# Introduction and context

Since the change of administration in August 2021, there has been a critical need for timely and frequently updated information on welfare in Afghanistan. While macroeconomic data shows a large economic contraction, the evolution of poverty levels over the last two years remains uncertain since no additional household expenditure survey has been conducted. The latest official estimates based on the 2019-2020 Expenditure and Labor Force Survey (IE-LFS) conducted by the National Statistics and Information Authority (NSIA) placed the national poverty rate at 47.1 percent, meaning that close to half of the country’s population was already living in poverty before 2021. Data collection for a new IE-LFS started in April 2021 (IE-LFS 2021-22) but fieldwork was suspended in June 2021 due to the deteriorating security situation. Poverty estimates based on first quarter data (spring season) of the IE-LFS 2021 showed that poverty for that period was 52 percent, very close to that reported for the same period in 2020.

The lack of comparable household budget survey data limits the ability to directly estimate the evolution of monetary poverty. We fill this knowledge gap by implementing a survey-to-survey imputation method using a SWIFT-plus approach. After the NSIA halted regular collection and reporting on key socioeconomic indicators in 2021, the World Bank continued welfare monitoring using phone surveys, and implemented the third round of the Afghanistan Welfare Monitoring Survey (AWMS) between April and June 2023. This round was expanded to include a module that allows estimating current monetary poverty levels through survey-to-survey imputation techniques.

We use the IE-LFS 2019-20 as it represents the last full-year household survey the NSIA conducted. Considering the strong seasonal dimension of poverty in Afghanistan and the timing of the AWMS R3 (April to June 2023), we trained the model using a quarter-representative sample of the IE-LFS 2019-20 corresponding to the same months of 2020. That is, we used data from the spring quarter of the IE-LFS 2019-20 to calibrate a model that predicts household expenditure and used this model to impute poverty in the AWMS R3. The SWIFT-plus methodology includes fast-changing consumption variables to better capture welfare changes in a context where large economic shocks have occurred. We implemented additional estimation steps to minimize model overfitting, account for estimation error through multiple imputation techniques, and improve out-of-sample performance. In line with previous SWIFT-plus tests done on Afghanistan, we trained separate models for urban and rural samples.

Our results show that poverty in Afghanistan has remained roughly the same as that observed in the spring of 2020 before the regime change. Overall, about 48.3 percent of the Afghan population was poor as of April-June 2023, representing a four-percentage point decline compared to poverty levels observed over the same months of 2020. Poverty trends differ between urban and rural areas. In rural areas, monetary poverty is estimated to have declined from 51 to 44.2 percent while marginally increasing in urban areas from 55.2 to 58 percent, although not statistically significant. These projected poverty rates are robust to in-sample and out-of-sample tests and potential biases from the AWMS R3 sampling.

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