Job categories:

• Jobs below the poverty line: Workers who earn less than $2.15 per day despite being employed (adjusted for purchasing power; this corresponds to the absolute poverty line). They work, but can barely afford the bare necessities.
• Gig jobs: Workers who are above the absolute poverty line but may need to take on several side jobs (“gigs”) to reach the income level of big jobs. Since the poverty line is defined in absolute terms, this allows for comparisons between different countries.
• Big jobs: Employees who have only one job and earn more than two-thirds of the country-specific median income. These jobs often correspond to the minimum wage and offer relative financial security. Depending on the country, the median income can be high or low; similar to the relatively defined poverty figures in the EU, this is a comparison within the country.

The Jobs Gap (the employment gap):

• Employed population who have neither a big job nor a gig job.
• This group therefore includes people who work but are poor/live in precarious circumstances, as well as people who do not work at all. This includes the unemployed, discouraged workers (who are unemployed but have given up looking for work), and people who are no longer in employment (e.g., people in education, people with disabilities, people who are unable or unwilling to work of their own accord).

• The various data sources are integrated into a common database in panel data form (i.e., country - year - scenario). Each input data set is restructured to fit into the structure of the overall data set. Country names and other identifiers are standardized so that the data sets can be combined. The imports, preparation, and processing are written entirely in program code in the Stata statistical software, so that updates are easy to make and run largely automatically. This ensures complete replicability.
• This database can then be used to determine the average correlation between job growth economic development, and population growth. For the past, we can use official survey data and estimates from the ILO. For the future, it is necessary to make assumptions about how key factors will develop. For this purpose, we use the SSP scenarios, specifically the SSP4-Scenario (Release 3.1 from July 2024). We use as few variables as possible, because for each variable we use to model the GJI over time and between countries, we have to make our own assumptions for the future. The two core variables are country-specific GDP per capita and the size of the male and female working-age population. All other variables are therefore implicitly assumed to remain unchanged—a necessary assumption to reduce complexity.
• We estimate the correlation between these variables and the employment rate (specifically by world region and gender) as well as between the size of the working population, per capita income, and the number of big jobs, gig jobs, and jobs that pay less than $2.15/day. This average correlation then allows us to project the GJI into the future. This gives us estimates of how the GJI will develop, for example, when comparing two countries. Each country has its own starting points calculated from the data, so that model estimates and projected changes result in a continuous time series.