Economics of data (Part 6: Data and poverty eradication)
Phuah Eng Chye (26 September 2020)
Poverty eradication remains an overarching objective. The United Nation’s Sustainable Development Goals calls for the ending of extreme poverty by 2030. Martin Ravallion notes poverty is perceived as “the most morally objectionable aspect of inequality, stemming mainly from economic and political forces rather than bad choices by poor people; a key material constraint on human freedom and social inclusion; a risk of deprivation, whether currently poor or not; and a cost to other valued goals, including economic efficiency, human development and environmental sustainability. The actions that might be motivated in response range from specific policies to efforts to help poor people organize collectively for things that matter to them. Thus, goal setting is seen as an incentive mechanism for attaining better outcomes. We can call this the motivating goal. Either way, it must first be agreed collectively that less poverty is a good thing”.
The onset of the data economy alters the dynamics of poverty. It also presents opportunities to develop new approaches towards its eradication. In this regard, the data economy has a tendency to aggravate inequalities. Governments have attempted to stem the erosion of the middle class but their efforts have been ineffectual due to the strong headwinds generated by the information effect of polarisation.
Governments should instead consider the alternative objective of raising baseline income levels; i.e. targeting poverty rather than inequality. Raising baseline income has many benefits. First, it ensures income reaches those who needs it most. Second, it expands inclusiveness as income is a necessary pre-condition or ante to participate in a monetised environment. Third, the existence of even minute income streams can create opportunities to catalyse monetised growth within the poorest communities. From a macroeconomic perspective, raising the income of the poorest communities has high multiplier effects on consumption and national income.
From a data perspective, the poor represents a source of untapped value from human capital. Using data to unlock this value can have a significant impact on eradicating poverty and boosting economic growth. Hence, poverty can be perceived as a form of under-investment in and under-utilisation of the data of the poor. Improved data availability is critical for diagnosis, organising participation and monetisation, tapping internal and external resources.
Hurdles to data use
For data to be a game changer in overcoming poverty, information and organisational hurdles need to be overcome so that data can be monetised. Susan Ariel Aaronson notes “many nations are transitioning to a new economy built on data…many middle-income and developing nations may be rich in data, but do not yet see their citizens’ personal data or their public data as an asset. Most states are learning how to govern and maintain trust in the data-driven economy; however, many developing countries are not well positioned to govern data in a way that encourages development”.
Steven Weber notes “while data are cheap and plentiful in many developing countries, data analysis is expensive because it is dependent on infrastructure and highly skilled labour”. As a result, policymakers “are instead focused on attracting foreign investment that creates many jobs, in particular for relatively unskilled workers. Citizens, business leaders and officials from many developing countries may not see leapfrogging to a data-driven economy as the best way to stimulate development”. “Without greater understanding of the economic and political use of data, these officials may hoard data or fail to advocate for their citizens’ interest. As a result, their citizens may miss an opportunity to use their data as leverage for development funding or economic diversification”.
This leads to risks of a widening of the digital divide. Steven Weber argues “since data products generate more data as they are used, the greater…data imbalance would become over time”. This leads to “a potential bleak future: the countries with large data pools and data analysis expertise will become a core, while those without data expertise become the periphery”. These developing economies will become dependent on developed countries; storing their data on external cloud servers and importing data-driven services.
Data availability on poverty is a major constraint. Eric Swanson and Lorenz Noe notes “after a decade of progress…the number of countries with two or more poverty estimates in a ten-year period has fell from 122 to 107…in 2017…missing data will severely hamstring efforts to fight poverty and deliver solutions in places where help is most needed”.
Pinelopi Goldberg notes while “technological advances improve the availability and use of data, data is still scarce where it’s most needed”. The World Bank plans to address data-related policy questions on how “the developing world can capture the economic opportunities offered by the data-driven economy while minimizing associated risks”. “Traditionally, development policy has relied on public data collected for specific purposes such as national accounts, household and firm surveys, and data collected through administrative systems such as birth records, pensions, tax records and census. Governments have been central to these efforts, but public data collection methods can be costly. As a result, surveys are performed infrequently, and often lack the granularity necessary to make meaningful inferences about small, sub-populations of interest”.
Pinelopi Goldberg points to the possibilities of integrating public and private efforts “with public data offering better coverage of populations of direct interest for development policy, and private data offering greater frequency, timeliness and granularity”. However, “the sheer variety, volume and sensitivity of digital records raises a host of issues related to the ownership, protection and security of data. And the global reach of digital platform enterprises places data at the center of current debates on anti-trust regulation, posing significant economic policy challenges in areas as diverse as taxation and international trade. Dealing with such a complex agenda will entail a new institutional framework for data governance; both at the national and international levels”.
Overall, data challenges vary according to the level of development. In many developing countries, the information infrastructure and the capabilities to generate value from data is under-developed. Nonetheless, the emergence of smartphones, graphic-based applications and AI can assist an informal economy to leapfrog traditional infrastructure. Another challenge is to overcome the concentration of data in global firms through efforts to address distributional bottlenecks and to widen data access to ensure a fairer distribution of value.
Management and ecosystem issues
The success of data strategies is dependent on good management and a supportive ecosystem. In this regard, the reputations of anti-poverty programs have been tarnished by poor design, mismanagement and corruption. From a corporate perspective, anti-poverty programs tend to be amateurish, standalone experiments with a narrow focus and limited time horizon. Time and money are often wasted on starting an operation or collecting data only to be subsequently shut down.
In my view, there is a need to conceptualise and manage anti-poverty programs as though they were businesses. This means anti-poverty programs should not be managed as temporary stand-alone projects. Thought should be given to creating a permanent and cohesive organisational framework to facilitate follow-through, continuous learning and innovation.
Rather than focus on alleviating hardships, it would be better to focus on developing business models that can create, increase and distribute economic value in impoverished communities on a sustained basis. Managing anti-poverty as a business requires organisational and management roles and accountabilities to be clearly set out. There should be career paths, key performance indicators (KPIs) and rewards to incentivise management to make things happen. Consideration should also be given to ensuring there are competitive elements to keep agencies and management on their toes.
Effective deployment of data strategies requires a supportive ecosystem – i.e. physical, digital and legal infrastructure. This is a major problem for poor communities. Governments need to finance some projects to meet basic infrastructure needs, particularly in rural areas, to enable data use. Mobility platforms are already playing a major role in making technology widely accessible at relatively low costs. A government platform can play a major role in monetising value from data on individuals and their activities, income and assets. The data provides the basis for program design, monitoring and follow-up. Consideration should be placed on launching cost-effective initiatives to improve access through provision of free internet services in key urban areas and in some strategically important rural centers.
Platforms have an essential role in integration. First, it provides a holistic data overview on whether anti-poverty programs are working or not. Second, it lowers the costs of authentication. Third, it facilitates coordination by different agencies and NGOs. Fourth, it facilitates self-organisation at the local level. Lastly, platforms provide a means to tap global demand and resources (expertise, talent, finance) to augment local demand and resources.
Data strategies should be reinforced by transparency. Information opaqueness provide a screen for corruption, exploitation and suppression and hinders poverty eradication. In relation to this, concentrated private (platform) ownership of data implies a reduction in the community’s access to data and stunts the growth of its human capital. Transparency is necessary to ensure the benefits from poverty programs reach the right recipients – that the poorest individuals are able to participate in or organise activities and the community is able to retain and accumulate income and assets. Transparency improves accountability by monitoring the conduct of officials and businesses which will reduce leakages of value from the community.
Poverty eradication strategies
Strategies to eradicate poverty can be viewed at three levels. At the highest level are the top-down strategies. China’s whole-of-government approach is regarded as the most successful. First, the government made poverty eradication a priority. He Huifeng notes President Xi Jinping “made the eradication of poverty a central tenet of his administration…In 2015, Xi set a deadline of 2020 to totally eradicate poverty in China.” Government support is extensive, ranging from direct subsidies to rebuild or renovate farmhouses or cost-free access to flats and enhanced public services including universal health insurance. “The pressure to help the poor is so great that local officials have repeatedly visited impoverished households”. In 2018, the number of Chinese living in absolute poverty had fallen to 16.6 million out of a population of 1.4 billion.
Second, multiple strategies should be deployed as it is unlikely a single approach would be sufficient and diverse strategies can be complementary and improve the odds of success. In 2017, China launched its “rural vitalization” strategy. Dexter Roberts notes this strategy aimed to induce migrant workers in cities to return to their hinterlands. “Workers would revitalize stagnating local economies by starting businesses and buying homes…simply by their absence, alleviating the overburdened roads, hospitals, and schools of China’s cities”. Elaine Chan highlights that rather than focus on traditional crops, China has explored “a new type of agriculture caters to the demand from China’s affluent urban consumers for high-quality products and services, from hairy crabs and organic vegetables to sightseeing experiences and comfortable accommodation in a rural setting”.
Third, the use of technology should be promoted. Chen Long points out “China’s e-commerce market has become the world’s largest…E-commerce and the complementary industries it spawns – mobile payments, cloud services, logistics – allow for the rapid spread of knowledge and wealth. What we see universally is that the more technology penetrates a society, the more inclusive that society becomes”. He cites the transformation of an impoverished town, Shaji, into the country’s largest producer of furniture sold over the internet as “living proof of how going digital presents an opportunity to pull the poor out of poverty and make an economy more inclusive”.
Most countries will likely find it difficult to replicate China’s whole-of-government approach. It should also be recognised this approach also has drawbacks. Sidney Leng cautions “financial subsidies and preferential policies favouring backwards areas, have distorted local governments’ incentives and misallocated resources”. “The failure to recognise geographic circumstances and factor in conditions…gave rise to a litany of problems such as irrational industrial arrangement, blind investment promotion and vicious competition”. The over-investment was characterised by low returns, overbuilding, “leaving some Western provinces burdened with massive debt and pollution problems”. It should be noted poverty eradication programs can be hobbled by depopulation.
Nonetheless, countries would still be able to make considerable headway against poverty providing it was a government priority. For example, Susanne Becken points out the amount of traveller spending in less developing economies are substantial but the impact on poverty is limited due to high leakages (such as repatriation of profits). She argues “governments can reduce leakage by thinking strategically about procurement, emphasising local business development, integrating supply chains and investing in education and training to prepare workers for tourism jobs…Such changes helped Samoa…By 2014, Samoa was no longer classified as a least-developed country. Making sure that visitor dollars benefit local people also depends on the commitment of foreign-owned companies, particularly hotel groups, to partner with and invest in local communities…Tourism will never end poverty. But if governments, industries and consumers start paying attention, they can make it a force for change”.
The second level consists of community or bottoms-up approaches. Raghuram Rajan suggests local community approaches to revive left-behind communities may be more effective. “Top-down solutions devised in remote capitals do little, however, to tackle the impediments to economic and social recovery. Locals typically know far more about what needs to be fixed – and they must be empowered to help their communities pull themselves up”. He suggests “successful small enterprises can help lift a sinking community, not just by providing jobs and an example but because they belong fully within the community and can help support its activities”. Large corporations could aid community revival by increasing allocations for community engagement and decentralizing corporate social responsibility activities.
Raghuram Rajan notes community revival could be assisted through “creative ways to draw able people back and increase the talent pool…Taxes could be reduced for those who live in stressed communities; the college loans of those who return to stressed communities for a number of years could be partly forgiven so that college becomes a route to training locals, not just a means of escape for the talented; and capable immigrants could be given residence visas if they agree to stay in communities that need them”. He argues communities should manage local assets to benefit the community in the future. “A far-sighted community will take ownership of local assets at the outset when they are cheap so it can then obtain greater funding resources as the community revives and local assets become more valuable”.
The third level is built around individuals – i.e. people or human capital centric. Personal data – the log of movements, communications, activities and assets of an individual – is already recognised as valuable. This suggests poverty eradication initiatives can be built around the data of individuals. Progress can be tracked by monitoring a family’s income inflows (including government transfers and others), expenses and their balance sheets (value of assets and liabilities).
The three levels of poverty eradication strategies are complementary. But anti-poverty efforts sometimes focus on one level and overlook the need for support at the other levels. In this regard, the limited focus of many programs – e.g. on income, employment, health, education and infrastructure – implies dispersion of efforts which limits effectiveness and results in wastage. Consideration should be given to coordinating the different government, NGO and corporate programs on a collective basis as this can have powerful and sustainable effects on creating, distributing and retaining value in impoverished communities.
An integrated approach to poverty eradication requires comprehensive data at the individual level; i.e. emphasis on human capital. At the moment, data on low-income individuals are held in isolated data silos. Ideally, data could be used to identify problems and to organise solutions. In real life, data is mainly used for administration (welfare, city councils) and enforcement purposes (police, immigration). Private sector firms have also built extensive profiles (customers, employees, suppliers) and databases (markets) but use the data to optimise profits. Data on the poor communities are also plagued by blind spots in relation to illegal immigrants and activities.
At this point, it is too difficult to even consider integrating data silos and individual anti-poverty programs on a nationwide basis. A more pragmatic approach is to launch smart villages or smart slums projects in areas with extreme poverty or high unemployment. A government platform should be established as the backbone for a pilot smart slum community. All government agencies should be required to operate through the platform. One essential condition is to ensure every individual has reasonable access to the platform. At the top level, several schemes can be coordinated to ensure a steady injection of resources into the community for a lengthy period. The platform can be used to facilitate self-organisation and promote local businesses and access to low-cost financial services. It can also be used to tap external resources (finance, technology, skills) and markets.
The tendency to view poverty in isolation from civic participation needs to be addressed. In this context, the platform can be used for community discussion and to facilitate local participation in projects. Local circulation of income can be strengthened by linking programs with local participation, skill development, community business and non-business activities. Grant allocations can be pooled into seeding public projects and commercial activities with local residents given priority. Civic participation can be monetised; with the option of offsetting the value of participation against local municipal fees, fines or traded for access to education, transport, food, housing and healthcare. Income can also be credited to individuals to participate in jobs (facing labour shortages) such as taking care of the elderly or in agriculture.
The platform should be positioned to support initiatives to raise the value of residents in the community. Redevelopment projects that increase property values without increasing the wealth of the low-income group can be considered to have failed because too much value have leaked outside the community. Success in poverty eradication can only be sustained by increasing the value of assets owned by a community. Hence, work initiatives should be linked with opportunities for local resident to own community assets. Additional endowment schemes can be created to provide opportunities for more in the community to benefit from capital gains.
Data transparency should be enhanced to provide visibility on who is doing what, how resources are being deployed, and on where targets are being met, and where shortfalls and leakages are occurring. KPIs can be set up to measure the changes in community value as measured by the value of properties, amenities and work opportunities. This allows the shaping of a comprehensive perspective on resource inflows and outflows, the value that is created within the community and the value that is ultimately retained by the poorest individuals.
A data platform, built around the data of individuals, can thus open the door to finding new solutions and creating opportunities. The platform itself could be used to provide pathways for education and technology opportunities with incentives provided local residents to develop community apps and tools. In particular, model smart villages or smart slums should be replicated and rolled out widely. Consideration could also be given to establishing principles that multinational and local firms should commit to if they wish to participate in the community.
One weakness of traditional anti-poverty solutions is that they are set up to fit into the existing economic system. In this context, approaches such as microfinance and cooperatives have enjoyed relative success. But their impact is limited at the macro level because they are not sufficiently disruptive. There is also a tendency to treat poverty eradication as a form of charity. Because of this, poverty eradication programmes often miss their mark – the bulk of benefits don’t reach intended recipients and when funding is exhausted, the programmes are shut down.
There is a need for game-changing business models for poverty eradication. A superior model is one that views the poor as representing a source of untapped value from human capital. In this context, data-driven strategies can be implemented by establishing platforms responsive to the need of local residents, that can contribute towards creating decent jobs and promote efficiency and innovation in public services. Unlocking the value of human capital at the bottom of the pyramid will undoubtedly have a positive impact on the aggregate value of an economy.
Chen Long (9 March 2019) “How digital technology can drive inclusive growth, starting with furniture producers in China”. SCMP. https://www.scmp.com/tech/article/2189182/how-digital-technology-can-drive-inclusive-growth-starting-furniture-producers
Dexter Roberts (16 May 2020) “China wants workers to stay in the countryside”. Foreign Policy. https://foreignpolicy.com/2020/05/16/china-wants-workers-to-stay-in-the-countryside/
Elaine Chan (8 February 2020) “China’s key agricultural sector starts to evolve, but can the whole country embrace the new rural economy?” SCMP. https://www.scmp.com/economy/china-economy/article/3049559/chinas-key-agricultural-sector-starts-evolve-can-whole
Eric Swanson, Lorenz Noe (October 2019) “Data deprivation: Progress has stalled”. Open Data Watch. https://opendatawatch.com/blog/data-deprivation-progress-has-stalled/
He Huifeng (3 November 2019) “China’s subsidies lifting rural villages out of poverty, but is Xi Jinping’s plan sustainable?”. SCMP. https://www.scmp.com/economy/china-economy/article/3035894/chinas-subsidies-lifting-rural-villages-out-poverty-xi
Janine Aron (2017) “Leapfrogging: A survey of the nature and economic implications of mobile money”. CSAE Working Paper. Centre for the Study of African Economies, University of Oxford. https://www.csae.ox.ac.uk/materials/papers/csae-wps-2017-02.pdf
Martin Ravallion (September 2020) “On the origins of the idea of ending poverty”. NBER. https://www.nber.org/papers/w27808
Michael Waters (12 June 2020) “Why a small town in Washington is printing its own currency during the pandemic”. The Hustle. https://thehustle.co/covid19-local-currency-tenino-washington/?utm_source=pocket-newtab
Pinelopi Goldberg (7 January 2020) “World Development Report 2021 – Data for development”. World Bank Blog. https://blogs.worldbank.org/developmenttalk/world-development-report-2021-data-development
Phuah Eng Chye (5 January 2019) “Future of work: Redefining work (Part 6: Monetising participation)”. http://economicsofinformationsociety.com/future-of-work-re-defining-work-part-6-monetising-participation/
Phuah Eng Chye (31 August 2019) “Information and development: The information path to development”. http://economicsofinformationsociety.com/information-and-development-the-information-path-to-development/
Phuah Eng Chye (4 July 2020) “Government of the Data (Part 3: The future of government platforms)”. http://economicsofinformationsociety.com/government-of-the-data-part-3-the-future-of-government-platforms/
Phuah Eng Chye (18 July 2020) “Economics of data (Part 1: What is data?)”.
Phuah Eng Chye (1 August 2020) “Economics of data (Part 2: Market approach to valuing data)”. http://economicsofinformationsociety.com/economics-of-data-part-2-market-approach-to-valuing-data/
Phuah Eng Chye (15 August 2020) “Economics of data (Part 3: Relationship between data and value and the monetisation framework)”.
Phuah Eng Chye (29 August 2020) “Economics of data (Part 4: The data economy)”.
Phuah Eng Chye (12 September 2020) “Economics of data (Part 5: Tax policies)”.
Raghuram Rajan (3 April 2020) “How to save global capitalism from itself”. Foreign Policy. https://foreignpolicy.com/2020/04/03/save-global-capitalism-localism-deglobalization/
Sidney Leng (24 June 2020) “Can Xi Jinping revive China’s dream of turning its poor west into an economic powerhouse?” SCMP. https://www.scmp.com/economy/china-economy/article/3090273/can-xi-jinping-revive-chinas-dream-turning-its-poor-west
Susan Ariel Aaronson (30 January 2020) “Data are a development issue”. Voxeu. https://voxeu.org/article/data-are-development-issue
Susanne Becken (24 July 2017) “Can tourism alleviate global poverty?” The Conversation. https://theconversation.com/can-tourism-alleviate-global-poverty-76581
Zheng Yongnian (17 Jun 2020) “Persistent poverty and a weak middle class: China’s fundamental challenge”. Thinkchina. https://www.thinkchina.sg/persistent-poverty-and-weak-middle-class-chinas-fundamental-challenge
 See Susan Ariel Aaronson.
 See Susan Ariel Aaronson.
 See “Information and development: The information path to development”.
 See Janine Aron.
 See “Government of the Data (Part 3: The future of government platforms)”.
 See “Future of work: Redefining work (Part 6: Monetising participation)”; “Economics of data (Part 3: Relationship between data and value and the monetisation framework)”.
 See Zheng Yongnian’s commentary that “alleviation of absolute poverty does not mean poverty has been eradicated. Many people will continue to live in relative poverty for a long time and could return to the state of absolute poverty”. These risks were reflected in the fact that China’s middle class remains below 30% of its population as compared with 60% to 70% in other countries.
 See “Government of the Data (Part 3: The future of government platforms)”.
 See Michael Waters’ story of a small US town that has started issuing its own wooden dollars that can only be spent at local businesses.