simulation, the Southwest region will be impacted most severely by the transition, followed by the South (Figure 10). In the South and Southwest regions, declines in household expenditure per capita relative to their 2013 peak will reach 10.8 and 12.4 percent, respectively.

Although the simulation results reported so far refer to average household expenditures at the regional level, the chosen approach also make it possible to compute statistics for subsets of the population in each region. In particular, by simulating the impact of the triple shock on each individual household in the NRVA sample, it is possible to assess which share of the population in each region will have expenditures per capita below any given expenditure threshold. When the chosen threshold is the poverty line, this procedure yields the estimated poverty rates over the simulation period (Figure 11).

Using this approach, poverty incidence in Afghanistan can be expected to increase from 35.8 percent in 2011 to 36.7 percent in 2016. Although this change may seem minor, the increase is much more substantial when comparing the predicted poverty rate for 2016 to that observed around 2013, when foreign aid levels and the presence of troops on the ground were at their peak. The increase will be more marked in the East, South, and Southwest regions.

# **8. Out-of-sample validation**

The results in this paper help understand the paradox of lower poverty in regions more severely affected by conflict, but the relevance of the forecasts on the dynamics triggered by the transition could be questioned. The estimated models ensure a good fit with the data during the estimation period, but they could be less relevant beyond it, when political processes and exogenous shocks may put the country, and conflict intensity, on a different trajectory relative to the forecasts. This is a valid concern. One way to address it is to assess how well the forecasts in this paper predict actuals for the year 2015.

Data recently made available on conflict intensity in recent months reveals a strong similarity between the actual intensity of conflict in 2015 and the forecasts from Model 1. The predicted number fell short of the actual by only 0.2 percent, suggesting that the proposed approach is quite accurate at the national level. There is also a reasonable closeness between predictions and actual figures at the provincial level, with the correlation coefficient between the two series reaching 0.96. However, there are also a few important discrepancies, with the model not fully anticipating the increase in conflict intensity in the Northeast, and overestimating conflict intensity in the Central region, and even more so in the Westcentral region (figure 12).

An out-of-sample validation can also be conducted in relation to expenditures per capita, Because Model 2 was estimated using data from 2011, forecasts and actuals can be compared for all the years since 2012. The exercise also allows to assess the performance of the empirical approach used in this paper relative to other forecasts produced building on the 2011 NRVA survey and other data sources issued at about the same time. Such analyses include the already mentioned landmark 24