The ticket to a better ride: How can Automated Fare Collection improve urban transport?

Leonardo Canon Rubiano's picture
Photo: Emily Jackson/Flickr
In both developed and developing countries, a growing number of cities are relying on automated systems to collect public transport fares and verify payment. Far from being a gimmick, Automated Fare Collection (AFC) can bring a wide range of benefits to local governments, transport planners, operators—and, of course, to commuters themselves.

The recent Transforming Transportation 2019 conference paid a great deal of attention to the applications and benefits of AFC, which have been at the heart of many World Bank and IFC-supported urban mobility projects.

For users, the development of AFC is a critical step toward making public transport more efficient, affordable, and accessible. The keywords here are integration and interoperability. AFC systems are now becoming compatible with an ever-increasing number of payment methods besides smart cards —near-field communication devices (including smartphones), debit and credit cards, e-commerce platforms (e.g PayPal, AliPay), and even printed QR codes and SMS, opening the way for integration with other transport services such as bikeshare schemes, paratransit, or even carpooling services.

Like manna from heaven? The sustainability of Open Source projects

Michael M. Lokshin's picture

Sustainability of OSS is an important, but often overlooked issue. The private sector is struggling to find the right model to maintain and sustain OSS. The International Development Agencies need viable long-term strategies to sustain the OSS projects they are developing, funding, or using.

Two young colleagues invited me for coffee to discuss their proposal to develop an open source software (OSS) system for administering government programs in developing countries. The idea of replacing costly, custom-built proprietary systems with open-source solutions tailored for specific country requirements was very appealing.

“Why pay millions of dollars for a proprietary solution when an open source system will be free?” exclaimed one of the colleagues.

I inquired cautiously, “Have you considered how to maintain these systems once they are deployed? Who will pay for customization and on-going support to the country clients? How do you consistently ensure the quality of the code?”

“The international OSS community will volunteer their time to maintain and improve these systems.” was the reply.

Accelerating Vietnam’s path to prosperity

Makhtar Diop's picture
Da Nang, Vietnam. © Pixabay
Da Nang, Vietnam. © Pixabay

Vietnam continues to boom. It is one of the most dynamic emerging markets in East Asia, marked, over the past thirty years, by a remarkable reduction in poverty and impressive economic growth—which has benefited the population of Vietnam. Few countries around the world could boast a 2018 growth rate of 7.1 percent, supported by strong exports and a growing share of formal employment, especially in manufacturing.
 
Infrastructure has been a central factor of Vietnam’s fast-paced economic development. Today, 99 percent of the population uses electricity as their main source of lighting, up from 14 percent in 1993. However, economic growth is putting increasing pressure on Vietnam’s infrastructure. Freight volumes are expanding rapidly. Road traffic has increased by an astounding 11 percent annually and the demand for energy is expected to grow by about 10 percent per year until 2030.

Kenya taps innovative digital mapping to enhance public participation

Rose Wanjiru's picture
OpenStreetMap of Kenya

Kenya is well known for its innovation in technology, particularly mobile technology in cash transfers. These innovations have largely been championed by the private sector and young entrepreneurs.

In contrast, the public sector tends to play catch up adopting new technology, and that has remained true in implementing Geographic Information Systems (GIS). GIS, also referred to as digital maps, is utilized to capture, store, analyze, manage, and present geographic data.

Data for development impact: Why we need to invest in data, people and ideas

Haishan Fu's picture
© Shutterstock
© Shutterstock


High quality development data is a must for development impact

We know that high quality development data is the foundation for meaningful policy-making, efficient resource allocation, and effective public service delivery. Unfortunately, even as new technology makes more data and wider uses of data possible, there are still many blank spaces on the global data map. A paper by my colleague Umar Serajuddin et al. (2015) describes this phenomenon as “data deprivation”, finding that as of just a few years ago, 77 countries still lacked the data needed to adequately measure poverty. What’s worse, data is often most scarce in the areas where it is most desperately needed. For one, the scarcity of individual-level data on issues like assets and consumption severely curtails our ability to make decisions to reduce gender disparities. Similarly, despite the urgency of the need to manage climate risk, significant voids remain with regards to climate data, such as impacts on freshwater resources. Education, health, food security, and infrastructure are just a few of the many other areas where more and better data is needed to deliver progress.

So what’s to be done? Looking forward, I propose three data priorities, which we are working to put into practice.


Pages