THE SPILLOVER EFFECTS OF CONFLICT ON ECONOMIC GROWTH IN NEIGHBOURING COUNTRIES IN AFRICA

Abstract

In this article, the influence of conflict on the economies of neighbouring countries is discussed. The results from previous papers show a strong negative effect for an entire area around a country suffering from conflict, but this paper reaches a different conclusion, by using more recent data and adjusting the methodology previously employed. Additionally, a new type of contiguity matrix is constructed and used in the actual analysis. The final analysis consists of a large number of regressions and concludes that conflict actually has two opposing effects. First, like conflict countries themselves, directly contiguous countries actually suffer from the negative effects of proximate conflict. Secondly, however, there is also a positive spillover of conflict, which affects non‐contiguous countries and this effect is larger for countries that are closer to the conflict country. The results from the paper predominantly hold for the most violent kind of conflict.

Keywords:

• Conflict
• Economic growth
• Spatial econometrics
• Africa

JEL Classifications:

• C21
• F51
• O11

ACKNOWLEDGEMENTS

The author is grateful to Guido Tabellini, Eliana La Ferrara, Kristian Skrede Gleditsch, Elsa Artadi, two anonymous referees and the participants of the Third IUE International Student Conference (April, 2007) in Izmir, Turkey and the NAKE Research Day (October 2007) in Utrecht, the Netherlands, for their useful comments. Most of the work took place while affiliated with Bocconi University, Milan, Italy.

Notes

¹ The phoenix effect is named after the proverbial phoenix rising from the ashes.

² For Africa, 100 km minimal distance is the optimal distance.

³ In particular, Murdoch and Sandler use the Penn World Tables (PWT) 5.7, but it is now possible to use the more recent version PWT 6.2 (Heston et al., 2006) or the World Development Indicators (Worldbank, 2007).

⁴ The Barro‐Lee dataset is available for download from http://www.cid.harvard.edu/ciddata/ciddata.html.

⁵ When, for a particular country, there is information available on conflict, growth, population size and schooling, it seems such a waste simply to discard all this valuable information, simply because investment data are not available from the exact same source as for all the other countries.

⁶ For economic growth, the Penn World Table 6.2 (Heston et al., 2006) is used as an alternative source (21 cases) or extrapolation of the WDI data (1 case). For the investment data, the Penn World table 5.7 is used as an alternative source (10 cases). Finally, the education data are supplemented with the other data collected by Barro and Lee (2000): education attainment for people over 15 (seven cases) and United Nations (2007) data on literacy rates (70 cases).

⁷ The UCDP/PRIO Armed Conflicts Dataset 4 is available for download from http://new.prio.no/CSCW-Datasets. More information is available in Harbom and Högbladh (2006).

⁸ As often in spatial econometrics, islands present a problem. I deal with the issue on a case‐by‐case basis and propose that for the dummy‐contiguity the following combinations are indicated as direct neighbours: Comoros‐Madagascar, Comoros‐Mozambique, Madagascar‐Mozambique, Madagascar‐Mauritius, Seychelles‐Madagascar, Cape Verde‐Senegal and Sao Tomé and Principe‐Gabon. As for border lengths, I employ several formulas. The assumed border length influence of i on j, when i is either a coastal nation or an island and j is an island is ; the border length influence of an island i on coastal nation j is δ_ij = coastline_i · , where k stands for all the islands that are within reach of j.

⁹ textit Google Earth can be downloaded from http://earth.google.com.

¹⁰ The difference between the reported outcome and regressions with a lower R ² is minimal, given the way of measuring conflict

¹¹ It should be noted that many different distance measures are, in fact, also significant, albeit with a lower explanatory power. For that reason, I have tried re‐running the regressions with three spillover mechanisms: one primary and two secondary, where one of the secondary mechanisms is kept fixed as the aforementioned md250. The new results show that for each specification, md250 continues to be highly significant, but the additional secondary effect rarely is. In the case of dummy violent conflict, only a few exceptions (md200, ms200, ms300 and md150) reach significance at the 10% level. This shows the particular strength of the 250 km minimum distance.

¹² It can be observed that the values for primary and secondary spillovers are of the same order of magnitude, yet with different signs. This should not be considered surprising, however, since the two are largely supposed to offset each other for primary neighbours, particularly with the optimal distances found in these results. With larger secondary distances, the point estimates of the absolute values of the coefficients diverge more.

¹³ The difference is due to rounding.

¹⁴ It must be recognised that, following Miguel et al. (2004), it could be argued that there is a causality issue in this case. However, as it is clear that growth is unlikely to have an effect on the intensity of the conflict, and the current results clearly show a correlation between intensity and growth, I believe this is not a particular problem in this case. In any case, the result should be interpreted in the sense of being able to compare the relative sizes of the different effects.

¹⁵ The data from the CoW project are available on http://www.correlatesofwar.org/.

¹⁶ I have a total of 300 observations, from 47 countries, while the paper by Murdoch and Sandler (2002b) that separates between different geographical regions has 235 observations from approximately 35–38 countries. The 2004 paper does not distinguish between different regions, but includes 217 observations for Africa, from 37 different countries.