Understanding Conflict in Africa: The Role of Economic Shocks and Spill-Over Effects

Transcription

1 Understanding Conflict in Africa: The Role of Economic Shocks and Spill-Over Effects Very early, incomplete draft Achim Ahrens Spatial Economics and Econometrics Centre (SEEC) Heriot-Watt University June 21, 2013 Abstract This study contributes to the understanding of violent conflict in Africa in two ways: First, I investigate the role of economic short-run fluctuations as a cause for conflict and, secondly, I examine the extent to which regional conflicts can be explained by spill-over effects. To this end, I construct a panel dataset of 742 African first-order administrative units covering the time period Sub-national gross domestic product (GDP) data for Africa is either unavailable or of bad quality, which is why I, as suggested by Henderson et al. (2012), utilize night-time light data from satellites to predict GDP at the sub-national level. To account for the endogeneity of GDP, I adopt the identification strategy from Miguel et al. (2004) and instrument with rainfall. The dependent variable is a casualty estimate from the UCDP Georeferenced Event Dataset (version 1.5). Preliminary results suggest that variations in output have a significant causal impact on violence, although small in magnitude, and spatio-temporal dynamics are important in explaining conflict. In fact, when omitting spatial and temporal lags the impact of income on conflict is over-estimated. Furthermore, there is evidence for substantial parameter heterogeneity across space. JEL: C33; C36; Q34. Keywords: Regional conflicts; Africa; economic shocks; night light; spatial; spill-over. aa1266@hw.ac.uk.

2 1 Introduction While the number of conflicts in Africa has been decreasing over the last two decades, violence still shapes the least developed continent and impedes economic growth and human well-being. In this study, I investigate the role of short-run income fluctuations as a cause for violence in African countries. There are multiple channels through which economic development may affect violence. Collier and Hoeffler (2004) argue that economic shocks facilitate conflicts by decreasing the opportunity costs for rebelling, whereas Fearon and Laitin (2003) focus on states capacity to prevent or repress insurgence, which is weak if income is low. In a seminal study, Miguel et al. (2004) estimate the effect of economic growth on conflict using a sample of African countries. To address the reverse causality in the relationship between economic growth and conflict, rainfall shocks (i.e., percentage change in rainfall) are used as an instrument. The authors find a significantly positive causal effect of growth on the likelihood of conflict. This study applies a similar identification strategy to a dataset of 742 African first-order administrative units covering Constrained by the lack of suitable sub-national data, empirical research usually focuses on states as units of observations. The need for econometric analysis using more disaggregated data has been stressed by authors from many disciplines, including conflict research (e.g. Blattman and Miguel, 2010; Jensen and Gleditsch, 2009). A recent study by Henderson et al. (2012) provides the framework for predicting gross domestic product (GDP) using night light data from satellites for countries with missing or low quality national accounts data, as well as for sub-national regions (see also Nordhaus and Chen, 2012). Based on Henderson et al. (2012), sub-national income levels are predicted using night light data and used to explain violence in Africa. Rather than using a binary conflict indicator, as widely employed, the dependent variable is an estimate of conflict-related casualties. Another focus of this study is on the role of spill-over effects in explaining violence. A conflict in one region may spread to other regions, or even trigger the emergence of a new conflict. While the spatial analysis of conflict has received some attention in conflict research (e.g. Jensen and Gleditsch, 2009; De Groot, 2011; Buhaug and Gleditsch, 2008), there is, to my knowledge, no study that accounts for spatial as well as temporal dynamics of violence and that focuses on the sub-national level. This study is an attempt to fill this gap and, in doing so, to provide further insights into the causes of conflict. Preliminary results suggest that rainfall levels are a strong instrument for GDP in African first-order administrative regions, variations in output 1

3 have a significant causal impact on violence, although small in magnitude, and spatio-temporal dynamics are important in explaining conflict. In fact, when omitting spatial and temporal lags the impact of income on conflict is over-estimated. The structure of this study is as follows: The following section outlines the econometric model and estimation method. In Section 3, the data is described. In Section 4, GDP predictions using night light data are obtained. Section 5.1 focuses on the validity and relevance of rainfall as an instrument. Section 5.2 shows preliminary main results and Section 5.3 discusses spatial heterogeneity. Concluding remarks are in Section 6. 2 Econometric Model The base model proposed by Miguel et al. (2004) is given by c it = α 0 g it + α 1 g it 1 + µ i + δ t + e it (1) where g it measures economic growth and c it is a binary conflict indicator. Specifically, following the classification of the Uppsala Conflict Data Program (UCDP), c it is equal to 1, if a conflict resulting in at least 25 battle deaths is recorded for country i and year t, 0 otherwise. µ i denotes region-specific, time-invariant unobserved fixed effects, and δ t is the common time-effect for time t. Miguel et al. (2004) employ two-stage least square estimation with time and country fixed effects and instrument economic growth with rainfall shocks. It is well known that omitting temporal or spatial dynamics may lead to inconsistent estimates. Therefore, I consider a spatio-temporal panel model that takes temporal persistence of violence as well as spill-over effects into account. Specifically, d it = γd it 1 +ρ 1 N j=1 w ij d jt +β 0 y it +β 1 y it 1 +µ i +u it, i = 1,..., N; t = 1,..., T. (2) The dependent variable is a log-transformation of the number of casualties in conflicts in sub-national region i and year t. A discrete dependent variable has at least two advantages vis-à-vis a binary dependent variable. First, it is not affected by an arbitrary conflict threshold and, secondly, a discrete dependent variable allows for capturing richer dynamics. y it is the logarithm of GDP which, due to lack of data at the sub-national level, is estimated utilizing satellite night light data. Due to reverse causality, income is expected 2

4 to be endogenous, but a set of valid instruments, r it = (r 1,it,..., r L,it ), using rainfall data is available. The specific definition of r it is discussed in Section 5.1. w ij is the (i, j)th element of the spatial weight matrix W N, which is an N dimensional square matrix with zeros on the diagonal. λ is the spatial autoregressive parameter whose parameter space is restricted, such that I N ρ 1 W N is nonsingular (for details see LeSage and Pace, 2010). Furthermore, it is assumed that the row and column norm of W N is bounded, which ensures that the degree of spatial dependence is finite (see e.g. Kapoor et al., 2007). The spatial weight matrix is typically treated as exogenous and a row-normalization is applied. I propose different specifications for the spatial weight matrix in Section 3. The rational for including a spatial lag is that a conflict, as measured by the number of casualties, may spread from one region to another. Although the spatial lag in (2) captures spatial dependence, the error term, u it, may still exhibit cross-sectional dependence. There are two major approaches for modelling cross-section dependence in panel models (see e.g. Bhattacharjee and Holly, 2011; Pesaran and Tosetti, 2011; Sarafidis and Wansbeek, 2012). First, the multi-factor error structure, u it = λ if t + ɛ it, (3) with λ i = (λ 1i,..., λ qi ) and f t = (f 1t,..., f qt ). This assumes the existence of a finite number, q, of time-varying, unobserved factors. Note that (3) allows for parameter heterogeneity, reflecting that the effect of a common shock is not the same for every i. Second, the spatial approach, ɛ it = ρ 2 N j=1 m ij ɛ jt + v it, (4) where m it is the (i, j)th element of M N. The assumptions, affecting W N and ρ 1, also apply to M N and ρ 2. The idiosyncratic error term, v it, is assumed to be independently and identically distributed with variance σv. 2 From the boundedness assumption follows that spatial processes constitute a form of weak cross-sectional dependence, whereas multi-factor structures generally establish strongly cross-sectionally dependent processes (Pesaran and Tosetti, 2011; Chudik et al., 2011). Intuitively, weak dependence requires that the degree of cross-sectional dependence does not increase indefinitely as N. 1 The distinction between weak and strong dependence 1 For a formal definition of weak dependence see Chudik et al. (2011) or Sarafidis and Wansbeek (2012). 3

5 is relevant for identifying spatial parameters. Since strongly dependent processes dominate weak dependence, it is not possible to estimate ρ 2 without accounting for the multi-factor structure in (3). The cross-section dependence test proposed by Pesaran (2012) can be interpreted as a test for weak vs. strong cross-section dependence and is employed in this study. 2 Estimation of econometric models with unobserved common factors is, among others, considered by Bai (2009), who suggests iterative procedures based on principal component analysis. Pesaran (2006) introduces the common correlated effects (CCE) estimator which is obtained by augmenting the model of interest by time-specific cross-section averages of the dependent and explanatory variables as proxies for unobserved common factors. Pesaran and Chudik (2013) extend the approach to weakly exogenous regressors and dynamic models. In an application to house market in the US, Holly et al. (2010) estimate a model similar to (2)-(4). However, the CCE estimator, as well as the approach by Bai (2009), require both N and T to be large, and are therefore not appropriate for this study. A widely applied method for estimating dynamic, small T panel models with cross-sectional independence (i.e., ρ = 0, λ i = 0) is based on Holtz- Eakin et al. (1988) and Arellano and Bond (1991) who suggest to eliminate unobserved fixed effects in (2) by first differencing. They introduce a two-step GMM estimator which exploits the moment conditions E[d it l u it ] = 0 i; l 2; t = 3,... T, (5) E[r k,im u it ] = 0 i, k; m = 1,..., T ; t = 3,..., T. (6) Note that the latter expression uses the instruments, r k,it, which are assumed to be strictly exogenous instead of y. Arellano and Bover (1995) and Blundell and Bond (1998) add further moment conditions, E[ d it 1 (µ i + u it )] = 0 i; t = 3,..., T, (7) which refer to equation (2) in levels and establish the system GMM estimator. Kapoor et al. (2007) derive a set of moment conditions for estimating ρ 2 in a static panel model without common factors (γ = ρ 1 = 0, λ i = 0) and suggest a spatial Cochrane-Orcutt-type transformation. Baltagi et al. (2012) combine the work of Kapoor et al. (2007) and Arellano and Bond (1991) 2 The test statistic is given by N 1 2T N T t=1 CD = ˆρ ij, ˆρ ij = e ite jt N(N 1) ( T i=1 j=i+1 t=1 e2 it and is N(0, 1) distributed under the null of weak dependence. 4 ) 1/2 ( T t=1 e2 jt) 1/2

6 and outline an estimation method for the spatial autoregressive panel model (i.e., ρ 1 0, ρ 2 0, but λ i = 0). The estimation strategy makes use of the additional moment conditions E w ij d jt l u it = 0 i; l 2; t = 3,..., T, (8) i j E w ij r k,jm u it = 0 i, k; m = 1,... T ; t = 3,..., T, (9) i j which identify γ and ρ 1. In addition, higher order spatial lags of exogenous variables are commonly employed as instruments (Kelejian and Prucha, 1998). A complication arises when λ i 0. Sarafidis and Robertson (2009) consider a model with a lagged dependent variable, common factor structure and β 0 = β 1 = ρ 1 = 0 and show that the moment conditions in (5) and (7) are invalid if u it follows a multi-factor structure. Sarafidis (2009) also shows that the spatial moment condition in (8) remain valid and can be used for estimating γ. In addition, the spatial analogue to (7) can be exploited, that is E w ij d jt 1 (µ i + u it ) = 0 i; t = 3,..., T. (10) i j In this preliminary draft, I focus on the estimation of the parameters γ, ρ 1, β 1 and β 0 using a parsimonious subset of the moment conditions in (5)-(10) and treating the error structure in (3)-(4) as nuisance. 3 Data Data for the dependent variable is taken from the Uppsala Conflict Data Program s Georeferenced Event Dataset (UCDP GED) v The UCDP GED provides a list of geo-coded violent events in Africa covering An event is defined as follows: The incidence of the use of armed force by an organised actor against another organized actor, or against civilians, resulting in at least 1 direct death in either the best, low or high estimate categories at a specific location and for a specific temporal duration. Sundberg et al. (2010, p. 4) 3 Melander and Sundberg (2011), Sundberg et al. (2010). 5

7 For each event, the UCDP collected information on the location and timing of the event, a high, a low and a best casualty estimate and the conflict type. Conflict type can be either state-based, non-state based or one-sided. If a formally organized group is involved in a violent incident with a state-based actor, the conflict type is denoted as state-based (9,225 events in the UCDP GED). If none of the actors are state-based, but both actors are formally organized, the conflict type is coded as non state-based (3,004 events). Accordingly, if one actor is not formally organized, the conflict is denoted as one-sided (5,738 events). The precision of geo-referencing varies from exact coordinates to event can only be related to the whole country (Sundberg et al., 2010, p. 12). Events that cannot be related to first order administrative units are discarded. This affects 1,192 of 19,159 events (6.2%) over the period. With respect to the temporal dimension, the lowest precision is given if only the year of the event is known, which is sufficient for the purpose of this study. Figure 1 shows the development of the total number of casualties and the number of events as listed in the UCDP GED. Figure 1: Best casualty estimate and number of violent events in Africa, Casualty estimate Event count Source: UCDP GED (v ) The dependent variable is the sum of UCDP s best casualty estimates associated with all events that fall into a specific region-year. In this preliminary draft, a logarithm plus 1 transformation is applied to account for the skewed distribution of the dependent variable. This method is only used for preliminary results and the implementation of more appropriate count data methods is in progress. Alternatively, a binary conflict indicator could be used as the dependent variable. This is problematic, as there exists no consensus as to how a conflict 6

8 is defined (see Sambanis, 2004). Another alternative would be to define the dependent variable as the number of events in a region-year. This, however, is not appropriate as the UCDP often aggregate multiple events into one event, if a clear separation is not possible (Sundberg et al., 2010, p. 6). The precipitation data used is from Willmott and Matsuura (2013, WM) and provided by NOAA/ESRL/PSD (2013) in a suitable data format (i.e., NetCDF). The advantage of the WM dataset over the classically employed Global Precipitation Climatology Project (GPCP) dataset is the higher resolution. The authors have generated a 0.5 degree 0.5 degree global dataset based on 20,782 weather stations which record monthly total precipitation throughout Night light data is made publicly available by the NOAA National Geophysical Data Center (2010, NOAA-NGDC). The NOAA-NGDC processes raw satellite data from the United States Air Force Defense Meteorological Satellite Program s Operational Linescan System (DMSP-OLS). The DMSP- OLS s satellites collect data at every location on a daily basis between 7 pm and 9 pm local time. The light intensity is measured on a scale from 0 to 63. However, only a negligible fraction of the light data in low income countries is censored. The highest region-year observation in this dataset is and only 7 region-year observations are above 60. Observation distorted by sunlight, moonlight, clouds, auroral activity and forest fires are identified and excluded, and the remaining observations are used to obtain annual averages for each 30 arc second 30 arc second pixel 4 and each satellite-year. 5 The end product is a raster image in TIF format for each satellite-year, covering -180 to 180 degree longitude and -65 to 75 latitude. Satellite night light data is available for There is data from one satellite per year for , and , and two satellites for the remaining years. The current draft of this paper is only using data for Light intensity as measured by satellites is not directly comparable across time and satellites, due to different, time-varying satellite settings. The framework by Henderson et al. (2012) accounts for this by the use of year dummies, which will be discussed in the next section. For further information on night light data see Henderson et al. (2012), Doll (2008) and Elvidge et al. (2009) arc seconds are appoximately 0.86 kilometres at the equator. 5 Another source of background noise arises from gas flaring which occurs during oil production. The NOAA-NGDC does not exclude observations affected by gas flaring from the dataset. Elvidge et al. (2009) provides a polygon dataset that can be used to exclude the locations where light emissions are predominantly from gas flaring. The correlation coefficient between average light intensity with and without excluding gas flaring is however close to one. In this draft, I only consider average light intensity obtained without excluding locations affected by gas flaring. 7

9 Table 1: Summary statistics Variable Obs. Mean Std. Dev. Min Max Average light intensity Predicted GDP (in log, ŷ it ) Event count Best casualty estimate Best casualty estimate Rainfall, conflict and light data is matched with first order administrative boundaries from Natural Earth (2013, NE). 6 The NE map reflects the present state of political boundaries on the earth. Thus, the NE dataset does not account for boundary changes over time. While this is clearly a limitation, it is unlikely to have a severe effect on the results. 7 There are in total 743 African first-order administrative units in the NE map. Due to insufficient data, one unit (i.e., Eastern Zambia) is excluded which leaves 742 units of observations. I consider three different specifications for the spatial weight matrix. First, the binary contiguity matrix is defined such that w n,ij = 1, if i and j share a common border, 0 otherwise. Second, the binary country matrix: w c,ij = 1, if i and j are in the same country, 0 otherwise. The third weight matrix is based on ethnic groups. Specifically, if i and j are populated by at least one common ethnic group, w e,ij = 1, 0 otherwise. The binary ethnic matrix is obtained based on the Geo-referencing of ethnic groups (GREG) dataset by Weidmann et al. (2010) who use the classical Atlas Narodov Mira (1964) to generate maps of ethnic groups. As pointed out by Weidmann et al. (2010), the Atlas Narodov Mira, although widely used, has at least two limitations: Firstly, it is outdated and, secondly, it does not provide a clear definition of ethnic groups. For a critical discussion, see Bridgman (2008). Note also that, as standard in the spatial econometrics literature, all spatial weight matrices are row-standardized prior to generating spatial lags. For the prediction of GDP growth for African first-order administrative regions, I use, following Henderson et al. (2012), GDP data in local constant currency from the World Bank s World Development Database (WDI). The dataset covers 188 countries over the period. 6 The data generation process was carried out in R (R Core Team, 2013), in particular using the package raster by Hijmans and van Etten (2012). 7 I am currently waiting to get access to the map datasets from the Food and Agriculture Organization s (FAO) Global Administrative Unit Layers (GAUL) project which provides annual boundaries for first and second order administrative units. 8

10 4 Predicting GDP with Light Data In order to obtain estimates for GDP using night light data, I follow the methodology proposed by Henderson et al. (2012). The authors (eq. 13) consider different flavours of y kt = ψl kt + c k + d t + e kt (11) where y is the logarithm of GDP in level as measured by national accounts and l is the logarithm of average light density. k and t are the country index and year index, respectively. d t accounts for variations in satellite settings across time as well as time-specific economic and technological conditions. c k controls for country-specific unobserved heterogeneity due to cultural and economic characteristics. As shown by Henderson et al. (2012), the OLS estimator of ψ is biased. Nevertheless, equation (11) is useful for predictive purposes. Table 2: Predicting GDP growth y it l it (8.86) NT 3015 N 188 T 17 Fixed effects Yes Year effects Yes R 2 (within) t statistics in parentheses. Based on cluster-robust standard errors. p < 0.05, p < 0.01, p < Table 2 replicates Henderson et al. (2012), Table 2, column 2. 8,9 The coefficient on average light intensity is and the correlation is strong as indicated by the R 2 statistic. A major concern for the purpose of this study is that the relationship between income and light growth is, due to fixed installation costs, asymmetric in the sense that light is more responsive to positive growth than to negative economic growth. The authors, however, show that 8 All regression results in this study are obtained using Stata 12 (StataCorp, 2011). 9 Following Henderson et al. (2012, fn. 16), Bahrain, Singapore, Equatorial Guinea and Serbia and Montenegro are excluded from the sample. 9

11 the coefficient on positive light growth is not significantly different from the coefficient on negative light growth (see Table 3, column 4). Furthermore, Henderson et al. (2012) demonstrate the predictive power of night-light data for short-run fluctuations as well as for the long-run. The regression estimates in Table 2 allow for obtaining GDP predictions for African first-order administrative units. Specifically, the GDP predictions are defined as ŷ ikt = ˆψl it + ĉ k + ˆd t where region i lies in country k and ĉ k is the estimate of the country fixed effect from the regression in Table 2. If, because country k is not in the sample, no estimate for ĉ k is available, ĉ k = 0 is assumed. Summary statistics are shown in Table 1. 5 Preliminary Results 5.1 First stage: Economic growth and rainfall Miguel et al. (2004) consider the model in equation (1) and instrument GDP growth with rainfall shocks which they define as the percentage change of rainfall from previous year. The authors (see Table 2, p. 735) find significantly positive coefficients on contemporaneous and lagged rainfall shocks (around 0.05 and 0.03, respectively) in the first stage with GDP growth as the dependent variable. This approach is critically discussed by Ciccone (2011) who argues that, because rainfall is strongly mean-reverting, a specification using rainfall in levels is more appropriate. In a response, Miguel and Satyanath (2010) and Miguel and Satyanath (2011) justify the use of rainfall shocks, arguing that economic actors often react to changes in economic conditions, and also show that the main results do not change when using rainfall in levels. I consider both rainfall in levels as well as rainfall growth as potential instruments. With respect to rainfall in levels, it is clearly expected that, all other things equal, higher rainfall levels are associated with higher output due to favourable conditions for agricultural production. However, very high rainfall levels may reflect extreme, adverse weather conditions, which is why the relationship between output and rainfall in levels is likely to be non-linear. Table 3, model 1 shows the first stage regression using rainfall in levels. The dependent variable is predicted GDP in levels. The coefficient on contemporaneous rainfall is highly significant, but negative, which would suggest that higher rainfall is associated with lower output. In model 2, I include squared rainfall in levels to account for the non-linear nature of the 10

12 Table 3: First stage: GDP and rainfall (1) (2) (3) (4) (5) ŷ it ŷ it ŷ it ŷ it ŷ it r it (-3.26) (3.97) (1.58) (0.62) rit (-6.11) (-4.21) (-4.39) r it (5.20) rit (-6.80) r it (-4.94) (2.21) N T Fixed effects Yes Yes Yes Yes Yes F t statistics in parentheses, based on cluster-robust standard errors. p < 0.05, p < 0.01, p < rainfall-gdp relationship. The coefficient on rainfall turns positive, whereas the coefficient on squared rainfall is negative, consistent with the notion that very high rainfall levels are associated with adverse weather conditions. The F statistic is 30.88, indicating that the instruments are not weak. Model 3 adds lagged rainfall and lagged squared rainfall. Contemporaneous rainfall is not significant, whereas rainfall squared, lagged rainfall and lagged rainfall squared are significant at the 1% level. The F statistic is slightly higher than in model 2, indicating that including lagged rainfall variables adds to the predictive power. Model 4 shows that contemporaneous rainfall shocks are significant, but with an unexpected negative sign. Model 5 considers a specification including rainfall shocks as well as rainfall in levels. Including rainfall and rainfall squared in levels renders rainfall shocks less significant and positive. Model 3 shows the best performance which is why I use, in the following analysis, the instrument set in model 3. Before turning to the second stage, I examine whether the first stage relationship is reasonably robust across lower and higher developed countries, and across time and space. Model 1 in Table 4 reproduces model 3 in Table 3 for convenience. Model 3 uses a sub-sample of countries that are in the upper quartile of the income distribution (using the base year 2000). Model 2 uses the remaining sample. Surprisingly, the first stage results are stronger for higher income countries. Miguel and Satyanath (2011) conclude that the explanatory power of rainfall in the first stage regression is weaker after 2000, which seems plausible 11

13 Table 4: First Stage: Low/medium and high income countries (1) (2) (3) ŷ it ŷ it ŷ it r it (1.58) (0.30) (3.30) rit (-4.21) (-1.67) (-6.12) r it (5.20) (4.39) (3.10) rit (-6.80) (-5.45) (-4.81) Fixed effects Yes Yes Yes Years ŷ all < Q 3 Q 3 F N NT t statistics in parentheses, based on cluster-robust standard errors. Q 3 denotes the 75th percentile of the cross-country income distribution. p < 0.05, p < 0.01, p < Table 5: First Stage: Robustness across time and space (1) (2) (3) (4) (5) (6) ŷ it ŷ it ŷ it ŷ it ŷ it ŷ it r it (1.58) (3.64) (2.66) (4.94) (-3.71) (-0.34) rit (-4.21) (-0.28) (-1.39) (-1.97) (2.46) (-2.92) r it (5.20) (8.99) (4.39) (5.84) (0.07) (1.90) rit (-6.80) (-2.51) (-3.18) (-5.05) (0.54) (-3.92) Fixed effects Yes Yes Yes Yes Yes Yes Years Latitude all all all >10 <-10 (-10,10 ) F N NT t statistics in parentheses, based on cluster-robust standard errors. p < 0.05, p < 0.01, p <

14 due to better use of irrigation systems and decreasing importance of the agricultural sector. For this reason, Model 2 and 3 in Table 5 consider the sub-sample and , respectively. The coefficients in both sub-samples have the expected signs, but the F statistic of 7.1 is indeed lower in the second sample, but still reasonably high. I also examine different climate regions. Based on a world map of Köppen- Geiger climate classifications by Kottek et al. (2006), I divide Africa roughly into three climate regions. (a) Northern Africa: All regions above 10 latitude which are classified as arid climates. (b) Equatorial/Central Africa: Regions in a ±10 interval around the equator are tropical/equatorial climates. (c) Southern Africa: below 10, is influenced by warm temperatures or arid climates. First-order administrative units are divided based on region centroids. It turns out that the first stage for Southern Africa is not satisfactory. The F statistic is high, but the coefficient on contemporaneous rainfall is negative. Rainfall squared, lagged rainfall and lagged rainfall squared are not significant. To summarize, it seems reasonable to conclude that the first stage estimation is rather stable across space, time, as well as between low and higher income countries. However, Southern Africa is an exception. In the next sub-section, I show results including and excluding Southern Africa. 5.2 Second stage: Conflict and economic growth Preliminary estimation results are shown in Table Model 1 is nondynamic and non-spatial (γ = ρ 1 = 0), and only includes contemporaneous income, as lagged income was found to be insignificant in all specifications. Model 1 is estimated by IV/2SLS with fixed effects. The point estimate is and is marginally significant at the 5% level. Models 2-4 correspond to equation (2) but employ three different specifications of the spatial weight matrix. w e,ij, w c,ij and w n,ij refer to the binary ethnic, binary country and binary neighbour weight matrix, respectively. Since too many instruments are harmful due to over-fitting the first stage and weakening the Hansen-Sargan J test (Roodman, 2009), the estimation exploits only a parsimonious subset of the moment conditions in (5)-(10). Specifically, I minimize the number of moment conditions by setting l = 2 in equation (5) and (8), m = t in equation (6) and (9) (for t = 3,..., T ). With coefficient estimates around 0.07, temporal persistence is not very strong, but the temporal lags are significantly different from zero. Spatial dynamics seem to be stronger in comparison. Interestingly, the coefficient on 10 Estimation results are obtained using the packages xtivreg2 (Schaffer, 2005) and xtabond2 (Roodman, 2003). 13

15 predicted GDP is much smaller in absolute value when allowing for spatiotemporal dynamics, i.e. between and compared to This suggests that accounting for dynamics is important, since omitting spatial and temporal lags leads to overestimating the influence of income on violence. As discussed above, the first stage is less satisfactory for Southern Africa which is why model 5 excludes Southern Africa from the analysis. The Hansen-Sargan J tests does not reject the null of the validity of moment conditions in any of the models, but the power of the test might be weak due to the still large number of instruments. Cross-sectional dependence is strong, as indicated by the CD test. This suggests the presence of unobserved factors, a topic which requires further exploration. Table 6: Second stage: Conflict and GDP (1) (2) (3) (4) (5) d it d it d it d it d it ŷ it (-1.95) (-3.39) (-2.16) (-2.40) (-2.34) d it (2.92) (3.05) (2.80) (2.87) we,ij ŷ jt (9.51) wc,ij ŷ jt (10.27) wn,ij ŷ jt (8.97) (8.29) Constant (3.38) (2.12) (2.41) (2.34) Fixed effects Yes Year effects No Yes Yes Yes Yes Instruments J-test (p-val.) AR(1) (p-val.) AR(2) (p-val.) Sample all all all all >-10 N N T CD test t statistics in parentheses, based on cluster-robust standard errors. p < 0.05, p < 0.01, p <

16 Figure 2: Spatial heterogeneity: Estimates of β 0 across space (.005,.01] (0,.005] (.005,0] (.01,.005] (.015,.01] (.02,.015] [.025,.02] 5.3 Spatial heterogeneity The models in Table 6 assume that β 0, the effect of income on conflict, is constant across space. This is a very strong assumption. It seems likely that in some regions conflict is driven by institutional or political factors rather than by economic variables. Figure 2 explores whether β 0 is constant across African regions and reveals substantial spatial heterogeneity. For each region i, a sub-sample is chosen such that only those regions are included for which the distance to i is less than 1,500 kilometres. The chosen distance of 1,500 kilometres provides that no sub-sample is substantially smaller than 50. For comparison, the African continent extends over approximately 8,000 kilometres North-South and 7,000 kilometres East-West. Then, model 4 in Table 6 is estimated, and the corresponding estimate ˆβ 0,i is obtained. The map shows these estimates of β 0,i. Darkish colours indicate that ˆβ 0,i > 0 which is the case for 68 regions. The mean is and the standard deviation is quite large at

17 6 Conclusion While the results in this early draft certainly have to be treated with caution, a few remarks seem appropriate. First, the framework proposed by Henderson et al. (2012) is valuable in that it allows for examining social and economic phenomena at a sub-national level, which is likely to provide new insights not only in conflict research. Second, rainfall in levels constitutes a valid and relevant instrument for GDP in African first-order administrative units over the period. However, the strengths of the relationship seems to decrease over time, which is in accordance with the results by Miguel and Satyanath (2011). Furthermore, the relationship is non-linear, possibly because very high rainfall levels are associated with extreme, adverse weather conditions. Third, the impact of income on conflict, which is measured by UCDP s casualty estimate, is statistically significant, but small in magnitude. Precisely, the point estimates suggest that a one percent increase in GDP is associated with a 0.02% to 0.04% fall in conflict-related deaths. Fourth, accounting for spatio-temporal dynamics is important, as omitting these dynamics leads to overestimating the impact of GDP on conflict. Finally, there is evidence of substantial parameter heterogeneity across space. While the coefficient estimate of GDP is negative in most regions, it turns positive in other regions of Africa. The issue of spatial heterogeneity requires further exploration. 16

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