# **3.2** **_Survey data_**

- Survey data from the Demographic and Health Survey (DHS) were used to examine child

- marriage. We reviewed the most recent standard or interim DHS survey for each country that had GPS identifiers available for the DHS. Countries were then selected based on: • Country had at least 100 conflict deaths (per UCDP) within the five years preceding the survey (the most recent standard or interim DHS survey with GPS). This criterion ensures that countries are substantially conflict-affected and also that there is enough variation for our identification strategy, discussed below.

- A rate of child marriage from the survey in question (the most recent standard or interim DHS with GPS) above 10%.

These selection criteria yielded a sample of nineteen countries: Bangladesh (2017-18); Burundi (2016-17); Cameroon (2018); Chad (2014-15); Colombia (2010); Côte d’Ivoire (2011-12); Democratic Republic of the Congo (2013-14); Arab Republic of Egypt (2014); Ethiopia (2016); Guinea (2018); India (2015-16); Indonesia (2002-3); Kenya (2014); Mali (2018); Myanmar (2015-16); Nigeria (2018); Pakistan (2017-18); Peru (2009); and Philippines (2017).<sup>6</sup>

# **_3.3 Geospatial boundaries data_**

To match conflicts and DHS clusters with locations, we relied on the Database of Global Administrative Areas (GADM) version 3.6 files. We downloaded the boundary files for the second level of administrative geography for each country.<sup>7</sup> Administrative boundaries were based on 2020 data; boundaries may have been different at the time of fielding, but using a constant set of boundaries is important to consistently measure conflict. It is, however, important to note that second level administrative geographies vary in size and number across countries,<sup>8</sup> which can introduce some variation in proximity to conflict across countries when discussing “local” (i.e. within an individual’s second level administrative geography) conflict events.

# **_3.4 Matching conflicts to locations_**

We matched each of the UCDP conflict events, based on latitude and longitude, to a unique second-level administrative geography location in the geospatial boundaries data.<sup>9</sup> We likewise matched the location of each DHS cluster to the same geographical unit and thus were able to merge the data.<sup>10</sup> Our analysis sample includes only those second-level administrative geography locations that, during some year in our data, had conflict. We thus are able to identify the relationship of between conflict and child marriage based on variation over time among

> 6 At the time the research was conducted in mid-2021, all data except Colombia (2010) were also the most recent DHS for the country.

> 7 We then used the Stata package shp2dta (Crow, 2006) to convert the administrative boundary shapefiles into a Stata readable format.

> 8 Within our analysis sample, the number of second-level administrative units per country is: 53 (Bangladesh); 48 (Myanmar); 99 (Burundi); 12 (Cameroon); 30 (Chad); 230 (Colombia); 120 (DRC); 53 (Ethiopia); 15 (Guinea); 337 (India); 22 (Indonesia); 18 (Cote d’Ivoire); 78 (Kenya); 24 (Mali); 248 (Nigeria); 31 (Pakistan); 107 (Peru); 301 (Philippines); 59 (Egypt). The population in a second level administrative unit has a median of 160,000, with a minimum of 44 individuals and a maximum of 22 million. The 25<sup>th</sup> percentile of administrative unit size in our sample is 66,267 individuals and the 75<sup>th</sup> percentile is 589,999 individuals.

> 9 Matching was based on the Stata package geoinpoly (Picard, 2015).

> 10 GPS coordinates in the DHS are available on the cluster level. To protect anonymity of respondents, GPS coordinates were displaced by a random distance, in a random direction, of up to two kilometers in urban areas and up to ten kilometers (usually less) in rural areas (Burgert, Colston, Roy, & Zachary, 2013).

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