To refine the assessment of publication bias (and derive the genuine risks of conflicts, if any), we then focus on our three main meta-regression subgroups: **_AS+_** , **_AS-_** , and **_HS+_** in columns [3] to [5] of Table 2 (the results for the other types of shocks are presented in the Appendix). The dependent variables are the _t_ -statistics value of the collected estimate, as opposed to the adjusted and absolute value used when considering the whole sample. Results associated with these more homogeneous sets of estimates show a significant intercept in column [4] only. This suggests the presence of a Type I publication bias in studies analyzing the effect of AS-. In other words, researchers tend to prefer studies in line with conventional views (Card and Krueger, 1995) when they study negative agricultural shocks, and in the present case, higher risks of conflicts. Our results also point to a rather “substantial” selectivity in that sub-literature, as supported by the FAT values.<sup>16</sup> Interestingly, as already suggested by the funnel plots (see Figure 4 above), we do not find any evidence of Type I publication bias for studies focusing on AS+ (column [3]) and HS+ (column [5]). Since the majority of the estimates collected for AS+ and HS+ are price shocks (see table A1), these results are consistent with those of Blair et al. (2021), who find no evidence of publication selection bias in the literature on price shocks and conflicts. One potential but not definitive explanation is the nature of shocks: while AS+, and HS+ largely rely on market and price shocks, AS- largely rely on natural events (see table A1). It is possible that the analysis of climate events allows for a more arbitrary selection of results. Climate shocks can be captured by a range of variables (precipitation, drought, temperature, etc.) and parameters (different indicators, measures of variance over different periods, etc.). These methodological choices have theoretical implications and can lead authors into various empirical pitfalls (Auffhammer et al., 2013).<sup>17</sup> The results for other types of shocks (see Table A2) also point toward this explanation. Indeed, they suggest the presence of selection bias only in the regressions of pure positive and negative climate shocks, which are also measured with a heterogeneous set of climate variables (see Figure A1 and Table A1 in the Appendix). These elements will be discussed further in the heterogeneity analysis section.

> 16A FAT absolute value smaller than 1 is synonymous of ”little to modest” selection bias, while a FAT test absolute value ranging between 1 and 2 rather signals “substantial” selectivity (Doucouliagos and Stanley, 2013).

> 17Additionally, the WAAP by Ioannidis et al. (2017) and Stanley et al. (2017) shows few adequately powered significant estimates for the subsample of baseline AS- estimates only. In other words, the greater variety of shocks associated with AS- is associated both with overall opportunities of (type I) publication selection bias _and_ genuinely more unconditional risks of conflicts for the few subsample (150 out of 481 observations) of adequately powered estimates.

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