this higher disaggregation comes at the cost of additional noise.<sup>10</sup> Additionally, disaggregated GDP-growth projections are associated with higher degree of noise and fluctuation. Section 4 compares our specification to alternate models with only three sectors.

- 4 compares our specification to alternate models with only three sectors.

- 3. Non-labor income has two components. The first is the income from BISP transfers ) . The value of the cash transfers made under BISP’s flagship UCT program has gradually BBCCBBCC

- (yy

- increased over time in nominal terms. We use the yearly change in the nominal value of payouts (as announced by BISP) to simulate the evolution of households’ income from public transfers, using the reported values in HIES 2018/19 as a base.<sup>11</sup> The second component includes other non-labor incomes such as pensions and private transfers ) . For simplicity, we model its evolution by using the headline CPI. While the model is highly flexible, and NNLL

- (yy

- additional components of non-labor income can be independently modeled to simulate their net impact on poverty under different scenarios (see component (b) in equation (1)), data availability limits the level of heterogeneity that we can model.

4. To capture the impact of differential price trends across 12 COICOP components, we estimate a household-specific inflation rate (Meyer, Kishwar, & Nasir, 2024). As shown in equation (1) and (2) 1h denotes the household-specific inflation rate between the baseline period and the year we project to . As noted in the previous section, differential price trends PP tt= 0 affect households' effective inflation rates depending on the composition of their tt= 1 consumption basket. 5. Finally, we use the propensity to consume of each household 0 at baseline to transform � 0 ~~�~~ cc the predicted household income into household expenditure at , which is the measure yy to estimate the incidence of poverty using the official and international poverty lines. tt= 1 The heterogeneity of household income growth is crucial to properly model its evolution over time. Our method introduces several characteristics that allow us to reflect this heterogeneity. First, we account for the significant variation in the growth rates across sectors by using information from eleven different sectors, which greatly differ in the growth trend, demand for labor, and initial welfare levels of their workforce (see details panels (a) and (b) in Figure 1). A second source of income growth heterogeneity comes from the modeling of non-labor income. BISPS transfers are not indexed to inflation, making them lose purchasing power over time.<sup>12</sup> This means that households with the same amount of non-labor income at baseline will have different projected expenditure levels depending on the composition of their non-labor income. Finally,

> 10 We model the evolution of labor income using the growth of real GDP per capita when a person with positive labor income has no sector identified; this represents 0.75% of the population with positive labor income.

> 11 The quarterly payment under BISP’s UCT program was Rs. 5,000 in FY19. This was increased to Rs. 6,000 effective February 1, 2020, to Rs. 6,500 effective February 1, 2022, and to Rs. 7,000 effective March 1, 2022. Finally, it was increased by 25 percent effective January 1, 2023, to reach Rs. 8,750 per quarter.

> 12 In 2018/19 about 8 percent of the households received BISP transfers, and 92 percent had some type of non-labor income.

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