The data have the advantage that recruits report not only their age and education but also where they resided before enrolling; of the 605 Tunisian recruits, we are able to map delegation of residence for 501, of whom 468 also have information on education and age. Our sample for the analysis thus consists of these 468 Daesh recruits.<sup>4</sup> The average number of recruits per delegation is 1.9, but the average masks a highly skewed distribution (see Figure 1a): recruits come from 94 delegations only (out of 263), whereas Bizerte, the northernmost city in Africa, had 30, the highest number. The Tunis area was also a recruitment hotspot. Of the 468 Tunisian recruits in our database, 128 are from the governorates of Tunis and Bizerte. In general, recruitment rates are much higher in urban areas.

Detailed information on the socio-economic characteristics of recruits (including their age and education), allows us to construct a variable of recruitment that counts the number of Tunisian recruits who resided in delegation _d_ , have education level _e_ , and have reported to be _a_ years old; we therefore created five-year age groups so that each cell (observation) in the dataset is characterized by the triplet _{d, e, a}_ . Table 1 provides both cell-level and delegation-level summary statistics for our variables of interest. The number of recruits in a given cell ranges from 0 to 8.

**Socio-economic data** The 2014 Tunisia Census of Population and Housing is the source of our main independent variables. The census collected information on demographics, educational attainment, marital status, unemployment, television and radio ownership, emigration, and

> et al., 2016a,b; Jayakumar and Sumpter, 2019; Morris, 2020; Evans et al., 2020; Brockmeyer et al., forthcoming), but to the best of our knowledge they have not been used for country case studies.

> 4Using available information on Daesh recruit age, education, occupation before joining, marital status, previous experience with jihad, and self-reported knowledge of Islam, Appendix Table A2 presents tests examining whether there are systematic differences in the characteristics of fighters who were included in the analysis and those who are not. For each variable grouping, the F-test of joint significance of the difference in means between our sample and the data universe fails to reject that these are equal to zero. Our analysis sample thus does not appear to suffer from selection bias. In addition, Brockmeyer et al. (forthcoming) show that the larger database exhibits high correlation with alternative sources of information on Daesh foreign recruits; they look at country aggregates and conclude that the data are representative at the country level. We conduct a similar exercise and compare the reported delegations of last residence for observations in our dataset with the ones obtained by Zelin (2018) on 121 Tunisian recruits who died in Syria and Iraq. The correlation between our data on recruitment per delegation and Zelin’s measure of deceased foreign fighters per delegation is 0.71. Our data thus appear to accurately represent the spatial distribution of Daesh recruitment in Tunisia.

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