- income changes based on productivity growth in each sector (3 parameters), skill premiums in each sector (3 parameters), pensions and social transfers (2 parameters).

Due to the uncertainty inherent in projecting these drivers, we work with a range of values for each of these parameters. These ranges are based on historical data and trends and on previous work on socioeconomic scenarios such as the development of the SSPs.

**Demography.** For demographic changes, these ranges were chosen based on population data (total population by age, sex, education) developed by IIASA for the SSP4 and SSP5 (Samir and Lutz 2014).

**Structural change.** The plausible ranges are based on the initial economic structure of each country and projected pathways, using the minimum and maximum change observed in historical data over the last 20 years to estimate the boundaries of possible future structural change.

**Participation** (i.e. the share of 15‐64 years who have a job). To define a plausible range of uncertainty for this variable, we look at available historical data for all countries and all periods of time and choose boundaries slightly larger than the lowest and highest rates of change over 20 years in employment (see Appendix).

**Productivity growth.** We use a different productivity growth rate for each the tree main sectors of the economy (agriculture, industry and services). This growth rate is applied to the income of unskilled workers. The income of skilled workers will then increase with regards to the income of unskilled workers with a skill premium (here again, different a priori for each sector). For unskilled workers productivity growth rate, we calculate a range based on GDP growth in the SSPs (Chateau et al. 2012) and on the working age population growth, to make sure the range of total income growth resulting from our micro‐ simulation is centered around (but larger than) the SSP range. For the skill premium we use a range of 1 to 5 (this is the current range across countries). The total productivity growth is an output of the model, as it depends on productivity growth for unskilled workers, on the skill premium and on the share of skilled workers in each sector (Rodrik 2011).

**Pensions and social transfers** . We model two types of redistribution. The first one is a universal cash transfer (or basic income, distributed to each individual aged more than 15), financed by a flat consumption tax. The second transfer represents the pensions: we model a flat tax on workers’ income and use it for cash transfers to individuals older than 65 years. The amount of the cash transfers distributed to each individual therefore depends on the level of the two taxes and on total consumption (or income) in the economy. We use a range of 0‐20% for each tax.

To generate the scenarios, we use a latin hypercube sampling algorithm<sup>5</sup> that selects a few hundred combinations of parameters within the chosen ranges. Based on each combination of different parameter values, we produce several hundred projections of one individual into the future. This process results in a database of 1,200 scenarios of future poverty and distribution outcomes per country.

> 5 <mark>The latin hypercube sampling algorithm maps the n‐dimension space of uncertainty so that a minimum number of scenarios cover as densely as possible the full space.</mark>

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