metadata: authors: 0: author: Laville,Camille
1: author: Mandon,Pierre Jean-Claude
count: World
docna: 0: docna: Internal Conflicts and Shocks. A Narrative Meta-Analysis
docty: Policy Research Working Paper
abstracts: cdata!: Do income shocks locally affect internal
            conflicts? To address this question, this paper employs a
            meta-regression analysis of 2,464 infranational estimates
            from 64 recent empirical studies on conflicts and
            income-related shocks in developing countries. After
            accounting for publication selection bias, the analysis
            finds that, on average, wealth-increasing shocks in the
            agriculture sector are negatively associated with the local
            risk of conflict. Nonetheless, the analysis finds no average
            effect of wealth-decreasing shocks in the agriculture sector
            or wealth-increasing shocks in the extractive sector on the
            local risk of conflict. The paper also shows that studies
            that fail to uncover empirical effects that conform to
            researchers&#x2019; expectations on the theoretical
            mechanisms are less likely to be published. Differences in
            the geographical area of study, the choice of control
            variables, and the way shocks are measured substantially
            explain the heterogeneity among estimates in the literature.
display_title: Internal Conflicts and Shocks. A
            Narrative Meta-Analysis
listing_relative_url: /research/2023/02/34008307/internal-conflicts-shocks-narrative-meta-analysis
topicv3: Economic Shocks and Vulnerability m1326506 702,Fragility, Conflict and Violence m1326586 2432,Community Driven Development and Fragile
            and Conflicted Affected States m1327188 927
publishtoextweb_dt: 2023-02-23T00:00:00Z
dois: 10.1596/1813-9450-10315
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 1492862
model_extractions: Policy Research Working Paper 10315 Internal Conflicts and Shocks. A Narrative Meta-Analysis _Camille Laville Pierre Mandon_ Macroeconomics, Trade and Investment Global Practice February 2023
Policy Research Working Paper 10315

# **Abstract**

Do income shocks locally affect internal conflicts? To address this question, this paper employs a meta-regression analysis of 2,464 infranational estimates from 64 recent empirical studies on conflicts and income-related shocks in developing countries. After accounting for publication selection bias, the analysis finds that, on average, wealth-increasing shocks in the agriculture sector are negatively associated with the local risk of conflict. Nonetheless, the analysis finds no average effect of wealth-decreasing shocks in the agriculture sector or wealth-increasing shocks in the extractive sector on the local risk of conflict. The paper also shows that studies that fail to uncover empirical effects that conform to researchers’ expectations on the theoretical mechanisms are less likely to be published. Differences in the geographical area of study, the choice of control variables, and the way shocks are measured substantially explain the heterogeneity among estimates in the literature.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pmandon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Internal Conflicts and Shocks. A Narrative Meta-Analysis<sup>*</sup> Camille Laville<sup></sup> Pierre Mandon<sup></sup> JEL Classification Numbers: C19; C83; D74; H12; H56; Q34 Keywords: Conflicts; climate shocks; commodity shocks; natural resources; income-driven conflicts; meta-regression analysis

> * Unit: EAWM2, Macroeconomics, Trade & Investment Global Practice. Activity: background research for Eco-

> nomic Updates and other ASAs. TTL: Pierre Mandon

>  Researcher, Chair of Defense Economics, IHEDN. Associate researcher, CERDI-CNRS-IRD.

>  Country Economist, Macro Trade Investment (MTI) Global Practice, World Bank Group.
# **1 Introduction**

In the last ten years, 528,763 violent events have taken place worldwide, resulting in the loss of 904,881 human lives.<sup>1</sup> This human toll, which does not account for indirect deaths (including from displacement, deprivation, and disease), surpasses the estimated number of battle-deaths from the Iran–Iraq War (1980–1988) or the Afghan Civil War (1978–2002) (Lacina and Gleditsch, 2005). In 35% of these battles, violence against civilians or riots were reported in low-income economies and only 3% in high-income countries. This gap widens further in terms of human toll, with only 1% of total casualties reported in high-income countries, compared to 58% in low-income economies. By 2030, up to two-thirds of the world’s extremely poor people are expected to live in countries affected by violence and conflict (World Bank, 2020). In other words, contexts combining extreme poverty and exposure to conflict may become more common in the upcoming years. While there is little doubt about the damaging consequences of conflict on development (see for example Abadie and Gardeazabal, 2003; Islam et al., 2016), the effect of local economic conditions (i.e. incomes and economic prospects) on the risk of conflict is subject to academic discussion about the explanatory mechanisms and the appropriate empirical strategies to model them (Bazzi and Blattman, 2014; Laville, 2019). Understanding how incomes and economic prospects locally affect the risk of conflict is therefore important in order to provide adequate policy recommendations to low to intermediate income countries concerned about “conflict traps”<sup>2</sup> and exposed to income shocks related to climate change and commodity price disruptions.

Several empirical studies find that low levels of national incomes are robustly correlated with higher risks of internal conflicts (Hegre and Sambanis, 2006; Blattman and Miguel, 2010). However, the direction of the causal relationship is difficult to establish, especially at the infra-national scale. First, there is an endogeneity issue; the long-term negative consequences of insecurity on the economies and institutions could largely influence the direction of the relationship (Corral et al., 2020). Second, aggregated measures of poverty are coarse proxies for the economic constraints faced by individuals, especially in conflict affected areas (Laville, 2019). Third (and corollary), there is a lack of micro data, since obtaining micro evidence through field surveys is limited by obvious security concerns (Axinn et al., 2012). Fourth, other hard to quantify individual considerations might

> 1Source: Authors’ compilation from ACLED for the period between November 18, 2012 and November 18, 2022.

> 2In essence, the idea that civil war begets more civil war (See Collier, 2003).

2
be at play. For example, the very idea that internal conflicts are linked to rational individualism is sometimes questioned given the importance of other social and historical conditions and constraints (see, e.g., Cramer, 2002). Since the 2010s, the use of conflict location data and satellite imagery has offered new avenues for understanding in which local contexts violence develops. Empirical frameworks based on small spatial units (especially grid-cells) and georeferenced data introduce key sources of heterogeneity at the local scale, like the location of mineral deposits (Maystadt et al., 2014; Berman et al., 2017) or the volume of production/exports in each area (Dube and Vargas, 2013; Berman and Couttenier, 2015; McGuirk and Burke, 2020). These variations can then be exploited using quasi-experimental frameworks to isolate and test the validity of one mechanism among others (Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015; Laville, 2019). For example, using grid-cells and georeferenced data on conflicts in Sub-Saharan Africa, Berman and Couttenier (2015) conclude that positive income shocks - proxied by shocks in global demand for the main good exported by the cell - decrease conflict probability in the cell,<sup>3</sup> and von Uexkull (2014) find that negative income shocks - proxied by the exposure of rainfed croplands to drought - significantly and substantially increase the risk of conflict. Although these two studies reach similar results, they present key methodological differences in the sign and proxies used to define the shock. This makes it difficult to establish income as a key local factor of conflict.

Figure 1 presents the distribution of the _t_ -students from 1.391 point estimates from 61 studies on income shocks and conflicts.<sup>4</sup> Figure 1a shows that the literature associates almost twice as often income changes with adverse rather than positive effects on conflict, suggesting a possible asymmetric research focus on the link between income and violence. In Figure 1b, we adjust the _t_ -students so that the _ex-ante_ assumed effects all work in the same direction.<sup>5</sup> It reveals that finding a result in line with _ex-ante_ expectations is twice more frequent (66.8 % of estimates) than finding one with an unexpected signs (33.2 % of counter-intuitive or null effects). This coincidence between the obtained and expected results may suggest the presence of genuine effects or manipulations by researchers, namely, publication selection bias (i.e., to prefer results with signs consistent with their expectations or prior understanding, or results with higher statistical significance).

> 3This effect disappears when spatial units are countries, suggesting a local nature of the explanatory mechanism.

> 4Figure 1 only presents the sample of direct estimates from the MRA database of this study.

> 5 We reverse the sign of _t_ -statistics for regressions focusing on theoretically pacifying mechanisms as they are the only channels with an _ex-ante_ expectation of reducing risks of conflicts.

3
<!-- Start of picture text --> 90.0%<br>80.0%<br>70.0%<br>oo<br>200%<br><!-- End of picture text --> <!-- Start of picture text --> 90.0%<br>80.0%<br>70.0% .<br>coon<br>no<br><!-- End of picture text -->
(Type I publication bias); and (ii) promote results with higher statistical significance (Type II publication bias). After filtering them out, we report the presence a genuine negative effect of positive agricultural shocks on conflicts. We also provide evidence that the section of the literature studying adverse or unspecified mechanisms favors empirical results showing a higher risk of conflict, independently of the sector of the economy affected by the shock. Using Bayesian model averaging techniques, we show that several methodological choices made by the authors explain diverging results in the literature. We find that the most influential moderators relate to the direction of the explanatory mechanism (stressful or pacifying/undefined), the characteristics of the spatial units (their size and the geographic region studied), the method of measuring the shock (through climatic variables, through price variations, in the area where the resource is produced/located), the inclusion of interactions, and the control variables included (GDP or population size).

This study is, to our knowledge, the first MRA covering the literature on the effect of income shocks on the risk of conflicts. Previous meta-analyses have examined how the risk of conflict responds to natural resource endowment (O’Brochta, 2019; Vesco et al., 2020), commodity price shocks (Blair et al., 2021), and climate change (Hsiang et al., 2013, 2014). The present metaanalysis aims to reconcile all these different approaches through the prism of income shocks; as highlighted by recent literature reviews (Couttenier and Soubeyran, 2015; Laville, 2019), natural resource endowment, (commodity) price shocks and climate change could affect the risk of conflicts through income shocks both for active (or potential) warring groups and individuals. Furthermore, scholars have repeatedly expressed concerns about the presence of biases (including the publication selection bias) in the conflict literature due to the great variety of tested mechanisms, outcomes and empirical strategies (Ioannidis, 2005; Dixon, 2009; Bazzi and Blattman, 2014), the selection of control variables according to statistical significance and not theory (Ward et al., 2010), or the fragility of published results in studies conducted at the country/year level (Hegre and Sambanis, 2006; Ward et al., 2010). This work takes up several of these criticisms and determines whether they are justified in the context of work done at the sub-national scale on income shocks and conflicts.

The paper is organized as follows. Section 2 presents the different mechanisms linking income shocks to conflicts according to the literature. Section 3 discuss the construction of the meta-sample. Section 4 deals with publication bias. Section 5 explores different sources of heterogeneity in the collected estimates. Section 6 summarizes our findings and concludes.

5
# **2 Review of the Mechanisms Involved in the Literature**

Among all the possible channels of transmission between local incomes and conflicts, the literature emphasizes the role of _opportunity cost_ and _rapacity_ (Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015).<sup>6</sup> The _opportunity cost_ mechanism posits that lower wages in productive sectors of the economy increase relative gains from violent appropriation, which lowers the opportunity cost of conflict and increases the risk of predatory behaviors (Grossman, 1991; Hirshleifer, 1995; Collier, 1998). Alternatively, the _rapacity_ effect states that a rise in contestable income increases the risk of conflict by raising gains from appropriation. Put differently, rising commodity prices increase the rent from their capture and the taxes collected in production areas, facilitating the financing and recruitment capabilities of armed groups (Reuveny and Maxwell, 2001; Collier et al., 2008) and increasing their capacity to sustain a rebel movement (Berman et al., 2017).<sup>7</sup> In sum, a positive wealth-increasing shock, typically a higher selling price for a given produced commodity, could both increase conflict through rapacity effects, or decrease it through the opportunity cost mechanism. This comes from the fact that wealth-increasing shocks can simultaneously affect wages and returns to conflict. In a simple general equilibrium model, Dal B ́o and Dal B ́o (2011) predict that a key consideration in determining whether an income shock will increase or decrease the risk of conflict is the nature of the economic sector where it occurs. They find that positive shocks to labor-intensive industries diminish the risk of conflict, while positive shocks to capital-intensive industries increase the risk. A shock to a capital-intensive sector expands the capital-intensive industry and contracts the labor-intensive one. As a result, labor is relatively less scarce, resulting in lower wages and lower costs of appropriation activities relative to the amount of appropriable resources. Empirical evidence supports their findings. In Colombia, Dube and Vargas (2013) find that a rise in the price of coffee, a labor-intensive commodity, decreases violence in production areas, while a rise in the price of oil, a capital-intensive commodity, increases violence.

> 6A third mechanism, the _state capacity_ , posits that adverse income shocks expose the state to negative growth, which constrains investment in national counterinsurgency capacities (Fearon and Laitin, 2003). This channel appears however off the scope of this paper since it takes place at the national level and/or supposes that the state largely finance its operations by collecting tax revenues locally (which is a strong hypothesis for many low to intermediate income countries).

> 7Other channels of transmission have been investigated by the literature, like separatist ambitions in resource-rich regions (Morelli and Rohner, 2015), lower incentives to develop sufficient state capacity to discourage or buy off rebellion in rentier and resource-dependent states (Fearon, 2005), and grievances from environmental degradation and lack of mining jobs (Ross, 2004).

6
They explain these results by the variation in wages affecting the opportunity cost of conflict, and by the rapacity of armed groups seeking to capture the higher oil rents. Fjelde (2015) and Berman and Couttenier (2015) also find results in line with the opportunity cost channel for labor-intensive agricultural commodities produced in Africa.

Recent empirical evidence suggests that the opportunity cost is not an exclusive transmission channel for labor-intensive goods, nor is rapacity exclusive to capital-intensive goods. Commodities’ lootability and producers’ taxation opportunities can also influence the nature of transmission channels. Rapacity can be a relevant transmission channel for labor-intensive export resources if armed groups can tax producers. In Colombia, Angrist and Kugler (2008) show that the increase in the world price of cocaine (whose production is labor intensive) in the 1990s did increase the quantity produced but had a modest impact on producers’ incomes. Indeed, the wealth created was captured by armed groups and the number of violent events increased in the producing regions. Crost and Felter (2020) also find a higher risk of conflict when the price of bananas (a high-value exported commodity) increases. In the Democratic Republic of Congo, S ́anchez de la Sierra (2020) finds that positive demand shocks on coltan, a labor-intensive mineral, increase violence and suggests that the effect of the opportunity cost channel can be overridden by a taxation-induced rapacity effect.

Income shocks can also have different effects on the risk of conflict when they impact producers or consumers. In Sub-Saharan Africa, McGuirk and Burke (2020) find that higher commodity prices reduce conflict over the control of territory (what they call “factor conflict”) in food-producing areas, and increase conflict over the appropriation of surplus (“output conflict”) in food-consuming areas. Using survey data in Nigeria, Abidoye and Cal`ı (2021) find that higher prices of consumed goods increase the risk of conflict by reducing consumers’ incomes (in line with the opportunity cost channel), while higher oil prices increase the risk of conflict in oil-producing areas (in line with the rapacity effect). Concerning less organized and violent forms of collective actions, a large body of empirical research finds that food prices can act as a trigger for urban riots and social unrest (Bush, 2010; Bellemare, 2015; Hendrix and Haggard, 2015). They generally assume that relative deprivation and grievances are the main explanatory channel, although it is difficult to provide empirical evidence of causality (Martin-Shields and Stojetz, 2019). Another explanatory mechanism is the breakdown of state authority and legitimacy when it fails to provide food security (Arezki and Brueckner, 2014; Buhaug et al., 2015).

7
# **3 Construction of the Meta-Sample**

## **3.1 Studies’ Collection Strategy and Inclusion Criteria**

The first step in meta-analysis is to identify a sample of relevant studies. We use three research methods. First, we use reference snowballing techniques on two seminal reviews of the conflict literature: Couttenier and Soubeyran (2015), and Theisen (2012). Second, we collect relevant references from three meta-analysis on related topics: Blair et al. (2021) (on price shocks and conflicts), Vesco et al. (2020) (on natural resources and conflicts), and Hsiang et al. (2013, 2014) (on climate changes and conflicts). Third, we run keyword searches on Google Scholar and extract the results by web scraping using the R package ‘rvest’ (Wickham, 2022). We search for keyword associations in the format ‘ _”keyword1 shocks and keyword2”_ ’ (in whole documents) and ‘ _”keyword1 shocks” keyword2_ ’ (in reference titles), where _keyword1_ specifies the type of shock ( ‘income’, ‘price’, ‘natural resource’, ‘climate’, ‘climatic’ or ‘environmental’) and _keyword2_ indicates the type of conflict outcome (‘conflict’, ‘war’, ‘violence’, ‘unrest’). To be as comprehensive as possible, we also looked for plural or singular forms of keywords. A total of 112 different keyword associations were searched on Google Scholar in August and September 2022.

Our searches identify 565 potentially relevant studies (22 from literature reviews, 138 from meta-analysis, and 405 from keyword searches). To ensure the coherence of our sample, we choose seven inclusion criteria. We only select papers (i) published in peer-reviewed academic journals,<sup>8</sup> (ii) between 2010 and 2021, and (iii) reporting exploitable empirical results (including regression coefficients, sample size, standard errors and/or _t_ -statistics). We focus on studies (iv) where the outcome variable is the onset (i.e. conflict start), the incidence (i.e. number of conflicts), or the duration of a form of internal conflict,<sup>9</sup> and (v) where units of analysis are sub-national (e.g., grid-cells, municipalities, states, prefectures, or districts). We only select studies (vi) analyzing at least one income-related channel of transmission and (vii) in one or several non-OECD high income countries.<sup>10</sup> Sixty-four studies meeting our selection criteria compose our final sample. Figure A3

> 8We therefore exclude working papers that may be of high quality in terms of content but have not been subjected to the peer review process.

> 9We leave aside their intensity (i.e., number of fatalities) to limit causal heterogeneity between studies since these analysis refer to quite different explanatory mechanisms (see, for example, Lacina, 2006).

> 10Source: World Bank Country and Lending Groups, `https://datahelpdesk.worldbank.org/knowledgebase/ articles/906519-world-bank-country-and-lending-groups` , accessed in November, 2022.

8
<!-- Start of picture text --> 200<br>| :<br>|<br>> |<br>rs)<br>()Cc : | : :<br>= 100 E |<br>®<br>Le : 5<br>;I<br>:I<br>0 SOs {Latilh | | Ot Pia<br>10 -9 8 7 6 5 4 3 2 1 012 3 4 5 6 7 8 9 10<br>t-statistics<br><!-- End of picture text -->
median values are 0.6 and 0.9. Of the _t_ -statistics, 22% are below -2 and 36% are above 2. Values exceeding 10 or inferior to -10 only represent 0.9% of the sample, with minimum and maximum values of -27.7 and 46.8. To prevent potential distortions caused by the presence of outliers, we winsorize _t_ -statistics and degrees of freedom at the top and bottom of 5% level (Lipsey and Wilson, 2001; Viechtbauer and Cheung, 2010).<sup>11</sup>

# **3.3 Grouping the Collected Estimates According to Their Main Transmission Channels**

The collected estimates test the relationship between the risk of internal conflict and different types of positive or negative shocks. As a result, our meta-sample is heterogeneous in terms of the variables of interest and the transmission channels implicitly tested by the authors. Analyzing this raw sample would complicate the interpretation of the MRA’s results and limit our contributions to two central debates in conflict economics, namely what economic mechanisms are at play and how to test them empirically. We therefore split the collected estimates into four meta-regression subgroups that differ in the direction of the shock (wealth increasing or decreasing) and the sector of activity that is affected (agriculture, extractive or other sectors). Indeed, the literature suggests that income shocks affecting the extractive or the agriculture sector do not refer to the same main channel of transmission (Dal B ́o and Dal B ́o, 2011).

The first group, **_Negative Agricultural Shock_** ( **AS-** ), contains all estimates of negative transitory agricultural shocks (e.g., droughts, floods, rain deficiencies, etc.). The second one, **_Positive Agricultural Shock_** ( **AS+** ), includes all estimates of positive transitory agricultural shocks (e.g., increased demand and international prices for the cultivated good, environmental conditions particularly suitable to its production, etc.). The third group, **_Positive Hydrocarbon/Mineral Shock_** ( **HS+** ), contains all estimates of transitory shocks on extractive goods (i.e., hydrocarbon and minerals), including increases of the international price of the resource and subsidies to mining concessions. The other estimates fall into the heterogeneous category **_Other Shocks_** , which includes estimates of (positive or negative) pure climatic shocks that are not explicitly related to agriculture, labor market shocks, financial crisis, or shocks to the drug sector.

> 11Winsorisation corrects biases linked to extreme values without losing observations. It consists in replacing the outliers by the highest values in given percentiles.

10
Following the literature, we may expect shocks affecting the agriculture sector to mainly test poverty related mechanisms (notably, the opportunity cost channel) as they affect more laborintensive commodities and as rapacity effects concern a specific subset of high-value agricultural exports. Alternatively, we may expect shocks affecting the extractive sector to mainly test rapacity channels as they focus on more capital-intensive commodities. As a matter of fact, Table 1 confirms our expectations. Of the 808 estimates focusing on positive agricultural shocks, 648 (80%) test poverty-related mechanism and 151 (19%) test rapacity effects. Of the 951 estimates focusing on negative agricultural shocks, 798 (84%) test poverty-related mechanism and only 5 (0.5%) test rapacity effects, and 139 (15%) test other channels of transmission (including 107 cases of regional destruction).

Table 1: Main Implicitly Tested Mechanism in Estimates According to their Meta-Regression Subgroup

||Agricultural shock (+)|Agricultural shock (-)|Hydrocarbon/Mineral shock (+)|Other shock (+ or -)|Total|
|---|---|---|---|---|---|
|_Mechanism_||||||
|Poverty-related|648|798|82|46|1574|
|Rapacity-related|151|5|423|4|583|
|Other|4|139|38|0|181|
|Not specifed|5|9|0|112|126|

Notes: Number of estimates in the total sample of 2,464 estimates. Other shock (+ or -): estimates of negative or positive pure climatic shocks, estimates of labor market shocks, financial crisis, and estimates of shocks to the drug sector. Poverty-related mechanisms include the dynamism of local economy, food scarcity, budget constrains and water insecurity. Rapacity-related mechanisms include rent capture and the funding of insurgents. Other mechanisms include exodus, grievance, imperfect information, regional destruction (incl. agricultural soils) and state capacity. _Source: Authors’ compilation from MRA database._ Figure 3 presents the proportion of collected estimates in each meta-regression subgroup. Our three main subgroups (AS-, AS+, and HS+) account for 90.2% of the baseline sample of 1,391 regressions and 66.8% of the baseline estimates concern agricultural shocks (71.4% of the total sample of 2,464 regressions). Other shocks only account for 6.5% of the total sample. Interestingly, while selected studies on the agriculture sector investigate equally positive and negative shocks, studies focusing on the hydrocarbon/mineral sector only investigate positive shocks. Detailed information on the types of shocks in each meta-regression subgroup is provided in Figure A1, Table A1, and Tables A5 to A8 in the Appendix.

11
<!-- Start of picture text --> AGRICULTURAL SHOCK (-)<br>34.6%<br>AGRICULTURAL SHOCK (+) _ 32.2%<br>HYDROCARBON & MINERAL SHOCK (+) __ van<br>OTHER l 1.4%<br>3 (+) 7 i<br><!-- End of picture text -->
tendency to select their models based on conventionally accepted results (Card and Krueger, 1995; Stanley and Doucouliagos, 2012). By filtering these publication biases, we can determine, if any, the ”true effect” (or ”genuine effect”) of income shocks on the risk of internal conflict.

# **4.1 At First Glance (Funnel Plots)**

To illustrate the distribution of observations, we produce a funnel chart by plotting the partial correlation against precision (the inverse of its standard error).<sup>12</sup> By construction, estimates with a larger standard error (less precision) are spread at the bottom of the graph while those that are more precise form the top of the funnel. In the absence of publication selection bias, the funnel plot should be symmetric, with observations randomly distributed around the “true” effect (Egger et al., 1997; Stanley, 2007; Stanley and Doucouliagos, 2012).

We have _ex-ante_ expectations about the selective report of scholars and researchers about higher or lower risks of conflicts, depending on the transmission channels. We may suspect lower risks of conflicts in case of positive agricultural shocks (AS+) (e.g., increase in demand and international producers’ prices, exceptional rainfalls). On the contrary, we may suspect higher risks of conflicts in case of negative agricultural shocks (AS-) (e.g., droughts, floods, rain deficiencies), and positive hydrocarbon/mineral shocks (HS+) (e.g., increase in mineral or oil international prices, subsidies to mining concessions). As AS- represent more than one third (38.6 percent, see Figure 3) of the 2,464 observations, we would expect either a funnel plot centered close to zero and/or an asymmetry towards higher risks of conflicts.

Figure 4 highlights a mix of both intuitions; the upper-left chart suggests potentially null average genuine effects as the more precise estimates (at the top of the funnel) are closely distributed around zero. Moreover, the funnel appears slightly right-skewed (i.e., an asymmetry towards higher risks of conflict) for less precise estimates (at the bottom of the funnel), indicating the likelihood of publication selection bias, altogether with potential genuine effects. Switching to subgroups analysis, we find a right-skewed distribution (i.e., an asymmetry towards higher risks of conflict) when focusing on the of impact of AS-, a surprisingly slightly left-skewed distribution (i.e., an

> 12Partial correlation is computed as _r_ = _~~√~~ t_<sup>2</sup> _<u>t</u>_ + _df_<sup>, where</sup><sup>_t_is the t-statistic of the regression coefficient and</sup><sup>_df_denotes</sup> the degrees of freedom. Partial correlation coefficients measure the strength and direction of the association between potential determinants of conflicts and conflicts’ outcomes, holding all other factors constant. The standard error of the partial correlation is computed as ~~�~~ (1 _− r_<sup>2</sup> ) _/df_ in line with Stanley and Doucouliagos (2012).

13
<!-- Start of picture text --> Funnel plot on baseline coefficients [N=1,391; skweness = 0.460]<br>1000 |<br>ao '<br>?\ 800 !<br>[o} 1<br>2 1<br>!<br>2 600 i<br>=@ 11<br>28 400 Pex<br>3 Es<br>5o =|"<br>‘3a2 200ol ten ee eee attFob+ Saye.tteAtte 1 LER tata ae + OtherAgr.Agelydr/Min. anockshock o(+)~shockke)(+<br>-0.20 -0.10 0 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->

<!-- Start of picture text --> Funnel plot on negative agricultural shocks [N=481; skweness = 1.175]<br>1000 t<br>1<br>Ww—?\ 00 11!<br>2°Q 1 1<br>1<br>£& 600 1<br>g 1<br>3 1<br>2 400 j<br>225\I<br>2 200 ra<br>a i r<br>0<br>-0.20 -0.10 0) 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text --> <!-- Start of picture text --> Funnel plot on positive agricultural shocks [N=448; skweness = 0.429]<br>500 I<br>ity I<br>2 400<br>fo} 4<br>° #1<br>= anal<br>2 300 1<br>=@ batt+|<br>28 200 &:|<br>rc) |<br>53 iineteeaf |e ae<br>img2 1006 + + te te eee.tetRiceeagreeett4a ee Fe a|1 ae”Raett 4+atotheete hte“ .+<br>-0.20 -0.10 0 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->

<!-- Start of picture text --> Funnel plot on positive hydrocarbon/mineral shocks [N=325; skweness = -0.386]<br>1000 I<br>1<br>Ww—21 800 11!<br>2°Q !1<br>= 1<br>2 600 1<br>g !<br>B 1<br>23 400 i\<br>25 Hli<br>2 200 :<br>a2 i l<br>1<br>0<br>-0.20 -0.10 0) 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->
(Balima et al., 2020). To capture the between-study heterogeneity while controlling for withinstudy dependence, a multilevel model<sup>13</sup> is most appropriate (see, e.g., Doucouliagos and Laroche, 2009; Doucouliagos and Stanley, 2009). The multilevel model accounts for within-study dependence through the inclusion of a random individual effect for each study, hence the reference to a multilevel random effect model. More specifically, the following Equation 1 is considered: where _effectij_ stands for the _i_<sup>_th_</sup> estimate from the _j_<sup>_th_</sup> study on the effect of income shocks on conflicts; _SEij_ for the standard error (or accuracy of the estimate) of _i_<sup>_th_</sup> estimate from the _j_<sup>_th_</sup> study. _λj_ is the study level random effect and _εij_ is a disturbance term. _β_ 1 stands for the ”true value” of the effect of income shocks on conflicts. _β_ 0 _SEij_ captures the ”noise” or very tendency from researchers to prefer results that are statistically significant, and thus to make use of alternative estimation techniques and/or model specifications to get high significance levels, notably in the face of a small sample. If the number of observations increases indefinitely, the standard error _SEij_ will tend towards zero (i.e., the smaller the standard error, the more accurate and reliable the estimate), and the estimated effect of income shocks will get closer to _β_ 1. In other words, if there is no publication selection bias, the collected estimates will vary randomly around the ”true value” _β_ 1, regardless of the standard error. We correct for heteroscedasticity owing to differences across studies in the sample size and model specifications used by dividing Equation 1 by _SEij_ , which becomes Equation 2: where _tij_ represents the collected t-values. We then assess the existence of **Type I publication selection bias** by testing the null hypothesis that the intercept in Equation (2) is equal to zero ( _β_ 0 = 0), which is also known as the ’Funnel Asymmetry Test’ - ’Precision Effect Test’ (FAT-PET). Basically, if the intercept is statistically significant, it means that the collected estimates do not vary symmetrically and randomly around the ”genuine effect” ( _β_ 0 _SEij_ in Equation 1 has an influence). In this case, it is as if part of the evidence (estimates in the literature) is missing, resulting in a

> 13In the present case, a model allowing for heterogeneity both at the study (or paper) level and the estimate level.

15
truncated plot.<sup>14</sup> Our MRA focuses on the link between positive and negative income shocks, hence on estimates with likely opposite signs. To ensure comparability of estimates, we replace the left-hand side of Equation 2 with the absolute t-student value and consider alternatively adjusted t-student values: we reverse the signs of _t_ -statistics for regressions focusing on pacifying mechanisms as they are the only channels with an _ex-ante_ expectation of reducing risks of conflicts. This gives us Equation 3: Testing the null hypothesis ( _β_ 0 = 0) in Equation 3 assesses the presence of **Type II publication selection bias** . To check the presence of a ”genuine” effect after filtering out potential publication bias, we follow Stanley and Doucouliagos (2012) and carry out the so-called Precision Effect Estimate with Standard Error (PEESE) test. Concretely, we test the null hypothesis that the parameter associated with the inverse standard error ( _β_ 1) in Equation 2 equals zero. In other terms, we test whether in Equation 1, the intercept ( _β_ 1 or genuine effect) has a statistically significant role regardless of the outcome of the publication selection bias (influence of _β_ 0 _SEij_ ). Rejecting the null hypothesis would thus signal that a genuine effect remains after filtering out the publication bias.

In addition to the FAT-PET-PEESE procedure, we estimate the bias-adjusted genuine effect using the Weighted Average of Adequately Powered (WAAP) estimator (Ioannidis et al., 2017; Stanley et al., 2017). The WAAP is a weighted average that uses optimal weights (1 _/SEij_<sup>2)onthe</sup> only ’adequately powered’ estimates, which are usually defined as having standard errors smaller than the multilevel mixed-effects model estimates divided by 2.8 (Stanley et al., 2017).

# **4.3 Results**

Table 2 reports the associated results for the whole sample of 2,464 estimates ( _Panel A_ ) and our baseline sample of 1,391 estimates ( _Panel B_ ). Columns [1] and [2] present results for **Type II** publication bias, using respectively the adjusted and the absolute _t_ -statistics. Columns [3] to [7] depict results for **Type I** publication bias considering continuous _t_ -statistics for each type of shock (i.e., each meta-regression subgroup).

> 14Hence the reference to a ”funnel asymmetry” test.

16
For both _Panel A_ and _Panel B_ , the intercepts (FAT) in columns [1] and [2] are positive<sup>15</sup> and highly significant, pointing to the existence of Type II publication bias. This suggests that researchers have incentives to promote results with higher statistical significance, in line with most MRA findings (Doucouliagos and Stanley, 2013).

Table 2: Publication Selection Bias and Genuine Effect Tests [Baseline Results]

||[1]|[2]|[3]|[4]|[5]|
|---|---|---|---|---|---|
||**All local**<br>Absolute t-student|**shocks**<br>Adjusted t-student|**Main**<br>Agr. shock (+)|**channels of tr**<br>Agr. shock (-)|**ansmissison**<br>Hydr./Min. shock (+)|
|**Panel A: whole sample**<br>||||||
|**[i] FAT-PET**||||||
|**_Mean beyond bias (PET)_**||||||
|Precision (1/SE)|**-0.000 ***<br>|0.000<br>|**-0.003 *****<br>|-0.001<br>|0.000<br>|
||**(0.000)**|(0.000)|**(0.001)**|(0.001)|(0.001)|
|**_Publication bias (FAT)_**||||||
|Constant|**2.383 *****|**1.381 *****|-0.312|**1.641 *****|0.566|
||**(0.119)**|**(0.184)**|(0.397)|**(0.296)**|(0.460)|
|**[ii] WAAP**||||||
|**_Mean beyond bias_**||||||
|Constant|-|-|-|-|-|
||-|-|-|-|-|
|#studies|64|64|25|34|14|
|Observations|2 464|2 464|808|951|543|
|%Observations|100%|100%|33%|39%|22%|
|**Panel B: baseline coefcient**|**s only**|||||
|**[i] FAT-PET**||||||
|**_Mean beyond bias (PET)_**||||||
|Precision (1/SE)|0.000|0.001|**-0.005 *****|0.000|0.001|
||(0.000)|(0.000)|**(0.001)**|(0.001)|(0.001)|
|**_Publication bias (FAT)_**||||||
|Constant|**2.502 *****|**1.745 *****|-0.084|**2.165 *****|0.708|
||**(0.129)**|**(0.223)**|(0.493)|**(0.324)**|(0.601)|
|**[ii] WAAP**||||||
|**_Mean beyond bias_**||||||
|Constant|-|-|0.000|**0.002 *****|0.001|
||-|-|(0.001)|**(0.000)**|(0.000)|
|#studies|61|61|23|32|13|
|Observations|1 391|1 391|448|481|325|
|%Observations|100%|100%|32%|35%|23%|

Notes: FAT-PET models are estimated with a multilevel mixed-effects model and the weighted average of the adequately powered (WAAP) is derived from Ioannidis et al. (2017) and Stanley et al. (2017). Panel A refers to the whole sample of 2,464 observations and 64 studies and Panel B refers to the sample of baseline coefficients, including 1,411 observations and 63 studies. The dependent variable is the _t_ -statistic of the estimate of interest on conflicts as dependent variable. Standard errors, derived from observed information matrix, are reported in parentheses. Columns [1] and [2] report the results for all local shocks, using respectively the absolute value of the _t_ -statistic of the estimate of interest and the adjusted _t_ -statistic of the estimate of interest as dependent variables. Columns [3] to [5] focus on the main channels of transmission for conflict, namely positive agricultural shocks (col [3]), negative agricultural shocks (col [4]) and positive hydrocarbon/mineral shocks (for commodity exporters, col [5]). Results on pure (positive and negative) climatic shocks (respectively 39 and 85 observations over the whole sample of 2,464 observations) and other potential shocks (50 observations over the whole sample of 2,464 observations) are presented in Table A2 from the Appendices section. A detailed description of all variables is available in Table A3. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 01. _Source: MRA database._

> 15The sign of the intercept is only meaningful for the absolute measure of the _t_ -Student, its absolute value being necessarily positive.

17
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.

18
When the adjusted and absolute _t_ -statistics are used as dependent variable, it is hard to interpret the coefficients associated with 1 _/SE_ (precision parameter) as genuine effects, as the meta-group consists of a synthesis of studies that do not rest on a single transmission channel. Columns [3] to [5] of Table 2 allow going beyond the publication bias and testing for the existence of genuine effects of income shocks on conflicts for each meta-subgroup. After filtering out the publication bias (slope coefficients reported in columns [3] and [5]), we find a negative effect of AS+ and no evidence of genuine effects for AS- and HS+. Put differently, after filtering out the publication bias, we find that only positive agricultural shocks genuinely influence (here, reduce) the risk of conflict. This does not mean that AS- and HS+ have no effect on conflicts, but that their effects depend on several factors that will be discussed in the heterogeneity analysis section.

To sum up, these results show that the literature on the effects of income shocks on conflicts suffers from two types of publication bias: researchers tend to prefer studies that (i) find higher risks of conflicts when they focus on AS- (Type I publication bias); and (ii) generally promote results with higher statistical significance (Type II publication bias). We also find evidence that AS+ are associated with lower risks of conflicts. In the next section, we look at how key interactive factors might affect the baseline results.

# **5 Heterogeneity Analysis**

As we already pointed out, individual studies on the link between income shocks and conflict vary greatly in terms of data and method used. The purpose of this section is to investigate whether authors’ methodological choices systematically influence the estimated partial correlation coefficients and whether the estimated coefficients of publication bias from Section 4 survive the addition of moderator variables. Our approach to heterogeneity analysis in divided into two steps. We start by examining if sections of the literature that focus specifically on pacifying or destabilizing shocks or use interaction models are affected differently by publication bias. We then use Bayesian methods to test whether systematic differences in the data and methodological choices made by the authors explain the variations in the results that they obtain.

19
# **5.1 Publication Bias and Authors’ Initial Assumptions on the Sign and Strength of the Mechanisms at Play**

Figure 5 illustrates how our sample of selected regressions breaks down according to (i) the authors’ initial assumptions about the effect of the mechanism they are testing, and (ii) the interaction format they apply to test its robustness. We identify regressions testing a theoretically pacifying mechanism by selecting the ones where the authors analyze the effect of AS+ through any other mechanism than rapacity,<sup>18</sup> the effect of AS- through rapacity only, or the effect of HS+ through the dynamism of the local economy or the state’s capacity to impede rebellion. Alternatively, regressions testing a theoretically stressful mechanism are all regressions where the tested mechanism does not have a pacifying effect.<sup>19</sup> Additionally, we identify three categories of key interactive terms: (i) _ex-ante_ risk enhancers, (ii) _ex-ante_ risk absorbers, and (iii) interaction terms with no specific _ex-ante_ expectations (undetermined moderator). The ”risk enhancers” moderator contains all interactive terms that the authors assume will increase the risk of conflict _ceteris paribus_ (e.g., not cultivating sweet potatoes, not living in a dam downstream district, living far from key infrastructure). Conversely, the ”risk absorbers” moderator contains estimates where the variable interacted with the shock is expected to mitigate the risk of conflict (e.g., cultivating sweet potatoes, living in a dam downstream district). The ”undetermined” moderator contains all other conditional characteristics. It is not assumed to have a homogenous impact on conflicts and authors are more agnostic on average. Table A11 in the Appendix summarizes the different key interactive terms and the rationale underpinning their introduction.

Figure 5 shows that 65% of the selected regressions test a stressful mechanism, 30% test a pacifying mechanism, and 5% test a mechanism with undetermined effects. This indicates that a majority of studies on income shocks and conflicts focus on the detrimental impact of income shocks, which narrows the relative amount of empirical evidence available on local income shocks as a way out of conflicts. The figure also shows that regressions where the income shock is interacted with one or more unit-specific variables account for 40.6% of the collected estimates (1,073 estimates),

> 18As noted in the literature review section, some studies find that a positive price shock in the agriculture sector can increase the risk of conflict if it concerns a good with a high export value (Crost and Felter, 2020; S ́anchez de la Sierra, 2020), or if it affects the purchasing power of consumers (McGuirk and Burke, 2020; Abidoye and Cal`ı, 2021).

> 19For this identification process, we only take into account regressions where the authors clearly state _ex-ante_ what is the mechanism they are testing. We therefore leave aside 121 regressions for which the authors do not specify which mechanism they are testing.

20
revealing a recurrent practice in empirical models of conflicts. For example, in a study on weather shocks and peasant revolts in historical China, Jia (2014) interacts indicators of droughts and floods with sweet potatoes as food production (known to be more resilient to bad weather than rice or wheat production) as a ”risk absorber” moderator. The inclusion of interaction terms can help researchers identify the mechanisms at play by highlighting heterogeneous effects depending on the characteristics of the geographical areas studied. These moderator variables are model-specific and rarely similar across studies. Because interaction models can help identify or mitigate the action of specific mechanisms, result searching bias is likely to be captured, if existent, in the choice of interacted variables.

<!-- Start of picture text --> Indirect (+)<br>Risk Enhancer (258)<br>Risk Absorber (99)<br>Undetermined (364)<br>(878) Higher risk of conflict<br>Direct (+) (1599)<br>SHOCK<br>Direct (-) Lower risk of conflict<br>(400) (744)<br>Indirect (-)<br>Risk Enhancer (159)<br>Risk Absorber (60)<br>Undetermined (125)<br>Stressfull<br>Theoretically Mechanisms<br>Pacifying<br>Theoretically Mechanisms<br><!-- End of picture text --> Notes: Expected signs of direct and indirect (interaction) estimates. The number of observations concerned is given in parenthesis. 121 observations (not shown) refer to mechanisms with uncertain sign (including 113 direct estimates, 5 indirect estimates with a risk enhancer, 3 indirect estimates with a risk absorber). _Source: Authors compilation from MRA database._ Figure 5: Expected Signs of Direct and Indirect Channels of Transmission To test if these methodological choices are a source of publication bias per se, we augment the baseline model with dummy variables indicating the type of mechanism or the type of interaction that is tested. When the multilevel random effect model includes covariates (or moderators) accounting for heterogeneity between studies, the model becomes best described as a ”multilevel mixed-effect model”. More specifically, Equations 2 and 3 become: 21
or where _x′ij_<sup>stands for a set of meta-independent variables capturing empirical study characteristics</sup> from the meta-sample that explain the differences in estimates between studies. _β_ 0 measures the severity of publication bias conditional on the inclusion of controls, and _β_ 1 is the mean _β_ estimate corrected for publication bias but also conditional on the variables included.

Several multilevel random-effect methods have been proposed in the MRA literature to estimate the between-study variance in meta-regressions. The most used method computes the unknown variance of the random-effect model through an iterative residual (restricted) maximum likelihood process (REML), with normal distributions assumed for both the within and between-study effects. We rely on the multilevel mixed-effect REML, given its properties, namely avoiding not only downward-biased estimates of the between-study variance, but also underestimated standard errors and anti-conservative inference.<sup>20</sup> In addition, the restricted maximum likelihood (REML) is preferred to the maximum likelihood (ML) because the latter does not account for the degrees of freedom employed when estimating the fixed-effect portion of the model, which is a key shortcoming, especially given the small number of studies included in the MRA (Thompson and Sharp, 1999). The regressions are performed on the extended sample of 2,464 observations (our 1,391 baseline estimates plus 1,073 interactive terms) to study the heterogeneity in the estimated effects of income shocks on conflicts due to interactions. But to study the heterogeneity due to the ( _ex-ante_ ) direction of tested mechanisms by the authors, we do the regressions only on the baseline sample of 1,391 estimates, to exclude the potential “noisy” effects of interactive terms in the analysis.

Table 3 presents the results we obtain for AS+, AS-, and HS+ when we control for the type of mechanism tested. Coefficients associated with the analysis of a supposedly pacifying mechanism are negative and statistically significant in Columns [2], [6], and [10]. Coefficients associated with the analysis of a theoretically stressful mechanism are positive and statistically significant in Columns

> 20Anti-conservative inference happens when scholars and researchers do not update their prior and beliefs when they face incomplete information, noise, fallible data or counter-intuitive results for instance.

22
[3], [7], and [11]. This indicates that studies focusing on pacifying mechanisms in each metasubgroup report a lower risk of conflict following an income shock, while studies focusing on stressful mechanisms report a higher risk of conflict. The presence of publication bias appears conditional to the type of mechanism tested. We find a positive and statistically significant intercept when we control for pacifying mechanisms in columns [2], [6] and [10]. When we control for stressful mechanisms, the intercept in column [11] is also statistically significant (at the 0.10 level) but negative. These results indicate the presence of a substantial Type I publication selection bias in studies that do not focus on pacifying mechanisms, and in studies encompassing HS+ that do not test stressful mechanisms. In other words, the current literature favors the publication of results showing: (i) the detrimental impacts of income shocks on the local risk of conflict through theoretically stressful/undefined mechanisms, (ii) that pacifying mechanisms in the extractive sector reduce the local risk of conflict.

Table 3: Explaining Heterogeneity in the Estimated Effects of Income Shocks on Conflicts [Direction of the Tested Mechanism]

||[1]|[2]<br>AS|[3]<br>+|[4]|[5]<br>**Main c**|[6]<br>**hannels of tr**<br>AS|[7]<br>**ansmission**<br>-|[8]|[9]|[10]<br>H|[11]<br>S+|[12]|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Whole sample**|||||||||||||
|Precision (1/SE)|**-0.005 *****|**-0.005 *****|**-0.005 *****|**-0.005 *****|0.000|0.000|0.000|0.000|0.001|0.001|0.001|0.001|
||**(0.001)**|**(0.001)**|**(0.001)**|**(0.001)**|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Constant|-0.084|**1.695 *****|-0.360|-0.084|**2.165 *****|**2.475 *****|0.575|**2.172 *****|0.708|**1.242 ****|**-2.108 ***|0.708|
||(0.493)|**(0.533)**|(0.451)|(0.493)|**(0.324)**|**(0.261)**|(0.712)|**(0.331)**|(0.601)|**(0.556)**|**(1.237)**|(0.601)|
|**_Type of mechanism_**|||||||||||||
|Mecha. pacifying=1||**-2.055 *****||||**-4.885 *****||||**-3.350 *****|||
|||**(0.333)**||||**(1.043)**||||**(1.353)**|||
|Mecha. stressful=1|||**2.055 *****||||**1.814 *****||||**3.350 ****||
||||**(0.333)**||||**(0.736)**||||**(1.353)**||
|Mecha. undefned=1||||-||||-0.123<br>||||-|
|||||-||||(0.930)||||-|
|#studies|23|23|23|23|32|32|32|32|13|13|13|13|
|Observations|448|448|448|448|481|481|481|481|325|325|325|325|
|%Observations|32%|32%|32%|32%|35%|35%|35%|35%|23%|23%|23%|23%|

Notes: All models are estimated with a multilevel mixed-effects model on the whole sample of 2,464 observations and 64 studies. The dependent variable is the t-statistic of the estimate of interest on conflicts as dependent variable. Standard errors are reported in parentheses. Columns [1] to [4] focus on positive agricultural shocks, columns [5] to [8] focus on negative agricultural shocks, and columns [9] to [12] focus on positive hydrocarbon/mineral shocks. Results on pure (positive and negative) climatic shocks and other potential shocks are presented in Table A2 from the Appendices section. A detailed description of all variables is available in Table A3. The detailed composition of mechanisms is available in Tables A5, A6, and A7. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 01. _Source: Authors’ compilation from MRA database._ Table 4 presents our results when we control for the type of interactive term in the regression. Coefficients associated with the inclusion of a risk absorber interaction term are negative and statistically significant in Columns [2], [6] and [10]. Conversely, coefficients associated with the inclusion of a risk enhancer interaction term appear positive and statistically significant in Columns [3], [7] and [11]. This indicates that studies implementing interactions conclude a higher (or lower) local risk of conflict in line with the authors’ expectations. Columns [4], [8] and [12] show that coeffi23
cients associated with the inclusion of an interaction term with uncertain effects are statistically significant for shocks to the agriculture sector, but not for shocks to the extractive sector. The coefficient is positive for AS+ and negative for AS-. Therefore, these uncertain moderators tend to behave like risk enhancers of AS+, and risk absorbers for AS-. Overall, our results suggest that the authors prefer to highlight contrasted results that support the mechanisms being tested. For instance, Jia (2014) provides evidence about the detrimental impact of droughts and other negative weather shocks (AS-) on conflicts in historical Chinese prefectures (from 1470-1900) but she finds that cultivating sweet potatoes (drought resistant, risk absorber) mitigates this impact. These findings support her hypothesis that weather shocks increase the risk of revolt due to their negative impact on food supply. Columns [2], [6], and [10] indicate that the presence of publication bias depends on the type of interaction tested. Studies focusing on AS+ prefer a result for the intercept that shows a lower risk of conflict when they include an interaction with a risk enhancer. Similarly, studies focusing on HS+ and AS- tend to prefer an intercept showing a higher risk of conflict when there is an interaction with a risk absorber. This indicates a small or modest Type I publication selection bias: a tendency to select interaction terms of opposite direction as long as they support the tested mechanism.<sup>21</sup> Table 4: Explaining Heterogeneity in the Estimated Effects of Income Shocks on Conflicts [Inclusion of Interactions]

||[1]|[2]<br>|[3]<br>|[4]|[5]<br>**Main **|[6]<br> **channels of **<br>|[7]<br>**transmission**<br>|[8]<br>|[9]|[10]<br>|[11]<br>|[12]|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||AS|+|||A|S-||||HS+||
|**Whole sample**|||||||||||||
|Precision (1/SE)|**-0.003 *****|**-0.003 ****|**-0.003 ****|**-0.003 *****|-0.001|0.000|-0.001|-0.001|0.000|0.000|0.000|0.000|
||**(0.001)**|**(0.001)**|**(0.001)**|**(0.001)**|(0.001)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Constant|-0.312|-0.124|**-0.714 ***|-0.332|**1.641 *****|**2.121 *****|**1.463 *****|**1.665 *****|0.566|**0.899 ***|0.287|0.549|
||(0.397)|(0.406)|**(0.390)**|(0.400)|**(0.296)**|**(0.270)**|**(0.321)**|**(0.294)**|(0.460)|**(0.474)**|(0.467)|(0.461)|
|**_Type of interaction_**|||||||||||||
|Risk absorber=1||**-2.175 *****||||**-3.435 *****||||**-2.726 *****|||
|||**(0.241)**||||**(0.107)**||||**(0.235)**|||
|Risk enhancer=1|||**2.923 *****||||**1.405 *****||||**2.385 *****||
||||**(0.179)**||||**(0.167)**||||**(0.287)**||
|Risks uncertain=1||||**0.743 ****||||**-0.912 ****||||-0.298|
|||||**(0.318)**||||**(0.442)**||||(0.304)|
|#studies|25|25|25|25|34|34|34|34|14|14|14|14|
|Observations|808|808|808|808|951|951|951|951|543|543|543|543|
|%Observations|33%|33%|33%|33%|39%|39%|39%|39%|22%|22%|22%|22%|

Notes: All models are estimated with a multilevel mixed-effects model on the whole sample of 2,464 observations and 64 studies. The dependent variable is the _t_ -statistic of the estimate of interest on conflicts as dependent variable. Standard errors are reported in parentheses. Columns [1] to [4] focus on positive agricultural shocks, columns [5] to [8] focus on negative agricultural shocks, and columns [9] to [12] focus on positive hydrocarbon/mineral shocks. Results on pure (positive and negative) climatic shocks and other potential shocks are presented in Table A2 from the Appendices section. A detailed description of all variables is available in Table A3. The detailed composition of risk absorbers, risk enhancers and risk uncertain is available in Tables A9 and A10. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 01. _Source: Authors’ compilation from MRA database._ 21 Empirically, a lower (or higher) value of the intercept (i.e. the effect of the shock when the interaction term is null) implies a seemingly smaller (or larger) risk of conflict when the conditions of interaction term are not met.

24
# **5.2 Do the Scientific Publication Process, Data, and Methodological Choices Affect the Results?**

## **5.2.1 Model Estimated and Description of the Moderators**

In order to investigate systematic differences among the reported estimates, we select and present some key study characteristics (also called _moderators_ ) that are likely to drive the results. We code 41 variables according to the following categories: transmission channels (see Section 3.3), mechanisms and interactive models (see Section 5.1), publication outlet, geography, model characteristics, conflict characteristics, measures of shocks, and model specifications. Descriptive statistics of the moderators are detailed in Table A3 in the Appendix. For the model specification, we follow Stanley and Doucouliagos (2012): where _PCC_ is the partial correlation coefficient between local income shocks and conflicts’ outcome of the _i_<sup>_th_</sup> estimate from the _j_<sup>_th_</sup> study, _SE_<sup>_pcc_</sup> denotes the standard error of the PCC, and _εij_ is the error term. The vector _x′ij_<sup>standsforasetofmeta-independentvariablescapturingstudy-</sup> and regression-specific characteristics associated with the _j_<sup>_th_</sup> estimate as discussed in Section 5.1, with potential bearing on risks of conflicts. In other words, heterogeneity introduced and detailed below can be identified and quantified by the coefficients _βk_ .

## **Publication Outlet**

To some extent, differences in the publication process to which the studies were subjected may explain the heterogeneity of their estimates. We verify the validity of this concern through several controls. First, a careful peer review process can limit the degree of imprecision in the estimates of published studies. We consider two measures that can reasonably reflect a stricter publishing process: the impact score of academic journals (proxied by their SJR<sup>22</sup> score), and publications in the top five journal in economics or political science. Second, academic journals specializing in conflict publish relatively more conflict-related work, including field studies with detailed contextual information. They may be more willing to publish insignificant and/or counter-intuitive estimates

> 22 Scimago Journal & Country Rank (SJR): `https://www.scimagojr.com/journalrank.php` .

25
if they are justified by a careful analysis of local contexts. Additionally, these unusual results may be reviewed by highly specialized peers, which limits the degree of imprecision in the estimates of published studies. Third, additional materials consisting of careful sensitivity and robustness checks can help identify significant relationships or, alternatively, mitigate them. Fourth, as the availability of disaggregated data and statistical methodologies have improved over the 2010s (Laville, 2019), we expect older studies to present, _ceteris paribus_ , a lower degree of precision in their estimates.

# **Geography**

A commonly held limitation of the conflict literature is that phenomena applying to one country or region hardly apply to the rest of the world since the formation of armed groups is often rooted in the long history of nation-building (de Mesquita, 1985; Cramer, 2002; Michalopoulos and Papaioannou, 2020). This point seems likely given our sample of studies: 55% of the selected estimates focus at least partially on a panel of African countries, and half of the selected articles focus on a single country (see Table A3 of the Appendices section). We introduce several geography covariates to control for this potential source of heterogeneity. Specifically, we include dummy variables to assess whether estimates from country case studies or works on specific geographic regions explain heterogeneity among results.

# **Model Characteristics**

Some choices made in model construction may also explain heterogeneity. First, we test if estimator choices affect the results. Maximum-likelihood (MLE) and Ordinary Least Squares (OLS) are the two most common classes of estimators used by the literature. We create a dummy variable taking the value _one_ if the estimator is from the MLE class (i.e., Logit, Negative binomial or Poisson pseudo maximum-likelihood), and _zero_ otherwise. Second, inclusion of specific effects (including fixed and random effects) can increase the statistical power of the estimates by reducing unobserved heterogeneity. We create a dummy taking the value _one_ when specific (fixed, random or mixed) effects are introduced in estimates, and _zero_ otherwise. Third, clustering has implications on statistical inference. We include a dummy variable indicating if estimates are clustered on the unit of reference. Fourth, we include a variable indicating whether the period of interest in the regression is before or after the end of the Cold War (i.e., after 1991). Finally, we include information on 26
the level of disaggregation of the estimates (regions, 0.5 ° x0.5 ° grid cells, 0.25 ° x0.25 ° grid cells, etc.). Several reviews of the literature suggest using small geographic units and quasi-experimental design to avoid biases arising from unobserved heterogeneity or competing mechanisms (Collier and Hoeffler, 2007; Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015). However, these gains for the identification strategy may also depend on the existence of subnational root causes of conflict and the degree of precision of the data at that scale (for a discussion, see Laville, 2019). To test for the influence of the size of the spatial unit on the results, we include a dummy variable indicating whether the estimates apply to grid cells of size less than or equal to 0.5 ° x0.5 ° at the equator (approx. 55km Ö 55km), and the natural logarithm of the sample size.

# **Conflict Characteristics**

We use several variables to control for heterogeneity in the estimates resulting from differences in the empirical definition of the conflict outcome. First, we control for the origin of the conflict data. Conflict studies often rely on data from the Uppsala Conflict Data Program (UCDP/PRIO) (Sundberg and Melander, 2013) or Armed Conflict Location and Event Data (ACLED) (Raleigh et al., 2010). The institutions behind these datasets have the capacity to collect daily data on the timing and location of violent events, while maintaining a consistent definition of violent phenomena (which supports the comparability of estimates across studies). However, their respective definition of violent phenomena and the potential omission, inflation or misrepresentation of events could systematically influence the literature due to their widespread use (Miller et al., 2022). We therefore include a dummy variable indicating if the regression relies on UCDP/PRIO or ACLED data. Second, we control for the type of conflict outcome by including three dummy variables indicating estimates of conflict incidence (if the outcome variable is the total number of conflict per day/month/year, or if it takes the value _one_ when at least one conflict is observed), onset (if the outcome variable only takes the value _one_ at the starting day/month/year of the conflict), or duration (if the outcome variable is the number of days, months or years of active conflict). Third, we include a dummy variable indicating if the spatial dimension of the dependent variable is local (below Admin1 level or at small grids level), or larger (above Admin2 level or at large grids level). Fourth, we include a dummy variable indicating if the conflict implied human casualties, indicating a relatively more intense form of conflict. Finally, we control for the type of conflict using dummy variables in27
dicating if the authors study armed conflicts, crimes, social unrest, and/or violence against civilians.

# **Measures of Shocks**

How the authors measure income shocks may influence their results. First, we include a dummy variable indicating if the estimate uses a quasi-experimental design, namely if a measure of shock (e.g., prices variations, rainfall variation, floods, etc.) is interacted with a measure of resource endowment in the cell (e.g., production areas of coal, coffee intensity, oil and gas reserves, etc.). Second, following Dal B ́o and Dal B ́o (2011) and Dube and Vargas (2013), we include a dummy variable indicating if the shock specifically concerns a labor-intensive agricultural commodity.<sup>23</sup> Agricultural shocks that do not concern the production of a labor-intensive good include general climatic shocks that could influence labor-intensive and capital-intensive productions simultaneously, shocks that concern the livestock sector,<sup>24</sup> shocks on consumer commodity prices, and shocks related to resource scarcity. Finally, we separate climatic shocks, price shocks and other types of shocks using dummy variables.

# **Model Specifications**

Estimates can be sensitive to the choice of explanatory variables. In a sensitivity analysis of the conflict literature, Hegre and Sambanis (2006) identify a robust correlation between conflict onset and several variables including low per capita income, slow income growth, recent political instability, large population size, and war-prone neighbors. As noted by Blattman and Miguel (2010), the inclusion of such correlates in conflict models may bias other estimates in unknown directions due to endogeneity or insufficient knowledge about the mechanisms involved. We control for the inclusion of these correlates (population size, GDP, past conflicts, and neighborhood conflicts) as

> 23Unfortunately, extending this variable to the case of mineral resources proved to be more complicated. A first issue is that a resource can be labor-intensive in one country, but not in another one if the latter has the necessary infrastructure for a larger scale production. An adverse selection problem also arises since we depend on the author’s knowledge of the labor-intensity in the production of the commodity. For example, Corvalan and Pazzona (2019) analyze the links between copper market shocks and crime in Chile. Although they focus on a transmission channel passing through the local labor market, they do not specify the extent to which this production is labor intensive. Therefore, we chose to limit ourselves to the case of agricultural goods that involves less subjective choices in our coding strategy. Nevertheless, the intensity of labor in the production of mineral resources is central to understand how artisanal mining influences the risk of conflict and future MRA should address these issues.

> 24Shocks in the livestock sector concern only a few regressions. We separate them so that the variable is homogeneous. Indeed, although livestock farming requires labor force, it requires relatively little compared to agriculture. Also, its effects on the labor market are less direct because the creation or abandonment of an entire herd cannot be immediate.

28
control variables using dummy variables.

# **5.2.2 Estimation Technique and Results**

Ideally, we would include all our 41 moderators in Equation 6. However, it is likely that some explanatory variables will prove redundant, and including too many covariates relative to the number of studies increases the risk of false positive conclusions (Thompson and Higgins, 2002). Additionally, including the wrong variables in the equation leads to misspecification bias and invalid inference. Therefore, a fundamental problem in estimating Equation 6 is model uncertainty associated with the variables to include. Two popular strategies employed in the literature to address model uncertainty are model selection and model averaging (Steel, 2020). Stepwise regression is the easiest and most commonly used approach for model selection. However, this method may erroneously exclude important variables in sequential _t_ -tests, and it does not account for the selection process when presenting the results of the final equation. To avoid results dependent on the selected model, we employ a model averaging technique, which takes into account all possible models. Following the recent literature on meta-analysis, we apply the Bayesian Model Averaging (BMA) approach to deal with model uncertainty (Havranek et al., 2017, 2018; Zigraiova et al., 2021). The goal of BMA is to find the best possible approximation of the distribution of regression parameters by running regressions based on different subsets of moderators. In our case, since we consider 41 variables, this yields 2<sup>41</sup> possible models to estimate. We therefore apply the Markov chain Monte Carlo algorithm, which approximates the model space and walks the part that contains the models with the highest posterior model probabilities (PMP), which measures the ‘goodness of fit’ of each model with the data. For each variable in the model, BMA reports three parameters: posterior mean, posterior standard deviation and posterior inclusion probability (PIP). PIP aggregates the PMPs of all the models in which the variable is included. A PIP above 0.5 is usually regarded as the threshold to include variables in the model (Jeffreys, 1961; Eicher et al., 2011).

Figure 6 is a graphical representation of the BMA results for the whole sample of 2,464 regressions. The vertical axis lists the explanatory variables sorted by PIP in descending order. The horizontal axis is the PMP of each model sorted in ascending order. The blue (dark) color indicates the positive sign of the variable in the model, and the red color (light) denotes the negative sign of the variable. The blank cell suggests that the variable is excluded from the regression model. Figure 29
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and confirms that the literature suffers from publication selection bias. Thirteen variables present a PIP higher than 0.5, indicating that they are relevant for explaining the differences in the estimates. We focus on the variables for which we have the most robust evidence across the two specifications: at least a strong PIP in BMA, and a significance level of at least 10% in OLS. As could be expected due to the meta-sample’s heterogeneity, studies testing theoretically detrimental mechanism or introducing interactions with a risk enhancer find a higher local risk of conflict subsequent to income shocks. Studies controlling for GDP find a higher risk of conflict and, conversely, controlling for the size of the population, using a quasi-experimental framework or studying small grid cells is associated with a smaller risk of conflict.

Table 6 reports the results of the BMA and OLS for the sub-samples of AS+ (808 regressions), AS- (951 regressions), and HS+ (543 regressions) studies. The results we obtain for publication bias after controlling for our set of moderators confirms the presence of a substantial positive publication bias in the literature focusing on AS-, and the absence of evidence for such biases in the literature on AS+. However, Table 6 shows a negative and statistically significant coefficient of publication bias for HS+, which differs from the statistically non-significant constant found in Table 2. This indicates that HS+ studies not published recently in a top 5 journal suffer from a substantial negative publication selection bias when they focus on a small sample and countries from different regions, do not control spatially for conflicts or for specific effects, and do not study armed conflicts and price shocks. We find that AS+ studies specifically testing for stressful mechanisms<sup>25</sup> or introducing interactions with a risk enhancer find a higher risk of conflict. They also find a higher risk of conflict when they go through a stricter publishing process or when they examine the post-Cold War period. Studies focusing on AS+ in Latin American and African countries find a generally lower risk of conflict. Studies encompassing negative agricultural shocks (AS-) approaching income shocks through climatic events or, to a lesser extent, price variations find a higher risk of conflict. They also find a stronger association between income shocks and local criminality (e.g., robberies, property trespassing, assaults). Finally, studies published in journals specialized in conflict analysis, controlling for the size of the population, or focusing on Latin American countries find a smaller risk of conflict. Studies focusing on positive hydrocarbon/mineral shocks (HS+) find a lower risk of conflict when they encompass larger samples or test for price shocks.

> 25A few studies of AS+ test agitating mechanisms through rapacity effects.

31
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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
Table 6: Explaining Heterogeneity in the Estimated Effects of Income Shocks on Conflicts [BMA - Sub-Samples]

|||Positive A<br>BMA|gricultura|l Shocks (|AS+)<br>OLS|||Negative <br>BMA|Agricultur|al Shocks (|AS-)<br>OLS||Positi|ve Hydroc<br>BMA|arbon and|Mineral S|hocks (H<br>OLS|S+)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**(i) Baseline**|Post Mean|Post SD|PIP|Coef.|SE|p-Value|Post Mean|Post SD|PIP|Coef.|SE|p-Value|Post Mean|Post SD|PIP|Coef.|SE|p-Value|
|Publication bias (_β_1)<br>Precision (_β_0)<br>**(ii) Mechanisms and interacti**<br>_Othermechanisms(Ref)_|**-0.482**<br>0.009<br>**ve models**|**NA**<br>0.013|**1.000**<br>0.374|-0.521<br>**0.022**|0.312<br>**0.007**|0.108<br>**0.004**|**1.629**<br>0.000|**NA**<br>0.002|**1.000**<br>0.047|**1.693**<br>-0.001|**0.262**<br>0.003|**0.000**<br>0.815|**-3.283**<br>**0.133**|**NA**<br>**0.078**|**1.000**<br>**0.846**|**-2.870**<br>**0.109**|**0.718**<br>**0.039**|**0.002**<br>**0.014**|
|_._<br>Mechanism stressful<br>_Others(Ref)_|**0.045**|**0.005**|**1.000**|**0.045**|**0.005**|**0.000**|0.000|0.001|0.037|-|-|-|0.001|0.006|0.071|-|-|-|
|_._<br>Risk enhancer<br>|**0.014**|**0.001**|**1.000**|**0.014**|**0.005**|**0.008**|**0.002**|**0.000**|**1.000**|0.002|0.002|0.155|**0.002**|**0.001**|**0.798**|0.002|0.002|0.315|
|**(iii) Publication outlet**|||||||||||||||||||
|_No top 5 reviews (Ref.)_<br>Top 5<br>|-0.001|0.004|0.158|-|-|-|0.000|0.006|0.091|-|-|-|**-0.032**|**0.019**|**0.923**|**-0.027**|**0.005**|**0.000**|
|_No confict reviews (Ref.)_<br>Confict review<br>_Coreresults(Ref)_|0.002|0.005|0.150|-|-|-|**-0.017**|**0.004**|**0.998**|**-0.016**|**0.003**|**0.000**|0.001|0.004|0.158|-|-|-|
|_._<br>Appendix results<br>_Ctiibl_|0.000|0.000|0.019|-|-|-|0.000|0.000|0.026|-|-|-|0.001|0.005|0.081|-|-|-|
|_onnuous varaes_<br>Age of study|0.000|0.000|0.407|-|-|-|0.000|0.000|0.082|-|-|-|**0.008**|**0.005**|**0.820**|**0.008**|**0.001**|**0.000**|
|SJR score<br>**(iv) Geography**|**0.001**|**0.000**|**0.972**|**0.001**|**0.000**|**0.000**|**-0.001**|**0.000**|**0.931**|**-0.001**|**0.000**|**0.003**|0.000|0.001|0.302||||
|_No country focus (Ref.)_<br>Country focus<br>_Worldwide(Ref)_|0.000|0.002|0.038|-|-|-|0.000|0.001|0.058|-|-|-|**-0.057**|**0.032**|**0.866**|**-0.061**|**0.012**|**0.000**|
|_._<br>Region: Africa|**-0.028**|**0.009**|**0.993**|**-0.034**|**0.006**|**0.000**|**-0.002**|**0.002**|**0.755**|**-0.003**|**0.001**|**0.001**|0.008|0.043|0.368|-|-|-|
|Region: EAP|0.002|0.007|0.078|-|-|-|0.000|0.002|0.082|-|-|-|**0.059**|**0.058**|**0.708**|**0.056**|**0.014**|**0.001**|
|Region: LAC<br>|**-0.033**<br>|**0.010**<br>|**0.988**<br>|**-0.036**<br>|**0.005**<br>|**0.000**<br>|**-0.018**<br>|**0.005**<br>|**0.974**<br>|**-0.020**<br>|**0.005**<br>|**0.001**<br>|**0.035**<br>|**0.050**<br>|**0.628**<br>|**0.035**<br>|**0.004**<br>|**0.000**<br>|
|Region: South Asia<br>|**-0.021**|**0.012**|**0.859**|**-0.025**|**0.007**|**0.003**|**-0.041**|**0.016**|**0.938**|**-0.046**|**0.012**|**0.000**|**0.081**|**0.058**|**0.753**|**0.101**|**0.015**|**0.000**|
|**(v) Model characteristics**<br>_NoMLE(Ref)_|||||||||||||||||||
|_._<br>Maximum likelihood estimator<br>|0.000|0.000|0.021|-|-|-|0.000|0.000|0.035|-|-|-|0.000|0.002|0.058|-|-|-|
|_No specifc efects (Ref.)_<br>Specifc efects<br>|0.003|0.005|0.260|-|-|-|0.000|0.000|0.034|-|-|-|**0.047**|**0.055**|**0.520**|**0.048**|**0.021**|**0.042**|
|_Others (Ref.)_<br>Cluster unit reference<br>_Precoldwar(Ref)_|0.000|0.000|0.017|-|-|-|0.000|0.000|0.031|-|-|-|0.000|0.013|0.155|-|-|-|
|_._<br>Post cold war<br>|**0.006**|**0.001**|**1.000**|**0.006**|**0.003**|**0.095**|0.000|0.001|0.072|-|-|-|-|-|-|-|-|-|
|_No small grids (Ref.)_<br>Small grids<br>|0.000|0.001|0.052|-|-|-|0.000|0.001|0.041|-|-|-|-|-|-|-|-|-|
|_Continuous variable_<br>Sample size|0.000|0.000|0.064|-|-|-|0.000|0.000|0.043|-|-|-|**-0.011**|**0.003**|**1.000**|**-0.009**|**0.002**|**0.001**|
|**(vi) Confict characteristics**<br>|||||||||||||||||||
|_Usual confict data (Ref.)_<br>Unusual confict data<br>_Confictincidence(Ref)_|0.000|0.002|0.079|-|-|-|0.000|0.000|0.041|-|-|-|-|-|-|-|-|-|
|_._<br>Confict duration|-|-|-|-|-|-|0.000|0.000|0.319|-|-|-|-|-|-|-|-|-|
|Confict onset|0.000|0.001|0.073|-|-|-|0.000|0.000|0.097|-|-|-|-|-|-|-|-|-|
|_Nolocalconficts(Ref)_|||||||||||||||||||
|_._<br>Confict local<br>_Nonlethal(Ref)_|0.000|0.001|0.044|-|-|-|0.000|0.001|0.096|-|-|-|0.003|0.011|0.242|-|-|-|
|_._<br>Lethal<br>|0.000|0.001|0.058|-|-|-|0.001|0.002|0.210|-|-|-|-0.001|0.002|0.134|-|-|-|
|_Others (Ref.)_<br>Armed conficts|**0.005**|**0.002**|**0.930**|**0.004**|**0.001**|**0.002**|0.001|0.002|0.307|-|-|-|**-0.001**|**0.001**|**0.595**|**-0.002**|**0.001**|**0.007**|
|Crimes|0.000|0.001|0.047|-|-|-|**0.036**|**0.004**|**1.000**|**0.035**|**0.005**|**0.000**|0.000|0.002|0.034|-|-|-|
|Social unrests|**-0.006**|**0.007**|**0.522**|**-0.010**|**0.006**|**0.088**|0.000|0.001|0.103|-|-|-|0.000|0.001|0.057|-|-|-|
|Violenceagainstcitizens|0.000|0.001|0.023|-|-|-|0.000|0.000|0.026|-|-|-|0.000|0.002|0.065|-|-|-|
|<br>**(vii) Measures of shocks**<br>|||||||||||||||||||
|_No quasi experimental (Ref.)_<br>Quasi experimental<br>_NlbitiR_|**-0.003**|**0.003**|**0.545**|**-0.005**|**0.002**|**0.034**|**-0.004**|**0.001**|**1.000**|-0.004|0.002|0.105|0.009|0.014|0.377|-|-|-|
|_o aor nensve (ef.)_<br>Labor intensive resources<br>_Others(Ref)_|0.000|0.002|0.042|-|-|-|**-0.005**|**0.001**|**1.000**|-0.005|0.003|0.104|-|-|-|-|-|-|
|_._<br>Climatic shock|0.000|0.001|0.030|-|-|-|**0.006**|**0.001**|**1.000**|**0.007**|**0.002**|**0.000**|-|-|-|-|-|-|
|Price shock|0.000|0.001|0.038|-|-|-|**0.016**|**0.005**|**0.968**|**0.018**|**0.004**|**0.000**|**-0.081**|**0.037**|**0.969**|**-0.063**|**0.017**|**0.003**|
|**(viii)Modelspecifcation**|||||||||||||||||||
|<br>_No past confict (Ref.)_|||||||||||||||||||
|Control for past conficts<br>_Ntilft(Rf)_|0.000|0.000|0.024|-|-|-|0.000|0.000|0.025|-|-|-|-0.001|0.005|0.100|-|-|-|
|_o spaa acor e._<br>Control for confict spatial factor<br>_Nopopulation(Ref)_|0.000|0.001|0.030|-|-|-|0.000|0.001|0.063|-|-|-|**0.017**|**0.005**|**0.995**|0.016|0.010|0.118|
|_._<br>Controlforoulation|0001|0002|0270|-|-|-|**-0002**|**0000**|**1000**|**-0002**|**0001**|**0088**|0000|0007|0072|-|-|-|
|pp<br>_No GDP (Ref.)_|.|.|.||||**.**|**.**|**.**|**.**|**.**|**.**|.|.|.||||
|Control for GDP|0.000|0.001|0.100|-|-|-|0.000|0.000|0.055|-|-|-|-0.004|0.014|0.090|-|-|-|

Notes: AS+ sample: 808 observations and 25 studies focusing on positive agricultural shocks; AS- sample: 951 observations and 34 studies focusing on negative agricultural shocks; HS+ sample: 543 observations and 14 studies focusing on positive hydrocarbon/mineral shocks. The weights are the inverse of _SE_<sup>_pcc_</sup> . The dependent variable is the partial correlation between the type of shock 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._ 34
# **6 Discussion and Conclusion**

Do income shocks locally affect the probability of internal conflicts? This paper uses meta-regression analysis (MRA) to review the recent empirical literature on local income shocks and conflicts in developing countries. Using a sample of 2,464 point estimates from 64 studies, we evaluate the presence of publication selection bias and discuss the relevance of a range of methodological limitations and advances reported in previous reviews of the conflict literature.

We find no evidence of an average and unconditional genuine effect of income-related shocks on the risk of conflict at the infra-national scale. Therefore, the effect of climate changes, commodity price fluctuations and natural resources endowment on the risk of conflict does not seem to systematically transit through variations in local incomes and economic prospects. Nonetheless, the fluctuation of local incomes may affect the risk of conflict under certain conditions. To account for heterogeneity in the authors’ narratives, we divide the meta-sample into coherent sets based on the type (agricultural, extractive, or other) and direction (wealth-increasing or -decreasing) of the shock. For these sub-samples, we find a positive and statistically significant effect of wealthincreasing shock in the agriculture sector on the risk of conflict, but no statistically significant effects of wealth-decreasing shocks or shocks in the extractive sector. Moreover, we find that the literature suffers substantially from two types of publication selection bias: it favors the publication of results with higher statistical significance (Type II), and showing the detrimental effects of income shocks (Type I). Studies of negative agricultural shocks (droughts, floods) are particularly affected by the latter type of bias, probably because 99.5% of the estimates in this subgroup test theoretically stressful mechanisms. We also suspect the influence of result searching bias in the choice of climaterelated variables, since we find that overestimation is more likely for studies focusing on climate shocks. Indeed, climatic variables are intricate (Auffhammer et al., 2013) and recent micro-evidence suggests that their effect on local poverty is heterogeneous (Azzarri and Signorelli, 2020).

Several reviews of the literature advocate for the use of highly disaggregated spatial units in conflict models based on methodological considerations (Collier and Hoeffler, 2007; Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015) or study-specific robustness checks (Berman and Couttenier, 2015). The results of this MRA, and more precisely the heterogeneities captured with the BMA, suggest that quasi-experimental models and small grid cells may indeed palliate the 35
overestimation tendency we find in the literature on income shocks as a whole. However, these moderators never strongly influence the estimates when we divide the literature into coherent sets. When decomposing for the nature of main income shocks (AS+, AS-, HS+), the BMA technique enables us to precisely detect how complex heterogeneities — related to (i) the publication outlet, (ii) geographical area encompassed, (iii) model characteristics, (iv) the nature of conflicts, (v) measurements of shocks, and (vi) covariates introduced — affect the results from the literature on the risks of conflicts.

This MRA complements the existing body of meta-analysis on the economic origins of conflict in developing countries. O’Brochta (2019) finds no aggregate relationship between conflicts and natural resources, and Blair et al. (2021) obtain similar conclusions for commodity prices. Our results complement theirs by showing that there is no aggregated effect of income shocks on the risk of conflict at the infra-national scale. Vesco et al. (2020) find that both extractive resource wealth and renewable resource scarcity (e.g., forest, water, vegetation) increase the risk of conflict. When decomposing for the nature of main income shocks (AS+, AS-, HS+), our results suggest that wealth-increasing and -decreasing shocks do not have a symmetric effect on conflict, backing supporters of non-wealth-related factors (including grievances and state capacity) as motives of conflicts (de Mesquita, 1985; Cramer, 2002; Michalopoulos and Papaioannou, 2020). Hsiang et al. (2013, 2014) find that deviations from normal precipitation and mild temperatures systematically increase the risk of conflict. In line with their conclusions and several scholars (Theisen et al., 2012; Sarsons, 2015), our results suggest that the effect of climatic events is unlikely to be operating solely through changes in local agricultural income. Finally, our results show that researchers’ expectations on the theoretical mechanisms at play (above all, opportunity cost and rapacity) distort statistical inference and the resulting understanding of research on the local causes of conflict. Overall, this MRA advocates for caution in generalizing the results of conflict studies, especially for recommending policies to countries outside the study area. The genuine effect of positive agricultural shocks and the asymmetries we find in the literature call for more analysis of the peacemaking mechanisms linking income shocks to local risk of conflict.

36