Jobs, Firms & Informality

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Overview

Measuring work accurately is essential for macroeconomic planning and policy formulation on employment creation, vocational training, income generation and poverty reduction. Traditional survey methods that work well in high income countries may not capture all types of work in low- and middle-income countries (LMICs), where informal, casual and irregular forms of work may be more prominent. In addition, potential undermeasurement of women and youth’s labor market outcomes may bias labor statistics, misguiding the design of policies.

The Living Standards Measurement Study (LSMS) has conducted methodological research aimed at improving the measurement of labor outcomes in LMICs in partnership with the International Labor Organization (ILO), the World Bank’s Gender Group, the Food and Agriculture Organization (FAO), National Statistical Offices and research institutes in partner countries.

Work Areas

1. Measuring Work and Employment in Household Surveys: Operationalization of the 19^th ICLS

The 19th International Conference of Labour Statisticians (ICLS) narrowed the definition of employment to work performed for pay or profit. In addition, it introduced a new category of work, which recognizes all productive activities - paid and unpaid - and proposed several measures of labor underutilization. These new definitions have had significant implications for the measurement of employment and labor force participation in household surveys, including labor force surveys, especially for women and the rural poor.

To test the implications on measurement of labor of the new definition of employment for smallholder farmers, women and youth, and to contribute with guidelines to operationalize the agreements of the 19^th ICLS, the LSMS partnered with the ILO, the World Bank’s Gender Group and national statistical offices to conduct methodological experiments in Ghana, Malawi and Sri Lanka.

2. Measuring Labor in Agriculture

In low-income countries, a large share of labor occurs in household farms. The traditional approach of collecting data on agricultural labor is through recall methods, which document the accumulated total number of days/hours a person(s) worked on the farm over the agricultural season. There is a general perception that this data, collected over long periods of recall and requiring difficult mental calculations, is fraught with measurement error, which may affect some populations more than others.

In addition, the details of the work and its intensity are rarely known. These issues have been explored in a series of methodological experiments - in Tanzania, Ghana and Malawi - to measure and compare the impact of diverse methods of collecting household agricultural labor data. The goals are twofold: assess the accuracy of the traditional recall surveys and explore the option of mobile phone updates as an intermediate approach.

3. Measuring Informality and Reducing the Mismeasurement of Women and Youth Employment

Accurate measurement of work is crucial for policymaking, especially in LMICs, where gender and age-based employment gaps are often widespread. These gaps may partly arise from underreporting labor market activities.

An experiment conducted in El Salvador shows that alternative survey methods, such as listing work activities in household surveys, can reduce misreporting of labor market outcomes for women and young adults. This approach yields more accurate data, providing a clearer measure of employment and helping to inform policies aimed at reducing gender and age-based employment disparities.

4. Measuring Youth Aspirations

This area of work is focused on designing tools to measure youth aspirations and their link to education attainment and employment opportunities. Our team designed a module to measure educational attainment, employment history and aspirations on education, employment, and migration, targeting youth aged 15 to 25.

This module was tested as part of the Living Standards Measurement Study High-Frequency Phone Surveys initiative (LSMS - HFPS) in Ethiopia, Nigeria, Malawi and Uganda. The survey explored youth’s goals of completing university degrees, pursuing Science, Technology, Engineering and Mathematics (STEM) careers and migrating for better opportunities during the COVID-19 pandemic. This area of work provides essential insights for better alignment of education and employment programs with labor market needs.

5. Measuring Household Businesses: Household Surveys vs. Enterprise Surveys

Characterizing household enterprises is challenging because many are informal and excluded from official records. Two common methods for capturing these enterprises are household surveys with integrated enterprise modules and specialized surveys focused on informality. However, limited research has examined how these approaches affect the measurement and profiling of informal household businesses.

To address this gap, our team, in collaboration with the World Bank’s Enterprise Survey team, conducted an experiment in urban Ghana. The study compares the insights and differences generated by each method, with the objective of improving the design of household surveys to better measure informal enterprises.

6. Measuring Casual Labor

The usual practice for measuring work activities in household surveys is to capture work conducted in the past 7 days. However, in low- and middle-income countries, a significant share of the working age population work on and off in various casual labor activities. This irregularity in work patterns may lead to underreporting of employment data, which hinders the accurate measurement of labor force participation, especially of rural paid work.

To address this challenge, the LSMS team is currently developing new survey methods to capture data on work that may not be gathered in traditional labor modules, given its irregularity and its occurrence over longer periods of time.

7. Measuring Labor with Mixed Mode Surveys

The increasing use of phone surveys has created new opportunities to integrate high-frequency phone data into nationally representative surveys. In Nigeria, our team partnered with the National Bureau of Statistics to implement a mixed-mode approach for collecting data on agricultural and casual labor.

A survey-to-survey imputation exercise is being conducted to derive improved, predicted individual-disaggregated data on labor. This initiative aims to develop best practices and tools for measuring the type of labor above, incorporating the design and implementation of a phone survey validation study to evaluate and compare the effectiveness of different data collection methods.

Resources  

Data

Youth aspirations data were collected in the following datasets:

Living Standards Measurement Study