Tateno et al., 2021). We address several limitations of other projection approaches. First, using a single growth rate for household income, usually from the GDP per capita, disregards the fact that total growth is rarely equally distributed across sectors and households. For instance, growth in labor-intensive sectors will have a greater effect on poverty, while, similarly, growth in sectors that employ lower-income individuals is more likely to reduce poverty. Second, not all inflation is created equal, as households across the expenditure distribution have distinct consumption baskets, making differential price trends in food, non-food, and energy create heterogeneous effective inflation rates (Nasir et al., 2023).<sup>3</sup> We present benchmarking results using an arrangement of forecasting models commonly used in the literature and a detailed description of their modeling and methodological assumptions. We address their limitations by implementing three main variations over traditional forecasting models. First, we use household-level inflation rates to incorporate inflation heterogeneity. Second, we introduce sectoral growth rates and assess the different results using three-sector and eleven-sector models. Third, we independently forecast labor and non-labor income using Pakistan-specific conditions on the evolution of public transfers' purchasing power and household-specific inflation rates. Our results provide an overview of the broader literature on poverty projection and update the poverty estimates for Pakistan during the period of FY20 to FY25 period. This study aims to contribute to the country's ongoing policy discussions on poverty reduction by providing up-to-date poverty estimates and projections.

We find that the projected poverty incidence rates mimic the broader economic upheavals caused by the pandemic, floods, and macroeconomic crises. From a baseline value of 21.9 percent in 2019, poverty is estimated to have increased to as high as 24.9 percent and 25.3 percent at the tail-end of the pandemic and period of high inflation in 2020 and 2023, respectively, with a period of recovery in between. Forecasts predict that, following economic recovery, the poverty headcount will decrease to 18.7 percent by 2025.

The paper proceeds as follows: Section 2 outlines the data and projection methodology, Section 3 discusses the key results and trends, Section 4 compares results to conventional projection methods, and Section 5 concludes.

# **2. DATA AND METHODOLOGY**

# **2.1 Data**

We use both micro and macroeconomic data for the modeling. Microeconomic data provides the baseline levels of poverty and inequality as well as a set of household characteristics. On the other hand, macroeconomic data on sectoral growth and prices are the basis for predicting the evolution of household expenditure and the corresponding welfare changes, including poverty and inequality. The FY19 Household Integrated Economic Survey (HIES 2018/19), conducted by the Pakistan Bureau of Statistics (PBS), provides the expenditure vector to calculate the official

> 3 Inflation rates differ systematically across different types of households as their consumption patterns and different types of goods and services have different inflation rates.

3