Thus, our key covariate is transformed into deaths per thousand population. We then calculated, across countries and years, the tertiles of conflict (low, medium, and high conflict as compared to none).<sup>14</sup> Tertiles are preferred to continuous measures of conflict, since outliers in continuous measures can be problematic. Previous studies that combine DHS data with conflict-intensity data have also looked at tertiles (Kelly, 2017; Kelly, Colantuoni, Robinson, & Decker, 2018, 2019). However, the population normalization is a new approach undertaken in this paper in order to account for the widely varying populations across countries and different locations within them.

Our main specification uses this contemporaneous<sup>15</sup> tertiles of conflict (compared to none) model. As sensitivity analyses, we also estimate a number of other models. One is a tertiles of conflict with the contemporaneous conflict and four years of lagged tertiles. The different demographic, economic, and social channels for the impact of conflict on child marriage may act with some lag. For example, the economic destruction wreaked by conflict may impact local economies for a number of years after conflict subsides. There may also be rebound patterns as conflict subsides. As a further sensitivity analysis in terms of tertiles, we also estimate a model with contemporaneous tertiles of conflict (compared to none) where the tertiles are countryspecific rather than global. Conflict intensity may be experienced relatively subjectively within a country and the intensity of conflict varies substantially across countries. In an additional sensitivity analysis, we estimate a model with non-normalized contemporaneous tertiles of conflict, that is, we do not divide by the population. The absolute (rather than relative) number of fatalities may be more salient.

Lastly, we estimate a model that measures the number of years with conflict out of the contemporaneous year and the preceding four years,<sup>16</sup> similar to our lagged tertiles model. The years of conflict covariate is categorized into none, one-two years, and three or more years (out of five). This model focuses on the duration of conflict as a form of intensity as an alternative approach to measuring conflict-affected contexts. Both the model with lags and the model with years of conflict necessarily limit our sample, effectively dropping observations for 1989-1993 since we do not observe sufficient preceding years of conflict.

# **_3.7 Controls_**

In all our models we control for calendar year (as described above), current first level administrative geography and current urban/rural residence (DHS surveys generally lack full residential histories so using locations of birth is not possible). The calendar year controls (entered as calendar year fixed effects) are particularly important for accounting for time-varying country characteristics that may be related to both conflict and child marriage, for example, poverty. Additional controls, although desirable, are not possible since most of the factors that influence age at marriage relate to the natal household. Unfortunately, the DHS surveys do not collect natal household data, and while the unmarried are generally still in their natal households, the married are not. Other characteristics, such as a woman’s education level, may also be population in the year, based on UN population estimates (World Bank, 2021). This assumes that the shares of the population remained constant at the second-level administrative geography over time.

> 14 The tertiles are calculated among observations with conflict; the low, medium, and high tertiles are thus equally sized among observations with conflict, across countries, but the category “no conflict” that the tertiles are compared to is much larger.

> 15 We use contemporaneous here to refer to conflict in the same year (e.g. conflict in year 2000, when the girl was 0, if she was born in 2000), as compared to past (lagged) years’ conflict.

> 16 We tested initially up to ten lags, but past four lags was rarely statistically significant so we focused on four lags.

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