The Data Landscape of an Informal Economy: Expectations for India's 90% Informal Sector.....

The formal economy is characterized by registered businesses, codified labor laws, traceable transactions, standardized contracts, and verifiable data that is collected, recorded, and regulated by government and official institutions. The informal economy, conversely, operates largely outside these structures. When an economy like India has an estimated 90% of its workforce and economic activity residing in this informal sphere, the nature, availability, and utility of "data" diverge significantly from what one would expect in highly formalized nations. The sheer scale of this informality in India does not just mean "missing data"; it signifies a fundamentally different data ecosystem—one that is fragmented, often qualitative rather than quantitative, and difficult to standardize.

What We Could Expect of Data in a 90% Informal Economy

If 90% of an economy operates informally, the expectations for data can be categorized into several key areas: data availability and quality, economic visibility, policy challenges, and alternative data sources.

1. Poor Data Availability and Quality

The most direct expectation is the absence of reliable, official data for the vast majority of economic activity.

Missing Core Economic Indicators: Standard metrics like GDP contributions by specific sub-sectors of the informal economy, precise employment figures, and real-time wage data become estimates at best. National statistical organizations must rely heavily on periodic, large-scale sample surveys rather than routine administrative data.

Lack of Firm-Level Data: Data on business formation, revenue, expenditure, and investment for millions of small, unregistered enterprises (e.g., street vendors, home-based workers, small-scale agriculture) are virtually non-existent in official registries.

Untaxed and Untracked Transactions: Because transactions are often cash-based and unregistered, administrative data gathered from tax receipts (like the Goods and Services Tax, or GST) captures only a fraction of the total economic flow, leading to a significant "dark figure" of economic activity [1].

2. Limited Economic Visibility and Inaccurate Policy Making

The lack of reliable data creates a visibility problem for economists and policymakers, leading to expectations of:

Inaccurate Official Narratives: Official economic growth figures may underrepresent the true economic resilience or vulnerability of the informal sector. The data fails to tell the full story of how most citizens live and work.

Ineffective Policy Calibration: When the data on the primary labor market is sparse, government interventions—whether minimum wage laws, credit availability schemes, or social security programs—struggle to effectively reach the intended beneficiaries. Policies designed for the formal sector often fail to translate to the needs of informal workers.

Vulnerability Assessment Difficulties: It becomes nearly impossible to accurately assess the impact of sudden shocks (like a pandemic or a natural disaster) on the most vulnerable populations without real-time, accurate data on their livelihoods and savings.

3. Fragmentation and Siloed Information

The existing data in such an economy is expected to be fragmented across different sources.

Siloed Data Sources: Instead of unified data systems, information is likely scattered across various local government bodies, non-governmental organizations (NGOs) focused on specific worker groups (e.g., waste pickers' unions), microfinance institutions, and academic research projects.

Reliance on Alternative and 'Big Data': There is an increased reliance on "proxy data" or "alternative data." This might include satellite imagery for agricultural tracking, mobile phone data for population movement and commerce patterns, or digital payment data from financial technology (FinTech) firms operating in the space. These sources offer new insights but come with their own biases and privacy concerns.

4. The Emergence of 'Qualitative' Data Importance

In the absence of robust quantitative data, qualitative research methods become crucial.

Case Studies and Ethnography: The data that is available often comes from in-depth case studies, ethnographic research, and localized surveys conducted by researchers to understand the nuances of informal markets, supply chains, and social networks. This data provides rich context but is not easily scalable or generalizable to the national level.

In an economy where 90% of activity is informal, the expectation of data must be fundamentally managed. The data ecosystem will be characterized by significant gaps, measurement challenges, and a reliance on fragmented, often non-traditional data sources. The journey toward greater formalization, as seen in recent initiatives in India like the push for digital payments and labor registries (such as the e-Shram portal), is essentially a journey to create better data. Until that formalization is achieved, policymakers must navigate a challenging landscape where broad national statistics tell an incomplete story, necessitating innovative approaches to data collection and a nuanced understanding of the vast, complex, and data-sparse informal reality.