We then use a scenario discovery algorithm<sup>6</sup> (Bryant and Lempert 2010) to identify a combination of input drivers that is most likely to put the scenario in the optimistic or pessimistic box. <mark>In other words, we identify the range of parameter values that are found in this subset of scenarios (we may find that the scenarios with lower poverty and lower inequality are typically those with low population: it means that most scenarios with lower poverty and lower inequality have also low population, and that most scenario with low population have also lower poverty and lower inequality).</mark> For instance, optimistic scenarios in Vietnam are mostly scenarios with high redistribution level, relatively high pension levels, low population growth and high education (SSP5 demography), and relatively high productivity growth for unskilled agricultural workers. Pessimistic scenarios are characterized by relatively low redistribution level, high population growth and low education (SSP4 demography), and low productivity growth for unskilled agricultural workers. The details are in Table 1. The other parameters (e.g., structural change or change in productivity in service or manufacturing) play only a secondary role.

_Table 1: sets of conditions that characterize the scenarios in the optimistic box (defined as in Figure 8) for Vietnam. Density is the probability of a scenario which matches the set of conditions to be in the optimistic box. Coverage is the probability of a scenario which is in the optimistic box to match the set of conditions._

|**Optimistic set**|‐<br>High redistribution (tax for cash transfers >8% of total consumption)|
|---|---|
|**of conditions**|‐<br>Relatively high pensions (tax >5% of total consumption)|
|78% density|‐<br>Low population growth, high education (SSP5)|
|40% coverage|‐<br>Productivity growth for unskilled agriculture workers >2%peryear|

_Table 2: drivers for the scenarios in the pessimistic box (defined as in Figure 8) for Vietnam. Density is the probability of a scenario which matches the set of conditions to be in the optimistic box. Coverage is the probability of a scenario which is in the optimistic box to match the set of conditions._

|**Pessimistic set**|‐<br>Relatively low redistribution tax for cash transfers <15% of total|
|---|---|
|**of conditions**|consumption)|
|47% density|‐<br>High population growth, low education (SSP4)|
|59% coverage|‐<br>Productivity growth for unskilled agriculture workers <5%peryear|

To select one representative scenario in each box, we select only scenarios that correspond to the main set of drivers and apply the following additional criteria. For the pessimistic scenario we selected a scenario with a total income growth that is close to GDP growth in the SSP4 (the SSP scenario in which poverty and inequality remain high) and minimized structure change (to represent stagnation). For the optimistic scenario, we selected a scenario with a total income growth that is close to GDP growth in the SSP5 (the SSP scenario with the largest GDP growth), while minimizing the share of workers in agriculture, maximizing the share of workers in industry, and making sure that skill premiums are not too different between sectors.

> 6 Here we use the EMA work bench developed at Delft University: http://simulation.tbm.tudelft.nl/ema‐ workbench/contents.html

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