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The Informal Sector

From Invisibility to Undeniability:
Why Informal Economies Can No Longer Be Ignored in Public Policy

Hari Srinivas
Policy Analysis Series E-250

Abstract:
For much of the post-war development period, the informal economy was treated as an invisible or "residual" sector, largely absent from official statistics and marginal to public policy. This perception has changed significantly over the past two decades. The expansion of national labour force surveys, household and enterprise surveys, and internationally harmonised datasets has made the scale, persistence, and economic significance of informal employment and enterprises empirically undeniable. In many countries, informality now represents the dominant form of work, a substantial share of economic activity, and a important component of urban service provision and resilience.

This paper examines how the growing visibility of the informal economy through national and international datasets has reshaped public policy responses. Rather than producing uniform policy models, these data have reframed informality as a structural and governance-relevant phenomenon, compelling governments to move beyond denial, neglect or repression.

Drawing on cross-country examples, the paper illustrates how evidence on scale, linkages, and vulnerability has enabled policy shifts toward legal recognition, adaptive regulation, inclusive social protection, and integration into urban and environmental systems. The analysis underscores that once informality is measured and acknowledged, inclusion becomes an explicit policy choice, raising new questions of accountability, governance, and development strategy.

Keywords:
informal economy, informal employment, public policy, governance, urban development, social protection, data and evidence, economic resilience

1. Introduction

For decades, the informal economy was treated as an invisible or "residual" sector, largely absent from official statistics and peripheral to mainstream policy debates. Where it was acknowledged, it was often framed as a temporary aberration associated with underdevelopment, regulatory non-compliance, or weak governance. This analytical invisibility made it possible for governments to sideline informal workers and enterprises without having to justify exclusionary policies or enforcement-led approaches.

Over the past few decades, this situation has changed fundamentally. National labour force surveys, household and enterprise surveys, and internationally harmonised datasets produced by international institutions such as the ILO, World Bank, UN-Habitat, and others, have progressively made the informal economy visible at scale. These data have established, beyond reasonable dispute that informal employment constitutes the majority of work in many countries, that informal enterprises account for a substantial share of economic activity, and that informal actors are deeply embedded in urban economies, supply chains, and service systems.

This growing body of evidence has not produced a single model for informal economy policy, nor has it eliminated political disagreement. What it has done is more consequential. It has transformed informality from an analytical blind spot into a policy-relevant reality. Once counted, mapped, and repeatedly measured, the informal economy can no longer be ignored or treated as marginal. The examples outlined in the annex illustrate how data-driven visibility has enabled governments to move, in diverse contexts, from denial or repression and towards recognition, accommodation, and, in some cases, integration.

To create a clear policy imperative for local governments, data on the informal economic sector has to do more than describe poverty or survival activity. It needs to demonstrate scale, economic contribution, system linkages, and policy leverage points. The most persuasive datasets usually fall into six mutually reinforcing clusters.

1. Scale and economic significance
Data on the informal sector's scale and economis significance establishes that the informal sector is not marginal. Examples of data types include:
  • Number of informal enterprises and workers, disaggregated by ward, neighborhood, and sector
  • Share of local employment generated by informal activities, including gender and age breakdowns
  • Estimated contribution to local GDP or value added, even using conservative proxies
  • Average enterprise size in terms of workers, turnover ranges, and survival rates
This kind of data helps local governments see informal enterprises as a core part of the local economy rather than a temporary or residual phenomenon.

2. Enterprise characteristics and dynamics
Data on enterprise characteristics and dynamics show that informal enterprises are heterogeneous and capable of upgrading. Examples of data types include:
  • Size distribution from own-account workers to micro and small firms employing others
  • Longevity of enterprises, seasonality, and growth trajectories
  • Capital intensity, skills used, and degree of specialization
  • Reasons for remaining informal such as regulatory barriers, costs, or lack of information
This moves policy away from one-size-fits-all enforcement toward differentiated support strategies.

3. Linkages with the formal economy
Data on the informal sector's linkages with the formal sector is often the most compelling evidence for policymakers. Examples of data types include:
  • Backward and forward linkages with formal firms, including subcontracting, input sourcing, and distribution
  • Role of informal enterprises in "last-mile" delivery, waste collection, repair, food supply, and construction
  • Dependence of formal SMEs and large firms on informal labor or services
  • Participation in value chains that serve export, tourism, or urban infrastructure
When informality is shown to be embedded in formal supply chains, repression starts to look economically self-defeating.

4. Spatial and infrastructure dependence
Data on spataial and infrastructure dependence of informal enterprises make the local government's roles more explicit. Examples of data types include:
  • Geographic concentration of informal activities in markets, streets, transport nodes, and residential areas
  • Dependence on public space, roads, drainage, electricity, water, and sanitation
  • Impacts of eviction, relocation, or infrastructure projects on enterprise survival and household income
  • Contribution of informal enterprises to neighborhood vitality and service provision
This frames informality as an urban management and planning issue, not just a labor or tax issue.

5. Fiscal and governance interactions
Data on fiscal and governance interactions counter the argument that informal enterprises are a simply a fiscal burden. Examples of data types include:
  • Fees, licenses, market charges, and informal payments already made to local authorities
  • Willingness to pay for predictable services, secure tenure, and basic infrastructure
  • Cost-benefit comparisons between enforcement-led approaches and support-oriented approaches
  • Existing forms of organization such as associations, cooperatives, and unions
This helps justify policy shifts from policing to service provision and co-regulation.

6. Vulnerability and resilience functions
Data on vulnerability and resilience issues of the informal sector connect informality to social stability and crisis response. Examples of data types include:
  • Role of informal enterprises in absorbing labor during economic downturns or disasters
  • Income smoothing functions for low-income households
  • Gendered and migrant dimensions of risk and opportunity
  • Adaptive responses to shocks such as pandemics, climate events, or supply disruptions
Local governments are more likely to act when informality is framed as part of urban resilience and risk management.

What turns data into an imperative

For local governments, the strongest case emerges when datasets are combined to show that informal enterprises are:
  • Large enough to matter
  • Economically productive and linked to formal systems
  • Spatially dependent on local government decisions and services
  • Already interacting fiscally and institutionally with the municipality and its responsibilities
  • Capable of responding positively to supportive, low-cost policy interventions In practice, even imperfect data, if locally grounded and spatially explicit, can be more persuasive than national-level estimates. Framing informal sector data around economic contribution, system dependence, and policy leverage is usually more effective than focusing on informality as a "problem to be eliminated".

    Availability of Informal Sector Data

    Availability, quality, and granularity of informal sector data varies widely depending on the country, statistical capacity, survey systems, and institutional priorities. Some of these variations are because informal economic activity is inherently harder to measure than formal economic activity.

    A compilation of informality data sources are listed below.

    1. International statistical estimates

    Global organizations compile cross-country estimates of key informal sector indicators:
    • The International Labour Organization regularly publishes estimates of the share of employment that is informal, showing that in many lower-income countries most employment is informal. WIEGO
    • The United Nations and World Bank also provide international estimates on informal employment and share of total employment for nearly all countries. United Nations
    These sources give policymakers a comparative picture of size and prevalence but tend to be at a relatively high level (e.g., percent of employment informal).

    2. World Bank Informal Economy Database

    The World Bank has constructed a database including up to 196 economies over time with multiple indicators: estimated informal output, informal employment, self-employment, labor without pension insurance, and perception-based indicators from enterprise or household surveys. World Bank

    This means that quantitative data on output and employment shares exist for most countries, though some are modeled estimates rather than direct counts.

    3. National surveys and administrative data

    Many countries routinely collect informal sector data through:
    • Labor force surveys that categorize employment as formal or informal
    • Household income and expenditure surveys with questions on unregistered business income
    • Establishment or enterprise surveys that capture whether firms are registered or not
    In developing economies these surveys may be less frequent or limited in geographic coverage, but they still provide useful empirical evidence of size and characteristics.

    4. Specialized case studies and local surveys

    In cities or regions with high informality, researchers and statistical offices sometimes conduct targeted surveys that reveal:
    • Ratios of informal to formal businesses in sampled urban areas, sometimes showing several informal firms for every formal one.
    • Detailed data on sales, profits, and barriers to formalization in specific sectors from enterprise surveys.

    5. National accounts and estimates of non-observed economy

    National statistical offices sometimes include estimates of the non-observed economy (a category capturing informal activity) in their national accounts or GDP estimates. Practices vary by country.

    3. What this means for evidence-based policy

    Existing data can justify supportive policies towards the informal sector. Even with imperfect data, evidence points to already available for many countries. For example:
    • Employment prevalence: Percent of workforce that is informal, by gender, age, sector, and income group.
    • Output contributions: Estimates of the informal economy's share of GDP or value added (usually model-based).
    • Sectoral patterns: Agriculture, trade, construction, and services often dominate informal activity in many countries.
    • Employer-worker linkages: Data showing how informal and formal segments interact in labor markets.
    • Local survey evidence: City-level enterprise data showing density and operational characteristics of informal firms.
    There are obviously limitations to these dtaasets, and should inform how data that used. Such limitations include ganularity, in terms of detailed firm-level or supply chain linkage data, which is scarcer, especially at local levels, or timeliness, where many surveys are not annual and can lag current conditions. Data may also be limited by definition differences, where countries vary in how they classify informal activity, so direct comparisons require care.

    Local governments can, however, still use existing sources to build a compelling case by combining a number of sourses such as ILO employment data to show the scale of informal work in their country or region; World Bank database indicators to compare trends over time and across peers; City or regional surveys to demonstrate the density of informal firms in local markets and spatial concentrations of economic activity; or Household or enterprise surveys to show how informal enterprises contribute to livelihoods and link to formal sectors.

    By triangulating these data sources, policymakers can back supportive policies with evidence even where formal measurement systems are weak. Below are examples where national or international informal economy datasets materially shaped policy directions.

    1. Informal employment data and social protection expansion
    Data Trigger
    • ILO Labour Force Survey harmonisation showed that 60-90 percent of workers in many low- and middle-income countries were informally employed.
    • Gender-disaggregated data showed women were overrepresented in the most vulnerable informal categories.
    Policy Outcomes
    • Expansion of non-contributory social protection schemes not tied to formal employment.
    • Examples:
    • India: justification for the National Social Assistance Programme and later portability of benefits under Aadhaar-based systems.
    • Thailand: universal health coverage explicitly justified by high informal employment shares.
    • Latin America: growth of tax-financed pensions and health schemes covering informal workers.
    Why the data mattered Without nationally comparable informal employment data, ministries of finance resisted universal schemes, arguing they would reward non-compliance. The data reframed informality as the dominant labour market condition, not an exception.

    2. Informal economy share of GDP and SME policy reform
    Data Trigger (World Bank informal economy and other economy estimates)
    • Estimates showing informal output ranging from 25 to 60 percent of GDP in many economies.
    • Evidence that informality persisted even during growth periods.
    Policy Outcomes
    • Shift from enforcement-led formalisation to incremental compliance models.
    • Examples:
    • Mexico: introduction of simplified tax regimes for micro-enterprises.
    • Indonesia: expansion of micro-enterprise registration combined with low flat taxes.
    • Rwanda: tiered business registration linked to turnover rather than legal form.
    Why the data mattered GDP-share estimates undermined the idea that informality would disappear with growth alone. Policymakers began designing graduated regulatory systems.

    3. Urban informality data and street vendor legislation
    Data Trigger (National urban employment and household surveys)
    • National surveys revealed millions depended on street vending and informal retail.
    • Spatial concentration data showed vendors clustered near transport hubs and markets.
    Policy Outcomes
    • Legal recognition and protection of street vendors.
    • Examples:
    • India: Street Vendors (Protection of Livelihood and Regulation of Street Vending) Act.
    • South Africa: municipal by-laws recognising informal trading zones.
    • Peru: municipal ordinances integrating vendors into market infrastructure plans.
    Why the data mattered Courts and city governments were confronted with employment scale data that made mass eviction politically and economically untenable.

    4. Informal sector resilience data and crisis-response policy
    Data Trigger (COVID-era rapid informal economy surveys by ILO and World Bank)
    • High-frequency surveys showed informal workers lost income immediately during lockdowns.
    • Data demonstrated informal enterprises had little or no access to credit or reserves.
    Policy Outcomes
    • Emergency cash transfers and wage support extended to informal workers.
    • Examples:
    • Brazil: Auxilio Emergencial explicitly included informal workers.
    • Kenya: urban cash transfers targeted informal settlements.
    • Philippines: social amelioration programmes justified using informal employment data.
    Why the data mattered Without internationally comparable rapid surveys, governments initially claimed informal workers could rely on family support. The data disproved this assumption.

    5. Waste picker data and circular economy policy
    Data Trigger (ILO, WIEGO, and UN-Habitat datasets)
    • Estimates of informal waste pickers contribution to recycling rates and landfill diversion.
    • Data showing cost savings for municipalities.
    Policy Outcomes
    • Formal integration of waste pickers into municipal solid waste systems.
    • Examples:
    • Colombia: constitutional court rulings mandating inclusion of waste pickers.
    • Brazil: national solid waste policy recognising waste picker cooperatives.
    • South Africa: municipal contracts with informal recycling groups.
    Why the data mattered Environmental ministries began to see informal workers as service providers, not informal nuisances.

    6. Gendered informality data and labour law reform
    Data Trigger (ILO gender-disaggregated informal employment data)
    • Persistent gender gaps in informal employment quality and earnings.
    • Concentration of women in home-based and unpaid informal work.
    Policy Outcomes
    • Extension of labour protections beyond standard employment.
    • Examples:
    • ILO Convention 190 on violence and harassment at work.
    • National recognition of home-based workers in labour codes.
    • Expansion of childcare and maternity benefits not tied to formal contracts.
    Why the data mattered Gendered informality data reframed labour law from a factory-centric model to a livelihood-centric one.

    Table 1: Illustrative Policy Responses Enabled by Informal Economy Data
    Policy area Key datasets and evidence Policy response enabled Core implication for local and national governments
    Street vendors and public space National urban employment surveys; city-level vendor counts; gender-disaggregated data Legal recognition of street vending; vending zones; participatory planning mechanisms Informal livelihoods are a structural part of urban economies and must be managed, not eliminated
    Informal enterprises and SMEs World Bank informal economy estimates; enterprise surveys on registration and compliance costs Simplified registration and taxation; tiered and turnover-based compliance regimes Formalisation is more effective as a gradual pathway than as a binary legal requirement
    Informal workers and social protection ILO informal employment statistics; labour force surveys Universal or tax-financed health, pension, and income support systems Employment-linked social protection models exclude the majority of workers
    Informal settlements and services Household surveys; informal settlement mapping; service access data In-situ upgrading; extension of basic services without tenure preconditions Counting informal residents creates accountability for service exclusion
    Waste pickers and circular economy ILO, WIEGO, and UN-Habitat data on recycling contributions and employment Inclusion of waste pickers in municipal waste systems; payment for environmental services Informal workers can function as cost-effective service providers in environmental policy
    Crisis response and resilience Rapid informal economy surveys during COVID-19 Emergency cash transfers including informal workers and enterprises Informality is central to economic resilience and social stability during shocks
    Gender and informal work Gender-disaggregated informal employment and income data Labour law extensions; maternity benefits; childcare support beyond formal contracts Informality is not gender-neutral and requires targeted policy responses

    Conceptually, what the above case studies illustrate is that national and international datasets rarely produce local policy blueprints, but they have three important impleications: one, They close the "denial gap", that is governments can no longer claim informality is marginal or temporary; two, they legitimise supportive policy, that is officials gain political cover to move away from punitive approaches; and three, they reshape institutional mandates, that is informality becomes relevant to finance, planning, health, and environment ministries, not just labour departments.

    4. Way Forward

    The experience captured in these examples suggests that the policy relevance of informal economy data lies less in technical precision than in strategic visibility. Even imperfect data, when systematically collected and publicly recognised, can shift the terms of policy debate. For national and local governments, the key challenge is not to wait for complete or flawless measurement, but to make deliberate use of existing datasets to inform more coherent, accountable, and context-sensitive policy responses.

    Going forward, the most promising direction is greater alignment between national and international data systems and local policy needs. This includes improving spatial and sectoral disaggregation, strengthening links between labour, enterprise, urban, and environmental datasets, and ensuring that informal actors are visible within routine planning, budgeting, and monitoring processes. Such alignment increases the likelihood that informal economy data will translate into practical decisions on land use, infrastructure, service provision, and social protection.

    Ultimately, the growing visibility of the informal economy places a responsibility on policymakers. When informality is empirically established as central to employment, service delivery, and economic resilience, inaction or exclusion becomes a conscious policy choice rather than an oversight. The way forward, therefore, is not simply better data, but more deliberate governance responses that recognise the informal economy as a permanent and integral feature of contemporary development trajectories.

    ANNEX 1: Case Studies used in the research

     This document is based on the study of a number of illustrative policy responses enabled by informal economy data. These are listed below, grouped by policy type, with an emphasis on what data triggered action and what resulted.

      
    1. Street vendors and public space management

    India

    South Africa

    Peru
    Data that triggered action
    • National urban employment and household surveys showing street vending as a major source of non-agricultural employment
    • City-level counts revealing hundreds of thousands of vendors concentrated around transport nodes and markets
    • Gender-disaggregated data showing high female participation and household dependence on vending income
    Policies that resulted
    • Legal recognition of street vending as a legitimate urban livelihood
    • Designation of vending zones and time-based access to public space
    • Participatory planning mechanisms involving vendor associations
    Examples
    • India: The Street Vendors (Protection of Livelihood and Regulation of Street Vending) Act institutionalised vendor surveys, vending certificates, and town vending committees
    • South Africa: Municipal informal trading by-laws shifted from eviction to managed trading areas
    • Peru: Municipal ordinances integrating street vendors into local market redevelopment projects
    Why the data mattered
      The sheer employment scale made eviction politically, economically, and legally indefensible.

        
    2. Informal enterprises and SME formalisation pathways

    Mexico

    Indonesia

    Rwanda
    Data that triggered action
    • World Bank and national estimates showing informal enterprises constituting the majority of firms
    • Enterprise surveys revealing that regulatory cost and complexity, not criminal intent, drove informality
    • Persistence of informality despite economic growth
    Policies that resulted
    • Simplified registration and taxation regimes
    • Turnover-based compliance rather than full legal incorporation
    • Graduated pathways from informal to semi-formal to formal
    Examples
    • Mexico: Introduction of simplified tax regimes for micro-enterprises
    • Indonesia: Low-cost micro-enterprise registration linked to local government databases
    • Rwanda: Tiered business licensing tied to firm size and revenue
    Why the data mattered
      Informality was reframed as a structural condition requiring adaptive regulation, not eradication.

        
    3. Informal workers and social protection systems

    Thailand

    Brazil

    India
    Data that triggered action
    • ILO labour force data showing most workers outside formal employment
    • Gender and sectoral disaggregation revealing exclusion from contributory schemes
    • Evidence of old-age poverty among informal workers
    Policies that resulted
    • Universal or tax-financed social protection decoupled from formal employment
    • Health coverage, pensions, and maternity benefits extended to informal workers
    Examples
    • Thailand: Universal Health Coverage explicitly designed for an informal labour market
    • Brazil: Expansion of social pensions and income transfers
    • India: Non-contributory pensions and portability of benefits
    Why the data mattered
      The formal employment model was shown to be empirically irrelevant for most workers.

        
    4. Informal settlements and urban service delivery

    Kenya

    Bangladesh

    Brazil
    Data that triggered action
    • Household surveys documenting informal settlement populations and service deficits
    • Mapping of informal areas excluded from municipal infrastructure plans
    • Links between service gaps and health outcomes
    Policies that resulted
    • In-situ upgrading rather than relocation
    • Extension of water, sanitation, and electricity without tenure preconditions
    • Recognition of informal settlements in city plans
    Examples
    • Kenya: Upgrading programmes in Nairobi informal settlements
    • Bangladesh: Slum improvement projects linked to national urban data
    • Brazil: Favela upgrading integrated into municipal planning
    Why the data mattered
      Once informal populations were counted, exclusion from basic services became indefensible.

        
    5. Informal waste pickers and environmental policy

    Colombia

    Brazil

    South Africa
    Data that triggered action
    • Estimates of informal recyclers contribution to waste recovery rates
    • Cost-benefit analysis showing savings to municipalities
    • Employment data on waste picker livelihoods
    Policies that resulted
    • Formal inclusion of waste picker cooperatives in municipal waste systems
    • Payment for environmental services
    • Legal recognition as service providers
    Examples
    • Colombia: Constitutional court rulings mandating inclusion of waste pickers
    • Brazil: National solid waste policy recognising cooperatives
    • South Africa: Municipal recycling contracts with informal groups
    Why the data mattered
      Environmental performance data reframed informal workers as assets, not obstacles.

        
    6. Crisis response and urban resilience

    Global, COVID-19 period
    Data that triggered action
    • Rapid surveys showing immediate income loss among informal workers
    • Evidence of zero access to unemployment insurance
    • Gendered and urban concentration of impacts
    Policies that resulted
    • Emergency cash transfers including informal workers
    • Temporary income support not linked to employment status
    Examples
    • Brazil: Auxilio Emergencial explicitly targeting informal workers
    • Kenya and Philippines: Urban cash assistance programmes
    Why the data mattered
      Informal workers were recognised as central to social stability during crises.

    The underlying pattern

    Across all these cases, the datasets did not provide technical blueprints. What they did was establish scale beyond dispute and demonstrate economic and service contributions. They also revealed dependence on local government decisions and exposed the cost of exclusion and repression.

    That is why these policies emerged - visibility did not automatically produce inclusion, but it made exclusion an explicit and accountable policy choice.

    • It demonstrates that informal economy data is not merely descriptive but has already shaped concrete policy outcomes across sectors and regions.
    • It reinforces the core argument that visibility leads to policy accountability, even when data is imperfect.
    • It provides credible, cross-country illustrations without turning the annex into a catalogue of best practices or prescriptive models.

    ANNEX 2: Data Sources and Organizations working on the Informal Sector

     A number of data sources and organizations working on policies related to the informal sector were researched for this document. Below is a listing of the organizations, the core datasets/data products and their policy relevance..

    Organisation Core datasets and data products Primary policy relevance
    International Labour Organization (ILO) Informal employment statistics derived from harmonised Labour Force Surveys; global and regional estimates disaggregated by gender, age, sector, and employment status Establishes scale and structure of informal employment and underpins labour, social protection, and gender policy
    World Bank Informal economy and non-observed economy estimates; enterprise surveys including informality modules; productivity and regulatory datasets Links informality to growth, regulation, taxation, and enterprise policy
    United Nations Statistics Division (UNSD) Methodological frameworks for measuring the non-observed economy; guidance on integrating informal activity into national accounts Enables inclusion of informal activity in GDP and improves cross-country comparability
    UN-Habitat Urban datasets on informal settlements, livelihoods, and access to services; spatial and SDG-linked urban indicators Frames informality as an urban planning, infrastructure, and service delivery issue
    Women in Informal Employment: Globalizing and Organizing (WIEGO) Sector-specific and gender-disaggregated data on street vendors, home-based workers, domestic workers, and waste pickers Provides detailed evidence for inclusive urban, labour, and environmental policies
    Organisation for Economic Co-operation and Development (OECD) Comparative datasets and analyses on informality, productivity, labour markets, and regulatory frameworks Supports policy design on formalisation pathways and institutional reform
    National Statistical Offices (NSOs) Labour force surveys, household income and expenditure surveys, enterprise surveys, population censuses Primary source of country-specific and sub-national informal economy data
    Regional development banks and UN regional commissions Regional datasets and analytical reports on informality, employment, urbanisation, and structural change Contextualises informality within regional development and policy priorities

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