Bridging Data Deficits in Emerging Economies

Ken Stibler
Sep 4, 2023
14 min read

Executive Summary

In an increasingly data-driven world, the availability of timely, reliable, and granular information is a critical determinant of economic development, investment flows, and effective policymaking. This report, prepared for the Center for Emerging Economies, provides a comprehensive analysis of the significant data deficit that exists between developed nations and smaller emerging economies. By systematically comparing the data ecosystems of the G7 countries with those of frontier and lower-middle-income markets, we identify critical gaps in data availability, quality, and accessibility that hinder economic progress and create information asymmetry for decision-makers.

Our research reveals a stark contrast in the data landscapes. Developed markets benefit from a rich and mature data infrastructure, offering a wide array of sophisticated data products and services across economic, financial, corporate, and consumer domains. In contrast, emerging economies grapple with a scarcity of reliable data, particularly in areas crucial for investment and strategic planning. This “data deficit” is not merely a technical issue but a fundamental barrier to unlocking the full potential of these economies.

This report maps the specific data gaps across key categories and provides a strategic framework for addressing them. We conclude with actionable recommendations for the Center for Emerging Economies to play a leading role in bridging this data divide, thereby fostering greater transparency, reducing investment risk, and promoting sustainable development in the world’s most dynamic and promising markets.

1. Introduction

The digital revolution has transformed the global economy, making data the lifeblood of modern commerce, finance, and governance. For developed economies, this has ushered in an era of unprecedented analytical capability, enabling businesses to optimize operations, investors to make informed decisions, and policymakers to design evidence-based interventions. However, this data-driven progress has not been evenly distributed. A significant and widening “data divide” separates the information-rich developed world from the data-scarce emerging economies.

This report examines the nature and extent of this data deficit, focusing on the gap between the G7 nations and the frontier and lower-middle-income emerging markets. While the “digital divide” is a well-documented phenomenon, the “data divide,” or the gap in access to high-quality data, represents a more subtle but equally profound challenge. It is a barrier that impedes capital flows, distorts risk perception, and hampers the ability of local and international actors to make sound decisions.

For CEE, understanding and addressing this data deficit is our core strategic imperative. By identifying the specific data and research products that are readily available for developed markets but absent in emerging ones, we can begin to formulate strategies to close this gap. This report aims to provide the foundational research to support such an endeavor, offering a clear-eyed assessment of the current landscape and a roadmap for future action.

2. Methodology

To construct a comprehensive and actionable analysis of the data gap between developed and emerging economies, we first established a baseline of data availability in a mature market context (the G7 nations ) and then to systematically identify the corresponding deficits in frontier and lower-middle-income countries. The methodology can be broken down into four key phases:

Phase 1: Baseline Establishment and Demand Analysis. We began by identifying the most critical and frequently utilized data and research products in developed economies. This was achieved through a two-pronged approach. First, we analyzed the publications and data products of leading financial institutions, government statistical agencies, and economic research firms to identify the core datasets that underpin their analysis. Second, we investigated search engine trends and patterns related to economic and financial data to gauge the real-time information demands of business leaders, investors, and other decision-makers. This allowed us to create a demand-driven framework of essential data categories.

Phase 2: G7 Data Ecosystem Mapping. With the essential data categories defined, we conducted an in-depth mapping of the data ecosystem within the G7 countries (United States, United Kingdom, Germany, France, Japan, Canada, and Italy). This involved identifying the primary public and private sector providers for each data category, the specific data products they offer, and the typical levels of granularity, frequency, and historical depth. This phase provided a clear benchmark of what a mature and robust data ecosystem looks like.

Phase 3: Emerging Market Data Gap Analysis. The core of our research involved a systematic comparison of the G7 data ecosystem with the data landscapes of frontier and lower-middle-income economies across all geographic regions. For each data category identified in Phase 2, we investigated the availability, quality, and accessibility of comparable data in our target emerging markets. This was accomplished through a combination of academic and industry research, analysis of reports from multilateral institutions like the World Bank and IMF, and an examination of the product offerings of global and local data vendors.

Phase 4: Synthesis and Framework Development. In the final phase, we synthesized our findings to create a detailed data gap matrix, which visually represents the disparities in data availability across the different market categories. This matrix serves as the foundation for our strategic analysis. Based on the identified gaps, we developed a strategic framework and a set of actionable recommendations for the Center for Emerging Economies, designed to guide its efforts in bridging the data divide.

3. The Data Landscape: A Tale of Two Worlds

Our research reveals a stark dichotomy in the global data landscape. Developed markets, exemplified by the G7 nations, are characterized by a rich, mature, and highly sophisticated data ecosystem. In stark contrast, frontier and lower-middle-income emerging economies grapple with a significant data deficit, marked by a scarcity of reliable, timely, and granular information.

The Rich Data Ecosystem of Developed Markets

The G7 countries benefit from a deeply entrenched data infrastructure that has evolved over decades. This ecosystem is a complex interplay of public institutions, private sector data vendors, and a vibrant research community. The result is a comprehensive and readily accessible supply of high-quality data that supports a wide range of economic and financial activities.

Key Characteristics of the G7 Data Ecosystem:

Robust Public Data Infrastructure: National statistical offices, such as the U.S. Bureau of Economic Analysis (BEA) and the U.S. Census Bureau, provide a wealth of high-quality, publicly available data on a regular and timely basis. This includes fundamental economic indicators like GDP, inflation, employment, and trade data, often with deep historical records and granular geographic breakdowns.
Sophisticated Private Sector Data Providers: A mature market of private data vendors, such as Bloomberg, Refinitiv, S&P Global, and FactSet, offers a vast array of specialized data products and analytical tools. These platforms provide real-time financial market data, detailed corporate financials, supply chain information, and sophisticated credit rating services. The competition among these providers ensures a high level of data quality, innovation, and customer service.
Comprehensive Credit Information Systems: Developed economies have well-established credit bureaus that provide comprehensive credit information on both individuals and firms. This reduces information asymmetry in lending markets, facilitates access to credit, and lowers the cost of capital.
Deep and Liquid Financial Markets: The financial markets in G7 countries are characterized by high levels of liquidity, transparency, and data availability. Real-time stock market data, detailed bond pricing information, and a wide range of derivatives data are readily accessible to investors.
Rich Consumer and Market Research Data: A thriving market research industry provides a wealth of data on consumer behavior, sentiment, and purchasing patterns. This data is crucial for businesses in making strategic decisions about product development, marketing, and sales.

The Data Deficit in Emerging Markets

In stark contrast to the data-rich environment of the G7, frontier and lower-middle-income emerging markets are characterized by a significant and pervasive data deficit. This scarcity of reliable and accessible information creates a challenging environment for investors, businesses, and policymakers, hindering economic growth and development.

Key Characteristics of the Emerging Market Data Deficit:

Under-resourced National Statistical Offices: National statistical offices in many emerging economies lack the funding, technical capacity, and institutional autonomy to produce high-quality, timely, and comprehensive economic data. This results in data that is often infrequent, unreliable, and lacking in historical depth and granular detail.
Limited Private Sector Data Infrastructure: The private sector data vendor market in emerging economies is significantly less developed than in the G7. While global providers like Bloomberg and Refinitiv offer some coverage, it is often less comprehensive and more expensive than in developed markets. The lack of a robust local data vendor ecosystem means that many niche data categories are simply not covered.
Inadequate Credit Information Systems: Credit bureau coverage in many frontier and lower-middle-income countries is limited or non-existent. This information asymmetry between lenders and borrowers makes it difficult for individuals and small businesses to access credit, stifling entrepreneurship and economic dynamism.
Illiquid and Opaque Financial Markets: Financial markets in emerging economies are often characterized by low liquidity, a lack of transparency, and poor data quality. This makes it difficult for investors to accurately price assets and manage risk, leading to higher costs of capital and lower investment flows.
Scarcity of Consumer and Market Research Data: The market research industry in many emerging economies is nascent, with limited availability of reliable data on consumer behavior, preferences, and sentiment. This makes it challenging for businesses to understand their target markets and make informed decisions about market entry and product development.

4. The Data Gap Matrix: A Comparative Analysis

To provide a clear and actionable overview of the data deficit, we have developed a Data Gap Matrix. This matrix compares the availability and quality of key data categories between the G7 nations and the frontier/lower-middle-income emerging markets. The matrix highlights the specific areas where the data deficit is most acute, providing a roadmap for targeted intervention.

Data Category	G7 Countries	Frontier & Lower-Middle-Income EMs	Key Implications of the Gap
Macroeconomic Data
GDP & Components	High-frequency (quarterly ), granular, long historical data.	Low-frequency (annual), often with significant lags and revisions.	Difficulty in real-time economic monitoring and forecasting.
Inflation (CPI)	Monthly, detailed breakdowns by category and region.	Often monthly, but with less granularity and potential quality issues.	Inaccurate inflation measurement can distort investment returns and policy decisions.
Employment & Labor	Monthly, detailed data on unemployment, wages, and sector employment.	Limited, often informal sector is poorly captured.	Poor understanding of labor market dynamics and human capital.
Financial Market Data
Public Equities	Real-time data, deep historical records, extensive company information.	Limited real-time data, poor data quality, lack of historical data.	Increased investment risk, difficulty in valuation, and market inefficiency.
Fixed Income	Comprehensive data on government and corporate bonds, including pricing and yields.	Opaque, limited data on corporate bonds, unreliable pricing.	Challenges in assessing credit risk and developing a corporate debt market.
Private Company Data	Extensive databases (e.g., PitchBook, Preqin) with detailed financials and funding data.	Extremely limited, often anecdotal or relationship-based.	Significant barrier to private equity, venture capital, and M&A activity.
Corporate & Business Intelligence
Company Financials	Standardized, audited, and easily accessible financial statements for public and many private companies.	Non-standardized, often unaudited, and difficult to obtain.	Lack of transparency, increased due diligence costs, and higher fraud risk.
Supply Chain Data	Increasingly available data on supply chain linkages and dependencies.	Almost non-existent, making it difficult to assess operational risks.	Inability to model and mitigate supply chain disruptions.
Credit Ratings	Comprehensive coverage of corporate and sovereign debt by multiple agencies.	Limited coverage, often only for the largest corporations and government debt.	Difficulty in assessing creditworthiness, leading to higher borrowing costs.
Consumer & Demographic Data
Consumer Spending	Granular data from credit card transactions, retail sales surveys, and e-commerce platforms.	Limited and often unreliable data, especially outside of major urban centers.	Challenges for businesses in understanding consumer behavior and market size.
Demographics	Detailed census data, regularly updated, with granular geographic and demographic breakdowns.	Infrequent and often outdated census data, with limited granularity.	Difficulty in market segmentation and social policy planning.

5. From Data Gaps to Research Gaps: A Meta-Analysis

The data deficit in emerging markets is not merely a technical or logistical challenge; it is a fundamental barrier to knowledge and understanding. The absence of reliable and granular data directly translates into significant gaps in academic, policy, and commercial research. This section provides a meta-analysis of how the data gaps identified in the previous section create corresponding research gaps, hindering our ability to fully comprehend and engage with these economies.

Thematic Research Gaps

The scarcity of high-quality data in emerging markets makes it difficult to conduct rigorous thematic research on a wide range of critical topics. This, in turn, limits the ability of policymakers and investors to make evidence-based decisions.

Understanding Economic Resilience: The lack of high-frequency economic data, such as quarterly GDP and monthly labor statistics, makes it challenging to assess the resilience of emerging economies to external shocks. This was particularly evident during the COVID-19 pandemic, where the lack of timely data hampered the ability of governments to design and implement effective policy responses.
Mapping Financial Contagion: The opacity of financial markets and the limited availability of data on cross-border financial flows make it difficult to model and predict the spread of financial crises. This research gap increases the risk of financial contagion and systemic crises in the interconnected global financial system.
Assessing the Impact of Climate Change: The absence of granular data on climate-related risks, such as a lack of detailed geographic data on physical climate risks, makes it challenging to assess the potential impact of climate change on emerging economies. This hinders the development of effective climate adaptation and mitigation strategies.

Country-Specific Research Gaps

At the country level, data gaps create a vicious cycle of under-research and mis-perception. The lack of reliable data discourages researchers from studying these economies, which in turn reinforces the perception that they are opaque and high-risk.

Private Sector Dynamics: The dearth of reliable data on private companies is a major obstacle to understanding the dynamics of the private sector in emerging markets. This research gap makes it difficult to identify promising investment opportunities, assess the competitiveness of different industries, and design effective private sector development policies.
Household Welfare and Poverty: The infrequency and limited granularity of household survey data make it challenging to track changes in poverty and inequality, and to assess the effectiveness of social programs. This research gap hinders the development of evidence-based policies to promote inclusive growth and reduce poverty.
Subnational Economic Activity: The lack of granular subnational data on economic activity, such as GDP by province or city, makes it difficult to understand the drivers of regional growth and to design targeted regional development policies. This research gap can lead to a misallocation of resources and a failure to address regional disparities.

6. Emerging Market Data Use Cases and Availability Shortfalls

To make the implications of the data deficit more concrete, this section explores specific data use cases in emerging markets and the corresponding availability shortfalls. These examples illustrate the practical challenges that decision-makers face and highlight the opportunities for value creation by bridging the data gap.

Use Case 1: Foreign Direct Investment (FDI) and Market Entry

Decision-Maker: A multinational corporation (MNC) considering a significant investment in a frontier market.

Data Needs: Granular data on market size, consumer spending patterns, local competition, labor market conditions, and the regulatory environment.

Availability Shortfall: The MNC finds that reliable data on consumer spending is limited to the capital city, there is no comprehensive database of local competitors, and labor market data is outdated and incomplete. This lack of data makes it difficult to build a robust business case for the investment, leading to a higher perceived risk and a potential decision to delay or cancel the investment.

Use Case 2: Private Equity and Venture Capital Investment

Decision-Maker: A private equity fund seeking to invest in mid-sized, privately-held companies in a lower-middle-income country.

Data Needs: Detailed financial statements, information on corporate governance, and data on comparable company valuations.

Availability Shortfall: The fund discovers that most private companies do not have audited financial statements, information on corporate governance is anecdotal, and there is a lack of reliable data on comparable transactions. This makes it extremely difficult to conduct due diligence and value potential investments, significantly increasing the cost and risk of the investment process.

Use Case 3: Infrastructure Project Finance

Decision-Maker: A development finance institution (DFI) considering financing a large-scale infrastructure project in a frontier market.

Data Needs: Long-term historical data on macroeconomic stability, political risk, and the legal and regulatory framework.

Availability Shortfall: The DFI finds that historical macroeconomic data is volatile and unreliable, there is a lack of independent data on political risk, and the legal framework for public-private partnerships is untested. This data deficit makes it challenging to assess the long-term viability of the project and to structure a financing package that is acceptable to all stakeholders.

Use Case 4: SME Lending by Local Banks

Decision-Maker: A local bank in a lower-middle-income country seeking to expand its lending to small and medium-sized enterprises (SMEs).

Data Needs: Reliable credit information on SMEs to assess creditworthiness and manage risk.

Availability Shortfall: The bank finds that there is no functioning credit bureau, and most SMEs lack formal financial records. This forces the bank to rely on relationship-based lending, which is costly, inefficient, and limits the bank’s ability to scale its SME lending portfolio.

7. Strategic Framework and Recommendations

Addressing the data deficit in emerging markets is a complex challenge that requires a concerted and multi-faceted effort. The Center for Emerging Economies is uniquely positioned to play a catalytic role in this process. We propose a strategic framework based on three key pillars: Data Advocacy and Standardization, Data Infrastructure Development, and Data-Driven Capacity Building. For each pillar, we offer a set of actionable recommendations.

Pillar 1: Data Advocacy and Standardization

The first step in bridging the data divide is to raise awareness of the issue and to promote the adoption of international data standards. In practice this means:

Developing and Promoting Data Standards: Partner with international organizations like the IMF and the World Bank to develop and promote the adoption of standardized data formats and methodologies in emerging markets. This will improve the quality and comparability of data across countries.
Publishing an Annual Data Gap Index: Create and publish an annual index that ranks countries based on the availability and quality of their economic and financial data. This will create a “race to the top” and incentivize governments to invest in their national statistical systems.

Pillar 2: Data Infrastructure Development

Closing the data gap will require significant investment in the development of data infrastructure in emerging markets. Some non-exhaustive ideas:

Launching a Data Innovation Fund: Establish a fund to provide seed funding and technical assistance to local entrepreneurs who are developing innovative solutions to data challenges in emerging markets. This could include startups that are using new technologies, such as satellite imagery or mobile phone data, to collect and analyze data.
Subsidizing he Development of Credit Bureaus: Partner with financial institutions and technology providers to support the establishment and expansion of credit bureaus in frontier and lower-middle-income countries. This will be a critical step in improving access to credit for individuals and SMEs.
Facilitating Public-Private Partnerships: Act as a neutral broker to facilitate public-private partnerships aimed at improving data infrastructure. This could involve connecting governments with private sector data providers who have the technology and expertise to help them modernize their statistical systems.

Pillar 3: Data-Driven Capacity Building

Ultimately, bridging the data divide will require building the human capital and institutional capacity to collect, analyze, and use data effectively. The Center for Emerging Economies can support this by:

Developing and Delivering Data Literacy Programs: Create and deliver training programs for policymakers, journalists, and civil society leaders in emerging markets to improve their data literacy and their ability to use data to make informed decisions.
Supporting Local Research Institutions: Provide funding and technical assistance to universities and research institutions in emerging markets to strengthen their capacity to conduct high-quality, data-driven research.
Creating a Fellowship Program: Launch a fellowship program that brings promising young data scientists and economists from emerging markets to the Center for Emerging Economies for a year of training and mentorship.

8. Conclusion

The data deficit in emerging markets is a significant and multifaceted challenge, but it is not insurmountable. By taking a strategic and collaborative approach, the Center for Emerging Economies can play a pivotal role in bridging this divide. The recommendations outlined in this report are as much our roadmap for greenfeilding new data as they are a broader call to action for interest organizations to build out the "digital public commons" in emerging markets.

While not as flashy as other programmatic investments, data advocacy, infrastructure, and capacity building have a high development multiplier. While on a longer lag, this type of work can go beyond unlocking invisable opportunities to create new opportunities for investors, businesses, and researchers around the world.