Published on Data Blog

Better jobs indicators for development

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In the aftermath of COVID-19, better jobs indicators are even more critical to guide evidence-based labor market policies. Yesterday's World Development Information Day reminds us of the crucial role information plays for better development. To fulfill this role for jobs and labor policies, quality data on jobs indicators such as employment levels, unemployment, or wages are key.

COVID-19 has further amplified the need for these and related jobs indicators. They can help policymakers understand the labor market situation before COVID-19 and monitor the jobs impacts of key mitigation policies as the pandemic evolves using specialized surveys. The indicators can also help to disentangle the underlying differences or inequalities between groups, for example: Do women earn more than men? Does education pay? This allows policymakers to tailor development policies for better jobs for more people.

The new Global Jobs Indicator database (JOIN) helps users explore labor markets in low- and middle-income countries.  JOIN contains more than 100 Jobs Indicators and survey quality assessments for 164 countries and 1,430 surveys worldwide and is now available as part of the World Bank’s DataBank. Bulk downloads (in CSV or Excel) and API endpoints are also available.

The global database provides users with a quick access to more than 100 jobs indicators. It comes with a tool plus accompanying video to facilitate cross-country comparisons. This JOIN benchmarking tool forms part of the Jobs Diagnostic tools. The indicators are nationally representative and available for different types of workers. This includes workers in urban or rural areas, men and women, younger (age 15-24) and older (age 25-64) workers, and workers with lower and higher education.

Wage gaps for different types of waged workers are substantial across low- and middle-income countries. Wages are particularly interesting: Foremost, they provide a stable source of income for workers and their families. But they can also indicate structural changes in a country’s economy. JOIN is one of the first databases that allows users to explore wages on such large scale for development. This includes calculating wage gaps among different types of workers.

As an analysis using JOIN shows: Women earn on average 12 percent less in wages than men across low- and middle-income countries. This difference is a little smaller in low-income groups where the gender wage gap is about 9 percent.  Young workers face the biggest wage gap relative to older workers: Workers in the age group of 25-64 years receive 53 percent higher wages than those between 15 and 24 years.

This difference decreases as countries’ GDP per capita increases and amounts to 27 percent for upper middle-income countries. Rural wage workers receive on average 19 percent less than those in urban areas in low- and middle-income countries. This gap also declines with increasing GDP. The education gaps are strong across the income groups, workers with lower education earn on average 29 percent less than those with higher education. Note that only 49 percent of workers hold jobs with wages in low- and middle-income countries.

wage gaps per income group
Note: Wage gaps in percent for different types of waged workers are calculated using median hourly wages in USD, ppp adjusted and deflated. The values represent simple averages from the most recent surveys across 31 low income, 46 lower- and 42 upper middle-income countries, totaling to 119 economies worldwide. The female gap refers to the wage difference between male and female waged workers, the youth gap to the difference between young (age 15-24) and older (age 25-64) waged workers, the rural gap to the difference between rural and urban area waged workers, and the low education gap to the difference between waged workers with higher and lower education. Lower education refers to those waged workers with lower than complete primary education and high(er) education to all waged workers above this threshold.
Source: JOIN

A quality check algorithm enables better jobs indicators. JOIN comes with a four-step filtering algorithm to improve the data quality. It excludes indicators or entire surveys that, for example, do not match international statistics or are not consistent. For example, for an anonymized country in the database, the below figure demonstrates the effectiveness of the filtering.

Before the data quality filtering process, indicators could be generated for 30 surveys spanning the years 1995–2016. However, automated quality checks detected several issues with the underlying data and removed the surveys that do not meet minimum quality criteria. This results in reporting only higher quality jobs indicators as shown for those on employment shares by sector before and after filtering.

The filtering approach removes outliers and improves the quality of the Jobs Indicators

The filtering approach removes outliers and improves the quality of the Jobs Indicators
Source: JOIN

Interested in more? Read this guide to JOIN which also describes the data quality checks in more detail. Otherwise, get started by exploring the Global Jobs Indicators Database here.


Jörg Langbein

Development Economist, Jobs GP, World Bank

Michael Weber

Senior Economist, Human Capital Project

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