Sara Balestri* and Mario A. Maggioni Blood Diamonds, Dirty Gold and Spatial Spill-overs Measuring Conflict Dynamics in West Africa

Transcription

1 Peace Econ. Peace Sci. Pub. Pol. 2014; 20(4): Sara Balestri* and Mario A. Maggioni Blood Diamonds, Dirty Gold and Spatial Spill-overs Measuring Conflict Dynamics in West Africa Abstract: Although conflict incidence is likely to be characterized by spatial dependence, the scientific literature on conflicts often neglects the issue thus, implicitly, assuming independence among observations. We argue that such assumption could lead to biased and inconsistent results and we provide an exemplary application to the case of the Mano River Region (MRR) in West Africa. Once we detected the existence of spatial dependence within the distribution of conflict incidence, we introduce spatial econometrics techniques in order to explore diffusion paths of violence within the region. We firstly project on a spatially disaggregated map, built as a regular grid, the conflict occurrence and several georeferenced determinants of civil conflicts. Then, we model spatial dependence through the introduction of spatial autoregressive terms on both dependent and independent variables (SAR and SD Models). Across several models, civil conflict is found steadily clustered in space with significant spill-over effects on contiguous locations. Among other determinants, natural resources namely diamonds and gold are confirmed as relevant drivers of conflict diffusion and show neighbouring effects since their location and proximity may affect conflict dynamics. Keywords: Africa, civil conflict, natural resources, spatial econometrics JEL classification numbers: C21, D74, N47, N57, Q34 DOI /peps Previously published online October 24, 2014 *Corresponding author: Sara Balestri, DISEIS (Department of International Economics, Institutions and Development) and CSCC (Cognitive Science and Communication Research Centre), Università Cattolica del Sacro Cuore, L.go Gemelli, 1. I Milano, Italy, sara.balestri@unicatt.it Mario A. Maggioni: DISEIS (Department of International Economics, Institutions and Development) and CSCC (Cognitive Science and Communication Research Centre), Università Cattolica del Sacro Cuore, L.go Gemelli, 1. I Milano, Italy

2 552 Sara Balestri and Mario A. Maggioni 1 Introduction A fast and large growth characterized the scientific literature on conflicts in the past two decades, spurred by an increasing availability of spatial disaggregated data on conflict occurrence and its determinants 1. Within this context, the opportunity to adopt a regional approach which goes beyond a country-based analysis has received a considerable attention, given the recognition that: the majority of conflicts are linked to conflicts in neighbouring countries (Buhaug and Gleditsch 2008); the reciprocal interaction among fighting factions across neighbouring countries is a common feature (Banegas and Marshall-Fratani 2007); the occurrence of a mutually reinforcing mechanism among overlapping violent conflicts is often the case (Salehyan 2009). Moreover, events of large-scale violence experienced by African countries are often characterized by interdependence between them, even across borders (Gersovitz and Kriger 2013). In addition, countries in proximity to states experiencing conflict are much more likely to become involved in violent conflicts (Anselin and O Loughlin 1992). Although linear regression models and statistical methods for continuous data outcome allowing for spatial correlation have been largely developed in several scientific fields and, most notably, in criminology, regional science, labour economics, innovation studies [Anselin 1988; Kelejian and Prucha 1998; Le Sage and Pace 2004; Maggioni, Nosvelli and Uberti 2007] conflict literature still largely neglects the existence of spatial dependence in empirical analysis. However, the increasing available data at high spatial resolution allows detecting the existence of spatial spill-overs and spatial patterns at the appropriate scale, thus calling for an analysis of conflict occurrences within regional systems, sharing geographical, political, socio-economic factors across a transnational space. With reference to the widespread violence experienced in the period in West Africa, we analyse the Mano River Region case to give evidence of the above reasoning and apply spatial econometric techniques to get unbiased and reliable estimates of the empirical determinants of civil conflicts. After introducing the context, we explain our methodology and model specifications; the variables applied as determinants of civil conflicts and present the main findings of the research. Special attention has been devoted to the role of natural resources, which location is found relevant for both spill-over and neighbouring effects in increasing conflict incidence. 1 For a recent survey of the literature see Blattman and Miguel (2010).

3 Blood Diamonds, Dirty Gold and Spatial Spill-overs The Mano River Region As many other regions across the continent, West Africa is characterized by the presence of ethnic, cultural and social relationships which go beyond national boundaries. Civil conflicts are likely to be fuelled and reinforced in such complex relationships. The violent events occurred in this region, indeed, share the same pattern and have not been bound by state borders (Silberfein and Conteh 2006). In the Mano River Region (MRR), sustained civil conflicts led to thousands of deaths and displaced people, and contributed to widespread economic and human underdevelopment. Originally established in 1973, the Mano River Union constitutes a custom and economic union between Liberia, Sierra Leone, Guinea and, more recently, Cote d Ivoire 2, meant to foster economic cooperation in order to increase wealth and stability in the region. However, due to repeated conflicts and humanitarian crises, it has never really played a role neither achieved expected results. Since 2004, it received a new reviving. Since the 1990s, indeed, violence occurrence in one country has sustained violence outbreak in neighbouring countries and the whole region has been involved in several conflict events. The interconnection among them has been supported by the existence of porous borders which allowed the easy flows of weapons, the spread of combats and the exploitation of natural resources across the entire region. In the literature it is customary to count five civil conflicts occurred in the region, namely the first and second Liberian civil war ( ; ); the Sierra Leone civil war ( ); the Guinea conflict ( ), and the first Ivorian civil war ( ). 3 Methodology and models specification Among most conflict literature papers it is assumed that conflict events are independent across space; conversely our key point is to remark that conflict events are likely to be spatially correlated. To take into account this dimension, and thus to obtain unbiased estimates, we introduce spatial econometrics techniques to detect the propagation of conflict in space. 2 The Mano River Union (MRU) was originally signed by Liberia and Sierra Leone, joint later by Guinea in Côte d Ivoire, as neighbouring country with close social and economic characteristics, has widely shared the internal dynamics of MRU members for several years. In 2008 it officially signed the agreement to be part of the organization.

4 554 Sara Balestri and Mario A. Maggioni In particular, the analysis focuses on the empirical determinants of civil conflicts by exploring local-scale and spatial spill-over effects with special regards to time invariant characteristics such as valuable extractive resources. In order to do that, the MRR has been projected on a spatially disaggregated map built as a degree resolution regular grid (Tollefsen, Strand and Buhaug 2012). We therefore construct a dataset covering 265 cells corresponding to Liberia, Sierra Leone, Guinea and Cote d Ivoire territories and combine georeferenced conflict data and a large set of cell-level covariates from the PRIO- GRID dataset. Over the period , we check whether each cell has been involved in a conflict and we collapse the cell-year observations to create a timeinvariant measure of conflict incidence in a given cell. Such measure is applied as dependent variable in the analysis. We initially perform a cross-sectional analysis to detect geographic correlates and spatial patterns of conflict incidence at local-scale, by estimating the base model: CONF_INC =α+β X +γ X + µ + ε (1) cit,, c ct, 1 i c,, it where c denotes the cell, i the country and t the collapsed years of observation; X a vector of controls time-invariant or measured at t-1, and μ country fixed effects. Tests on OLS estimation s residuals effectively confirm high spatial autocorrelation 3. This first result corroborates our methodological approach, confirming the necessity to account for spatial correlation in conflict analysis. Following major findings of development literature (Harari and La Ferrara 2013), we re-estimate Model (1) by OLS with Conley (1999) standard errors which are robust to spatial correlation in the error term. However, it is reasonable to expect that conflict incidence itself as well as other covariates, being assumed as determinants of a conflict, may produce spillover effects on neighbouring areas, thus we firstly introduce a spatial-lag of the dependent variable in our base model 4 : CONF_INC =α+β X +γ X + ρ W*CONF_INC+ µ + ε (2) cit,, c ct, 1 i c,, it 3 Moran s I error of , significant at 1% level. 4 Spatial dependence is modelled through a first order queen contiguity matrix which defines a location s neighbours as the cells with either a shared border or a vertex of the unit of analysis, and therefore it is composed by maximum eight contiguous cells for any given cell. The matrix is then row standardized. Such weight matrix reflects the exogenous definition of the space we are analyzing.

5 Blood Diamonds, Dirty Gold and Spatial Spill-overs 555 secondly, we include a spatial autoregressive term for each independent variable: CONF_INC =α+β X +γ X + ρ W*CONF_INC+φ W*X+ µ + ε (3) cit,, c ct, 1 i c,, it where ρ and φ denote the spatial autoregressive parameters, W denotes the spatial weight (contiguity) matrix, and all the other terms are defined as above. Models (2) and (3) are estimated by Maximum Likelihood (ML). 4 Variables description Four categories of determinants of civil conflict are included in the analysis: lootable natural resources and terrain characteristics, geographical proximity to political power, climate effects, social dimension such as population and ethnic diversity. From an empirical review of the literature about MRR conflicts, we included gold and diamonds in the analysis as relevant natural resources. Plotting deposits locations, we code two dummy variables which take the value of 1 if a deposit is placed within a cell at the time of conflict start, 0 otherwise. We gathered georeferenced data on resource locations from DIADATA (Gilmore et al. 2005) and GOLDATA (Balestri 2013). Extractive resources with high economic value and limited traceability have been largely exploited by fighting groups in order to sustain conflict costs or cumulate private wealth, thus we expect a positive correlation with conflict diffusion. Following the main literature on civil conflicts, we controlled for terrain characteristics (as the percentage of mountainous and forested areas within each cell, and the land area in each cell). As proximity to political power we include the distance (in kilometres) from the capital city and from the border of the nearest continuous neighbouring country. All variables are drawn from PRIO-GRID. These first two categories are time invariant for the period of observation. Then, we added two climate variables: an indicator of the average amount of precipitations in any given cell for the whole period and a second built on the Standardized Precipitation Index referring to deviations from normal long-term rainfall levels. This covariate indicates the sum of yearly periods of at least moderate drought occurred within each cell for the period of observation. We used data originally coming from the Global Precipitation Climatology Centre (Rudolf et al. 2010) and adapted on a grid space suitable for our analysis by Tollefsen, Strand and Buhaug (2012) (Table 1).

6 556 Sara Balestri and Mario A. Maggioni Table 1: Summary statistics. Variable Description Obs Mean Std. dev. Min Max Source CONFL_INC Conflict Incidence by cell over the period PRIO-GRID, 2012 N_EVENTS Cumulative number of conflict events per cell over the period 265 8, , UCDP-GED, CELLAREA Land area in the cell (sq km) , ,7454 1, ,102 PRIO-GRID, 2012 BRD_DIST Distance to border (km) , , PRIO-GRID, 2012 CAP_DIST Distance to capital city (km) , , PRIO-GRID, 2012 MNT Mountainous terrain as share of cell area (percentage) 265 0, , ,9417 UNEP, 2002 FRST Forested terrain as share of cell area (percentage) , ,6434 0,029 98,185 FAO, 2000 AVG_PREC Average precipitation per cell over the period (mm) , , , ,4 NOAA, 2011 SPI6 Number of drought periods in the cell, measured through the Standardized Precipitation Index 265 2,849 1, GPCC, 2010 DIA Dummy variable for the presence of lootable diamonds 265 0,1358 0, DIADATA, 2005 GOLD Dummy variable for the presence of lootable gold deposits 265 0, , GOLDATA, 2013 N_GRP Number of different ethnic groups per cell 265 1,5094 0, GeoEPR-ETH, 2010 POP Population size for each cell (1990) , ,1339 0, ,069 CIESIN, 2005 BRD Dummy variable for proximity to border 265 0, PRIO-GRID, 2012 CAP Dummy variable for proximity to capital city 265 0, PRIO-GRID, 2012

7 Blood Diamonds, Dirty Gold and Spatial Spill-overs 557 We also included a cell measure for population size and the number of ethnic groups settled in each cell as computed in the GeoEPR-ETH v.2 dataset (Weidmann et al. 2010). We apply this measure as proxy of ethnic diversity: on average, each cell is characterized by the presence of almost two different ethnic groups. To avoid issues of reverse causality, we introduced estimates corresponding to the beginning of the period of observation. As alternative measure of conflict occurrence we used georefenced data on conflict events locations drawn from the UCDP-GED (Sundberg and Melander 2013) dataset which codes events of armed violence resulting in at least 1 related death. The alternative measure corresponds to the cumulative number of events occurred in any cell. Finally, we controlled for regime type and state capacity, by introducing country fixed effects. 5 Major findings Conflict incidence in any given cell is our dependent variable and it is meaningful to assess patterns of spatial diffusion of conflict over time. In our sample, a cell has been involved in conflict dynamics for 12% of the years over the period of observation, on average. This suggests a high prevalence of conflict events in the area. We firstly estimate Model (1) by OLS with Conley standard errors to correct for spatial correlation the linear model and we find that natural resources are significantly correlated with conflict incidence. Table 2 summarizes the results. We then model the spatial correlation by assuming that the involvement of a specific area in conflict could have spill-over effects on the neighbouring locations. Model (2), estimated by ML, confirms such assumption with the autoregressive term significant at 1% level, meaning that the value taken by conflict incidence in a given cell is affected by the values taken in neighbouring cells. Since in our sample each cell is contiguous, on average, to neighbouring cells, each neighbour s influence is Therefore, we multiply the coefficient of the autoregressive term by the single neighbour s influence in order to obtain the average effect of conflict incidence on the cell under study. The prevalence of conflict in any given cell has a positive impact on conflict incidence in neighbouring cells of 5.4 percentage points. Diamonds are positively and significantly correlated to conflict incidence. The total effect of their presence in any given cell, on average, has a positive impact of 1.9 percentage points in conflict incidence. Since the existence of spill-over effects, it is arguable that even the determinants of conflict may have neighbouring effects and contribute in defining the

8 558 Sara Balestri and Mario A. Maggioni Table 2: Spatial spill-overs of conflict incidence. Dependent variable: proportion of years with at least one conflict over sample period OLS (1) a (2) (3) W. Y *** *** (0.0437) (0.0439) Area b *** *** ** (0.0007) (0.0003) (0.0004) Border distance b (0.0685) (0.0041) (0.0055) Capital distance b *** (0.0030) (0.0021) (0.0028) Mountains ** (0.0179) (0.0125) (0.0160) Forest (0.0129) (0.0078) (0.0001) Precipitation b * (0.0011) (0.0007) (0.0016) SPI (0.0021) (0.0016) (0.0016) Diamonds *** *** * (0.0079) (0.0060) (0.0059) Gold ** (0.0083) (0.0113) (0.0106) Ethnic groups (0.0041) (0.0034) (0.0033) Population b (0.0009) (0.0014) (0.0014) W. Area b (0.0012) W. Border (0.0155) W. Capital *** (0.0119) W. Mountains (0.0262) W. Forest b (0.0002) W. Precipitation b (0.0019) W. SPI *** (0.0035) W. Diamonds *** (0.0128)

9 Blood Diamonds, Dirty Gold and Spatial Spill-overs 559 (Table 2: Continued) Dependent variable: proportion of years with at least one conflict over sample period OLS (1) a (2) (3) W. Gold ** (0.0258) W. Ethnic groups (0.0073) W. Population b *** (0.0032) Observations AIC Country fixed effects X X X Standard errors in parentheses, p < 0.15, *p < 0.1, **p < 0.05, ***p < W refers to first order queen contiguity matrix. a Standard errors in parentheses corrected for spatial dependence, following T. Conley (1999). b Coefficients and standard errors multiplied by spatial propagation of conflict. We test this hypothesis through a Spatial Durbin specification, namely Model (3). Conflict is confirmed as clustered in space with a positive impact of conflict incidence on neighbouring cells of 4.6 percentage points 5. The distance to the political power is found negatively correlated to conflict incidence, suggesting that civil conflicts in this area are mainly associated with the tentative of taking control of government 6. Diamonds are confirmed as meaningful driver of conflict prevalence since their location affects the cell itself and the neighbouring ones. Moreover, the presence of gold deposits in primary neighbours is positively associated to conflict incidence in the observed cell. Simple exploitation techniques 7 and high economic value, indeed, make gold a potential conflict resource by facilitating conflict occurrence and duration, since 5 As for Model (2), we calculate the average effect for a neighbouring cell by multiplying the coefficient of the autoregressive term by the cell average influence. 6 A neighbouring cell could belong to a different country, thus the distance to the capital city is measured in relation to a different national capital. This case raises the possibility of heterogeneous and conflicting effects of capital distance on contiguous cells. Thus, we changed the definition of the spatially lagged variable by using a dummy variable which takes the value of 1 if the cell belongs to the capital city area, 0 otherwise. Capital city area is defined by a cut-off distance of 110 km, approximately corresponding to the length of two cells. 7 We included only lootable gold deposits in the analysis, that is superficial and placer deposits.

10 560 Sara Balestri and Mario A. Maggioni it may provide a source of private profit as wells as being a tool for military financing. The experience of drought periods in neighbouring cells increase conflict incidence, probably through a rise in social tensions and grievances. Finally, a higher population in the neighbouring cells is negatively correlated with conflict incidence suggesting that the population size may play as obstacle to the propagation of conflict, especially in case of balanced fighters groups. Looking more closely to variations within the period, supported by LISA map plotting exercise, data suggest the existence of two different spatial patterns across the period. We model this evidence by building two sub-periods namely (A) and (B) and performing the same analysis as before (Table 3). In both sub-periods conflict incidence is strongly characterized by spatial dependence, as expected. In period (A), Model (2A) basically confirms the main findings, highlighting the role of diamonds. It also introduces a negative correlation with forested areas; however, this relation does not appear stable and is not confirmed in subsequent models. When spatial-lag for the covariates are introduced (Model 3A) for this sub-period, natural resources are the most powerful regressors of conflict incidence. In this model, the presence of gold in neighbouring areas positively affects the conflict prevalence in the observed cell, probably raising greed opportunities. In sub-period (B) the distance from the power centre is even more relevant, whereas diamonds are significant only in the spatial-lag definition. Conversely, in this period, beyond a raising attention to diamond markets transparency 8, gold increases its significance as conflict resource. 6 Robustness checks The robustness of our estimates has been verified through several sensitivity tests. Being aware that the measure applied for conflict incidence may incorporate to some extent a spatial effect given by its construction algorithm, we used an alternative measure of conflict occurrence: the number of conflict events reported for any given cell. As a point variable, conflict events are less susceptible to incorporate spatial dependence by the definition used to code them, thus this proxy is useful to verify our results. Performing Models (2) and (3) by ML estimation, not only the spatial 8 We remind that the Kimberley Certification Scheme for blood diamonds was established in 2003 with a large legacy to the facts occurred in the area during the 1990s.

11 Blood Diamonds, Dirty Gold and Spatial Spill-overs 561 Table 3: Spatial spill-overs of conflict incidence, by periods. Dependent variable: proportion of years with at least one conflict over a sub-period First period, Second period, OLS (1A) a (2A) (3A) OLS (1B) a (2B) (3B) W. Y *** *** *** *** (0.0415) (0.0421) (0.0463) (0.0499) Area b ** *** *** *** *** *** (0.0011) (0.0005) (0.0006) (0.0008) (0.0004) (0.0006) Border distance b * * (0.0076) (0.0060) (0.0082) (0.0120) (0.0055) (0.0077) Capital distance b * ** *** *** *** (0.0037) (0.0030) (0.0042) (0.0052) (0.0029) (0.0040) Mountains (0.0185) (0.0180) (0.0247) (0.0321) (0.0166) (0.0231) Forest ** *** (0.0001) (0.0001) (0.0001) (0.0002) (0.0001) (0.0001) Precipitation b (0.0016) (0.0010) (0.0023) (0.0021) (0.009) (0.0022) SPI (0.0034) (0.0023) (0.0025) (0.0032) (0.0021) (0.0023) Diamonds 0.292*** *** * ** (0.0108) (0.0087) (0.0088) (0.0134) (0.0080) (0.0084) Gold * ** ** (0.0164) (0.0163) (0.0158) (0.0139) (0.0150) (0.0147) Ethnic groups (0.0065) (0.0049) (0.0049) (0.0071) (0.0044) (0.0046) Population b * (0.0017) (0.0020) (0.0021) (0.0017) (0.0018) (0.0020) W. Area b (0.0017) (0.0016) W. Border (0.0231) (0.0216) W. Capital *** (0.0179) (0.0166) W. Mountains *** (0.0392) (0.0366) W. Forest b (0.0003) (0.0281) W. Precipitation b (0.0028) (0.0026) W. SPI (0.0052) (0.0049) W. Diamonds *** *** (0.0186) (0.0175) W. Gold ** (0.0386) (0.0359)

12 562 Sara Balestri and Mario A. Maggioni (Table3: Continued) Dependent variable: proportion of years with at least one conflict over a sub-period First period, Second period, OLS (1A) a (2A) (3A) OLS (1B) a (2B) (3B) W. Ethnic groups (0.0109) (0.0102) W. Population b *** (0.0048) (0.0045) Observations AIC Country fixed effects X X X X X X Standard errors in parentheses, p < 0.15, *p < 0.1, **p < 0.05, ***p < W refers to first order queen contiguity matrix. a Standard errors in parentheses corrected for spatial dependence, following T. Conley (1999). b Coefficients and standard errors multiplied by autoregressive coefficients are confirmed in sign and significance, but also their magnitude is larger than before. Since we apply the same weight matrix we can compare the two and confirm the goodness of the analysis 9. Then, we explore the estimates robustness by applying different choices of weight matrix: using a second and third order of queen contiguity matrix in Models (2) and (3), the spatial autoregressive terms remain steadily significant and, as expected, decrease in magnitude as the distance increases, since the explaining power of proximity progressively drops. Other results are consistent with the major findings. Finally, we apply a different specification for natural resources locations by switching cell identification for deposits located in the grid border. This modification does not change the consistency of the results. 7 Final remarks and future research agenda As a matter of fact, civil conflict incidence is likely to be characterized by spatial correlation: in our sample we clearly find that conflicts are persistent in space, and this feature confirms the necessity to apply a spatial analysis in order to get 9 Spatial autoregressive coefficient for Model (2) is , significant at 1% level. Spatial autoregressive coefficient for Model (3) is , significant at 1% level.

13 Blood Diamonds, Dirty Gold and Spatial Spill-overs 563 unbiased estimates. OLS estimations, largely applied by conflict literature, may produce biased and inconsistent results when spatial correlation is not taken into account. Once controlled for spatial correlation, we detect the presence of spill-overs and neighbouring effects of conflict incidence, suggesting that the spread of violence follows a spatial pattern affecting contiguous areas. Assuming that the proximity of the determinants of a conflict may shape neighbouring effects, we accordingly model spatial dependence and we find that even the correlates of civil conflict have a robust local dimension: natural resources are confirmed as powerful driver since their location and proximity may affect conflict dynamics. However it is worth noting that by distinguishing among specific resources we are able to detect different pattern of correlation. Future extensions of this paper will explicitly model the diffusion patterns of conflicts through spatio-temporal analysis in order to better fit the dynamics of civil warfare. References Anselin, L., (1988), A Test for Spatial Autocorrelation in Seemingly Unrelated Regressions, Economics Letters, vol. 28, no. 4, pp Anselin, L., O Loughlin, D., (1992), Geography of International Conflict, in Chatterji M., Kuenne R. E., (eds.), Dynamics and Conflict in Regional Structural Change, New York University Press, New York, NY, pp Balestri, S., (2013), GOLDATA. The Gold Deposits Dataset. UCSC Milan, CSCC Working Paper(1). Banegas, R., Marshall-Fratani, R., (2007), Cote d Ivoire: Negotiating Identity and Citizenship, in Bøås M., Dunn K. C., (eds.), Africa Guerrillas: Raging Against the Machine, Lynne Rienner, Boulder, CO, pp Blattman, C., Miguel, E., (2010), Civil War, Journal of Economic Literature, vol. 48, no. 1, pp Buhaug, H., Gleditsch, K. S., (2008), Contagion or Confusion? Why Conflicts Cluster in Space, International Studies Quarterly, vol. 52, no. 2, pp Conley, T. G., (1999), GMM Estimation with Cross Sectional Dependence, Journal of Econometrics, vol. 92, no. 1, pp Gersovitz, M., Kriger, N., (2013), What is A Civil War? A Critical Review of its Definition and (Econometric) Consequences, World Bank Policy Research Working Paper, Gilmore, E., Gleditsch, N. P., Lujala, P., Ketil Rod, J., (2005), Conflict Diamonds: A New Dataset, Conflict Management and Peace Science, vol. 22, no. 3, pp Harari, M., LaFerrara, E., (2013), Conflict, Climate and Cells: A Disaggregated Analysis, CEPR Discussion Paper DP9277. Kelejian, H. H., Prucha, I. R., (1998), A Generalized Spatial Two-Stage Least Squares Procedure for Estimating A Spatial Autoregressive Model with Autoregressive Disturbances, Journal of Real Estate Finance and Economics, vol. 17, no. 1, pp

14 564 Sara Balestri and Mario A. Maggioni LeSage, J. P., Pace, K. R., (2004), Spatial Statistics and Real Estate, The Journal of Real Estate Finance and Economics, vol. 29, no. 2, pp Maggioni, M. A., Nosvelli, M., Uberti, T. E., (2007), Space Versus Networks in the Geography of Innovation: A European Analysis, Papers in Regional Science, vol. 86, no. 3, pp Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., Ziese, M., (2010), GPCC Status Report, Global Precipitation Climatology Centre, Salehyan, I., (2009), Rebels Without Borders: Transnational Insurgencies in World Politics, Cornell University Press, Ithaca, NY. Silberfein, M., Conteh, A., (2006), Boundaries and Conflict in the Mano River Region of West Africa, Conflict Management and Peace Science, vol. 23, no. 4, pp Sundberg, R., Melander, E., (2013), Introducing the UCDP Georeferenced Event Dataset, Journal of Peace Research, vol. 50, no. 4, pp Tollefsen, A. F., Strand, H., Buhaug, H., (2012), PRIO-GRID: A Unified Spatial Data Structure, Journal of Peace Research, vol. 49, no. 2, pp Weidmann, N. B., Rød, J. K., Cederman, L., (2010), Representing Ethnic Groups in Space: A New Dataset, Journal of Peace Research, vol. 47, no. 4, pp