locations that experienced conflict, which are a more appropriate comparison group than all locations.

# **_3.5 Outcomes_**

The outcome of concern is female child marriage, a formal marriage or informal union before age 18. Since we are interested in how time-varying conflict may affect child marriage, we construct our analysis sample so that an observation is a child and her age in years (e.g. age 15, age 16). The age range for the DHS woman’s module restricts our analysis sample to women aged 15-49 at the time of the survey. Girls are theoretically at risk of child marriage and thus potentially have observations from age zero to age 17.<sup>11</sup> Based on the age at first marriage question, we know the age a girl aged 15 or over first married, if she has ever married. We use this retrospective question to construct our “failure” indicator (our outcome). It is one if the girl marries at that observation’s particular age and zero if, at that particular age, a girl is not yet married. Girl-age observations after she has married are excluded from our analysis sample (“failure” has already occurred), consistent with the concept of a hazard in survival analysis terms. So, for example, if a girl marries at age 15, she would have observations from ages zero to 15, including the indicator for getting married on her age 15 observation, and then she will no longer be in the sample as she is no longer “at risk” for the event of child marriage, since she has been married. The appendix and Table 12 provide an illustration and further discussion of this data structure.

# **_3.6 Key covariates: Conflict_**

Our focus covariate of concern is conflict. From the UCDP data (Pettersson & Öberg, 2020; Sundberg & Melander, 2013), we calculated total fatalities for each calendar year. We matched and merged this conflict data to the girl-age analysis sample based on the DHS cluster location (second level administrative geography from GADM) and the calendar year corresponding to a particular time-varying year of age for that girl. Since the UCDP data are calendar year data, we match calendar years to years of age based on the birth year<sup>12</sup> plus the year of age for that observation. This means that if, for example, a girl was born on March 1, 2000, age zero is considered to have occurred in 2000, age one in 2001, etc. Although age zero actually spanned March 2000-February 2001, this means that conflict data always overlap with or slightly precede the year of age (never after that age). We excluded from our analyses observations where the calendar year was equal to the year of the interview, since these would be only a partial year. The appendix and Table 12 provide an illustration and further discussion of this data structure.

After thus calculating the total number of fatalities at the second-level administrative geography in each year, we normalized relative to the second-level administrative population.<sup>13</sup>

> 11 In our data, children marry as young as age zero (N=24). When modeling (see below) the hazard of marriage at each age is accounted for; there are lower hazards for very young ages. Since the model is effectively multiplicative, including all ages does not bias our estimates.

> 12 In countries (Bangladesh, Egypt, Indonesia, Pakistan) where the women’s questionnaire was only asked of evermarried women, we use the current age from the household roster, the day and month of the interview, and randomly assign a month of birth to calculate the year of birth.

> 13 We used the DHS weights, which account for the sampling strategy (selection probability) and household response rate in the stratum in order to be representative (Rutstein & Rojas, 2006). We calculate the share of the population (all individuals) in each second-level administrative geography and multiplied this by the national

7