transfer, is positive, significant, and strikingly high when self-reported information about demobilization status is cross-checked against official demobilization registers (columns [1] to [4]). It suggests that the consumption of demobilized FNL households is between 77% and 144% higher<sup>23</sup> than the consumption levels of FNL _returnee_ households that did not benefit from reinsertion allowances but had the same consumption level in 2006 as the demobilized households. This effect is also positive when using the definition of demobilization status based on self-reported information (columns [5] and [6]), but it loses its significance. This reduced precision can be explained by the presence of noise due to misreporting (Hausman, 2001).

The long-run evolution of total consumption per adult equivalent does not seem to differ between civilian households and CNDD+ _returnee_ households that did not benefit from the DDR. In contrast, the long-run evolution of total consumption turns out to be negative for households that benefited from the first wave of the DDR program from 2004 (hypothesis 2). The coefficient of the variable _D_<sup>_CNDD_</sup> _i_<sup>+</sup> is significant and large but negative. These estimates suggest that the consumption of CNDD+ households that benefited from the first wave of the program is between 40% and 50% lower in 2010 than the total consumption of nonbeneficiary CNDD+ households that had the same consumption level in 2006 as beneficiary households. This observation holds for all three definitions. The coefficients associated with the variables _Di_<sup>_FNL_</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+</sup> are of similar size, but of opposite sign (the F-test _p_ -values reported at the bottom of table 3 are above 0.48 for all specifications).

Table 3 also highlights the presence of spillovers (hypothesis 3). The coefficient associated with the proportion of ex-FNLs in collines _S i_<sup>_FNL_</sup> is positive and significant. This result suggests that households living in collines with a large number of demobilized ex-combatants benefited from positive spillovers. By contrast, the coefficient of the variable _S_<sup>_CNDD_</sup> _i_<sup>+</sup> is negative and significant, showing that in the long run, households living in areas with numerous CNDD+ ex-combatants consumed on average less in 2010 than households who had a similar standard of living in 2006 and who lived in areas with fewer CNDD+ ex-combatants. The coefficients associated with the variables _S i_<sup>_FNL_</sup> and _S_<sup>_CNDD_</sup> _i_<sup>+</sup> are of similar size, but of opposite sign across definitions (F-test _p_ -values are all above 0.78).

Finally, the coefficient associated with the lagged dependent variable is negative and significant. This further illustrates the importance of including the lagged dependent variables in the regressions to account for low auto-correlation, noise, or catch-up effects in the outcomes of interest.<sup>24</sup> Table 4 displays the estimates of the lagged dependent variable model for the five other

> 23As the dependent variables of the regressions are expressed in log, the interpretation of the coefficients in terms of percentage should be corrected according to the formula: _e_<sup>coef.</sup> − 1.

> 24As shown in table S.4 in appendix, the coefficients associated with the long-run indirect effect lose significance when we do not control for the lagged dependent variables.

18