Data are a development issue

Susan Ariel Aaronson 30 January 2020

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Many nations are transitioning to a new economy built on data. Individuals and firms in these states have expertise in using data to create new goods and services as well as in using data to solve complex problems. Meanwhile, 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. 

According to the political scientist Steven Weber, while data are cheap and plentiful in many developing countries, data analysis is expensive because it is dependent on infrastructure and highly skilled labour.  It is therefore hard to build these sectors in countries where infrastructure is inadequate and the supply of highly skilled labour is small. Weber also argues that there are not enough incentives in many developing countries to create firms to conduct data analysis in lieu of importing (Weber 2017: 410). Moreover, “since data products generate more data as they are used, the greater…data imbalance would become over time” (Weber 2017: 411).   

Given these impediments, executives at data-driven firms are unlikely to see these countries as effective locales for their operations.  Meanwhile, policymakers in the developing world are instead focused on attracting foreign investment that creates many jobs, in particular for relatively unskilled workers (Ernst et al. 2018). 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. 

Hence, Weber posits 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 states could, over time, become less capable of developing further innovation (Weber 2017: 412-13). Along these lines, the World Bank warned that to get the most out of the digital revolution, countries also need to strengthen regulations that ensure competition among businesses, by adapting workers’ skills to the demands of the new economy and by ensuring that institutions are accountable (World Bank 2016).  UNCTAD warned that digitalisation could also lead to increased polarisation and widening income inequalities, as productivity gains may accrue mainly to a few, already wealthy and skilled individuals. Consequently, UNCTAD suggested policymakers should deepen their understanding of the issues at the interface of trade logistics, digitalisation and e-commerce (UNCTAD 2017a: 2).  For this reason, the Center for Global Development, the Heinrich Boll Foundation, the Digital Trade and Data Governance Hub, and the Institute for International Economic Policy are holding a conference on 31 January 2020 to examine these questions from a multidisciplinary perspective.1  

Recent research

In a recent paper (Aaronson 2019), I examine whether developing countries are ready for this new data-driven economy and how development organisations might help them. I use a wide range of metrics to show that most developing and middle-income countries are not ready or able to provide an environment where their citizens’ personal data are protected and where public data are open and readily accessible. But they will need to improve governance in these areas if they want to use data to solve complex problems, maintain infrastructure, or create jobs in the manufacturing or services sector. Data have become an essential input.  

Meanwhile, although developing countries are rich in data, many developing-country officials do not yet see data as a resource. Without greater understanding of the economic and political use of data, these officials may hoard data or fail to advocate for their citizens’ interest (Aaronson 2018). As a result, their citizens may miss an opportunity to use their data as leverage for development funding or economic diversification. Moreover, these same citizens do not own the infrastructure; instead, in many developing countries, the infrastructure is in the cloud and the cloud servers are located abroad — most likely in industrialised countries (Pinto 2018). 

Unfortunately, countries that do not accommodate the data-driven economy may find their development will suffer, nonetheless. UNCTAD (2017b: 7) reports that these countries will be less well-positioned to trade without data-driven expertise. These countries may, over time, export their data, but because data are plentiful, they are unlikely to yield significant export earnings. At the same time, these nations will be importers of data-driven services such as health-care protocols or consumer predictive analytics. Moreover, these states will need to use data analytics to ensure that the other goods and services that they produce remain competitive. 

Analysis 

My research strategy is designed to capture a broad and representative sample of developing country capacity on data. I carry out two types of analysis: average scores for the 42 states on four key metrics and country-specific analysis on multiple metrics. Forty-two countries were chosen from the World Bank listing of low-income, lower-middle and upper-middle countries. I also try to broadly represent various world regions as described by the Bank. The World Bank classifies 34 countries as low income, 47 as lower-middle income, 56 as upper-middle income, and the remaining 81 as high income.2 The 42 states in my analysis represent 31% of the 137 developing states (the low, lower-middle and upper middle income states). The 42 countries are then divided into three income groups, with 14 in each group. This division makes it possible to see if income is associated with better performance on various metrics of governance of data. The analysis then relies on the Economist Intelligence Unit to classify each country by type of regime (Economist Intelligence Unit 2016).

I next assess each country using World Bank metrics and other widely used perception metrics. I use the Global Human Capital Report from the World Economic Forum, which tells us something about whether a country has enough expertise to build data-driven sectors such as AI. Next I use the World Bank’s Global Indicators of Regulatory Governance, which examines how and how well nations regulate.3 I then use another World Bank metric, Statistical Capacity, as a metric of producing quality public data.4  Finally, I used the Open Data Index as a metric for using public data to feed artificial intelligence and other forms of data analytics.5

Findings

Not surprisingly, greater wealth is associated with better scores on all the metrics.11 Richer countries are generally more open and have more data to use to solve problems and develop wealth. They are also more likely to protect personal data and provide public data for use by businesses.  Yet, as noted above, many industrialised countries are also struggling to govern the many different types and uses of data. 

Mexico was the best performer on all metrics; this solid performance cannot be attributed simply to the influence of the US and Canada, its neighbours and key trade partners. Mexico is a member of the OECD and the Group of Twenty (G20), and is also a signatory to the CPTPP, an agreement with binding data flow provisions and the EU/Mexican trade agreement. Mexico is active in the digital economy, excelling in app development (Popescu 2016, Di Ionnoy and Mandel 2016). Mexico is also a standout in e-government and open government (Cesar et al. 2018). 

Possible ways to help developing countries include encouraging states to develop plans for data governance and encouraging experimentation through technical assistance, regulatory sandboxes and collaboration.  Finally, development agencies and advocates need to wrestle with important questions about data-driven growth. Should development countries do a development policy rethink, given the rising import of data to both manufacturing and services? 

References 

Aaronson, S A (2017), “Information Please: A Comprehensive Approach to Digital Trade Provisions in NAFTA 2.0”, CIGI Paper No. 154..

Aaronson, S A (2018), “Data Is Different: Why the World Needs a New Approach to Cross Border Data Flows”, CIGI Paper No. 197.

Aaronson, S A (2019), “Data Is a Development Issue”, CIGI Paper No. 223.

Cesar, M, A Chaia, A de Oliveira Vaz, G Garcia-Muñoz and P Haugwitz (2018), “How Mexico can become Latin America’s digital-government powerhouse”, McKinsey Digital, November.

Di Ionnoy, M and M Mandel (2016), “The Rise of the Mexican App Economy”, Progressive Policy Institute, August.

Economist Intelligence Unit (2016), Democracy Index 2015: Democracy in an age of anxiety.

Onifade, A (2018), “How Prepared Are We for the 4th Industrial Revolution — A Case Study in Africa Part II.” Business Day, 5 July.

Pinto, R A (2018), “Digital Sovereignty or Digital Colonialism?”, Sur International Journal on Human Rights 27.

Popescu, A (2016), “Is Mexico the Next Silicon Valley? Tech Boom Takes Root in Guadalajara”, The Washington Post, 14 May.

UNCTAD (2017a), “Friends of E-commerce for Development Launch Roadmap for International Trade and Development Policy”, 4 May.

UNCTAD (2017b), Information Economy Report 2017: Digitalization, Trade and Development, United Nations.

Weber, S (2017), “Data, Development and Growth”, Business and Politics 19 (3): 397–423.

World Bank (n.d), “Global Indicators of Regulatory Governance: Trends in Participatory Rulemaking: A Case Study.”

World Bank (2016),  World Development Report 2016: Digital Dividends. 

World Bank (2018), Information and Communications for Development: Data-Driven Development.

World Economic Forum (2017), The Global Human Capital Report 2017.

Endnotes

1 Conference information (it is free and open to all) is at https://datagovhub.org/data-as-a-development-issue/.  Livestream at https://livestream.com/internetsociety/digitaltrade2

 2 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

 3 https://rulemaking.worldbank.org/

 4 http://datatopics.worldbank.org/statisticalcapacity/SCIdashboard.aspx

 5 https://index.okfn.org/

 6 For country-specific scores, see https://datagovhub.org/data-governance-in-developing-and-middle-income-countries-visualization-overview

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Topics:  Development Frontiers of economic research

Tags:  data, data analysis, developing countries, digital revolution

Research Professor and Cross-Disciplinary Fellow, Elliott School of International Affairs, George Washington University

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