Inclusive disruption: harnessing the social power of technology
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With the adoption of the 2030 Agenda for Sustainable Development, 193 nations around the world pledged to “endeavor to reach the furthest behind first” so that “no one will be left behind”. Governments often face barriers reaching the most vulnerable populations. Remoteness, low-literacy rates, lack of electricity and connectivity, and traditional gender norms and biases can limit the hardest to reach communities from accessing information and opportunities.
Nonetheless, mobile phones, the internet, and new innovative digital technologies have created unprecedented opportunities to leapfrog current constraints. Effective use of disruptive technologies is a major priority for the World Bank and reaching those furthest behind has been a core mandate of the World Bank’s Social Development Global Practice. In our work, we help the poorest and most marginalized to overcome their deprivations and barriers.
Thus, we have been working on initiatives utilizing technology to empower citizens to create better futures for themselves and their communities. Here are some early findings:
1. Even low-tech can be disruptive and deliver results
Amplio Network, a nonprofit whose mission is to share knowledge with the world’s hardest to reach communities, created an easy-to-use Talking Book audio device designed for people who can’t read and who live in places where there’s no electricity or network. It’s built to withstand the elements and works with the locally available batteries that people use for radios or flashlights. Their cloud-based technology platform includes an audio content manager, an app that downloads new content playlists and collects user feedback and usage data from Talking Books in the field, and an analytics dashboard for monitoring and evaluating data for each community. These devices are brought to communities by mobilizers who visit periodically. They can hold hundreds of hours of audio and have an interface designed to work well across cultures, regardless of literacy or disability.
It is currently used throughout Africa, particularly in Ghana and Kenya, to disseminate health and livelihood information and to collect feedback from remote communities using audio-based messages. This technology can extend the life of a consultation well beyond the in-person phase, and the data collected can be subsequently uploaded to the cloud to provide a rich source of data analytics about citizen engagement and local needs. The Bank has initiated pilots using this tool in Cameroon and is planning similar pilots in Nigeria, Kenya and Uganda.
2. The potential of new data sources and analytics is significant
Often excluded from traditional forms of data collection due to various biases, much of the population remain outside the global digital ecosystem. Thankfully this is about to change. , even in the most remote corners of the globe. For example, we are working with Orbital Insight to improve various aspects of resettlement planning.
Along with the rapid advances in Artificial Intelligence (AI) analytics, lack of data will not be a justifiable excuse for exclusion. Given these new advances, we are now able to better analyze qualitative information - gathered from consultations and grievance redress mechanisms, among other activities - in a more comprehensive manner. AI algorithms that commercial firms use to respond to consumer complaints and feedback or conduct targeted advertising based on qualitative information based on social media posts, can also be used for social good and citizen engagement. This could yield important insights on what matters most to the poor, best practices that reflect citizen needs and how to best target development interventions based on local circumstances.
Also, given the increased use of online search and social media where there is connectivity, there is a rich and underexploited environment of information from citizens. Not only can we use these platforms to digitally engage citizens, but we can mine the content of these networks using AI-driven search and social media analytics to take a pulse of sentiments, identify development preferences, and ensure that technology amplifies the voices of the poor.
3. Where there is opportunity, there is also risk
While providing many benefits, technology, if not carefully deployed, can also exacerbate the digital divide and negatively affect social cohesion. The World Bank’s Social Development Team is starting to address this by learning from experts in this space and educating ourselves on how to be smart consumers and users of AI/ Machine Learning (ML) and other technology tools. For example, forums such as the Ethics, Policy, and Governance in AI and Digital Civil Society conferences at Stanford have highlighted that algorithms that build on the past often internalize bias and discrimination. Similarly, as broadband penetration widens, the expansion of social media networks can disrupt longstanding social structures and can also be exploited to target and marginalize vulnerable groups and minorities.
Overall, we are excited about the opportunity to transform development and reach those furthest behind with technology. In addition to our current pilots and potential future pilots in 2020, we are also exploring partnerships with technology companies, innovators, universities, and foundations to pilot and scale up promising technologies to enhance impact of social and economic services in vulnerable communities and lagging areas. As we continue to embrace and leverage disruptive technology to improve the lives of those in the most vulnerable communities, we are also bringing a critical eye – asking tough questions, assessing risk, and encouraging inclusive development.
Social communication is greatly helped when we understand more exactly how our social system of macroeconomics works. Today this subject is not well understood, although we all like to think that we are able to work with it. Perhaps my article and book will help such workers to realize that there is far better knowledge available if they follow my proposals.
Making Macroeconomics a Much More Exact Science
Today macroeconomics is treated inexactly within the humanities, because at a first look it appears to be a very complex and easily confused matter. But this does not give it fair justice, because we should be trying to find an approach to the topic and examine it in a better way that avoids these problems of complexity and confusion. Suppose we ask ourselves the question: “how many different KINDS of financial (business) transaction occur within our society?” Then the simple and direct answer shows that that only a limited number of them are possible or necessary.
Although our sociological system comprises of many millions of participants, to properly answer this question we should be ready to consider the averages of the various kinds of activities (no matter who performs them), and simultaneously to idealize these activities so that they fall into a number of commonly shared ones. This employs some general terms for expressing the various types of these transactions, into what becomes a relatively small number of operations. Here, each activity is found to apply between a particular pair of agents—each one having individual properties. Then to cover the whole sociological system of a country, the author finds that it requires only 19 kinds of exchanges of the goods, services, access rights, taxes, credit, investment, valuable legal documents, etc., verses the mutual opposing flows of money. Also these flows need to pass between only 6 different types of representative agents.
The analysis that led to this initially unexpected result was prepared by the author and it may be found in his working paper (on the internet) as SSRN 2865571 “Einstein’s Criterion Applied to Logical Macroeconomics Modeling”. In this model these 19 double flows of money verses goods, etc., are shown to pass between the 6 kinds of role-playing entities. Of course, there are a number of different configurations that are possible for this type of simplification, but if one tries to eliminate all the unnecessary complications and sticks to the more basic activities, then these particular quantities and flows provide the most concise result, which is presentable in a comprehensive and seamless manner, and one that is suitable for further analysis of the whole system.
Surprisingly, past representation of our sociological system by this kind of an interpretation model has neither been properly derived nor presented before. Previously, other partial versions have been modeled (using up to 4 agents, as by Professor Hudson), but they are inexact due to their being over-simplified. Alternatively, in the case of econometrics, the representations are far too complicated and almost impossible for students to follow. These two reasons of over-simplification and of over-complexity are why this non-scientific confusion is created by many economists and explains their failure to obtain a good understanding about how the whole system works.
The model being described here in this paper is unique, in being the first to include, along with some additional aspects, all the 3 factors of production, in Adam Smith's “Wealth of Nations” book of 1776. These factors are Land, Labor and Capital, along with their returns of Ground-Rent, Wages and Interest/Dividends, respectively. All of them are all included in the model, as a diagram in the paper.
(Economics’ historians will recall, as originally explained by Adam Smith and David Ricardo, that there are prescribed independent functions of the land-owners and the capitalists. The land-owners speculate in the land-values and rent it to tenants, whilst the capitalists are actually the owners/managers of the durable capital goods used in industry. These items may be hired out for use. Regrettably, for political reasons, these 2 different functions were deliberately combined by John Bates Clark and company about 1900, resulting in the later neglect of their different influences on our sociological system-- the terms landlord and capitalist becoming virtually synonymous along with the expression for property as real-estate.)
The diagram of this model is in my paper (noted above). A mention of the related teaching process is also provided in my short working paper SSRN 2600103 “A Mechanical Model for Teaching Macroeconomics”. With this model in its different forms, the various parts and activities of the Big Picture of our sociological system can be properly identified and defined. Subsequently by analysis, the way our sociological system works can then be properly seen, calculated and illustrated.
This analysis is introduced by the mathematics and logic that was devised by Nobel Laureate Wassiley W. Leontief, when he invented the important "Input-Output" matrix methodology (that he originally applied only to the production sector). This short-hand method of modeling the whole system replaces the above-mentioned block-and-flow diagram. It enables one to really get to grips with what is going-on within our sociological system. Subsequently it will be found that it is the topology of the matrix which actually provides the key to this. The logic and math are not hard and is suitable for high-school students, who have been shown the basic properties of square matrices.
By this technique it is comparatively easy to introduce a change to a preset sociological system that is theoretically in equilibrium (even though we know that this ideal is never actually attained--it simply being a convenient way to begin the study). This change creates an imbalance and we need to regain equilibrium again. Thus, sudden changes or policy decisions may be simulated and the effects of them determined, which will point the way to what policy is best. In my book about it, (see below) 3 changes associated with taxation are investigated in hand-worked numerical examples. In fact when I first worked it out, the irrefutable logical results were a surprise, even to me!
Developments of these ideas about making our subject more truly scientific (thereby avoiding the past pseudo-science being taught at universities), may be found in my recent book: “Consequential Macroeconomics—Rationalizing About How Our Social System Works”. Please write to me at chesterdh@hotmail.com for a free e-copy of this 310 page book and for additional information.
Technology can improve life of most vulnerable communities.