# 3. Methodology

Constructing a non-COVID counterfactual household income distribution for FY20 The first part of the estimation strategy adopted in this paper aims to generate an updated baseline household income distribution for FY20, in the absence of the COVID-19 shock. The starting point is Egypt’s Household Expenditure and Income Survey (HIECS) 2017/18, which collected information about households’ incomes and consumption and is the official data source for poverty estimation. The survey is representative of the Egyptian population, and data were collected over a full year to capture seasonality in income patterns. The survey covers three quarters of FY18; data collection started in Q2 of 2017 and was completed in Q3 of 2018. For this analysis, we treat this baseline information as FY18.

To create a counterfactual income distribution for FY20, we start from the observed trends provided by the Labor Force Survey (LFS) for the period 2017 to June 2020. We take the trends in labor force participation, unemployment, and earnings across different sectors to update the HIECS 2017/18 employment and labor income distributions. This analysis focuses on the early pandemic impacts for FY20. That means that the income shock considered covers the last quarter (Q4) of FY20 (April to June 2020). The LFS data already captures the impact of COVID-19 in that last quarter. Therefore, to create the counterfactual non-COVID income distribution, we assume that, in the absence of the COVID-19 shock, labor market conditions would have been like those observed in Q3 of FY20. Projections done before the pandemic indicate that there was no expectation of another economic shock or break for Q4 (see Annex A, Figure A.1). Trends using LFS also show that the structure of Egypt’s labor market changes little from quarter to quarter, so using employment and income measures for Q3 may be a sensible way to model the counterfactual labor market situation. However, one concern is seasonality. When we compare trends in FY19 across quarters, the indication is that Q4 employment composition and earnings are not very different from those observed in Q3 (Annex A, Table A.0). The ratio of monthly earnings between Q3 and Q4 in FY19 ranged between 1.02 and 1.06, depending on the sector, suggesting a positive and not a very large difference between the two quarters, on average.

We update workers’ labor market status (extensive margin), based on the changes observed in activity status and employment by sector. Due to sample size limitations in modeling transitions, we use six states for males (not working, which combines out of the labor force and unemployed; working in agriculture; working in industry as an informal worker; working in industry as a formal worker; working in services as an informal worker; working in services as a formal worker). For females, we consider three states (not working, formal workers, and informal workers). Table 1 shows the percentage of workers across these states by gender in HIECS 2017/18.

To produce the most accurate update, workers had to be remapped into different labor market states to reflect the changes in labor force participation and unemployment and the increases in informality observed over the period. The transition model parameters are estimated using the Egypt Labor Market Panel Survey (ELMPS) for the period 2012 to 2018. The ELMPS is a key source of information for the Egyptian labor force, and main trends and patterns align closely with the LFS. The value added 6