# **1. Introduction**

Pakistan has faced various shocks in recent years, including the COVID-19 pandemic, natural disasters, and economic shocks. These shocks are expected to profoundly impact household welfare and poverty rates in the country. Yet, the latest official poverty rates are available for fiscal year 2019 (FY19),<sup>1</sup> the year of the last Household Income and Integrated Survey (HIES). Due to resource constraints and the time it takes to collect and process household survey data, surveys are only implemented every few (2-5) years, undermining data availability. This presents challenges in providing real-time welfare (namely poverty, vulnerability, inequality) analysis and informing the design of policies to mitigate the impact of shocks like economic crises (e.g., price shocks) or natural disasters (such as floods) on people’s well-being and their risk to fall into or stay in poverty. To address this issue, policy makers rely on "nowcasting" and "forecasting"<sup>2</sup> poverty rates using aggregate measures of national welfare to estimate poverty incidence levels. This approach is based on the fundamental principle that the incidence of poverty and aggregate measures of national welfare, like GDP per capita, are intertwined.

Our approach explicitly models change in the welfare distribution by using information from sectoral growth and household-specific inflation rates, producing a more nuanced picture of the incidence of poverty and the distributional changes experienced in the country. We model the evolution of labor and non-labor incomes. First, we account for the differential growth trends and concentration of labor across the economy by modeling labor income using information on the growth rates of 11 different sectors. This is key since there are significant variations in sectors' contribution to overall growth, their labor intensity, and the welfare level of individuals employed in different sectors. Second, we include specific conditions to mimic the evolution of non-labor income transfers, with a particular focus on the changes in the real value of the transfers from the main anti-poverty program in the country. Finally, we account for the differential price trends across different good categories and their impact on the effective inflation rate households experience given their consumption basket. We implement this modeling in the case of Pakistan and show how it captures how the differential sectoral growth rates, the loss in purchasing power of public transfers, and the inflation gap between urban and rural areas all together impact the evolution of poverty since the last official measure in FY19 was published. The model is specifically tailored to the conditions of Pakistan, but its methodological approach offers complete flexibility to extend it to other country-specific conditions in terms of relevant economic sectors, non-labor income components, and available inflation information.

Our results are aligned with past evidence on how accounting for the distributional effects of growth produces more accurate predictions of the evolution of poverty (Caruso et al., 2017;

> 1 In Pakistan, the fiscal year covers the 12-month period from July to June of the next year. For example, FY19 covers the period from July 2018 to June 2019 inclusive.

> 2 Nowcasting refers to the practice of using recently published data to update key economic indicators that are published with a significant lag. Poverty nowcasting relies on actual GDP data, its accuracy depends only on the features of each nowcasting method (reference). Forecasting refers to the practice of predicting what will happen in the future by taking into consideration events in the past and present.

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