Measuring the informal sector through mixed surveys: Past experiences and challenges ahead

While in developing countries around 50% of total employment are informal sector jobs, and much more among the poorest, its statistical measurement is a major issue. Having long remained a black hole of national statistical systems, sound methodologies have been developed to measure the informal sector in its different dimensions (employment, production, and more broadly articulation with the rest of the economy) in a reliable manner. Taking stock on nearly four decades of experiences accumulated around the world, this article presents a system of original surveys, specially designed for this objective: the Informal Sector Mixed (households-businesses) Surveys (ISMSs). After exposing their theoretical principles, the paper addresses the main challenges of implementation in the field, detailing their advantages and limitations. In particular, we question the compatibility of ISMSs with the recommendations of the new Resolution concerning statistics on the informal economy, adopted in 2023 during the 21st International Conference of Labor Statisticians. We also identify avenues to address a certain number of challenges that still arise with this type of survey, both in the methodological field and on the institutional level. We illustrate our discussion with a set of selected empirical results drawn from surveys conducted in various contexts.

Authors

Institutions

Publication Details

Journal
Statistical Journal of the IAOS
Published
2024-09-20
DOI
https://doi.org/10.3233/sji-240055
Primary Topic
Income, Poverty, and Inequality
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring the informal sector through mixed surveys: Past experiences and challenges ahead

François Roubaud
Statistical Journal of the IAOS
Income, Poverty, and Inequality
article

Measuring the informal sector through mixed surveys: Past experiences and challenges ahead

François Roubaud
article en

Abstract

While in developing countries around 50% of total employment are informal sector jobs, and much more among the poorest, its statistical measurement is a major issue. Having long remained a black hole of national statistical systems, sound methodologies have been developed to measure the informal sector in its different dimensions (employment, production, and more broadly articulation with the rest of the economy) in a reliable manner. Taking stock on nearly four decades of experiences accumulated around the world, this article presents a system of original surveys, specially designed for this objective: the Informal Sector Mixed (households-businesses) Surveys (ISMSs). After exposing their theoretical principles, the paper addresses the main challenges of implementation in the field, detailing their advantages and limitations. In particular, we question the compatibility of ISMSs with the recommendations of the new Resolution concerning statistics on the informal economy, adopted in 2023 during the 21st International Conference of Labor Statisticians. We also identify avenues to address a certain number of challenges that still arise with this type of survey, both in the methodological field and on the institutional level. We illustrate our discussion with a set of selected empirical results drawn from surveys conducted in various contexts.

Statistical Journal of the IAOSVol. 40(4)
Universidade Federal do Rio de Janeiro (BR)
Decent work and economic growth
Openalex Percentile: Top 75%
Income, Poverty, and Inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.