Table 5: Explaining Heterogeneity in the Estimated Effects of Income Shocks on Conflicts [BMA - Total Sample]

|||BMA|Full sam|ple|OLS||
|---|---|---|---|---|---|---|
|**(i) Baseline**|Post Mean|Post SD|PIP|Coef.|SE|p-Value|
|Publication bias (_β_1)<br>|**0.789**<br>|**NA**<br>|**1.000**<br>|**0.737**<br>|**0.393**<br>|**0.065**<br>|
|Precision (_β_0)<br>**iiTiihl**|0.000|0.002|0.052|-0.000|0.003|0.893|
|**() ransmsson cannes**<br>|||||||
|_Positive agricultural shock (Ref.)_<br>Negative agricultural shock<br>Positivehdrocarbonshock|0.001<br>-0002|0.002<br>0002|0.127<br>0492|-<br>-|-<br>-|-<br>-|
|y <br>Othhk|.<br>0000|.<br>0001|.<br>0096||||
|er soc<br>**(iii) Mechanisms and interactive models**|.|.|.|-|-|-|
|_Other mechanisms (Ref.)_<br>Mechanism stressful<br>_OthR_|**0.007**|**0.001**|**0.994**|**0.006**|**0.002**|**0.008**|
|_ers (ef.)_<br>Risk enhancer<br>|**0.004**|**0.000**|**1.000**|**0.004**|**0.002**|**0.093**|
|**(iv) Publication outlet**<br>_Notop5reviews(Ref)_|||||||
|_._<br>Top 5<br>_NR_|**0.003**|**0.003**|**0.627**|**0.005**|**0.002**|**0.011**|
|_o confict reviews (ef.)_<br>Confict review<br>_Clt(Rf)_|0.000|0.000|0.046|-|-|-|
|_ore resus e._<br>Appendix results<br>Continuous variables|0.000|0.000|0.024|-|-|-|
|Age of study|0.000|0.000|0.079|-|-|-|
|SJR score<br>|0.000|0.000|0.418|-|-|-|
|**(v) Geography**<br>_No country focus (Ref.)_|||||||
|Country focus<br>_WldidR_|**-0.009**|**0.002**|**0.992**|-0.004|0.003|0.228|
|_orwe (ef.)_<br>|||||||
|Region: Africa<br>Region: East Asia Pacifc (EAP)|-0.001<br>0.002|0.002<br>0.003|0.127<br>0.457|-<br>-|-<br>-|-<br>-|
|Region: Latin America & the Caribbean (LAC)|**-0.003**|**0.003**|**0.548**|**-0.005**|**0.002**|**0.025**|
|Region: South Asia<br>|**-0.019**|**0.005**|**0.995**|**-0.020**|**0.012**|**0.089**|
|**(vi) Model characteristics**<br>_No MLE (Ref.)_|||||||
|Maximum likelihood estimator<br>|0.000|0.001|0.060|-|-|-|
|_No specifc efects (Ref.)_<br>Specifc efects|0.000|0.000|0.016|-|-|-|
|_No cluster unit reference (Ref.)_<br>Cluster unit reference<br>_PldR_|0.000|0.001|0.221|-|-|-|
|_re co war (ef.)_<br>Post cold war<br>_Nosmallgrids(Ref.)_|**0.004**|**0.001**|**0.994**|0.002|0.002|0.268|
|<br>Small grids<br>|**-0.008**|**0.002**|**1.000**|**-0.006**|**0.003**|**0.043**|
|Continuous variable<br>Sample size|0.000|0.000|0.140|-|-|-|
|**(vii) Confict characteristics**<br>_Usualconfictdata(Ref)_|||||||
|_._<br>Unusual confict data<br>_Confict incidence (Ref.)_|0.000|0.001|0.042|-|-|-|
|Confict duration|0.000|0.000|0.103|-|-|-|
|Confict onset<br>|0.000|0.000|0.028|-|-|-|
|_No local conficts (Ref.)_<br>Confict local<br>_Nonlethal(Ref)_|0.001|0.002|0.140|-|-|-|
|_._<br>Lethal<br>|0.000|0.001|0.087|-|-|-|
|_Others (Ref.)_<br>Armed conficts<br>Crimes|0.000<br>0000|0.000<br>0001|0.018<br>0089|-<br>-|-<br>-|-<br>-|
|Sil|.<br>0000|.<br>0001|.<br>0062||||
|oca unrests<br>Violence against citizens|.<br>0.000|.<br>0.000|.<br>0.020|-<br>-|-<br>-|-<br>-|
|**(viii) Measures of shocks**|||||||
|_Noquasiexperimental(Ref)_|||||||
|_._<br>Quasi experimental<br>|**-0.006**|**0.001**|**1.000**|**-0.005**|**0.002**|**0.028**|
|_No labor intensive (Ref.)_<br>Labor intensive resources<br>_Oth(Rf)_|0.000|0.000|0.014|-|-|-|
|_ers e._<br>Climatic shock<br>|**0.003**|**0.001**|**0.809**|**0.003**|**0.002**|**0.096**|
|Price shock|0.000|0.001|0.133|-|-|-|
|**(ix) Model specifcation**|||||||
|_No past confict (Ref.)_<br>|||||||
|Control for past conficts<br>|0.000|0.001|0.139|-|-|-|
|_No spatial factor (Ref.)_<br>Control for confict spatial factor<br>_Nooulation(Ref)_|-0.006|0.002|0.929|-|-|-|
|_pp ._<br>Control for population<br>|**-0.003**|**0.000**|**1.000**|**-0.003**|**0.002**|**0.036**|
|_No GDP (Ref.)_<br>Control for GDP|**0.007**|**0.001**|**1.000**|**0.004**|**0.002**|**0.042**|

Notes: All models are estimated on the whole sample of 2,464 observations and 64 studies focusing on positive agricultural shocks, with the weight being the inverse of _SE_<sup>_pcc_</sup> . The dependent variable is the partial correlation between shocks and conflicts. SD = standard deviation. SE = standard error. PIP = posterior inclusion probability. Bayesian model averaging (BMA) employs the unit information and the dilution prior (UIP g-prior; uniform model prior on the BMS package by Zeugner and Feldkircher (2015)) suggested by George (2010). The frequentist check (OLS) includes only explanatory variables with a PIP above 50 percent ( _≥_ 0.50) in the BMA and is estimated using clustered standard errors (cluster at the study level). In bold, explanatory variable with a PIP above 50 percent ( _≥_ 0.50) (BMA) and/or statistically significant at 10 percent or less (OLS). _Source: Authors’ compilation from MRA database._ 33