Abstract
This paper emphasizes the importance of accurately measuring informal economic activity to support inclusive and sustainable economic growth policies. A major advance in this area is the integration of internationally agreed labor-statistical definitions of informality into the System of National Accounts 2025 (2025 SNA) and the seventh edition of the Balance of Payments and International Investment Positions Manual (BPM7). For the first time, these standards enable consistent measurement of informal production, employment, and external-sector activities within macroeconomic accounts. Recognizing informal activities ensures that the contributions of millions of workers and enterprises are properly reflected in economic analysis, thereby supporting more equitable policy responses and fostering development that leaves no one behind. The paper details recent methodological progress in capturing informal economic activity through integrated frameworks that link informal economic production from both enterprise and employment perspectives. It highlights ongoing challenges, including data gaps, inconsistent national practices, and limited capacity in many countries. Improved statistics can inform a range of policy applications, from poverty reduction and labor market regulation to macroeconomic planning. The paper concludes by identifying priorities for further research, including incorporating data into mainstream economic indicators and improving comparability of informality statistics across countries.
Keywords
Introduction
Activities such as small-scale trading, family farming, home-based production, and casual jobs often operate outside formal regulatory frameworks, providing livelihoods for millions of individuals. However, for decades, these activities remained largely unrecognized in international statistical standards—either because the concepts themselves were not defined or because statisticians had not yet developed robust estimation measures for these difficult-to-capture activities. Historically, the System of National Accounts (SNA) and the Balance of Payments Manual (BPM) were designed to capture the structure and flows of modern economies. Although national statistical agencies have made efforts to estimate informal activities, these have not been separately identified in official statistics. The emphasis of data collection on registered enterprises, official transactions, and formal institutions resulted in significant underestimation of production and labor occurring outside these structures if not appropriately taken into account. Consequently, a persistent gap emerged between the realities of economic participation and the representations found in official statistics, highlighting the need for an update to better capture informal activities.
Tracing the history of the SNA illustrates this conceptual gap in the treatment of informality. The first internationally agreed SNA, published in 1953, was designed in the context of post-war reconstruction and industrial development. It comprised six standard accounts and twelve tables that assumed most production occurred within formal, organized institutions, with no explicit recognition of unregistered or informal activities. 1 The 1968 SNA expanded the framework through the introduction of input–output tables and a more detailed institutional sectoring, yet it continued to focus primarily on formal economic production and did not introduce any concept comparable to today's “informal economy”. 2
Greater attention to coverage and exhaustiveness appeared in the 1993 SNA, which emphasized the importance of measuring all economic production within the production boundary, even where it might not be directly observed by traditional data sources. 3 However, the notion of a non-observed economy (NOE)—encompassing underground, illegal, informal, and household production that escapes conventional measurement—was developed more explicitly in a companion publication, the Handbook on Measuring the Non-Observed Economy. 4
It is important to distinguish the informal economy from the non-observed economy (NOE). The NOE is a statistical construct capturing all activities missed by standard data sources—including illegal, underground, and informal production—whereas the informal economy focuses specifically on productive activities carried out outside formal regulatory arrangements. While the two overlap, they serve different analytical and policy purposes.
The 2008 SNA advanced the discussion further by including an entire chapter on “Informal aspects of the economy” and by clarifying the distinction between underground production (legal activities deliberately concealed to evade taxes or regulations) and illegal production (activities prohibited by law). 5 Paragraphs 6.40–6.48 of the 2008 SNA emphasize the need for exhaustive coverage of production while recognizing that the statistical boundary of the informal economy remains only loosely defined. The 2008 SNA framework thus acknowledges informality conceptually but stops short of providing a comprehensive operational definition or measurement methodology.
The BPM followed a similar trajectory. The first BPM, published by the International Monetary Fund in 1948, was designed to record official trade and financial transactions between residents and non-residents. Subsequent editions expanded coverage of services, personal transfers, and financial flows, but still assumed that most cross-border activity passed through formal systems such as customs and banks. The Balance of Payments and International Investment Position Manual, sixth edition (BPM6), aligned conceptually with the 2008 SNA and provided clearer guidance on remittances and household transfers. 6 Nevertheless, it did not address in detail the measurement of informal cross-border trade or unrecorded remittance channels—issues that remain particularly significant in developing economies where substantial external transactions occur outside formal institutions. 7
This historical evolution reveals a persistent gap: while labor statistics progressively refined the concept of informality, macroeconomic standards lagged in operationalizing it. The 2025 SNA and BPM7 revisions explicitly respond to this gap by integrating internationally agreed labor-statistical concepts into the core macroeconomic framework. The following sections examine how these revisions fundamentally change the measurement, presentation, and policy relevance of informal economic activity. This paper aims to: (1) trace the evolution of informality concepts in international statistical standards; (2) explain how the 2025 SNA and BPM7 address longstanding gaps in measuring informal economic activity; (3) demonstrate the policy relevance of improved informality statistics; and (4) identify priority areas for future research and implementation.
International standards on informality
Labor statisticians—particularly through the International Labour Organization (ILO)—had been steadily advancing the concept of informality. At the 15th International Conference of Labour Statisticians (ICLS) in 1993, the Resolution concerning statistics of employment in the informal sector defined the informal sector in terms of production units that (i) operate at a low level of organization, (ii) are unincorporated household enterprises, and (iii) undertake market production of goods or services for sale or barter. 8 The 17th ICLS (2003) expanded this to “informal employment,” emphasizing job characteristics such as lack of contracts, social protection, or legal recognition.9–11 The 20th ICLS (2018) refined these concepts further through the International Classification of Status in Employment (ICSE-18), introducing categories such as dependent contractors that became particularly relevant in the digital platform economy. 9 These advances provided a robust labor-statistical framework, but because they were not yet integrated into the SNA or BPM, a disconnect persisted between labor-force-survey statistics and macroeconomic accounts.8,10
The 21st ICLS in 2023 was a significant update that replaced both the 15th ICLS resolution on informal sector statistics and the 17th ICLS guidelines regarding the statistical definition of informal employment.10–12 It established a comprehensive statistical framework for the informal economy by integrating definitions for several key concepts. It covers (i) the informal economy, which encompasses all informal productive activities, whether paid or unpaid; (ii) the informal market economy, which focuses on informal employment and the informal sector; and (iii) informal work, which are activities not regulated by formal arrangements in either law or practice.
Importantly, the framework recognizes various forms of informal production undertaken by both enterprises and households. This includes own-use production, unpaid trainee work, and volunteer work, acknowledging the breadth of informal activities contributing to the economy.
The new framework combines two perspectives—economic units and persons/jobs/work—into a unified statistical standard. It categorizes all economic units and workers into three sectors: the formal sector, the informal sector, and the household own-use production and community sector. Within this system, all jobs and work activities are classified as either formal or informal, with particular emphasis on activities within the informal sector and informal employment.
The resolution also provides clear definitions and boundaries. Informal production is defined as activities conducted by economic units and individuals, whether for pay, profit, or otherwise, and considers their relationship to the SNA production boundary. The informal sector is identified as all production by informal household unincorporated market enterprises, while the household and community sector includes own-use production, volunteer work, and the activities of non-formal non-profit organizations.
The framework was developed in close collaboration with the update of the SNA and BPM. The resolution clarifies that the informal economy is distinct from, though it sometimes overlaps with, other concepts such as the non-observed, undeclared, and illegal economies.,
The 2025 revisions to the SNA and the BPM represent a major step forward. These updates introduce a coherent statistical framework for measuring the informal economy, incorporating ILO definitions and integrating them into both national accounts and external-sector statistics.13–15 This unified framework positions the measurement of informality as a central component of modern economic analysis.
Informal activity and the 2025 SNA and BPM7
The updated System of National Accounts (SNA) and Balance of Payments Manual (BPM) define informal productive activities as all productive activities carried out by persons or economic units that are, in law or in practice, not covered by formal arrangements.12,14,15 This broad framing is deliberate: it captures informality from both the economic unit perspective and the worker perspective, ensuring that measurement is not restricted to small, unregistered firms but extends to the employment conditions of workers engaged in production across the economy.8,9,12
From the economic unit perspective, informal units are unincorporated businesses owned by households that are not registered and do not maintain complete accounts. This category includes, for instance, household unincorporated market enterprises that are active in selling goods or services but fall outside the purview of registration and taxation systems.1,15 From the employee perspective, informal employment refers to work that is not subject to labor regulations, contracts, or social protection. Such employment can exist in both informal and formal enterprises—for example, undeclared labor in hotels or seasonal agricultural workers without contracts in advanced economies. 8 By adopting this dual perspective, the new framework addresses a long-standing limitation: earlier statistical frameworks approached informality from either the enterprise or the job perspective, resulting in partial and fragmented coverage built on two slightly different foundations.
Increasingly, the rise of digitalization and the expansion of platform-based jobs—such as gig work, freelance digital services, and app-based delivery—are accelerating this trend.7,13 As more individuals engage in work mediated by digital platforms, many find themselves outside the scope of traditional labor protection and social security systems. This shift is amplifying the prevalence of informality, with a growing segment of the workforce at risk of lacking access to legal safeguards, employment contracts, and social benefits, underscoring the need for updated measurement and policy responses.7,16
While the SNA and BPM frameworks already include all activities, whether formal and informal, as long as they fall within the production boundary, the updated frameworks distinguish three major domains of informal activity, thereby clarifying the scope of informality within macroeconomic statistics as shown in Table 1. The first is the informal sector, which consists of household unincorporated market enterprises not recognized by government authorities because they are not registered.5,15 This includes street vending in Lagos Nigeria and home-based workshops in Dhaka Bangladesh. The second domain is informal employment in formal units, capturing situations where enterprises are formally registered, but workers remain informally engaged, such as casual day laborers in construction firms in Brazil or undeclared cleaning staff in hotels in Turkey.8,9,12,15 The third domain is the household own-use production and community sector, which covers goods and services (excluding owner-occupied housing services) produced for final use by the household itself or for the use of other households without the purpose of generating income or profit for the producing household(s), such as water collection in rural Nepal or subsistence farming in Ethiopia.10,15 The SNA retains a single production boundary. However, for analytical purposes, the informal economy framework explicitly identifies household own-use production and community activities, including some services that lie outside the core SNA production boundary. This distinction does not alter GDP but enhances visibility of informal activity for policy analysis. The updated SNA makes clear that, while certain elements of own-use production were already included within the production boundary, the explicit recognition of this domain and extension strengthens the analytical coherence of the informal economy as a statistical category. 15
Informal productive activities in the informal economy.
Source: 2025 SNA Table 39.1 (pre-edited version).
Components of the informal market economy.
The production boundary applied in the integrated framework of the SNA also includes illegal activities that are out of scope in the informal economy framework.
Equally important is the distinction between the informal economy and the NOE. The NOE is a pragmatic construct that covers all activities not captured in regular statistical inquiries, such as illegal, underground, or missed production.4,15 While there is overlap between the two, the focus of the informal economy is to highlight productive activities and the associated policy relevance for employment, poverty reduction, and inclusion, rather than illicit or criminal flows.12,15 1 For example, undeclared production within registered enterprises would be counted in the NOE but not the informal economy, while subsistence agriculture or domestic services where the household employs paid staff (e.g., cooks, chauffeurs, etc.) may be included in both.12,15 This distinction ensures that informality is not conflated with tax evasion or smuggling and is recognized as a legitimate component of economic life requiring visibility in macroeconomic statistics.12,15
The updated SNA and BPM frameworks are now closely aligned with the ILO's definitions of informality, ensuring consistency between macroeconomic and labor statistics.12–15 This integration bridges previous gaps, making key economic measures such as GDP and external-sector accounts directly comparable with labor force survey results.
One of the most notable innovations of the updated framework is the recommendation to produce a set of indicators on the informal economy reflecting national context, priorities and objectives, as presented in Table 2. 12 The Informal Economy Indicator Framework is designed to help countries collect, interpret, and utilize data related to informality. While more countries are gathering such data, many still face challenges in determining which indicators to use and how to apply them. The framework aims to be flexible and adaptable to national contexts and priorities, supporting countries regardless of their stage in addressing informality.
Informal economy – key recommendations, policy uses, and impact on key aggregates.
Source: Authors.
The framework consists of evolving questions and indicators, alongside guidance for linking and analyzing them for policy and intervention development. Key questions focus on the extent and types of informality, working conditions, productivity, gender disparities, and the identification of priority groups for formalization. Further, it addresses how to improve protections, prevent informalization of formal jobs, and support gender-transformative transitions. These questions are relevant across different groups, sectors, and economic units.
These innovations manifest in several key ways and extend information relating to activities undertaken informally, in terms of the integrated framework of the national accounts, only as far as the production account and the generation of earned income account. First, the framework can quantify the size of the informal economy in relation to GDP and employment, that can be extended with detailed breakdowns by institutional sector and by types of informal labor input.10,15 Second, it provides cross-classifications of informal activity that distinguish between production units—such as the informal sector, the formal sector utilizing informal labor, and the household own-use and community sector—and different categories of employment, including employees, dependent contractors, and contributing family workers and independent workers (including employers and independent workers without employees). 15 Third, the framework establishes explicit linkages between household surveys and national accounts, enabling data on informal activity collected at the micro level to be systematically integrated into macroeconomic aggregates.4,12,15 Finally, the framework extends its scope to cover presentations of cross-border informal activity, such as shuttle trade, informal tourism services, and remittances sent through informal transfer systems, which are now explicitly recognized by the revised Balance of Payments Manual (BPM7).14,15
While there is no conceptual impact on the macroeconomic aggregates both the System of National Accounts 2025 and Balance of Payments Manual, Seventh Edition (BPM7) shed light on the informal economy by explicitly incorporating the concept into the international statistical framework.14,15 They adopt a dual perspective—covering both informal economic units and informal employment—and align with ILO standards.12,14,15 The aim is to ensure that informal activities, which may account for a significant share of employment and production, especially in developing economies, are properly measured and integrated into macroeconomic aggregates.
Collectively, these enhancements represent a significant departure from viewing informality merely as a statistical adjustment; instead, they firmly establish it as an integral and analytically robust component of national accounts and balance of payments frameworks. Through the introduction of dedicated indicators, the updated SNA actively encourages countries to systematically document and present informality as a standard statistical product depending on national context, priorities, and objectives. Importantly, the role of statistical standards extends beyond the mere establishment of definitions and classifications—they also serve as authoritative guides on the optimal presentation and communication of information to diverse user groups. The tables that follow exemplify various methodological approaches that compilers of official statistics may employ to effectively convey the multifaceted nature of informal economic activity to end users.
Standard tables for the analysis of informal activities: A user perspective
The
Production by type of unit and employment status (illustrative). (percent of GDP and employment, country X, year)
Source: Authors’ example based on fictitious data.
The
Cross-classification of informal employment by status in employment. (number of jobs, country X, year, in thousands)
Source: Authors’ example based on fictitious data.
The
Informal economy in external accounts (illustrative, country X, year).
Source: Author's example based on fictitious data.
The
Household thematic account for informal production (illustrative, country X, year).
Source: Author's example based on fictitious data.
Why measuring informality matters for policy
Recognizing and measuring the informal economy is not simply a matter of improving the accuracy of statistics; it is critical to effective policymaking across multiple domains.7,10,11,15,16 When informality is not visible in statistics or not completely captured, policymakers work with an incomplete picture of the economy, leading to distorted productivity measures, underestimation of employment, and policy designs that may exclude large segments of the population. By shedding light on informality in the SNA and the BPM, countries can use the information to strengthen fiscal frameworks, refine labor-market interventions, design more inclusive social protection systems, and better manage external accounts.10,14,15
One key area is economic growth and productivity. In countries with high levels of informality, aggregate productivity statistics are systematically distorted.16,17 Mexico provides a clear example: a significant portion of its labor force works informally. With the new SNA framework, Mexico can distinguish formal and informal output, allowing policymakers to identify where productivity bottlenecks are concentrated and design policies that encourage formalization, skills upgrading, and sectoral reallocation. 15 Such measures are critical for long-term competitiveness and for raising living standards.
Informal employment provides a welcome means of support in countries where the growth of the labor force outstrips the pace of job creation in the formal sector. The level of informal employment also reflects the accessibility and degree of social protection available to workers. Informal employment therefore varies substantially across countries, as presented in Figure 1. Uganda, Nigeria, and India stand out, with more than 85% of workers employed informally, while Mexico's share is about 55% and Brazil is just under 40% (ILO.stat). In contrast, Germany and other EU economies record shares of less than 5%. These differences underscore why integrating informality into official statistics matters: in countries where informality is pervasive, omitting it from macroeconomic aggregates means that much of the economy—and thus the majority of workers—remains invisible in policy debates.12,15
Informal employment share of total employment (percentage). Source: Statistics on the Informal Economy—ILOSTAT. Notes: Selected Countries—Denmark (2019), Uganda (2021) Germany (2022), All other selected countries (2024).
A second critical area is fiscal capacity. Informality limits the tax base, as unregistered businesses and undeclared workers fall outside tax systems.16,18,19 Informal trade and services are prevalent in urban economies but contribute little to government revenue, weakening fiscal space for infrastructure and social spending. With better statistics, governments can tailor policies to address these gaps.8,18 By making informality visible, the new SNA/BPM frameworks help tax authorities design more effective systems that expand the revenue base without stifling small-scale enterprise. 7
Social protection is another domain where measurement matters. Informal workers are typically excluded from pensions schemes, health insurance, and unemployment benefits, leaving them highly vulnerable to shocks.8,9,19 The COVID-19 pandemic starkly revealed this fragility: informal workers lost their jobs or income but lacked access to formal safety nets.19,20 By distinguishing formal and informal employment, the updated framework provides the basis for building more inclusive safety nets. Policymakers can identify vulnerable groups, design social protection schemes tailored to them, and monitor coverage more accurately over time.8,15
Gender and equity dimensions further highlight the importance of measuring informality. Women are disproportionately represented in informal work, often in its most precarious forms.8,9 The ILO estimates that in developing economies, more women than men are informally employed. 9 The informal economy therefore provides opportunities to increase the participation of women in the economy and provides a source of income. However, women tend to be in the type of employment relationships that have lower earnings and a higher risk of poverty. 9 In Bangladesh, many women in the garment industry are subcontracted to home-based workshops without contracts or protections; in sub-Saharan Africa, women dominate petty trade and services in local markets. 9 Without disaggregated statistics, their contributions and vulnerabilities remain invisible. By explicitly classifying informal jobs, the new SNA/BPM frameworks enable gender-sensitive analysis that can inform policies promoting women's entrepreneurship, childcare access, and legal protections for domestic workers.8,9
Globalization and digitalization add another dimension. Digital platforms can be a useful source of data on informality as they can provide a link between formal enterprises and the informal economy. They serve as a channel through which households can provide goods and services mainly to other households. Since they facilitate this interaction, the platforms may collect information on the income and residence of the service providers. For instance, ride-hailing services may be able to provide information on the value of transactions, income, and location of the service providers. Likewise, accommodation hosting services collect information on the revenue from the sale of accommodation services. However, compilers may need to explore alternative sources to collect data on the expenses relating to these activities. Data on expenses may be gathered from surveys of households to collect data on the income and related expenses of these service providers. Targeted surveys of these service providers may be feasible if the digital platforms are able to provide a listing of these providers. However, data collection on digital platforms and digital products may be complicated by the fact that providers are based in a limited number of economic territories.
Mobile-money and digital payment systems have expanded rapidly in economies with high informality, where there is limited penetration of traditional financial services. Likewise, many small-scale online sellers and freelance digital workers may operate informally through digital platforms. 7 Without recognition in macroeconomic statistics, these activities distort measures of services, productivity, and employment. By explicitly including any impact due to digitalization on informal activity, the updated SNA and BPM ensure continued relevance in an economy increasingly mediated by digital platforms, while providing regulators with data to balance innovation, consumer protection, and fiscal integrity.13–15
Integrating informal-economy measures into national accounts and balance-of-payments statistics opens new applications for policymaking. In macroeconomic management, distinguishing formal and informal GDP improves understanding of structural dynamics.13–15 For example, if headline growth reflects expansion in informal agriculture and services it will mask weak performance in formal industry. In poverty and inequality analysis, linking household-survey and production data clarifies how informal incomes sustain livelihoods and shape inequality.8,21 For labor-market policy, informality statistics identify where interventions are needed—Brazil's simplified registration reforms in the 2000s being a notable example.16,18 In the external sector, BPM7 acknowledges informal cross-border flows—such as shuttle trade, informal tourism, and unregistered remittances—captured in improved balance-of-payments surveys. 11 This enhances trade statistics and supports regional integration.
Finally, informality is central to monitoring the Sustainable Development Goals (SDGs). Indicator 8.3.1 requires data on informal employment and embedding informality in the SNA ensures consistent production of this indicator, aligning SDG monitoring with national accounts.13,15
Official statistics on the level of informal output are generally not available; therefore, model-based measures are often used. Estimates by the World Bank (Figure 2) using Multiple Indicators Multiple Causes (MIMIC) model show that informal output as a percentage of GDP remains high in Sub-Saharan Africa and Latin America. 22 These patterns suggest that failing to account for informality may distort assessments of the size and structure of the economy, therefore depriving policymakers of the evidence needed for sound fiscal, trade, and social policies.13,15 However, while the MIMIC model has been used in a large number of papers to develop cross-country estimates, such research has faced criticism over model specification and identification, broad assumptions underlying the estimates, and the reliability of results.23–25 Depending on the variables included, the models may not clearly delineate where the informal economy starts or where it ends. Therefore, MIMIC models may sometimes include some activities that are part of the broader non-observed economy but that are not informal. Further, official GDP estimates may already include some components of informality, whereas MIMIC estimates imply that official GDP data do not capture any of the informal economy. 26
Informal output as percentage of GDP, 2020. Source: Elgin, C., M. A. Kose, F. Ohnsorge, and S. Yu. 2021. “Understanding Informality”, C.E.P.R. Discussion Paper 16497, Centre for Economic Policy Research, London; MIMIC Model Estimates.
Model-based estimates such as those derived from MIMIC models provide useful cross-country perspectives where official statistics are unavailable. However, these estimates should be interpreted cautiously. Critiques highlight sensitivity to model specification, reliance on proxy variables, and ambiguity in separating informal activity from other components of the non-observed economy. Moreover, MIMIC estimates often implicitly assume that official GDP excludes all informal activity, which may overstate informality in countries that already apply exhaustiveness adjustments. Accordingly, the MIMIC estimates shown in Figure 2 are illustrative rather than definitive, underscoring the need for improved official measurement consistent with the 2025 SNA and BPM7.23–25
Estimation options and challenges
Statistical agencies conduct various data collection activities to estimate parts of the economy not covered by regular data gathering. These efforts mainly involve surveys of individuals or households, such as household income and expenditure surveys. However, the underlying data often lack adequate coverage, detail, and timeliness. Although there's an aim to run these surveys every five or ten years, some countries fall short due to limited resources.
Enterprise surveys, designed primarily for constructing national accounts, are not always effective in capturing informal economic activity. These surveys usually depend on business registers derived from official records, meaning businesses without formal addresses—often those in the informal sector—are missed. Moreover, enterprise surveys generally do not collect enough detailed labor information to reveal informal jobs within formal settings, making tailored surveys necessary to fully understand activities and employment in the informal sector.
Some countries have introduced mixed or modular approaches, like 1-2-3 surveys, targeting both informal household and enterprise activities. Typically, these involve first identifying households engaged in informal work, then assessing their related business activities. For instance, Vietnam's General Statistics Office, with the support of the French Research Institute for Sustainable Development, has developed methods for tracking informal economic contributions to GDP. 27 Labor force surveys may need adjustments to better capture informal employment. Most standard labor force surveys only ask about primary jobs, which means secondary informal jobs can go unreported. Given the sporadic nature of informal work, these surveys might miss certain types of employment outside the reference period. As with other data collection endeavors, how often these surveys are conducted may depend on available resources.
The methods used to compile informal economy estimates generally depend on the type of source data available. Supply and use tables are useful tools to identify gaps in the basic statistics. These tables reconcile the amount used of a given product with the supply. For example, the output of hairdresser services, unregulated taxi services, and small-scale agriculture can be estimated based on the consumption of these goods and services by households. As compilation is costly and time consuming, developing countries are unable to produce these tables at regular intervals. Many countries attempt to produce supply and use tables at regular five-year intervals—to coincide with the rebasing and benchmarking process—but in practice, the time span may be longer. In addition, because of the technical requirements to complete the intricate balancing process, there is usually a lag of over one year between the reference period and when the data becomes available.
Supply and use tables help spot gaps in basic statistics by reconciling the supply and use of goods and services. For example, the production of hairdressing services or small-scale farm production can be estimated from household consumption. However, compiling supply and use tables is costly and time-consuming, so developing countries often cannot produce them regularly. Many countries aim to compile these tables at five-year intervals, but delays are common due to complex balancing and technical requirements.
Weak statistical capacity, mainly due to the unavailability of resources for data collection, remains a key impediment to developing reliable estimates. As noted above, coverage of the informal economy requires source data that are collected through surveys. Many developing countries attempt to undertake household income and expenditure surveys at regular five to ten years, but in practice, the frequency may be irregular and there may be significant gaps. Further, the surveys may be designed for a range of purposes, such as to derive household expenditure patterns, and may therefore lack the income/revenue detail required to derive comprehensive estimates of the informal economy.
Conclusion and future research
This paper set out to examine how the 2025 System of National Accounts (SNA) and the seventh edition of the Balance of Payments Manual (BPM7) address the long-standing challenge of measuring informal economic activity. Specifically, it traced the evolution of informality concepts in international statistical standards, explained the key innovations introduced in the 2025 revisions, demonstrated why improved measurement of informality matters for policy, and highlighted areas where further methodological and empirical work is needed.
The 2025 SNA and BPM7 represent a significant advance in the treatment of the informal economy. For the first time, internationally agreed labor-statistical definitions—developed under the auspices of the International Labour Organization—are fully integrated into the macroeconomic accounting framework. This integration resolves a long-standing disconnect between labor statistics and national accounts by adopting a dual perspective that captures informality both in terms of production units (informal sector enterprises) and employment relationships (informal jobs, including within formal enterprises). Importantly, the updated standards do not alter the core production boundary or aggregate measures such as GDP; rather, they enhance transparency and analytical power by explicitly identifying informal activities within supplementary frameworks and tables.
A key innovation of the revised standards is their emphasis on presentation and use, not merely conceptual completeness. By recommending supplementary tables and indicators—covering production, employment, household income, and cross-border flows—the 2025 SNA and BPM7 provide statistical offices with practical tools to make informality visible in ways that are directly relevant to users. These presentations enable policymakers to distinguish between formal and informal sources of growth, assess labor-market vulnerability, evaluate the fiscal implications of informality, and better understand the role of informal activity in sustaining household livelihoods and external balances.
The policy relevance of these improvements is substantial. In economies where informality accounts for a large share of employment and production, failure to make it visible can distort productivity analysis, weaken fiscal planning, and undermine the design of social protection systems. By aligning macroeconomic statistics with labor-market realities, the updated standards support more inclusive economic policymaking—ranging from labor regulation and tax policy to poverty reduction, gender equality, and the monitoring of Sustainable Development Goals. They also provide a clearer framework for interpreting model-based estimates of informality, reinforcing the need for official statistics grounded in transparent concepts and data sources.
Despite these advances, important challenges remain. Measuring informal economic activity continues to depend on the availability and quality of underlying data, including household surveys, labor force surveys, enterprise surveys, and administrative sources. Resource constraints, especially in low- and lower-middle-income countries, limit the frequency and coverage of such data collections. Moreover, emerging forms of informality associated with digital platforms, cross-border services, and informal financial channels pose new measurement challenges that will require methodological innovation and institutional cooperation.
Looking ahead, several priority areas for future research and implementation emerge. First, statistical offices need practical guidance on integrating informal-economy estimates into supply and use tables and household satellite accounts in a consistent and sustainable manner. Second, further work is needed to improve the valuation of informal services and own-use production, particularly where market prices are absent or unreliable. Third, the use of alternative data sources—such as digital platform records and administrative data—offers promise but raises issues of access, coverage, and confidentiality that must be addressed. Fourth, greater emphasis should be placed on integrating informal-economy measures into distributional and well-being accounts, strengthening their relevance for equity-focused policies. Finally, sustained investment in statistical capacity building is essential to ensure that the conceptual advances of the 2025 SNA and BPM7 translate into robust, comparable, and policy-relevant statistics across countries.
By explicitly incorporating the informal economy into the international statistical framework, the 2025 SNA and BPM7 move beyond treating informality as a residual or adjustment and instead recognize it as an integral component of economic life. In doing so, they bring official statistics closer to the lived reality of millions of workers and households and strengthen the evidence base for policies aimed at inclusive and sustainable development.
Footnotes
Acknowledgments
This paper is an adapted version of a policy paper issued to the IMF Executive Board: 
. The authors thank colleagues for helpful discussions. The views expressed in this article are those of the authors and do not necessarily represent the views of the International Monetary Fund, its Executive Board, or its management.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.