representative at the national level and residence areas: Ndjamena (capital city), rural areas, and other urban areas. So far, one round of the survey has been published.

We first present a few employment and income-related characteristics of female-headed households in Chad, making these households uniquely vulnerable to shocks. For this purpose, we use data from the full sample of ECOSIT 4. Next, to make inferences about COVID-19’s economic impacts, we concentrate on the four modules in the HFPS related to employment and revenue, loss of income, shocks and coping strategy, and COVID-19 impact. We use simple descriptive statistics and test of proportions (prtest) to identify the most affected income channels and coping strategies adopted by female-headed households and use probit model regressions to identify outcomes significantly different from male-headed households. We use weights associated with households in the survey so that the reported results are representative at the national level. The weights also correct for any selection bias associated with not interviewing households that do not own mobile phones or that cannot be reached despite repeated call attempts.

The probit model, which is well-suited to model dichotomous or binary outcome variables, allows us to determine if differences in binary outcomes between female and male-headed households observed through descriptive statistics and proportion tests are significant. It allows us to methodically document trends and impact instead of relying on anecdotes and assumptions. However, our method and survey data do not tell us about the underlying mechanisms, i.e., why there are differences. We draw inferences from the existing literature to explain possible reasons for these differences.

The main binary outcome that we examine through the probit regression is the reduction in household income from various sources in the past 12 months and the adoption of different coping strategies. For household income reduction, we constructed binary variables for the decline in household income from any source (value one if “yes” and zero if unchanged or increased) and for each income source: wage employment, non-farm enterprise income, farm income, remittance. The households are asked if they experienced a reduction in income during the time of the survey. Reporting loss of income from a given source is conditional on receiving income from that source prior to the COVID-19 pandemic. As it is a short survey, most variables are categorical or binary, and we cannot make quantitative inferences about the income loss amount.

Unlike Josephson et al. (2020), which uses a univariate reduced form regression to assess socioeconomic dimensions of COVID-19 impact, we use a probit regression with the gender of the household head, ruralurban location, education level of household head, and household size as independent variables. The assumption of exogeneity is difficult to maintain for the gender of household head (hhgender), and there could be omitted variable bias. A possible way to check for omitted variable bias is to examine if the significance and sign of the coefficient of interest changes with variation in independent variables. We first run the probit regressions with only the gender of the household head as an independent variable and add the other independent variables one by one in separate regressions. The significance of the coefficients of our variable of interest remains the same.

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