Main results

The estimation results of one-year mortality (Z = 12) of the specification (1) are reported in Table 2. The analysis in column (a) separated the sample into 10 categories based on the value of malaria endemicity, and the corresponding indicators for each category are included (the reference group is the lowest percentile). Based on the result, wartime pregnancies in the groups of 60―100 percentiles of

20The data and relevant documents are freely available from https://www.prio.org/Data/Armed-Conflict/

Armed-Conflict-Location-and-Event-Data/.

malaria endemicity experienced significantly increased one-year mortality rates by approximately 4―

5 percentage points. The estimation in column (b), which alternatively exploited an indicator for the highest 50 percentiles of malaria endemicity, also revealed a 3% increase in the mortality rate in the corresponding areas.

As the one-year mortality rate of children conceived before the war was approximately 17% based on the exploited data, the 3-percent increase in infant mortality accounts for about 17.6% of the pre-war mortality. In addition, the estimated mean proportion of the infected population aged 2―10 years (i.e., values of malaria endemicity index) is about 0.34 and 0.49 in areas belonging to the lowest and highest 50 percentiles of malaria endemicity, respectively. By combining these numbers with the 17.6% change in the mortality rate, therefore, it can be determined that the war might have caused a 0.44-percent increase in the infant mortality rate in response to a one-percent increase in the infection risk measured by the infected population.

It is difficult to speculate on this magnitude in terms of the number of infants killed by maternal malaria infection during the war and to find estimates comparable to this elasticity. However, the impact size is non-negligible, considering the presumption that the estimated coefficient does not necessarily capture the full effect of maternal malaria infection. This is because not all women residing in malaria-endemic areas may contract the disease. Since the infection probability is typically less than unity, the treatment effect of malaria infection is still likely to be larger than the ITT estimate (i.e., treatment effect of malaria infection risk) shown in the present analysis.

The exercise in column (c) replaced the dummy for the upper 50% quantile of malaria endemicity with the continuous value of the index. Exploiting the continuous measure also confirmed the view that after the war, children conceived by mothers residing in malaria-endemic areas more clearly lost their life within one year after birth compared to those conceived by mothers in low endemic areas.

By including the fixed effects of year-of-conception, the previous estimations controlled for all time-varying factors that affected areas of high and low malaria endemicity in a similar manner. However, it is possible that the war noticeably changed the health-related infrastructure across areas having different levels of malaria endemicity while biasing the estimated α2. To mitigate this concern to some extent, this

study grouped the sample data into 66 categories based on administration areas of “districts” (see Figure A.1 for the boundaries) and in the analysis of column (d) in Table 2, controlled for a time trend (over 40 years) specific to each district.²¹ The exercise exploiting the continuous measure of malaria endemicity yielded similar estimates to those obtained in column (c).

To alleviate the potential bias attributed to possible pregnancy cases that were not reported to the DHS team, the analysis in column (e) included an indicator equal to one if a mother had ever experienced a pregnancy that terminated in a miscarriage, abortion, or stillbirth. This additional control left the implications obtained from the previous estimations almost unchanged.

As discussed in subsection 3.2.2, wartime childbearing might have been possible only for those with particular parental characteristics (e.g., health safety). To control for such (only) time-invariant charac-teristics, this study replaced the previously exploited covariates relevant to mothers and community-fixed effects with mother-fixed effects and then re-estimated the specification (1). Column (f) reports the esti-mation result, which again highlights the adverse mortality effect of wartime pregnancy associated with high malaria infection risk.

Another empirical challenge noted in subsections 3.1.2 and 3.2.2 is that the respondents might have resided in different locations from the current DHS communities because the crisis induced massive pop-ulation displacement. In this case, the measured malaria endemicity includes some noise. To determine whether previous findings were sensitive to this concern, two exercises were performed. First, this study exploited data pertaining only to children born to mothers who were identified as permanent residents of the surveyed community in the 2007 DHS and re-estimated the specification (1) in column (g). Data for the 2013 DHS were not exploited in this estimation because the relevant information was not available in that round. Second, during the conflict, most IDPs headed towards the capital, Monrovia (Nilsson (2003)). In addition, Liberian refugees were repatriated to the capital under a program run by the UN refugee agency after the war. Because some of those people preferred to stay there instead of returning to their original homes in the post-war periods, the current residents of the capital area must show a

21It was not possible to identify a district corresponding to each community from the DHS data alone. To achieve this purpose, therefore, this study matched a community’s GPS latitude/longitude coordinates with a map of Liberia sourced from DIVA-GIS (http://www.diva-gis.org/datadown). Consequently, the communities were categorized into 65 districts plus one group for which the ArcGIS failed to identify the corresponding district. The analysis in column (d) in Table 2 included the unidentified group as one district because it constituted only 3% of the sample.

great tendency to have resided in different places during the years of the conflict. This is particularly true if they no longer had any relatives living in their original communities (Jesuit Refugee Service (2007);

Omata (2012)). Given this likelihood, the other exercise conducted in column (h) excluded from the esti-mated sample those currently living in the Greater Monrovia District. As is evident from the estimation results, the additional analyses based on these sub-samples of the data yielded similar implications to that obtained before.²²

[Here, Table 2]