and seasonal/temporary workers. The vector for the type of enterprise is farm, non-farm, farm and non-farm, and no enterprise.

_Ghpc_ is a vector of dummy variables for income group of the household, proxied by imputed household welfare measures.<sup>19</sup> We construct four levels of income, based on the World Bank’s new poverty thresholds: below 2.15 USD-a-day, between 2.15 and 3.65 USD-a-day, between 3.65 and 6.85, and above 6.85, in 2017 PPP terms (Jolliffe et al. 2022). The imputation method and validation of the poverty proxy is provided in Annex B.

_Xhpc_ is a vector of individual characteristics of the respondent, comprising gender and age. Age is defined as a binary variable, where “adults” are older than 24 years of age and “youth” are between 16 and 24 years old. Unfortunately, the data do not include information on the education level of the respondent. This vector is only relevant when using the household head definition.

_Ipc_ are fixed effects specified at the provincial-country level.<sup>20</sup> εhpc is a standard classical error term clustered on country and province.

**Second, we examine which types of household enterprises were more economically affected by the COVID-19 pandemic, according to estimated household welfare and characteristics of the owner** .<sup>21</sup> Again, we estimate an LPM. This specification differs from Equation (1) in that we drop the _E_ vector, limit the sample to household with enterprises (or are self-employed, when using the household head definition), and use a different dependent variable. The LPM specification is:

We use two outcome variables: decline in farm income and decline in non-farm income.<sup>22</sup> First, _Yhpc_ takes the value 1 if the household ( _h)_ in province p within country _c_ reported a decline in farm income since the pandemic started and 0 if it did not. Second, _Yhpc_ takes the value 1 if the household ( _h)_ in province _p_ within country _c_ reported a decline in non-farm income since the pandemic started and 0 if it did not. The vectors _Ghpc_ , _Xhpc,_ and _Ipc_ are defined as in Equation (1).

We estimate this model in two samples. First, we use the household business definition of urban household enterprise to estimate how farm and non-farm household enterprise income changes by poverty status of the household. We restrict the sample to households owning a farm business when modeling declines in farm income, and similarly to households owning a nonfarm business when modeling declines in nonfarm income. This model does not include the X vector control variables. We then use the household head definition of urban household enterprise and include the X vector to explore how farm and non-farm income changes among the self-employed who

> 19 <mark>We use the survey-to-survey imputation method. Using household surveys with individual level data, we run a model for each country where the welfare measure is the dependent variable (per capita consumption) controlling for demographic characteristics. Then we use the coefficients of the regression to predict the welfare level in the HFPS. The variables used to impute the poverty proxy are not included in the control variables in the regression.</mark>

> 20 The survey asks for location at the 1st level administrative unit. We would have preferred to control for fixed effects at the city level to allow for differences between capital and secondary cities, but the data do not include a city variable.

> 21 <mark>The exercise only observes change in enterprise earnings so we cannot rule out that factors other than the pandemic affected enterprise income. However, given the magnitude of the pandemic shock, it is likely that the pandemic played a significant role.</mark>

> 22 The survey questions are: (i) "Has this source of household income changed since last 12 months?": Family farming, livestock or fishing, and (ii) "Has this source of household income changed since the pandemic started?": Non-farm family business.

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