The nature of the data on conflict makes applying an ordinary linear regression model problematic. The conflict measures are all positive integers, so they will likely exhibit non-normal distribution. This is confirmed by a summary of the state-year conflict measures (Table 2). The number of conflict events in a year and number of conflict events with fatalities range from 0 to 118 and 0 to 79, respectively. There are only an average of 6.9 and 2.2 conflict events and conflict events with fatalities per year.

The Poisson and Negative Binomial models are the two commonly used models for this kind of data characteristic (count data), because they ensure a positive conditional mean of the conflict variables. The Poisson model has the advantage that it does not require that the data be distributed Poisson for it to be used – that is, the model requires a weaker distributional assumption than the negative binomial model. However, the negative binomial model is designed to handle over-dispersion in our data and will lead to higher efficiency in estimation.

**Table 2: Summary statistics of the dependent variables (2004-11)**

|Variable|Obs|Mean|Std.<br>Dev.|Min|Max|% of<br>Zeroes|
|---|---|---|---|---|---|---|
|Nr. of fatalities from conflict episodes|296|18.4|85.5|0|1001|41.6|
|Nr. of conflict events in a year|296|6.9|12.7|0|118|21.3|
|Number of conflict events with fatalities|296|2.2|6.0|0|79|41.6|

_Source: ACLED_ Within the negative binomial model option in STATA, we use the population-averaged (PA) option that relaxes the assumption of independence to allow for different correlations over time of the conflict. The relaxation of this assumption is useful for the purpose of our analysis so the influence of conflict in 2003 is not restricted to be the same on all the other years. The other two options, Random effect (RE) and fixed effect (FE), do not adequately capture our data and impose additional structure on the data that cannot be validated. Also, they attempt to model over dispersion rather than capturing fixed effects at the state level and tend to cause convergence issues given the size of our data.

One may argue that unobserved characteristics of the states can potentially bias the estimates when state fixed effects are not controlled for. For example, that may be the case if the initial composition of consumption and production baskets is correlated with unobserved characteristics of the states which also affect the propensity for conflict. We employ a variety of controls to attenuate this concern. First we use region-time effects, which capture any time varying factors in each of the six Nigerian regions. This is an important control as many of the cultural and socio-economic traits as well as the geo-climatic areas in the country are defined at the regional level. In addition, this control captures changes over time that are common across states within a region.

Second, we use a large variety of state-level controls. The vector Z includes variables measured at the beginning of the period of analysis (from the NBS), i.e. population, population density, and measures of poverty and inequality, including the headcount poverty rate, poverty gap 14