Humanitarian food aid and civil conflict

World Development 126 (2020) 104713

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Humanitarian food aid and civil conflict Sébastien Mary a,c,⇑, Ashok K. Mishra b a

The University of Kansas, Department of Economics, Lawrence, KS 66045, United States Arizona State University, W.P. Carey School of Business, Morrison School of Agribusiness, Mesa, AZ 85212, United States c DePaul University, Department of Economics, Chicago, IL 60604, United States b

a r t i c l e

i n f o

Article history: Accepted 11 October 2019

JEL codes: F35 Q18 D74 Keywords: Civil conflict Humanitarian food aid Sustainable development goals Africa

a b s t r a c t Humanitarian food aid has long been considered to be an effective tool towards conflict mitigation among donors and policymakers. Within the Sustainable Development Goals that have the objectives of ending hunger before 2030 (SDG#2) and bringing peace and justice (SDG#16), humanitarian food assistance may play a critical role in delivering progress in developing countries. However, there have been growing concerns that it may actually have counter-intended effects by aggravating civil conflicts in recipient countries. We estimate the effect of humanitarian food aid on civil conflict using a sample of 79 recipient countries between 2002 and 2017. Our analysis exploits cross-sectional and time variation in between-country humanitarian food aid displacements. Our baseline instrumental variables estimates imply that a 10 percent increase in humanitarian food aid per capita decreases the incidence of civil conflict by about 0.2 percentage point (or by about 0.9 per cent at the mean conflict incidence). Humanitarian food aid also decreases the incidence of small-scale and large-scale civil conflicts, and the onset and duration of civil conflicts. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Does humanitarian food aid save lives in the developing world? The question has received increased scientific (e.g. Nunn & Qian, 2014), media (e.g. The Economist, 2014), and policy attention (e.g. CIA, 2014). The recent worsening of food insecurity in subSaharan Africa and South Eastern and Western Asia caused by a resurgence of civil conflicts (FAO, Ifad, UNICEF, WFP, & WHO, 2017), despite having received large amounts of humanitarian food assistance, reinforces the legitimacy of the question (and by implication, the need to provide an answer). Humanitarian food aid1 is typically provided in emergency and conflict situations, and includes the provision and distribution of food, cash and vouchers for food purchases, as well as non-medical nutritional interventions for the benefit of crisis-affected people (OECD, 2019)2. It has long been considered to be an effective tool

⇑ Corresponding author. E-mail addresses: [email protected] (S. Mary), [email protected] (A.K. Mishra). Humanitarian or emergency food aid (coded 72,040 under the Credit Reporting System) is different from food aid (coded 52,010 under the Credit Reporting System). 2 Humanitarian aid includes humanitarian food aid, as well as several other aid types: (1) material relief and assistance and services related to shelter, water, sanitation and health services, supply of medicines; (2) relief coordination, protection and support services; (3) reconstruction relief and rehabilitation, especially for preexisting infrastructures (e.g. roads, bridges, water and sanitation); (4) disaster prevention and preparedness, including disaster risk reduction activities, early warning systems, emergency contingency stocks; (5) administrative costs and other. 1

https://doi.org/10.1016/j.worlddev.2019.104713 0305-750X/Ó 2019 Elsevier Ltd. All rights reserved.

towards hunger reduction and conflict prevention and mitigation among policymakers (e.g. Open letter to US congress, 2017). There are indeed several mechanisms through which humanitarian food aid may mitigate civil conflict. If civil conflicts arise because of competition for scarce resources crucial to survival (i.e., food), then the delivery of humanitarian food aid increases the opportunity cost of joining a rebellion (Collier and Hoeffler, 1998, 2004) and reduces the incentive to fight by reducing scarcity. Also, to the extent that the distribution of humanitarian food aid reduces the actual grievances of population mostly at risk of fighting, the conflict-reducing effect may be increased (e.g. Messer & Cohen, 2004). In addition, the provision of humanitarian food assistance in collaboration with domestic governments may win over the ‘‘hearts and minds” of local populations and/or be seen as a reward for sharing critical information that could result in a reduced probability of conflict or conflict duration (Zurcher, 2017). However, others have argued that humanitarian food aid instead promotes conflict. Food assistance can be hijacked by rebels allowing them to fight longer (Messer, Cohen, & Marchione, 2000; Hendrix & Brinkman, 2013; Nunn & Qian, 2014). There is indeed anecdotal evidence supporting this channel (Polman, 2010; Magone, Neuman, & Weissman, 2012), though it is not clear how much of humanitarian food aid is sensitive to theft. In addition, the information sharing resulting from the delivery of humanitarian food aid can result in insurgents targeting local populations to deter them from further engaging with the government, and thus in additional deaths and injuries (Zurcher, 2017). Also, if

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humanitarian food assistance is politicized in the recipient country, this may add to local resentment based on pre-existing perceptions on unfairness and inequality (Messer, Cohen, & D’Costa, 1998). Given the existence of multiple but opposite channels, it is analytically impossible to provide a clear conclusion on the net impact of humanitarian food aid on civil conflict. It is also important to highlight that these mechanisms are specific to each aid type, and that they likely play out differently for other aid types (e.g. food aid and medical humanitarian aid inflows). More fundamentally, these conceptual mechanisms are also dependent on the allocation mode of humanitarian food aid. Food assistance is typically provided through two ways: in-kind aid (shipping commodities produced in donors’ countries to countries in need) and cash-based assistance. The latter includes local and regional purchases, direct cash transfers, and food vouchers. Historically, most donors have largely, if not exclusively, relied on in-kind aid, but in the mid/late 2000s, international food assistance has shifted toward a combination of in-kind and cash-based assistance3. While both aid modalities, in-kind and cash-based, have advantages and challenges4, studies comparing the effectiveness of food aid modalities (in-kind versus cash-based transfers) have found that all modalities support food security outcomes, but more interestingly, that their effectiveness is on average similar (World Bank, 2016; GAO, 2016). However, most of these studies have evaluated the impact of various aid modalities in non-emergency settings. A notable exception is Tranchant et al. (2019) that provides an impact evaluation of food assistance in conflict-affected areas of Mali. Policymakers cannot reform humanitarian food assistance on the basis of anecdotal evidence without a more robust evidence base (nor, candidly, should they believe that humanitarian food aid prevents or mitigates conflict either). The evidence base has remained scarce in the face of uncertain and complex conceptual links. Recently, a much-publicized paper by Nunn and Qian (2014) – NQ after that – has stirred controversy because the authors find that an increase in US humanitarian food aid increases the incidence and duration of civil conflicts in recipient countries using a sample of 125 developing countries between 1971 and 2006. These results have been disputed by USAID (2014) that shows that their results are sensitive to the exclusion of unreliable food aid data and the inclusion of additional controls. More fundamentally, Christian and Barrett (2017) argue that the identification strategy in Nunn and Qian (2014) may be susceptible to spurious trends that further cast doubt on NQ’s findings. In a related literature on development aid, a systematic literature review by Zurcher (2017) suggests that overall humanitarian aid increases violence. Yet, a careful re-examination of the studies sampled in this literature review may not support such conclusion. For example, the interpretation of the econometric results is invalid in Narang (2014, 2015) leading the author to misconclude about the negative effects of humanitarian aid (see Mary, 2019). In particular, the author fails to interpret the interaction term as a ratio of hazard ratios. In addition, Mary (2019) shows that conclusions 3 For instance, about 45 per cent of humanitarian food assistance from the European Commission was provided in cash or vouchers in 2018. In a more dramatic fashion, since 2008, Canada exclusively relies on cash-based assistance for food aid. However, it is noteworthy that the US, the largest donor of food aid, still relies primarily on in-kind aid due to US law requirements (Casey, 2018). 4 In-kind aid is often criticized for its costs, delays, and potential disruptions to local markets. Local and regional purchases are much quicker and arguably create fewer side effects to local markets (Lentz et al., 2013), but are dependent on the calorific and nutritional quality of the available food supply in the region or country. Furthermore, in-kind aid and LRP may also be difficult to deliver when it is unsafe to operate in conflict zones. On the contrary, cash transfers and food vouchers are appropriate when there are security concerns about transporting in-kind aid, but they can also be stolen or used by recipients to purchase non-food items in poorly controlled settings (e.g. World Bank, 2016; GAO, 2016).

in Wood and Molfino (2015) and Wood and Sullivan (2014) may be affected by the inclusion of year dummies that capture common shocks. Finally, a few studies (e.g. De Ree & Nillesen, 2009; Nielsen, 2011) have examined the effect of total aid on conflict, but the use of total aid, rather than sector aid, makes it difficult to isolate the causal transmission channels (Zurcher, 2017). Despite the political importance of this topic, little is known of the effect of humanitarian food aid on civil conflict. This is particularly relevant given the Sustainable Development Goals (SDG) #2 ‘‘end hunger, achieve food security and improved nutrition” and #16 [promote] ‘‘peace, justice, and strong institutions.” If humanitarian food aid indeed aggravates conflict, food assistance policies must be reformed to target only the environments meeting the conditions where we can ensure the efficacy of humanitarian food aid must be scaled down or ended (e.g. Lentz & Barrett, 2008). Therefore, the objective of this study is to examine the causal effect of humanitarian food aid on civil conflicts. To accomplish this objective we use data from 2002 to 2017 for a sample of 79 recipient countries, from the Credit Reporting System (CRS) of the Organization for Economic Development and Cooperation (OECD), the Peace Research Institute Oslo (PRIO) and Uppsala Conflict Data Program (UCDP), and the World Development Indicators (WDI) of the World Bank. Our identification strategy exploits between-country humanitarian food aid displacements within the sample (between the domestic country and the rest of the sample) and uses the latter to instrument domestic food aid per capita using a two-stage least squares (2SLS) instrumental variables (IV) estimation. These displacements result from the emergence of a loud (i.e., attracting media and donors’ attention) food crisis happening in the rest of the world (Barrett, 2002). The validity of the strategy relies on donors’ responses to loud emergencies and food aid budget constraints (e.g. Barrett, 2002; Neumayer, 2005; World Food Programme, 2005; United Nations, 2010; Kuhlgatz & Abdulai, 2012; Reuters, 2014; World Hunger, 2018). Our modeling specification explicitly accounts for time-invariant omitted variables, time fixed effects, and country-specific time trends. We also run a battery of robustness analyses accounting for, among others, country-specific linear and non-linear time trends and timevarying omitted variables affecting civil conflict and humanitarian food aid. Our baseline 2SLS-IV estimates suggest that a 10 percent increase in humanitarian food aid per capita results in a 0.2 percentage point decrease in conflict incidence. In other words, humanitarian food aid saves lives in the developing world. Also, we find that humanitarian food aid decreases the onset and duration of civil conflicts. Our results are important for policymakers in that they confirm the use of humanitarian food aid as an effective tool in promoting security in developing countries. To the extent the pass-through between conflicts and food insecurity is strong (FAO, Ifad, UNICEF, WFP and WHO, 2017), increasing humanitarian food aid inflows would likely result in much-improved food security, especially considering the direct effect of humanitarian food aid on nutrition (e.g. Mary, Saravia-Matus, & Gomez y Paloma, 2018). Implicitly, our results point out the need to reallocate foreign sector aid towards humanitarian food aid if donors want to achieve SDG#2 and SDG#16. In addition, our results raise important concerns with respect to the modeling of civil conflict in previous studies. In particular, we show that when our baseline model does not include countryspecific time trends, we find that humanitarian food aid does not affect civil conflict incidence. Researchers studying the links between humanitarian food aid and conflict should, at the least, test the robustness of their results to the inclusion of linear and non-linear time trends as the latter seem to be a driving force behind recent results (i.e., Christian & Barrett, 2017). Lastly, our paper provides preliminary insights about the existence of

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between-country humanitarian food aid displacements within recipient countries and extends the empirical literature about health aid displacements to a different aid type (e.g. Lordan, Ki Tang, & Carmignani, 2011). In particular, we find that a 1 basis percentage point increase in the average share of humanitarian food aid in the rest of the world (excluding the domestic country) decreases humanitarian food aid per capita in the domestic country by about 0.7 USD. The rest of the paper is structured as follows. Section 2 describes the empirical strategy. Section 3 presents the data and descriptive statistics. Section 4 analyses the main results. Section 5 examines heterogeneous effects. Section 6 concludes. 2. Empirical strategy To examine the impact of humanitarian food aid on conflict the empirical model builds upon the linear framework that has been used by Nunn and Qian (2014) and can be expressed as:

C it ¼ bAIDit þ cit X it þht þ di þ eit

ð1Þ

AIDit ¼ aSjt þ lit X it þ pt þ qi þ
it

ð2Þ

Eq. (1) is the second stage of our 2SLS system and Eq. (2) is the first stage. The use of 2SLS estimation is justified by the existence of reverse causality and simultaneity between conflict and humanitarian food aid5. Note that we do not lag humanitarian food aid in our model. Lagging a suspected endogenous explanatory variable is common practice in applied research to avoid potential endogeneity biases. However, Reed (2015) and Bellemare, Maski, and Pepinsky (2017) find that this practice may result in inconsistent estimates and misleading inference. More fundamentally, the use of contemporaneous aid is in line with the belief that humanitarian food aid will have a within-year effect (e.g. Lentz, Passarelli, & Barrett, 2013)6. Throughout Eqs. (1) and (2), countries are indexed by i and year is indexed by t; C it is the existence of a civil conflict in country i in year t; AIDit is humanitarian food aid per capita in US dollars; di (qi Þ are country-specific (time-constant unobserved) fixed effects controlling for time invariant heterogeneity. ht ðpt ) are time fixed effects controlling for common shocks that affect both conflict and humanitarian food aid, such as global or region-wide business cycle, food price or natural resource price shocks. The instrument Sjt is the share of humanitarian food aid out of total aid averaged across all sampled countries other than country i. The share is calculated on the monetary amount of humanitarian food aid, rather than on its quantity. Before we discuss the identification strategy, it is important to highlight that NQ (2014) has come under scrutiny because their identification strategy relying on a continuous difference-indifference estimation with Bartik-shift instruments may be susceptible to spurious nonlinear trends (Christian & Barrett, 2017). To prevent trend effects to bias our estimates, we add countryspecific time trends in Eqs. (1) and (2):

C it ¼ bAIDit þ cit X it þht þ uit þ di þ eit

ð3Þ

AIDit ¼ aSjt þ lit X it þ pt þ xit þ qi þ
it

ð4Þ

where uit and xit are country-specific time trends. 5 In this paper, we cannot address questions with respect to the modality composition of food aid over time, and thus implicitly assume that the effectiveness of a 1$ of humanitarian food aid per capita is constant throughout the study period and is not affected by its modality structure. 6 We run additional estimations replacing current aid with one-period lagged aid and find no significant effect, which supports the argument that the effect of humanitarian food aid does not last past a year in our sample (see column 4 Table A3).

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The importance of controlling for country-specific time trends (as well as time fixed effects) has already been highlighted in the climate-conflict literature (see Burke et al., 2009; Buhaug, 2010; Couttenier & Soubeyran, 2014; Hsiang & Meng, 2014). Countryspecific time trends7 have been used to control for unobserved time-varying heterogeneity, that is, variables that could be evolving over time (such as economic performance or political institutions) and altering conflict risk (e.g. Burke et al., 2009). In contrast, a common, rather than country-specific, time trend requires the assumption that all countries are comparable over time in the factors that influence conflict risk, e.g. geopolitics, natural resources, colonial history, international trade patterns, and geographic constraints (Hsiang & Meng, 2014). Intuitively, there is no reason to believe that these factors would be the same across countries. Hsiang and Meng (2014) suggest using a F-statistic test that jointly tests whether trends in conflict are statistically different from zero, and thus countries have the same trends in conflict. Furthermore, in line with Christian and Barrett (2017), we check for the presence of nonlinear country-specific trends in conflict. The visual inspection of conflict and aid variables, the Akaike Information Criterion (AIC), and a F-statistic test can help inform model selection, i.e. which trend specification best fits the data, and all suggest that country-specific cubic time trends should be included in our baseline specification. This is in line with the conflict literature that has shown the importance of modeling time dependence in binary (conflict) data to the cubic degree (e.g. Carter & Signorino, 2010). X it is a vector of independent variables that includes food aid that is not humanitarian food aid, the logarithm of non-food foreign aid (defined as total aid minus humanitarian food aid and other food aid), the logarithm of GDP per capita (based on Purchasing Power Parity), the logarithm of inflation rate, and a set of climate variables controlling for the average temperature and precipitation in each month of the year. These monthly variables capture the fact that different countries have different cropspecific growing seasons and different sensitivities to weather variations (Nunn & Qian, 2014). We also include two weighted averages, respectively, of humanitarian food aid and conflict incidence in neighboring countries to account for potential spillover effects. The choice of entering specific variables in levels or logarithms is driven our intuition behind what the relationship is between, let’s say, non-food aid and conflict risk. In particular, we believe that non-food aid has marginally increasing/decreasing effects on conflict risk. Such modeling for non-food aid has been used in the literature (e.g. Dube & Naidu, 2015). We also test whether logging the aid variables or using the levels of other non-food aid affects the results in robustness analyses and we confirm the baseline results using these alternative models (see Table A3). We also include an indicator of the level of ethnic tensions and an indicator of the political regime. The inclusion of these variables is common in the conflict literature (e.g. Burke et al., 2009). One concern about the inclusion of the vector X it relates to the issue of bad controls caused by the inclusion of potentially endogenous and proxy regressors (Angrist & Pischke, 2008), so we estimate parsimonious models in robustness analyses to examine the extent to which bad controls potentially affect inference. The identification strategy exploits cross-sectional and time variation in between-country humanitarian food aid displacements between country i and all other recipient countries included in the sample. We borrow from the health economics literature to design our IV strategy (e.g. Lordan et al., 2011). In our context, 7 In practice, country-specific time trends are constructed by interacting the time variable (‘‘year”) with each country (via the country-specific fixed effect). This allows each country to have their own time trend.

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these displacements result from the emergence of a (relatively) loud(er) food crisis happening in the rest of the world (Barrett, 2002). The validity of the strategy relies on two main elements: donors’ responses to loud emergencies and food aid budget constraints. First, there is evidence that donors respond to media coverage (Kuhlgatz & Abdulai, 2012). As the media coverage of a food crisis is amplified in a particular country, donors will redirect and refocus their efforts towards this area, while humanitarian food aid for the country in a ‘silent’ emergency or in a situation that is relatively less strategic in terms of the donors’ interests might receive less humanitarian food aid (Barrett, 2002; Neumayer, 2005). Second, donors’ budgets for food aid may not be increased during the fiscal or calendar year, therefore putting more stress on the inter-country competitive use and (re-)allocation of resources, especially in a context where donors are slow to pledge and deliver humanitarian food aid (Kuhlgatz & Abdulai, 2012). There are indeed numerous and recurrent policy and media reports about the World Food Programme had to reduce or cut food aid in developing countries during the year because of a lack of funds (e.g. World Food Programme, 2005; Reuters, 2014; United Nations, 2010; The Washington Post, 2018; World Hunger, 2018). In addition, the coordination between multiple donors of humanitarian food aid responses may also exacerbate such between-country displacements (Kuhlgatz, Abdulai, & Barrett, 2010). Also, NonGovernmental Organizations and aid delivery organizations may contribute to this phenomenon by redirecting aid funds to particularly distressed regions. Conditional on country-specific time trends, country fixed effects, time fixed effects, and the vector of controls X it , the instrument Sjt that is defined as the humanitarian food aid funding received by all countries other than i, (AIDt  AIDit ), to total aid funding received by all countries other than i, (TOTAIDt  TOTAIDit ), averaged across all sampled countries other than i, represents the donors’ response to the occurrence of a (relatively) loud(er) food emergency in the rest of the world. a in Eq. (4) captures the between-country displacement effect, or in other words, translate the redirection of humanitarian food aid resources towards the rest of the world, but away from country i. Thus we expect a to be negative, especially in a context of limited budget constraints, donors’ fatigue, or long funding authorization and allocation procedures. What about b? Nunn and Qian (2014) suggest that increased humanitarian food aid may cause increased civil conflict risks because humanitarian food aid is vulnerable to theft allowing rebels to fight longer. However, Blattman and Miguel (2010) highlight exogenous sources of income growth are generally associated with lower conflict risks, thus making this channel unlikely. In addition, while some humanitarian food aid may be captured by rebels, it is equally likely that most humanitarian food aid ends up being delivered and arguably reduces conflict risks (through reduced starvation), so we could now expect b to be negative in such context. On the whole, it is not analytically clear what to expect for b. Our strategy to causal identification depends on the credibility of the exclusion restriction behind the IV strategy, which is that the instrument, conditional on the set of fixed effects, time trends, and controls, included in Eqs. (3) and (4), is correlated with conflict in country i only through the domestic humanitarian food aid channel. A potential threat to our identification strategy exists if the instrument influences domestic conflict through other channels that are not controlled for. For example, the potential redirection of food aid resources away from the domestic country may also affect other types of aid that are related to conflict risks (e.g. Gupta et al., 2018). Equally, the redirection of aid may affect economic growth and/or governance that have been both linked to

conflict. In a related manner, humanitarian food aid in neighboring countries could have spillover effects in the domestic country via cross-border trade, while civil conflicts in neighboring countries could have spillover effects via migration. To control for these channels, we include a number of covariates, including other non-food aid, other food aid, GDP per capita, an indicator of the political regime, as well as two weighted averages, respectively, of conflict incidence8 and humanitarian food aid in neighboring countries (based on the relative border’s length of a recipient country with its neighbors). Furthermore, the instrument may lead to changes in food prices that may contribute to social unrest and conflict risks (Bellemare, 2015). However, we include time fixed effects controlling for global or region-wide economic shocks affecting all countries in the region, lessening these concerns. We also include the inflation rate to control for the fact that these price shocks may be domestic, as well as region-wide. Moreover, it is possible that the instrument affects the country’s reliance on humanitarian food aid, but again this is likely to be mostly controlled via the country fixed effects and the time trends. Still, to be sure of the validity of the identification strategy, we provide an additional analysis in which we include lagged humanitarian food aid to account for time-varying heterogeneity and aid inertia. A more serious problem to our strategy may come from the fact that conflict in a large country may create substantial migratory flows that result in a loud emergency crisis in other countries. In other words, there could be reverse causality between our dependent variable in Eq. (3) and the instrument in Eq. (4). To doublecheck the validity of our approach, we drop the five largest populated countries of our sample, i.e., China, India, Indonesia, Nigeria, Pakistan, in sensitivity analyses, and confirm this does not affect our baseline estimates. Another threat to our identification strategy might come from potential reverse causality between the dependent variable AIDit and the instrument Sjt in Eq. (4). However, an endogeneity test fails to reject the null that the instrument can be treated as exogenous in Eq. (4), thus indicating this is not a problem here. The approach also relies on the fact that humanitarian food aid increases in the rest of world due to a food crisis, but not because food aid directed at the country i has decreased following changes in conflict risks in this country. In other words, conflict in country i should not affect the instrument. We run additional estimations and indeed show that conflict in country i does not affect the instrument, thus providing supporting evidence for this assumption. Moreover, if donors increase overall aid as a response to conflict, the IV estimate of humanitarian food aid on conflict will be biased upwards. This is not a serious concern though, because it means our baseline estimate may be viewed as a lower bound to the positive effect of food aid. Nonetheless, we can examine this problem through a robustness analysis where data, when total aid per capita changes by more than 10 per cent (decrease and increase), is excluded. Additionally, we implement a test in support of the validity of the exclusion restriction following Brückner (2013). We can introduce the instrument Sjt as a right hand side variable in Eq. (3) and use six-period lagged humanitarian food aid as a new instrument in Eq. (4). If the coefficient associated to Sjt in the second stage is statistically significant, this may indicate the violation of the exclusion restriction. We also perform several sensitivity analyses while

8 The weighted average is linked to the dependent variable. For example, if we use the number of battle-related deaths as dependent variable in the structural model, we include the weighted average of the number of battle-related deaths in neighboring countries.

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relaxing the exclusion restriction using the Union of Confidence Intervals approach designed by Conley, Hansen, and Rossi (2012). Another concern potentially affecting our estimates of Eq. (3) is that the latter do not fully account for time-varying heterogeneity. Arguably, country-specific time trends capture some of this heterogeneity (Burke et al., 2009), but it is likely some may be left out. To account for this problem, we estimate a model with lagged conflict. The use of a lagged dependent variable explicitly controls for timevarying omitted variable biases (Mary et al., 2018). If these results remain in line with our static estimates of Eq. (3), time-varying omitted variables biases are likely unimportant in driving our baseline results. Through the estimations, the strength of instruments is examined by reporting the Kleibergen-Paap rk Wald F statistic (or F-statistic) that we compare with the critical values from Stock and Yogo (2005) for testing weak instruments. We test the null hypothesis that the maximum size distortion is greater than 10, 15 or 20 percent and (if needed) the null that the relative bias is larger than 10, 15, or 20 percent. We report inference robust to weak instruments, based on the Stock-Wright S Lagrange Multiplier (LM) statistic (Pflueger & Wang, 2015). Following Young (2018), we test the inference relative to our main results to bootstrapping and proceed to leave-one (cluster)-out tests to examine the robustness of our panel IV regressions. Last, it is important to note that 2SLS estimators typically apply when both the dependent variables in the structural model (Eq. (3)) and the reduced form for the endogenous regressor (Eq. (4)) are continuously distributed. This is neither the case here because conflict incidence is binary and humanitarian food aid cannot be negative. Ideally, one would like to extend the 2SLS reasoning to nonlinear models. Unfortunately, there is no commonly accepted and easily adaptable approach that allows doing so, especially where the first stage would be a tobit model and the secondstage a probit model. Trying to extend such reasoning often results in (infamous) forbidden regressions. Thus, the most often applied approach has been to use linear 2SLS estimation. This choice is supported by evidence that show little differences between partial effects from more plausibly correct nonlinear models to partial effects from linear models (e.g. Papke & Wooldridge, 2008). More fundamentally, whether the dependent variable in the second stage is binary, non-negative, or continuously distributed, the 2SLS-IV approach captures the local average treatment effect, that is, in this context, the effect of humanitarian food aid for countries receiving such aid, which is the main question of interest in our paper (Angrist & Pischke, 2008). There are, of course, more complex alternatives to estimating binary models with endogenous regressors such as control function (CF) methods or maximum likelihood (ML) estimators. The former is typically used when the endogenous variable is continuously distributed while the latter is used when the endogenous regressor is binary. However, neither CF methods nor ML estimators can accommodate limited regressors. Both CF and ML approaches also come at the cost of additional assumptions with respect to the joint distribution of errors in the structural model and the reduced form for the endogenous regressor. Moreover, it is noteworthy that, unlike CF or ML estimators9 that become generally inconsistent if an appropriate instrument is not included, the 2SLS estimator only loses efficiency (Baum, Dong, Lewbel, & Yang, 2012). Given these difficulties, one might be tempted to change the dependent variable and/or the independent variable of interest

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Furthermore, these alternatives rarely allow for the inclusion of fixed effects. A potential exception is the approach developed by Papke and Wooldridge (2008), but the approach only fits the case where the endogenous regressor is continuous. Also, these approaches do not converge under more advanced model specifications, i.e. when we include country-specific nonlinear time trends.

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(humanitarian food aid) to make the estimation more fitting to the 2SLS framework. For instance, we could use the number of battle-related deaths or civilian casualties for the dependent variable (in levels or logs), and/or use the logged value of aid. Note, though, both changes would represent a change in the functional form of the structural model, and the choice of the new baseline model would then have to be informed by AIC and prior beliefs. More importantly, while this may lessen some of the issues for the estimations discussed above, this would also bring about another new set of issues, related to the zero values in both the dependent and the aid variables. If we log variables whose values often take 0, we risk losing a lot of information. The traditional solution to this problem has been to use a transformation such as lnðAID þ xÞ with x being a small number. As a way to test the robustness our results, we also provide these alternative estimations in robustness analyses (see Table 3 and A3). 3. Data and descriptive statistics The sample is composed of 79 humanitarian food aid recipient countries. Data are collected for the period 2002–2017. The availability and quality of data restricts the selection of countries and time coverage. Especially, data on sector foreign aid before 2002 is not suitable for empirical analysis (Mary et al., 2018)10. The full list of countries can be found in the Appendix while descriptive statistics can be found in Table 1. The indicator of civil conflict incidence is from the PRIO/UCDP database (version 19.1) (Gleditsch, Wallensteen, Eriksson, Sollenberg, & Strand, 2002; Pettersson et al., 2019), where civil conflict (or state-based armed conflict) is defined as ‘‘a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battlerelated deaths in a calendar year.” (Pettersson, 2019). In practice, civil conflict is captured by a binary variable taking the value 1 if the conflict has resulted in more than 25 battle-related deaths in year t, 0 otherwise. We also use several alternative conflict variables in robustness analyses, especially a binary variable for the onset11 of civil conflicts. We also test the conflict incidence for minor and major civil conflicts. A minor conflict is defined by the number of battle-related deaths being between 25 and 999. If a conflict results in more than 999 battle-related deaths, the conflict is a major conflict. We also run additional estimations where the dependent variable is the number of battle-related deaths and the number of civilian casualties12. Battle-related deaths refer to those deaths caused by the warring parties that can be directly related to combat during an armed conflict, but exclude deaths due to disease and starvation, criminality, or attacks deliberately directed against civilians only. Civilian fatalities result from the use of armed force by the government of a state against civilians which results in at least 25 deaths. Non-humanitarian food aid is defined as the supply of edible human food under national or international programmes including transport costs; cash payments made for food supplies; project food aid and food aid for market sales (CRS purpose code 52010). Unlike humanitarian food aid, it typically targets hunger and food insecurity in the medium run. Other aid is defined as total aid minus food aid and humanitarian food aid. Aid inflows are gross disbursements in constant 2017 USD (OECD, 2019). 10 We do not rely on FAO data, unlike Nunn and Qian (2014), because our IV approach requires total aid which is not available from the FAO dataset. Moreover, using the same database has the advantage of consistency across aid measurements. 11 The onset (offset) indicator takes the value of 1 in the year where the conflict started (ended), 0 otherwise. 12 In both cases, we use the best estimates provided by the UCDP databases.

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Table 1 Descriptive statistics of the baseline estimation sample (N = 1158). Mean

Std. deviation

Min.

Max.

Variables

(1)

(2)

(3)

(4)

Civil conflict – more than 25 battle-related deaths Civil conflict – more than 25 battle-related deaths but less than 1000 Civil conflict – more than 999 battle-related deaths Onset of civil conflict – more than 25 battle-related deaths Offset of civil conflict – more than 25 battle-related deaths Number of battle-related deaths due to conflicts Number of battle-related deaths due to conflicts, log Number of civilian deaths due to government attacks Number of civilian deaths due to government attacks, log Weighted average of conflict incidence (more than 25 battle-related deaths) in neighboring countries Weighted average of conflict incidence (between 25 and 999 battle-related deaths) in neighboring countries Weighted average of conflict incidence (more than 999 battle-related deaths) in neighboring countries Weighted average of number of battle-related deaths in neighboring countries Weighted average of number of civilian fatalities in neighboring countries

0.194 0.180 0.0259 0.0501 0.0415 120.20 0.778 15.06 1.797 0.199 0.176 0.0318 313.9 19.51

0.396 0.385 0.159 0.218 0.199 577.20 3.140 118.50 1.819 0.259 0.251 0.116 2,649 104.6

0 0 0 0 0 0.00 2.303 0.00 2.303 0 0 0 0 0

1 1 1 1 1 10,165 9.227 2,595 7.861 1 1 1 57,160 2,553

Humanitarian food aid per capita, constant 2017 USD Other non-food aid per capita, constant 2017 USD Other non-food aid per capita, log Food aid per capita, constant 2017 USD Weighted average of per capita humanitarian food aid in neighboring countries Share of humanitarian food aid out of total aid, rest of sample

0.986 49.58 3.330 0.833 1.166 2.262

3.117 55.26 1.198 1.645 3.004 0.340

0 0.92 0.0825 0 0 1.520

49.37 455.90 6.122 27.35 43.92 2.938

GDP per capita, PPP (constant 2011 international $) GDP per capita, log Inflation, GDP deflator (annual %) Inflation, log Polity 2 score Ethnic tensions

7,425 8.540 9.317 1.792 3.606 0.379

5,856 0.936 11.22 1.013 5.289 0.207

545.3 6.301 0.0195 3.937 9 0

29,494 10.29 197.0 5.283 10 1

Monthly average precipitation January February March April May June July August September October November December

77.55 70.96 79.19 86.86 109.9 119.2 129.5 135.9 128.0 119.5 92.53 85.15

87.21 78.60 78.07 79.18 105.8 121.8 134.5 141.5 126.6 118.6 94.50 90.13

0 0 0 0 0 0.100 0 0 0 0.100 0 0

462.3 400 443.1 419.9 619 639.7 849.7 944.6 880.4 1,070 528.8 445.6

Monthly average temperature January February March April May June July August September October November December

18.74 19.83 21.56 23.01 23.77 24.04 24.11 24.25 23.84 22.90 21.01 19.25

10.48 10.02 8.303 6.466 5.528 5.302 5.139 4.605 4.505 5.868 7.865 9.739

25.70 24.30 11.50 1.600 5.100 3.100 3.600 3.600 6 0.900 12.70 21.10

28.70 30.60 33 33.60 34.50 34.70 35.30 35 31.70 30.60 29.30 28.60

GDP per capita (in 2011 constant dollars, purchasing power parity), total population and the inflation rate are taken from the WDI database (WDI, 2019). The index on the level of ethnic tensions captures the existence of tensions attributable to racial, nationality or language divisions in a country. We rescale an index taken from the International Country Risk Guide from the Political Risk Services from 0 to 1 (ICRG, 2018), with 0 (1) indicating the lowest (highest) level of ethnic tensions. Ethnic divides have long been suspected to be a cause of conflict (e.g. Alesina, Devleeschauwer, Easterly, Kurlat, & Wacziarg, 2003). The Polity2 score is taken from the Polity IV database from the Center for Systemic Peace. The variable captures the type and quality of political regime and has been previously used in the

literature (e.g. Burke et al., 2009). The monthly values for average temperature and precipitation are taken from the Climate Research Unit database of the University of East Anglia (version TS v 4.01) (CRU, 2019). Temperature data are expressed in degree Celsius (°C) while precipitation data are expressed in millimeters (mm). Fig. 1 shows the humanitarian food aid and civil conflict trends over the period 2002–2017 for the sample. It is clear that the average incidence of civil conflict has been overall increasing since 2012, while it had fallen down significantly between 2007 and 2012. Interestingly, the increasing conflict trend seems to coincide with several years of decreased humanitarian aid inflows (though with some delay).

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4. Results

Table 2 The effect of humanitarian food aid on civil conflict incidence.

4.1. Baseline estimates Table 2 displays the Ordinary Least Squares (OLS) results of Eq. (3) in column 1 and the 2SLS-IV estimation results of the system of Eqs. (3) and (4) in columns 2 (first stage) and 3 (second stage). 13As explained in Section 2, our baseline modeling specification includes country and time fixed effects, country-specific cubic time trends as well as a set of monthly weather variables and other covariates. Column 1 Table 2 suggests that humanitarian food aid is associated with civil conflict. The coefficient is negative (0.008) and statistically significant, suggesting that humanitarian food aid reduces civil conflict incidence. This estimate is, however, likely biased because of reverse causality and simultaneity, so we use 2SLS-IV estimation to control for endogeneity. The first stage of the 2SLS-IV estimation is displayed in column 2 Table 2. The instrument is clearly correlated with humanitarian food aid. The displacement coefficient is largely negative (71.029) and statistically significant. This confirms the existence of between-country aid displacements in the context of humanitarian food aid. In the face of a new food crisis, donors reallocate funds away from the domestic country to another country (or area). As the reallocation takes place, the share of humanitarian food aid out of total aid averaged across all other recipient countries goes up. These displacements are economically large as a 1 basis percentage point increase in the average share of humanitarian food aid for the rest of the sample would result approximately in a 0.7 USD decrease in domestic humanitarian food aid per capita (or about 72 percent of the mean humanitarian food aid per capita). While this is the first study, to the best of our knowledge, to explicitly model such displacements in the humanitarian food aid context, there is some literature that has highlighted that foreign aid targeting HIV/AIDS may have displaced aid for other health concerns (e.g. Lordan et al., 2011). Column 3 shows the second stage of the 2SLS-IV estimation and the estimate for humanitarian food aid is negative (-0.018), statistically significant, and larger in absolute values than the OLS estimate. This implies that humanitarian food aid is even more so effective at mitigating civil conflict. A 10 percent increase in humanitarian food aid per capita would decrease the incidence of civil conflict by 0.2 percentage point or by 0.9 percent14. Following Young (2018)’s recommendation, note that the estimate remains significant (p-value 0.09), even when we rely on bootstrapping and check the robustness of the inference by using leave-one (cluster)out tests. As explained in Section 2, humanitarian food aid may have negative and positive impacts (e.g. sabotage, theft and predation versus direct nutrition, reduction in grievances and informationsharing), but our findings suggest that on average the positive effects dominate the negative ones. The effect of humanitarian food aid is economically substantial, and is, to some extent, in contrast with the findings of NQ (2014) who find that US food aid promotes conflict in recipient countries. However, it is important to highlight that our paper is quite different from theirs. First, our model is different in that we explicitly model country-specific time trends. Also, NQ (2014) estimate the effect of US food aid using a sample of 125 developing countries, and food aid data from the Food and Agriculture Organization over a much longer period. It is important to note that most humanitarian food aid during the period covered by NQ (2014) was in-kind

13 14

Coefficients for weather variables are omitted from all tables for the sake of space. This is calculated as: 0:098  0:018  0:0017. Then: ð0:0025=0:194Þ  100  0:91.

OLS-FE

2SLS-IV

Dependent variable

Conflict (1)

First stage HFA (2)

Humanitarian food aid per capita (HFA)

0.008***

Other non-food aid per capita, log Food aid per capita Ethnic tensions Polity 2 score GDP per capita, log Inflation, log HFA in neighbor countries, weighted average Conflict in neighbor countries, weighted average

0.018**

(0.003) 0.011 (0.023) 0.016# (0.011) 0.662** (0.290) 0.013# (0.008) 0.616** (0.276) 0.014 (0.011) 0.003

0.634** (0.281) 0.099 (0.169) 0.031 (1.161) 0.023 (0.043) 2.054# (1.284) 0.032 (0.076) 0.124

[0.026] 0.010 (0.023) 0.017# (0.011) 0.664** (0.293) 0.014# (0.008) 0.701*** (0.268) 0.014 (0.011) 0.001

(0.008) 0.103*

(0.096) 0.223

(0.009) 0.100*

(0.059)

(0.289) 71.029*** (20.359)

(0.060)

1,158 79 YES YES CUBIC YES n.a.

1,158 79 YES YES CUBIC YES n.a.

1,158 79 YES YES CUBIC YES 12.17

Displacement (instrument) Observations Number of countries Country FE Year FE Country-specific time trends Weather controls First-stage, F-stat

Second stage Conflict (3)

Notes: Robust country-clustered standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.10, # p < 0.15. Stock-Wright LM p-values in square brackets. n.a.: not applicable.

aid while the period covered in this paper has seen dramatic changes in the modality structure of humanitarian food aid (towards cash and vouchers). Moreover, we use humanitarian food aid data from the OECD over a much shorter and recent period. In a related literature on humanitarian aid, our results somewhat relate to Gupta et al. (2018) who find that US health aid contributes to security in recipient countries. Furthermore, our OLS estimate is higher than our 2SLS estimate suggesting that the reverse causality is positive. This is in line with the stated objective of the donors, since humanitarian food aid is intended to respond to conflict situations. While we acknowledge the differences in the study period and design, this is also in contrast with NQ (2014). Next, we can reject the null hypothesis that the maximum size distortion is greater than 20 percent as the Kleibergen-Paap statistic is 12.17, well above the 20 percent Stock-Yogo critical value (6.66). F-tests fail to reject the (joint and individual) relevance or inclusion of time fixed effects (pvalue: 0.000) and country-specific cubic time trends (p.value: 0.000). Last, we can briefly discuss other independent variables. Economic growth is associated with decreased conflict incidence. Food aid per capita and conflict in neighboring countries would seem to decrease conflict incidence but both coefficients do not pass Young’s test. The coefficient for ethnic tensions is somewhat surprisingly negative, indicating that increased tensions would reduce the incidence of civil conflict. Perhaps, this result implies that further tensions would only dampen the risk for conflict, given the already high level of tensions, or possibly that the relationship is nonlinear.

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4.2. Test of exclusion restriction The validity in the IV estimation in Table 2 relies on assuming that the between-country ‘displacement’ instrument affects domestic civil conflict only through domestic humanitarian food aid per capita. Table A1 implements an intuitive check for the validity of the exclusion restriction following Brückner (2013). Column 1 Table A1 first presents the direct impact of the ‘displacement’ instrument on civil conflict. The OLS coefficient is positive (1.273) and significant. This suggests that the redirection of food assistance funds away from (towards) the rest of the world is associated with lower (higher) conflict incidence in the domestic country. This implicitly confirms our previous results and the dominance of the positive mechanisms with respect to the impact of humanitarian food aid. Column 2 introduces the ‘displacement’ instrument as a righthand side variable in Eq. (3) and uses instead six-period lagged humanitarian food aid per capita15 to instrument current humanitarian food aid per capita. The ‘displacement’ coefficient is now negative, in comparison with the direct positive ‘displacement’ effect in column 1 Table A1, and statistically insignificant. In other words, our preferred instrument does not have any direct effects on conflict other than through the humanitarian food aid channel. This intuitive check, therefore, provides reassuring evidence for the exclusion restriction. Let’s note that the coefficient for humanitarian food aid per capita (0.032) is negative, twice the estimate in Table 2, and statistically significant. We also perform several sensitivity analyses while relaxing the exclusion restriction using the Union of Confidence Intervals approach designed by Conley et al. (2012). We use the support restrictions ½2db; 2db where d takes the value 0 or 1. b is the estimate of the effect of food aid on conflict. The 95 per cent confidence intervals are respectively [0.027;0.007] and [0.028;0.006] for d ¼ 0 andd ¼ 1. Given that this approach is considered conservative, it is clear that the relaxation of the exclusion restriction does not invalidate our inference.

4.3. Alternative specifications Table A2 summarizes the 2SLS-IV estimation results across several alternative specifications. First, column 1 displays the estimation results of a model in which we exclude country-specific time trends. The 2SLS coefficient for humanitarian food aid per capita is now positive and insignificant. This implies that humanitarian food aid has no effect on conflict incidence in the sample. When we include country-specific linear time trends in column 2, the estimate turns negative but is still insignificant. When we include country-specific quadratic time trends in column 3 Table A2, the estimate is negative and is somewhat imprecise. This sensitivity analysis somewhat resonates with Christian and Barrett (2017). Notably, the AIC clearly point towards the specification with country-specific cubic time trends being the preferred model over all specifications, as the AIC for our baseline model in column 3 Table 2 is 846 (against 73, 349, 596, respectively, for the models without time trends, with linear time trends, and with quadratic time trends). Overall, these findings illustrate the need to account for country-specific time trends in modeling conflict, as had been previously argued in previous studies (e.g. Burke et al., 2009). It also sends a warning to future researchers in that the validity of empirical results should be tested to the inclusion of country-specific nonlinear time trends. 15 We use six-period lagged humanitarian food aid because estimations based on earlier lags suffer from weak instruments.

Furthermore, one of the issues of our baseline specification is that our results may be affected by the issue of bad controls (Angrist & Pischke, 2008). We thus propose a couple of parsimonious models in columns 4 and 5 Table 4. Column 4 excludes all variables from the model but humanitarian food aid. Column 5 only excludes ethnic tensions and polity2 for which data are often missing. Overall, these additional results support our baseline results. For example, the coefficient for humanitarian food aid is negative in column 4 (and a bit imprecise), though somewhat lower than in column 3 Table 2. In particular, a 10 percent increase in humanitarian food aid per capita would decrease the probability of civil conflict approximately by slightly less than 0.1 percentage point or by about 0.6 percent. Still, even if this estimate is lower than our baseline estimate in Table 2, humanitarian food aid appears as a ‘‘bargain” towards conflict mitigation in terms of cost-effectiveness. Column 6 Table 4 controls for time-varying heterogeneity in Eq. (3) by adding a lagged civil conflict variable. Again, the coefficient for humanitarian food aid remains negative and is statistically significant (0.013). Table A3 provides alternative aid specifications. Column 1 logs all aid variables in the model. Colum 2 uses the transformation ln(AID + 0.1) to avoid the loss of information related to zero aid values in the case of humanitarian food aid (and food aid). Column 3 uses the levels of aid variables in the model. Across these alternative aid specifications, columns 1–3 all suggest that humanitarian food aid would decrease the incidence of civil conflict in the sample. When we replace current humanitarian food aid by its one-period lag, we find no effect of lagged humanitarian food aid on civil conflict. This suggests that the effect of humanitarian food aid is short-lived. Next, column 5 weighs the humanitarian food aid variable by the country’s relative land area. Conflict is likely to be located away from the capital (Findley et al., 2011). Humanitarian food aid is targeted at populations in conflict zones. However, humanitarian food aid may not always and/or fully reach the areas most affected by conflict (or at least not proportionally to the conflict intensity) (e.g. Briggs, 2018). This arguably means that our estimated effect of humanitarian food aid could be underestimating the true effect16. The estimate in column 5 Table A3 is 0.052, and much larger than previously estimated in Table 2. This result suggests that humanitarian food aid is likely to be more effective at mitigating conflict than what our baseline results indicate. Finally, column 1 Table A4 drops the five largest countries from the sample. The estimate is virtually the same than in Table 2 column 3. Column 2 drops data where total aid responses to conflict have been larger than 10 per cent. This allows checking whether the potential endogeneity between overall aid budgets to conflict affects our IV estimates. The IV estimate in column 2 (0.044) is negative and much larger than in Table 2. As explained in the modeling section, this is in line with our IV estimate in Table 2 being a lower bound to the positive effect of humanitarian food aid towards mitigating civil conflict. 4.4. Persistence in humanitarian food aid Kuhlgatz and Abdulai (2012) show that humanitarian food aid is relatively persistent. Our instrumentation strategy does not explicitly account for aid inertia, though the country-specific time trends likely account for some time-varying heterogeneity in Eq. (4). To check whether our instrumentation strategy is affected by 16 To simplify the exposition, let’s assume that 10 per cent of humanitarian food aid is given to non-conflict areas (rather than 0 per cent), the only way populations in conflict areas will benefit from such humanitarian food aid is through indirect market effects. The additional food in non-conflict areas lowers the overall food demand and puts fewer pressures on food prices in other (conflict) areas.

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S. Mary, A.K. Mishra / World Development 126 (2020) 104713 Table 3 The effect of humanitarian food aid on civil conflict: alternative conflict outcomes. 2SLS-IV Second stage Dependent variable

Humanitarian food aid per capita Other non-food aid per capita, log Food aid per capita Ethnic tensions Polity 2 score GDP per capita, log Inflation, log HFA in neighbor countries (w.a.) Conflict in neighbor countries (w.a.) Observations Number of countries Country FE Year FE Country-specific time trends Weather controls First-stage, F-stat

Weibull-IV

Minor

Major

Ln (Battle-related deaths + 0.1)

Ln (Civilian fatalities + 0.1)

Onset conflict

Offset conflict

(1)

(2)

(3)

(4)

(5)

(6)

0.015* [0.062] 0.011 (0.025) 0.020* (0.010) 0.562* (0.299) 0.007 (0.009) 0.047 (0.162) 0.013 (0.012) 0.007 (0.010) 0.072 (0.057) 1,158 79 YES YES CUBIC YES 12.25

0.005* [0.099] 0.003 (0.007) 0.001 (0.003) 0.200 (0.151) 0.005 (0.006) 0.707*** (0.233) 0.002 (0.006) 0.000 (0.007) 0.021 (0.027) 1,158 79 YES YES CUBIC YES 12.49

0.133* [0.062] 0.126 (0.176) 0.132* (0.072) 5.176*** (1.952) 0.105# (0.065) 6.647** (3.080) 0.090 (0.075) 0.004 (0.071) 0.000 (0.000) 1,158 79 YES YES CUBIC YES 12.65

0.037 [0.499] 0.364* (0.216) 0.032 (0.029) 1.412 (1.230) 0.081* (0.045) 7.437*** (1.351) 0.066 (0.073) 0.121 (0.106) 0.000 (0.000) 1,158 79 YES YES CUBIC YES 12.36

0.010# [0.102] 0.006 (0.026) 0.008 (0.007) 1.002*** (0.270) 0.012 (0.009) 0.765*** (0.216) 0.017# (0.012) 0.009 (0.010) 0.111** (0.051) 1,158 79 YES YES CUBIC YES 12.17

1.096# (0.066) 0.706* (0.127) 1.061 (0.149) 10.374** (12.31) 0.992 (0.038) 0.675 (0.261) 1.082 (0.186) 1.046 (0.045) 1.274 (1.178) 934 79 n.a. n.a. n.a. YES n.a.

Notes: Robust country-clustered standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.10, # p < 0.15. The IV in column 6 relies on a control function approach. We insert the residuals from estimating Eq. (4) into the Weibull model. The estimate in column 6 is a hazard ratio. W.a.: weighted average.

Table 4 The effect of humanitarian food aid on civil conflict: regional estimations. 2SLS-IVSecond stage Africa Asia Conflict

(1)

(2)

Humanitarian food aid per capita (HFA)

0.030** [0.012] 0.023 (0.036) 0.009 (0.013) 0.791 (0.557) 0.006 (0.016) 0.919*** (0.230) 0.016 (0.014) 0.005 (0.010) 0.221** (0.100) 504 36 YES YES CUBIC YES 90.92

0.084** [0.037] 0.035 (0.033) 0.013 (0.011) 1.219*** (0.222) 0.013 (0.010) 0.309 (0.237) 0.034 (0.029) 0.041 (0.032) 0.014 (0.115) 232 15 YES YES CUBIC YES 12.60

Other non-food aid per capita, log Food aid per capita Ethnic tensions Polity 2 score GDP per capita, log Inflation, log HFA in neighbor countries, weighted average Conflict in neighbor countries, weighted average Observations Number of countries Country FE Year FE Country-specific time trends Weather controls First-stage, F-stat

Notes: Robust country-clustered standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.10, #p < 0.15.

aid inertia, we re-estimate the results in columns 2 and 3 Table 2, but we marginally modify Eq. (4) by including one-period lagged humanitarian food aid. Results are provided in Table A5. The main pattern of results remains in line with Table 2. The estimated displacement effect in column 1 Table A5 is close but

slightly somewhat larger to that of column 2 Table 2. The estimate is statistically significant and negative (73.75). In addition, the lagged aid coefficient is positive (0.247) and statistically significant, suggesting moderate inertia. This is in line with previous findings (e.g. Kuhlgatz & Abdulai, 2012). More importantly, column 2 Table A5 implies that humanitarian food aid decreases conflict incidence, even we account explicitly for humanitarian aid inertia. The coefficient in column 2 is 0.013 and statistically significant, implying relatively lesser effects of humanitarian food aid than those estimated in Table 2.

5. Heterogeneous effects of humanitarian food aid 5.1. Alternative conflict indicators Table 3 provides 2SLS-IV estimates using alternative civil conflict indicators. First, the coefficients for humanitarian food aid in columns 1 and 2 remain negative and statistically significant. This implies that humanitarian food aid reduces the incidence of smallscale and large-scale civil conflicts in the sample. Second, if we use the logged number of battle related deaths as the dependent variable17, we also confirm our baseline results. A 10 per cent increase in humanitarian food aid per capita would decrease the number of battle-related deaths by about 1.5 deaths. If we use the logged number of civilian fatalities as dependent variable in column 4, we find no effect of humanitarian food aid per capita on the number of civilian fatalities, though the estimate is positive and insignificant. Next, we also examine the impact of humanitarian food aid on the onset and offset of conflicts. The estimate for the effect of humanitarian food aid on the onset of civil conflicts is column 5 is negative, implying that humanitarian food 17 AIC suggest that the log-level specification is best when using the number of battle-related deaths or civilian casualties as dependent variables.

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aid is effective at preventing new civil conflicts. A 10 per cent increase in per capita humanitarian food aid would decrease the onset of civil conflict by 2.3 per cent. To study the effect of humanitarian food aid on the duration of conflicts, we follow previous studies that use survival models (e.g. Narang, 2014). We use the control function approach used by Nunn and Qian (2014) to account for potential endogeneity using the ‘displacement’ instrument. Column 6 Table 3 provides estimates for the Weibull model (we find the latter is preferred to a Cox model). However, the unusual negative AIC value suggests that this framework may not be applicable to the outcome of interest and these results should be taken with caution. This caveat aside, the main message is that humanitarian food aid seems to be associated with a shorter civil war survival, since the hazard ratio in column 6 is above 1. In other words, humanitarian food aid seems to reduce the duration of civil conflicts. 5.2. Contextual factors Table A6 examines whether the effect of humanitarian food aid is heterogeneous across different contexts. Especially, Table A6 examines whether the impact of food aid on conflict depends on the level of ethnic tensions in the recipient country (columns 1–2) as might be implicitly suggested by previous studies (e.g. Schleussner, Donges, Donner, & Schellnhuber, 2016), on the inflation rate via food prices (columns 3–4) as suggested by Bellemare (2015), and on the precipitation level (columns 5–6). High/low is defined with respect to being above/below the mean sample level of each mitigating variable. For example, according to column 1 Table 3, when ethnic tensions are higher than the mean sample level, humanitarian food aid is found to have a statistically significant and large effect on conflict reduction (coefficient: 0.033). However, when ethnic tensions are lower, the effect of food aid on conflict subsides as the coefficient is negative but statistically insignificant. Further, the main message from columns 3–4 is that humanitarian food aid largely reduces conflict incidence in situations of low inflation; when inflation is high, food aid is found not to affect conflict incidence. This is somewhat surprising, but arguably this could be due to the fact that price-driven riots are an urban phenomenon and might likely happen away from conflict areas. Columns 5–6 suggest that humanitarian food aid is quite effective at reducing conflict risks as precipitation levels are especially low. This is line with the scarcity channel discussed in the introduction (e.g. Hendrix & Brinkman, 2013). When precipitations are above the mean level (or in situations of relative food abundance), the coefficient for humanitarian food aid is found positive though insignificant. This somewhat relates to recent studies linking food scarcity/abundance to conflict (e.g. Koren, 2018; Koren & Bagozzi, 2017). 5.3. Regional estimations Last, Table 4 presents regional estimations for Africa and Asia, two regions that have suffered a great deal because of hunger and civil conflicts. When we limit the sample to African or Asian countries, the coefficients for humanitarian food aid seem to be much larger than estimated in Table 2. For Africa (sub-Saharan Africa and Northern Africa), a 10 per cent increase in humanitarian food aid per capita (about 0.15USD) in the region would result in a 2.7 per cent decrease in conflict incidence. This is more than twice the relative effect estimated for the entire sample. For Asia, a 10 per cent increase in humanitarian food aid per capita (about 0.15USD) in the region would result in a 0.3 per cent decrease in conflict incidence. We

are unable to provide estimations for other regions18. Overall, Table 4 suggests that the humanitarian food aid is particularly effective in the African context.

6. Conclusions Humanitarian food aid is designed to save lives and alleviate human suffering in conflict situations. Within the Sustainable Development Goals that have the objectives of ending hunger before 2030 (SDG#2) and bringing peace and justice (SDG#16), humanitarian food assistance may play a critical role in delivering progress in developing countries. However, there have been growing concerns that humanitarian food aid may actually have counter-intended effects by aggravating conflicts in recipient countries. Given the importance of humanitarian food assistance policies, this paper has revisited the effect of humanitarian food aid on conflict in developing countries. We find that, on average, an increase in humanitarian food aid is associated with decreased incidence of civil conflicts in the sample. Overall, the provision of humanitarian food aid in conflict situations seems to provide some solace to the populations that may be more likely to join the rebellion and fight, and weaken the risk of civil war. The positive effect of humanitarian food aid towards conflict mitigation is economically large. Our empirical results are also robust to the use of alternative conflict indicators, in particular the onset and duration of conflicts. These additional results convey much political importance and support the sustained provision of food assistance in high-risk countries as an effective tool for conflict prevention and mitigation. We also find that humanitarian food aid is particularly effective in situations of (likely) weather-related food scarcity and high ethnic tensions. We also find that humanitarian food aid is particularly effective in Africa. More fundamentally, we raise a major concern in empirical studies about the modeling of conflict. In particular, we show that the inclusion of country-specific nonlinear time trends is critical in identifying the causal impact of humanitarian food aid on conflict. This resonates with Christian and Barrett (2017) and Burke et al. (2009) in somewhat related literature. We invite future researchers to double-check the robustness of their empirical results to the inclusion of country-specific time trends. The implication of our findings for policymakers and development practitioners is clear. Humanitarian food aid overall saves lives in recipient countries, and as such, the continuation of humanitarian food assistance will make the developing world, especially Africa, safer, and by extension, less food insecure (see FAO, Ifad, UNICEF, WFP and WHO. , 2017). At a global level, the stability of developing countries may contribute to security and lesser terrorist threats worldwide (Piazza, 2008; Steinwand, 2015; Open letter to US congress, 2017). On a more practical level, our findings indicate the relatively good ‘value for money’ of humanitarian food assistance towards conflict prevention and mitigation. Furthermore, the existence of heterogeneous effects offers some guidance to better target the locations and times where the efficacy of humanitarian food aid can be improved. The SDG aims to end hunger and make the world a safer place. However, our results are at odds with the reality of foreign aid sectoral allocation in recent years. Mary et al. (2018) show that the share of humanitarian food aid has relatively decreased in the last two decades. According to our findings, this may put at risk the progress towards achieving the SDG #2 and #16. In addition, in the US (the major humanitarian food aid donor), the Global Food 18 This is because we do not have enough countries per region or the estimations suffer from weak instruments.

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Security Act of 2016 recommends ‘‘reducing reliance upon emergency food assistance” upon evidence that ‘‘insurgent groups can exploit international food aid” (Global Food Security, 2016). Our findings suggest these concerns are not warranted on aggregate. Given that there are still millions of people in the world, who suffer from conflicts and its awful consequences, e.g. injuries, deaths, or extreme hunger, it is a moral imperative to continue researching the effect of humanitarian aid. Studies adding more (data) granularity as well as cross-national studies are equivalently needed to capture the complex relationships between humanitarian food assistance and civil conflict in the developing world and to further inform policymakers. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors gratefully acknowledge the data assistance from Marissa Sirico. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.worlddev.2019.104713. References Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., & Wacziarg, R. (2003). Fractionalization. Journal of Economic Growth, 8(2), 155–194. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton Univ. Press. Barrett, C. (2002). Food aid effectiveness: It’s the targeting, stupid. Cornell University Applied Economics and Management Working Paper No. 2002-43. Baum, C. F., Dong, Y., Lewbel, A., & Yang, T. (2012). Binary choice models with endogenous regressors. State Conference, 2012, San Diego http://fmwww.bc.edu/ EC-C/S2013/823/baum.san2012.pdf. Bellemare, M. F. (2015). Rising food prices, food price volatility, and social unrest. American Journal of Agricultural Economics, 97(1), 1–21. Bellemare, M. F., Maski, T., & Pepinsky, T. B. (2017). Lagged explanatory variables and the estimation of causal effect. Journal of Politics, 79(3), 949–963. Blattman, C., & Miguel, E. (2010). Civil war. Journal of Econic Litwerature, 48(1), 3–57. Briggs, R. C. (2018). Poor targeting: A gridded spatial analysis of the degree to which aid reaches the poor in Africa. World Development, 103, 133–148. Brückner, M. (2013). On the simultaneity problem in the aid and growth debate. Journal of Applied Econometrics, 28(1), 126–150. Buhaug, H. (2010). Climate not to blame for African civil wars. PNAS, 107, 16477–16482. Burke, M., Miguel, E., Satyanath, S., Dykema, J. A., & Lobell, D. B. (2009). Warming increases the risk of civil war in Africa. PNAS, 106(49), 20670–20674. https:// doi.org/10.1073/pnas.0907998106. Carter, D. B., & Signorino, C. S. (2010). Back to the future: Modeling time dependence in binary data. Political Analysis, 13(3), 271–292. Casey, A., 2018. US International Food Assistance: An Overview, 2018. Congressional Research Service Report R45422, December 6. Center for Systemic Peace, Accessed 25.01.2019 https://www.systemicpeace.org/. Christian, P., Barrett, C. (2017). Revisiting the effect of food aid on conflict: a methodological caution, Policy Research Working Paper 8171, World Bank, August. https://sites.tufts.edu/neudc2017/files/2017/10/paper_42.pdf. CIA (2014). World Threat Assessment of the US Intelligence Community, Senate Select Committee on Intelligence, statement for the record, James Clapper. https://www.dni.gov/files/documents/Intelligence%20Reports/2014%20WWTA %20%20SFR_SSCI_29_Jan.pdf. Climate Research Unit (2019). University of East Anglia, Accessed 6.28.2019. http:// www.cru.uea.ac.uk/data. Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. In Oxford Economic Papers 56 (pp. 563–595). Collier, P., & Hoeffler, A. (1998). On economic causes of civil war 50, No. 4 (Oct., 1998). In Oxford Economic Papers (pp. 563–573). Conley, T., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. The Review of Economics and Statistics, 94(1), 260–272. Couttenier, M., & Soubeyran, R. (2014). Drought and civil war in sub-Saharan Africa. The Economic Journal, 124, 201–244.

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