# 4.5 Model Specification

We test the hypothesis that, in the absence of the BH insurgency, changes in the rates of IPV before and during the BH insurgency would have been the same in the BH affected areas and in areas unaffected by BH (hereafter non-BH areas). To estimate the effect of the BH insurgency on women’s experience of IPV, we specify the following difference-in-difference (DD) model. I i = 0 + 1 P + 2 P B B + 3 P * P B B + 4 i + i (1) ii ii ii ii ii ii ii The DD model exploits the variation in timing and location of the BH insurgency. This allows for II ββ ββ IIP PP ββ ββ IIP PP ββ XX εε the avoidance of biases from comparisons of IPV rates before and after 2009 that could be the results of factors other than BH attacks. The unit of observation for equation (1) is a woman. I i is the outcome variable indicating if a woman _i_ , interviewed in an area _l_ , and NDHS year _y_ , ii experienced IPV. P is a binary variable indicating the NDHS year the woman was II interviewed. It is 1 for observations from the 2013 NDHS during the BH insurgency and 0 ii otherwise. P B B IIP indicates whether the woman resides in a BH affected area. It is a binary PP variable equal to 1 if the woman resides within a 10 km radius of any BH event between 2009 and ii 2013 and 0 otherwise. The covariates listed in Table 2 are represented by i . Finally, i stochastic error term. ii ii<sup>is the</sup> XX εε The parameter of interest is 3, which measures the average treatment on the treated. That is, the effect of the BH insurgency on women’s experiences of IPV, as measured by the change in IPV between BH affected areas and non-BH areas before and after the BH insurgency. In other words, ββ it estimates the effect of the presence of the BH insurgency on IPV in the BH affected areas.<sup>21</sup>

# 4.6 Estimation Bias

In standard DD models, there are two common causes of estimator bias. First, selection bias across time and groups (Stuart et al. 2014). Second, using data from repeated cross sections as opposed to longitudinal data can cause estimates to be biased due to terms that must remain unchanged to ensure before-after comparability (Blundell and Dias 2009). For our model, selection bias across time could occur if the composition of the sample in the BH and non-BH affected areas changes in the period before and during the BH insurgency due to the BH insurgency. Alternatively, selection bias across groups could occur if the composition of the sample changes across the BH and non-BH affected areas. Population movements to and from the BH affected area because of the operations of law enforcement, forced returns and flight from the conflict could affect the composition of women in the BH affected area before and after the BH insurgency. This could cause selection biases to occur both across groups and time.

We address these potential sources of bias as follows. First, as discussed in Section 4.1, we exclude from our analysis observations from the 2008 NDHS from areas not surveyed in the 2013 NDHS on the account of the BH insurgency to ensure before-after comparability. Second, we employ a

> 21 We also check if the intensity of exposure to BH may affect the likelihood of increased IPV beyond residing in an area where the BH insurgency is present, i.e., we determine the marginal effect of BH events in the BH affected area, by accounting for the number of BH events in the affected area within the study period. However, we find that each additional BH event has no significant effect on the outcome variables, except in the case of earnings decision-making autonomy where the impact is 0.1 percent. This is likely because we do not have observations from the areas of the country where the largest number of BH attacks took place.

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